Modeling techniques for simulating total forage biomass in rangelands of the Caatinga Biome, Brazil

ABSTRACT From 2018 to 2019, total forage biomass (TFBM) harvests were carried out in four transects established in the rangelands of the Caatinga biome, which exhibited varying woody density. In northeastern Brazil, these transects are located in the municipalities of Tauá (Ceará state) and Ouricuri (Pernambuco state). The objective was to use artificial neural network (ANN) and phytomass growth (Phygrow) models to simulate rangeland production. ANNs acquired by Sentinel 2-A remote sensing data were used to obtain six vegetation indices (VIs): normalized difference vegetation index (NDVI); soil-adjusted vegetation index (SAVI); enhanced vegetation index (EVI); leaf area index (LAI); modified soil-adjusted vegetation index 2 (MSAVI2); and normalized difference water index (NDWI). These indices were calculated in QGis software (version 2.18). The development of the ANN model incorporated both measured TFBM and the data collected by the aforementioned VIs. The Phygrow model was performed using the virtual platform Phyweb. The validation models summary revealed that the Phygrow model overestimated TFBM by more than 7.5 %. A comparison of the ANNs for all sites with the Phygrow model revealed that Phygrow had lower root mean square error (RMSE) values (88 vs 462 kg ha–1 of dry matter) and a higher Willmott index (0.84 vs 0.52). However, the ANN model exhibited a lower percentage of bias (0.5 vs 7.5 %) and lower mean bias error (MBE of 6.9 vs 13.2). Phygrow demonstrated superior performance in simulating TFBM when compared to the ANN model, thereby substantiating its efficacy in estimating TFBM in rangelands of the Caatinga biome. In regions characterized by variable woody density, such as the Caatinga, the Phygrow model emerges as a useful tool for the optimization of rangeland management.