Evaluation of CRU-JRA gridded meteorological dataset for modeling of wheat production systems in Iran.

Meteorological variables are essential inputs for agricultural simulation models and the lack of measured data is a big challenge for the application of these models in many agricultural zones. Studies indicated that gridded meteorological datasets can be proper replacements for measured data. This paper aimed to examine a new gridded meteorological dataset namely CRU-JRA for crop modeling intents. The CRU-JRA is a 6-hourly dataset with a spatial resolution of 0.5° × 0.5° that was primarily constructed for modeling purposes. The CERES-Wheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) was used for the simulation of irrigated and rainfed wheat production systems in Iran. Results showed that the CRU-JRA maximum and minimum temperature values had a relatively fine accuracy with a normalized root mean square error (NRMSE) of 14% for the simulated grain yield. The performance of the CRU-JRA solar radiation values for the simulation of grain yield was similar with a NRMSE of 14.4%. The weakest performance was found for the CRU-JRA precipitation values with a NRMSE of 18.9%. Overall, the CRU-JRA dataset performed comparatively acceptable and similar to existing gridded meteorological datasets for crop modeling purposes in the study area, however further calibrations can improve the accuracy of the next versions of this dataset. More research is necessary for the investigation of the CRU-JRA dataset for agricultural modeling purposes across diverse climates.

Effect of wind speed variation on rainfed wheat production evaluated by the CERES-Wheat model.

Climate is one of the major factors affecting crop phenology and yield. In most previous studies, impacts of temperature (T) and rainfall (R) on crop development, growth, and yield were investigated, while the effect of wind speed (WS) has so far not been assessed. In this study, the influence of WS alteration on rainfed wheat production was evaluated in arid and semi-arid environments during a 25-year period in northeast Iran. In so doing, various climatic scenarios were defined using T, R, and WS changes, and then applied to the CERES-Wheat model included in DSSAT v4.7.5. The results showed that WS variation can alter total ET (planting to harvest) from -12.1 to +8.9%, aboveground biomass from -8.4 to +11.0%, water use efficiency from -13.4 to +19.7%, and grain yield from -11.2 to +15.3%. These changes were in many cases related to the climatic conditions. It was also revealed that in a greater amount of rainfall and shorter growing season (i.e., less drought stress), the WS variation had the stronger impact on total ET; while for aboveground biomass, water use efficiency, and grain yield, the greatest effect of WS variation was detected under the water scarcity conditions (i.e., low rainfall). The results demonstrate that wind speed needs to be better considered in climate change impact studies, in particular in water-scarce regions.

Association of Genetic Structure and Diversity in Iranian Wild Germplasms of Mentha longifolia L. Based on Phenotypical, Biochemical, and Molecular Markers.

Mentha longifolia L. is well-known to be one of the most pervasive wild-growing species of the Lamiaceae family, which has extensive beneficial properties in the fields of pharmacology and biological products. In the present study, the correlation between Inter-simple sequence repeat (ISSR) markers and morpho-chemical parameters of twenty different M. longifolia accessions (MLACs) were assessed. The geographic information system (GIS) has been employed to interpret the original habitat of the accessions in Iran. ISSR analysis indicated a remarkable difference in the studied accessions, segregated them into three main groups, constructed by an unweighted pair-group method with arithmetic (UPGMA) and principal coordinate analysis (PCoA). A total of 89 bands were generated by 12 ISSR primers, among which 82 (91.97 %) of them were polymorphic. The cluster analysis based on agro-morphological data scattered MLACs into two main groups. The essential oils (EOs) were analyzed through GC/FID/MS, and four chemotypes were characterized according to the major constituents. Pulegone ranged from 0.17 to 69.50 % was the main oil constituent with the highest content. Also, HPLC-PDA was employed to identify and to quantify the phenolic compounds in the MeOH extracts of MLACs. Heatmap cluster based on phenolic compounds produced three main categories of accessions. The components identified in the extracts were rosmarinic acid, rutin, vanillic acid, ferulic acid, chlorogenic acid, caffeic acid, 3,4-dihydroxybenzoic acid, 2,5-dihydroxybenzoic acid, and p-coumaric acid, which among them rosmarinic acid (RA) varied from 39.16 to 261.55 mg/100 g (DW) as a predominant constituent. Subsequently, multiple regression analyses between ISSR fragments and morpho-chemical data illustrated considerable relationships in the plant materials. The high variation and correlation observed in metabolic and phenotypic traits of MLACs establish an adequate source to conduct reserves conservation programs.

Analyzing trends of days with low atmospheric visibility in Iran during 1968-2013.

Atmospheric visibility (AV) is an indicator for assessing air quality and is measured in standard weather stations. The AV can change as a result of two main factors: air pollution and atmospheric humidity. This study aimed to investigate trends in the number of days with AV equal or less than 2 km (DAV2) in Iran during 1968-2013. Consequently, 43 weather stations with different climates were evaluated across the country, using the Mann-Kendall (MK) trend test. The results show that the number of stations with positive (i.e., significant or non-significant) MK z values was equal to, or greater than, those with negative MK z values, in all months and seasons of the year, as well as annually. Furthermore, summer and autumn had, respectively, the least and most stations with positive MK z values. Fewer trends in DAV2 were detected in the central, east, and northeast regions of the country. Analyzing the DAV2 and relative humidity together indicated that over 30% of stations had at-risk air quality in January, and that the largest number of stations with at-risk air quality was in the autumn and winter. These results are useful for better environmental planning to improve air quality, especially in developing countries such as Iran, where reduced air quality has been a major problem in recent decades.