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There is an urgent need to ensure regional food security and increase irrigation water productivity in response to water shortages in arid and semi-arid regions. Previous studies of the optimal allocation of irrigation water did not consider simultaneously optimizing across multiple crops or at different growth stages. This paper describes the development of an irrigation water optimization model that uses a crop water allocation priority (CWAP) model. The CWAP value was determined by quantifying the changes in three indicators: yield, economic benefits, and irrigation water productivity. Maximum yield, maximum economic benefits, and minimum irrigation shortage (at the critical crop and growth stage) were used as the objective functions of a non-linear multi-objective optimization model. The largest irrigation district in the northern arid area of China, Hetao Irrigation District (HID), was chosen to prototype this model. The optimization results, using CWAP, showed that yield, economic benefits, irrigation water productivity, and water productivity could be increased, respectively, by up to 13.38%, 13.40%, 2.30%, and 6.29%, for most crops when compared with optimization results without CWAP. Comparison of the optimized net irrigation quantities with the actual net irrigation quantities showed that optimization reduced water usage by up to 60.77% for wheat, 51.24% for corn, and 63.59% for sunflower. Blue water utilization under optimal irrigation conditions decreased by 1.12% for wheat, 2.91% for corn, and 9.91% for sunflower, compared with those in actual irrigation scenario. This method of optimizing irrigation water allocation in arid areas using CWAP provides decision-makers with accurate water-saving irrigation protocols that will reduce demand for water resources and promote sustainable agriculture. [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]

Precision agriculture has been increasingly practised in recent years. Under precision management, farmland is divided into several management zones to implement different strategies and improve irrigation water productivity and revenues. In this research, six soil properties (silt, sand, soil moisture content, available nitrogen, electrical conductivity and elevation) were selected, and fuzzy c-means clustering was used to delineate management zones. The field was divided into three zones. The differences in the mean of the soil properties among the zones were large and within a zone were small. The coefficient of variation of the properties and yield were also smaller than that before classification. The optimization was carried out by using a genetic algorithm based on the Jensen model. Three objective functions were set as maximum yield, maximum irrigation water productivity (IWP) and maximum revenue, and the weights were kept equal to 1/3. The WHCNS (soil water heat carbon nitrogen simulator) model was used to simulate the maize yield under optimized irrigation schedule for the three management zones and to calculate IWP and revenue. Compared with uniform management, IWP and revenue were increased by 0.6 kg m−3 and $61 ha−1, respectively. The optimized irrigation schedule can be used as a reference for the actual irrigation management. It can increase the IWP and revenue under the premise of achieving the target yield. The results show that the method can guide precision agricultural production and management in large-scale farmland. [ABSTRACT FROM AUTHOR]

• PET and ground-based ET 0 estimation are integrated to reflect the spatiotemporal variability and improve ET 0 accuracy. • A distributed interval nonlinear multiobjective programming (DINMP) model is proposed. • Uncertainties, conflicting objectives, spatial information, and marginal benefit are fully considered in DINMP model. • DINMP is applied to a real-world case and the results can contribute to the sustainable development of agriculture. Considering the uncertainties in agricultural system and spatiotemporal variability in evapotranspiration and precipitation, a distributed interval nonlinear multiobjective programming (DINMP) model was developed for optimal allocation of limited irrigation water resources in the middle reaches of Heihe River basin. The meteorological data from meteorological stations were used to estimate reference crop evapotranspiration (ET 0) through FAO56 Penman-Monteith (PM) equation, and then the remote sensing MOD16/PET data were fitted by linear regression model according to the FAO56 PM results. The 95% confidence interval was used to further improve the accuracy of the fitting results. Thus, satellite-based potential evapotranspiration (PET) and ground-based ET 0 estimation were integrated to not only reflect the spatial and temporal variability but also guarantee the accuracy of the ET 0. In the terms of precipitation, spatial interpolation was used to spatial information of precipitation. Based on these spatiotemporal data, a DINMP with three objectives, including maximizing economic benefits and water saving as well as minimizing water shortage of critical growth periods, was formulated, and further solved by fuzzy coordination method. The optimal allocation scheme improves the irrigation water productivity by [0.50, 0.66] kg/m3, and decreases net irrigation water allocation by [0.33, 1.01] ×108 m3. These results show that DINMP can not only consider the uncertainties and multiple objectives in agricultural water management, but also improve the spatial resolution of optimal water allocation strategies. The framework of this study can provide a reference for agricultural water managers in similar areas to obtain more reasonable water allocation schemes. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel regional grid-scale crop dynamic layout optimization model was constructed. • Multiyear crop layout plan was obtained by considering inter-annual impacts. • Spatial variance of parameters and neighbourhood effects of crop units were noted. • Overall government planning and farmers' crop rotation behaviour were regarded. • Stakeholders and their multi-objective planting preferences were balanced. Rapid population growth has led to an increasing demand for food, accompanied by an increase in the resources invested in producing food, and the extensive food production practices have placed a huge burden on the environment. There is an urgent desire of need to find a sustainable model of food production. Therefore, this study attempted to develop a dynamic regional grid-scale crop layout model (ABCAMODP-Crop) combining agent-based model, multi-objective programming, dynamic programming and cellular automata model to promote more sustainable food production from a supply-side perspective. The model can: (1) take into account the planting preferences and practical behaviours of government and farmer as stakeholders in crop layout; (2) further weigh the conflicts among the various objectives that stakeholders are concerned with from resource, environmental, nutritional and economic dimensions; (3) consider the impact of the previous year's crop layout on the next year's crop placement; and (4) obtain grid-scale accurate crop layout solutions by considering the spatial variability of optimization parameters and the neighborhood effects of crop layout cells. The model was applied to the Shiyang River Basin in China to verify its usefulness. Over the entire planning period, crop layout schemes optimized with the objective of integrating irrigation water productivity, environmental impact, nutrient availability and economic benefits have higher comprehensive benefits than the original scheme, with an increase in average irrigation water productivity per unit area of 14.55%, an increase in total yield of 14.45%, an increase in total carbohydrate availability of 7.39%, an increase in total calcium availability of 18.74%, an increase in total net carbon sequestration of 8.05%, an increase in total net benefit of 20.08%. The model developed allows for a holistic view of crop layout and guides decision-makers in a rational distribution of regional crops, thus promoting more sustainable food production. It can equally be extended to other regions as a generic modelling framework. [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]