{"paper_id":"40affca7-4457-469e-b2b5-aaa99ee7e296","body_text":"From Soil to Spikelet: The Integrated Impact of AWCI on Rice Growth Under Heat and Drought Stress | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article From Soil to Spikelet: The Integrated Impact of AWCI on Rice Growth Under Heat and Drought Stress Wenping Zhang, Jiangyuan Zhang, Feiyu Peng, Zhuying Liu, Xin Liu, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8497875/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract The intricate interplay among soil physicochemical properties, root physiological traits, and flowering gene expression fundamentally governs rice reproductive success and grain yield. This study elucidates the effects of the Aerobic and water-controlled irrigation (AWCI) method on rhizosphere soil parameters, root biochemical responses, pollen viability, and temporal expression of the circadian-related flowering gene OsFKF1 during the heading and flowering stage, illuminating their integrated impact on rice productivity. Application of the AWCI regime modulated soil pH and nitrogen dynamics at critical growth intervals, enhanced rhizosphere oxygen availability, and shifted redox potential, thereby optimizing nutrient bioavailability. Simultaneously, AWCI treatment influenced root antioxidant enzyme activities—including peroxidase (POD) and catalase (CAT)—as well as abscisic acid (ABA) and MDA concentrations, which positively correlated with elevated pollen viability and upregulated OsFKF1 expression. Multivariate analyses identified key determinants of yield enhancement, notably augmented root surface area during the jointing-booting phase, balanced soil nitrogen content, and finely regulated oxidative stress markers at the mid-tillering stage. Hierarchical clustering robustly designated the T 3 treatment as the optimal AWCI protocol for maximizing reproductive performance and grain yield. Collectively, these findings underscore the pivotal nexus among irrigation management, soil biochemical milieu, root physiology, and floral gene regulation in modulating rice yield, offering a theoretical foundation for precision water management strategies tailored to sustainable productivity enhancement. Future research should extend to encompass additional flowering-related genes and a broader spectrum of rice cultivars to generalize these mechanistic insights. AWCI method Rhizosphere soil physicochemical properties Root antioxidant enzymes OsFKF1 gene expression Grain yield Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Highlights • Aerobic and water-controlled irrigation(AWCI) method increased pollen activity, OsFKF1 expression levels at the heading and flowering stage, and rice grain yield. • soil pH values, and soil total nitrogen(TN) content, malondialdehyde(MDA) content in rice roots, and total rice root surface area increase Pollen vitality,OsFKF1 expression level at the heading and flowering stages, as well as rice grain yield. • 0-30 mm at the mid-tillering stage, 60%-80% at the late tillering stage, and 0-30 mm at the jointing-booting stage of water control mode was adopted as a theoretically satisfactory scheme to regulate the flowering period of rice and improve rice grain yield. 1. Introduction The escalating frequency and intensity of extreme heat and drought phenomena exert profound disturbances on vegetation structure and physiological processes, posing formidable challenges to plant growth, survival, and, ultimately, global food security [ 1 – 2 ]. Rice, as a staple crop of paramount importance worldwide, exhibits pronounced sensitivity to elevated temperatures and water scarcity throughout its developmental continuum [ 3 ]. Exposure to ambient temperatures exceeding 35°C for durations surpassing two hours during anthesis precipitates precipitous declines in spikelet fertility and thousand-grain weight, accompanied by an increased proportion of abortive grains [ 4 ]. Concurrent drought stress further exacerbates these deleterious effects by impairing photosynthetic performance and curtailing vegetative growth, with severe instances capable of inducing yield reductions of up to 93.6% [ 5 ]. The detrimental impacts of heat and drought stress are distinctly stage-specific: the early tillering stage experiences reductions in plant height, tiller number, and shoot biomass; during panicle initiation and heading, there is heightened susceptibility manifested as diminished light-use efficiency, panicle biomass, seed setting rate, and grain filling; the grain-filling phase, crucial for assimilate accumulation, is significantly compromised by impaired photosynthetic capacity [ 6 – 7 ]. Within the middle and lower reaches of the Yangtze River basin in China, recurrent heat and drought episodes during summer and autumn impose substantial threats to rice production. For example, Hunan Province, situated within this region, endures protracted heatwaves lasting up to 41.6 consecutive days, culminating in severe yield losses and, in extreme cases, complete crop failure. Despite the rice-growing season predominantly spanning June to August—encompassing the critical panicle initiation to flowering interval—the mechanisms by which combined heat and drought stresses converge to influence yield during this vulnerable window remain insufficiently elucidated [ 8 ]. The exquisite synchronization and timely deployment of the rice spikelet’s lemma and palea at anthesis constitute a pivotal determinant in the orchestration of grain yield [ 9 ]. This finely regulated phenological event arises from a complex interplay among soil microenvironmental parameters, root physiological status, and the spatiotemporal expression of flowering-associated gene networks [ 10 – 11 ]. Edaphic factors such as soil moisture content, oxygen availability, alkali-hydrolyzable nitrogen, and bioavailable phosphorus critically modulate energy fluxes and nutrient cycling within the soil-plant-atmosphere continuum. Enzymatic activities within the rhizosphere, alongside concentrations of soil nitrate nitrogen (SNN) and ammonium nitrogen (SAN), correlate strongly with root morphological attributes and physiological functionality [ 12 – 13 ], thereby influencing overall rice growth and developmental trajectories. Key soil enzymes—including CAT, urease, and invertase—mediate essential biogeochemical transformations of nitrogen and phosphorus; their activities are intricately modulated by soil pH dynamics. Root morphology and vitality not only dictate functional capacity and growth dynamics but also regulate flowering phenology and yield formation by enabling rapid and adaptive responses to drought-induced perturbations. Antioxidant defense systems within roots—comprising POD, CAT, superoxide dismutase (SOD), and soluble proteins (SP)—escalate under abiotic stress, efficiently scavenging reactive oxygen species (ROS) to mitigate cellular oxidative damage induced by thermal and hydric stresses [ 14 – 15 ]. Furthermore, root endogenous ABA orchestrates antioxidant enzyme activation, modulates cellular hydration status, and regulates stomatal conductance, collectively enhancing rice resilience to the synergistic challenges of heat and drought [ 16 – 17 ]. Hence, elucidating rhizosphere soil milieu and root physiological dynamics during flowering is of paramount theoretical and practical significance for safeguarding sustainable grain productivity in southern China. Irrigation practices wield profound influences on soil pH, enzymatic activities, and nitrogen-phosphorus nutrient transformations, thereby fostering root vitality and optimal morphological development, which in turn modulate flowering phenology and yield outcomes [ 18 ]. The efficacy of irrigation modalities in adjusting soil pH across acidic and alkaline contexts follows the hierarchy: flooding irrigation > alternate wetting and drying irrigation > moist irrigation [ 19 – 20 ]. Innovative interventions such as micro-nano bubble-aerated irrigation markedly enhance root antioxidant enzyme activities (SOD, CAT) and elevate MDA levels, reflecting augmented oxidative stress responses in rice roots [ 11 ]. Oxygen-enriched irrigation regimes consistently elevate soil dissolved oxygen concentrations, stimulate urease, SOD, and CAT activities, and bolster microbial biomass, thereby refining root architectural complexity and metabolic vigor [ 21 – 22 ]. Alternate wetting and drying irrigation increases root dry biomass, root oxidation capacity, and antioxidant enzyme activities within the rhizosphere [ 23 ]. Water-controlled irrigation enhances soil oxygen diffusion and dissolution capacities, accelerates nitrification processes in rhizosphere soils, and consequently augments nitrate nitrogen availability, positioning it as a salient water-saving and yield-enhancing agronomic practice [ 24 ]. Although numerous studies have examined the impacts of disparate irrigation regimes on rice root physiology and rhizosphere soil characteristics [ 25 – 26 ], a comprehensive evaluation of the integrated effects of AWCI method on rice phenology, antioxidant enzyme profiles, and grain yield remains conspicuously lacking. This study endeavors to dissect the influence of AWCI method on rice root physiological indices, rhizosphere soil environment, pollen viability, flowering-related gene expression, and yield formation. Specifically, it aims to: (1) unravel the regulatory mechanisms by which AWCI method modulates root physiological performance and rhizosphere soil attributes; (2) elucidate the interdependencies among root physiological traits, rhizosphere milieu, pollen viability, gene expression dynamics, and final grain yield. We hypothesize that the AWCI technique substantially enhances these parameters, culminating in elevated rice productivity. The insights gleaned are anticipated to furnish a robust theoretical framework for the selection and breeding of heat-resilient, high-yielding, and high-quality rice cultivars, thereby decisively contributing to food security and the stability of agroecosystems(Fig. 1 ). 2. Materials and Methods 2.1 Pot Experiments The soil utilized in the pot experiments was procured from a paddy field located at Hunan Agricultural University (28°10′N, 113°04′E, Changsha, China). Prior to experimentation, the soil samples were air-dried, sieved through a 5 mm mesh to ensure homogeneity, and thoroughly mixed [ 26 ]. The physicochemical characterization of the soil revealed a pH of 6.19 and a moisture content of 42.93%. Total nitrogen (TN) and phosphorus (TP) contents in soil were quantified at 1.18 mg/kg and 0.97 mg/kg, respectively. The concentrations of SAN and SNN measured 14.42 mg/kg and 32.29 mg/kg, correspondingly. Two treatment modalities were established, both employing the oxygation technique. The control group (CK) maintained soil moisture at approximately 80%–100% during the late tillering stage, with a standing water layer of 0–30 mm above the soil surface during all other rice developmental stages. In contrast, the treatment group (T) implemented a water-controlled irrigation regime, in which soil moisture was regulated between 60%–80% during the mid and late tillering stages, rising to 80%–100% during the booting stage (see Table 1 for detailed moisture parameters). Each treatment was replicated three times under greenhouse conditions and was subject to ambient temperature fluctuations. Each pot contained 7.5 kg of prepared soil and was planted with a single rice seedling of the cultivar Zhongzao 39. Seedlings were initially sown on May 8, 2022, followed by transplantation into pots on May 21, 2022. Flowering and maturity stages were defined as 44 and 75 days after post-transplantation, respectively. During the experimental period, N fertilizer [CO(NH 2 ) 2 ] was applied at a total rate of 2.56 g per pot, fractionated into basal, tillering, and grain-filling applications at a ratio of 4:3:3. Potassium was supplied in the form of potassium chloride (KCl), with the amount calculated as 1.00 g K 2 O equivalent per pot, divided between pre-transplanting and jointing stages. P applicaiton was administered exclusively as basal fertilizer at a rate of 4.17 g per pot [ 27 ]. Table 1 Water management measures under AWCI method Treatments Rice growth stages Green-up stage Early tillering stage Mid-tillering stage Late tillering stage Jointing- booting stage Heading and flowering stage Milky stage Yellow maturity stage T 1 0–30 0–30 0–30 80–100% 0–30 0–30 0–30 0 T 2 0–30 0–30 0–30 80–100% 80–100% 0–30 0–30 0 T 3 0–30 0–30 0–30 60–80% 0–30 0–30 0–30 0 T 4 0–30 0–30 0–30 60–80% 80–100% 0–30 0–30 0 T 5 0–30 0–30 80–100% 80–100% 0–30 0–30 0–30 0 T 6 0–30 0–30 80–100% 80–100% 80–100% 0–30 0–30 0 T 7 0–30 0–30 80–100% 60–80% 0–30 0–30 0–30 0 T 8 0–30 0–30 80–100% 60–80% 80–100% 0–30 0–30 0 CK 0–30 0–30 0–30 0–30 0–30 0–30 0–30 0 2.2 Plant and Soil Analyses Experimental samples were collected in triplicate from individual pots for subsequent plant and soil analyses. Rice plants were meticulously excavated to preserve root integrity, then temporarily stored in a low-temperature incubator before transfer to the laboratory for processing. The roots, stems, and leaves of the rice plants were carefully separated, rinsed thoroughly with distilled water, and gently blotted dry using filter paper. POD activity in rice roots was quantified using a colorimetric POD activity assay kit, while CAT activity was assessed employing the CheKine™ CAT activity analysis kit, also based on colorimetric detection. ABA concentrations in root tissues were measured via enzyme-linked immunosorbent assay (ELISA). MDA content in root tissues were measured via enzyme-linked immunosorbent assay (ELISA) [ 28 ] . Root morphological parameters—including root length, surface area, and number of lateral roots—were analyzed utilizing the EPSON Expression 11000XL root scanner. Grain yield per plant was recorded following harvest. Soil pH was determined using an Ohaus 868 pH meter. TN content in soil was measured through the Kjeldahl digestion method, whereas TP content in soil was quantified via alkali fusion followed by inductively coupled plasma optical emission spectrometry (ICP-OES). SAN and SNN concentrations were analyzed with a flow analyzer (Fink TFIA-102-TP/P). For SAN and SNN analyses, 10.0 g of soil was combined with 100 mL of 2 M potassium chloride (KCl) solution in a sealed plastic bottle, shaken vigorously for 30 minutes, and immediately filtered into a 50 mL conical flask, following protocols established in previous investigations [ 26 ]. 2.3 Gene Quantification Analysis During the heading and flowering stages, three phenotypically uniform rice plants were selected for sampling. Male anthers were excised from rice panicles to evaluate pollen viability using the Alexander staining method. Subsequently, rice pollen and floral bud samples were dispatched to Ouyi Biomedical Technology Co., Ltd., Shanghai, for molecular analysis. The expression levels of the OsFKF1 gene were quantified by real-time quantitative polymerase chain reaction (qPCR), utilizing a fluorescent dye-based detection system [ 9 ]. 2.4 Statistical analysis The dataset was subjected to statistical analysis using IBM SPSS Statistics version 24.0. One-way analysis of variance (ANOVA) was performed employing Duncan’s new multiple range test to discern significant differences among treatment means. Additionally, Origin 2021 software was utilized to conduct further variance analyses and to generate correlation plots, facilitating comprehensive data visualization and interpretation. 3. Results 3.1 Soil characteristics As presented in Fig. 2 , the concentrations of SAN and SNN exhibited significant increases ( p < 0.05) across various rice growth stages under the AWCI regime. Correspondingly, soil pH values in treatment groups T 1 through T 4 demonstrated a biphasic trend, initially declining before rising as the rice developmental stages progressed. Notably, the highest soil pH was observed in the T 2 group (6.437), whereas the lowest value occurred in the T 6 group (5.563) during the late tillering stage (Fig. 2 ). Regarding nutrient content, SAN peaked at 19.107 mg·kg⁻¹ within the CK group during the mid-tillering stage, contrasting with its nadir of 3.490 mg·kg⁻¹ in T 1 at the jointing- booting stage. SNN content reached a maximum of 214.797 mg·kg⁻¹ in the T 7 group at mid-tillering, while the minimum of 12.650 mg·kg⁻¹ was recorded in T 3 during the jointing-booting phase. TN content exhibited its highest concentration (0.122%) in T 8 and the lowest (0.103%) in T 1 at the jointing-booting stage. TP content was greatest (1.603 g·kg⁻¹) in the T 7 group at mid-tillering, and lowest (0.466 g·kg⁻¹) in the CK group during late tillering. Table 2 further elucidates these dynamics: compared with the CK group at mid-tillering, soil pH values in T 1 -T 4 increased marginally (ΔpH/pH CK = 0.11% to 0.59%), whereas T 5 –T 8 exhibited decreases ranging from 0.27% to 6.47%. Simultaneously, SAN content diminished across all treatments (ΔSAN/SAN CK = 17.75% to 71.17%). During the late tillering stage, soil pH universally declined in T 1 –T 8 (ΔpH/pH CK = 0.02% to 10.84%), while SAN and TP contents increased markedly (ΔSAN/SAN CK = 0.01% to 135.97%; ΔTP/TP CK = 5.26% to 38.17%). At the heading and flowering stages, soil pH in T 1 –T 4 rose modestly (ΔpH/pH CK = 1.18% to 3.54%), contrasting with decreases in T 5 –T 8 (1.18% to 10.51%). Notably, SAN content decreased in T 1 –T 7 (ΔSAN/SAN CK = 12.08% to 36.87%), SNN content declined across T 1 –T 8 (ΔSNN/SNN CK = 3.70% to 86.18%), while TP content exhibited substantial increases (ΔTP/TP CK = 16.15% to 109.77%) (Fig. 2 ). These observations collectively underscore the intricate alterations in soil chemical properties mediated by varying irrigation regimes and developmental stages, with potential implications for nutrient availability and rice growth dynamics. Table.2 Analysis of differences in pH values of rice soils.