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1

A new model for hazard evaluation of vegetation degradation using DPSIR framework, a case study: Sadra Region, Iran

100%
Masoudi M., Amiri E.
Polish Journal of Ecology
|
2015
|
tom 63
|
nr 1
Vegetation degradation causes reduction in the available biomass, and decline in the vegetative cover. The Sadra watershed which covers the upper reaches of Marharlu basin, in southern Iran, has been chosen for a test hazard assessment of this type of degradation. The different kinds of data for indicators of vegetation degradation were gathered from collecting of field data like percent canopy and biomass and also records and published reports of the governmental offices of Iran. A new model has been developed for assessing the hazard of vegetation degradation using DPSIR (Driving forces, Pressures, State, Impacts and Responses) framework. The approach is based on the use of indicators, which may be direct or indirect, ecological, technical, socioeconomic or cultural causes of environmental hazard. Taking into consideration fourteen indicators of vegetation degradation the model identifies areas with different hazard class. The preparation of hazard maps based on the Geographic Information System (GIS) analysis of these indicators will be helpful for prioritizing the areas to initiate remedial measures. By fixing the thresholds of severity classes of the fourteen indicators, a hazard map for each indicator was first prepared in GIS. The hazard classes were defined on the basis of hazard scores arrived at by assigning the appropriate attributes to the indicators and the final hazard map was prepared by intersecting fourteen hazard maps in five main hazard layers including anthropogenic, natural, current state of hazard, livestock pressure and trend of degradation in the GIS. Results show among the five main hazard maps used in the model, the most main effective indicator in vegetation degradation of the study area is ‘Current State of Hazard’. Also areas under severe hazard class have been found to be widespread (58%) and areas under moderate hazard class have been found (42%) in the Sadra watershed.
2

The feasibility of using vegetation indices and soil texture to predict rice yield

81%
Yaghouti H., Pazira E., Amiri E., Masihabadi M. H.
Polish Journal of Environmental Studies
|
2019
|
tom 28
|
nr 4
Identifying plant-environment interactions along with remote sensing provides grounds for designing management methods as well as predicting rice yield in different conditions; accordingly, it is very helpful to use vegetation indices for identifying the vegetation and greenness of farms. The regression between the local and high-yield varieties of rice in 2012 and the NDVI, SAVI, LAI, DVI, and RVI indices derived from Landsat 7 in northern Iran indicate the superiority of the NDVI index in the flowering stage of rice. Results show that the coefficient of determination of the fitted model for local and high-yielding varieties is 0.71 and 0.70, respectively, which indicates the good consistency of the results with the regional data. We evaluated the models for the local and high-yielding varieties in crop year 2013 with RMSE of 406 and 272 kg ha-1 and NRMSE of 12% and 6%, respectively. Moreover, the simulation results show that the yield of the models is well fitted with the observed values; besides, there is high correlation (R>0.80) between the real and predicted yield values. As shown by the investigation of the region’s soil texture, the fine-texture paddy fields have better yield.
3

Estimate of peanut production function under irrigated conditions and salinity

81%
Gohari A. A., Babazadeh H., Amiri E., Sedghi H.
Polish Journal of Environmental Studies
|
2018
|
tom 27
|
nr 4
Salinity is one of the main and common challenges in the world. This study was conducted to determine the effect of salinity and water requirements of peanuts, with main factors including water requirement 40% (WR₄), 60% (WR₃), 80% (WR₂), and 100% (WR₁); sub-treatment including salinity with values of 1 (S₁), 3 (S₂), 5 (S₃), and 7 (S₄) dS/m; and sub-sub-treatment including four cultivars Guil (V₁), Gorgani (V₂), Jonobi (V₃), and Mesri (V₄). The experimental design was conducted in the form of split factorial in a completely randomized block with 3 replications in 2015 and 2016. The results showed that the maximum seed yields in salinity levels of 1 dS m⁻¹ in 2015 and 2016 were 1,142 and 987 kg ha⁻¹, respectively. On the interaction effect of irrigation and salinity, maximum seed yields in 80% water requirement and salinity of 1 dS m⁻¹ in 2015 and 2016 were 1,393 and 1,265 kg ha⁻¹, respectively. The maximum seed yields in Jonobi cultivar with salinity of 1 dS m⁻¹ and in Guil cultivar in salinity of 5 dS m⁻¹ were 1,254 and 1,127 kg ha⁻¹, respectively. The Guil cultivar in 100% water requirement and with salinity of 1 dS m⁻¹ in 2015 and 2016 had the highest seed yield, with 1,883 and 1,710 kg ha⁻¹, respectively.
4

The effects of drought stress on yield, yield components, and yield stability at different growth stages in bread wheat cultivar (Triticum aestivum L.)

61%
Mehraban A., Tobe A., Gholipouri A., Amiri E., Ghafari A., Rostaii M.
Polish Journal of Environmental Studies
|
2019
|
tom 28
|
nr 2
The current study examined the response of yield, yield components, and other physiological traits to drought in bread wheat. A field experiment was conducted in 2013-2015 at the the Agriculture and environmental research center of Ardabili located in Moghan, Iran. The experimental design was a split-plot experiment based on randomized complete block design with three replications under five drought stress (no irrigation) regimes: rainfed (T1), tillering stage (T2), booting stage (T3), after anthesis (T4), full irrigation (T5), and 10 bread wheat cultivars. Combined analysis of variance revealed significant genotypic differences for all measured trait aspects of TN, FTN, SN, and TDM. Significant differences were also observed between drought treatments for PH, DHE, DMA, GFP, NGS, TGW, HI, SW, TDM, GWP, and GW. The interaction between stress treatments and cultivars was also significant for DHE, DMA, GFP, NGS, TGW, and GY. The stability measuring of the GGE biplot polygon showed that the performance of cultivars G3, G7, and G8 are highly variable (less stable), whereas cultivars G5, G1, G2, G10, G9, and G4 are highly stable. Cultivars G9 and G10 are more desirable than other cultivars that has both high mean yield and high stability.
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