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The worsening water scarcity has imposed a significant stress on food production in many parts of the world. This stress becomes more critical when countries seek self-sufficiency. A literature review shows that food self-sufficiency has not been assessed as the main factor in determining the optimal cultivation patterns. However, food self-sufficiency is one of the main policies of these countries and requires the most attention and concentration. Previous works have focused on the virtual water trade to meet regional food demand and to calculate trade flows. The potential of the trade network can be exploited to improve the cropping pattern to ensure food and water security. To this end, and based on the research gaps mentioned, this study develops a method to link intra-country trade networks, food security, and total water footprints (WFs) to improve food security. The method is applied in Iran, a water-scarce country. The study shows that 781 × 106 m3 of water could be saved by creating a trade network. Results of the balanced trade network are input to a multi-objective optimization model to improve cropping patterns based on the objectives of achieving food security and preventing water crises. The method provides 400 management scenarios to improve cropping patterns considering 51 main crops in Iran. Results show a range of improvements in food security (19-45%) and a decrease in WFs (2-3%). The selected scenario for Iran would reduce the blue water footprint by 1207 × 106 m3, and reduce the cropland area by 19 × 103 ha. This methodology allows decision makers to develop policies that achieve food security under limited water resources in arid and semi-arid regions.

Groundwater quality monitoring and simulation is of great importance in arid and semi-arid regions due to water scarcity in such regions. Geostatistical methods are appropriate techniques for interpolating and spatial mapping of groundwater qualitative parameters. In the present study, geostatistic-based Kriging and Co-Kriging methods were used and compared with data-driven artificial neural network (ANN) and ANFIS models for predicting spatial distribution of groundwater electrical conductivity (EC), then the best model was selected for further sampling in the studied region. Data from 24 wells in the Keshit, Bam Normashir, and Rhmtabad plains (Kerman province, Iran) during the period of 2002-2011 were used for analysis. Root mean square error (RMSE), R 2 , and mean absolute error (MAE) statistical indices were used for assessing the applied models. Results showed that for both the models, i.e. triple input model (including longitude, latitude, and the number of months) and the quadruple-input model (including longitude, latitude, number of months, and Cl), ANN model presented the most accurate results with the lowest RMSE and MAE and the highest R 2 values. Attending to the geostatistical methods, Co-Kriging method outperformed the Kriging method. Finally, using the tested geostatistical model, the EC data were produced for the regions without observational values. [ABSTRACT FROM AUTHOR]

Soil is one of the main elements of natural resources. Accurate estimation of soil erosion is very important in optimum soil resources development and management. Analyzing soil erosion by water on cultivated lands is an important task due to the numerous problems caused by erosion. In this study, the performance of three different data-driven approaches, e.g. multilayer perceptron artificial neural network (ANN), grid partitioning (GP), and subtractive neuro-fuzzy (NF) models were evaluated for estimating soil erosion. Land use, slope, soil and upland erosion amount were used as input parameters of the applied models and the erosion values obtained by MPSIAC method were considered as the benchmark for evaluating the ANN and NF models. The applied models were assessed using the coefficient of determination (R2), the root mean square error (RMSE), the BIAS, and the variance accounted for (VAF) indices. The results showed that the subtractive NF model presented the most accurate results with the minimum RMSE value (3.775) and GP, NF and ANN models were ranked successively. [ABSTRACT FROM AUTHOR]

First of all, the authors thank the distinguished discusser for providing helpful comments on the original paper. But the following answers and explanations are essential. [ABSTRACT FROM AUTHOR]