Optimization Of Planting Structure In Medium-sized Irrigation Areas Based On Water Footprint

The uneven spatial and temporal distribution of water resources and the imbalance between supply and demand are important factors affecting agricultural development,and the spatial layout of more north and less east and west has caused greater pressure on regional water resources,and improving water use efficiency is of great significance to alleviate water resource pressure and promote agricultural development.The water footprint of crop production can reflect the consumption of water resources,and reducing the water footprint of crop production is an effective way to reduce the pressure on water resources,and optimising the cropping pattern is an effective way to achieve it.Therefore,the temporal and spatial evolution trend of crop production water footprint and its driving factors are analyzed in depth,and a multi-objective planting structure optimisation model is established based on the water footprint theory to obtain the optimization scheme of crop planting structure in irrigated areas,which aims to ensure food security while improving economic benefits,reducing water waste and further promoting sustainable agricultural development.Taking Sanliuzhai Irrigation District as the research area,this paper selects the planting structures of three main crops,such as wheat,maize and peanuts,combines meteorological data with production inputs and other data,the CROPWAT model was used to analyse inter-annual variability in blue water footprint,green water footprint and grey water footprint of crops in the irrigated area from 2010 to 2020,and visually analyzes the spatial layout of crop production water footprint in combination with Arc GIS software.Secondly,combined with the extended STIRPAT model,the response mechanism of crop production water footprint and four influencing factors such as population,economy,technology and meteorology was analyzed by ridge regression analysis.Finally,based on the water footprint theory,a multi-objective planting structure optimization model with maximizing economic benefits,minimizing blue water utilization rate and minimizing grey water footprint is constructed,and the particle swarm-cosine algorithm is used to solve the problem,and the solution results of sine and cosine algorithm and particle swarm optimization are compared and analyzed to obtain a suitable crop planting layout scheme.The results of this study are as follows:(1)A method to quantify the water footprint of crop production based on the CROPWAT model was developed and its spatio-temporal variability characteristics analysed.Combining meteorological data,agricultural statistics and crop data,the CROPWAT model was used to calculate the water footprint of plant evapotranspiration and crop production in the Sanliuzhai Irrigation District from 2010 to 2020,and its spatial and temporal distribution characteristics were analysed using Arc GIS software.The results indicate that the water footprint of crop production present a decreasing trend from the terms of time,and the mean values were arranged in order of size as wheat(1677.05 m~3/t),maize(1573.0 m~3/t)and peanuts(1007.76m~3/t).Among them,the blue water footprint of crops indicate a fluctuating trend of first increasing and then decreasing.The interannual variation characteristics of crop green water footprint are opposite to crop blue water footprint,presenting a fluctuation trend of first decreasing and then increasing.The crop grey water footprint present a fluctuating and decreasing trend during the study period.From the spatial point of view,the areas with high crop production water footprint were distributed in the east and south,among which the blue water footprint and green water footprint of crops in the southern region accounted for a higher proportion,as high as 40.95%and 47.45%.The crop grey water footprint accounted for 11.81%of the crop production water footprint in the eastern region.(2)Factors affecting the water footprint of crop production in the Sanliuzhai Irrigation District were identified.Combining demographic,economic,technological data,an extended STIRPAT model was created to analyze factors affecting the water footprint of crop production,and the influence of nine factors was qualitatively analyzed using SPSS software,population,urbanisation level,net income per farmer,fertiliser application,average wind speed,average precipitation,average sunshine hours,average relative humidity and average temperature.The highest impact is on average precipitation with an impact coefficient of 2.97,followed by average wind speed with an impact coefficient of 0.61 and average relative humidity with the lowest impact coefficient of 0.17;while population,urbanisation level,per capita net income of farmers,fertiliser application,average sunshine hours and average temperature have a suppressive effect on the water footprint of crop production.The largest inhibitory effect on the water footprint is on fertiliser application with a coefficient of-2.52,followed by average temperature with a coefficient of-1.94 and the smallest is on average sunshine hours with a coefficient of-0.22.(3)The planting structure optimisation model based on water footprint will be solved using the particle swarm sine algorithm,and a planting structure optimisation scheme will be created for the Sanliuzhai Irrigation District.Based on the water footprint theory,the optimization model of plantation structure is created and solved with different algorithms based on the water footprint theory and the efficient use of water resources,and the particle swarm-sine algorithm is found to be superior,and the results indicate that the planting area of wheat,maize and peanut after optimization adjustment becomes 17.23 hm~2,14.99 hm~2 and9.06 hm~2,respectively,the added value of crop economic benefits was about 22,500 yuan,blue water utilization rate decreased by 5.21%,and the water footprint of crop production was reduced by 700.56 m~3/t,of which the crop blue water footprint crop was reduced by 845.86m~3/t,the crop green water footprint increased by 498.01 m~3/t compared with the optimization,and the crop grey water footprint decreased by 352.72 m~3/t.