(B),Different capital letters in the same line indicated significant difference in different growth periods of rice ( p < 0.05); Different lowercase letters in the same column indicated significant differences among rice treatments ( p < 0.05). Intervention Soil pH value Mid- tillering stage Late tillering stage Jointing-booting stage CK 6.233 ± 0.045aB 6.393 ± 0.035abA 6.217 ± 0.078bcB T 1 6.240 ± 0.040aB 6.217 ± 0.050cB 6.430 ± 0.020aA T 2 6.250 ± 0.026aB 6.130 ± 0.040dC 6.437 ± 0.021aA T 3 6.253 ± 0.035aA 5.700 ± 0.020eB 6.290 ± 0.123abA T 4 6.270 ± 0.040aB 6.133 ± 0.021dC 6.393 ± 0.025aA T 5 6.217 ± 0.031aB 6.423 ± 0.067aA 6.127 ± 0.061cB T 6 6.203 ± 0.029aA 6.250 ± 0.040cA 5.563 ± 0.146dB T 7 5.857 ± 0.083bC 6.407 ± 0.045abA 6.100 ± 0.056cB T 8 5.830 ± 0.087bC 6.340 ± 0.026bA 6.143 ± 0.083cB R SNN -0.906** -0.158 0.011 SAN 0.640** -0.493** -0.221 TN -0.473* -0.045 -0.462* TP -0.696** -0.324 -0.005 Table.4 Root physiological characteristics of rice Figure.3 Root physiological characteristics of rice.(a),POD in roots(U·g − 1 )(b),CAT in roots(µg·g − 1 )(c),ABAin roots(ng·g-1)(d), MDA in roots(nmol·g − 1 ). C).CK and T represent conventional irrigation, AWCI method, respectively. The indexes of ABA and POD were sampled on June 20, July 3 and July 16, respectively. Data are described as mean ± standard deviation (n = 3). Different lowercase letters in the same column indicate significant differences in 0.05 levels between different treatment periods, and the uppercase letters in the same column indicate significant differences in 0.05 levels between different fertility periods. 3.2 Root Physiological Growth Characteristics Under the AWCI regime, ABA concentrations and MDA content in rice roots demonstrated significant elevations ( p < 0.05) across different developmental stages (Fig. 3). POD activity reached its apex in the T 7 treatment at the late tillering stage, reaching 90.13 U·g − 1 , whereas the lowest activity was observed in T 1 at mid-tillering, measuring 9.75 U·g − 1 . CAT activity attained its maximum (0.68 µg·g⁻¹) within the T 6 group at late tillering, while the minimum (0.19 µg·g⁻¹) was recorded in T4 during the jointing-booting stage. ABA concentration was highest in T 1 at late tillering (61.50 ng·g − 1 ) and lowest in T 2 at mid-tillering (10.81 ng·g − 1 ). Notably, MDA content was elevated at mid-tillering compared to late tillering and jointing-booting stages, with the maximum value occurring in T 1 (23.80nmol·g − 1 ) and the minimum in T 2 (8.09 nmol·g⁻¹) at jointing- booting. Comparative analysis relative to the CK group(Fig. 3) reveals that, during mid-tillering, POD and CAT activities in the roots of T 1 –T 4 treatments declined significantly (ΔPOD/POD CK = 70.36% to 71.55%; ΔCAT/CAT CK = 4.86% to 8.91%), whereas these enzymatic activities in T 5 –T 8 increased moderately (ΔPOD/POD CK = 2.53% to 7.00%; ΔCAT/CAT CK = 21.70% to 28.73%). Concurrently, ABA concentrations across T 1 –T 8 diminished (ΔABA/ABA CK = 16.73% to 23.69%), while MDA content exhibited a pronounced increase (ΔMDA/MDA CK = 17.62% to 63.17%). At the late tillering stage, POD activity in rice roots rose markedly (ΔPOD/POD CK = 1.77% to 83.70%), contrasted by a decline in CAT activity within T 1 –T 8 treatments (ΔCAT/CAT CK = 0.12% to 62.73%), accompanied by elevated MDA content (ΔMDA/MDA CK = 17.61% to 72.09%). During the heading and flowering stages, a general reduction in both POD (ΔPOD/POD CK = 4.59% to 48.36%) and CAT (ΔCAT/CAT CK = 10.59% to 69.69%) activities was observed across treatments T 1 –T 8 . Conversely, both ABA concentration(ΔABA/ABA CK =1.37% to 63.52%) and MDA content (ΔMDA/MDA CK = 7.03% to 35.53%) demonstrated appreciable increments. Collectively, these findings elucidate a dynamic modulation of antioxidative enzyme activities and stress-related metabolites in rice roots under varying irrigation regimes and developmental phases, reflecting complex physiological adaptations to environmental and agronomic factors. The total root surface area and cumulative root length of rice progressively increased in tandem with the advancement of the growth period(Fig. 4 ). The largest total root surface area was observed in the CK group during the jointing- booting stage, measuring 1240.547 cm² per plant, whereas the smallest area occurred in T 1 at mid-tillering, amounting to 959.047 cm² per plant. Regarding total root length, T 6 at the late tillering stage attained a maximum of 3225.415 cm per plant, in stark contrast to the minimum length of 1090.138 cm per plant recorded in T 4 at mid-tillering. Average root diameter peaked at 3.730 mm per plant in T 6 during the jointing-booting phase, while the most diminutive diameter (1.005 mm per plant) was noted in T 1 at mid-tillering. The highest number of root tips was recorded in T 3 at the jointing-booting stage (84,274.50 per plant), with the lowest count found in T 1 at mid-tillering (1,090.00 per plant). Analysis of Fig. 4 reveals that, relative to the CK group, total root surface area decreased across all T 1 –T 8 treatments during the mid-tillering, late tillering, and jointing -booting stages. Specifically, at mid-tillering stage, total root length declined in T 1 –T 8 groups by 37.54% to 46.55% (ΔLength/Length CK ). Concurrently, average root diameter exhibited an increase in T 1 –T 4 (ΔDiameter/Diameter CK =20.14% to 64.74%), whereas a reduction was observed in T 5 –T 8 (2.76% to 6.60%). During the late tillering and jointing-booting stages, average root diameter consistently decreased across all T 1 –T 8 treatments. Notably, at the jointing-booting stage, the number of root tips increased markedly in T 1 –T 8 groups, ranging from 9.49% to 102.97% above the CK baseline (ΔTip/Tip CK ). These findings delineate nuanced modifications in root morphological parameters influenced by irrigation treatments and developmental timing, potentially impacting nutrient uptake efficiency and overall plant vigor. 3.3 Pollen Viability, OsFKF1 Expression, and Rice Grain Yield The AWCI treatment elicited a marked enhancement in rice grain yield, pollen viability, and the expression of the OsFKF1 gene during the critical heading and flowering stages (Table 3 ). Notably, the highest rice grain yield was observed in the T 8 group, reaching 83.908 g per plant, whereas the CK group exhibited the lowest yield at 69.64 g per plant. Pollen viability peaked in the T 3 group at 83.28% during these developmental phases, contrasting with the minimum value of 69.42% documented in the CK group. Concurrently, OsFKF1 expression attained its maximum (2.96%) in the T 2 group, with the CK group registering the lowest level (1.00%). This augmentation in reproductive parameters appears inversely correlated with a decline in root physiological growth characteristics. Nevertheless, a statistically significant increase ( p < 0.05) was observed in pollen viability at the heading and flowering stages, rising from 69.42% to 83.28% (Table 3 ). Multiple linear regression analysis, investigating the interrelationships among rice grain yield, pollen viability, and OsFKF1 expression levels, identified pollen viability during heading and flowering as the principal determinant influencing grain yield. Collectively, these findings substantiate the efficacy of the AWCI methodology in enhancing rice grain yield, pollen viability, and OsFKF1 gene expression at pivotal reproductive stages. Table 3 Pollen viability, OsFKF1 expression level and rice grain yield. (B), y is the dependent variable of grain yield; x is the independent variables,x1 is pollen activity,and x2 is OsFKF1 expression level at heading and flowering stage.* means significant difference between different variables ( p < 0.05), ** means extremely significant difference between different variables ( p < 0.01), the same as below. Treatment Pollen vitality(%) OsFKF1 expression Grain yield(g/Plant) CK 69.42 ± 0.30c 1.00 ± 0.04d 69.640 ± 2.88e T 1 81.79 ± 2.26a 1.94 ± 0.015b 80.490 ± 3.62abc T 2 80.25 ± 2.88a 2.96 ± 0.30a 81.644 ± 2.31ab T 3 83.28 ± 1.42a 1.27 ± 0.38d 82.183 ± 4.36ab T 4 69.96 ± 0.90c 1.91 ± 0.33bc 75.069 ± 5.17cde T 5 76.06 ± 1.28b 1.43 ± 0.17bcd 76.521 ± 1.85bcd T 6 80.20 ± 3.59a 1.37 ± 0.57cd 72.757 ± 1.14de T 7 74.04 ± 1.68b 1.20 ± 0.12d 78.750 ± 0.96abc T 8 81.11 ± 2.40a 1.85 ± 0.28bc 83.908 ± 3.33a R Pollen vitality(%) 1 0.217 0.565** OsFKF1 expression 0.217 1 0.445* Multiple linear regression equation y = 34.496 + 56.097x 1 (R = 0.565, P < 0.01) Table 4 Correlation analysis of rice pollen activity and rhizosphere soil environment factors.(B) ,y represents the dependent variables—pollen vitality, OsFKF1 expression level, and rice grain yield, respectively. x₁ to x₁₅denote independent variables: soil pH (x₁–x₃), nitrate nitrogen (x₄–x₆), ammonium nitrogen (x₇–x₉), total nitrogen (x₁₀–x₁₂), and total phosphorus (x₁₃–x₁₅) measured at mid-tillering, late tillering, and jointing-booting stages, respectively. index Soil pH value Soil nitrate nitrogen content/(mg·kg − 1 ) Soil ammonium nitrogen content/(mg·kg − 1 ) Growth stage Mid- tillering stage Late tillering stage Jointing -booting stage Mid- tillering stage Late tillering stage Jointing -booting stage Mid- tillering stage Late tillering stage Jointing -booting stage Pollen vitality R -0.039 -0.447* -0.071 0.014 -0.102 -0.475* -0.197 -0.05 -0.209 MLR analysis y = 1.442-0.108x 2 (R = 0.447, P < 0.05) y = 0.82-0.002x 6 (R = 0.475, P < 0.05) / Soil total nitrogen content/(%) Total phosphorus content in soil/(g·kg − 1 ) Mid- tillering stage Late tillering stage Jointing -booting stage Mid- tillering stage Late tillering stage Jointing -booting stage R 0.327 -0.256 0.354 -0.14 0.005 0.355 MLR analysis / / OsFKF1 expression level Soil pH value Soil nitrate nitrogen content/(mg·kg − 1 ) Soil ammonium nitrogen content/(mg·kg − 1 ) Mid- tillering stage Late tillering stage Jointing -booting stage Mid- tillering stage Late tillering stage Jointing -booting stage Mid- tillering stage Late tillering stage Jointing -booting stage R 0.166 -0.15 0.436* -0.236 0.207 -0.129 0.199 0.209 -0.291 MLR analysis y=-4.611 + 1.013x 3 (R = 0.436, P < 0.05) / / Soil total nitrogen content/(%) Total phosphorus content in soil/(g·kg − 1 ) Mid- tillering stage Late tillering stage Jointing -booting stage Mid- tillering stage Late tillering stage Jointing -booting stage R 0.402* -0.239 -0.205 -0.302 -0.076 0.223 MLR analysis y=-2.712 + 38.24x 10 (R = 0.402, P < 0.05) / Rice grain yield Soil pH value Soil nitrate nitrogen content/(mg·kg − 1 ) Soil ammonium nitrogen content/(mg·kg − 1 ) Mid- tillering stage Late tillering stage Jointing -booting stage Mid- tillering stage Late tillering stage Jointing -booting stage Mid- tillering stage Late tillering stage Jointing -booting stage R -0.295 -0.333 0.322 0.306 -0.282 -0.137 -0.215 0.224 0.104 MLR analysis / / / Soil total nitrogen content/(%) Total phosphorus content in soil/(g·kg − 1 ) Mid- tillering stage Late tillering stage Jointing -booting stage Mid- tillering stage Late tillering stage Jointing -booting stage R 0.573** 0.25 0.193 -0.127 0.378 0.216 MLR analysis y = 24.923 + 463.219x 13 (R = 0.573, P < 0.01) / 3.4 Relationship Analysis 3.4.1 Relationship Among Pollen Viability, Gene Expression, Grain Yield, and Rhizosphere Soil Environmental Factors The AWCI method modulates soil pH across different stages of rice development, thereby orchestrating the N and P dynamics within the rhizosphere. This regulation is pivotal in shaping pollen viability, OsFKF1 gene expression during heading and flowering, and ultimately, rice grain yield. The correlation analysis results, elucidating the relationships among pollen viability, OsFKF1 expression, grain yield, and rhizospheric environmental parameters, are summarized in Table 4 . Specifically, pollen viability during the heading and flowering stages exhibited a significant inverse correlation with soil pH at the late tillering stage and SNN content during the jointing-booting stages ( p < 0.05). Conversely, OsFKF1 expression correlated positively with soil pH at the jointing-booting stage and TN content at the tillering stage ( p < 0.05). Rice grain yield was positively associated with TN content at the late tillering stage, with a higher level of significance ( p < 0.01). Multiple linear regression (MLR) analyses further substantiated that the AWCI approach finely regulates soil pH and TN content. The predictive models indicated that a reduction in soil pH at the late tillering stage, coupled with an elevation of soil pH at booting and increased TN content during mid-tillering, synergistically contribute to enhanced pollen viability, augmented OsFKF1 expression, and improved grain yield. These insights affirm the critical influence of soil pH and nitrogen availability under AWCI-mediated cultivation. 3.4.2 Relationship Among Pollen Viability, Gene Expression, Grain Yield, and Root Physiological Growth Characteristics of Rice Correlation analyses exploring the interplay between pollen viability, gene expression, grain yield, and root physiological parameters are also presented in Table 5 . Pollen viability at the heading and flowering stages showed a robust positive correlation with ABA levels in roots during the late tillering phase ( p < 0.01), while demonstrating significant negative correlations with root ABA concentrations at mid-tillering and MDA content during the jointing-booting stage ( p < 0.05). OsFKF1 expression was inversely associated with POD, ABA, and MDA content in roots at mid-tillering ( p < 0.01). Rice grain yield also manifested a negative correlation with CAT activity in roots at booting, as well as with root ABA and MDA levels during mid-tillering ( p < 0.05). Regarding root morphology, pollen viability was significantly and negatively correlated with the total root surface area during late-tillering and jointing-booting stages ( p < 0.01). Negative correlations were also observed between pollen viability and total root length at mid-tillering, as well as average root diameter at late tillering ( p < 0.05). In contrast, pollen viability correlated positively with average root diameter and total root tip count during the jointing-booting stage ( p < 0.01 and p < 0.05, respectively). OsFKF1 expression showed a negative correlation with total root surface area during the jointing-booting phase ( p < 0.05). Grain yield exhibited significant negative correlations with root surface area at late tillering and jointing-booting stages, and total root length at mid-tillering, yet a positive correlation with average root diameter at jointing-booting ( p < 0.01). MLR analyses revealed that the AWCI method governs root surface area alongside MDA content in roots. The constructed models suggest that an expansion in root surface area during the jointing-booting stage together with elevated MDA content at mid-tillering enhance pollen viability, OsFKF1 expression, and rice grain yield. These findings underscore the pivotal role of root morphological traits and oxidative stress markers modulated by the AWCI method in optimizing reproductive success and productivity. Table 5 correlation analysis of Pollen viability, flowering specific gene expression, and grain yield with POD,CAT,ABA, MDA in rice root.(B),y represents the dependent variables—pollen vitality, OsFKF1 expression, and rice grain yield, respectively. x denotes independent variables including POD in roots, CAT in stems, MDA in leaves, root surface area, total root length, average root diameter, and total root tip count, each measured at mid-tillering, late tillering, and jointing-booting stages. Root Physiological Growth Characteristics POD in roots CAT in roots ABA in roots Mid- tillering stage Late tillering stage Jointing-booting stage Mid- tillering stage Late tillering stage Jointing-booting stage Mid- tillering stage Late tillering stage Jointing-booting stage Pollen vitality(%) -0.229 -0.253 -0.275 0.017 0.278 -0.269 -0.388* 0.491** -0.229 OsFKF1eexpression -0.526** 0.115 -0.248 -0.291 0.067 -0.278 -0.493** 0.148 0.252 Grain yield(g/plant) -0.288 0.061 -0.056 -0.118 -0.158 -0.404* -0.416* 0.051 0.011 MDA in roots root surface area total root length Mid- tillering stage Late tillering stage Jointing-booting stage Mid- tillering stage Late tillering stage Jointing-booting stage Mid- tillering stage Late tillering stage Jointing-booting stage Pollen vitality(%) 0.354 -0.151 0.432* -0.06 -0.662** -0.743** -0.381* -0.148 0.066 OsFKF1eexpression 0.569** 0.209 -0.211 0.038 -0.333 -0.476* -0.359 -0.095 0.136 Grain yield(g/plant) -0.288 0.061 -0.056 -0.118 -0.158 -0.404* -0.416* 0.051 0.011 average root diameter total root tip count Mid- tillering stage Late tillering stage Jointing-booting stage Mid- tillering stage Late tillering stage Jointing-booting stage Pollen vitality(%) 0.14 0.394* 0.560** 0.192 0.227 0.432* OsFKF1eexpression 0.142 0.099 0.132 0.2 0.171 0.042 Grain yield(g/plant) 0.141 0.374 0.549** 0.094 0.234 0.373 MLR analysis Pollen vitality(%) y = 2.433-0.001x 12 (R=-0.743, P < 0.01) OsFKF1eexpression y=-0.228 + 0.095x 7 (R = 0.569, P < 0.01) Grain yield(g/plant) y = 209.596-0.112x 12 (R=-0.592, P < 0.01) 3.5 Cluster Analysis Outcomes Hierarchical cluster analysis alongside descriptive statistical evaluations(Metcalf et al., 2025) was conducted on rice grain yield indicators (Fig. 5 ). The factor sets incorporated into the analysis comprised physiological growth characteristics, rice grain yield, rhizospheric soil parameters, pollen viability, and OsFKF1 expression levels measured during the heading and flowering stages. Decision categories were delineated as satisfied, moderately satisfied, and dissatisfied. Utilizing a distance threshold spanning 20,000 to 32,000, treatments were stratified into three distinct clusters: the satisfied cluster (solely treatment T 3 ), the moderately satisfied cluster (including T 2 , T 5 , T 7 , and T 8 ), and the dissatisfied cluster (comprising T 1 , T 4 , T 7 , and the control, CK). The T 3 treatment emerged as the theoretically optimal regimen within the AWCI framework, demonstrating superior regulation of root physiological growth and concomitant enhancement of grain yield. Notably, the T 3 cohort exhibited the highest pollen viability and grain yield, attaining 83.28% and 82.183 g per plant, respectively, underscoring its efficacy as a prime agricultural intervention for augmenting reproductive success and productivity in rice cultivation. 