Evaluating Compost Effects on Tomato (Lycopersicon esculentum (L.) Mill) Under Drought: An Integrated Soil fertility index (SFI), Monte Carlo Simulation (MCS), and Multivariate Soil–Plant Interaction Modelling in Sandy Loam and Silty Clay soils

Evaluating Compost Effects on Tomato (Lycopersicon esculentum (L.) Mill) Under Drought: An Integrated Soil fertility index (SFI), Monte Carlo Simulation (MCS), and Multivariate Soil–Plant Interaction Modelling in Sandy Loam and Silty Clay soils | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluating Compost Effects on Tomato (Lycopersicon esculentum (L.) Mill) Under Drought: An Integrated Soil fertility index (SFI), Monte Carlo Simulation (MCS), and Multivariate Soil–Plant Interaction Modelling in Sandy Loam and Silty Clay soils Majda Oueld Lhaj, Rachid Moussadek, Hatim Sanad, Abdelmjid Zouahri, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9439169/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Soil fertility decline and increasing water scarcity threaten horticultural production systems in arid and semi-arid regions, particularly in North Africa. This study aimed to evaluate whether compost can enhance soil fertility and sustain tomato ( Lycopersicon esculentum (L.) Mill) performance under controlled water stress (WS) in contrasting soil textures. The objectives were to assess compost effects on soil physicochemical properties, plant growth and physiology, nutrient uptake, biomass and yield, and to identify key drivers of productivity using multivariate and probabilistic modelling. A greenhouse experiment was conducted on sandy loam and silty clay soils amended with compost at 1% and 3%, chemical fertilizer, or left untreated, combined with 40%, 60%, and 80% field capacity (FC). Soil and plant data across all growth phases were analyzed using SFI, statistical analysis and MCS. Results showed that compost 3% × 80% FC produced the highest SFI in both soils, reaching 0.42 in sandy loam and 0.92 in silty clay, compared to 0.06–0.10 in controls. Compost significantly increased plant height (by 35–55%), leaf area (by 40–70%), Relative Water Content (RWC) (by 15–28%), chlorophyll content (by 20–45%), and fruit yield (by 45–75%) relative to control treatments under drought. PCA and PLSR identified soil moisture retention, chlorophyll stability, and Ca–Mg nutrition as the major predictors of yield, while MCS demonstrated reduced fertility risk and higher probability of achieving optimal SFI under compost. Overall, compost application markedly improved soil fertility and tomato productivity under WS, offering a sustainable strategy for resilient horticultural systems in drought-prone regions. Organic amendment Water stress Sustainable horticulture production systems Physiological responses agricultural productivity yield performance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Global horticultural production systems are increasingly threatened by the accelerating impacts of climate change, particularly the intensification of water scarcity. Rising temperatures, altered precipitation regimes, and increasing evapotranspiration have reduced the reliability of freshwater supplies essential for irrigated agriculture. According to the (Arulingam et al., 2022 ), approximately 1.8 billion people already live in areas with severe WS, and global agricultural water demand is projected to increase by 60% by 2050. Mediterranean and North African regions are considered climate-change hotspots where drought frequency and intensity continue to rise. Recent analyses indicate that average annual temperatures in the Mediterranean Basin have already increased by 1.54°C since the pre-industrial period, exceeding the global average warming rate (Azzopardi et al., 2020 ). Such climatic shifts directly undermine horticultural systems, which rely on precise soil–water–plant interactions for stable yields and product quality. Under these conditions, WS has emerged as one of the principal factors constraining productivity, root development, and physiological processes in high-value horticultural crops. Soil fertility degradation compounds the effects of climate-induced WS, particularly in arid and semi-arid countries like Morocco. More than 70% of Moroccan agricultural soils exhibit low soil organic carbone content (< 2%), leading to reduced nutrient availability, poor aggregate stability, and low water retention capacity (Devkota et al., 2022 ). In addition, accelerated soil erosion, salinization in irrigated zones, and nutrient mining driven by intensive cultivation have further weakened soil productivity. According to the (Laamouri and Khattabi, 2025 ), Morocco loses approximately USD 2.1 billion annually (1.77% of GDP) due to land degradation, including declines in soil fertility and agricultural productivity. These fertility constraints are especially severe in sandy loam soils of coastal and inland horticultural areas, where limited water-holding capacity (WHC) and rapid nutrient leaching interact with drought to suppress crop growth. The increasing unpredictability of rainfall and irrigation water availability in Morocco elevates the need for sustainable soil amendment practices that enhance long-term soil resilience. Tomato ( Lycopersicon esculentum (L.) Mill) is one of the most widely cultivated horticultural crops globally, with an annual production exceeding 189 million tons in 2021 (Szabo et al., 2025 ). Morocco ranks among the top tomato exporters in the Mediterranean region, with a significant share derived from greenhouse systems in water-limited environments (Benabderrazik et al., 2021 ; Santeramo and Lamonaca, 2024 ). Tomato is particularly sensitive to both moisture deficits and nutrient imbalance due to its shallow root system, high evapotranspiration demands, and rapid fruit development cycle. WS reduces leaf expansion, photosynthetic rate, chlorophyll content, and fruit set, leading to yield losses that may exceed 30–50% under severe drought (Wahab et al., 2022 ). Soil fertility degradation further intensifies these constraints by limiting nutrient uptake (particularly N, P, K, Ca, Mg) essential for cell division, fruit enlargement, and metabolic functioning. Moreover, water-deficient and nutrient-poor soils reduce fruit quality attributes such as size, firmness, soluble solids, and antioxidant activity. This dual vulnerability underscores the need for soil management strategies that simultaneously enhance water retention and nutrient availability. Organic amendments such as compost have emerged as pivotal tools in climate-smart agriculture due to their ability to enrich soil OM, enhance cation exchange capacity (CEC), improve soil structure, and strengthen microbial activity. Compost application increases soil WHC, reduces bulk density (BD), and enhances nutrient retention through stable organic–mineral complexes, thereby mitigating the impacts of WS on crop growth. Recent studies have reported that compost can increase soil moisture retention and improve tomato yield by 20% or greater depending on soil type and application rate (Tao et al., 2024 ). In sandy-textured soils, compost provides a particularly important structural benefit by reducing rapid infiltration losses and improving pore continuity, and in clay-rich soils, compost enhances aggregate stability and nutrient cycling, improving root aeration and sustained nutrient release during water-limited phases (Oueld Lhaj et al., 2024b ). Given the increasing water scarcity in the Mediterranean region, compost represents a sustainable and ecologically robust strategy for stabilizing horticultural productivity (Oueld Lhaj et al., 2025 ). Quantifying soil fertility under complex stress conditions requires integrated assessment frameworks that account for physical, chemical, and biological indicators simultaneously. The SFI provides a composite measure that synthesizes multiple soil attributes into a single numerical value, allowing for objective comparison of treatments and soil types. Multivariate statistical tools such as Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), Linear Discriminant Analysis (LDA), and Partial Least Squares Regression (PLSR), offer powerful analytical frameworks for identifying the dominant factors driving tomato performance under variable soil and water conditions. These tools have increasingly been used to evaluate soil–plant interactions and to predict productivity under stress (Zandi et al., 2025 ). Furthermore, MCS enables probabilistic assessment of fertility outcomes by incorporating uncertainty in soil properties, moisture status, and amendment effects. MCS based approaches allow quantification of risk levels associated with crop production and have been adopted in recent sustainability assessments to support decision-making under climatic uncertainty (Liman Harou et al., 2021 ). Despite substantial progress in understanding compost’s role in drought mitigation and nutrient management, few studies have simultaneously examined its effects across contrasting soil textures under controlled WS conditions while integrating SFI, plant nutrient status, multivariate models, and probabilistic simulation. Existing research has either focused on isolated soil types, single physiological traits, or limited statistical approaches, leaving a gap in holistic assessments of soil–plant–water interactions. The innovative aspect of the present study lies in its comprehensive combination of SFI quantification, multivariate modeling (PCA, HCA, LDA, PLSR), and MCS across two contrasting soil textures providing a robust and multidimensional evaluation of compost performance under WS. The overall aim of this study is to evaluate the effectiveness of compost amendment in improving soil fertility, plant physiological functioning, growth performance, and yield of tomato under different irrigation levels in contrasting soil textures. Specifically, this study seeks to: (1) Assess the impact of compost on soil physicochemical properties and SFI under WS conditions in sandy loam and silty clay soils. (2) Investigate the influence of compost on plant morphological, physiological, and nutrient responses during all growth phases leading to final biomass and yield at harvest. (3) Apply multivariate statistical techniques (PCA, HCA, LDA, PLSR) to identify key soil–plant variables driving productivity under varying amendment and irrigation treatments, (4) Employ MCS to quantify uncertainty and probabilistic fertility outcomes associated with compost use in water-limited horticultural systems. 2. Materials and Methods 2.1. Soil preparation and description The experiment was carried out between February and July 2024 under controlled greenhouse conditions at the Agronomic Research Station of the National Institute of Agronomic Research (INRA) within the Research Unit for Environment and Natural Resources Conservation (URECRN) in the capital Rabat, Morocco. Two contrasting soils were selected to represent distinct agroecological contexts, namely a sandy loam soil collected from the Tiflet region located about 60 km east of Rabat and a silty clay soil obtained from the agricultural area of Temara situated roughly 30 km south of Rabat. Soil sampling was performed using a manual auger, targeting the 0–20 cm surface horizon, which constitutes the biologically active and agronomically most responsive layer, particularly in terms of nutrient cycling, salinity dynamics, and structural variability. For each soil type, multiple subsamples were collected across the site, homogenized into composite samples to ensure representativeness, and transported to the laboratory for analysis. Upon arrival, the soils were air-dried at room temperature, manually cleaned of visible debris, and sieved to 2 mm for physical characterization and 0.25 mm for chemical analyses. The complete initial soils characterization are presented in Table 1 . Table 1 Initial physical and chemical properties of the soils used in the experiment. Parameter Unit Silty Clay Soil Sandy Loam Soil Sand % 13.30 57.90 Silt % 34.10 27.24 Clay % 52.60 14.86 Texture class – Silty clay Sandy loam pH – 7.81 7.30 Electrical conductivity (EC) dS/m 0.200 0.201 OM % 1.32 1.29 CEC cmol/kg 0.65 11 Total N ( TN) % 0.078 0.072 Available P (Av. P) mg/kg 120 31.85 Exchangeable K (Ex. K) mg/kg 229 41.25 Sodium (Na) mg/kg 1.50 1.50 Calcium (Ca) mg/kg 5.20 16.50 Magnesium (Mg) mg/kg 5.00 21.50 2.2. Compost characterization and chemical properties The organic amendment used in this study consisted of a mature compost produced at the INRA botanical garden in Rabat through a controlled co-composting process. The feedstock was composed of green plant residues blended with sheep manure, providing a balanced carbon–nitrogen matrix conducive to sustained aerobic microbial activity. Composting was carried out over a 120-day period under monitored aeration and moisture conditions to ensure efficient OM decomposition, stabilization of the material, and preservation of nutrient integrity, following the methodological framework described by (Oueld Lhaj et al., 2024a , 2026). Upon completion of the composting cycle, the final product was air-dried, homogenized, and sieved to 2 mm prior to its incorporation into the experimental pots. Comprehensive chemical analyses were subsequently performed to determine its suitability as an organic amendment. The main physicochemical properties of the compost are presented in Table 2 , confirming its classification as a nutrient-rich, well-stabilized organic amendment appropriate for greenhouse tomato cultivation. Table 2 Physical and chemical characteristics of the compost used in the experiment. Parameter Unit Value pH – 6.8 EC mS/cm 2.92 OM % DM 29 Total N % DM 1.98 Total P % DM 3.22 Total K % DM 0.61 WHC % 122 C/N ratio – 16.15 Zinc (Zn) mg/kg DM 83 Copper (Cu) mg/kg DM ND Iron (Fe) mg/kg DM 321 Manganese (Mn) mg/kg DM 230 Cadmium (Cd) mg/kg DM ND Lead (Pb) mg/kg DM ND Nickel (Ni) mg/kg DM ND Arsenic (As) mg/kg DM ND Note: “ ND ” non-detected, “DM” dry matter 2.3. Experimental Design The experiment was conducted under controlled greenhouse conditions to evaluate the effect of compost and drought stress on the growth and physiological performance of tomato ( Lycopersicon esculentum (L.) Mill.). The study followed a full-factorial arrangement combining two soil types, four amendment regimes, and three irrigation levels, implemented within a randomized complete block design (RCBD). The two soils used were a sandy loam and a silty clay, both air-dried, sieved to 4 mm, and characterized physicochemically prior to the trial. Each treatment was replicated four times, with one pot per treatment per block, giving a total of 96 experimental units. Plants were grown in rigid plastic pots with a nominal volume of 8 L, filled to 7.5 L to maintain irrigation headspace. Based on BD, each pot contained approximately 10.5 kg (sandy loam) or 9.0 kg (silty clay) of dry soil. For the compost treatments, compost was incorporated on a dry-weight basis at 1% or 3% ( w/w ), corresponding respectively to 105 g and 315 g per pot for the sandy loam, and 90 g and 270 g per pot for the silty clay. In addition to these organic amendments, two control treatments were included for each soil, a negative control (no compost or fertilizer) and a positive control receiving a balanced solid mineral fertilizer. For the positive control, nutrient supply was standardized using granular 12–12–17 at 15 g/pot (equivalent to 200 Kg N/ha) for the sandy loam and 20–10–10 at 9 g/pot (equivalent to 200 Kg N/ha) for the silty clay, based on the initial soil fertility status of each substrate. In both cases, 40% of the dose was uniformly incorporated into the upper 10–12 cm of substrate at transplanting, while the remaining 60% was applied in six equal weekly top-dressings to ensure steady nutrient availability throughout the vegetative and early reproductive stages. Irrigation regimes were imposed following an initial establishment phase. All pots were maintained at 85–90% of FC for the first 14 days after transplanting to ensure uniform root establishment. Thereafter, three drought levels were implemented using gravimetric control of substrate moisture including 80% FC, 60% FC, and 40% FC, representing well-watered, moderate, and severe deficit conditions, respectively. For each pot, FC was determined by saturating the soil to incipient drainage, allowing a 48-h drainage period, and subsequently recording the pot weight. Daily target irrigation weight was calculated individually for every pot using the expression using the Eq. ( 1 ): $$\:\:{\mathbf{W}}_{\mathbf{T}\mathbf{a}\mathbf{r}\mathbf{g}\mathbf{e}\mathbf{t}}=\:{\mathbf{W}}_{\mathbf{D}\mathbf{r}\mathbf{y}}+\:\mathbf{F}\mathbf{C}\:\times\:\:{(\mathbf{W}}_{\mathbf{F}\mathbf{C}}\:-\:{\mathbf{W}}_{\mathbf{D}\mathbf{r}\mathbf{y}})$$ 1 Where “W Dry ” denotes the pre-irrigation dry weight of the pot-soil system. Irrigation was applied exclusively by surface watering, supplying the exact volume required to restore the pot to its targeted weight, thereby preventing confounding leaching effects. This pot-specific approach ensured that differences in water retention due to soil texture and compost rate were fully incorporated into the drought imposition strategy. All plants were grown under controlled greenhouse conditions where temperature was maintained at 23 ± 2.5°C and relative humidity at 62 ± 8%, with illumination relying exclusively on natural sunlight. Tomato seedlings at the 4–5 true-leaf stage (approximately 25–30 days old) were transplanted individually into the center of each pot and supported with a uniform vertical stake. During the establishment phase (0–14 days after transplanting), all pots were maintained at approximately 90% of FC to ensure homogeneous early root development across treatments. The drought treatments were imposed from day 15 onward and maintained until the conclusion of the experiment. Irrigation was applied by surface watering only, avoiding any bottom irrigation or sub-irrigation that could alter soil moisture gradients. Each pot was weighed daily in the morning, and the volume of water required to restore it to the assigned target weight was applied. When pot weights exceeded the target, irrigation was omitted to maintain consistent soil water deficits. Leaching events were minimized to maintain stable moisture profiles and prevent unintended nutrient losses. Pots were spaced at 30 cm intervals in all directions to minimize shading, edge interference, and competition for light. To minimize positional heterogeneity within the greenhouse, pots were rotated periodically to mitigate microclimatic variation associated with bench effects. Within each pot, technical repetitions were performed for several measurements to ensure data reliability but were treated statistically as subsamples rather than independent replicates. The main components of the experimental design are summarized in Table 3 . Table 3 Summary of the experimental design. Component Description Experimental design Full factorial arrangement in a RCBD with 4 blocks and 96 pots total Greenhouse conditions - Temperature: 23 ± 2.5°C - Relative humidity: 62 ± 8% - Light: natural sunlight only (no supplementary lighting) Factors and levels - Soil type (2): Sandy loam, Silty clay - Control (2): Negative control, Positive control (mineral fertilizer) - Amendments (2): Compost 1%, Compost 3% - Irrigation (3): 80% FC, 60% FC, 40% FC Replicates Four replicates per treatment (one per block) Pot characteristics - Rigid plastic pots (8 L nominal; filled to 7.5 L) - Soil mass/pot: 10.5 kg (sandy loam) and 9.0 kg (silty clay) Compost rates - 1% (w/w ) : 105 g/pot (sandy loam), 90 g/pot (silty clay) - 3% (w/w): 315 g/pot (sandy loam), 270 g/pot (silty clay) Positive control fertilization - Sandy loam: 12–12–17 at 15 g/pot - Silty clay: 20–10–10 at 9 g/pot - Application: 40% basal, 60% split into 6 weekly doses Transplanting material - Tomato seedlings at 4–5 true leaves - 25–30 days old - One plant per pot - Centrally positioned and staked Drought treatments - Initiated on day 15 - Irrigation controlled gravimetrically to maintain 80%, 60%, or 40% FC 2.4. Sampling Periods and Experimental Phases To monitor the temporal dynamics of soil and plant responses under WS and compost application, sampling was carried out at four distinct experimental phases including baseline phase (prior to stress induction), drought initiation phase, mid-drought stress phase and final harvest phase. During the baseline period (0–14 days after transplanting (DAT)), plants were maintained at approximately 90% of FC to ensure uniform root establishment. Drought treatments were introduced on day 15, corresponding to 80%, 60%, and 40% FC levels, and maintained until the end of the experiment. Soil and plant samples were collected at each phase to evaluate both the short-term physiological responses and the long-term agronomic and nutritional effects of compost and drought interaction. The first two sampling phases focused on early plant responses and soil moisture dynamics, while the mid-drought and final harvest stages captured the cumulative impact on soil fertility, water retention, biomass accumulation, and nutrient uptake. All measurements were performed on fully randomized pots within each treatment, and destructive sampling (shoot and root biomass, root length and volume, fruit yield and leaf nutrient contents) was restricted to the final harvest phase, whereas non-destructive parameters (plant height, stem diameter, leaf number, leaf area, RWC, chlorophyll and transpiration) were monitored across all sampling phases to preserve treatment integrity. 2.5. Soil Laboratory Analyses Comprehensive soil analyses were performed to assess the initial physicochemical properties and the variations induced by compost application and drought stress. Soil samples were collected at each of the four experimental phases (baseline, drought initiation, mid-drought stress, and final harvest) from the 0–15 cm layer of each pot. Samples were air-dried, gently ground, and sieved to < 2 mm for physical determinations, while a subsample was sieved to < 0.25 mm for chemical analysis. 2.5.1. Physical and Hydro-physical Properties Soil texture was determined by the hydrometer method (Babur et al., 2021 ), to classify the soils according to the USDA textural triangle. The BD was measured using the core method (Obidike-Ugwu et al., 2023 ) and total porosity (TP) was calculated assuming a particle density of 2.65 g/cm 3 . FC was determined gravimetrically by saturating soil samples and allowing them to drain freely for 48 h at room temperature. Because specialized equipment was unavailable, the permanent wilting point (PWP) was estimated empirically using a pedotransfer function that relates PWP to soil texture and OM content following Eq. 2 by (Saxton and Rawls, 2006 ): $$\:\mathbf{P}\mathbf{W}\mathbf{P}\:\left(\mathbf{\%}\right)=\:-0.024\:\times\:\mathbf{S}\mathbf{a}\mathbf{n}\mathbf{d}+0.487\:\times\:\mathbf{C}\mathbf{l}\mathbf{a}\mathbf{y}+0.006\:\times\:\mathbf{O}\mathbf{M}+0.005\:\times\:\left(\mathbf{S}\mathbf{a}\mathbf{n}\mathbf{d}\:\times\:\mathbf{O}\mathbf{M}\right)-0.013\:\times\:\left(\mathbf{C}\mathbf{l}\mathbf{a}\mathbf{y}\:\times\:\mathbf{O}\mathbf{M}\right)+0.068$$ 2 Where “Sand”, “Clay”, and OM are expressed in percent (%). The available water content (AWC) was then calculated using Eq. 3 : $$\:\mathbf{A}\mathbf{W}\mathbf{C}\:\left(\mathbf{\%}\right)=\mathbf{F}\mathbf{C}\:\left(\mathbf{\%}\right)-\mathbf{P}\mathbf{W}\mathbf{P}\:\left(\mathbf{\%}\right)$$ 3 This empirical approach provides a reliable estimation of soil water availability when direct measurements are not feasible. Additionally, the soil WHC was determined gravimetrically by saturating 100 g of dry soil, allowing drainage for 48 h, and expressing the retained water as a percentage of dry soil weight. Gravimetric moisture content (GMC) was periodically determined by oven-drying subsamples at 105°C for 24 h to validate irrigation accuracy and maintain the target FC levels. 2.5.2. Chemical and Fertility Analyses Soil pH and EC were determined in a 1:2 soil-to-water suspension using a calibrated pH/EC meter. OM was analyzed by the Walkley–Black dichromate oxidation method, and TN was quantified using the Kjeldahl digestion method. The C/N ratio was calculated from organic carbon and TN values. The Av. P was extracted using the Olsen method and quantified colorimetrically at 880 nm. The Ex. K, Ca, Mg, and Na were extracted with 1 M ammonium acetate (pH 7.0) and determined using flame photometry (for K and Na) and atomic absorption spectrophotometry (for Ca and Mg). The CEC was measured using the ammonium acetate saturation method and expressed in cmol/kg. To provide an integrative evaluation of soil nutrient status, a SFI was calculated following a normalized scoring approach, combining key fertility attributes (OM, TN, av P, Ex K, and CEC). Each variable was normalized between 0 and 1 using the min–max method, and the overall index was computed using Eq. ( 4 ): $$\:\mathbf{S}\mathbf{F}\mathbf{I}=\:\frac{1}{\mathbf{n}}\:\times\:\:\sum\:_{\mathbf{i}=1}^{\mathbf{n}}\frac{{\mathbf{X}}_{\mathbf{i}}\:-\:{\mathbf{X}}_{\mathbf{m}\mathbf{i}\mathbf{n}}}{{\mathbf{X}}_{\mathbf{m}\mathbf{a}\mathbf{x}}-\:{\mathbf{X}}_{\mathbf{m}\mathbf{i}\mathbf{n}}}$$ 4 where “X i ​” represents the measured value of each fertility parameter, “X min ” and “X max ” are the minimum and maximum values across all treatments, and “n” is the number of fertility parameters included (n = 5). The resulting SFI ranges between 0 (lowest fertility) and 1 (highest fertility), allowing quantitative comparison of fertility improvements induced by compost addition under varying drought levels. All analyses were conducted in triplicate, and results were expressed on an oven-dry weight basis. Quality assurance was ensured through analytical blanks, internal standards, and replicate control samples. 2.6. Plant Laboratory Analyses Plant analyses were conducted to evaluate the morphological, physiological, and nutritional responses of tomato ( Lycopersicon esculentum (L.) Mill.) to compost application under varying drought stress in two contrasting soil types. Sampling was performed at the four experimental phases to assess both short-term physiological reactions and cumulative agronomic performance. Plant measurements were carried out on all replicates, and destructive sampling (biomass and nutrient analysis) was limited to designated pots within each treatment. 2.6.1. Morphological and Growth Measurements Plant height was measured from the collar to the apical meristem using a graduated ruler, while stem diameter was determined at 2 cm above the soil surface with a digital caliper. The number of fully expanded leaves per plant was recorded at each sampling phase. Leaf area was determined non-destructively using the method described by (Castro-Valdecantos et al., 2022 ), applying the following Eq. ( 5 ): $$\:\mathbf{L}\mathbf{e}\mathbf{a}\mathbf{f}\:\mathbf{A}\mathbf{r}\mathbf{e}\mathbf{a}\:\left({\mathbf{c}\mathbf{m}}^{2}\right)=\mathbf{L}\:\times\:\mathbf{W}\:\times\:\mathbf{k}$$ 5 Where “L” is the leaf length (cm), “W” is the maximum width (cm), and “k” is a correction factor equal to 0.75 for tomato leaves. The Leaf Area Index (LAI) was then calculated using Eq. ( 6 ): $$\:\mathbf{L}\mathbf{A}\mathbf{I}=\:\frac{\mathbf{T}\mathbf{o}\mathbf{t}\mathbf{a}\mathbf{l}\:\mathbf{L}\mathbf{e}\mathbf{a}\mathbf{f}\:\mathbf{A}\mathbf{r}\mathbf{e}\mathbf{a}\:\left({\mathbf{c}\mathbf{m}}^{2}\right)}{\mathbf{G}\mathbf{r}\mathbf{o}\mathbf{u}\mathbf{n}\mathbf{d}\:\mathbf{A}\mathbf{r}\mathbf{e}\mathbf{a}\:\left({\mathbf{c}\mathbf{m}}^{2}\right)}$$ 6 At harvest, plants were carefully uprooted, washed with distilled water, and separated into shoots, roots, and fruits. Fresh weights were recorded, and subsamples were oven-dried at 70°C to constant weight to determine dry biomass. Root length was measured manually from the base of the stem to the tip of the longest root, while root volume was determined by the water displacement method using a graduated cylinder, with displaced water volume (mL) expressed as root volume (cm³). Fruit yield was calculated as the total number and fresh weight of fruits per plant at harvest. 2.6.2. Physiological Parameters Leaf relative water content (RWC) was determined using Eq. ( 7 ) following the procedure of (Barrs and Weatherley, 1962 ): $$\:\mathbf{R}\mathbf{W}\mathbf{C}\:\left(\mathbf{\%}\right)=\:\frac{\mathbf{F}\mathbf{W}-\mathbf{D}\mathbf{W}}{\mathbf{T}\mathbf{W}-\mathbf{D}\mathbf{W}}\:\times\:100$$ 7 Where “FW”, “TW”, and “DW” represent fresh, turgid, and dry weights, respectively. Chlorophyll pigments (chlorophyll a, chlorophyll b, and total chlorophyll) were quantified according to (Arnon, 1949 ). Fresh leaf samples (0.2 g) were homogenized in 10 mL of 80% acetone and centrifuged. The absorbance of the supernatant was measured at 663 nm and 645 nm with a UV–Vis spectrophotometer, and chlorophyll contents were calculated using the following Equations (8)-(10): \(\:\mathbf{C}\mathbf{h}\mathbf{l}\mathbf{o}\mathbf{r}\mathbf{o}\mathbf{p}\mathbf{h}\mathbf{y}\mathbf{l}\mathbf{l}\:\mathbf{a}\:\left(\mathbf{m}\mathbf{g}/\mathbf{g}\:\mathbf{F}\mathbf{W}\right)=12.7\:\left({\mathbf{A}}_{663}\right)-2.69\:\left({\mathbf{A}}_{645}\right)\) (8) \(\:\mathbf{C}\mathbf{h}\mathbf{l}\mathbf{o}\mathbf{r}\mathbf{o}\mathbf{p}\mathbf{h}\mathbf{y}\mathbf{l}\mathbf{l}\:\mathbf{b}\:\left(\mathbf{m}\mathbf{g}/\mathbf{g}\:\mathbf{F}\mathbf{W}\right)=22.9\:\left({\mathbf{A}}_{645}\right)-4.68\:\left({\mathbf{A}}_{663}\right)\) (9) \(\:\mathbf{T}\mathbf{o}\mathbf{t}\mathbf{a}\mathbf{l}\:\mathbf{C}\mathbf{h}\mathbf{l}\mathbf{o}\mathbf{r}\mathbf{o}\mathbf{p}\mathbf{h}\mathbf{y}\mathbf{l}\mathbf{l}\:\left(\mathbf{m}\mathbf{g}/\mathbf{g}\:\mathbf{F}\mathbf{W}\right)=20.2\:\left({\mathbf{A}}_{645}\right)+8.02\:\left({\mathbf{A}}_{663}\right)\) (10) 2.6.3. Transpiration Rate The transpiration rate was estimated using the gravimetric water balance method, which provides a reliable measure of plant water use under controlled greenhouse conditions. The total water applied to each pot was recorded daily, and since leaching and surface evaporation were minimized by careful irrigation management, the cumulative water loss was considered to represent plant transpiration. The transpiration rate was calculated by Eq. ( 11 ): $$\:\mathbf{T}\mathbf{r}\mathbf{a}\mathbf{n}\mathbf{s}\mathbf{p}\mathbf{i}\mathbf{r}\mathbf{a}\mathbf{t}\mathbf{i}\mathbf{o}\mathbf{n}\:(\mathbf{m}\mathbf{L}/\mathbf{p}\mathbf{l}\mathbf{a}\mathbf{n}\mathbf{t}/\mathbf{d}\mathbf{a}\mathbf{y})=\:\frac{{\mathbf{V}}_{\mathbf{i}}-\:{\mathbf{V}}_{\mathbf{f}}}{\mathbf{A}\:\times\:\mathbf{t}}$$ 11 Where “V i ”​ and “V f ”​ are the total irrigation volumes at two consecutive weighing intervals (mL), “A” is the pot surface area (m²), and “t” is the time interval (days). 2.7. Multivariate Statistical Analysis Comprehensive statistical and multivariate analyses were carried out to evaluate the interactive effects of soil type, compost application, and irrigation regime on soil physicochemical properties and plant responses. All datasets were first screened for outliers, and missing values were handled through mean substitution when necessary. Prior to analysis, data were standardized (z-score normalization) to ensure equal weighting of variables with different measurement scales. Descriptive statistics (mean ± standard deviation) were computed for all measured parameters. A three-way analysis of variance (ANOVA) was performed to assess the main and interactive effects of soil type, compost rate, and irrigation level on each variable. Normality and homogeneity of variances were verified using the Shapiro–Wilk and Levene’s tests, respectively. When assumptions were not met, data were log- or arcsine-transformed prior to analysis. Mean separation was conducted using Tukey’s Honestly Significant Difference (HSD) test at a 5% probability level. Bivariate relationships among soil and plant variables were examined using Pearson’s correlation analysis, allowing the identification of significant linear associations between soil fertility parameters, physiological traits, and performance indices. The correlation matrix served as an input for further multivariate analyses. A Principal Component Analysis (PCA) was employed to reduce data dimensionality and identify the main components explaining variability among treatments. Variables with high loading coefficients (≥ 0.70) were considered major contributors to component variance. The PCA biplots were used to visualize associations among soil types, compost levels, irrigation regimes, and the corresponding plant responses. Complementary to PCA, a HCA using Ward’s linkage method and Euclidean distance was conducted to group treatments according to similarity in multivariate responses. The resulting dendrogram allowed classification of soil–compost–irrigation combinations into distinct performance clusters, highlighting treatments with comparable physiological or fertility characteristics. To enhance discrimination among experimental groups, LDA was performed on the standardized dataset to identify the parameters that best separated treatment categories. The discriminant functions were validated through cross-validation using the leave-one-out method, and the accuracy of group classification was quantified as the percentage of correctly assigned cases. PLSR was applied to model the relationships between predictor variables (soil fertility and physiological traits) and response variables. PLSR was chosen for its robustness in handling multicollinearity among predictors and its capacity to rank the importance of variables through the Variable Importance in Projection scores. The number of latent components was optimized based on the lowest Root Mean Square Error of Prediction (RMSEP) using a 10-fold cross-validation procedure. To account for stochastic variability and parameter uncertainty, a MCS approach was applied to the SFI parameters for both soils. Each simulation was iterated 10,000 times using random sampling from the normal distribution of input variables (mean ± SD) to estimate the probability distributions and confidence intervals of these indices under varying soil and irrigation conditions. The resulting probability density functions provided a robust probabilistic assessment of treatment performance and uncertainty propagation. All statistical analyses including ANOVA, correlation, PCA, HCA, LDA, and PLSR were conducted using XLSTAT software (Version 2024.1; Addinsoft, Paris, France), while the MCS procedures were implemented in Python (Version 3.11)(Sanad et al., 2025a , 2026a ). All quantitative results obtained in this study, including soil physicochemical properties, plant morphological and physiological traits, nutrient contents, and root parameters across treatments and growth phases, are comprehensively presented in the tables provided in the Supplementary Materials. The flow-sheet of our study is represented in Fig. 1 . 