Foreknowledge about sugarcane crop size can help industry members make more informed decisions. There exists many different combinations of climate variables, seasonal climate prediction indices, and crop model outputs that could prove useful in explaining sugarcane crop size. A data mining method like random forests can cope with generating a prediction model when the search space of predictor variables is large. Research that has investigated the accuracy of random forests to explain annual variation in sugarcane productivity and the suitability of predictor variables generated from crop models coupled with observed climate and seasonal climate prediction indices is limited. Simulated biomass from the APSIM (Agricultural Production Systems sIMulator) sugarcane crop model, seasonal climate prediction indices and observed rainfall, maximum and minimum temperature, and radiation were supplied as inputs to a random forest classifier and a random forest regression model to explain annual variation in regional sugarcane yields at Tully, in northeastern Australia. Prediction models were generated on 1 September in the year before harvest, and then on 1 January and 1 March in the year of harvest, which typically runs from June to November. Our results indicated that in 86.36 % of years, it was possible to determine as early as September in the year before harvest if production would be above the median. This accuracy improved to 95.45 % by January in the year of harvest. The R-squared of the random forest regression model gradually improved from 66.76 to 79.21 % from September in the year before harvest through to March in the same year of harvest. All three sets of variables-(i) simulated biomass indices, (ii) observed climate, and (iii) seasonal climate prediction indices-were typically featured in the models at various stages. Better crop predictions allows farmers to improve their nitrogen management to meet the demands of the new crop, mill managers could better plan the mill's labor requirements and maintenance scheduling activities, and marketers can more confidently manage the forward sale and storage of the crop. Hence, accurate yield forecasts can improve industry sustainability by delivering better environmental and economic outcomes.

No abstract

No abstract

No abstract

Climate is a key driver of sugarcane production and all its by-products. Consequently, it is important to understand how climate change will influence sugarcane crop productivity. Ensembles from a crop model and climate projections form part of the dual ensemble methodology to assess climate change impacts on sugarcane productivity for three major sugarcane-growing regions in Australia-Burdekin, Mackay and New South Wales (NSW). Different parameterisations of a crop model injected with climate outputs from eleven statistically downscaled general circulation models (GCM) were used to estimate regionally averaged sugarcane yields for the base period 1971 to 2000. The forward stagewise algorithm selected crop model parameterisations that best explained the observed yields. Leave-one-out cross validation assessed the predictive capability of the equally weighted crop ensemble members characterised by the selected crop model parameterizations. A Monte Carlo permutation testing procedure was employed to measure the significance of the predictive correlations. The predictive correlations between historical yields and simulated historical yields for the Burdekin, Mackay and NSW were 0.69 (p = 0.030), 0.83 (p < 0.001) and 0.70 (p = 0.034), respectively. Simulations were run based on GCM projections for 2046 to 2065 for a low (B1) and a high (A2) emission scenario, with and without elevated CO2 levels. We found it was plausible for industry to consider an increase in yields to all three regions under the B1 scenario and highly plausible for NSW under the A2 scenario. Higher CO2 levels resulted in lower demand of water for the crop, particularly in the Burdekin region and suggested that industry could expand into regions currently considered as marginal owing to the benefits of increased transpiration efficiency that are associated with increased CO2. Although this study favoured neutral or positive impacts on sugarcane production, industry should not overlook negative impacts when developing a risk management framework in response to a changing climate.