Developing Active Canopy Sensor-Based Precision Nitrogen Management Strategies for Maize in Northeast China

Xinbing Wang,Xuezhi Yue,Yanjie Guan,Zhichao Chen,Yuxin Miao,Zheng Fang,Rui Dong,David Mulla

doi:10.3390/su11030706

Abstract

Precision nitrogen (N) management (PNM) strategies are urgently needed for the sustainability of rain-fed maize (Zea mays L.) production in Northeast China. The objective of this study was to develop an active canopy sensor (ACS)-based PNM strategy for rain-fed maize through improving in-season prediction of yield potential (YP0), response index to side-dress N based on harvested yield (RIHarvest), and side-dress N agronomic efficiency (AENS). Field experiments involving six N rate treatments and three planting densities were conducted in three growing seasons (2015–2017) in two different soil types. A hand-held GreenSeeker sensor was used at V8-9 growth stage to collect normalized difference vegetation index (NDVI) and ratio vegetation index (RVI). The results indicated that NDVI or RVI combined with relative plant height (NDVI*RH or RVI*RH) were more strongly related to YP0 (R2 = 0.44–0.78) than only using NDVI or RVI (R2 = 0.26–0.68). The improved N fertilizer optimization algorithm (INFOA) using in-season predicted AENS optimized N rates better than the N fertilizer optimization algorithm (NFOA) using average constant AENS. The INFOA-based PNM strategies could increase marginal returns by 212 $ ha−1 and 70 $ ha−1, reduce N surplus by 65% and 62%, and improve N use efficiency (NUE) by 4%–40% and 11%–65% compared with farmer’s typical N management in the black and aeolian sandy soils, respectively. It is concluded that the ACS-based PNM strategies have the potential to significantly improve profitability and sustainability of maize production in Northeast China. More studies are needed to further improve N management strategies using more advanced sensing technologies and incorporating weather and soil information.

Highlights

To achieve high grain yield and meet globally increasing food demand, over-application of nitrogen (N) fertilizer has been common in Chinese crop production [1,2,3]
The grain yield and RIHarvest were more variable across fields (CV = 36–37%) than the relative plant height and response index (RI) based on plant height (RIHeight) (CV = 14–17%)
The results indicated that including plant height information together with normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) would improve the prediction of YP0 and RIHarvest than NDVI or RVI alone for two different soil types

Summary

Introduction

To achieve high grain yield and meet globally increasing food demand, over-application of nitrogen (N) fertilizer has been common in Chinese crop production [1,2,3]. Nitrogen fertilizer beyond the amount required by maize can escape from agricultural soils as reactive N (Nr), which can result in unintended adverse environmental and human health impacts [5,6]. Some of these environmental consequences, such as climate change and tropospheric ozone pollution, can negatively affect crop yields [7,8]. Optimizing N management for maize production to minimize the adverse environmental impacts is crucially important for sustainable development of agriculture [5,10]

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