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Remote sensing technology is widely used to obtain evapotranspiration (ETa), but whether it can distinguish the differences in farmland energy balance components and ETa under different irrigation methods has not been studied. We used Landsat 8 data as the primary dataset to drive the METRIC model and inverted the surface parameters and ETa of the Shiyang River Basin from 2014 to 2018. After improving the METRIC model using Ta obtained by the regression method instead of interpolation to calculate the net radiation flux (Rn), R2 was improved from 0.45 to 0.53, and the RMSE was reduced from 61 W/m2 to 51 W/m2. The ETa estimation results on satellite overpass days performed well, with R2 equal to 0.93 and RMSE equal to 0.48 mm when compared with the Eddy covariance method (EC) observations. Subsequently, the different growth stages and daily average ETa estimates of maize were compared with three observations (water balance, WB; Bowen ratio and energy balance method, BREB; and EC). The daily estimates of ETa correlate well with the observations of BREB (R2BI = 0.82, R2DI = 0.92; RMSEBI = 0.46 mm/day, RMSEDI = 0.32 mm/day) and EC (R2BI = 0.85, R2DI = 0.92; RMSEBI = 0.45 mm/day, RMSEDI = 0.34 mm/day), and the estimation for drip irrigation was found to be better than for border irrigation. The total accuracy of the ETa estimation on the five-year overpass day of maize farmland reached R2 = 0.93 and RMSE = 0.48 mm. With sufficient remote sensing data, the 4-year average ETa of maize was 31 mm lower for DI than for BI, and the mean value of ETa obtained from the three observation methods was 40 mm. The METRIC model can be used to distinguish ETa differences between the two irrigation methods in maize farmlands. [ABSTRACT FROM AUTHOR]

The allocation of crop spatial pattern has an appreciable impact on achieving sustainable agricultural development. Crop spatial pattern should be adjusted in accordance with the agricultural environment factors and climate change so that we make full use of agricultural resources to produce more food. However, crop suitability has not been adequately considered in crop patterns, ignoring the fact that the fundamental solution to agricultural water scarcity is to promote crop water productivity (CWP). This study aims to reallocate crop spatial patterns while synergistically improving irrigation benefits and water productivity. To achieve this, we developed a minimum cumulative resistance (MCR) model based on ecological movement processes to simulate crop suitability distribution and thus determine crop spatial patterns. The results show that the adjusted crop pattern could increase the regional water productivity by 1.35 times and the net benefits of unit water by 0.94%, and maintained crop yields. Under future climate scenarios, this approach can still ensure irrigation benefits and food security. Therefore, the MCR model provides effective crop patterns and solutions for sustainable agricultural development when facing a complex agricultural environment. [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]

P1, P3, P5 are the irrigation intervals at 6, 10, 14 days, respectively. P5D are treatments with an irrigation interval of P5 The original article has been corrected. [Extracted from the article]

Modelling crop evapotranspiration (ET) response to different planting scenarios in an irrigation district plays a significant role in optimizing crop planting patterns, resolving agricultural water scarcity and facilitating the sustainable use of water resources. In this study, the SWAT model was improved by transforming the evapotranspiration module. Then, the improved model was applied in Qingyuan Irrigation District of northwest China as a case study. Land use, soil, meteorology, irrigation scheduling and crop coefficient were considered as input data, and the irrigation district was divided into subdivisions based on the DEM and local canal systems. On the basis of model calibration and verification, the improved model showed better simulation efficiency than did the original model. Therefore, the improved model was used to simulate the crop evapotranspiration response under different planting scenarios in the irrigation district. Results indicated that crop evapotranspiration decreased by 2.94% and 6.01% under the scenarios of reducing the planting proportion of spring wheat (scenario 1) and summer maize (scenario 2) by keeping the total cultivated area unchanged. However, the total net output values presented an opposite trend under different scenarios. The values decreased by 3.28% under scenario 1, while it increased by 7.79% under scenario 2, compared with the current situation. This study presents a novel method to estimate crop evapotranspiration response under different planting scenarios using the SWAT model, and makes recommendations for strategic agricultural water management planning for the rational utilization of water resources and development of local economy by studying the impact of planting scenario changes on crop evapotranspiration and output values in the irrigation district of northwest China. [ABSTRACT FROM AUTHOR]

It is urgent to determine the crop critical irrigation period and the priority of crop water irrigation in arid and semi-arid regions to cope with agricultural water shortage. In this study, a method combining Agricultural Production Systems Simulator (APSIM) crop model with grey relational analysis was proposed to determine the irrigation priority of wheat, maize, and sunflower in the irrigation water distribution to ensure food security, and applied to the Hetao Irrigation District (HID) in China. Crop irrigation priority was determined by crop yield, water supply demand ratio (SDR), and water productivity (WP). The maximum yield, maximum water productivity and minimum water stress (crop growth stage) were evaluated. The obtained results indicate that: (1) When the irrigation scenario was 100%, the critical irrigation period for wheat was the first 10 days after sowing and 40–61 days after sowing; The critical irrigation priority for maize was 20–30 days after sowing and 61–81 days after sowing; And the critical irrigation priority for sunflower was 30–61 days after sowing. (2) In early May, irrigation priority was wheat priority to maize, while maize was a priority to wheat in mid to late May. In June, sunflower priority to wheat and maize irrigation. From early to middle July, irrigation priority to maize, and from late July to late August, sunflower water irrigation priority to maize. The method of to identify irrigation priority and crop priority to provide irrigation decision for allocating agriculture water resources. [Display omitted] • A novel method based on crop model and grey relational analysis is proposed. • The best irrigation time after sowing wheat, maize, and sunflower is identified. • Priority for intercrop water irrigation at the same time is proposed. • The effects of water stress, water productivity, and yield is considered. [ABSTRACT FROM AUTHOR]

The pressures of frequent extreme weather, water shortages and increasing food demand pose a continued challenge of maintaining the sustainable development of irrigated agricultural systems. Although rational arable land management is fundamental to alleviating these pressures, the relationship between cropping patterns and irrigation sustainability is understudied. Using the Hexi Corridor as an example, a maximum entropy machine learning model was used to determine the optimal cropping pattern based on crop suitability and to explore the impact mechanism of the optimal cropping pattern on the irrigation sustainability index (SI) from the perspective of reliability, resilience, and vulnerability. An optimal cropping pattern was conducive to sustainable irrigation and reduced irrigation water use by 21.03% from 1960 s to 2010 s with no continued agricultural expansion. Thus, the challenges of food security and sustainability for similar regions, and globally, can be met but will require major changes in cropping planning and management. [Display omitted] • Crop suitability is an important basis for determining optimal cropping patterns. • Irrigation sustainability is assessed under optimal cropping patterns. • Adjusting the cropping pattern is an effective way to alleviate water scarcity. • Sustainable irrigation development needs more attention. [ABSTRACT FROM AUTHOR]