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基于土壤和作物数据的玉米生产氮素空间管理策略。

Spatial management strategies for nitrogen in maize production based on soil and crop data.

机构信息

Department of Agricultural, Forest and Food Sciences, University of Turin, Grugliasco, Italy.

St-Jean-sur-Richelieu R&D Centre, Agriculture and Agri-Food Canada/Government of Canada, Saint-Jean-sur-Richelieu, QC, Canada.

出版信息

Sci Total Environ. 2019 Dec 20;697:133854. doi: 10.1016/j.scitotenv.2019.133854. Epub 2019 Aug 9.

Abstract

Nitrogen (N) fertilisation determines maize grain yield (MGY). Precision agriculture (PA) allows matching crop N requirements in both space and time. Two approaches have been suggested for precision N management, i.e. management zones (MZ) delineation and crop remote and proximal sensing (PS). Several studies have demonstrated separately the advantages of these approaches for precision N application. This study evaluated their convenient integration, considering the influence of different PA techniques on MGY, N use efficiency (NUE), and farmer's net return, then providing a practical tool for choosing the fertilisation strategy that best applies in each agro-environment. A multi-site-year experiment was conducted between 2014 and 2016 in Colorado, USA. The trial compared four N management practices: uniform N rate, variable N rate based on MZ (VR-MZ), variable N rate based on PS (VR-PS), and variable N rate based on both PS and MZ (VR-PSMZ), based on their effect on MGY, partial factor productivity (PFP), and net return above N fertiliser cost (RANC). Maize grain yield and PFP maximisation conflicted in several situations. Hence, a compromise between obtaining high yield and increasing NUE is needed to enhance the overall sustainability of maize cropping systems. Maximisation of RANC allowed defining the best N fertilisation practice in terms of profitability. The spatial range in MGY is a practical tool for identifying the best N management practice. Uniform N supply was suitable where no spatial pattern was detected. If a high spatial range (>100 m) existed, VR-MZ was the best approach. Conversely, VR-PS performed better when a shorter spatial range (<16 m) was detected, and when maximum variability in crop vigour was observed across the field (range of variation = 0.597) leading to a larger difference in MGY (range of variation = 13.9 Mg ha). Results indicated that VR-PSMZ can further improve maize fertilisation for intermediate spatial structures (43 m).

摘要

氮(N)施肥决定了玉米籽粒产量(MGY)。精准农业(PA)允许在空间和时间上匹配作物的 N 需求。已经提出了两种用于精准 N 管理的方法,即管理区(MZ)划定和作物远程和近程感知(PS)。多项研究分别证明了这些方法在精准 N 应用方面的优势。本研究评估了它们的便捷整合,考虑了不同 PA 技术对 MGY、N 利用效率(NUE)和农民净收益的影响,然后为选择最适合每个农业环境的施肥策略提供了一种实用工具。2014 年至 2016 年在美国科罗拉多州进行了一项多地点-多年度试验。该试验比较了四种 N 管理措施:均匀 N 率、基于 MZ 的可变 N 率(VR-MZ)、基于 PS 的可变 N 率(VR-PS)和基于 PS 和 MZ 的可变 N 率(VR-PSMZ),基于它们对 MGY、偏生产力(PFP)和肥料氮成本以上的净收益(RANC)的影响。在几种情况下,玉米籽粒产量和 PFP 的最大化存在冲突。因此,需要在获得高产量和提高 NUE 之间取得折衷,以提高玉米种植系统的整体可持续性。RANC 的最大化允许根据盈利能力来定义最佳的 N 施肥实践。MGY 的空间范围是识别最佳 N 管理实践的实用工具。在没有检测到空间模式的情况下,均匀的 N 供应是合适的。如果存在高空间范围(>100 m),则 VR-MZ 是最佳方法。相反,当检测到较短的空间范围(<16 m)时,VR-PS 的效果更好,并且当田间作物活力的最大变异性观察到(变化范围=0.597)导致 MGY 更大差异(变化范围=13.9 Mg ha)时,情况也是如此。结果表明,VR-PSMZ 可以进一步改善中间空间结构(43 m)的玉米施肥。

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