Submitted by Vinicius Rafael Bianchi (vini_bianchi@hotmail.com) on 2015-12-14T12:09:16Z No. of bitstreams: 1 Dissertação Vinicius Rafael Bianchi.pdf: 4843088 bytes, checksum: acd51851a8b271398245547f942d984e (MD5) / Approved for entry into archive by Cássia Adriana de Sant ana Gatti (cgatti@marilia.unesp.br) on 2015-12-17T17:58:46Z (GMT) No. of bitstreams: 1 Dissertação Vinicius Rafael Bianchi.pdf: 4843088 bytes, checksum: acd51851a8b271398245547f942d984e (MD5) / Made available in DSpace on 2015-12-17T17:58:46Z (GMT). No. of bitstreams: 1 Dissertação Vinicius Rafael Bianchi.pdf: 4843088 bytes, checksum: acd51851a8b271398245547f942d984e (MD5) Previous issue date: 2015-09-25 / Este estudo teve como principal objetivo fazer uma análise dos sistemas agrários do município de Palmital/SP, localizado na região centro oeste do estado de São Paulo, mais precisamente na região do Médio Paranapanema, por meio de uma contextualização histórica, da análise da paisagem e dos estudos existentes sobre este município. Em seguida se estabeleceu a tipologia dos produtores rurais de Palmital/SP e dos sistemas de produção por eles praticados. Tal abordagem está baseada em um enfoque sistêmico, pautadas nas premissas da ferramenta do diagnóstico de sistemas agrários. Definidas as tipologias, foram estabelecidas as distintas formas de geração de renda daqueles sistemas produtivos na referida localidade. Assumindo a importância da variável renda na manutenção e reprodução dos sistemas de produção agrícola, por meio de análise estatística multivariada (modelo de regressão múltiplo) foram identificados quais são os fatores que interferem positiva ou negativamente na renda agrícola dos produtores rurais. A coleta dos dados para análise foi feita por meio de um formulário de pesquisa aplicado a uma amostra de produtores rurais estratificada, proporcional à condição do estabelecimento rural. Todos os dados coletados estão compreendidos entre o período de agosto de 2013 a julho de 2014. Os resultados deste trabalho permitiram apontar as transformações dos sistemas agrários da região estudada bem como a presença de distintos tipos dos produtores rurais: patronais e familiares, com diversificação nos sistemas produtivos destes, respectivamente, e com casos em que se evidenciaram aqueles fatores que influenciavam no resultado obtido na renda agrícola de maneira positiva ou negativamente. Enfim, possibilitando elaborar propostas de desenvolvimento rural ao município de Palmital/SP. / This study aimed to make an analysis of agrarian systems of the city of Palmital / SP, located in the center west region of São Paulo, more precisely in the Middle Paranapanema, through a historical context, landscape analysis and of existing studies on it. Then it was established the typology of farmers of the municipality and of production systems by them. Such an approach is based on a systemic approach, guided the premises of the diagnosis of agrarian systems tool. Defined typologies, the different forms of income generation of productive systems in that locality were established. Assuming the importance of equity in the maintenance and reproduction of agricultural production systems by means of multivariate statistical analysis (multiple regression model) were analyzed what are the factors that interfere positively or negatively in the agricultural income of farmers. Data collection for analysis was done through a survey form applied to a stratified sample of farmers, proportional (to the condition of the farm). All data collected are between the period August 2013 to July 2014. These results point allowed the transformation of agrarian systems of the study area and the presence of different types of farmers: employers and family, with diversification in these production systems, respectively; in cases in which it showed those factors that influence the result obtained in agricultural income positive or negative way. Anyway, enabling elaborate proposals for rural development to the city of Palmital / SP.

Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgico / The Brazilian agricultural sector has made in the period of stabilization after the Real Plan (1996-2009) a satisfactory economic dynamics, in which the level of agricultural products began an upward trend and virtually uninterrupted growth. This performance suggests that state economies are undergoing a process of catching up, which in the long run there would be a tendency for poorer economies achieve the same level of economic growth (in terms of per capita agricultural GDP) of the richest economies, setting a process of convergence to steady state. Accordingly, this paper seeks to analyze the convergence of per capita agricultural income between the states of Brazil, making sure that the dynamics of the agricultural sector had contributed to the reduction of inequalities existing interstate. To this end, it was used the first-order Markov process. The results indicate the occurrence of movements backward economies to levels of income per capita agricultural lower, indicating that the economies under review showed a trend of impoverishment, despite the global economic growth presented by the sector over the period. Among the factors that led these economies to tread a path of impoverishment, one can cite the emphasis of public policy to export crops, not covered by all the federating units of the country, which would result in the strengthening of the state economies have developed, expense of which are under development; beyond the migration of manpower for the agricultural production centers in more developed agricultural, causing the "Red Queen Effect," in which the growth of agricultural GDP does not translate into growth of income per capita in the field. However, the focus of this study is to identify the occurrence of convergence / divergence, no inferences about the causes that led to the initiation of such a movement, since these factors make room for new studies that seek to investigate them, in order to provide tools for the formulation of agricultural policies aimed at minimizing or even reversal of the causes that lead to poverty in the countryside. / O setor agropecuÃrio brasileiro tem apresentado no perÃodo de pÃs estabilizaÃÃo do Plano Real (1996-2009) uma dinÃmica econÃmica satisfatÃria, em que o nÃvel de produto agropecuÃrio iniciou uma trajetÃria ascendente e praticamente ininterrupta de crescimento. Tal performance sugere que as economias estaduais estejam passando por um processo de catching up, em que no longo prazo existiria uma tendÃncia das economias mais pobres alcanÃarem o mesmo nÃvel de crescimento econÃmico (em termos de PIB per capita agropecuÃrio) das economias mais ricas, configurando um processo de convergÃncia no steady state. Eom efeito, este, trabalho busca analisar a convergÃncia da renda agropecuÃria per capita entre os estados do Brasil, verificando se a dinÃmica do setor agrÃcola teria contribuÃdo para a reduÃÃo das desigualdades interestaduais preexistentes. Para tal, fez-se uso do processo markoviano de primeira ordem. Os resultados apontaram a ocorrÃncia de movimentos de retrocesso das economias para nÃveis de renda per capita agropecuÃria inferiores, indicando que as economias em anÃlise apresentaram uma tendÃncia de empobrecimento, apesar do crescimento econÃmico global do setor ao longo do perÃodo. Dentre os fatores que levariam tais economias a trilharem uma trajetÃria de empobrecimento, pode-se citar a Ãnfase das polÃticas pÃblicas Ãs culturas de exportaÃÃo, nÃo contempladas por todas as unidades federativas do PaÃs, o que resultaria no fortalecimento das economias estaduais jà desenvolvidas, em detrimento das que se encontram em desenvolvimento; alÃm dos movimentos migratÃrios da mÃo-de-obra agropecuÃria para os centros produtores agrÃcolas mais desenvolvidos, causando o âEfeito Rainha Vermelhaâ, em que o crescimento do PIB agropecuÃrio nÃo se traduziria em crescimento das rendas per capita no campo. Contudo, o foco deste estudo consiste na identificaÃÃo da ocorrÃncia do processo de convergÃncia/divergÃncia, sem inferir sobre as causas que levariam ao desencadeamento de tal movimento, jà que tais fatores abrem espaÃo para novos estudos que busquem investigÃ-los, a fim de poder fornecer instrumentos de formulaÃÃo de polÃticas pÃblicas agropecuÃrias direcionadas à minimizaÃÃo ou mesmo reversÃo das causas que levam à pobreza no campo.

