首页> 外文期刊>Precision Agriculture >Use of soil moisture data for refined GreenSeeker sensor based nitrogen recommendations in winter wheat (Triticum aestivum L.)
【24h】

Use of soil moisture data for refined GreenSeeker sensor based nitrogen recommendations in winter wheat (Triticum aestivum L.)

机译:利用土壤水分数据为基于改良的GreenSeeker传感器的冬小麦氮素推荐(Triticum aestivum L.)

获取原文
获取原文并翻译 | 示例
获取外文期刊封面目录资料

摘要

Previous studies have shown the importance of soil moisture (SM) in estimating crop yield potential (YP). The sensor based nitrogen (N) rate calculator (SBNRC) developed by Oklahoma State University utilizes the Normalized Difference Vegetation Index (NDVI) and the in-season estimated yield (INSEY) as the estimate of biomass to assess YP and to generate N recommendations based on estimated crop need. The objective was to investigate whether including the SM parameter into SBNRC could help to increase the accuracy of YP prediction and improve N rate recommendations. Two experimental sites (Lahoma and Perkins) in Oklahoma were established in 2006/07 and 2007/08. Wheat spectral reflectance was measured using a GreenSeeker (TM) 505 hand-held optical sensor (N-Tech Industries, Ukiah, CA). Soil-water content measured with matric potential 229-L sensors (Campbell Scientific, Logan, UT) was used to determine volumetric water content and fractional water index. The relationships between NDVI, INSEY and SM indices at planting and sensing at 5, 25, 60 and 75-cm depths versus grain yield (GY) were evaluated. Wheat GY, NDVI at Feekes 5 and soil WC at planting and as sensed at three depths were also analyzed for eight consecutive growing seasons (1999-2006) for Lahoma. Incorporation of SM into NDVI and INSEY calculations resulted in equally good prediction of wheat GY for all site-years. This indicates that NDVI alone was able to account for the lack of SM information and thus lower crop YP. Soil moisture data, especially at the time of sensing at the 5-cm depth could assist in refining winter wheat YP prediction.
机译:先前的研究表明,土壤水分(SM)在估算作物单产潜力(YP)中的重要性。俄克拉荷马州立大学开发的基于传感器的氮(N)速率计算器(SBNRC)利用归一化植被指数(NDVI)和季节估算的产量(INSEY)作为生物量的估算值,以评估YP并基于N提出建议估计的作物需求。目的是研究将SM参数包含在SBNRC中是否有助于提高YP预测的准确性并改善N率建议。 2006/07年和2007/08年在俄克拉荷马州建立了两个实验点(拉奥马和珀金斯)。使用GreenSeeker TM 505手持式光学传感器(N-Tech Industries,Ukiah,CA)测量小麦光谱反射率。用基质电势229-L传感器(Campbell Scientific,洛根,犹他州)测量的土壤含水量用于确定体积含水量和分数水分指数。评估了在5、25、60和75厘米深度种植和感测时NDVI,INSEY和SM指数与谷物产量(GY)的关系。还对拉赫马的连续八个生长季节(1999-2006年)进行了小麦GY,Fekes 5的NDVI和种植时的土壤WC以及在三个深度的感测。将SM纳入NDVI和INSEY计算可以对所有站点年的小麦GY进行同样好的预测。这表明仅NDVI就能解决缺少SM信息的问题,从而降低了作物的YP。土壤水分数据,特别是在5厘米深度的土壤湿度数据,可以帮助完善冬小麦的YP预测。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号