Agricultural Water Management最新文献

{"title":"Automated irrigation of apple trees based on dendrometer sensors","authors":"Thainná Waldburger ,&nbsp;Thomas Anken ,&nbsp;Marianne Cockburn ,&nbsp;Achim Walter ,&nbsp;Matthias Hatt ,&nbsp;Camilo Chiang ,&nbsp;Hassan-Roland Nasser","doi":"10.1016/j.agwat.2025.109398","DOIUrl":"10.1016/j.agwat.2025.109398","url":null,"abstract":"<div><div>This study evaluates the efficiency of an automated irrigation system using dendrometer sensors in apple orchards and compares it to a standard grower commercial irrigation approach based on soil moisture sensors. An algorithm was developed to balance daily stem shrinkage (water loss) and expansion (water uptake), aiming for a stable dendrometer signal. The dendrometer-based irrigation system (DENDRO) significantly reduced water use—by 38 % in 2022 and more than 45 % in 2023—while maintaining yields similar to those of the soil moisture-based system (SOIL). The DENDRO responded quite well to plant water stress, as indicated by stem water potential (WP). Although the tested algorithm proved to be efficient, the results also indicated the potential for optimization. One example is shortening the averaging period used to calculate stem recovery (R_Δ). The SOIL method was effective in fruit production but proved to be less efficient in reflecting water needs. Alternative approaches, including FAO-based irrigation (FAO) and a linear regression model combining dendrometer parameters and climatic data (MODEL), were also assessed. The FAO method tended to overestimate water requirements, while the MODEL method showed promise for dynamic irrigation adjustment based on climatic conditions and dendrometer values. Overall, the findings highlight the advantage of integrating plant-based sensors, such as dendrometers, for more precise irrigation management in orchard systems, leading to more sustainable water use without compromising crop yield.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"311 ","pages":"Article 109398"},"PeriodicalIF":5.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized fertilizer management strategy based on ridge–furrow planting pattern enhances dryland wheat yield and water utilization on the Loess Plateau","authors":"Jun Xing ,&nbsp;Guojun Liu ,&nbsp;Wenbo Zhai ,&nbsp;Tong Gou ,&nbsp;Zuoyan Zhou ,&nbsp;Ai Hu ,&nbsp;Kai Zhang ,&nbsp;Dong Bai ,&nbsp;Aixia Ren ,&nbsp;Zhiqiang Gao ,&nbsp;Min Sun","doi":"10.1016/j.agwat.2025.109391","DOIUrl":"10.1016/j.agwat.2025.109391","url":null,"abstract":"<div><div>Rain-fed agroecosystems require integrated strategies to synchronize water and nitrogen use for sustainable production. To investigate the mechanisms by which ridge–furrow planting (RP), coupled with optimal N rate (90, 135, 180 kg N ha⁻¹), enhances soil water utilization, yield formation, and water productivity (WP) in dryland wheat systems, a three-year split-plot field experiment (2018–2021) was designed to compare RP against flat planting (FP) under semi-arid rainfall variability. Results showed that RP improved rainwater infiltration into deeper soil layers, increasing soil water storage by 4.3–8.0 % at jointing and elevating soil water use rate by prioritizing deep-layer extraction during critical growth stages. RP combined with optimized N rates achieved the highest grain yield (25.9 %, 15.3 %, and 10.8 % increases in dry, normal, and wet years) and WP by harmonizing water-N synergies. Enhanced post-anthesis water extraction from 160–200 cm layers under RP significantly boosted dry matter accumulation. Correlation analyses revealed that spike number in dry years correlated with pre-anthesis water use in the 80–160 cm layer (P &lt; 0.01), kernels per spike in normal years aligned with balanced pre-/post-anthesis allocation across 0–200 cm (P &lt; 0.05), and 1000-grain weight in wet years depended on post-anthesis extraction from 160–200 cm (P &lt; 0.01), synergistically driving yield gains. RP integrated with adaptive nitrogen thresholds (90–180 kg ha⁻¹) is recommended to stabilize yields and maximize WP in rain-fed systems. This strategy provides a scalable pathway to strengthen climate resilience and sustainable resource utilization in water-limited agroecosystems.