Climate change and
agricultural food production of Bangladesh: an impact assessment using
gis-based biophysical crop simulation model Afzal Ahmed and Ryosuke
SHIBASAKI Center for Spatial Information Science University of Tokyo, 4-6-1 Komaba Meguro-Ku, Tokyo 153-8505, JAPAN Phone: +81-3-5452-6417, Fax: +81-3-5452-6414 Email: afzal@skl.iis.u-tokyo.ac.jp Key Words: Global warming, crop production, Crop simulation model, Spatial-EPIC Abstract Agriculture is always vulnerable to unfavorable weather events and climatic conditions. Despite technological advances such as improved crop varieties and irrigation systems, weather and climate are still key factors in agricultural productivity. Often the linkages between these key factors and production losses are obvious, but sometimes the linkages are less direct. The impacts of climate change on agricultural food production are global concerns, and they are very important for Bangladesh. Agriculture is the single most and the largest sector of Bangladesh's economy which accounts for about 35% of the GDP and about 70% of the labor force. Agriculture in Bangladesh is already under pressure both from huge and increasing demands for food, and from problems of agricultural land and water resources depletion. The prospect of global climate change makes the issue particularly urgent. It is well established that atmospheric concentration of CO2 is increasing and this would be beneficial for crop growth and productivity. But the nature and magnitude of climatic change associated with the increase of CO2 and other radioactive trace gases are uncertain. Thus, it is difficult to predict the combined impact of increasing atmospheric CO2 on agricultural productivity. Crop simulation models are used widely to predict the crop growth and development in studies of the impact of climatic change. So far, climate-based models for estimating potential productivity are used for this purpose. These models are relatively easy to apply, but they fail to estimate actual productivity and possible effects of mitigation measures. On the other hands, process-based crop models are advantageous to estimate actual productivity, but they are applied to only limited numbers of test sites due to heavy data requirement in applying them for wide areas. A comprehensive GIS-based biophysical crop simulation model, Spatial-EPIC, was used in order to demonstrate the crop growth response to the combined effects of CO2 concentration increase and CO2-induced climate change at the national level. Modeling within a GIS offers a mechanism to integrate the many scales of data developed in and for agricultural research. Rice and wheat cropping system in Bangladesh were studied for this purpose. Introduction The balance of scientific evidence now suggests that over the last century humans have begun to have a discernible influence on the earth's climate, causing it to warm (IPCC, 1996, 1998). Since the beginning of the industrial age, the concentration of CO2 in the atmosphere has increased from 280 to 350 parts per million (Bazzaz and Fajer, 1992). The increase of CO2 in the atmosphere has been more rapid in recent years. The major reason for this increase may be attributable to the extensive use of fossil fuels, such as oil, coal and gas. The destruction of carbon sink by excessive land use and deforestation might be another important cause for the atmospheric CO2 increase over the last 100 years (Houghton et. al., 1990). It has been projected from the historical data and simulation models that the CO2 level in the atmosphere will reach 600 ppm in the last half of this century (Strain, 1987). The increase of CO2 and several other green house gases such as methane, nitrous oxide, chlorofluorocarbons (CFCs) could cause an increase global temperature of about 4.2°C and possibly a change in precipitation patterns and amounts in some regions (Kimball et. al., 1993). Global warming due to increasing concentrations of green house gases poses a threat to human society by changing the living and working environment to which society has adapted over many generations (Jodha, 1989). Agricultural impacts of climate change could have profound effect in poor and developing countries. Bangladesh, a developing country in South Asia, is primarily a deltaic flood plains, and elevations in most of the country do not exceed 10m. The country has a humid tropical climate. Average rainfall in drier and wetter regions are 1500mm and 5000mm per year respectively. In winter, the average minimum and maximum daily temperatures are about 9.7 and 26.6°C respectively. In the summer, the average maximum temperature is about 32.2°C (BARC, 1991). Despite technological advances such as improved crop varieties and irrigation system, weather and climate are still key factors in agricultural productivity. The rise of CO2 level in the atmosphere and the concomitant climate change will have a direct impact on agriculture. It is generally well accepted that this increase will have beneficial effects on plant productivity. What remains most uncertain is the nature and magnitude of the climate changes that will occur as a result of the increase of CO2 and other radioactively trace gases. Thus it is difficult to predict the combined impact on agricultural productivity. Crop simulation models can be used predict the impact. These models can provide a way to estimate crop production under climate-change condition. Research on crop simulation has concentrated on determining the relationships between crop growth, yields and environmental variables through field experiments as well as simulated experiments. Geographic Information System (GIS) provide another technology for crop modeling researches. GIS is a unique tool on solving spatial related problems. In recent times, several scientists/researchers have attempted to link crop models with GIS. For the research purpose of this paper, a spatial biophysical crop model- "Spatial-EPIC" (Satya et. al., 1998) is chosen. This model is the modification of EPIC (Erosion Productivity Impact Calculator) developed by USDA/ARS, which offers a spatial dimension to the original one. Introduction To Spatial-Epic Traditional decision support systems based on crop simulation models are normally site-specific. In order to address the effects of spatial variability of soil conditions and weather variables on crop production from one place/region to other, GIS is linked with biophysical agricultural management simulation model EPIC, which is known as "Spatial-EPIC". Wit the development of this model any size of agroecosystem starting from a field to a country and even bigger ca be modeled. "Spatial-EPIC" system file structure is comprised of text files, which contain estimate of parameters of different physical processes modeled by "Spatial-EPIC". These files include Basic User-Supplied