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Investigating of energy flow and greenhouse gas emissions is one of the methods to compare energy efficiency, production efficiency and environmental hazards of agroecosystems. This study investigates the patterns of energy consumption, greenhouse gas emissions and global warming potential of dryland chickpea production in rural areas of Qoshkhaneh region of Shirvan city. Data for this experiment were collected through a face-to-face questionnaire. Farms were selected by random sampling. In this study, energy inputs included: labor, machinery, diesel, chemical fertilizers, chemical pesticides and seeds, and the study output was grain yield and shoot yield (straw). The results showed that the average labor used per hectare of dryland chickpea farms in rural areas of North Khorasan was 21 people, nitrogen fertilizer 50 and phosphorus and potassium fertilizers 70 kg/ha, as well as diesel fuel consumption of 35 liters and the use of machinery 8 h/ha. Evaluation of energy consumption showed that among the inputs, nitrogen fertilizer with 3307 MJ/ha accounted for 40.06% of the total energy input and diesel fuel with 1970.85 MJ/ha, Consumed 23.23% of the total input energy. labor and seeds also had the lowest share of energy inputs with 0.49 and 1.87 percent, respectively. Total energy consumption efficiency (chickpea + straw) was 1.91, energy productivity was 0.08 kg/MJ and specific energy was 12.32 MJ/kg. In terms of greenhouse gas emissions was 399.20 kg/ha, the share of CO2 was 398.60, the share of N2O and CH4 was 0.02 and 0.57 kg/ha respectively. Global warming production potential per hectare of dryland chickpeas in North Khorasan Province was 419.13 kg CO2 equivalence. In general, the results showed that dryland chickpea fields have low energy consumption due to dependence on rainwater and lack of electricity consumption, as well as low consumption of chemical fertilizers. The most important strategies to reduce energy consumption in the production of dryland chickpeas is the use of appropriate crop rotation to reduce the use of herbicides and chemical fertilizers and also to observe the rules of farm traffic to reduce the movement of agricultural implements on farms. [ABSTRACT FROM AUTHOR]

Oilseed production can be affected in arid and semi-arid regions that are exposed to water shortages. Nevertheless, cultivation of drought-resistant crops such as Indian mustard is a suitable way to gain acceptable yields. Effects of different levels of irrigation and sulfur (S) fertilizer on the quantity and quality parameters of mustard (Brassica juncea) were assessed in a trial at the Indian Agricultural Research Institute, New Delhi, during 2007–2008 and 2008–2009 growing seasons. The experiment was carried out in a split plot as randomized complete block design with three replications. The experiment treatments consisted of three irrigation levels [no irrigation, one irrigation at 45 days after sowing (DAS), and two irrigations at 45 DAS and 90 DAS] in main plots and four sulfur levels (S; 0, 15, 30, and 45 kg S ha−1) in subplots. The results showed that in the two successive years of the experiment, the number of siliquae/plant, length of siliqua, number of seeds/siliqua, and 1000-seed weight increased significantly with increasing the level of irrigation, which resulted in greater seed yield than no irrigation. Also, mustard plants irrigated two times showed the greatest values of nitrogen (N), protein, and oil content of the seeds over no irrigation in both years of this study. The oil yield and S content in the seed of mustard increased significantly with increasing the level of irrigation in both years. Number of siliquae/plant, siliqua length, number of seed/siliqua, and 1000-seed weight increased significantly with increasing the rates of S up to 45 kg S ha−1in both years of experimentation; however, in 2008–2009 the difference between application of 30 and 45 kg ha−1S was not statically significant. Also, the seed yield, N, protein, oil, and S contents in seeds of mustard increased significantly with increase in the level of applied S in both years. [ABSTRACT FROM AUTHOR]

The predicted models for crops yield are developing rapidly by the creation of new statistical techniques and neural networks. For this purpose, a research was carried out in the Torbat-e-Heydarieh region for predicting yield and water use efficiency of saffron by using an artificial neural network model. The model was calibrated and validated by using crop yield and climate parameters data during 2009-2010. The models were evaluated by using indices of correlation coefficient (R2), root mean squares error normalized (RMSEn), and mean squares error (MSE). The results showed that the suggested neural network (model No. 9) with having 2 hidden layers, 8 neurons, and R2= 0.97 (for saffron yield); and 1 hidden layer, 7 neurons, and R2= 0.90 (for water use efficiency) had a high accommodation with these two factors. Also, according to the indices RMSEn and MSE, model No. 9 simulated the yield and WUE of saffron with a high accuracy, such that RMSEn and MSE for yield in this model obtained were 2.78% and 0.0041, respectively; and for WUE they were calculated to be 5.41% and 0.0073, respectively. Also, the results of sensitivity analysis indicated that irrigation is the most important parameter for predicting yield and WUE, and after that is precipitation and solar radiation. Generally, use of the suggested neural network in this research can improve saffron cultivation in the Torbat-e-Heydarieh region. [ABSTRACT FROM AUTHOR]