|Type of Project||Applied research project|
|Keyword||simulation tools; decision support; production yields|
|Location||South Africa; Mauritius; Botswana; Kenya|
|Main Funder||African Union Commission (AURG 1)|
|Coordinator||Durban University of Technology (DUT)|
|Project Web Site||https://www.dut.ac.za/iss/fsia/|
|Documents||AURG projects phase 1 booklet|
Developing Agricultural Production Decision Support Simulation Tools for Increasing Agricultural Production and Food Security in Africa
The Institute of Systems Science (iSS) through a multi-disciplinary research group have developed methodologies for understanding a variety of complex questions from the sciences, engineering and management. In particular we research sustainable environmental and social systems and use mathematical and computer models to predict their dynamics.
The overarching aim of this project is to provide African farmers with decision support tools based on scientifically tested computer and mathematical models to improve agricultural production yields.
Approach of the Project
• Investigations into appropriate computer and mathematical models to enable yield predictions based on field and human data inputs.
• Investigations into the pressing needs and existing practices of African farmers.
• Development of user friendly interactive application program interface to assist farmer decision processes.
Major Results Achieved
• The Agricultural Production Systems sIMulator (APSIM) program has been selected as the best resource to assist farmers.
• We have investigated the effects of varying within season daily rainfall distributions on crop yields using simulations. Results show that within season distributions can affect yields in low rainfall seasons but this is also dependent on the use of fertilizer.
• To improve our understanding of the impact of foliar diseases, we formulated a mathematical model based on data. Qualitative analyses were carried out and methods developed to reduce the spread of foliar diseases through effective control measures with minimum cost.
• We have established that planting date, variety, and sowing density are better predicted by the farmer, or extension personnel, especially where computer resources and expertise are stretched.
• Soil conditions are an important aspect of farming as they determine the crops suitable to grow and also offer a platform for the interaction of fertilizer, crops and water. Thus soil conditions have a bearing on yields.
• Fertilizer has a clear impact in optimal soil conditions for all the rainfall conditions tested. Applying fertilizer improves yields. The relationship between soil, rain and fertilizer is more subtle in other conditions and this is where predictive models are helpful.
Farmers can use decision support tools and models to assist in pre-season and in-season management decisions on cultivation practices, fertilization, irrigation, pesticide use, etc. This is expected to result in optimizing production and hence overall food security. It should also prevent the negative environmental effects of unnecessary agricultural inputs.