Molecular characterization and fertilizer optimization in soybean to maximize productivity and minimize environmental impact

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This situation necessitates the optimization of macronutrients, soil moisture, and drought indexing to enhance resilience, moving beyond molecular characterization of germplasm. The molecular characterization and correlation analysis of germplasm elucidates its genetic potential, variability, and diversity, while productivity depends on essential nutrients provided by soil or the application of NPK fertilizers. However, indiscriminate use of excessive NPK fertilizers, devoid of strategic optimization, compromises soil vitality and accelerates environmental damage. The central composite design (CCD) was used to examine soybean yield with four independent variables: moisture level from 0 to 200 mm and macronutrients (NPK) from 0 to 100kg per acre for each with RSM model and contour plot. A screening experiment utilizing PEG-6000 on forty-eight soybean accessions demonstrated significant correlations between growth indicators. SSR markers were employed to assess genetic diversity, with principal component analysis (PCA) accounting for up to 73.8% of the variation The RSM model predicts the optimal conditions, which include the application rates of nitrogen, phosphorus, and potassium (65, 40, and 20 kg/acre), while maintaining soil moisture levels between 100 and 150 mm. In the validation experiment, eleven out of forty-eight soybean accessions improved up to 70% more yield than control plants when the above optimum conditions were applied. The results of this study demonstrate that optimizing fertilizer application rates can significantly decrease emissions of greenhouse gases and alleviate soil and environmental pollution linked to agricultural practices. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Biological sciences/Plant sciences Climate Soil Molecular Characterization Fertilizer Optimization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Soybean (Glycine max) is an essential oil-producing crop and a major player in the supply chain of vegetable oil. Its important position in the global oilseed market is further supported by the abundance of beneficial lipids, which highlight its worth in bioenergy production, industrial applications, and culinary uses 1–3 . The anticipated rise in global population is expected to lead to heightened food consumption, presenting considerable challenges to the food supply chain and requiring innovative strategies to maintain food security. However, the ramifications of climate change profoundly affect soybean production, resulting in diminished yields and endangering the world's food supply 4–6 . Due to ongoing changes in weather patterns, it is indisputable that climate change presents a significant long-term threat to soybean yield including unpredictable rainfall, sharp temperature swings, and water scarcity 7,8 . These variables affect the dynamics of pests and diseases, interfere with planting and harvesting schedules, decrease photosynthetic efficiency, and exacerbate biotic and abiotic stressors 9–12 . As noted by 13,14 , drought stress significantly impairs various physiological and biochemical processes in soybeans, including germination, seedling, flowering, and maturation. One of the most important issues in the world is improving the ability of crops to withstand biotic and abiotic stresses. To fight climate change and global warming necessitates the development of long-term strategies that are grounded in the principles of adaptation, avoidance, and evasion 15,16 . As a result of water scarcity and unpredictable weather, traditional breeding methods have a hard time for keeping consistency in potential of plant production, which limits agricultural resilience and prosperity 17,18 . A combination of conventional and recombinant breeding techniques has led to genetic improvements of agricultural crops, serving as a powerful weapon in the battle against the effects posed by climate change and drought stress. A great number of soybean-specific QTLs have been identified by scientists because of recent advances in genetics and genomics 19,20 . Although numerous genetic maps have been developed to study drought tolerance QTLs in soybeans, it is essential to verify and validate these QTLs before application to crop breeding program. Next-generation sequencing (NGS) has improved the cost-effectiveness of marker-assisted selection (MAS) for attaining genetic progress. GWAS that use SNPs are great for finding out how markers and traits are related in marker-assisted selection (MAS) breeding programs. They are also useful for finding out how genes are linked and how genetic diversity is measured 21–23 . The consequences of human activity are aggravating the phenomena of global warming at an alarming rate. This trend points to a future with longer and more intense droughts that will test the ability of our planet's ecosystems and food production systems to withstand shocks 24,25 . Thus, dependence exclusively on traditional breeding methods, even with the incorporation of molecular markers, is inadequate for enhancing crop resilience to abiotic stresses. An integrated approach, incorporating advanced crop management strategies, is crucial for tackling issues associated with soil health, water scarcity, and pest management 26 . The molecular characterisation of soybean production offers valuable insights; yet it may not entirely capture the complex array of factors influencing yield and other critical features. Molecular methods such as marker-assisted selection, while promising in breeding programs, often overlook the intricate interactions among genetic composition, environmental influences, and management strategies. A holistic strategy that incorporates molecular data, phenotypic evaluations, agronomic experience, and optimal nutrient management through precise fertilizer administration is crucial for achieving a thorough understanding of soybean productivity 27,28 . The advancement, efficiency, and quality of soybean seeds are contingent upon the judicious application of fertilizers that provide essential nutrients such as nitrogen, phosphorus, and potassium. They facilitate the enhancement of disease resistance, the process of protein synthesis, and the advancement of root development 29 . Nonetheless, excessive application may contaminate water sources, impede nitrogen fixation, and adversely affect soil health. Glycine max, the continuity of production is upheld while simultaneously minimizing the ecological footprint via the adoption of sustainable fertilizer management practices 30,31 . A crucial statistical method for optimizing intricate agricultural variables such soil humidity, drought indexing, and NPK fertilizer amounts is Response Surface Methodology (RSM). RSM assists in determining the ideal circumstances for crop growth and yield by examining the interplay between these variables. It is essential for comprehending how nutrient and environmental elements impact plant performance, which makes it a useful tool for boosting crop production tactics, increasing the effectiveness of resource utilization, and tackling issues brought on by climatic unpredictability 32–34 . This study is notable for its application of the response surface methodology to soybeans, encompassing the processes of screening, characterizing, optimizing, and validating them. The goals of this research were to examine soybean germplasm in terms of its genetic diversity, find the best ways to meet crop needs, and assess the efficacy of management approaches and field output. The RSM methodology underwent refinement and validation concerning soil moisture, drought intensity, and primary macronutrients by assessing models through correlation analysis. The findings of this study can enhance drought resistance in soybean varieties and facilitate the development of more effective agricultural management strategies. The principal recommendations for enhancing sustainable agriculture encompassed strategies focused on augmenting crop yields, refining fertilizer application in soybean farming, and reducing the likelihood of agricultural pollutants. Experimental procedures Plant Material This study examined 48 genotypes of Glycine max (L.) plants exhibiting varying responses to drought, ranging from susceptibility to resilience. Dried seeds from the specified genotypes were obtained from the Plant Genetic Resource Institute (PGRI) and the Oilseed Research Program (ORP) at the National Agricultural Research Centre (NARC) in Islamabad (https://www.parc.gov.pk/PlantDivision/PGRI). The seeds were subjected to cleaning and manual sorting before grinding and sieving. Only soybeans that were processed through a sieve with a mesh size ranging from 0.40 to 1.0 mm were included in the analysis. Table S1 provides a detailed enumeration of the genotypes collected. In-Vitro Screening Experiment To induce osmotic stress, a total of forty-eight genotypes were initially subjected to screening using Polyethylene Glycol (PEG-6000) before being subsequently planted in the field. Following a series of surface sterilization steps utilizing a diluted sodium hypochlorite solution, the seeds underwent multiple rinses with distilled water. The experiment was conducted using a completely randomized design (CRD) and was performed within plastic trays. The experiment consisted of four treatments, namely Control, T1, T2, and T3. Each treatment consisted of varying concentrations of PEG-6000, ranging from 0–20%. The data collection process encompassed various factors and was conducted 20 days after sowing (DAS). The measurements of root and shoot lengths were recorded from the apex to the base of the plant and from the crown to the distal end of the root, respectively. For each of the four treatments, measurements were based on three plants at random from each replication. The study involved assessing the masses of plants' aboveground and belowground structures, both in their hydrated and dehydrated states, under each experimental condition. Fresh shoot weights were determined using an analytical scale, while dry shoot weights were measured after drying in an oven set to 70°C. Root development assessment included quantification of fresh weights and obtaining dry weights through oven drying. Root weight was measured in grams. The Root-to-Shoot Ratio (RSR) was found by dividing the dry root weight (DRW) by the dry shoot weight (DSW), as described by 35 . Phenotypical Analysis of morphological characteristics The study was carried out within a controlled environment with daytime temperature was regulated between 25–30℃ and the nighttime temperature