Accurate prediction of sugarcane yield using a random forest algorithm

International audienceAbstractForeknowledge 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

New APSIM-Sugar features and parameters required to account for high sugarcane yields in tropical environments

Sugarcane in field plot experiments in tropical Brazil (Guadalupe, Piaui State, 6.6 degrees S), produced very high yields under non-limiting water and nutrients. Mean stalk dry mass at 8, 11.5 and 15 months were 40, 51 and 70 t/ha respectively for six varieties and six planting dates. These yields were explained by high but not excessive temperatures allowing the canopy to close after 73 days on average. Substantial changes were required to enable the APSIM-Sugar model to simulate canopy and yield gain processes in Brazilian genotypes for the purpose of optimising variety, planting and harvest date options. A new modelling feature was proposed to deal with the observed growth slowdown when crop was about 7-8 months old and dry mass yields higher than 40 t/ha. All new parameters and features were validated with independent experiments as well as with the original dataset used for developing APSIM-Sugar. Future studies involving irrigation, yield gap analysis and climate change in environments where high yields are expected, should consider these modifications

Forecasting water allocations for Bundaberg sugarcane farmers

Limited water availability in dry cane growing regions poses a challenge to sugarcane farmers. Water allocations tend to be lower at the beginning of the water season, and are increased during the season when inflows are captured. Probabilistic information reflecting the likelihood of specified increases in water allocation is not available to sugarcane farmers. The present paper describes how seasonal climate forecasts were used to provide this information for the 2001/2002 season as part of a case study involving sugarcane farmers in Bundaberg, Australia. Water allocation forecasts were then supplied to an irrigation simulation scheduling system to provide guidance about when and how much water could be applied. This research was underpinned by a cross-institutional collaboration that engaged industry, extension officers, engineers from the water authority and scientists from agriculture and climatology. The key findings from this investigation were 2-fold: the participatory approach (1) contributed to the development of information needed by industry, and (2) demonstrated the potential usefulness of climate forecasting models, hydrological models and cropping system simulators to contribute to enhancing knowledge about water availability and application. Additional investigations are required before this technology can be operationalised

Genetic adjustment to changing climates: sugarcane

Climate change could affect sugarcane production directly through biophysical processes and indirectly as governments start implementing energy and environmental policies. Life cycle analyses indicate that sugarcane offers large advantages over other crops such as corn for biofuel production. As the fiber component of sugarcane becomes more valuable as an energy source, selection indices may change with a focus on higher total biomass rather than sucrose production alone. With climate change, the Australian sugar industry at least is expected to experience increases in atmospheric [CO2], rainfall variability, temperature, evapo-transpiration and water stress. Sugarcane photosynthesis and biomass accumulation is reported to increase between 7% and 40% in 2x normal CO2 and transpiration efficiency (TE) could increase up to 60%. Increased CO2 is likely to mitigate the negative effects of water stress by improving TE even if the direct effects on biomass yields are modest. Simulation of various drought resistance or avoidance traits indicated that traits such as leaf senescence and decreased leaf or root conductance, were mostly negative for biomass yields in rainfed Australian and South African climates. However simulations of increased TE resulted in increased biomass yields of 1 to 11% even though increased TE was combined with reduced conductance. Experiments with Sugarcane in a region in Australia where the mean annual temperature is 3°C higher than the next hottest sugarcane region in the country indicated that specific selection will be required for hotter climates. Little in known about the physiology of sugarcane at elevated temperatures and we suggest that research now be focused on both elevated CO2 and temperature in the search for germplasm that can contribute to adaptation to climate

A dual ensemble agroclimate modelling procedure to assess climate change impacts on sugarcane production in Australia

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

Improving the participatory development of decision support systems for the sugar industry

Sugar production systems are characterized by complex interactions between a range of economic, environmental and social factors. This complexity has lead to a search for ways in which scientific knowledge can be incorporated into forms that industry stakeholders can use to assist their management decisions. Decision support systems (DSSs) are one of the ways in which scientists have attempted to make agricultural systems science more accessible and useful for industry stakeholders. Recently, there has been a shift towards more participatory research and development of DSSs. We have analysed the participatory development of DSSs using concepts from the sociology of science and technology, as part of a study examining the adoption of knowledge intensive technologies in the Australian sugar industry. In this paper, we develop a framework for describing the phases of the participatory process, and the likely outcomes of the process. Understanding these phases allows those involved to be more confident that the participatory process will result in the beneficial relationships and greater mutual understanding that are desired from these processes. This work also highlights that the subsequent use of the DSS is not a guaranteed outcome of participatory development. We identify two likely outcomes of participatory DSS development: DSSs may lead to practice change even if they are used only to build capacity, or DSSs may be used directly to improve practice on an ongoing basis. Our analysis so far suggests that successful DSS development should be viewed as a participatory process leading to practice change, which results in improved farm or agricultural supply chain management, irrespective of whether or not this involves ongoing DSS use. We illustrate this framework with case studies of DSSs for irrigation management and climate forecasting

