Does Conservation Agriculture Enhance Household Food Security? Evidence from Smallholder Farmers in Nkhotakota in Malawi
Bibliographic record
Abstract
<p>The paper identified factors that influence the adoption and contribution of conservation agriculture (CA) on household food security using household-level data collected in 2010 from Nkhotakota District, Central Malawi where Total Land Care (TLC) a local Non-governmental Organization (NGO) has been promoting CA. To determine factors that influence adoption of CA, a Probit regression model was used. Then, the paper compared estimated production function between adopters and non- adopters of CA. The Probit results show that age and education level of the household head, number of extension visits, and land holding size are important factors that influence farmers’ adoption of CA in the study area. Further results showed that CA adopters had more than 50% higher maize production than that of non-adopters from the Cobb-Douglas production estimates. From the findings, there should be improvement in the delivery of extension services in the promotion and dissemination of agricultural technology to foster wider adoption and improve food security status in the study areas. This can be achieved through increased number of extension workers, increase number of demonstrations when introducing CA technology and improved access to formal education. Our overall results show consistently that CA adopters are better off than non-adopters in various aspects such as maize production, per capita maize requirements and meal frequency. As such, the promoting and up-scaling of CA technologies to smallholder farmers should be intensified as an effective strategy for addressing household food insecurity than the promotion of chemical fertilizers use through programs such as the Farm Inputs Subsidy Program, which is not only unsustainable, but also inappropriate for poor resource farmers.</p>
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".