Agricultural Water Management最新文献

{"title":"Interpretable across-period ensemble learning with multi-stress dataset fusion to enhance early-stage yield prediction under combined water and nitrogen stress using hyperspectral sensing","authors":"Bohan Mao ,&nbsp;Xiaoxiao Sun ,&nbsp;Hao Li ,&nbsp;Feng Wu ,&nbsp;Xiaohui Kuang ,&nbsp;Ying Feng ,&nbsp;Wanna Fu ,&nbsp;Weiguang Zhai ,&nbsp;Yafeng Li ,&nbsp;Fuyi Duan ,&nbsp;Qian Cheng ,&nbsp;Xiuqiao Huang ,&nbsp;Zhen Chen","doi":"10.1016/j.agwat.2026.110204","DOIUrl":"10.1016/j.agwat.2026.110204","url":null,"abstract":"<div><div>Machine learning models fitted with remote sensing data facilitate accessible and timely crop yield prediction. Since crop canopy structure influences dry matter accumulation and signals physiological status, the predictive accuracy of dependent models improve with accumulating morphogenetic change, which limits their applicability in early-stage task. Moreover, interannual variations in combined stress (i.e., the simultaneous occurrence of multiple stressors) introduce generalization errors. which refer to the errors a model produces when applied to data that differs from its training set. Consequently, substantial research achieve limited practical adoption. This study takes winter wheat under combined water-nitrogen stress as the research object, conducted a two-year field trial both two experimental sites, utilizing raw canopy reflectance acquired through UAV-based hyperspectral sensing as modeling features. Quantification of model performance degradation under combined stress, revealed an average R² reduction of 13.6 % across Random Forest Regression (RFR), Light Gradient Boosting Machine, and Partial Least Squares Regression (PLSR) models at both sites. To this, models trained on the multi-stress dataset fusion strategy we proposed, achieved an average 8.51 % R² improvement compared to the best-performing single-stress dataset. Building upon the characteristic improvement of model accuracy with advancing growth periods, this study developed the Across-Period Ensemble Learning (APEL) framework. In heading-stage prediction across both regions, the APEL-PLSR models achieved R² of 0.72 and 0.647, outperforming its base learner PLSR models, while reducing RMSE by 78.2 % and 44.4 %. Compared to RFR, APEL-PLSR demonstrated an average R² increase of 0.389 and an RMSE reduction of 1.21 t/ha. We conducted Ablation Study on the APEL framework and integrated Variable Importance in the Projection and SHapley Additive exPlanations, further enhancing model interpretability. This study proposes a novel solution for early-stage yields prediction under complex stresses.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"325 ","pages":"Article 110204"},"PeriodicalIF":6.5,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coupled effects of meteorological and irrigation factors differentiate spatiotemporal variability and seasonal fluctuations of groundwater levels","authors":"Qize Gao ,&nbsp;Jingsi Zhu ,&nbsp;Long Sun ,&nbsp;Wentan Chen ,&nbsp;Yong Zhang ,&nbsp;Bo Liu ,&nbsp;Chengpeng Lu","doi":"10.1016/j.agwat.2026.110196","DOIUrl":"10.1016/j.agwat.2026.110196","url":null,"abstract":"<div><div>Given the scarcity of groundwater resources in the North China Plain (NCP) – a region important to both ecological integrity and socioeconomic development – understanding the spatiotemporal evolution and seasonal fluctuations of groundwater levels (GWLs) helps support effective groundwater management. This study integrates the Self-Organizing Map (SOM), an improved Innovative Trend Analysis (ITA), the Geographically Weighted Regression (GWR) model, and the Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors (SARIMAX) model to analyze GWL variations in the NCP affected by meteorological and irrigation factors from 2018 to 2023. Winter wheat irrigation was estimated based on daily water stress factors, and spring irrigation demand across the region was effectively quantified. The results reveal significant spatial variability in GWLs, with levels generally higher in the northeast and lower in the southwest. The improved ITA method shows that the shallow GWLs rose by 26% and 39% in the low and high value parts and the deep GWLs rose by 11% and 20% in the low and high value parts, respectively. This indicates an overall upward trend in GWLs across the NCP. Shallow aquifers exhibit greater sensitivity to environmental changes and increasing spatial heterogeneity, while deep aquifers are relatively stable, with decreasing spatial variability. Quantitative analysis using the GWR model confirms that precipitation is the main source of groundwater recharge, while potential evaporation and irrigation are the main causes of discharge. Spring irrigation, in particular, exerts a strong influence on shallow aquifer GWLs. The SARIMAX model further demonstrates clear seasonal patterns in GWLs, highlighting the lagged effects of different influencing factors. Precipitation and irrigation are identified as the dominant drivers of seasonal groundwater fluctuations in the NCP. These findings provide useful insights for improving the prediction and adaptive management of groundwater resources in the region.