## 4 Discussion  
The present study looked at the relationship between several VIs derived from Sentinel-2 and the LWC of cattle with linear mixed-effects models in a commercial breeding herd in northern Australia. It also used machine learning to create a predictive model of the LWC in breeding cows from a suite of VIs along with date and rainfall. To the author's best knowledge, this was the first study to show the relationships between these variables in extensive commercial beef breeding operations using remote sensing technologies. On the one hand, this knowledge could be used to predict the growth rate of cattle from satellite imagery, which is of importance to manage livestock production systems, including nutrition and fertility [35; 36]. On the other hand, the fact that this information can be collected in near-real time and remotely with the set of technologies used in the present study, supports the hypothesis that the approach can be used to help in making more informed and timely decisions regarding animal management. Such decisions could involve the time to start and finish feed supplementation and the amount to be provided, the weaning time and age of calves, and paddock changes where possible [8]. For example,  
Figure 5: Variable importance of a random forest model predicting the live weight change of breeder cows under commercial conditions in northern Australia over a 2-year trial period. NDVI-Normalised Difference Vegetation Index; NDRE-Normalised Difference Red Edge; EVI- Enhanced Vegetation Index; Clr- Coloured Infrared; VREI- Vogelmann Red Edge Index; BI- Bare Soil Index; Rainfall- monthly cumulative rainfall data near trial site; Month- the numeric month of the trial from 1 to 24; Day- the Julian day of the trial for each year; Year- the numeric year of the trial,1 or 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**R2** & *** LCCC** & *** RMSE (kg)** & **Bias** \\ \hline
0.44 & 0.62 & 0.67 & \(-\)0.02 \\ \hline \hline \end{tabular}  
* LCCC—Lin’s concordance correlation coefficient; RMSE—root mean squared error.  
\end{table} 
Table 5: Validated model statistics for machine learning model predicting live weight change of breeding cows from vegetation indices, date, and rainfall under extensive conditions. if the weight loss could be predicted by the VI the month before, early management interventions could include the onset of supplementary feed prior to weight loss, or the weaning of calves earlier than planned in a breeding system. Similarly, stocking rates could be modified based on predictive modelling, with potential for productivity gains as well as improved animal welfare. These results could also have substantial impact on rotational grazing systems, where predictions of the LWC and pasture availability are of high importance. This could also increase the productivity of breeding herds by determining threshold values beyond which negative impacts on production and animal welfare could occur, such as excessive weight loss and the survival of animals. However, further research is needed to determine these thresholds under multiple environmental conditions and management practices. The strong relationships between VIs and animal performance demonstrate the importance of vegetation quality and quantity in extensive production systems. Despite the broad range of studies in which machine learning has been utilised for beef [37; 38; 39] and dairy cattle [40], to date, there are no studies which incorporate the use of VIs to explain the LWC of animals under commercial conditions of northern Australia. In addition to the VIs presented, other VIs were also used in the preliminary analysis, but not included in the paper due to their low correlation with the LWC. These included the Canopy Chlorophy Content Index (CCCI) [41], Modified Soil-Adjusted VI (MAVI) [42], Normalized Difference Moisture Index (NDMI) [43], and Modified Chlorophyll Absorption Ratio Index (MCARI) [44].