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Reference evapotranspiration (ET0), an essential variable used to estimate crop evapotranspiration, is expected to change significantly under climate change. Detecting and attributing the change trend in ET0 to underlying drivers is therefore important to the adoption of agricultural water management under climate change. In this study, we focus on a typical agricultural region of the Fenwei Plain in northern China and use the Mann–Kendall test and contribution rate to detect the change and trend in ET0 at annual and seasonal scales and determine the major contribution factors to ET0 change for the baseline period (1985–2015) and the future period (2030–2060) based on high-resolution gridded data and climatic data from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results indicate that the annual ET0 of the Fenwei Plain showed a significant decreasing trend in the baseline period but insignificant and significant increasing trends in the future period under the SSP245 and SSP585 scenarios, respectively. The annual ET0 of the plain under the SSP245 and SSP585 scenarios increase by 4.6% and 3.0%, respectively, compared to the baseline period. The change and trend in ET0 between the four seasons are different in the baseline and future periods. Winter and autumn show clear increases in ET0. VPD is the major contribution factor to the ET0 change in the plain. The change in ET0 is mainly driven by the climatic variables that change the most rather than by the climatic variables that are the most sensitive to the ET0 change. The change and trend in ET0 in the plain showed clear spatial differences, especially between the eastern and western area of the plain. To adapt to the impact of climate change on ET0, the irrigation schedule of the crops cultivated in the plain, the cropping system and management of the irrigation district in the plain need to be adjusted according to the change characteristics of spatial and temporal ET0 in the future. These results contribute to understanding the impacts of climate change on evapotranspiration in the study region and provide spatial and temporal references for adaptation in managing agricultural water use and crop cultivation under climate change. [ABSTRACT FROM AUTHOR]

Achieving global food security requires an understanding of the current status and the future trends in the yield gap for cropping systems worldwide. The aim of this study was to scientifically understand the existing yield gap research, recognize the knowledge base and influential articles, and uncover key research themes and how these have evolved over the past three decades. Bibliometric methods were used to analyze articles related to the yield gap available in the largest scientific database, the Web of Science. A total of 6049 relevant articles published from 1993 to 2023 were numerically analyzed for patterns, trends, and clusters. The findings identified a few primary authors of widely cited publications. Geographically, the United States and China were the two major contributors to the publication pool, with articles from China mostly affiliated with the Chinese Academy of Sciences and China Agricultural University, while the United States had a more scattered distribution of affiliated institutions. The research on yield gaps primarily focused on biological factors, such as the effects of crop varieties, agronomic management, climate change, and soil conditions, with a limited exploration of social and economic factors. Within the auspices of the current food issues worldwide, this study provides a thorough view of the progress and key topics in crop yield gap research, contributing to the existing body of knowledge and providing guidance for researchers, policymakers, and stakeholders involved in agricultural productivity enhancement and sustainable food production. Amid the increasing trend in hunger worldwide over the past decade, we thus concluded that, by establishing appropriate benchmarks, re-prioritizing research needs, and focusing on transforming natural resources rather than inputs, the crop yield gap approach can be useful in terms of the clear inclusion of local contexts and socioeconomic constraints. [ABSTRACT FROM AUTHOR]

Agricultural droughts cause a great reduction in winter wheat productivity; therefore, timely and precise irrigation recommendations are needed to alleviate the impact. This study aims to assess drought stress in winter wheat with the use of an unmanned aerial system (UAS) with multispectral and thermal sensors. High-resolution Water Deficit Index (WDI) maps were derived to assess crop drought stress and evaluate winter wheat actual evapotranspiration rate (ETa). However, the estimation of WDI needs to be improved by using more appropriate vegetation indices as a proximate of the fraction of vegetation cover. The experiments involved six irrigation levels of winter wheat in the harvest years 2019 and 2020 at Luancheng, North China Plain on seasonal and diurnal timescales. Additionally, WDI derived from several vegetation indices (VIs) were compared: near-infrared-, red edge-, and RGB-based. The WDIs derived from different VIs were highly correlated with each other and had similar performances. The WDI had a consistently high correlation to stomatal conductance during the whole season (R2 between 0.63–0.99) and the correlation was the highest in the middle of the growing season. On the contrary, the correlation between WDI and leaf water potential increased as the season progressed with R2 up to 0.99. Additionally, WDI and ETa had a strong connection to soil water status with R2 up to 0.93 to the fraction of transpirable soil water and 0.94 to the soil water change at 2 m depth at the hourly rate. The results indicated that WDI derived from multispectral and thermal sensors was a reliable factor in assessing the water status of the crop for irrigation scheduling. [ABSTRACT FROM AUTHOR]

