0 IR 36 with improved tolerance to temperature stress
3.11 Mitigation Efforts: Use of Simulation Studies
The global climate change is not a new phenomenon and every new anthropogenic driving force can affect the rate of the climate change. The changes, which might be observed over a geologic time period, could happen over a shorter time span, since the start of Industrial Revolution. Apparently, human activities are causing rapid climate change. The concentrations of key anthropogenic greenhouse gases, such as carbon dioxide (CO2), methane, nitrous oxide and tropospheric ozone, have reached their highest levels, primarily due to the combustion of fossil fuels, agriculture, and land-use changes (Korner and Bazzaz. 1996; Rosenzweig and Hillel 1998). These greenhouse gases stay in the atmosphere for a long time, as the atmospheric life time for these chemicals vary (5-200 years for CO2, 12 years for CH4 and 114 years for N2O). If the current greenhouse gas emission rates continue into the future, agriculture and crop production will face enormous pressure from the stresses caused by these heat trapping gases.
Rice paddy fields are one of the major sources of atmospheric CH4 and N2O. There is a need for careful evaluation of the source strength of this ecosystem, and of the influence of soil, water and crop management practices on both grain yield and greenhouse gas fluxes. A number of models have been developed in recent years to predict the rate of CH4 emission from rice fields, each model having its own strategy or philosophy. Some models tried to use the least number of input parameters and more empirical equations to capture basic pattern of gas fluxes, so that these models could be easily used at the regional or global scale. Several models, such as DNDC (Li et al. 2004), Expert-N, CASA, CENTURY, NLOOS, MERES, MEM and DAYCENT have been developed. Recently, a process-based model INFOCROP (Aggarwal et al. 2006a, b) has been developed for scaling-up gas emission estimates from tropical agriculture. These models can be used to demonstrate a number of mitigation options for reducing methane emissions from rice soil, based on the significant influence of soil texture and pH, moderate influence of organic C content, little effect on short-term seasonal simulations due to the variation in the quantity of aboveground biomass returning to the soil, increasing the length of mid-season aeration, and addition of sulphate fertilizer reducing CH4 emissions from rice soil.
The climate models indicate that greater warming can occur in the next century; all land areas will warm more rapidly than the global average, particularly at high northern latitudes in the winter season. The projected climate change will have beneficial and adverse effects on both environmental and socio-economic systems, but the larger and more abrupt changes in climate will cause more adverse effects on crop production and thus affecting the regional food security. Hence, there is a continual need to identify and develop mitigation options to minimize the rate of adverse climate changes.
The major challenge in crop modeling is to develop user friendly and economically viable technology that is readily adoptable by the decision makers as well as farmers. More interactive communication between the model developers and all the stakeholders including extension staff, consultants, farmers, researchers and policy decision makers is required for improving the transferability of models from laboratories to various modes of applications. The crop modeling can be best used as an aid for on-farm decisions. The crop growth simulator, when incorporated with important physiological process and appropriately addressed various physical process, such as water uptake, sunlight etc into one package, would correctly predict growth and yield under varying climate change conditions. For practical use, the farmers require less complicated decision support systems. Hence, any model based on reasoning system, which simplifies the information input and provides a user friendly output format for crop management decisions, such as when to irrigate and how much fertilizer to apply, the extent of land to lease, etc., is very useful to the farmers. But, an extensive consultation with farmers, while testing and validation of the model before it is released for public use, is necessary (Whisler et al. 1986). Every positive feed back from farmers during various stages of model building can help to its successful adoption in various locations and wider use by farmers.
