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

Xinbing Wang, Yuxin Miao, Rui Dong, Zhichao Chen, Yanjie Guan, Xuezhi Yue, Zheng Fang and David J. Mulla
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Xinbing Wang: College of Resources and Environmental Sciences, China Agricultural University, Beijing 10093, China
Yuxin Miao: College of Resources and Environmental Sciences, China Agricultural University, Beijing 10093, China
Rui Dong: College of Resources and Environmental Sciences, China Agricultural University, Beijing 10093, China
Zhichao Chen: School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
Yanjie Guan: College of Resources and Environmental Sciences, China Agricultural University, Beijing 10093, China
Xuezhi Yue: School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
Zheng Fang: School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
David J. Mulla: Precision Agriculture Center, Department of Soil, Water and Climate, University of Minnesota, St. Paul, MN 55108, USA

Sustainability, 2019, vol. 11, issue 3, 1-26

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 (YP 0 ), response index to side-dress N based on harvested yield (RI Harvest ), and side-dress N agronomic efficiency (AE NS ). 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 YP 0 (R 2 = 0.44–0.78) than only using NDVI or RVI (R 2 = 0.26–0.68). The improved N fertilizer optimization algorithm (INFOA) using in-season predicted AE NS optimized N rates better than the N fertilizer optimization algorithm (NFOA) using average constant AE NS . 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.

Keywords: Precision nitrogen management; soil type; plant height; profitability; sustainability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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