ranged from 18–22℃. Soil samples were collected from the upper 20 centimetres of an experimental station. The river sand was subjected to a washing process, subsequently air-dried, and then sieved using a 2-mm mesh size to remove coarse fragments and microarthropods from both the river sand and field soil. Consequently, the soil from the field and the sand from the river were subjected to a process of sterilisation. The combination of sterilised river sand and field soil was executed in a ratio of 3:7 (w/w), with each pot measuring 85 cm × 85 mm × 180 mm containing 1.2 kg of the resultant soil mixture. Deionised water was administered to each pot to attain 80% of its capacity, with measurements recorded bi-daily. The process of data collection involved the meticulous measurement and documentation of various morphological parameters, such as plant height (cm), the number of pods per plant, hundred seed weight (g), days to maturity, chlorophyll content, proline accumulation, oil content, protein content, and yield per plant (g). The height of the genotypes was evaluated by measuring the distance from the ground to the apex of a juvenile leaf on five randomly chosen plants, recorded in centimetres (cm). The calculation of the average number of both filled and unfilled pods per plant involved the selection of five random specimens from each genotype. The period leading to maturity was evaluated from the emergence of seedlings until the moment when 57% of the pods displayed a yellow hue. The seed yield per plant was determined by assessing the average yield in grammes (g) from five randomly selected plants for each genotype. The mass of 100 seeds was measured after threshing and recorded in grammes (g). A SPAD 502 instrument quantified chlorophyll concentrations in the foliage before the onset of flowering in the plants. Mean values were derived from three leaves of five chosen plants for each genotype and documented in SPAD units36. DNA Extraction and Polymorphism DNA Extraction and Polymorphism A recognized technique was improved for the extraction of genomic DNA. Young leaves, when freshly harvested, yield a visually appealing powder. Following meticulous grinding, the plant material was combined with CTAB buffer for the purposes of incubation and precipitation. The DNA pellet was subsequently washed with 70% ethanol following separation. The measurement of DNA content and purity was conducted using a Nano-Drop (ND-1000) spectrophotometer. DNA samples were diluted to a concentration of 20 ng/µl in preparation for PCR/SSR analysis using randomly selected SSR markers. Table S2 presents a summary of the primer parameters 37,38 . Field Experimental Design The effect of the four independent variables on quantitative yield and qualitative yield were investigated using the central composite design (CCD) and response surface method ology (RSM). A total of twelve experimental runs for the optimization of the yield correlated parameters were carried out. Five levels (-2, -1, 0, +1, +2) were used for each independent variable. The six independent variables are moisture level/drought percentage, nitrogen level, phosphorous level, potassium level, extraction temperature, extraction time, while the quantitative and qualitative yield are dependent variables. The independent variables with their levels and codes are shown in Table 1. Table 1: Independent variables and their levels in CCD. Measurement of Soil Moisture The quantity of water can be quantified as the weight fraction of water relative to the weight of the soil in which it is contained according to the equation reported by 39 . The common measuring units are typically expressed as a percentage or grams of water per 100 grams of soil, denoted by the symbol Pw, which represents the percentage of water on a weight basis. The soil appears moist upon measurement; however, the calculation relies on the dry weight of the soil that contains the water. The measurement involves collecting a soil sample from the designated depth and location, which is then placed in a watertight container to prevent drying during the collection of additional samples or transport to the laboratory. The sample is weighed upon receipt in the laboratory and subsequently after being dried in an oven for 24 hours at 1050 °C. (1) Drought Index Agricultural drought is characterized by water deficiency in crops due to various external factors, resulting in impaired growth and development of plants 40 . This study introduces a regional agricultural drought index that utilizes the percentage of effective storage capacity of soil reservoirs as a variable, grounded in soil reservoir theory. We present the expression below. (2) Where AWCI is the available water content of the soil index, θ t is the average soil volume moisture percentage that was measured during the time period, θ fc is the field capacity, and θ wp is the wilting coefficient. The effective available water content of the soil is found by subtracting θ fc from θ wp . Response Surface Methodology The screening and molecularly characterisation of germplasm emphasises genetic diversity, indicating that certain germplasm might provide resistance to stress. However, there are a few outside variables that also affect crop production, including nutrients availability, drought severity, and soil moisture content. Here, we applied RSM for the following purposes: (1) to optimize the level of irrigation, drought, nitrogen, phosphorous and potassium for attaining of soybean quantitative yield to obtain the best results, (2) to optimize the level of extraction time and temperature for soybean qualitative yield and (3) to obtain a predictive model that adequately represents the variation in response to the input variables 41 . A central composite design (CCD) was used to construct second-order mathematical models relating to the observed variables with irrigation levels/drought due to their high efficiency in terms of the number of runs required. In CCD, all process variables contain five levels: a total of thirteen experimental runs for each treatment used for the optimization of the soy yield were carried out. Five levels (-2, -1, 0, +1, +2) were used for each independent variable. The four independent variables are moisture level/drought (X1), nitrogen level (X2), phosphorous level (X3) and potassium level (X4), with dependent variable soybean yield (Y1). The number of design points (N) is determined by the following equation: (3) where k is the number of variables and Nc is the number of central points. The variables here are irrigation levels and drought levels (k = 2). When Nc takes one central point, a total of 13 designed points for each of all treatments are generated through CCD. The following formula was used for the analysis of response surface and counter plot of level of moisture and macronutrients. (4) Where Y is the response variable, X i and X j is the independent variable, β 0 is intercept, β i is linear coefficient,β ii is the quadric coefficient,β ij is interaction coefficient and ε is the random error. Statistical Analysis The phenotypic data collected from both the control and on-site experiments underwent further analysis using various statistical methods such as Analysis of Variance (ANOVA) and principal component analysis (PCA Biplot) conducted using the Minitab software with the use of the following formulas 42 . (5) (6) Where in formula 5, αi is the effect of factor A, βj is the effect of factor B, and (αβ)ij is the interaction effect. Formula 6 represents that Y is the dependent variable (X1, X2, …). Xn is the independent variable, β0 is the intercept, βn is the regression coefficient, and ε is the random error. Results Analysis of growth parameters of soybean We did this study to see how forty-eight genotypes responded to drought stress and different amounts of PEG at the molecular lab of our research institute during the kharif season of 2022–2023. The analysis of variance (ANOVA) exhibited significant differences and confirmed the presence of variations and radar plot analysis among genotypes for the traits, namely shoot length, fresh and dry shoot weight, root length, and fresh and dry root weight. The findings of the radar plot indicate that Treatment 2 demonstrated notably superior performance under PEG treatment, followed by Treatment 3, whereas Treatment 4 was substantially affected by PEG-induced drought stress. Root length (RL) decreased by 33% and 56% in Treatments 3 and 4, respectively, due to drought stress, while it experienced a slight increase in Treatment 2 compared to the control (Figure 1). The percentage decrease in shoot length (SL) under PEG treatment, as compared to the control, was 5%, 23%, and 43% in Treatments 2, 3, and 4, respectively. Conversely, the smallest decrease of 31% in fresh shoot weight (FSW) was observed in Treatment 3, followed by a 59% decrease in Treatment 4, while Treatment 2 exhibited an 8% increase compared to the control. Furthermore, fresh root weight (FRW) decreased by 37%, 64%, and 79% in Treatments 2, 3, and 4, respectively, compared to the control under PEG-induced drought treatment. On average, a 13% decrease in dry shoot weight (DSW) was recorded in Treatment 2, whereas Treatments 3 and 4 experienced decreases of 39% and 71%, respectively, compared to the control. The percentage decrease in dry root weight (DRW) was 45%, 89%, and 90% in Treatments 2, 3, and 4 under PEG treatment. Polymorphisms of soybean cultivars In the course of the initial experiment, ten SSR markers were employed to amplify DNA from sixty distinct genotypes of soybeans that had been previously identified. Initially, a mere five SSR markers demonstrated the capability to effectively amplify the DNA, encompassing all sixty genotypes. Despite further endeavours employing a diverse array of annealing temperatures, the residual markers failed to achieve successful DNA amplification across all populations. It was found that 60.67% of these SSR markers exhibited polymorphism. The subsequent illustration, designated as Figure 2, presents the results of the polymerase chain reaction pertaining to five distinct molecular markers. The markers in question are Satt373, Satt454, Satt471, Satt478, and Satt581. Principal Component Analysis The principal Component Analysis (PCA) is a multivariate statistical technique utilized to analyze and streamline intricate and extensive datasets. It aims to assess the diversity of soybean genotypes and their relationship to observed traits. A PCA biplot ellipses analysis is to identify traits that can be grouped into main and subcategories based on their similarities and differences. The PCA biplot ellipses reveal the presence of four distinct trait groups, considering both PC1 