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Intensive irrigation has been proven to profoundly impact climate through the surface energy budget. However, the impacts of irrigation and climate interactions on gross primary productivity (GPP) in maize cultivated areas remain uncertain. Here we quantified the irrigation effects on maize GPP (∆GPP) across China by combining a land surface model and a light‐use efficiency model and using satellite‐based irrigation water use. We show that irrigation significantly contributed to an increase in maize GPP by an average of 430 gC · m−2 · yr−1, equivalent to 28% of the irrigated maize GPP in China. These benefits (∆GPP) were attributed to irrigation effects (water supply, surface cooling) and climate interactions based on a machine learning framework (eXtreme Gradient Boosting model‐SHapley Additive exPlanations). Irrigation water supply and surface cooling explained 54% ± 19% and 23% ± 20% of ∆GPP respectively, the rest being due to strong climate interactions with irrigation through water and energy balance. Assuming business‐as‐usual irrigation levels, changing climate both increases and decreases ΔGPP over different regions, driven primarily by temperature changes. The irrigation benefits in those areas under heat stress are greatly threatened due to changing climate. The roles of climate change on ∆GPP are reversed from beneficial to detrimental in the North China Plain, dominated by different warming levels of future scenarios. Our analysis provides new insights into assessing irrigation potential with the climate interactions and future irrigation priority regions. Plain Language Summary: Irrigation is the most important water use sector accounting for about 70% of the global freshwater withdrawals and contributing 75% of China's total grain production. Application of irrigation water significantly alters agroecosystem water cycle, energy cycle and carbon cycle. The magnitudes of irrigation impact are also influenced by climate variability. In this study, we simulated irrigation benefits for maize gross primary productivity (GPP) using land surface models and satellite observations. The effects of climate interactions on the irrigation benefits were untangled using an explainable machine learning framework (eXtreme Gradient Boosting model‐SHapley Additive exPlanations). We found that alterations in water cycle (water supply) and energy cycle (surface cooling) accounted for 54% ± 19% and 23% ± 20% of the irrigation benefits variability respectively, the rest being due to temperature‐dominated climate interactions. With the climate change driven by persist warming, the irrigation benefits will be substantially reduced for those regions under ongoing heat stress, such as North China Plain. Future maize productivity will be greatly threatened by changing climate, considering the prevailing negative trends for the irrigation benefits throughout the nation. Reinforcing anthropogenic activities such as irrigation water management and planting structure adjustment are strongly required to improve crop resilience to climate variability. Key Points: Irrigation contribution to the maize gross primary productivity across 15 agricultural regions is quantified in ChinaThe irrigation benefits explained by water supply and cooling are identified, and the rest being due to climate interactionsChanging climate both increases and decreases irrigation benefits for maize over different regions, contingent on heat conditions [ABSTRACT FROM AUTHOR]

Drip irrigation combined with film mulching significantly improved water saving and agricultural efficiency in arid and semi-arid regions. However, the impact of various irrigation technical parameters under plastic film-mulched (PM) drip irrigation on water consumption and maize yield (Zea mays L.) for seed production is still unclear. Field experiments were conducted between 2016 and 2018 in Northwest China to investigate the effects of irrigation technical parameters (dripper discharge and irrigation interval) on biomass, grain yield, water, and nitrogen (N) use efficiency of the PM-maize for seed production. Four dripper discharges (1.38 L h−1, 2.0 L h−1, 2.5 L h−1, 3.0 L h−1) and five irrigation intervals (6 days, 8 days, 10 days, 12 days, 14 days) were selected. The normalized yield was used to analyze the effect of different treatments on the yield to eliminate the differences caused by uncontrollable factors, such as precipitation and temperature between different years. The results indicated a quadratic function among the final aboveground biomass, grain yield of maize for seed production, and irrigation intervals, with the highest value observed at 8 days irrigation interval. Both yield and nitrogen use efficiency measured as partial factor productivity of nitrogen (PFPN) decreased rapidly as the irrigation interval exceeded 10 days. When the irrigation interval increased from 10 to 14 days, the relative grain yield decreased between 6.5 and 16.0%, while the PFPN decreased between 1.1 and 99.2 kg kg−1. Crop evapotranspiration (ET) initially decreased and then increased as the irrigation interval increased. The water productivity (WPY-ET) was the highest at the irrigation interval of 8 days. The PFPN increased between 6.6 and 42.8 kg kg−1 at the dripper discharge of 3.0 L h−1 when compared with dripper discharge of 2.5 L h−1. The study showed an optimal dripper discharge–irrigation interval combination for maximizing water-resources utilization and yield increase. When biomass, grain yield, ET, and WPY-ET are considered simultaneously, the most suitable irrigation technical parameters of maize for seed production in Northwest China are the combination of 2.5 L h−1 and 8 days. Furthermore, a combination of 3.0 L h−1 and 8 days is more conducive to reducing fertilizer costs. [ABSTRACT FROM AUTHOR]

