Pub Date : 2026-04-01Epub Date: 2026-02-21DOI: 10.1016/j.agwat.2026.110242
Shou-Chen Ma , Zhen-Hao Gao , Jia-Ju Dong , Shou-Tian Ma
<div><div>To address the low estimation accuracy of the Crop Water Stress Index (CWSI) directly induced by imprecise extraction of plant canopy temperature (T<sub>c</sub>) from thermal infrared (TIR) imagery, this study used UAV visible imagery of winter wheat under different water and nitrogen regimes to calculate the Green Leaf Index (GLI) for canopy mask construction, which was then overlaid with TIR imagery to extract T<sub>c</sub>, and subsequently multi-gradient extreme pixel elimination ratios were applied to identify the optimal method for T<sub>c</sub> extraction. Subsequently, the extracted T<sub>c</sub> are categorized into distinct pixel distribution intervals based on the standard normal distribution, and the interval-specific Crop Water Stress Index (CWSI<sub>F</sub>) is calculated using the mean canopy temperature (T<sub>F</sub>) of each interval. Thereafter, rigorous regression analysis was performed for the derived CWSI<sub>F</sub> variants against key crop physiological indicators to determine the most sensitive CWSI<sub>F</sub> values corresponding to each indicator for subsequent practical applications. The results indicate that proper removal of extreme pixels enhanced the consistency between UAV TIR-retrieved temperature and in-situ measured temperature. Excluding 3 % of extreme pixels from both ends of the T<sub>c</sub> distribution histogram yielded a relatively optimal level of this consistency, thus enabling more accurate characterization of the actual T<sub>c</sub> of crop. CWSI<sub>F</sub> values derived from the T<sub>F</sub> across different T<sub>c</sub> pixel distribution intervals differed significantly. Regression analysis showed that the sensitive CWSI<sub>F</sub> corresponding to stomatal conductance (G<sub>s</sub>), transpiration rate (T<sub>r</sub>), and net photosynthetic rate (P<sub>n</sub>) differed significantly, requiring a comprehensive evaluation integrating multiple physiological indicators. For the scientific diagnosis of crop water status, the entropy weight method was employed to assign weights to the evaluation indicators of G<sub>s</sub>, T<sub>r</sub>, and P<sub>n</sub>. Based on these weights, a linear weighted summation model was used to obtain the comprehensive score. and the optimal CWSI<sub>F</sub> that reflects the characteristics of multiple physiological indexes was determined for each growth stage: the optimal index was CWSI<sub>-0.5</sub> during the jointing stage and flowering stage, and CWSI<sub>-0.3</sub> during the filling stage. This solves the problem of inconsistent evaluation of CWSI<sub>F</sub> by different physiological indicators and improves the pertinence and accuracy of water stress diagnosis. Across all growth stages, the coefficient of determination (R²) between the optimal CWSI<sub>F</sub> and plant water content (PWC) was consistently higher than that between the traditional CWSI<sub>T</sub> (CWSI calculated based on the average value of all T<sub>c</sub>) and PWC, wh
{"title":"Optimization study on diagnostic methods for winter wheat water stress using UAV-borne thermal infrared imagery","authors":"Shou-Chen Ma , Zhen-Hao Gao , Jia-Ju Dong , Shou-Tian Ma","doi":"10.1016/j.agwat.2026.110242","DOIUrl":"10.1016/j.agwat.2026.110242","url":null,"abstract":"<div><div>To address the low estimation accuracy of the Crop Water Stress Index (CWSI) directly induced by imprecise extraction of plant canopy temperature (T<sub>c</sub>) from thermal infrared (TIR) imagery, this study used UAV visible imagery of winter wheat under different water and nitrogen regimes to calculate the Green Leaf Index (GLI) for canopy mask construction, which was then overlaid with TIR imagery to extract T<sub>c</sub>, and subsequently multi-gradient extreme pixel elimination ratios were applied to identify the optimal method for T<sub>c</sub> extraction. Subsequently, the extracted T<sub>c</sub> are categorized into distinct pixel distribution intervals based on the standard normal distribution, and the interval-specific Crop Water Stress Index (CWSI<sub>F</sub>) is calculated using the mean canopy temperature (T<sub>F</sub>) of each interval. Thereafter, rigorous regression analysis was performed for the derived CWSI<sub>F</sub> variants against key crop physiological indicators to determine the most sensitive CWSI<sub>F</sub> values corresponding to each indicator for subsequent practical applications. The results indicate that proper removal of extreme pixels enhanced the consistency between UAV TIR-retrieved temperature and in-situ measured temperature. Excluding 3 % of extreme pixels from both ends of the T<sub>c</sub> distribution histogram yielded a relatively optimal level of this consistency, thus enabling more accurate characterization of the actual T<sub>c</sub> of crop. CWSI<sub>F</sub> values derived from the T<sub>F</sub> across different T<sub>c</sub> pixel distribution intervals differed significantly. Regression analysis showed that the sensitive CWSI<sub>F</sub> corresponding to stomatal conductance (G<sub>s</sub>), transpiration rate (T<sub>r</sub>), and net photosynthetic rate (P<sub>n</sub>) differed significantly, requiring a comprehensive evaluation integrating multiple physiological indicators. For the scientific diagnosis of crop water status, the entropy weight method was employed to assign weights to the evaluation indicators of G<sub>s</sub>, T<sub>r</sub>, and P<sub>n</sub>. Based on these weights, a linear weighted summation model was used to obtain the comprehensive score. and the optimal CWSI<sub>F</sub> that reflects the characteristics of multiple physiological indexes was determined for each growth stage: the optimal index was CWSI<sub>-0.5</sub> during the jointing stage and flowering stage, and CWSI<sub>-0.3</sub> during the filling stage. This solves the problem of inconsistent evaluation of CWSI<sub>F</sub> by different physiological indicators and improves the pertinence and accuracy of water stress diagnosis. Across all growth stages, the coefficient of determination (R²) between the optimal CWSI<sub>F</sub> and plant water content (PWC) was consistently higher than that between the traditional CWSI<sub>T</sub> (CWSI calculated based on the average value of all T<sub>c</sub>) and PWC, wh","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"326 ","pages":"Article 110242"},"PeriodicalIF":6.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146777204","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}
