A Full-Scale Optimization of a Crop Spatial Planting Structure and its Associated Effects

Driven by the concept of agricultural sustainable development,crop planting structure optimization(CPSO)has become an effective measure to reduce regional crop water demand,ensure food security,and protect the environment.However,traditional optimization of crop planting structures often ignores the impact on regional food supply–demand relations and interprovincial food trading.Therefore,using a system analysis concept and taking virtual water output as the connecting point,this study proposes a theoretical CPSO framework based on a multi-aspect and full-scale evaluation index system.To this end,a water footprint(WF)simulation module denoted as soil and water assessment tool–water footprint(SWAT-WF)is constructed to simulate the amount and components of regional crop WFs.A multi-objective spatial CPSO model with the objectives of maximizing the regional economic water productivity(EWP),minimizing the blue water dependency(BWFrate),and minimizing the grey water footprint(GWFgrey)is established to achieve an optimal planting layout.Considering various benefits,a fullscale evaluation index system based on region,province,and country scales is constructed.Through an entropy weight technique for order preference by similarity to an ideal solution(TOPSIS)comprehensive evaluation model,the optimal plan is selected from a variety of CPSO plans.The proposed framework is then verified through a case study of the upper–middle reaches of the Heihe River Basin in Gansu province,China.By combining the theory of virtual water trading with system analysis,the optimal planting structure is found.While sacrificing reasonable regional economic benefits,the optimization of the planting structure significantly improves the regional water resource benefits and ecological benefits at different scales.

Analysis and Optimization of Crop Planting Structure in Ningxia

[Objectives]To analyze and optimize the crop planting structure in Ningxia based on the shortage of water resources and the large proportion of agricultural water consumption in Ningxia.[Methods]The change trend of crop planting area and planting structure in Ningxia in 2004-2018 was analyzed,and a multi-objective optimization model was constructed with the objectives of maximum crop profit and minimum water demand.The STEM method was applied to solve the problem,and the optimization scheme of crop planting in Ningxia was obtained.[Results]In Ningxia in 2004-2018,the planting area showed the characteristics of"increase-decrease-increase";the area and proportion of cash crops were increasing,and the proportion of grain crops was gradually decreasing,but the proportion of crops with high water consumption was still high.After the planting structure was optimized,the economic benefit was increased by 34.85×10^(8) yuan,and the water demand was reduced by 3.9×10^(8) m^(3).[Conclusions]Under the premise of ensuring food security,the optimized scheme not only saves water resources but also obtains higher economic benefits.It provides a reference for alleviating water shortage and increasing farmers'income.

Soil moisture response to land use and topography across a semi-arid watershed: Implications for vegetation restoration on the Chinese Loess Plateau

Soil moisture is a limiting factor of ecosystem development in the semi-arid Loess Plateau. Characterizing the soil moisture response to its dominant controlling factors, such as land use and topography, and quantifying the soil-water carrying capacity for revegetation is of great significance for vegetation restoration in this region. In this study, soil moisture was monitored to a depth of 2 m in three land use types(native grassland, introduced grassland,and forestland), two landforms(hillslope and gully),and two slope aspects(sunny and shady) in the Nanxiaohegou watershed of the Loess Plateau,Northwest China. The MIKE SHE model was then applied to simulate the soil moisture dynamics under different conditions and determine the optimal plant coverage. Results showed that the average soil moisture was higher in native grassland than in introduced grassland and Platycladus orientalis forestland for a given topographic condition;however,a high soil moisture content was found in Robinia pseudoacacia forestland, with a value that was even higher than the native grassland of a sunny slope. The divergent results in the two forestlands were likely attributed to the differences in plant coverage. Gully regions and shady slopes usually had higher soil moisture, while lower soil moisture was usually distributed on the hillslope and sunny slope.Furthermore, the mean absolute relative error and Nash-Sutcliffe efficiency coefficient of the MIKE SHE model ranged between 2.8%–7.8% and 0.550–0.902,respectively, indicating that the model could effectively simulate the soil moisture dynamics. The optimal plant coverage was thus determined for hillslope P. orientalis by the model, corresponding to a leaf area index(LAI) value of 1.92. Therefore, for sustainable revegetation on the Loess Plateau,selecting suitable land use types(natural vegetation),controlling the planting density/LAI, and selecting proper planting locations(gully and shady slope regions) should be considered by local policy makers to avoid the over-consumption of soil water resources.

Integrated uncertain models for runoff forecasting and crop planting structure optimization of the Shiyang River Basin,north-west China

To improve the accuracy of runoff forecasting,an uncertain multiple linear regression(UMLR) model is presented in this study. The proposed model avoids the transfer of random error generated in the independent variable to the dependent variable, as this affects prediction accuracy. On this basis, an inexact two-stage stochastic programming(ITSP) model is used for crop planting structure optimization(CPSO) with the inputs that are interval flow values under different probabilities obtained from the UMLR model. The developed system, in which the UMLR model for runoff forecasting and the ITSP model for crop planting structure optimization are integrated, is applied to a real case study. The aim of the developed system is to optimize crops planting area with limited available water resources base on the downstream runoff forecasting in order to obtain the maximum system benefit in the future. The solution obtained can demonstrate the feasibility and suitability of the developed system, and help decision makers to identify reasonable crop planting structure under multiple uncertainties.