The global increasing food demand can be met by efficient energy utilization in mechanized agricultural productions. In this study, input–output energy flow along with CO2 emissions for different wheat production cases (C-I to C-V) were investigated to identify the one that is most energy-efficient and environment-friendly case. Data and information about input and output sources were collected from farmers through questionnaires and face-to-face interviews. Input and output sources were converted into energy units by energy equivalents while CO2 emissions were calculated by emission equivalents. Data envelopment analysis (DEA) was conducted to compare technical efficiencies of the developed cases for optimization of inputs in inefficient cases. Results revealed that case C-Ⅴ (higher inputs, larger fields, the tendency of higher fertilizer application and tillage operations) has the highest energy inputs and outputs than the rest of the cases. Moreover, it possesses the lowest energy use efficiency and energy productivity. The highest CO2 emissions (1548 kg-CO2/ha) referred to C-Ⅴ while lowest emissions per ton of grain yield were determined in C-Ⅳ (higher electricity water pumping, moderate energy input). The grain yield increases directly with input energy in most of the cases, but it does not guarantee the highest values for energy indices. C-Ⅲ (moderate irrigations, educated farmers, various fertilizer applications) was found as an optimum case because of higher energy indices like energy use efficiency of 4.4 and energy productivity of 153.94 kg/GJ. Optimum input and better management practices may enhance energy proficiency and limit the traditionally uncontrolled CO2 emissions from wheat production. Therefore, the agricultural practices performed in C-Ⅲ are recommended for efficient cultivation of wheat in the studied area.

An organization's environmental performance is affected by its suppliers' environmental performance, and selecting green suppliers is a strategic decision in order to be more competitive in today's global market. By developing green movement across the globe, organizations are under pressure to reduce the emissions across their supply chain. Formerly the food production systems was oriented and optimized to satisfy economic demands and the nutritional needs of a rapidly growing world population. The food production industry requires large inputs of resources and causes several negative environmental effects. In recent years, environment factors rapidly emerging as an important issue for decision makers in food industries. This study identifies best supplier in holistic perspective for edible oil production, and proposes a hybrid model using Delphi method and Green Data Envelopment Analysis (GDEA). Delphi method identifies the main criteria influenced in supplier selection process based on opinion of company purchase experts. GDEA evaluates the overall performances of suppliers and choose green supplier. Proposed hybrid model applied to a well-known company who produce edible oil (palm, soybean and olive oil) to evaluate green suppliers (among 13 main potential suppliers). Delphi questionnaire included 17 factors which were from financial, services, qualitative and environmental factors. The factors with the highest Delphi score (raw material price, quality, delivery and carbon footprint) entered in DEA model and high efficiency suppliers selected. Results showed that the most efficient raw oil suppliers of company are: S4 among soybean oil suppliers, P1 among palm oil suppliers and O3 among olive oil suppliers. Also palm oil not only has fewer price and carbon footprint but also the highest mean efficiency.

This study examined energy, greenhouse gas emission and ecological footprint analysis (EFA) of chickpea and lentil cultivation with different mechanization production systems. In lentil production, except for tillage operations, other operations are performed manually and the remaining straw is burned in the field; while in chickpea production, most of the agricultural operations are mechanized and residues are collected, baled and transferred to the warehouse for animal feed. In this paper, for the first time, some of the sustainability indicators are investigated and compared in two different legume production systems. Energy productivity and net energy for chickpea and lentil production were calculated at 0.036, 0.161 and 2373 and 5900 MJ per hectare, respectively. The CO₂emission and ecological carbon footprint were 173 kg CO_2−eqand 0.15 global hectare for lentil and 484 and 0.87 for chickpea production. Totally, due to excessive consumption of diesel fuel and lack of proper management, the social cost of emission from straw baling in chickpea production (27.65 dollars per hectare) was higher than burning straw in lentil production (8.77). Multi-objective genetic algorithm results showed the potential of minimizing diesel fuel and fertilizer consumption and no chemical for chickpea production. Overall audition results of two different production systems revealed that traditional lentil production is more sustainable. Therefore, implementations of modern agricultural practices alone are not enough to achieve sustainability in agricultural production systems.

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.

Mechanization in the agriculture sector requires well-informed technology selection decisions and a balance of financial and non-financial considerations. This study integrates sustainability and technical considerations into farm machinery selection decisions. Using an industry case study where technology selection for the mechanization process is imperative a multi-method decision-making framework is developed to select the best rice harvest machinery. For this purpose, the Delphi method is used to systematically identify the most relevant decision criteria; the Analytic Hierarchy Process (AHP) determines the criteria's importance, and the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is applied to rank the alternatives. Given the wide variety of available harvesting technologies in the competitive market, it is shown that the sustainability criteria are receiving recent recognition among agriculture practitioners. The findings provide insights for the producers to help improve production, and exportation of the local brands in accordance with the international market needs.