Pub Date : 2026-04-01Epub Date: 2026-02-21DOI: 10.1016/j.agwat.2026.110246
Lintao Liu, Hongxia Cao, Guoteng Du, Jiaxuan Liu
In semi-arid dryland systems on the Loess Plateau, rainfall is highly variable within the season and drought frequently occurs around drought-sensitive stages, making stage-targeted micro-supplemental irrigation essential for stabilizing sorghum yield. However, the optimal timing and allocation of micro-supplemental irrigation, and the root–nitrogen mechanisms linking stage-targeted water inputs to yield formation and water use efficiency (WUE), remain insufficiently quantified. To address these gaps, a two-year field experiment (2022–2023) was conducted in a National High-Standard Farmland, Loess Plateau. Based on regional precipitation patterns (1957–2017), locally available irrigation water for sorghum, and crop water-demand characteristics, the experiment included single- and dual-phase micro-supplemental irrigation treatments, along with rainfed controls (CK0: no mulch; CK1: mulched). Single-stage irrigation included two jointing-stage treatments (15 mm: I15–0; 24 mm: I24–0) and one tasseling-stage treatment (15 mm: I0–15). Double irrigation was applied at both stages: 15–15 mm (I15–15), 24–15 mm (I24–15), 9–15 mm (I9–15), and 15–9 mm (I15–9). Relative to CK0 and CK1, I15–15 significantly increased sorghum yield by 98.1 %–138.4 %, WUE by 63.7 %–95.1 %, and harvest index by 17.4 %–34.3 %. It also increased grain starch content by 0.9 %–10.5 % and changed grain fat content by –5.3–13.4 %, while reducing protein content by 7.7 %–18.8 %. In addition, I15–15 reduced NO3⁻–N residues in the 0–100 cm soil layer by 33.6 %–47.2 %, concurrent with higher aboveground nitrogen accumulation (9.6 %–77.3 %). Structural equation modeling and Random Forest analyses indicated that micro-supplemental irrigation improved root morphology (higher root length density and root surface area density), which in turn enhanced nitrogen uptake, leaf area index, and chlorophyll content, ultimately leading to increased yield and WUE. Therefore, applying micro-supplemental irrigation at both jointing and tasseling is recommended for dryland sorghum in semi-arid regions to maximize yield and WUE while limiting post-harvest soil nitrate accumulation. This study quantifies an actionable split irrigation strategy for dryland sorghum and, based on multiple analytical approaches, demonstrates that improvements in root morphology constitute a key pathway linking limited water inputs to enhanced nitrogen uptake, canopy function, and yield.
{"title":"Stage-targeted micro-supplemental irrigation at jointing and tasseling enhances sorghum yield and nitrogen uptake by optimizing root morphology in semi-arid dryland systems","authors":"Lintao Liu, Hongxia Cao, Guoteng Du, Jiaxuan Liu","doi":"10.1016/j.agwat.2026.110246","DOIUrl":"10.1016/j.agwat.2026.110246","url":null,"abstract":"<div><div>In semi-arid dryland systems on the Loess Plateau, rainfall is highly variable within the season and drought frequently occurs around drought-sensitive stages, making stage-targeted micro-supplemental irrigation essential for stabilizing sorghum yield. However, the optimal timing and allocation of micro-supplemental irrigation, and the root–nitrogen mechanisms linking stage-targeted water inputs to yield formation and water use efficiency (WUE), remain insufficiently quantified. To address these gaps, a two-year field experiment (2022–2023) was conducted in a National High-Standard Farmland, Loess Plateau. Based on regional precipitation patterns (1957–2017), locally available irrigation water for sorghum, and crop water-demand characteristics, the experiment included single- and dual-phase micro-supplemental irrigation treatments, along with rainfed controls (<em>CK</em><sub><em>0</em></sub>: no mulch; <em>CK</em><sub><em>1</em></sub>: mulched). Single-stage irrigation included two jointing-stage treatments (15 mm: <em>I</em><sub><em>15–0</em></sub>; 24 mm: <em>I</em><sub><em>24–0</em></sub>) and one tasseling-stage treatment (15 mm: <em>I</em><sub><em>0–15</em></sub>). Double irrigation was applied at both stages: 15–15 mm (<em>I</em><sub><em>15–15</em></sub>), 24–15 mm (<em>I</em><sub><em>24–15</em></sub>), 9–15 mm (<em>I</em><sub><em>9–15</em></sub>), and 15–9 mm (<em>I</em><sub><em>15–9</em></sub>). Relative to <em>CK</em><sub><em>0</em></sub> and <em>CK</em><sub><em>1</em></sub>, <em>I</em><sub><em>15–15</em></sub> significantly increased sorghum yield by 98.1 %–138.4 %, WUE by 63.7 %–95.1 %, and harvest index by 17.4 %–34.3 %. It also increased grain starch content by 0.9 %–10.5 % and changed grain fat content by –5.3–13.4 %, while reducing protein content by 7.7 %–18.8 %. In addition, <em>I</em><sub><em>15–15</em></sub> reduced NO<sub>3</sub>⁻–N residues in the 0–100 cm soil layer by 33.6 %–47.2 %, concurrent with higher aboveground nitrogen accumulation (9.6 %–77.3 %). Structural equation modeling and Random Forest analyses indicated that micro-supplemental irrigation improved root morphology (higher root length density and root surface area density), which in turn enhanced nitrogen uptake, leaf area index, and chlorophyll content, ultimately leading to increased yield and WUE. Therefore, applying micro-supplemental irrigation at both jointing and tasseling is recommended for dryland sorghum in semi-arid regions to maximize yield and WUE while limiting post-harvest soil nitrate accumulation. This study quantifies an actionable split irrigation strategy for dryland sorghum and, based on multiple analytical approaches, demonstrates that improvements in root morphology constitute a key pathway linking limited water inputs to enhanced nitrogen uptake, canopy function, and yield.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"326 ","pages":"Article 110246"},"PeriodicalIF":6.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778237","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}
