Dear Editor,
Sugar cane is one of the most important crops in Guyana; it contributes nearly 20% to GDP and hence is very important to the Guyanese economy. The 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 sugar cane and sugar yields.
Rainfall is the single most important factor responsible for sugar cane production. As rainfall is significantly affected by extreme events such as El Niño and La Niña so is cane production. The annual rainfall in Guyana averages between 1500 mm to 2800 mm of which most of falls in May-June and November-December. Some cane production areas receive more than the average annual rainfall and sometimes at 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, the formation of water shoots and an increase in the tissue moisture. It also hampers harvesting and transport operations.
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 regards 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.
The rate of photosynthesis is dependent on temperature as well as many other biochemical processes controlling meristematic activity for leaf and bud development. The photosynthesis efficiency of sugar-cane increases linearly between temperatures ranging from 8oC to 34oC 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.
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 the growth rate increases more than the photosynthetic rate.
The 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. There are several well-known crop modelling groups across the world, of which the Wageningen Group is probably the strongest in terms of concentration and strength of the 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. All the modelling groups are networked within the ICASA (International Consortium for Agricultural Systems Analysis) network. While all the groups [Editor’s note: list not included] 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 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, experiment 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, the 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.
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 socio-economic 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 Sugar-cane industry could use this model to study the uncertainty in sugar-cane production associated with the climate variables, and predict the potential impacts on overall productivity for the improved management of the industry.
Yours faithfully,
Bissasar Chintamanie
Climate-Crop Modelling Specialist
Triple ‘C’ Consultancy Consortium