4. Discussion 4.1. Effects and Mechanisms of Irrigation Methods on Soil Properties and Rice Grain Yield Pollen viability released at the precise stage and time is essential for sexual reproduction and rice production [ 8 ], soil physicochemical properties and irrigation strategies represent fundamental determinants of pollen viability during the critical heading and flowering phases, as well as rice grain yield [ 29 – 31 ]. Our findings demonstrate that the AWCI method significantly enhances pollen viability and OsFKF1 gene expression during these developmental stages, culminating in notable increases in grain yield. Moreover, positive correlations were identified among rice grain yield, pollen viability, and OsFKF1 expression levels (Table 3 ). Irrigation regimes induce a cascade of soil biochemical transformations, prominently influencing redox reactions that modulate soil pH, redox potential (Eh), and stimulate soil enzyme activities. Various irrigation practices—flooding, alternating wet and dry cycles, and wetting irrigation—affect soil pH differently, with the magnitude of change following the order: flooding > alternating wet-dry > wetting [ 19 ]. Flooding typically diminishes soil oxygen availability, fostering anaerobic conditions [ 32 ], whereas oxygation and water-controlled irrigation bolster soil oxygen content over extended periods [ 32 ]. Under flooded, reduced conditions, the soil environment becomes anaerobic, promoting a gradual neutralization of soil pH irrespective of its initial acidity or alkalinity.This phenomenon is primarily driven by alterations in cation exchange capacity (CEC), degradation of organic matter, and decarboxylation of organic anions during anaerobic microbial metabolism. Flooding enhances CEC, which raises the pH toward neutrality in acidic soils. Concurrently, the microbial breakdown of organic acids elevates soil pH through organic acid decarboxylation [ 30 ]. Additionally, soil microorganisms utilize alternative electron acceptors, such as nitrate, during organic matter oxidation, contributing to reductions in Eh [ 33 ]. The AWCI method further elevates soil dissolved oxygen content and modulates soil pH [ 22 ], thereby influencing the transformation and availability of nitrogen (N) and phosphorus (P) by regulating soil water and nutrient dynamics throughout different rice growth stages [ 21 , 34 ]. Changes in redox potential and pH impact the mobility of N and P species. Correlation analyses revealed a negative association between soil pH and total nitrogen (TN), soil nitrate nitrogen (SNN), and total phosphorus (TP) at mid-tillering, while soil alkaline nitrogen (SAN) was positively correlated with pH. During late tillering, SAN was inversely correlated with pH, and at jointing-booting, TN content exhibited a negative relationship with pH. Insufficient N and P availability in soil can constrain their uptake and assimilation by rice plants, ultimately limiting growth and reducing yield. Multiple linear regression (MLR) analyses confirmed that the AWCI method induces a decrease in soil pH during late tillering, an increase in pH at jointing-booting, and elevates TN content at mid-tillering, collectively enhancing pollen viability, OsFKF1 expression, and grain yield. Additionally, soil water potential and temperature emerge as critical factors influencing these outcomes. Soil water potential governs water distribution within the soil matrix and plant water acquisition capacity, while excessive soil temperatures can impair root function and reduce pollen viability. Maintaining optimal temperature regimes accelerates growth, increases tiller number, and ultimately boosts yield [ 35 – 36 ]. 4.2. Effects and Mechanisms of Water Management on Root Physiological Traits and Rice Grain Yield Rice roots extract water and oxygen from the rhizosphere, triggering adaptive responses that include modulation of antioxidant enzyme activities and synthesis of osmolytes. These processes facilitate cellular water uptake, regulate stomatal function, and enhance photosynthetic efficiency, thereby mitigating the deleterious effects of heat and drought stress [ 37 – 38 ]. Key root biochemical indicators—POD, CAT, ABA, and MDA—significantly influence pollen viability, OsFKF1 expression during heading and flowering, and grain yield(Table.5). This study comprehensively characterized the temporal dynamics of POD, CAT, ABA, and MDA in rice roots under the AWCI regime, alongside root morphological traits, to elucidate their impact on reproductive parameters and yield. AWCI method resulted in reductions of root ABA by 16.73%–23.69% at mid-tillering, increased ABA by 1.37%–63.52% at jointing-booting, and elevated POD by 1.77%–83.70% at late tillering. Conversely, POD activity decreased by 4.59%–48.36% at jointing-booting, with treatments T 1 –T 4 showing a 70.36%–71.55% reduction at mid-tillering, while T 5 –T 8 exhibited a modest increase of 2.53%–7.00%. Root CAT activity declined 0.12%–62.73% during late tillering and jointing-booting stages, whereas MDA levels rose by 17.62%–63.17%, 17.61%–72.09%, and 7.03%–35.53% across mid-tillering, late tillering, and jointing-booting stages, respectively(Figure 3). Root morphological and physiological traits critically underpin root functionality and vigor [ 39 ]. Reductions in traits such as total root length and surface area can paradoxically enhance root vitality by stimulating antioxidant defense systems (Table 5 ), including ABA, POD, and CAT, increasing soluble sugar and auxin content. This cascade enhances leaf cell antioxidant capacity, promotes water uptake, regulates stomatal aperture and photosynthesis, and effectively scavenges reactive oxygen species (ROS), collectively supporting robust rice growth. Water demand varies markedly across rice growth stages. Implementing judicious water-saving strategies during organogenesis optimizes rhizospheric conditions, modulates root activity, elevates antioxidant enzyme activities (SOD, CAT, MDA), suppresses oxidative kinase activities, and fortifies ROS detoxification mechanisms, thereby ensuring stable grain yields [ 17 ]. The AWCI method enhances rice root vigor and enzymatic activity, thereby influencing spikelet opening and closing during flowering and grain yield formation. Moreover, energy and material fluxes within the soil–plant–atmosphere continuum are shaped by soil temperature and moisture, which AWCI modulates by altering rhizospheric water and nutrient availability. MLR results revealed that increases in total root surface area at jointing–booting and elevated root MDA at mid-tillering under AWCI correspond to improved pollen viability, OsFKF1 expression, and grain yield. Hierarchical cluster analysis further identified T3 as the theoretically optimal AWCI treatment for regulating flowering and enhancing yield. 5. Conclusions This investigation elucidates the multifaceted effects of the AWCI method on soil physicochemical properties, root physiological traits, pollen viability, and OsFKF1 gene expression during heading and flowering stages, with significant implications for rice grain yield. Key determinants include total root surface area at jointing-booting stage, root MDA content at mid-tillering stage, soil pH, and TN content during mid-tillering stage. Treatment T 3 emerges as an optimal strategy for modulating rice flowering and boosting yield under AWCI management. Future research should explore additional flowering-related gene expressions to delineate their relative contributions alongside soil properties throughout the rice growth cycle. Furthermore, the allocation of root physiological traits suggests that enhanced antioxidant enzyme functionality, reduced oxidative kinase activity, and improved ROS scavenging in aboveground tissues warrant deeper investigation to ensure yield stability. Integrating soil characteristics, root physiology, and gene expression profiling will afford a more comprehensive understanding of rice developmental dynamics. Given that this study focused on a single rice cultivar, subsequent studies should assess whether these findings generalize across diverse genotypes with respect to soil-root-flowering gene interactions. Abbreviations ABA Abscisic acid AWCI Aerobic and water-controlled irrigation CAT Catalase CK Control (conventional irrigation) ELISA Enzyme-linked immunosorbent assay MDA Malondialdehyde POD Peroxidase qPCR Quantitative real-time polymerase chain reaction ROS Reactive oxygen species SAN Soil ammonium nitrogen SNN Soil nitrate nitrogen SOD Superoxide dismutase SP Soluble protein TN Total nitrogen TP Total phosphorus. Declarations Ethics approval and consent to participate: Not applicable. This study did not involve human participants, human data, or animals. Consent for publication: Not applicable. Availability of data and materials: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing Interests: The authors declare that they have no competing interests. Funding: This work was supported by the Natural Science Foundation of Hunan Province, China (Grant No. 2023JJ30311), and the Water Resources Science and Technology Project of Hunan Province, China (Grant No. XSKJ2025056-16). Authors’ contributions: W.Z. (Wenping Zhang): Conceptualization, Methodology, Writing – original draft, Supervision, Project administration, Funding acquisition. J.Z. (Jiangyuan Zhang): Investigation, Formal analysis, Data curation. F.P. (Feiyu Peng): Investigation, Validation. Z.L. (Zhuying Liu): Resources. X.L. (Xin Liu): Software, Visualization. W.X. (Weihua Xiao): Methodology. D.H. (Deyong Hu): Resources. T.L. (Tongcheng Luo): Investigation. X.S. (Xinyi Su): Investigation. S.Z. (Shuhan Zhang): Formal analysis. G.W. (Genyi Wu): Writing – review & editing, Supervision. All authors read and approved the final manuscript. Acknowledgements: Not applicable. References Marković, M., šoštarić, J., Josipović, M., Atilgan, A., 2021. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-8497875\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":590899147,\"identity\":\"e3dedde9-4886-4931-9f9b-8fccfe26c3cb\",\"order_by\":0,\"name\":\"Wenping Zhang\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"\",\"institution\":\"Hunan Agricultural University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Wenping\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"},{\"id\":590899150,\"identity\":\"36f7c5cf-a6f3-43aa-b73b-0f0913912e71\",\"order_by\":1,\"name\":\"Jiangyuan Zhang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Hunan Agricultural University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jiangyuan\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"},{\"id\":590899151,\"identity\":\"db76dbfa-dc3c-45c9-9f3e-5fe2430c809b\",\"order_by\":2,\"name\":\"Feiyu Peng\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Hunan Agricultural University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Feiyu\",\"middleName\":\"\",\"lastName\":\"Peng\",\"suffix\":\"\"},{\"id\":590899152,\"identity\":\"a072c91c-6b7d-40d7-9ec3-6d18385f1bdf\",\"order_by\":3,\"name\":\"Zhuying Liu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Hunan Agricultural University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Zhuying\",\"middleName\":\"\",\"lastName\":\"Liu\",\"suffix\":\"\"},{\"id\":590899153,\"identity\":\"83b4c9ef-be23-450a-86dd-b87004fc1fde\",\"order_by\":4,\"name\":\"Xin Liu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Hunan Agricultural University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xin\",\"middleName\":\"\",\"lastName\":\"Liu\",\"suffix\":\"\"},{\"id\":590899154,\"identity\":\"01312df0-6ba9-4b77-9d82-d7e901f4736b\",\"order_by\":5,\"name\":\"Weihua Xiao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Hunan Agricultural University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Weihua\",\"middleName\":\"\",\"lastName\":\"Xiao\",\"suffix\":\"\"},{\"id\":590899155,\"identity\":\"d6523a72-c104-439d-9eba-a0436fc0ff7a\",\"order_by\":6,\"name\":\"Deyong Hu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Hunan Agricultural University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Deyong\",\"middleName\":\"\",\"lastName\":\"Hu\",\"suffix\":\"\"},{\"id\":590899156,\"identity\":\"f397d73e-acc6-4500-a242-16a59b0d7ed3\",\"order_by\":7,\"name\":\"Tongcheng Luo\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Hunan Agricultural University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Tongcheng\",\"middleName\":\"\",\"lastName\":\"Luo\",\"suffix\":\"\"},{\"id\":590899157,\"identity\":\"8dc9e198-f966-4a0b-823a-383c921fe826\",\"order_by\":8,\"name\":\"Xinyi Su\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Hunan Agricultural University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xinyi\",\"middleName\":\"\",\"lastName\":\"Su\",\"suffix\":\"\"},{\"id\":590899158,\"identity\":\"ac4605fc-8040-41cd-ad20-1af77301dd25\",\"order_by\":9,\"name\":\"Shuhan Zhang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Hunan Agricultural University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Shuhan\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"},{\"id\":590899159,\"identity\":\"316569c8-4e62-485a-b4a7-a8a34edc0768\",\"order_by\":10,\"name\":\"Genyi Wu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Hunan Agricultural University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Genyi\",\"middleName\":\"\",\"lastName\":\"Wu\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-01-02 03:53:23\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-8497875/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-8497875/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":102963487,\"identity\":\"a326f43d-2147-47e5-8041-3e6e8cf1f4aa\",\"added_by\":\"auto\",\"created_at\":\"2026-02-19 04:18:19\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":329062,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSoil physicochemical properties, root physiological traits, and flowering gene expression collectively govern rice grain yield under AWCI method.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8497875/v1/9f2d628df9215dadcbbec0a0.png\"},{\"id\":102838429,\"identity\":\"5c06f4ac-c861-4886-aa53-058678c3d25e\",\"added_by\":\"auto\",\"created_at\":\"2026-02-17 11:37:47\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":317992,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAnalysis on the difference of nitrogen form and content in rice soil (a),Soil nitrate nitrogen content(SNN)/(mg·kg\\u003csup\\u003e-1\\u003c/sup\\u003e)(b),Soil ammonium nitrogen content(SAN) /(mg·kg\\u003csup\\u003e-1\\u003c/sup\\u003e) (c) ,Soil total nitrogen content(TN)/(%) (d),Soil total phosphorus content (TP)/(g·kg\\u003csup\\u003e-1\\u003c/sup\\u003e).C).Different capital letters in the same industry indicate significant differences in rice growth stages (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026lt;0.05); Different lowercase letters in the same column indicate significant differences among different treatments of rice (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026lt;0.05), the same as below.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8497875/v1/b11124c183f7499701c39522.png\"},{\"id\":102963585,\"identity\":\"3d8319ef-8a14-4439-8958-1658b87c319b\",\"added_by\":\"auto\",\"created_at\":\"2026-02-19 04:19:08\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":359599,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eRoot physiological characteristics of rice.(a),POD in roots(U·g\\u003csup\\u003e-1\\u003c/sup\\u003e)(b),CAT in roots(μg·g\\u003csup\\u003e-1\\u003c/sup\\u003e)(c),ABAin roots(ng·g-1)(d), MDA in roots(nmol·g\\u003csup\\u003e-1\\u003c/sup\\u003e). C).CK and T represent conventional irrigation，AWCI method，respectively. The indexes of ABA and POD were sampled on June 20, July 3 and July 16, respectively. Data are described as mean±standard deviation (n=3). Different lowercase letters in the same column indicate significant differences in 0.05 levels between different treatment periods, and the uppercase letters in the same column indicate significant differences in 0.05 levels between different fertility periods.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8497875/v1/8d9190b0ad64a6261ea0b4fc.png\"},{\"id\":102838431,\"identity\":\"01a2fe75-f59a-48d1-8f22-77fe98bc891c\",\"added_by\":\"auto\",\"created_at\":\"2026-02-17 11:37:47\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":374003,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eRoot growth characteristics of rice(a),Total root surface area (cm² per plant)(b),Total root length (cm per plant) (c) ,Average root diameter (mm per plant) (d),Root tips number.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8497875/v1/e2c52312d95d9bb368211b3c.png\"},{\"id\":102963289,\"identity\":\"54271c56-b6d5-4a78-b3be-78b1eda3d05b\",\"added_by\":\"auto\",\"created_at\":\"2026-02-19 04:15:14\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":71658,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eResults of cluster analysis on rice grain yield and rice physiological indexes\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8497875/v1/13b29033a68cb5463fe1274e.png\"},{\"id\":102965444,\"identity\":\"9630c1dd-3288-483d-86d3-af2ad86a5d85\",\"added_by\":\"auto\",\"created_at\":\"2026-02-19 04:31:31\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2893307,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8497875/v1/111385a3-e572-4fdd-aadb-22e24d6ad3ff.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"From Soil to Spikelet: The Integrated Impact of AWCI on Rice Growth Under Heat and Drought Stress\",\"fulltext\":[{\"header\":\"Highlights\",\"content\":\"\\u003cp\\u003e• Aerobic and water-controlled irrigation(AWCI) method increased pollen activity, OsFKF1 expression levels at the heading and flowering stage, and rice grain yield.\\u003c/p\\u003e\\n\\u003cp\\u003e• soil pH values, and soil total nitrogen(TN) content, malondialdehyde(MDA) content in rice roots, and total rice root surface area increase Pollen vitality,OsFKF1 expression level at the heading and flowering stages, as well as rice grain yield.\\u003c/p\\u003e\\n\\u003cp\\u003e• 0-30 mm at the mid-tillering stage, 60%-80% at the late tillering stage, and 0-30 mm at the jointing-booting stage of water control mode was adopted as a theoretically satisfactory scheme to regulate the flowering period of rice and improve rice grain yield.