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Showing 2 results for Farsi
Allelic Variation of Hardness Genes (puroindoline a and b) in Iranian Commercial and Landrace Wheats
Kh. Malekzadeh, F. Shahriari, M. Farsi , E. Mohsenifard,
Volume 12, Issue 45 (fall 2008)
Abstract
Kernel hardness is one of the most important characterizations on end-use quality of bread wheat and also used for their marketing classification. Kernel texture, mainly controlled by one major locus (Ha) located on the short arm of chromosome 5D. Two tightly linked genes as puroindolin a , and b covered by this major locus and designed as Pina and Pinb respectively. When both puroindolines are in their ‘functional’ wild state, grain texture is soft. When either of the puroindoline alleles is absent or alter by mutation, then the result is hard texture. In this study, 61 Iranian commercial cultivars and 92 landraces were investigated for their kernel hardness and puroindoline alleles using SKCS and, PCR and cleaved amplified polymorphic sequences (CAPS) techniques respectively. Specific primers were used to amplify Pina and Pinb. The results indicated that frequency of hard, mixed and soft genotypes were 65.6, 19.6 and 14.8% respectively, in commercial cultivars and 58.7, 13 and 28.3% in landraces varieties. Among hard type of commercial cultivars, 18 and 5, genotypes have identified as Pina-D1b and Pinb-D1b respectively. Kavir was only cultivar with Pinb-D1e allele. Pinb-D1b allele was identified in two hard types of landrace varieties. Surprisingly, Pinb-D1c was not found in any varieties. Influence of the above proindoline alleles on kernel hardness showed that the SKCS hardness index of Pina-D1b was significantly higher than that of Pinb-D1b. Our knowledge about the genetic basis of kernel hardness could provide useful information in breeding programs of bread wheat.
Integrating Remote Sensing with SCS and ICONA Models for Mapping Land Degradation in Fars Province
S. Dehghan Farsi, R. Jafari, A.r. Mousavi,
Volume 26, Issue 2 (ُSummer 2022)
Abstract
The objective of the present study was to investigate the performance of some of the extracted information for mapping land degradation using remote sensing and field data in Fras province. Maps of vegetation cover, net primary production, land use, surface slope, water erosion, and surface runoff indicators were extracted from MOD13A3, MOD17A3, Landsat TM, SRTM, ICONA model, and SCS model, respectively. The rain use efficiency index was obtained from the net primary production and rainfall map, which was calculated from meteorological stations. The final land degradation map was prepared by integrating all the mentioned indicators using the weighted overlay method. According to the ICONA model, 5.1, 9, 47.21, 27.91, and 10.73 percent of the study area were classified as very low, low, moderate, severe, and very severe water erosion; respectively. Overlaying the ICONA map with other indicators showed that very high and high classes, moderate, and low and very low classes of land degradation covered 1.3, 18.7, 70, 0.9, and 9.1 percent of the study area, respectively. According to the results, integrating remote sensing with ICONA and SCS models increases the ability to identify land degradation.

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