To figure out the driving factors of irrigation water demand and an effective way for relieving the sharp contradiction between the supply and demand of agriculture water resources in the Heihe River basin of Northwest China, a conventional method in estimating irrigation water demand was modified by incorporating the simulations of Soil and Water Assessment Tool (SWAT) model with its distributed hydrological response units (HRUs). Simultaneously, a new factor decomposition model for irrigation water demand was created using the extended Kaya identity. Moreover, the additive and multiplicative forms of the logarithmic mean Divisia index (LMDI) decomposition were used to quantify the variation of driving factors in irrigation water demand from 1985 to 2014. The results show that total irrigation water demand in this arid region had increased by 3.249 × 10 8  m 3 over the past 30 years. These contributions are arising from the following four driving factors - planting scale, planting pattern, climate change and water saving technology (e.g., drip irrigation, sprinkler irrigation, etc.) with respective contribution of irrigation water demand of 1.981 × 10 8  m 3 , 0.933 × 10 8  m 3 , 1.523 × 10 8  m 3 and −1.188 × 10 8  m 3 . The corresponding average contribution rates of these driving factors are 60.96%, 28.72%, 46.86% and −36.53%, respectively. Although the effects of the four drivers on the irrigation water demand for various crops over three periods (i.e., 1985–1994; 1995–2004; 2005–2014) are inconsistent, both the planting scale and the cropping pattern increase the irrigation water demand. Water saving technologies as known have inhibited water demand, but climate change turns out to increase the demand and then inhibit it. Therefore, to reduce the irrigation water demand and develop a proper irrigation water planning in Heihe River basin, it is beneficial to regulate the scale of agricultural development, adjust the agriculture pattern by reducing the area planted with crops that consume relatively large amounts of water (i.e. spring corn and vegetables), identify agricultural water saving potential and decrease the impact of climate change. [ABSTRACT FROM AUTHOR]

Water shortage and serious soil salinization are two general features in arid regions. Crop models can provide constructive suggestions for scientific soil water-salt regulations. In the study an ecohydrological crop model WAVES (the WAter Vegetation Energy and Solute model) was employed to examine the crop responses to soil water-salt dynamics in a severely dry and saline region of southern Xinjiang, China. Observation data of film-mulched cotton with the initial soil salinity of 7.19–9.80 g kg-1 (EC 1:5 of 1.56–2.12 dS m-1) were collected in 2009 and 2010 with five different irrigation water salinity treatments 0–11.05 g L-1 (EC of 0–11.85 dS m-1). Results showed that the relative absolute errors of simulated soil water and salt were less than 4.1% and 12.4% in 2010 (calibration), and 11.1% and 13.2% in 2009 (validation). The coefficient of determination of yield in calibration and validation was 0.93 and 0.74, respectively. Simulated dynamics indicated soil salt accumulation at a depth of 40 cm. Results of scenario analysis showed that crop water productivity was the highest when the total irrigation volume was 275–325 mm in all scenarios. Saline water with a certain degree of salinity can be used for irrigation without causing a severe yield reduction (20% reduction). This study provided a scientific basis for water management in areas with severe drought and salinization. • WAVES simulates soil water-salt dynamics and cotton response well in severe salinization. • Salt accumulated at the depth of 40 cm. • Salt at the end of the growing season was 136–157% of the original soil profile. • Optimal crop water productivity was within the total irrigation volume of 275–325 mm. [ABSTRACT FROM AUTHOR]

Crude protein (CP) and fiber are the two most important quality traits of forage crops, and accurately estimating them under water and salt stress is of great significance to the production of high-quality alfalfa. However, there is seldom an applicable quality model. Here, we conducted pot and plot experiments under three irrigation amounts (full irrigation W1, moderate water deficit W2, and severe water deficit W3) and six soil salinity levels (S0, S1, S2, S3, S4, and S5, indicating 0, 1, 2, 3, 4, and 5‰ mass ratio of salt per unit soil mass, respectively), to examine the responses of CP and relative feed value (RFV) and develop CP and RFV models in alfalfa at different growth ages under combined water and salt stress. We found that there was a negative parabolic relationship between relative alfalfa CP (CP r) and relative soil electrical conductivity (EC r) with a EC r threshold (EC rth) for maximum CP r. Moderate water stress improved the CP r , while cutting number related to growth age had the opposite effect. The EC rth tended to decrease with increasing water stress and growth age, i.e., they increased the sensitivity of CP r to salt stress. Relative RFV (RFV r) of alfalfa was improved by water and salt stress, however, at the expense of biomass reduction, and the rate of increase declined with increasing growth age. Based on the above, crude protein and fiber models of alfalfa incorporating growth age under water and salt stress were developed, and performed well under pot (CP model: r 2 = 0.94; RFV model: r 2 = 0.98) and plot conditions (CP model: r 2 = 0.95; RFV model: r 2 = 0.88). Overall, the developed CP and RFV models would provide an essential framework for the production of high-quality forage in saline soil, conservation of water resources, and suppression of salinization. • There was a parabolic relationship between relative crude protein (CP r) and relative soil electrical conductivity (EC r). • Moderate water stress improved the CP r , while cutting number had the opposite effect. • Relative feed value (RFV r) was increased by water and salt stress at the expense of biomass loss. • CP and RFV models incorporating growth age under water and salt stress were developed and validated. [ABSTRACT FROM AUTHOR]

