An Integrated Yield-Based Methodology for Improving Soil Nutrient Management at a Regional Scale

Mingkai Qu,Biao Huang,Hongbo Liu,Jinfen Li,Yongcun Zhao,Xu Guang

doi:10.3390/agronomy12020298

Mingkai Qu, Biao Huang + Show 4 more

Open Access

https://doi.org/10.3390/agronomy12020298

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Abstract

The relationships between crop yield and its selected related impact factors has often been explored using ordinary least squares regression (OLSR). However, this model is non-spatial and non-robust. This study first used stepwise regression to identify the main factors affecting winter wheat yield from twelve potential related factors in Yucheng County, China. Next, robust geographically weighted regression (RGWR) was used to explore the spatially non-stationary relationships between wheat yield and its main impact factors. Then, its modeling effect was compared with that of GWR and OLSR. Last, robust geostatistical analysis was conducted for spatial soil management measures in low-yield areas. Results showed that: (i) three main impact factors on wheat yield were identified by stepwise regression, namely soil organic matter, soil total phosphorus, and pH; (ii) the spatially non-stationary effects of the main impact factors on wheat yield were revealed by RGWR but were ignored by OLSR; (iii) RGWR obtained the best modeling effect (RI = 52.31%); (iv) robust geostatistics obtains a better spatial prediction effect and the low-yield areas are mainly located in the northeast and the middle east of the study area. Therefore, the integrated yield-based methodology effectively improves soil nutrient management at a regional scale.

Highlights

With a growing human population [1], global food demand will double by 2050 [2]
This study mainly focused on improving soil nutrient management at the county scale to increase wheat yield
robust geographically weighted regression (RGWR) was established to explore the relationships between winter wheat yield and its main related soil factors, and its modeling effect was further compared with the traditionally-used ordinary least squares regression (OLSR) and Geographically weighted regression (GWR)

Summary

Introduction

In. China, 30% to 50% more food will be needed to meet the country’s demands in the two to three decades [3]. Wheat is a staple food in China, and increasing wheat yield per unit area is crucial to ensuring the country’s food supply. Wheat yield may be influenced by many factors, such as soil physical and chemical properties, climate, irrigation, tillage, and so on [5,6,7,8,9,10,11,12]. Since the influence degree of environmental factors in each agricultural area is not identical, the main limiting factors for crop yield are usually different. In an agricultural areas rich in a certain soil nutrient, this nutrient is often not the key limiting factor for crop growth and development

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