3. Results 3.1. Impact of compost amendment on soil properties under water stress All detailed values of physicochemical properties for each soil across all phases and treatment are provided in table S2, S3, S4 and S5 in the Supplementary Materials. In the sandy loam soil, compost amendment markedly modified both fertility and hydro-physical attributes across the four sampling phases, with clear differences among irrigation regimes. Across all treatments and phases, soil OM in the sandy loam ranged from 1.13% in the unfertilized control at 40% FC during the baseline phase to 2.47% under the 3% compost treatment at 80% FC during mid-drought. This pattern indicates that compost, particularly at 3%, effectively increased OM even under water deficit, and that the best expression of this effect occurred under the highest moisture level. Under 3% compost, OM was consistently highest at 80% FC in all phases, with mean values of about 2.03% at baseline, 2.09% at drought start, 2.22% at mid-drought and 2.29% at harvest, whereas the control at 40% FC systematically recorded the lowest OM values at each phase, close to 1.25–1.30%. In the sandy loam, TN varied between 0.058% (control, 40% FC, harvest) and 0.110% (3% compost, 80% FC, harvest). The combination of 3% compost and 80% FC at harvest thus provided the highest N enrichment, indicating that organic N release from compost was favored under near-optimal moisture conditions. In contrast, the lowest TN values were consistently associated with the non-amended control under 40% FC at advanced phases, revealing that both the absence of external N inputs and severe drought reduced N availability. The C/N ratio in the sandy soil remained within a relatively narrow range (8.3–15.9), suggesting that compost addition did not induce excessive N immobilization, under compost 3% at 80% FC. The Av. P and Ex. K showed a complementary pattern between compost and mineral fertilization in the sandy loam. Overall, Av.P ranged from 25.2 mg/kg (control, 40% FC, drought start) to 50.9 mg/kg (chemical fertilizer, 40% FC, drought start). At each phase, the highest mean Av. P was recorded in the chemically fertilized treatment, especially at 80% FC at baseline (46.4 mg/kg) and 40% FC at drought start and later phases (around 45–43 mg/kg), confirming the immediate solubility and availability of mineral P sources. Compost did increase Av. P relative to the unfertilized control, particularly at 3% and under 60–80% FC, but it did not reach the peaks obtained with mineral fertilizer. The Ex. K in sandy soil varied between 36.3 mg/kg (control, 40% FC, drought start) and 71.4 mg/kg (chemical fertilizer, 40% FC, drought start). As for Av.P, the chemical fertilizer at 40–60% FC consistently produced the highest Ex.K values at all phases, while compost 3% produced intermediate Ex.K levels clearly above the control but below the mineral fertilization. This indicates that in the sandy loam, compost is particularly effective for building OM and Av.N, while mineral fertilizer dominates the short-term Av.P and Ex.K enrichment. The CEC of the sandy loam, initially low, ranged from 10.4 to 15.6 cmol/kg. The largest CEC values were observed under the 3% compost treatment, particularly at 60–80% FC during mid-drought and drought start. For instance, the maximum CEC of 15.6 cmol/kg was recorded in the 3% compost treatment at 40% FC during drought start, whereas the minimum values were associated with the control at 40–60% FC. The increase in CEC with compost and adequate moisture reflects the combined effect of added organic colloids and their partial oxidation, which enhances negative charge density and the soil’s capacity to retain nutrient cations (X. Zhang et al., 2025 ). These fertility improvements are well summarized by the SFI. In sandy loam, SFI varied widely from 0.025 to 0.406. The lowest values were found in the non-amended control under 80% FC at drought start and 60% FC at harvest (SFI = 0.03), indicating that maintaining water alone was not sufficient to sustain fertility in the absence of amendments. In contrast, the highest SFI (0.406) occurred under 3% compost at 60% FC during drought start, while 3% compost at 80% FC also achieved very high SFI values across all phases. Under severe drought (40% FC), compost still improved SFI relative to the control, but its effect was attenuated compared with 60–80% FC, highlighting that organic inputs mitigate but do not fully compensate for strong water limitation. Compost also had a pronounced effect on the hydro-physical properties of the sandy loam. The FC in this soil ranged from 20.1% (chemical fertilizer, 40% FC, harvest) to 27.8% (3% compost, 80% FC, mid-drought). The AWC ranged between 9.8% (control, 60% FC, baseline) and 17.6% (3% compost, 80% FC, mid-drought), while WHC varied from 26.7% (chemical fertilizer, 60% FC, drought start and harvest) to 37.2% (3% compost, 60% FC, baseline). For nearly all phases, the 3% compost treatment under 80% FC exhibited the highest FC and AWC, for example, at baseline FC, AWC and WHC reached about 25.1%, 14.5% and 35.5%, respectively. This indicates that in sandy loam, compost improved not only chemical fertility but also the soil’s capacity to store plant-available water, particularly when combined with moderate to high irrigation levels. The modest fluctuations of FC, AWC and WHC across phases suggest that compost-induced structural improvements remained relatively stable throughout the cropping cycle, whereas the lowest water storage capacities were associated with the mineral-fertilized soil under 40–60% FC, where structure benefited less from organic inputs. In the silty clay soil, baseline fertility and water retention were intrinsically higher, and compost further reinforced these advantages under WS. At each phase, the highest OM values were consistently associated with 3% compost, typically under 40–60% FC: for instance, 2.24% at baseline (3% compost, 60% FC), 2.15% at drought start (3% compost, 60% FC), 2.44% at mid-drought (3% compost, 40% FC) and 2.47% at harvest (3% compost, 40% FC). This suggests that in the finer-textured soil, compost-derived OM is better preserved under moderate rather than maximal irrigation, probably because lower leaching and slower decomposition rates favor OM accumulation under 40–60% FC. The TN in silty clay varied between 0.067% and 0.119%.The upper range was mainly reached under 3% compost at 40–60% FC in mid-drought and harvest phases, while the lower values were recorded in the control or mineral treatments under 40% FC. The generally higher TN content compared with the sandy soil reflects the higher inherent fertility and greater capacity of silty clay to stabilize organic N. As in the sandy loam, C/N ratios remained within agronomically favorable ranges, and compost tended to maintain ratios around 10–11 in the best treatment combinations, supporting a balanced N supply under stress. The Av.P and Ex.K in the clay soil were an order of magnitude higher than in the sandy loam. Overall, Av.P were between 90.0 mg/kg (control, 40% FC, drought start) and 190.9 mg/kg (chemical fertilizer, 80% FC, harvest). Compost 3% increased Av.P relative to the control in all irrigation regimes but did not exceed the mineral fertilizer peaks, mirroring the pattern observed in sandy loam. The Ex.K ranged from 188.9 mg/kg (control, 60% FC, harvest) to 373.0 mg/kg (chemical fertilizer, 80% FC, drought start). Again, the chemically fertilized 80% FC treatment produced the highest Ex.K values, especially at early phases, whereas compost raised Ex.K to intermediate levels, confirming its role as a slower-release source of base cations rather than a short-term equivalent to mineral fertilization. The CEC of the silty clay soil was much larger than that of the sandy loam, ranging from 25.7 to 36.6 cmol/kg. The maximum CEC values were reached under 3% compost, particularly at baseline and mid-drought, confirming the synergistic effect of clay and OM on charge development. Even the control treatments exhibited relatively high CEC, reflecting the inherent buffering capacity of the clay matrix, but compost amplified this capacity, thereby enhancing nutrient retention under fluctuating moisture conditions. The SFI in silty clay reflected this high fertility background. Values ranged from 0.419 (control, 60% FC, baseline) to 0.913 (3% compost, 80% FC, mid-drought). On average, SFI was 0.65 at baseline, 0.64 at drought start, 0.66 at mid-drought and 0.61 at harvest, pointing to a generally stable but slightly decreasing fertility towards the end of the cycle, likely due to crop uptake. The highest SFI values were systematically observed under 3% compost at 40–80% FC, with a particularly pronounced peak under 3% compost and 80% FC at mid-drought (0.913) and 3% compost and 40% FC at harvest (0.80). This indicates that in the clay soil, compost not only maintains but substantially enhances an already fertile system, even under moderate water limitation. Hydro-physical properties of the silty clay soil were also strongly favorable to WS mitigation, and compost further improved them. AWC varied between 21.2% and 33.1%, and WHC ranged from 41.0% to 55.1%. The highest WHC values across phases were generally attained in the 3% compost treatment at 40–60% FC: for example, at baseline WHC reached 51.9% under 3% compost at 40% FC, and at drought start AWC peaked at 29.3% under 3% compost at 80% FC. In this context, compost acted more as a fine-tuner, further increasing the already WHC, particularly under moderate water regimes, whereas the effect of irrigation level on water retention was less pronounced than in the coarse-textured sandy loam. 3.2. Impact of Compost Amendment on Plant Responses Under Water Stress 3.2.1. Morphological and growth responses across all phases Table S6, S7, S8, S9, S10, S11 and S12 in the Supplementary Materials presents all detailed results values of plant morphological and physiological traits, nutrient contents, and root parameters across treatments and growth phases for each soil. In the sandy loam soil, plant growth trajectories showed a clear increase from baseline to harvest, with strong modulation by both compost amendment and irrigation regime. At baseline, plant height had the minimum value in the control at 60% FC (21.36 cm), and the maximum value in the chemical fertilizer treatment at 80% FC (47.47 cm). This already indicates that, even before drought imposition, mineral fertilization under optimal moisture promoted initial elongation, whereas the unfertilized substrate, especially under suboptimal FC, constrained early growth. Stem diameter at the same phase varied between 2.00 mm (chemical fertilizer at 60% FC) and 4.98 mm (compost 3% at 60% FC), showing that the 3% compost at 60% FC was already able to thicken stems more than the purely mineral treatment at this early stage. Leaf number ranged between 7 leaves (control, 40% FC) and 16 leaves (compost 3% at 60% FC). Leaf area at baseline extended from 427.2 cm² (compost 3% at 60% FC) to 841.9 cm² (compost 3% at 80% FC), and LAI from 0.475 (compost 3% at 60% FC) to 0.935 (compost 3% at 80% FC). Thus, at baseline, the largest canopy and LAI were obtained with compost 3% under 80% FC, while the lowest canopy development was associated with the unfertilized or suboptimally irrigated combinations, highlighting the positive effect of both organic inputs and adequate water on early vegetative vigor in the sandy substrate. At drought start, all morphological variables responded upward relative to baseline. Plant height in sandy loam increased to a range of 37.60–82.15 cm, with the minimum in the control at 40% FC and the maximum in compost 3% at 80% FC. Stem diameter varied from 4.32 mm (control at 80% FC) to 8.76 mm (compost 3% at 80% FC), indicating that compost 3% × 80% FC was clearly the most favorable combination for early drought-stage radial growth. Leaf number ranged between 17 and 29 leaves, with the highest value recorded in compost 3% at 60% FC and the lowest encountered across several combinations including control at 40% and 80% FC, and chemical fertilizer at 60% FC. Leaf area attended 877.7 cm² in control at 80% FC and 1501.1 cm² in compost 3% at 80% FC, while LAI ranged between 0.975 (control at 80% FC) and 1.668 (compost 3% at 80% FC). These patterns show that, once stress is initiated, compost 3% under 80% FC maximizes height, stem thickening, leaf area, and LAI, whereas unfertilized plants, especially under lower FC, quickly lag behind in vegetative expansion. During the mid-drought phase, plant height in sandy loam ranged between 63.99 cm (control at 40% FC) and 114.43 cm (chemical fertilizer at 80% FC). This indicates that in the intermediate phase, chemical fertilizer at 80% FC produced the tallest plants, while the unfertilized 40% FC combination remained the most penalized. Stem diameter attended 6.08 mm in control at 80% FC and 13.02 mm in compost 3% at 80% FC, confirming that 3% compost × 80% FC continued to be the most effective for radial growth under sustained stress. Leaf number ranged between 24 (control at 40% FC) and 41 leaves (chemical fertilizer at 60% FC), and leaf area between 1207.0 cm² (control at 80% FC) and 2189.0 cm² (compost 1% at 80% FC). LAI simultaneously varied between 1.341 (control at 80% FC) and 2.432 (compost 1% at 80% FC). Interestingly, at this stage the largest canopy (leaf area and LAI) in sandy loam was associated with compost 1% at 80% FC, while stem thickening peaked under compost 3% and plant height under mineral fertilization at 80% FC. This divergence suggests that a moderate organic rate (1%) under high FC can favor canopy development, whereas higher compost rates (3%) enhance stem robustness, and mineral fertilization fronts plant height under optimal moisture. By harvest, morphological differences among treatments became most pronounced. Plant height in sandy loam ranged between 72.02 cm (control at 40% FC) and 143.25 cm (chemical fertilizer at 80% FC). Thus, the tallest plants were obtained with mineral fertilizer at 80% FC, while the shortest corresponded to the unfertilized, severely stressed treatment. Stem diameter at harvest varied between 7.73 mm (chemical fertilizer at 60% FC) and 16.73 mm (compost 3% at 60% FC), indicating that compost 3% under 60% FC produced the thickest stems, which likely enhanced mechanical support and drought resilience. Leaf number ranged between 30 leaves (control at 40% FC) and 50 leaves, the latter achieved under both chemical fertilizer at 60% FC and compost 3% at 80% FC. Leaf area at harvest spanned 1554.9–2738.5 cm², the minimum being recorded in the control at 60% FC and the maximum in compost 1% at 80% FC. LAI ranged between 1.728 (control at 60% FC) and 3.043 (compost 1% at 80% FC). These harvest results demonstrate that severe water deficit in the absence of amendments (control 40–60% FC) strongly restricts plant size and canopy development, while combinations involving high FC and compost or chemical fertilizer at moderate to high FC foster maximal vegetative growth. In the silty clay soil, the same parameters showed higher absolute values and a somewhat different ranking of treatments, reflecting the higher intrinsic fertility and WHC of this substrate. At baseline, plant height ranged between 27.30 cm (control at 40% FC) and 44.54 cm (compost 3% at 80% FC). Stem diameter varied between 2.21 mm (control at 40% FC) and 5.43 mm (compost 1% at 80% FC). Leaf number at this phase spanned 9–17 leaves, the minimum observed in chemical fertilizer at 40% FC and the maximum across chemical fertilizer at 60–80% FC and compost 1% at 40% FC. Leaf area ranged between 507.9 cm² (compost 1% at 60% FC) and 942.8 cm² (chemical fertilizer at 60% FC). LAI similarly ranged from 0.564 (compost 1% at 60% FC) to 1.048 (chemical fertilizer at 60% FC). This implies that in clay soil, chemical fertilization at moderate FC (60%) already produced the largest canopy at baseline, whereas compost 3% at 80% FC maximized height and compost 1% at 80% FC maximized stem diameter, confirming that the clay matrix enables more balanced growth under a range of fertilization strategies. At drought start, plant height in silty clay varied from 57.28 cm (control at 60% FC) to 81.71 cm (compost 1% at 80% FC). Stem diameter ranged between 4.25 mm (control at 40% FC) and 9.63 mm (compost 1% at 80% FC), indicating that compost 1% under 80% FC was particularly efficient for early drought-stage thickening in the clay soil. Leaf number at this phase ranged between 17 leaves (control at 40% FC) and 29 leaves, with the maximum recorded under compost 3% at 40% FC. Leaf area ranged from 959.0 cm² (compost 1% at 60% FC) to 1745.1 cm² (compost 3% at 80% FC), while LAI varied between 1.066 (compost 1% at 60% FC) and 1.939 (compost 3% at 80% FC). These data show that in the clay soil, high compost rates (3%) under 80% FC already produced the largest leaf area and LAI at drought initiation, while lower compost rates (1%) at optimal FC enhanced stem thickness and plant height. The lowest values for height, leaf number and stem diameter remained consistently associated with the control at 40–60% FC, underlining the risk of insufficient fertilization even in a fertile clay context. Under mid-drought, the clay soil maintained its advantage in supporting plant growth. Plant height ranged between 78.96 cm (control at 60% FC) and 116.31 cm (compost 3% at 40% FC). Stem diameter varied from 6.44 mm (control at 40% FC) to 14.44 mm (compost 1% at 80% FC), showing that compost 1% × 80% FC yielded the thickest stems during prolonged drought. Leaf number fluctuated between 27 and 42 leaves, with the minimum shared by chemical fertilizer at 80% FC and control at 40% FC, and the maximum observed under chemical fertilizer at 40% FC and compost 1% at 60% FC. Leaf area ranged from 1351.9 cm² (compost 1% at 60% FC) to 2614.2 cm² (compost 3% at 80% FC), while LAI varied between 1.502 and 2.905 for the same treatments. Thus, under intermediate stress, compost 3% under 80% FC consistently maximized canopy area and LAI in clay soil, demonstrating a strong capacity of the system to maintain leaf expansion under combined organic inputs and adequate water. At harvest, morphological performance in silty clay was clearly superior to sandy loam. Plant height ranged from 103.09 cm (control at 40% FC) to 145.27 cm (compost 3% at 80% FC). Stem diameter varied between 7.08 mm (control at 40% FC) and 17.97 mm (compost 1% at 80% FC), confirming that compost 1% at 80% FC promoted the greatest radial growth at maturity. Leaf number ranged between 34 leaves (control at 40% FC) and a maximum of 51 leaves, reached across several treatments at moderate to high FC (chemical fertilizer 80% FC, compost 1% 60% FC, compost 3% 60% FC and even control 60% FC). Leaf area at harvest ranged from 1710.0 cm² (compost 1% at 60% FC) to 3221.5 cm² (compost 3% at 80% FC), while LAI varied between 1.90 and 3.579 with minima and maxima in the same treatments. These harvest data clearly indicate that compost 3% under 80% FC generated the largest canopy and LAI in silty clay, while compost 1% under 80% FC maximized stem diameter, and control at 40% FC consistently produced the smallest plants. 3.2.2. Physiological responses across all phases under water stress In the sandy loam soil, leaf water status and photosynthetic pigments showed a clear response to both irrigation level and amendment type along the crop cycle. At the baseline phase, RWC varied between 66.4% in the chemical fertilizer treatment at 40% FC and 97.1% in the 3% compost treatment at 80% FC. This already shows that under the same sandy texture, supplying 3% compost together with high moisture allowed leaves to approach full turgor, whereas mineral fertilization without enough water reduced hydration. At the same phase, chlorophyll a ranged from 1.085 mg/g FW (control, 80% FC) to 2.067 mg/g FW (chemical fertilizer, 60% FC), while chlorophyll b ranged between 0.769 mg/g FW (control, 80% FC) and 1.380 mg/g FW (chemical fertilizer, 60% FC). Total chlorophyll therefore spanned 1.977–3.447 mg/g FW, with the minimum under control at 80% FC and the maximum under chemical fertilizer at 60% FC. Baseline transpiration in sandy loam ranged from 60.2 ml/day in the control at 40% FC to 180.2 ml/day in compost 3% at 80% FC, confirming that combining OM with high FC strongly stimulates gas exchange from the very beginning of the cycle. At drought start, in sandy loam soil, RWC decreased slightly, with values between 64.8% in the control at 40% FC and 94.5% under compost 3% at 80% FC. The lowest RWC now clearly appears in the most stressed and unfertilized combination, while the compost 3% × 80% FC maintains the highest hydration. Chlorophyll a ranged from 0.947 mg/g FW (control at 60% FC) to 2.006 mg/g FW (compost 3% at 40% FC), chlorophyll b from 0.726 to 1.416 mg/g FW (same minimum in control 60% FC and maximum in compost 3% at 80% FC), and total chlorophyll between 1.743 mg/g FW (control, 60% FC) and 3.300 mg/g FW (compost 3% at 60% FC). This indicates that, once stress is initiated, chlorophyll degradation is most severe in the unfertilized or poorly irrigated plants, whereas both 3% compost and relatively high FC (60–80% FC) sustain pigment content. Transpiration at drought start ranged from 118.4 ml/day in the control at 40% FC to 327.7 ml/day in compost 1% at 80% FC. Interestingly, at this phase the maximum water flux is observed in the 1% compost × 80% FC combination rather than 3% compost, suggesting that a moderate organic rate under optimal FC can support very active stomatal conductance in sandy soil. During mid-drought, stress intensity increased, and this was reflected in both water status and pigment levels. RWC in sandy loam ranged from 58.3% in the 1% compost treatment at 40% FC to 91.7% under 3% compost at 80% FC. The lowest RWC at this stage thus appears not in the unfertilized control but in the low-rate compost under severe deficit, emphasizing that insufficient water can negate part of the benefit of organic inputs. Chlorophyll a at mid-drought ranged between 1.094 mg/g FW (chemical fertilizer, 60% FC) and 1.997 mg/g FW (3% compost, 60% FC), while chlorophyll b ranged from 0.723 mg/g FW to 1.307 mg/g FW; both minimum and maximum chlorophyll b values occurred in the 1% compost at 40% FC, reflecting high variability in that treatment. Total chlorophyll spanned 1.938–3.124 mg/g FW, with the minimum again in chemical fertilizer at 60% FC and the maximum in 3% compost at 60% FC. Transpiration at mid-drought ranged from 185.4 ml/day in 1% compost at 40% FC to 494.2 ml/day in 1% compost at 80% FC. These data confirm that in sandy loam, high FC strongly promotes transpiration regardless of compost level, but that 3% compost is particularly efficient in maintaining high RWC and chlorophyll under sustained stress. By harvest, physiological divergence among treatments was largest. In sandy loam, RWC ranged between 58.2% in 1% compost and 40% FC to 91.9% in 3% compost at 80% FC. Leaf hydration at harvest thus remained highest when both OM and water were abundant, while the lowest hydration consistently occurred under low moisture combined with insufficient organic supply. Chlorophyll a varied between 1.245 mg/g FW (chemical fertilizer at 60% FC) and 2.115 mg/g FW (3% compost at 60% FC), chlorophyll b between 0.749 and 1.462 mg/g FW, and total chlorophyll between 2.073 mg/g FW (chemical fertilizer at 40% FC) and 3.577 mg/g FW (3% compost at 60% FC). Transpiration at harvest ranged from 247.9 ml/day in the control at 40% FC to 655.9 ml/day in 1% compost at 80% FC. Taken together, these results show that in sandy loam, compost, especially at 3%, is crucial to maintaining RWC and chlorophyll throughout the cycle, while high FC levels (80% FC, sometimes 60% FC) are critical for sustaining transpiration and thus carbon assimilation. The combinations with low FC, particularly 1% compost at 40% FC and the control at 40% FC, repeatedly show the lowest physiological performance. In the silty clay soil, the same variables show higher absolute values and a more buffered response to stress, due to the improved water and nutrient retention of the fine-textured matrix. At baseline, RWC in clay soil ranged from 67.2% in the chemical fertilizer treatment at 40% FC to 98.0% under 3% compost at 80% FC, slightly higher than in sandy loam. Chlorophyll a at this phase varied from 1.329 mg/g FW (control, 60% FC) to 2.245 mg/g FW (3% compost, 60% FC), chlorophyll b from 0.800 to 1.636 mg/g FW, and total chlorophyll from 2.233 to 3.798 mg/g FW, with minima consistently associated with the control at 60% FC and maxima with 3% compost at 80% FC. Baseline transpiration in clay ranged from 68.2 ml/day in the control at 40% FC to 188.1 ml/day in chemical fertilizer at 80% FC, indicating slightly higher baseline gas exchange compared with the sandy soil. At drought start, RWC in silty clay ranged from 67.9% in 1% compost at 40% FC to 98.0% in 1% compost at 80% FC. This shows that, under the same clay texture, modifying FC and compost rate modulates leaf hydration, but even the minimum RWC remains slightly higher than the corresponding minima in sandy soil at the same phase. Chlorophyll a varied between 1.220 and 2.437 mg/g FW, chlorophyll b between 0.872 and 1.610 mg/g FW, and total chlorophyll between 2.091 and 4.047 mg/g FW, with all minima observed in the control at 40% FC and maxima consistently in 3% compost at 80% FC. Transpiration at drought start in clay extended from 135.3 ml/day (control, 40% FC) to 370.2 ml/day (3% compost, 80% FC), thus exceeding the average rate in sandy loam and confirming that clay soil better supports gas exchange under early stress when combined with organic inputs. During mid-drought, silty clay continued to buffer stress effects. RWC ranged from 62.9% in the chemical fertilizer treatment at 40% FC to 95.2% under 3% compost at 80% FC, compared with 76.1% in sandy loam. Chlorophyll a ranged between 1.365 and 2.372 mg/g FW, chlorophyll b between 0.828 and 1.589 mg/g FW, and total chlorophyll between 2.329 and 3.876 mg/g FW, with the lowest total chlorophyll recorded in control at 60% FC and the highest in 3% compost at 60% FC. Transpiration ranged from 222.4 ml/day (control, 40% FC) to 606.6 ml/day (3% compost, 80% FC), again higher than the 305.9 ml/day observed in sandy soil. This confirms that, under intermediate stress, 3% compost at 80% FC is particularly effective in sustaining both water flux and chlorophyll in the clay matrix. By harvest, clay soil still exhibited higher physiological resilience than sandy soil. RWC ranged between 60.0% in the control at 40% FC and 89.5% in 1% compost at 80% FC and 3% compost at 80% FC. Chlorophyll a varied between 1.162 and 2.338 mg/g FW, chlorophyll b from 0.730 to 1.616 mg/g FW, and total chlorophyll from 1.892 to 3.954 mg/g FW, with all minima associated with chemical fertilizer at 40% FC and maxima consistently in 3% compost at 80% FC. Transpiration at harvest ranged between 283.6 ml/day (control, 40% FC) and 700.0 ml/day (3% compost, 80% FC), clearly higher than the 399.9 ml/day observed in sandy loam at the same phase. 3.2.3. Leaf nutrient content across all phases under water stress In the sandy loam soil, leaf macronutrient contents showed relatively stable averages across phases but clear differences in minima and maxima according to amendment and irrigation. For leaf N, at the baseline phase values ranged between 2.20% in the control at 80% FC and 3.24% in compost 3% at 80% FC. At drought start, leaf N varied between 2.18% in chemical fertilizer at 60% FC and 3.15% in compost 3% at 60% FC. During mid-drought, the minimum was 2.21% in the control at 80% FC and the maximum 3.14% in compost 3% at 60% FC. At harvest, N ranged from 2.15% (control at 60% FC) to 3.26% (chemical fertilizer at 60% FC). Across the cycle, sandy loam leaf N therefore remained in a narrow band around 2.7–2.8%, with the highest values systematically in compost 3% or chemical fertilizer at moderate or high FC, and the lowest values mostly in unfertilized or suboptimally irrigated combinations. For leaf P in sandy loam, at baseline the minimum was 0.279% in the chemical fertilizer treatment at 80% FC and the maximum 0.406% in the control at 60% FC. At drought start, P ranged between 0.273% (control at 40% FC) and 0.390% (chemical fertilizer at 80% FC). At mid-drought, the minimum was 0.270% (control at 40% FC) and the maximum 0.404% (compost 3% at 60% FC), while at harvest values varied between 0.261% (chemical fertilizer at 80% FC) and 0.401% (compost 1% at 60% FC). These results show that leaf P was quite stable in average terms (= 0.33–0.34%), but peak values shifted between control and compost or mineral treatments depending on FC, with high P often associated with intermediate water levels where growth dilution is less pronounced. For leaf K in sandy loam, at baseline the range was 2.08–3.34%, with the minimum in the control at 80% FC and the maximum in compost 3% at 80% FC. At drought start, K ranged between 2.08% (control at 80% FC) and 3.439% (compost 3% at 40% FC). During mid-drought, the minimum was 2.123% in the control at 40% FC, the maximum 3.219% in chemical fertilizer at 40% FC. At harvest, the minimum K was 2.107% (control at 80% FC) and the maximum 3.307% (compost 3% at 40% FC). Thus, leaf K in sandy loam remained around 2.7% on average, but compost 3% and, in some phases, mineral fertilizer produced substantially higher K contents, especially under non-severe FC, while the control at 80% FC repeatedly returned the lowest K values. The Ca in sandy loam showed a similar pattern of moderate but consistent enrichment under compost. At baseline, leaf Ca ranged between 1.066% in the control at 40% FC and 1.987% in compost 3% at 40% FC. At drought start, the minimum was 1.173% (control at 80% FC) and the maximum 1.89% (compost 3% at 80% FC). During mid-drought, Ca varied between 1.133% (chemical fertilizer at 60% FC) and 1.974% (compost 1% at 40% FC), while at harvest it ranged from 1.312% in compost 3% at 40% FC to 1.932% in compost 3% at 80% FC. These values indicate that compost, particularly at 3% under 80% FC, tended to maximize Ca accumulation, whereas control and some mineral combinations under lower FC frequently gave the lowest Ca values. For leaf Mg in sandy loam, at baseline concentrations ranged between 0.345% (control at 80% FC) and 0.576% (control at 40% FC). At drought start, Mg varied between 0.397% in compost 1% at 60% FC and 0.707% in compost 3% at 60% FC. During mid-drought, Mg ranged from 0.346% (control at 60% FC) to 0.628% (compost 3% at 80% FC), at harvest, it varied between 0.388% (chemical fertilizer at 80% FC) and 0.626% (compost 3% at 40% FC). Leaf Mg thus remained relatively stable in average around 0.48%, with higher values often associated with compost 3%, and lower values with unfertilized or mineral-fertilized treatments at high FC, where greater biomass production likely diluted Mg concentration. In the silty clay soil, leaf nutrient contents were generally higher and more homogeneous, reflecting the higher buffering capacity and fertility of this substrate. For leaf N, at baseline values ranged between 2.579% in the control at 40% FC and 3.885% in compost 1% at 80% FC. At drought start, the minimum N (2.699%) occurred in the control at 60% FC, whereas the maximum (3.864%) was observed in compost 3% at 60% FC. During mid-drought, N varied between 2.669% (control at 40% FC) and 3.76% (compost 3% at 80% FC). At harvest, the minimum N was 2.65% in the control at 40% FC and the maximum 3.82% in compost 3% at 40% FC. Compared with sandy loam, leaf N in clay was consistently higher and most enhanced under compost treatments, especially 3% compost at 60–80% FC. For leaf P in silty clay, at baseline the minimum of 0.306% occurred in the control at 80% FC, and the maximum of 0.441% in compost 3% at 40% FC. At drought start, P ranged from 0.287% (control at 60% FC) to 0.428% (both compost 3% at 40% FC and control at 40% FC). At mid-drought, values varied between 0.278% in the control at 60% FC and 0.449% in compost 3% at 80% FC, at harvest they ranged from 0.311% (control at 60% FC) to 0.428% (compost 3% at 60% FC). In clay soil, leaf P thus remained slightly higher than in sandy loam on average, with peaks consistently reached in compost 3% under moderate or high FC. Leaf K in silty clay was also higher than in sandy loam. At baseline, values ranged from 2.537% in the chemical fertilizer at 80% FC to 3.623% in the control at 80% FC. At drought start, K varied between 2.717% (compost 3% at 80% FC) and 3.606% (control at 60% FC). During mid-drought, the minimum value 2.574% occurred in chemical fertilizer at 80% FC, while the maximum 3.473% was observed in compost 3% at 60% FC. At harvest, K ranged between 2.227% (compost 3% at 80% FC) and 3.525% (compost 3% at 60% FC). These results confirm that silty clay maintained high K levels across treatments and phases, but 3% compost, particularly at 60% FC, was most effective in maximizing foliar K at later stages. For leaf Ca in silty clay, at baseline the minimum of 1.484% occurred in chemical fertilizer at 80% FC, while the maximum of 1.982% was in chemical fertilizer at 60% FC. At drought start, Ca ranged from 1.302% (compost 1% at 40% FC) to 1.973% (compost 1% at 80% FC). During mid-drought, the minimum was 1.225% (compost 1% at 40% FC) and the maximum 2.03% (chemical fertilizer at 80% FC). At harvest, Ca ranged between 1.421% in the control at 40% FC and 1.973% in compost 3% at 80% FC. Thus, the clay soil allowed higher Ca contents than the sandy soil, with peaks occurring in either mineral or compost treatments depending on phase, but the lowest Ca values consistently associated with low FC and low or moderate compost rates. Finally, leaf Mg in silty clay showed moderately higher average values than in sandy loam. At baseline, Mg ranged between 0.366% and 0.685%, both minima and maxima occurring in the control at 40% FC due to within-treatment variability. At drought start, Mg varied between 0.419% (control at 40% FC) and 0.637% (compost 1% at 80% FC). During mid-drought, Mg ranged from 0.415% to 0.635%, both extremes in the control at 40% FC. At harvest, the minimum Mg was 0.393% in compost 1% at 80% FC, the maximum 0.634% in compost 3% at 80% FC. Compared with sandy loam, leaf Mg in clay was slightly higher and more stable, with compost 3% at 80% FC tending to produce the highest concentrations at the end of the cycle. 3.2.4. Plant morphological, root, and yield responses at harvest under water stress The harvest stage revealed the cumulative effects of soil texture, compost amendment, and irrigation regime on above and below ground biomass allocation and fruit productivity. In the sandy loam soil, shoot biomass exhibited substantial variability, ranging between 70.92 g in the compost 3% treatment at 40% FC and 152.63 g under the control at 80% FC. The lowest shoot biomass in compost 3% at severe deficit suggests that high OM cannot compensate for limited water availability in coarse-textured soils. Conversely, the highest shoot mass in the unfertilized 80% FC treatment reflects the strong influence of adequate moisture on above-ground development despite the absence of nutrient inputs, indicating that moisture was a stronger limiting factor than fertility for shoot biomass at harvest in sandy loam. Root dry weight in sandy loam ranged between 12.00 g in the 3% compost treatment at 80% FC and 41.17 g in the chemical fertilizer treatment at 60% FC. This pattern suggests that mineral fertilization under moderate irrigation promotes more robust root mass compared with high compost rates under high FC, where plants tended to allocate fewer resources below ground, likely due to reduced need for root exploration when nutrient and water availability were ample. Root length varied between 37.60 cm in the control at 60% FC and 94.50 cm in the compost 1% treatment at 40% FC, revealing that moderate compost doses under WS incentivize deeper or more extensive root systems. Root volume ranged from 11.68 cm³ in compost 3% at 40% FC to 38.77 cm³ under compost 3% at 60% FC, confirming that compost enhances root structural development, particularly when sufficient water (60% FC) is available. Fruit yield in sandy loam showed the greatest dispersion, ranging from 523 g in the control at 60% FC to 2066 g in compost 3% at 80% FC. The extremely high yield under compost 3% × 80% FC demonstrates the synergistic effect of OM and adequate water supply on reproductive performance. The lowest yield under control with 60% FC underscores that in coarse soils, moisture deficit combined with nutrient scarcity severely limits fruit production. These patterns highlight that in sandy loam, compost amendments and high FC not only improved root and shoot development but were especially effective in enhancing fruit productivity, with compost 3% at 80% FC providing the optimal harvest performance across all measured parameters. In the silty clay soil, biomass and yield values were higher overall, reflecting the greater fertility and WHC of the fine-textured matrix. Shoot dry weight ranged from 74.85 g under compost 1% at 60% FC to 195.60 g in compost 3% at 80% FC. Contrary to sandy loam, the maximum shoot biomass in clay occurred under the highest compost and FC combination, indicating that the clay matrix can fully exploit the added OM to support above-ground growth. Root dry weight varied between 15.69 g in the compost 1% under 40% FC and 52.52 g under compost 3% at 80% FC, confirming that compost 3% under high FC conditions maximized root biomass in clay soil. Root length values ranged between 55.80 cm (chemical fertilizer, 40% FC) and 105.60 cm (the control at 80% FC). Interestingly, the longest roots were observed in the unfertilized but well-irrigated treatment, suggesting that in clay soils, high moisture availability alone can drive extensive root elongation independently of amendment rates. Root volume varied between 12.84 cm³ (control at 40% FC) and 41.55 cm³ (compost 3% at 80% FC), showing that compost 3% × 80% FC produced the most structurally developed root systems. Fruit yield in silty clay ranged between 553 g in the control at 80% FC and 1322 g under chemical fertilizer at 80% FC. Unlike sandy loam, the highest yield in clay soil occurred with mineral fertilization rather than compost, suggesting that nutrient release dynamics interact differently with this soil’s physicochemical properties. The lowest yield did not occur under the most severe stress (40% FC) but under control with 80% FC, indicating that while clay soils buffer WS effectively, nutrient limitations can still restrict reproductive output even when moisture is abundant. 