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Agriculture is required to produce more food while reduce water consumption at the same time. To improve the irrigation water use efficiency in agricultural production, this study proposed a fuzzy multiobjective optimal modeling approach based on Moderate-resolution Imaging Spectroradiometer (MODIS) global evapotranspiration products for optimizing spatial water footprint of crop planting. This study attempts to accurately estimate the spatial crop water footprint based on remote sensing information, and effectively plan crop planting structure considering conflicting targets, spatial water footprint, and limited irrigation water resources under fuzzy uncertainty. To test its feasibility, the proposed approach is employed to a typical semi-arid region in Northwest China. Three performance evaluation indicators, including synthetic degree (SD), sustainability index (SI), and approximation degree of ideal objective value (AD), are applied for measuring model performance and try to give a more comprehensive picture of agricultural land use strategies. Results show that (1) the remote sensing data can effectively improve the accuracy of water footprint estimation, and identify crop planting advantage distribution in spatial; (2) the planting area of crops with high evapotranspiration should be increased in irrigation districts near the upstream with high precipitation, which can improve economic benefit, increase utilization rate of green water, and fairness of allocation; (3) through optimization, the average total, blue, and gray water footprint among IDs are drop while green water utilization rates is raised comparing with status quo (2014); (4) Water scarcity will not only damage the coordinated development degree of economy, society and environment, but also hurt the regional sustainable development ability and the realization of development objectives. The proposed optimal modeling framework can help optimize the spatial water footprint of crop planting and improve irrigation water use efficiency, which can be applied to similar regions that suffer from water scarcity.

An enhanced radial interval programming approach was developed for supporting the identification of sound agricultural production plans. It was designed to tackle dual uncertainties in both the economic and environmental subsystems as well as reflect differential aspiration levels for 'immunity' against such uncertainty. By adding protection functions to both the objective functions and constraints susceptible to dual uncertainties without any distributions, it improved upon previous methods through simultaneously measuring and manipulating expected return risk and resource-environmental compliance risk. Moreover, this study made novel attempts to explore system-wide optimal levels of robustness based on proposed performance evaluation criteria and investigations over the interactive effects of varied robustness aspirations for different model components. Based on analyzed tradeoffs among system optimality, robustness and uncertainty degree of solutions pertaining to the study case, a unified protection level of 13 (or 10) was recommended for risk-averse (or risk-neutral) decision making. The model outputs were compared to those from three alternatives, revealing that it could achieve a high level of system reliability at a minor price of economic return. This approach could be generalized for wide-scale applications.

Agriculture plays a central role in maintaining food security and achieving sustainable development for human society. It is a challenge for the agricultural sector to mitigate greenhouse gas (GHG) emissions and maintain agricultural production. However, dual-level uncertainties exist in the processes of agricultural GHG accounting and emission reduction management. In this research, an integrated approach for identifying adaptation strategies in agricultural GHG emission reduction management was developed through incorporating life cycle analysis (LCA) of agricultural production into a general mathematical programming model. This approach strengthened the applicability of LCA and the comprehensiveness of programming models in generating agricultural adaptive actions under different GHG emission restriction targets. Also, dual-level uncertainties of LCA and adaptation management can be effectively addressed. A case study was proposed to illustrate application of the approach in Dalian City, China. The results indicated that farming patterns in eight districts would change significantly. The total area of maize fields would account for the primary proportion (i.e., 40–45 %) in its agricultural sector. Rice, peanut and cabbage fields would be the minor contributors in terms of GHG emissions. In addition to effective rainfall (i.e., [156, 259] mm/ha), a certain amount of water would be supplied for agricultural irrigation to maximize the city’s agricultural yields. Compared with other agricultural crops, rice fields would need the largest amount of irrigation water (i.e., [153.72, 277.98] Mt) to meet the requirements of local government plans.

Sustainable development is of great significance in rural areas of China, which are under coupled pressures of poverty reduction, environmental protection and economic development. In these areas, agriculture is the primary sector in supporting their economies, where a number of the relevant production and processing practices cause many adverse impacts on environment and ecosystem. This poses many challenges for decision makers in formulating sustainable development strategies without invoking potential controversies or objections from the public. In this study, a two-level expert system (TES) for facilitating the investigation of public's perception towards sustainable development in rural areas was developed. Interactive relationships among objectives of socio-economic development and environmental protection, as well as the related policy implications and public responses, were comprehensively examined and incorporated within TES. A two-level system was particularly adopted for facilitating participation of various stakeholders, including decision makers and farmers. The Yongxin County of the Shanjianghu Region in Jiangxi Province, China was selected to demonstrate the applicability of the developed TES. A series of questionnaire surveys were conducted both for acquiring knowledge about the interrelationships among sustainable development, agricultural production, poverty reduction and environmental protection. Public's perception towards various system components of rural sustainable development were efficiently acquired and analyzed. The results indicated that sustainable development would be very significant on rural areas in China. Also, public awareness to the concept and profile of rural sustainable development was still at a low level, especially for farmers. The developed TES can be not only used for acquiring knowledge of sustainable development, but also providing basic information for the formulation of relevant policies and strategies in rural areas of China.