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"325 ","pages":"Article 110196"},"PeriodicalIF":6.5,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of irrigation and nitrogen application strategies on cotton yield, agronomic nitrogen use efficiency, and environmental nitrogen fate","authors":"Zhen Luo ,&nbsp;Wei Tang ,&nbsp;Xiyuan Duan ,&nbsp;Hequan Lu ,&nbsp;Cundong Li ,&nbsp;Liantao Liu ,&nbsp;Xiangqiang Kong","doi":"10.1016/j.agwat.2026.110194","DOIUrl":"10.1016/j.agwat.2026.110194","url":null,"abstract":"<div><div>This study evaluated the impacts of different irrigation-nitrogen (N) application strategies and N application rates on cotton growth and yield, agronomic N use efficiency (ANUE), and fertilizer-N fate in arid region. A split-plot design was employed to compare traditional irrigation and N application (TIN) with alternate partial root-zone irrigation combined with root-zone fertilization (ADI-RZF), under three N rates (0, 220, and 275 kg ha⁻¹). The results revealed that the seed cotton yield, harvest index (HI), ANUE, irrigation water productivity (WP_I) and fertilizer N recovery efficiency (FNRE) significantly increased under the ADI-RZF treatment relative to TIN. The expression of nitrate transporter genes (<em>GhNRT1.1</em> and <em>GhNRT1.5</em>) was upregulated by 2.3- and 2.7-fold in hydrated root zones under ADI-RZF, explaining the 34.7–52.9 % enhancement in FNRE. At N_220, the optimized ADI-RZF system achieved 95 % of the maximum yield potential (equivalent to the N₂₇₅ yield under ADI-RZF), while it reduced N input by 20 % and lowered the fertilizer N loss rate (FNLR) by 38.5–42.7 % compared to TIN at equivalent N rates. This reduction is attributed to the spatially targeted N placement in ADI-RZF, which minimized the soil residual N by 11.9–30.3 % through enhanced root foraging precision. In conclusion, ADI-RZF at the N₂₂₀ rate represents a sustainable strategy for cotton production in arid regions such as Xinjiang, China, effectively balancing high yield with a reduced environmental N footprint.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"325 ","pages":"Article 110194"},"PeriodicalIF":6.5,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated organic-inorganic fertilization enhances soil microbial diversity and mitigates the yield-quality trade-off in pakchoi (Brassica chinensis L.)","authors":"Shudong Lin ,&nbsp;Xiaole Zhao ,&nbsp;Qiuping Fu ,&nbsp;Zhenghu Ma ,&nbsp;Yingjie Ma ,&nbsp;Tingrui Yang","doi":"10.1016/j.agwat.2026.110189","DOIUrl":"10.1016/j.agwat.2026.110189","url":null,"abstract":"<div><div>The sustainability of agricultural production systems is increasingly constrained by the trade-off between yield and quality, largely driven by declines in soil microbial diversity under conventional intensive management characterized by excessive synthetic fertilizer inputs. This study elucidates the synergistic mechanisms through which integrated low-rate inorganic-organic fertilization (IO1) alleviates this trade-off in pakchoi (<em>Brassica chinensis L</em>.) by regulating rhizosphere microbial communities. A hierarchical pathway model was developed to quantify the linkages among soil microbial diversity, crop growth dynamics, yield formation, and quality attributes. Compared with inorganic-only fertilization (I1), the IO1 treatment significantly enhanced bacterial Shannon diversity and Chao1 richness, which accelerated the average growth rates of plant height (0.736 cm/d) and leaf area index (0.121 cm²/(cm²·d)). As a result, pakchoi yield increased to 5.58 kg/m², representing a 24.77 % improvement over I1. At the mechanistic level, improved microbial functional balance optimized nitrogen metabolic pathways, leading to substantial increases in soluble sugars (64.37 %), soluble proteins (39.21 %), and vitamin C content (82.04 %), while simultaneously reducing nitrate accumulation by 14.78 %. Mantel test results further revealed that bacterial communities primarily governed biomass accumulation through fresh weight dynamics (4.239 g/(plant·d)), whereas fungal communities played a key role in regulating photosynthate redistribution via organic matter catabolism, thereby establishing a \"growth prioritization-quality compensation\" dynamic equilibrium. Model predictions indicated that each unit increase in bacterial Shannon diversity corresponded to a 0.534 kg/m² increase in yield, while each unit rise in the Pielou evenness index resulted in a 2.218 mg/g reduction in nitrate content. Overall, these findings provide a robust theoretical basis for microbial driven precision fertilization strategies aimed at enhancing yield, quality, and sustainability in vegetable production systems.