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"311 ","pages":"Article 109391"},"PeriodicalIF":5.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing crop growth models across the contiguous USA with a focus on dry and warm spells","authors":"Sneha Chevuru ,&nbsp;Gambhir Lamsal ,&nbsp;L.P.H. (Rens) van Beek ,&nbsp;Michelle T.H. van Vliet ,&nbsp;Landon Marston ,&nbsp;Marc F.P. Bierkens","doi":"10.1016/j.agwat.2025.109403","DOIUrl":"10.1016/j.agwat.2025.109403","url":null,"abstract":"<div><div>Efficient water use and sustainable crop production are vital for ensuring both food and water security amidst the growing global population and changing climate. With expected increases in hydroclimatic extremes under climate change, irrigated agriculture is projected to rise, further interlinking food and water security. Hence, the need for future crop yield projections has become more urgent, including knowledge about the accuracy of crop growth modeling in terms of crop yield and water consumption. To this end, our study evaluates two crop growth models—AquaCrop-OS and PCR-GLOBWB 2-WOFOST. The coupled hydrological-crop growth model PCR-GLOBWB 2-WOFOST was applied using both its original settings and harmonized with AquaCrop-OS input, resulting in two model experiments that were compared to the AquaCrop-OS results, allowing analyses of differences in both model structure and parametrizations. AquaCrop-OS and PCR-GLOBWB 2-WOFOST (with original and harmonized input) were used to simulate the yield of irrigated maize, soybean, winter wheat and spring wheat across the contiguous United States (CONUS) from 2001 to 2019. Model outputs were compared in terms of crop water consumption, biomass production, and crop yield and the model sensitivity under dry and warm conditions was evaluated.</div><div>For all considered crops, PCR-GLOBWB 2-WOFOST (original) simulated yields align closely with mean reported yields, with the harmonized version showing some underestimates for soybeans and winter wheat (∼23 %). AquaCrop-OS generally overestimates crop yields (up to ∼34 %). Our results also highlight significant differences in spatial patterns of crop water consumption, biomass, and crop yield between AquaCrop-OS and PCR-GLOBWB 2-WOFOST (original and harmonized) models. Specifically, differences between the AquaCrop-OS and PCR-GLOBWB 2-WOFOST (harmonized) models are attributed to variations in how biomass is converted to yield within each model. Also, AquaCrop-OS is found to be much less sensitive to higher air temperatures than the PCR-GLOBWB 2-WOFOST model. Finally, the differences between PCR-GLOBWB 2-WOFOST original and its harmonized settings emphasize the importance of using local- and up-to-date information on crop-specific parametrization in crop growth modeling. These findings underscore the importance of model selection and parameterization in accurately simulating crop yield and crop water consumption under climate extremes, which is essential for improving agricultural practices under climate change.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"311 ","pages":"Article 109403"},"PeriodicalIF":5.9,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Benchmarking farm-level cotton water productivity using on-farm irrigation measurements and remotely sensed yields","authors":"Zitian Gao ,&nbsp;Danlu Guo ,&nbsp;Dongryeol Ryu ,&nbsp;Andrew W. Western","doi":"10.1016/j.agwat.2025.109384","DOIUrl":"10.1016/j.agwat.2025.109384","url":null,"abstract":"<div><div>Benchmarking farm-level irrigation water productivity (WP_I) and water productivity (WP) can assist in understanding the irrigation effectiveness of individual farms and in developing strategies to improve their irrigation management. This study introduces a method to integrate on-farm irrigation measurements, remotely sensed yields and publicly available rainfall data for multi-year farm-level WP_I and WP benchmarking. The method was tested over cotton farms located in south-eastern Australia during the 2011–19 cropping seasons. We trained remote sensing (RS)-based