Use of satellite remote-sensing techniques to predict the variation of the nutritional composition of corn (Zea mays L) for silage

The nutritional composition of corn (Zea mays L) silage can vary substantially within a same silo. Environmental differences within the cornfield could contribute to this variability. We explored using green vegetation index maps, known as normalized difference vegetation index (NDVI) maps, to identify differences in the nutritional composition of corn at the field level. We hypothesized that the nutritional composition of the corn plant differs within the cornfield according to the vegetation index maps as detected by satellite remote-sensing techniques. Three cornfields from 3 commercial dairy farms located within the state of Virginia were utilized in this study. Landsat satellite data were obtained from the US Geological Survey to develop NDVI maps. Each cornfield was segregated in 3 regions classified as low NDVI, mid NDVI, and high NDVI. Corn plants from each region were harvested to determine their nutritional composition. At harvesting, corn plants were cut, weighed, chopped, and analyzed in the laboratory. Data were analyzed as for a complete block design, where fields and NDVI regions were considered blocks and treatments, respectively. The concentrations of ash (40 g kg(-1)), crude protein (102 g kg(-1)), neutral detergent fiber (398 g kg(-1)), acid detergent fiber (232 g kg(-1)), acid detergent lignin (14 g kg(-1)), and starch (304 g kg(-1)) did not differ at different NDVI regions. In our study none of the cornfields seemed to be environmentally stressed during the growing season of 2014. Therefore, it is plausible that the intrinsic variation of the cornfields was minimum due to the adequate growing conditions.