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Ground based active remote sensors for precision nitrogen management in irrigated maize production.

机译:地面有源遥感器,用于灌溉玉米生产中的精确氮管理。

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摘要

Precision agriculture can increase farm input efficiency by accurately quantifying variability within a field. Remotely sensed normalized difference vegetation index (NDVI) has been shown to quantify maize (Zea mays ) N variability. Ground-based active remote sensors that can determine NDVI are commercially available and have been shown to accurately distinguish N variability in maize. There are several active sensors available but no studies directly comparing active sensors have been reported. Therefore, a study was conducted to evaluate active sensor performance and develop an in-season maize N recommendation algorithm for use in Colorado using NDVI. Previous studies have demonstrated an association of active sensor NDVI with maize N content and height. However, the NDVI from a GreenSeeker(TM) green NDVI prototype active sensor had not yet been tested when our study began. Therefore, the green sensor was evaluated to determine if differences in plant growth across MZ could be determined by the active sensor. Results show that the prototype active sensor did not record NDVI values that were associated with MZ. The NDVI from two different sensors (Crop Circle(TM) amber NDVI and GreenSeeker(TM) red NDVI) were then examined under greenhouse and field conditions. Results show that NDVI from the amber and red sensors equally distinguished applied N differences in maize. Each active sensor's NDVI values had high R2 values with applied N rate and plant N concentration. Results also show that each sensor's NDVI readings had high R2 values with applied N rate and yield at the V12 and V14 maize growth stages. An N recommendation algorithm was then created for use at the V12 maize growth stage for both the amber and red sensors using NDVI. These algorithms yielded N recommendations that were not significantly different across sensor type suggesting that the amber and red NDVI sensors performed equally. Also, each N recommendation algorithm yielded unbiased N recommendations suggesting that each was a valid estimator of required N at maize growth stage V12. Overall results show that the amber and red sensors equally determine N variability in irrigated maize and could be very important tools for managing in-season application of N fertilizer.
机译:精确农业可以通过准确量化田间的变异性来提高农场的投入效率。遥感归一化差异植被指数(NDVI)已显示可量化玉米(Zea mays)N的变异性。可以确定NDVI的基于地面的有源远程传感器已在市场上出售,并已显示出可准确区分玉米中N的变异性。有几种有源传感器可用,但尚无直接比较有源传感器的研究报告。因此,进行了一项研究,以评估有源传感器的性能,并开发使用NDVI在科罗拉多州使用的季节玉米N推荐算法。先前的研究表明,有源传感器NDVI与玉米N含量和高度相关。然而,当我们的研究开始时,尚未对GreenSeeker™绿色NDVI原型有源传感器的NDVI进行测试。因此,对绿色传感器进行了评估,以确定主动传感器是否可以确定MZ上植物生长的差异。结果表明,原型有源传感器未记录与MZ相关的NDVI值。然后在温室和田间条件下检查来自两个不同传感器(Crop Circle™琥珀色NDVI和GreenSeeker™红色NDVI)的NDVI。结果表明,来自琥珀色传感器和红色传感器的NDVI同样可以区分玉米中施用的N差异。每个有源传感器的NDVI值在施氮量和植物氮浓度下均具有较高的R2值。结果还表明,在V12和V14玉米生长阶段,每个传感器的NDVI读数均具有较高的R2值,以及施加的N速率和产量。然后创建了一个N推荐算法,供使用NDVI的琥珀色传感器和红色传感器在V12玉米生长阶段使用。这些算法产生的N条建议在各个传感器类型上均无显着差异,表明琥珀色和红色NDVI传感器性能相同。另外,每种N推荐算法均产生无偏N推荐,表明每个推荐都是玉米生长阶段V12所需N的有效估计量。总体结果表明,琥珀色传感器和红色传感器同样确定了灌溉玉米中的氮变异性,并且可能是管理氮肥季节施用的非常重要的工具。

著录项

  • 作者

    Shaver, Timothy Michael.;

  • 作者单位

    Colorado State University.;

  • 授予单位 Colorado State University.;
  • 学科 Agriculture Agronomy.;Remote Sensing.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 185 p.
  • 总页数 185
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 农学(农艺学);遥感技术;
  • 关键词

  • 入库时间 2022-08-17 11:38:28

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