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