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"325 ","pages":"Article 110189"},"PeriodicalIF":6.5,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of wetting patterns for surface drip irrigation using moment analysis and interpretable PSO-SVM-AdaBoost model","authors":"Ge Li,&nbsp;Weibo Nie,&nbsp;Yuchen Li","doi":"10.1016/j.agwat.2026.110177","DOIUrl":"10.1016/j.agwat.2026.110177","url":null,"abstract":"<div><div>A thorough understanding of soil wetting patterns during infiltration is essential for designing surface drip irrigation systems and placing soil moisture sensors. This study systematically evaluated variations in centroid depth (<em>z</em>_<em>c</em>), horizontal (<em>σ</em>_<em>x</em>) and vertical (<em>σ</em>_<em>z</em>) standard deviations across nine soil textures, three discharge rates (1, 2, and 3 L·h⁻¹), and three initial soil water contents (30 %, 50 %, 70 % of maximum available water) using Hydrus-2D/3D numerical simulations combined with spatial moment analysis. Based on these results, a machine learning model combining particle swarm optimization (PSO), support vector machine (SVM), and adaptive boosting (AdaBoost) was developed and compared with multiple linear regression (MLR), SVM, and PSO-SVM models. Soil texture and initial water content had greater influence on <em>z</em>_<em>c</em>, <em>σ</em>_<em>x</em>, and <em>σ</em>_<em>z</em> than discharge rates. The PSO-SVM-AdaBoost model achieved the highest accuracy, with <em>Bias</em>, Root Mean Square Error (<em>RMSE</em>), and the Coefficient of Determination (<em>R</em>²) for the test set of −0.129 cm, 1.139 cm, and 0.989 for <em>z</em>_<em>c</em>; −0.034 cm, 0.366 cm, and 0.996 for <em>σ</em>_<em>x</em>; and −0.169 cm, 1.426 cm, and 0.984 for <em>σ</em>_<em>z</em>. Furthermore, to address concerns regarding the “black-box” nature of the model, the explainable artificial intelligence (XAI) framework SHapley Additive exPlanations (SHAP) was applied, revealing that cumulative infiltration flux (Q_3D) contributed most significantly to <em>z</em>_<em>c</em>, <em>σ</em>_<em>x</em>, and <em>σ</em>_<em>z</em>, while discharge rates contributed the least. The PSO-SVM-AdaBoost model and its interpretability framework proposed in this study provide technical support for the design of surface drip irrigation systems and the optimal placement of soil moisture sensors.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"325 ","pages":"Article 110177"},"PeriodicalIF":6.5,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stomatal conductance modeling for drip-irrigated kiwifruit in seasonal drought regions of South China: Evaluation of improved empirical models and interpretable machine learning approaches","authors":"Shunsheng Zheng ,&nbsp;Ningbo Cui ,&nbsp;Quanshan Liu ,&nbsp;Shouzheng Jiang ,&nbsp;Daozhi Gong ,&nbsp;Xiaoxian Zhang","doi":"10.1016/j.agwat.2026.110153","DOIUrl":"10.1016/j.agwat.2026.110153","url":null,"abstract":"<div><div>Accurate modeling of stomatal conductance (g_s) enhances understanding of plant water relations and supports advancements in eco-physiological modeling and adaptive irrigation practices. This study provides a comprehensive evaluation of g_s modeling for drip-irrigated kiwifruit through parallel development of three Jarvis-type empirical models (JV, JV1, JV2) and five machine learning algorithms (XGBoost, LightGBM, CatBoost, SVR, LR) based on three years of field measurements comprising synchronized records of g_s and key environmental drivers. Models were assessed via year-wise grouped cross-validation, with performance measured by R², RMSE, and MAE, and interpretability analyzed using SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDPs). Results showed that deficit irrigation significantly reduced g_s, with sensitivity being most pronounced during stage II. The incorporation of soil water content (SWC) substantially improved the accuracy of both empirical and machine learning models. Among empirical models, JV2, featuring a stage-specific nonlinear SWC response function, demonstrated the highest accuracy (R² ranging from 0.736 to 0.814) and minimized bias under extreme SWC conditions. Using vapor pressure deficit (VPD), air temperature (Ta), photosynthetically active radiation (PAR), and SWC as input variables, CatBoost outperformed both empirical models and other machine learning algorithms across all growth stages (R² = 0.815–0.839; RMSE = 0.065–0.076 mol m⁻² s⁻¹; MAE = 0.054–0.064 mol m⁻² s⁻¹). SHAP analysis and PDPs identified VPD as the dominant driver of g_s variation, followed by SWC. Overall, the improved JV2 model offers a structurally transparent framework for g_s estimation with acceptable accuracy, while CatBoost combined with SHAP analysis and PDPs provides superior predictive performance and robust interpretability under complex environmental conditions. These findings support the reliable modeling and regulation of kiwifruit g_s under varying SWC scenarios in drip-irrigated orchards.