Comparing phase based seasonal climate forecasting methods for sugarcane growing regions

Climate forecasting systems that group years on the basis of a climate forecasting index like the Southern Oscillation Index (SOI) or sea surface temperatures (SSTs) are quite simple to explain to industry personnel. Phase systems identify a subset of years (analogues) that have the same phase for a particular month. Industries can then investigate how the response of interest varied historically by the SOI or SST phase and self-validate the system. This is possible because industry members will remember the big wet and big dry years. Phase systems also allow industry personnel to visualise distributional shifts in rainfall and other responses (e.g. yield) between the different phases. These components spark a great deal of interest and enthusiasm at case study meetings. The simplicity of phase systems contributes to increased understanding of the forecasting approach, and highlights both the strengths and limitations associated with seasonal climate forecasting. Given that climate forecasts are not a perfect science, it is important that industries understand the risks and probability concepts so they can better integrate forecasts into a decision-making framework. The Australian sugar industry has predominantly used the five-phase SOI climate forecasting system as its benchmark in recent years. The purpose of this paper is to compare the performance of the benchmark system with other phase-based climate forecasting systems. Three-phase and nine-phase SST forecasting systems and a three-phase SOI system formed part of the investigation. An assessment is made across the sugarcane growing regions and across the calendar year, simultaneously. This is done for seven sugar growing regions that collectively produce approximately 90% of Australia's sugar. A methodology that enables a fair comparison of the systems is presented. This methodology caters for the different number of phases with each forecasting system. We consider three performance measures: P-values of (i) the Kruskal-Wallis (KW) test statistic, (ii) a linear error in probability space (LEPS) skill score and (iii) a relative operating characteric (ROC) skill score for above and below median rainfall. P-values are used to overcome obstacles associated with the different numbers of phases. This is important since, by chance alone, it is easier to get a higher or better categorical LEPS score for systems that have more phases. Results can vary with the performance measure. If ROC- and LEPS-based performance measures were preferred, then the three-phase SST system produced a higher number of significant results across the regions and three-month rolling periods. If performance measures that reflect the degree of distributional shifts or discriminatory ability between phases are preferred, then the five-phase SOI system produced the highest number of significant fields. Taking into consideration dependencies and auto-correlations associated with the response measurements across the calendar year and across coastal regions which essentially differ in latitudinal positioning, it is important to assess the likelihood that the number of significant fields could have occurred purely by chance. Whilst a methodology for comparing different phase systems, where the number of phases varies from system to system is presented, the dilemma as to which performance measures to base decisions remains. Users must carefully consider which performance measures are most appropriate for their investigation

Identifying climate variables having the greatest influence on sugarcane yields in the tully mill area

Large fluctuations in cane yield from one season to the next are problematic for all sectors of the sugar industry. The Wet Tropics region is characterised by high rainfall, excessive soil wetness, low solar radiation and vulnerability to extreme climatic variability. Although many different factors influence productivity, annual fluctuations in cane yield at the farm level in this region are believed to be strongly associated with changes in climatic conditions. To investigate this further, a stepwise linear regression model used atmospheric variables at different times of the growing season to explain Tully mill detrended cane yield data for eight different time blocks. These time blocks ranged from 10 to 80 years. The regression models explained between 32.2 and 94.1% of the variation in detrended cane yields for the Tully mill area. Rainfall, most commonly around spring and summer, was always the first variable entered into the models making it an important predictor. However, the other variables selected for late entry changed over time. Improved yield forecasts coupled with greater knowledge of the influence of climatic conditions on cane yields could be used for a range of management decisions across all sectors of the industry

An introduction to multivariate adaptive regression splines\ud for the cane industry

Industries strive to find the balance between increased productivity and future sustainability of production. To this end, the sugar cane industry maintains records from each farm about CCS (commercial cane sugar content (%)), total cane yield, cane varieties and growing conditions throughout each region. A challenge that the cane industry faces is how to accurately extract useful information from this vast array of data to better understand and improve the production system. Data mining methods have been developed to search large data sets for hidden patterns. This paper introduces a powerful data mining method known as Multivariate Adaptive Regression Splines (MARS). By applying the MARS methodology to model CCS production data from the Herbert district, a model was produced for the 2005 harvest period. This model produced a north-south geographic separation between low and high CCS producing farms in line with recorded CCS values. The model was also able to identify farm groupings which contributed to lower, modelled CCS values, relative to other farms. A brief investigation on the isolated effects of variety was also conducted