and PC2 simultaneously (Figure 3A). Notably, in the laboratory experiment, there is no overlap between the control and combined stress treatments, indicating significant trait variation. Dimension-1 (Dim-1) contributes to 73.6% of the total variance, while Dimension-2 (Dim-2) contributes to 11.8% (Figure 3A). Traits such as RL, SL, FRW, DRW, and FSW are associated with Treatment-1, while RL, SL, FRW, DSW, DRW, and FSW are linked to Treatments 2 and 3. Additionally, FSW and DSW are associated with Treatment 4. In the field experiment, the PCA biplot demonstrates that PC1 represents approximately 44.7% and PC2 approximately 17.3% of the total variation (Figure 3B). Under control conditions, all traits except PA remain closely clustered, displaying maximal parallelism in their expression patterns. Moreover, the PCA analysis indicates that trait associations vary under drought conditions compared to the control, suggesting distinct influences of drought and control conditions on trait pairings. Consequently, as a result, genotypes that exhibit differential expressions of traits are segregated and categorized into each quadrant of the biplot based on their trait expressions. Regression Models for quantitative characteristics A multiple regression analysis was performed on the data that is shown in Table 2, and the linear interaction coefficients for yield (Y1) were utilized in the data analysis. In addition to this, we show the results of the goodness of fit and lack of fit tests that were extracted from the regression model. To get the regression coefficient, which illustrates the relationship between the amount of nitrogen, phosphorus, and water present in the soil and the yield (Y1), the equations of the regression model were utilized. Based on the statistical analysis, it was determined that only the linear interaction coefficients exhibited statistical significance (p<0.05). On the other hand, the two-way coefficients exhibited a p-value of 0.070, which was not significant. Table 2: Summary of analysis of variance (ANOVA) between dependent and independent variables for model interactions. Quantitative Yield analysis (Y1) We made a contour plot of the determination coefficients of linear regression between one part of soil moisture, three parts of macronutrients (nitrogen, phosphorous, and potassium), and yield using the "plsregress" function in the Minitab procedure and the data from the experiment. The results of the contour plot indicated that the higher the moisture level (up to 150 mm), the higher the yield (up to 600 kg/acre). Figure. 4a and 4c show how the different parts of the contour plot showed a link between different amounts of macronutrients and how they affected the soybeans grown when the soil was wet or dry. In this study, the optimum conditions were selected using surface plots. To determine the optimum yield at any point, two of the four independent variables were fixed while varying the remaining one and predicting the response variables. The triangle-shaped yield production surface plots revealed that there is a reciprocal relationship between soil moisture and intensity of drought. Results revealed that the maximum quantitative yield was attained at a moisture level and nitrogen level of 100-150 mm and 65 kg per acre, respectively (Figure 4b). The results also reported that the higher the moisture level, the more it can lead to lowering the drought intensity and improving the yield of soybean. The results of the surface plot indicated that a higher amount of nitrogen can’t produce a positive impact on yield in the presence of higher drought intensity. As shown in Figure 4d, interactions between the phosphorous and potassium levels caused an increase in yield, resulting in a maximum yield of about 550-600 kg per acre at 40 kg/acre of phosphorous and 20 kg/acre potassium, respectively. However, a further increase in the amount of phosphorus and potassium after reaching the optimum caused a corresponding decrease in yield. Validation of optimal conditions The experiment was repeated in optimal conditions, with a moisture level of 150 mm and macronutrient application rates of 65 kg/acre of nitrogen, 40 kg/acre of phosphorus, and 20 kg/acre of potassium. This was done in accordance with the conclusions of our research focused on optimization. When compared to the control, eleven out of forty-eight soybean accessions were able to increase the experimental yield ranges by as much as seventy percent under the conditions that were adjusted above. Because of this finding, the predictions made by the regression model that were based on the RSM regression equation (Figure 5) were validated. Discussion The production and yield stability of soybean (Glycine max L.) are significantly compromised by the challenges posed by drought stress. According to 43 , the best way to grow drought-resistant high-yielding cultivars is to use direct selection to ensure stable seed and oil yields. It is essential to molecularly isolate drought-tolerant and susceptible genotypes in soybeans to breed new varieties with higher yield potential. Molecular data can elucidate genetic distances and variants 44 , 45 . As noted by 46 , the application of soybean genotyping has proven effective in enhancing seed quality, improving resistance to rhizoctonia root rot, and increasing tolerance to the yellow mosaic virus. We amplified 48 genotypes (Table S1 ) using five drought-tolerant SSR markers (Table S2) to identify soybeans with higher yield. Due to its elevated molecular weight and solubility in water, PEG-6000 serves as an effective osmotic regulator that is unable to penetrate plant cell walls. A nutritional solution with PEG-6000 concentrations ranging from 0–20% simulated mild drought stress over a four-day period (Fig. 1 ). The radar plot results indicate that Treatment 2 was the most effective regarding PEG, followed by Treatment 3 in second place. Treatment 4 exhibited more pronounced effects from PEG-induced drought stress. The second treatment resulted in a 33% reduction in root length, the third treatment led to a 56% reduction, and the fourth treatment caused a 33% reduction. A reduction in fresh shoot weight of 37%, 64%, and 79% was observed in treatments 2, 3, and 4, respectively. A 13% reduction in dry shoot weight was noted following the second treatment (Fig. 1 ). Similar to the findings of 47 , we observed a comparable correlation. Among the forty-eight soybean genotypes analyzed in this study, five out of ten randomly selected markers demonstrated significant variation. Five molecular markers, specifically Satt373, Satt451, Satt471, Satt478, and Satt531, were identified as the most effective in distinguishing highly tolerant soybean cultivars with enhanced edible oil among forty-eight tested varieties. The SSR markers effectively distinguished the soybean cultivars by producing four distinct banding patterns. Figure 2 illustrates the polymorphism of five of the ten single sequence repeat biomarkers. The findings challenge the conclusions of 48 , who found that none of the seven primers could distinguish among 16 different cultivars of Brassica juncea in a single study. The findings from our studies were correlated with the results reported by 49 . The PCA biplot results from the in vitro lab experiment indicate that approximately 73.8% and 14.9% of the total variation is explained (Fig. 3 A & B). The PCA analysis indicates that trait associations differ under drought conditions relative to control conditions. The results indicate that two environments exert distinct effects on trait pairings. The findings of our study were correlated with those reported by 50 . The expression of traits relies on soil resource availability; however, marker-assisted selection enhances drought resilience in soybeans and increases the efficiency of breeding programs. The overuse of NPK fertilizer, driven by erroneous yield projections, poses a threat to soil integrity and environmental health. The use of balanced fertilizer enhances soil fertility, reduces runoff and leaching, and decreases greenhouse gas emissions, thereby supporting sustainable soybean cultivation in drought conditions. The analysis of variance results for the optimization of NPK and moisture levels, presented in Table 2 , reveal a non-significant lack of fit for NPK and moisture levels (F = 1.63; p = 0.110 > 0.05), indicating that the regression model for yield (Y₁) adequately represented the experimental data. A correlation was identified between our results and those reported by 51 . The goodness-of-fit of the ANOVA was evaluated through the coefficient of determination (R²), which quantifies the proportion of variance in the response variable accounted for by the independent variables and their interactions. R² values nearing 1 signify a robust correspondence between the statistical model and the data. The regression model forecasts yield (Y₁) based on moisture level (X₁) and NPK (X₂, X₃, X₄), yielding R² and predicted R² values of 0.725 and 0.715, respectively, indicating the model's adequacy. Another study found that linear regression models effectively estimate crop yields in various contexts. This indicates that reliance on traditional methods based on factors such as soil type, historical data, and vegetation indicators is unnecessary 52 – 54 . The research indicates that optimal soybean yield occurs with nitrogen application at 65 kg/ha, alongside soil moisture levels ranging from 100 to 150 mm, suggesting a relationship between soil moisture and the severity of drought conditions. Soil moisture management effectively mitigated drought severity and enhanced yield; conversely, nitrogen fertilizer did not influence yield under conditions of severe drought. The application of 40 kg/acre of phosphorus and 20 kg/acre of potassium led to the highest yield, ranging from 550 to 600 kg/acre, influenced by the synergistic interaction between these two nutrients. The results obtained from the response surface methodology confirm the synergistic effects of nutrient management and moisture optimization on soybean output, as illustrated by the contour plot analysis in Fig. 4 a-d. The research highlights the critical role of water availability and the strategic application of fertilizers in improving drought resilience and optimizing agricultural productivity. The findings of our research correspond with those reported by 55 , 56 , indicating that the vegetative growth response of soybeans in acidic soil specifically plant height, leaf count, and root length can be enhanced through the application of an optimized quantity of organic fertilizer. In light of the results, the experiment was re-done with the following parameters: 150 mm of moisture, 65 kg/acre of