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"325 ","pages":"Article 110153"},"PeriodicalIF":6.5,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145969402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of irrigation and fertilization on the yield of traditional and modern foxtail millet varieties","authors":"Ke Ma ,&nbsp;Zheng Jia ,&nbsp;Xinya Wen ,&nbsp;Fu Chen","doi":"10.1016/j.agwat.2026.110168","DOIUrl":"10.1016/j.agwat.2026.110168","url":null,"abstract":"<div><div>To elucidate the mechanisms underlying yield formation in response to water and fertilizer management in traditional and modern foxtail millet varieties, a field experiment was conducted using the traditional cultivar Jingu 6 and the modern cultivar Changsheng 13. Three irrigation regimes (rainfed, pre‑sowing supplemental irrigation, and full growth‑stage irrigation) and two fertilizer levels (high and low) were implemented. Phenotypic traits, photosynthetic parameters, dry matter accumulation and translocation, photosynthate content, and yield were measured. Multivariate statistical analysis was performed to reveal the intrinsic factors responsible for yield differences between the varieties under varying water and fertilizer conditions. The results indicated that the traditional cultivar exhibited low yield potential but high stability, with minimal inter‑annual variation and low sensitivity to water and fertilizer inputs. Under rainfed conditions, its yield decreased by 16.98–39.18 %, which was maintained primarily through optimized photoprotective mechanisms and pre‑flowering dry matter allocation. In contrast, the modern cultivar showed high yield potential but poor stability, with yield increases ranging from 39.09 % to 272.42 % under conditions of high water and high fertilizer inputs. Its high yield depended on full growth‑stage irrigation, achieved mainly through improved plant architecture, enhanced photosynthetic efficiency, and strengthened source‑sink coordination. Therefore, traditional cultivars are suitable for rainfed dryland agriculture, whereas modern cultivars require reliable irrigation. Future breeding strategies should integrate the water‑saving and stress‑tolerance traits of traditional cultivars with the high‑yield potential of modern cultivars to develop water‑efficient and high‑yielding hybrids, which is crucial for building a climate‑resilient foxtail millet production system.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"325 ","pages":"Article 110168"},"PeriodicalIF":6.5,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146000594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drip irrigation-mediated application of multi-walled carbon nanotubes and Bacillus subtilis improves maize salt tolerance in saline agricultural ecosystems","authors":"Yi Liu ,&nbsp;Wenzhi Zeng ,&nbsp;Chang Ao ,&nbsp;Yutian Zuo ,&nbsp;Ying Luo ,&nbsp;Zhen Li","doi":"10.1016/j.agwat.2026.110192","DOIUrl":"10.1016/j.agwat.2026.110192","url":null,"abstract":"<div><div>Soil salinization impairs fertility and reduces crop productivity across more than 6 % of the world’s arable land. Traditional remediation approaches, like chemical amendments, are often costly and involve ecological compromises. This study investigates an innovative nano-bio strategy that integrates multi-walled carbon nanotubes (MWCNTs) with Bacillus subtilis (<em>B. subtilis</em>) under drip irrigation to boost maize tolerance in saline environments. Germination tests and field studies were conducted in soils treated with 50 mM NaCl. The results from four comparative treatments revealed that MWCNTs markedly improved seed germination (achieving 52 % by day two versus 24 % in controls) and enhanced root elongation by 52.36 %. These effects were linked to the upregulation of key ion transporters (<em>ZmSKOR</em>). Furthermore, MWCNTs application enhanced the expression of aquaporin genes <em>ZmPIP1;1</em> and <em>ZmPIP2;1</em>. Although <em>B. subtilis</em> alone had a minimal impact on germination, its combination with MWCNTs fostered stronger soil-microbe-nanomaterial interactions under drip irrigation. This synergy increased maize yield by 20.6 %, raised the 1000-grain weight by 3.08 %, lowered the leaf Na⁺/K⁺ ratio by 19.93 %, and improved antioxidant defense mechanisms, such as a 10.44 % rise in SOD activity. Importantly, while MWCNTs alone decreased soil nitrogen in non-saline conditions, adding <em>B. subtilis</em> helped rebalance nutrients, an effect that was reinforced by the uniform distribution provided by drip irrigation. The mechanism involves improved nutrient assimilation, better stomatal control, and reduced reactive oxygen species under salt stress. These findings indicate that the MWCNTs and <em>B. subtilis</em> act synergistically with drip irrigation via molecular soil-root interactions to mitigate salt toxicity. This integrated approach, which combines nanotechnology, microbiome engineering, and water-efficient irrigation, offers a sustainable and effective solution for reclaiming saline soils and advancing stress-resistant agriculture.