489 research outputs found

    Técnicas de otimização na agricultura : o problema de rotação de culturas

    Get PDF
    Orientadores: Akebo Yamakami, Priscila Cristina Berbert RampazzoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Rotação de culturas é o futuro da agricultura sustentável. Diversidade na sequência de rotação melhora as propriedades físicas e químicas do solo sem demandar todas as exaustivas práticas convencionais de manejo do solo ou grandes quantidades de insumos agrícolas. Cultivar plantas de cobertura ao longo da rotação também desempenha um papel fundamental no controle de pestes e ervas daninhas, melhora a fertilidade do solo e reduz os processos erosivos. Embora esta pesquisa concentre-se na promoção de práticas agrícolas mais sustentáveis, as propriedades rurais precisam ser lucrativas e resilientes para prosperar num futuro incerto. Então, o planejamento das rotações de culturas precisa equilibrar os cenários econômicos potenciais e a conservação ambiental, sendo que as técnicas de otimização conseguem realizar este balanço naturalmente. Após considerar o fluxo de nutrientes nos campos cultiváveis e muitas vantagens do cultivo das plantas de rotação, foram propostos novos modelos para o Problema de Rotação de Culturas (PRC). A pesquisa prosseguiu com a avaliação das técnicas de otimização disponíveis para o PRC e com a proposta de novos métodos. Das abordagens clássicas, foram analizados métodos de otimização multiobjetivo, tais como o método da soma ponderada e as técnicas de escalarização. Em busca de métodos mais eficientes, os algoritmos evolutivos (AE), que são baseados na evolução biológica, tais como herança genética e mutação, são alternativas interessantes. Foram desenvolvidos algoritmos genéticos para otimização mono-objetivo e para otimização multi-objetivo. Após a realização de diversos testes utilizando dados reais do PRC, os resultados encontrados confirmam que os algoritmos propostos têm desempenho satisfatório. Esta pesquisa contribuiu para os campos da Agricultura, com os modelos propostos para o PRC, e da Otimização, com o desenvolvimento de algoritmos evolutivosAbstract: Crop rotation is the future of sustainable agriculture. Diversity in the cropping sequence can improve soil physical and chemical properties without demanding all the conventional tillage practices or large amounts of agricultural chemicals. Growing cover crops along the rotation also plays a fundamental role in controlling pests and weeds, improving soil fertility and reducing erosion. Although we have focused on bringing about more sustainable agrarian practices, farms ought to be profitable and resilient to thrive in an uncertain future. Therefore, planning crop rotations needs to balance the potential economic scenarios and the environmental conservation, which optimization techniques can manage this balance naturally. Our main effort in this research is to develop the crop rotation¿s concepts in the optimization perspective. After carefully considering the nutrient flow in agricultural fields and many advantages of seeding cover crops, we have proposed new models for the Crop Rotation Problem (CRP). Our research proceeds with evaluating optimization techniques for the CRP and proposing new alternatives. From classical methodologies, we have analyzed multiobjective optimization methods such as the weighted sum and the achievement scalarizing function technique. Looking for more efficient methods, evolutionary algorithms (EAs), which are based on biological evolution, such as genetic inheritance and mutation, are interesting alternatives. We have developed a mono-objective genetic algorithm and a multiobjective one. After running several tests using real data of the CRP, the achieved results confirm that the proposed algorithms have satisfactory performance. This research contributed to the fields of Agriculture, with the proposed models of CRP and Optimization, with the development of evolutionary algorithmsMestradoAutomaçãoMestre em Engenharia Elétrica88882.329362/2019-01CAPE

    Development of Enhanced Emission Factor Through the Identification of an Optimal Combination of Input Variables Using Artificial Neural Network

    Get PDF
    A great deal of attention is being paid worldwide to particulate matter (PM), which is now considered a significant component of air pollution. Specifically, in this thesis, road dust is a primary source of PM that is having a significant impact on human health and air quality. For example, impaired visibility due to road dust can cause more vehicle accidents. Hence, in order to efficiently develop PM control strategies, it is critical to improve the estimation of PM concentration levels generating from paved and unpaved roads. Since 1979, the U.S. Environmental Protection Agency (EPA) has developed emission factor equations to quantify the magnitude of PM for paved and unpaved roads based on multiple linear regression (MLR) models. However, the MLR models are not suitable for PM data that exhibit the characteristics of complexity and non-linearity, thereby limiting the predictive accuracy of MLR to estimate PM. The objective of this thesis is to present a method to improve the quality of the existing EPA emission factor equations for paved and unpaved roads by employing an artificial neural network (ANN). The proposed method consists of the following steps: data processing for outliers, data normalization, data classification, ANN model training to determine the weights of emission factors identified, and method validation through additional data testing. This thesis included a case study using the data retrieved from the database used by the EPA to generate their emission factor equations for paved and unpaved roads. The proposed method was evaluated by demonstrating its improved performance as shown in the coefficient of determination (R2) and the root mean square error (RMSE) values compared to the values obtained with the existing EPA emission equations. The empirical findings of the case study verified that the proposed method using the ANN model is capable of improving the quality of the EPA emission equations, resulting in higher R 2 and lower RMSE values for both paved and unpaved roads. The expected significance of this thesis is that the proposed method improves the ability to develop more reliable emission factors for predictable PM levels that can help agencies establish enhanced PM control strategies. In addition, the method may have application in other fields that require a selection process to identify an optimal combination of input variables

