# Machine Learning Regression Model for Predicting Honey Harvestis
## 2 Materials and Methods
### Results
(**a**) Average maximum temperature for October to January (**b**) evapotranspiration for January (**c**) Gross Primary Productivity from March to January (**d**) Net Photosynthesis For March to January (**e**) Solar Radiation for January (**f**) Total Potential Evapotranspiration For August to January (**g**) rainfall from December to January (**h**) average maximum temperature in January (**i**) maximum Enhanced Vegetation Index from December to January (**j**) rainfall from August to January. In order to determine whether the dimensionality of the input features could be reduced without compromising the predictive accuracy of our models, the GBR function was run multiple times with fewer input features for each run. The selection of input features for each run was on the basis of the highest feature importance from the preceding model. Table 6 shows the VIs and predictive errors for this process. The MaT4 feature is clearly the most important input, with a VI of 37.7% even when all 115 features are used in the regression modelling. This is followed by ET1 and GPP, both at below 10% when all feature inputs are used. The columns highlighted in green show the models with the lowest error, by class (five input features) and by weight (two input features). These reduced input feature models both have slightly higher honey weight prediction errors than the full feature model (increased error of 0.52 kg versus a honey harvest range of 0.0 kg to 71.0 kg in the training and testing datasets). If this simplified approach were to be applied to larger datasets for the prediction of honey harvest weight, a significantly smaller number of feature inputs can be used, with a minimal reduction in predictive accuracy, reducing the size of the multidimensional dataset required. The 'Honey class. (1-2)' row in Table 6 is calculated by classifying the predicted honey weight as either 'good year' or 'below good year', based on the high degree of clustering found with key weather and satellite inputs for 'good years' by [PERSON] and [PERSON] (2018). This tight clustering for 'good years', and the 0% error in the predictive regression classification, means that for this dataset the 'good year' prediction is both an accurate and a robust model. While this cannot assist apiarists with preparation for 'poor years', when their bees can sometimes starve due to the lack of nectar, the apiarists can prepare for a 'good year' in advance and therefore increase the honey production, compared with being unprepared for a 'good year' and having insufficient hives or other equipment to facilitate efficient use of the abundant resource. Although the predictive regression model developed solely from the MaT4 and ET1 features has a relatively low error and is shown to be quite robust at predicting harvests that are 'good years', both input features require data from the month immediately preceding the honey flow. If the predictive model developed here was used operationally by beekeepers to adaptively manage their beehives, this timing of the key input data would limit the time available for apiarists to prepare for a 'good year'. To determine whether a predictive model could be generated with a longer lead time into the honey flow, mean maximum temperatures for the individual months from October to January were also tested in the GBR, as well as combinations of these months. The VIs from this process are summarised in Table 7. While the original MaT4 and ET1 features retained a high importance, MaT3 (mean maximum temperature for November) was also assessed as a key feature input, based on a visual assessment of