machine learning (ML) models – Random Forest Regression (RFR), Gradient Boosting Regression (GBR) and Support Vector Regression (SVR) – to predict yields for over 400 cotton fields with ground-truth yield data. Predicted cotton yields from the best-performing model were then combined with irrigation and rainfall data for WP_I and WP benchmarking. We also examined: 1) if the yield model is transferable to unseen years and 2) if sub-field-scale yield data from a harvester over a small number of fields are effective for training ML models, in case field-scale yield data are insufficient. The results showed that field-scale cotton yield could be predicted with the best accuracy using the GBR model (R² = 0.7, RMSE = 235 kg/ha, mean absolute error = 176 kg/ha and Pearson correlation = 0.84) when applied to the period of training. The average WP_I and WP varied between 0.18–0.36 kg/m³ and 0.16–0.23 kg/m³, respectively. However, the RS-based yield model showed reduced performance outside of the training period. In addition, when field-scale yield samples were used in combination with many sub-field-scale samples for calibration, the model performance was biased to favour the sub-field-scale samples. Our findings demonstrate the ability of RS and ML models to provide yields for benchmarking analysis but highlight the potential risk of reduced accuracy of yield prediction in future years.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"311 ","pages":"Article 109384"},"PeriodicalIF":5.9,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remote sensing characterisation of cropping systems and their water use to assess irrigation management from field to canal command scale","authors":"Jorge L. Peña-Arancibia ,&nbsp;Mobin-ud Din Ahmad ,&nbsp;Yingying Yu","doi":"10.1016/j.agwat.2025.109374","DOIUrl":"10.1016/j.agwat.2025.109374","url":null,"abstract":"<div><div>Remote sensing (RS) plays a crucial role in water resources management. Irrigated areas have undergone substantial changes globally. This research utilises RS to characterise irrigation from 2010 to 2020 within five canal commands in the Indus Basin Irrigated System (IBIS), the world's largest contiguous irrigation system (∼16 million hectares). Cropping systems, water use and supply assessments are conducted primarily through estimations of 30 m actual evapotranspiration (ET_a) and seasonal land cover classification maps – for both the wet summer 'Kharif' and dry winter 'Rabi' seasons. ET_a estimates are required to match the 10-day period in which supply is adjusted to balance shortages in the canal commands. The multiannual 10-day frequency is achieved through blending of 'low spatial resolution-high temporal frequency' MODIS images (500 m and daily) and 'high spatial resolution-low temporal frequency' Landsat images (30 m and every 16 days). ET_a estimates show reasonable spatiotemporal agreement (R²&gt;0.7) when compared against locally calibrated ET_a estimates. Seasonal crop maps generated with a Random Forest classification show reasonable accuracy (R²&gt;0.9) when compared against agricultural survey statistics. The crop maps and associated ET_a provide valuable insights into cropping and water use dynamics. While Kharif ET_a and total cropped area exhibit relatively low year-to-year variability, large shifts from cotton (49% decrease) to rice (125% increase), other crops, and aquaculture are observed in some areas. During Rabi, ET_a and total cropped area variations are less pronounced compared to Kharif, with winter cereals dominating the landscape. ET_a generally exceeds water supply in the canal commands, with the disparity being higher during Rabi (36% on average), indicating groundwater augmentation as a significant contributor to groundwater depletion. The integration of ET_a crop maps and canal water deliveries offers novel and essential knowledge for agriculture and water management policymaking in the IBIS and similar regions, from field to canal command scales.