The crop simulation models are increasingly available as a part of Decision Supporting Systems (DSS). These DSS can be used to extrapolate results for strategic decision making tasks, such as regional planning, policy analysis and poverty alleviation. The crop systems models have played a useful role in agricultural planning, more commercially in developed countries like USA, France and Australia, and in developing countries as a part of internationally funded projects in the last two decades. Regional or national planning involves analysis of information that covers different crop production systems for making decisions like best land use to meet the specific development goals. One of the successful approaches is coupling of crop simulation models with GIS containing land and water characteristics of a region. In India, Selvarajan et al. (1997) used the ORYZA1 and WTGROW models for analyzing trade offs between water use, farm income, and adoption risks at the district level. Jansen (2001) developed a methodology called SOLUS (Sustainable Options for Land Use) in which crop simulation model MACROS was coupled with a Geographic Information Systems (GIS) and a Linear Program Model (LP) to define crop options and associated management practices in Costa Rica. Using the same approach, Schipper et al. (2001) evaluated the policy issues, such as taxing chemicals to reduce environmental contamination and maintenance of forests through subsidies. The SARP and SYSNET projects of IRRI, involved scientists from India, Malaysia, Vietnam and the Philippines to develop and evaluate methodologies and tools for land use analysis and apply them at regional levels to support agricultural and environmental policy making. The land units of India were defined by agro-ecological zones based on soil and weather characteristics and WTGROW and CERES-RICE models were used to explore possible combinations of integrated farming to achieve the goals of maximizing food, minimizing water use and controlling environmental degradation and soil salinity (Aggarwal 2000).
The researchers and the decision makers have begun to apply the results of crop simulation models to strategic policy analysis. The integration of crop simulation models with GIS and expert systems facilitated it as a useful tool for investment decisions. Beinroth et al. (1998) described the development and use of AEGIS (Agricultural and Environmental GIS) for application with DSSAT type models in land use analysis, as a part of rural development in Columbia. The outputs from crop models were used as inputs to other models of second order effects. Other examples in India (Singh and Thornton 1992) and Putero Rico (Hanson et al. 1999) are also reported. Parry et al. (1985) used CERES models to study the first-order effects of climate on cereal yields. Then, the farm level profitability was investigated as a second order effect of the climate induced yield change by balancing the gross return per unit of production. The implications of changes in crop yields and production for agricultural policy were examined both at the national and international levels. The crop models in combination with spatial analysis tools have the potential to develop rural poverty reduction strategies and evaluate changes in government support programs in different agro-climatic zones. The use of crop simulation models in this way could help to reduce poverty in Kenya (McCown et al. 1994). Due to increasing population pressure, nutrient depletion, soil degradation, low crop yields, and income reduction, were described as a spiraling "poverty trap." The use of a crop simulation model, complimented by a small set of on-farm trials, showed that a use of small amount of fertilizer was an efficient strategy to break the poverty cycle. As the farmers in that area never used fertilizer for crop production, extensive field research conducted past several years did not consider this as an option.
There are many potential uses of crop simulation models to support strategic policy decisions at the regional as well as national level (Anbumozhi et al. 2003). The policy makers and aid agencies like World Bank can greatly benefit from the use of crop simulation models in evaluating the type of interventions, including the conservation of the natural resource base. Some specific areas where the systems analysis will help in this endeavor are (i) to assess the changes in natural resource base because of new policies; (ii) to evaluate advantages and disadvantages of different policy packages, such as changing cropping pattern, shifting production basins, etc; (iii) to analyze farmer responses to policy changes; and (iv) to design new policies based on sustainable rural development by determining acceptable level of trade-off between development and natural resource depletion. Any well informed decision for natural resource management has the potential to reduce rural poverty and for this purpose, crop simulation models will be of high relevance and useful for developmental assistance programs.
3.14 Integration of Climate Prediction and Agricultural Models
The interest in integrating crop simulation models with dynamic seasonal climate forecast models is expanding in response to a perceived opportunity to add value to seasonal climate forecasts for agriculture. Integrated modeling may help to address some obstacles to the effective agricultural use of climate information. Firstly, the modeling can address the mismatch between farmers' needs and available operational forecasts. The probabilistic crop yield forecasts are directly relevant to farmers' livelihood decisions and, at a different scale, to early warning and market applications. Secondly, the credible evidence of livelihood benefits, using integrated climate-crop-economic modeling in a value-of information framework, may assist in the challenge of obtaining institutional, financial and political support; and targeting for greatest benefit. Thirdly, the integrated modelling can reduce the risk and learning time associated with adaptation and adoption, and related uncertainty on the part of advisors and advocates. It can provide insights to advisors and enhance site-specific interpretation of recommendations when driven by spatial data. The modelbased "discussion support systems" contribute to learning and farmer-researcher dialogue. The integrated climate-crop modelling may play a genuine, but limited role in efforts to support climate risk management in agriculture, but only if they are used appropriately, with understanding of their capabilities and limitations, and with cautious evaluation of model predictions and of the insights that arise from model-based decision analysis.