Threshold levels of soil moisture and salinity in the plant root zone can guide crop planting and farming practices by providing a baseline for adjusting irrigation and modifying soil salinity. This study describes a method of soil water and salinity control based on an optimized model for growing cotton in an arid area. Experiments were conducted in Akesu Irrigation District, southern Xinjiang, northwest China, to provide data for cotton yield and soil water content and salinity in the root zone at different growth stages. The sensitivity of cotton to soil water content and salinity was predicted for different growth periods using a modified Jensen model. An optimization model with 480 boundary conditions was created, with the objective of maximizing yield, to obtain the dynamically varying water and salt threshold levels in the root zone for scenarios that included three initial soil moisture content values (W0), eight irrigation quantities (M), five initial soil salt content values (S0), and four irrigation water salinity levels (K). Results showed that the flowering–boll stage is the crucial period for cotton yield, and the threshold levels of soil water content and salinity in the cotton root zone varied with the boundary conditions. The scenario chosen for the research area in this study was W0 = 0.85θfc (θfc is field capacity), S0 = 4 g kg−1, M = 400 mm, K = 0 g L−1. The predicted threshold levels of soil water for different growth stages (seedling, bud, flowering–boll, and boll-opening) were respectively 0.75–0.85θfc, 0.65–0.75θfc, 0.56–0.65θfc, and 0.45–0.56θfc. Corresponding threshold levels of salt were 4–4.16, 4.16–4.39, 4.39–4.64, and 4.64–4.97 g kg−1 when no action was taken to remove salt from the root zone. This study provides an innovation method for the determination of dynamically varying soil water content and salt thresholds. [ABSTRACT FROM AUTHOR]

• The strengths and weaknesses of DSSAT-CERES-Maize and AquaCrop were identified. • DSSAT-CERES-Maize with the two evapotranspiration (ET) options was tested. • DSSAT-CERES-Maize was inferior to handle ET simulation with drought than AquaCrop. • DSSAT-CERES-Maize showed greater potential to simulated maize yield than AquaCrop. • Both crop models had greater uncertainties under severe drought conditions. As water scarcity becomes more acute in many parts of the world, crop modeling tools that effectively simulating crop response to deficit irrigation strategies to help investigate management improvement are needed. Identifying the strengths and weaknesses of the crop models with different growth-engines are therefore of great importance. The objective of this study was to investigate the capability and improvements of the new version of a solar energy-driven crop model (DSSAT-CERES-Maize, v4.7.5.0) in simulating water consumption and yield of hybrid seed maize under different soil water conditions, and its comparison with a water-driven crop model (AquaCrop, v4.0). Data obtained from a 4-year (2012–2015) field trial on maize grown under different irrigation treatments at Wuwei, Northwest China, was used for this assessment. These models were calibrated and validated using measured daily evapotranspiration (ET), leaf area index (LAI), aboveground biomass, yield (Y), harvest index (HI) and soil water content (SWC). Daily ET was measured using a combination of an eddy covariance (EC) system, sap flow sensors, and micro-lysimeter cylinders. The ability of DSSAT-CERES-Maize using the two different ET options, i.e., Priestley-Taylor/Ritchie (PT) and FAO-56 Penman-Monteith (PM) was analyzed. The results showed that DSSAT-CERES-Maize with the PT approach had fair agreement with measured daily ET of maize under non-water stress condition (R2=0.85; NRMSE=26.7%), but poor agreement with ET under water stress conditions (R2=0.51; NRMSE=43.8%). DSSAT-CERES-Maize with the PM approach systematically underestimated ET by up to 13% under non-water stress condition, which was mainly attributed to that the maximum static CERES-Maize crop coefficient (EORATIO) was currently hard coded to 1.0. Using the PT or FAO-56 PM approach as ET input in DSSAT-CERES-Maize showed no different effect on final biomass (B) and Y simulation for full irrigation. But for water stress conditions, DSSAT-CERES-Maize with the FAO-56 PM approach simulated B and Y with higher overestimation to the measured data than those simulated using the PT approach. The simulated LAI, biomass and SWC by DSSAT-CERES-Maize using the PT approach generally well followed the trend of the measured values for most irrigation treatments. The model with the PT approach showed acceptable prediction for B and Y of different irrigation treatments across years, with NRMSE of 15.5% and 26.2%, respectively, but the accuracy decreased with an aggravation of water stress. Furthermore, the strengths and weaknesses of DSSAT-CERES-Maize and AquaCrop, and their different cores of growth-engines, i.e. RUE and normalized water productivity (WP*) were carefully discussed. It was concluded that DSSAT-CERES-Maize was a superior estimate of maize yield than was AquaCrop, especially when the climate varied dramatically between years. But DSSAT-CERES-Maize, for the simulation of maize water consumption in an arid region where drought often occurs, was inferior to AquaCrop. These results contribute to recommend the appropriate crop model for specific modeling goals. [ABSTRACT FROM AUTHOR]

