High-efficiency identification method for salt resistance of cotton germplasms

Abstract

The invention provides a high-efficiency identification method for salt resistance of cotton germplasms. By utilizing a flower pot soil seedling cultivation method, cotton seedings are cultivated to a three-leaf-and-one-leaflet stage. The materials with normal and consistent growth vigour are selected and subjected to salt stress treatment and clear water treatment with the same volume respectively. The second true leaves are selected, the PSII maximum photochemical efficiencies of the true leaves with different treatments are measured respectively, the relative values are calculated, and brought into the standard equation of salt resistant indexes: D=1.943X-0.882 to calculate the salt resistant indexes and determine the salt resistance, wherein X is for a relative value of a PSII maximum photochemical efficiency and D is for a salt resistant index. The larger the salt resistant index, the stronger the salt resistance. When the method is compared to the prior art, the results of the obtained standard equation of the method are accurate and credible. The measurement method of PSII maximum photochemical efficiencies is simple and fast, can be performed in vivo, does not harm plants, and can ensure normal running of downstream experiments. The measurement time is the cotton seedling stage, so the identification time can be shortened effectively, the efficiency is raised, and the method is suitable for large scale germplasm resource identification.

Description

technical field [0001] The invention belongs to the technical field of agriculture, and in particular relates to a high-efficiency identification method for the salt tolerance of cotton germplasm. Background technique [0002] Cotton is the most important economic crop and occupies an important position in the world and our country's national economy. With the growth of the world's population, the demand for food, fiber, and oil is increasing. Land becomes a restrictive factor, about 100 million hm in my country 2 The saline-alkali land in cultivated land occupies 6.66 million hm 2 , and about 200 million hm 2 Saline-alkaline wasteland resources have not been developed and utilized, and these saline-alkali lands are mostly distributed in North China, Northwest China, Northeast China and the eastern coastal areas (Yu Shuxun, Ji Daofan, etc. China Cotton Genetics and Breeding [M]. Shandong Science and Technology Press, 2003). With the reduction of arable land, cotton planti...

AI Technical Summary

Problems solved by technology

Moreover, the identification of salt ponds is time-consuming and laborious, which is extremely unfavorable for large-scale identification of germplasm resources. Therefore, it is urgent to find a fast and accurate method for identifying the salt tolerance of cotton germplasm resources.

[0005] In the early days, it was difficult to fully and accurately reflect the true salt tolerance of cotton germplasm through a single index or a small number of indexes, and then proposed to use the comprehensive evaluation of the membership function (Li Changyan. Study on salt tolerance of coastal saline-alkali land plants [D]. Hohhot: Inner Mongolia University, 2011), principal component analysis (Liu Shaoqing, He Shoupu et al. Identification of heat tolerance of different cotton germplasm resources [J]. Journal of Plant Genetic Resources, 2013,14(2):219-220) and gray correlation analysis (Yu Meiling. Comprehensive evaluation of salt-tolerant physiological and agronomic traits of 20 safflower varieties [D]. Xinxiang: Henan Normal University, 2010) and other methods to evaluate plant stress resistance, although the one-sidedness of using individual indicators alone can be improved, there are still limitations

For example, the principal component analysis method simplifies multiple indicators by using dimensionality reduction, but ignores the close internal relationship between some indicators, and their changing trends are basically the same. These indicators actually belong to the same type, while the principal component analysis generally Reduce the dimensionality of all indicators without considering the weight differences between different types of indicators

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