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In this paper, we considered an agricultural production planning problem with multiple objectives and having fuzziness in input information parameters. This fuzziness in parameters is handled by the concept of fuzzy set theory by considering input information parameters as trapezoidal fuzzy numbers. α– Cut and ranking function approach are used to get the crisp value of the fuzzy parameters. An Interactive Fuzzy Goal Programming (IFGP) approach has been applied with linear, exponential function and hyperbolic membership function respectively. In IFGP approach we focus on maximizing the minimum membership function to determine the optimum land allocation for the vegetable crop under fuzzy environment. This paper presents an interactive goal programming model to help agricultural economist for allocating different kinds of available resources for agricultural production planning in an optimum manner. An agricultural numerical example is solved to illustrate the solution procedure. [ABSTRACT FROM AUTHOR]

This research designs and develops a software innovation for Pattavia pineapple cultivation and productivity distribution planning to increase income for farmers. This research formulated and introduced an innovative machine learning (ML) model, the new model is called a new intelligent particle swarm optimization algorithm with extreme learning machine (NIPSO-ELM), to forecast Pattavia pineapple productivity with a notable degree of precision and dependability. In this work, the artificial neural network (ANN) and the standard ELM was built, and assessed for its ability to forecast the productivity of Pattavia pineapples. The findings indicate that the ELM neural network is an innovative model characterized by its straightforward architecture and exceptional performance. Moreover, the utilization of particle swarm optimization (PSO), ant colony optimization (ACO) and the NIPSO algorithms significantly enhanced ELM performance when forecasting the productivity of Pattavia pineapples. The NIPSO-ELM model emerged as the most optimal ML model for accurately, reliably and stably forecasting the productivity of Pattavia pineapples in practical scenarios. The most optimal NIPSO-ELM models for the Loei provinces, Thailand exhibit the following performance metrics: RMSE = 304.36389, MAE = 243.29531, MAPE = 0.03753 and MASE = 0.93157. The most optimal NIPSO-ELM models for the Nong Khai provinces, Thailand exhibit: RMSE = 304.57352, MAE = 244.67834, MAPE = 0.03756 and MASE = 0.93296, respectively. [ABSTRACT FROM AUTHOR]

In this paper a Multiple Criteria Mathematical Programming model has been developed integrating vulnerability maps; its purpose is to optimize and facilitate the decision making process relating to the problems of water management, land use and environmental protection. The model is used to support irrigation water use and eco-friendly decision processes in agricultural production planning. It has been developed in the context of the research project entitled EU Water (transnational integrated management of water resources in agriculture for European water emergency control). For accessing the vulnerability of agricultural land to water and nitrogen losses, a set of indices were utilised. It was implemented in the Sarigkiol basin in the north-eastern part of the Kozani prefecture in Northern Greece, combining different criteria to a utility function under a set of constraints concerning different categories of labour, land, available capital, etc. On the same time the model is taking into account the vulnerability maps which have been developed for the area. The aim of the study was to achieve optimum crop plans in the aforementioned area and it was further used to simulate different scenarios and policies, due to changes on different social, economic and environmental parameters (e.g. different levels of chemicals or water consumption per crop). The decision maker can get alternative production plans and agricultural land uses as well as the social, economic and environmental impact of different policies. Results show that the model can be considered as an important planning tool enabling the regional authorities to design optimal spatial development policies and protect groundwater from the excessive fertilizer and other pollution agents use. [ABSTRACT FROM AUTHOR]

Abstract: This paper explains the process followed to generate the model used as a decision support tool for agricultural production planning in the most agrarian areas in Galicia (NW Spain). The model comprises three procedures that use 44 social, environmental and economic indicators developed using both monographic information and field data. The indicators allow for the selection of the most suitable crops and land uses for each agrarian area, and allow decision makers to define key factors for performing a diagnostic analysis and proposing relevant actions regarding agricultural production planning. The potential of the tool for defining a hierarchy of potential crops and land uses according to their degree of suitability has been illustrated by applying the model to one of the 88 Ecological and Economic Units studied (EEUs). The proposed model can be a useful tool for production managers, agricultural associations, governmental agencies and even non-governmental organizations in underdeveloped countries. The most innovative aspects of the model are the feasibility of grouping indicators to perform a diagnostic analysis of different scenarios, and that it can be adapted to any other region in the world, by adjusting the objectives of agricultural production planning and the corresponding indicators. [Copyright &y& Elsevier]

Abstract: Since 2002, the Galician Administration (NW Spain) has been working in the development of a decision support system for Agricultural Production Planning. To this end, an initial decision tool, Gestop®, was developed. However, the tool had a number of limitations for practical use because of the lack of differentiated data about the real situation of farms. With a view to improving the initial tool, a combinatorial method (CM) was developed. Such a combinatorial method used official census data to classify farms based on the different production processes, sizes and geographical locations of farms, among other factors. The combinatorial method proved very useful as a source of differentiated data for the decision support system. Yet, validating the results using robust statistical methods was considered essential in order to guarantee the suitability of the model for use. This paper uses discriminant analysis (DA) techniques to validate the types of dairy farms obtained from the combinatorial method for Galicia. Results clearly validate the combinatorial method and its use as a data source for decision support systems in Agricultural Production Planning. [Copyright &y& Elsevier]