Mudanças na política agrícola brasileira têm preconizado a adoção de mecanismos de mercado para o fortalecimento da comercialização, financiamento à produção e mitigação dos riscos agropecuários, tanto o climático, quanto o de mercado. O seguro rural é um dos instrumentos mais promissores nesse novo estágio da política agrícola, pois permite a administração do risco agrícola, ao mesmo tempo em que lastreia as operações de comercialização e financiamento agrícola. O seguro de receita emerge como um instrumento ainda mais robusto de estabilização a receita agrícola, na medida em que garante a variação de produtividade e preço, simultaneamente. O instrumento já é consolidado nos Estados Unidos e começa a ser estudado no Brasil. Por essa razão, a primeira parte do estudo, capítulo 2, visa analisar os planos de seguro existentes e indicar os modelos que devem ser fomentados no Brasil. Constatou-se que os modelos estadunidenses baseados no plano de Proteção de Renda (IP - Income Protection) e Receita Garantida (RA - Revenue Assurance), substituídos pelo plano Proteção de Receita (RP - Revenue Protection), são os mais adequados para iniciar o desenvolvimento dessa modalidade de seguro no Brasil. Na segunda parte do trabalho, capítulo 3, é apresentado modelo teórico de plano de seguro de receita, bem como procedimento metodológico de cálculo da taxa de prêmio, de modo univariado e bivariado. Aplicada a metodologia ao caso da soja no Paraná, concluiu-se que as taxas calculadas no estudo são inferiores às praticadas nos dois projetos experimentais existentes. O distanciamento entre as taxas praticadas no mercado e a diferença em relação às estimadas na nesta pesquisa indicam imprecisão no cálculo das taxas de prêmio e são evidências de superestimação das taxas pelas seguradoras. / Changes in Brazilian agricultural policies have advocated the adoption of market mechanisms for strengthening the marketing, the financing to production, and both climate and market farming risk mitigation. Rural insurance is one of the most promising instruments in this new stage of agricultural policy, for crop risk administration at the same time it serves as collateral to marketing operations and agricultural funding. The insurance revenue emerges as an even more robust stabilization of agricultural revenue instrument to the extent that it ensures the variation of productivity and price simultaneously. The instrument is already consolidated in the United States and begins to be studied in Brazil. For this reason, the first part of the study, Chapter 2, aims to analyze existing insurance plans and indicate the models that should be encouraged in Brazil. It was found that models based on U.S. Income Protection (IP) and Revenue Assurance (RA), replaced by the plan Revenue Protection are best suited to start the development of this type of insurance in Brazil. In the second part of the dissertation, Chapter 3, the theoretical model of revenue insurance plan is presented, as well as a methodology for univariate and bivariate premium ratemaking. The methodology was applied to the case of soybean in Paraná, and it was concluded that the rates calculated in this study are lower than those of the two existing experimental projects. The gap between the market rates and the difference in relation to the rates estimated in the study indicate inaccuracy in the calculation of premium rates and are evidence of rate overestimation by insurers.

The aim of the study was to investigate the impact of agricultural infrastructure on agricultural productivity and agricultural income of smallholder farmers in the North West Province, South Africa. Factors that contribute to the availability, accessibility and satisfaction of smallholder farmers with regards to agricultural infrastructure were also assessed in the study. Using cross sectional data from the North West Province of South Africa, 150 smallholder farmers were selected using stratified sampling to group farmers into those who had agricultural infrastructure and those who did not have. Data were collected using a structured questionnaire, divided into six sections as follows: personal socio-economic characteristics of farmers; characteristics of the land; agricultural infrastructure of smallholder farmers; agricultural production and markets; and production activities and financial support rendered to farmers. The data were coded, captured and analysed using STATA 14.0. Data were analysed through descriptive analyses, Principal Components Analysis (PCA), Stochastic Frontier Analysis, Heckman selection procedure and Tobit Regression Models. This result revealed that most of the farmers were male, aged between 41 and 60 years of age, had contact with extension services, had contact with extension services only