Government policy and agricultural production: a scoping review to inform research and policy on healthy agricultural commodities
Bibliographic record
Abstract
Unhealthy foods and tobacco remain the leading causes of non-communicable disease (NCDs). These are key agricultural commodities for many countries, and NCD prevention policy needs to consider how to influence production towards healthier options. There has been little scholarship to bridge the agriculture with the public health literature that seeks to address the supply of healthy commodities. This scoping review synthesizes the literature on government agricultural policy and production in order to 1) present a typology of policies used to influence agricultural production, 2) to provide a preliminary overview of the ways that impact is assessed in this literature, and 3) to bring this literature into conversation with the literature on food and tobacco supply.This review analyzes the literature on government agricultural policy and production. Articles written in English and published between January 1997 and April 2018 (20-year range) were included. Only quantitative evaluations were included. Studies that collected qualitative data to supplement the quantitative analysis were also included. One hundred and three articles were included for data extraction. The following information was extracted: article details (e.g., author, title, journal), policy details (e.g., policy tools, goals, context), methods used to evaluate the policy (e.g., outcomes evaluated, sample size, limitations), and study findings. Fifty four studies examined the impact of policy on agricultural production. The remaining articles assessed land allocation (n = 25) (e.g., crop diversification, acreage expansion), efficiency (n = 23), rates of employment including on- and off-farm employment (n = 18), and farm income (n = 17) among others. Input supports, output supports and technical support had an impact on production, income and other outcomes. Although there were important exceptions, largely attributed to farm level allocation of labour or resources. Financial supports were most commonly evaluated including cash subsidies, credit, and tax benefits. This type of support resulted in an equal number of studies reporting increased production as those with no effects.This review provides initial extrapolative insights from the general literature on the impact of government policies on agricultural production. This review can inform dialogue between the health and agricultural sector and evaluative research on policy for alternatives to tobacco production and unhealthy food supply.
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 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 | 0.019 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.020 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".