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Showing 3 results for Dadjou
Influence of Nano-Priming on Festuca ovina Seed Germination and Early Seedling Traits under Drought Stress, in Laboratory Condition

Volume 7, Issue 3 (Summer 2019)
Abstract
Aims: Germination stage is one of the most sensitive stages to drought stresses and if the plant is able to tolerate stresses in this stage, it can pass the later growth stages. Priming could improve germination of seeds under stress.
Materials and Methods: Experiment was carried out using a randomized complete block design. 25 seeds were placed in petri dish under drought stress with polyethylene glycol 6000 in three levels of 0, -6 and -12 bar. After 14 days, it was found that the germination percent in these seeds was low (>40%). Then nano priming was used to improve seed germination attributes. Treatments were; control, silver nanoparticles with the concentrations of 25, 50 and 75%.
Findings: Data analysis of variance indicated that influence of nano priming, drought stress, and their interaction was significant on root and shoot length, wet and dry weight, vigor index, allometric coefficient and mean germination time (p≤0.01). Results showed that PEG stress had a negative effect on seeds germination. And an increase of silver nanoparticles concentration improves F. ovina seed germination and seedling traits. The maximum of GP (86%), SG (6N/D), Vi (5), AC (6) and MGT (7.08d) were recorded for seeds nano primed in the stress of 0 level.
Conclusion: Nano-priming is an impressive technique to the betterment of seedlings germination and growth of F. ovina. In the most studied indices, nano-priming 75% had the greatest influence. Before planting to restore of rangelands, to promote the establishment and growth of planted F. ovina it is recommended to prime seeds with nano-silver particles.

Determinization of Environmental Factors Effects on Plants Production in QezelOzan-Kosar Rangelands, Ardabil Province Factors Effect on Rangelands Production

Volume 8, Issue 1 (Winter 2020)
Abstract
Aims: The aim of the present study was to determine the most important environmental factors affecting aboveground net primary production (ANPP) of plants along the altitude gradient in QezelOzan-Kosar rangelands, Iran.
Materials & Methods: Eight sites along the altitude gradient were selected, in each of which three transects parallel and perpendicular to the slope were established. Along each transect (totally 240 plots), ANPP and soil samples were measured. Using digital elevation model (DEM) map, the maps of slope, aspect, elevation, topographic index (CTI), stream power index (SPI), plan curvature (PC), precipitation and temperature were extracted. The soil parameters measured in soil laboratory. To determine the important effective factors, principal component analysis (PCA) was used. Moreover, the ANPP prediction equation was simulated using the parameter which had the greatest impact and correlation with ANPP (precipitation), using 2nd-order polynomial model and mapped further.
Findings: The results of PCA revealed that six components had the highest effect on the ANPP variations (76.35% of ANPP variations). The result of simulated equation and map indicated acceptable accuracy (R2= 0.95, RMSE= 0.73).
Conclusion: The results of the present study highlight the importance of topographic, climatic, and soil factors in ANPP variations, and can be used to manage QezelOzan-Kosar rangelands for establishing balance between biomass and carbon of the ecosystem and ecosystem supply and demand.

Habitat Suitability of Dorema ammoniacum D. Don. Using Maximum Entropy and Logistic Regression Modeling in Central Region of Iran

Volume 10, Issue 1 (Winter 2022)
Abstract
Aims The purpose of this study was to evaluate the competency of logistic regression (LR) and maximum entropy (MaxEnt) models to predict the distribution of Dorema ammoniacum D. Don. in rangeland habitats in the central region of Iran, Yazd province.
Materials & Methods The potential distribution map of Dorema ammoniacum D. Don. was prepared. The homogenous habitats were identified, and vegetation sampling was conducted using a systematic random method. The data including: soil (physical and chemical properties), physiographic (slope, aspect and altitude), and vegetation data (presence and absence) were used. Soil sampling was performed at two depths of 0-30, and 30-60 cm. The required maps were prepared using interpolation method. Statistics were taken from 90 plots along 9 transect both in the presence and absence area. Response curve and Jackknife test (for MaxEnt method) were employed to identify the most important environmental predictive factors. The kappa index was used to determine the agreement between the actual and predicted maps.
Findings The accuracy of predicted map was weak in LR Model (AUC= 0.65), but it was considerably high in the MaxEnt model (AUC=0.87). The agreement between the predicted map of MaxEnt model, and ground truths was very good (kappa=0.74), and the agreement between predicted map generated by LR with the ground-truths was medium (kappa=0.5).
Conclusion This plant has a limited ecological niche; therefore, the MaxEnt model could take precedence over the LR model because the only data it employs is the presence of the species.
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