# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 2 Measurement of Fruit Number
### Correlative Methods
#### 2.5.6 Prediction Based on Multiple Season Attributes
Higher mean maximum temperatures during April-June were associated with increased yield in southern California orchards, while a larger amount of precipitation in March reduced the yield, especially in northern orchards. Annual maximum vegetation indices were dominant variables in the prediction of yield potential. Light (photosynthetically active radiation, PAR) interception is a key component affecting both the quality and quantity of yield [85]. [PERSON] et al. [86] noted that at any given level of light interception of an almond orchard, yield can fall markedly below full yield potential because of factors such as tree age, tree density, location, winter temperature and summer mean daily maximum Vapor Pressure Deficit (VPD\({}_{\text{max}}\)). Warmer winter conditions and summer VPD\({}_{\text{max}}\) beyond 40 hPa resulted in decreased yield. A random forest model based on these inputs explained 82% of yield variation in a sixfold cross validation, with an RMSE of 88.3 kg/ha, for average orchard yields around 3000 kg/ha. When light interception was not used as an input, the model still explained 78% of yield variation. In another example, the national coconut production of Sri Lanka was forecast through prediction in seven production regions based on climate variables [87]. The absolute error on production estimates for 2003 and 2004 was 6.5 and 7.0%, respectively. [PERSON] et al. [88] reported the prediction of macadamia yield using a large number (90+) of climate measures for times of the year related to key physiological events for macadamia trees, with a target of \(\pm\)10% of actual. Of general linear models, partial least squares regression and Least Absolute Shrinkage and Selection Operator (LASSO) penalized regression models, and ensembles of these models; LASSO models performed best. The lowest MAE rates were 9.0% and 5.9% for two Australian production regions, respectively. Region 1 was noted to predominantly rely on rainfall, while region 2 farms were mostly irrigated. The six key variables for region 1 were price (lagged-two-years), rainfall (Jan in previous year), rainfall (Apr/May), rain--days (Sep/Oct), solar radiation (Dec) and soil water index (summer). For region 2, the key variables were price, annual soil water index, winter solar radiation, day--degrees from optimal through Sep/Oct, minimum temperature (Nov) and maximum temperature (Dec). It was rationalized that price (lagged-two-years) was a useful variable as orchard management inputs decrease during periods of low prices, resulting in a later season yield decrease. [PERSON] and [PERSON] [89] also reported on the use of climate data and remote sensing vegetative indices in the prediction of macadamia yields. Imagery in the second, third and fourth quarters of the year was associated with flower initiation, spring flush and nut growth, respectively. The variable with the highest correlation was the green normalized difference vegetation index (NIR/Green) collected in the flower initiation period. A ridge regularized regression was recommended over the machine learning algorithms of LASSO, support vector regression and random forest. Most information came from the remote sensing variables (79%), with little improvement in model performance noted from the addition of meteorological variables (1%). At the orchard level, the RMSE of the 2019 forecast was 0.8 t/ha, with a mean absolute percentage error of 20.9%. At the region level, aggregating block level predictions, prediction errors were between 0-15% across the 2016-2019 seasons. The potential for yield estimation without input of ground-collected inputs is encouraging, providing for scalability in widespread implementation. However, such models are indirect estimates of yield, and will fail if seasons' conditions exceed that encompassed by the training set.