nitrogen, 40 kg/acre of phosphorus, and 20 kg/acre of potassium. The findings provide credence to the RSM regression equation-based regression model. A 70% increase in production at the best level was observed when eleven different types of soybeans were grown in soil that had been affected by drought, as shown in Fig. 5 . In order to avoid negative impacts, it is crucial to avoid applying too much of any nutrient, as this research explains plants' basic needs. According to 57 , the results of the screening and molecular characterization did not meet expectations, highlighting the necessity of optimizing soil conditions. The study's results underscore the importance of understanding various factors such as soil type, moisture level, drought severity, optimal macronutrient ratios, and levels of soil and ambient pollution in order to promote sustainable farming techniques. Conclusion This research emphasized the importance of thorough soil analysis prior to soybean cultivation, as the performance of different genotypes, as determined through screening and molecular characterization methods, does not always correspond with expectations. The latest research strongly suggested that a full analysis of soil moisture, drought severity, and nutrient concentrations be done to see if a field is ready to plant or to figure out what changes need to be made to improve soybean production. The productivity of soybean crops is adversely affected by erratic soil and environmental conditions. These fluctuated conditions lead to premature blooming, hindered photosynthesis, leaf loss, and diminished seed germination rates, all of which culminate in reduced yields. The strategic application of fertilizers (NPK) markedly enhances crop yield by fostering robust plant growth, facilitating pod formation, and improving seed quality. The ongoing maintenance of soil fertility, the reduction of environmental impacts, and the continuation of productivity all depend on the careful management of fertilizer. This study established the ideal circumstances for optimizing soybean yield, which consist of maintaining soil moisture levels between 100 and 150 mm, alongside application rates of nitrogen, phosphorus, and potassium at 65, 40, and 20 kg/acre, respectively. The findings of this study indicate that further investigation into optimal nitrogen application rates could contribute to reducing nitrous oxide emissions and mitigating the environmental impact of agriculture. Declarations Author Contributions: Kamran Shehzad Bajwa: designed the research activity, wrote the manuscript, Sabahat Noor : provided materials and proofreading of manuscript Zaheer Abbas : edited the manuscript Muhammad Rizwan Aliand Muhammad Umer bin Muhammad Ishaq : data analysis, Raja Sheraz Rafique, Zeeshan Abbas and Shagufta Parveen: performed the experiments Shaukat Ali: reviewed manuscript. All authors have read and agreed to the published version of the manuscript. Funding: Not applicable Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed at the corresponding author. Acknowledgments: The authors would like to extend their sincere appreciation to the researchers of the Plant Genetic Resource Institute (PGRI) and the Oil Seed Program of the National Agriculture Research Centre, Islamabad, Pakistan. Conflicts of Interest: The authors declare no conflicts of interest. References Lakhiar, I. A. et al. Plastic Pollution in Agriculture as a Threat to Food Security, the Ecosystem, and the Environment: An Overview. Agronomy 14 , 548 (2024). Mani, V. et al. Metabolic Perspective on Soybean and Its Potential Impacts on Digital Breeding: An Updated Overview. Journal of Plant Biology 67 , 87-98 (2024). https://doi.org:10.1007/s12374-023-09419-z Amanullah, Khan, J. A. & Yasir, M. 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1","display":"","copyAsset":false,"role":"figure","size":261034,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRadar plot depicts alterations in genotype traits across all treatments induced by PEG-induced drought\u003c/strong\u003e \u003cstrong\u003estress in controlled environment;\u003c/strong\u003e SL: Shoot Length (cm), RL: Root Length (cm), FSW: Fresh Shoot Weight, DSW: Dry Shoot Weight, FRW: Fresh Root Weight (g), DRW: Dry Root Weight (g),\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7487341/v1/19e78c6924f1303938d60b52.png"},{"id":92109725,"identity":"1c3ae581-225e-4fbb-834d-b94a7042eadd","added_by":"auto","created_at":"2025-09-24 17:58:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":433652,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRepresentative gel image of forty-eight soybean genotypes with SSR marker on 2.5% agarose gel; A) \u003c/strong\u003epolymorphic amplification of soybean genotypes with Satt373 marker;\u003cstrong\u003e B) \u003c/strong\u003ePCR based detection with Satt454 marker;\u003cstrong\u003e C) \u003c/strong\u003epolymorphism with Satt471 SSR marker;\u003cstrong\u003e D) \u003c/strong\u003ePCR amplification using Satt478 marker;\u003cstrong\u003e E) \u003c/strong\u003edetection through polymerase chain reaction of soybean genotypes with Satt581 marker;\u003cstrong\u003e \u003c/strong\u003eLane 1-60: genotypes of soybean; Lane M: 1 kb plus DNA ladder\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7487341/v1/3fb6b570a46d77cc24af9b3b.png"},{"id":92109726,"identity":"1451f246-d088-4a85-98ea-883aefdbb1ef","added_by":"auto","created_at":"2025-09-24 17:58:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":254003,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrincipal Component Analysis Biplot Ellipses illustrating the extent of similarity and dissimilarity of Morpho-physiological analysis A) \u003c/strong\u003eBiplot analysis of all genotypes under pot experiment, \u003cstrong\u003eB) \u003c/strong\u003ePrincipal component analysis of soybean genotypes under field experiment.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7487341/v1/7a464e4835a4fc1ab750ee33.png"},{"id":92109727,"identity":"cfbbbe04-75f2-406d-8ce9-bb3a45a66df6","added_by":"auto","created_at":"2025-09-24 17:58:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":225067,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eContour and Response Surface plots of determination of linear regression coefficient of yield; A) \u003c/strong\u003eEffect of moisture level and nitrogen level on yield with contour plot, \u003cstrong\u003eB) \u003c/strong\u003eEffect of moisture level and nitrogen level on yield with RSM,\u003cstrong\u003e C) \u003c/strong\u003eEffect of phosphorous level and level of potassium on yield with contour plot, \u003cstrong\u003eD) \u003c/strong\u003eEffect of phosphorous level and level of potassium on yield with RSM.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7487341/v1/02743e2bebc059ef9f8cec36.png"},{"id":92109730,"identity":"9477f700-5a88-45ac-bb32-cfd23e0b78e3","added_by":"auto","created_at":"2025-09-24 17:58:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":53504,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of quantitative yield of eleven soybean accessions under optimized field conditions\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7487341/v1/e7d719baca2f034bdaf06a1b.png"},{"id":98243780,"identity":"197d2c94-ca0e-4395-8183-bb3e4fa79369","added_by":"auto","created_at":"2025-12-15 16:10:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2482081,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7487341/v1/57725f6b-cb32-4f47-8d22-001362809c7a.pdf"},{"id":92109220,"identity":"17c3723c-0ffc-4d41-bd8e-773d9abb1ba9","added_by":"auto","created_at":"2025-09-24 17:50:39","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":48128,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile.doc","url":"https://assets-eu.researchsquare.com/files/rs-7487341/v1/ab2a874c06e3803622e2cf31.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Molecular characterization and fertilizer optimization in soybean to maximize productivity and minimize environmental impact","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSoybean (Glycine max) is an essential oil-producing crop and a major player in the supply chain of vegetable oil. Its important position in the global oilseed market is further supported by the abundance of beneficial lipids, which highlight its worth in bioenergy production, industrial applications, and culinary uses\u003csup\u003e1–3\u003c/sup\u003e. The anticipated rise in global population is expected to lead to heightened food consumption, presenting considerable challenges to the food supply chain and requiring innovative strategies to maintain food security. However, the ramifications of climate change profoundly affect soybean production, resulting in diminished yields and endangering the world's food supply\u003csup\u003e4–6\u003c/sup\u003e. Due to ongoing changes in weather patterns, it is indisputable that climate change presents a significant long-term threat to soybean yield including unpredictable rainfall, sharp temperature swings, and water scarcity\u003csup\u003e7,8\u003c/sup\u003e. These variables affect the dynamics of pests and diseases, interfere with planting and harvesting schedules, decrease photosynthetic efficiency, and exacerbate biotic and abiotic stressors\u003csup\u003e9–12\u003c/sup\u003e. As noted by\u003csup\u003e13,14\u003c/sup\u003e, drought stress significantly impairs various physiological and biochemical processes in soybeans, including germination, seedling, flowering, and maturation.