Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.
University of Illinois Extension, Urbana, Illinois, United States of America.
PLoS One. 2024 Apr 2;19(4):e0297027. doi: 10.1371/journal.pone.0297027. eCollection 2024.
Sustainable crop production requires adequate and efficient management practices to reduce the negative environmental impacts of excessive nitrogen (N) fertilization. Remote sensing has gained traction as a low-cost and time-efficient tool for monitoring and managing cropping systems. In this study, vegetation indices (VIs) obtained from an unmanned aerial vehicle (UAV) were used to detect corn (Zea mays L.) response to varying N rates (ranging from 0 to 208 kg N ha-1) and fertilizer application methods (liquid urea ammonium nitrate (UAN), urea side-dressing and slow-release fertilizer). Four VIs were evaluated at three different growth stages of corn (V6, R3, and physiological maturity) along with morphological traits including plant height and leaf chlorophyll content (SPAD) to determine their predictive capability for corn yield. Our results show no differences in grain yield (average 13.2 Mg ha-1) between furrow-applied slow-release fertilizer at ≥156 kg N ha-1 and 208 kg N ha-1 side-dressed urea. Early season remote-sensed VIs and morphological data collected at V6 were least effective for grain yield prediction. Moreover, multivariate grain yield prediction was more accurate than univariate. Late-season measurements at the R3 and mature growth stages using a combination of normalized difference vegetation index (NDVI) and green normalized difference vegetation index (GNDVI) in a multilinear regression model showed effective prediction for corn yield. Additionally, a combination of NDVI and normalized difference red edge index (NDRE) in a multi-exponential regression model also demonstrated good prediction capabilities.
可持续的作物生产需要充分和有效的管理措施，以减少过量施氮对环境的负面影响。遥感作为一种低成本、高效率的监测和管理作物系统的工具，已经引起了人们的关注。本研究利用无人机（UAV）获取的植被指数（VIs），来检测玉米（Zea mays L.）对不同氮素用量（0-208 kg N ha-1）和施肥方式（液体尿素硝铵（UAN）、尿素侧施和缓释肥）的响应。在玉米的三个不同生长阶段（V6、R3 和生理成熟），评估了四种 VIs 以及形态特征，包括株高和叶片叶绿素含量（SPAD），以确定它们对玉米产量的预测能力。结果表明，在 156 kg N ha-1和 208 kg N ha-1下，沟施缓释肥与侧施尿素的产量（平均 13.2 Mg ha-1）没有差异。在 V6 期早期收集的早期遥感 VIs 和形态数据对产量预测的效果最差。此外，多元谷物产量预测比单变量更准确。在 R3 期和成熟生长阶段后期，使用归一化差异植被指数（NDVI）和绿色归一化差异植被指数（GNDVI）的组合，在多元线性回归模型中，对玉米产量进行了有效的预测。此外，在多指数幂回归模型中，NDVI 和归一化差异红边指数（NDRE）的组合也表现出了良好的预测能力。
