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ABSTRACT:Optimization of N management is one of the great challenges to be overcome in grain production, as it is directly related to productivity and can also cause environmental damage. Precision agriculture aims to solve this problem by applying nitrogen fertilizer at varying rates. Reflectance sensors are instruments capable of estimating N needs in various crops, including grain crops. However, it is not clear how these sensors perform under varying solar radiation and cloud cover, due to a lack of research on their temporal variability. Thus, this study examined the temporal variability of the NDVI (normalized difference vegetation index), as measured by an active reflectance sensor, in both soybean and wheat crops. The NDVI data were collected using a GreenSeeker sensor every 15 minutes over 12 or 14 consecutive hours. Incident solar radiation was recorded using an Instrutherm MES-100 pyranometer. In all experiments in soybean and wheat, NDVI was negatively influenced by irradiation, showing higher values at the beginning and end of the day. Changes in cloud cover also affected NDVI values during the experiments.

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During the last few years, yield maps have become economically feasible for farmers due to technological advances in precision agriculture. However, evidence of yield profitability is still uncertain, and variability in yield has seldom been correlated to variability in profits. Differently from yield maps, profit maps can supply additional information about the economic return for each particular area of a field. The objective of the present work was to study how management decisions can be facilitated by transforming yield-map datasets into profit maps and the importance of the selection of interpolator type. Yield and profit maps were generated for each data set (three soybean fields and one corn field) using the inverse of the distance (ID), the inverse of the square of the distance (IDS) and kriging (KRG) as interpolation methods. It can be concluded that profit maps are an important tool for the diagnosis of the spatial variability of economic return because they can assist farmers with management decision-making. The impact of the interpolator type was less than 200 kg ha -1 for the yield and US$ 30 ha -1 for the profit, indicating that the choice of interpolator type is of secondary importance. In addition, the profit maps showed large variability that would not be easily found if only yield maps were analyzed.