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"311 ","pages":"Article 109374"},"PeriodicalIF":5.9,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated soil moisture fusion for enhanced agricultural drought monitoring in China","authors":"Aifeng Lv ,&nbsp;Xianglei Yang ,&nbsp;Wenxiang Zhang ,&nbsp;Yan Han","doi":"10.1016/j.agwat.2025.109401","DOIUrl":"10.1016/j.agwat.2025.109401","url":null,"abstract":"<div><div>Frequent drought events have a profound impact on the natural environment and socio-economics. Therefore, accurate drought monitoring is essential to prevent and minimize drought losses. In this study, we developed an improved soil moisture dataset (Merged-SM) by using Triple Collocation (TC) and Linear Weight Fusion (LWF) methods to fuse soil moisture data from ERA5-Land, ESA CCI, and MERRA-2. The dataset was validated against in-situ data and applied to investigate the spatiotemporal dynamics of agricultural droughts across China. Results show that (1) Merged-SM exhibits superior accuracy and spatial coverage in comparison to individual datasets, achieving a higher correlation with in-situ data (R = 0.573) and a reduced unbiased root mean square error (ubRMSE = 0.027–0.047). (2) The Merged-SM accurately identified the onset, duration, and spatial extent of agricultural drought events, showing a significant negative correlation with agricultural disaster area (R = −0.418, P = 0.006). (3) Temporally, agricultural droughts across most regions of China displayed stable or alleviating trends, with particularly notable relief observed in Region VI. Spatially, 58.25 % of China's territory experienced a decrease in drought intensity, especially in the Qinghai-Tibetan Plateau, North China Plain, and southern regions, while certain areas in northern and southwestern China recorded an intensification of drought conditions. (4) The correlation between meteorological drought and agricultural drought was found to be stronger during the summer (R = 0.68) and autumn (R = 0.63) compared to winter and spring. The propagation time from meteorological drought to agricultural drought varied seasonally, being shortest in summer (2.54 months) and longest in winter (6.54 months). These findings highlight the potential of the Merged-SM dataset for improving agricultural drought monitoring and provide critical insights into the spatiotemporal dynamics and propagation mechanisms of droughts in China.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"311 ","pages":"Article 109401"},"PeriodicalIF":5.9,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remote sensing-based analysis of yield and water-fertilizer use efficiency in winter wheat management","authors":"Weiguang Zhai ,&nbsp;Qian Cheng ,&nbsp;Fuyi Duan ,&nbsp;Xiuqiao Huang ,&nbsp;Zhen Chen","doi":"10.1016/j.agwat.2025.109390","DOIUrl":"10.1016/j.agwat.2025.109390","url":null,"abstract":"<div><div>Winter wheat is one of the world’s most important food crops, and effective water and fertilizer management is crucial for optimizing its yield and water-fertilizer use efficiency. Unmanned aerial vehicle remote sensing provides a reliable tool for accurately monitoring winter wheat growth and dynamically adjusting water and fertilizer strategies to enhance yield. In this study, a water and fertilizer experiment was conducted in Xinxiang County, Henan Province, a region with a warm temperate continental monsoon climate, characterized by hot, humid summers and cold, dry winters. Various water (W1: 0 mm, W2: 50 mm, W3: 100 mm, W4: 150 mm) and nitrogen (N1: 0 kg/ha, N2: 90 kg/ha, N3: 210 kg/ha, N4: 330 kg/ha) treatments were applied. Subsequently, the effects of different water and fertilizer treatments on winter wheat yield and water-fertilizer use efficiency were evaluated, and the response patterns between winter wheat spectral features (normalized difference vegetation index, NDVI) and texture features (Contrast) and yield and water-fertilizer use efficiency were analyzed. The main findings are: (1) Winter wheat yield increased with higher irrigation and nitrogen levels but plateaued when irrigation reached 120 mm and nitrogen application was 225 kg/ha, beyond which further increases showed no significant improvement; (2) Water-fertilizer use efficiency decreased with increasing irrigation and