\\u003c/p\\u003e\"},{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eThe escalating frequency and intensity of extreme heat and drought phenomena exert profound disturbances on vegetation structure and physiological processes, posing formidable challenges to plant growth, survival, and, ultimately, global food security [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. Rice, as a staple crop of paramount importance worldwide, exhibits pronounced sensitivity to elevated temperatures and water scarcity throughout its developmental continuum [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Exposure to ambient temperatures exceeding 35\\u0026deg;C for durations surpassing two hours during anthesis precipitates precipitous declines in spikelet fertility and thousand-grain weight, accompanied by an increased proportion of abortive grains [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. Concurrent drought stress further exacerbates these deleterious effects by impairing photosynthetic performance and curtailing vegetative growth, with severe instances capable of inducing yield reductions of up to 93.6% [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. The detrimental impacts of heat and drought stress are distinctly stage-specific: the early tillering stage experiences reductions in plant height, tiller number, and shoot biomass; during panicle initiation and heading, there is heightened susceptibility manifested as diminished light-use efficiency, panicle biomass, seed setting rate, and grain filling; the grain-filling phase, crucial for assimilate accumulation, is significantly compromised by impaired photosynthetic capacity [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. Within the middle and lower reaches of the Yangtze River basin in China, recurrent heat and drought episodes during summer and autumn impose substantial threats to rice production. For example, Hunan Province, situated within this region, endures protracted heatwaves lasting up to 41.6 consecutive days, culminating in severe yield losses and, in extreme cases, complete crop failure. Despite the rice-growing season predominantly spanning June to August\\u0026mdash;encompassing the critical panicle initiation to flowering interval\\u0026mdash;the mechanisms by which combined heat and drought stresses converge to influence yield during this vulnerable window remain insufficiently elucidated [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe exquisite synchronization and timely deployment of the rice spikelet\\u0026rsquo;s lemma and palea at anthesis constitute a pivotal determinant in the orchestration of grain yield [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. This finely regulated phenological event arises from a complex interplay among soil microenvironmental parameters, root physiological status, and the spatiotemporal expression of flowering-associated gene networks [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. Edaphic factors such as soil moisture content, oxygen availability, alkali-hydrolyzable nitrogen, and bioavailable phosphorus critically modulate energy fluxes and nutrient cycling within the soil-plant-atmosphere continuum. Enzymatic activities within the rhizosphere, alongside concentrations of soil nitrate nitrogen (SNN) and ammonium nitrogen (SAN), correlate strongly with root morphological attributes and physiological functionality [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e], thereby influencing overall rice growth and developmental trajectories. Key soil enzymes\\u0026mdash;including CAT, urease, and invertase\\u0026mdash;mediate essential biogeochemical transformations of nitrogen and phosphorus; their activities are intricately modulated by soil pH dynamics. Root morphology and vitality not only dictate functional capacity and growth dynamics but also regulate flowering phenology and yield formation by enabling rapid and adaptive responses to drought-induced perturbations. Antioxidant defense systems within roots\\u0026mdash;comprising POD, CAT, superoxide dismutase (SOD), and soluble proteins (SP)\\u0026mdash;escalate under abiotic stress, efficiently scavenging reactive oxygen species (ROS) to mitigate cellular oxidative damage induced by thermal and hydric stresses [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. Furthermore, root endogenous ABA orchestrates antioxidant enzyme activation, modulates cellular hydration status, and regulates stomatal conductance, collectively enhancing rice resilience to the synergistic challenges of heat and drought [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. Hence, elucidating rhizosphere soil milieu and root physiological dynamics during flowering is of paramount theoretical and practical significance for safeguarding sustainable grain productivity in southern China.\\u003c/p\\u003e \\u003cp\\u003eIrrigation practices wield profound influences on soil pH, enzymatic activities, and nitrogen-phosphorus nutrient transformations, thereby fostering root vitality and optimal morphological development, which in turn modulate flowering phenology and yield outcomes [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. The efficacy of irrigation modalities in adjusting soil pH across acidic and alkaline contexts follows the hierarchy: flooding irrigation\\u0026thinsp;\\u0026gt;\\u0026thinsp;alternate wetting and drying irrigation\\u0026thinsp;\\u0026gt;\\u0026thinsp;moist irrigation [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. Innovative interventions such as micro-nano bubble-aerated irrigation markedly enhance root antioxidant enzyme activities (SOD, CAT) and elevate MDA levels, reflecting augmented oxidative stress responses in rice roots [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. Oxygen-enriched irrigation regimes consistently elevate soil dissolved oxygen concentrations, stimulate urease, SOD, and CAT activities, and bolster microbial biomass, thereby refining root architectural complexity and metabolic vigor [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Alternate wetting and drying irrigation increases root dry biomass, root oxidation capacity, and antioxidant enzyme activities within the rhizosphere [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. Water-controlled irrigation enhances soil oxygen diffusion and dissolution capacities, accelerates nitrification processes in rhizosphere soils, and consequently augments nitrate nitrogen availability, positioning it as a salient water-saving and yield-enhancing agronomic practice [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eAlthough numerous studies have examined the impacts of disparate irrigation regimes on rice root physiology and rhizosphere soil characteristics [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e], a comprehensive evaluation of the integrated effects of AWCI method on rice phenology, antioxidant enzyme profiles, and grain yield remains conspicuously lacking. This study endeavors to dissect the influence of AWCI method on rice root physiological indices, rhizosphere soil environment, pollen viability, flowering-related gene expression, and yield formation. Specifically, it aims to: (1) unravel the regulatory mechanisms by which AWCI method modulates root physiological performance and rhizosphere soil attributes; (2) elucidate the interdependencies among root physiological traits, rhizosphere milieu, pollen viability, gene expression dynamics, and final grain yield. We hypothesize that the AWCI technique substantially enhances these parameters, culminating in elevated rice productivity. The insights gleaned are anticipated to furnish a robust theoretical framework for the selection and breeding of heat-resilient, high-yielding, and high-quality rice cultivars, thereby decisively contributing to food security and the stability of agroecosystems(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\"},{\"header\":\"2. Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1 Pot Experiments\\u003c/h2\\u003e \\u003cp\\u003eThe soil utilized in the pot experiments was procured from a paddy field located at Hunan Agricultural University (28\\u0026deg;10\\u0026prime;N, 113\\u0026deg;04\\u0026prime;E, Changsha, China). Prior to experimentation, the soil samples were air-dried, sieved through a 5 mm mesh to ensure homogeneity, and thoroughly mixed [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. The physicochemical characterization of the soil revealed a pH of 6.19 and a moisture content of 42.93%. Total nitrogen (TN) and phosphorus (TP) contents in soil were quantified at 1.18 mg/kg and 0.97 mg/kg, respectively. The concentrations of SAN and SNN measured 14.42 mg/kg and 32.29 mg/kg, correspondingly.\\u003c/p\\u003e \\u003cp\\u003eTwo treatment modalities were established, both employing the oxygation technique. The control group (CK) maintained soil moisture at approximately 80%\\u0026ndash;100% during the late tillering stage, with a standing water layer of 0\\u0026ndash;30 mm above the soil surface during all other rice developmental stages. In contrast, the treatment group (T) implemented a water-controlled irrigation regime, in which soil moisture was regulated between 60%\\u0026ndash;80% during the mid and late tillering stages, rising to 80%\\u0026ndash;100% during the booting stage (see Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e for detailed moisture parameters). Each treatment was replicated three times under greenhouse conditions and was subject to ambient temperature fluctuations. Each pot contained 7.5 kg of prepared soil and was planted with a single rice seedling of the cultivar Zhongzao 39.\\u003c/p\\u003e \\u003cp\\u003eSeedlings were initially sown on May 8, 2022, followed by transplantation into pots on May 21, 2022. Flowering and maturity stages were defined as 44 and 75 days after post-transplantation, respectively. During the experimental period, N fertilizer [CO(NH\\u003csub\\u003e2\\u003c/sub\\u003e)\\u003csub\\u003e2\\u003c/sub\\u003e] was applied at a total rate of 2.56 g per pot, fractionated into basal, tillering, and grain-filling applications at a ratio of 4:3:3. Potassium was supplied in the form of potassium chloride (KCl), with the amount calculated as 1.00 g K\\u003csub\\u003e2\\u003c/sub\\u003eO equivalent per pot, divided between pre-transplanting and jointing stages. P applicaiton was administered exclusively as basal fertilizer at a rate of 4.17 g per pot [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eWater management measures under AWCI method\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"9\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eTreatments\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"8\\\" nameend=\\\"c9\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eRice growth stages\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGreen-up stage\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eEarly tillering stage\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMid-tillering stage\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eLate tillering stage\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eJointing- booting stage\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eHeading and flowering stage\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eMilky stage\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eYellow maturity stage\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eT\\u003csub\\u003e1\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e80\\u0026ndash;100%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e 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\\u003cp\\u003e80\\u0026ndash;100%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eT\\u003csub\\u003e3\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e60\\u0026ndash;80%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eT\\u003csub\\u003e4\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e60\\u0026ndash;80%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e80\\u0026ndash;100%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eT\\u003csub\\u003e5\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e80\\u0026ndash;100%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e80\\u0026ndash;100%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eT\\u003csub\\u003e6\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e80\\u0026ndash;100%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e80\\u0026ndash;100%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e80\\u0026ndash;100%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eT\\u003csub\\u003e7\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e80\\u0026ndash;100%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e60\\u0026ndash;80%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eT\\u003csub\\u003e8\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e80\\u0026ndash;100%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e60\\u0026ndash;80%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e80\\u0026ndash;100%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCK\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2 Plant and Soil Analyses\\u003c/h2\\u003e \\u003cp\\u003eExperimental samples were collected in triplicate from individual pots for subsequent plant and soil analyses. Rice plants were meticulously excavated to preserve root integrity, then temporarily stored in a low-temperature incubator before transfer to the laboratory for processing. The roots, stems, and leaves of the rice plants were carefully separated, rinsed thoroughly with distilled water, and gently blotted dry using filter paper.\\u003c/p\\u003e \\u003cp\\u003ePOD activity in rice roots was quantified using a colorimetric POD activity assay kit, while CAT activity was assessed employing the CheKine\\u0026trade; CAT activity analysis kit, also based on colorimetric detection. ABA concentrations in root tissues were measured via enzyme-linked immunosorbent assay (ELISA). MDA content in root tissues were measured via enzyme-linked immunosorbent assay (ELISA) [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e] .\\u003c/p\\u003e \\u003cp\\u003eRoot morphological parameters\\u0026mdash;including root length, surface area, and number of lateral roots\\u0026mdash;were analyzed utilizing the EPSON Expression 11000XL root scanner. Grain yield per plant was recorded following harvest.\\u003c/p\\u003e \\u003cp\\u003eSoil pH was determined using an Ohaus 868 pH meter. TN content in soil was measured through the Kjeldahl digestion method, whereas TP content in soil was quantified via alkali fusion followed by inductively coupled plasma optical emission spectrometry (ICP-OES). SAN and SNN concentrations were analyzed with a flow analyzer (Fink TFIA-102-TP/P).\\u003c/p\\u003e \\u003cp\\u003eFor SAN and SNN analyses, 10.0 g of soil was combined with 100 mL of 2 M potassium chloride (KCl) solution in a sealed plastic bottle, shaken vigorously for 30 minutes, and immediately filtered into a 50 mL conical flask, following protocols established in previous investigations [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3 Gene Quantification Analysis\\u003c/h2\\u003e \\u003cp\\u003eDuring the heading and flowering stages, three phenotypically uniform rice plants were selected for sampling. Male anthers were excised from rice panicles to evaluate pollen viability using the Alexander staining method. Subsequently, rice pollen and floral bud samples were dispatched to Ouyi Biomedical Technology Co., Ltd., Shanghai, for molecular analysis.\\u003c/p\\u003e \\u003cp\\u003eThe expression levels of the OsFKF1 gene were quantified by real-time quantitative polymerase chain reaction (qPCR), utilizing a fluorescent dye-based detection system [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.4 Statistical analysis\\u003c/h2\\u003e \\u003cp\\u003eThe dataset was subjected to statistical analysis using IBM SPSS Statistics version 24.0. One-way analysis of variance (ANOVA) was performed employing Duncan\\u0026rsquo;s new multiple range test to discern significant differences among treatment means. Additionally, Origin 2021 software was utilized to conduct further variance analyses and to generate correlation plots, facilitating comprehensive data visualization and interpretation.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1 Soil characteristics\\u003c/h2\\u003e \\u003cp\\u003eAs presented in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, the concentrations of SAN and SNN exhibited significant increases (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) across various rice growth stages under the AWCI regime. Correspondingly, soil pH values in treatment groups T\\u003csub\\u003e1\\u003c/sub\\u003e through T\\u003csub\\u003e4\\u003c/sub\\u003e demonstrated a biphasic trend, initially declining before rising as the rice developmental stages progressed. Notably, the highest soil pH was observed in the T\\u003csub\\u003e2\\u003c/sub\\u003e group (6.437), whereas the lowest value occurred in the T\\u003csub\\u003e6\\u003c/sub\\u003e group (5.563) during the late tillering stage (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eRegarding nutrient content, SAN peaked at 19.107 mg\\u0026middot;kg⁻\\u0026sup1; within the CK group during the mid-tillering stage, contrasting with its nadir of 3.490 mg\\u0026middot;kg⁻\\u0026sup1; in T\\u003csub\\u003e1\\u003c/sub\\u003e at the jointing- booting stage. SNN content reached a maximum of 214.797 mg\\u0026middot;kg⁻\\u0026sup1; in the T\\u003csub\\u003e7\\u003c/sub\\u003e group at mid-tillering, while the minimum of 12.650 mg\\u0026middot;kg⁻\\u0026sup1; was recorded in T\\u003csub\\u003e3\\u003c/sub\\u003e during the jointing-booting phase. TN content exhibited its highest concentration (0.122%) in T\\u003csub\\u003e8\\u003c/sub\\u003e and the lowest (0.103%) in T\\u003csub\\u003e1\\u003c/sub\\u003e at the jointing-booting stage. TP content was greatest (1.603 g\\u0026middot;kg⁻\\u0026sup1;) in the T\\u003csub\\u003e7\\u003c/sub\\u003e group at mid-tillering, and lowest (0.466 g\\u0026middot;kg⁻\\u0026sup1;) in the CK group during late tillering.