Roland Schulze of the University of KwaZulu-Natal (UKZN), for their dedicated guidance and assistance during the course of this study. Doug Hellman of SASRI is thanked for his valuable support and comments during the study.
Problem Statement in Summary
Problem Statement
Background to the South African Sugar Industry
A synthesis was compiled highlighting the potential use of these technologies in the context of the South African sugar industry. Validation of the CANESIM sugarcane growth model using GIS within the South African sugar industry.
Yield Forecasting in the South African Sugar Industry
Benefits from Climate and Yield Forecasting
Hansen (2002) produced Figure 1.1 to illustrate the determinants on which benefits can be derived from seasonal climate forecasts by adapting information for decision making. Nevertheless, the probabilistic nature of climate forecasting and translated into yield forecasting requires risk-related decision-making capabilities among stakeholders (Thornton and Wilkes, 1998; Hammer, 2000a).
Aim and Objectives of this Study
The simulation environment had to be representative of the entire sugar industry in South Africa, providing enough information to delineate sugarcane production regions into logical and manageable sub-units. As a fifth objective, information transfer and user adoption of the above forecasting system had to be evaluated against existing forecasts.
Introduction
Statistical Forecasting Methods
The reason for this is that important driving factors, such as soil water balance, were often neglected. 2000) overcame this problem by developing a soil moisture stress index for wheat using daily precipitation and temperature data and weekly radiation to drive a simple water balance algorithm. 1991) linked crop yields with integrated climate indices, only in their case using existing indices from other applications. Integrated climate indices assimilate large amounts of data, such as precipitation, evapotranspiration, and soil properties, into a single number that is representative of the “big picture” (Hayes, 2001).
Yield Forecasts using Crop Yield Models
Hansen and Jones (1999) noted that crop yield models have been developed using information from intensive management trial experiments. Potential disadvantages of plant yield models are the lack of genetic coefficients to simulate yields from different cultivars (Ogoshi, 1995) and the lack of reliability among industry stakeholders (Meinke et al., 2001).
Remote Sensing for Crop Yield Estimation and Forecasting
Bannayan and Crout (1999) note that a key advantage of crop models is the ability to quantify risk under uncertain conditions after performing frequency analyzes on the outcomes of repeated simulations. Higher resolution vegetation index information, such as that obtained from LANDSAT and SPOT, could also theoretically be used to estimate sugarcane conditions at farm and field scales (Schmidt et al. concluded that there is significant scope for remote sensing information to use for predicting yields in the South African sugar industry.
Combined Methods for Crop Yield Forecasting
Various studies have resulted in improved crop forecasts after combining remote sensing technologies with crop modeling (Maas, 1988; Horie et al., 1992; Roebeling et al., simulation models coupled with sensing technologies to predict sugarcane yields over large areas in Thailand. Maas (1988) confirmed that relatively simple crop yield models performed well, but noted that the combination of remote sensing information and crop modeling compensated for each other's weaknesses.1992) also concluded that this technique resulted in the most effective yield prediction method investigated.
A Synthesis of Yield Forecasting Methods for the South African Sugar
Remote sensing techniques seem the most appropriate way to overcome some of the above limitations of crop yield models. However, the development of a model-based yield prediction system would allow later integration with remote sensing technologies.
An Overview of Stakeholder Requirements for Yield Forecasts
There are many areas in which decision making can be improved once yield forecasts have been transferred and interpreted correctly (Horie et al., 1992; In the marketing sector, yield forecasts can be used to plan an overall marketing strategy , including forward sales, transportation and warehouse requirements (Gadekar, 1998; Everingham et al., 2002a).
A Questionnaire Survey for Industry Stakeholders
Results from the Questionnaire Survey
A small majority of stakeholders proposed that yields be expressed as a percentage of an equivalent crop grown in the previous season (Figure 3.4c). Although not reflected in Figure 3.4(c), it should be noted that most grower representatives would also be interested in a comparison between the current crop and the previous crop grown in the same field.
Discussion and Conclusions
All predicted parameters should include confidence bands and values should be compared with those of the previous season. Local extension officers and agricultural economists should be encouraged, where possible, to integrate yield forecasts with agronomic, business and risk management approaches.
