Pasture quality monitoring based on proximal and remote optical sensors: case study in the Montado Mediterranean ecosystem

Abstract

Permanent dryland pastures are the basis of animal feed in extensive grazing systems. Seasonality and inter-annual climatic variability, associated with shallow, acidic and not very fertile soils result in low productivity and rapid degradation of pasture quality, which requires the supplementation of animal feed. In this study, carried out in a biodiverse pasture field in the Mediterranean region of Southern Portugal, the vegetation index (NDVI, Normalized Difference Vegetation Index) obtained from measurements performed by a proximal optical sensor (PS) and satellite images (RS) was used to assess pasture quality parameters (pasture moisture content, PMC, crude protein, CP and neutral detergent fiber, NDF). The monitoring was carried out throughout the 2021/2022 pasture growing season. Significant correlations were obtained between the NDVI obtained by PS and RS (R2 of 0.8372) and between the NDVI (PS and RS) and the reference values of pasture parameters obtained in laboratory protocols: PMC (R2 of 0.9094 and 0.8147, respectively), CP (R2 of 0.6675 and 0.6329, respectively), and NDF (R2 of 0.4966 and 0.4576, respectively). These results show the pertinence of these technologies in supporting the decision making process of the farm manager, namely to estimate the supplementation needs of animals in critical phases, especially after the spring production peak and before the autumn production peak.