Biplot analysis of test environments of quinoa (Chenopodium quinoa Willd.) in Burkina Faso

Abstract

The identification of stable and adaptable high yielding quinoa (Chenopodium quinoa Willd.) and, highly discriminative environments are worthwhile for a successful introduction and adoption of this crop in Burkina Faso. The objectives of this study were to determine the relationship among test environments, to identify the most discriminative and representative test environment(s), and to identify high yielding and stable quinoa variety. The study highlighted that prevailing agrometeorological conditions in an area determine the specificity of the environment. Thus, quinoa growth and productivity is affected by differences in pedological and meteorological conditions. Emerging findings showed that environment E1 at Farako-Bâ characterized by a relative low wind speed (2.03 m/s), no rainfall (0 mm) and moderate temperature (25.07°C), was efficient discriminative and representative of quinoa growing conditions in Burkina Faso for both grain yield and grain yield per plant. Quinoa varieties, Puno and Titicaca were the highest yielding (1132 and 892 kg/ha, respectively) and stable across the environments, while Pasankalla, with an average yield of 779 kg/ha, showed a specific adaptation in two environments having a short day length located at Saria and Lanfiera. The photoperiodicity and temperature were key factors determining the adaptation of this variety in an environment. Plant height and number of branches of Negra Collana were highly stable but its yield performance was low (121 kg/ha). The research implications of this study are numerous, including tailoring quinoa growing calendars and screening a large number of genotypes under the best test environment identified, prior a multi-location trial.

Genotype by Environment Interaction by AMMI and GGE Bi-Plot Analysis for Maize (Zea Mays L.) for Transitional High Land Agroecology of Ethiopia.

“…Only one test environment (Bako) is both representative and discriminating, making it a good place to nd genotypes that are generally adapted. Similar results as many authors Y Matana [4]; SALEEM [26], Dagnechew [27], Ndlala [28], Dao [29] and Moges [30], who used GGE biplot to identify high representativeness and discriminating for different crops.…”