SUGARCANE is one of the most important crops in Guyana; it contributes nearly 20% to GDP and hence is very important to the Guyanese economy. Productivity of sugar like rice is highly dependent on climatic changes and therefore it is essential to understand the major changes in climate patterns that affect sugarcane and sugar yields. Climatic factors that affect cane and sugar production:

RAINFALL
Rainfall is the single most important factor responsible for sugarcane production. As rainfall is significantly affected by extreme events such as El Nino and La Nina, so is cane production. The annual rainfall in Guyana averages between 1500 mm to 2800 mm of which most of it falls in May-June and November – December. Some cane production areas receive more than the average annual rainfall and sometimes in different times of the year. This has a major effect on production. During the active growth period, rainfall encourages rapid cane growth, cane elongation and internode formation. But during the ripening period high rainfall is not desirable because it leads to poor juice quality, encourages vegetative growth, formation of water shoots and increases in the tissue moisture. It also hampers harvesting and transport operations.

RADIATION
Solar radiation is the main source of energy for photosynthesis and is also responsible for the loss of water from soil and plants. Being a C4 plant, sugarcane is capable of high photosynthetic rates and the process shows a high saturation range with regard to light. High light intensity and long duration promote tillering, while cloudy and short days affect it adversely. As radiation cannot be conserved it is important to have management strategies to intercept most of the radiation by appropriate times of planting and planting densities.

TEMPERATURE
The rate of photosynthesis is dependent on temperature as well as many other biochemical processes controlling meristematic activity for leaf and bud development.  Photosynthesis efficiency of sugarcane increases linearly between temperatures ranging from 80C to 340C degrees. Cool nights and early morning temperatures affect photosynthesis. In Guyana there is little variation in temperature and thus, sprouting and emergence are not affected.

TEMPERATURE AND PURE OBTAINABLE SUGAR
It is generally accepted that leaf growth is constrained at low temperatures. Cool night temperatures and sunny days slow down growth and carbon consumption, while photosynthesis may continue thus enhancing sucrose accumulation. The stalk elongation is more sensitive to lower temperature than are photosynthetic rates. Thus the accumulation of sucrose is not favoured at high temperatures as growth rate increases more than photosynthetic rate.

NEED FOR MODELLING
Agricultural systems are complex, and understanding this complexity requires systematic research, but resources for agricultural research are shrinking. Field experimentation can only be used to investigate a very limited number of variables under a few site-specific conditions. Crop models are useful tools for integrating knowledge of the bio-physical processes governing the plant-soil-atmosphere system, and for extrapolating research results to other locations or sites. Where there are long sequences of daily weather data, crop models can be used to evaluate the production uncertainties associated with these management scenarios. Models can thus be used to extend research results both spatially and temporally.

There are many crop growth simulation models. Some are more generic in nature while others are built for specific purposes. Most of these models simulate crop growth and soil processes using daily time steps. All models are developed with some assumptions and hypotheses, and all have strengths, weaknesses and limitations for appropriate application. Well-known crop modelling groups across the world include IBSNAT/IFDC (International Benchmark Sites Network for Agrotechnology Transfer/International Fertilizer Development Centre) in USA, WAU/AB-DLO (Wageningen Agriculture University/Centre for Agrobiological Research) in the Netherlands, and APSRU (Agriculture Production Systems Research Unit) in Australia. The IBSNAT project was initiated in 1982, and over the past 20 years it has developed more than 15 models, including CERES (Crop Estimation through Resource and Environment Synthesis) Rice and CERES Wheat, which are available either as stand-alone models or within the DSSAT (Decision Support System for Agrotechnology Transfer) shell. All the DSSAT models are continuously being refined, calibrated, validated, and applied across the world by the scientists who developed the models and their collaborators and others.

The Wageningen Group is probably the strongest modelling group in the world in terms of concentration and strength of modellers and the range of models being developed. Most of the models that are available today across the world have some sort of origin from Wageningen. WAU/AB-DLO also has projects and collaborators, mostly in Asia and South America, the most notable being the SARP (Simulation and Systems Analysis for Rice Production) project with IRRI and collaborators in Asian countries and the REPOSA (Research Programme On Sustainability in Agriculture) project in Costa Rica. The APSRU group has developed and applied APSIM (Agriculture Production Systems Simulator) with a range of models initially targeted for rain-fed cropping, but also applicable to irrigated conditions. All these modelling groups are networked within the ICASA (International Consortium for Agricultural Systems Analysis) network. While all these groups have developed several models, it has been suggested that the DSSAT models have had the biggest impacts in developing countries in terms of their applicability, diffusion, and adoption.

DSSAT
DSSAT was developed by an international network of scientists, cooperating in the International Benchmark Sites Network for Agrotechnology Transfer project (IBSNAT) to facilitate the application of crop models in a systems approach to agronomic research. Its initial development was motivated by a need to integrate knowledge about soil, climate, crops, and management for making better decisions about transferring production technology from one location to others where soils and climate differed. The DSSAT is a collection of independent programmes that operate together; crop simulation models are at its centre.  Databases describe weather, soil, experimental conditions and measurements, and genotype information for applying the models to different situations. This software helps users to prepare these databases and compare simulated results with observations, to give them confidence in the models or to determine if modifications are needed to improve accuracy. In addition, programmes contained in DSSAT allow users to simulate options for crop management over a number of years to assess the risks associated with each option.

How can DSSAT help with sugarcane crop management?
The DSSAT simulates growth, development and yield of a crop growing on a uniform area of land under prescribed or simulated management as well as the changes in soil water, carbon, and nitrogen that take place under the cropping system over time. DSSAT crop models have been widely used over the last 15 years by many researchers for many different applications. Many of these applications have been done to study management options at study sites, including fertilizer, irrigation, pest management, and site-specific farming. These applications have been conducted by agricultural researchers from different disciplines, frequently working in teams to integrate cropping systems analysis using models with field agronomic research and socioeconomic information to answer complex questions about production, economics, and the environment.

An important aspect of many of these studies is a consideration that weather influences the performance of crops, interacting in complex ways with soil and management. Researchers have thus applied these models to study uncertainty in crop production associated with weather variability and the associated economic risks that farmers face under such climate variability. Researchers from all continents have used these models in studying potential impacts of climate change on agricultural production. The models have also been widely used in studying the potential use of climate forecasts for improving management of different cropping systems, and the value and risks associated with the use of this information.

The sugarcane industry can use this model to study the uncertainty in sugarcane production associated with the climate variables and predict the potential impacts on overall productivity for the improved management of the industry.