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In conditions where don't have enough water for irrigation, intercropping of forage plants is one of the ways to achieve sustainable agriculture. Biochemical and physical properties of soil tillage system can affect the yield of plants in intercropping. Therefore, with the aim of investigating the forage yield and competitive indices of intercropping, this experiment was performed in the cropping seasons of 2020-2021. Treatments included conventional, minimum, and no tillage system. Intercropping patterns also included monoculture of forage sorghum and maize, alternate row cultivation maize and forage sorghum and alternate cultivation on planting rows. The results showed that the highest plant height (295 cm) and leaf area (6.88) was obtained in intercropping and conventional tillage. The highest forage yield was also observed in sorghum monoculture. However, the land equivalent ratio of mix intercropping with 1.17 showed the superiority of intercropping over monoculture. Evaluation of competitive indicators such as dominance index, relative crowding coefficient, and competitive ratio showed that forage maize is the predominant plant in intercropping conditions. The system productivity index with 8.11 showed that intermediate cultivation on planting rows was superior to other treatments in conventional tillage conditions. The results showed that in conventional tillage and intercropping conditions, acceptable yield was obtained; this advantage can increase water efficiency and use of sorghum in intercropping. [ABSTRACT FROM AUTHOR]

The nonparametric method of data envelopment analysis (DEA) was used to investigate the energy efficiency and CO2 emission of barley farm in Hamedan province of Iran. The method was used based on eight energy inputs including human labor, machinery, diesel fuel, fertilizers, farmyard manure, biocide, electricity and seed energy and single output of barley yield and technical, pure technical, scale and cross efficiencies were calculated using CCR and BCC models. The results showed that the average values of technical, pure technical and scale efficiency scores of farmers were 0.788, 0.941 and 0.833, respectively. Also, energy saving target ratio for barley production was calculated as 11.45%, indicating that by following the recommendations of this study, about 2,865 MJ ha-1 of total input energy could be saved with the same constant level of barley yield. Moreover the contribution of chemical fertilizer input from total saving energy was 34.88% which was the highest share followed by diesel fuel (25.88%) and electricity (20.89%) energy inputs. On one hand, optimization of energy use improved the energy use efficiency, energy productivity and net energy by 12.94%, 15.55% and 6.16%, respectively. On the other hand, total greenhouse gases (GHG) emission was 885.56 kg CO2eq ha-1, which indicated that, the total CO2 emissions can be reduced by 11.06%. [ABSTRACT FROM AUTHOR]

The objective of this study is to examine input--output energy and CO2 emission of almond production in Shahrekord region, Iran. This article presents a comprehensive picture of the current status of energy consumption and some energy indices like energy use efficiency, energy productivity, specific energy and net energy gain. Sensitivity analysis of energy was carried out using the marginal physical productivity (MPP) technique. For this propose data were collected from 29almond farms using a face to face questionnaire. The results revealed that total energy input for almond production was found to be 106.61GJ/ha where the electricity was the major energy consumer (59.58%). The direct energy shared about (50.98%) whereas the indirect energy did (49.02%). Energy use efficiency, energy productivity, and net energy were 0.37, 0.016 kg/MJ, and -67350.16MJ/ha, respectively. The regression results revealed that the contribution of energy inputs on crop yield (except for farmyard manure and water energies) was insignificant. Water energy was the most significant input (0.674) which affects the output level. The results also showed that the impacts of direct, indirect and renewable energies on yield are significant. The GHG emissions were indicated a high CO2 output in diesel fuel consumption. [ABSTRACT FROM AUTHOR]

In this study, various Artificial Neural Networks (ANNs) were developed to estimate the output energy for corn silage production in Esfahan province, Iran. For this purpose, the data on 65 corn silage production farms in the Esfahan province, were collected and analyzed. The results indicated that total energy input for corn silage production was about 83126 MJ ha-1; machinery (with 38.8 %) and chemical fertilizer (with 24.5 %) were amongst the highest energy inputs for corn silage production. The developed ANN was a multilayer perceptron (MLP) with eight neurons in the input layer (human power, machinery, diesel fuel, chemical fertilizer, water for irrigation, seed, farm manure and pesticides), one, two, three, four and five hidden layer(s) of various numbers of neurons and one neuron (output energy) in the output layer. The results of ANNs analyze showed that the (8-5-5-1)-MLP, namely, a network having five neurons in the first and second hidden layer was the best-suited model estimating the corn silage output energy. For this topology, MAB, MAE, RMSE and R² were 0.109, 0.001, 0.0464 and 98%, respectively. The sensitivity analysis of input parameters on output showed that diesel fuel and seeds had the highest and lowest sensitivity on output energy with 0.0984 and 0.0386, respectively. The ANN approach appears to be a suitable method for modeling output energy, fuel consumption, CO2 emission, yield, and energy consumption based on social and technical parameters. This method would open new doors to advances in agriculture and modeling. [ABSTRACT FROM AUTHOR]

This paper studies the energy balance between the input and the output energies per unit area for greenhouse tomato production. For this purpose, the data on 30 tomato production greenhouses in Isfahan province, Iran were collected and analyzed. The results indicated that a total specific input energy of 116,768.4 MJ ha-1 was consumed for tomato production. Diesel fuel (with 40%) and chemical fertilizers and manure (with 30%) were amongst the highest input energies for tomato production. The energy productivity was estimated to be 1.16 kg MJ-1. The ratio of output energy to input energy was approximately 0.92. 19% and 81% of total energy input was in renewable and non-renewable forms, respectively. The regression results revealed that the contribution of input energies on crop yield for human power, machinery, pesticides and electricity inputs was significant. The human power energy had the highest impact (1.45) among the other inputs in greenhouse tomato production. The marginal physical productivity of diesel fuel, seed and total chemical fertilizer with manure was negative. It can be because of applying the inputs more than required or improperly applying. The highest shares of expenses were found to be 34% and 21% for human power and total diesel fuel and machinery, respectively. Cost analysis revealed that total cost of production for 1 ha greenhouse tomato production was around US$34939. Accordingly, the benefit-cost ratio was estimated as 2.74. Results of greenhouse gas emission indicated that tomato production is mostly depended on diesel fuel sources. Diesel fuel had the highest share (2,719.98 kg CO2eq.ha-1) followed by electricity (729.6 kg CO2eq.ha-1) and nitrogen fertilizer (409.5 kg CO2eq.ha-1). [ABSTRACT FROM AUTHOR]