This study uses data from a special tabulation of the USDA Farm and Ranch Irrigation Survey to examine the relationship between farm size and adoption of a variety of water management practices across seven Colorado Basin states. Parametric (Cochran-Armitage trend test) and non-parametric (Goodman-Kruskal gamma) methods were used to estimate associations between farm size and adoption of water management practices, use of water management information, and participation in conservation programs. Farms were divided into five categories: small farms, medium farms, large farms and very large farms, based on their gross sales. In all seven states, very large farms relied on a greater number of different information sources for water management than small farms. The relationship between farm size and information source use was not always monotonic, however. Small farms were more likely to rely more on their neighbors and irrigation district staff for water management information. Large and very large farms relied on a more diverse set of information sources and relied more on privately provide sources, such as consultants. In very few cases was a public or private information source used by more than half of any group of farmers. There is no "one-stop shopping" for irrigation management information. Smaller farms were more likely to not have investigated ways to improve water or energy conservation practices in the previous five years. Farmers cited economic factors as the most important largest constraints on adoption of conservation investments. Larger farms were more likely to participate in government (federal, state, or local) other conservation programs. These farms, though, account for the greatest share of water use. Many smaller farms do not have control over the timing of their irrigation applications, but rather depend on irrigation districts to supply water "in turn." Extension messaging to improve irrigation timing may be more effective if they target irrigation district staff that control irrigation scheduling.

The world’s population is growing at an average of 1.2 percent per annum and forecasts see the global population reaching 9.6 billion by 2050. This places great demands on the sustained production capacity of agricultural organisations to meet the desperate need for nutrition. This problem will continue to persist if production methods do not evolve to improve production and quality. Hydroponics and Controlled Environment Agriculture (CEA) was first seen in Rome during the 1st Century. Then sixteen Centuries later Greenhouses were developed in France and England as experimental hydroponics for basic laboratory research. Rapid expansion took place from about the 1950’s in areas where traditional openenvironment agriculture was difficult or impossible such as the deserts of Iran, Abu Dhabi and California. Sixty-five years later in 2015 hydroponics and CEA are well established around the world with thousands of hectares under propagation. Hydroponics is a method of agricultural production that has been refined over the years to become an exact science. Through the application of technology and know-how the physiological processes within plants can be manipulated and controlled to produce superior results. These results require less land area and less water to accomplish. It can be seen, based on this development, that hydroponics is such an evolution that has the capacity to meet the needs of a growing global population and its nutritional needs. The challenge lies, though, in the scientific understanding and application of knowledge in growing and managing a hydroponics farm. This study seeks to determine the internal data and external information needs of farmers in the hydroponics industry. This data and information will be integrated into a Farm Management Information System (FMIS) model that will be used for decision making, report generation and documentation. The problem leading to this study is the dissemination of data and information sources that are currently underutilised and difficult to access. In determining the internal data and external information needs, an empirical study was conducted using structured interview. Thirty farm managers were interviewed to assess what their current information system consisted of, whether they had a need for an FMIS and what internal data and external information was needed which related to four functional components of hydroponic farming. The results of this study indicate that there is a need for an FMIS for the hydroponic industry in South Africa. The results also indicate that managers are not fully satisfied with the performance of their current information system and would be interested in considering alternative information systems. Data points relating to the four functional components were assessed and integrated into an FMIS model for the hydroponic industry. This model sets out to integrate internal data and external information for purposes of decision making, report generation and documentation.

With the world's population expected to reach nine billion by 2050, agricultural production will have to double to meet this growing demand. Hence, a need for better infrastructure to enhance farming efficiency becomes apparent. There are a number of solutions that have been developed to date that are commercially available. They range from genetically modified seeds and bio/green fertilizers to advanced farming machinery amongst others. However most of the farming equipment developed has drawbacks such as: heavy weight – this leads to reduced yields due to soil compacting; human dependency – constant monitoring and controlling is needed; light dependency – excludes usage during the night or when visibility is poor. Therefore, a possible solution will be researched to enhance the evolution of farming equipment. Furthermore, a model will be developed for testing and verifying the research.

