Drastic increases in the cost of N fertilizer and increased public scrutiny have encouraged development and implementation of improved N management practices. This study evaluated the relationship between corn (Zea mays L.) grain yield and early season normalized difference vegetation index (NDVI) sensor readings using the Green-Seeker sensor. The relationships between grain yield and several predictor variables were determined using linear and nonlinear regression analysis. Categorizing NDVI measurement by leaf stage indicated that growth stage was critical for predicting grain yield potential. Poor exponential relationships existed between NDVI from early sensor measurements (V6-V7 leaf stage) and grain yield. By the V8 stage, a strong relationship (R 2 5 0.77) was achieved between NDVI and grain yield. Later sensor measurements (V9 and later) failed to distinguish variation in green biomass as a result of canopy closure. Normalizing the NDVI with GDD (growing degree days) did not significantly improve yield potential prediction (R 2 5 0.73), but broadened the yield potential prediction equation to include temperature and allowed for adaptation into various climates. Sensor measurements at the range of 800 to 1000 GDD resulted in a significant exponential relationship between grain yield and NDVI (R 2 5 0.76) similar to the V8 leaf stage categorization. Categorizing NDVI by GDD (800-1000 GDD) extended the sensing time by two additional leaf stages (V7-V9) to allow a practical window of opportunity for sidedress N applications. This study showed that yield potential in corn could be accurately predicted in season with NDVI measured with the GreenSeeker sensor.

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Improving crop management inputs with remote sensing devices is an emerging technology. This study documented the progression of the normalized difference vegetative index (NDVI) during the life cycle of corn (Zea mays L.), evaluated the spatial variability of corn growth in terms of the CV (calculated from NDVI readings), and documented the relationships between NDVI, CV (calculated from NDVI), and grain and biomass yields and plant density. Four rows, 30 m in length, from two locations during 2 yr were randomly selected for this study. An optical sensor was used to collect NDVI readings at multiple growth stages during the life cycle of corn. The NDVI increased with progression of vegetative growth stages until V10, where a plateau was encountered, followed by a decline in NDVI after the VT growth stage. Coefficient of variation data from the NDVI readings revealed two dominant peaks during the life cycle of corn, one between the V6 and V8 growth stages and the second during the late reproductive growth stages. The CV data illustrated that the greatest variation expressed by corn during the vegetative growth stages was between the V6 and V8 growth stages. The highest correlation of NDVI with corn grain yield was found at the V7 to V9 growth stages; likewise, CV and plant density were also more highly correlated from V7 to V9. The CV from NDVI readings was highly correlated with grain and biomass yields at all growth stages.

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)(2000)(2001)(2002)(2003)(2004)(2005)(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.

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