Pub Date : 2026-04-01Epub Date: 2026-02-22DOI: 10.1016/j.agwat.2026.110154
Shaofeng Yan , Guilin Liu , Jinwei Dong , Zhuojian Wen , Xueru Qiu , Dacheng Li
Winter irrigation (WI) is a vital practice for mitigating soil salinization and replenishing soil water storage in arid agroecosystems. However, accurate spatiotemporal monitoring of WI remains challenging because of the transient nature of flood events and spectral interference from snow and ice, which limit the applicability of traditional threshold-based methods. To address these issues, in this study, an automated, event-driven detection framework was developed by integrating the LandTrendr temporal segmentation algorithm with dense Sentinel-2 and Landsat time series. Instead of relying on static thresholds, the model explicitly identifies the abrupt spectral rise associated with irrigation onset, thereby decoupling irrigation signals from background noise. Additionally, a dynamic dual-index strategy (MNDWI/NDWI), guided by ERA5-Land meteorological data, was employed to minimize snowfall interference. Validated across major oases in southern Xinjiang from 2020 to 2024, the framework demonstrated robust performance, achieving an overall accuracy of > 95 % for spatial extent and > 72 % for irrigation timing within a 7-day tolerance. The results further indicate that the pixel-based sensitivity of the method effectively characterizes intrafield irrigation variability, revealing the fine-scale dynamics of water distribution. Furthermore, the threshold-free nature of the algorithm enhances its potential for transferability to other dryland regions. This study provides a reliable, high-resolution solution for supporting precision water management and salinity control strategies in water-limited environments.
{"title":"Mapping winter irrigation areas and timing in arid regions using time series remote sensing data","authors":"Shaofeng Yan , Guilin Liu , Jinwei Dong , Zhuojian Wen , Xueru Qiu , Dacheng Li","doi":"10.1016/j.agwat.2026.110154","DOIUrl":"10.1016/j.agwat.2026.110154","url":null,"abstract":"<div><div>Winter irrigation (WI) is a vital practice for mitigating soil salinization and replenishing soil water storage in arid agroecosystems. However, accurate spatiotemporal monitoring of WI remains challenging because of the transient nature of flood events and spectral interference from snow and ice, which limit the applicability of traditional threshold-based methods. To address these issues, in this study, an automated, event-driven detection framework was developed by integrating the LandTrendr temporal segmentation algorithm with dense Sentinel-2 and Landsat time series. Instead of relying on static thresholds, the model explicitly identifies the abrupt spectral rise associated with irrigation onset, thereby decoupling irrigation signals from background noise. Additionally, a dynamic dual-index strategy (MNDWI/NDWI), guided by ERA5-Land meteorological data, was employed to minimize snowfall interference. Validated across major oases in southern Xinjiang from 2020 to 2024, the framework demonstrated robust performance, achieving an overall accuracy of > 95 % for spatial extent and > 72 % for irrigation timing within a 7-day tolerance. The results further indicate that the pixel-based sensitivity of the method effectively characterizes intrafield irrigation variability, revealing the fine-scale dynamics of water distribution. Furthermore, the threshold-free nature of the algorithm enhances its potential for transferability to other dryland regions. This study provides a reliable, high-resolution solution for supporting precision water management and salinity control strategies in water-limited environments.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"326 ","pages":"Article 110154"},"PeriodicalIF":6.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778243","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}
Intensifying extreme precipitation (EP) due to global climate change poses severe challenges to controlling watershed non-point source pollution. However, the performance of best management practices (BMPs) under EP and its associated timescale-dependent mechanisms are poorly understood. We develop a multiscale, long-term assessment framework to quantify BMP effectiveness and resilience under EP at annual, seasonal, and monthly scales, and apply it to a tributary watershed of the Pearl River, China’s third-largest river, using 1970–2022 data. Our findings reveal that EP significantly amplifies performance variability, with the mean coefficient of variation (CV) for total nitrogen (TN) reduction surging by 110 %. Application of the resilience index (RI) indicates substantial divergence in BMP resilience: vegetated filter strips (VFS) prove highly resilient (RI < 0.15), with TN removal enhanced under EP (increasing from 30.83 % to 35.95 %), whereas fertilizer reduction is vulnerable, with its TN reduction declining from 8.18 % to 5.45 %. Crucially, BMP responses are strongly scale-dependent. For instance, conservation tillage shows improved annual TN removal but degrades performance during the rainy season, demonstrating that annual-level assessments can mask critical seasonal vulnerabilities. This study underscores the necessity of multiscale analysis to develop climate-adaptive watershed management. It provides decision-relevant evidence for designing resilient BMP portfolios, such as prioritizing stable measures like VFS, to ensure long-term pollution control in an era of increasing climate extremes.
