The research was carried out with 9 triticale, 3 bread wheat, 3 durum wheat and 3 barley varieties and advanced lines in Tekirdag, Edime and Silivri locations during three years. In the study, the data obtained from c...The research was carried out with 9 triticale, 3 bread wheat, 3 durum wheat and 3 barley varieties and advanced lines in Tekirdag, Edime and Silivri locations during three years. In the study, the data obtained from combined variance analysis were performed and the significance of the differences between the averages was determined by LSD multiple comparison test. GGE biplot analysis and graphics were made by using the statistical package program. The genotypes G2 and G3 for thousand kernel weight, genotype G1 for the heading time and test weight, genotypes G14 and G15 for the maturation time, number of spikelets per spike and grain weight per spike and G13 for the plant height, spike length and grain yield per hectare decare revealed the highest values. The genotypes G6, GS, G4, G14, G9, G8 and G7 gave lower values than the average in terms of grain yield, whereas the other genotypes gave higher values than the general average. According to biplot graphical results, while locations 1 and 8 were closely related, locations 9, 2 and 7 were positively related to these environments. Although the location 7 is slightly different from the other 4 locations, these 5 locations can be seen as a mega environment. Genotypes G12, G2, G3 and G10 for this mega-environment showed the best performances. According to the results of grain yields obtained from 9 different locations, the location 5 was the most discriminating area while the location 1 was the least discriminating. Location 2 was the best representative location, while locations 4 and 7 were with the lowest representation capability. The locations that are both descriptive and representative are good test locations for the selection of adapted genotypes. Test environments, such as location 8, with low ability to represent are useful for selecting genotypes that perform well in specific regions if the target environments can be subdivided into sub-environments.展开更多
It is important to study the contributions of climate change and human activities to cropland changes in the fields of both climate change and land use change. Relationships between cropland changes and driving forces...It is important to study the contributions of climate change and human activities to cropland changes in the fields of both climate change and land use change. Relationships between cropland changes and driving forces were qualitatively studied in most of the previous researches. However, the quantitative assessments of the contributions of climate change and human activities to cropland changes are needed to be explored for a better understanding of the dynamics of land use changes. We systematically reviewed the methods of identifying the contributions of climate change and human activities to cropland changes at quantitative aspects, including model analysis, mathematical statistical method, framework analysis, index assessment and difference comparison. Progress of the previous researches on quantitative evaluation of the contributions was introduced. Then we discussed four defects in the assessment of the contributions of climate change and human activities. For example, the methods were lack of comprehensiveness, and the data need to be more accurate and abundant. In addition, the scale was single and the explanations were biased. Moreover, we concluded a clue about quantitative approach to assess the contributions from synthetically aspect to specific driving forces. Finally, the solutions of the future researches on data, scale and explanation were proposed.展开更多
文摘The research was carried out with 9 triticale, 3 bread wheat, 3 durum wheat and 3 barley varieties and advanced lines in Tekirdag, Edime and Silivri locations during three years. In the study, the data obtained from combined variance analysis were performed and the significance of the differences between the averages was determined by LSD multiple comparison test. GGE biplot analysis and graphics were made by using the statistical package program. The genotypes G2 and G3 for thousand kernel weight, genotype G1 for the heading time and test weight, genotypes G14 and G15 for the maturation time, number of spikelets per spike and grain weight per spike and G13 for the plant height, spike length and grain yield per hectare decare revealed the highest values. The genotypes G6, GS, G4, G14, G9, G8 and G7 gave lower values than the average in terms of grain yield, whereas the other genotypes gave higher values than the general average. According to biplot graphical results, while locations 1 and 8 were closely related, locations 9, 2 and 7 were positively related to these environments. Although the location 7 is slightly different from the other 4 locations, these 5 locations can be seen as a mega environment. Genotypes G12, G2, G3 and G10 for this mega-environment showed the best performances. According to the results of grain yields obtained from 9 different locations, the location 5 was the most discriminating area while the location 1 was the least discriminating. Location 2 was the best representative location, while locations 4 and 7 were with the lowest representation capability. The locations that are both descriptive and representative are good test locations for the selection of adapted genotypes. Test environments, such as location 8, with low ability to represent are useful for selecting genotypes that perform well in specific regions if the target environments can be subdivided into sub-environments.
基金National Natural Science Foundation of China,No.41401113,No.41371002,No.41471091The Science and Technology Strategic Pilot of the Chinese Academy of Sciences,No.XDA05090310The Key Project of Physical Geography of Hebei Province
文摘It is important to study the contributions of climate change and human activities to cropland changes in the fields of both climate change and land use change. Relationships between cropland changes and driving forces were qualitatively studied in most of the previous researches. However, the quantitative assessments of the contributions of climate change and human activities to cropland changes are needed to be explored for a better understanding of the dynamics of land use changes. We systematically reviewed the methods of identifying the contributions of climate change and human activities to cropland changes at quantitative aspects, including model analysis, mathematical statistical method, framework analysis, index assessment and difference comparison. Progress of the previous researches on quantitative evaluation of the contributions was introduced. Then we discussed four defects in the assessment of the contributions of climate change and human activities. For example, the methods were lack of comprehensiveness, and the data need to be more accurate and abundant. In addition, the scale was single and the explanations were biased. Moreover, we concluded a clue about quantitative approach to assess the contributions from synthetically aspect to specific driving forces. Finally, the solutions of the future researches on data, scale and explanation were proposed.
