J. J. Nuvunga, Cristian Tiago Erazo Mendes, Alessandra Querino da Silva, Luciano Antonio de Oliveira, Carlos Pereira da Silva, Lídia Andarusse, J. S. S. B. Filho
{"title":"利用AMMI贝叶斯模型评价莫桑比克北部常见花生品种(Arachis hypogaea L.)的适应性和稳定性","authors":"J. J. Nuvunga, Cristian Tiago Erazo Mendes, Alessandra Querino da Silva, Luciano Antonio de Oliveira, Carlos Pereira da Silva, Lídia Andarusse, J. S. S. B. Filho","doi":"10.21475/ajcs.22.16.07.p3442","DOIUrl":null,"url":null,"abstract":"This study evaluated the stability and adaptability of common peanut cultivars (Arachis hypogaea L.) in three locations across northern Mozambique over four years, using the additive main effects and multiplicative interaction model (AMMI) under a Bayesian approach. The multi-environmental data consisted of 20 genotypes evaluated in three locations. We analyzed grain yield in tons per hectare in a complete randomized block design for each location. The results indicated that genotypes with higher marginal yield contribute to the genotype by environment interaction (GEI) and thus are not largely recommended for the entire target environment. The Namapa (NMP) location showed consistent behavior and did not contribute to the GEI effect, and in this sense, G6 and G7 would be the best indications for this location. Moreover, genotypes considered stable, with emphasis on the G20 genotype, did not have a good average yield. Mapupulo (MPPL) and Nampula (NLP) had a significant contribution to GEI, and the best genotypes for these locations were G7 and G3, respectively. In this sense, the results of the analysis specified that using genotypes in specific environments would be the best strategy to decrease the effect of GEI and increase peanut productivity in the environments considered","PeriodicalId":8581,"journal":{"name":"Australian Journal of Crop Science","volume":"206 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Evaluating the adaptability and stability of common peanut varieties (Arachis hypogaea L.) in Northern Mozambique using the AMMI Bayesian model\",\"authors\":\"J. J. Nuvunga, Cristian Tiago Erazo Mendes, Alessandra Querino da Silva, Luciano Antonio de Oliveira, Carlos Pereira da Silva, Lídia Andarusse, J. S. S. B. Filho\",\"doi\":\"10.21475/ajcs.22.16.07.p3442\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study evaluated the stability and adaptability of common peanut cultivars (Arachis hypogaea L.) in three locations across northern Mozambique over four years, using the additive main effects and multiplicative interaction model (AMMI) under a Bayesian approach. The multi-environmental data consisted of 20 genotypes evaluated in three locations. We analyzed grain yield in tons per hectare in a complete randomized block design for each location. The results indicated that genotypes with higher marginal yield contribute to the genotype by environment interaction (GEI) and thus are not largely recommended for the entire target environment. The Namapa (NMP) location showed consistent behavior and did not contribute to the GEI effect, and in this sense, G6 and G7 would be the best indications for this location. Moreover, genotypes considered stable, with emphasis on the G20 genotype, did not have a good average yield. Mapupulo (MPPL) and Nampula (NLP) had a significant contribution to GEI, and the best genotypes for these locations were G7 and G3, respectively. In this sense, the results of the analysis specified that using genotypes in specific environments would be the best strategy to decrease the effect of GEI and increase peanut productivity in the environments considered\",\"PeriodicalId\":8581,\"journal\":{\"name\":\"Australian Journal of Crop Science\",\"volume\":\"206 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Australian Journal of Crop Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21475/ajcs.22.16.07.p3442\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Agricultural and Biological Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Australian Journal of Crop Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21475/ajcs.22.16.07.p3442","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Agricultural and Biological Sciences","Score":null,"Total":0}
Evaluating the adaptability and stability of common peanut varieties (Arachis hypogaea L.) in Northern Mozambique using the AMMI Bayesian model
This study evaluated the stability and adaptability of common peanut cultivars (Arachis hypogaea L.) in three locations across northern Mozambique over four years, using the additive main effects and multiplicative interaction model (AMMI) under a Bayesian approach. The multi-environmental data consisted of 20 genotypes evaluated in three locations. We analyzed grain yield in tons per hectare in a complete randomized block design for each location. The results indicated that genotypes with higher marginal yield contribute to the genotype by environment interaction (GEI) and thus are not largely recommended for the entire target environment. The Namapa (NMP) location showed consistent behavior and did not contribute to the GEI effect, and in this sense, G6 and G7 would be the best indications for this location. Moreover, genotypes considered stable, with emphasis on the G20 genotype, did not have a good average yield. Mapupulo (MPPL) and Nampula (NLP) had a significant contribution to GEI, and the best genotypes for these locations were G7 and G3, respectively. In this sense, the results of the analysis specified that using genotypes in specific environments would be the best strategy to decrease the effect of GEI and increase peanut productivity in the environments considered