3.3. Multivariate Statistical Analysis 3.3.1. Correlation Structure Among Soil Properties, Plant Functional Traits, and Yield Components The correlation analysis revealed a highly structured set of relationships linking soil physicochemical properties, plant morphological and physiological traits, nutrient status, and final biomass and yield components ( Fig. 2 ) . Together, these patterns demonstrate how soil quality, water availability, and amendment induced changes cascade through the plant system to influence performance under WS. The soil fertility indicators showed strong positive associations with growth and yield parameters, indicating that nutrient-rich soils provide a physiological advantage throughout plant development. Exchangeable cations such as Ca, Mg, and K were positively correlated with plant height, leaf area, and LAI, with coefficients generally exceeding r > 0.60, reflecting their essential role in turgor regulation, stomatal function, and chloroplast stability. The soil CEC, which integrates clay content and OM quality, displayed some of the strongest correlations with vegetative growth, particularly with leaf area and stem diameter (up to r = 0.70–0.85, p < 0.001). This suggests that soils capable of retaining and releasing nutrients steadily throughout the drought period provided plants with a more buffered supply of essential ions, reducing the physiological stress associated with declining soil moisture. Water-related soil parameters, including FC, AWC, and WHC, were strongly linked to both physiological performance and yield outcomes. AWC in particular demonstrated high correlations with chlorophyll content and RWC, often exceeding r = 0.75, indicating that soils capable of retaining more plant-available water enabled the maintenance of cellular hydration and chloroplast function under drought. The correlation between AWC and fruit yield was also robust (r ≈ 0.70–0.80), highlighting that water availability throughout the reproductive stage was a primary determinant of fruit filling and final productivity. This pattern was reinforced by the strong positive association between WHC and root length, showing that soils with greater moisture retention stimulated deeper or more expansive root systems, which enhanced the plant’s capacity to acquire water under increasing deficit. Physiological traits were tightly interlinked with biomass accumulation and yield, underscoring their role as sensitive integrators of plant stress and resource availability. RWC demonstrated one of the strongest correlations with fruit yield (r = 0.80–0.90, p < 0.001), emphasizing that maintaining internal water status under drought was a key determinant of reproductive success. Similarly, chlorophyll a, chlorophyll b, and total chlorophyll were positively correlated with shoot biomass and fruit yield, with coefficients frequently above r = 0.70, indicating that photosynthetic capacity under stress directly influenced carbon assimilation and allocation. The strong correlation between RWC and chlorophyll indices (r > 0.80) demonstrates the physiological co-regulation of water status and pigment stability, plants capable of sustaining hydration were also capable of maintaining chlorophyll integrity, resulting in more sustained photosynthetic activity. Nutrient content in leaves revealed additional insights into yield drivers under WS. Leaf N and K levels were positively correlated with fruit yield (r = 0.60–0.75), consistent with their roles in protein synthesis, osmotic adjustment, and stomatal control. Leaf Ca and Mg showed moderate to strong correlations with both root volume and shoot biomass, often above r = 0.65, supporting their importance in cell wall stability, membrane integrity, and enzyme activation during stress (Xie et al., 2024 ). These patterns suggest that nutrient uptake was not only influenced by soil supply but also by root system development, which in turn was shaped by moisture availability and soil structure. Yield was positively associated with nearly all growth and physiological variables, confirming that productive plants were those that maintained both structural development and physiological function throughout drought progression. Fruit yield displayed particularly strong correlations with leaf area, LAI, chlorophyll content, and RWC, typically exceeding r = 0.80, highlighting that well-developed canopies and the maintenance of photosynthetic pigments were essential for assimilate production during fruit filling. The correlations between root traits and yield, especially root volume and root length, underscore the pivotal role of root system architecture in supporting water and nutrient uptake, with coefficients ranging from r = 0.55–0.70. These findings corroborate the view that deep or voluminous roots mitigate the negative impacts of water deficit by facilitating access to subsurface moisture and enabling the continued supply of essential ions to the shoot. The combined correlation structure reveals a coherent mechanism: soils with higher fertility and greater water retention promoted stronger root systems, which in turn supported improved physiological resilience, leading to enhanced vegetative development and ultimately higher fruit yield. WS markers such as declining RWC and chlorophyll degradation were tightly linked with reductions in biomass and reproductive output, confirming their utility as integrative indicators of drought severity. At the same time, nutrient-linked variables such as leaf K, Ca, and N demonstrated that mineral nutrition remained a central component of drought tolerance by reinforcing osmotic stability, metabolic activity, and tissue integrity. 3.3.2. PCA of Integrated Soil–Plant–Yield Relationships at Harvest. The PCA of the harvest dataset revealed a well-structured multivariate pattern linking soil physicochemical properties, plant functional traits, root system development, and final yield ( Fig. 3 ) . The first two principal components explained approximately 81.67% of the overall variance, with PC1 accounting for 57.33% and PC2 contributing 24.34%. Together, these components provide a clear dimensional reduction that captures the dominant gradients shaping tomato performance under differing soil types, organic amendment levels, and irrigation regimes. PC1 represented the major productivity and resource-status axis, with strong positive loadings from biomass and yield-related traits including Shoot Biomass, Root Biomass, Root Volume, Root Length, and Yield, as well as plant functional parameters such as Leaf Area, LAI, Plant Height, and physiological variables (RWC, Chlorophyll a, Chlorophyll b, Chlorophyll total). Leaf nutrient concentrations, particularly Leaf N, Leaf K, and Leaf Ca, also loaded positively on PC1, indicating that nutrient assimilation and physiological resilience co-varied with biomass accumulation at harvest. Soil water-related properties including AWC, WHC, and FC displayed positive associations with PC1, which reflects the fundamental influence of soil moisture retention in supporting plant water status, photosynthetic function, and carbon allocation to yield. Conversely, negative loadings on PC1 were associated with soil parameters indicative of weaker fertility or structure, such as lower OM, CEC, and reduced macro-nutrient availability, showing that nutrient-poor soil profiles clustered toward the negative dimension of PC1. The strong coupling between physiological variables and biomass traits along PC1 highlights that RWC and chlorophyll stability were key determinants of yield outcomes. Their high loadings suggest that plants maintaining hydration and photosynthetic pigment concentration under stress were able to sustain assimilate production, which translated directly into higher fruit biomass. The joint positioning of root and shoot traits on the positive PC1 axis indicates that vigorous root systems enhanced uptake of both water and nutrients, improving canopy development and thereby boosting yield. PC2 captured a secondary but meaningful axis related to soil physicochemical variation and its influence on plant nutrient status. Variables such as soil pH, EC, Mg, Ca, and to a lesser extent, Na contributed to variation along this axis. While these did not strongly influence yield relative to PC1, they describe an orthogonal gradient related to inherent soil mineral composition and salinity-related properties. Leaf nutrient variables including Leaf Mg and Leaf P also showed moderate loadings on PC2, suggesting that specific nutrient dynamics independent of global biomass and yield processes contributed to treatment differentiation. The spatial distribution of observations in the PCA biplot demonstrates that treatments receiving 3% compost under 80% FC clustered along the positive extremes of PC1, consistent with the high biomass and yield values observed in univariate analyses. Treatments under 40% FC, especially in the control and chemical fertilizer groups, tended to cluster on the negative side of PC1, reflecting reduced physiological performance, compromised nutrient uptake, and limited biomass allocation under severe water deficit. Intermediate irrigation treatments (60% FC) occupied mid-range positions along PC1, with compost-amended samples shifting toward the positive axis relative to mineral and unfertilized treatments. Soil-type separation was also evident, with silty clay soils generally positioned toward the positive directions of both PC1 and PC2 owing to their greater water retention and nutrient-buffering capacity, while sandy loam treatments populated the lower-loading axes, reflecting limited structural and fertility attributes associated with coarse textures. 3.3.3. HCA of Integrated Soil–Plant–Yield Relationships at Harvest The hierarchical cluster analysis revealed a highly structured multivariate organization reflecting the combined influence of soil quality, compost amendment, irrigation regime, and plant physiological functioning on biomass and yield outcomes ( Fig. 4 ) . The dendrogram distinguished two major sample clusters representing clear contrasts in plant performance and environmental conditions at harvest. The first large cluster consisted predominantly of samples originating from silty clay soils under moderate to high moisture levels (60–80% FC) and especially those receiving 3% compost amendment. These samples grouped together because they expressed consistently elevated values for physiological indicators such as RWC and total chlorophyll, and for structural and functional parameters such as leaf area, LAI, plant height, and shoot biomass. Their shared position in the dendrogram demonstrates that heavy-textured soils with superior water-retention capacity supported more stable physiological functioning, enabling plants to maintain hydration, preserve photosynthetic pigments, and allocate substantial biomass to both root and shoot systems. This cluster also included nearly all high-yielding observations, indicating that the multivariate signatures of hydraulic stability, nutrient availability, and robust canopy development were the dominant determinants of fruit yield. The second major cluster comprised mainly sandy soil samples, which segregated clearly due to their lower soil water-holding characteristics, reduced nutrient-retention capacity, and the associated decline in physiological stability. Within this cluster, treatments subjected to 40% FC consistently grouped together, regardless of amendment type, underscoring the overarching influence of severe WS in driving multivariate dissimilarity. These samples were characterized by low RWC, lower chlorophyll concentrations, smaller leaf area, and reduced shoot and root biomass. Their grouping in the dendrogram is consistent with the physiological distress caused by limited soil water availability, leading to impaired photosynthetic function and restricted assimilate allocation. Control and mineral fertilizer treatments under sandy soils further consolidated within this cluster, reflecting the insufficient nutrient-buffering capacity of low OM soils when not supplemented with compost. The parameter-based clustering from the heatmap revealed biologically coherent patterns that aligned with known soil–plant interactions under WS ( Fig. 5 ) . A tightly grouped cluster of biomass and yield parameters (Shoot biomass, Root biomass, Root Length, Root Volume, Yield) emerged alongside canopy development traits (Leaf area, LAI, Plant height ) , indicating that structural growth and carbon allocation are strongly co-regulated. These variables also clustered close to physiological indicators such as RWC and total chlorophyll, confirming that plants maintaining hydration and photosynthetic pigments were those capable of sustaining growth and fruit filling. This parameter cluster also associated strongly with soil hydraulic indicators (AWC, WHC, FC), demonstrating that soil moisture dynamics influenced plant performance at multiple levels, from water relations to biomass formation. A separate soil-chemistry cluster included variables such as soil Ca, Mg, K, CEC, and OM, reflecting the fertility gradient between silty clay and sandy soils. These parameters clustered away from stress indicators and biomass traits, indicating that the chemical richness and cation exchange properties of soils are foundational attributes that indirectly shape plant functioning by determining nutrient supply. Leaf nutrient concentrations also formed a coherent group, reflecting synchronized nutrient uptake processes and their dependence on root function and soil fertility. Their intermediate position between the soil-fertility cluster and physiological parameters suggests their mediating role in linking soil chemistry to physiological resilience under water deficit. 3.3.4. LDA of Treatment Classes Based on Integrated Soil–Plant–Yield Variables at Harvest The LDA applied to the combined harvest dataset (soil, plant, physiological, nutrient, biomass, and yield variables) produced a clear multivariate discrimination of the experimental groups defined by soil type, compost amendment, and irrigation level ( Fig. 6 ) . The first two discriminant functions (LD1 and LD2) captured the main structure of between group variance and provided an efficient separation of treatment categories in the reduced LD1–LD2 space. Overall, the leave-one-out cross-validation (LOOCV) procedure yielded a classification accuracy of 80.2%, indicating that the selected variables possess a strong discriminatory power to distinguish among the 24 soil–amendment–irrigation combinations. LD1 represented the dominant discrimination axis, contrasting highly productive, physiologically stable treatments with those characterized by WS and suboptimal resource status. High positive coefficients on LD1 were associated with biomass and yield variables and with canopy development traits such as Leaf area, LAI, and Plant height, while physiological variables including RWC, Chlorophyll a, Chlorophyll b, and Chlorophyll total also contributed positively. Leaf nutrient variables, in particular Leaf N and Leaf K, loaded positively on LD1, reinforcing their role in supporting growth and yield under favorable conditions. Soil hydrological properties (AWC, WHC, FC) and fertility indicators (CEC, Ex Ca, K, OM) also showed positive loadings, confirming that soils with higher water- and nutrient-retention capacity underpinned the high LD1 scores. Negative loadings on LD1 were linked to combinations of low water availability and poorer soil structure, corresponding to stressed, low-yielding treatments. LD2 captured a secondary gradient related more to soil-type specific properties and the balance between root allocation and above-ground performance. Some soil chemical variables such as pH, EC, Na, and specific cation patterns showed contrasting contributions on LD2, together with more moderate loadings from leaf nutrients like Leaf Mg and Leaf P. This indicates that LD2 separated treatments according to subtler differences in mineral composition and rooting strategies, rather than overall productivity. Together, LD1 and LD2 provided a two-dimensional representation in which high-yielding, well-irrigated treatments were clearly separated from low yielding, water stressed ones, while also distinguishing the influence of soil texture and amendment strategy. In the LD1–LD2 scatterplot, treatments on silty clay clustered mainly on the positive side of LD1, especially those receiving 3% compost at 60% and 80% FC. These groups combined high soil water retention, strong physiological performance, and high biomass and yield values, reflecting their position as optimal or near-optimal management combinations. Conversely, sandy loam treatments, particularly under 40% FC and in control or mineral fertilizer plots, occupied the negative extremities of LD1. Their position corresponded to low RWC, reduced chlorophyll content, limited root and shoot biomass, and poor yield, highlighting the combined effect of coarse texture and water deficit. Intermediate treatments, such as compost 1% or 60% FC combinations, occupied mid-range positions along LD1, reflecting transitional performance between stressed and optimal conditions. The classification table derived from LOOCV shows that most misclassifications occurred among treatments with similar irrigation and amendment levels within the same soil type, underlining that treatments with comparable soil water status and amendment rate tend to converge in their multivariate response. In contrast, confusion between highly contrasting groups (SC–C3–80% FC versus SL–C0–40% FC) was minimal, confirming that the combined soil–plant variable set captured the major differences between favorable and limiting environments. The relatively high overall classification accuracy (around 80%) and the clear separation of clusters in LD space confirm that integrating soil physicochemical data with plant functional traits, biomass, and yield provides a robust basis for discriminating management scenarios under water stress. 3.4. PLSR Analysis 3.4.1. PLSR of Soil–Plant–Yield Relationships in Sandy Loam Soils at Harvest The PLSR analysis performed on the harvest-phase dataset for sandy loam soil revealed a clear multivariate gradient linking soil fertility and water-retention properties with plant physiological performance, structural growth, and final biomass–yield outcomes ( Fig. 7 a ) . The first two latent components (T1 and T2) captured the major axes of variation, with latent variable 1 (LV1) explaining approximately 34.1% of the variance and latent variable 2 (LV2) explaining about 8.9%. LV1 represented the primary productivity and water–nutrient status axis. Positive LV1 loadings were strongly associated with soil moisture-related variables, particularly AWC, WHC, and FC, reflecting the pivotal role of water retention capacity in coarse-textured soils. These soil parameters clustered closely with key physiological attributes such as RWC and chlorophyll parameter, indicating that sandy-loam treatments benefiting from higher moisture retention maintained superior hydration and chlorophyll stability during the drought cycle. The alignment of these variables with structural growth metrics (Leaf area, LAI, Plant height) and biomass traits (Shoot biomass, Root biomass, Root Volume) demonstrates that LV1 effectively captures the continuum from stressed, low-performing treatments to highly productive, physiologically stable plants. Fruit yield aligned positively with LV1, confirming that this axis represents the integrative eco-physiological gradient driving harvest performance in sandy loam. Conversely, negative LV1 associations were linked to lower soil fertility indicators (e.g., reduced OM, CEC, and base cation availability) and diminished physiological performance. Treatments with strong negative T1 scores corresponded to combinations involving 40% FC and minimal amendment (control or chemical fertilizer), where reduced water availability and the poor nutrient-buffering capacity of sandy soil restricted plant function. These treatments exhibited low canopy development, reduced root biomass, and limited assimilate allocation to fruits traits captured clearly by their negative projections on LV1. LV2 captured a secondary differentiation among treatments, primarily reflecting contrasts in physiological adjustments and specific soil chemical signatures rather than broad productivity trends. Variables such as soil EC, Na, Mg, and certain leaf nutrient concentrations contributed more strongly to LV2 than to LV1. The orientation of these variables suggests that LV2 identifies subtle metabolic adjustments or nutrient imbalances under particular amendment–irrigation combinations. While LV2 explained less variance than LV1, it nonetheless helped discriminate between intermediate treatments (e.g., compost 1% at 60% FC vs chemical fertilizer at 80% FC), where plants displayed moderate physiological stability but differed in nutrient assimilation patterns. The T1–T2 score plot clearly illustrated treatment separation across sandy-loam environments. Treatments receiving 3% compost at 80% FC achieved the highest positive scores along LV1, indicating that enhanced water retention and nutrient enrichment provided by compost substantially improved plant physiology and yield potential even in coarse-textured soils. Moderate irrigation (60% FC) combined with compost amendments positioned treatments in the central-to-positive region of LV1, reflecting partial alleviation of moisture stress. By contrast, 40% FC treatments clustered on the negative side of LV1, regardless of amendment type, demonstrating that soil moisture limitation remained the dominant constraint in sandy soils. The most stressed groups control and chemical fertilizer under 40% FC fell in the lower-left quadrant of the T1–T2 space, where both LV1 (productivity gradient) and LV2 (nutrient imbalance axis) indicated severe eco-physiological strain. 3.4.2. PLSR of Soil–Plant–Yield Relationships in Silty Clay Soil at Harvest The PLSR for silty clay soil revealed a distinct multivariate structure that differed markedly from the sandy-loam system, reflecting the inherently superior hydraulic and nutrient-buffering properties of fine-textured soils ( Fig. 7 b ) . The LV1, which explained approximately 23.7% of the predictor variance, captured the principal productivity and water-regulation gradient influencing tomato performance in silty clay soil. LV1 loadings were dominated by hydrological and fertility-related predictors such as AWC, WHC, FC, CEC, Ca, and OM, confirming that even in a moisture-conserving soil, water retention and CEC remained key drivers of plant functioning. These soil variables were closely aligned with physiological traits including RWC, and chlorophylls containt, indicating that silty clay soils favored the maintenance of water status and photosynthetic stability throughout the drought cycle. On the response side, strong positive LV1 contributions were observed for Shoot biomass, Root biomass, Root volume, LAI, and Fruit yield, demonstrating that LV1 represents the axis of integrated physiological resilience, structural growth, and yield formation. Negative LV1 scores were associated with treatments exhibiting lower physiological stability or reduced biomass allocation, although these cases were far less extreme than those observed in sandy soils. Even under 40% FC, silty clay treatments did not cluster strongly on the negative LV1 side, suggesting that soil texture buffered plants from severe WS. Instead, negative LV1 associations reflected relatively moderate declines in canopy expansion, root elongation, and chlorophyll content under low-irrigation conditions, highlighting the soil’s capacity to mitigate drought intensity through improved water storage. The LV2 explaining approximately 11.5% of predictor variance, captured a secondary gradient related to nutrient assimilation strategies and subtle shifts in eco-physiological balance. LV2 was characterized by higher loadings for Mg, Na, EC, and leaf nutrient concentrations such as Leaf Mg and Leaf P. These variables indicated that LV2 differentiated treatments not by overall productivity but by nutrient-specific influences on physiological functioning and biomass distribution. LV2 also distinguished treatments receiving higher compost rates at 60–80% FC from those under mineral fertilizer, suggesting that organic inputs modulated nutrient uptake patterns and physiological adjustments through improved soil organic structure and cation balance. The score plot of T1 versus T2 demonstrated clear segregation of treatment combinations. High-performing treatments (particularly 3% compost under 80% FC and 60% FC) clustered on the positive side of LV1, indicating strong soil–physiology–yield linkages and optimal water availability. These treatments formed tightly grouped clusters, reflecting consistent performance across replicates and confirming that silty clay provides a highly stable environment for tomato growth. Intermediate treatments (e.g., compost 1% under 60% or 80% FC) occupied the central region of LV1, indicating moderate but sustained physiological stability. Even the lowest-performing groups (40% FC under control or mineral fertilizer) remained closer to the origin rather than occupying extreme negative LV1 values, reinforcing the soil’s inherent buffering capacity. The greater spread along LV2 compared with sandy soil suggests that nutrient-driven physiological adjustments played a more prominent role in silty clay, where water availability was less limiting and nutrient-driven differentiation became more apparent. 3.4.3. Comparative Interpretation of PLSR in Sandy and Silty Soils The contrast between sandy loam and silty clay PLSR structures highlights the crucial role of soil texture in mediating drought responses and determining the multivariate architecture of plant performance. In sandy loam, LV1 represented a steep gradient of WS, where inadequate moisture retention and low fertility drove strong negative projections for low-irrigation and unamended treatments. Conversely, in silty clay, LV1 captured a more moderated gradient, with even low-irrigation treatments maintaining physiological function due to the soil’s higher AWC, WHC, and CEC. As a result, the multivariate separation between stressed and high-performing treatments was much sharper in sandy loam and more compact in silty clay. Key physiological variables such as RWC and chlorophyll indices aligned positively with LV1 in both soils, but in sandy loam their loadings were stronger and more directly tied to yield, highlighting their sensitivity to water limitation. In silty clay, the same variables contributed positively to LV1, but their influence was more moderated by soil fertility and structural properties rather than by water scarcity alone. This distinction demonstrates that in sandy soils, physiological collapse under drought is the dominant mechanism limiting yield, whereas in silty soils, nutrient buffering and moderated moisture release shape the physiological state. Yield drivers also differed between soils. In sandy loam, yield was tightly linked with soil water properties (AWC, WHC) and chlorophyll maintenance, forming a strong LV1 association with compost and irrigation synergy. In silty clay, yield aligned with a more complex combination of water retention, nutrient availability, and physiological stability, indicating that no single constraint dominated performance. Compost in sandy soils primarily improved water availability, whereas in silty soils it augmented nutrient cycling and structural growth. These differences explain why sandy soils showed large treatment separation along both LV1 and LV2, while silty soils displayed more clustered and stable patterns. 3.5. MCS of the SFI Under Silty Clay and Sandy Loam Conditions 3.5.1. MCS of SFI in Sandy Loam soil at Harvest The MCS revealed a clear and consistent probabilistic hierarchy among the SFI outcomes in sandy loam soil at harvest ( Fig. 8 a ) . The distribution of simulated values for each amendment–irrigation combination reflected the underlying differences in soil fertility and moisture availability, enabling a robust evaluation of uncertainty and treatment performance beyond point estimates. Across all treatments, simulated SFI values ranged from a minimum of approximately 0.03 (Control × 60% FC) to a maximum close of 0.42 (Compost 3% × 80% FC). These extreme values reflect the strong contrast between treatments exhibiting minimal fertility support and those benefiting from enhanced organic inputs combined with near-optimal water availability. The ordering of simulated SFI distributions followed a logical fertility gradient: Control < Chemical fertilizer < Compost 1% < Compost 3%, and within each amendment class, 40% FC < 60% FC < 80% FC. This ordering emerged consistently across the means, medians, 95% confidence intervals, and the shape of the distributions produced by the Monte Carlo iterations. Treatments receiving 3% compost exhibited the highest simulated distributions across all moisture regimes, particularly under 80% FC, where mean SFI values approached 0.33–0.35 with upper simulated extremes exceeding 0.40. The 95% confidence interval for this treatment was the widest among all groups, reflecting both the high fertility potential and the natural variability induced by enhanced microbial and nutrient cycling processes in organic-rich sandy soil (Liu et al., 2025 ). Importantly, there was minimal overlap between the confidence intervals of Compost 3% with 80% FC and any other treatment category, indicating a statistically and ecologically meaningful superiority in fertility performance. Intermediate treatments such as Compost 1% with 80% FC and Compost 3% with 60% FC formed the next tier in the probability ranking. Their simulated mean SFI values clustered within the 0.23–0.30 range, with confidence intervals that partially overlapped with each other but remained distinctly above those of chemical fertilizer treatments. This pattern suggests that even moderate organic amendment levels strongly improve fertility in coarse-textured soils, especially under sufficient irrigation (Liu et al., 2025 ). Chemical fertilizer treatments occupied an intermediate but lower probability space relative to the compost treatments. Their simulated distributions rarely exceeded 0.20 and exhibited narrower confidence intervals, indicating stable but limited fertility contributions. The lack of OM addition restricted improvements in water retention, CEC, and microbial-mediated nutrient release, all of which are critical for sustaining fertility in sandy soils (Yang et al., 2024 ). Control treatments consistently produced the lowest SFI distributions, with mean simulated values between 0.06 and 0.10 depending on irrigation level. Their confidence intervals frequently overlapped with those of chemical fertilizer at 40% FC, reflecting minimal improvement in soil fertility under moisture deficit. The lowest simulated values (down to − 0.03) were restricted exclusively to the control treatments, demonstrating the high susceptibility of sandy soils to fertility decline in the absence of organic inputs. The superior performance of the Compost 3% with 80% FC treatment can be explained by the synergistic interaction between OM enrichment and high soil moisture availability. In sandy soils, compost additions substantially enhance WHC, AWC, CEC, and nutrient retention, all of which directly feed into the SFI calculation. At 80% FC, these compost-mediated improvements are fully expressed because increased moisture enhances nutrient diffusion, microbial activity, and root uptake efficiency. OM decomposition is also accelerated under adequate moisture, releasing mineral nutrients and promoting aggregate stability. The MCS by sampling from the observed variation in SFI, therefore captures both the deterministic fertility improvements provided by compost and the stochastic components associated with biological and environmental variability, leading to a higher and broader distribution of simulated SFI values. 3.5.2. MCS of SFI in Silty Clay Soil at Harvest The MCS conducted for the silty clay soil revealed a distinct probability structure of SFI values that reflects the intrinsic fertility advantages of fine-textured soils relative to sandy loam ( Fig. 8 a ) . Simulated SFI values ranged from a minimum of approximately 0.38 (Control × 40% FC) to a maximum exceeding 0.92 (Compost 3% × 80% FC), demonstrating substantially higher fertility potential and lower risk of fertility collapse under constrained moisture conditions. Unlike sandy soils, where low compost and low irrigation levels produced negative or near-zero simulated SFI values, the silty clay system maintained all simulated SFI values within a moderate to high fertility range, reflecting the strong buffering capacity of this soil against nutrient and moisture stress. A clear probabilistic hierarchy emerged from the simulation results. Compost 3% with 80% FC, produced the highest simulated mean SFI (= 0.78–0.82) and the widest distribution envelope, with a 95% confidence interval extending approximately from 0.69 to 0.92. This distribution showed minimal overlap with all other treatments, indicating a statistically dominant fertility performance. The presence of outliers reaching values above 0.95 in the simulated distribution reflects the synergistic effects of high OM input and near-optimal soil moisture on nutrient mineralization, cation-exchange enhancement, and structural aggregation in the fine-textured matrix. The second tier of treatments included Compost 3% with 60% FC, Compost 1% with 80% FC, and Compost 1% with 60% FC, with simulated mean SFI values ranging between approximately 0.61 and 0.75. Their confidence intervals partially overlapped with each other but remained distinctly above the ranges associated with mineral fertilizer treatments. These results support the strong contribution of compost amendments to soil structure, base saturation, and organic nutrient release, particularly under high moisture availability. Chemical fertilizer treatments ranked below compost treatments but above the control group. Simulated mean SFI values for chemical fertilizer treatments ranged from 0.54 to 0.63, with narrow confidence intervals that reflect consistent but modest fertility improvements. The absence of organic inputs limited their effect on moisture retention and CEC, which are critical structural fertility components in silty clay soils. Control treatments occupied the lowest position in the probabilistic ranking, with simulated mean SFI values between 0.46 and 0.49, and the broadest uncertainty among low-performing treatments. Their 95% confidence interval ranged from approximately 0.40 to 0.52, showing substantial overlap across all irrigation levels. Even so, their simulated distributions remained significantly higher than the corresponding control treatments in sandy loam, highlighting the inherent fertility advantage provided by the clay-rich, organic-buffering matrix of silty clay soils. 