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The study used methods based on remote sensing to analyze the dynamics of a silage corn crop and predict biomass production. The corn crop, the Micado hybrid, was organized within the Didactic and Experimental Station of the "King Mihai I" University of Life Sciences in Timisoara, under the specific conditions of the 2021-2022 agricultural year. The soil in the crop plot was of chernozem type, and the crop was in a non-irrigated system. To obtain the satellite data, the Sentinel 2 system was used. To characterize the plot and the crop of silage corn, 14 sets of images were taken, in the interval March - July 2022. From the analysis of the images, the spectral data were obtained, and based on the consecrated formulas MSAVI, NDMI, NDVI and NBR indexes were calculated. The ANOVA test confirmed the reliability of the data and the presence of variance in the data set (F>Fcrit, p<0.001, for Alpha=0.001). The level of correlations identified between the calculated indices was between r=0.967 (NBR with NDVI) and r=0.996 (NDVI with MSAVI). The variation of the NBR index, in relation to the other indices, was described by linear equations, in terms of statistical safety (R2=0.965, p<0.001, in relation to MSAVI; R2=0.989, p<0.001 in relation to NDMI; R2=0.934, p<0.001 in relation to NDVI). The variation of indices in relation to time (t, days) during the study period was described by polynomial equations of the 3rd degree, in terms of statistical safety (R2=0.964 for MSAVI, R2=0.934 for NDMI, R2=0.941 for NDVI, and R2=0.961 for NBR). Regression analysis facilitated obtaining some equations, as models for predicting biomass production, under conditions of statistical safety. Based on the RMSEP parameter, it was found that the most reliable level of production prediction was obtained in the case of MSAVI and NDMI indices (RMSEP=0.05923).
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