Evaluating Performance of Sixteen Upland Rice Genotypes under Field Conditions for Further Breeding Process
Bibliographic record
Abstract
Rice is second most consumed cereal after wheat and it is considered as staple food in Thailand and most of the ASEAN countries. Analyzing yield performance of upland rice genotypes under field conditions is a key step in rice crop breeding program for improved rice production and ensuring food security. The objective of this research was to conduct a yield trial to assess performance of sixteen Thai upland rice genotypes under field conditions for further breeding process. Data from field trial that was conducted in 2016 in field research area of Faculty of Natural Resources, Prince of Songkla University, Hat Yai, Thailand was used in this assessment. Randomized complete block design (RCBD) with three replications was used to layout the experiment in field. Soil was analyzed upto 50 cm soil depth for its fertility and nutrient status to check the fertilizer requirements before planting. Soil moisture contents were recorded using PR2/6 probe. Observed moisture contents indicated that there was 60-70% water available to all genotypes based on filed capacity (FC). Results indicated that genotypes, Hawm Mali Doi, Jao Khao Pichit, Jao Daeng, Sahm Deuan and Ma-led-nai-fai were shorter in plant height while genotypes Ma-led-nai-fai, Goo Meung Lung, Jao Daeng, Dawk Pa-yawm and Hawm Jet Ban had higher number of tillers m-2. Genotypes Ma-led-nai-fai, Goo Meung Lung, Jao Daeng, Dawk Pa-yawm and Hawm Jet Ban had higher number of panicles m-2. Genotypes Jao Daeng, Ma-led-nai-fai, Sahm Deuan, Hawm Mali Doi and Jao Khao Pichit were early flowering and maturity considering as early maturity genotypes. Genotypes Goo Meung Lung, Hawm Mali Doi, Jao Khao Pichit, Jao Daeng and Mai Tahk had higher 1000 GW while genotypes Dawk Kham, Dawk Kha, Hawm Jet Ban, Dawk Pa-yawm and Bow Leb Nahag were superior in grain yield response. Performance of genotypes indicated that these genotypes can be used for further breeding process for acquiring desired traits.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".