{"title":"Timescale-dependent impacts of extreme precipitation on watershed nutrient removal: Insights from five decades (1970–2022)","authors":"Ying Xing, Yuxian Li, Jiahui Zhu, Shuai Wang, Feifei Dong","doi":"10.1016/j.agwat.2026.110226","DOIUrl":"10.1016/j.agwat.2026.110226","url":null,"abstract":"<div><div>Intensifying extreme precipitation (EP) due to global climate change poses severe challenges to controlling watershed non-point source pollution. However, the performance of best management practices (BMPs) under EP and its associated timescale-dependent mechanisms are poorly understood. We develop a multiscale, long-term assessment framework to quantify BMP effectiveness and resilience under EP at annual, seasonal, and monthly scales, and apply it to a tributary watershed of the Pearl River, China’s third-largest river, using 1970–2022 data. Our findings reveal that EP significantly amplifies performance variability, with the mean coefficient of variation (<em>CV</em>) for total nitrogen (TN) reduction surging by 110 %. Application of the resilience index (<em>RI</em>) indicates substantial divergence in BMP resilience: vegetated filter strips (VFS) prove highly resilient (<em>RI</em> < 0.15), with TN removal enhanced under EP (increasing from 30.83 % to 35.95 %), whereas fertilizer reduction is vulnerable, with its TN reduction declining from 8.18 % to 5.45 %. Crucially, BMP responses are strongly scale-dependent. For instance, conservation tillage shows improved annual TN removal but degrades performance during the rainy season, demonstrating that annual-level assessments can mask critical seasonal vulnerabilities. This study underscores the necessity of multiscale analysis to develop climate-adaptive watershed management. It provides decision-relevant evidence for designing resilient BMP portfolios, such as prioritizing stable measures like VFS, to ensure long-term pollution control in an era of increasing climate extremes.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"326 ","pages":"Article 110226"},"PeriodicalIF":6.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146209954","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}
Pub Date : 2026-04-01Epub Date: 2026-02-13DOI: 10.1016/j.agwat.2026.110217
Shaobo Wang, Yingji Lian, Hongwei Pan, Muhammad Zain, Jiankun Ge, Hongjun Lei
Aeration irrigation (AI) is a promising method for improving crop quality and efficiency. Based on 156 peer-reviewed articles encompassing 1294 data pairs, this meta-analysis quantified the effects of AI on yield, yield components, and fruit quality. The results showed that AI significantly increased crop yield by 18.6 % (95 %CI: 17.2–19.7 %) compared with non-aerated irrigation (NAI). The most substantial benefits were observed in warm temperate climates, clayey and alkaline soils, and in greenhouse and fruit production systems. Yield gains were further enhanced under low nitrogen input and combined organic-inorganic fertilization. This technique also significantly improved grain yield components, increasing productive panicles, thousand kernel weight, grains per panicle, and seed setting rate by 7.2 %, 2.9 %, 4.3 %, and 1.0 %, respectively. For fruits and vegetables, AI enhanced the contents of vitamin C, soluble protein, soluble sugar, and lycopene by 11.6 %, 14.2 %, 11.3 %, and 31.1 %, respectively. It also improved the sugar-acid ratio by 13.1 % and reduced nitrate content by 10.6 %. Random Forest analysis identified soil organic matter, mean annual temperature, and irrigation amount as the dominant factors influencing the effectiveness of AI. Targeted application of AI under specific environmental and soil conditions can support the sustainable intensification of irrigated agriculture by improving both yield and fruit quality.
