International Journal of Horticultural Science and Technology، جلد ۷، شماره ۲، صفحات ۱۱۹-۱۳۷

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عنوان انگلیسی Assessment of Salinity Indices to Identify Mint Ecotypes using Intelligent and Regression Models
چکیده انگلیسی مقاله Despite recent development in producing chemical medicines, associated side effects have led to increased use of medicinal plants and natural compounds. Soil salinity, especially in arid and semi-arid regions, is a serious threat to global agriculture. Nowadays, efforts have been made to find benchmarks that can effectively select salt-tolerant or salt-resistant genotypes. In this regard, the use of computer software to predict the indices can help us for screening the most tolerant ecotypes. The objectives of the present study were to determine the best indicators of salinity tolerance using intelligent and regression models for eighteen commercial ecotypes of mint. The seedlings were planted in plastic pots and arranged in a split factorial experiment in a randomized complete block design with four replicates. The treatments consisted of four levels of salinity (0, 2.5, 5 and 7.5 dS m-1), two levels of harvesting time, and 18 ecotypes. The plants were grown until the flowering stage and then harvested. There was a significant difference between ecotypes in terms of calculated indices at all three levels of salinity. Indicators such as TOL, MP, GMP, YSI, STI and HM showed a significant positive correlation with YS and YP at all three levels of salinity. The cluster analysis divided the ecotypes into three distinct groups based on the calculated indices at all levels of salinity. The principal component analysis revealed that the YP, YS, TOL, MP, GMP, YSI, STI and HM were more suitable among others salt stress indices. The sensitivity analysis at 2.5 dS m-1 salinity level showed that the HM, STI, YSI, YI, SSI and MP indices were of higher importance than the others. At 5 dS m-1 salinity level, the HM, STI, YSI, YI, GMP and MP indices showed the highest importance whereas at 7.5 dS m-1 salinity level, the STI, YSI, YI, GMP and YP indices indicated the highest importance. In general, the results suggest that ANN(MLP) model (R2 = 0.999) is the best model to predict at all salinity levels. E13, E14, E15, E16 and E18 ecotypes are the most salt tolerant ecotypes which can be used for the future breeding program.
کلیدواژه‌های انگلیسی مقاله Mint, Predict, Regression model, salinity

نویسندگان مقاله Seyyed Jaber Hosseini |
Department of Agronomy, Faculty of Agriculture, Tarbiat Modares University, Tehran, 14115-336, Iran

Zeinolabedin Tahmasebi-Sarvestani |
Department of Agronomy, Faculty of Agriculture, Tarbiat Modares University, Tehran, 14115-336, Iran

Hematollah Pirdashti |
Department of Agronomy, Genetics and Agricultural Biotechnology Institute of Tabarestan, Sari Agricultural Sciences and Natural Resources University, Sari, Iran.

Seyyed Ali Mohammad Modarres-Sanavy |
Department of Agronomy, Faculty of Agriculture, Tarbiat Modares University, Tehran, 14115-336, Iran

Ali Mokhtassi-Bidgoli |
Department of Agronomy, Faculty of Agriculture, Tarbiat Modares University, Tehran, 14115-336, Iran

Saeid Hazrati |
Department of Agronomy, Faculty of Agriculture, Azarbaijan Shahid Madani University, 53714-161, Iran

Silvana Nicola |
Department of Agricultural, Forest and Food Sciences, VEGMAP, University of Turin, Grugliasco (TO) 10095, Italy


نشانی اینترنتی https://ijhst.ut.ac.ir/article_76145_39665541d3dffafbacb0a789dcbfae9a.pdf
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