\\u003c/p\\u003e \\u003cp\\u003eTable\\u0026nbsp;2 further elucidates these dynamics: compared with the CK group at mid-tillering, soil pH values in T\\u003csub\\u003e1\\u003c/sub\\u003e-T\\u003csub\\u003e4\\u003c/sub\\u003e increased marginally (ΔpH/pH\\u003csub\\u003eCK\\u003c/sub\\u003e = 0.11% to 0.59%), whereas T\\u003csub\\u003e5\\u003c/sub\\u003e\\u0026ndash;T\\u003csub\\u003e8\\u003c/sub\\u003e exhibited decreases ranging from 0.27% to 6.47%. Simultaneously, SAN content diminished across all treatments (ΔSAN/SAN\\u003csub\\u003eCK\\u003c/sub\\u003e = 17.75% to 71.17%). During the late tillering stage, soil pH universally declined in T\\u003csub\\u003e1\\u003c/sub\\u003e\\u0026ndash;T\\u003csub\\u003e8\\u003c/sub\\u003e (ΔpH/pH\\u003csub\\u003eCK\\u003c/sub\\u003e = 0.02% to 10.84%), while SAN and TP contents increased markedly (ΔSAN/SAN\\u003csub\\u003eCK\\u003c/sub\\u003e = 0.01% to 135.97%; ΔTP/TP\\u003csub\\u003eCK\\u003c/sub\\u003e = 5.26% to 38.17%). At the heading and flowering stages, soil pH in T\\u003csub\\u003e1\\u003c/sub\\u003e\\u0026ndash;T\\u003csub\\u003e4\\u003c/sub\\u003e rose modestly (ΔpH/pH\\u003csub\\u003eCK\\u003c/sub\\u003e = 1.18% to 3.54%), contrasting with decreases in T\\u003csub\\u003e5\\u003c/sub\\u003e\\u0026ndash;T\\u003csub\\u003e8\\u003c/sub\\u003e (1.18% to 10.51%). Notably, SAN content decreased in T\\u003csub\\u003e1\\u003c/sub\\u003e\\u0026ndash;T\\u003csub\\u003e7\\u003c/sub\\u003e (ΔSAN/SAN\\u003csub\\u003eCK\\u003c/sub\\u003e = 12.08% to 36.87%), SNN content declined across T\\u003csub\\u003e1\\u003c/sub\\u003e\\u0026ndash;T\\u003csub\\u003e8\\u003c/sub\\u003e (ΔSNN/SNN\\u003csub\\u003eCK\\u003c/sub\\u003e = 3.70% to 86.18%), while TP content exhibited substantial increases (ΔTP/TP\\u003csub\\u003eCK\\u003c/sub\\u003e = 16.15% to 109.77%) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThese observations collectively underscore the intricate alterations in soil chemical properties mediated by varying irrigation regimes and developmental stages, with potential implications for nutrient availability and rice growth dynamics.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eTable.2\\u003c/b\\u003e Analysis of differences in pH values of rice soils.(B),Different capital letters in the same line indicated significant difference in different growth periods of rice (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05); Different lowercase letters in the same column indicated significant differences among rice treatments (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"No\\\" id=\\\"Taba\\\" border=\\\"1\\\"\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" morerows=\\\"1\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eIntervention\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eSoil pH value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMid- tillering stage\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLate tillering stage\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eJointing-booting stage\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eCK\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.233\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.045aB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.393\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.035abA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6.217\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.078bcB\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eT\\u003csub\\u003e1\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.240\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.040aB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.217\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.050cB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6.430\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.020aA\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eT\\u003csub\\u003e2\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.250\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.026aB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.130\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.040dC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6.437\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.021aA\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eT\\u003csub\\u003e3\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.253\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.035aA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5.700\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.020eB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6.290\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.123abA\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eT\\u003csub\\u003e4\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.270\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.040aB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.133\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.021dC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6.393\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.025aA\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eT\\u003csub\\u003e5\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.217\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.031aB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.423\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.067aA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6.127\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.061cB\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eT\\u003csub\\u003e6\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.203\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.029aA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.250\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.040cA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5.563\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.146dB\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eT\\u003csub\\u003e7\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.857\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.083bC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.407\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.045abA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6.100\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.056cB\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eT\\u003csub\\u003e8\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.830\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.087bC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.340\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.026bA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6.143\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.083cB\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSNN\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.906**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.158\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.011\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSAN\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.640**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.493**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.221\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTN\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.473*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.045\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.462*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTP\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.696**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.324\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.005\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable border=\\\"1\\\"\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eTable.4 Root physiological characteristics of rice\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eFigure.3\\u003c/b\\u003e Root physiological characteristics of rice.(a),POD in roots(U\\u0026middot;g\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)(b),CAT in roots(\\u0026micro;g\\u0026middot;g\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)(c),ABAin roots(ng\\u0026middot;g-1)(d), MDA in roots(nmol\\u0026middot;g\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e). C).CK and T represent conventional irrigation, AWCI method, respectively. The indexes of ABA and POD were sampled on June 20, July 3 and July 16, respectively. Data are described as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation (n\\u0026thinsp;=\\u0026thinsp;3). Different lowercase letters in the same column indicate significant differences in 0.05 levels between different treatment periods, and the uppercase letters in the same column indicate significant differences in 0.05 levels between different fertility periods.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2 Root Physiological Growth Characteristics\\u003c/h2\\u003e \\u003cp\\u003eUnder the AWCI regime, ABA concentrations and MDA content in rice roots demonstrated significant elevations (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) across different developmental stages (Fig.\\u0026nbsp;3). POD activity reached its apex in the T\\u003csub\\u003e7\\u003c/sub\\u003e treatment at the late tillering stage, reaching 90.13 U\\u0026middot;g\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, whereas the lowest activity was observed in T\\u003csub\\u003e1\\u003c/sub\\u003e at mid-tillering, measuring 9.75 U\\u0026middot;g\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e. CAT activity attained its maximum (0.68 \\u0026micro;g\\u0026middot;g⁻\\u0026sup1;) within the T\\u003csub\\u003e6\\u003c/sub\\u003e group at late tillering, while the minimum (0.19 \\u0026micro;g\\u0026middot;g⁻\\u0026sup1;) was recorded in T4 during the jointing-booting stage. ABA concentration was highest in T\\u003csub\\u003e1\\u003c/sub\\u003e at late tillering (61.50 ng\\u0026middot;g\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e) and lowest in T\\u003csub\\u003e2\\u003c/sub\\u003e at mid-tillering (10.81 ng\\u0026middot;g\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e). Notably, MDA content was elevated at mid-tillering compared to late tillering and jointing-booting stages, with the maximum value occurring in T\\u003csub\\u003e1\\u003c/sub\\u003e (23.80nmol\\u0026middot;g\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e) and the minimum in T\\u003csub\\u003e2\\u003c/sub\\u003e (8.09 nmol\\u0026middot;g⁻\\u0026sup1;) at jointing- booting.\\u003c/p\\u003e \\u003cp\\u003eComparative analysis relative to the CK group(Fig.\\u0026nbsp;3) reveals that, during mid-tillering, POD and CAT activities in the roots of T\\u003csub\\u003e1\\u003c/sub\\u003e\\u0026ndash;T\\u003csub\\u003e4\\u003c/sub\\u003e treatments declined significantly (ΔPOD/POD\\u003csub\\u003eCK\\u003c/sub\\u003e = 70.36% to 71.55%; ΔCAT/CAT\\u003csub\\u003eCK\\u003c/sub\\u003e = 4.86% to 8.91%), whereas these enzymatic activities in T\\u003csub\\u003e5\\u003c/sub\\u003e\\u0026ndash;T\\u003csub\\u003e8\\u003c/sub\\u003e increased moderately (ΔPOD/POD\\u003csub\\u003eCK\\u003c/sub\\u003e = 2.53% to 7.00%; ΔCAT/CAT\\u003csub\\u003eCK\\u003c/sub\\u003e = 21.70% to 28.73%). Concurrently, ABA concentrations across T\\u003csub\\u003e1\\u003c/sub\\u003e\\u0026ndash;T\\u003csub\\u003e8\\u003c/sub\\u003e diminished (ΔABA/ABA\\u003csub\\u003eCK\\u003c/sub\\u003e = 16.73% to 23.69%), while MDA content exhibited a pronounced increase (ΔMDA/MDA\\u003csub\\u003eCK\\u003c/sub\\u003e = 17.62% to 63.17%).\\u003c/p\\u003e \\u003cp\\u003eAt the late tillering stage, POD activity in rice roots rose markedly (ΔPOD/POD\\u003csub\\u003eCK\\u003c/sub\\u003e = 1.77% to 83.70%), contrasted by a decline in CAT activity within T\\u003csub\\u003e1\\u003c/sub\\u003e\\u0026ndash;T\\u003csub\\u003e8\\u003c/sub\\u003e treatments (ΔCAT/CAT\\u003csub\\u003eCK\\u003c/sub\\u003e = 0.12% to 62.73%), accompanied by elevated MDA content (ΔMDA/MDA\\u003csub\\u003eCK\\u003c/sub\\u003e = 17.61% to 72.09%). During the heading and flowering stages, a general reduction in both POD (ΔPOD/POD\\u003csub\\u003eCK\\u003c/sub\\u003e = 4.59% to 48.36%) and CAT (ΔCAT/CAT\\u003csub\\u003eCK\\u003c/sub\\u003e = 10.59% to 69.69%) activities was observed across treatments T\\u003csub\\u003e1\\u003c/sub\\u003e\\u0026ndash;T\\u003csub\\u003e8\\u003c/sub\\u003e. Conversely, both ABA concentration(ΔABA/ABA\\u003csub\\u003eCK\\u003c/sub\\u003e=1.37% to 63.52%) and MDA content (ΔMDA/MDA\\u003csub\\u003eCK\\u003c/sub\\u003e = 7.03% to 35.53%) demonstrated appreciable increments.\\u003c/p\\u003e \\u003cp\\u003eCollectively, these findings elucidate a dynamic modulation of antioxidative enzyme activities and stress-related metabolites in rice roots under varying irrigation regimes and developmental phases, reflecting complex physiological adaptations to environmental and agronomic factors.\\u003c/p\\u003e \\u003cp\\u003eThe total root surface area and cumulative root length of rice progressively increased in tandem with the advancement of the growth period(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). The largest total root surface area was observed in the CK group during the jointing- booting stage, measuring 1240.547 cm\\u0026sup2; per plant, whereas the smallest area occurred in T\\u003csub\\u003e1\\u003c/sub\\u003e at mid-tillering, amounting to 959.047 cm\\u0026sup2; per plant. Regarding total root length, T\\u003csub\\u003e6\\u003c/sub\\u003e at the late tillering stage attained a maximum of 3225.415 cm per plant, in stark contrast to the minimum length of 1090.138 cm per plant recorded in T\\u003csub\\u003e4\\u003c/sub\\u003e at mid-tillering. Average root diameter peaked at 3.730 mm per plant in T\\u003csub\\u003e6\\u003c/sub\\u003e during the jointing-booting phase, while the most diminutive diameter (1.005 mm per plant) was noted in T\\u003csub\\u003e1\\u003c/sub\\u003e at mid-tillering. The highest number of root tips was recorded in T\\u003csub\\u003e3\\u003c/sub\\u003e at the jointing-booting stage (84,274.50 per plant), with the lowest count found in T\\u003csub\\u003e1\\u003c/sub\\u003e at mid-tillering (1,090.00 per plant).\\u003c/p\\u003e \\u003cp\\u003eAnalysis of Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e reveals that, relative to the CK group, total root surface area decreased across all T\\u003csub\\u003e1\\u003c/sub\\u003e\\u0026ndash;T\\u003csub\\u003e8\\u003c/sub\\u003e treatments during the mid-tillering, late tillering, and jointing -booting stages. Specifically, at mid-tillering stage, total root length declined in T\\u003csub\\u003e1\\u003c/sub\\u003e\\u0026ndash;T\\u003csub\\u003e8\\u003c/sub\\u003e groups by 37.54% to 46.55% (ΔLength/Length\\u003csub\\u003eCK\\u003c/sub\\u003e). Concurrently, average root diameter exhibited an increase in T\\u003csub\\u003e1\\u003c/sub\\u003e\\u0026ndash;T\\u003csub\\u003e4\\u003c/sub\\u003e (ΔDiameter/Diameter\\u003csub\\u003eCK\\u003c/sub\\u003e=20.14% to 64.74%), whereas a reduction was observed in T\\u003csub\\u003e5\\u003c/sub\\u003e\\u0026ndash;T\\u003csub\\u003e8\\u003c/sub\\u003e (2.76% to 6.60%). During the late tillering and jointing-booting stages, average root diameter consistently decreased across all T\\u003csub\\u003e1\\u003c/sub\\u003e\\u0026ndash;T\\u003csub\\u003e8\\u003c/sub\\u003e treatments. Notably, at the jointing-booting stage, the number of root tips increased markedly in T\\u003csub\\u003e1\\u003c/sub\\u003e\\u0026ndash;T\\u003csub\\u003e8\\u003c/sub\\u003e groups, ranging from 9.49% to 102.97% above the CK baseline (ΔTip/Tip\\u003csub\\u003eCK\\u003c/sub\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThese findings delineate nuanced modifications in root morphological parameters influenced by irrigation treatments and developmental timing, potentially impacting nutrient uptake efficiency and overall plant vigor.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3 Pollen Viability, OsFKF1 Expression, and Rice Grain Yield\\u003c/h2\\u003e \\u003cp\\u003eThe AWCI treatment elicited a marked enhancement in rice grain yield, pollen viability, and the expression of the OsFKF1 gene during the critical heading and flowering stages (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Notably, the highest rice grain yield was observed in the T\\u003csub\\u003e8\\u003c/sub\\u003e group, reaching 83.908 g per plant, whereas the CK group exhibited the lowest yield at 69.64 g per plant. Pollen viability peaked in the T\\u003csub\\u003e3\\u003c/sub\\u003e group at 83.28% during these developmental phases, contrasting with the minimum value of 69.42% documented in the CK group. Concurrently, OsFKF1 expression attained its maximum (2.96%) in the T\\u003csub\\u003e2\\u003c/sub\\u003e group, with the CK group registering the lowest level (1.00%).\\u003c/p\\u003e \\u003cp\\u003eThis augmentation in reproductive parameters appears inversely correlated with a decline in root physiological growth characteristics. Nevertheless, a statistically significant increase (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) was observed in pollen viability at the heading and flowering stages, rising from 69.42% to 83.28% (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Multiple linear regression analysis, investigating the interrelationships among rice grain yield, pollen viability, and OsFKF1 expression levels, identified pollen viability during heading and flowering as the principal determinant influencing grain yield.\\u003c/p\\u003e \\u003cp\\u003eCollectively, these findings substantiate the efficacy of the AWCI methodology in enhancing rice grain yield, pollen viability, and OsFKF1 gene expression at pivotal reproductive stages.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003ePollen viability, OsFKF1 expression level and rice grain yield. (B), y is the dependent variable of grain yield; x is the independent variables,x1 is pollen activity,and x2 is OsFKF1 expression level at heading and flowering stage.* means significant difference between different variables (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), ** means extremely significant difference between different variables (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01), the same as below.