• Projecting the future climate trends in the Heihe agricultural region under three RCP scenarios. • Separating the effects of future climate change and human activity on total ET. • An effective way has been presented to reduce agricultural water consumption. Evapotranspiration (ET) is the component of the hydrological cycle that accounts for most water consumption across the agricultural region of the Heihe River basin in Northwest China. It is of great practical importance in the basin to allocate water resources for ensuring the security of food production, and to formulate effective science-based agro-ecological management policies to address climate change impacts due to the probable effects of future changing environment on regional ET. In this study, the Soil and Water Assessment Tool (SWAT) model were first driven by the downscaled climate data from three IPCC representative concentration pathways (RCPs) that have been projected by five General circulation models (GCMs) in the Coupled model intercomparison project phase 5 (CMIP5) and the water-saving irrigation scenario to project ET per unit area, we then added to the model the upper and lower limits of the appropriate agricultural scale that considered the ecological health to simulate the behaviour of total ET for the period 2021–2050. Finally 12 case study scenarios were used to separate the effects of climate change and human activity on total ET. The results showed that total ET can be significantly decreased by >1.22 × 108 m3 when the RCP-water-saving irrigation scenarios were combined with the appropriate agricultural scale. The results suggest that the best set of strategies to reduce agricultural water consumption is to reduce the area of arable land through consideration of ecosystem health and aggressively utilize water-saving agricultural technology to counter the increased ET brought about by future climate change in the Heihe agricultural region. [ABSTRACT FROM AUTHOR]

• N alleviated reductions of tomato transpiration caused by deficit irrigation. • N enhanced effects of deficit irrigation in tomato water use efficiency. • There were synergistic effects of irrigation and N on transpiration and water use. Nitrogen (N) application affects the relationships between tomato transpiration (T), water use efficiency (WUE) and soil water content (SWC), and is important for practical agricultural management. This study investigated T (hourly, T h , and daily, T C) and WUE (leaf scale, WUE L , and plant scale, WUE P) differences of tomato grown in a solar greenhouse in response to different N and irrigation treatments. Three N application rates (0, 100 and 200 mg N kg−1 soil) were applied with four irrigation levels (full irrigation, Wck; and deficit irrigation, half amount of Wck, happened in different growth stage, W1, W2 and W3 respectively). Results showed that N application partly alleviated reductions in T associated with deficit irrigation, and it enhanced the promotion of deficit irrigation in WUE L. In tomato fruit late growth stage (stage 3), a balance is needed between irrigation and N application rate, higher N application can improve T and WUE L under higher irrigation water available. The positive effects of N2 and N3 on T were more pronounced when SWC was in the range of 0.19 − 0.22 cm3 cm−3 and 0.22 − 0.26 cm3 cm−3, respectively in this study. In tomato fruit early growth stage (stage 1), N application aggravated the reduction in WUE P due to deficit irrigation, while in tomato fruit middle and late growth stages (stage 2 and 3), N application enhanced the promotion of deficit irrigation on WUE P. Generally, in tomato early growth stage, there should be no water shortage and N excess; in fruit middle and late growth stages, more N can be applied along with the increase of SWC. This study proposes a prospect to implement integrative management of irrigation and N application during tomato growth, promoting transpiration and water use efficiency in a scientific way. [ABSTRACT FROM AUTHOR]

Abstract: Regulated deficit irrigation (RDI) was applied on field-grown pear-jujube trees in 2005 and 2006 and its effects on crop water-consumption, yield and fruit quality were investigated. Treatments included severe, moderate and low water deficit treatments at bud burst to leafing, flowering to fruit set, fruit growth and fruit maturation stages. Different deficit irrigation levels at different growth stages had significant effects on the fruit yield and quality. Moderate and severe water deficits at bud burst to leafing and fruit maturation stages increased fruit yield by 13.2–31.9% and 9.7–17.5%, respectively. Fruit yield under low water deficit at fruit growth and fruit maturation stages was similar to that of full irrigation (FI) treatment. All water deficit treatments reduced water consumption by 5–18% and saved irrigation water by 13–25% when compared to the FI treatment. During the bud burst to leafing stage, moderate and severe water deficits did not have effect on the fruit quality, but significantly saved irrigation water and increased fruit yield. Low water deficit during the fruit growth stage and low, moderate and severe water deficits during the fruit maturation stage had no significant effect on the fruit weight and fruit volume but reduced fruit water content slightly, which led to much reduced rotten fruit percentage during the post-harvest storage period. Such water deficit treatments also shortened the fruit maturation period by 10–15d and raised the market price of the fruit. Fruit quality shown as fruit firmness, soluble solid content, sugar/acid ratio and vitamin C (VC) content were all enhanced as a result of deficit irrigation. Our results suggest that RDI should be adopted as a beneficial agricultural practice in the production of pear-jujube fruit. [Copyright &y& Elsevier]

