Yield and Disease Responses of Improved Groundnut Genotypes Under Natural Disease Infection in Northern Uganda: Implication for Groundnut Disease Management
Bibliographic record
Abstract
Groundnut production in Uganda is constrained by groundnut rosette disease (GRD), the main cause of yield loss experienced by farmers. We conducted the current study to assess the responses of improved groundnuts to diseases (rosette and late leaf spot) and yield under local conditions. Four released groundnut genotypes (Serenut 5R, Serenut 8R, Serenut 9T and Serenut 14R) were evaluated in four locations in northern Uganda for two seasons in 2019. We established the experiment following randomised complete block design with three replications. GRD severity (harvest) and late leaf spot (LLS) severity (harvest) on the four genotypes were not significantly (P > 0.05) different but positively correlated with the Area Under Disease Progress Curve (AUDPC). Genotype-by-location interaction for LLS AUDPC, GRD AUDPC and dry pod yield were significant (P < .001). Season-by-genotype interaction was not significant (P = 0.367). Days to 50% flowering were also not significant (P > 0.05). Highest and lowest yields were recorded for Serenut 9T in the Omoro district (1,291 kg/acre) and the Amuru district (609 kg/acre), respectively. Dry pod yield was significantly (P < 0.001) negatively correlated with GRD severity and GRD AUDPC. Yield performance of the four genotypes was not significantly (P < 0.05) different in the districts, except for Kitgum, where yields of Serenut 9T and Serenut 8R were significantly (P < 0.05) higher. These genotypes could be used to manage GRD by smallholder farmers in Northern Uganda. Special consideration should therefore be given to these four groundnut genotypes for GRD management in the Acholi sub-region.
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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) | 0.001 | 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".