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eTreatment\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePollen vitality(%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eOsFKF1 expression\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eGrain yield(g/Plant)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eCK\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e69.42\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.30c\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.04d\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e69.640\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.88e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eT\\u003csub\\u003e1\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e81.79\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.26a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.94\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.015b\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e80.490\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.62abc\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eT\\u003csub\\u003e2\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e80.25\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.88a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.96\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.30a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e81.644\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.31ab\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eT\\u003csub\\u003e3\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e83.28\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.42a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.27\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.38d\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e82.183\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.36ab\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eT\\u003csub\\u003e4\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e69.96\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.90c\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.91\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.33bc\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e75.069\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.17cde\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eT\\u003csub\\u003e5\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e76.06\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.28b\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.43\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.17bcd\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e76.521\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.85bcd\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eT\\u003csub\\u003e6\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e80.20\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.59a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.37\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.57cd\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e72.757\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.14de\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eT\\u003csub\\u003e7\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e74.04\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.68b\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.20\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.12d\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e78.750\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.96abc\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eT\\u003csub\\u003e8\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e81.11\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.40a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.85\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.28bc\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e83.908\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.33a\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePollen vitality(%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.217\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.565**\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOsFKF1 expression\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.217\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.445*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eMultiple linear regression equation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003ey\\u0026thinsp;=\\u0026thinsp;34.496\\u0026thinsp;+\\u0026thinsp;56.097x\\u003csub\\u003e1\\u003c/sub\\u003e (R\\u0026thinsp;=\\u0026thinsp;0.565, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eCorrelation analysis of rice pollen activity and rhizosphere soil environment factors.(B) ,y represents the dependent variables\\u0026mdash;pollen vitality, OsFKF1 expression level, and rice grain yield, respectively. x₁ to x₁₅denote independent variables: soil pH (x₁\\u0026ndash;x₃), nitrate nitrogen (x₄\\u0026ndash;x₆), ammonium nitrogen (x₇\\u0026ndash;x₉), total nitrogen (x₁₀\\u0026ndash;x₁₂), and total phosphorus (x₁₃\\u0026ndash;x₁₅) measured at mid-tillering, late tillering, and jointing-booting stages, respectively.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"11\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eindex\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eSoil pH value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c8\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003eSoil nitrate nitrogen content/(mg\\u0026middot;kg\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c11\\\" namest=\\\"c9\\\"\\u003e \\u003cp\\u003eSoil ammonium nitrogen content/(mg\\u0026middot;kg\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eGrowth stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMid- tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLate tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eJointing -booting stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMid- tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eLate tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eJointing -booting stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eMid- tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eLate tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003eJointing -booting stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"5\\\" rowspan=\\\"6\\\"\\u003e \\u003cp\\u003ePollen vitality\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.039\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.447*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.071\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.014\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.102\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e-0.475*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.197\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e-0.05\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e-0.209\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMLR analysis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003ey\\u0026thinsp;=\\u0026thinsp;1.442-0.108x\\u003csub\\u003e2\\u003c/sub\\u003e(R\\u0026thinsp;=\\u0026thinsp;0.447, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c8\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003ey\\u0026thinsp;=\\u0026thinsp;0.82-0.002x\\u003csub\\u003e6\\u003c/sub\\u003e(R\\u0026thinsp;=\\u0026thinsp;0.475, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c11\\\" namest=\\\"c9\\\"\\u003e \\u003cp\\u003e/\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eSoil total nitrogen content/(%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c8\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003eTotal phosphorus content in soil/(g\\u0026middot;kg\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c11\\\" namest=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMid- tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLate tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eJointing -booting stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMid- tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eLate tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eJointing -booting stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.327\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.256\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.354\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.005\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.355\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMLR analysis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e/\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c8\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e/\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c11\\\" namest=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"7\\\" rowspan=\\\"8\\\"\\u003e \\u003cp\\u003eOsFKF1 expression level\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eSoil pH value\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c8\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003eSoil nitrate nitrogen content/(mg\\u0026middot;kg\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c11\\\" namest=\\\"c9\\\"\\u003e \\u003cp\\u003eSoil ammonium nitrogen content/(mg\\u0026middot;kg\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMid- tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLate tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eJointing -booting stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMid- tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eLate tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eJointing -booting stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eMid- tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eLate tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003eJointing -booting stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.166\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.436*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.236\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.207\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e-0.129\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.199\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.209\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e-0.291\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMLR analysis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003ey=-4.611\\u0026thinsp;+\\u0026thinsp;1.013x\\u003csub\\u003e3\\u003c/sub\\u003e(R\\u0026thinsp;=\\u0026thinsp;0.436, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c8\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e/\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c11\\\" namest=\\\"c9\\\"\\u003e \\u003cp\\u003e/\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eSoil total nitrogen content/(%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c8\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003eTotal phosphorus content in soil/(g\\u0026middot;kg\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c11\\\" namest=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMid- tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLate tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eJointing -booting stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMid- tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eLate tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eJointing -booting stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.402*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.239\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.205\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.302\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.076\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.223\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMLR analysis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003ey=-2.712\\u0026thinsp;+\\u0026thinsp;38.24x\\u003csub\\u003e10\\u003c/sub\\u003e(R\\u0026thinsp;=\\u0026thinsp;0.402, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c8\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e/\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c11\\\" namest=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"7\\\" rowspan=\\\"8\\\"\\u003e \\u003cp\\u003eRice grain yield\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eSoil pH value\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c8\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003eSoil nitrate nitrogen content/(mg\\u0026middot;kg\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c11\\\" namest=\\\"c9\\\"\\u003e \\u003cp\\u003eSoil ammonium nitrogen content/(mg\\u0026middot;kg\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMid- tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLate tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eJointing -booting stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMid- tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eLate tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eJointing -booting stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eMid- tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eLate tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003eJointing -booting stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.295\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.333\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.322\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.306\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.282\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e-0.137\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.215\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.224\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.104\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMLR analysis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e/\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c8\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e/\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c11\\\" namest=\\\"c9\\\"\\u003e \\u003cp\\u003e/\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eSoil total nitrogen content/(%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c8\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003eTotal phosphorus content in soil/(g\\u0026middot;kg\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c11\\\" namest=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMid- tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLate tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eJointing -booting stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMid- tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eLate tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eJointing -booting stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.573**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.193\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.127\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.378\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.216\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMLR analysis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003ey\\u0026thinsp;=\\u0026thinsp;24.923\\u0026thinsp;+\\u0026thinsp;463.219x\\u003csub\\u003e13\\u003c/sub\\u003e(R\\u0026thinsp;=\\u0026thinsp;0.573, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c8\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e/\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.4 Relationship Analysis\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e3.4.1 Relationship Among Pollen Viability, Gene Expression, Grain Yield, and Rhizosphere Soil Environmental Factors\\u003c/h2\\u003e \\u003cp\\u003eThe AWCI method modulates soil pH across different stages of rice development, thereby orchestrating the N and P dynamics within the rhizosphere. This regulation is pivotal in shaping pollen viability, OsFKF1 gene expression during heading and flowering, and ultimately, rice grain yield. The correlation analysis results, elucidating the relationships among pollen viability, OsFKF1 expression, grain yield, and rhizospheric environmental parameters, are summarized in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003eSpecifically, pollen viability during the heading and flowering stages exhibited a significant inverse correlation with soil pH at the late tillering stage and SNN content during the jointing-booting stages (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Conversely, OsFKF1 expression correlated positively with soil pH at the jointing-booting stage and TN content at the tillering stage (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Rice grain yield was positively associated with TN content at the late tillering stage, with a higher level of significance (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01).\\u003c/p\\u003e \\u003cp\\u003eMultiple linear regression (MLR) analyses further substantiated that the AWCI approach finely regulates soil pH and TN content. The predictive models indicated that a reduction in soil pH at the late tillering stage, coupled with an elevation of soil pH at booting and increased TN content during mid-tillering, synergistically contribute to enhanced pollen viability, augmented OsFKF1 expression, and improved grain yield. These insights affirm the critical influence of soil pH and nitrogen availability under AWCI-mediated cultivation.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e3.4.2 Relationship Among Pollen Viability, Gene Expression, Grain Yield, and Root Physiological Growth Characteristics of Rice\\u003c/h2\\u003e \\u003cp\\u003eCorrelation analyses exploring the interplay between pollen viability, gene expression, grain yield, and root physiological parameters are also presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e. Pollen viability at the heading and flowering stages showed a robust positive correlation with ABA levels in roots during the late tillering phase (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01), while demonstrating significant negative correlations with root ABA concentrations at mid-tillering and MDA content during the jointing-booting stage (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). OsFKF1 expression was inversely associated with POD, ABA, and MDA content in roots at mid-tillering (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01). Rice grain yield also manifested a negative correlation with CAT activity in roots at booting, as well as with root ABA and MDA levels during mid-tillering (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05).