Saline water irrigation presents a viable solution to alleviate freshwater scarcity. However, it's crucial to consider the threshold of salinity, as excessive levels can adversely affect crop yield, crop productivity and soil sustainability. Additionally, due to varying surface conditions and management practices at the regional level, a distributed approach to saline water irrigation management is necessary. The primary goal of this research was to develop a regional agro-hydrological crop model. The validated regional model was then employed to identify the spatial water salinity threshold for saline water irrigation. In this research, we utilized the WAVES (WAter Vegetation Energy and Solute model), which was adapted for mulching environment and validated in severely arid and saline fields. To account for spatial variations, we applied a distributed method to upscale the WAVES to the regional level. Multisource and multiscale data were used for regional model validation. Different scenarios of salinity levels for saline water irrigation were simulated, and a comprehensive criterion to determine water salinity threshold was proposed considering cotton yield, water productivity and soil salt accumulation. Model evaluation indicated a high level of accuracy for the developed regional model. The comparison between simulated and measured values for soil salt content, soil water content, cotton leaf area index, measured lint yield, and statistical yield resulted in indices of agreement of 0.90, 0.71, 0.93, 0.58, and 0.70, respectively. The validated regional model was employed to simulate various saline water irrigation scenarios. The findings revealed that under identical irrigation water salinity, cotton cultivation on sandy loam soil is more productive but has a larger yield reduction percentage compared to silty loam soil. Moreover, the research indicates a groundwater depth of 1.5 m is associated with higher cotton production. Additionally, a spatial pattern of the irrigation water salinity threshold for saline water irrigation was determined, suggesting that the water salinity in most areas of the Tarim Irrigation District should not exceed 6 g L−1. Notably, in the current practice of pumping groundwater in situ for irrigation, approximately 46% of the areas are at risk, indicating that it's not suitable for saline water irrigation. This study developed a distributed model under a typical irrigation district characterized by mulching, severe drought and salinization and defined the distributed spatial threshold for saline water irrigation. These research results hold significant implications for guiding regional-scale saline water irrigation and groundwater extraction. [Display omitted] • A regional distributed hydrological crop model was established considering the spatial heterogeneity. • The developed regional model demonstrates good performance, validated with multisource and multiscale data. • For every 1 dS m−1 (0.74 g kg−1) increase in soil salt content, the relative cotton yield decreased by 4.94%. • The threshold mode of irrigation water salinity in the Tarim Irrigation District is 6 g L−1. • 46% of the area are not suitable using in-situ groundwater for irrigation. [ABSTRACT FROM AUTHOR]

• Nonlinear relationship between g s and CO 2 concentration is considered by modified VIC model. • Evapotranspiration responses on elevated CO 2 concentration for typical crop are quantified. • The historical/future evapotranspiration evolution for inland agricultural land is evaluated. The increase in atmospheric carbon dioxide ( C O 2) concentration is changing plant physiology, thus affecting terrestrial hydrological response. A nonlinear stomatal conductance response to carbon dioxide concentration (g s - C O 2) was incorporated in the VIC model for better representation of the evapotranspiration (ET) response to the elevated C O 2. The annual ET of maize and wheat over the agricultural land in Northwest China was found to decrease by 0.54% and 0.21% during 1980–2010, respectively. Under doubled C O 2 concentration (660 ppm), the ET reduction of maize and wheat was 23.3 mm and 8.9 mm, which accounted for 4.3% and 1.8% of the corresponding annual ET. The annual ET reduction of maize, under the four future scenarios (RCP4.5_2040s, RCP4.5_2080s, RCP8.5_2040s, and RCP8.5_2080s), was about 1.1–6.4%, resulted from an ensemble mean of eight general circulation models. The effects of elevated C O 2 offset part of ET increase caused by the precipitation and temperature changes. This study has practical implications for precise irrigation. The ET response of maize should be paid more attention for its larger potential in saving irrigation water for the studied region. The elevated C O 2 concentration will be beneficial for saving irrigation water to a certain degree. [ABSTRACT FROM AUTHOR]

