The applicability of the agricultural production systems simulator (APSIM) model to decision-making in small-scale, resource-constrained farming systems : a case study in the Lower Gweru Communal area, Zimbabwe.
Masere, Tirivashe Phillip.
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Small-scale farmers rarely get enough yields to sustain themselves to the next harvest. Most of these farmers are located in marginal areas with poor soils and in semi-arid areas which receive little rainfall yet the farmers practice rainfed agriculture. A number of reasons can be attributed to the low yields characterizing these farms. Lack of relevant knowledge for decision-making and climate change are among the major reasons for poor yields. Whilst there is not much the small-scale farmers can do to influence climate, they can at least make informed decisions to improve their yields. The information necessary for agricultural decision-making include the climate forecast information and information about performance of new technologies be it fertilisers, varieties or other practices. The study aimed to answer the primary research question: What is the applicability of the APSIM model in decision-making by small-scale resource constrained farmers? This question was supported by secondary research questions namely: - How useful is the APSIM model in small-scale farmers' adaptation to future climate change? - What are the current farming systems of Lower Gweru farmers with regards to maize production? - What are farmers' perceptions of climate change and what changes have they noticed in the last 10 years? - How do small-scale farmers make crop management decisions? Data was gathered through five methods namely, Focus Group Discussions, resource allocation mapping technique, APSIM simulations, on-farm experimentation, and semi-structured interviews. Data was collected from a group of 30 small-scale farmers of Lower Gweru Communal area. The study concentrated on maize production due to the fact that it is the staple food and was grown by all farmers. All the farmers perceived climate to be changing. The changes noted included late start of the rain season, early cessation of rain season and temperature extremes. The majority of farmers highlighted that they were using local indicators to make decisions about climate or to forecast the nature of the coming season before they were exposed to SCF and APSIM. The data gathered from three selected resource allocation maps were used to run the APSIM model. For which farmers were convinced that the model was credible in yield prediction based on the simulated results which reasonably compared to observed yields. The what if questions raised by farmers during the discussions were also assessed and this further increased the farmers' confidence with the model, as they viewed it as a planning and guiding tool before one can actually commit resources. The semi-structured interviews showed that most farmers will continue to use the model outputs in their decision-making. The reasons being that it was a good planning and budgeting tool, it is cheaper and faster since one can assess a lot of options in a short time and would then decide on which options are viable in a given season. The few farmers who said they would not use the model or its outputs in decision-making cited reasons including lack of a computer to install the model and that it was complex for them. Semi-structured interviews confirmed the data collected in resource allocation mapping, focused group discussions and APSIM sessions.
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