3.15 Pertinent Issues to be Considered while Using Simulation Models
While interpreting results from the scenarios predicted by the GCMs, some considerations are necessary. The most significant limitations are their poor resolution, inadequate coupling of atmospheric and oceanic processes, poor simulation of cloud processes and inadequate representation of the biosphere and its feedbacks. The poor resolution is likely to be significant in north-eastern parts of India where the relief is varied and local climate may be quite different from the average across the area used by a GCM. Most GCMs have difficulty in even describing the current climate adequately (Bachelet et al. 1995). The current GCMs are able to predict neither the changes in the variability of the weather nor the frequency of catastrophic events, such as hurricanes, floods or even the intensity of monsoons, all of which can be important in determining crop yields as the average climatic data. The GCMs can, at best, be used to suggest the likely direction and rate of change of future climates.
According to Long et al. (2005), fertilization effect of CO2 has probably been overestimated. The omission of O3 effects from the most models could have led to 20% overestimation of crop production in the Northern Hemisphere. The database of chamber studies is the mechanistic basis for crop yield models. Hence, these models overestimate the yield gain due to elevated CO2 compared to those observed under fully open-air condition (FACE) experiments in the field. The current FACE experiments are, however, not adequate enough to reparameterize the existing models (Long et al. 2005). In a recent study, Bannyayan et al. (2005) evaluated ORYZA 2000 (Bouman and Van Laar 2006) against the observed growth and yield of rice in a 3-year field experiment in Japan where rice plants were subjected to the elevated CO2 in FACE under varying N fertilizer rates. The simulation results showed that the model overestimated the increases in green leaf area indices due to the elevated CO2 concentration, but the enhancement of total biomass was only a minor overesti-mation. While the model was successful in simulating the increase in rice yield due to the CO2 enrichment, it failed to reproduce the observed interaction with N in the rice yield response to elevated CO2 concentration. The lack of complete understanding of the effects and the potential interactions of environment variables on plant processes preludes the definitive predictions of the effects of global climate change. Despite the limitations imposed by the assumptions made in both the GCM and the crop simulation models, the models provide significant progress in our understanding of how future climates are likely to affect crop production. Nevertheless, the use of simulation models to predict the likely effects of climate change on crop production is an evolving process.
3.16 Future Research Priorities Using Simulation Studies
Assessment on agriculture and policy response to manage climate change impacts will not be complete unless the biophysical, environmental and socio-economic sectors of agro-ecosystems are studied together. The global integrated impact assessment models provide such a framework, but they are inadequate for regional policy planning. Often, these are not validated at that scale due to their inherent inter-and intra-sectoral conflicts. There is an urgent need to develop the integrated assessment simulation models in which cropping systems; water use and socio-economic parameters are brought together for assessing the impact of climate change in diverse regions of the country. The collaboration with several stakeholders including policy makers, agricultural and environmental scientist, climatologist, economist, administrators, industry and farmers organization, is very much essential. In future studies, unless the uncertainties and limitations discussed above are considered in the crop simulation modeling and climate change scenarios, the assessment of climate change on agriculture cannot provide sound basis for regional policy planning.
The crop simulation models offer many opportunities and can enhance natural resource management decisions in several ways. At the field level, these models can investigate the long-term changes in the environmental quality of air, soil and water, and yield stability. At the farm level, the applications of the models can include selection of new cropping systems that adapt to micro-climate change, socio-economic viability, and analysis of yield gaps between experimental stations and field production. At the regional level, these models allow the aggregation of crop production responses to various environmental changes.
Many uncertainties exist in modeling studies, partly due to the quality of the predictions by the models, from the use of limited sites for which historical weather are available, due to the quality of the crop simulation models, especially when applied under the rain-fed conditions (Bachelet et al. 1995), and due to the quality of the climate models used to predict future weather scenarios. These uncertainties may be reduced only when a large number of scenarios for different locations are compared and evaluated. Improvement in models has been a continuous process and more scientific understanding is needed to deal with sensitivity of crop production to dynamic changes taking place in the natural resource base. The global, regional and local information sharing can be highly complementary and the information generated from such efforts will serve as a sound basis to make refined models, to develop policy interventions, and to attain food security at the regional as well as the national level.
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