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This systematic review critically analyzes the literature on the study of energy-use patterns in agricultural crop systems in Iran. We examine the relevant methodologies and research trends from 2008 to 2019, a particularly active and productive period. Initially, we find researchers using energy audits and regression modeling to estimate energy-use patterns. Then economic and environmental-emissions audits are more commonly incorporated into analyses. Finally, the application of different Artificial Intelligence (AI) methods are observed in papers. The main focus of this study is on energy-use patterns, economic modelling, and environmental emissions. We then address critical issues, including sample size, energy equivalents, and additional practical energy-saving recommendations which can be considered by researchers in future analyses. The application of AI in the analysis of agricultural systems, and how it can be used to achieve sustainable agriculture, is discussed with the aim of providing guidelines for researchers interested in energy flow in agricultural systems, especially in Iran. To achieve sustainable agriculture systems, we recommend more attention be given toward considering the impact of social factors in addition to energy, environmental and economic factors. Finally, this review should guide other researchers in choosing appropriate crop types and regions in need study to avoid repetitive studies.

Potatoes are the single most important agricultural commodity in Hamadan province of Iran, where 25,503 ha of this crop were planted in 2008 under irrigated conditions. This paper compares results of the application of two different approaches, parametric model (PM) and artificial neural networks (ANNs), for assessing economical productivity (EP), total costs of production (TCP) and benefit to cost ratio (BC) of potato crop. In this comparison, Cobb-Douglas function for PM and multilayer feedforward for implementing ANN models have been used. The ANN, having 8-6-12-1 topology with R 2 = 0.89, resulted in the best-suited model for estimating EP. Similarly, optimal topologies for TCP and BC were 8-13-15-1 (R 2 = 0.97) and 8-15-13-1 (R 2 = 0.94), respectively. In validating the PM and ANN models, mean absolute percentage error (MAPE) was used as performance indicator. The ANN approach allowed to reduce the MAPE from -184% for PM to less than 7% with a +30% to -95% variability range. Since ANN outperformed PM model, it should be preferred for estimating economical indices.Additional key words: artificial neural networks; benefit to cost ratio; Cobb-Douglas production function; economical productivity; estimation error; Solanum tuberosum; total cost of production. ResumenEstudio comparativo entre enfoques paramétricos y de redes neuronales artificiales para la evaluación económica de la producción de patata en Irán La patata es el producto agrícola más importante en la provincia de Hamadan (Irán), donde se plantaron 25.503 ha de este cultivo en 2008 bajo condiciones de riego. Este trabajo compara los resultados de aplicar dos enfoques diferentes, un modelo paramétrico (PM) y redes neuronales artificiales (ANN), para evaluar la productividad económica (EP), los costos totales de producción (TCP) y el coeficiente beneficio/costo (BC) del cultivo de la patata. En esta comparación se han utilizado la función Cobb-Douglas como PM y el proceso "feedforward" multicapa para implementar modelos de ANN. Las ANN, con una topología 8-6-12-1 con R 2 = 0,89, resultaron ser el modelo más adecuado para estimar la EP. Del mismo modo, las topologías óptimas para TCP y BC fueron 8-13-15-1 (R 2 = 0,97) y 8-15-13-1 (R 2 = 0,94), respectivamente. Para validar los modelos PM y ANN, se utilizó como indicador de desempeño el error porcentual medio absoluto (MAPE). El enfoque de ANN permitió reducir el MAPE desde -184% para PM a menos del 7% con un rango de variabilidad de +30% a-95%. Dado que ANN fue mejor que el modelo PM, debe ser preferido para la estimación de los índices económicos.Palabras clave adicionales: coeficiente beneficio/costo; costo total de producción; error de estimación; función de producción Cobb-Douglas; productividad económica; redes neuronales artificiales; Solanum tuberosum.

The main goal of this paper is to construct objectives and attributes of the service centers location problem. This problem is often found within production management as a location problem, when designing for example supply chains or manufacturing layouts. The main contribution of this paper is constructing related location indicators. Since there was no similar literature in this discipline, so a Delphi survey applied to quantify expert's attitudes about location problem of Agricultural Service Center (ASC) and construct location selection attributes and also ASC objectives. A TOPSIS survey is done to rank extracted attributes to import in fuzzy analytical hierarchy process (AHP) study. Then a fuzzy AHP technique is applied to compute the weight of these most important attributes using four objectives, which obtained by Delphi technique too. In the simplest form with this assumption that all objectives have the same priority, the results illustrated that the service, cost, speed, and ASC profit are first to last important objectives, respectively. At the end of this paper, the multi-choice goal programming method recommended to use when the priorities of objectives are not the same. Finally, the weight of ASC location attributes computed to consider in ASC location problem.