The deep drainage of irrigated cropland, which is one of the main causes of water losses in irrigation, plays an important role in water resource management. However, its magnitudes and variations remain unclear at regional scales. In this study, we established a remote sensing deep drainage model for irrigated cropland (ICDD_RS model) based on satellite observations and a deep neural network (DNN). First, deep drainage for natural vegetation was calculated by the water balance equation. Then, the ICDD_RS model was established by linking the calculated deep drainage and remote sensing data for natural vegetation based on a DNN. Finally, the model was used to predict the deep drainage of irrigated cropland through remote sensing data. The model was tested in the Heihe River Basin of China and the results showed the following: (1) The ICDD_RS model can well describe the relationship between deep drainage and selected influencing factors (0.83 < R2 < 0.86). At the site scale, it is highly consistent with in-situ observations with R2 values of 0.86, 0.74, and 0.71 for the Daman, Yingke, and Pingchuan stations, respectively. At the catchment scale, a low bias of 12% is obtained. (2) Deep drainage is large, accounting for 6–17% of the irrigation water. This model should be valuable in providing more detailed and effective information on agricultural water resource management. • Development of ICDD_RS model to simulate the deep drainage for irrigated cropland. • ICDD_RS captures the dynamic of deep drainage at both site and watershed scales. • Deep drainage accounts for 6–17% of irrigation in the Heihe River Basin. [ABSTRACT FROM AUTHOR]

Abstract Crop production is expected to increase by more than 50% to meet the demand of population growth in China in 2050 (FAO, 2017). Crop production in North China largely depends on irrigation, which is mainly from groundwater in Northwest China. Over-extraction of groundwater is decreasing groundwater levels, and threatening the fragile ecological systems of arid regions. How groundwater levels will change in order to meet the irrigation water requirement in Northwest China has not been extensively investigated to evaluate sustainability of agriculture and the cost to maintain groundwater levels. Here, we examine the dynamic relations between groundwater levels and the amount of irrigation water, by employing the Variable Infiltration Capacity model and an irrigation scheme, for the last three decades in Heihe River basin of China. The results show that on the average about 1.86 m decline of groundwater is attributable to the irrigation water consumption for the farmland area in Heihe River over the past three decades. In the scenario of ceasing irrigation activities, the groundwater level will be prevented to further decline about 3.06 ± 0.4 m under the future climate scenarios till 2050, but at the cost of crop production valued 64.2 ± 8.4 billion CNY. Effective water-saving measures and strategies are expected to adopt to maintain both groundwater levels and agricultural productivity for the coming decades. Highlights • The production sustainability on irrigation water in the Heihe Oasis are assessed. • The groundwater level (GWL) declines attributed to crop production are examined. • The cost we need to pay for ceasing GWL declines in farmland region is quantified. [ABSTRACT FROM AUTHOR]

Water scarcity and salt stress are two main limitations for agricultural production. Groundwater evapotranspiration (ET g ) with upward salt movement plays an important role in crop water use and water productivity in arid regions, and it can compensate the impact of deficit irrigation on crop production. Thus, comprehensive impacts of shallow groundwater and deficit irrigation on crop water use results in an improvement of irrigation water productivity (IWP). However, it is difficult to quantify the effects of groundwater and deficit irrigation on IWP. In this study, we built an IWP evaluation model coupled with a water and salt balance model and a crop yield estimation model. As a valuable tool of IWP simulation, the calibrated model was used to investigate the coupling response of sunflower IWP to irrigation water depths (IWDs), groundwater table depth (GTDs) and groundwater salinities (GSs). A total of 210 scenarios were run in which five irrigation water depths (IWDs) and seven groundwater table depths (GTDs) and six groundwater salinities (GSs) were used. Results indicate that increasing GS clearly increases the negative effect on a crop's actual evapotranspiration (ET a ) as salt accumulation in root zone. When GS is low (0.5–1 g/L), increasing GTD produces more positive effect than negative effect. In regard to relatively high GS (2–5 g/L), the negative effect of shallow-saline groundwater reaches a maximum at 2 m GTD. Additionally, the salt concentration in the root zone maximizes its value at 2.0 m GTD. In most cases, increasing GTD and GS reduces the benefits of irrigation water and IWP. The IWP increases with decreasing irrigation water. Overall, in arid regions, capillary rise of shallow groundwater can compensate for the lack of irrigation water and improve IWP. By improving irrigation schedules and taking advantages of shallow saline groundwater, we can obtain higher IWP. [ABSTRACT FROM AUTHOR]

The analysis of irrigation water productivity (IWP) can provide insights into taking measures to improve water-efficient irrigation. This study examines the temporal IWP trend of cereal crops over the Hexi Corridor in Northwest China by employing descriptive analysis, trend analysis, and change-point analysis. Spatial patterns of different typical years (dry, average and wet year) are analyzed by the spatial interpolation method and spatial autocorrelation method. The regional average IWP significantly increased from 0.51 kg/m 3 to 1.29 kg/m 3 during the period of 1981–2012 and no change point was detected. Spatial distribution of IWP reveals that IWP was higher in the plain oasis region, while lower in the mountainous and desert oasis region. The IWP ranged from 0.72 to 1.60 kg/m 3 for the dry year 2004, 0.77–1.66 kg/m 3 for the average year 2008, and 0.81–1.93 kg/m 3 for the wet year 2011, respectively. No significant spatial autocorrelation was observed. By 2012, there were still 3.9% of the area with IWP less than 1.0 kg/m 3 , which implied an opportunity to increase IWP through better water management practices. The grey relational analysis of the influences of major driving factors (area supported by unit of irrigation water use, fertilization, agricultural film, agricultural pesticide, and annual mean temperature) on IWP showed that area supported by unit of irrigation water use, fertilization, and agricultural film had dominant impacts during the whole period. [ABSTRACT FROM AUTHOR]

