Investigation of agricultural mechanization status in corn production of Iran

For determination and assessing the effect of agricultural mechanization in irrigated corn of Iran, two indicators have been used: cultivated area (ha) and yield (kg/ha).  Several regression models have been built, using Mechanization Level (ML) and Mechanization Ownership (MO) of all agricultural operations, as input, and cultivated area and yield as output, separately.  The survey was carried out by means of data obtained from Agricultural Ministry of Iran in the period of 2001-2008.  The results revealed that mechanization ownership of planting and harvesting have a significant effect on cultivated area of corn in Iran with 95% and 99% confidence, respectively.  Based on obtained results, agricultural mechanization has an important role in improvement of corn production in Iran.  Levels of mechanization in each agricultural operation have different effects on yield improvement.  Policy makers can consider important factors between mechanization inputs to improve the corn production of Iran.   Keywords: yield, regression model, mechanizatio

Investigation of input and output energy for wheat production : a comprehensive study for Tehsil Mailsi (Pakistan)

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.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.King Saud University, Riyadh, Saudi Arabi

Do the cattle farms of Iran produce economically efficient or not

Abstract: The technical efficiencies of a sample of extensive Iranian cattle farms collected by Ministry of Agriculture were analyzed using a nonparametric Data Envelopment Analysis (DEA) model. Mean pure technical efficiency in a DEA model in which all variables were normalized with the number of cows was 0.79, indicating that there is ample potential for more efficient input utilization in cattle farming. The normalized measurement of variables captures the fact that most scale-inefficient farms (52%) are operating under decreasing returns to scale. This implies that even smaller farms have expanded to a size larger than is optimal relative to the number of cows in their herds. A comparison conducted in all region of Iran, Yazd and Khuzestan provinces were the most efficient and inefficient region of Iran in cattle farming, respectively. In an attempt to explain variation in efficiency scores, the study focuses on certain managerial practices often not accounted for. Results showed that dairy cattle are more efficient than beef cattle; also ANOVA test revealed that the choices of Holstein breed and herd with more than 200 cows have a positive significant impact on the efficiency level of cattle farms

Management and Production Engineering Review GREEN SUPPLIER SELECTION IN EDIBLE OIL PRODUCTION BY A HYBRID MODEL USING DELPHI METHOD AND GREEN DATA ENVELOPMENT ANALYSIS (GDEA)

Abstract Accepted: 22 September 2014 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

Meta-Analysis on Energy-Use Patterns of Cropping Systems in Iran

We present a meta-analysis of energy-consumption and environmental-emissions patterns in Iranian cropping systems using data collected from articles published between 2008 and 2018 for 21 different crops. The results show that the crops consuming the most energy per hectare are tomato, sugarcane, cucumber and alfalfa, while sunflower consumed the least. The average total energy input for all crops in Iran during the study period was 48,029 MJ ha−1. Our analysis revealed that potato has the highest potential to reduce energy consumption and that electricity and fertilizer inputs have the most potential for energy savings in cropping systems. Not all studies reviewed addressed the factors that create energy consumption patterns and environmental emissions. Therefore, eight indicators were modeled in this meta-analysis, which include Total Energy Input, Energy Productivity, Energy Use Efficiency, Net Energy, Greenhouse Gas Emissions, Technical Efficiency, Pure Technical Efficiency and Scale Efficiency. The effects of region (which was analyzed in terms of climate), year and crop or product type on these eight indicators were modeled using meta-regression and the nonparametric Kruskal–Wallis test. To create a comprehensive picture and roadmap for future research, the process of the agricultural-systems analysis cycle is discussed. This review and meta-analysis can be used as a guide to provide useful information to researchers working on the energy dynamics of agricultural systems, especially in Iran, and in making their choices of crop types and regions in need of study

Criteria definition and approaches in green supplier selection – a case study for raw material and packaging of food industry

This paper formulates an integrated framework for deciding about the green supplier selection criteria in food supply chain and also proposes different methods that account for single and multiple sourcing of supplier selection. Green supplier selection relies on green criteria, so determination of suitable set of criteria will affect decision-maker results directly. In this research, an operational model including combination of general and environmental criteria is introduced for green supplier selection criteria in raw material and packaging of food industry. This model reviewed a literature on general and environmental criteria that help us to know and make a set of common green criteria. Afterward, weighting criteria and collecting of sub-criteria are done by an expert team using the Analytic Hierarchy Process and Delphi method. The expert team tried to propose important and practical sub-criteria which are well fitted to food industry. Finally, in the section of supplier selection, two kinds of qualitative and quantitative data are discussed when single and multiple sourcing are required, respectively. Fuzzy Grey Relational Analysis is proposed to ranking suppliers in presence of qualitative and imprecise data. Also linear programming is used to present a model which can select the best suppliers and allocate the orders among them optimally

Green supplier selection in edible oil production by a hybrid model using Delphi method and Green Data Envelopment Analysis (GDEA)

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