3.5.3. Comparative Interpretation between Sandy Loam and Silty Clay based Monte Carlo Outputs The MCS comparison between sandy loam and silty clay underscores the dominant influence of soil texture on the probabilistic behavior of soil fertility. In sandy loam, simulated SFI values ranged from negative values to a maximum of 0.42, with extensive overlap between low and mid performing treatments. This reflects the high sensitivity of coarse-textured soils to water limitation, nutrient leaching, and low OM retention. In contrast, the silty clay soil exhibited a significantly higher fertility baseline, with all simulated SFI values remaining above 0.38 and reaching maxima above 0.92. This difference illustrates the superior WHC, cation-exchange potential, and nutrient buffering behavior of the clay-rich matrix. The magnitude of treatment separation was also markedly different between the two soils. Sandy loam displayed a large spread in simulated SFI distributions, with wide confidence intervals among low-performing treatments, indicating high vulnerability to fertility decline. Silty clay exhibited tighter and more stable distributions even at low irrigation levels, reflecting structural persistence of fertility under WS. While compost 3% with 80% FC was the top-performing treatment in both soils, the simulated SFI under this treatment was nearly double in silty clay compared to sandy loam. This highlights the soil dependent expression of compost benefits: in sandy loam, compost primarily offsets water scarcity, whereas in silty clay, compost synergistically enhances OM cycling, aggregate formation, and nutrient adsorption–desorption processes. 4. Discussion 4.1. Compost-Mediated Enhancement of Soil Fertility Under Water Stress in Sandy Loam and Silty Clay Soils The results of this study clearly demonstrate that compost amendment substantially improves soil fertility under water-limited conditions, and that the magnitude of this improvement is strongly modulated by soil texture and irrigation regime. Across both sandy loam and silty clay soils, compost at 3% consistently achieved the highest SFI values, outperforming both chemical fertilizer and the unamended control. However, the pathways and magnitude of fertility enhancement differed considerably between soil types, reflecting the distinct hydrological and physicochemical constraints characteristic of coarse-textured and fine-textured systems. In sandy loam soil, the fertility response to compost application was closely tied to improvements in soil moisture retention and nutrient-holding capacity. The inherently low WHC, low AWC, and reduced CEC of sandy loam create a soil environment in which nutrients are prone to leaching and water deficits develop rapidly under reduced irrigation. Compost addition substantially altered this matrix by increasing soil OM content, enhancing aggregate formation, and improving water retention parameters, particularly under 60% and 80% FC. These structural improvements were reflected in the pronounced increases in SFI observed in compost-amended sandy soil compared to both chemical fertilizer and the control. Chemical fertilizer, while contributing to short-term nutrient enrichment, did not improve soil structure, water retention, or microbial functioning, resulting in significantly lower SFI values and greater variability under WS. The MCS confirmed this pattern, showing a large spread in simulated SFI values for low-irrigation control and mineral fertilizer treatments, indicating high susceptibility to fertility decline. In contrast, the simulated distributions for compost 3% with 80% FC treatments reached maxima around 0.42 and displayed minimal overlapping with other treatments, illustrating the robustness of compost-induced fertility gains even under stochastic water availability. In silty clay soils, compost amendments similarly enhanced SFI but through mechanisms driven more by nutrient retention and biological activity rather than structural water limitations. This soil exhibited intrinsically high AWC, WHC, and CEC values, which buffered plants from drastic fertility losses under deficit irrigation. Consequently, even the control and chemical fertilizer treatments maintained SFI values well above those seen in sandy loam. Compost further amplified these inherent advantages by strengthening the organic-mineral complex, stimulating microbial nutrient cycling, and improving structural aggregation. These mechanisms produced exceptionally high SFI values for compost 3% (80% FC) treatments, with simulated maxima exceeding 0.92 nearly double the peak values observed in sandy soil. The MCS outcomes revealed narrow, high-fertility distributions across all silty clay treatments, evidencing the soil’s resilience to WS and its capacity to maintain fertility within a stable probabilistic envelope (Wang et al., 2022 ). These results indicate that compost benefits are not limited to water-limited environments but are magnified in clay-rich soils through synergistic interactions between organic matter and fine mineral fractions (S. Zhang et al., 2025 ). Comparative analyses across soil types further highlight the pivotal role of soil texture in determining fertility trajectories under WS (Neubert and Brüggemann, 2025 ; Steiner et al., 2025 ). Sandy loam exhibited a large separation among treatments, reflecting strong dependence on irrigation level and OM inputs to maintain fertility (Sun et al., 2026 ). Silty clay, by contrast, showed far more clustered fertility responses, with all treatments positioned within a moderate-to-high fertility range. This difference is consistent with PCA, HCA, and LDA results, which showed broader structural divergence among sandy soil treatments and tighter clustering in silty clay. In essence, compost acts as a stabilizing agent in sandy soil by mitigating WS and nutrient loss (Castellini et al., 2025 ), whereas in silty clay soil, it enhances an already fertile and structurally robust system, raising the entire fertility baseline (Villa et al., 2021 ). The strong irrigation-amendment interactions observed for both soil types emphasize that fertility outcomes under compost amendment are not solely determined by OM input but emerge from the coordinated interaction between soil structure, moisture availability, and nutrient dynamics (Suvendran et al., 2025 ). 4.2 Effect of Compost on Plant Growth and Yield Production Under Water Stress in Sandy Loam and Silty Clay Soils The response of plant growth and yield to compost amendment under WS followed a clear gradient shaped by the interaction between soil texture, irrigation regime, and amendment type. Across all phases, compost particularly at 3% consistently enhanced morphological, physiological, and yield-related parameters in both sandy loam and silty clay soils, although the mechanisms and magnitude of improvement varied substantially between the two soil types. In sandy loam soil, compost exerted its strongest influence by improving plant water status and photosynthetic functioning, two domains that are highly sensitive to water deficits in coarse-textured substrates (Delval et al., 2025 ). The limited water retention capacity of sandy loam (low WHC and AWC) translated into rapid declines in RWC, chlorophyll a, chlorophyll b, and total chlorophyll under 40% FC, particularly in the control and mineral fertilizer treatments. These physiological declines were reflected in reduced leaf area, leaf number, and overall canopy development, ultimately suppressing shoot and root biomass accumulation and lowering fruit yield. Compost improved these responses primarily by enhancing soil moisture availability and reducing the rate of water loss, which sustained leaf turgor pressure, stomatal conductance, and chlorophyll integrity throughout the drought cycle (Abdou et al., 2023 ; Sanad et al., 2025b , 2025c ; Wang et al., 2025 ; Sanad et al., 2026c ). The 3% compost (80% FC) treatment consistently produced the highest values of plant height, stem diameter, leaf area, LAI, root volume, and fruit yield, indicating that compost significantly enhanced root–shoot balance and plant resource acquisition capacity (Wang et al., 2025 ). The multivariate analyses further confirmed the centrality of soil moisture and physiological status in determining plant performance in sandy loam. PCA and PLSR both revealed strong loadings of RWC, chlorophylls, LAI, and root metrics on the principal components associated with yield, demonstrating the mechanistic coupling between plant water status and productivity. Compost-modified treatments were strongly aligned with positive PC1 and LV1 values, while stressed control and mineral fertilizer treatments under 40% FC clustered toward negative values, illustrating the high sensitivity of sandy soils to irrigation deficits. LDA and HCA also emphasized the clear separation between compost-amended and non-amended treatments, highlighting compost’s role in maintaining physiological resilience under moisture stress (Kamanga et al., 2024 ). In silty clay soils, the effect of compost on plant growth and yield followed similar directional trends but exhibited smaller relative differences and a more stable performance across irrigation levels. The fine-textured matrix of silty clay provided inherently higher water retention and CEC, which buffered plants from rapid moisture loss and nutrient fluctuation even under 40% FC. As a result, baseline physiological parameters such as RWC and chlorophyll concentrations remained substantially higher compared to sandy loam across all treatments, including the control. Despite this inherent resilience, compost further enhanced nutrient availability, microbial activity, and soil structural properties, which translated into significantly improved growth and yield metrics (Abdou et al., 2023 ). The 3% compost with 80% FC treatment again achieved the highest values for growth and yield traits, but the overall gradient between treatments was less steep than in sandy soil, reflecting the soil’s buffering capacity. The physiological and morphological improvements observed in silty clay under compost amendment can be attributed to enhanced nutrient uptake efficiency and improved root system development rather than solely moisture conservation. This interpretation is supported by leaf nutrient data, which showed increased leaf N, P, K, Ca, and Mg concentrations under compost treatments, indicating that OM facilitated sustained nutrient supply and improved ion balance (Suvendran et al., 2025 ). These nutrient-driven pathways were also reflected in the multivariate analyses. PLSR for silty clay showed stronger loadings for nutrient-related variables on the latent structures associated with yield. These findings suggest that in silty clay, yield improvement is governed by compost-mediated nutrient optimization rather than moisture buffering alone. Biomass and yield results further underscore the differential role of compost across soils. In sandy loam soil, compost improved both shoot and root dry matter by mitigating WS (Sisouvanh et al., 2021 ), which enhanced assimilate allocation and biomass partitioning to fruit (Oueld Lhaj et al., 2024b ). In silty clay soil, compost enhanced total biomass and fruit yield through increased nutrient supply and root proliferation within a structurally stable, well-aggregated soil matrix (Hassan and Strezov, 2025 ). The superior effectiveness of compost at 3% in both soils highlights its capacity to simultaneously address water limitation in sandy soil and nutrient limitation in silty clay. 4.3. Impact and Benefits of Applying Compost on Tomato and Horticultural Crops Under Water Stress The application of compost as an organic amendment has long been recognized for its ability to improve soil fertility, enhance plant growth (Oueld Lhaj et al., 2025 ), and mitigate the adverse effects of WS (Boutasknit et al., 2022 ). This study confirmed the multifaceted role of compost in enhancing tomato productivity and soil health under controlled water deficit conditions, specifically in sandy loam and silty clay soils. Compost not only increased soil water retention, CEC, and nutrient availability, but also mitigated WS induced physiological impairments, leading to significant improvements in tomato growth, root development, and fruit yield (Cozzolino et al., 2023 ). These findings align with the conclusions of (Tahiri et al., 2022 ), who demonstrated that compost, in combination with beneficial microorganisms, substantially improved shoot biomass and fruit yield under water-stressed conditions, enhancing the osmotic and mineral accumulation processes in tomato plants. In their study, compost treatments increased shoot biomass by 160–180%, similar to the 45–75% increase in tomato yield observed in our experiment under drought stress. Compost's ability to enhance antioxidant capacity was also observed in our study, where compost treatments significantly improved RWC, chlorophyll concentration, and reduced the negative effects of water scarcity on physiological parameters such as stomatal conductance and transpiration rate. The role of compost in enhancing plant water relations and improving soil microbial health was similarly noted by (Lahbouki et al., 2024 ), who found that organic amendments, especially when combined with mycorrhizal fungi, increased nutrient uptake and improved WS tolerance in tomato plants, particularly under reduced irrigation. The improvement in soil health, particularly in silty clay, highlights the importance of soil texture in modulating the effects of compost. Compost enhanced aggregate stability, microbial diversity, and nutrient cycling, facilitating consistent plant growth even under moderate WS. This is in contrast to the study by (Wang et al., 2025 ), where compost improved root length density and transpiration efficiency in corn plants, resulting in increased root biomass and better water use efficiency under water deficit conditions. In our study, similar benefits were seen in tomato root systems, with compost significantly increasing root biomass and promoting deeper root growth, contributing to better water and nutrient uptake. In tomato crops, compost application increased total protein content, reduced oxidative damage, and enhanced enzyme activities involved in stress tolerance, further supporting the role of compost as a biostimulant that improves resilience to water deficit (Soussani et al., 2023 ). 4.4. Mitigation Strategies and Future Perspectives for the Use of Compost in Sustainable Horticultural Production Systems The results of this study demonstrate that compost application represents a powerful and context-dependent mitigation strategy capable of stabilizing soil fertility and improving tomato productivity under constrained water availability (González-Hernández et al., 2025 ). By enhancing soil physicochemical properties, improving water retention, increasing nutrient accessibility, and strengthening plant physiological resilience, compost directly addresses the multifaceted vulnerabilities associated with water-limited horticultural systems (Sanad et al., 2024d , 2025d ; Suvendran et al., 2025 ; Sanad et al., 2026b ). In tomato production, where root-zone moisture dynamics and nutrient–water interactions strongly influence growth and fruit development (Li et al., 2025 ), compost offers an integrated soil management tool that can simultaneously buffer environmental stress and optimize resource efficiency (Cozzolino et al., 2023 ). The mitigation potential of compost arises from its ability to modify soil–plant processes that are highly sensitive to WS (Zgallai et al., 2024 ; Sanad et al., 2026b , 2024a , 2024b , c ). In sandy loam soils, compost acts primarily by compensating for structural limitations through increased WHC, aggregate stability, and CEC (Castellini et al., 2025 ). These improvements reduce the rate of soil drying, enhance root hydration, and help maintain stomatal conductance and chlorophyll stability during drought periods (Bondì et al., 2022 ). In silty clay soils, compost strengthens nutrient cycling and microbial activity (Pérez-Piqueres et al., 2006 ), reducing the negative effects of nutrient immobilization and enhancing root penetration and soil aeration (Haufiku et al., 2025 ). These mechanisms collectively mitigate WS by sustaining nutrient and water uptake pathways that are essential for tomato growth and fruit filling (Boutasknit et al., 2024 ). The sharp improvements in shoot and root biomass, leaf physiological traits, and fruit yield observed in compost-amended treatments underscore compost’s multifunctional role as both a fertility enhancer (Su et al., 2022 ) and a stress-moderating agent (Manhou et al., 2026 , 2025b , 2025a , 2024 ; Wahab et al., 2022 ). The observed differences between soil types indicate that future mitigation strategies should adopt a soil-specific approach in which compost application rates, timing, and combinations with irrigation scheduling are optimized according to soil texture and hydraulic properties. In coarse-textured soils where drought stress is rapid and severe, compost application should be integrated with deficit irrigation strategies to maintain productivity while reducing water use. In fine-textured soils, compost may be best positioned to balance nutrient availability and support microbial communities that enhance nutrient mineralization under moisture fluctuations. These insights highlight the importance of adjusting compost management within precision agriculture frameworks to maximize water productivity and ensure consistent crop performance under increasingly variable climatic conditions. Future perspectives for compost use in sustainable horticultural systems extend beyond simple OM input. The integration of compost into soil health management strategies can be further advanced through microbial inoculation, and controlled-release organic fertilizers that enhance nutrient synchronization with tomato growth stages. Moreover, the adoption of site-specific compost application guided by soil mapping, remote sensing, and machine learning models offers a promising avenue for scaling up compost-based mitigation while limiting waste and maximizing agronomic efficiency. The results of the multivariate analyses in this study, including PCA, HCA, PLSR, and MCS, provide a strong basis for predictive frameworks that can guide compost recommendations and optimize input combinations under water-limited conditions in arid and semi-arid regions. Future research should focus on quantifying long-term compost effects on soil carbon dynamics, microbial networks, and nutrient legacy effects, as well as evaluating compost performance under combined stress scenarios such as heat stress, and irregular rainfall patterns. Additionally, the development of standardized compost quality indices and the assessment of compost derived from diverse organic waste streams will be critical for ensuring consistent agronomic outcomes and environmental safety. 5. Conclusion Tomato growth and physiological functioning were similarly responsive to compost amendment. Across both soil types, compost improved plant height, leaf area, stem diameter, and root system development, while also maintaining higher relative water content and chlorophyll concentrations during drought periods. These physiological improvements supported robust biomass accumulation and significantly higher fruit yields, especially under moderate to high irrigation levels. The multivariate analyses, including PCA, HCA, LDA, and PLSR, confirmed that compost-mediated enhancements in soil water retention, nutrient status, and physiological resilience were the dominant drivers of yield under variable moisture conditions. In sandy loam, compost mitigated the constraints of rapid soil drying and nutrient loss, while in silty clay it strengthened nutrient cycling efficiency and promoted stable root functioning even under reduced irrigation. The MCS further reinforced the reliability of compost as a fertility-enhancing strategy by showing that treatments receiving 3% compost under 80% FC produced the highest simulated SFI distributions with minimal overlap across treatments. This probabilistic robustness highlights the capacity of compost to reduce the uncertainty of soil fertility outcomes under WS. Notably, silty clay soils exhibited higher overall simulated fertility levels across all treatments, demonstrating a stronger resilience compared to sandy loam soils. However, compost improved fertility trajectories significantly in both soil types and consistently reduced the risk of fertility decline. Compost represents a promising and sustainable amendment for improving tomato production in diverse agroecosystems facing water scarcity and climate variability. Declarations CRediT authorship contribution statement Majda Oueld Lhaj: Conceptualization, Methodology, Resources, Validation, Formal analysis, Writing—original draft, Writing – review and editing. Rachid Moussadek: Formal analysis, Writing – review and editing, Funding acquisition. Hatim sanad: Conceptualization, Methodology, Software, Visualization, Writing—original draft, Writing – review and editing. Abdelmjid Zouahri: Methodology, Validation, Writing—original draft, Writing – review and editing, Supervision. Khadija Manhou: Resources, Writing – review and editing. Meriem Mdarhri Alaoui: Validation, Writing—original draft, Writing – review and editing, Supervision. Latifa Mouhir: Validation, Writing—original draft, Writing – review and editing, Supervision. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 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Nutr Cycl Agroecosystems 120:343–361. https://doi.org/10.1007/s10705-021-10154-5 Wahab A, Abdi G, Saleem MH, Ali B, Ullah S, Shah W, Mumtaz S, Yasin G, Muresan CC, Marc RA (2022) Plants’ Physio-Biochemical and Phyto-Hormonal Responses to Alleviate the Adverse Effects of Drought Stress: A Comprehensive Review. Plants 11:1620. https://doi.org/10.3390/plants11131620 Wang L, He Z, Zhao W, Wang C, Ma D (2022) Fine Soil Texture Is Conducive to Crop Productivity and Nitrogen Retention in Irrigated Cropland in a Desert-Oasis Ecotone, Northwest China. Agronomy 12, 1509. https://doi.org/10.3390/agronomy12071509 Wang X, Sale P, Hunt J, Clark G, Wood JL, Franks AE, Reddy P, Jin J, Joseph S, Tang C (2025) Enhancing growth and transpiration efficiency of corn plants with compost addition and potential beneficial microbes under well-watered and water-stressed conditions. Plant Soil 514:2475–2493. https://doi.org/10.1007/s11104-025-07527-y Xie K, Pan Y, Meng X, Wang M, Guo S (2024) Critical Leaf Magnesium Thresholds for Growth, Chlorophyll, Leaf Area, and Photosynthesis in Rice (Oryza sativa L.) and Cucumber (Cucumis sativus L). Agronomy 14:1508. https://doi.org/10.3390/agronomy14071508 Yang K, Hu J, Ren Y, Zhang Z, Tang M, Shang Z, Zhen Q, Zheng J (2024) Enhancement of Soil Organic Carbon, Water Use Efficiency and Maize Yield (Zea mays L.) in Sandy Soil through Organic Amendment (Grass Peat) Incorporation. Agronomy 14:353. https://doi.org/10.3390/agronomy14020353 Zandi A, Hosseinirad S, Zadeh K, Tavakolian H, Cho K, Vasefi B-K, Kim F, Tavakolian MS, P (2025) A systematic review of multi-mode analytics for enhanced plant stress evaluation. Front Plant Sci 16. https://doi.org/10.3389/fpls.2025.1545025 Zgallai H, Zoghlami RI, Annabi M, Zarrouk O, Jellali S, Hamdi H (2024) Mitigating soil water deficit using organic waste compost and commercial water retainer: a comparative study under semiarid conditions. Euro-Mediterr J Environ Integr 9:377–391. https://doi.org/10.1007/s41207-023-00437-4 Zhang S, Chen X, Shi A, Xu M, Zhang F, Zhang L, Zang J, Xu X, Gao J (2025) Effect of Compost Addition on Carbon Mineralization and Kinetic Characteristics in Three Typical Agricultural Soils. Agronomy 15:1559. https://doi.org/10.3390/agronomy15071559 Zhang X, Yu Q, Gao B, Hu M, Chen H, Liang Y, Yi H (2025) Organic Amendments Enhance the Remediation Potential of Economically Important Crops in Weakly Alkaline Heavy Metal-Contaminated Bauxite Residues. Agriculture 15:15. https://doi.org/10.3390/agriculture15010015 Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9439169","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":624378453,"identity":"219e9ddc-9d76-49a2-b53b-0ef73d794c49","order_by":0,"name":"Majda Oueld Lhaj","email":"data:image/png;base64,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","orcid":"","institution":"Laboratory of Process Engineering and Environment, Faculty of Science and Technology Mohammedia, University Hassan II of Casablanca, Mohammedia 28806, Morocco","correspondingAuthor":true,"prefix":"","firstName":"Majda","middleName":"Oueld","lastName":"Lhaj","suffix":""},{"id":624378909,"identity":"49257c04-baf0-427f-8e86-6461021e9459","order_by":1,"name":"Rachid Moussadek","email":"","orcid":"","institution":"International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat 10100, Morocco","correspondingAuthor":false,"prefix":"","firstName":"Rachid","middleName":"","lastName":"Moussadek","suffix":""},{"id":624380219,"identity":"52f91508-a0a2-4132-a19b-d29432d402a6","order_by":2,"name":"Hatim Sanad","email":"","orcid":"","institution":"Laboratory of Process Engineering and Environment, Faculty of Science and Technology Mohammedia, University Hassan II of Casablanca, Mohammedia 28806, Morocco","correspondingAuthor":false,"prefix":"","firstName":"Hatim","middleName":"","lastName":"Sanad","suffix":""},{"id":624380220,"identity":"eabb86c6-2a1f-4285-b3a8-ec3e801051e1","order_by":3,"name":"Abdelmjid Zouahri","email":"","orcid":"","institution":"Research Unit on Environment and Conservation of Natural Resources, Regional Center of Rabat, National Institute of Agriculture Research (INRA), Avenue Ennasr, Rabat 10101, Morocco","correspondingAuthor":false,"prefix":"","firstName":"Abdelmjid","middleName":"","lastName":"Zouahri","suffix":""},{"id":624380221,"identity":"ec1540c6-c471-4a7c-908c-3985bed76e0d","order_by":4,"name":"khadija Manhou","email":"","orcid":"","institution":"Research Unit on Environment and Conservation of Natural Resources, Regional Center of Rabat, National Institute of Agricultural Research, AV. 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design\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9439169/v1/c1f678a26bbe4efe2ceac65a.png"},{"id":107134595,"identity":"9b4d4164-805a-41fa-87ad-0665777c8645","added_by":"auto","created_at":"2026-04-17 07:46:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":352613,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix illustrating the relationships among soil physicochemical properties, plant morphological and physiological traits, leaf nutrient contents, and biomass–yield components across all treatments, irrigation regimes, and phases.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9439169/v1/da5e1d2bf261884e4bf57e08.png"},{"id":107134593,"identity":"2968d55e-7807-4bc4-a411-11235c526f54","added_by":"auto","created_at":"2026-04-17 07:46:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":240208,"visible":true,"origin":"","legend":"\u003cp\u003ePCA biplot illustrating the multivariate relationships among soil physicochemical parameters, plant morphological and physiological traits, leaf nutrient concentrations, and biomass–yield components at harvest.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9439169/v1/a0688106807e3b86c9bcc315.png"},{"id":107134599,"identity":"7ea63a7f-f442-4d29-8c4a-ae6d8c0f26c1","added_by":"auto","created_at":"2026-04-17 07:46:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":583146,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram resulting from Ward’s hierarchical clustering method applied to samples at harvest.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9439169/v1/32b313f1fe3233addea4b31d.png"},{"id":107134594,"identity":"42f9d7a5-5fc2-4417-b51b-14fb3f039fac","added_by":"auto","created_at":"2026-04-17 07:46:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5113157,"visible":true,"origin":"","legend":"\u003cp\u003eClustered correlation heatmap showing multivariate associations among simplified soil physicochemical properties, plant morphological and physiological traits, leaf nutrient content, and biomass–yield variables at harvest.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9439169/v1/d528b7991322e9ff52762c7c.png"},{"id":107134596,"identity":"f58ee9a4-85ae-42b6-9ac1-aec29f7ddbf9","added_by":"auto","created_at":"2026-04-17 07:46:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":471809,"visible":true,"origin":"","legend":"\u003cp\u003eLDA scatterplot of treatment groups at harvest based on combined soil physicochemical properties, plant morphological and physiological traits, leaf nutrient concentrations, and biomass–yield components.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9439169/v1/402dd51b3d32ca98a4d75e50.png"},{"id":107134598,"identity":"d4f11256-8c2a-425d-9fa9-9b8314c29a63","added_by":"auto","created_at":"2026-04-17 07:46:11","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2259428,"visible":true,"origin":"","legend":"\u003cp\u003ePLSR scores plot at harvest, based on integrated soil physicochemical properties, physiological traits, and biomass–yield parameters for (a) Sandy loam and (b) Silty clay soils.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9439169/v1/492dc6ab7d43600aaf0a65bd.png"},{"id":107134597,"identity":"5789f879-18bb-4149-8da2-2ed3b849819d","added_by":"auto","created_at":"2026-04-17 07:46:11","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2432172,"visible":true,"origin":"","legend":"\u003cp\u003eMCS (10,000 iterations) of the SFI for each amendment–irrigation combination in sandy loam and silty clay soils at harvest.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-9439169/v1/49b90126cda9a887035fa0f1.png"},{"id":107482153,"identity":"ed0f90e2-235e-473f-8ca9-5394040ae5a3","added_by":"auto","created_at":"2026-04-22 02:22:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":17175640,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9439169/v1/827b8173-1c2f-46d3-8142-6e406e16c92c.pdf"},{"id":107134592,"identity":"71f4ed14-f4b1-4c88-a6e2-7c217195df10","added_by":"auto","created_at":"2026-04-17 07:46:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":98358,"visible":true,"origin":"","legend":"","description":"","filename":"3SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-9439169/v1/85799c0559c7bdcee3309bc5.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eEvaluating Compost Effects on Tomato (Lycopersicon esculentum (L.) Mill) Under Drought: An Integrated Soil fertility index (SFI), Monte Carlo Simulation (MCS), and Multivariate Soil–Plant Interaction Modelling in Sandy Loam and Silty Clay soils\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobal horticultural production systems are increasingly threatened by the accelerating impacts of climate change, particularly the intensification of water scarcity. Rising temperatures, altered precipitation regimes, and increasing evapotranspiration have reduced the reliability of freshwater supplies essential for irrigated agriculture. According to the (Arulingam et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), approximately 1.8\u0026nbsp;billion people already live in areas with severe WS, and global agricultural water demand is projected to increase by 60% by 2050. Mediterranean and North African regions are considered climate-change hotspots where drought frequency and intensity continue to rise. Recent analyses indicate that average annual temperatures in the Mediterranean Basin have already increased by 1.54\u0026deg;C since the pre-industrial period, exceeding the global average warming rate (Azzopardi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Such climatic shifts directly undermine horticultural systems, which rely on precise soil\u0026ndash;water\u0026ndash;plant interactions for stable yields and product quality. Under these conditions, WS has emerged as one of the principal factors constraining productivity, root development, and physiological processes in high-value horticultural crops.\u003c/p\u003e \u003cp\u003eSoil fertility degradation compounds the effects of climate-induced WS, particularly in arid and semi-arid countries like Morocco. More than 70% of Moroccan agricultural soils exhibit low soil organic carbone content (\u0026lt;\u0026thinsp;2%), leading to reduced nutrient availability, poor aggregate stability, and low water retention capacity (Devkota et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, accelerated soil erosion, salinization in irrigated zones, and nutrient mining driven by intensive cultivation have further weakened soil productivity. According to the (Laamouri and Khattabi, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), Morocco loses approximately USD 2.1\u0026nbsp;billion annually (1.77% of GDP) due to land degradation, including declines in soil fertility and agricultural productivity. These fertility constraints are especially severe in sandy loam soils of coastal and inland horticultural areas, where limited water-holding capacity (WHC) and rapid nutrient leaching interact with drought to suppress crop growth. The increasing unpredictability of rainfall and irrigation water availability in Morocco elevates the need for sustainable soil amendment practices that enhance long-term soil resilience.\u003c/p\u003e \u003cp\u003eTomato (\u003cem\u003eLycopersicon esculentum\u003c/em\u003e (L.) Mill) is one of the most widely cultivated horticultural crops globally, with an annual production exceeding 189\u0026nbsp;million tons in 2021 (Szabo et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Morocco ranks among the top tomato exporters in the Mediterranean region, with a significant share derived from greenhouse systems in water-limited environments (Benabderrazik et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Santeramo and Lamonaca, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Tomato is particularly sensitive to both moisture deficits and nutrient imbalance due to its shallow root system, high evapotranspiration demands, and rapid fruit development cycle. WS reduces leaf expansion, photosynthetic rate, chlorophyll content, and fruit set, leading to yield losses that may exceed 30\u0026ndash;50% under severe drought (Wahab et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Soil fertility degradation further intensifies these constraints by limiting nutrient uptake (particularly N, P, K, Ca, Mg) essential for cell division, fruit enlargement, and metabolic functioning. Moreover, water-deficient and nutrient-poor soils reduce fruit quality attributes such as size, firmness, soluble solids, and antioxidant activity. This dual vulnerability underscores the need for soil management strategies that simultaneously enhance water retention and nutrient availability.