The Relationship between Satellite-Derived Vegetation Indices and Live Weight Changes of Beef Cattle in Extensive Grazing Conditions

Abstract

The live weight (LW) and live weight change (LWC) of cattle in extensive beef production is associated with pasture availability and quality. The remote monitoring of pastures and cattle LWC can be achieved with a combination of satellite imagery and walk-over-weighing (WoW) stations. The objective of the present study is to determine the association, if any, between vegetation indices (VIs) (pasture availability) and the LWC of beef cattle in an extensive breeding operation in Northern Australia. The study also tests a suite of VIs along with variables such as rainfall and Julian day to predict the LWC of breeding cows. The VIs were calculated from Sentinel-2 satellite imagery over a 2-year period from a paddock with 378 cattle. Animal LW was measured remotely using a weighing scale at the water point. The relationship between VIs, the LWC, and LW was assessed using linear mixed-effects regression models and random forest modelling. Findings demonstrate that all VIs calculated had a significant positive relationship with the LWC and LW (p < 0.001). Machine learning predictive modelling showed that the LWC of breeding cows could be predicted from VIs, Julian day, and rainfall information, with a Lin’s Concordance Correlation Coefficient of 0.62 when using the leave-one-month-out cross-validation. The LW and LWC were greater during the wet season when VIs were higher compared to the dry season (p < 0.001). Results suggest that the remote monitoring of pasture availability, the LWC and LW is possible under extensive grazing conditions. Further, the use of VIs and other readily available data such as rainfall can be used to predict the LWC of a breeding herd in extensive conditions. Such information could be used to increase the productivity and land management in extensive beef production. The integration of these data streams offers great potential to improve the monitoring, management, and productivity of grazing or cropping enterprises.

Research, development and adoption for the north Australian beef cattle breeding industry: an analysis of needs and gaps

“…A recent study conducted under extensive commercial conditions in the Victoria River Downs region of Northern Territory found significant positive relations between various indexes of pasture availability assessed by satellite imagery and liveweight change of breeding cows assessed remotely by WOW over a 2-year period (Pearson et al 2021). Further, machine-learning predictive modelling was used to show that liveweight change could be predicted with reasonable confidence by a combination of information on pasture availability, calendar date and rainfall.…”

Perspectives on the Special Issue for Applications of Remote Sensing for Livestock and Grazingland Management

“…By combining MODIS NDVI data and the USDA soil survey geographic (SSURGO) database, they were able to correlate the model to ground-truthed biomass data. Pearson et al [103] coupled remotely sensed vegetation indices with electronic identification of cattle via automatic weighing stations at watering points to show that cattle live weights, and live weight change can be modeled from a combination of vegetation indices, Julian day, and rainfall data. Finally, Irisarri et al [104] present a novel way to estimate crude protein content of forages using the MODIS platform.…”

A Technological Approach to Support Extensive Livestock Management in the Portuguese Montado Ecosystem

“…According to Teague et al [17], there are four factors that can be manipulated to achieve the desired management goals in rangelands: stocking rate, grazing season, livestock distribution, and frequency of grazing. These variables have impact on the availability and quality of pasture and, therefore, on the calculation of feed supplementation needs [18]. However, the vicious cycle of low profit margins leading to low investments, determines the little existing knowledge about the relationship between dynamic grazing and the response of pasture in extensive ecosystems (namely in beef cattle production).…”

“…An emerging possibility is indirect measurement of plant growth through vegetation indices [11]. In recent years, some studies have been carried out to evaluate the potential of NDVI (Normalized Difference Vegetation Index) or NDWI (Normalized Difference Water Index), obtained from satellite images (Sentinel-2), as low-cost tools, high temporal resolution (5 days) and acceptable spatial resolution (10 m × 10 m or 20 m × 20 m) to monitor pasture development (productivity) and vigor (quality) throughout the vegetative cycle [18,[20][21][22][23][24]. NDVI could be used to support grazing management decisions, to manage animal nutrition, through paddock change, feed supplementation and, also, identify early warning indicators of poor animal performance [18].…”

“…In recent years, some studies have been carried out to evaluate the potential of NDVI (Normalized Difference Vegetation Index) or NDWI (Normalized Difference Water Index), obtained from satellite images (Sentinel-2), as low-cost tools, high temporal resolution (5 days) and acceptable spatial resolution (10 m × 10 m or 20 m × 20 m) to monitor pasture development (productivity) and vigor (quality) throughout the vegetative cycle [18,[20][21][22][23][24]. NDVI could be used to support grazing management decisions, to manage animal nutrition, through paddock change, feed supplementation and, also, identify early warning indicators of poor animal performance [18]. Some limitations due to the presence of clouds or the existence of trees can be overcome with the complementary use of proximal sensors, for example capacitance probes ("Grassmaster II" or Grassmaster Pro"; [25,26]) for biomass estimation, or optical sensors ("OptRx") for crude protein or fiber estimation [27].…”