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"325 ","pages":"Article 110192"},"PeriodicalIF":6.5,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantification of water in ginseng roots (Panax ginseng C. A. Meyer) in soil using 3D neutron imaging","authors":"Cheul Muu Sim ,&nbsp;TaeJoo Kim ,&nbsp;Hwasuk Oh ,&nbsp;Robert Bellarmin Nshimirimana ,&nbsp;Michael Frei ,&nbsp;Bernd Honermeier","doi":"10.1016/j.agwat.2026.110183","DOIUrl":"10.1016/j.agwat.2026.110183","url":null,"abstract":"<div><div>The ginseng plant is threatened with extinction owing to the prevalence of soil-borne pathogens and water shortage in field cultivation due to climate change. To optimize water management in controlled cultivation that can sustain ginseng production, a 3D neutron imaging method was developed to quantitatively measure water content of roots growing in soil. It was determined that, according to a Monte Carlo simulation, the neutron penetration rate is 32 %, which allows quantitative measurement of water thicknesses up to 30 mm in aluminum phantom using 3D neutron imaging. In the simulation, the aluminum phantom was buried in soil with 12 % moisture content contained in a 50 mm diameter aluminum pot. In practical experiments, the neutron penetration rate of an aluminum phantom buried in soil with a moisture content of 7.7 % was 18 % at a water thickness of 30 mm. A calibration curve was created to quantitatively measure the water content of aluminum phantom buried in aluminum pot soil with 1.3∼7.7 % moisture. The water content of 3-year-old ginseng roots growing in aluminum pot soil with a moisture content of 7.7 % was quantitatively determined to be 70.0 % (±5 %), 55.0 % (±5 %) and 70.0 % (± 5 %) on the basis of the calibration curve. It is concluded that, the <em>in vivo</em> 3D neutron imaging is a unique way to analyze the hydrology throughout the seedling and culturing stages of plant roots in soil for controlled cultivation.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"325 ","pages":"Article 110183"},"PeriodicalIF":6.5,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Responses of tree water uptake to soil water content variability under combined threshold effects","authors":"Xiao Zhang ,&nbsp;Ziqiang Liu ,&nbsp;Xinxiao Yu ,&nbsp;Longqi Zhang ,&nbsp;Guodong Jia","doi":"10.1016/j.agwat.2026.110197","DOIUrl":"10.1016/j.agwat.2026.110197","url":null,"abstract":"<div><div>In the context of global climate change, seasonal droughts significantly impact tree water use and soil water source contribution (WSC) in forest ecosystems. However, studies on the threshold effects of the soil water content (SWC) and previous precipitation on tree water uptake are scarce. In the study, the focus was placed on the two tree species <em>Platycladus orientalis</em> and <em>Quercus variabilis</em>, which grow in the mountainous areas of northern China. Using eco-hydrological monitoring data from 3 years, we investigated the nonlinear threshold relationship between the SWC and the WSC in different precipitation treatments (zero, half, natural, and double precipitation). A combination of structural equation modeling, random forest modeling, and S-shaped curve threshold analysis was applied to evaluate the indirect effects of previous precipitation on the SWC and WSC. S-shaped threshold analysis identified SWC thresholds spanning approximately 3–26 %; <em>Quercus variabilis</em> showed a consistent deep-layer transition at low SWC (φ1 = 6.3–8.4 % across zero, half and natural treatments), whereas <em>Platycladus orientalis</em> exhibited clear cross-layer thresholds mainly under half precipitation (φ1 = 7.0–9.8 %). Thresholds generally shifted upward under wetter conditions. Based on the results, SWC, as the dominant factor influencing the WSC, exhibited complex and significant threshold effects at different soil depths. The two species displayed contrasting water use strategies at similar threshold levels, effectively reducing competition for water. The indirect influence of previous precipitation on the SWC and WSC also varied significantly with soil depth and precipitation amount. The results of this study highlight the complex threshold effects of the SWC on WSC in different precipitation scenarios, providing a scientific basis for understanding forest water dynamics under climate change and developing adaptive management strategies.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"325 ","pages":"Article 110197"},"PeriodicalIF":6.5,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}