\u003c/p\u003e\n\u003cp\u003eOne of the most important issues in the world is improving the ability of crops to withstand biotic and abiotic stresses. To fight climate change and global warming necessitates the development of long-term strategies that are grounded in the principles of adaptation, avoidance, and evasion\u003csup\u003e15,16\u003c/sup\u003e. As a result of water scarcity and unpredictable weather, traditional breeding methods have a hard time for keeping consistency in potential of plant production, which limits agricultural resilience and prosperity\u003csup\u003e17,18\u003c/sup\u003e. A combination of conventional and recombinant breeding techniques has led to genetic improvements of agricultural crops, serving as a powerful weapon in the battle against the effects posed by climate change and drought stress. A great number of soybean-specific QTLs have been identified by scientists because of recent advances in genetics and genomics\u003csup\u003e19,20\u003c/sup\u003e. Although numerous genetic maps have been developed to study drought tolerance QTLs in soybeans, it is essential to verify and validate these QTLs before application to crop breeding program. Next-generation sequencing (NGS) has improved the cost-effectiveness of marker-assisted selection (MAS) for attaining genetic progress. GWAS that use SNPs are great for finding out how markers and traits are related in marker-assisted selection (MAS) breeding programs. They are also useful for finding out how genes are linked and how genetic diversity is measured\u003csup\u003e21–23\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe consequences of human activity are aggravating the phenomena of global warming at an alarming rate. This trend points to a future with longer and more intense droughts that will test the ability of our planet's ecosystems and food production systems to withstand shocks\u003csup\u003e24,25\u003c/sup\u003e. Thus, dependence exclusively on traditional breeding methods, even with the incorporation of molecular markers, is inadequate for enhancing crop resilience to abiotic stresses. An integrated approach, incorporating advanced crop management strategies, is crucial for tackling issues associated with soil health, water scarcity, and pest management\u003csup\u003e26\u003c/sup\u003e. The molecular characterisation of soybean production offers valuable insights; yet it may not entirely capture the complex array of factors influencing yield and other critical features. Molecular methods such as marker-assisted selection, while promising in breeding programs, often overlook the intricate interactions among genetic composition, environmental influences, and management strategies. A holistic strategy that incorporates molecular data, phenotypic evaluations, agronomic experience, and optimal nutrient management through precise fertilizer administration is crucial for achieving a thorough understanding of soybean productivity\u003csup\u003e27,28\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe advancement, efficiency, and quality of soybean seeds are contingent upon the judicious application of fertilizers that provide essential nutrients such as nitrogen, phosphorus, and potassium. They facilitate the enhancement of disease resistance, the process of protein synthesis, and the advancement of root development\u003csup\u003e29\u003c/sup\u003e. Nonetheless, excessive application may contaminate water sources, impede nitrogen fixation, and adversely affect soil health. Glycine max, the continuity of production is upheld while simultaneously minimizing the ecological footprint via the adoption of sustainable fertilizer management practices\u003csup\u003e30,31\u003c/sup\u003e. A crucial statistical method for optimizing intricate agricultural variables such soil humidity, drought indexing, and NPK fertilizer amounts is Response Surface Methodology (RSM). RSM assists in determining the ideal circumstances for crop growth and yield by examining the interplay between these variables. It is essential for comprehending how nutrient and environmental elements impact plant performance, which makes it a useful tool for boosting crop production tactics, increasing the effectiveness of resource utilization, and tackling issues brought on by climatic unpredictability\u003csup\u003e32–34\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThis study is notable for its application of the response surface methodology to soybeans, encompassing the processes of screening, characterizing, optimizing, and validating them. The goals of this research were to examine soybean germplasm in terms of its genetic diversity, find the best ways to meet crop needs, and assess the efficacy of management approaches and field output. The RSM methodology underwent refinement and validation concerning soil moisture, drought intensity, and primary macronutrients by assessing models through correlation analysis. The findings of this study can enhance drought resistance in soybean varieties and facilitate the development of more effective agricultural management strategies. The principal recommendations for enhancing sustainable agriculture encompassed strategies focused on augmenting crop yields, refining fertilizer application in soybean farming, and reducing the likelihood of agricultural pollutants.\u003c/p\u003e"},{"header":"Experimental procedures","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003ePlant Material\u003c/h2\u003e\n \u003cp\u003eThis study examined 48 genotypes of Glycine max (L.) plants exhibiting varying responses to drought, ranging from susceptibility to resilience. Dried seeds from the specified genotypes were obtained from the Plant Genetic Resource Institute (PGRI) and the Oilseed Research Program (ORP) at the National Agricultural Research Centre (NARC) in Islamabad (https://www.parc.gov.pk/PlantDivision/PGRI). The seeds were subjected to cleaning and manual sorting before grinding and sieving. Only soybeans that were processed through a sieve with a mesh size ranging from 0.40 to 1.0 mm were included in the analysis. Table S1 provides a detailed enumeration of the genotypes collected.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eIn-Vitro Screening Experiment\u003c/h3\u003e\n\u003cp\u003eTo induce osmotic stress, a total of forty-eight genotypes were initially subjected to screening using Polyethylene Glycol (PEG-6000) before being subsequently planted in the field. Following a series of surface sterilization steps utilizing a diluted sodium hypochlorite solution, the seeds underwent multiple rinses with distilled water. The experiment was conducted using a completely randomized design (CRD) and was performed within plastic trays. The experiment consisted of four treatments, namely Control, T1, T2, and T3. Each treatment consisted of varying concentrations of PEG-6000, ranging from 0\u0026ndash;20%. The data collection process encompassed various factors and was conducted 20 days after sowing (DAS). The measurements of root and shoot lengths were recorded from the apex to the base of the plant and from the crown to the distal end of the root, respectively. For each of the four treatments, measurements were based on three plants at random from each replication. The study involved assessing the masses of plants\u0026apos; aboveground and belowground structures, both in their hydrated and dehydrated states, under each experimental condition. Fresh shoot weights were determined using an analytical scale, while dry shoot weights were measured after drying in an oven set to 70\u0026deg;C. Root development assessment included quantification of fresh weights and obtaining dry weights through oven drying. Root weight was measured in grams. The Root-to-Shoot Ratio (RSR) was found by dividing the dry root weight (DRW) by the dry shoot weight (DSW), as described by\u003csup\u003e35\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003ePhenotypical Analysis of morphological characteristics\u003c/h3\u003e\n\u003cp\u003eThe study was carried out within a controlled environment with daytime temperature was regulated between 25\u0026ndash;30℃ and the nighttime temperature ranged from 18\u0026ndash;22℃. Soil samples were collected from the upper 20 centimetres of an experimental station. The river sand was subjected to a washing process, subsequently air-dried, and then sieved using a 2-mm mesh size to remove coarse fragments and microarthropods from both the river sand and field soil. Consequently, the soil from the field and the sand from the river were subjected to a process of sterilisation. The combination of sterilised river sand and field soil was executed in a ratio of 3:7 (w/w), with each pot measuring 85 cm \u0026times; 85 mm \u0026times; 180 mm containing 1.2 kg of the resultant soil mixture. Deionised water was administered to each pot to attain 80% of its capacity, with measurements recorded bi-daily. The process of data collection involved the meticulous measurement and documentation of various morphological parameters, such as plant height (cm), the number of pods per plant, hundred seed weight (g), days to maturity, chlorophyll content, proline accumulation, oil content, protein content, and yield per plant (g). The height of the genotypes was evaluated by measuring the distance from the ground to the apex of a juvenile leaf on five randomly chosen plants, recorded in centimetres (cm). The calculation of the average number of both filled and unfilled pods per plant involved the selection of five random specimens from each genotype. The period leading to maturity was evaluated from the emergence of seedlings until the moment when 57% of the pods displayed a yellow hue. The seed yield per plant was determined by assessing the average yield in grammes (g) from five randomly selected plants for each genotype. The mass of 100 seeds was measured after threshing and recorded in grammes (g). A SPAD 502 instrument quantified chlorophyll concentrations in the foliage before the onset of flowering in the plants. Mean values were derived from three leaves of five chosen plants for each genotype and documented in SPAD units36.\u003c/p\u003e\n\u003ch3\u003eDNA Extraction and Polymorphism\u003c/h3\u003e\n\u003cdiv\u003eDNA Extraction and Polymorphism\u003c/div\u003e\n\u003cp\u003eA recognized technique was improved for the extraction of genomic DNA. Young leaves, when freshly harvested, yield a visually appealing powder. Following meticulous grinding, the plant material was combined with CTAB buffer for the purposes of incubation and precipitation. The DNA pellet was subsequently washed with 70% ethanol following separation. The measurement of DNA content and purity was conducted using a Nano-Drop (ND-1000) spectrophotometer. DNA samples were diluted to a concentration of 20 ng/\u0026micro;l in preparation for PCR/SSR analysis using randomly selected SSR markers. Table S2 presents a summary of the primer parameters\u003csup\u003e37,38\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eField Experimental Design\u003c/h3\u003e\n\u003cp\u003eThe effect of the four independent variables on quantitative yield and qualitative yield were investigated using the central composite design (CCD) and response surface method ology (RSM). A total of twelve experimental runs for the optimization of the yield correlated parameters were carried out. Five levels (-2, -1, 0, +1, +2) were used for each independent variable. The six independent variables are moisture level/drought percentage, nitrogen level, phosphorous level, potassium level, extraction temperature, extraction time, while the quantitative and qualitative yield are dependent variables. The independent variables with their levels and codes are shown in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Independent variables and their levels in CCD.