{"title":"The effects of aerated irrigation on crop yield and fruit quality: A meta-analysis","authors":"Shaobo Wang, Yingji Lian, Hongwei Pan, Muhammad Zain, Jiankun Ge, Hongjun Lei","doi":"10.1016/j.agwat.2026.110217","DOIUrl":"10.1016/j.agwat.2026.110217","url":null,"abstract":"<div><div>Aeration irrigation (AI) is a promising method for improving crop quality and efficiency. Based on 156 peer-reviewed articles encompassing 1294 data pairs, this meta-analysis quantified the effects of AI on yield, yield components, and fruit quality. The results showed that AI significantly increased crop yield by 18.6 % (95 %CI: 17.2–19.7 %) compared with non-aerated irrigation (NAI). The most substantial benefits were observed in warm temperate climates, clayey and alkaline soils, and in greenhouse and fruit production systems. Yield gains were further enhanced under low nitrogen input and combined organic-inorganic fertilization. This technique also significantly improved grain yield components, increasing productive panicles, thousand kernel weight, grains per panicle, and seed setting rate by 7.2 %, 2.9 %, 4.3 %, and 1.0 %, respectively. For fruits and vegetables, AI enhanced the contents of vitamin C, soluble protein, soluble sugar, and lycopene by 11.6 %, 14.2 %, 11.3 %, and 31.1 %, respectively. It also improved the sugar-acid ratio by 13.1 % and reduced nitrate content by 10.6 %. Random Forest analysis identified soil organic matter, mean annual temperature, and irrigation amount as the dominant factors influencing the effectiveness of AI. Targeted application of AI under specific environmental and soil conditions can support the sustainable intensification of irrigated agriculture by improving both yield and fruit quality.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"326 ","pages":"Article 110217"},"PeriodicalIF":6.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172779","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}
Pub Date : 2026-04-01Epub Date: 2026-02-19DOI: 10.1016/j.agwat.2026.110239
Zahra Jahandideh , Shahrokh Zand-Parsa , Ali Reza Sepaskhah , Paula Paredes , Luis S. Pereira
Developed by FAO, AquaCrop is one of the most important generic crop growth and yield simulation models. The current research investigated the hypothesis that the AquaCrop model's normalized maize water productivity (WP*) remains constant or not under different water stress conditions and it was found that WP* decreases with increasing water stress. To calibrate and evaluate the model, diverse experimental field data sets on maize were used, which included various water stress levels and different irrigation methods (sprinkler, furrow and basin). The performance evaluation of results showed that the original AquaCrop-OS model is accurate in estimating biomass and grain yield for the treatments with mild or no water stress. Conversely, the model showed inaccuracy in estimating biomass and grain yield under severe water stress conditions. The normalized root mean square error (NRMSE) values for simulating both maize biomass and grain yield for the entire datasets and all treatments were 20.0 % and 19.5 %, respectively, when validating the original model. When, in the following, the model was modified using its open-source MATLAB version and rewritten with a purposefully modified WP*, i.e. the AquaCrop-WM version, led to NRMSE not exceeding 8.5 % and 11 % for biomass and grain yield, respectively. The new AquaCrop-WM is user-friendly and consists of an independent executable version accessible to all users. Thus, it is proposed its adoption when severe water stress conditions are to be assessed, namely to assess and select appropriate irrigation scheduling practices.
{"title":"Using the AquaCrop model for maize in arid water deficient conditions: A new version for improved accuracy when adopting a varied normalized water productivity","authors":"Zahra Jahandideh , Shahrokh Zand-Parsa , Ali Reza Sepaskhah , Paula Paredes , Luis S. Pereira","doi":"10.1016/j.agwat.2026.110239","DOIUrl":"10.1016/j.agwat.2026.110239","url":null,"abstract":"<div><div>Developed by FAO, AquaCrop is one of the most important generic crop growth and yield simulation models. The current research investigated the hypothesis that the AquaCrop model's normalized maize water productivity (<em>WP*</em>) remains constant or not under different water stress conditions and it was found that <em>WP*</em> decreases with increasing water stress. To calibrate and evaluate the model, diverse experimental field data sets on maize were used, which included various water stress levels and different irrigation methods (sprinkler, furrow and basin). The performance evaluation of results showed that the original AquaCrop-OS model is accurate in estimating biomass and grain yield for the treatments with mild or no water stress. Conversely, the model showed inaccuracy in estimating biomass and grain yield under severe water stress conditions. The normalized root mean square error (<em>NRMSE</em>) values for simulating both maize biomass and grain yield for the entire datasets and all treatments were 20.0 % and 19.5 %, respectively, when validating the original model. When, in the following, the model was modified using its open-source MATLAB version and rewritten with a purposefully modified <em>WP*</em>, i.e. the AquaCrop-WM version, led to <em>NRMSE</em> not exceeding 8.5 % and 11 % for biomass and grain yield, respectively. The new AquaCrop-WM is user-friendly and consists of an independent executable version accessible to all users. Thus, it is proposed its adoption when severe water stress conditions are to be assessed, namely to assess and select appropriate irrigation scheduling practices.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"326 ","pages":"Article 110239"},"PeriodicalIF":6.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146777223","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}
Pub Date : 2026-04-01Epub Date: 2026-02-11DOI: 10.1016/j.agwat.2026.110214
Rozita Soltani Tehrani , Xiaomei Yang , Jos van Dam
Soil contamination with microplastics is an emerging challenge that may affect soil-water interactions, infiltration processes, and ultimately agricultural water management. However, the mechanisms controlling microplastic transport under transient and unsaturated flow conditions remain insufficiently understood, particularly under repeated rainfall or irrigation events. To better quantify how water flow conditions control microplastic mobility in soils, we used data from a controlled column experiment including two agricultural soil textures (sandy loam and loamy sand), three microplastic types: LDPE (low-density polyethylene), PBAT (butylene adipate terephthalate), and a starch-based polymer, and two rainfall intensities: 22 and 35 mm/h. Rainfall was applied during two successive imbibition–drainage cycles to mimic realistic transient flow conditions in agricultural soils. Microplastics were quantified in effluent and soil layers to construct breakthrough curves and retention profiles. HYDRUS-1D, a numerical model for simulating water flow and solute/particle transport in soil, was employed to simulate transport under transient, unsaturated flow conditions. The simulated water contents showed strong agreement with sensor measurements, confirming a reliable representation of water flow in both soil types. Simulations reproduced observed retention profiles accurately when depth-dependent deposition was included. Results show that soil texture, rainfall intensity, and polymer type strongly influence microplastic leaching and retention, affecting downward movement with percolating water. Loamy sand exhibited higher breakthrough concentrations than sandy loam, indicating enhanced transport in coarser-textured soil, while LDPE showed the highest mobility among the tested polymers due to its lower density and surface characteristics. These findings offer insight into how irrigation or rainfall regimes may influence microplastic transport in agricultural soils, informing risk assessment and water management strategies.
