Technological development for monitoring pasture quality and supporting grazing management decisions – a review

Abstract

Effective grazing management depends on accurate and timely information on pasture biomass and nutritive value. This review examines recent advances in sensing technologies for estimating forage quality parameters, particularly crude protein (CP) and neutral detergent fiber (NDF), in pasture-based systems. Emphasis is placed on two complementary indicators: Dry Matter (DM) and Crude Protein (CP). Proximal tools such as rising plate meters (RPM) have evolved to incorporate optical components capable of estimating vegetation indices like the Normalized Difference Vegetation Index (NDVI), which may correlate with forage quality. Meanwhile, satellite-based remote sensing (Sentinel-2) offers broader spatial coverage and access to multiple spectral bands. These allow for the computation of several indices that can be explored using statistical and machine learning (ML) models to predict CP and NDF content. The integration of sensor-derived data with artificial intelligence (AI) represents a promising avenue for developing predictive models and decision-support systems (DSS), improving rotational grazing, supplementation planning, and reducing the occurrence of metabolic and nutritional disorders. This review highlights key findings in the literature and identifies knowledge gaps, particularly in the validation of new technologies across different pasture types and seasons. Future research should focus on combining multi-source data for real-time, on farm applications.