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cimg width=\"529\" height=\"170\" 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alt=\"image\"\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement of Soil Moisture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe quantity of water can be quantified as the weight fraction of water relative to the weight of the soil in which it is contained according to the equation reported by\u003csup\u003e39\u003c/sup\u003e. The common measuring units are typically expressed as a percentage or grams of water per 100 grams of soil, denoted by the symbol Pw, which represents the percentage of water on a weight basis. The soil appears moist upon measurement; however, the calculation relies on the dry weight of the soil that contains the water. The measurement involves collecting a soil sample from the designated depth and location, which is then placed in a watertight container to prevent drying during the collection of additional samples or transport to the laboratory. The sample is weighed upon receipt in the laboratory and subsequently after being dried in an oven for 24 hours at 1050 \u0026deg;C.\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"295\" height=\"33\" 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\" alt=\"image\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (1)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDrought Index\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAgricultural drought is characterized by water deficiency in crops due to various external factors, resulting in impaired growth and development of plants\u003csup\u003e40\u003c/sup\u003e. This study introduces a regional agricultural drought index that utilizes the percentage of effective storage capacity of soil reservoirs as a variable, grounded in soil reservoir theory. We present the expression below.\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"163\" height=\"33\" 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\" alt=\"image\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (2)\u003c/p\u003e\n\u003cp\u003eWhere AWCI is the available water content of the soil index, \u0026theta;\u003csub\u003et\u003c/sub\u003eis the average soil volume moisture percentage that was measured during the time period, \u0026theta;\u003csub\u003efc\u003c/sub\u003e is the field capacity, and \u0026theta;\u003csub\u003ewp\u003c/sub\u003e is the wilting coefficient. The effective available water content of the soil is found by subtracting \u0026theta;\u003csub\u003efc\u003c/sub\u003efrom \u0026theta;\u003csub\u003ewp\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResponse Surface Methodology\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe screening and molecularly characterisation of germplasm emphasises genetic diversity, indicating that certain germplasm might provide resistance to stress. However, there are a few outside variables that also affect crop production, including nutrients availability, drought severity, and soil moisture content. Here, we applied RSM for the following purposes: (1) to optimize the level of irrigation, drought, nitrogen, phosphorous and potassium for attaining of soybean quantitative yield to obtain the best results, (2) to optimize the level of extraction time and temperature for soybean qualitative yield and (3) to obtain a predictive model that adequately represents the variation in response to the input variables\u003csup\u003e41\u003c/sup\u003e. A central composite design (CCD) was used to construct second-order mathematical models relating to the observed variables with irrigation levels/drought due to their high efficiency in terms of the number of runs required. In CCD, all process variables contain five levels: a total of thirteen experimental runs for each treatment used for the optimization of the soy yield were carried out. Five levels (-2, -1, 0, +1, +2) were used for each independent variable. The four independent variables are moisture level/drought (X1), nitrogen level (X2), phosphorous level (X3) and potassium level (X4), with dependent variable soybean yield (Y1). The number of design points (N) is determined by the following equation:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"119\" height=\"23\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;(3)\u003c/p\u003e\n\u003cp\u003ewhere k is the number of variables and Nc is the number of central points. The variables here are irrigation levels and drought levels (k = 2). When Nc takes one central point, a total of 13 designed points for each of all treatments are generated through CCD. The following formula was used for the analysis of response surface and counter plot of level of moisture and macronutrients.\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"296\" height=\"23\" 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\" alt=\"image\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(4)\u003c/p\u003e\n\u003cp\u003eWhere Y is the response variable, X\u003csub\u003ei\u003c/sub\u003e and X\u003csub\u003ej\u003c/sub\u003e is the independent variable, \u0026beta;\u003csub\u003e0\u0026nbsp;\u003c/sub\u003eis intercept, \u0026beta;\u003csub\u003ei\u0026nbsp;\u003c/sub\u003eis linear coefficient,\u0026beta;\u003csub\u003eii\u0026nbsp;\u003c/sub\u003eis the quadric coefficient,\u0026beta;\u003csub\u003eij\u0026nbsp;\u003c/sub\u003eis interaction coefficient and \u0026epsilon; is the random error.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe phenotypic data collected from both the control and on-site experiments underwent further analysis using various statistical methods such as Analysis of Variance (ANOVA) and principal component analysis (PCA Biplot) conducted using the Minitab software with the use of the following formulas\u003csup\u003e42\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"215\" height=\"23\" 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\" 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\" alt=\"image\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (6)\u003c/p\u003e\n\u003cp\u003eWhere in formula 5, \u0026alpha;i is the effect of factor A, \u0026beta;j is the effect of factor B, and (\u0026alpha;\u0026beta;)ij is the interaction effect. Formula 6 represents that Y is the dependent variable (X1, X2, \u0026hellip;). Xn is the independent variable, \u0026beta;0 is the intercept, \u0026beta;n is the regression coefficient, and \u0026epsilon; is the random error.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eAnalysis of growth parameters of soybean\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe did this study to see how forty-eight genotypes responded to drought stress and different amounts of PEG at the molecular lab of our research institute during the kharif season of 2022\u0026ndash;2023. The analysis of variance (ANOVA) exhibited significant differences and confirmed the presence of variations and radar plot analysis among genotypes for the traits, namely shoot length, fresh and dry shoot weight, root length, and fresh and dry root weight. The findings of the radar plot indicate that Treatment 2 demonstrated notably superior performance under PEG treatment, followed by Treatment 3, whereas Treatment 4 was substantially affected by PEG-induced drought stress. Root length (RL) decreased by 33% and 56% in Treatments 3 and 4, respectively, due to drought stress, while it experienced a slight increase in Treatment 2 compared to the control (Figure 1). The percentage decrease in shoot length (SL) under PEG treatment, as compared to the control, was 5%, 23%, and 43% in Treatments 2, 3, and 4, respectively. Conversely, the smallest decrease of 31% in fresh shoot weight (FSW) was observed in Treatment 3, followed by a 59% decrease in Treatment 4, while Treatment 2 exhibited an 8% increase compared to the control. Furthermore, fresh root weight (FRW) decreased by 37%, 64%, and 79% in Treatments 2, 3, and 4, respectively, compared to the control under PEG-induced drought treatment. On average, a 13% decrease in dry shoot weight (DSW) was recorded in Treatment 2, whereas Treatments 3 and 4 experienced decreases of 39% and 71%, respectively, compared to the control. The percentage decrease in dry root weight (DRW) was 45%, 89%, and 90% in Treatments 2, 3, and 4 under PEG treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePolymorphisms of soybean cultivars\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the course of the initial experiment, ten SSR markers were employed to amplify DNA from sixty distinct genotypes of soybeans that had been previously identified. Initially, a mere five SSR markers demonstrated the capability to effectively amplify the DNA, encompassing all sixty genotypes. Despite further endeavours employing a diverse array of annealing temperatures, the residual markers failed to achieve successful DNA amplification across all populations. It was found that 60.67% of these SSR markers exhibited polymorphism. The subsequent illustration, designated as Figure 2, presents the results of the polymerase chain reaction pertaining to five distinct molecular markers. The markers in question are Satt373, Satt454, Satt471, Satt478, and Satt581.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrincipal Component Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe principal Component Analysis (PCA) is a multivariate statistical technique utilized to analyze and streamline intricate and extensive datasets. It aims to assess the diversity of soybean genotypes and their relationship to observed traits. A PCA biplot ellipses analysis is to identify traits that can be grouped into main and subcategories based on their similarities and differences. The PCA biplot ellipses reveal the presence of four distinct trait groups, considering both PC1 and PC2 simultaneously (Figure 3A). Notably, in the laboratory experiment, there is no overlap between the control and combined stress treatments, indicating significant trait variation. Dimension-1 (Dim-1) contributes to 73.6% of the total variance, while Dimension-2 (Dim-2) contributes to 11.8% (Figure 3A). Traits such as RL, SL, FRW, DRW, and FSW are associated with Treatment-1, while RL, SL, FRW, DSW, DRW, and FSW are linked to Treatments 2 and 3. Additionally, FSW and DSW are associated with Treatment 4. In the field experiment, the PCA biplot demonstrates that PC1 represents approximately 44.7% and PC2 approximately 17.3% of the total variation (Figure 3B). Under control conditions, all traits except PA remain closely clustered, displaying maximal parallelism in their expression patterns. Moreover, the PCA analysis indicates that trait associations vary under drought conditions compared to the control, suggesting distinct influences of drought and control conditions on trait pairings. Consequently, as a result, genotypes that exhibit differential expressions of traits are segregated