{"title":"Simulating microplastic transport in unsaturated soil using HYDRUS-1D","authors":"Rozita Soltani Tehrani , Xiaomei Yang , Jos van Dam","doi":"10.1016/j.agwat.2026.110214","DOIUrl":"10.1016/j.agwat.2026.110214","url":null,"abstract":"<div><div>Soil contamination with microplastics is an emerging challenge that may affect soil-water interactions, infiltration processes, and ultimately agricultural water management. However, the mechanisms controlling microplastic transport under transient and unsaturated flow conditions remain insufficiently understood, particularly under repeated rainfall or irrigation events. To better quantify how water flow conditions control microplastic mobility in soils, we used data from a controlled column experiment including two agricultural soil textures (sandy loam and loamy sand), three microplastic types: LDPE (low-density polyethylene), PBAT (butylene adipate terephthalate), and a starch-based polymer, and two rainfall intensities: 22 and 35 mm/h. Rainfall was applied during two successive imbibition–drainage cycles to mimic realistic transient flow conditions in agricultural soils. Microplastics were quantified in effluent and soil layers to construct breakthrough curves and retention profiles. HYDRUS-1D, a numerical model for simulating water flow and solute/particle transport in soil, was employed to simulate transport under transient, unsaturated flow conditions. The simulated water contents showed strong agreement with sensor measurements, confirming a reliable representation of water flow in both soil types. Simulations reproduced observed retention profiles accurately when depth-dependent deposition was included. Results show that soil texture, rainfall intensity, and polymer type strongly influence microplastic leaching and retention, affecting downward movement with percolating water. Loamy sand exhibited higher breakthrough concentrations than sandy loam, indicating enhanced transport in coarser-textured soil, while LDPE showed the highest mobility among the tested polymers due to its lower density and surface characteristics. These findings offer insight into how irrigation or rainfall regimes may influence microplastic transport in agricultural soils, informing risk assessment and water management strategies.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"326 ","pages":"Article 110214"},"PeriodicalIF":6.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146161068","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}
Pub Date : 2026-03-31Epub Date: 2026-01-22DOI: 10.1016/j.agwat.2026.110179
Yanan Chen , Dongqiang Chen , Jiaoyue Wang , Li Yao , Jianguang Wen , Xuguang Tang
Multiple satellite remote sensing-based gross primary productivity (GPP) and evapotranspiration (ET) products have been developed to simulate the spatiotemporal patterns of carbon and water cycles in terrestrial ecosystems. However, the performance of these products in monitoring cropland water use efficiency (WUE) has rarely been evaluated. In this study, a total of 140 site-years of flux data across the maize, soybean, rice and winter wheat ecosystems were used as a benchmark to assess the performance of such products over 8-day, monthly and annual time scales, including the Breathing Earth System Simulator (BESS), Global Land Surface Satellite (GLASS), Moderate Resolution Imaging Spectroradiometer (MODIS), and the Penman-Monteith-Leuning V2 (PML), respectively. Our site-level evaluation demonstrated that the performance of such satellite-based products for monitoring WUE varied considerably across diverse agroecosystems. BESS WUE outperformed the other three products at all time scales when mixing all crop types together. However, the optimal product varied for specific crop across 8-day, monthly and yearly scales. At the 8-day scale, BESS performed best in simulating WUE for maize, while PML was superior for the other three crops. It indicated that the models that closely coupled carbon and water cycles tend to yield more robust WUE estimates. At the monthly scale, the BESS again provided the most accurate WUE for maize and soybean, whereas the PML product performed best for rice and winter wheat. Nevertheless, all products showed limitations, particularly in capturing the interannual variations of cropland WUE. On an annual scale, BESS exhibited the best accuracy (2.50 vs 2.51 g C kg−1 H2O), followed by PML (2.49 g C kg−1 H2O), MODIS (2.03 g C kg−1 H2O) and GLASS (1.53 g C kg−1 H2O) models. Our evaluation underscores the potential of integrating carbon and water cycles into models to enhance WUE performance, thereby providing a direction for future improvements in model structures.