An introduction to multivariate adaptive regression splines for the cane industry

Industries strive to find the balance between increased productivity and future sustainability of production. To this end, the sugar cane industry maintains records from each farm about CCS (commercial cane sugar content (%)), total cane yield, cane varieties and growing conditions throughout each region. A challenge that the cane industry faces is how to accurately extract useful information from this vast array of data to better understand and improve the production system. Data mining methods have been developed to search large data sets for hidden patterns. This paper introduces a powerful data mining method known as Multivariate Adaptive Regression Splines (MARS). By applying the MARS methodology to model CCS production data from the Herbert district, a model was produced for the 2005 harvest period. This model produced a north-south geographic separation between low and high CCS producing farms in line with recorded CCS values. The model was also able to identify farm groupings which contributed to lower, modelled CCS values, relative to other farms. A brief investigation on the isolated effects of variety was also conducted

Forecasting Australian sugar yields using phases of the Southern Oscillation Index

Yields for the Australian sugar industry can vary seasonally and regionally. Advanced knowledge of likely sugar productivity levels for mill regions in a particular season would assist marketers to forward sell Australian sugar, and allow mill managers and harvest operators to better plan for the coming season. Given that climate is a key driver of productivity, the purpose of this paper is to investigate the potential usefulness of a climate forecast system which incorporates five patterns or phases of the Southern Oscillation Index (SOI) to forecast sugar yields. The chance of obtaining a sugar yield above the long-term median was computed for each SOI phase across eight regions which span the coastline of Queensland, where most of Australia's sugarcane is grown. Results indicate that for certain regions, the chance of obtaining an above average crop can be greatly increased, and in some cases decreased depending on the phase of the SOI. Since many decisions in the Australian sugar industry are based on crop size, the SOI phases provide a useful tool for enhancing decision making and risk management capability for the industry

On measuring quality of a probabilistic commodity forecast for a system that incorporates seasonal climate forecasts

Regional commodity forecasts are being used increasingly in agricultural industries to enhance their risk management and decision-making processes. These commodity forecasts are probabilistic in nature and are often integrated with a seasonal climate forecast system. The climate forecast system is based on a subset of analogue years drawn from the full climatological distribution. In this study we sought to measure forecast quality for such an integrated system. We investigated the quality of a commodity (i.e. wheat and sugar) forecast based on a subset of analogue years in relation to a standard reference forecast based on the full climatological set. We derived three key dimensions of forecast quality for such probabilistic forecasts: reliability, distribution shift, and change in dispersion. A measure of reliability was required to ensure no bias in the forecast distribution. This was assessed via the slope of the reliability plot, which was derived from examination of probability levels of forecasts and associated frequencies of realizations. The other two dimensions related to changes in features of the forecast distribution relative to the reference distribution. The relationship of 13 published accuracy/skill measures to these dimensions of forecast quality was assessed using principal component analysis in case studies of commodity forecasting using seasonal climate forecasting for the wheat and sugar industries in Australia. There were two orthogonal dimensions of forecast quality: one associated with distribution shift relative to the reference distribution and the other associated with relative distribution dispersion. Although the conventional quality measures aligned with these dimensions, none measured both adequately. We conclude that a multi-dimensional approach to assessment of forecast quality is required and that simple measures of reliability, distribution shift, and change in dispersion provide a means for such assessment. The analysis presented was also relevant to measuring quality of probabilistic seasonal climate forecasting systems. The importance of retaining a focus on the probabilistic nature of the forecast and avoiding simplifying, but erroneous, distortions was discussed in relation to applying this new forecast quality assessment paradigm to seasonal climate forecasts

