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The approach of water, food and energy nexus is a comprehensive view of sustainability; and agriculture plays a key role in the use of these resources and food security. Considering that the identification of the indicators of the water, food and energy security nexus in the agricultural sector is the main criterion for checking the status of the deployment of these resources in order to establish security in the agricultural sector. The gap in this field has caused this article to compile and evaluate the indicators of the approach of water, food and energy security nexus in the agricultural sector. To achieve this goal, the method of qualitative content analysis was used, using MAXQDA 18 software and manual analysis. The research sample included 228 articles related to the research topic, which were published in national and international journals during the years 2007-2022. The findings showed; Water, food, and energy security have eight, fourteen, and ten indicators, respectively, which were categorized into four categories: access, availability, usability, and sustainability. According to the findings, we conclude that these indicators include the dimensions of sustainable development, including economic, social and environmental dimensions, and are interdependent. Therefore, these indicators are a comprehensive tool to measure the approach of water, food and energy nexus in the direction of sustainable development. [ABSTRACT FROM AUTHOR]

Dependency of water demands on the climate variation occurs especially in regions where agricultural demand has a significant share of the total water demands. The variability between demands that are based on annual climate conditions may be larger than the uncertainty associated with other explanatory variables in long-term operation of an irrigation dam. This paper illustrates certain benefits of using variable demands for long-term reservoir operation to help manage water resources system in Zayandeh-rud river basin in Iran. A regional optimal allocation of water among different crops and irrigation units is developed. The optimal allocation model is coupled with a reservoir operating model, which is developed based on the certain hedgings that deals with the available water and the water demands mutually. This coupled model is able to activate restrictions on allocating water to agricultural demands considering variation of inflow to the reservoir, variation of demands, and the economic value of allocating water among different crops and irrigation units. Using this model, long-term operation of Zayandeh-rud dam is evaluated considering different scenarios of inflow to the reservoir as well as agricultural demands. The results indicate that the use of operating rules which consider variable demands could significantly improve the efficiency of a water resources system in long-term operation, as it improves the benefit of Zayandeh-rud reservoir operation in comparison with conventional water supply approaches. DOI: 10.1061/(ASCE)IR.1943-4774.0000128 CE Database subject headings: Irrigation; Water demand; Reservoirs; Iran; Case studies. Author keywords: Irrigation demands; Reservoir operation; Demand management; Water demand variability.

Frequent drought amplifies the need for a warning system and forecasting models for damage to crop yields. This study developed an operational model to assess agricultural drought impact. The dryland areas of Kermanshah Province (Iran) were selected to test the proposed modelling system. The model predicted the consequences of drought damage on wheat crop during critical stages of growth (emergence, vegetative growth, initiation of flowering, grain filling, and maturity) as a drought loss indicator. Two types of input were evaluated to correlate climate conditions versus drought losses. The first group comprises the Palmer Drought Severity Index, Z-index, Crop Moisture Index, Crop-Specific Drought Index (CSDI), Standardized Precipitation Index, and Effective Drought Index with one- to three-month timescales used as meteorological indices. The second group, which is consistent of the vegetation condition index and temperature condition index, is based on satellite data. Also a new satellite-based version of CSDI, so-called standardized CSDI (S-CDSI), where evapotranspiration was estimated using surface energy balance algorithm for land, is used. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) technique was used for forecasting with genetic algorithms applied to select appropriate inputs from among the large number of indices. It was concluded that the combination of meteorological and satellite indices performed best in forecasting crop yield. As expected, accuracy improved over the growth stages as the crop developed. Enhancement of the model with a GIS platform made it possible to present the results more suitably, hence helping users to make more realistic decisions. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]