Study on factors affecting corn yield based on the Cobb-Douglas production function

This paper presents an analysis of the quantitative correlations between corn yield and its influencing factors in Daqing City, China by establishing a Cobb–Douglas production function model. The effective precipitation, corn planting area and chemical fertilizer and pesticide application rates were selected as the influencing factors of corn yield. Using the Cobb–Douglas production function model, the output elasticity and degree of influence for each factor on increasing grain yield were calculated. The current fertilizer and pesticide application rates, effective precipitation and planting area had positive effects on increasing corn yield, and the Daqing City area has the potential to produce more corn. Among the four influencing factors, the amounts of pesticide applications had the greatest impact on corn yield, followed by planting area, amounts of chemical fertilizer applications and, finally, effective precipitation. In this study, we used remote-sensing images combined with meteorological station data to calculate the effective precipitation in corn fields in Daqing City. The accuracy of this method was 0.01%–11.0% greater than that of the traditional effective precipitation calculation method. The innovation was the use of Thiessen polygons to calculate regional precipitation by combining satellite images with ground meteorological station data. The insufficient sensitivity of satellite inversion for precipitation (short and heavy rainfalls cannot be detected) and insufficient temporal resolution were avoided by using precipitation data from ground meteorological stations. Using satellite image interpretations, the weight coefficient of precipitation could be confirmed according to the location and size of the study area, improving the accuracy of Thiessen polygons in calculating regional precipitation. However, this method still has limitations. When calculating precipitation over a short time, it can be limited by the cloudiness of satellite images. When calculating the long-term precipitation trend, it can be limited by incomplete precipitation data from surface meteorological stations.