| dc.description.abstract | Choosing the optimal cropping or enterprise mix has undoubtedly been one of the greatest challenges facing farmers due to multiple objectives such as food security, cash requirements, profit maximization etc. Farmers find it difficult to choose an optimal cropping or enterprise mix given the multiple objectives faced by farmers and often continuous changing farming systems and profitability. As a result, decision making has become more and more difficult in agriculture requiring sophisticated techniques and methods to help in decision making.
Several methods have been used in empirical studies to help farmers in decision making especially on optimal crop mix. This critical decision is made by each farmer at the beginning of each season. Maximizing profits leads to the same decision rule as minimizing costs of production. The production theory defines output or production as a function of several inputs such as land, labor, capital and management. These factors influence production and household resource allocation. There are several research methods that have been identified in the literature. The methods reviewed vary from gross margin to linear programming models. Linear programming might be preferred where the choice was made among many alternatives and high accuracy needed because it enables even a less skilled worker to reach optimum solution.
Chapter 3 presented the research methods which were going to be used to achieve my objectives. Introduction of a new enterprise affected resource allocation as farmers re-organized resource use to accommodate new enterprise and increase income. The analytical framework consisted of gross margin and linear programming analysis. The main objective of this study was to estimate the optimal cropping or enterprise mix that would result with the introduction of organic chili production in the districts of Goromonzi and Marondera, in Mashonaland-East province in Zimbabwe. Preliminary analysis showed chili, ground nuts, and sugar beans and maize with about US$380, US$349, US$180 and US$53 gross margin budgets respectively.
Although preliminary analysis was necessary to understand the socio-economic characteristics of the two districts, and have these socio-economic characteristics would affect agricultural output given the level of function of production and given level of technology. Further detailed analysis was required to understand the optimal cropping enterprise mix in the two districts. Linear programming estimation was therefore carried out to estimate the optimal crop mix for an average farmer in the two districts.
Linear programming analysis was used to explore optimum crop mix for the average farmer.
The optimum crop mix is 0.2 acres, 0.3 acres, 5.5 acres and 0 acres of ground nuts, sugar beans, chili and maize respectively. The optimal crop gross return is US$3082. Finally, sensitivity analysis was carried out. It showed that a percentage increase in land resulted in increase in area under. Areas under the other crops remained the same. Further sensitivity analysis showed that a percentage change in labor resulted in a decrease in the gross return. However there were no factor movements both at 5 % and 10 % change.
Important recommendations from the empirical findings were that there is need for the government to provide extension services and support services such as road networks and an enabling environment for production of crops. NGOs to increase extension and training programmes to farmers in contract negotiations and also they should continue to source for better markets and training to increase production performances. Farmers should form marketing groups. | en_ZW |
