Modelling Fertilizer Use in Relation to Farmers’ Household Characteristics in Three Gorges Reservoir Area, China

Lihua Ma, Jiupai Ni, Luuk Fleskens, Han Wang and Yunqing Xuan
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Lihua Ma: College of Resources and Environment, Southwest University, Beibei, Chongqing 400715, China
Jiupai Ni: College of Resources and Environment, Southwest University, Beibei, Chongqing 400715, China
Luuk Fleskens: Soil Physics and Land Management Group, Wageningen University, 6708 PB Wageningen, The Netherlands
Han Wang: College of Engineering, Swansea University, Swansea SA2 8PP, UK
Yunqing Xuan: College of Engineering, Swansea University, Swansea SA2 8PP, UK

Agriculture, 2021, vol. 11, issue 6, 1-17

Abstract: Non-point source pollution from excessive use of fertilizers in agriculture is a major cause of the eutrophication problem in China. Understanding farmers’ decision-making concerning fertilization and identifying the influencing factors in this process are key to tackling overfertilization and related pollution issues. This paper reports a study on modelling decisions about fertilizer use based on data collected from 200 farmer households in the Three Gorges Reservoir area of China, using a well-fitted artificial neural network (ANN) with incorporated variance-based sensitivity analysis. The rate of fertilizer use estimated from the model is in good agreement with observed data. The model is further validated and tested by comparing the simulated and observed values. Results show that the model is able to identify the influencing factors and their interactions causing the variation in fertilizer use and to help pinpoint the underlying reasons. It is found that the farmers’ fertilization behavior is greatly affected by the area of cultivated land, followed by the interaction among farmers’ education level, annual income, and awareness of the importance of environmental protection. Future land consolidation is one of several ways to achieve more sustainable fertilization strategies.

Keywords: farmer behavior; fertilizer use; artificial neural networks; uncertainty analysis (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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