Abstract:The objective of this study was to identify by microhistological technique the reference chemical components for use as indicators of the nutritive value of Caatinga plants forage grazed by sheep throughout the year. A flock of twenty mixed-race meat ewes, multiparous, in production, with an average 34.84 ± 1.75 kg live weight and 36 months of age was assigned to supplement treatment of 0, 200, 350, and 500 g concentrate/head/day for 3 years. The experiment was designed as a randomized complete design with rep… Show more
“…Changes in the Caatinga biome have already resulted in it reaching approximately 50% of its original vegetation, making it the third most degraded biome in Brazil, behind only the Atlantic forest and Cerrado [1,2]. Although cattle raising is one of the main activities in the region, it is among the main sources of interference in the Caatinga due to the extensive practice of grazing, non-adjustment of grazing pressure, and grazing at inappropriate times, which are driven by the difficulty of monitoring and estimating yield parameters of forage plants [3][4][5].…”
The environmental changes in the Caatinga biome have already resulted in it reaching levels of approximately 50% of its original vegetation, making it the third most degraded biome in Brazil, due to inadequate grazing practices that are driven by the difficulty of monitoring and estimating the yield parameters of forage plants, especially in agroforestry systems (AFS) in this biome. This study aimed to compare the predictive ability of different indexes with regard to the biomass and leaf area index of forage crops (bushveld signal grass and buffel grass) in AFS in the Caatinga biome and to evaluate the influence of removing system components on model performance. The normalized green red difference index (NGRDI) and the visible atmospherically resistant index (VARI) showed higher correlations (p < 0.05) with the variables. In addition, removing trees from the orthomosaics was the approach that most favored the correlation values. The models based on classification and regression trees (CARTs) showed lower RMSE values, presenting values of 3020.86, 1201.75, and 0.20 for FB, DB, and LAI, respectively, as well as higher CCC values (0.94). Using NGRDI and VARI, removing trees from the images, and using CART are recommended in estimating biomass and leaf area index in agroforestry systems in the Caatinga biome.
“…Changes in the Caatinga biome have already resulted in it reaching approximately 50% of its original vegetation, making it the third most degraded biome in Brazil, behind only the Atlantic forest and Cerrado [1,2]. Although cattle raising is one of the main activities in the region, it is among the main sources of interference in the Caatinga due to the extensive practice of grazing, non-adjustment of grazing pressure, and grazing at inappropriate times, which are driven by the difficulty of monitoring and estimating yield parameters of forage plants [3][4][5].…”
The environmental changes in the Caatinga biome have already resulted in it reaching levels of approximately 50% of its original vegetation, making it the third most degraded biome in Brazil, due to inadequate grazing practices that are driven by the difficulty of monitoring and estimating the yield parameters of forage plants, especially in agroforestry systems (AFS) in this biome. This study aimed to compare the predictive ability of different indexes with regard to the biomass and leaf area index of forage crops (bushveld signal grass and buffel grass) in AFS in the Caatinga biome and to evaluate the influence of removing system components on model performance. The normalized green red difference index (NGRDI) and the visible atmospherically resistant index (VARI) showed higher correlations (p < 0.05) with the variables. In addition, removing trees from the orthomosaics was the approach that most favored the correlation values. The models based on classification and regression trees (CARTs) showed lower RMSE values, presenting values of 3020.86, 1201.75, and 0.20 for FB, DB, and LAI, respectively, as well as higher CCC values (0.94). Using NGRDI and VARI, removing trees from the images, and using CART are recommended in estimating biomass and leaf area index in agroforestry systems in the Caatinga biome.
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