\u003c/p\u003e \u003cp\u003eOrganic amendments such as compost have emerged as pivotal tools in climate-smart agriculture due to their ability to enrich soil OM, enhance cation exchange capacity (CEC), improve soil structure, and strengthen microbial activity. Compost application increases soil WHC, reduces bulk density (BD), and enhances nutrient retention through stable organic\u0026ndash;mineral complexes, thereby mitigating the impacts of WS on crop growth. Recent studies have reported that compost can increase soil moisture retention and improve tomato yield by 20% or greater depending on soil type and application rate (Tao et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In sandy-textured soils, compost provides a particularly important structural benefit by reducing rapid infiltration losses and improving pore continuity, and in clay-rich soils, compost enhances aggregate stability and nutrient cycling, improving root aeration and sustained nutrient release during water-limited phases (Oueld Lhaj et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). Given the increasing water scarcity in the Mediterranean region, compost represents a sustainable and ecologically robust strategy for stabilizing horticultural productivity (Oueld Lhaj et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eQuantifying soil fertility under complex stress conditions requires integrated assessment frameworks that account for physical, chemical, and biological indicators simultaneously. The SFI provides a composite measure that synthesizes multiple soil attributes into a single numerical value, allowing for objective comparison of treatments and soil types. Multivariate statistical tools such as Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), Linear Discriminant Analysis (LDA), and Partial Least Squares Regression (PLSR), offer powerful analytical frameworks for identifying the dominant factors driving tomato performance under variable soil and water conditions. These tools have increasingly been used to evaluate soil\u0026ndash;plant interactions and to predict productivity under stress (Zandi et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, MCS enables probabilistic assessment of fertility outcomes by incorporating uncertainty in soil properties, moisture status, and amendment effects. MCS based approaches allow quantification of risk levels associated with crop production and have been adopted in recent sustainability assessments to support decision-making under climatic uncertainty (Liman Harou et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite substantial progress in understanding compost\u0026rsquo;s role in drought mitigation and nutrient management, few studies have simultaneously examined its effects across contrasting soil textures under controlled WS conditions while integrating SFI, plant nutrient status, multivariate models, and probabilistic simulation. Existing research has either focused on isolated soil types, single physiological traits, or limited statistical approaches, leaving a gap in holistic assessments of soil\u0026ndash;plant\u0026ndash;water interactions. The innovative aspect of the present study lies in its comprehensive combination of SFI quantification, multivariate modeling (PCA, HCA, LDA, PLSR), and MCS across two contrasting soil textures providing a robust and multidimensional evaluation of compost performance under WS.\u003c/p\u003e \u003cp\u003eThe overall aim of this study is to evaluate the effectiveness of compost amendment in improving soil fertility, plant physiological functioning, growth performance, and yield of tomato under different irrigation levels in contrasting soil textures. Specifically, this study seeks to:\u003c/p\u003e \u003cp\u003e(1) Assess the impact of compost on soil physicochemical properties and SFI under WS conditions in sandy loam and silty clay soils.\u003c/p\u003e \u003cp\u003e(2) Investigate the influence of compost on plant morphological, physiological, and nutrient responses during all growth phases leading to final biomass and yield at harvest.\u003c/p\u003e \u003cp\u003e(3) Apply multivariate statistical techniques (PCA, HCA, LDA, PLSR) to identify key soil\u0026ndash;plant variables driving productivity under varying amendment and irrigation treatments,\u003c/p\u003e \u003cp\u003e(4) Employ MCS to quantify uncertainty and probabilistic fertility outcomes associated with compost use in water-limited horticultural systems.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Soil preparation and description\u003c/h2\u003e \u003cp\u003eThe experiment was carried out between February and July 2024 under controlled greenhouse conditions at the Agronomic Research Station of the National Institute of Agronomic Research (INRA) within the Research Unit for Environment and Natural Resources Conservation (URECRN) in the capital Rabat, Morocco. Two contrasting soils were selected to represent distinct agroecological contexts, namely a sandy loam soil collected from the Tiflet region located about 60 km east of Rabat and a silty clay soil obtained from the agricultural area of Temara situated roughly 30 km south of Rabat. Soil sampling was performed using a manual auger, targeting the 0\u0026ndash;20 cm surface horizon, which constitutes the biologically active and agronomically most responsive layer, particularly in terms of nutrient cycling, salinity dynamics, and structural variability. For each soil type, multiple subsamples were collected across the site, homogenized into composite samples to ensure representativeness, and transported to the laboratory for analysis.\u003c/p\u003e \u003cp\u003eUpon arrival, the soils were air-dried at room temperature, manually cleaned of visible debris, and sieved to 2 mm for physical characterization and 0.25 mm for chemical analyses. The complete initial soils characterization are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInitial physical and chemical properties of the soils used in the experiment.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSilty Clay Soil\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSandy Loam Soil\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSilt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTexture class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSilty clay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSandy loam\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectrical conductivity (EC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edS/m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecmol/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal N ( TN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvailable P (Av. P)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExchangeable K (Ex. K)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium (Na)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium (Ca)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium (Mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Compost characterization and chemical properties\u003c/h2\u003e \u003cp\u003eThe organic amendment used in this study consisted of a mature compost produced at the INRA botanical garden in Rabat through a controlled co-composting process. The feedstock was composed of green plant residues blended with sheep manure, providing a balanced carbon\u0026ndash;nitrogen matrix conducive to sustained aerobic microbial activity. Composting was carried out over a 120-day period under monitored aeration and moisture conditions to ensure efficient OM decomposition, stabilization of the material, and preservation of nutrient integrity, following the methodological framework described by (Oueld Lhaj et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e, 2026). Upon completion of the composting cycle, the final product was air-dried, homogenized, and sieved to 2 mm prior to its incorporation into the experimental pots. Comprehensive chemical analyses were subsequently performed to determine its suitability as an organic amendment. The main physicochemical properties of the compost are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, confirming its classification as a nutrient-rich, well-stabilized organic amendment appropriate for greenhouse tomato cultivation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhysical and chemical characteristics of the compost used in the experiment.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emS/cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC/N ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZinc (Zn)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/kg DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCopper (Cu)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/kg DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eND\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIron (Fe)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/kg DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManganese (Mn)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/kg DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCadmium (Cd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/kg DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eND\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLead (Pb)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/kg DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eND\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNickel (Ni)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/kg DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eND\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArsenic (As)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/kg DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eND\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: \u0026ldquo;\u003cem\u003eND\u003c/em\u003e\u0026rdquo; non-detected, \u0026ldquo;DM\u0026rdquo; dry matter\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Experimental Design\u003c/h2\u003e \u003cp\u003eThe experiment was conducted under controlled greenhouse conditions to evaluate the effect of compost and drought stress on the growth and physiological performance of tomato (\u003cem\u003eLycopersicon esculentum\u003c/em\u003e (L.) Mill.). The study followed a full-factorial arrangement combining two soil types, four amendment regimes, and three irrigation levels, implemented within a randomized complete block design (RCBD). The two soils used were a sandy loam and a silty clay, both air-dried, sieved to 4 mm, and characterized physicochemically prior to the trial. Each treatment was replicated four times, with one pot per treatment per block, giving a total of 96 experimental units.\u003c/p\u003e \u003cp\u003ePlants were grown in rigid plastic pots with a nominal volume of 8 L, filled to 7.5 L to maintain irrigation headspace. Based on BD, each pot contained approximately 10.5 kg (sandy loam) or 9.0 kg (silty clay) of dry soil. For the compost treatments, compost was incorporated on a dry-weight basis at 1% or 3% (\u003cem\u003ew/w\u003c/em\u003e), corresponding respectively to 105 g and 315 g per pot for the sandy loam, and 90 g and 270 g per pot for the silty clay. In addition to these organic amendments, two control treatments were included for each soil, a negative control (no compost or fertilizer) and a positive control receiving a balanced solid mineral fertilizer. For the positive control, nutrient supply was standardized using granular 12\u0026ndash;12\u0026ndash;17 at 15 g/pot (equivalent to 200 Kg N/ha) for the sandy loam and 20\u0026ndash;10\u0026ndash;10 at 9 g/pot (equivalent to 200 Kg N/ha) for the silty clay, based on the initial soil fertility status of each substrate. In both cases, 40% of the dose was uniformly incorporated into the upper 10\u0026ndash;12 cm of substrate at transplanting, while the remaining 60% was applied in six equal weekly top-dressings to ensure steady nutrient availability throughout the vegetative and early reproductive stages.\u003c/p\u003e \u003cp\u003eIrrigation regimes were imposed following an initial establishment phase. All pots were maintained at 85\u0026ndash;90% of FC for the first 14 days after transplanting to ensure uniform root establishment. Thereafter, three drought levels were implemented using gravimetric control of substrate moisture including 80% FC, 60% FC, and 40% FC, representing well-watered, moderate, and severe deficit conditions, respectively. For each pot, FC was determined by saturating the soil to incipient drainage, allowing a 48-h drainage period, and subsequently recording the pot weight. Daily target irrigation weight was calculated individually for every pot using the expression using the Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\:{\\mathbf{W}}_{\\mathbf{T}\\mathbf{a}\\mathbf{r}\\mathbf{g}\\mathbf{e}\\mathbf{t}}=\\:{\\mathbf{W}}_{\\mathbf{D}\\mathbf{r}\\mathbf{y}}+\\:\\mathbf{F}\\mathbf{C}\\:\\times\\:\\:{(\\mathbf{W}}_{\\mathbf{F}\\mathbf{C}}\\:-\\:{\\mathbf{W}}_{\\mathbf{D}\\mathbf{r}\\mathbf{y}})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u0026ldquo;W\u003csub\u003eDry\u003c/sub\u003e\u0026rdquo; denotes the pre-irrigation dry weight of the pot-soil system. Irrigation was applied exclusively by surface watering, supplying the exact volume required to restore the pot to its targeted weight, thereby preventing confounding leaching effects. This pot-specific approach ensured that differences in water retention due to soil texture and compost rate were fully incorporated into the drought imposition strategy.\u003c/p\u003e \u003cp\u003eAll plants were grown under controlled greenhouse conditions where temperature was maintained at 23\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u0026deg;C and relative humidity at 62\u0026thinsp;\u0026plusmn;\u0026thinsp;8%, with illumination relying exclusively on natural sunlight. Tomato seedlings at the 4\u0026ndash;5 true-leaf stage (approximately 25\u0026ndash;30 days old) were transplanted individually into the center of each pot and supported with a uniform vertical stake. During the establishment phase (0\u0026ndash;14 days after transplanting), all pots were maintained at approximately 90% of FC to ensure homogeneous early root development across treatments. The drought treatments were imposed from day 15 onward and maintained until the conclusion of the experiment. Irrigation was applied by surface watering only, avoiding any bottom irrigation or sub-irrigation that could alter soil moisture gradients. Each pot was weighed daily in the morning, and the volume of water required to restore it to the assigned target weight was applied. When pot weights exceeded the target, irrigation was omitted to maintain consistent soil water deficits. Leaching events were minimized to maintain stable moisture profiles and prevent unintended nutrient losses. Pots were spaced at 30 cm intervals in all directions to minimize shading, edge interference, and competition for light. To minimize positional heterogeneity within the greenhouse, pots were rotated periodically to mitigate microclimatic variation associated with bench effects. Within each pot, technical repetitions were performed for several measurements to ensure data reliability but were treated statistically as subsamples rather than independent replicates. The main components of the experimental design are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the experimental design.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperimental design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFull factorial arrangement in a RCBD with 4 blocks and 96 pots total\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreenhouse conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e- Temperature: 23\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u0026deg;C\u003c/p\u003e \u003cp\u003e- Relative humidity: 62\u0026thinsp;\u0026plusmn;\u0026thinsp;8%\u003c/p\u003e \u003cp\u003e- Light: natural sunlight only (no supplementary lighting)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors and levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e- Soil type (2): Sandy loam, Silty clay\u003c/p\u003e \u003cp\u003e- Control (2): Negative control, Positive control (mineral fertilizer)\u003c/p\u003e \u003cp\u003e- Amendments (2): Compost 1%, Compost 3%\u003c/p\u003e \u003cp\u003e- Irrigation (3): 80% FC, 60% FC, 40% FC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReplicates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFour replicates per treatment (one per block)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePot characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e- Rigid plastic pots (8 L nominal; filled to 7.5 L)\u003c/p\u003e \u003cp\u003e- Soil mass/pot: 10.5 kg (sandy loam) and 9.0 kg (silty clay)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompost rates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e- 1% (w/w\u003cem\u003e)\u003c/em\u003e: 105 g/pot (sandy loam), 90 g/pot (silty clay)\u003c/p\u003e \u003cp\u003e- 3% (w/w): 315 g/pot (sandy loam), 270 g/pot (silty clay)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive control fertilization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e- Sandy loam: 12\u0026ndash;12\u0026ndash;17 at 15 g/pot\u003c/p\u003e \u003cp\u003e- Silty clay: 20\u0026ndash;10\u0026ndash;10 at 9 g/pot\u003c/p\u003e \u003cp\u003e- Application: 40% basal, 60% split into 6 weekly doses\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransplanting material\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e- Tomato seedlings at 4\u0026ndash;5 true leaves\u003c/p\u003e \u003cp\u003e- 25\u0026ndash;30 days old\u003c/p\u003e \u003cp\u003e- One plant per pot\u003c/p\u003e \u003cp\u003e- Centrally positioned and staked\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrought treatments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e- Initiated on day 15\u003c/p\u003e \u003cp\u003e- Irrigation controlled gravimetrically to maintain 80%, 60%, or 40% FC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Sampling Periods and Experimental Phases\u003c/h2\u003e \u003cp\u003eTo monitor the temporal dynamics of soil and plant responses under WS and compost application, sampling was carried out at four distinct experimental phases including baseline phase (prior to stress induction), drought initiation phase, mid-drought stress phase and final harvest phase. During the baseline period (0\u0026ndash;14 days after transplanting (DAT)), plants were maintained at approximately 90% of FC to ensure uniform root establishment. Drought treatments were introduced on day 15, corresponding to 80%, 60%, and 40% FC levels, and maintained until the end of the experiment. Soil and plant samples were collected at each phase to evaluate both the short-term physiological responses and the long-term agronomic and nutritional effects of compost and drought interaction. The first two sampling phases focused on early plant responses and soil moisture dynamics, while the mid-drought and final harvest stages captured the cumulative impact on soil fertility, water retention, biomass accumulation, and nutrient uptake. All measurements were performed on fully randomized pots within each treatment, and destructive sampling (shoot and root biomass, root length and volume, fruit yield and leaf nutrient contents) was restricted to the final harvest phase, whereas non-destructive parameters (plant height, stem diameter, leaf number, leaf area, RWC, chlorophyll and transpiration) were monitored across all sampling phases to preserve treatment integrity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Soil Laboratory Analyses\u003c/h2\u003e \u003cp\u003eComprehensive soil analyses were performed to assess the initial physicochemical properties and the variations induced by compost application and drought stress. Soil samples were collected at each of the four experimental phases (baseline, drought initiation, mid-drought stress, and final harvest) from the 0\u0026ndash;15 cm layer of each pot. Samples were air-dried, gently ground, and sieved to \u0026lt;\u0026thinsp;2 mm for physical determinations, while a subsample was sieved to \u0026lt;\u0026thinsp;0.25 mm for chemical analysis.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1. Physical and Hydro-physical Properties\u003c/h2\u003e \u003cp\u003eSoil texture was determined by the hydrometer method (Babur et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), to classify the soils according to the USDA textural triangle. The BD was measured using the core method (Obidike-Ugwu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and total porosity (TP) was calculated assuming a particle density of 2.65 g/cm\u003csup\u003e3\u003c/sup\u003e. FC was determined gravimetrically by saturating soil samples and allowing them to drain freely for 48 h at room temperature. Because specialized equipment was unavailable, the permanent wilting point (PWP) was estimated empirically using a pedotransfer function that relates PWP to soil texture and OM content following Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e by (Saxton and Rawls, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2006\u003c/span\u003e):\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\mathbf{P}\\mathbf{W}\\mathbf{P}\\:\\left(\\mathbf{\\%}\\right)=\\:-0.024\\:\\times\\:\\mathbf{S}\\mathbf{a}\\mathbf{n}\\mathbf{d}+0.487\\:\\times\\:\\mathbf{C}\\mathbf{l}\\mathbf{a}\\mathbf{y}+0.006\\:\\times\\:\\mathbf{O}\\mathbf{M}+0.005\\:\\times\\:\\left(\\mathbf{S}\\mathbf{a}\\mathbf{n}\\mathbf{d}\\:\\times\\:\\mathbf{O}\\mathbf{M}\\right)-0.013\\:\\times\\:\\left(\\mathbf{C}\\mathbf{l}\\mathbf{a}\\mathbf{y}\\:\\times\\:\\mathbf{O}\\mathbf{M}\\right)+0.068$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u0026ldquo;Sand\u0026rdquo;, \u0026ldquo;Clay\u0026rdquo;, and OM are expressed in percent (%).\u003c/p\u003e \u003cp\u003eThe available water content (AWC) was then calculated using Eq.\u0026nbsp;\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:\\mathbf{A}\\mathbf{W}\\mathbf{C}\\:\\left(\\mathbf{\\%}\\right)=\\mathbf{F}\\mathbf{C}\\:\\left(\\mathbf{\\%}\\right)-\\mathbf{P}\\mathbf{W}\\mathbf{P}\\:\\left(\\mathbf{\\%}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThis empirical approach provides a reliable estimation of soil water availability when direct measurements are not feasible.\u003c/p\u003e \u003cp\u003eAdditionally, the soil WHC was determined gravimetrically by saturating 100 g of dry soil, allowing drainage for 48 h, and expressing the retained water as a percentage of dry soil weight. Gravimetric moisture content (GMC) was periodically determined by oven-drying subsamples at 105\u0026deg;C for 24 h to validate irrigation accuracy and maintain the target FC levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2. Chemical and Fertility Analyses\u003c/h2\u003e \u003cp\u003eSoil pH and EC were determined in a 1:2 soil-to-water suspension using a calibrated pH/EC meter. OM was analyzed by the Walkley\u0026ndash;Black dichromate oxidation method, and TN was quantified using the Kjeldahl digestion method. The C/N ratio was calculated from organic carbon and TN values. The Av. P was extracted using the Olsen method and quantified colorimetrically at 880 nm. The Ex. K, Ca, Mg, and Na were extracted with 1 M ammonium acetate (pH 7.0) and determined using flame photometry (for K and Na) and atomic absorption spectrophotometry (for Ca and Mg). The CEC was measured using the ammonium acetate saturation method and expressed in cmol/kg.\u003c/p\u003e \u003cp\u003eTo provide an integrative evaluation of soil nutrient status, a SFI was calculated following a normalized scoring approach, combining key fertility attributes (OM, TN, av P, Ex K, and CEC). Each variable was normalized between 0 and 1 using the min\u0026ndash;max method, and the overall index was computed using Eq.\u0026nbsp;(\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e):\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:\\mathbf{S}\\mathbf{F}\\mathbf{I}=\\:\\frac{1}{\\mathbf{n}}\\:\\times\\:\\:\\sum\\:_{\\mathbf{i}=1}^{\\mathbf{n}}\\frac{{\\mathbf{X}}_{\\mathbf{i}}\\:-\\:{\\mathbf{X}}_{\\mathbf{m}\\mathbf{i}\\mathbf{n}}}{{\\mathbf{X}}_{\\mathbf{m}\\mathbf{a}\\mathbf{x}}-\\:{\\mathbf{X}}_{\\mathbf{m}\\mathbf{i}\\mathbf{n}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u0026ldquo;X\u003csub\u003ei\u003c/sub\u003e​\u0026rdquo; represents the measured value of each fertility parameter, \u0026ldquo;X\u003csub\u003emin\u003c/sub\u003e\u0026rdquo; and \u0026ldquo;X\u003csub\u003emax\u003c/sub\u003e\u0026rdquo; are the minimum and maximum values across all treatments, and \u0026ldquo;n\u0026rdquo; is the number of fertility parameters included (n\u0026thinsp;=\u0026thinsp;5).\u003c/p\u003e \u003cp\u003eThe resulting SFI ranges between 0 (lowest fertility) and 1 (highest fertility), allowing quantitative comparison of fertility improvements induced by compost addition under varying drought levels.\u003c/p\u003e \u003cp\u003eAll analyses were conducted in triplicate, and results were expressed on an oven-dry weight basis. Quality assurance was ensured through analytical blanks, internal standards, and replicate control samples.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Plant Laboratory Analyses\u003c/h2\u003e \u003cp\u003ePlant analyses were conducted to evaluate the morphological, physiological, and nutritional responses of tomato (\u003cem\u003eLycopersicon esculentum\u003c/em\u003e (L.) Mill.) to compost application under varying drought stress in two contrasting soil types. Sampling was performed at the four experimental phases to assess both short-term physiological reactions and cumulative agronomic performance. Plant measurements were carried out on all replicates, and destructive sampling (biomass and nutrient analysis) was limited to designated pots within each treatment.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.6.1. Morphological and Growth Measurements\u003c/h2\u003e \u003cp\u003ePlant height was measured from the collar to the apical meristem using a graduated ruler, while stem diameter was determined at 2 cm above the soil surface with a digital caliper. The number of fully expanded leaves per plant was recorded at each sampling phase. Leaf area was determined non-destructively using the method described by (Castro-Valdecantos et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), applying the following Eq.\u0026nbsp;(\u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e5\u003c/span\u003e):\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:\\mathbf{L}\\mathbf{e}\\mathbf{a}\\mathbf{f}\\:\\mathbf{A}\\mathbf{r}\\mathbf{e}\\mathbf{a}\\:\\left({\\mathbf{c}\\mathbf{m}}^{2}\\right)=\\mathbf{L}\\:\\times\\:\\mathbf{W}\\:\\times\\:\\mathbf{k}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u0026ldquo;L\u0026rdquo; is the leaf length (cm), \u0026ldquo;W\u0026rdquo; is the maximum width (cm), and \u0026ldquo;k\u0026rdquo; is a correction factor equal to 0.75 for tomato leaves. The Leaf Area Index (LAI) was then calculated using Eq.\u0026nbsp;(\u003cspan refid=\"Equ6\" class=\"InternalRef\"\u003e6\u003c/span\u003e):\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$\\:\\mathbf{L}\\mathbf{A}\\mathbf{I}=\\:\\frac{\\mathbf{T}\\mathbf{o}\\mathbf{t}\\mathbf{a}\\mathbf{l}\\:\\mathbf{L}\\mathbf{e}\\mathbf{a}\\mathbf{f}\\:\\mathbf{A}\\mathbf{r}\\mathbf{e}\\mathbf{a}\\:\\left({\\mathbf{c}\\mathbf{m}}^{2}\\right)}{\\mathbf{G}\\mathbf{r}\\mathbf{o}\\mathbf{u}\\mathbf{n}\\mathbf{d}\\:\\mathbf{A}\\mathbf{r}\\mathbf{e}\\mathbf{a}\\:\\left({\\mathbf{c}\\mathbf{m}}^{2}\\right)}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAt harvest, plants were carefully uprooted, washed with distilled water, and separated into shoots, roots, and fruits. Fresh weights were recorded, and subsamples were oven-dried at 70\u0026deg;C to constant weight to determine dry biomass.\u003c/p\u003e \u003cp\u003eRoot length was measured manually from the base of the stem to the tip of the longest root, while root volume was determined by the water displacement method using a graduated cylinder, with displaced water volume (mL) expressed as root volume (cm\u0026sup3;). Fruit yield was calculated as the total number and fresh weight of fruits per plant at harvest.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.6.2. Physiological Parameters\u003c/h2\u003e \u003cp\u003eLeaf relative water content (RWC) was determined using Eq.\u0026nbsp;(\u003cspan refid=\"Equ7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) following the procedure of (Barrs and Weatherley, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1962\u003c/span\u003e):\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$\\:\\mathbf{R}\\mathbf{W}\\mathbf{C}\\:\\left(\\mathbf{\\%}\\right)=\\:\\frac{\\mathbf{F}\\mathbf{W}-\\mathbf{D}\\mathbf{W}}{\\mathbf{T}\\mathbf{W}-\\mathbf{D}\\mathbf{W}}\\:\\times\\:100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u0026ldquo;FW\u0026rdquo;, \u0026ldquo;TW\u0026rdquo;, and \u0026ldquo;DW\u0026rdquo; represent fresh, turgid, and dry weights, respectively.\u003c/p\u003e \u003cp\u003eChlorophyll pigments (chlorophyll a, chlorophyll b, and total chlorophyll) were quantified according to (Arnon, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1949\u003c/span\u003e). Fresh leaf samples (0.2 g) were homogenized in 10 mL of 80% acetone and centrifuged. The absorbance of the supernatant was measured at 663 nm and 645 nm with a UV\u0026ndash;Vis spectrophotometer, and chlorophyll contents were calculated using the following Equations (8)-(10):\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mathbf{C}\\mathbf{h}\\mathbf{l}\\mathbf{o}\\mathbf{r}\\mathbf{o}\\mathbf{p}\\mathbf{h}\\mathbf{y}\\mathbf{l}\\mathbf{l}\\:\\mathbf{a}\\:\\left(\\mathbf{m}\\mathbf{g}/\\mathbf{g}\\:\\mathbf{F}\\mathbf{W}\\right)=12.7\\:\\left({\\mathbf{A}}_{663}\\right)-2.69\\:\\left({\\mathbf{A}}_{645}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mathbf{C}\\mathbf{h}\\mathbf{l}\\mathbf{o}\\mathbf{r}\\mathbf{o}\\mathbf{p}\\mathbf{h}\\mathbf{y}\\mathbf{l}\\mathbf{l}\\:\\mathbf{b}\\:\\left(\\mathbf{m}\\mathbf{g}/\\mathbf{g}\\:\\mathbf{F}\\mathbf{W}\\right)=22.9\\:\\left({\\mathbf{A}}_{645}\\right)-4.68\\:\\left({\\mathbf{A}}_{663}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mathbf{T}\\mathbf{o}\\mathbf{t}\\mathbf{a}\\mathbf{l}\\:\\mathbf{C}\\mathbf{h}\\mathbf{l}\\mathbf{o}\\mathbf{r}\\mathbf{o}\\mathbf{p}\\mathbf{h}\\mathbf{y}\\mathbf{l}\\mathbf{l}\\:\\left(\\mathbf{m}\\mathbf{g}/\\mathbf{g}\\:\\mathbf{F}\\mathbf{W}\\right)=20.2\\:\\left({\\mathbf{A}}_{645}\\right)+8.02\\:\\left({\\mathbf{A}}_{663}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.6.3. Transpiration Rate\u003c/h2\u003e \u003cp\u003eThe transpiration rate was estimated using the gravimetric water balance method, which provides a reliable measure of plant water use under controlled greenhouse conditions. The total water applied to each pot was recorded daily, and since leaching and surface evaporation were minimized by careful irrigation management, the cumulative water loss was considered to represent plant transpiration. The transpiration rate was calculated by Eq.\u0026nbsp;(\u003cspan refid=\"Equ8\" class=\"InternalRef\"\u003e11\u003c/span\u003e):\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$$\\:\\mathbf{T}\\mathbf{r}\\mathbf{a}\\mathbf{n}\\mathbf{s}\\mathbf{p}\\mathbf{i}\\mathbf{r}\\mathbf{a}\\mathbf{t}\\mathbf{i}\\mathbf{o}\\mathbf{n}\\:(\\mathbf{m}\\mathbf{L}/\\mathbf{p}\\mathbf{l}\\mathbf{a}\\mathbf{n}\\mathbf{t}/\\mathbf{d}\\mathbf{a}\\mathbf{y})=\\:\\frac{{\\mathbf{V}}_{\\mathbf{i}}-\\:{\\mathbf{V}}_{\\mathbf{f}}}{\\mathbf{A}\\:\\times\\:\\mathbf{t}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e11\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u0026ldquo;V\u003csub\u003ei\u003c/sub\u003e\u0026rdquo;​ and \u0026ldquo;V\u003csub\u003ef\u003c/sub\u003e\u0026rdquo;​ are the total irrigation volumes at two consecutive weighing intervals (mL), \u0026ldquo;A\u0026rdquo; is the pot surface area (m\u0026sup2;), and \u0026ldquo;t\u0026rdquo; is the time interval (days).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Multivariate Statistical Analysis\u003c/h2\u003e \u003cp\u003eComprehensive statistical and multivariate analyses were carried out to evaluate the interactive effects of soil type, compost application, and irrigation regime on soil physicochemical properties and plant responses. All datasets were first screened for outliers, and missing values were handled through mean substitution when necessary. Prior to analysis, data were standardized (z-score normalization) to ensure equal weighting of variables with different measurement scales. Descriptive statistics (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation) were computed for all measured parameters. A three-way analysis of variance (ANOVA) was performed to assess the main and interactive effects of soil type, compost rate, and irrigation level on each variable. Normality and homogeneity of variances were verified using the Shapiro\u0026ndash;Wilk and Levene\u0026rsquo;s tests, respectively. When assumptions were not met, data were log- or arcsine-transformed prior to analysis. Mean separation was conducted using Tukey\u0026rsquo;s Honestly Significant Difference (HSD) test at a 5% probability level. Bivariate relationships among soil and plant variables were examined using Pearson\u0026rsquo;s correlation analysis, allowing the identification of significant linear associations between soil fertility parameters, physiological traits, and performance indices. The correlation matrix served as an input for further multivariate analyses. A Principal Component Analysis (PCA) was employed to reduce data dimensionality and identify the main components explaining variability among treatments. Variables with high loading coefficients (\u0026ge;\u0026thinsp;0.70) were considered major contributors to component variance. The PCA biplots were used to visualize associations among soil types, compost levels, irrigation regimes, and the corresponding plant responses. Complementary to PCA, a HCA using Ward\u0026rsquo;s linkage method and Euclidean distance was conducted to group treatments according to similarity in multivariate responses. The resulting dendrogram allowed classification of soil\u0026ndash;compost\u0026ndash;irrigation combinations into distinct performance clusters, highlighting treatments with comparable physiological or fertility characteristics. To enhance discrimination among experimental groups, LDA was performed on the standardized dataset to identify the parameters that best separated treatment categories. The discriminant functions were validated through cross-validation using the leave-one-out method, and the accuracy of group classification was quantified as the percentage of correctly assigned cases. PLSR was applied to model the relationships between predictor variables (soil fertility and physiological traits) and response variables. PLSR was chosen for its robustness in handling multicollinearity among predictors and its capacity to rank the importance of variables through the Variable Importance in Projection scores. The number of latent components was optimized based on the lowest Root Mean Square Error of Prediction (RMSEP) using a 10-fold cross-validation procedure. To account for stochastic variability and parameter uncertainty, a MCS approach was applied to the SFI parameters for both soils. Each simulation was iterated 10,000 times using random sampling from the normal distribution of input variables (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) to estimate the probability distributions and confidence intervals of these indices under varying soil and irrigation conditions. The resulting probability density functions provided a robust probabilistic assessment of treatment performance and uncertainty propagation. All statistical analyses including ANOVA, correlation, PCA, HCA, LDA, and PLSR were conducted using XLSTAT software (Version 2024.1; Addinsoft, Paris, France), while the MCS procedures were implemented in Python (Version 3.11)(Sanad et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2026a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAll quantitative results obtained in this study, including soil physicochemical properties, plant morphological and physiological traits, nutrient contents, and root parameters across treatments and growth phases, are comprehensively presented in the tables provided in the Supplementary Materials. The flow-sheet of our study is represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Impact of compost amendment on soil properties under water stress\u003c/h2\u003e \u003cp\u003eAll detailed values of physicochemical properties for each soil across all phases and treatment are provided in table S2, S3, S4 and S5 in the Supplementary Materials.