## 4 Discussion
Furthermore, CIr seemed to be the most suitable VI to predict animal performance due to its greatest importance in the random forest model. The machine learning predictive modelling dataset only included breeding cows, with bulls and calves removed from the data. This was because the objective of the predictive model was to predict the growth for breeding cows in a given month, so sufficient live weight data were necessary to ensure this model was a robust prediction of the LWC. However, results from linear mixed-effects models included the whole herd data with bulls and calves. The aim of the linear mixed-effects models was to establish whether there were relationships at the herd level with the VI. Since there were significant relationships with VIs and herd data, the presence of only cows with sufficient live weight data would only enhance the strength of these relationships with the VI. It is important to point out that the association between VIs and the LWC could also have been affected by other factors affecting energy and nutrient demands of the animals, including the lactation status, weaning, and feed supplementation, amongst others. It was clear that cows responded with an increasing LWC immediately after weaning in October and November, and before rainfall and pastures started to green up, suggesting that the lactation status may be more important in affecting the LWC than the pasture biomass and availability and, indirectly, the VI. Interestingly, VIs were higher in the second wet season of the trial, which experienced a higher rainfall, even though cow live weight gains and the LW were lower than the first year. There could be several reasons for this, and one possible explanation is the lack of wet season phosphorus supplementation in year 2. Phosphorus supplementation during the wet season is critical to maintain the growth rate and fertility of cattle in northern Australia due to deficiency in soils and vegetation [45; 46]; therefore, demonstrating that there may be other factors influencing cow LWC. Satellite imagery enabling predictions of vegetation quantity and quality are not new [47], and their roles in agriculture have been proven useful, for example, for estimating crop health and productivity [17; 41; 48], and the forage quantity of grasslands alike [49; 50]. However, results of the present trial relating VIs with important profit drivers in the cattle industry, such as animal performance, added considerable value to the information that satellite data can provide. The predictive modelling was applied under these conditions specific to this region and at this time. It is important to note that the independent variables of the predictive model and the parameters presented in the present study may not be applicable to other regions or operations. For example, regions with a larger variation in VIs such as more arid areas may show stronger associations with the LWC. Measuring the relative importance of other variables in each production system would also be important to quantify in future, to enable more robust predictive modelling in future research. Traditionally, the productivity of extensive beef breeding operations in northern Australia has been measured through weaning or branding percentages (number of calves branded or weaned from every 100 cows exposed to a bull). It has previously been noted that new technologies could enhance the productivity and profitability in beef breeding herds of northern Australia [8; 51]. This study offers an approach which utilises technology linking cattle production with potential indicators such as VIs and, hence, could also be linked to reproduction and calf loss [6]. However, further research on the use of this information to predict, monitor, and improve the reproductive performance and cow and calf survival is needed. Importantly, the present study emphasizes the broad range of factors which may contribute to calf loss and the overall productivity of a breeding herd.