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The use of remote sensing in agriculture presents some practical applications in crop production forecast. In this context, studies with remote sensing are scarce for crops such as white oats, which may indicate the capacity of using this technique in the crop. The aim of this study was to evaluate the accuracy in validation of white oat biomass and grain yield estimates by spectral models previously calibrated using two vegetation indices (NDVI and IRVI) at three phenological stages. The mean values of NDVI and IRVI were correlated with the grain and biomass yield of white oats to obtain regression equations. The accuracy was verified by the determination coefficient (R2), root mean square error (RMSE) and mean bias error (MBE). The models were calibrated using data from a field experiment carried out in 2017 and validated with data from the same experiment, but conducted in 2018. The models had good generalization capacity for estimating yield of white oats, especially for biomass yield. Parametrized models in more advanced phenological stages, showed lower error of estimation. Models calibrated with the vegetation index IRVI had lower error of estimation than when calibrated with NDVI. [ABSTRACT FROM AUTHOR]

One of the benefits of irrigation with wastewater in agriculture is the reduction in the use of mineral fertilizers and the increase in crop yield. For wastewater application, the use of remote sensing can help to define specific management areas. The aim of this study was to evaluate the yield and the growth of irrigated white oats (Avena sativa L.) with varying treated sewage effluent (TSE) levels. These attributes were then correlated with Normalized Difference Vegetation Index (NDVI) values obtained at four phenological stages of the crop and used to define critical limits of NDVI. The experiment consisted of five TSE treatments differing in irrigation depths (11, 31, 60, 87, and 100%). Mean NDVI values in the crop were determined with an active terrestrial sensor (GreenSeeker), which were then correlated with height, maximum leaf area index (LAI), panicles per square meter, number of grains per panicle, grain mass, grain yield (GY), and biomass yield (BY). In order to define the NDVI critical limits, NDVI values were generated as a function of relative BY in four phenological stages; NDVI values at 90% and 110% of relative BY were considered critical. Maximum GY (5.775 kg.ha-1) and BY (20,731 kg.ha-1) of white oats were achieved with TSE-induced nitrogen rates of 45 kg.ha-1 and 88 kg.ha-1, respectively. NDVI readings were highly correlated (greater than 0.85) with growth and yield of white oats. In this study, we defined the critical NDVI limits of white oats and obtained maximum precision at the phenological stage 10.5.4. [ABSTRACT FROM AUTHOR]