\u003c/p\u003e \u003cp\u003eIn the sandy loam soil, compost amendment markedly modified both fertility and hydro-physical attributes across the four sampling phases, with clear differences among irrigation regimes. Across all treatments and phases, soil OM in the sandy loam ranged from 1.13% in the unfertilized control at 40% FC during the baseline phase to 2.47% under the 3% compost treatment at 80% FC during mid-drought. This pattern indicates that compost, particularly at 3%, effectively increased OM even under water deficit, and that the best expression of this effect occurred under the highest moisture level. Under 3% compost, OM was consistently highest at 80% FC in all phases, with mean values of about 2.03% at baseline, 2.09% at drought start, 2.22% at mid-drought and 2.29% at harvest, whereas the control at 40% FC systematically recorded the lowest OM values at each phase, close to 1.25\u0026ndash;1.30%.\u003c/p\u003e \u003cp\u003eIn the sandy loam, TN varied between 0.058% (control, 40% FC, harvest) and 0.110% (3% compost, 80% FC, harvest). The combination of 3% compost and 80% FC at harvest thus provided the highest N enrichment, indicating that organic N release from compost was favored under near-optimal moisture conditions. In contrast, the lowest TN values were consistently associated with the non-amended control under 40% FC at advanced phases, revealing that both the absence of external N inputs and severe drought reduced N availability. The C/N ratio in the sandy soil remained within a relatively narrow range (8.3\u0026ndash;15.9), suggesting that compost addition did not induce excessive N immobilization, under compost 3% at 80% FC.\u003c/p\u003e \u003cp\u003eThe Av. P and Ex. K showed a complementary pattern between compost and mineral fertilization in the sandy loam. Overall, Av.P ranged from 25.2 mg/kg (control, 40% FC, drought start) to 50.9 mg/kg (chemical fertilizer, 40% FC, drought start). At each phase, the highest mean Av. P was recorded in the chemically fertilized treatment, especially at 80% FC at baseline (46.4 mg/kg) and 40% FC at drought start and later phases (around 45\u0026ndash;43 mg/kg), confirming the immediate solubility and availability of mineral P sources. Compost did increase Av. P relative to the unfertilized control, particularly at 3% and under 60\u0026ndash;80% FC, but it did not reach the peaks obtained with mineral fertilizer. The Ex. K in sandy soil varied between 36.3 mg/kg (control, 40% FC, drought start) and 71.4 mg/kg (chemical fertilizer, 40% FC, drought start). As for Av.P, the chemical fertilizer at 40\u0026ndash;60% FC consistently produced the highest Ex.K values at all phases, while compost 3% produced intermediate Ex.K levels clearly above the control but below the mineral fertilization. This indicates that in the sandy loam, compost is particularly effective for building OM and Av.N, while mineral fertilizer dominates the short-term Av.P and Ex.K enrichment.\u003c/p\u003e \u003cp\u003eThe CEC of the sandy loam, initially low, ranged from 10.4 to 15.6 cmol/kg. The largest CEC values were observed under the 3% compost treatment, particularly at 60\u0026ndash;80% FC during mid-drought and drought start. For instance, the maximum CEC of 15.6 cmol/kg was recorded in the 3% compost treatment at 40% FC during drought start, whereas the minimum values were associated with the control at 40\u0026ndash;60% FC. The increase in CEC with compost and adequate moisture reflects the combined effect of added organic colloids and their partial oxidation, which enhances negative charge density and the soil\u0026rsquo;s capacity to retain nutrient cations (X. Zhang et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese fertility improvements are well summarized by the SFI. In sandy loam, SFI varied widely from 0.025 to 0.406. The lowest values were found in the non-amended control under 80% FC at drought start and 60% FC at harvest (SFI\u0026thinsp;=\u0026thinsp;0.03), indicating that maintaining water alone was not sufficient to sustain fertility in the absence of amendments. In contrast, the highest SFI (0.406) occurred under 3% compost at 60% FC during drought start, while 3% compost at 80% FC also achieved very high SFI values across all phases. Under severe drought (40% FC), compost still improved SFI relative to the control, but its effect was attenuated compared with 60\u0026ndash;80% FC, highlighting that organic inputs mitigate but do not fully compensate for strong water limitation.\u003c/p\u003e \u003cp\u003eCompost also had a pronounced effect on the hydro-physical properties of the sandy loam. The FC in this soil ranged from 20.1% (chemical fertilizer, 40% FC, harvest) to 27.8% (3% compost, 80% FC, mid-drought). The AWC ranged between 9.8% (control, 60% FC, baseline) and 17.6% (3% compost, 80% FC, mid-drought), while WHC varied from 26.7% (chemical fertilizer, 60% FC, drought start and harvest) to 37.2% (3% compost, 60% FC, baseline). For nearly all phases, the 3% compost treatment under 80% FC exhibited the highest FC and AWC, for example, at baseline FC, AWC and WHC reached about 25.1%, 14.5% and 35.5%, respectively. This indicates that in sandy loam, compost improved not only chemical fertility but also the soil\u0026rsquo;s capacity to store plant-available water, particularly when combined with moderate to high irrigation levels. The modest fluctuations of FC, AWC and WHC across phases suggest that compost-induced structural improvements remained relatively stable throughout the cropping cycle, whereas the lowest water storage capacities were associated with the mineral-fertilized soil under 40\u0026ndash;60% FC, where structure benefited less from organic inputs.\u003c/p\u003e \u003cp\u003eIn the silty clay soil, baseline fertility and water retention were intrinsically higher, and compost further reinforced these advantages under WS. At each phase, the highest OM values were consistently associated with 3% compost, typically under 40\u0026ndash;60% FC: for instance, 2.24% at baseline (3% compost, 60% FC), 2.15% at drought start (3% compost, 60% FC), 2.44% at mid-drought (3% compost, 40% FC) and 2.47% at harvest (3% compost, 40% FC). This suggests that in the finer-textured soil, compost-derived OM is better preserved under moderate rather than maximal irrigation, probably because lower leaching and slower decomposition rates favor OM accumulation under 40\u0026ndash;60% FC.\u003c/p\u003e \u003cp\u003eThe TN in silty clay varied between 0.067% and 0.119%.The upper range was mainly reached under 3% compost at 40\u0026ndash;60% FC in mid-drought and harvest phases, while the lower values were recorded in the control or mineral treatments under 40% FC. The generally higher TN content compared with the sandy soil reflects the higher inherent fertility and greater capacity of silty clay to stabilize organic N. As in the sandy loam, C/N ratios remained within agronomically favorable ranges, and compost tended to maintain ratios around 10\u0026ndash;11 in the best treatment combinations, supporting a balanced N supply under stress.\u003c/p\u003e \u003cp\u003eThe Av.P and Ex.K in the clay soil were an order of magnitude higher than in the sandy loam. Overall, Av.P were between 90.0 mg/kg (control, 40% FC, drought start) and 190.9 mg/kg (chemical fertilizer, 80% FC, harvest). Compost 3% increased Av.P relative to the control in all irrigation regimes but did not exceed the mineral fertilizer peaks, mirroring the pattern observed in sandy loam. The Ex.K ranged from 188.9 mg/kg (control, 60% FC, harvest) to 373.0 mg/kg (chemical fertilizer, 80% FC, drought start). Again, the chemically fertilized 80% FC treatment produced the highest Ex.K values, especially at early phases, whereas compost raised Ex.K to intermediate levels, confirming its role as a slower-release source of base cations rather than a short-term equivalent to mineral fertilization.\u003c/p\u003e \u003cp\u003eThe CEC of the silty clay soil was much larger than that of the sandy loam, ranging from 25.7 to 36.6 cmol/kg. The maximum CEC values were reached under 3% compost, particularly at baseline and mid-drought, confirming the synergistic effect of clay and OM on charge development. Even the control treatments exhibited relatively high CEC, reflecting the inherent buffering capacity of the clay matrix, but compost amplified this capacity, thereby enhancing nutrient retention under fluctuating moisture conditions.\u003c/p\u003e \u003cp\u003eThe SFI in silty clay reflected this high fertility background. Values ranged from 0.419 (control, 60% FC, baseline) to 0.913 (3% compost, 80% FC, mid-drought). On average, SFI was 0.65 at baseline, 0.64 at drought start, 0.66 at mid-drought and 0.61 at harvest, pointing to a generally stable but slightly decreasing fertility towards the end of the cycle, likely due to crop uptake. The highest SFI values were systematically observed under 3% compost at 40\u0026ndash;80% FC, with a particularly pronounced peak under 3% compost and 80% FC at mid-drought (0.913) and 3% compost and 40% FC at harvest (0.80). This indicates that in the clay soil, compost not only maintains but substantially enhances an already fertile system, even under moderate water limitation.\u003c/p\u003e \u003cp\u003eHydro-physical properties of the silty clay soil were also strongly favorable to WS mitigation, and compost further improved them. AWC varied between 21.2% and 33.1%, and WHC ranged from 41.0% to 55.1%. The highest WHC values across phases were generally attained in the 3% compost treatment at 40\u0026ndash;60% FC: for example, at baseline WHC reached 51.9% under 3% compost at 40% FC, and at drought start AWC peaked at 29.3% under 3% compost at 80% FC. In this context, compost acted more as a fine-tuner, further increasing the already WHC, particularly under moderate water regimes, whereas the effect of irrigation level on water retention was less pronounced than in the coarse-textured sandy loam.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Impact of Compost Amendment on Plant Responses Under Water Stress\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Morphological and growth responses across all phases\u003c/h2\u003e \u003cp\u003eTable S6, S7, S8, S9, S10, S11 and S12 in the Supplementary Materials presents all detailed results values of plant morphological and physiological traits, nutrient contents, and root parameters across treatments and growth phases for each soil.\u003c/p\u003e \u003cp\u003eIn the sandy loam soil, plant growth trajectories showed a clear increase from baseline to harvest, with strong modulation by both compost amendment and irrigation regime. At baseline, plant height had the minimum value in the control at 60% FC (21.36 cm), and the maximum value in the chemical fertilizer treatment at 80% FC (47.47 cm). This already indicates that, even before drought imposition, mineral fertilization under optimal moisture promoted initial elongation, whereas the unfertilized substrate, especially under suboptimal FC, constrained early growth. Stem diameter at the same phase varied between 2.00 mm (chemical fertilizer at 60% FC) and 4.98 mm (compost 3% at 60% FC), showing that the 3% compost at 60% FC was already able to thicken stems more than the purely mineral treatment at this early stage. Leaf number ranged between 7 leaves (control, 40% FC) and 16 leaves (compost 3% at 60% FC). Leaf area at baseline extended from 427.2 cm\u0026sup2; (compost 3% at 60% FC) to 841.9 cm\u0026sup2; (compost 3% at 80% FC), and LAI from 0.475 (compost 3% at 60% FC) to 0.935 (compost 3% at 80% FC). Thus, at baseline, the largest canopy and LAI were obtained with compost 3% under 80% FC, while the lowest canopy development was associated with the unfertilized or suboptimally irrigated combinations, highlighting the positive effect of both organic inputs and adequate water on early vegetative vigor in the sandy substrate.\u003c/p\u003e \u003cp\u003eAt drought start, all morphological variables responded upward relative to baseline. Plant height in sandy loam increased to a range of 37.60\u0026ndash;82.15 cm, with the minimum in the control at 40% FC and the maximum in compost 3% at 80% FC. Stem diameter varied from 4.32 mm (control at 80% FC) to 8.76 mm (compost 3% at 80% FC), indicating that compost 3% \u0026times; 80% FC was clearly the most favorable combination for early drought-stage radial growth. Leaf number ranged between 17 and 29 leaves, with the highest value recorded in compost 3% at 60% FC and the lowest encountered across several combinations including control at 40% and 80% FC, and chemical fertilizer at 60% FC. Leaf area attended 877.7 cm\u0026sup2; in control at 80% FC and 1501.1 cm\u0026sup2; in compost 3% at 80% FC, while LAI ranged between 0.975 (control at 80% FC) and 1.668 (compost 3% at 80% FC). These patterns show that, once stress is initiated, compost 3% under 80% FC maximizes height, stem thickening, leaf area, and LAI, whereas unfertilized plants, especially under lower FC, quickly lag behind in vegetative expansion.\u003c/p\u003e \u003cp\u003eDuring the mid-drought phase, plant height in sandy loam ranged between 63.99 cm (control at 40% FC) and 114.43 cm (chemical fertilizer at 80% FC). This indicates that in the intermediate phase, chemical fertilizer at 80% FC produced the tallest plants, while the unfertilized 40% FC combination remained the most penalized. Stem diameter attended 6.08 mm in control at 80% FC and 13.02 mm in compost 3% at 80% FC, confirming that 3% compost \u0026times; 80% FC continued to be the most effective for radial growth under sustained stress. Leaf number ranged between 24 (control at 40% FC) and 41 leaves (chemical fertilizer at 60% FC), and leaf area between 1207.0 cm\u0026sup2; (control at 80% FC) and 2189.0 cm\u0026sup2; (compost 1% at 80% FC). LAI simultaneously varied between 1.341 (control at 80% FC) and 2.432 (compost 1% at 80% FC). Interestingly, at this stage the largest canopy (leaf area and LAI) in sandy loam was associated with compost 1% at 80% FC, while stem thickening peaked under compost 3% and plant height under mineral fertilization at 80% FC. This divergence suggests that a moderate organic rate (1%) under high FC can favor canopy development, whereas higher compost rates (3%) enhance stem robustness, and mineral fertilization fronts plant height under optimal moisture.\u003c/p\u003e \u003cp\u003eBy harvest, morphological differences among treatments became most pronounced. Plant height in sandy loam ranged between 72.02 cm (control at 40% FC) and 143.25 cm (chemical fertilizer at 80% FC). Thus, the tallest plants were obtained with mineral fertilizer at 80% FC, while the shortest corresponded to the unfertilized, severely stressed treatment. Stem diameter at harvest varied between 7.73 mm (chemical fertilizer at 60% FC) and 16.73 mm (compost 3% at 60% FC), indicating that compost 3% under 60% FC produced the thickest stems, which likely enhanced mechanical support and drought resilience. Leaf number ranged between 30 leaves (control at 40% FC) and 50 leaves, the latter achieved under both chemical fertilizer at 60% FC and compost 3% at 80% FC. Leaf area at harvest spanned 1554.9\u0026ndash;2738.5 cm\u0026sup2;, the minimum being recorded in the control at 60% FC and the maximum in compost 1% at 80% FC. LAI ranged between 1.728 (control at 60% FC) and 3.043 (compost 1% at 80% FC). These harvest results demonstrate that severe water deficit in the absence of amendments (control 40\u0026ndash;60% FC) strongly restricts plant size and canopy development, while combinations involving high FC and compost or chemical fertilizer at moderate to high FC foster maximal vegetative growth.\u003c/p\u003e \u003cp\u003eIn the silty clay soil, the same parameters showed higher absolute values and a somewhat different ranking of treatments, reflecting the higher intrinsic fertility and WHC of this substrate. At baseline, plant height ranged between 27.30 cm (control at 40% FC) and 44.54 cm (compost 3% at 80% FC). Stem diameter varied between 2.21 mm (control at 40% FC) and 5.43 mm (compost 1% at 80% FC). Leaf number at this phase spanned 9\u0026ndash;17 leaves, the minimum observed in chemical fertilizer at 40% FC and the maximum across chemical fertilizer at 60\u0026ndash;80% FC and compost 1% at 40% FC. Leaf area ranged between 507.9 cm\u0026sup2; (compost 1% at 60% FC) and 942.8 cm\u0026sup2; (chemical fertilizer at 60% FC). LAI similarly ranged from 0.564 (compost 1% at 60% FC) to 1.048 (chemical fertilizer at 60% FC). This implies that in clay soil, chemical fertilization at moderate FC (60%) already produced the largest canopy at baseline, whereas compost 3% at 80% FC maximized height and compost 1% at 80% FC maximized stem diameter, confirming that the clay matrix enables more balanced growth under a range of fertilization strategies.\u003c/p\u003e \u003cp\u003eAt drought start, plant height in silty clay varied from 57.28 cm (control at 60% FC) to 81.71 cm (compost 1% at 80% FC). Stem diameter ranged between 4.25 mm (control at 40% FC) and 9.63 mm (compost 1% at 80% FC), indicating that compost 1% under 80% FC was particularly efficient for early drought-stage thickening in the clay soil. Leaf number at this phase ranged between 17 leaves (control at 40% FC) and 29 leaves, with the maximum recorded under compost 3% at 40% FC. Leaf area ranged from 959.0 cm\u0026sup2; (compost 1% at 60% FC) to 1745.1 cm\u0026sup2; (compost 3% at 80% FC), while LAI varied between 1.066 (compost 1% at 60% FC) and 1.939 (compost 3% at 80% FC). These data show that in the clay soil, high compost rates (3%) under 80% FC already produced the largest leaf area and LAI at drought initiation, while lower compost rates (1%) at optimal FC enhanced stem thickness and plant height. The lowest values for height, leaf number and stem diameter remained consistently associated with the control at 40\u0026ndash;60% FC, underlining the risk of insufficient fertilization even in a fertile clay context.\u003c/p\u003e \u003cp\u003eUnder mid-drought, the clay soil maintained its advantage in supporting plant growth. Plant height ranged between 78.96 cm (control at 60% FC) and 116.31 cm (compost 3% at 40% FC). Stem diameter varied from 6.44 mm (control at 40% FC) to 14.44 mm (compost 1% at 80% FC), showing that compost 1% \u0026times; 80% FC yielded the thickest stems during prolonged drought. Leaf number fluctuated between 27 and 42 leaves, with the minimum shared by chemical fertilizer at 80% FC and control at 40% FC, and the maximum observed under chemical fertilizer at 40% FC and compost 1% at 60% FC. Leaf area ranged from 1351.9 cm\u0026sup2; (compost 1% at 60% FC) to 2614.2 cm\u0026sup2; (compost 3% at 80% FC), while LAI varied between 1.502 and 2.905 for the same treatments. Thus, under intermediate stress, compost 3% under 80% FC consistently maximized canopy area and LAI in clay soil, demonstrating a strong capacity of the system to maintain leaf expansion under combined organic inputs and adequate water.\u003c/p\u003e \u003cp\u003eAt harvest, morphological performance in silty clay was clearly superior to sandy loam. Plant height ranged from 103.09 cm (control at 40% FC) to 145.27 cm (compost 3% at 80% FC). Stem diameter varied between 7.08 mm (control at 40% FC) and 17.97 mm (compost 1% at 80% FC), confirming that compost 1% at 80% FC promoted the greatest radial growth at maturity. Leaf number ranged between 34 leaves (control at 40% FC) and a maximum of 51 leaves, reached across several treatments at moderate to high FC (chemical fertilizer 80% FC, compost 1% 60% FC, compost 3% 60% FC and even control 60% FC). Leaf area at harvest ranged from 1710.0 cm\u0026sup2; (compost 1% at 60% FC) to 3221.5 cm\u0026sup2; (compost 3% at 80% FC), while LAI varied between 1.90 and 3.579 with minima and maxima in the same treatments. These harvest data clearly indicate that compost 3% under 80% FC generated the largest canopy and LAI in silty clay, while compost 1% under 80% FC maximized stem diameter, and control at 40% FC consistently produced the smallest plants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Physiological responses across all phases under water stress\u003c/h2\u003e \u003cp\u003eIn the sandy loam soil, leaf water status and photosynthetic pigments showed a clear response to both irrigation level and amendment type along the crop cycle. At the baseline phase, RWC varied between 66.4% in the chemical fertilizer treatment at 40% FC and 97.1% in the 3% compost treatment at 80% FC. This already shows that under the same sandy texture, supplying 3% compost together with high moisture allowed leaves to approach full turgor, whereas mineral fertilization without enough water reduced hydration. At the same phase, chlorophyll a ranged from 1.085 mg/g FW (control, 80% FC) to 2.067 mg/g FW (chemical fertilizer, 60% FC), while chlorophyll b ranged between 0.769 mg/g FW (control, 80% FC) and 1.380 mg/g FW (chemical fertilizer, 60% FC). Total chlorophyll therefore spanned 1.977\u0026ndash;3.447 mg/g FW, with the minimum under control at 80% FC and the maximum under chemical fertilizer at 60% FC. Baseline transpiration in sandy loam ranged from 60.2 ml/day in the control at 40% FC to 180.2 ml/day in compost 3% at 80% FC, confirming that combining OM with high FC strongly stimulates gas exchange from the very beginning of the cycle.\u003c/p\u003e \u003cp\u003eAt drought start, in sandy loam soil, RWC decreased slightly, with values between 64.8% in the control at 40% FC and 94.5% under compost 3% at 80% FC. The lowest RWC now clearly appears in the most stressed and unfertilized combination, while the compost 3% \u0026times; 80% FC maintains the highest hydration. Chlorophyll a ranged from 0.947 mg/g FW (control at 60% FC) to 2.006 mg/g FW (compost 3% at 40% FC), chlorophyll b from 0.726 to 1.416 mg/g FW (same minimum in control 60% FC and maximum in compost 3% at 80% FC), and total chlorophyll between 1.743 mg/g FW (control, 60% FC) and 3.300 mg/g FW (compost 3% at 60% FC). This indicates that, once stress is initiated, chlorophyll degradation is most severe in the unfertilized or poorly irrigated plants, whereas both 3% compost and relatively high FC (60\u0026ndash;80% FC) sustain pigment content. Transpiration at drought start ranged from 118.4 ml/day in the control at 40% FC to 327.7 ml/day in compost 1% at 80% FC. Interestingly, at this phase the maximum water flux is observed in the 1% compost \u0026times; 80% FC combination rather than 3% compost, suggesting that a moderate organic rate under optimal FC can support very active stomatal conductance in sandy soil.\u003c/p\u003e \u003cp\u003eDuring mid-drought, stress intensity increased, and this was reflected in both water status and pigment levels. RWC in sandy loam ranged from 58.3% in the 1% compost treatment at 40% FC to 91.7% under 3% compost at 80% FC. The lowest RWC at this stage thus appears not in the unfertilized control but in the low-rate compost under severe deficit, emphasizing that insufficient water can negate part of the benefit of organic inputs. Chlorophyll a at mid-drought ranged between 1.094 mg/g FW (chemical fertilizer, 60% FC) and 1.997 mg/g FW (3% compost, 60% FC), while chlorophyll b ranged from 0.723 mg/g FW to 1.307 mg/g FW; both minimum and maximum chlorophyll b values occurred in the 1% compost at 40% FC, reflecting high variability in that treatment. Total chlorophyll spanned 1.938\u0026ndash;3.124 mg/g FW, with the minimum again in chemical fertilizer at 60% FC and the maximum in 3% compost at 60% FC. Transpiration at mid-drought ranged from 185.4 ml/day in 1% compost at 40% FC to 494.2 ml/day in 1% compost at 80% FC. These data confirm that in sandy loam, high FC strongly promotes transpiration regardless of compost level, but that 3% compost is particularly efficient in maintaining high RWC and chlorophyll under sustained stress.\u003c/p\u003e \u003cp\u003eBy harvest, physiological divergence among treatments was largest. In sandy loam, RWC ranged between 58.2% in 1% compost and 40% FC to 91.9% in 3% compost at 80% FC. Leaf hydration at harvest thus remained highest when both OM and water were abundant, while the lowest hydration consistently occurred under low moisture combined with insufficient organic supply. Chlorophyll a varied between 1.245 mg/g FW (chemical fertilizer at 60% FC) and 2.115 mg/g FW (3% compost at 60% FC), chlorophyll b between 0.749 and 1.462 mg/g FW, and total chlorophyll between 2.073 mg/g FW (chemical fertilizer at 40% FC) and 3.577 mg/g FW (3% compost at 60% FC). Transpiration at harvest ranged from 247.9 ml/day in the control at 40% FC to 655.9 ml/day in 1% compost at 80% FC. Taken together, these results show that in sandy loam, compost, especially at 3%, is crucial to maintaining RWC and chlorophyll throughout the cycle, while high FC levels (80% FC, sometimes 60% FC) are critical for sustaining transpiration and thus carbon assimilation. The combinations with low FC, particularly 1% compost at 40% FC and the control at 40% FC, repeatedly show the lowest physiological performance.\u003c/p\u003e \u003cp\u003eIn the silty clay soil, the same variables show higher absolute values and a more buffered response to stress, due to the improved water and nutrient retention of the fine-textured matrix. At baseline, RWC in clay soil ranged from 67.2% in the chemical fertilizer treatment at 40% FC to 98.0% under 3% compost at 80% FC, slightly higher than in sandy loam. Chlorophyll a at this phase varied from 1.329 mg/g FW (control, 60% FC) to 2.245 mg/g FW (3% compost, 60% FC), chlorophyll b from 0.800 to 1.636 mg/g FW, and total chlorophyll from 2.233 to 3.798 mg/g FW, with minima consistently associated with the control at 60% FC and maxima with 3% compost at 80% FC. Baseline transpiration in clay ranged from 68.2 ml/day in the control at 40% FC to 188.1 ml/day in chemical fertilizer at 80% FC, indicating slightly higher baseline gas exchange compared with the sandy soil.\u003c/p\u003e \u003cp\u003eAt drought start, RWC in silty clay ranged from 67.9% in 1% compost at 40% FC to 98.0% in 1% compost at 80% FC. This shows that, under the same clay texture, modifying FC and compost rate modulates leaf hydration, but even the minimum RWC remains slightly higher than the corresponding minima in sandy soil at the same phase. Chlorophyll a varied between 1.220 and 2.437 mg/g FW, chlorophyll b between 0.872 and 1.610 mg/g FW, and total chlorophyll between 2.091 and 4.047 mg/g FW, with all minima observed in the control at 40% FC and maxima consistently in 3% compost at 80% FC. Transpiration at drought start in clay extended from 135.3 ml/day (control, 40% FC) to 370.2 ml/day (3% compost, 80% FC), thus exceeding the average rate in sandy loam and confirming that clay soil better supports gas exchange under early stress when combined with organic inputs.\u003c/p\u003e \u003cp\u003eDuring mid-drought, silty clay continued to buffer stress effects. RWC ranged from 62.9% in the chemical fertilizer treatment at 40% FC to 95.2% under 3% compost at 80% FC, compared with 76.1% in sandy loam. Chlorophyll a ranged between 1.365 and 2.372 mg/g FW, chlorophyll b between 0.828 and 1.589 mg/g FW, and total chlorophyll between 2.329 and 3.876 mg/g FW, with the lowest total chlorophyll recorded in control at 60% FC and the highest in 3% compost at 60% FC. Transpiration ranged from 222.4 ml/day (control, 40% FC) to 606.6 ml/day (3% compost, 80% FC), again higher than the 305.9 ml/day observed in sandy soil. This confirms that, under intermediate stress, 3% compost at 80% FC is particularly effective in sustaining both water flux and chlorophyll in the clay matrix.\u003c/p\u003e \u003cp\u003eBy harvest, clay soil still exhibited higher physiological resilience than sandy soil. RWC ranged between 60.0% in the control at 40% FC and 89.5% in 1% compost at 80% FC and 3% compost at 80% FC. Chlorophyll a varied between 1.162 and 2.338 mg/g FW, chlorophyll b from 0.730 to 1.616 mg/g FW, and total chlorophyll from 1.892 to 3.954 mg/g FW, with all minima associated with chemical fertilizer at 40% FC and maxima consistently in 3% compost at 80% FC. Transpiration at harvest ranged between 283.6 ml/day (control, 40% FC) and 700.0 ml/day (3% compost, 80% FC), clearly higher than the 399.9 ml/day observed in sandy loam at the same phase.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Leaf nutrient content across all phases under water stress\u003c/h2\u003e \u003cp\u003eIn the sandy loam soil, leaf macronutrient contents showed relatively stable averages across phases but clear differences in minima and maxima according to amendment and irrigation. For leaf N, at the baseline phase values ranged between 2.20% in the control at 80% FC and 3.24% in compost 3% at 80% FC. At drought start, leaf N varied between 2.18% in chemical fertilizer at 60% FC and 3.15% in compost 3% at 60% FC. During mid-drought, the minimum was 2.21% in the control at 80% FC and the maximum 3.14% in compost 3% at 60% FC. At harvest, N ranged from 2.15% (control at 60% FC) to 3.26% (chemical fertilizer at 60% FC). Across the cycle, sandy loam leaf N therefore remained in a narrow band around 2.7\u0026ndash;2.8%, with the highest values systematically in compost 3% or chemical fertilizer at moderate or high FC, and the lowest values mostly in unfertilized or suboptimally irrigated combinations.\u003c/p\u003e \u003cp\u003eFor leaf P in sandy loam, at baseline the minimum was 0.279% in the chemical fertilizer treatment at 80% FC and the maximum 0.406% in the control at 60% FC. At drought start, P ranged between 0.273% (control at 40% FC) and 0.390% (chemical fertilizer at 80% FC). At mid-drought, the minimum was 0.270% (control at 40% FC) and the maximum 0.404% (compost 3% at 60% FC), while at harvest values varied between 0.261% (chemical fertilizer at 80% FC) and 0.401% (compost 1% at 60% FC). These results show that leaf P was quite stable in average terms (=\u0026thinsp;0.33\u0026ndash;0.34%), but peak values shifted between control and compost or mineral treatments depending on FC, with high P often associated with intermediate water levels where growth dilution is less pronounced.\u003c/p\u003e \u003cp\u003eFor leaf K in sandy loam, at baseline the range was 2.08\u0026ndash;3.34%, with the minimum in the control at 80% FC and the maximum in compost 3% at 80% FC. At drought start, K ranged between 2.08% (control at 80% FC) and 3.439% (compost 3% at 40% FC). During mid-drought, the minimum was 2.123% in the control at 40% FC, the maximum 3.219% in chemical fertilizer at 40% FC. At harvest, the minimum K was 2.107% (control at 80% FC) and the maximum 3.307% (compost 3% at 40% FC). Thus, leaf K in sandy loam remained around 2.7% on average, but compost 3% and, in some phases, mineral fertilizer produced substantially higher K contents, especially under non-severe FC, while the control at 80% FC repeatedly returned the lowest K values.\u003c/p\u003e \u003cp\u003eThe Ca in sandy loam showed a similar pattern of moderate but consistent enrichment under compost. At baseline, leaf Ca ranged between 1.066% in the control at 40% FC and 1.987% in compost 3% at 40% FC. At drought start, the minimum was 1.173% (control at 80% FC) and the maximum 1.89% (compost 3% at 80% FC). During mid-drought, Ca varied between 1.133% (chemical fertilizer at 60% FC) and 1.974% (compost 1% at 40% FC), while at harvest it ranged from 1.312% in compost 3% at 40% FC to 1.932% in compost 3% at 80% FC. These values indicate that compost, particularly at 3% under 80% FC, tended to maximize Ca accumulation, whereas control and some mineral combinations under lower FC frequently gave the lowest Ca values.\u003c/p\u003e \u003cp\u003eFor leaf Mg in sandy loam, at baseline concentrations ranged between 0.345% (control at 80% FC) and 0.576% (control at 40% FC). At drought start, Mg varied between 0.397% in compost 1% at 60% FC and 0.707% in compost 3% at 60% FC. During mid-drought, Mg ranged from 0.346% (control at 60% FC) to 0.628% (compost 3% at 80% FC), at harvest, it varied between 0.388% (chemical fertilizer at 80% FC) and 0.626% (compost 3% at 40% FC). Leaf Mg thus remained relatively stable in average around 0.48%, with higher values often associated with compost 3%, and lower values with unfertilized or mineral-fertilized treatments at high FC, where greater biomass production likely diluted Mg concentration.\u003c/p\u003e \u003cp\u003eIn the silty clay soil, leaf nutrient contents were generally higher and more homogeneous, reflecting the higher buffering capacity and fertility of this substrate. For leaf N, at baseline values ranged between 2.579% in the control at 40% FC and 3.885% in compost 1% at 80% FC. At drought start, the minimum N (2.699%) occurred in the control at 60% FC, whereas the maximum (3.864%) was observed in compost 3% at 60% FC. During mid-drought, N varied between 2.669% (control at 40% FC) and 3.76% (compost 3% at 80% FC). At harvest, the minimum N was 2.65% in the control at 40% FC and the maximum 3.82% in compost 3% at 40% FC. Compared with sandy loam, leaf N in clay was consistently higher and most enhanced under compost treatments, especially 3% compost at 60\u0026ndash;80% FC.\u003c/p\u003e \u003cp\u003eFor leaf P in silty clay, at baseline the minimum of 0.306% occurred in the control at 80% FC, and the maximum of 0.441% in compost 3% at 40% FC. At drought start, P ranged from 0.287% (control at 60% FC) to 0.428% (both compost 3% at 40% FC and control at 40% FC). At mid-drought, values varied between 0.278% in the control at 60% FC and 0.449% in compost 3% at 80% FC, at harvest they ranged from 0.311% (control at 60% FC) to 0.428% (compost 3% at 60% FC). In clay soil, leaf P thus remained slightly higher than in sandy loam on average, with peaks consistently reached in compost 3% under moderate or high FC.\u003c/p\u003e \u003cp\u003eLeaf K in silty clay was also higher than in sandy loam. At baseline, values ranged from 2.537% in the chemical fertilizer at 80% FC to 3.623% in the control at 80% FC. At drought start, K varied between 2.717% (compost 3% at 80% FC) and 3.606% (control at 60% FC). During mid-drought, the minimum value 2.574% occurred in chemical fertilizer at 80% FC, while the maximum 3.473% was observed in compost 3% at 60% FC. At harvest, K ranged between 2.227% (compost 3% at 80% FC) and 3.525% (compost 3% at 60% FC). These results confirm that silty clay maintained high K levels across treatments and phases, but 3% compost, particularly at 60% FC, was most effective in maximizing foliar K at later stages.\u003c/p\u003e \u003cp\u003eFor leaf Ca in silty clay, at baseline the minimum of 1.484% occurred in chemical fertilizer at 80% FC, while the maximum of 1.982% was in chemical fertilizer at 60% FC. At drought start, Ca ranged from 1.302% (compost 1% at 40% FC) to 1.973% (compost 1% at 80% FC). During mid-drought, the minimum was 1.225% (compost 1% at 40% FC) and the maximum 2.03% (chemical fertilizer at 80% FC). At harvest, Ca ranged between 1.421% in the control at 40% FC and 1.973% in compost 3% at 80% FC. Thus, the clay soil allowed higher Ca contents than the sandy soil, with peaks occurring in either mineral or compost treatments depending on phase, but the lowest Ca values consistently associated with low FC and low or moderate compost rates.\u003c/p\u003e \u003cp\u003eFinally, leaf Mg in silty clay showed moderately higher average values than in sandy loam. At baseline, Mg ranged between 0.366% and 0.685%, both minima and maxima occurring in the control at 40% FC due to within-treatment variability. At drought start, Mg varied between 0.419% (control at 40% FC) and 0.637% (compost 1% at 80% FC). During mid-drought, Mg ranged from 0.415% to 0.635%, both extremes in the control at 40% FC. At harvest, the minimum Mg was 0.393% in compost 1% at 80% FC, the maximum 0.634% in compost 3% at 80% FC. Compared with sandy loam, leaf Mg in clay was slightly higher and more stable, with compost 3% at 80% FC tending to produce the highest concentrations at the end of the cycle.