The sustainability of growing a maize—winter wheat double crop rotation in the North China Plain (NCP) has been questioned due to its high nitrogen (N) fertiliser use and low N use efficiency. This paper presents field data and evaluation and application of the soil–vegetation–atmosphere transfer model Daisy for estimating crop production and nitrate leaching from silty loam fields in the NCP. The main objectives were to: i) calibrate and validate Daisy for the NCP pedo-climate and field management conditions, and ii) use the calibrated model and the field data in a multi-response analyses to optimise the N fertiliser rate for maize and winter wheat under different field managements including straw incorporation. The model sensitivity analysis indicated that a few measurable crop parameters impact the simulated yield, while most of the studied topsoil parameters affect the simulated nitrate leaching. The model evaluation was overall satisfactory, with root mean squared residuals ( RMSR ) for simulated aboveground biomass and nitrogen content at harvest, monthly evapotranspiration, annual drainage and nitrate leaching out of the root zone of, respectively, 0.9 Mg ha −1 , 20 kg N ha −1 , 30 mm, 10 mm and 10 kg N ha −1 for the calibration, and 1.2 Mg ha −1 , 26 kg N ha −1 , 38 mm, 14 mm and 17 kg N ha −1 for the validation. The values of mean absolute deviation, model efficiency and determination coefficient were also overall satisfactory, except for soil water dynamics, where the model was often found erratic. Re-validation run showed that the calibrated Daisy model was able to simulate long-term dynamics of crop grain yield and topsoil carbon content in a silty loam field in the NCP well, with respective RMSR of 1.7 and 1.6 Mg ha −1 . The analyses of the model and the field results showed that quadratic, Mitscherlich and linear-plateau statistical models may estimate different economic optimal N rates, underlining the importance of model choice for response analyses to avoid excess use of N fertiliser. The analyses further showed that an annual fertiliser rate of about 300 kg N ha −1 (100 for maize and 200 for wheat) for the double crop rotation with straw incorporation is the most optimal in balancing crop production and nitrate leaching under the studied conditions, given the soil replenishment with N from straw mineralisation, atmospheric deposition and residual fertiliser. This work provides a sound reference for determining N fertiliser rates that are agro-environmentally optimal for similar and other cropping systems and regions in China and extends the application of the Daisy model to the analyses of complex agro-ecosystems and management practices under semi-arid climate. [ABSTRACT FROM AUTHOR]

Irrigation schedule is essential for improving crop production and allocating water resources in agricultural regions that heavily rely on irrigation. This study designs a framework based on the AquaCrop model to optimize the irrigation schedule of winter wheat under dry, normal and wet hydrologic scenarios over a large region in China. The model parameters were calibrated for one cultivar using observed data from three locations in the Fenwei Plain, northern China, and were shown to slightly vary spatially across this region. Regional weather data at high spatio-temporal resolution were generated by interpolation and were combined with regional soil data on a 2 × 2 km grid to drive the model. The irrigation schedule for the study area was optimized by combining a multi-objective algorithm with the exponential efficacy coefficient method. The optimization objectives included crop yield, water use efficiency (WUE), irrigation WUE and economic irrigation benefit. The results showed that the optimized irrigation schedule performed better than the current irrigation schedule applied by the farmers under studied hydrologic scenarios, resulting in increased crop yield, WUE, irrigation WUE and irrigation economic benefit by 1.1–9.7% and decreased irrigation amount by 4.2–5.7%, depending on regions within the study area. The framework developed in this study reallocated irrigation water amounts between regions, thereby improving water allocation to achieve optimal crop yield, water use and economic benefit for the Fenwei Plain. The results can also serve as a guide for local farmers and irrigation district managers. ga1 • A framework is constructed to optimize irrigation schedule over a large region. • Regional weather data is used for hydrologic year division and driving the crop model. • Multiple objectives including crop yield and water use and economic cost are considered in the optimization. • Optimized irrigation schedule improves the performance compared to the local current schedule. [ABSTRACT FROM AUTHOR]

