ABSTRACT
Barley cultivation for drought areas requires a reliable assessment of drought tolerance variability among the breeding germplasms. Hence, 121 barley landraces, advanced breeding lines, and varieties were evaluated under both moisture non-stress and stress field conditions using a lattice square (11×11) design with two replications for each set of the trials. Twelve drought tolerance indices (SSI, TOL, MP, GMP, STI, YI, YSI, HM, SDI, DI, RDI, and SSPI) were used based on grain yield under normal (Yp) and drought (Ys) conditions. Analysis of variance showed a significant genetic variation among genotypes for all indices except for TOL and SSPI indices. Yp had a very strong association with Ys (r = 0.92**) that indicates high yield potential under non-stress can predict better yield under stress conditions. Yp and Ys were positively and significantly correlated with MP, GMP, STI, YI, HM, and DI indices, whereas they were negatively correlated with SSI and SDI. In principal component analysis (PCA), the first PC explained 64% of total variation with Yp, Ys, MP, GMP, STI, YI, HM, and DI. The second PC explained 35.6% of the total variation and had a positive correlation with SSI, TOL, SDI, and SSPI. It can be concluded that MP, GMP, STI, YI, HM and DI indices with the most positive and significant correlation with the yield at both non-stress and stress environments would be better indices to screen barley genotypes, although none of the indices could undoubtedly identify high yield genotypes under both conditions.
O cultivo de cevada para áreas secas exige uma avaliação confiável da variabilidade da tolerância à seca entre os germoplasmas reprodutores. Assim, 121 linhagens crioulas de cevada (linhas de reprodução avançada e variedades) foram avaliadas em campo sob condições sem estresse e com estresse de umidade do solo, utilizando-se para isso um arranjo experimental de malha quadrada (11×11), com duas repetições para cada conjunto de ensaios. Foram utilizados 12 índices de tolerância à seca (SSI, TOL, MP, GMP, STI, YI, YSI, HM, SDI, DI, RDI e SSPI), com base no rendimento de grãos sob condições normais sem estresse (Yp) e com estresse de seca (Ys). A análise de variância mostrou uma variação genética significativa entre os genótipos para todos os índices, com exceção dos índices TOL e SSPI. Yp teve uma associação muito forte com Ys (r = 0,92**), o que indica que o potencial de alto rendimento sob condições sem estresse pode prever melhor rendimento sob condições de estresse. Yp e Ys foram positivamente e significativamente correlacionados com os índices MP, GMP, STI, YI, HM e DI, enquanto, foram correlacionados negativamente com os índices SSI e SDI. Na análise de componentes principais (PCA), o primeiro PC explicou 64% da variação total com Yp, Ys, MP, GMP, STI, YI, HM e DI. O segundo PC explicou 35,6% da variação total e apresentou correlação positiva com SSI, TOL, SDI e SSPI. Pode-se concluir que, os índices MP, GMP, STI, YI, HM e DI com a correlação mais positiva e significativa com a produção nos ambientes sem estresse e com estresse seriam melhores índices para a seleção de genótipos de cevada, embora nenhum dos índices pudesse concretamente identificar genótipos de alto rendimento sob ambas as condições.
Subject(s)
Hordeum , Seed BankABSTRACT
Drought is a global phenomenon that can occur in any ecological zone and render significant damages to both the natural environment and human lives. However, hydro-climatic stresses are growing distinctly in the arid zones across the globe. Literature suggests that the analysis of a long-term data-set could help in strengthening of mitigation planes and rationalization of disaster management policies. Thus, the present study is aimed to analyze the evidence-based historical drought events happened in arid-zone Badin, Pakistan and predict its occurrence and severity for the next 82 years (2018-2099). Drought indices viz standardized precipitation index and reconnaissance drought index have been used to detect the severity of the drought events. Thirty years (1988 to 2017) past data of precipitation and temperature were used to categorize the drought severity and validated against the local data. Climate projections based on RCP 4.5 and 8.5 made at 25x25 km resolution used for future drought analysis. The results demonstrate that the region faced severe to extreme drought in 1990-91 and 2001-04. While, in future 2020-21, 2036-37, 2038-39 would be the extreme driest years under RCP 4.5 and 2029-30, 2089-90 under RCP 8.5. Further insight revealed that the average annual temperature has increased and precipitation has decreased w.r.t the base year 1988. It is concluded that drought detection with SPI and RDI is suitable and drought prediction with the RCP 4.5 and 8.5 could be a better option.