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4. Plant morphological, root, and yield responses at harvest under water stress\u003c/h2\u003e \u003cp\u003eThe harvest stage revealed the cumulative effects of soil texture, compost amendment, and irrigation regime on above and below ground biomass allocation and fruit productivity. In the sandy loam soil, shoot biomass exhibited substantial variability, ranging between 70.92 g in the compost 3% treatment at 40% FC and 152.63 g under the control at 80% FC. The lowest shoot biomass in compost 3% at severe deficit suggests that high OM cannot compensate for limited water availability in coarse-textured soils. Conversely, the highest shoot mass in the unfertilized 80% FC treatment reflects the strong influence of adequate moisture on above-ground development despite the absence of nutrient inputs, indicating that moisture was a stronger limiting factor than fertility for shoot biomass at harvest in sandy loam.\u003c/p\u003e \u003cp\u003eRoot dry weight in sandy loam ranged between 12.00 g in the 3% compost treatment at 80% FC and 41.17 g in the chemical fertilizer treatment at 60% FC. This pattern suggests that mineral fertilization under moderate irrigation promotes more robust root mass compared with high compost rates under high FC, where plants tended to allocate fewer resources below ground, likely due to reduced need for root exploration when nutrient and water availability were ample. Root length varied between 37.60 cm in the control at 60% FC and 94.50 cm in the compost 1% treatment at 40% FC, revealing that moderate compost doses under WS incentivize deeper or more extensive root systems. Root volume ranged from 11.68 cm\u0026sup3; in compost 3% at 40% FC to 38.77 cm\u0026sup3; under compost 3% at 60% FC, confirming that compost enhances root structural development, particularly when sufficient water (60% FC) is available.\u003c/p\u003e \u003cp\u003eFruit yield in sandy loam showed the greatest dispersion, ranging from 523 g in the control at 60% FC to 2066 g in compost 3% at 80% FC. The extremely high yield under compost 3% \u0026times; 80% FC demonstrates the synergistic effect of OM and adequate water supply on reproductive performance. The lowest yield under control with 60% FC underscores that in coarse soils, moisture deficit combined with nutrient scarcity severely limits fruit production. These patterns highlight that in sandy loam, compost amendments and high FC not only improved root and shoot development but were especially effective in enhancing fruit productivity, with compost 3% at 80% FC providing the optimal harvest performance across all measured parameters.\u003c/p\u003e \u003cp\u003eIn the silty clay soil, biomass and yield values were higher overall, reflecting the greater fertility and WHC of the fine-textured matrix. Shoot dry weight ranged from 74.85 g under compost 1% at 60% FC to 195.60 g in compost 3% at 80% FC. Contrary to sandy loam, the maximum shoot biomass in clay occurred under the highest compost and FC combination, indicating that the clay matrix can fully exploit the added OM to support above-ground growth. Root dry weight varied between 15.69 g in the compost 1% under 40% FC and 52.52 g under compost 3% at 80% FC, confirming that compost 3% under high FC conditions maximized root biomass in clay soil. Root length values ranged between 55.80 cm (chemical fertilizer, 40% FC) and 105.60 cm (the control at 80% FC). Interestingly, the longest roots were observed in the unfertilized but well-irrigated treatment, suggesting that in clay soils, high moisture availability alone can drive extensive root elongation independently of amendment rates. Root volume varied between 12.84 cm\u0026sup3; (control at 40% FC) and 41.55 cm\u0026sup3; (compost 3% at 80% FC), showing that compost 3% \u0026times; 80% FC produced the most structurally developed root systems.\u003c/p\u003e \u003cp\u003eFruit yield in silty clay ranged between 553 g in the control at 80% FC and 1322 g under chemical fertilizer at 80% FC. Unlike sandy loam, the highest yield in clay soil occurred with mineral fertilization rather than compost, suggesting that nutrient release dynamics interact differently with this soil\u0026rsquo;s physicochemical properties. The lowest yield did not occur under the most severe stress (40% FC) but under control with 80% FC, indicating that while clay soils buffer WS effectively, nutrient limitations can still restrict reproductive output even when moisture is abundant.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Multivariate Statistical Analysis\u003c/h2\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. Correlation Structure Among Soil Properties, Plant Functional Traits, and Yield Components\u003c/h2\u003e \u003cp\u003eThe correlation analysis revealed a highly structured set of relationships linking soil physicochemical properties, plant morphological and physiological traits, nutrient status, and final biomass and yield components \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Together, these patterns demonstrate how soil quality, water availability, and amendment induced changes cascade through the plant system to influence performance under WS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe soil fertility indicators showed strong positive associations with growth and yield parameters, indicating that nutrient-rich soils provide a physiological advantage throughout plant development. Exchangeable cations such as Ca, Mg, and K were positively correlated with plant height, leaf area, and LAI, with coefficients generally exceeding r\u0026thinsp;\u0026gt;\u0026thinsp;0.60, reflecting their essential role in turgor regulation, stomatal function, and chloroplast stability. The soil CEC, which integrates clay content and OM quality, displayed some of the strongest correlations with vegetative growth, particularly with leaf area and stem diameter (up to r\u0026thinsp;=\u0026thinsp;0.70\u0026ndash;0.85, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This suggests that soils capable of retaining and releasing nutrients steadily throughout the drought period provided plants with a more buffered supply of essential ions, reducing the physiological stress associated with declining soil moisture.\u003c/p\u003e \u003cp\u003eWater-related soil parameters, including FC, AWC, and WHC, were strongly linked to both physiological performance and yield outcomes. AWC in particular demonstrated high correlations with chlorophyll content and RWC, often exceeding r\u0026thinsp;=\u0026thinsp;0.75, indicating that soils capable of retaining more plant-available water enabled the maintenance of cellular hydration and chloroplast function under drought. The correlation between AWC and fruit yield was also robust (r\u0026thinsp;\u0026asymp;\u0026thinsp;0.70\u0026ndash;0.80), highlighting that water availability throughout the reproductive stage was a primary determinant of fruit filling and final productivity. This pattern was reinforced by the strong positive association between WHC and root length, showing that soils with greater moisture retention stimulated deeper or more expansive root systems, which enhanced the plant\u0026rsquo;s capacity to acquire water under increasing deficit.\u003c/p\u003e \u003cp\u003ePhysiological traits were tightly interlinked with biomass accumulation and yield, underscoring their role as sensitive integrators of plant stress and resource availability. RWC demonstrated one of the strongest correlations with fruit yield (r\u0026thinsp;=\u0026thinsp;0.80\u0026ndash;0.90, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), emphasizing that maintaining internal water status under drought was a key determinant of reproductive success. Similarly, chlorophyll a, chlorophyll b, and total chlorophyll were positively correlated with shoot biomass and fruit yield, with coefficients frequently above r\u0026thinsp;=\u0026thinsp;0.70, indicating that photosynthetic capacity under stress directly influenced carbon assimilation and allocation. The strong correlation between RWC and chlorophyll indices (r\u0026thinsp;\u0026gt;\u0026thinsp;0.80) demonstrates the physiological co-regulation of water status and pigment stability, plants capable of sustaining hydration were also capable of maintaining chlorophyll integrity, resulting in more sustained photosynthetic activity.\u003c/p\u003e \u003cp\u003eNutrient content in leaves revealed additional insights into yield drivers under WS. Leaf N and K levels were positively correlated with fruit yield (r\u0026thinsp;=\u0026thinsp;0.60\u0026ndash;0.75), consistent with their roles in protein synthesis, osmotic adjustment, and stomatal control. Leaf Ca and Mg showed moderate to strong correlations with both root volume and shoot biomass, often above r\u0026thinsp;=\u0026thinsp;0.65, supporting their importance in cell wall stability, membrane integrity, and enzyme activation during stress (Xie et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These patterns suggest that nutrient uptake was not only influenced by soil supply but also by root system development, which in turn was shaped by moisture availability and soil structure.\u003c/p\u003e \u003cp\u003eYield was positively associated with nearly all growth and physiological variables, confirming that productive plants were those that maintained both structural development and physiological function throughout drought progression. Fruit yield displayed particularly strong correlations with leaf area, LAI, chlorophyll content, and RWC, typically exceeding r\u0026thinsp;=\u0026thinsp;0.80, highlighting that well-developed canopies and the maintenance of photosynthetic pigments were essential for assimilate production during fruit filling. The correlations between root traits and yield, especially root volume and root length, underscore the pivotal role of root system architecture in supporting water and nutrient uptake, with coefficients ranging from r\u0026thinsp;=\u0026thinsp;0.55\u0026ndash;0.70. These findings corroborate the view that deep or voluminous roots mitigate the negative impacts of water deficit by facilitating access to subsurface moisture and enabling the continued supply of essential ions to the shoot.\u003c/p\u003e \u003cp\u003eThe combined correlation structure reveals a coherent mechanism: soils with higher fertility and greater water retention promoted stronger root systems, which in turn supported improved physiological resilience, leading to enhanced vegetative development and ultimately higher fruit yield. WS markers such as declining RWC and chlorophyll degradation were tightly linked with reductions in biomass and reproductive output, confirming their utility as integrative indicators of drought severity. At the same time, nutrient-linked variables such as leaf K, Ca, and N demonstrated that mineral nutrition remained a central component of drought tolerance by reinforcing osmotic stability, metabolic activity, and tissue integrity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. PCA of Integrated Soil\u0026ndash;Plant\u0026ndash;Yield Relationships at Harvest.\u003c/h2\u003e \u003cp\u003eThe PCA of the harvest dataset revealed a well-structured multivariate pattern linking soil physicochemical properties, plant functional traits, root system development, and final yield \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe first two principal components explained approximately 81.67% of the overall variance, with PC1 accounting for 57.33% and PC2 contributing 24.34%. Together, these components provide a clear dimensional reduction that captures the dominant gradients shaping tomato performance under differing soil types, organic amendment levels, and irrigation regimes.\u003c/p\u003e \u003cp\u003ePC1 represented the major productivity and resource-status axis, with strong positive loadings from biomass and yield-related traits including Shoot Biomass, Root Biomass, Root Volume, Root Length, and Yield, as well as plant functional parameters such as Leaf Area, LAI, Plant Height, and physiological variables (RWC, Chlorophyll a, Chlorophyll b, Chlorophyll total). Leaf nutrient concentrations, particularly Leaf N, Leaf K, and Leaf Ca, also loaded positively on PC1, indicating that nutrient assimilation and physiological resilience co-varied with biomass accumulation at harvest. Soil water-related properties including AWC, WHC, and FC displayed positive associations with PC1, which reflects the fundamental influence of soil moisture retention in supporting plant water status, photosynthetic function, and carbon allocation to yield. Conversely, negative loadings on PC1 were associated with soil parameters indicative of weaker fertility or structure, such as lower OM, CEC, and reduced macro-nutrient availability, showing that nutrient-poor soil profiles clustered toward the negative dimension of PC1.\u003c/p\u003e \u003cp\u003eThe strong coupling between physiological variables and biomass traits along PC1 highlights that RWC and chlorophyll stability were key determinants of yield outcomes. Their high loadings suggest that plants maintaining hydration and photosynthetic pigment concentration under stress were able to sustain assimilate production, which translated directly into higher fruit biomass. The joint positioning of root and shoot traits on the positive PC1 axis indicates that vigorous root systems enhanced uptake of both water and nutrients, improving canopy development and thereby boosting yield.\u003c/p\u003e \u003cp\u003ePC2 captured a secondary but meaningful axis related to soil physicochemical variation and its influence on plant nutrient status. Variables such as soil pH, EC, Mg, Ca, and to a lesser extent, Na contributed to variation along this axis. While these did not strongly influence yield relative to PC1, they describe an orthogonal gradient related to inherent soil mineral composition and salinity-related properties. Leaf nutrient variables including Leaf Mg and Leaf P also showed moderate loadings on PC2, suggesting that specific nutrient dynamics independent of global biomass and yield processes contributed to treatment differentiation.\u003c/p\u003e \u003cp\u003eThe spatial distribution of observations in the PCA biplot demonstrates that treatments receiving 3% compost under 80% FC clustered along the positive extremes of PC1, consistent with the high biomass and yield values observed in univariate analyses. Treatments under 40% FC, especially in the control and chemical fertilizer groups, tended to cluster on the negative side of PC1, reflecting reduced physiological performance, compromised nutrient uptake, and limited biomass allocation under severe water deficit. Intermediate irrigation treatments (60% FC) occupied mid-range positions along PC1, with compost-amended samples shifting toward the positive axis relative to mineral and unfertilized treatments. Soil-type separation was also evident, with silty clay soils generally positioned toward the positive directions of both PC1 and PC2 owing to their greater water retention and nutrient-buffering capacity, while sandy loam treatments populated the lower-loading axes, reflecting limited structural and fertility attributes associated with coarse textures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3. HCA of Integrated Soil\u0026ndash;Plant\u0026ndash;Yield Relationships at Harvest\u003c/h2\u003e \u003cp\u003eThe hierarchical cluster analysis revealed a highly structured multivariate organization reflecting the combined influence of soil quality, compost amendment, irrigation regime, and plant physiological functioning on biomass and yield outcomes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The dendrogram distinguished two major sample clusters representing clear contrasts in plant performance and environmental conditions at harvest. The first large cluster consisted predominantly of samples originating from silty clay soils under moderate to high moisture levels (60\u0026ndash;80% FC) and especially those receiving 3% compost amendment. These samples grouped together because they expressed consistently elevated values for physiological indicators such as RWC and total chlorophyll, and for structural and functional parameters such as leaf area, LAI, plant height, and shoot biomass. Their shared position in the dendrogram demonstrates that heavy-textured soils with superior water-retention capacity supported more stable physiological functioning, enabling plants to maintain hydration, preserve photosynthetic pigments, and allocate substantial biomass to both root and shoot systems. This cluster also included nearly all high-yielding observations, indicating that the multivariate signatures of hydraulic stability, nutrient availability, and robust canopy development were the dominant determinants of fruit yield.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe second major cluster comprised mainly sandy soil samples, which segregated clearly due to their lower soil water-holding characteristics, reduced nutrient-retention capacity, and the associated decline in physiological stability. Within this cluster, treatments subjected to 40% FC consistently grouped together, regardless of amendment type, underscoring the overarching influence of severe WS in driving multivariate dissimilarity. These samples were characterized by low RWC, lower chlorophyll concentrations, smaller leaf area, and reduced shoot and root biomass. Their grouping in the dendrogram is consistent with the physiological distress caused by limited soil water availability, leading to impaired photosynthetic function and restricted assimilate allocation. Control and mineral fertilizer treatments under sandy soils further consolidated within this cluster, reflecting the insufficient nutrient-buffering capacity of low OM soils when not supplemented with compost.\u003c/p\u003e \u003cp\u003eThe parameter-based clustering from the heatmap revealed biologically coherent patterns that aligned with known soil\u0026ndash;plant interactions under WS \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. A tightly grouped cluster of biomass and yield parameters (Shoot biomass, Root biomass, Root Length, Root Volume, Yield) emerged alongside canopy development traits (Leaf area, LAI, Plant height\u003cb\u003e)\u003c/b\u003e, indicating that structural growth and carbon allocation are strongly co-regulated. These variables also clustered close to physiological indicators such as RWC and total chlorophyll, confirming that plants maintaining hydration and photosynthetic pigments were those capable of sustaining growth and fruit filling. This parameter cluster also associated strongly with soil hydraulic indicators (AWC, WHC, FC), demonstrating that soil moisture dynamics influenced plant performance at multiple levels, from water relations to biomass formation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA separate soil-chemistry cluster included variables such as soil Ca, Mg, K, CEC, and OM, reflecting the fertility gradient between silty clay and sandy soils. These parameters clustered away from stress indicators and biomass traits, indicating that the chemical richness and cation exchange properties of soils are foundational attributes that indirectly shape plant functioning by determining nutrient supply. Leaf nutrient concentrations also formed a coherent group, reflecting synchronized nutrient uptake processes and their dependence on root function and soil fertility. Their intermediate position between the soil-fertility cluster and physiological parameters suggests their mediating role in linking soil chemistry to physiological resilience under water deficit.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e3.3.4. LDA of Treatment Classes Based on Integrated Soil\u0026ndash;Plant\u0026ndash;Yield Variables at Harvest\u003c/h2\u003e \u003cp\u003eThe LDA applied to the combined harvest dataset (soil, plant, physiological, nutrient, biomass, and yield variables) produced a clear multivariate discrimination of the experimental groups defined by soil type, compost amendment, and irrigation level \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe first two discriminant functions (LD1 and LD2) captured the main structure of between group variance and provided an efficient separation of treatment categories in the reduced LD1\u0026ndash;LD2 space. Overall, the leave-one-out cross-validation (LOOCV) procedure yielded a classification accuracy of 80.2%, indicating that the selected variables possess a strong discriminatory power to distinguish among the 24 soil\u0026ndash;amendment\u0026ndash;irrigation combinations.\u003c/p\u003e \u003cp\u003eLD1 represented the dominant discrimination axis, contrasting highly productive, physiologically stable treatments with those characterized by WS and suboptimal resource status. High positive coefficients on LD1 were associated with biomass and yield variables and with canopy development traits such as Leaf area, LAI, and Plant height, while physiological variables including RWC, Chlorophyll a, Chlorophyll b, and Chlorophyll total also contributed positively. Leaf nutrient variables, in particular Leaf N and Leaf K, loaded positively on LD1, reinforcing their role in supporting growth and yield under favorable conditions. Soil hydrological properties (AWC, WHC, FC) and fertility indicators (CEC, Ex Ca, K, OM) also showed positive loadings, confirming that soils with higher water- and nutrient-retention capacity underpinned the high LD1 scores. Negative loadings on LD1 were linked to combinations of low water availability and poorer soil structure, corresponding to stressed, low-yielding treatments.\u003c/p\u003e \u003cp\u003eLD2 captured a secondary gradient related more to soil-type specific properties and the balance between root allocation and above-ground performance. Some soil chemical variables such as pH, EC, Na, and specific cation patterns showed contrasting contributions on LD2, together with more moderate loadings from leaf nutrients like Leaf Mg and Leaf P. This indicates that LD2 separated treatments according to subtler differences in mineral composition and rooting strategies, rather than overall productivity. Together, LD1 and LD2 provided a two-dimensional representation in which high-yielding, well-irrigated treatments were clearly separated from low yielding, water stressed ones, while also distinguishing the influence of soil texture and amendment strategy.\u003c/p\u003e \u003cp\u003eIn the LD1\u0026ndash;LD2 scatterplot, treatments on silty clay clustered mainly on the positive side of LD1, especially those receiving 3% compost at 60% and 80% FC. These groups combined high soil water retention, strong physiological performance, and high biomass and yield values, reflecting their position as optimal or near-optimal management combinations. Conversely, sandy loam treatments, particularly under 40% FC and in control or mineral fertilizer plots, occupied the negative extremities of LD1. Their position corresponded to low RWC, reduced chlorophyll content, limited root and shoot biomass, and poor yield, highlighting the combined effect of coarse texture and water deficit. Intermediate treatments, such as compost 1% or 60% FC combinations, occupied mid-range positions along LD1, reflecting transitional performance between stressed and optimal conditions.\u003c/p\u003e \u003cp\u003eThe classification table derived from LOOCV shows that most misclassifications occurred among treatments with similar irrigation and amendment levels within the same soil type, underlining that treatments with comparable soil water status and amendment rate tend to converge in their multivariate response. In contrast, confusion between highly contrasting groups (SC\u0026ndash;C3\u0026ndash;80% FC versus SL\u0026ndash;C0\u0026ndash;40% FC) was minimal, confirming that the combined soil\u0026ndash;plant variable set captured the major differences between favorable and limiting environments. The relatively high overall classification accuracy (around 80%) and the clear separation of clusters in LD space confirm that integrating soil physicochemical data with plant functional traits, biomass, and yield provides a robust basis for discriminating management scenarios under water stress.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.4. PLSR Analysis\u003c/h2\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1. PLSR of Soil\u0026ndash;Plant\u0026ndash;Yield Relationships in Sandy Loam Soils at Harvest\u003c/h2\u003e \u003cp\u003eThe PLSR analysis performed on the harvest-phase dataset for sandy loam soil revealed a clear multivariate gradient linking soil fertility and water-retention properties with plant physiological performance, structural growth, and final biomass\u0026ndash;yield outcomes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. The first two latent components (T1 and T2) captured the major axes of variation, with latent variable 1 (LV1) explaining approximately 34.1% of the variance and latent variable 2 (LV2) explaining about 8.9%.\u003c/p\u003e \u003cp\u003eLV1 represented the primary productivity and water\u0026ndash;nutrient status axis. Positive LV1 loadings were strongly associated with soil moisture-related variables, particularly AWC, WHC, and FC, reflecting the pivotal role of water retention capacity in coarse-textured soils. These soil parameters clustered closely with key physiological attributes such as RWC and chlorophyll parameter, indicating that sandy-loam treatments benefiting from higher moisture retention maintained superior hydration and chlorophyll stability during the drought cycle. The alignment of these variables with structural growth metrics (Leaf area, LAI, Plant height) and biomass traits (Shoot biomass, Root biomass, Root Volume) demonstrates that LV1 effectively captures the continuum from stressed, low-performing treatments to highly productive, physiologically stable plants. Fruit yield aligned positively with LV1, confirming that this axis represents the integrative eco-physiological gradient driving harvest performance in sandy loam. Conversely, negative LV1 associations were linked to lower soil fertility indicators (e.g., reduced OM, CEC, and base cation availability) and diminished physiological performance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTreatments with strong negative T1 scores corresponded to combinations involving 40% FC and minimal amendment (control or chemical fertilizer), where reduced water availability and the poor nutrient-buffering capacity of sandy soil restricted plant function. These treatments exhibited low canopy development, reduced root biomass, and limited assimilate allocation to fruits traits captured clearly by their negative projections on LV1.\u003c/p\u003e \u003cp\u003eLV2 captured a secondary differentiation among treatments, primarily reflecting contrasts in physiological adjustments and specific soil chemical signatures rather than broad productivity trends. Variables such as soil EC, Na, Mg, and certain leaf nutrient concentrations contributed more strongly to LV2 than to LV1. The orientation of these variables suggests that LV2 identifies subtle metabolic adjustments or nutrient imbalances under particular amendment\u0026ndash;irrigation combinations. While LV2 explained less variance than LV1, it nonetheless helped discriminate between intermediate treatments (e.g., compost 1% at 60% FC vs chemical fertilizer at 80% FC), where plants displayed moderate physiological stability but differed in nutrient assimilation patterns.\u003c/p\u003e \u003cp\u003eThe T1\u0026ndash;T2 score plot clearly illustrated treatment separation across sandy-loam environments. Treatments receiving 3% compost at 80% FC achieved the highest positive scores along LV1, indicating that enhanced water retention and nutrient enrichment provided by compost substantially improved plant physiology and yield potential even in coarse-textured soils. Moderate irrigation (60% FC) combined with compost amendments positioned treatments in the central-to-positive region of LV1, reflecting partial alleviation of moisture stress. By contrast, 40% FC treatments clustered on the negative side of LV1, regardless of amendment type, demonstrating that soil moisture limitation remained the dominant constraint in sandy soils. The most stressed groups control and chemical fertilizer under 40% FC fell in the lower-left quadrant of the T1\u0026ndash;T2 space, where both LV1 (productivity gradient) and LV2 (nutrient imbalance axis) indicated severe eco-physiological strain.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2. PLSR of Soil\u0026ndash;Plant\u0026ndash;Yield Relationships in Silty Clay Soil at Harvest\u003c/h2\u003e \u003cp\u003eThe PLSR for silty clay soil revealed a distinct multivariate structure that differed markedly from the sandy-loam system, reflecting the inherently superior hydraulic and nutrient-buffering properties of fine-textured soils \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e. The LV1, which explained approximately 23.7% of the predictor variance, captured the principal productivity and water-regulation gradient influencing tomato performance in silty clay soil. LV1 loadings were dominated by hydrological and fertility-related predictors such as AWC, WHC, FC, CEC, Ca, and OM, confirming that even in a moisture-conserving soil, water retention and CEC remained key drivers of plant functioning. These soil variables were closely aligned with physiological traits including RWC, and chlorophylls containt, indicating that silty clay soils favored the maintenance of water status and photosynthetic stability throughout the drought cycle. On the response side, strong positive LV1 contributions were observed for Shoot biomass, Root biomass, Root volume, LAI, and Fruit yield, demonstrating that LV1 represents the axis of integrated physiological resilience, structural growth, and yield formation.\u003c/p\u003e \u003cp\u003eNegative LV1 scores were associated with treatments exhibiting lower physiological stability or reduced biomass allocation, although these cases were far less extreme than those observed in sandy soils. Even under 40% FC, silty clay treatments did not cluster strongly on the negative LV1 side, suggesting that soil texture buffered plants from severe WS. Instead, negative LV1 associations reflected relatively moderate declines in canopy expansion, root elongation, and chlorophyll content under low-irrigation conditions, highlighting the soil\u0026rsquo;s capacity to mitigate drought intensity through improved water storage.\u003c/p\u003e \u003cp\u003eThe LV2 explaining approximately 11.5% of predictor variance, captured a secondary gradient related to nutrient assimilation strategies and subtle shifts in eco-physiological balance. LV2 was characterized by higher loadings for Mg, Na, EC, and leaf nutrient concentrations such as Leaf Mg and Leaf P. These variables indicated that LV2 differentiated treatments not by overall productivity but by nutrient-specific influences on physiological functioning and biomass distribution. LV2 also distinguished treatments receiving higher compost rates at 60\u0026ndash;80% FC from those under mineral fertilizer, suggesting that organic inputs modulated nutrient uptake patterns and physiological adjustments through improved soil organic structure and cation balance.\u003c/p\u003e \u003cp\u003eThe score plot of T1 versus T2 demonstrated clear segregation of treatment combinations. High-performing treatments (particularly 3% compost under 80% FC and 60% FC) clustered on the positive side of LV1, indicating strong soil\u0026ndash;physiology\u0026ndash;yield linkages and optimal water availability. These treatments formed tightly grouped clusters, reflecting consistent performance across replicates and confirming that silty clay provides a highly stable environment for tomato growth. Intermediate treatments (e.g., compost 1% under 60% or 80% FC) occupied the central region of LV1, indicating moderate but sustained physiological stability. Even the lowest-performing groups (40% FC under control or mineral fertilizer) remained closer to the origin rather than occupying extreme negative LV1 values, reinforcing the soil\u0026rsquo;s inherent buffering capacity. The greater spread along LV2 compared with sandy soil suggests that nutrient-driven physiological adjustments played a more prominent role in silty clay, where water availability was less limiting and nutrient-driven differentiation became more apparent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e3.4.3. Comparative Interpretation of PLSR in Sandy and Silty Soils\u003c/h2\u003e \u003cp\u003eThe contrast between sandy loam and silty clay PLSR structures highlights the crucial role of soil texture in mediating drought responses and determining the multivariate architecture of plant performance. In sandy loam, LV1 represented a steep gradient of WS, where inadequate moisture retention and low fertility drove strong negative projections for low-irrigation and unamended treatments. Conversely, in silty clay, LV1 captured a more moderated gradient, with even low-irrigation treatments maintaining physiological function due to the soil\u0026rsquo;s higher AWC, WHC, and CEC. As a result, the multivariate separation between stressed and high-performing treatments was much sharper in sandy loam and more compact in silty clay.\u003c/p\u003e \u003cp\u003eKey physiological variables such as RWC and chlorophyll indices aligned positively with LV1 in both soils, but in sandy loam their loadings were stronger and more directly tied to yield, highlighting their sensitivity to water limitation. In silty clay, the same variables contributed positively to LV1, but their influence was more moderated by soil fertility and structural properties rather than by water scarcity alone. This distinction demonstrates that in sandy soils, physiological collapse under drought is the dominant mechanism limiting yield, whereas in silty soils, nutrient buffering and moderated moisture release shape the physiological state.\u003c/p\u003e \u003cp\u003eYield drivers also differed between soils. In sandy loam, yield was tightly linked with soil water properties (AWC, WHC) and chlorophyll maintenance, forming a strong LV1 association with compost and irrigation synergy. In silty clay, yield aligned with a more complex combination of water retention, nutrient availability, and physiological stability, indicating that no single constraint dominated performance. Compost in sandy soils primarily improved water availability, whereas in silty soils it augmented nutrient cycling and structural growth. These differences explain why sandy soils showed large treatment separation along both LV1 and LV2, while silty soils displayed more clustered and stable patterns.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e3.5. MCS of the SFI Under Silty Clay and Sandy Loam Conditions\u003c/h2\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003e3.5.1. MCS of SFI in Sandy Loam soil at Harvest\u003c/h2\u003e \u003cp\u003eThe MCS revealed a clear and consistent probabilistic hierarchy among the SFI outcomes in sandy loam soil at harvest \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. The distribution of simulated values for each amendment\u0026ndash;irrigation combination reflected the underlying differences in soil fertility and moisture availability, enabling a robust evaluation of uncertainty and treatment performance beyond point estimates.