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Record W2591715795 · doi:10.1186/s40066-017-0096-6

Mitigating dry season food insecurity in the subtropics by prospecting drought-tolerant, nitrogen-fixing weeds

2017· article· en· W2591715795 on OpenAlex
Finlay Small, Manish N. Raizada

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAgriculture & Food Security · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLegume Nitrogen Fixing Symbiosis
Canadian institutionsUniversity of Guelph
FundersInternational Development Research Centre
KeywordsAgronomyDry seasonNitrogen fixationWet seasonAgroforestryEnvironmental scienceSubtropicsAgricultureSoil fertilityCover cropBiologyCroppingSoil waterEcology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.218
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it