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

Author

Listed:
  • 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
    Precision Agriculture Center, Department of Soil, Water and Climate, University of Minnesota, St. Paul, MN 55108, USA)

  • 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)

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.

Suggested Citation

  • Xinbing Wang & Yuxin Miao & Rui Dong & Zhichao Chen & Yanjie Guan & Xuezhi Yue & Zheng Fang & David J. Mulla, 2019. "Developing Active Canopy Sensor-Based Precision Nitrogen Management Strategies for Maize in Northeast China," Sustainability, MDPI, vol. 11(3), pages 1-26, January.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:3:p:706-:d:201760
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    References listed on IDEAS

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    2. Xin Zhang & Eric A. Davidson & Denise L. Mauzerall & Timothy D. Searchinger & Patrice Dumas & Ye Shen, 2015. "Managing nitrogen for sustainable development," Nature, Nature, vol. 528(7580), pages 51-59, December.
    3. Wu, Dali & Xu, Xinxing & Chen, Yanling & Shao, Hui & Sokolowski, Eldad & Mi, Guohua, 2019. "Effect of different drip fertigation methods on maize yield, nutrient and water productivity in two-soils in Northeast China," Agricultural Water Management, Elsevier, vol. 213(C), pages 200-211.
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    Cited by:

    1. Guopeng Jiang & Miles Grafton & Diane Pearson & Mike Bretherton & Allister Holmes, 2019. "Integration of Precision Farming Data and Spatial Statistical Modelling to Interpret Field-Scale Maize Productivity," Agriculture, MDPI, vol. 9(11), pages 1-22, November.
    2. Zhao, Jin & Yang, Xiaoguang & Liu, Zhijuan & Pullens, Johannes W.M. & Chen, Ji & Marek, Gary W. & Chen, Yong & Lv, Shuo & Sun, Shuang, 2020. "Greater maize yield improvements in low/unstable yield zones through recommended nutrient and water inputs in the main cropping regions, China," Agricultural Water Management, Elsevier, vol. 232(C).

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