nitrogen levels but improved with synergistic water-fertilizer interactions. The N3W3 treatment achieved high yield while maintaining superior water-fertilizer use efficiency (irrigation water use efficiency: 1.28 kg/m³, agronomic nitrogen efficiency: 13.33 kg/kg, and fertilizer benefit: 5961.30 RMB/ha), making it the most effective management strategy; (3) NDVI exhibited saturation under high-density conditions, limiting its sensitivity to subtle differences in winter wheat. Conversely, Contrast provided complementary insights into canopy structure, revealing variations in uniformity and resource efficiency under excessive water and nitrogen inputs. Integrating NDVI with Contrast enabled a more accurate assessment of yield and water-fertilizer use efficiency, offering actionable insights for optimizing water-fertilizer management strategies.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"311 ","pages":"Article 109390"},"PeriodicalIF":5.9,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Does water-saving irrigation truly conserve water? Yes and No","authors":"Nan Zhao ,&nbsp;Xinjun Zheng ,&nbsp;Bin Zhang ,&nbsp;Shengchuan Tian ,&nbsp;Lan Du ,&nbsp;Yan Li","doi":"10.1016/j.agwat.2025.109399","DOIUrl":"10.1016/j.agwat.2025.109399","url":null,"abstract":"<div><div>Irrigation is by far the largest consumer of freshwater, and is thus widely acknowledged as a major contributor to water scarcity. Consequently, water-saving technologies (WST) are considered to be effective in reducing irrigation water use and alleviating water scarcity. However, growing evidence indicates that looking at the larger spatial scale, these technologies may exacerbate water scarcity, particularly in arid regions. This study evaluated the water-saving effect at the field and regional scales based on a water accounting framework for an arid oasis region in Northwest China. The results showed that, with the application of WST, irrigation volume decreased by 1012.95 m³/ha over 20 years, with reduced soil evaporation for 80.4 % of the cropland. However, the perceived water saving gives the misleading impression that overall water use is declining, encouraging farmers to expand irrigated areas in pursuit of higher profits. Our results confirmed that the expansion leads to more water consumption at a regional scale. More importantly, this study highlighted that not all water losses are wasteful. Drainage plays a crucial ecological role in salt leaching and nourishing adjacent desert vegetation. Its significant reduction has occurred alongside noticeable drops in groundwater levels in the oasis-desert ecotone, which has subsequently led to vegetation degradation. These findings provide valuable insights for implementing water-saving measures in arid regions worldwide and serve as a warning that the overuse of WST in such areas could exacerbate water scarcity and ecological crises.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"311 ","pages":"Article 109399"},"PeriodicalIF":5.9,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of different data quality control on evapotranspiration of winter wheat with Bowen ratio method","authors":"Yingnan Wu,&nbsp;Qiaozhen Li,&nbsp;Xiuli Zhong,&nbsp;Daozhi Gong,&nbsp;Xiaoying Liu","doi":"10.1016/j.agwat.2025.109379","DOIUrl":"10.1016/j.agwat.2025.109379","url":null,"abstract":"<div><div>The Bowen ratio energy balance (BREB) method is widely used to study surface evapotranspiration, but its major drawback is the uncertainty when Bowen ratio (β)→ −1. Various approaches have been employed to address this issue, but their performances were less evaluated via long-term field observations. Using data from three growing cycles, this study investigated the effect of five screening methods (Mth1 to Mth5 for −1 − |ε₁| &lt; β &lt; −1 + |ε₁|, −1.05 &lt; β &lt; −0.95, β &lt; −0.75, −1.3 &lt; β &lt; −0.75 and β &lt; −0.7 or β &gt; 10 or Δe ≤ 0, Δe denotes the measured vapor pressure gradient, and ε₁ is a coefficient depending on sensor resolution and Δe) on winter wheat evapotranspiration in northern China. On diurnal, daily and seasonal basis, the effect was in the order of Mth5 &gt; Mth3 &gt; Mth1 &gt; Mth2 &gt; Mth4, and the seasonal mean daily value of the gap-filled was 0.38, 0.22, 0.11, 0.01, and 0.01 mm d⁻¹ higher than the unfilled ones, yielding a seasonal total of 96.0, 53.5, 26.0, −0.9, and 0.4 mm, or 18.9 %, 11.4 %, 6.5 %, −0.2 %, and 0.1 % higher than the unfilled ones, respectively. These values resulted from the large difference in data rejection ranking as Mth5 &gt; Mth3 &gt; Mth1 &gt; Mth4 &gt; Mth2, seasonal mean daily 10-min rejection rate ranging from 15.4–73.2 %, 10.3–48.9 %, 5.3–44.9 %, 1.6–10.4 %, and 0.5–7.3 %, respectively (averaging 42.4 %, 30.5 %, 23.2 %, 5.7 %, and 2.6 %, respectively). The corresponding daily rejected hours ranged from 6.83–8.88, 3.60–6.11, 1.85–3.49, 0.10–0.39, and 0.07–0.33 h/day, respectively (averaging 7.53, 4.77, 2.90, 0.28, and 0.24 h/day, respectively), resulting in large data gaps for Mth5 (58.8 %), Mth3 (38.2 %), and Mth1 (17.5 %). Nighttime deletion dominated for Mth2 to Mth4, accounting for 61.1 %, 64.4 %, 68.3 %, and 63.2 % of the total deletion, whereas daytime deletion dominated for Mth1, accounting for 58.1 %. A large portion of invalid rejections of Mth1 (40.4 %–77.6 %), Mth3 (54.3 %–90.9 %) and Mth5 (61.8 %–92.7 %) was observed at the selected period, which was probably a consequence of the sensor’s error cancellation effect, questioning the traditional a priori assumption that small vapor gradients within instrumental error should be discarded. Overall, large differences were observed and the simple Mth4 performed better than the more restrictive ones. These findings are expected to guide the selection of post-data processing in the application of BREB method.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"311 ","pages":"Article 109379"},"PeriodicalIF":5.9,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Irrigation technology, irrigation dose, and crop genetic impacts on alfalfa yield and quality","authors":"Bradley S. Crookston ,&nbsp;Dakota Boren ,&nbsp;Matt Yost ,&nbsp;Tina Sullivan ,&nbsp;Earl Creech ,&nbsp;Burdette Barker ,&nbsp;Cheyenne Reid","doi":"10.1016/j.agwat.2025.109366","DOIUrl":"10.1016/j.agwat.2025.109366","url":null,"abstract":"<div><div>In water limited environments, alfalfa (<em>Medicago sativa)</em> is often criticized for its high water use, prompting interest in optimizing irrigation technologies, deficit irrigation, and drought-tolerant genetics. However, potential cumulative benefits from combining water-saving strategies have not been previously identified. This study evaluated the independent and combined effects of five irrigation technologies (low-elevation Nelson advantage, low-elevation precision application, low-elevation spray application, mid-elevation spray application, and mobile drip irrigation), four irrigation doses (growers’ typical full dose, a 25 % reduction, and two 50 % reductions, uniform and growth stated-targeted), and two alfalfa varieties (growers’ conventional and drought-tolerant) across three Utah sites from 2020 to 2022. No interaction effects were found among these factors, indicating that stacking multiple water-saving strategies did not enhance yield or forage quality. Low-elevation sprinkler technologies generally outperformed mid-elevation and mobile drip irrigation, though results varied by environment. Deficit irrigation at 25 % reduction often maintained yields similar to growers’ Full irrigation dose, while 50 % reductions consistently decreased yield by 22–54 %. However, deficit irrigation improved forage quality and water use efficiency. Decision tree models revealed that maximizing relative feed value-adjusted water use efficiency primarily depended on matching irrigation dose and technology to site-specific climate demand rather than applying Full irrigation. These findings suggest that moderate deficit irrigation and low-elevation sprinkler technologies can improve forage quality and water resource efficiency without substantial yield loss that occurs with 50 % deficit irrigation.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"311 ","pages":"Article 109366"},"PeriodicalIF":5.9,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}