Use of Climatic Variables, Ndvi and Stocking Rate in Estimating the Ranch Level Grazing Coefficient

Abstract

The value of grasslands worldwide has been underestimated; however, they sustain more than 800 million people. The state of Zacatecas has 5.4 million hectares dedicated to livestock, of which 2.3 million hectares are highly degraded. To improve grassland conditions, it is necessary to dynamically assess biomass production and have technically and economically feasible measurement tools that allow for informed decisions regarding conservation and production through adjustments to the grazing coefficient. The objective of this study was to evaluate the influence of climatic factors, the Normalized Difference Vegetation Index (NDVI), and stocking rate on the estimation of the grazing coefficient in three ranches, based on the premise that these factors are related to the grazing coefficient. Vegetation status (biomass) was measured using the NDVI index. Precipitation, temperature, and evapotranspiration data were obtained monthly from the climatological stations of the INIFAP (National Institute of Forestry, Agriculture and Livestock Research) network in Zacatecas, from January 2010 to 2024. Area and livestock carrying capacity data for the three ranches were also collected annually. Statistical linear regression analysis shows that climate data, NDVI, and livestock carrying capacity are correlated with the Grazing Coefficient, with r²=0.63 for NDVI in two ranches and r²=0.72 for the third ranch. The variables studied were statistically significant (P<0.05), revealing that two of the ranches exhibit overgrazing when compared to data from the Technical-Consultative Commission on Rangeland Coefficients (COTECOCA), the government agency responsible for establishing recommended animal units per hectare by region throughout the country. This method represents a simple and easy-to-implement tool for livestock producers, offering lower financial and time costs with an acceptable level of precision. It also allows for in-situ dry matter estimation to provide a more accurate assessment of pasture condition.