and categorized into each quadrant of the biplot based on their trait expressions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegression Models for quantitative characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA multiple regression analysis was performed on the data that is shown in Table 2, and the linear interaction coefficients for yield (Y1) were utilized in the data analysis. In addition to this, we show the results of the goodness of fit and lack of fit tests that were extracted from the regression model. To get the regression coefficient, which illustrates the relationship between the amount of nitrogen, phosphorus, and water present in the soil and the yield (Y1), the equations of the regression model were utilized. Based on the statistical analysis, it was determined that only the linear interaction coefficients exhibited statistical significance (p\u0026lt;0.05). On the other hand, the two-way coefficients exhibited a p-value of 0.070, which was not significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Summary of analysis of variance (ANOVA) between dependent and independent variables for model interactions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cimg width=\"529\" height=\"353\" 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”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alt=\"image\"\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantitative Yield analysis (Y1)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe made a contour plot of the determination coefficients of linear regression between one part of soil moisture, three parts of macronutrients (nitrogen, phosphorous, and potassium), and yield using the \u0026quot;plsregress\u0026quot; function in the Minitab procedure and the data from the experiment. The results of the contour plot indicated that the higher the moisture level (up to 150 mm), the higher the yield (up to 600 kg/acre). Figure. 4a and 4c show how the different parts of the contour plot showed a link between different amounts of macronutrients and how they affected the soybeans grown when the soil was wet or dry. In this study, the optimum conditions were selected using surface plots. To determine the optimum yield at any point, two of the four independent variables were fixed while varying the remaining one and predicting the response variables. The triangle-shaped yield production surface plots revealed that there is a reciprocal relationship between soil moisture and intensity of drought. Results revealed that the maximum quantitative yield was attained at a moisture level and nitrogen level of 100-150 mm and 65 kg per acre, respectively (Figure 4b). The results also reported that the higher the moisture level, the more it can lead to lowering the drought intensity and improving the yield of soybean. The results of the surface plot indicated that a higher amount of nitrogen can\u0026rsquo;t produce a positive impact on yield in the presence of higher drought intensity. As shown in Figure 4d, interactions between the phosphorous and potassium levels caused an increase in yield, resulting in a maximum yield of about 550-600 kg per acre at 40 kg/acre of phosphorous and 20 kg/acre potassium, respectively. However, a further increase in the amount of phosphorus and potassium after reaching the optimum caused a corresponding decrease in yield.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;of optimal conditions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe experiment was repeated in optimal conditions, with a moisture level of 150 mm and macronutrient application rates of 65 kg/acre of nitrogen, 40 kg/acre of phosphorus, and 20 kg/acre of potassium. This was done in accordance with the conclusions of our research focused on optimization. When compared to the control, eleven out of forty-eight soybean accessions were able to increase the experimental yield ranges by as much as seventy percent under the conditions that were adjusted above. Because of this finding, the predictions made by the regression model that were based on the RSM regression equation (Figure 5) were validated.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe production and yield stability of soybean (Glycine max L.) are significantly compromised by the challenges posed by drought stress. According to\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, the best way to grow drought-resistant high-yielding cultivars is to use direct selection to ensure stable seed and oil yields. It is essential to molecularly isolate drought-tolerant and susceptible genotypes in soybeans to breed new varieties with higher yield potential. Molecular data can elucidate genetic distances and variants\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. As noted by\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, the application of soybean genotyping has proven effective in enhancing seed quality, improving resistance to rhizoctonia root rot, and increasing tolerance to the yellow mosaic virus.\u003c/p\u003e\u003cp\u003eWe amplified 48 genotypes (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) using five drought-tolerant SSR markers (Table S2) to identify soybeans with higher yield. Due to its elevated molecular weight and solubility in water, PEG-6000 serves as an effective osmotic regulator that is unable to penetrate plant cell walls. A nutritional solution with PEG-6000 concentrations ranging from 0\u0026ndash;20% simulated mild drought stress over a four-day period (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The radar plot results indicate that Treatment 2 was the most effective regarding PEG, followed by Treatment 3 in second place. Treatment 4 exhibited more pronounced effects from PEG-induced drought stress. The second treatment resulted in a 33% reduction in root length, the third treatment led to a 56% reduction, and the fourth treatment caused a 33% reduction. A reduction in fresh shoot weight of 37%, 64%, and 79% was observed in treatments 2, 3, and 4, respectively. A 13% reduction in dry shoot weight was noted following the second treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Similar to the findings of\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, we observed a comparable correlation.\u003c/p\u003e\u003cp\u003eAmong the forty-eight soybean genotypes analyzed in this study, five out of ten randomly selected markers demonstrated significant variation. Five molecular markers, specifically Satt373, Satt451, Satt471, Satt478, and Satt531, were identified as the most effective in distinguishing highly tolerant soybean cultivars with enhanced edible oil among forty-eight tested varieties. The SSR markers effectively distinguished the soybean cultivars by producing four distinct banding patterns. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the polymorphism of five of the ten single sequence repeat biomarkers. The findings challenge the conclusions of\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, who found that none of the seven primers could distinguish among 16 different cultivars of Brassica juncea in a single study. The findings from our studies were correlated with the results reported by\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. The PCA biplot results from the in vitro lab experiment indicate that approximately 73.8% and 14.9% of the total variation is explained (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA \u0026amp; B). The PCA analysis indicates that trait associations differ under drought conditions relative to control conditions. The results indicate that two environments exert distinct effects on trait pairings. The findings of our study were correlated with those reported by\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe expression of traits relies on soil resource availability; however, marker-assisted selection enhances drought resilience in soybeans and increases the efficiency of breeding programs. The overuse of NPK fertilizer, driven by erroneous yield projections, poses a threat to soil integrity and environmental health. The use of balanced fertilizer enhances soil fertility, reduces runoff and leaching, and decreases greenhouse gas emissions, thereby supporting sustainable soybean cultivation in drought conditions. The analysis of variance results for the optimization of NPK and moisture levels, presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, reveal a non-significant lack of fit for NPK and moisture levels (F\u0026thinsp;=\u0026thinsp;1.63; p\u0026thinsp;=\u0026thinsp;0.110\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating that the regression model for yield (Y₁) adequately represented the experimental data. A correlation was identified between our results and those reported by\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. The goodness-of-fit of the ANOVA was evaluated through the coefficient of determination (R\u0026sup2;), which quantifies the proportion of variance in the response variable accounted for by the independent variables and their interactions. R\u0026sup2; values nearing 1 signify a robust correspondence between the statistical model and the data. The regression model forecasts yield (Y₁) based on moisture level (X₁) and NPK (X₂, X₃, X₄), yielding R\u0026sup2; and predicted R\u0026sup2; values of 0.725 and 0.715, respectively, indicating the model's adequacy. Another study found that linear regression models effectively estimate crop yields in various contexts. This indicates that reliance on traditional methods based on factors such as soil type, historical data, and vegetation indicators is unnecessary\u003csup\u003e\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe research indicates that optimal soybean yield occurs with nitrogen application at 65 kg/ha, alongside soil moisture levels ranging from 100 to 150 mm, suggesting a relationship between soil moisture and the severity of drought conditions. Soil moisture management effectively mitigated drought severity and enhanced yield; conversely, nitrogen fertilizer did not influence yield under conditions of severe drought. The application of 40 kg/acre of phosphorus and 20 kg/acre of potassium led to the highest yield, ranging from 550 to 600 kg/acre, influenced by the synergistic interaction between these two nutrients. The results obtained from the response surface methodology confirm the synergistic effects of nutrient management and moisture optimization on soybean output, as illustrated by the contour plot analysis in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea-d. The research highlights the critical role of water availability and the strategic application of fertilizers in improving drought resilience and optimizing agricultural productivity. The findings of our research correspond with those reported by\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, indicating that the vegetative growth response of soybeans in acidic soil specifically plant height, leaf count, and root length can be enhanced through the application of an optimized quantity of organic fertilizer.