occasionally and did not engage in non-farming activities. Smallholder farmers had less than 10 years of farming experience, a household size of less than or equal to five members, had about one household member assisting in the day-to-day farming activities. Most of the farmers did not belong to any organisation. Generally, the farmers indicated that they were involved in dry land farming. Farmers who irrigated their farms, did so on approximately 15 and 45 hectares of land. Farmers also indicated that they received agricultural support from CASP and used commercial seeds, fertilizers and animal vaccines as their production inputs. Furthermore, smallholder farmers in the study area received support for inputs while majority indicated they did not have to repay for the inputs. Majority of farmers indicated that infrastructure impacted on their farming enterprises through increases in productivity in their farming enterprises. The study found that the factors influencing agricultural income for smallholder farmers with agricultural infrastructure were: Physical infrastructure index (Coef=0.78: P=0.01); Social infrastructure availability index (Coef=0.61: P<0.01); Institutional infrastructure availability index (Coef=1.05: P<0.01); Level of education of farmers (Coef=0.96: P<0.01); Access to extension services (Coef=1.05: P<0.01); Membership of farmers’ organisations (Coef=0.59: P<0.05); Age of smallholder famers in the study area (Coef=0.05: P<0.01); and Household members assisting in farming activities (Coef=0.24: P<0.05). In terms of smallholder farmers with accessible agricultural infrastructure, factors influencing agricultural income were: Physical infrastructure access index (Coef=1.29: P<0.01); Social infrastructure access index (Coef=0.38: P<0.1); Equipment infrastructure access index (Coef=0.62: P<0.01); Level of education for smallholder farmers (Coef=1.21: P<0.01); Access to agricultural extension services (Coef=1.64: P<0.01); Membership of Farmers’ organisations (Coef=0.77: P<0.05); Age of smallholder farmer (Coef=0.01: P<0.01); and Household members assisting in the farming enterprises (Coef=0.39: P<0.01). In terms of satisfaction of smallholder farmers with agricultural infrastructure, factors influencing agricultural income were: Physical infrastructure satisfaction index (Coef=0.35: P<0.1); Social infrastructure satisfaction index (Coef=0.37: P<0.1); Institutional infrastructure satisfaction index (Coef=1.25: P<0.01); Equipment infrastructure satisfaction index (Coef=1.04: P<0.01); Level of education of respondents (Coef=1.24: P<0.01); Access to extension services (Coef=1.58: P<0.01); Age of smallholder farmers in the study area (Coef=0.05: P<0.01); Number of years farming (Coef = -0.57: P<0.1); and Number of household members assisting in farming (Coef=0.19: P<0.1). The results of the Heckman selection model revealed that the variables impacting on agricultural income were: agricultural infrastructure availability index (Coef=1.12: P<0.01); and access to extension services (Coef=0.62: P<0.05). Furthermore, those impacting on agricultural production were: infrastructure satisfaction index (Coef=-1.31: P<0.01); infrastructure accessibility index (Coef=-0.59: P<0.05); Level of education of smallholder farmers (Coef=0.64: P<0.01); access to extension services (Coef=1.29: P<0.01); and membership of farmers’ organisations (Coef=0.66: P<0.01). The results of the Tobit Regression Model showed that among others factors influencing availability of agricultural infrastructure, the following variables played a critical role: assistance of household members in farming enterprise (Coef=0.702: P<0.01); farm ownership (Coef=0.962: P<0.01); farm acquisition (Coef=0.323: P<0.01)farmer occupation (Coef=0.785: P<0.01); member of farmers’ organisations (Coef=2.066: P<0.01); sources of labour (Coef=1.283: P<0.01); farming experience (Coef=0.100: P<0.01); and agricultural production inputs (Coef=-0.763: P<0.05). In terms of accessibility to agricultural infrastructure, the following variables played a critical role: engagement in non-farming activities Coef=1.275: P<0.01); contact with extension services (Coef=1.205: P<0.01); farm ownership (Coef=0.403: P<0.01); farmer occupation (Coef=0.456: P<0.01); membership of farmers’ organisations (Coef=1.111: P<0.01); sources of labour (Coef=0.653: P<0.01); farming experience (Coef=0.045: P<0.05) and land tenure (Coef=0.156: P<0.01). In terms of satisfaction with agricultural infrastructure, among other factors influencing satisfaction with agricultural infrastructure, the following variables played a critical role: organisation for extension services (Coef=1.779: P<0.01); assistance of household members in farming enterprise (Coef=0.411: P<0.01); government agricultural support to farmers (Coef=0.419: P<0.01); farm ownership (Coef=0.464: P<0.01); membership of farmers’ organisations (Coef=1.011: P<0.01); age of farmer (Coef= 0.030: P<0.01); level of education (Coef= 0.483: P<0.01); marital status (Coef=0.290: P<0.01); and gender (Coef= -0.576: P<0.01). The results of the analysis were used to close the knowledge gap with regards to the impact of agricultural infrastructure, availability, accessibility and satisfaction on the productivity and agricultural income of smallholder farmers in the North West Province. In terms of recommendations, the study highlighted that agricultural industries and government should commit in assisting smallholder farmers to be productive and to participate in economic activities. This could be achieved through collaboration with industries in implementing initiatives that assist and accelerate the development of smallholder farming and also through assisting smallholder farmers access agricultural infrastructure. / Agriculture and Animal Health / Ph. D. (Agriculture)

The aim of the study was to investigate the impact of agricultural infrastructure on agricultural productivity and agricultural income of smallholder farmers in the North West Province, South Africa. Factors that contribute to the availability, accessibility and satisfaction of smallholder farmers with regards to agricultural infrastructure were also assessed in the study. Using cross sectional data from the North West Province of South Africa, one hundred and fifty smallholder farmers were selected using stratified sampling to group farmers into those who had agricultural infrastructure and those who did not have. Data were collected using a structured questionnaire, divided into six sections as follows: personal socio-economic characteristics of farmers; characteristics of the land; agricultural infrastructure of smallholder farmers; agricultural production and markets; and production activities and financial support rendered to farmers. The data were coded, captured and analysed using STATA 14.0. Data were analysed through descriptive analyses, Principal Components Analysis (PCA), Stochastic Frontier Analysis, Heckman selection procedure and Tobit Regression Models. This result revealed that most of the farmers were male, aged between 41 and 60 years of age, had contact with extension services only occasionally and did not engage in non-farming activities. Most of the smallholder farmers had less than 10 years of farming experience, had household sizes of less than or equal to five members, had about one household member assisting in the day-to-day farming activities. Most of the farmers did not belong to any farmer organisation. Generally, the farmers were involved in dry land farming. Farmers who irrigated their farms, did so on approximately 15 and 45 hectares of land. Farmers also received agricultural support from CASP and used commercial seeds, fertilizers and animal vaccines as their production inputs. Furthermore, smallholder farmers in the study area received support for inputs and majority did not have to repay for the inputs. Majority of farmers indicated that infrastructure impacted on their farming enterprises through increases in both productivity and sizes of their farming enterprises. The study found that the factors influencing agricultural income for smallholder farmers with agricultural infrastructure were: Physical infrastructure index (Coef=0.78: P<0.01); Social infrastructure availability index (Coef=0.61: P<0.01); Institutional infrastructure availability index (Coef=1.05: P<0.01); Level of education of farmers (Coef=0.96: P<0.01); Access to extension services (Coef=1.05: P<0.01); Membership of farmers’ organisations (Coef=0.59: P<0.05); Age of smallholder famers in the study area (Coef=0.05: P<0.01); and Household members assisting in farming activities (Coef=0.24: P<0.05). In terms of farmers without agricultural infrastructure available, factors influencing agricultural income were: physical infrastructure availability index (Coef = 0.74; P<0.01); social infrastructure availability index (Coef = 0.77: P<0.01); institutional infrastructure availability index (Coef = 0.61: P<0.01); level of education (Coef = 0.89: P<0.01); access to extension services (Coef=1.24: P<0.01); age of farmers (Coef = 0.06: P<0.01) and assistance of household members in farming enterprises (Coef=0.33: P<0.01). In terms of smallholder farmers with accessible agricultural infrastructure, factors influencing agricultural income were: Physical infrastructure access index (Coef=1.29: P<0.01); Social infrastructure access index (Coef=0.38: P<0.1); Equipment infrastructure access index (Coef=0.62: P<0.01); Level of education for smallholder farmers (Coef=1.21: P<0.01); Access to agricultural extension services (Coef=1.64: P<0.01); Membership of Farmers’ organisations (Coef=0.77: P<0.05); Age of smallholder farmer (Coef=0.01: P<0.01); and Household members assisting in the farming enterprises (Coef=0.39: P<0.01). With regards to smallholder farmers without accessible agricultural infrastructure, factors influencing agricultural income were: Physical infrastructure accessibility index (Coef=0.92, P<0.01); Equipment accessibility index (Coef=0.43, P<0.05); Level of education (Coef=1.25: P<0.01P); access to extension services (Coef = 1.63; P<0.01); membership of farming organisations (Coef = 0.86; p<0.01); age of farmers (Coef= 0.07; P<0.01) and assistance of household members in farming enterprises (Coef = 0.34; P<0.05). In terms of satisfaction of smallholder farmers with agricultural infrastructure, factors influencing agricultural income were: Physical infrastructure satisfaction index (Coef=0.35: P<0.1); Social infrastructure satisfaction index (Coef=0.37: P<0.1); Institutional infrastructure satisfaction index (Coef=1.25: P<0.01); Equipment infrastructure satisfaction index (Coef=1.04: P<0.01); Level of education of respondents (Coef=1.24: P<0.01); Access to extension services (Coef=1.58: P<0.01); Age of smallholder farmers in the study area (Coef=0.05: P<0.01); Number of years farming (Coef = -0.57: P<0.1); and Number of household members assisting in farming (Coef=0.19: P<0.1). The results of the Heckman selection model revealed that the variables impacting on agricultural income were: agricultural infrastructure availability index (Coef=1.12: P<0.01); and access to extension services (Coef=0.62: P<0.05). With regards to farmers not satisfied with agricultural infrastructure, factors influencing agricultural income were: institutional infrastructure satisfaction index (Coef = 0.54: P< 0.05); level of education (Coef=1.25: P<0.01); access to extension services (Coef = 1.77: P<0.01); age of farmers (Coef = 0.06: P<0.01) and assistance of household members in farming enterprises (Coef = 0.34: P<0.01). Furthermore, those impacting on agricultural production were: infrastructure satisfaction index (Coef=-1.31: P<0.01); infrastructure accessibility index (Coef=-0.59: P<0.05); Level of education of smallholder farmers (Coef=0.64: P<0.01); access to extension services (Coef=1.29: P<0.01); and membership of farmers’ organisations (Coef=0.66: P<0.01). The results of the Tobit Regression Model showed that among others factors influencing availability of agricultural infrastructure, the following variables played a critical role: assistance of household members in farming enterprise (Coef=0.702: P<0.01); farm ownership (Coef=0.962: P<0.01); farm acquisition (Coef=0.323: P<0.01) farmer occupation (Coef=0.785: P<0.01); member of farmers’ organisations (Coef=2.066: P<0.01); sources of labour (Coef=1.283: P<0.01); farming experience (Coef=0.100: P<0.01); and agricultural production inputs (Coef=-0.763: P<0.05). In terms of accessibility to agricultural infrastructure, the following variables played a critical role: engagement in non-farming activities Coef=1.275: P<0.01); contact with extension services (Coef=1.205: P<0.01); farm ownership (Coef=0.403: P<0.01); farmer occupation (Coef=0.456: P<0.01); membership of farmers’ organisations (Coef=1.111: P<0.01); sources of labour (Coef=0.653: P<0.01); farming experience (Coef=0.045: P<0.05) and land tenure (Coef=0.156: P<0.01). In terms of satisfaction with agricultural infrastructure, among other factors influencing satisfaction with agricultural infrastructure, the following variables played a critical role: organisation for extension services (Coef=1.779: P<0.01); assistance of household members in farming enterprise (Coef=0.411: P<0.01); government agricultural support to farmers (Coef=0.419: P<0.01); farm ownership (Coef=0.464: P<0.01); membership of farmers’ organisations (Coef=1.011: P<0.01); age of farmer (Coef= 0.030: P<0.01); level of education (Coef= 0.483: P<0.01); marital status (Coef=0.290: P<0.01); and gender (Coef= -0.576: P<0.01). The results of the analysis were used to close the knowledge gap with regards to the impact of agricultural infrastructure, availability, accessibility and satisfaction on the productivity and agricultural income of smallholder farmers in the North West Province. In terms of recommendations, the study highlighted that agricultural industries and government should commit in assisting smallholder farmers to be productive and to participate in economic activities. This could be achieved through collaboration with industries in implementing initiatives that assist and accelerate the development of smallholder farming and also through assisting smallholder farmers access agricultural infrastructure. / Agriculture and  Animal Health / D. Litt et Phil. (Agriculture)