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Complex innovations in agriculture, environment, and health – the perceptions of rice farmers in the Jequetepeque Valley, Peru

2021· article· en· W4205303550 on OpenAlexfundno aff
Renata Távora, José Augusto Drummond, Alain Santandreu, Anita Luján, Ernesto Ráez-Luna, Ester Montalvan, Elena Ogusuko, Frédéric Mertens

Bibliographic record

VenueSustainability in Debate · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Cultural Studies in Latin America and Beyond
Canadian institutionsnot available
FundersMinisterio de SaludCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorInternational Development Research Centre
KeywordsAgricultureSustainabilityBusinessSocioeconomic statusEnvironmental planningIrrigationNatural resource economicsAgricultural economicsGeographyEnvironmental healthEconomicsMedicineAgronomy

Abstract

fetched live from OpenAlex

The increased use of water in irrigated rice monocultures in the Jequetepeque Valley, on the northern coast of Peru, has exacerbated environmental, socioeconomic and health problems. The Alternate Wetting and Drying (AWD) irrigation technique aims to increase water management efficiency in rice cultivation. The objective of the present article is to understand farmers’ perceptions about the benefits and risks of implementing AWD. Data from interviews with 319 farmers showed that they recognise nine interactions between AWD's economic, environmental and health aspects but prioritise economic factors when assessing its benefits. We also identified the main channels and spaces of communication and debate on issues related to agriculture and health that are likely to be effective in promoting the diffusion of AWD. The study demonstrated the relevance of integrated actions to encourage the adoption of agricultural innovations which consider the interactions between environmental sustainability, health issues, and producers' economic priorities.

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.

How this classification was reachedexpand

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.271
Teacher spread0.255 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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