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Showing 3 results for Siasar
Investigating the Diversity and Relationship between Agronomical Traits and Seed Yield in Barley Advanced lines Using Factor Analysis
M. Iravani, M. Solouki, A.m. Rezai, B.a. Siasar, S.a. Kohkan,
Volume 12, Issue 45 (fall 2008)
Abstract
In order to investigate the diversity and relationship between agronomical traits with seed yield components in barley, twenty advanced barley lines were evaluated in a randomized complete block design with 3 replications at Research Center of Agriculture in Sistan in 2006. Each plot consisted of six rows spaced 20 cm apart and 5 meters long. In this research, 24 Agronomic traits were measured on five randomly selected plants in the central rows of each plot. Analysis of variance showed that there were significant differences among the lines for most of the traits. Line No.7 had the highest (406 grs/m2) and line No.5 had the lowest (309 grs/m2) seed yield. There were high correlation between seed yield and number of panicle/m2. Factor analysis results indicated that 7 independent factors explained 82 percent of the total variation. The first two factors, namely yield components and tillering capacity, explained 41 percent of the total variation. Therefore, it can be concluded that the traits are related to seed yield and tillering capacity, i.e., number of seed per main panicle. 1000 seed weight, number of seed per plant, number of days to physiological maturity and days to heading are the most important characteristics in selecting lines with high seed yield. Number of fertile tiller, total number of tillers and peduncle length were also next set of important traits. Number of days to emergence, nodule number and number of panicle per m2 were also important as selection criteria. Seed weight per plant, biological yield, awn length and the traits that were related to flag leaf had lower importance for selection of lines with high seed yield.
Comparing of Generalized Linear Models, Random Forest and Gradient Boosting Trees in Estimation of Reference Crop Evapotranspiration (Case Study: The Sistan Plain)
H. Siasar, T. Honar, M. Abdolahipour,
Volume 23, Issue 4 (winter 2020)
Abstract
The estimation of reference crop evapotranspiration (ETo) is one the important factors in hydrological studies, irrigation planning, and water resources management. This study attempts to explore the possibility of predicting this key component using three different methods in the Sistan plain: Generalized Linear Models (GLM), Random Forest (RF) and Gradient Boosting Trees (GBT). The maximum and minimum temperature, mean temperature, maximum and minimum humidity, mean humidity, rainfall, sunshine hours, wind speed, and pan evaporation data were applied for years between 2009 to 2018. Using various networks, the ETo as output parameter was estimated for different scenarios including the combination of daily scale meteorological parameters. In order to evaluate the capabilities of different models, results were compared with the ETo calculated by FAO Penman-Monteith as the standard method. Among studied scenarios, M1 covering the maximum number of input parameters (10 parameters) showed the highest accuracy for GBT model, with the lowest RMSE (0.633) and MAE (0.451) and the maximum coefficient of regression (R = 0.993). Air temperature was found as the most sensitive parameters during sensitivity analysis of studied models. It indicated that accuracy and precision of temperature data can improve the results. Application of the GBT model could decrease the time consumed to run the model by 70%. Therefore, the GBT model is recommended for estimation of ETo in the Sistan plain.

Evaluation of Precipitation Data using CHIRPS and PERSIANN Models (Case Study: Bandar Abbas)
H. Siasar, A. Salari,
Volume 27, Issue 1 (Spring 2023)
Abstract
Access to large precipitation data with appropriate accuracy can play an effective role in irrigation planning and water resources management. Satellite images generate high, wide, cheap, and up-to-date data is a good way to estimate precipitation. In this research, the Google Earth engine system and precipitation products from satellite images of PERSIANN and CHIRPS models in daily, monthly, and annual time intervals were used to evaluate and validate the amount of precipitation in Bandar Abbas station during the statistical period of 1983-2020. The results showed that the precipitation estimation by PERSIANN and CHIRPS satellites on a monthly and annual scale is more accurate than the daily scale. The highest correlation coefficient and the least RMSE belonged to the PERSIANN algorithm on monthly and annual scales. The value of the correlation coefficient in the PERSIANN algorithm on daily, monthly, and annual scales is equal to 0.32, 0.83, and 0.94, respectively. The correlation coefficient in the CHIRPS algorithm in daily, monthly, and annual scales is equal to 0.24, 0.71, and 0.90, respectively. The coefficient of determination (R2) of PERSIANN and Chrips algorithms on a monthly scale were 0.89 and 0.70, respectively, and for an annual scale were 0.88 and 0.80, respectively. The general conclusion of this study indicated that the accuracy of the two algorithms in determining the spatial pattern of rainfall on a monthly and annual scale is appropriate, and the PERSIANN algorithm had a higher accuracy on a monthly time scale.

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