Sugarcane yield future scenarios in Brazil as projected by the APSIM-Sugar model

Crop models like APSIM-Sugar have been used to assess the impacts of climate variability and change on sugarcane. APSIM-Sugar was recently upgraded to simulate sugarcane in Brazil and to cater for climate impacts under water-limited environments. In this context, our first objective was to evaluate the recent upgrade on determined traits for the most cultivated Brazilian variety under water-limited conditions. As a second objective, we applied these model settings to project the sugarcane crop performance under likely changing climates in important producing regions. The stalk mass estimates for four published experiments across Brazil were satisfactory, with a R2 of 0.96, the agreement d index of 0.99, and root mean square error (RMSE) of 3.8 t/ha. Future climate projections (until 2099), considered two emission scenarios (RCP4.5 and RCP8.5), were derived based on a recently proposed subset approach of five climate models (GCMs) from CMIP5. The subset aimed to capture the full ensemble of temperature and rainfall changes by considering basic classes of climate changes (relatively cool/wet, cool/dry, middle, hot/wet, and hot/dry). A typical system with 12-month old crops was simulated for rainfed and irrigated sites. The ensemble of future projections indicated that cane yields would decline compared to the present average simulations (1980−2009) for rainfed sites, mainly due to increased water stress. Yields at fully irrigated sites are also projected to decline slightly. However, the expected changes were highly variable between GCMs with the End-of-Century period presenting large uncertainty. In short, while providing an up-to-date projection of future climate change impacts on sugarcane in Brazil, such results should be interpreted with caution

Most Profitable Use of Irrigation Supplies: A Case Study of A Bundaberg Cane Farm

A range of biophysical and financial factors, including the crop response to available water and the cost of irrigation, significantly impact on the economic benefits from using irrigation. Research tools have been developed in a multi-disciplinary environment to allow for the assessment of the economic benefits associated with using irrigation. This paper adopts the 1996-1997 season in Bundaberg as a case study and develops arguments for best use of limited water based on current economic and biophysical modelling capability. A selection of irrigation ‘options’ were chosen for investigation based on combinations of soil type, allocation, critical fraction of available soil water (FASW) to irrigate, irrigation amount, and age of crop for irrigation commencement. The influence of these options on cane production is explored in a farm-level linear programming model. There appears to be a sound economic argument for further biophysical research into the crop response to irrigation, based on the sensitivity of farm incomes to choice of irrigation strategy

Traits for canopy development and light interception by twenty-seven Brazilian sugarcane varieties

Since new varieties are released continuously in the Brazilian sugarcane agro-industry, the understanding of their growth, development and yields are necessary. In Brazil, there is a lack of studies on sugarcane variety traits for canopy development and yields, especially those employed by the sugarcane modelling community. This paper assessed the canopy development and light interception by 27 sugarcane varieties grown at two tropical sites (Sao Romao, MG, and Guadalupe, PI) under non-limiting (potential) conditions in Brazil and tested the capability of the well-known APSIM-Sugar model to distinguish these varieties. Parameters for APSIM-Sugar canopy traits (leaf size, green leaf number, tillering and stalk emergence) and the light extinction coefficient were derived for each variety from field experiments and by calibration for the plant cane cycle. Trait parameters were then validated satisfactorily against independent datasets from the same two sites (first racoon cycle of 27 varieties) and a row spacing experiment at Sao Romao (plant and racoon for six varieties). A validation was also done using published experiments in other five sites across Brazil (four varieties). After APSIM-Sugar parameters were calibrated and validated, long-term simulations were run for each variety at the two sites. APSIM-Sugar outputs of thermal time to reach 50% of canopy closure were employed to group the varieties in terms of canopy formation by clustering analysis. The four major clusters corresponded well with promotional information from breeding companies in Brazil about canopy formation. These findings suggest it is reasonable to hypothesise that the APSIM-Sugar parameters are plausible and are an important step for unravelling genetic x environment x management interactions to improve yields and quality in the Brazilian sugarcane agro-industry

High-yielding sugarcane in tropical Brazil - integrating field experimentation and modelling approach for assessing variety performances

Aiming to gain an understanding of how the genotype × environment × management (G×E×M) interaction influences the yield accumulation by elite sugarcane varieties in Brazil, a large dataset from field plot experiments carried out in two tropical sites (Guadalupe, 6.8 °S; São Romão, 16.4 °S) involving distinct planting dates, varieties and harvest ages, was analysed with statistical techniques and with the APSIM-Sugar model. Radiation use efficiency (RUE) was determined via a series of regressions and employed in an analysis of variance to investigate site, seasonal, developmental, and varietal differences. Outstanding yields were achieved at both sites. RUE declined as the crop progressed, confirming previous observations on declining RUE with age, known as the reduced growth phenomenon (RGP). RUE was always greater at Guadalupe than São Romão, evidencing that Guadalupe is a more suitable environment for sugarcane production, favoured by higher air temperatures during crop establishment and canopy formation. Varietal differences in RUE appeared only after the early developmental stage, and the observed growth slowdown with age was consistent across the two experimental sites, indicating that RGP is a varietal trait that should be considered for high-yielding environments. The process-based APSIM-Sugar model was set up with recently determined canopy traits and a new RGP feature based on leaf appearance. RGP parameters were obtained for each variety and site through calibration. The calibrated model was accurate to account for yield accumulation by the varieties in both experiments. The new parameters were evaluated with independent datasets from other local experiments at each tropical site as well as from published rainfed experiments in sub-tropical Southeast Brazil. Independent verification of the RGP traits added confidence in the new way of dealing with RGP based on leaf stage. The G×E×M interaction on yield accumulation can now be explored more confidently with APSIM-Sugar for the purpose of optimising the choice of varieties, planting dates and harvest ages for sugarcane industries in favourable irrigated lands in tropical Brazil