Introduction
CANEGRO
CANEGRO simulates plant, atmospheric and soil properties with relatively high levels of complexity on a daily time step. The model requires a wide range of input variables, from cultivar- and soil-specific coefficients to daily climate and irrigation records.
ACRU-Thompson
Various mechanistic processes, such as radiation-driven biomass accumulation, leaf development, canopy expansion and groundwater movement are modeled (Bezuidenhout, 2000). Singels and Bezuidenhout (2002) compared the model results with observed data from a wide range of experiments within the South African sugar industry.
Canesim
1999) added a degree-based yield coefficient development routine to the ACRU-Thompson model during a sugarcane yield forecast study for the Eston plant in the KwaZulu-Natal highlands. Relative simulated yield (%) Relative actual mill yield (%) Figure 4.3 Simulated Canesima yield and measured cane yield (expressed as . percent of previous season's yield) for the Darnall mill supply area on the North Coast of KwaZulu-Natal (from Gers et. al., 2001).
Discussion
Introduction
1998) stated that a crop forecasting system should be sensitive to prevailing sugarcane cropping cycles in a region. The purpose of this chapter is to obtain the necessary spatial information and input data to justify the development of a crop forecasting system for the South African sugar industry.
Climate Regionalisation
- Methods of Delineating New Climate Zones for the Sugarcane Belt
- Results and Discussion
- An Introduction to the Spatial Collation of Soil and Crop
- Soil and Harvest Age Information
- Irrigation Information
- Sugarcane Delivery Information
The aim of this part of the study is to establish reasonably homogeneous climatic zones for the sugar cane producing areas of South Africa. A template of new zones was based on the boundaries of the relatively homogeneous hydrological response zones of Dent et al.
Discussion and Conclusions
Introduction
A Review of Climate Data Options Available to the South African Sugar
- Use of Data from Climate and Rainfall Stations
- The Application of Empirical Interpolation and Substitution
- Emulating Climate Variables using Stochastic Weather Generators
- Use of Downscaled Results from General Circulation Models
- Remote Sensing Applications for Producing Climate Data
The H&S equation has received significant recognition, including support from the FAO (Allen et al., 1998). These techniques can rely on regression analyses, canonical correlation analyses, statistical analogs, artificial neural networks, and the use of topography and weather classification schemes (Hewitson and Crane, 1996; Bates et al., 2000).
The Derivation of Climate Data for Crop Yield Modelling Purposes
Background
However, these methods are laborious and require high levels of image calibration and refinement and analysis before the data can be made available for crop modeling purposes (pers comm.
The BEEH Climate Dataset
This variable could not be calculated by the conventional Penman-Monteith method as used by McGlinchey and Inman-Bamber (1996), since no estimates of solar radiation and wind speed were available in the BEEH climate database. For this study, data from 15 automatic weather stations (AWS) located in the sugar cane belt (see Table 6.2) were used to relate ETO values derived from the H&S and Linacre equations to ECref values derived from the Penman-Monteith equation (McGlinchey and Inman)-Bamber, 1996).
The SASRI Climate Dataset
A complete climate record for most HCZs from 1978 to 2002 was compiled using data from various available SASRI climate stations shown in Table 6.1. Particular attention was paid to representativeness, and notable trends between neighboring climate stations were first removed before combining data.
Results
The rainfall station data were not combined in a similar way, but fewer simulations were performed when rainfall data were not available. Indicates that some parameters in the source data were linearly adjusted before being added to the record.
Discussion and Conclusions
Introduction
Likewise, different seasonal climate forecasts and interpretations of these climate forecasts can affect yield forecasting skills. The first is to review general methods that underlie seasonal climate forecasts and to examine the way in which climate indices and forecasts are imported into yield forecasting systems.
A Review of Seasonal Climate Outlooks and their Adoption for Yield
Climate outlooks should not be taken as true images of the future, but as a way to statistically reduce a priori uncertainty (Todini, 1999). In these studies, climate outlooks and indices, such as the SOI, were used to select more than one suitable analog from the historical record.
System Configuration
The SAWS seasonal precipitation forecast was used to select either nine or ten appropriate analog seasons from each HCZ's climate history. Then, nine or ten analog seasons were selected from the midpoints of each category, these being at the 17th, 50th, and 84th percentiles, respectively.
