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Showing 2 results for Pistacia Atlantica
Physiological Responses to Cold Stress in Different Provenances of Pistacia atlantica Seedlings
Somayeh Homayounfar , Roghaiyeh Zolfaghari , Payam Fayyaz ,
Volume 3, Issue 2 (3-2017)
Abstract
Background and objectives: Plant species with wide geographic ranges, exhibit high tolerance to cold stress. Trees in temperate regions must cope with freezing temperatures, especially in late autumn and winter. One of the mechanisms to tolerate cold stress is through decreasing photoperiod and temperature during acclimation. In addition, provenance of a species could be one of the main factors contributing to cold resistance. Therefore, in the present study, the effect of cold stress on physiological responses of Pistacia atlantica seedlings from three provenances was investigated in southern Zagros to select the most cold-resistant provenance.Material and methods: Seeds of three provenances, namely Margon, Noor Abad and Yasuj, from southern Zagros, were collected and planted in pots. After germination of seeds, induction of acclimation was carried out at two stages for four weeks. This was accomplished through the simulation of temperature and photoperiod of Yasuj region from September to November. Following that, the indexes of photosystem efficiency and chlorophyll content were measured. After each acclimation stage, leaves and stems of seedlings were exposed to three levels of temperature, +4°C (control), -20°C for one hour, and -20 for two hours. Then, relative water content (RWC) and electrolyte leakage (EL) of stems and leaves were measured. This study was conducted as a factorial with three factors of provenance (three levels), cold treatments (three levels) and acclimation (two levels), adopting a completely random design.Results: The results showed that EL increased with decreases in temperature and this increase was the highest in the case of the Noor Abad provenance. In addition, the stem RWC of Yasuj and Margoon provenances decreased in the second cold acclimation stage. Chlorophyll content, photosystem efficiency and electron transport rate were lower in Noor Abad provenance, compared with other two provenances. Moreover, except the electron transport rate, all fluorescence parameters significantly decreased during the cold acclimation. Conclusion: The results showed that measuring EL and fluorescence parameters could be a useful indicator for identifying cold-tolerant provenances. The seedlings of Noor Abad provenance exhibited the least and Margoon provenance showed the highest resistance to cold stress. This could be due to high altitude and cold climate of Margoon provenance.
 

Modeling the Relationship Between Climatic Fluctuations and Diameter Growth of Pistacia atlantica in Zagros Forests Using Neural Network
Dr. Ali Mahdavi, Msc. Sahel Ramezani, Dr. Hamidreza Naji,
Volume 4, Issue 2 (9-2025)
Abstract
Extended Abstract
Background and Objectives: Climate change, manifested through fluctuations in key variables such as temperature and precipitation, poses a significant threat to forest ecosystems, particularly in semi-arid regions like the Zagros. Pistacia atlantica (Wild Pistachio) is a valuable native species in these forests, whose growth is highly sensitive to water availability. Quantifying the impact of climatic variables on its radial growth is essential for adaptive forest management. While traditional statistical methods have been used, advanced artificial intelligence models like the Group Method of Data Handling (GMDH) neural network offer superior capabilities for modeling complex, non-linear relationships. The main objectives of this study were to: 1) evaluate the effect of climatic variables (precipitation, temperature, relative humidity) on the diameter growth of P. atlantica, 2) determine the most influential climatic factors, and 3) assess the accuracy of the GMDH neural network model in predicting growth based on climate data.
Materials and Methods: The study was conducted in three habitats of P. atlantica (Darreh Shahr, Abdanan, and Majin) in Ilam province, Iran. A total of 18 tree discs (6 from each site, divided into two diameter classes: <30 cm and >30 cm) were collected from trees with similar topographic conditions. After surface preparation and polishing, high-resolution images were taken, and annual ring widths were precisely measured using Motic image software. Wood density was also determined for each sample. Climatic data (annual precipitation, average, minimum, and maximum temperature, relative humidity) for the past 15 years were obtained from the nearest meteorological stations. The relationship between ring width indices and climatic variables was first analyzed using Pearson correlation in SPSS software. Subsequently, the GMDH neural network model was implemented in MATLAB (R2014a) to predict radial growth based on climatic inputs. The data were randomly divided into training (70%), validation (15%), and test (15%) sets. Model performance was evaluated using statistical indices: Root Mean Square Error (RMSE), Mean Square Error (MSE), and Correlation Coefficient (R).
Results: Statistical analysis revealed a significant positive correlation between annual ring width and both annual precipitation (r = 0.188, p=0.002) and relative humidity (r = 0.173, p=0.004). In contrast, a significant negative correlation was found with average annual temperature (r = -0.185, p=0.002) and maximum annual temperature (r = -0.152, p=0.013). No significant correlation was observed with minimum annual temperature. The GMDH neural network model demonstrated high accuracy in predicting radial growth from climatic variables, with performance metrics on the total dataset as follows: RMSE = 3.86, MSE = 14.88, and R = 0.90. The model's predictions closely matched the observed growth trends, confirming its effectiveness.
Conclusion: The radial growth of Pistacia atlantica in the semi-arid Zagros forests is significantly influenced by climatic fluctuations. Increased precipitation and relative humidity positively enhance growth, while rising temperatures, particularly maximum temperatures, have a suppressive effect, likely due to increased evapotranspiration and water stress. The successful application of the GMDH neural network model, with its high predictive accuracy (R=0.90), establishes it as a powerful and reliable tool for modeling climate-growth relationships in complex forest ecosystems. These findings provide critical insights for developing climate-adaptive conservation and management strategies to enhance the resilience of P. atlantica stands against future climate variability.

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