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This study provided insight regarding the climate change impacts on maize production and the efficacy of adaptation strategies. © 2016 Society of Chemical Industry.

Bagherzadeh A., Paymard P. (2015): Assessment of land capability for different irrigation systems by parametric and fuzzy approaches in the Mashhad Plain, northeast Iran. Soil & Water Res., 10: 90-98.Water quality and quantity in agricultural systems of arid and semi-arid regions of the world are of great importance. In this regard the trend to pressurized irrigation systems compared to surface irrigation, elevating water use efficiency, has drastically increased in the agriculture sector. The present study aimed to assess land capability for different types of irrigation systems including surface, drip, and sprinkler practices by parametric and fuzzy approaches to evaluate the capability of cultivated lands on 6131 km 2 of the Mashhad Plain, Khorasan Razavi Province, northeast Iran. In this regard land qualities (drainage and slope), soil physical and chemical properties (texture, depth, salinity, drainage, calcium carbonate and gypsum percentage) and climate conditions (wind velocity) were evaluated by using the Geographic Information System (GIS). Based on parametric approach, some 1116.5 ha of the study area were classified as highly suitable (S1 class) for surface irrigation, while the corresponding values by fuzzy approach accounted for 6099.7 ha of the region. The moderately suitable class of S2, assessed by parametric and fuzzy approaches, included 5014.5 and 31.3 ha of the plain, respectively. It was revealed that the land capability indices were in higher classes (S1 to S2) by drip and sprinkler irrigation compared to the surface irrigation system and the soil texture was detected as the most limiting factor for using the surface irrigation system. With respect to current soil and climate conditions in the study area, the most efficient irrigation systems are drip and sprinkler practices.

The accuracy of daily output of satellite and reanalysis data is quite crucial for crop yield prediction. This study has evaluated the performance of APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation), PERSIANN (Rainfall Estimation from Remotely Sensed Information using Artificial Neural Networks), TRMM (Tropical Rainfall Measuring Mission), and AgMERRA (The Modern-Era Retrospective Analysis for Research and Applications) precipitation products to apply as input data for CSM-CERES-Wheat crop growth simulation model to predict rainfed wheat yield. Daily precipitation output from various sources for 7 years (2000-2007) was obtained and compared with corresponding ground-observed precipitation data for 16 ground stations across the northeast of Iran. Comparisons of ground-observed daily precipitation with corresponding data recorded by different sources of datasets showed a root mean square error (RMSE) of less than 3.5 for all data. AgMERRA and APHRODITE showed the highest correlation (0.68 and 0.87) and index of agreement (d) values (0.79 and 0.89) with ground-observed data. When daily precipitation data were aggregated over periods of 10 days, the RMSE values, r, and d values increased (30, 0.8, and 0.7) for AgMERRA, APHRODITE, PERSIANN, and TRMM precipitation data sources. The simulations of rainfed wheat leaf area index (LAI) and dry matter using various precipitation data, coupled with solar radiation and temperature data from observed ones, illustrated typical LAI and dry matter shape across all stations. The average values of LAI were 0.78, 0.77, 0.74, 0.70, and 0.69 using PERSIANN, AgMERRA, ground-observed precipitation data, APHRODITE, and TRMM. Rainfed wheat grain yield simulated by using AgMERRA and APHRODITE daily precipitation data was highly correlated (r ≥ 70) with those simulated using observed precipitation data. Therefore, gridded data have high potential to be used to supply lack of data and gaps in ground-observed precipitation data.

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