Allocation and management of agricultural water resources is an emerging concern due to diminishing water supplies and increasing water demands. To achieve economic, social, and environmental goals in a specific irrigation district, decisions should be made subject to the changing water supply and water demand—the two critical random parameters in agricultural water resources management. This paper presents the foundations of a systematic framework for agricultural water resources management, including determination of distribution functions, joint probability of water supply and water demand, optimal allocation of agricultural water resources, and evaluation of various schemes according to agricultural water resources carrying capacity. The maximum entropy method is used to estimate parameters of probability distributions of water supply and demand, which is the basic for the other parts of the framework. The entropy-weight-based TOPSIS method is applied to evaluate agricultural water resources allocation schemes, because it avoids the subjectivity of weight determination and reflects the dynamic changing trend of agricultural water resources carrying capacity. A case study using an irrigation district in Northeast China is used to demonstrate the feasibility and applicability of the framework. It is found that the framework works effectively to balance multiple objectives and provides alternative schemes, considering the combinatorial variety of water supply and water demand, which are conducive to agricultural water resources planning.

Agricultural water scarcity is a global problem and this reinforces the need for optimal allocation of irrigation water resources. However, decision makers are challenged by the complexity of fluctuating stream condition and irrigation quota as well as the dynamic changes of the field water cycle process, which make optimal allocation more complex. A two-stage chance-constrained programming model with random parameters in the left- and right-hand sides of constraints considering field water cycle process has been developed for agricultural irrigation water allocation. The model is capable of generating reasonable irrigation allocation strategies considering water transformation among crop evapotranspiration, precipitation, irrigation, soil water content, and deep percolation. Moreover, it can deal with randomness in both the right-hand side and the left-hand side of constraints to generate schemes under different flow levels and constraint-violation risk levels, which are informative for decision makers. The Yingke irrigation district in the middle reaches of the Heihe River basin, northwest China, was used to test the developed model. Tradeoffs among different crops in different time periods under different flow levels, and dynamic changes of soil moisture and deep percolation were analyzed. Scenarios with different violating probabilities were conducted to gain insight into the sensitivity of irrigation water allocation strategies on water supply and irrigation quota. The performed analysis indicated that the proposed model can efficiently optimize agricultural irrigation water for an irrigation district with water scarcity in a stochastic environment.

An evaluation of soil quality sustainability can support decision making for the sustainable use of land resources. However, certain current problems associated with these evaluations remain unaddressed, e.g., the evaluation indicators do not fully reflect soil quality risks and the evaluation scale is not sufficiently small. In this study, 25,000 spatial grids of dimensions 3 km × 3 km are used to divide the major grain-producing regions in China, namely, the Sanjiang Plain and the Songnen Plain of Heilongjiang. Then, the soil erosion modulus, nutrient balance index, soil organic carbon (SOC) storage, heavy metal soil pollution index and crop productivity are calculated for each grid using the RULSE model, nutrient balance index model, soil type method, geoaccumulation index method and mechanism method, respectively. A spatial grid cluster analysis method is used to thoroughly evaluate and analyze the sustainability of soil quality in each grid. The results show that the overall soil status of the study area is good. The soil and water conservation levels are high, the soils show low levels of contamination, the crop production potential is high and the ratio of highly sustainable to moderately sustainable soils is approximately 2:1. Only 2.74% of the land is rated extremely unsustainable and needs to be restored to a basic level of productivity before subsequent functional restoration can be carried out. This study provides a new method for the fine-scale evaluation of soil quality and contributes to the management of land resources.