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Monitoring pasture variability: optical OptRx® crop sensor versus Grassmaster II capacitance probe

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Abstract

Estimation of pasture productivity is an important step for the farmer in terms of planning animal stocking, organizing animal lots, and determining supplementary feeding needs throughout the year. The main objective of this work was to evaluate technologies which have potential for monitoring aspects related to spatial and temporal variability of pasture green and dry matter yield (respectively, GM and DM, in kg/ha) and support to decision making for the farmer. Two types of sensors were evaluated: an active optical sensor (“OptRx®,” which measures the NDVI, “Normalized Difference Vegetation Index”) and a capacitance probe (“GrassMaster II” which estimates plant mass). The results showed the potential of NDVI for monitoring the evolution of spatial and temporal patterns of vegetative growth of biodiverse pasture. Higher NDVI values were registered as pasture approached its greatest vegetative vigor, with a significant fall in the measured NDVI at the end of Spring, when the pasture began to dry due to the combination of higher temperatures and lower soil moisture content. This index was also effective for identifying different plant species (grasses/legumes) and variability in pasture yield. Furthermore, it was possible to develop calibration equations between the capacitance and the NDVI (R 2 = 0.757; p < 0.01), between capacitance and GM (R 2 = 0.799; p < 0.01), between capacitance and DM (R 2 = 0.630; p < 0.01), between NDVI and GM (R 2 = 0.745; p < 0.01), and between capacitance and DM (R 2 = 0.524; p < 0.01). Finally, a direct relationship was obtained between NDVI and pasture moisture content (PMC, in %) and between capacitance and PMC (respectively, R 2 = 0.615; p < 0.01 and R 2 = 0.561; p < 0.01) in Alentejo dryland farming systems.

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Serrano, J.M., Shahidian, S. & Marques da Silva, J.R. Monitoring pasture variability: optical OptRx® crop sensor versus Grassmaster II capacitance probe. Environ Monit Assess 188, 117 (2016). https://doi.org/10.1007/s10661-016-5126-5

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