    Space minimization in agricultural production planning by column generation

    No full text
    We deal in this paper with an agricultural production planning problem where crops must be scheduled on land plots so as to satisfy crop demands every period of time and to minimize the overall surface of land used for cultivation. This problem can be formulated as a covering integer program with a huge number of variables. A resolution scheme based on column generation is thus proposed, where the resulting pricing problem is efficiently solved by dynamic programming. The numerical experiments show that the method is all the more so efficient and robust as the planning horizon is long and plot sizes are small

    Using improved climate forecasting in cash crop planning

    Get PDF
    Developments in meteorology over the last couple of decades have enabled significant improvements to be made in the accuracy of seasonal forecasts. This paper focuses on developing a model for cash crop planning that utilises these forecasts. It does this by determining the rate of growth of each crop as a function of heat units accumulated. This enables time to maturity to be determined and used in planning, particularly for planting new crops, removing unprofitable immature crops, and harvesting mature crops for profits. The proposed model is solved on a rolling horizon basis. To illustrate the advantage to be gained from improved seasonal forecasts the model is first applied to a problem using long-term temperature averages (climatology). Solutions to the same problem utilising improved seasonal forecasts for temperature are then obtained. This forecast proves to be a valuable input to the model and makes the second approach outperform the first consistently in our simulations

    Modelling regional cropping patterns under scenarios of climate and socio-economic change in Hungary

    Get PDF
    Impacts of socio-economic, political and climatic change on agricultural land systems are inherently uncertain. The role of regional and local-level actors is critical in developing effective policy responses that accommodate such uncertainty in a flexible and informed way across governance levels. This study identified potential regional challenges in arable land use systems, which may arise from climate and socio-economic change for two counties in western Hungary: Veszprém and Tolna. An empirically-grounded, agent-based model was developed from an extensive farmer household survey about local land use practices. The model was used to project future patterns of arable land use under four localised, stakeholder-driven scenarios of plausible future socio-economic and climate change. The results show strong differences in farmers' behaviour and current agricultural land use patterns between the two regions, highlighting the need to implement focused policy at the regional level. For instance, policy that encourages local food security may need to support improvements in the capacity of farmers to adapt to physical constraints in Veszprém and farmer access to social capital and environmental awareness in Tolna. It is further suggested that the two regions will experience different challenges to adaptation under possible future conditions (up to 2100). For example, Veszprém was projected to have increased fallow land under a scenario with high inequality, ineffective institutions and higher-end climate change, implying risks of land abandonment. By contrast, Tolna was projected to have a considerable decline in major cereals under a scenario assuming a de-globalising future with moderate climate change, inferring challenges to local food self-sufficiency. The study provides insight into how socio-economic and physical factors influence the selection of crop rotation plans by farmers in western Hungary and how farmer behaviour may affect future risks to agricultural land systems under environmental change

    Dynamics of Crop Evapotranspiration of Four Major Crops on a Large Commercial Farm: Case of the Navajo Agricultural Products Industry, New Mexico, USA

    Get PDF
    Crop evapotranspiration (ETa) is the main source of water loss in farms and watersheds, and with its effects felt at a regional scale, it calls for irrigation professionals and water resource managers to accurately assess water requirements to meet crop water use. On a multi-crop commercial farm, different factors affect cropland allocation, among which crop evapotranspiration is one of the most important factors regarding the seasonally or annually available water resources for irrigation in combination with the in-season effective precipitation. The objective of the present study was to estimate crop evapotranspiration for four major crops grown on the Navajo Agricultural Products Industry (NAPI) farm for the 2016–2010 period to help crop management in crop plant allocation based on the different objectives of the NAPI. The monthly and seasonal satellite-based ETa of maize, potatoes, dry beans, and alfalfa were retrieved and compared using the analysis of variance and the least significant difference (LSD) at 5% of significance. Our results showed the highly significant effects of year, months, and crops. The year 2020 obtained the highest crop ETa, and July had the most evapotranspiration demand, followed by August, June, September, and May, and the pool of April, March, February, January, December, and November registered the lowest crop ETa. Maize monthly ETa varied from 17.5 to 201.7 mm with an average seasonal ETa of 703.8 mm. The monthly ETa of potatoes varied from 9.8 to 207.5 mm, and their seasonal ETa averaged 600.9 mm. The dry bean monthly ETa varied from 10.4 to 178.4 mm, and the seasonal ETa averaged 506.2 mm. The alfalfa annual ETa was the highest at 1015.4 mm, as it is a perennial crop. The alfalfa monthly ETa varied from 8.2 to 202.1 mm. The highest monthly crop ETa was obtained in July for all four crops. The results of this study are very critical for cropland allocation and irrigation management under limited available water across a large commercial farm with multiple crops and objectives
    corecore