Abstract:
Agricultural modelling has played a significant role in the development of Africa by providing valuable insights, informing decision-making, and supporting sustainable agricultural practices. This research was focused towards analysing the contribution of mathematical modelling in agriculture through systematics and bibliometric literature review. A systematic literature review was carried out on the following areas, mathematical modelling, application of mathematical modelling in crop planning, mathematical modelling in crop breeding, modelling agricultural marketing and risk modelling in agriculture. The results of the review revealed that mathematical models are powerful tool for addressing agricultural problems which in turns improve the standard of living of Africans. While bibliometric data extracted from web of science which was analysed with web-based software called biblioshiny revealed that there were 6326 articles published in the field between the period of 2012 and 19/08/2023 available from 266 articles sources, published by 15527 authors, the annual growth rate in the field was 3.48%, average citation per document was 17.52 and total references of 217306. Furthermore, the study also established that USA who had 4272 articles was the most productive nation in the field globally while South Africa who had 613 articles were the most productive African country. The study also identified five research gaps
Description:
Agricultural modelling has played a significant role in the development of Africa by providing valuable insights, informing decision-making, and supporting sustainable agricultural practices. These models simulate various aspects of agricultural systems, such as crop growth, yield prediction, pest and disease management, soil health, and climate impacts. Here are some key contributions of agricultural modelling to the development of Africa, along with references to relevant sources:
Improved Crop Management: Agricultural models help farmers make informed decisions about planting, irrigation, fertilization, and pest management. By considering various factors like weather, soil conditions, and crop characteristics, these models optimize resource use and increase agricultural productivity (Thornton, et al., 2006).
