Average Canadian farm incomes tend to be consistently lower than non-farm incomes. Many reasons, including aggregate overproduction are advanced as possible explanations of the above problem. This thesis attempts to determine whether overproduction has been one of the causes of the farm income problem. The bulk of information used in this study was time series data as prepared by the Dominion Bureau of Statistics for the years 1935 to 1965. A model was constructed to test two related hypotheses regarding the presence of excess capital formation and its effect on income and overproduction. The basic findings of the study were that capital formation was not greater than required to produce an aggregate supply of agricultural products equal to aggregate demand. The growth in aggregate supply and aggregate demand were found to be very similar for the period 1935 to 1965, thus indicating that the farm income problem was not aggravated during this era by overproduction. In subsequent analysis, a broader view of the problem was undertaken. To begin, it was established that per capita farm incomes have been growing at a rate similar to that of non-farm incomes. In addition the so called "cost-price squeeze" was not found when the entire 1935 to 1965 period was observed but rather was only found in subperiods. Factor share analysis was used to show that agricultural capital offered returns at least equal to the opportunity costs of capital. / Land and Food Systems, Faculty of / Graduate

This research was undertaken to study the effect of education on small farm revenues and profits in Kenya. Schooling (defined as the number of school standards completed by the farm operator) was used as the most important source of education. It was hypothesized that schooling has a positive effect on farm revenues and profits. The effect of other sources of information viz; extension contact, demonstration attendance and baraza attendance on farm revenues and profits were also investigated. The research was done using regression analysis where these variables and other farm activity relevant variables were fitted in regression equations. The choice of these variables were based on economic theory, Kenya small farm characteristics and the objective of the study. Several factors would qualify as supporting evidence for the argument that educated farmers are more productive. We expect educated farmers to be more informed in terms of use of new production technologies. Education as a source of human capital also enhances the productive abilities of human beings and also enables those who have invested in education to use their resources more efficiently as well as adjusting to new "ways of producing more efficiently". In the current study we find that schooling of the farm operator is positively related to level of expenditure on farm purchased variable inputs. This indicates that education enhances adoption of new technologies and innovativeness. Further it was shown that farmers with more education earned more value added per acre from their farm business compared to their less educated counterparts. On the overall farm activity, farmers with eight or more standards of schooling earned upto 80.2% in value added per acre compared to those who had no schooling. The regression estimates were done on a stepwise procedure where farm specific enterprises were estimated separately and then aggregated and estimated as one farm sector. Thus a crop equation, a livestock equation and a total farm output equation were estimated. This model was then developed into a variable profit function. A simple linear function procedure was used in the regression analysis. In all the estimated value added equations the schooling coefficient was positive and significant at 5% level two tail t-test. As we move from farm specific activities to a farm aggregate output model and lastly to value added model the schooling coefficient increased in size confirming the positive role of education in allocative effect. These results show that schooling plays an important role in allocation of other purchased inputs and also choice of crop mix and input selection. The estimated marginal return to schooling of farm operator in the profit function was Kshs.281. In an earlier function where schooling of the farm operator was fitted into a total farm income equation the estimated marginal return to schooling was Kshs.778.89. When schooling of the farm operator is allowed to interact with extension service the estimated interaction variable coefficient is negative showing the two act as substitute sources of knowledge, and the schooling coefficient increased in size showing that those who had both schooling and extension service earned comparatively more farm revenues. The role of other educative factors like extension service, demonstration attendance, and baraza attendance in influencing agricultural production was investigated. Regression results showed that extension contact had a negative and significant effect on farm revenues and profits. Demonstration and baraza attendance had similar effects on farm revenues and profits. In the value added function hired labour variable was fitted as the cost of hired labour per day. The estimated coefficient for this variable was positive and significant at 5%. The estimated coefficient for this variable shows hired labour is not optimally used, and farmers can increase their farm profits by hiring more labour. When this variable was fitted as the wage rate paid to hired labour per day the estimated coefficient was positive and significant. These results indicate that cost of hired labour depends on its quality. In the sales function hired labour was specified as mandays of hired labour per year and the estimated coefficient which reflects the shadow price of labour was higher than average hired labour wage rate implying that this factor is underemployed. In the sales function the estimated coefficient for the value of purchased inputs variable indicates that there is an element of underutilization of these inputs. This variable is fitted in value terms and in profit maximizing conditions the estimated coefficient is expected to be no different from unit. However, the estimated coefficient for this variable is approximately 2.5 showing a shilling spent on purchased inputs will bring forth 2.5 shillings. Thus an increase in the use of purchased inputs will increase farm revenues. Results show evidence of regional differences in farmer productivity and utilization of purchased inputs in favour of Central province. The study is based on the 1982 CBS-IDS-World Bank Household Survey of Rural Kenya data set. / Land and Food Systems, Faculty of / Graduate

This study uses linear programming and econometric tools to determine the impact of agricultural productivity (technology) on agricultural exports. The study determines total factor productivity (TFP) using the Malmquist index method for a panel of 64 countries. Productivity impact on exports is determined by a two-stage estimation procedure. The results show agricultural productivity affects agricultural exports. This has important implications for developing countries. A 1 unit change in cumulative TFP increases agricultural output by .79% and a 1% increase in estimated agricultural output increases exports by .37%. Therefore, the total effect of technology on exports of primary and processed commodities is .29%. Developed countries generally have higher TFP rates, leading to higher export earnings; meanwhile, developing countries are not getting the benefits from agricultural exports because they have a relatively lower level of agricultural productivity. Investing in research and development for agriculture can improve technology, which, in turn, can Increase agricultural exports.

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手稿本, 複本據手稿本影印. / Thesis (M.A.)--香港中文大學硏究院歷史學部. / Shou gao ben, fu ben ju shou gao ben ying yin. / Includes bibliographical references (leaves 245-253). / Thesis (M.A.)--Xianggang Zhong wen da xue yan jiu yuan li shi xue bu. / Chapter I --- 引言 --- p.1 / Chapter II --- 重整農村的社會經濟秩序 --- p.25 / Chapter III --- 責任制政策的演變過程 --- p.62 / Chapter 一 --- 定額計酬時期 --- p.65 / Chapter 二 --- 聯產計酬時期 --- p.74 / Chapter 三 --- 家庭聯產承包時期 --- p.88 / (小結) --- p.97 / Chapter IV --- 責任制的推廣情況 --- p.120 / Chapter V --- 有中國特色的社會主義農村經濟發展道路 --- p.189 / Chapter 一 --- 基本策略 --- p.190 / Chapter 二 --- 專業戶 --- p.195 / Chapter 三 --- 鄉鎮企業和小城鎮 --- p.205 / (小結) --- p.214 / Chapter VI --- 結語 --- p.238 / 參考資料 --- p.245

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This item was digitized as part of the Million Books Project led by Carnegie Mellon University and supported by grants from the National Science Foundation (NSF). Cornell University coordinated the participation of land-grant and agricultural libraries in providing historical agricultural information for the digitization project; the University of Arizona Libraries, the College of Agriculture and Life Sciences, and the Office of Arid Lands Studies collaborated in the selection and provision of material for the digitization project.