Enhanced risk management and decision-making capability across the sugarcane industry value chain based on seasonal climate forecasts

Everingham, Yvette; Muchow, R.C.; Stone, Roger; Inman-Bamber, N. G.; Singels, A.; Bezuidenhout, C. N.

doi:10.1016/s0308-521x(02)00050-1

Cited by 104 publications

(82 citation statements)

References 18 publications

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“…In the case of sugarethanol a lot of effort has been made in different ways around all the countries that produce sugar cane, sugar and ethanol. Sugar-ethanol industry is particularly challenging due to interdependencies of climate uncertain and political-economic decisions, what directly impacts its value chain (Everingham et al, 2002) .…”

Section: Introductionmentioning

confidence: 99%

Is There a Bitter Flavor in Sustainability for the Sugar-Alcohol Industry?

Paiva

Biazzin

Sério

et al. 2015

JOSCM

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Biofuel has been pointed out as one "green" option for traditional fossil fuels like petroleum. Brazil is one the leading countries within this proposal competing globally with its sugar ethanol.. Nevertheless, there is a debate between corn ethanol and sugar ethanol .regarding the appropriateness to produce fuel instead of food in highly fertile regions. This industry is also severely criticized regarding its socio-environmental practices. We analyzed three cases identified as featuring best practices in this industry in Brazil. Two of the companies are located in the state of São Paulo, Southeastern Brazil, the most important region in sugar-ethanol production. The third is situated in the Brazilian Midwest, a region with fast growth in this industry. In this region, cattle, soy and corn have been substitute by sugarcane plantation. The results suggest that, when a company in this industry pursues a sustainable approach to its operations, it is possible to mitigate environmental impacts and to improve local social conditions. Such practices are potential sources of competitive advantage. However, clear gaps are still present regarding integration and collaboration along the supply chain. Nevertheless, sustainable business policies have also created new business opportunities. The continuous challenge is to develop synergetic operational capabilities that are able to mitigate environmental impacts, to decrease operational costs and to add value to the products.

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Section: Introductionmentioning

confidence: 99%

Is There a Bitter Flavor in Sustainability for the Sugar-Alcohol Industry?

Paiva

Biazzin

Sério

et al. 2015

JOSCM

View full text Add to dashboard Cite

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“…The common method of issuing operational seasonal climate forecasts as shifted probabilities of each of the climatological terciles, can be used directly to assign weights to analog years or to resample past years in proportion to the forecast probabilities. For example, Everingham et al (2002) and Bezuidenhout & Singles (2006) sampled analog years in proportion to tercile forecasts from the South African Weather Service to forecast sugarcane production.…”

Section: Classification and Analog Methodsmentioning

confidence: 99%

Translating climate forecasts into agricultural terms: advances and challenges

Hansen¹,

Challinor²,

Ines³

et al. 2006

Clim. Res.

240

145

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Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate.'

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“…Tercile hit rates are useful for decision-making purposes compared to above median hit rates, since they provide a better guide to higher or lower rainfall. For more information on terciles and decision-making processes used in seasonal forecasting, see Everingham et al (2002) and Ritchie et al (2004). LEPS scores, which are described by Potts et al (1996), are calculated as the LEPS percentage score in SCOPIC and range from −100 per cent to +100 per cent, where values above zero indicate Zhao and Hendon 2009) and it is capable of simulating the spatial and temporal variability of tropical rainfall associated with ENSO (Cottrill et al 2013b).…”

Section: Introductionmentioning

confidence: 99%

An assessment of rainfall seasonal forecasting skill from the statistical model SCOPIC using four predictors

Cottrill¹,

Kulesho²

2014

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The statistical model SCOPIC (Seasonal Climate Outlook for Pacific Island Countries) has been used to produce seasonal forecasts in ten Pacific Island nations since mid-2007 to improve their seasonal forecasting capacity and to provide timely warnings to changes in rainfall. However, to date there has been no detailed hindcast validation study to compare the forecast skill from the different predictors used to produce the seasonal forecasts at different stations from across the Pacific region. Here, we compare the rainfall forecasts created by the linear discriminant analysis model within SCOPIC using the four predictors: the Southern Oscillation Index (SOI); empirical orthogonal functions of sea surface temperature anomalies (SST1&9) and the NINO3.4 and the 5VAR index. This indicates that skill varies from season to season across the Pacific, with the highest skill in the austral summer and lowest skill in the austral winter. This study using tercile hit rates and LEPS percentage scores shows the 5VAR index has slightly superior skill compared to the NINO3.4, SOI and the SST1&9 indices, but results will vary depending on the station location, analysis period and the number of months used to calculate the predictor value.

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Enhanced risk management and decision-making capability across the sugarcane industry value chain based on seasonal climate forecasts

Cited by 104 publications

References 18 publications

Is There a Bitter Flavor in Sustainability for the Sugar-Alcohol Industry?

Is There a Bitter Flavor in Sustainability for the Sugar-Alcohol Industry?

Translating climate forecasts into agricultural terms: advances and challenges

An assessment of rainfall seasonal forecasting skill from the statistical model SCOPIC using four predictors

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