Comparison of Ground-Based, Unmanned Aerial Vehicles and Satellite Remote Sensing Technologies for Monitoring Pasture Biomass on Dairy Farms

• Autores: Gargiulo, Juan; Lyons, Nicolas; Masía, Fernando; Beale, Peter; Insua, Juan Ramón; Correa Luna, Martín; Garcia, Sergio C.
• Año de publicación: 2023
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: AUTOMATION; PRODUCTIVITY; CALIBRATION; AUSTRALIA; GRAZING MANAGEMENT
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

Systematic measurement of pasture biomass (kg DM/ha) is crucial for optimising pasture utilisation and increasing dairy farm profitability. On-farm pasture monitoring can be conducted using various sensors, but calibrations are necessary to convert the measured variable into pasturebiomass. In this study, we conducted three experiments in New South Wales (Australia) to evaluate the use of the rising plate meter (RPM), pasture reader (PR), unmanned aerial vehicles (UAV) and satellites as pasture monitoring tools. We tested various calibration methods that can improve the accuracy of the estimations and be implemented more easily on-farm. The results indicate that UAV and satellite-derived reflectance indices (e.g., Normalised Difference Vegetation Index) can be indirectly calibrated with height measurements obtained from an RPM or PR. Height measurements can be then converted into pasture biomass ideally by conducting site-specific sporadic calibrations cuts. For satellites, using the average of the entire paddock, root mean square error (RMSE) = 226 kg DM/ha for kikuyu (Pennisetum clandestinum Hochst. ex Chiov) and 347 kg DM/ha for ryegrass (Lolium multiflorum L.) is as effective as but easier than matching NDVI pixels with height measurement using a Global Navigation Satellite System (RMSE = 227 kg DM/ha for kikuyu and 406 kg DM/ha for ryegrass). For situations where no satellite images are available for the same date, the average of all images available within a range of up to four days from the day ground measurements were taken could be used (RMSE = 225 kg DM/ha for kikuyu and 402 kg DM/ha for ryegrass). These methodologies aim to develop more practical and easier-to-implement calibrations to improve the accuracy of the predictive models in commercial farms. However, more research is still needed to test these hypotheses under extended periods, locations, and pasture species.
Fil: Gargiulo, Juan. University Of Sidney. Faculty Of Science; Australia
Fil: Lyons, Nicolas. No especifíca;
Fil: Masía, Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina
Fil: Beale, Peter. No especifíca;
Fil: Insua, Juan Ramón. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Innovación para la Producción Agropecuaria y el Desarrollo Sostenible - Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce. Instituto de Innovación para la Producción Agropecuaria y el Desarrollo Sostenible; Argentina
Fil: Correa Luna, Martín. University Of Sidney. Faculty Of Science; Australia
Fil: Garcia, Sergio C.. University Of Sidney. Faculty Of Science; Australia