Determinants of smallholder farmers’ adoption of short-term and long-term sustainable land management practices
Bibliographic record
Abstract
Abstract Despite increasing land degradation in sub-Saharan Africa, investment in sustainable land management (SLM) remains low. Empirical evidence show that smallholder farmers tend to prioritize investing in SLM practices with short-term turnover—e.g., composting and crop residue integration—in order to improve soil fertility and yields to the neglect of practices like agroforestry whose benefits tend to materialize in a relatively longer period. While it is crucial for farmers to prioritize both short-term and long-term SLM practices for the maintenance of overall ecosystem health, the factors that shape the concurrent adoption of short-term and long-term SLM practices remain underexplored. Using data from a cross-sectional survey with smallholder farming households ( n = 512) in Malawi, we employed logistic regression to examine the determinants of the concurrent adoption of short-term and long-term SLM practices. Our findings show that plot size, farmer-to-farmer knowledge sharing, presence of a chronically ill person in the household, active household labor size, wealth and women's autonomy are noteworthy determinants. A unit increase in plot size was associated with increased odds (OR = 1.41, p < 0.01) of simultaneously adopting short-term and long-term SLM practices. Similarly, a unit increase in the active labor size of the household (OR = 1.30, p < 0.001) was positively associated with the concurrent adoption of short-term and long-term SLM practices. Households with no chronically sick person were 3.2 times more likely to adopt short-term and long-term SLM practices simulataneously compared to those with chronically sick persons. Farming households that exchanged farming information (OR = 2.50, p < 0.001) with other households had significantly higher odds of adopting short-term and long-term SLM practices concurrently than those that did not share farming information. Compared to households in the poorest wealth category, those in the richer (OR = 3.14, p < 0.001) and richest (OR = 3.64, p < 0.001) wealth categories were both significantly more likely to adopt short-term and long-term SLM practices concurrently. These findings suggest that initiatives targeted at promoting the holistic adoption of SLM practices—a combination of both short-term and long-term practices—must pay attention to contextual nuances including household wealth, gender, farmer training and land access dynamics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".