Data file, Crop Parameter File, Tillage Parameter File, Pesticide Parameter File, Fertilizer Parameter File, Miscellaneous Parameter File, Multi-Run File, Output Variables File and Daily Weather Data File. ArcView 3.1 is used as a pre and post processor for data furnishing as well as graphical display of Spatial-EPIC. "Spatial-EPIC" is composed of physically based sub models for simulating weather, hydrology, erosion, plant nutrients, plant growth, soil tillage and management, and plant environment control. The model runs on daily time-step therefore, each model is linked subsequently and interactively with other sub models. In brief, the each sub module is dealt with their computation procedure. Weather: daily rain, maximum and minimum temperature, solar radiation, wind and relative humidity can be based on measured and data and/or generated stochastically. Hydrology: runoff, percolation, lateral subsurface flow are simulated. Erosion: it simulates soil erosion by wind and water (for this paper the erosion part has not been included). Nutrient Cycling: the model simulates, nitrogen and phosphorus fertilization, transformations, crop uptake and nutrient movement. Nutrient can be applied as mineral fertilizers, in irrigation water, or as animal manure. Soil: soil temperature responds to weather, soil water content and bulk density. It is computed daily in each soil layer. Tillage: the equipment used affects soil hydrology and nutrient cycling. The user can change the characteristics of simulated tillage equipment, if needed. Crop Growth: A single crop model capable of simulating major agronomic crops. Crop-specific parameters are available for most crops. The model also simulates crop grown in complete rotations. Plant Environment: It is capable of variety of cropping variables, management practices, and other naturally occurring processes. These include different crop characteristics, plant population, and dates of planting and harvesting, fertilization, irrigation, tillage and many more those are normally practiced in the field. Climate Change Scenarios A climate change scenario is defined as a physically consistent set of changes in meteorological variables, based on generally accepted projections of concentrations of carbon dioxide (CO2 thought to be the likely cause of future climate change). Scenarios of climate change were developed in order to estimate their effects on crop yields which may be severe in coming days as per the speculations going on throughout the world. The set of scenarios used is intended to capture a range of possible effects and set limits on the associated uncertainty. The scenarios for this study were created by changing observed daily data from the current daily-observed historical climate data (1975-1995). The scenarios are the combination of a range of temperature (-2°C, 0°C and +2°C) and precipitation (+/-25%). For simplicity, solar radiation, vapor pressure and wind speed were assumed to remain unchanged for all scenarios although some changes associated with temperature and precipitation changes is to be expected. Simulations for each cropping system were made for 6 climate scenarios and two atmospheric concentrations of 350 and 550ppm. Conclusion The attempt of this paper is to simulate the country level Bangladeshi agroecosystem with the help of Spatial-EPIC. Modeling within a GIS offers a mechanism to integrate the many scales of data developed in and for agricultural research. Data access, including modeling results, expands to a "decision system" or decision tool which uses a mix of process models (where appropriate/possible) and biophysical data (growing season climate characteristics, soils, terrain). An accurate spatial (and temporal) database enables the characterization of agroecosystem. This ability is vital in the developing world for efficient resource allocation in agricultural research. Agroecosystem are complex entities, which span several levels or scales, with different processes dominating each scale. Geographic data are either as a vector model or raster model. In general both the data structure can be used to represent any type of geographical data, depending on the scale desired for analysis. However, raster model fit best for modeling type of analysis involving natural resources data such as land use, soils and vegetation as their is spatial control for continuous variable and also uniform sampling of the surface being modeled. When the different thematic layers exist as grids with a common resolution, there is computation efficiency in overlaying those grids. Spatial analytical functionality is easy to implement and fast and efficient especially for operation such as spatial averaging and intersections which pose problems in vector systems. The soil characteristics of Bangladesh were obtained after digitization of survey of Bangladesh soil map with many properties like soil texture, soil pH and soil depth. Weather data were obtained and their surfaces were generated using World Meteorological Organization station falling around 22 in number scattered throughout Bangladesh. Agricultural management data were obtained at district level. All these data were used for whole country simulation at 10 km cell size. The simulation and validation result is not complete yet. These would likely to be presented at the conference. References BARC: 1991, Agroecological database, BARC Computer Center, Bangladesh Agricultural Research Council, Dhaka. Bazzaz, F. A., Fajer, E. D. (1992). Plant life in a CO2-rich world. Scient. Am. 1992: 1821 Houghton, J. T., Jenkins, G. J., and Ephrauma, J. J.: 1990, Climate Change, The IPCC Scientific Assessment, Cambridge University Press, Cambridege, UK, 365 pp. IPCC, 1996. Watson, R.T., Zinyowera, M.C., Moss, R.H. (Eds.) Climate Change 1995: Impacts, Adaptations and Mitigation of Climate Change: Scienti"c-Technical Analyses. Contribution of Working Group II to the Second Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge. IPCC, 1998. Watson, R.T., Zinyowera, M.C., Moss, R.H. (Eds.) The regional impacts of climate change: an assessment of vulnerability. A Special Report of IPCC Working Group II, Cambridge Univer-sity Press, Cambridge. Jodha N.S., 1989, Potential strategies for adapting to green house warming: Perspective from the developing world, in: Green House warming: Abatement and Adaptation, Rosenberg N.L., Earterling III W.E., Crosson P.R., and Darmstadter J. (eds), RFF Proceedings, Climate Resources Division, pp.147-158 Kimball, B. A., J. R. Mauney, F. S. Nakayama and S. B. Idso,: 1983, Effects of Increasing Atmospheric CO2 on Vegetation. Vegetatio 104/105:65-70 Satya Priya, Shibasaki Ryosuke and Shiro Ochi (1998) Modeling Spatial Crop Production: A GIS approach, Proceedings of the 19th Asian Conference on Remote Sensing, 16-20 Nov, 1998 held at Manila. pp A-9-1 to A-9-6. |