Launch of the Ethiopian Digital AgroClimate Advisory Platform (EDACaP) Progress Report on EDACaP Development and Hosting

This brief outlines progress achieved with the establishment of the Ethiopian Digital AgroClimate Advisory Platform (EDACaP) under the CCAFS project P263 (Regional and national engagement, synthesis and strategic research) with support from P1605 (Capacitating African Stakeholders with Climate Advisories and Insurance Development). EDACaP aims to build farmers' resilience through agro-climate advisories that digitally integrate climate, soil, crop and agronomic data and are delivered through SMS, IVRS and radio to development agents and farmers in local languages. It builds on a partnership between the Ethiopian Institute of Agricultural Research (EIAR), the Ministry of Agriculture (MoA), the National Meteorological Agency, CIAT, ILRI, CIMMYT with additional support from ICRISAT, IRI, and University of Florida

Senior Thesis ST 2011-02

Agriculture in the Arctic is often limited by the low receipt of heat energy, which is often measured in growing degree days (GDD). With the advent of increasingly powerful climate modeling, projection and downscaling techniques, it is becoming possible to examine future climates in high resolution. Recent availability in Alaska has prompted interest in examining the distribution of current and the potential future of local agriculture. The goal of this study was to utilize Scenarios Network for Alaska Planning (SNAP) downscaled, ensemble projections to examine this in terms of GDDs in the Fairbanks North Star Borough of Alaska. Historic and projected monthly mean temperatures were utilized to calculate GDDs and then map the borough at a 4 km2 scale. Additionally, local agriculturalists were interviewed in order to put these theoretical calculations into context. Ultimately, projections of the examined agricultural locations showed an average of a 2% increase in GDD per decade and a 26% increase in GDDs from 1949 to 2099. This project indicated that the North Star Borough will receive increased heat energy due to climate change over the next century that may further enable increased yields and varieties of crops

Comparison of Two Detailed Models of Aedes aegypti Population Dynamics

The success of control programs for mosquito-­borne diseases can be enhanced by crucial information provided by models of the mosquito populations. Models, however, can differ in their structure, complexity, and biological assumptions, and these differences impact their predictions. Unfortunately, it is typically difficult to determine why two complex models make different predictions because we lack structured side-­by-­side comparisons of models using comparable parameterization. Here, we present a detailed comparison of two complex, spatially explicit, stochastic models of the population dynamics of Aedes aegypti, the main vector of dengue, yellow fever, chikungunya, and Zika viruses. Both models describe the mosquito?s biological and ecological characteristics, but differ in complexity and specific assumptions. We compare the predictions of these models in two selected climatic settings: a tropical and weakly seasonal climate in Iquitos, Peru, and a temperate and strongly seasonal climate in Buenos Aires, Argentina. Both models were calibrated to operate at identical average densities in unperturbedconditions in both settings, by adjusting parameters regulating densities in each model (number of larval development sites and amount of nutritional resources). We show that the models differ in their sensitivityto environmental conditions (temperature and rainfall) and trace differences to specific model assumptions.Temporal dynamics of the Ae. aegypti populations predicted by the two models differ more markedly under strongly seasonal Buenos Aires conditions. We use both models to simulate killing of larvae and/or adults with insecticides in selected areas. We show that predictions of population recovery by the models differ substantially, an effect likely related to model assumptions regarding larval development and (director delayed) density dependence. Our methodical comparison provides important guidance for model improvement by identifying key areas of Ae. aegypti ecology that substantially affect model predictions, and revealing the impact of model assumptions on population dynamics predictions in unperturbed and perturbed conditions.Fil: Legros, Mathieu. University of North Carolina; Estados UnidosFil: Otero, Marcelo Javier. Universidad de Buenos Aires; ArgentinaFil: Romeo Aznar, Victoria Teresa. Universidad de Buenos Aires; ArgentinaFil: Solari, Hernan Gustavo. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Gould, Fred. National Institutes of Health; Estados UnidosFil: Lloyd, Alun L.. National Institutes of Health; Estados Unido

The role of social interaction in farmers' climate adaptation choice

Adaptation to climate change might not always occur, with potentially\ud catastrophic results. Success depends on coordinated actions at both\ud governmental and individual levels (public and private adaptation). Even for a “wet” country like the Netherlands, climate change projections show that the frequency and severity of droughts are likely to increase. Freshwater is an important factor for agricultural production. A deficit causes damage to crop production and consequently to a loss of income. Adaptation is the key to decrease farmers’ vulnerability at the micro level and the sector’s vulnerability at the macro level. Individual adaptation decision-making is determined by the behavior of economic agents and social interaction among them. This can be best studied with agentbased modelling. Given the uncertainty about future weather conditions and the costs and effectiveness of adaptation strategies, a farmer in the model uses a cognitive process (or heuristic) to make adaptation decisions. In this process, he can rely on his experiences and on information from interactions within his social network. Interaction leads to the spread of information and knowledge that causes learning. Learning changes the conditions for individual adaptation decisionmaking. All these interactions cause emergent phenomena: the diffusion of adaptation strategies and a change of drought vulnerability of the agricultural sector. In this paper, we present a conceptual model and the first implementation of an agent-based model. The aim is to study the role of interaction in a farmer’s social network on adaptation decisions and on the diffusion of adaptation strategies\ud and vulnerability of the agricultural sector. Micro-level survey data will be used to parameterize agents’ behavioral and interaction rules at a later stage. This knowledge is necessary for the successful design of public adaptation strategies, since governmental adaptation actions need to be fine-tuned to private adaptation behavior

Scoping report: current status of index-based insurance in Bangladesh

With current and anticipated increases in magnitude of extreme weather events and a declining consistency in weather patterns, particularly challenging for agriculture, there has been a growing interest in weather index-based insurance (IBI) schemes in Bangladesh. A number of weather index-based insurance products have already been tested and applied across Asia and Africa, with varying degrees of success, as a mechanism to improve livelihood security by enabling vulnerable populations to transfer risk associated with climate change, extreme weather events and other hazards. In the process, these efforts have generated important new knowledge on how these schemes can be designed and implemented for optimal results. However, the practice of index-based insurance is still limited in Bangladesh, and the experience and knowledge generated by the different stakeholders involved needs to be better communicated