In his review of the current status and future challenges for climate prediction applications in agriculture, Sivakumar (2006) proposed several priorities to advance the use of forecasts for climate risk management in agriculture in the near future.
Improve the Accuracy of Prediction Models
Because the behavior of the atmosphere is chaotic, results from even well performing models can diverge, or develop increasing uncertainty at longer time ranges. Good science and support tools are fundamental prerequisites for ensuring a higher percentage of adoption of climate forecasts by farmers. As Doblas-Reyes et al. (2006) explained, several avenues are likely to enhance the quality of forecasts of agricultural impacts of climate variations over the next five to ten years. First, dynamically coupling crop models within climate models will support refined two-way interaction between the atmosphere and agricultural land use. Resulting predictions will continue to require calibration for the foreseeable future. Second, remote sensing and proliferation of spatial environmental databases provide substantial opportunities to expand the use and enhance the quality and resolution of climate-based crop forecasts. Third, empirical evaluation across a range of crops and locations will help establish the robustness and relative merits of alternative approaches. The fourth area where we expect to see significant progress is in advancing consistent methods for assessing the uncertainties associated with climate-based crop forecasts. Finally, climate-based crop forecasts will benefit from climate research in the emerging area of "weather within climate."
Generate Quantitative Evidence of the Usefulness of Forecasts
Although there have been several case studies on the application of climate forecasts for better managing risks under a variable climate, wider and more consistent applications of the forecasts can only be promoted when more quantitative evidence can be generated about the usefulness of climate forecasts. Current research on climate forecast applications needs to focus more on impact assessment of climate forecasts through the development of new tools to assist in the evaluation of impacts of climate forecasts (Thorton 2006). These include the specification of comprehensive behavioral frameworks that go beyond current notions of risk theory, so that impacts on food security, reduction of vulnerability, and increases in household adaptive capacity can be addressed. A third area is in the development of hybrid approaches that combine the quantitative with the qualitative, the top-down with the bottom-up, and the socioeconomic with the biophysical aspects. A fourth area is to make the process of impact assessment as participatory as possible.
Give Greater Priority to Extension and Communication
Hansen (2002) argued that sustained use of climate prediction to improve decisions depends on adequate communication. Proper communication of information implies that the user is receptive to "proper" channels i.e. sources that they already know and trust. Hence agricultural extension agencies must be involved from an early stage since they are in regular contact with farmers. Another aspect of the "proper" information is related to the communication process of translating the probabilistic forecasts into easily understandable language for the farmers. Improper interpretation of the probabilities can lead to loss of trust and exposing farmers to unnecessary risks. Appropriate and beneficial production decisions are often related to timing and hence the communication of climate forecast information also must also be made in a timely manner.
Respond to Users' Needs and Involve Them More Actively
The use of climate forecasts requires that right audience receives and correctly interprets the right information at the right time, in a form that can be applied to the decision problem(s). Understanding who the potential clients are, what characterizes them, how they are linked to relevant and appropriate institutions, how information flows between the major actors in the system - these are questions that can be answered with solid baseline information, and improvements in spatial and non-spatial databases will help greatly in more effective targeting of potential forecast users (Thorton 2006). Forecasts are only useful if they are skillful, timely and relevant to actions, which potential users can incorporate into production decisions to improve potential outcomes (Stern and Easterling 1999). Roncoli (2006) advocates the use of ethnographic and participatory methods to provide a roadmap through the intricacies of climate information processing and agricultural decision-making and to help enhance the role that climate forecasts can play in improving rural livelihoods. Ethnography must go beyond portrayals of culture as a static configuration of categories and norms uniformly shared across society, to account for social diversity and cultural change. It also needs to extend its scope to elucidate the workings of culture in scientific circles as well as in rural communities. Likewise, participatory research should go beyond the deployment of a fixed repertoire of tools and techniques. It needs to examine the participation process itself through ethnographic and sociolinguistic analysis of group dynamics, including the interactions between farmers and scientists. It has been demonstrated that when stakeholders are well informed about the utility of climate prediction information and when they are more directly involved in testing the benefits of such information, they tend to offer more direct support for climate prediction applications.
Learn From Non-Adoption Situations
As Rubas et al. (2006) explained, improved climate forecasts are relatively new, so there has been little research on the adoption path over time or the optimal levels of adoption. It is important that when efforts are made to promote adoption by rural communities, plans for studying the reasons for non-adoption are built into the project implementation framework. Reasons for the adoption or otherwise of new technologies can be identified through farmer surveys, model-based analysis of farming systems, and through studying farmer motivations and behavior. Combining research on technology adoption, climate forecasts, and the economics of information would allow researchers to combine cutting-edge issues from different disciplines. Such research could build on the important survey studies on the use of climate forecasts by decision-makers.
Create Better Institutional and Policy Environment
As Rubas et al. (2006) explained, climate information in isolation has relatively little value beyond basic science unless it is integrated into managerial and policy processes. This requires an integrated research program that robustly interacts with and identifies the needs and environment in which decision-makers function. One of the major challenges to promotion of climate forecast applications in most of the economic sectors at the national level is the lack of a clear national climate agenda. Absence of appropriate policy documents leads to problems such as lack of a clear guidance as to which institutions have the main responsibility to produce and distribute climate products, inadequate research capacity and lack of a critical mass to deal with the key climate issues.
An important policy implication is that climate information and forecasts can be combined with research from other physical and social sciences to mitigate natural disasters by helping financial institutions, development agencies, and insurance corporations better identify resiliency strategies that enhance development and reduce risk. Climate change is another area that can benefit from seasonal forecast research. There is increasing recognition that economic analysis of climate change must occur at a finer spatial scale. Research on climate forecasts as an adaptation strategy for climate change holds great promise. Education in general as well as education on using climate forecasts is closely related to this issue and provides yet another place to look for research that can be combined with climate forecast research to advance the economics of information and lead to a better understanding of the decision to adopt climate forecasts (Rubas et al. 2006).
Derive Economic Benefit through Applications to Trade and Storage
According to Hallstrom (2001), trade and storage are especially important instruments for responding to agricultural production shocks caused by climate variation. Trade can mitigate the negative impacts of a climatic disturbance in a given location by allowing demands to be met by production that took place elsewhere. Similarly, storage allows demands at one point in time to be met by production that occurred at an earlier point in time.
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