M. Farshadfar, E. Farshadfar,
Volume 8, Issue 2 (summer 2004)
Abstract
Agropyron is one of the most resistant plants to biotic and abiotic stresses. It plays an important role in forage yield in rangelands. Genetic variability based on different markers is the important step in crop improvement. In order to evaluate the genetic variation of different Agropyron species based on morphological and chemical traits, this experiment was carried out in the Agriculture and Natural Resources Research Center of Kermanshah. The tiller numbers, spike length, spikelet numbers, length of flag leaf, width of flag leaf, plant height, peduncle length, ash percentage, organic matter, fiber, dry matter, fat and crude protein were registered. Statistical analysis of data was done by SPSS software. According to cluster analysis the tetraploid genotypes were classified into 5 clusters. Based on principal components analysis the length of flag leaf, spike length and plant height, and among the chemical traits of the ash percentage, organic matter, and crude protein had the highest portions of variance. The genetic parameters such as PCV, GCV, ECV, Hb and Ga for length of flag leaf were 0.274, 0.169, 0.215, 0.382, 3.05 and for spike length were 30.96, 21.64,22.139, 0.48, 5.786 and for plant height were 0.16, 0.084, 0.136,0.276, 6.054, respectively.
H. Zali, S.h. Sabaghpour, E. Farshadfar, P. Pezeshkpour, M. Safikhani, R. Sarparast, A. Hashem Beygi,
Volume 11, Issue 42 (winter 2008)
Abstract
Presence of genotype × environment interaction necessitates evaluation of genotypes in a wide range of environments to find desirable genotypes. This study was carried out to determine the stability and adaptability of grain yield of 17 chickpea genotypes, in RCBD with four replications at Kermanshah, Lorestan, Ilam, Gachsaran and Gorgan Research Stations during two seasons (2003-2004). The genotype × environment interaction effect analyzed using the additive main effects and multiplicative interaction (AMMI) statistical model was significant at 1% level of probability. The sum of squares of G × E interaction was partitioned by AMMI model into four significant interaction principal component axes (IPCA). The first four principal component axes (IPCA 1, 2, 3 and 4) cumulatively contributed to 94% of total genotype by environment interaction. A biplot generated using genotypic and environmental scores of the first two AMMI components also showed that genotypes FLIP 97- 79, X95TH1 and FLIP 97- 114 were selected as stable genotypes, among which the genotype FLIP 97- 114 was outstanding for high yield stability.
I. Saleh, S. M. Soleimanpour, M. Khazaei, O. Rahmati, S. Shadfar,
Volume 29, Issue 4 (Winter 2025)
Abstract
Soil loss and extensive degradation caused by gully erosion have always caused serious damage. Because direct field measurement and monitoring of gully erosion are costly and time-consuming, it is very difficult to determine the amount of soil loss caused by gully erosion. The present research was conducted to calculate the volume of soil loss due to gully erosion using machine learning models in the Abgendi watershed of Kohgiluyeh and Boyar Ahmad province based on field studies. Machine learning models include random forest, support vector machine, artificial neural network, and adaptive neural fuzzy inference system. The location of 68 gullies in the area was recorded. Hence, initially, digital layers of factors affecting the expansion of gullies, including topography, pedology, lithology, and hydrology, were prepared as independent variables to model soil loss caused by gullies. Then, representative gullies were selected in the studied watershed, and the volume of soil loss due to gully erosion was directly measured in the field as a dependent variable. The measured gullies were randomly divided into two training and validation groups. The results of the models were evaluated using root mean square error (RMSE) and R2, and the models were compared. According to the results, gully erosion in the Abgendi watershed of Kohgiluyeh and Boyar Ahmad province is increasing every year. Also, the amount of erosion and soil loss will increase when the amount of rainfall and the frequency of intense rainfall (≥5mm) are high. Among the machine learning models used in the present research, the random forest (RF) model was selected as the best model to predict soil loss generated by gully erosion.
