Economic Optimal Nitrogen Rate Variability of Maize in Response to Soil and Weather Conditions: Implications for Site-Specific Nitrogen Management

Xinbing Wang,Rui Dong,Krzysztof Kusnierek,David J Mulla,Guohua Mi,Zhichao Chen,Yuxin Miao

doi:10.3390/agronomy10091237

Abstract

The dynamic interactions between soil, weather and crop management have considerable influences on crop yield within a region, and should be considered in optimizing nitrogen (N) management. The objectives of this study were to determine the influence of soil type, weather conditions and planting density on economic optimal N rate (EONR), and to evaluate the potential benefits of site-specific N management strategies for maize production. The experiments were conducted in two soil types (black and aeolian sandy soils) from 2015 to 2017, involving different N rates (0 to 300 kg ha−1) with three planting densities (55,000, 70,000, and 85,000 plant ha−1) in Northeast China. The results showed that the average EONR was higher in black soil (265 kg ha−1) than in aeolian sandy soil (186 kg ha−1). Conversely, EONR showed higher variability in aeolian sandy soil (coefficient of variation (CV) = 30%) than in black soil (CV = 10%) across different weather conditions and planting densities. Compared with farmer N rate (FNR), applying soil-specific EONR (SS-EONR), soil- and year-specific EONR (SYS-EONR) and soil-, year-, and planting density-specific EONR (SYDS-EONR) would significantly reduce N rate by 25%, 30% and 38%, increase net return (NR) by 155 $ ha−1, 176 $ ha−1, and 163 $ ha−1, and improve N use efficiency (NUE) by 37–42%, 52%, and 67–71% across site-years, respectively. Compared with regional optimal N rate (RONR), applying SS-EONR, SYS-EONR and SYDS-EONR would significantly reduce N application rate by 6%, 12%, and 22%, while increasing NUE by 7–8%, 16–19% and 28–34% without significantly affecting yield or NR, respectively. It is concluded that soil-specific N management has the potential to improve maize NUE compared with both farmer practice and regional optimal N management in Northeast China, especially when each year’s weather condition and planting density information is also considered. More studies are needed to develop practical in-season soil (site)-specific N management strategies using crop sensing and modeling technologies to better account for soil, weather and planting density variation under diverse on-farm conditions.

Highlights

Improper nitrogen (N) management in current crop production systems has become a growing concern among governments, scientists and farmers around the world [1,2,3]
Two field locations within the study site with contrasting soil types were selected for this study: one field with a black soil equivalent to typical Haploboroll and the other field with an aeolian sandy soil equivalent to typical Cryopsamments according to the United States Department of Agriculture (USDA) Soil Taxonomy [42]
According to the results of ANOVA (Table 1), maize yield, aboveground biomass (AGB), PNC, and nutrition index (NNI), were all significantly affected by soil type, year with its weather pattern, N rate, and their interactions

Summary

Introduction

Improper nitrogen (N) management in current crop production systems has become a growing concern among governments, scientists and farmers around the world [1,2,3]. Optimizing N management in agriculture is crucially important for food security, environmental protection, and sustainable development [3,4,5] This is true for China, the world’s largest producer, consumer and importer of chemical fertilizers [6,7]. Chinese scientists have been promoting a regional optimal N management (RONM) strategy to avoid significant over- or under-application problems [5,8]. If it were adopted for maize (Zea mays L.) production across China, more than 1.4 million tons N fertilizer and 18.6 million tons of greenhouse gas (GHG) emission would be reduced [9]. Due to the significant field-to-field and within-field variability of indigenous soil N supply and crop N demand, this fixed rate and timing strategy will unavoidably result in sub-optimal N management in different fields within a region [8,10]

Objectives

Methods

Discussion

Conclusion