Breeding Chickpea for Early Phenology: Perspectives, Progress and Prospects
Bibliographic record
Abstract
Chickpea (Cicer arietinum L.) is currently grown in over 50 countries representing a wide range of environments and cropping systems. Phenology (time to flowering, podding and maturity) is an important component of crop adaptation in these environments. Crop maturity ranges from 80 to 180 days depending on genotype, soil moisture, time of sowing, latitude and altitude. However, in at least two-thirds of the chickpea growing area, the available crop-growing season is short (90-120 days) due to risk of drought or temperature extremities at the end of season (pod filling stage of the crop). About 73% of the global chickpea area is in South and Southeast Asia where chickpea is largely grown rainfed in the post-rainy season on receding soil moisture and often experiences terminal drought and heat stresses. Early phenology is also important in autumn-sown rainfed crop in Mediterranean-type environments for escape from terminal drought, as in Australia; and in summer-grown crop in the temperate environments for escape from frost at the end of season, as in Canada. Early phenology is also needed for promotion of chickpea to rice-fallows and other late sown conditions of south Asia. Hence, development of early maturing cultivars is one of the major objectives in chickpea breeding programs of International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India and in several countries, including India, Myanmar, Bangladesh, Ethiopia, Australia and Canada. Several short-duration cultivars with resistance to fusarium wilt have been developed which have made significant impacts on enhancing chickpea area and production in central and southern India, Myanmar and Ethiopia. Efforts are being made to combine earliness with resistance to ascochyta blight and chilling tolerance for enhancing adaptation of chickpea to short-season Mediterranean regions and temperate environments. Early and extra-early cultivars are expected to play key role in expanding chickpea area in new niches where available crop growing season is short.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".