Editorial: Accelerating Genetic Gains in Pulses
Bibliographic record
Abstract
Legumes, members of the Fabaceae/Leguminosae family, are the third largest family of higher plants with almost 20,000 species belonging to 650 genera, and are ubiquitous all over the world. Among all legumes, pulse crops or food legumes fall into the four clades of the subfamily Papilionoideae which include aeschynomenoids/dalbergiods, genistoids, hologalegina, and phaseoloids/millettoids. They are distinctive due to their positive impact on agricultural and environmental sustainability and have a prominent role in promoting human and animal health, soil amelioration, cropping system diversification, and sustenance of rural livelihoods (Pratap et al., 2021a). These also provide protein isolates that are increasingly being used in the food industry as functional ingredients suitable for vegan diets (Robinson et al., 2019). The inclusion of pulses in rotation with cereals helps to improve system yields, enhance net carbon sequestration, and lower the carbon footprint. Nonetheless, in addition to being an excellent source of protein, starch, and micronutrients, pulses also contain anti-nutritional compounds that can interfere with the absorption of minerals (Moore et al., 2018) and also the digestion of protein (Clemente et al., 2015). Realizing their importance, significant research has been dedicated to their genetic amelioration, thereby turning them into mainstream crops from so-called “orphan legumes”. Classical plant breeding methods led to the development of more than 3,800 improved varieties of different pulse crops globally, with improved attributes of grain yield, crop duration, stress resistance, nutrition quality, etc. However, despite this effort, the increase in average pulse yields (from 637 to 1,009 kg/ha) has been modest compared to dramatic increases in cereal productivity (from 1,353 to 4,074 kg/ha) between 1961 and 2017 (Kumar et al., 2020). Among legumes, Koester et al. (2014) studied 80 years of historical data of soybean breeding at the Crop Research and Education Center in Urbana, USA and reported a genetic gain of 26.5 kg ha−1 year−1 , attributing the gain in grain yield to increases in light interception, energy conversion, and partitioning efficiencies. Productivity gains in pulses have been recorded when especially considered along with the markedly reduced duration of the improved varieties, leading to increased cropping intensity, while genetic gains have been recorded for traits imparting resistance to major biotic and abiotic stresses, herbicide tolerance, larger seeds, and improved nutritional quality. This resulted in the growth, in terms of production and productivity, in major pulse-producing countries. For example, India witnessed the highest growth in production in mung bean (178%), followed by chickpea (125%), urdbean (90%), pigeonpea (51%), and lentil (34%) in the last 15 years (Gaur, 2021). Notably, breeding in most pulses has remained confined to the exploitation of genetic variation within the primary gene pool, which has resulted in a narrow genetic base in most of them.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".