Preliminary Studies of the Performance of Quinoa (Chenopodium quinoa Willd.) Genotypes under Irrigated and Rainfed Conditions of Central Malawi
Bibliographic record
Abstract
The goal of sustainable intensification of agriculture in Malawi has led to the evaluation of innovative, regionally novel or under-utilized crop species. Quinoa (Chenopodium quinoa Willd.) has the potential to provide a drought tolerant, nutritious alternative to maize. We evaluated 11 diverse varieties of quinoa for their yield and agronomic performance at two locations, Bunda and Bembeke, in Malawi. The varieties originated from Ecuador, Chile and Bolivia in South America; the United States and Canada in North America; and, Denmark in Europe, and were chosen based on their variation in morphological and agronomic traits, and their potential for adaptation to the climate of Malawi. Plant height, panicle length, days to maturity, harvest index, and seed yield were recorded for each variety under irrigation at Bunda and Bembeke, and under rainfed conditions at Bunda. Plant height was significantly influenced by both genotype and environment. There were also significant differences between the two locations for panicle length whereas genotype and genotype × environment (G×E) interaction were not significantly different. Differences were found for genotype and genotype × environment interaction for harvest index. Notably, differences for genotype, environment and G×E were found for grain yield. Seed yield was higher at Bunda (237 to 3019 kg/ha) than Bembeke (62 to 692 kg/ha) under irrigated conditions. The highest yielding genotype at Bunda was Titicaca (3019 kg/ha) whereas Multi-hued was the highest (692 kg/ha) at Bembeke. Strong positive correlations between seed yield and 1) plant height (r=0.74), 2) days to maturity (r=0.76), and 3) biomass (r=0.87) were found under irrigated conditions. The rainfed evaluations at Bunda revealed significant differences in seed yield, plant biomass and seed size among the genotypes. The highest yielding genotype was Black Seeded (2050 kg/ha) followed by Multi Hued (1603 kg/ha) and Bio-Bio (1446 kg/ha). Ecuadorian (257 kg/ha) was the lowest yielding genotype. In general the seed yields of the genotypes were lower under rainfed conditions than under irrigated conditions at Bunda. The results also highlight the need to continue evaluating a diverse number of cultivars to select for genotypes adapted to specific agro-ecological areas and across seasons in Malawi.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".