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAcross all treatments, simulated SFI values ranged from a minimum of approximately 0.03 (Control \u0026times; 60% FC) to a maximum close of 0.42 (Compost 3% \u0026times; 80% FC). These extreme values reflect the strong contrast between treatments exhibiting minimal fertility support and those benefiting from enhanced organic inputs combined with near-optimal water availability. The ordering of simulated SFI distributions followed a logical fertility gradient: Control\u0026thinsp;\u0026lt;\u0026thinsp;Chemical fertilizer\u0026thinsp;\u0026lt;\u0026thinsp;Compost 1% \u0026lt; Compost 3%, and within each amendment class, 40% FC\u0026thinsp;\u0026lt;\u0026thinsp;60% FC\u0026thinsp;\u0026lt;\u0026thinsp;80% FC. This ordering emerged consistently across the means, medians, 95% confidence intervals, and the shape of the distributions produced by the Monte Carlo iterations.\u003c/p\u003e \u003cp\u003eTreatments receiving 3% compost exhibited the highest simulated distributions across all moisture regimes, particularly under 80% FC, where mean SFI values approached 0.33\u0026ndash;0.35 with upper simulated extremes exceeding 0.40. The 95% confidence interval for this treatment was the widest among all groups, reflecting both the high fertility potential and the natural variability induced by enhanced microbial and nutrient cycling processes in organic-rich sandy soil (Liu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Importantly, there was minimal overlap between the confidence intervals of Compost 3% with 80% FC and any other treatment category, indicating a statistically and ecologically meaningful superiority in fertility performance.\u003c/p\u003e \u003cp\u003eIntermediate treatments such as Compost 1% with 80% FC and Compost 3% with 60% FC formed the next tier in the probability ranking. Their simulated mean SFI values clustered within the 0.23\u0026ndash;0.30 range, with confidence intervals that partially overlapped with each other but remained distinctly above those of chemical fertilizer treatments. This pattern suggests that even moderate organic amendment levels strongly improve fertility in coarse-textured soils, especially under sufficient irrigation (Liu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChemical fertilizer treatments occupied an intermediate but lower probability space relative to the compost treatments. Their simulated distributions rarely exceeded 0.20 and exhibited narrower confidence intervals, indicating stable but limited fertility contributions. The lack of OM addition restricted improvements in water retention, CEC, and microbial-mediated nutrient release, all of which are critical for sustaining fertility in sandy soils (Yang et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eControl treatments consistently produced the lowest SFI distributions, with mean simulated values between 0.06 and 0.10 depending on irrigation level. Their confidence intervals frequently overlapped with those of chemical fertilizer at 40% FC, reflecting minimal improvement in soil fertility under moisture deficit. The lowest simulated values (down to \u0026minus;\u0026thinsp;0.03) were restricted exclusively to the control treatments, demonstrating the high susceptibility of sandy soils to fertility decline in the absence of organic inputs.\u003c/p\u003e \u003cp\u003eThe superior performance of the Compost 3% with 80% FC treatment can be explained by the synergistic interaction between OM enrichment and high soil moisture availability. In sandy soils, compost additions substantially enhance WHC, AWC, CEC, and nutrient retention, all of which directly feed into the SFI calculation. At 80% FC, these compost-mediated improvements are fully expressed because increased moisture enhances nutrient diffusion, microbial activity, and root uptake efficiency. OM decomposition is also accelerated under adequate moisture, releasing mineral nutrients and promoting aggregate stability. The MCS by sampling from the observed variation in SFI, therefore captures both the deterministic fertility improvements provided by compost and the stochastic components associated with biological and environmental variability, leading to a higher and broader distribution of simulated SFI values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003e3.5.2. MCS of SFI in Silty Clay Soil at Harvest\u003c/h2\u003e \u003cp\u003eThe MCS conducted for the silty clay soil revealed a distinct probability structure of SFI values that reflects the intrinsic fertility advantages of fine-textured soils relative to sandy loam \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. Simulated SFI values ranged from a minimum of approximately 0.38 (Control \u0026times; 40% FC) to a maximum exceeding 0.92 (Compost 3% \u0026times; 80% FC), demonstrating substantially higher fertility potential and lower risk of fertility collapse under constrained moisture conditions. Unlike sandy soils, where low compost and low irrigation levels produced negative or near-zero simulated SFI values, the silty clay system maintained all simulated SFI values within a moderate to high fertility range, reflecting the strong buffering capacity of this soil against nutrient and moisture stress.\u003c/p\u003e \u003cp\u003eA clear probabilistic hierarchy emerged from the simulation results. Compost 3% with 80% FC, produced the highest simulated mean SFI (=\u0026thinsp;0.78\u0026ndash;0.82) and the widest distribution envelope, with a 95% confidence interval extending approximately from 0.69 to 0.92. This distribution showed minimal overlap with all other treatments, indicating a statistically dominant fertility performance. The presence of outliers reaching values above 0.95 in the simulated distribution reflects the synergistic effects of high OM input and near-optimal soil moisture on nutrient mineralization, cation-exchange enhancement, and structural aggregation in the fine-textured matrix.\u003c/p\u003e \u003cp\u003eThe second tier of treatments included Compost 3% with 60% FC, Compost 1% with 80% FC, and Compost 1% with 60% FC, with simulated mean SFI values ranging between approximately 0.61 and 0.75. Their confidence intervals partially overlapped with each other but remained distinctly above the ranges associated with mineral fertilizer treatments. These results support the strong contribution of compost amendments to soil structure, base saturation, and organic nutrient release, particularly under high moisture availability.\u003c/p\u003e \u003cp\u003eChemical fertilizer treatments ranked below compost treatments but above the control group. Simulated mean SFI values for chemical fertilizer treatments ranged from 0.54 to 0.63, with narrow confidence intervals that reflect consistent but modest fertility improvements. The absence of organic inputs limited their effect on moisture retention and CEC, which are critical structural fertility components in silty clay soils.\u003c/p\u003e \u003cp\u003eControl treatments occupied the lowest position in the probabilistic ranking, with simulated mean SFI values between 0.46 and 0.49, and the broadest uncertainty among low-performing treatments. Their 95% confidence interval ranged from approximately 0.40 to 0.52, showing substantial overlap across all irrigation levels. Even so, their simulated distributions remained significantly higher than the corresponding control treatments in sandy loam, highlighting the inherent fertility advantage provided by the clay-rich, organic-buffering matrix of silty clay soils.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003e3.5.3. Comparative Interpretation between Sandy Loam and Silty Clay based Monte Carlo Outputs\u003c/h2\u003e \u003cp\u003eThe MCS comparison between sandy loam and silty clay underscores the dominant influence of soil texture on the probabilistic behavior of soil fertility. In sandy loam, simulated SFI values ranged from negative values to a maximum of 0.42, with extensive overlap between low and mid performing treatments. This reflects the high sensitivity of coarse-textured soils to water limitation, nutrient leaching, and low OM retention. In contrast, the silty clay soil exhibited a significantly higher fertility baseline, with all simulated SFI values remaining above 0.38 and reaching maxima above 0.92. This difference illustrates the superior WHC, cation-exchange potential, and nutrient buffering behavior of the clay-rich matrix.\u003c/p\u003e \u003cp\u003eThe magnitude of treatment separation was also markedly different between the two soils. Sandy loam displayed a large spread in simulated SFI distributions, with wide confidence intervals among low-performing treatments, indicating high vulnerability to fertility decline. Silty clay exhibited tighter and more stable distributions even at low irrigation levels, reflecting structural persistence of fertility under WS. While compost 3% with 80% FC was the top-performing treatment in both soils, the simulated SFI under this treatment was nearly double in silty clay compared to sandy loam. This highlights the soil dependent expression of compost benefits: in sandy loam, compost primarily offsets water scarcity, whereas in silty clay, compost synergistically enhances OM cycling, aggregate formation, and nutrient adsorption\u0026ndash;desorption processes.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec36\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Compost-Mediated Enhancement of Soil Fertility Under Water Stress in Sandy Loam and Silty Clay Soils\u003c/h2\u003e \u003cp\u003eThe results of this study clearly demonstrate that compost amendment substantially improves soil fertility under water-limited conditions, and that the magnitude of this improvement is strongly modulated by soil texture and irrigation regime. Across both sandy loam and silty clay soils, compost at 3% consistently achieved the highest SFI values, outperforming both chemical fertilizer and the unamended control. However, the pathways and magnitude of fertility enhancement differed considerably between soil types, reflecting the distinct hydrological and physicochemical constraints characteristic of coarse-textured and fine-textured systems.\u003c/p\u003e \u003cp\u003eIn sandy loam soil, the fertility response to compost application was closely tied to improvements in soil moisture retention and nutrient-holding capacity. The inherently low WHC, low AWC, and reduced CEC of sandy loam create a soil environment in which nutrients are prone to leaching and water deficits develop rapidly under reduced irrigation. Compost addition substantially altered this matrix by increasing soil OM content, enhancing aggregate formation, and improving water retention parameters, particularly under 60% and 80% FC. These structural improvements were reflected in the pronounced increases in SFI observed in compost-amended sandy soil compared to both chemical fertilizer and the control. Chemical fertilizer, while contributing to short-term nutrient enrichment, did not improve soil structure, water retention, or microbial functioning, resulting in significantly lower SFI values and greater variability under WS. The MCS confirmed this pattern, showing a large spread in simulated SFI values for low-irrigation control and mineral fertilizer treatments, indicating high susceptibility to fertility decline. In contrast, the simulated distributions for compost 3% with 80% FC treatments reached maxima around 0.42 and displayed minimal overlapping with other treatments, illustrating the robustness of compost-induced fertility gains even under stochastic water availability.\u003c/p\u003e \u003cp\u003eIn silty clay soils, compost amendments similarly enhanced SFI but through mechanisms driven more by nutrient retention and biological activity rather than structural water limitations. This soil exhibited intrinsically high AWC, WHC, and CEC values, which buffered plants from drastic fertility losses under deficit irrigation. Consequently, even the control and chemical fertilizer treatments maintained SFI values well above those seen in sandy loam. Compost further amplified these inherent advantages by strengthening the organic-mineral complex, stimulating microbial nutrient cycling, and improving structural aggregation. These mechanisms produced exceptionally high SFI values for compost 3% (80% FC) treatments, with simulated maxima exceeding 0.92 nearly double the peak values observed in sandy soil. The MCS outcomes revealed narrow, high-fertility distributions across all silty clay treatments, evidencing the soil\u0026rsquo;s resilience to WS and its capacity to maintain fertility within a stable probabilistic envelope (Wang et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These results indicate that compost benefits are not limited to water-limited environments but are magnified in clay-rich soils through synergistic interactions between organic matter and fine mineral fractions (S. Zhang et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eComparative analyses across soil types further highlight the pivotal role of soil texture in determining fertility trajectories under WS (Neubert and Br\u0026uuml;ggemann, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Steiner et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Sandy loam exhibited a large separation among treatments, reflecting strong dependence on irrigation level and OM inputs to maintain fertility (Sun et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Silty clay, by contrast, showed far more clustered fertility responses, with all treatments positioned within a moderate-to-high fertility range. This difference is consistent with PCA, HCA, and LDA results, which showed broader structural divergence among sandy soil treatments and tighter clustering in silty clay. In essence, compost acts as a stabilizing agent in sandy soil by mitigating WS and nutrient loss (Castellini et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), whereas in silty clay soil, it enhances an already fertile and structurally robust system, raising the entire fertility baseline (Villa et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The strong irrigation-amendment interactions observed for both soil types emphasize that fertility outcomes under compost amendment are not solely determined by OM input but emerge from the coordinated interaction between soil structure, moisture availability, and nutrient dynamics (Suvendran et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e4.2 Effect of Compost on Plant Growth and Yield Production Under Water Stress in Sandy Loam and Silty Clay Soils\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe response of plant growth and yield to compost amendment under WS followed a clear gradient shaped by the interaction between soil texture, irrigation regime, and amendment type. Across all phases, compost particularly at 3% consistently enhanced morphological, physiological, and yield-related parameters in both sandy loam and silty clay soils, although the mechanisms and magnitude of improvement varied substantially between the two soil types.\u003c/p\u003e \u003cp\u003eIn sandy loam soil, compost exerted its strongest influence by improving plant water status and photosynthetic functioning, two domains that are highly sensitive to water deficits in coarse-textured substrates (Delval et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The limited water retention capacity of sandy loam (low WHC and AWC) translated into rapid declines in RWC, chlorophyll a, chlorophyll b, and total chlorophyll under 40% FC, particularly in the control and mineral fertilizer treatments. These physiological declines were reflected in reduced leaf area, leaf number, and overall canopy development, ultimately suppressing shoot and root biomass accumulation and lowering fruit yield. Compost improved these responses primarily by enhancing soil moisture availability and reducing the rate of water loss, which sustained leaf turgor pressure, stomatal conductance, and chlorophyll integrity throughout the drought cycle (Abdou et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sanad et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025c\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sanad et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2026c\u003c/span\u003e). The 3% compost (80% FC) treatment consistently produced the highest values of plant height, stem diameter, leaf area, LAI, root volume, and fruit yield, indicating that compost significantly enhanced root\u0026ndash;shoot balance and plant resource acquisition capacity (Wang et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe multivariate analyses further confirmed the centrality of soil moisture and physiological status in determining plant performance in sandy loam. PCA and PLSR both revealed strong loadings of RWC, chlorophylls, LAI, and root metrics on the principal components associated with yield, demonstrating the mechanistic coupling between plant water status and productivity. Compost-modified treatments were strongly aligned with positive PC1 and LV1 values, while stressed control and mineral fertilizer treatments under 40% FC clustered toward negative values, illustrating the high sensitivity of sandy soils to irrigation deficits. LDA and HCA also emphasized the clear separation between compost-amended and non-amended treatments, highlighting compost\u0026rsquo;s role in maintaining physiological resilience under moisture stress (Kamanga et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn silty clay soils, the effect of compost on plant growth and yield followed similar directional trends but exhibited smaller relative differences and a more stable performance across irrigation levels. The fine-textured matrix of silty clay provided inherently higher water retention and CEC, which buffered plants from rapid moisture loss and nutrient fluctuation even under 40% FC. As a result, baseline physiological parameters such as RWC and chlorophyll concentrations remained substantially higher compared to sandy loam across all treatments, including the control. Despite this inherent resilience, compost further enhanced nutrient availability, microbial activity, and soil structural properties, which translated into significantly improved growth and yield metrics (Abdou et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The 3% compost with 80% FC treatment again achieved the highest values for growth and yield traits, but the overall gradient between treatments was less steep than in sandy soil, reflecting the soil\u0026rsquo;s buffering capacity.\u003c/p\u003e \u003cp\u003eThe physiological and morphological improvements observed in silty clay under compost amendment can be attributed to enhanced nutrient uptake efficiency and improved root system development rather than solely moisture conservation. This interpretation is supported by leaf nutrient data, which showed increased leaf N, P, K, Ca, and Mg concentrations under compost treatments, indicating that OM facilitated sustained nutrient supply and improved ion balance (Suvendran et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These nutrient-driven pathways were also reflected in the multivariate analyses. PLSR for silty clay showed stronger loadings for nutrient-related variables on the latent structures associated with yield. These findings suggest that in silty clay, yield improvement is governed by compost-mediated nutrient optimization rather than moisture buffering alone.\u003c/p\u003e \u003cp\u003eBiomass and yield results further underscore the differential role of compost across soils. In sandy loam soil, compost improved both shoot and root dry matter by mitigating WS (Sisouvanh et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which enhanced assimilate allocation and biomass partitioning to fruit (Oueld Lhaj et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). In silty clay soil, compost enhanced total biomass and fruit yield through increased nutrient supply and root proliferation within a structurally stable, well-aggregated soil matrix (Hassan and Strezov, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The superior effectiveness of compost at 3% in both soils highlights its capacity to simultaneously address water limitation in sandy soil and nutrient limitation in silty clay.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec37\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Impact and Benefits of Applying Compost on Tomato and Horticultural Crops Under Water Stress\u003c/h2\u003e \u003cp\u003eThe application of compost as an organic amendment has long been recognized for its ability to improve soil fertility, enhance plant growth (Oueld Lhaj et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and mitigate the adverse effects of WS (Boutasknit et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This study confirmed the multifaceted role of compost in enhancing tomato productivity and soil health under controlled water deficit conditions, specifically in sandy loam and silty clay soils. Compost not only increased soil water retention, CEC, and nutrient availability, but also mitigated WS induced physiological impairments, leading to significant improvements in tomato growth, root development, and fruit yield (Cozzolino et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These findings align with the conclusions of (Tahiri et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who demonstrated that compost, in combination with beneficial microorganisms, substantially improved shoot biomass and fruit yield under water-stressed conditions, enhancing the osmotic and mineral accumulation processes in tomato plants. In their study, compost treatments increased shoot biomass by 160\u0026ndash;180%, similar to the 45\u0026ndash;75% increase in tomato yield observed in our experiment under drought stress.\u003c/p\u003e \u003cp\u003eCompost's ability to enhance antioxidant capacity was also observed in our study, where compost treatments significantly improved RWC, chlorophyll concentration, and reduced the negative effects of water scarcity on physiological parameters such as stomatal conductance and transpiration rate. The role of compost in enhancing plant water relations and improving soil microbial health was similarly noted by (Lahbouki et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who found that organic amendments, especially when combined with mycorrhizal fungi, increased nutrient uptake and improved WS tolerance in tomato plants, particularly under reduced irrigation.\u003c/p\u003e \u003cp\u003eThe improvement in soil health, particularly in silty clay, highlights the importance of soil texture in modulating the effects of compost. Compost enhanced aggregate stability, microbial diversity, and nutrient cycling, facilitating consistent plant growth even under moderate WS. This is in contrast to the study by (Wang et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), where compost improved root length density and transpiration efficiency in corn plants, resulting in increased root biomass and better water use efficiency under water deficit conditions. In our study, similar benefits were seen in tomato root systems, with compost significantly increasing root biomass and promoting deeper root growth, contributing to better water and nutrient uptake. In tomato crops, compost application increased total protein content, reduced oxidative damage, and enhanced enzyme activities involved in stress tolerance, further supporting the role of compost as a biostimulant that improves resilience to water deficit (Soussani et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec38\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Mitigation Strategies and Future Perspectives for the Use of Compost in Sustainable Horticultural Production Systems\u003c/h2\u003e \u003cp\u003eThe results of this study demonstrate that compost application represents a powerful and context-dependent mitigation strategy capable of stabilizing soil fertility and improving tomato productivity under constrained water availability (Gonz\u0026aacute;lez-Hern\u0026aacute;ndez et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). By enhancing soil physicochemical properties, improving water retention, increasing nutrient accessibility, and strengthening plant physiological resilience, compost directly addresses the multifaceted vulnerabilities associated with water-limited horticultural systems (Sanad et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024d\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025d\u003c/span\u003e; Suvendran et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sanad et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2026b\u003c/span\u003e). In tomato production, where root-zone moisture dynamics and nutrient\u0026ndash;water interactions strongly influence growth and fruit development (Li et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), compost offers an integrated soil management tool that can simultaneously buffer environmental stress and optimize resource efficiency (Cozzolino et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe mitigation potential of compost arises from its ability to modify soil\u0026ndash;plant processes that are highly sensitive to WS (Zgallai et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sanad et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2026b\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003ec\u003c/span\u003e). In sandy loam soils, compost acts primarily by compensating for structural limitations through increased WHC, aggregate stability, and CEC (Castellini et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These improvements reduce the rate of soil drying, enhance root hydration, and help maintain stomatal conductance and chlorophyll stability during drought periods (Bond\u0026igrave; et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In silty clay soils, compost strengthens nutrient cycling and microbial activity (P\u0026eacute;rez-Piqueres et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), reducing the negative effects of nutrient immobilization and enhancing root penetration and soil aeration (Haufiku et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These mechanisms collectively mitigate WS by sustaining nutrient and water uptake pathways that are essential for tomato growth and fruit filling (Boutasknit et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The sharp improvements in shoot and root biomass, leaf physiological traits, and fruit yield observed in compost-amended treatments underscore compost\u0026rsquo;s multifunctional role as both a fertility enhancer (Su et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and a stress-moderating agent (Manhou et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2026\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wahab et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe observed differences between soil types indicate that future mitigation strategies should adopt a soil-specific approach in which compost application rates, timing, and combinations with irrigation scheduling are optimized according to soil texture and hydraulic properties. In coarse-textured soils where drought stress is rapid and severe, compost application should be integrated with deficit irrigation strategies to maintain productivity while reducing water use. In fine-textured soils, compost may be best positioned to balance nutrient availability and support microbial communities that enhance nutrient mineralization under moisture fluctuations. These insights highlight the importance of adjusting compost management within precision agriculture frameworks to maximize water productivity and ensure consistent crop performance under increasingly variable climatic conditions.\u003c/p\u003e \u003cp\u003eFuture perspectives for compost use in sustainable horticultural systems extend beyond simple OM input. The integration of compost into soil health management strategies can be further advanced through microbial inoculation, and controlled-release organic fertilizers that enhance nutrient synchronization with tomato growth stages. Moreover, the adoption of site-specific compost application guided by soil mapping, remote sensing, and machine learning models offers a promising avenue for scaling up compost-based mitigation while limiting waste and maximizing agronomic efficiency. The results of the multivariate analyses in this study, including PCA, HCA, PLSR, and MCS, provide a strong basis for predictive frameworks that can guide compost recommendations and optimize input combinations under water-limited conditions in arid and semi-arid regions. Future research should focus on quantifying long-term compost effects on soil carbon dynamics, microbial networks, and nutrient legacy effects, as well as evaluating compost performance under combined stress scenarios such as heat stress, and irregular rainfall patterns. Additionally, the development of standardized compost quality indices and the assessment of compost derived from diverse organic waste streams will be critical for ensuring consistent agronomic outcomes and environmental safety.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eTomato growth and physiological functioning were similarly responsive to compost amendment. Across both soil types, compost improved plant height, leaf area, stem diameter, and root system development, while also maintaining higher relative water content and chlorophyll concentrations during drought periods. These physiological improvements supported robust biomass accumulation and significantly higher fruit yields, especially under moderate to high irrigation levels. The multivariate analyses, including PCA, HCA, LDA, and PLSR, confirmed that compost-mediated enhancements in soil water retention, nutrient status, and physiological resilience were the dominant drivers of yield under variable moisture conditions. In sandy loam, compost mitigated the constraints of rapid soil drying and nutrient loss, while in silty clay it strengthened nutrient cycling efficiency and promoted stable root functioning even under reduced irrigation. The MCS further reinforced the reliability of compost as a fertility-enhancing strategy by showing that treatments receiving 3% compost under 80% FC produced the highest simulated SFI distributions with minimal overlap across treatments. This probabilistic robustness highlights the capacity of compost to reduce the uncertainty of soil fertility outcomes under WS. Notably, silty clay soils exhibited higher overall simulated fertility levels across all treatments, demonstrating a stronger resilience compared to sandy loam soils. However, compost improved fertility trajectories significantly in both soil types and consistently reduced the risk of fertility decline. Compost represents a promising and sustainable amendment for improving tomato production in diverse agroecosystems facing water scarcity and climate variability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMajda Oueld Lhaj:\u003c/strong\u003e Conceptualization, Methodology, Resources, Validation, Formal analysis, Writing\u0026mdash;original draft, Writing \u0026ndash; review and editing. \u003cstrong\u003eRachid Moussadek:\u003c/strong\u003e Formal analysis, Writing \u0026ndash; review and editing, Funding acquisition. \u003cstrong\u003eHatim sanad:\u003c/strong\u003e Conceptualization, Methodology, Software, Visualization, Writing\u0026mdash;original draft, Writing \u0026ndash; review and editing. \u003cstrong\u003eAbdelmjid Zouahri:\u003c/strong\u003e Methodology, Validation, Writing\u0026mdash;original draft, Writing \u0026ndash; review and editing, Supervision. \u003cstrong\u003eKhadija Manhou:\u0026nbsp;\u003c/strong\u003eResources,\u0026nbsp;Writing \u0026ndash; review and editing. \u003cstrong\u003eMeriem Mdarhri Alaoui:\u003c/strong\u003e Validation, Writing\u0026mdash;original draft, Writing \u0026ndash; review and editing, Supervision. \u003cstrong\u003eLatifa Mouhir:\u0026nbsp;\u003c/strong\u003eValidation, Writing\u0026mdash;original draft, Writing \u0026ndash; review and editing, Supervision.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to all collaborators who participated in field sampling, laboratory analyses, and manuscript preparation. The authors are also grateful to the MCGP INRA-ICARDA and EiA projrots for their financial support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdou NM, Roby MHH, AL-Huqail AA, Elkelish A, Sayed AAS, Alharbi BM, Mahdy HAA, Abou-Sreea AIB (2023) Compost Improving Morphophysiological and Biochemical Traits, Seed Yield, and Oil Quality of Nigella sativa under Drought Stress. Agronomy 13:1147. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/agronomy13041147\u003c/span\u003e\u003cspan address=\"10.3390/agronomy13041147\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArnon DI (1949) Copper Enzymes in Isolated Chloroplasts. Polyphenoloxidase in Beta Vulgaris. 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Agriculture 15:15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/agriculture15010015\u003c/span\u003e\u003cspan address=\"10.3390/agriculture15010015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Organic amendment, Water stress, Sustainable horticulture production systems, Physiological responses, agricultural productivity, yield performance","lastPublishedDoi":"10.21203/rs.3.rs-9439169/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9439169/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSoil fertility decline and increasing water scarcity threaten horticultural production systems in arid and semi-arid regions, particularly in North Africa. This study aimed to evaluate whether compost can enhance soil fertility and sustain tomato (\u003cem\u003eLycopersicon esculentum\u003c/em\u003e (L.) Mill) performance under controlled water stress (WS) in contrasting soil textures. The objectives were to assess compost effects on soil physicochemical properties, plant growth and physiology, nutrient uptake, biomass and yield, and to identify key drivers of productivity using multivariate and probabilistic modelling. A greenhouse experiment was conducted on sandy loam and silty clay soils amended with compost at 1% and 3%, chemical fertilizer, or left untreated, combined with 40%, 60%, and 80% field capacity (FC). Soil and plant data across all growth phases were analyzed using SFI, statistical analysis and MCS. Results showed that compost 3% \u0026times; 80% FC produced the highest SFI in both soils, reaching 0.42 in sandy loam and 0.92 in silty clay, compared to 0.06\u0026ndash;0.10 in controls. Compost significantly increased plant height (by 35\u0026ndash;55%), leaf area (by 40\u0026ndash;70%), Relative Water Content (RWC) (by 15\u0026ndash;28%), chlorophyll content (by 20\u0026ndash;45%), and fruit yield (by 45\u0026ndash;75%) relative to control treatments under drought. PCA and PLSR identified soil moisture retention, chlorophyll stability, and Ca\u0026ndash;Mg nutrition as the major predictors of yield, while MCS demonstrated reduced fertility risk and higher probability of achieving optimal SFI under compost. Overall, compost application markedly improved soil fertility and tomato productivity under WS, offering a sustainable strategy for resilient horticultural systems in drought-prone regions.\u003c/p\u003e","manuscriptTitle":"Evaluating Compost Effects on Tomato (Lycopersicon esculentum (L.) Mill) Under Drought: An Integrated Soil fertility index (SFI), Monte Carlo Simulation (MCS), and Multivariate Soil–Plant Interaction Modelling in Sandy Loam and Silty Clay soils","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-17 07:46:05","doi":"10.21203/rs.3.rs-9439169/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5f4a39ba-46c0-4b53-a9e4-ff71271fddb7","owner":[],"postedDate":"April 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-17T07:46:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-17 07:46:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9439169","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9439169","identity":"rs-9439169","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}