Spatialization of Actual Grain Crop Yield Coupled with Cultivation Systems and Multiple Factors: From Survey Data to Grid

Jingxin Li,Erqi Xu,Hongqi Zhang

doi:10.3390/agronomy10050675

Abstract

The spatialization of actual grain crop yield helps to understand the spatial heterogeneity of yield and support for the precise farming and targeted farmland management. However, inadequate consideration and quantification of anthropogenic factors affecting the estimation of actual yield distribution easily cause uncertainties in recent researches. Here, we developed a new grain crop yield spatialization (GCYS) model in order to downscale the yield from county to grid scale. The GCYS model is composed of four modules: (a) cultivated land Net Primary Productivity (NPP) calculation module, (b) comprehensive agricultural system construction module, (c) key factors establishment module, and (d) integration and validation module. Its novelty is to realize the actual grain crop yield spatialization from county scale to grid scale by quantifying and spatializing the comprehensive agricultural system when considering the diversity of cultivated structure and field management factors. Taking Guizhou and Guangxi Karst Mountains Region as a study-area, the GCYS model is developed and tested. The determination coefficients of regression analysis between agricultural survey data and spatialization results of paddy rice yield calculated by our model reach 0.72 and 0.76 in 2000 and 2015, respectively (p < 0.01). The results visualize the spatial pattern of actual grain crop yield at the grid scale, which show a gradually decreasing trend from southeast to northwest. With an increase in potential yield and improvement of field management technologies, the actual average yield of grain crops per unit increased form 4745.10 kg/ha of 2000 to 5592.89 kg/ha of 2015. Especially in high-yield zones in southeast area, mechanized cultivation became the dominated factor, rather than chemical fertilizer application. This demonstrates that our model can provide a reference for agricultural resource utilization and policy-making.

Highlights

The actual grain crop yield is affected to different degrees by the physical environment and social factors [1,2,3,4], such as irregular topography [1,3], irrigation [5,6], soil nutrients [1,7], and the application of pesticides and chemical fertilizers [8,9]
The aim at our research is to develop a novel grain crop yield spatialization (GCYS) model, which included: (1) a comprehensive agricultural cultivation system that is suitable for local farming conditions, (2) the spatialization of the core physical and anthropogenic factors correlated to actual grain crop yield, and (3) an accuracy assessment and calibration process at the grid and county scale
Net Primary Productivity (NPP) calculation is the first step for estimating the potential yield of grain crops

Summary

Introduction

The actual grain crop yield is affected to different degrees by the physical environment and social factors [1,2,3,4], such as irregular topography [1,3], irrigation [5,6], soil nutrients [1,7], and the application of pesticides and chemical fertilizers [8,9]. The downscaled grid data can contribute to understanding the spatial heterogeneity of agricultural resource distribution. Identifying the major obstacles or opportunities at Agronomy 2020, 10, 675; doi:10.3390/agronomy10050675 www.mdpi.com/journal/agronomy

Objectives

Methods

Results

Discussion

Conclusion