Yield Stability of Sweet Sorghum Genotypes for Bioenergy Production Under Contrasting Temperate and Tropical Environments
Bibliographic record
Abstract
Forty-three sweet sorghum accessions were grown in two contrasting environments; Nigeria (tropical environment) and Denmark (temperate environment). The objectives were to determine the interaction between genotype and environment on grain yield, fresh biomass and stem sugar, and to assess yield stability of sweet sorghum and identify the best genotypes for biofuel production. The sweet sorghum originating from a Dutch and ICRISAT collection was grown in randomized complete block design in three replicates for two years (2014 and 2015). The combined analysis of variance of the sweet sorghum genotypes in two years over the two contrasting environments revealed that year (Y), genotype (G), environment (E) and genotype by environment interaction (GEI) were significant in the entire biofuel yield attributes obtained from both Dutch and ICRISAT collections except the degree of Brix and fresh biomass respectively across the year. The year and genotype interaction (Y×G) was not significant in all the biofuel attributes of Dutch accessions. Additive main effect and multiplicative interaction (AMMI) analysis of variance showed significant effect of G, E and the GEI. The AMMI was used to identify the best performing, adaptable and more stable genotypes. Twenty-two genotypes of both ICRISAT and Dutch accessions were identified to be stable across the two locations with respect to different biofuel attributes. Nine, seven, and six genotypes were found to be stable for grain yield, biomass yield and brix value, respectively. The best performing genotypes for stem sugar across locations were identified. From the available data collected, the performance of the sweet sorghum was attributed to both genetic and environmental effects. High GE was observed to influence stability, hence will influence the selection criteria of the sweet sorghum genotypes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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".