Supervised hierarchical clustering using CART

The size and complexity of current data mining data sets have eclipsed the limits of traditional statistical techniques. Such large datasets frequently require some form of cluster analysis, usually in the form of a hierarchical cluster analysis. However the implementation of a traditional hierarchical scheme on large datasets requires an additional cluster validation analysis. Classification and Regression Trees (CART) are a non-parametric regression and classification technique that have become popular within the biotechnology and ecological fields. CARTs intuitive interpretation, and ability to handle large datasets make it easily accessible to the non-statistician by presenting the statistical relationships found in the form of a binary tree. This paper proposes a supervised clustering algorithm capable of finding real clusters within large datasets by using CART as a means of filtering the clusters found using any hierarchical technique. The supervision performed by CART acts as a filter of the results from a hierarchical cluster analysis by merging or removing poorly defined groups. It is common practice to validate a cluster analysis using descriminant analysis, however this assumes that the correct number of clusters is known. CART implements a selective classification of groups allowing for some groups not to be explicitly classified, a feature not supported by standard descriminant analysis. This selective classification acts in two fold, firstly by filtering or merging clusters that are not validated by the data, and secondly, as a relationship model for the clusters found and provides statistical measures of certainty over the analysis. An example of this method is presented using Sea Surface Temperatures (SST). This is an ideal choice as very little statistical cluster analysis has been implemented on this dataset, yet knowledge of such structure is in high demand. The analysis is performed for one month November for the years 1940 through to 2002, where some of the most useful variation is expected. The supervised clustering technique successful extracted seven meaningful clusters, which predicted with a cross-validated classification rate of 0.50

RAIN FORECASTER - a seasonal climate forecasting tool

Like most agricultural industries, the Australian sugar industry is exposed to the elements of the climate. Knowing if the season ahead is likely to be wetter or drier can assist industry decision makers plan for the future. RAIN FORECASTER is a computer program that can forecast rainfall and wet days using phases of the Southern Oscillation Index (SOI) and anomalies in sea surface temperatures in the Niño 3.4 region which forms part of the central equatorial Pacific Ocean. RAIN FORECASTER also incorporates the long lead forecasting model developed by Florida State University (FSU). The Florida State University model allows industry to assess early in the year (e.g. January, February) if there is likely to be a wet finish to the harvest season. Whilst RAIN FORECASTER can be used as an operational prediction tool, it is also a learning tool that can be used to understand how climate indices influence rainfall patterns. For example, by experimenting with RAIN FORECASTER, three key rules emerge common to most sugarcane growing regions located along the eastern coast of Australia. These rules are: (i) consistently positive SOI phases and/or negative Niño 3.4 anomalies (La Niña) during the harvest season favour wetter harvest conditions; (ii) consistently negative SOI phases and/or positive Niño 3.4 anomalies (El Niño) during the harvest season favour drier harvest conditions, and (iii) La Niña projections for the harvest season made by the FSU model early in the year, increase the risk of a wetter finish to the harvest. RAIN FORECASTER can be used for forecasting conditions outside the harvest season, but different results may appear for different regions. RAIN FORECASTER is a simple tool that provides an excellent basis for exploring how climate conditions are influenced by variations in atmospheric and oceanic conditions

An assessment of the 5 phase SOI climate forecasting system to improve harvest management decisions

Extreme rainfall events during the harvest season can cause major disruptions to harvesting operations. Of critical importance is unseasonably high rainfall in the May-June and October-November periods which affect crush start time, timing of sugar supply, harvest season length and ratooning capability for the following season. If the likely climate conditions for these periods can be forecast with a reasonable degree of certainty, and with sufficient lead times, then local industry decision makers can better plan harvesting operations to coincide with the likely forecast, and thereby reduce the risk and uncertainty associated with start and finish times of the harvest season. The aim of this paper is to investigate the potential usefulness of the climate forecasting system that incorporates the 5 phases of the Southern Oscillation Index (SOl) in aiding decision making capability and risk management associated with harvesting operations. Interaction with the sugar industry indicated a clear need to identify critical 'wetday' occurrence. The likelihood of wetdays occurring during May-June and October-November were calculated using the '5 phase SOL system'. Results from this analysis indicate that this climate forecast system offers some benefits associated with forecasting 'wetdays' during October-November, whereas benefits associated with forecasting 'wetdays' during May-June are currently more limited. The usefulness of the climate forecast was found to vary with lead-time and location