• The percentile method in statistics was introduced into the crop growth monitor. • Many years' crop NDVI data make the percentile lookup tables more reasonable. • Division of large monitoring areas reduced the effects of spatial heterogeneity. • Crop NDVI percentiles had a good relationship with its aboveground part dry weight. The Normalized Difference Vegetation Index (NDVI) is a widely used remote sensing indicator for crop growth monitoring, farmland management and crop production prediction. Traditional monitoring methods that are based on the NDVI difference (d NDVI) are sensitive to variations in the crop growth status in the reference year and to the classification standard to obtain crop growth "grades", however, they are only applicable for the same crop area. To overcome this limitation, this study developed a new approach that is based on crop NDVI percentiles (p NDVI) for real-time monitoring of crop growth. The method utilizes NDVI data for the same day in the past five years over the same region and is based on crop distribution maps of these years, thereby creating a large sample of crop NDVI. According to the lookup table for each NDVI and its percentile (P %), which were retrieved from this sample, the current NDVI value is converted to its P and used for real-time growth evaluation, and the P score indicates that the growth of the crop at this pixel on the monitoring day is better than the P % of the crops for the whole monitoring region over the last 5 years. This approach was applied and validated for a representative agricultural region in the North China Plain (NCP), and winter wheat was used as the test crop. The results were satisfactory, with a significant and sound correlation between the scores that were obtained by the p NDVI method and the sampled winter wheat aboveground biomass. The correlation was higher for the p NDVI compared to d NDVI , although the correlation was significant for both methods. [ABSTRACT FROM AUTHOR]

Achieving a pathway for green development is a critically important challenge for agriculture in China and beyond. The current study evaluates the effects of a range of management interventions including planting, fertilizer nitrogen (N) rate optimization and increasing farm size to promote agricultural green development across the North China Plain (NCP) based on large-scale farm surveys. Our results showed that the mean annual N fertilizer rate for wheat-soybean rotation was much lower than that of wheat-maize and wheat-peanut. Interestingly, our study indicated strong pre-crop effects of summer soybean (Glycine max (Linn.) Merr.) on the following winter wheat (Triticum aestivum Linn.) in N saving compared to summer maize (Zea mays Linn.) and summer peanuts (Arachis hypogaea Linn.), the low N rate for summer soybean and its 'legume' carryover effects led to the low N rate, N surplus and N footprint, and high N use efficiency (NUE) in wheat-soybean. The survey results showed that the optimal N rates for achieving maximum yield of summer maize, summer peanuts and winter wheat were 229, 249 and 236–260 kg ha−1 across the NCP, respectively. Moreover, better N management is beneficial for reducing the N surplus and leads to higher NUE and lower N footprint. Generally, large farms applied less N fertilizer than small farms, thus leading to a lower N surplus and higher N partial factor productivity with the same yield level. Here we show for the first time that the combinations of crop rotation design, optimizing N rate application and increasing farm size are very efficient in reducing N fertilizer applications and the N footprint with stable crop yields. N management should play a more important role in agricultural green development across the NCP and similar regions around the world. • The mean annual N rate, N surplus and N footprint of wheat-soybean was lowest among the three major crop rotations. • Pre-crop effects of soybean on winter wheat increased PFP and reduced N footprint without affecting wheat yield. • The optimal N rate for the maximum crop yield of maize, peanut, wheat were 229, 249, 236–260 kg N ha−1, respectively. • The NUE was higher and N footprint was lower in large farms compared with small farms. [ABSTRACT FROM AUTHOR]

• Water use strategies of three economic tree species were investigated. • Peach, walnut, and apple trees have different water use strategies. • Walnut trees have greater ecological plasticity by absorbing more deep soil water. • Peach and apple trees have higher water use efficiencies than walnut trees. Economic tree species growing in the semi-arid Loess Plateau of China are important for maintaining sound economic viability and protecting the fragile hydro-ecological environment. However, their water use strategies and ecological adaptation to dryland environments remain poorly understood. In this study, we measured variations in soil water content, root distribution, water isotopic signature for hydrogen (δ2H) and oxygen (δ18O) in xylem and soil water of peach (Prunus persica), walnut (Juglans regia L.), and apple (Malus pumila) trees, as well as carbon isotopic signature (δ13C) in their leaves during two consecutive growing seasons—2018 (normal year) and 2019 (wet year). The results showed all species consumed less deep soil water in 2019 than in 2018 due to the increased precipitation. Peach trees used more shallow soil water compared to walnut and apple trees that used more deep soil water during high water-demanding periods. Walnut trees depended more on deep soil water throughout the growing season, contributing 46.2% in 2018, compared to 26.0% for peach trees and 24.0% for apple trees. Moreover, walnut trees had lower deep soil water and leaf δ13C than peach and apple trees. The findings suggest differences in phenology periods of the three rainfed economic tree species to affect the water source used. The three tree species differed in their water use strategies: walnut trees had ecological plasticity for reducing water stress by absorbing deep soil water, and peach and apple trees had higher water use efficiencies. This study has significance for evaluating water use characteristics and drought stress responses of economic trees in water-limited environments. [ABSTRACT FROM AUTHOR]