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.

Why some countries are so much richer than others is a question of central interest in economics. Low aggregate income per worker in poor countries is mostly accounted for by low labor productivity and high employment in agriculture. This thesis attempts to understand cross-country income difference through examining productivity differences at the sector level - in agriculture and in non-agriculture. Between rich and poor countries, there is a 45-fold difference in agricultural output per worker and a 34-fold difference in mean farm size. In the first chapter, I argue farmer's skill as a plausible explanation for these differences. The model features heterogeneity in innate agricultural skill, on-the-job skill accumulation, and span-of-control in agricultural production. I show that low total factor productivity (TFP) in poor countries not only induces more individuals with low innate skill to choose farming, but also reduces the incentive to accumulate skill. Between rich and poor countries, the model generates substantial difference in farmer's skill, which translates into differences in agricultural productivity and farm size distribution. Quantitatively, the calibrated model explains half of the cross-country differences in agricultural output per worker, and successfully replicates the size distribution of farms in both rich and poor countries. Cross-country productivity differences are asymmetric across sectors. The labor productivity gap between rich and poor countries in agriculture is twice as large as that in the aggregate, and ten times larger than that in non-agriculture. The second chapter shows that these sectoral productivity differences can arise solely from difference in aggregate TFP. I extend the framework in the first chapter to allow for different skill in non-agricultural production as well. Low TFP distorts the allocation of skills across sectors and discourages skill accumulation on the job. To discipline the initial skill distribution and skill accumulation, the model is calibrated to match earnings distribution and age-earnings profiles in both agriculture and non-agriculture in the U.S. The model's implications are then examined using a sample of 70 countries that covers a wide range of development. Between rich and poor countries, the model accounts for most of the productivity differences at the sector level - productivity difference in agriculture in the model is 1.8 times larger than those in the aggregate and 6 times larger than those in non-agriculture. As in the data, the share of farmer in the labor force in the model declines from 85 percent in the poorest countries to less than 2 percent in the richest countries. These results suggest that policy aiming at improving overall efficiency should be prioritized.

Tese (Magister Scientiae)--Universidade Federal de Viçosa. / Bibliography: leaves 60-61.

"Tesis de grado que presentan como requisito parcial para optar al título de economistas." / Bibliography: leaves 90-91.

Thesis--University of Wisconsin. / eContent provider-neutral record in process. Description based on print version record. Bibliography: p. 341-350.

Small-scale sugar-cane farming came to Mpumalanga Province in the 1990s. As result of the Nkomazi Irrigation Expansion Programme, 34 projects with farms of on average size of seven hectares were initially allocated by Government to potential farmers in rural areas. This was done to enable them to generate income from sugar-cane to support their families. The initial expectations for the success of the programme were high, but they did not realise as anticipated. The yield results for the first decade of the 21st millennium showed a declining trend. Over the same period the large-scale sugar-cane growers (LSGs) performed better. This added impetus to the on-going debate on the relationship between farm size and efficiency in South Africa. It also raised the question whether small-scale farming has a future. Four hypotheses were formulated and tested with regard to the Mpumalanga sugar-cane growers’ land productivity. Regression analysis on land productivity, stakeholders’ inputs, production budget analysis and macro-economic analysis, by applying the Social Accounting Matrix of Mpumalanga, were used to address the hypotheses. The first hypothesis states: ‘There exits an inverse relationship between farm size and land productivity amongst sugar-cane growers in Mpumalanga.’ It was rejected but qualifications were added. For the sugar-cane cultivated until farm size groups of 4 000 ha in the 2009 season, there was a direct relationship between farm size and land productivity which was highly significant. If this study only focussed on farm sizes up to 7 ha, the hypothesis would have been accepted as there was a high significance of an inverse relationship of the small-scale growers (SSGs) until 7 ha. Despite the inverse relationship of certain larger farm size groups, of which regression analysis suggested no evidence of such a relationship, the LSGs average yield was still approximately 25 t/ha higher than SSGs yield of about 64 t/ha. The second hypothesis, namely, that land productivity has declined amongst SSGs and not so amongst LSGs, was tested by observing partial productivity over different time periods. The LSGs had a negative growth rate during 2001–2005 but showed positive growth during 2005–2009. The whole period of 2001–2009 showed marginal positive growth for the LSG while the SSGs growth rate declined by 4.6%. For the SSGs the land productivity was about 20 t/ha lower compared to the LSGs, at the data points, 2002, 2007 and 2011, as well as over the period 2002–2011. This confirmed the second hypothesis. The third hypothesis, namely that the performance of SSGs in the 2009 season indicated financial sustainability, was evaluated by means of production cost analyses for SSG farm size groups, individual farmers and a breakeven point scenario. If the net farm income (NFI) per hectare was the only consideration to measure financial feasibility, the hypothesis would have been accepted. The analyses however showed that the SSGs had much difficulty to cover their living costs from a farm of less than 6.29 ha, resulting in a rejection of the hypothesis. Testing of the fourth hypothesis, namely that SSGs are an important and essential part of the Mpumalanga economy, and make a critical economic contribution to the region, revealed that SSGs’ direct contribution in terms of agricultural production represents 20% of the involvement in the sugar-cane industry and 0.03% of the economy of Mpumalanga Province. Its economic contribution consisted of about R110 million of total GDP, about 2 800 total employment opportunities, and income distribution to households of almost R50 million. The fourth hypothesis can be rejected when considering the magnitude of the SSGs’ production only constitutes 0.03% of the total economy of Mpumalanga. However, to assess the real importance of the SSGs, other factors besides production magnitude should also be considered. A major contribution of the SSG sector is the amount of labour opportunities they offer. If this is taken into account, there is reason enough to accept the hypothesis. When the focus shifts from Mpumalanga as a whole to the Nkomazi region, the contribution of the SSGs is substantial. It is therefore possible to confirm the hypothesis, especially due to the contribution to the Nkomazi region. This study found that SSGs on the whole did not perform as well as LSGs. It however found that some of the SSGs performed sufficiently, and have potential for a sustainable future. Continued support from institutions such as local, provincial and national government, Tsb Sugar, the Cane Growers’ Association and Akwandze Agricultural Finance will remain indispensable. With such aid it can be anticipated that the SSGs contribution to society will continue and should be with co-operative ventures as implemented at the irrigation project, Langeloop II, assist the SSGs in being more financially sustainable and providing an even greater economic contribution. / Dissertation (MCom)--University of Pretoria / Agricultural Economics, Extension and Rural Development / unrestricted

