Optimization of Grain for Green Program based on sediment reduction and economic benefits

The Grain for Green Program (GGP) is an effective way to control soil erosion and improve the eco-environment in China. How to develop the most cost-effective GGP scheme, which can balance the contradiction among ecology, economy, and food security, is the key point for the sustainable development of the GGP. In this study, Xixian watershed located in the upper reaches of the Huaihe River is taken as the study area. Based on the current land use situation, the distributed hydrological model SWAT (Soil and Water Assessment Tool) was used to simulate the runoff and sediment processes. Then sediment reduction resultants per unit area of GGP, which defined as the sediment reduction coefficients, were obtained by simulating the successive GGP operations in each sub-basin based on the validated SWAT model. Meanwhile, considering the spatial correspondence between GDP (gross domestic product) and land use type, GDP loss coefficients at sub-basin scale were obtained by overlapping the GDP map and current land use map. On this basis, the ecological benefit and economic benefit of the GGP operation was expressed by sediment reduction coefficients and GDP loss coefficients respectively. Finally, the multi-objective genetic algorithm NSGA-II was used to optimize the GGP scheme at sub-basin scale. The results showed that 1) The SWAT model performed high simulation accuracy for runoff and sediment modeling. The Nash–Sutcliffe coefficients were above 0.90 and 0.70, the deterministic coefficients were both greater than 0.80, and the percentage deviation of the total amount is controlled within −20% to 20%, respectively. It can be conclude that the SWAT model can be used to evaluate the impact of the GGP on sediment reduction. 2) The sediment reduction coefficients ranged from 26.70 to 2 675.85 t/km², decreasing gradually from the upper reaches to the lower reaches, which indicated that implementation of GGP per unit area can reduce sediment more effectively in the upstream river source area. 3) The GDP loss coefficient presented spatial differences significantly with the range from −5 756.83 yuan/km² to 136.26 yuan/km², showing that both increased GDP (i.e GDP loss coefficient values were greater than 0) and decreased GDP (i.e GDP loss coefficient values were less than 0) could be observed among sub-basins. Notably, sub-basins where the values of the GDP loss coefficient appeared to be the smallest were mainly concentrated in the main residential areas of cities and towns. That is, the GGP in these sub-basins would prove more costly. 4) The GGP schemes obtained by multi-objective optimization maintained the per capita cultivated land area between 1.04×10⁻³ and 1.54×10⁻³ km², which was significantly higher than the warning level of food security. Meanwhile, the Pareto-optimal set was able to reduce sediment yield by 53.54% to 69.86% of the initial value and still achieve the sustainable soil erosion level, while only losing 30.13% to 37.67% of the initial economic output. The GGP optimization method proposed in this study based on ecological sediment reduction benefits and economic benefits can provide reference and guidance for scientific planning GGP and other soil and water conservation measures.