Discussion and Conclusions
An increase in the number of selected analog seasons and skillful rainfall forecasts with longer lead times could be useful to address these issues. For hindcasting purposes, the simulation environment was set up so that any date in the period could be treated as the end of the record, after which data from analogue seasons could be used to complete the relevant season.
Introduction
The purpose of this chapter is to evaluate various components of the Canesim yield prediction system and provide recommendations for future refinements. Assessing the accuracy and proficiency of the system at different times of the year, and.
Methods
- Industry Production Data Corrections
- Corrections Resulting from Mill Closures and Sugarcane
- Conversions from Total Tonnes Crushed to Cane Yield 75
- Evaluation Parameters to Assess Forecast Accuracy
- Assessment of the Value to Accuracy from Additional Raingauge
- Assessment of the Value of Climate Data
- Model Forecast Accuracy in an Operational Context and
A positive value indicates better results obtained by the BEEH climate dataset, while a negative value supports the use of the SASRI climate dataset. The simulations were conducted for each HCZ between 1980 and 2002 for sugarcane crops harvested in each month of the milling season (April–December).
Results and Discussion
- The Value of Additional Raingauge Information
- The Value of Climate Data
- Accuracy of Operational Forecasts
- The Value of Seasonal Rainfall Outlook Information
The difference in forecast skill between simulations based on neutral seasonal precipitation forecasts and simulations based on actual seasonal precipitation forecasts was used to quantify the value of precipitation forecast information. The selection of analogous seasons in this study was based on information about accumulated rainfall over three months.
Discussion and Conclusions
The assessment showed significant differences in accuracy between model simulations based on two different sets of climate data. In contrast, Malelane and Umzimkulu mills were better represented by more generically derived climate data derived from temperature and precipitation datasets maintained by BEEH.
Introduction
9 A review of historical yield forecasting and the transfer of related information in the South African sugar industry. unlike climate data, which can take up to six weeks to become available. Specific objectives are (1) to verify and compare the accuracy of MGB forecasts with Canesim-based forecasts, (2) to assess information transfer methodologies, and (3) to investigate the potential role of the Canesim model-based yield predictions as a source of additional information. in the South African sugar industry.
Methods
Operational Model-Based Forecasts of Sugarcane Production
The consolidated MGB forecast, for example, appears regularly as the official production forecast on the main website of the Sugar Association of South Africa (cf. www.sugar.org.za). The purpose of this chapter is to evaluate operational Canesim sugarcane yield predictions in light of the existing and most widely used MGB predictions.
An Evaluation of Mill Group Board and Model-Based Forecasts
This relative value is used to project the current known production figures from the previous season to the current season.
Results of Operational Forecast Accuracies
Large differences in average forecast skills of the MGB forecasts were observed between different mills over the period (cf. Table 9.2). It should also be noted that model-based yield forecasts outperformed MGB forecasts during the January to April period at six of the 15 mills (cf. Table 9.4).
A Synthesis on Forecast Accuracies and Information Transfer
The forecast error will therefore be exacerbated by expressing an expected yield in relative terms with a simulated yield from the previous season as a benchmark. The timing of the Canesim dividend forecasts may also hamper the absorption of the information.
Conclusions
It is believed that the accuracy of model-based forecasts of annual production at both mill and industrial scales can be greatly improved if the correct feedback mechanisms of production to date are incorporated into the system. Canesim model-based forecasts of annual production and MGB forecasts cannot be considered independent sources of information, as model-based forecasts have been distributed to MGBs for consideration since 2001.
Main Conclusions
The system's ability to provide predictions of sugarcane production at climate zone, mill and industry scales was not only demonstrated but also operationally implemented in South Africa for several years. The study provided the first system, to the author's knowledge, to produce model-based operational forecasts of sugarcane production at an industry scale for one of the world's 15 major sugarcane producing countries.
Recommendations for Future Research
In the Proceedings of the Workshop on Computer Techniques of Meteorological Data Applications for Problems in Agriculture and Forestry. In Proceedings of the SASTA Workshop on Burning/Harvest Delays for Crushing and Yield Estimation, South African Association of Sugar Technologists, 20-29