\\u003c/p\\u003e \\u003cp\\u003eRegarding root morphology, pollen viability was significantly and negatively correlated with the total root surface area during late-tillering and jointing-booting stages (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01). Negative correlations were also observed between pollen viability and total root length at mid-tillering, as well as average root diameter at late tillering (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). In contrast, pollen viability correlated positively with average root diameter and total root tip count during the jointing-booting stage (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01 and \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, respectively). OsFKF1 expression showed a negative correlation with total root surface area during the jointing-booting phase (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Grain yield exhibited significant negative correlations with root surface area at late tillering and jointing-booting stages, and total root length at mid-tillering, yet a positive correlation with average root diameter at jointing-booting (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01).\\u003c/p\\u003e \\u003cp\\u003eMLR analyses revealed that the AWCI method governs root surface area alongside MDA content in roots. The constructed models suggest that an expansion in root surface area during the jointing-booting stage together with elevated MDA content at mid-tillering enhance pollen viability, OsFKF1 expression, and rice grain yield. These findings underscore the pivotal role of root morphological traits and oxidative stress markers modulated by the AWCI method in optimizing reproductive success and productivity.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003ecorrelation analysis of Pollen viability, flowering specific gene expression, and grain yield with POD,CAT,ABA, MDA in rice root.(B),y represents the dependent variables\\u0026mdash;pollen vitality, OsFKF1 expression, and rice grain yield, respectively. x denotes independent variables including POD in roots, CAT in stems, MDA in leaves, root surface area, total root length, average root diameter, and total root tip count, each measured at mid-tillering, late tillering, and jointing-booting stages.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"11\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" morerows=\\\"1\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eRoot Physiological Growth Characteristics\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003ePOD in roots\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c8\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003eCAT in roots\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c11\\\" namest=\\\"c9\\\"\\u003e \\u003cp\\u003eABA in roots\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMid- tillering stage\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLate tillering stage\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eJointing-booting stage\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMid- tillering stage\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eLate tillering stage\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eJointing-booting stage\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eMid- tillering stage\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eLate tillering stage\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003eJointing-booting stage\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003ePollen vitality(%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.229\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.253\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.275\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.017\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.278\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e-0.269\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.388*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.491**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e-0.229\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eOsFKF1eexpression\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.526**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.115\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.248\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.291\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.067\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e-0.278\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.493**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.148\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.252\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eGrain yield(g/plant)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.288\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.061\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.056\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.118\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.158\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e-0.404*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.416*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.051\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.011\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" morerows=\\\"1\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\" rowspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eMDA in roots\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c8\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003eroot surface area\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c11\\\" namest=\\\"c9\\\"\\u003e \\u003cp\\u003etotal root length\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMid- tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLate tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eJointing-booting stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMid- tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eLate tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eJointing-booting stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eMid- tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eLate tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003eJointing-booting stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003ePollen vitality(%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.354\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.151\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.432*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.06\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.662**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e-0.743**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.381*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e-0.148\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.066\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eOsFKF1eexpression\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.569**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.209\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.211\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.038\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.333\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e-0.476*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.359\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e-0.095\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.136\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eGrain yield(g/plant)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.288\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.061\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.056\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.118\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.158\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e-0.404*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.416*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.051\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.011\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" morerows=\\\"1\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\" rowspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eaverage root diameter\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c8\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003etotal root tip count\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMid- tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLate tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eJointing-booting stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMid- tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eLate tillering stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eJointing-booting stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003ePollen vitality(%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.394*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.560**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.192\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.227\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.432*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eOsFKF1eexpression\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.142\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.099\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.132\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.171\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.042\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eGrain yield(g/plant)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.141\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.374\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.549**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.094\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.234\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.373\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eMLR analysis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePollen vitality(%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"9\\\" nameend=\\\"c11\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003ey\\u0026thinsp;=\\u0026thinsp;2.433-0.001x\\u003csub\\u003e12\\u003c/sub\\u003e (R=-0.743, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOsFKF1eexpression\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"9\\\" nameend=\\\"c11\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003ey=-0.228\\u0026thinsp;+\\u0026thinsp;0.095x\\u003csub\\u003e7\\u003c/sub\\u003e (R\\u0026thinsp;=\\u0026thinsp;0.569, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGrain yield(g/plant)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"9\\\" nameend=\\\"c11\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003ey\\u0026thinsp;=\\u0026thinsp;209.596-0.112x\\u003csub\\u003e12\\u003c/sub\\u003e (R=-0.592, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.5 Cluster Analysis Outcomes\\u003c/h2\\u003e \\u003cp\\u003eHierarchical cluster analysis alongside descriptive statistical evaluations(Metcalf et al., 2025) was conducted on rice grain yield indicators (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). The factor sets incorporated into the analysis comprised physiological growth characteristics, rice grain yield, rhizospheric soil parameters, pollen viability, and OsFKF1 expression levels measured during the heading and flowering stages. Decision categories were delineated as satisfied, moderately satisfied, and dissatisfied.\\u003c/p\\u003e \\u003cp\\u003eUtilizing a distance threshold spanning 20,000 to 32,000, treatments were stratified into three distinct clusters: the satisfied cluster (solely treatment T\\u003csub\\u003e3\\u003c/sub\\u003e), the moderately satisfied cluster (including T\\u003csub\\u003e2\\u003c/sub\\u003e, T\\u003csub\\u003e5\\u003c/sub\\u003e, T\\u003csub\\u003e7\\u003c/sub\\u003e, and T\\u003csub\\u003e8\\u003c/sub\\u003e), and the dissatisfied cluster (comprising T\\u003csub\\u003e1\\u003c/sub\\u003e, T\\u003csub\\u003e4\\u003c/sub\\u003e, T\\u003csub\\u003e7\\u003c/sub\\u003e, and the control, CK). The T\\u003csub\\u003e3\\u003c/sub\\u003e treatment emerged as the theoretically optimal regimen within the AWCI framework, demonstrating superior regulation of root physiological growth and concomitant enhancement of grain yield.\\u003c/p\\u003e \\u003cp\\u003eNotably, the T\\u003csub\\u003e3\\u003c/sub\\u003e cohort exhibited the highest pollen viability and grain yield, attaining 83.28% and 82.183 g per plant, respectively, underscoring its efficacy as a prime agricultural intervention for augmenting reproductive success and productivity in rice cultivation.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.1. Effects and Mechanisms of Irrigation Methods on Soil Properties and Rice Grain Yield\\u003c/h2\\u003e \\u003cp\\u003ePollen viability released at the precise stage and time is essential for sexual reproduction and rice production [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e], soil physicochemical properties and irrigation strategies represent fundamental determinants of pollen viability during the critical heading and flowering phases, as well as rice grain yield [\\u003cspan additionalcitationids=\\\"CR30\\\" citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. Our findings demonstrate that the AWCI method significantly enhances pollen viability and OsFKF1 gene expression during these developmental stages, culminating in notable increases in grain yield. Moreover, positive correlations were identified among rice grain yield, pollen viability, and OsFKF1 expression levels (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eIrrigation regimes induce a cascade of soil biochemical transformations, prominently influencing redox reactions that modulate soil pH, redox potential (Eh), and stimulate soil enzyme activities. Various irrigation practices\\u0026mdash;flooding, alternating wet and dry cycles, and wetting irrigation\\u0026mdash;affect soil pH differently, with the magnitude of change following the order: flooding\\u0026thinsp;\\u0026gt;\\u0026thinsp;alternating wet-dry\\u0026thinsp;\\u0026gt;\\u0026thinsp;wetting [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. Flooding typically diminishes soil oxygen availability, fostering anaerobic conditions [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e], whereas oxygation and water-controlled irrigation bolster soil oxygen content over extended periods [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eUnder flooded, reduced conditions, the soil environment becomes anaerobic, promoting a gradual neutralization of soil pH irrespective of its initial acidity or alkalinity.This phenomenon is primarily driven by alterations in cation exchange capacity (CEC), degradation of organic matter, and decarboxylation of organic anions during anaerobic microbial metabolism. Flooding enhances CEC, which raises the pH toward neutrality in acidic soils. Concurrently, the microbial breakdown of organic acids elevates soil pH through organic acid decarboxylation [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. Additionally, soil microorganisms utilize alternative electron acceptors, such as nitrate, during organic matter oxidation, contributing to reductions in Eh [\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe AWCI method further elevates soil dissolved oxygen content and modulates soil pH [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e], thereby influencing the transformation and availability of nitrogen (N) and phosphorus (P) by regulating soil water and nutrient dynamics throughout different rice growth stages [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]. Changes in redox potential and pH impact the mobility of N and P species. Correlation analyses revealed a negative association between soil pH and total nitrogen (TN), soil nitrate nitrogen (SNN), and total phosphorus (TP) at mid-tillering, while soil alkaline nitrogen (SAN) was positively correlated with pH. During late tillering, SAN was inversely correlated with pH, and at jointing-booting, TN content exhibited a negative relationship with pH. Insufficient N and P availability in soil can constrain their uptake and assimilation by rice plants, ultimately limiting growth and reducing yield.\\u003c/p\\u003e \\u003cp\\u003eMultiple linear regression (MLR) analyses confirmed that the AWCI method induces a decrease in soil pH during late tillering, an increase in pH at jointing-booting, and elevates TN content at mid-tillering, collectively enhancing pollen viability, OsFKF1 expression, and grain yield. Additionally, soil water potential and temperature emerge as critical factors influencing these outcomes. Soil water potential governs water distribution within the soil matrix and plant water acquisition capacity, while excessive soil temperatures can impair root function and reduce pollen viability. Maintaining optimal temperature regimes accelerates growth, increases tiller number, and ultimately boosts yield [\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.2. Effects and Mechanisms of Water Management on Root Physiological Traits and Rice Grain Yield\\u003c/h2\\u003e \\u003cp\\u003eRice roots extract water and oxygen from the rhizosphere, triggering adaptive responses that include modulation of antioxidant enzyme activities and synthesis of osmolytes. These processes facilitate cellular water uptake, regulate stomatal function, and enhance photosynthetic efficiency, thereby mitigating the deleterious effects of heat and drought stress [\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]. Key root biochemical indicators\\u0026mdash;POD, CAT, ABA, and MDA\\u0026mdash;significantly influence pollen viability, OsFKF1 expression during heading and flowering, and grain yield(Table.5).