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


  • Zhang, Qichen
  • Dong, Weihong
  • Wen, Chuanlei
  • Li, Tong


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.

Suggested Citation

  • Zhang, Qichen & Dong, Weihong & Wen, Chuanlei & Li, Tong, 2020. "Study on factors affecting corn yield based on the Cobb-Douglas production function," Agricultural Water Management, Elsevier, vol. 228(C).
  • Handle: RePEc:eee:agiwat:v:228:y:2020:i:c:s0378377419312120
    DOI: 10.1016/j.agwat.2019.105869

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    References listed on IDEAS

    1. Jagustović, Renata & Zougmoré, Robert B. & Kessler, Aad & Ritsema, Coen J. & Keesstra, Saskia & Reynolds, Martin, 2019. "Contribution of systems thinking and complex adaptive system attributes to sustainable food production: Example from a climate-smart village," Agricultural Systems, Elsevier, vol. 171(C), pages 65-75.
    2. Yoshikawa, Natsuki & Shiozawa, Sho, 2006. "Estimating variable acreage of cultivated paddy fields from preceding precipitation in a tropical watershed utilizing Landsat TM/ETM," Agricultural Water Management, Elsevier, vol. 85(3), pages 296-304, October.
    3. Irmak, Suat & Kukal, Meetpal S. & Mohammed, Ali T. & Djaman, Koffi, 2019. "Disk-till vs. no-till maize evapotranspiration, microclimate, grain yield, production functions and water productivity," Agricultural Water Management, Elsevier, vol. 216(C), pages 177-195.
    4. Marek, Gary & Gowda, Prasanna & Marek, Thomas & Auvermann, Brent & Evett, Steven & Colaizzi, Paul & Brauer, David, 2016. "Estimating preseason irrigation losses by characterizing evaporation of effective precipitation under bare soil conditions using large weighing lysimeters," Agricultural Water Management, Elsevier, vol. 169(C), pages 115-128.
    5. Amita Majumder & Ranjan Ray & Kompal Sinha, 2012. "Calculating Rural-Urban Food Price Differentials from Unit Values in Household Expenditure Surveys: A Comparison with Existing Methods and A New Procedure," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 94(5), pages 1218-1235.
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