The objective of the thesis was to construct a deterministic single year, farm planning model that would enable vegetable producers to select an optimal farm plan from among alternative crops and crop production methods so as to maximize farm income consistent with technological and resource constraints and other goals. The model was to be readily adaptable to a wide range of commercial vegetable farmers in Canada but sufficiently flexible to be adaptable to the particular situation of a specific farm. A multiperiod linear programming model was built and validated through its application to a large commercial vegetable farm. The relevant theory of the firm was reviewed with special attention made to the theory's application to vegetable farms. The structure of a linear programming problem was discussed and related to the theory of the firm and vegetable farms. Special emphasis was placed on the problem of modeling the machinery used in vegetable production. The work of agricultural engineers was examined to determine the technological relationships involved in machine operation. Other crop budgeting models which involved the construction of similar planning models for a different sector of the agricultural community, especially the Purdue Crop Budgeting Models were reviewed. The model constructed was able to deal with machinery constraints by building a number of machine operating activities and tractor transfers so that the time constraint for a particular job would consist of any subset of the predefined set of time periods. Standard coefficients were prepared based on engineering formulae for fuel consumption and repair and maintenance costs for tractors. All inputs in the model except repair and maintenance costs were in physical units. This made it necessary to build several different types of purchasing or renting activities but facilitates the interpretation of data and the use of the model in a large number of different situations. The model was validated through its application to a large commercial vegetable farm in British Columbia. The model was run in simulation mode by forcing the model to follow the farm's 1974 crop plan and altering yields and prices to yields and prices that actually occured in that year. In this manner the reliability of the cost coefficients of the input data and the relationships between resources could be evaluated and compared with the results recorded in the farm's CANFARM records. The model was run in optimization mode with expected 1976 prices and yields to demonstrate the use of the model in selecting an optimal farm plan. A total of six plans were prepared based on alternate market and risk constraints and yields. These were compared with the plan selected by the farmer without the aid of the model. A detailed report on one of the farm plans v/as also prepared. / Land and Food Systems, Faculty of / Graduate

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Bibliography: pages166-179. / Changes in the choice of technique in the harvesting and delivery of maize and weeding, the causes of these changes, and their consequences for employment and unemployment, were investigated. Data was collected for 61 farms in six magisterial districts of the Western Transvaal for the period 1968-1981. In 1968 about 30 percent of the crop was being harvested mechanically; by 1981, 95 percent. Sacks, in which almost half of total output was being delivered to depots in 1968, had, by 1977, been almost completely displaced by bulk handling. From 15 percent of the total crop area of 1968, weed sprays were being applied to 95 percent in 1981. There was an increase of about 75 percent in the average surface area of farms, and more than a doubling in the average yield per hectare. The greatest part of most of these changes occurred between 1973 and 1977. The causes of changes in the choice of technique were sought both in theory - neo-classical and historical-materialist - and in empirical evidence. A growing shortage of men but not of women, the convenience and controllability of combine-harvesting, and economies of scale generated by the increasing size of farms, were all important causes. But the cost advantage of mechanical over hand-harvesting on all except the smallest farms is what appears to have been crucial. For various reasons, this was not fully exploited until the middle '70s. Exogenous developments in technology, rather than changes in relative factor prices, should be seen as the fundamental cause of changes in harvesting techniques. Seasonal workers were still employed to glean after combine-harvesting and to hoe spray-resistant weeds, but whereas in the late '60s seasonal harvesting teams consisted typically of a comparatively large number of workers from black rural areas, mainly adults - women in the majority - and a few children, by the late '70s they were composed of a comparatively small number, most of whom were the wives and children of permanent farm workers, living on white farms. Between 1968 and 1981, the number of seasonal jobs per 1 000 hectares of maize fell by about 70 percent in harvesting and delivery, and by 60 percent in weeding. Rough estimates show total seasonal employment to have fallen from about 105 000 to 43 000 annually, the mechanization of reaping being the most important single cause. The employment of permanent workers in harvesting and delivery declined by almost 50 percent per 1 000 hectares, enabling the total number of workers to contract from about 30 000 in 1969 to 25 000 in 1976. Since 1977 employment patterns have stabilized. Though few men appear to have become unemployed because of changes in technology, women from black rural areas, chiefly in Bophuthatswana, have generally not been able to find other jobs. The real wages of permanent workers rose by 150 percent between 1970 and 1981, while those of seasonal workers increased only marginally. Changes in both wage and employment patterns have greatly narrowed the distribution of agricultural income.

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