开发了基于卫星遥感的多种总初级生产力（GPP）和蒸散发（ET）产品，用于模拟陆地生态系统碳和水循环的时空格局。然而，这些产品在监测农田水分利用效率（WUE）方面的性能很少得到评价。本研究以140个站点年的玉米、大豆、水稻和冬小麦生态系统通量数据为基准，分别使用呼吸地球系统模拟器（BESS）、全球陆地表面卫星（GLASS）、中分辨率成像光谱仪（MODIS）和Penman-Monteith-Leuning V2 (PML)，在8天、月和年时间尺度上评估这些产品的性能。我们的站点级评估表明，用于监测WUE的这种基于卫星的产品的性能在不同的农业生态系统中差异很大。当混合所有作物类型在一起时，BESS WUE在所有时间尺度上都优于其他三种产品。然而，特定作物的最佳产量在8天、月和年尺度上有所不同。在8 d尺度上，BESS对玉米水分利用效率的模拟效果最好，而PML对其他3种作物的模拟效果最好。它表明，碳和水循环紧密耦合的模式往往产生更可靠的用水效率估计。在月尺度上，BESS对玉米和大豆的水分利用效率最准确，而PML对水稻和冬小麦的水分利用效率最好。然而，所有产品都存在局限性，特别是在捕捉农田水分利用效率的年际变化方面。在年尺度上，BESS表现出最好的精度（2.50 vs 2.51 g C kg−1 H2O），其次是PML(2.49 g C kg−1 H2O)， MODIS（2.03 g C kg−1 H2O）和GLASS（1.53 g C kg−1 H2O）模型。我们的评估强调了将碳和水循环整合到模型中以提高WUE性能的潜力，从而为未来模型结构的改进提供了方向。
{"title":"Divergent performance of multiple satellite-based products for monitoring water use efficiency across diverse agroecosystems","authors":"Yanan Chen , Dongqiang Chen , Jiaoyue Wang , Li Yao , Jianguang Wen , Xuguang Tang","doi":"10.1016/j.agwat.2026.110179","DOIUrl":"10.1016/j.agwat.2026.110179","url":null,"abstract":"<div><div>Multiple satellite remote sensing-based gross primary productivity (GPP) and evapotranspiration (ET) products have been developed to simulate the spatiotemporal patterns of carbon and water cycles in terrestrial ecosystems. However, the performance of these products in monitoring cropland water use efficiency (WUE) has rarely been evaluated. In this study, a total of 140 site-years of flux data across the maize, soybean, rice and winter wheat ecosystems were used as a benchmark to assess the performance of such products over 8-day, monthly and annual time scales, including the Breathing Earth System Simulator (BESS), Global Land Surface Satellite (GLASS), Moderate Resolution Imaging Spectroradiometer (MODIS), and the Penman-Monteith-Leuning V2 (PML), respectively. Our site-level evaluation demonstrated that the performance of such satellite-based products for monitoring WUE varied considerably across diverse agroecosystems. BESS WUE outperformed the other three products at all time scales when mixing all crop types together. However, the optimal product varied for specific crop across 8-day, monthly and yearly scales. At the 8-day scale, BESS performed best in simulating WUE for maize, while PML was superior for the other three crops. It indicated that the models that closely coupled carbon and water cycles tend to yield more robust WUE estimates. At the monthly scale, the BESS again provided the most accurate WUE for maize and soybean, whereas the PML product performed best for rice and winter wheat. Nevertheless, all products showed limitations, particularly in capturing the interannual variations of cropland WUE. On an annual scale, BESS exhibited the best accuracy (2.50 <em>vs</em> 2.51 g C kg<sup>−1</sup> H<sub>2</sub>O), followed by PML (2.49 g C kg<sup>−1</sup> H<sub>2</sub>O), MODIS (2.03 g C kg<sup>−1</sup> H<sub>2</sub>O) and GLASS (1.53 g C kg<sup>−1</sup> H<sub>2</sub>O) models. Our evaluation underscores the potential of integrating carbon and water cycles into models to enhance WUE performance, thereby providing a direction for future improvements in model structures.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"325 ","pages":"Article 110179"},"PeriodicalIF":6.5,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025043","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}
Pub Date : 2026-03-31Epub Date: 2026-01-23DOI: 10.1016/j.agwat.2026.110128
Zhengguang Xu , Bo Jiang , Xiao Guo , Zhiyong Wu , Siqi Fan
Agricultural drought, typically triggered by meteorological drought, poses a significant threat to crop production and regional water resources. Understanding the propagation from meteorological to agricultural drought is therefore crucial for improving drought early warning and agricultural water management. In this study, we investigated event-scale drought propagation in the Yellow River Basin using the Standardized Precipitation Evapotranspiration Index and Standardized Soil Moisture Index to characterize meteorological and agricultural droughts, respectively. Variations in drought characteristics (duration and intensity) across the entire drought event and during its development, persistence, and recovery stages were analyzed based on matched drought events. We further identified the dominant drivers and constructed predictive models of propagation time using the eXtreme Gradient Boosting (XGBoost) algorithm. The results indicate that agricultural droughts occur less frequently and with lower intensity but persist longer than meteorological droughts. Approximately 49.5 % of meteorological droughts propagate into agricultural droughts, with the one-to-one propagation type being dominant. Lengthening of duration and attenuation of intensity were observed during drought propagation across different drought stages. Initial soil moisture conditions emerged as the dominant driver of event-scale propagation time, followed by the timing of meteorological drought occurrence and its development duration. Based on the identified dominant influencing factors, a propagation time prediction model was constructed for each subregion using the XGBoost algorithm, enabling reliable prediction of propagation time. These findings underscore the critical role of initial soil moisture in regulating drought propagation, offering valuable insights for the development of agricultural drought early warning systems and the optimization of irrigation scheduling.
