Understanding the temporal and spatial variability in a crop yield is viewed as one of the key\nsteps in the implementation of precision agriculture practices. Therefore, a study on a center\npivot irrigated 23.5 ha field in Saudi Arabia was conducted to assess the variability in alfalfa\nyield using Landsat-8 imagery and a hay yield monitor data. In addition, the study was\ndesigned to also explore the potential of predicting the alfalfa yield using vegetation indices.\nA calibrated yield monitor mounted on a large rectangular hay baler was used to measure\nthe actual alfalfa yield for four alfalfa harvests performed in the period from October 2013 to\nMay 2014. A total of 18 Landsat-8 images, representing different crop growth stages, were\nused to derive different vegetation indices (VIs). Data from the yield monitor was used to\ngenerate yield maps, which illustrated a definite spatial variation in alfalfa yield across the\nexperimental field for the four studied harvests as indicated by the high spatial correlation\nvalues (0.75 to 0.97) and the low P-values (4.7E-103 to 8.9E-27). The yield monitor-measured\nalfalfa actual yield was compared to the predicted yield form the Vis. Results of the\nstudy showed that there was a correlation between actual and predicted yield. The highest\ncorrelations were observed between actual yield and the predicted using NIR reflectance,\nSAVI and NDVI with maximum correlation coefficients of 0.69, 0.68 and 0.63, respectively.
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