Identification of high-yielding stable promising rice lines and determination of suitable areas for rice lines would be done by additive main effects and multiplicative interaction(AMMI) model. Seven promising rice ge...Identification of high-yielding stable promising rice lines and determination of suitable areas for rice lines would be done by additive main effects and multiplicative interaction(AMMI) model. Seven promising rice genotypes plus two check varieties Shiroudi and 843 were analyzed using a randomized complete block design with three replications in three consecutive years(2012, 2013 and 2014). Homogenous error variance was indicated in the nine environments for grain yield. The combined analysis of variance indicated significant effects of environment, genotype and genotype × environment(GE) interactions on grain yield. The significant effect of GE interaction reflected on the differential response of genotypes in various environments and demonstrated that GE interaction had remarkable effect on genotypic performance in different environments. The application of AMMI model for partitioning the GE interaction effects showed that only the first two terms of AMMI were significant based on Gollob's Ftest. The lowest AMMI-1 was observed for G7, G2 and G6. G7 and G6 had higher grain yield. According to the first eigenvalue, which benefits only the first interaction principal component scores, G1, G6, G2 and G9 were the most stable genotypes. The values of the sum of first two interaction principal component scores could be useful in identifying genotype stability, and G6, G5 and G2 were the most dynamic stable genotypes. AMMI stability value introduced G6 as the most stable one. According to AMMI biplot view, G6 was high yielding and highly stable genotype. In conclusion, this study revealed that GE interactions were an important source of rice yield variation, and its AMMI biplots were forceful for visualizing the response of genotypes to environments.展开更多
The analysis of energy-use patterns and carbon footprint is useful in achieving sustainable development in agriculture.Energy-use indices and carbon footprint for rain-fed watermelon production were studied in the Kia...The analysis of energy-use patterns and carbon footprint is useful in achieving sustainable development in agriculture.Energy-use indices and carbon footprint for rain-fed watermelon production were studied in the Kiashahr region of Northern Iran.Data were collected from 58 farmers using a self-structured questionnaire during the growing season of 2013.The Cobb–Douglas model and sensitivity analysis were used to evaluate the effects of energy input on rain-fed watermelon yield.The findings demonstrated that chemical fertilizers consumed the highest percentage of total energy input(75.2%),followed by diesel fuel(12.9%).The total energy input was 16594.74 MJ ha^-1 and total energy output was 36275.24 MJ ha^-1.The results showed that the energy-use ratio was 2.19,energy productivity was 1.15 kg MJ1,energy intensity was 0.87 MJ kg1,and net energy gain was 19680.60 MJ ha^-1.Direct and indirect energy for watermelon production were calculated as 2374.4 MJ ha^-1(14.3%)and 14220.3 MJ ha^-1(85.7%),respectively.The share of renewable energy was 1.4%.This highlights the need to reduce the share of non-renewable energy and improve the sustainability of rain-fed watermelon production in Northern Iran.The study of carbon footprint showed that the chemical fertilizer caused the highest percentage of greenhouse gas emissions(GHG)followed by machinery with 52.6%and 23.8%of total GHG emissions,respectively.The results of the Cobb–Douglas model and sensitivity analysis revealed that increasing one MJ of energy input of human labor,machinery,diesel fuel,chemical fertilizers,biocides,and seed changed the yield by 1.03,0.96,0.19,0.97,0.16,and 0.22 kg,respectively,in the Kiashahr region of Northern Iran.Providing some of the nitrogen required for crop growth through biological alternatives,renewing old power tillers,and using conservation tillage machinery may enhance energy efficiency and mitigate GHG emissions for rain-fed watermelon production in Northern Iran.展开更多
文摘Identification of high-yielding stable promising rice lines and determination of suitable areas for rice lines would be done by additive main effects and multiplicative interaction(AMMI) model. Seven promising rice genotypes plus two check varieties Shiroudi and 843 were analyzed using a randomized complete block design with three replications in three consecutive years(2012, 2013 and 2014). Homogenous error variance was indicated in the nine environments for grain yield. The combined analysis of variance indicated significant effects of environment, genotype and genotype × environment(GE) interactions on grain yield. The significant effect of GE interaction reflected on the differential response of genotypes in various environments and demonstrated that GE interaction had remarkable effect on genotypic performance in different environments. The application of AMMI model for partitioning the GE interaction effects showed that only the first two terms of AMMI were significant based on Gollob's Ftest. The lowest AMMI-1 was observed for G7, G2 and G6. G7 and G6 had higher grain yield. According to the first eigenvalue, which benefits only the first interaction principal component scores, G1, G6, G2 and G9 were the most stable genotypes. The values of the sum of first two interaction principal component scores could be useful in identifying genotype stability, and G6, G5 and G2 were the most dynamic stable genotypes. AMMI stability value introduced G6 as the most stable one. According to AMMI biplot view, G6 was high yielding and highly stable genotype. In conclusion, this study revealed that GE interactions were an important source of rice yield variation, and its AMMI biplots were forceful for visualizing the response of genotypes to environments.
基金The financial support provided by Rasht Branch,Islamic Azad University is duly acknowledged.
文摘The analysis of energy-use patterns and carbon footprint is useful in achieving sustainable development in agriculture.Energy-use indices and carbon footprint for rain-fed watermelon production were studied in the Kiashahr region of Northern Iran.Data were collected from 58 farmers using a self-structured questionnaire during the growing season of 2013.The Cobb–Douglas model and sensitivity analysis were used to evaluate the effects of energy input on rain-fed watermelon yield.The findings demonstrated that chemical fertilizers consumed the highest percentage of total energy input(75.2%),followed by diesel fuel(12.9%).The total energy input was 16594.74 MJ ha^-1 and total energy output was 36275.24 MJ ha^-1.The results showed that the energy-use ratio was 2.19,energy productivity was 1.15 kg MJ1,energy intensity was 0.87 MJ kg1,and net energy gain was 19680.60 MJ ha^-1.Direct and indirect energy for watermelon production were calculated as 2374.4 MJ ha^-1(14.3%)and 14220.3 MJ ha^-1(85.7%),respectively.The share of renewable energy was 1.4%.This highlights the need to reduce the share of non-renewable energy and improve the sustainability of rain-fed watermelon production in Northern Iran.The study of carbon footprint showed that the chemical fertilizer caused the highest percentage of greenhouse gas emissions(GHG)followed by machinery with 52.6%and 23.8%of total GHG emissions,respectively.The results of the Cobb–Douglas model and sensitivity analysis revealed that increasing one MJ of energy input of human labor,machinery,diesel fuel,chemical fertilizers,biocides,and seed changed the yield by 1.03,0.96,0.19,0.97,0.16,and 0.22 kg,respectively,in the Kiashahr region of Northern Iran.Providing some of the nitrogen required for crop growth through biological alternatives,renewing old power tillers,and using conservation tillage machinery may enhance energy efficiency and mitigate GHG emissions for rain-fed watermelon production in Northern Iran.
