Fast, accurate methods to determine in-season corn (Zea mays L.) nitrogen (N) requirements are needed to provide more precise and economical management and potentially decrease groundwater N contamination. The objectives of this study were to: (i) develop a methodology for predicting in-season N requirement for corn at the V7 and VT stages using aerial color infrared (CIR) photography; (ii) validate the RGDVI-based remote sensing technique for determining in-season N requirements for corn at VT; (iii) determine the relationships between corn agronomic parameters and spectral parameters that influence the prediction of optimum NV7 and NVT rates. Field studies were conducted for four years over a wide range of soil conditions and water regimes in the North Carolina Coastal Plain. A two-way factorial experimental design was implemented as a split-plot in randomized complete blocks with NPL as the main plot factor and NV7 or NVT as the sub-plot factor. Corn agronomic parameters were measured and aerial CIR photographs were obtained for each site at V7 or VT prior to N application. Spectral radiation of corn measured using the Green Difference Vegetation Index (GDVI) relative to high-N reference strips using a linear-plateau model was the best predictor of optimum NVT (R2 = 0.67). Very weak correlations were observed between optimum rates of N V7 and band combinations with significant correlations for relative G, RGDVI, and relative difference vegetation index (RDVI). In the VT validation study, the difference between predicted and observed optimum NVT rates ranged from -30 to 90 kg N ha-1. Overall, the remote sensing technique was successful (r2 = 0.85) in predicting optimum NVT rates despite the inherent constraints of predicting yield potential in any particular year. The spectral index RGDVI showed consistently significant relationships with corn agronomic parameters measured at VT. By assessing corn N requirements late in the season during the period of maximum N uptake and applying fertilizer appropriately, application of large amounts of N early in the season when corn uptake is low and leaching potential high might be avoided, thus minimizing groundwater pollution.
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机译：需要快速，准确的方法来确定季节玉米（Zea mays L.）的氮（N）需求，以提供更精确，更经济的管理，并有可能减少地下水对氮的污染。这项研究的目的是：（i）开发一种使用航空彩色红外（CIR）摄影技术预测V7和VT阶段玉米季节氮需求的方法； （ii）验证基于RGDVI的遥感技术，以确定VT玉米的季节氮需求； （iii）确定影响最佳NV7和NVT速率预测的玉米农艺参数与光谱参数之间的关系。在北卡罗莱纳州沿海平原的广泛土壤条件和水分状况下进行了为期四年的田间研究。在随机完整块中，以NPL为主要图因子，NV7或NVT为子图因子，在分开的完整块中实现了双向分解实验设计。测量玉米的农艺参数，并在施用氮肥之前在V7或VT获得每个部位的空中CIR照片。使用绿色差异植被指数（GDVI）相对于使用线性高原模型的高氮参考条测量的玉米光谱辐射是最佳NVT的最佳预测因子（R2 = 0.67）。在N V7的最佳速率和波段组合之间观察到非常弱的相关性，相对G，RGDVI和相对差异植被指数（RDVI）具有显着相关性。在VT验证研究中，预测和观察到的最佳NVT速率之间的差异为-30至90 kg N ha-1。总体而言，尽管在任何特定年份中都无法预测产量潜力，但遥感技术成功地预测了最佳NVT率（r2 = 0.85）。光谱指数RGDVI与VT测得的玉米农学参数始终具有显着的相关性。通过在最大氮素吸收时期的季节后期评估玉米N需求量并适当地施肥，可以避免在玉米吸收率低且浸出潜力高的季节早时施用大量N，从而将地下水污染降至最低。
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