Biochar has been proven to have great potential for mitigating greenhouse gas emissions and improving crop growth. However, the effects of biochar addition on yield-scaled N 2 O emissions in a mulched and drip-irrigated maize field using different irrigation water qualities and quantities remain unclear. A two-year field experiment was conducted to examine the effects of biochar addition, irrigation level and water salinity on soil N 2 O emissions, maize yield and grain N uptake in Northwest China. Eight treatments in total included the combination of two biochar addition rates of 0 t/ha (B0) and 60 t/ha (B1), two irrigation levels of full (W1) and deficit irrigation (W2, W2 = 1/2 W1) and two water salinity levels of freshwater (S0, 0.71 g/L) and brackish water (S1, 4 g/L). The soil properties, N 2 O emission fluxes, maize yield and grain N uptake, along with the abundances of N-cycle functional marker genes were measured during the two growing seasons. Soil water-filled pore space (WFPS), soil NH 4 +-N and NO 3 --N contents accounted for the majority of the variation in the dynamic changes of N 2 O fluxes. Deficit irrigation had lower N 2 O emissions compared with full irrigation, which was more pronounced in the second year. Relative to freshwater irrigation, brackish water irrigation increased N 2 O emissions under W1, but freshwater and brackish water irrigation had similar N 2 O emissions under W2 in both years. Biochar addition effectively mitigated N 2 O emissions by 29.4–31.0% and 17.9–29.2% under different irrigation treatments in 2020 and 2021, respectively. The continuous drought induced by deficit irrigation weakened the effectiveness of biochar in reducing N 2 O emissions. The differences in N-cycle functional marker genes could not reflect the variation in cumulative N 2 O emissions among treatments. Under all irrigation treatments excluding W2S1, biochar addition improved the total maize yield and grain N uptake over the two-year period by 4.06–10.74% and 5.57–8.63%, respectively. Brackish water and deficit irrigation increased grain and grain N yield-scaled N 2 O emissions, whereas biochar addition reduced both of them. Overall, these findings suggest that biochar addition can effectively reduce N 2 O emissions and increase yield and grain N uptake under brackish water and deficit drip irrigation with mulching in a maize field system, and thus adding biochar can help with achieving the sustainability of agricultural production in areas with limited freshwater resources. • The effects of brackish water on N 2 O emissions varied between irrigation levels. • Biochar addition mitigated N 2 O emissions for all irrigation treatments. • The continuous drought weakened biochar's effectiveness in reducing N 2 O emission. • Biochar improved yield and N uptake under proper water and salinity treatments. • Biochar addition reduced yield-scaled N 2 O emissions for all irrigation treatments. [ABSTRACT FROM AUTHOR]

Improving productivity of saline soils under drought condition is critical for sustainable agricultural development in arid areas. Whether biochar addition can interact with drought and salinity on fruit yield and quality remains unclear. A pot study was conducted to examine the effects of water, salinity and biochar addition on tomato yield and quality in a solar greenhouse of northwest China. There were twelve treatments combining two irrigation levels of high (W1) and low irrigation (W2 = 2/3 W1), three salinity levels with 0%, 0.3% and 0.6% of soil dry weight salts, referred to S0, S1, and S2, respectively, and two biochar addition levels with 0 (B0) and 1% (B1) of soil dry weight. Biochar, water, salinity, and the interaction between water and salinity were found significant affecting yield and irrigation water productivity (IWP). Biochar addition reduced yield and IWP, ranging from by 7% of W2S0 to 43% of W1S2. The difference in yield and IWP between W1 and W2 was greater for lower salinity treatments. The reduction percentage of W2, relative to W1, was 70%, 38%, and 29% for yield, 58%, 14%, and 0.9% for IWP under S0, S1, and S2, respectively. The effects of water, salinity and biochar treatments was found inconsistent for different quality parameters. Adding biochar had no significant effect on firmness, and slightly increased total soluble solids (TSS) and Vitamin C (VC) at both irrigation levels, while lower irrigation and higher salinity generally led to higher TSS and VC. The absolute slope value of the linear regression of yield and quality parameters with soil electrical conductivity was smaller under W2, relative to W1, indicating that the salinity effect was less pronounced when water stress was greater. The results are valuable in developing and evaluating remedy measures for improving saline soil productivity in arid areas. • Adding biochar reduced tomato yield and IWP under water and salinity stresses. • Biochar addition had a limited effect on tomato TSS, shape index, VC and fruit firmness. • Effect of salinity on yield was more pronounced at relatively higher irrigation level. • Responses of tomato quality to salinity differed for different parameters and at different water levels. [ABSTRACT FROM AUTHOR]

