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In this paper, the Northeast China including the 3 provinces of Heilongjiang, Liaoning and Jilin was regarded as study area where maize, rice and soybean were main food crops. Simulation of spatial competition of the 3 crops under 4 future combined scenarios of IPCC CO2 emission and socio-economic development (urbanization rate and governmental purchasing price for food crops) was conducted for the year of 2030 by application of the FCS (farmer crop selection) model () and EPIC (environmental policy integrated climate) model. The 4 final scenarios were A2C1D1, A2C1D2, B2C2D1 and B2C2D2. A2C1D1 represented a combination scenario by IPCC A2 option, farmers having no income when going to city to do temporary work and prices for food crop purchased by government doubling. A2C1D2 meant a combination scenario by IPCC A2 option, farmers having no income when going to city to do temporary work and prices for food crop purchased by government quadrupling. B2C2D1 was a combination scenario by IPCC B2 option, farmers having income when going to city to do temporary work and prices for food crop purchased by government doubling. B2C2D2 denoted a combination scenario by IPCC B2 option, farmers having income when going to city to do temporary work and prices for food crop purchased by government quadrupling. In comparison with simulation results in 2009, the conclusions were: 1) For the first scenario of A2C1D1, percentage of maize acreage to total arable land decreased to 77.36% from 82.3%, and meanwhile there was an increase from 7.7% and 10% to 8.93% and 13.71% for soybean and paddy rice, respectively; spatially, maize acreage declined in the fringe areas between the Lesser Xing'an Mountain and the Haerbin Plain, but in the Liaohe Plain acreages of soybean and rice went up respectively. 2) For the second scenario of A2C1D2, percentage of maize acreage to total arable land decreased to 75.56% and meanwhile that of soybean and paddy rice increased to 9.52% and 14.92%, respectively; spatially the change was very similar to that of the scenario of A2C1D1. 3) For the third scenario of B2C2D1, due to a little bit higher urbanization rate than present and present purchasing price by government doubling, percentage of maize acreage to total arable land increased to 84.16%, and meanwhile soybean and paddy rice increased to 9.52% and 14.92%, respectively; maize acreage rose in the fringe areas between the Lesser Xing'an Mountain and the Haerbin Plain, while in the Liaohe Plain acreages of soybean and rice went down respectively, which was contrary to that of the A2C1D1scenario. 4) For the fourth scenario of IPCC B2C2D2, due to a little bit higher urbanization rate than present and present purchasing price by government quadrupling, percentage of maize acreage to total food crops acreage decreased to 80.06%, and meanwhile soybean and paddy rice increased to 9.01% and 10.93%, respectively. There was not obvious change in space. Since 2030 is not far from present, there are not very dramatic changes of food crops in space for all the 4 scenarios. We also don't consider extreme events' effect on spatial competition of food crops because cognition and judge of farmers will not be influenced by that. In terms of present practical situation in China, we deduce the scenarios of B2C2D1 and B2C2D2 maybe more accord with reality. Moreover, B2C2D2 will be more beneficial to optimizing agricultural layout. [ABSTRACT FROM AUTHOR]

Energy is a major component in enhancing agricultural productivity for the rapidly growing world population. From that fact, a comprehensive analysis of energy inputs and outputs is required to conserve energy for future generations without threatening the food supply. Therefore, this study was performed in wheat production across important cropping zones of Punjab, Pakistan. In this study, the energy use pattern of wheat production was analyzed, and the degrees of technical efficiency of Decision Making Units (DMUs) were examined using Data Envelopment Analysis (DEA). Based on the results of the DEA analysis, the inefficient energy inputs were identified and further explored with the core objective of a significant reduction of excess valuable resources. Data were collected from conducting a face-to-face questionnaire of 200 farmers. The farms for sample were chosen randomly by a stratified normal approach. The results disclosed that the input energy of 34,430.98 MJ ha−1 was used up for wheat production with an output energy of 48,267.05 MJ ha−1. Energy use efficiency, specific energy, energy productivity, and net energy gain in wheat production were calculated as 1.4 MJ kg−1, 9.27 MJ kg−1, 0.10 MJ kg−1 and 13,836.07 MJ kg−1, respectively. The average technical, pure technical, and scale efficiency of DMUs were 0.668, 0.776, and 0.828, respectively, and 0.74% of consulted DMUs were functioning at decreasing returns to scale. Additionally, the significant energy consumption belongs to fertilizer, and diesel fuel, which contribute 65% of the total energy input. If these inputs are applied and managed in line with ours optimize value (29,388.5 MJ ha−1) could save 14.65% resources, which will eventually add the equal quantity in wheat-yield. The total Greenhouse Gas (GHG) emissions were calculated to be 866.43 kg CO2-eq ha−1. In conclusion, the results of the present study suggest that there is sensible capacity for enhancing the energy efficiency of wheat production in Pakistan by accompanying the recommendations for economical energy management, sustainable and efficient use of energy is extremely encouraged. [ABSTRACT FROM AUTHOR]