Crude Protein as an Indicator of Pasture Availability and Quality: A Validation of Two Complementary Sensors

This study evaluated the possibility of using two complementary electronic sensors (rising plate meter (RPM) and active optical sensor (AOS)) to obtain a global indicator, pasture crude protein (CP) in kg ha−1. This parameter simultaneously integrates two essential dimensions: pasture dry matter availability (dry matter (DM) in kg ha−1) measured by RPM, and pasture quality (measured by AOS), and supports management decisions, particularly those related to the stocking rates, supplementation, or rotation of animals between grazing parks. The experimental work was carried out on a dryland biodiverse and representative pasture, and consisted of sensor measurements, followed by the collection of a total of 144 pasture samples, distributed between three dates of the pasture vegetative cycle of 2023/2024 (Autumn—December 2023; Winter—February 2024; and Spring—May 2024). These samples were subjected to laboratory reference analysis to determine DM and CP. Sensor measurements (compressed height (HRPM) in the case of RPM, and normalized difference vegetation index (NDVI) in the case of AOS) and the results of reference laboratory analysis were used to develop prediction models. The best correlations between CP (kg ha−1) and “HRPM × NDVI” were obtained in the initial and intermediate phases of the cycle (autumn: R2 = 0.86 and LCC = 0.80; and Winter; R2 = 0.74 and LCC = 0.81). In the later phase of the cycle (spring), the accuracy of the forecasting model decreased dramatically (R2 = 0.28 and LCC = 0.42), a trend that accompanies the decrease in the pasture moisture content (PMC) and CP. The results of this study show not only the importance of extending the database to other pasture types in order to enhance the process of feed supplement determination, but also the potential for the research and development of proximal and remote sensing tools to support pasture monitoring and animal production management.