Development of an Agent-Based Model for Weather Forecast Information Exchange in Rural Area of Bahir Dar, Ethiopia

Abstract

Smallholder farmers in Ethiopia are vulnerable to climate change impacts due to their low adaptive capacity and dependence on rainfed agriculture. Thus, a successful weather forecast system may bring significant economic and social value to the community. The main objectives of this study were to identify the key information exchange agents, understand the information flow path, rank the relative importance of the different information dissemination pathways, and determine weather forecast adoption. We conducted a household survey in five villages of Rim Kebele in Bahir Dar area and found that farmers communicate with four main agents with regard to information exchange. We developed an agent-based model to learn the adoption rates of weather forecast information. Agriculture extension agents were found to be the most influential members of the community. Farmers’ communicating with neighboring village farmers showed higher adoption. Our results show at least twice that improvements in communication network attain higher adoption rates. Radio has also demonstrated positive uptake of information. We also found that forecast accuracy of 70% is sufficient to achieve high adoption rates. Our findings might help decision-makers recognize critical information flow pathways and their relative importance, and identify barriers to disseminating weather forecast information in the community.

Application of Agent-Based Modeling in Agricultural Productivity in Rural Area of Bahir Dar, Ethiopia

“…Rim Kebele is located in Mecha Woreda, West Gojjam Zone of Amhara Region, Ethiopia [64] (Figure 1). We chose this Kebele due to the high reliance of farmers on using rainfed agriculture systems.…”

“…Education level of the smallholder farmers shows only 28% of farmers can read and write and completed grades 1-12 of the school system, whereas 77% of the farmers are unable to read and write. Average family size of the farmers is five people per household [64]. The climate of Ethiopia varies geographically [65] and is classified into five climatic zones based on elevation and temperature [66].…”

“…The average amount of land of the smallholder farmers is 0.75 ha. Farmers tend to prefer traditional weather forecast methods such as looking at clouds (55% of farmers) and wind directions (58% of farmers) compared to scientific weather forecasting methods broadcasted on TV and radios and in newspapers [64].…”

“…were used, and the interviewer listed the farming activities and farmers' behaviors as responses to such rainfall conditions. We used agents' adoption rates [64] coupled with a crop productivity model to assess the impact of farmers' decisions on agricultural productivity (Figure 3). Farmers' decisions such as planting date, seed type, fertilizer use, pesticide use, and tillage frequencies were used to crop model (DSSAT).…”

“…An agent-based model was developed based on the household survey to assess the impact of seasonal weather forecast information used by smallholder farmers. ABM helped calculate adoption rates based on the information flow through these main agents and reported trust values from [64] (Table 2). Influential weather communicators are neighboring farmers, other farmers in the Kebele, agriculture extension workers, and radio.…”