Narrowing Yield Gaps and Enhancing Nitrogen Utilization for Summer Maize (Zea mays L) by Combining the Effects of Varying Nitrogen Fertilizer Input and Planting Density in DSSAT Simulations.

Hao Ren,Qinglong Yang,Yi Cheng,Rongfa Li,Peng Liu,Jibo Zhang,Shuting Dong,Jiwang Zhang,Zhenhai Li,Bin Zhao

doi:10.3389/fpls.2020.560466

Abstract

In China, the most common grain crop is maize (Zea mays). The increasing pressure to meet the food demands of its growing population has pushed Chinese maize farmers toward an excessive use of chemical fertilizers, a practice which ultimately leads to a massive waste of resources and widespread environmental pollution. As a result, increasing the yield and improving the nitrogen (N) use efficiency of maize has become a critical issue for agriculture in China. This study, which analyzes the combined data from a simulation carried out using the Decision Support System for Agrotechnology Transfer (DSSAT), a field experiment, and a household survey, explored the effectiveness of several approaches aimed at narrowing the maize yield gap and improving the N utilization efficiency in the Huang-Huai-Hai Plain (HHHP), the most important area for the production of summer maize in China. The various approaches we studied deploy different methods for the integrated management of N fertilizer input and the planting density. The study produced the following results: (1) For the simulated and actual maize yields, the root mean square error (RMSE), the normalized root mean squared errors (NRMSE) and the index of agreement (d) were 1,171 (kg ha–1), 12% and 0.84, respectively. These results show that the model is viable for the experiment included in the study; (2) The potential yield was 15.58 t ha–1, and the yields achieved by the super-high-yield cultivation pattern (SH), the optimized nutrient and density management pattern (ONM), the simulated farmer’s practice cultivation pattern (FP) and actual farmer’s practice (AFP) were 11.43, 11.06, 10.33, and 7.95 t ha–1, respectively. The yield gaps associated with the different yield levels were large; (3) For summer maize, the high yield and a high N partial factor productivity (NPFP) was found when applying a planting density of 9 plants m–2 and an N application amount of 246 kg ha–1. These results suggest that the maximum yield that can actually be achieved by optimizing the N application and planting density is less than 73% of the potential yield. This implies in turn that in order to further narrow the observed yield gaps, other factors, such as irrigation, sowing dates and pest control need to be considered.

Highlights

Between 2000 and 2050, the global maize production is expected to grow by more than 450 million tons in order to meet the demands posed by population growth and improving living standards (Hubert et al, 2010)
We set up three experimental treatments and defined the following three corresponding yield levels: SH, defined as the maximum yield under local climatic conditions with unlimited supply of nutrients and water; ONM, defined as the yield obtained after optimizing the fertilization program and planting density; and FP, the yield achieved by simulating the farmers’ management practices at the experimental station
The DSSAT model has been widely used to provide an accurate basis for cultivation measures like N application, planting density and irrigation amount, and it has been improved constantly over the years

Summary

Introduction

Between 2000 and 2050, the global maize production is expected to grow by more than 450 million tons in order to meet the demands posed by population growth and improving living standards (Hubert et al, 2010). China’s actual maize yield is only 58% of the average potential yield of 13,875 kg ha−1, which makes for a yield gap of 42% (Global Yield Gap Atlas, 2019). This yield gap is the result of farmers not using suitable cultivars and production techniques. Narrowing the yield gap is a logical and modern strategy that has been touted as a solution to the world’s need to increase global food production (Lobell et al, 2009; Liu et al, 2016; Marloes et al, 2019). All studies agree that quantifying the potential yield and the yield gap for maize could help to reveal the factors that limit the yield, and lead to suggestions for technical management measures to narrow the existing yield gap (Wang et al, 2014; Liu et al, 2016; Agus et al, 2019; Marloes et al, 2019)

Objectives

Methods

Results

Discussion

Conclusion