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<p>The results of previous studies generally show that organic production is more profitable than conventional production. However, as a source of these results, the trials are either of short duration or do not specifically examine the benefits during the transition period, suggesting that previous work may not have captured the full variability of crop net returns. The purpose of this study was to compare the net returns and risks of conventional, transition, and organic cropping systems using long-term crop budgets and stochastic simulations. Conventional crop rotations were represented by a corn / soybean rotation and a corn / soybean / wheat rotation. The organic crop rotation produced corn, soybean and wheat. Historical prices, crop budgets, and FINBIN data are used to develop the long-term crop budgets. The organic crop rotation included a three-year transition period. Ten-year enterprise budgets were created for each crop and rotation so that we could compare the net returns of the transition year of organic crop rotation and the year of organic production. This thesis summarizes the enterprise budget information for each crop in the conventional and organic cropping systems, and summarizes the information used for the simulation analysis. Using @ risk, differences in annual net returns to land between crop rotations were summarized. Results are as follows. First, the transition and organic cropping system was found to have higher net returns to land than the two conventional crop rotations under most of the simulation iterations. Second, the annual net returns during the transition period were relatively low. Third, the highest average annual net return was for organic corn.</p><div><br></div>

<p>  </p> <p>Data is the foundation of digital agriculture. Data from a wide variety of sensors in the soil, in machinery, or from remote sensing can inform decisions including site-specific land and crop management but capitalizing on these data requires metadata that captures the full story related to production. Answers to metadata questions such as who, what, where, when, and how are often unavailable when aggregated data are analyzed. These metadata are crucial for making accurate operation and management decisions and certainly for developing AI models. Since farmers and researchers exhibit human behavior of forgetting to take notes or entering incorrect information, even with digital means, missing and erroneous records are common. To address this issue, a metadata collection Android app – Meta Ag for agricultural activities was created that automatically appends the operator’s name, time, and space information to an in-field event, and provides a user-friendly interface to gather information with more details describing which activity was done and how. Meta Ag has six main modules, including user registration, geofence construction, accessed geofence recognition, an infobot for extensive activity data collection, setting options for Infobot and data access. By design, manual data input, with automatic validation, when possible, was used for information collection. To achieve this, Meta Ag uses dynamically constructed, interactive option lists for fewer data entry errors. The collected data were stored in a Google Firebase database as central storage for multiple users. To facilitate data interoperability, stored data were made accessible in CSV and JSON format. The Android app collects extensive metadata from database interactive option lists and the infobot as a data collection wizard provides a dynamic environment for data collection in a short time with minimal manual input. The app was also able to reduce missing data as it automatically records the accessed fields and activity time in that field. The Meta-Ag app can work both as a standalone tool and an integration into any other farm information management system.</p>

The factors affecting the supply of rubber from Sarawak, East Malaysia, were identified and reviewed in an historical framework. A methodical framework for the general analysis of economic relationships between variables was reviewed and a practical application of the methodology to the supply of rubber from Sarawak was carried out. An econometric analysis of the long run factors affecting the production of rubber was carried out. (1) Two log-differenced autoregressive models of the rubber supply were formulated. (2) The models were tested for parameter constancy to identify structural breaks in the time series and for structural invariance to determine whether they were suitable for policy analysis, forecasting and backcasting. (3) The variables were tested for bivariate Granger Causality to determine the relationships between the factors of production and the output of rubber. (4) Forecast Error Variance Decomposition analysis of multivariate Granger Causality was carried out using a Vector Autoregressive Model. The results confirm the a priori economic theory that long run changes in supply have been affected primarily by changes in area under rubber production and long term price trends. The area planted to rubber has depended upon price incentives and the availability of scarce labour resources. Prices have been affected by the supply of rubber from Sarawak but this is posited to be a reflection of global supply trends affecting prices. While the results generally confirm the economic theory, caution is urged when interpreting the results. The severe inadequacies of the data used highlights the need for more accurate time series and the mainly methodological approach of this study.