{"title":"Initial soil moisture conditions dominate variation in event-scale propagation time from meteorological to agricultural drought","authors":"Zhengguang Xu , Bo Jiang , Xiao Guo , Zhiyong Wu , Siqi Fan","doi":"10.1016/j.agwat.2026.110128","DOIUrl":"10.1016/j.agwat.2026.110128","url":null,"abstract":"<div><div>Agricultural drought, typically triggered by meteorological drought, poses a significant threat to crop production and regional water resources. Understanding the propagation from meteorological to agricultural drought is therefore crucial for improving drought early warning and agricultural water management. In this study, we investigated event-scale drought propagation in the Yellow River Basin using the Standardized Precipitation Evapotranspiration Index and Standardized Soil Moisture Index to characterize meteorological and agricultural droughts, respectively. Variations in drought characteristics (duration and intensity) across the entire drought event and during its development, persistence, and recovery stages were analyzed based on matched drought events. We further identified the dominant drivers and constructed predictive models of propagation time using the eXtreme Gradient Boosting (XGBoost) algorithm. The results indicate that agricultural droughts occur less frequently and with lower intensity but persist longer than meteorological droughts. Approximately 49.5 % of meteorological droughts propagate into agricultural droughts, with the one-to-one propagation type being dominant. Lengthening of duration and attenuation of intensity were observed during drought propagation across different drought stages. Initial soil moisture conditions emerged as the dominant driver of event-scale propagation time, followed by the timing of meteorological drought occurrence and its development duration. Based on the identified dominant influencing factors, a propagation time prediction model was constructed for each subregion using the XGBoost algorithm, enabling reliable prediction of propagation time. These findings underscore the critical role of initial soil moisture in regulating drought propagation, offering valuable insights for the development of agricultural drought early warning systems and the optimization of irrigation scheduling.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"325 ","pages":"Article 110128"},"PeriodicalIF":6.5,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025111","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}
Rice cultivation in the Guadalquivir River marshes of southern Spain is increasingly constrained by irrigation water salinity, exacerbated by drought and seawater intrusion. This study assessed the agronomic, physiological, and spectral responses of indica and japonica cultivars under commercial farming conditions across a natural salinity gradient (mean electrical conductivity of irrigation water ranging from 3.1 to 6.9 dS m⁻¹). Field measurements included yield, growth traits, and leaf ion concentrations, complemented with Sentinel-2 vegetation indices and integrated using Generalized Additive Models (GAMs). Rice yield declined steeply with salinity, with up to 70 % losses between 3 and 7 dS m⁻¹ . Rice grown in medium-salinity fields maintained Na/K ratios comparable to low-salinity fields, suggesting that compensatory K⁺ uptake mitigated yield penalties. By contrast, high salinity led to marked ionic imbalance, particularly in japonica cultivars, which consistently exhibited higher Na/K ratios than indica. Spectral data revealed that broad-band greenness indices (NDVI, GNDVI, EVI, SAVI, NDRE) effectively captured early osmotic effects (<60 DAS), while MCARI uniquely detected late-stage ionic stress during reproductive phases. GAM analysis confirmed two phenological windows of higher sensitivity to salinity—vegetative establishment and reproductive development—while demonstrating the predictive utility of combined physiological and spectral indicators (LOOCV R² = 0.867). These findings underscore the need for growth phase-specific management and the potential of integrating physiological and remote sensing data to support adaptation strategies in Mediterranean rice systems.
{"title":"Integrating remote sensing and ion balance to predict yield losses under saline irrigation in rice","authors":"Gregorio Egea , Annkathrin Rosenbaum , Mathias Becker , José Rodolfo Quintana-Molina , Shyam Pariyar","doi":"10.1016/j.agwat.2026.110164","DOIUrl":"10.1016/j.agwat.2026.110164","url":null,"abstract":"<div><div>Rice cultivation in the Guadalquivir River marshes of southern Spain is increasingly constrained by irrigation water salinity, exacerbated by drought and seawater intrusion. This study assessed the agronomic, physiological, and spectral responses of <em>indica</em> and <em>japonica</em> cultivars under commercial farming conditions across a natural salinity gradient (mean electrical conductivity of irrigation water ranging from 3.1 to 6.9 dS m⁻¹). Field measurements included yield, growth traits, and leaf ion concentrations, complemented with Sentinel-2 vegetation indices and integrated using Generalized Additive Models (GAMs). Rice yield declined steeply with salinity, with up to 70 % losses between 3 and 7 dS m⁻¹ . Rice grown in medium-salinity fields maintained Na/K ratios comparable to low-salinity fields, suggesting that compensatory K⁺ uptake mitigated yield penalties. By contrast, high salinity led to marked ionic imbalance, particularly in <em>japonica</em> cultivars, which consistently exhibited higher Na/K ratios than <em>indica</em>. Spectral data revealed that broad-band greenness indices (NDVI, GNDVI, EVI, SAVI, NDRE) effectively captured early osmotic effects (<60 DAS), while MCARI uniquely detected late-stage ionic stress during reproductive phases. GAM analysis confirmed two phenological windows of higher sensitivity to salinity—vegetative establishment and reproductive development—while demonstrating the predictive utility of combined physiological and spectral indicators (LOOCV R² = 0.867). These findings underscore the need for growth phase-specific management and the potential of integrating physiological and remote sensing data to support adaptation strategies in Mediterranean rice systems.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"325 ","pages":"Article 110164"},"PeriodicalIF":6.5,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014579","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}
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