\\u003c/p\\u003e \\u003cp\\u003eThis study comprehensively characterized the temporal dynamics of POD, CAT, ABA, and MDA in rice roots under the AWCI regime, alongside root morphological traits, to elucidate their impact on reproductive parameters and yield. AWCI method resulted in reductions of root ABA by 16.73%\\u0026ndash;23.69% at mid-tillering, increased ABA by 1.37%\\u0026ndash;63.52% at jointing-booting, and elevated POD by 1.77%\\u0026ndash;83.70% at late tillering. Conversely, POD activity decreased by 4.59%\\u0026ndash;48.36% at jointing-booting, with treatments T\\u003csub\\u003e1\\u003c/sub\\u003e\\u0026ndash;T\\u003csub\\u003e4\\u003c/sub\\u003e showing a 70.36%\\u0026ndash;71.55% reduction at mid-tillering, while T\\u003csub\\u003e5\\u003c/sub\\u003e\\u0026ndash;T\\u003csub\\u003e8\\u003c/sub\\u003e exhibited a modest increase of 2.53%\\u0026ndash;7.00%. Root CAT activity declined 0.12%\\u0026ndash;62.73% during late tillering and jointing-booting stages, whereas MDA levels rose by 17.62%\\u0026ndash;63.17%, 17.61%\\u0026ndash;72.09%, and 7.03%\\u0026ndash;35.53% across mid-tillering, late tillering, and jointing-booting stages, respectively(Figure 3).\\u003c/p\\u003e \\u003cp\\u003eRoot morphological and physiological traits critically underpin root functionality and vigor [\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e]. Reductions in traits such as total root length and surface area can paradoxically enhance root vitality by stimulating antioxidant defense systems (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e), including ABA, POD, and CAT, increasing soluble sugar and auxin content. This cascade enhances leaf cell antioxidant capacity, promotes water uptake, regulates stomatal aperture and photosynthesis, and effectively scavenges reactive oxygen species (ROS), collectively supporting robust rice growth.\\u003c/p\\u003e \\u003cp\\u003eWater demand varies markedly across rice growth stages. Implementing judicious water-saving strategies during organogenesis optimizes rhizospheric conditions, modulates root activity, elevates antioxidant enzyme activities (SOD, CAT, MDA), suppresses oxidative kinase activities, and fortifies ROS detoxification mechanisms, thereby ensuring stable grain yields [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. The AWCI method enhances rice root vigor and enzymatic activity, thereby influencing spikelet opening and closing during flowering and grain yield formation. Moreover, energy and material fluxes within the soil\\u0026ndash;plant\\u0026ndash;atmosphere continuum are shaped by soil temperature and moisture, which AWCI modulates by altering rhizospheric water and nutrient availability. MLR results revealed that increases in total root surface area at jointing\\u0026ndash;booting and elevated root MDA at mid-tillering under AWCI correspond to improved pollen viability, OsFKF1 expression, and grain yield. Hierarchical cluster analysis further identified T3 as the theoretically optimal AWCI treatment for regulating flowering and enhancing yield.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"5. Conclusions\",\"content\":\"\\u003cp\\u003eThis investigation elucidates the multifaceted effects of the AWCI method on soil physicochemical properties, root physiological traits, pollen viability, and OsFKF1 gene expression during heading and flowering stages, with significant implications for rice grain yield. Key determinants include total root surface area at jointing-booting stage, root MDA content at mid-tillering stage, soil pH, and TN content during mid-tillering stage. Treatment T\\u003csub\\u003e3\\u003c/sub\\u003e emerges as an optimal strategy for modulating rice flowering and boosting yield under AWCI management.\\u003c/p\\u003e \\u003cp\\u003eFuture research should explore additional flowering-related gene expressions to delineate their relative contributions alongside soil properties throughout the rice growth cycle. Furthermore, the allocation of root physiological traits suggests that enhanced antioxidant enzyme functionality, reduced oxidative kinase activity, and improved ROS scavenging in aboveground tissues warrant deeper investigation to ensure yield stability.\\u003c/p\\u003e \\u003cp\\u003eIntegrating soil characteristics, root physiology, and gene expression profiling will afford a more comprehensive understanding of rice developmental dynamics. Given that this study focused on a single rice cultivar, subsequent studies should assess whether these findings generalize across diverse genotypes with respect to soil-root-flowering gene interactions.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cdiv class=\\\"DefinitionList\\\"\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eABA\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eAbscisic acid\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eAWCI\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eAerobic and water-controlled irrigation\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eCAT\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eCatalase\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eCK\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eControl (conventional irrigation)\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eELISA\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eEnzyme-linked immunosorbent assay\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eMDA\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eMalondialdehyde\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003ePOD\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003ePeroxidase\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eqPCR\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eQuantitative real-time polymerase chain reaction\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eROS\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eReactive oxygen species\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eSAN\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eSoil ammonium nitrogen\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eSNN\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eSoil nitrate nitrogen\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eSOD\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eSuperoxide dismutase\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eSP\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eSoluble protein\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eTN\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eTotal nitrogen\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eTP\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eTotal phosphorus.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate:\\u0026nbsp;\\u003c/strong\\u003eNot applicable. This study did not involve human participants, human data, or animals.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication:\\u0026nbsp;\\u003c/strong\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials:\\u0026nbsp;\\u003c/strong\\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting Interests:\\u0026nbsp;\\u003c/strong\\u003eThe authors declare that they have no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding:\\u0026nbsp;\\u003c/strong\\u003eThis work was supported by the Natural Science Foundation of Hunan Province, China (Grant No. 2023JJ30311), and the Water Resources Science and Technology Project of Hunan Province, China (Grant No. XSKJ2025056-16).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors’ contributions:\\u0026nbsp;\\u003c/strong\\u003eW.Z. (Wenping Zhang): Conceptualization, Methodology, Writing – original draft, Supervision, Project administration, Funding acquisition. J.Z. (Jiangyuan Zhang): Investigation, Formal analysis, Data curation. F.P. (Feiyu Peng): Investigation, Validation. Z.L. (Zhuying Liu): Resources. X.L. (Xin Liu): Software, Visualization. W.X. (Weihua Xiao): Methodology. D.H. (Deyong Hu): Resources. T.L. (Tongcheng Luo): Investigation. X.S. (Xinyi Su): Investigation. S.Z. (Shuhan Zhang): Formal analysis. G.W. (Genyi Wu): Writing – review \\u0026amp; editing, Supervision. All authors read and approved the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements:\\u0026nbsp;\\u003c/strong\\u003e Not applicable.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eMarković, M., \\u0026scaron;o\\u0026scaron;tarić, J., Josipović, M., Atilgan, A., 2021. 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Change Biol. 30 (11), e17577. https://10.1111/gcb.17577.\\u003c/li\\u003e\\n\\u003cli\\u003eWu, Q., Mou, X., Wu, H., Tong, J., Sun, J., Gao, Y., Shi, J., 2021. Water management of alternate wetting and drying combined with phosphate application reduced lead and arsenic accumulation in rice. Chemosphere 283, 131043. https://10.1016/j.chemosphere.2021.131043.\\u003c/li\\u003e\\n\\u003cli\\u003eZhou, J., Tang, S., Pan, W., Liu, X., Han, K., Si, L., Ma, Q., Mao, X., Fu, H., Wu, L., 2024. Long-term non-flooded cultivation with straw return maintains rice yield by increasing soil pH and soil quality in acidic soil. Eur. J. Agron. 159, 127208. https://https://doi.org/10.1016/j.eja.2024.127208.\\u003c/li\\u003e\\n\\u003cli\\u003eOuyang, Z., Tian, J., Yan, X., Yang, Z., 2023. Micro-nano oxygenated irrigation improves the yield and quality of greenhouse cucumbers under-film drip irrigation. Sci. 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Dynamics of soil penetration resistance, moisture depletion pattern and crop productivity determined by mechanized cultivation and lifesaving irrigation in zero till blackgram. Heliyon 10 (7), e28625. https://10.1016/j.heliyon.2024.e28625.\\u003c/li\\u003e\\n\\u003cli\\u003eChen, H., Wang, Y., Yuan, J., Zhu, W., Chen, G., Wang, S., 2021. Effect of P fertilizer reduction regime on soil Olsen-P, root Fe-plaque P, and rice P uptake in rice-wheat rotation paddy fields. Pedosphere 31 (1), 94-102. https://10.1016/S1002-0160(20)60052-2.\\u003c/li\\u003e\\n\\u003cli\\u003eXiao, W., Liao, Q., Fu, J., Liu, Y., Zhang, C., Zhang, W., 2023. Effects of micro-nano bubble aerated irrigation and levels of nitrogen fertilizer on nitrogen accumulation and metabolism in super rice. Irrig. Drain. 72 (1), 138-147. https://10.1002/ird.2765.\\u003c/li\\u003e\\n\\u003cli\\u003eZhang, W., Li, H., Tan, X., Li, Z., Zhong, C., Xiao, W., Xiong, Y., Zhang, W., Yang, L., Wu, G., 2021. Fe-Mn plaque formation mechanism underlying the inhibition of cadmium absorption by rice under oxygation conditions. Environ. Eng. Sci. 38 (7), 676-684. https://10.1089/ees.2020.0434.\\u003c/li\\u003e\\n\\u003cli\\u003eUllah, S., Adeel, M., Zain, M., Rizwan, M., Irshad, M.K., Jilani, G., Hameed, A., Khan, A., Arshad, M., Raza, A., Baluch, M.A., Rui, Y., 2020. Physiological and biochemical response of wheat (Triticum aestivum) to TiO(2) nanoparticles in phosphorous amended soil: A full life cycle study. J. Environ. Manage. 263, 110365. https://10.1016/j.jenvman.2020.110365.\\u003c/li\\u003e\\n\\u003cli\\u003eMetcalf, K.J., Wo, G., Zaragoza, J.P., Raoufi, F., Baker, J., Chen, D., Derebe, M., Hogan, J., Hsu, A., Kofman, E., Leigh, D., Li, M., Malashock, D., Mann, C., Motlagh, S., Park, J., Sathiyamoorthy, K., Shidhore, M., Tang, Y., Teng, K., Williams, K., Waight, A., Yilmaz, S., Zhang, F., Zhong, H., Fayadat-Dilman, L., Bailly, M., 2025. 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Cadmium transfer in contaminated soil-rice systems: Insights from solid-state speciation analysis and stable isotope fractionation. Environ. Pollut. 269, 115934. https://10.1016/j.envpol.2020.115934.\\u003c/li\\u003e\\n\\u003cli\\u003eHemkemeyer, M., Schwalb, S.A., Heinze, S., Joergensen, R.G., Wichern, F., 2021. Functions of elements in soil microorganisms. Microbiol. Res. 252, 126832. https://https://doi.org/10.1016/j.micres.2021.126832.\\u003c/li\\u003e\\n\\u003cli\\u003eSouza, R., Hartzell, S., Freire Ferraz, A.P., de Almeida, A.Q., de Sousa Lima, J.R., Dantas Antonino, A.C., de Souza, E.S., 2021. Dynamics of soil penetration resistance in water-controlled environments. Soil and Tillage Research 205, 104768. https://https://doi.org/10.1016/j.still.2020.104768.\\u003c/li\\u003e\\n\\u003cli\\u003eCai, G., Ahmed, M.A., Abdalla, M., Carminati, A., 2022. Root hydraulic phenotypes impacting water uptake in drying soils. Plant Cell Environ. 45 (3), 650-663. https://10.1111/pce.14259.\\u003c/li\\u003e\\n\\u003cli\\u003eSonkar, I., Kotnoor, H.P., Sen, S., 2019. Estimation of root water uptake and soil hydraulic parameters from root zone soil moisture and deep percolation. Agric. Water Manag. 222.\\u003c/li\\u003e\\n\\u003cli\\u003ePeter Mantilen Ludong, D., Nanlohy, F.N., Ai Nio, S., 2020. Physiological Responses to Drought in Six Rice (Oryza sativa L.) Cultivars Cultivated in North Sulawesi, Indonesia. Pak J Biol Sci 23 (12), 1666-1675. https://10.3923/pjbs.2020.1666.1675.\\u003c/li\\u003e\\n\\u003cli\\u003eWang, H., Ye, T., Guo, Z., Yao, Y., Tu, H., Wang, P., Zhang, Y., Wang, Y., Li, X., Li, B., Xiong, H., Lai, X., Xiong, L., 2024. A double-stranded RNA binding protein enhances drought resistance via protein phase separation in rice. Nat. Commun. 15 (1), 2514. https://10.1038/s41467-024-46754-2.\\u003c/li\\u003e\\n\\u003cli\\u003eGao, Y., Zhang, Z., Zeng, F., Ma, X., 2023. Root morphological and physiological traits are committed to the phosphorus acquisition of the desert plants in phosphorus-deficient soils. BMC Plant Biol. 23 (1), 188. https://10.1186/s12870-023-04178-y.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-plant-biology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"pbio\",\"sideBox\":\"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/pbio/default.aspx\",\"title\":\"BMC Plant Biology\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"AWCI method, Rhizosphere soil physicochemical properties, Root antioxidant enzymes, OsFKF1 gene expression, Grain yield\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8497875/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8497875/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThe intricate interplay among soil physicochemical properties, root physiological traits, and flowering gene expression fundamentally governs rice reproductive success and grain yield. This study elucidates the effects of the Aerobic and water-controlled irrigation (AWCI) method on rhizosphere soil parameters, root biochemical responses, pollen viability, and temporal expression of the circadian-related flowering gene OsFKF1 during the heading and flowering stage, illuminating their integrated impact on rice productivity. Application of the AWCI regime modulated soil pH and nitrogen dynamics at critical growth intervals, enhanced rhizosphere oxygen availability, and shifted redox potential, thereby optimizing nutrient bioavailability. Simultaneously, AWCI treatment influenced root antioxidant enzyme activities—including peroxidase (POD) and catalase (CAT)—as well as abscisic acid (ABA) and MDA concentrations, which positively correlated with elevated pollen viability and upregulated OsFKF1 expression. Multivariate analyses identified key determinants of yield enhancement, notably augmented root surface area during the jointing-booting phase, balanced soil nitrogen content, and finely regulated oxidative stress markers at the mid-tillering stage. Hierarchical clustering robustly designated the T\\u003csub\\u003e3\\u003c/sub\\u003e treatment as the optimal AWCI protocol for maximizing reproductive performance and grain yield. Collectively, these findings underscore the pivotal nexus among irrigation management, soil biochemical milieu, root physiology, and floral gene regulation in modulating rice yield, offering a theoretical foundation for precision water management strategies tailored to sustainable productivity enhancement. Future research should extend to encompass additional flowering-related genes and a broader spectrum of rice cultivars to generalize these mechanistic insights.\\u003c/p\\u003e\",\"manuscriptTitle\":\"From Soil to Spikelet: The Integrated Impact of AWCI on Rice Growth Under Heat and Drought Stress\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-02-17 11:37:38\",\"doi\":\"10.21203/rs.3.rs-8497875/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2026-04-10T11:22:47+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-04-10T04:18:16+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-04-10T00:43:39+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"59607843541085437609392042662284367185\",\"date\":\"2026-04-03T06:57:46+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"38548066242163789817763786892277043611\",\"date\":\"2026-04-02T11:58:17+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"218419529637403265683929714532446158821\",\"date\":\"2026-03-05T08:01:44+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-03-05T06:51:31+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"76652330261715811311246048646584874322\",\"date\":\"2026-02-23T08:51:44+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"84377321234014551440774208027406924218\",\"date\":\"2026-02-13T05:34:56+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-02-12T02:38:53+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2026-02-03T09:22:51+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-01-09T13:48:08+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-01-09T11:46:56+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Plant Biology\",\"date\":\"2026-01-09T11:33:08+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-plant-biology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"pbio\",\"sideBox\":\"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/pbio/default.aspx\",\"title\":\"BMC Plant Biology\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"8bf2df82-aba3-4d47-8e3a-e00dfebf26d4\",\"owner\":[],\"postedDate\":\"February 17th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-05-19T07:39:35+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-02-17 11:37:38\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8497875\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8497875\",\"identity\":\"rs-8497875\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}