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Tentar entender porque algumas empresas têm melhor desempenho que outras é uma questão que pode auxiliar na compreensão do potencial existente em determinado setor. Um importante arcabouço teórico da estratégia que subsidia condições para este tipo de análise é a Teoria Baseada em Recursos ou da Visão Baseada em Recursos - VBR. Ela propõe o entendimento dos fatores-chave de sucesso que levam as organizações a adquirir vantagens competitivas pelo conjunto de habilidades e rotinas essenciais e pela coerência entre habilidades e know-how de propriedade única. Nesse contexto o objetivo da pesquisa foi de levantar e analisar, a partir da teoria da Visão Baseada em Recursos, quais são os recursos estratégicos e a sua influência no desempenho das propriedades rurais no setor leiteiro. Para alcançar esse objetivo foi realizada uma etapa de entrevista com especialistas no setor leiteiro no Brasil e na França onde foram levantados os recursos considerados estratégicos pelos especialistas e discutidas informações importantes sobre medidas de desempenho em propriedades leiteiras. Posteriormente foram realizadas entrevistas com 199 produtores de leite na bacia leiteira do estado de Pernambuco. A partir desses dados foram realizadas as análises: Fatorial, de Cluster e Regressão Múltipla. Observou-se que no caso das propriedades produtoras de leite os recursos mais importantes foram os Recursos Organizacionais, Humanos e Físicos, respectivamente. Esse resultado difere das empresas urbano industriais ao invés do destaque nos recursos físicos é observado o recurso tecnológico. O destaque fica com os Recursos Organizacionais e Humanos que além de muito relacionados são destaque no sucesso dos empreendimentos agropecuários de produção leiteira. Os indicadores mais importantes foram os indicadores de gestão do rebanho, gestão de custos, produtividade dos animais, produtividade da terra, formação, abertura a novidades (via treinamentos e viagens), mostrando fazer a diferença entre os grupos com melhor rendimento e os com pior. Contudo, um elemento considerado essencial: a reserva alimentar se relacionou negativamente com a renda, provavelmente porque a reserva é mal planejada e insuficiente para dar conta dos períodos de estiagem, bem como pode gerar certa acomodação por parte dos produtores em não investir em outros aspectos alimentares que garantam os ganhos em todos os períodos, inclusive os de estiagem. Desta forma os resultados elucidam aspectos interessantes e emergem novas proposições de pesquisa com diferentes níveis de aprofundamento para o entendimento dessas questões mais qualitativas e possível investigação em diferentes cadeias produtivas. / In trying to understand why some companies have better performance than others is a question that can help understand the potential existing in a given sector. An important theoretical framework of the strategy subsidizing conditions for this kind of analysis is the Resource-Based Theory or Resource-Based View – RBV. It proposes the understanding of success key-factors that lead organizations to gain competitive advantages by the set of skills, essential routines and consistency between skills and individual know-how. The aim of this study was to search and analyze, from the Resource-Based View theory, the strategic resources and their influence on the performance of farms in the dairy sector. In order to reach this goal, were carried out interviews with experts of the dairy sector in Brazil and France in which the resources considered strategic by the experts were surveyed and important situations on performance evaluation in dairy properties were discussed. Then, interviews with 199 milk producers in the dairy region of Pernambuco state were conducted. From these data, the Factorial, Cluster and Multiple Regression Analyses were done. It was observed that, in the case of dairy properties, the organizational, human and physical resources, respectively, were the most important ones. This result differs from the urban industrial companies; instead of highlighting the physical resources, the technological are considered. The organizational and human resources are highlighted, which, besides being related, are important for the success of farming enterprises of milk production. The herd and cost management, animals and land productivity, training and opening to new aspects (via training and travel) were the most important indicators, showing that they make the difference between the groups with the best and the worst performance. However, an essential element, the food reserve, was negatively correlated with income, probably because the reserve is poorly planned and insufficient to account for the periods of droughts, as well as it can generate some accommodation of farmers in not investing in other food aspects which may ensure gains in all periods, including drought. Thus, the results elucidate interesting aspects, giving rise to new research proposals with different levels of depth for the understanding of those more qualitative questions and a possible investigation in different production chains.