Mitigating dry season food insecurity in the subtropics by prospecting drought-tolerant, nitrogen-fixing weeds
Bibliographic record
Abstract
Subtropical regions experience an extended dry season, which inhibits the growth of most crops, and as a result there is seasonal scarcity of food and fodder. Globally, almost 600 million smallholders and landless laborers experience hunger in the dry season. This situation is expected to worsen, as water shortages are expected to impact up to two-thirds of humanity between 2010 and 2050. A second challenge is that 45% of the world’s agricultural land is sloped and vulnerable to intense surface runoff during the transition from the dry to rainy season (e.g., monsoon). Erosion, along with nutrient mining, contributes to a net loss of soil fertility. Drought-tolerant legumes can mitigate these challenges. Legumes form symbiotic relationships with microbes that can sequester atmospheric nitrogen gas as ammonia, a process termed biological nitrogen fixation (BNF). As a result of BNF, legumes are rich in nitrogen, which is a building block of edible protein and organic nitrogen fertilizer to replenish soils. Leguminous cover crops can be used as food/feed, and as a tool to reduce the need for synthetic fertilizers, prevent erosion, and suppress undesired weeds that grow on bare, dry soil that otherwise cause female drudgery. Unfortunately, cover cropping is not a traditional practice in most subtropical regions and BNF is inhibited by drought (dry season). Subsistence farmers around the world would benefit from nutritious and drought-tolerant cover crops that can sustain nitrogen fixation in the dry season. Here, we propose that neglected crops in addition to native and naturalized plants that persist in the dry season, often considered to be weeds, may be utilized for the development of new cover crops. A detailed framework is presented for the identification, characterization, and selection of such species. As a case study, the framework was applied to the mid-hills of Nepal. A literature review, stakeholder interviews, and field site visits with farmers informed the selection of 78 candidate dry season leguminous cover crop species. It is hoped that this innovative approach will serve as a model to help alleviate food/feed shortages and improve the livelihoods of subsistence farmers in the global subtropics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".