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Record W1980067860 · doi:10.3763/ijas.2009.0452

Agronomic considerations for urban agriculture in southern cities

2010· article· en· W1980067860 on OpenAlexaff
Nikita S. Eriksen‐Hamel, George Danso

Bibliographic record

VenueInternational Journal of Agricultural Sustainability · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsAgriculture Food and Rural Development
Fundersnot available
KeywordsFood securityBusinessAgricultureEnvironmental planningProduction (economics)Urban agricultureNatural resource economicsFood processingPsychological interventionAgricultural economicsEnvironmental scienceEnvironmental protectionGeographyEconomics

Abstract

fetched live from OpenAlex

Urban and peri-urban agriculture (UA) provide a significant contribution to the total food requirements of cities, especially in southern cities of the developing world. Increasing food production in UA is therefore a necessity for increasing the food security of the urban poor. Urban environments are inherently different from rural environments and these differences in environmental conditions are expected to impact differently on crop growth. This review describes agronomic issues that are unique to UA and identifies possible interventions to address them. The constraints that can significantly differ include temperature, air quality, solar radiation and climate. The growth-limiting and growth-reducing factors that affect actual production in UA include water availability, nutrient supply, soil degradation, pests and soil pollution. The interventions addressing these constraints require action at both field level, and municipal or regional levels. The food security of the urban poor will therefore require coordinated efforts and cooperation between the farmers who produce food and the planners and policy makers who manage the supporting systems such as markets, inputs and land registration.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.584
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.009
GPT teacher head0.226
Teacher spread0.217 · 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

Citations50
Published2010
Admission routes1
Has abstractyes

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