## 2 Materials and Methods
### Predictive Machine Learning Model  
A predictive model of cattle LWC was created using a random forest machine learning model. Specifically, the 'ranger' function within the ranger package in R [30] was used, which is a fast implementation of a random forest model. Live weight change was the response variable, and the predictor variables were monthly rainfall, year, month, Julian day, and the suite of VIs. Rainfall data were measured at the closest Bureau of Meteorology weather station in Kununurra, 50 km from the trial site. Random forest models have an ability to deal with non-linear relationships and are well-suited to diverse and high  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**VI** & **Red** & **Green** & **Blue** & **NIR** & \begin{tabular}{c} **Red** \\ **Edge** \\ \end{tabular} & \multicolumn{2}{c}{**Description**} \\ \hline NDVI & X & & & X & & Chlorophyll-sensitive \\  & & & & & Identifying canopy structure \\ \hline NDRE & & & X & X & \begin{tabular}{c} Good to estimate late season biomass and tree crops \\ Does not oversaturate images \\ \end{tabular} \\ \hline EVI & X & & X & X & & \begin{tabular}{c} Reduces soil and atmospheric noise \\ Sensitive to changes in high biomass \\ Detects variations in plant structure \\ \end{tabular} \\ \hline Clr & X & X & & X & & \
\begin{tabular}{c} Assesses soil moisture \\ Assesses soil composition \\ \end{tabular} \\ \hline VREI & & & & X & Chlorophyll-sensitive \\  & & & & & Sensitive to small changes in vegetation \\ \hline BI & X & X & X & &
\begin{tabular}{c} Identifies areas of bare soil \\ Good for detection of degradation and \\ drought-affected areas \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Vegetation indices included in the study, and associated bands used to calculate the vegetation indices, and description.  
-dimensional datasets [31; 32]. The dataset used for the random forest modelling included only breeding cows with sufficient WoW data across the trial period. Cows which had less than 90 days of data recorded, or less than 10 observations on the WoW system, were not included. The dataset contained 65,000 rows of data over a 24-month period from 148 cows. This data subset with cows that had a more complete time-series of data was selected, as the aim of this modelling was to predict or nowcast the LWC of those animals for the subsequent month. The predictor variable importance was assessed using the 'permutation' approach in the 'ranger' function in R [33].  
To test the quality of the models and validate the predictions, a leave-one-month-year-out approach was implemented in this study. This involved removing all data from a particular year and month, and then building a model with the remaining data. This model was then used to predict LWC of the removed data, and the predicted LWC was retained. This procedure was implemented for all years/months in the dataset (n = 24 months). The relationship between all of the observed live weight change data and independently predicted LWC data was then assessed using R\({}^{2}\), Lin's concordance correlation coefficient (LCCC) [34], root mean square error (RMSE), and bias.  
The aim of creating this predictive LWC model was to test the ability to predict or nowcast the LWC of cattle. The robust validation implemented provided a realistic insight into whether LWC could be predicted in the future using machine learning in combination with readily available data such as satellite VIs, rainfall data, and dates.