MétaCan
Menu
Back to cohort
Record W4403804191 · doi:10.1080/21683565.2024.2419407

Manure contribution to rural livelihoods at farm and landscape levels: a systemic approach in semi-arid Central Tunisia

2024· article· en· W4403804191 on OpenAlexaff
Véronique Alary, Aymen Frija, Mohamed Arbi Abdeladhim, M. Sghaier, Crystèle Léauthaud, Manel Farhat, Mongi Sghaïer

Bibliographic record

VenueAgroecology and Sustainable Food Systems · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsÉcole de Technologie Supérieure
FundersConsortium of International Agricultural Research Centers
KeywordsLivelihoodAridGeographyAgroforestryManureAgricultureEnvironmental scienceAgronomyEcologyBiology

Abstract

fetched live from OpenAlex

Manure valorization through on-farm use or market transactions is an ancient and widespread practice in the mixed crop-livestock systems of the semi-arid areas of North Africa. While research has long focused on the manure contribution to soil fertility at the plot level, little has been done concerning livelihood conditions. The present paper aims to assess the contribution of manure use and exchange on the livelihoods of rural communities using an original dataset collected in 2021 among 150 farmers in Central Tunisia. This analysis is carried out within the analytical agroecology framework combined with factor analysis methods. Results showed that manure use and valorization differ along the watershed, from a socioeconomic perspective in small farms operating under rainfed tree-pastoral systems, to an environmental and agronomic perspective in the mixed rainfed-irrigated systems downstream. Manure flow analysis confirmed that on-farm manure balance is positively correlated to economic wealth. However, the manure fluxes questioned the environmental sustainability of the vulnerable zones. Its use and management could significantly impact livelihood discrepancies in the future, with the increasing of demand and use of manure in more favorable zones such as irrigated lands at the detriment of the rainfed zones.

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 score0.275
Threshold uncertainty score0.373

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 studies0.0000.000
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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAgroecology and Sustainable Food SystemsSame topicAgriculture and Rural Development ResearchFrench-language works237,207