Diss. (sammanfattning) Uppsala : Sveriges lantbruksuniv., 2007. / Härtill 4 uppsatser.

Diss. (sammanfattning) Uppsala : Sveriges lantbruksuniv. / Härtill 4 uppsatser.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1964. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves [194]-197).

Submitted by Nicoli Carolini de Lázari null (nicolicarol@hotmail.com) on 2017-04-21T22:58:31Z No. of bitstreams: 1 NicoliCaroliniDeLázari_PGAD_TUPA_Fevereiro2017.pdf: 2566566 bytes, checksum: 7acca8a7e2294f9242da9387a0ceda7d (MD5) / Approved for entry into archive by Luiz Galeffi (luizgaleffi@gmail.com) on 2017-04-25T19:25:32Z (GMT) No. of bitstreams: 1 lazari_nc_me_tupa.pdf: 2566566 bytes, checksum: 7acca8a7e2294f9242da9387a0ceda7d (MD5) / Made available in DSpace on 2017-04-25T19:25:32Z (GMT). No. of bitstreams: 1 lazari_nc_me_tupa.pdf: 2566566 bytes, checksum: 7acca8a7e2294f9242da9387a0ceda7d (MD5) Previous issue date: 2017-02-21 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / O objetivo deste estudo é medir o crescimento da produtividade total dos fatores (PTF) segundo o tamanho dos estabelecimentos agropecuários na região Sudeste entre os Censos Agropecuários de 1985, 1995/96 e 2006, assim como decompor a mudança da PTF em dois componentes, mudança tecnológica e mudança na eficiência técnica. Este objetivo está fundamentado nas hipóteses de que exista heterogeneidade no crescimento da PTF segundo o tamanho dos estabelecimentos, e que distintas fontes expliquem essa heterogeneidade. A metodologia está baseada na análise de fronteira estocástica de produção. Os dados são representativos para os municípios da macrorregião, considerando cinco classes de área: 0-5 ha, 5-20 ha, 20-100 ha, 100-500 ha, 500 e mais ha. Observou-se crescimento da PTF para a região Sudeste. A decomposição deste crescimento apontou a mudança tecnológica como a principal fonte de ganho de produtividade. A mudança na eficiência técnica foi negativa. O crescimento da PTF entre os tamanhos de estabelecimentos e entre as unidades da federação da região Sudeste foi heterogêneo. Os maiores estabelecimentos, 100-500 ha e 500 e mais ha, alcançaram maior crescimento da PTF do que os estabelecimentos das três primeiras classes de área, 0-5 ha, 5-20 ha e 20-100 ha. Para as unidades da federação, notou-se que a PTF do estabelecimento de São Paulo cresceu relativamente mais rápido. / The aim of this paper is to measure the total factor productivity (TFP) growth by farm size in Southeast, from the 1985, 1995/96 and 2006 Agricultural Census, as well as to decompose TFP change into two components, technical change and technical efficiency change. This aim is based on the hypothesis that there is heterogeneity in TFP growth by farm size, and that different sources explain such heterogeneity. The methodology is based on stochastic frontier analysis. The data are aggregated at the municipality level into five size classes: 0-5 ha, 5-20 ha, ha 20-100, 100-500 ha, greater than 500 ha. It was observed productivity growth in Southeast. The decomposition of this growth showed to technical change as the main source of productivity gain. The technical efficiency change was negative. This growth was heterogeneous among size class and among states of the Southeast region. The largest farms, 100-500 ha and greater than 500 ha, achieved higher TFP growth than the first three size classes, 0-5 ha, 5-20 ha and 20-100 ha. Among states, it was noticed that the TFP in São Paulo farm grew relatively faster.