Oteng-Frimpong Richard, Kassim Yussif Baba, Puozaa Doris Kanvenaa, Nboyine Jerry Asalma, Issah Abdul-Rashid, Rasheed Masawudu Abdul, Adjebeng-Danquah Joseph, Kusi Francis
Council for Scientific and Industrial Research (CSIR) - Savanna Agricultural Research Institute, Tamale, Ghana.
Front Plant Sci. 2021 Mar 15;12:637860. doi: 10.3389/fpls.2021.637860. eCollection 2021.
In this study, the differential rankings of 36 groundnut genotypes under varying environmental conditions were studied at various levels of phenotype. Locations that are generally accepted by the crop- and soil-based research community to represent the entire Guinea and Sudan Savanna agro-ecological zones in Ghana were characterized, this time using a crop. The characterization was done based on four farmer-preferred traits (early and late leaf spot disease ratings, and haulm and pod yields) using three models (i.e., AMMI, GGE, and Finlay-Wilkinson regression). These models were used to capture specific levels of phenotype, namely, genotype-by-environment interaction (GE), genotype main effect plus GE (G+GE), and environment and genotype main effects plus GE (E+G+GE), respectively. The effect of three major environmental covariables was also determined using factorial regression. Location main effect was found to be highly significant ( < 0.001), confirming its importance in cultivar placement. However, unlike genotypes where the best is usually adjudged through statistical ranking, locations are judged against a benchmark, particularly when phenotyping for disease severity. It was also found that the locations represent one complex mega-environment, justifying the need to test new technologies, including genotypes in all of them before they can be approved for adoption nationally. Again, depending on the phenotypic level considered, genotypic rankings may change, causing environmental groupings to change. For instance, all locations clustered to form one group in 2017 for early and late leaf spot diseases and pod yield when GE was considered, but the groupings changed when G+GE was considered for the same traits in the same year. As a result, assessing genotypic performance at the various levels to arrive at a consensus decision is suggested. Genotypes ICGV-IS 141120 and ICGV-IS 13937 were found to be the best performing.
在本研究中，在不同表型水平下研究了36个花生基因型在不同环境条件下的差异排名。此次以一种作物为依据，对加纳作物与土壤研究界普遍认可的代表整个几内亚和苏丹稀树草原农业生态区的地点进行了特征描述。基于四个农民偏好的性状（早叶斑病和晚叶斑病评级以及茎蔓和荚果产量），使用三种模型（即AMMI、GGE和芬利 - 威尔金森回归）进行了特征描述。这些模型分别用于捕捉特定的表型水平，即基因型与环境互作（GE）、基因型主效应加GE（G + GE）以及环境和基因型主效应加GE（E + G + GE）。还使用因子回归确定了三个主要环境协变量的影响。发现地点主效应高度显著（<0.001），证实了其在品种布局中的重要性。然而，与通常通过统计排名来评判最佳基因型不同，地点是根据一个基准来评判的，特别是在对病害严重程度进行表型分析时。还发现这些地点代表一个复杂的大环境，这证明有必要在新技术（包括基因型）被批准在全国采用之前，在所有这些地点对其进行测试。同样，根据所考虑的表型水平，基因型排名可能会发生变化，从而导致环境分组发生变化。例如，在2017年，当考虑GE时，所有地点在早叶斑病、晚叶斑病和荚果产量方面聚为一组，但在同一年对相同性状考虑G + GE时，分组发生了变化。因此，建议在不同水平评估基因型表现以达成共识决策。发现基因型ICGV - IS 141120和ICGV - IS 13937表现最佳。