Agriculture has by far the largest water footprint of human activities. Improving the water productivity of crop production is key to enhancing both water and food security. Irrigation is a significant pressure on water resources in the arid region of Northwest China. Here, we examine optimizing the regional cropping distribution within the Heihe River basin for achieving the dual goals of improving productivity and reducing irrigation water demand. The Soil and Water Assessment Tool (SWAT) and a cellular automata model are employed to model the processes of cropping pattern changes. The optimization takes the maximum of crop water productivity (CWP), the maximum economic water productivity (EWP) and the maximum nutrient water productivity (NWP) as the objective function. The model optimizes the spatial distribution of six crops including corn, spring wheat, spring barley, spring canola-Polish, alfalfa and upland cotton. Results show that under the premise of considering food security, the maximization of water productivity for CWP, EWP, and NWP, leads to the reduction of corn planting area and the eastward shift of corn planting region. A significant decrease in the proportion of wheat planting occurs in the objective of EWP maximization, while the planting proportion of barley and canola increased significantly. All three optimization objectives yield cropping distributions that reduce the irrigation water demand of cultivated land and improve the water productivity of the basin, among which maximizing CWP scenario has the greatest water-saving intensity. Furthermore, from the perspective of ecosystem services, the cropping distribution of maximizing EWP is more reasonable for the basin. Different cropping change scenarios provide effective references for decision makers to make a reasonable cropping distribution in the region. • A spatial optimization model is proposed for cropping pattern. • The optimization improves water productivity and reduces irrigation water demand. • Productivity of crop water (CWP), economic water (EWP) and nutrient water (NWP) were evaluated. • The cropping distribution is optimized based on maximization of CWP, EWP, and NWP. • Different optimization scenarios provide effective references for decision makers. [ABSTRACT FROM AUTHOR]

Conventionally, canal water is distributed empirically regardless of soil water status at the time in irrigation districts of China, and the mismatch between the timing of water delivery and crop water demand often leads to the reduction in total production and irrigation water efficiency. In the study, an optimization model with minimizing both the difference between the time of water delivery and water demand, and fluctuation of discharge of the main canal as objectives was developefd for a two-stage canal system. The genetic algorithm was used to solve the problem. The irrigation time to meet the water demand in the optimization model was determined using a field water balance model for the command area of secondary canals. The optimization model was applied to the Xixun irrigation district located at Zhangye, Gansu Province, Northwest China. The performance of the model was evaluated by comparing crop water consumption and water productivity (WP) between optimized with conventional scheduling. The results showed that the optimized scheduling could achieve greater total production, less water loss, higher water use efficiency (WUE) and irrigation water productivity (IWP) under both full and reduced capacity conditions. Specifically, the optimized scheduling led to 20% greater total production, 33% less water loss, and more than 20% larger IWP than the conventional scheduling under both conditions. The optimization model could be a valuable tool in improving WP over the command area by adjusting water delivery and irrigation scheduling for irrigation districts. • A canal water delivery model for optimizing irrigation scheduling was established. • The model could increase crop ET and maintain stable canal discharge rate. • The optimization model could improve WUE and IWP. • The optimization model worked under both full and reduced capacity scenarios. [ABSTRACT FROM AUTHOR]

Abstract: Partial root-zone irrigation (PRI) is an effective water-saving irrigation method but the heterogeneous soil moisture distribution that may affect soil enzymatic activities and crop water use. With pot-grown maize, we investigated the dry mass accumulation, crop water-use efficiency and the activities of four major soil enzymes from jointing to grain filling stages of maize plants subjected to PRI and also different ratios of inorganic to organic N fertilizers. Three irrigation methods, i.e. conventional irrigation (CI), alternate PRI (APRI) and fixed PRI (FPRI) and three ratios of inorganic to organic N, i.e. 100% inorganic (F1), 70% inorganic+30% organic (F2) and 40% inorganic+60% organic (F3), were applied. Compared to CI, PRI reduced total dry mass and water consumption of maize by 9.5 and 15.7%, respectively, which led to an increase of canopy water-use efficiency by 7.4%. Within the same irrigation method (CI, APRI or FPRI), added organic N increased total dry mass and canopy WUE. During the whole period, maximal soil catalase, urease and acid-phosphatase activities occurred in the wet root-zone of PRI, but maximal invertase activity occurred in the dry root-zone of PRI. When organic N was the most (F3), APRI increased soil catalase, urease and invertase activities at jointing stage if compared to CI, but PRI reduced the acid-phosphatase activity from jointing to filling stages. Soil catalase, urease and invertase activities generally increased with more organic manure, but the maximal acid-phosphatase activities occurred under moderate amount of organic N (F2). Our results indicate that APRI increases canopy WUE and the catalase, urease and invertase activities in its wet zone and organic N plays a major role in enhancing canopy WUE and soil enzymatic activities. [Copyright &y& Elsevier]