\u003c/p\u003e\u003cp\u003eIn light of the results, the experiment was re-done with the following parameters: 150 mm of moisture, 65 kg/acre of nitrogen, 40 kg/acre of phosphorus, and 20 kg/acre of potassium. The findings provide credence to the RSM regression equation-based regression model. A 70% increase in production at the best level was observed when eleven different types of soybeans were grown in soil that had been affected by drought, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. In order to avoid negative impacts, it is crucial to avoid applying too much of any nutrient, as this research explains plants' basic needs. According to\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, the results of the screening and molecular characterization did not meet expectations, highlighting the necessity of optimizing soil conditions. The study's results underscore the importance of understanding various factors such as soil type, moisture level, drought severity, optimal macronutrient ratios, and levels of soil and ambient pollution in order to promote sustainable farming techniques.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis research emphasized the importance of thorough soil analysis prior to soybean cultivation, as the performance of different genotypes, as determined through screening and molecular characterization methods, does not always correspond with expectations. The latest research strongly suggested that a full analysis of soil moisture, drought severity, and nutrient concentrations be done to see if a field is ready to plant or to figure out what changes need to be made to improve soybean production. The productivity of soybean crops is adversely affected by erratic soil and environmental conditions. These fluctuated conditions lead to premature blooming, hindered photosynthesis, leaf loss, and diminished seed germination rates, all of which culminate in reduced yields. The strategic application of fertilizers (NPK) markedly enhances crop yield by fostering robust plant growth, facilitating pod formation, and improving seed quality. The ongoing maintenance of soil fertility, the reduction of environmental impacts, and the continuation of productivity all depend on the careful management of fertilizer. This study established the ideal circumstances for optimizing soybean yield, which consist of maintaining soil moisture levels between 100 and 150 mm, alongside application rates of nitrogen, phosphorus, and potassium at 65, 40, and 20 kg/acre, respectively. The findings of this study indicate that further investigation into optimal nitrogen application rates could contribute to reducing nitrous oxide emissions and mitigating the environmental impact of agriculture.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eKamran Shehzad Bajwa: designed the research activity, wrote the manuscript,\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSabahat Noor\u003csup\u003e:\u0026nbsp;\u003c/sup\u003e provided materials and proofreading of manuscript\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZaheer Abbas\u003csup\u003e:\u0026nbsp;\u003c/sup\u003eedited the manuscript \u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMuhammad Rizwan Aliand Muhammad Umer bin Muhammad Ishaq\u003csup\u003e:\u0026nbsp;\u003c/sup\u003edata analysis,\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRaja Sheraz Rafique, Zeeshan Abbas and Shagufta Parveen: performed the experiments\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eShaukat Ali: reviewed manuscript.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e The original contributions presented in the study are included in the article, further inquiries can be directed at the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e The authors would like to extend their sincere appreciation to the researchers of the Plant Genetic Resource Institute (PGRI) and the Oil Seed Program of the National Agriculture Research Centre, Islamabad, Pakistan.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLakhiar, I. 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An interaction regression model for crop yield prediction. \u003cem\u003eScientific Reports\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 17754 (2021). https://doi.org:10.1038/s41598-021-97221-7\u003c/li\u003e\n\u003cli\u003eSantos, L. B.\u003cem\u003e et al.\u003c/em\u003e Soybean yield prediction using machine learning algorithms under a cover crop management system. \u003cem\u003eSmart Agricultural Technology\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 100442 (2024). https://doi.org:https://doi.org/10.1016/j.atech.2024.100442\u003c/li\u003e\n\u003cli\u003eLestari, P. G., Sinaga, A. O. Y., Marpaung, D. S. S., Nurhayu, W. \u0026amp; Oktaviani, I. Application of organic fertilizer for improving soybean production under acidic stress. \u003cem\u003eOil Crop Science\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 46-52 (2024). https://doi.org:https://doi.org/10.1016/j.ocsci.2024.02.001\u003c/li\u003e\n\u003cli\u003eRen, C., He, L. \u0026amp; Rosa, L. Integrated irrigation and nitrogen optimization is a resource-efficient adaptation strategy for US maize and soybean production. \u003cem\u003eNature Food\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 389-400 (2025). https://doi.org:10.1038/s43016-024-01107-6\u003c/li\u003e\n\u003cli\u003eSaha, S. \u0026amp; Bhardwaj, A. A narrative review of artificial intelligence to optimize the use of fertilizers: A game changing opportunity. \u003cem\u003eCrop, Forage \u0026amp; Turfgrass Management\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, e70027 (2025). https://doi.org:https://doi.org/10.1002/cft2.70027\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Climate, Soil, Molecular Characterization, Fertilizer, Optimization","lastPublishedDoi":"10.21203/rs.3.rs-7487341/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7487341/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change and deteriorating soil conditions have exacerbated water resource challenges, significantly limiting soybean yields. This situation necessitates the optimization of macronutrients, soil moisture, and drought indexing to enhance resilience, moving beyond molecular characterization of germplasm. The molecular characterization and correlation analysis of germplasm elucidates its genetic potential, variability, and diversity, while productivity depends on essential nutrients provided by soil or the application of NPK fertilizers. However, indiscriminate use of excessive NPK fertilizers, devoid of strategic optimization, compromises soil vitality and accelerates environmental damage. The central composite design (CCD) was used to examine soybean yield with four independent variables: moisture level from 0 to 200 mm and macronutrients (NPK) from 0 to 100kg per acre for each with RSM model and contour plot. A screening experiment utilizing PEG-6000 on forty-eight soybean accessions demonstrated significant correlations between growth indicators. SSR markers were employed to assess genetic diversity, with principal component analysis (PCA) accounting for up to 73.8% of the variation The RSM model predicts the optimal conditions, which include the application rates of nitrogen, phosphorus, and potassium (65, 40, and 20 kg/acre), while maintaining soil moisture levels between 100 and 150 mm. In the validation experiment, eleven out of forty-eight soybean accessions improved up to 70% more yield than control plants when the above optimum conditions were applied. The results of this study demonstrate that optimizing fertilizer application rates can significantly decrease emissions of greenhouse gases and alleviate soil and environmental pollution linked to agricultural practices.\u003c/p\u003e","manuscriptTitle":"Molecular characterization and fertilizer optimization in soybean to maximize productivity and minimize environmental impact","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-24 17:50:34","doi":"10.21203/rs.3.rs-7487341/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-07T17:43:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-01T10:53:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"297044587664551698493170585878059597828","date":"2025-09-29T11:58:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-25T07:03:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-24T05:13:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"113302696125382744720253479661680287200","date":"2025-09-24T03:58:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"198713048155733682584338642126763185525","date":"2025-09-22T10:45:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144713258997141847576594606717153283525","date":"2025-09-22T09:03:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143695321457268510468650887459991156586","date":"2025-09-22T08:38:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-18T10:08:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"134930456066211788173643585195529714928","date":"2025-09-16T09:02:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-16T08:33:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-16T08:31:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-04T12:16:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-03T07:57:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-03T07:52:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a297745a-2482-4871-8978-bdbfac434207","owner":[],"postedDate":"September 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":55111726,"name":"Biological sciences/Ecology"},{"id":55111727,"name":"Earth and environmental sciences/Ecology"},{"id":55111728,"name":"Earth and environmental sciences/Environmental sciences"},{"id":55111729,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2025-12-15T16:02:35+00:00","versionOfRecord":{"articleIdentity":"rs-7487341","link":"https://doi.org/10.1038/s41598-025-27572-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-11 15:57:01","publishedOnDateReadable":"December 11th, 2025"},"versionCreatedAt":"2025-09-24 17:50:34","video":"","vorDoi":"10.1038/s41598-025-27572-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-27572-y","workflowStages":[]},"version":"v1","identity":"rs-7487341","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7487341","identity":"rs-7487341","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}