Determinants of adoption and intensity of adoption of Fall Armyworm management practices in Southwestern Ghana
Bibliographic record
Abstract
The fall armyworm (FAW) continues to cause substantial economic losses to cereal crop farmers in sub–Saharan Africa (SSA), including Ghana. Specifically, FAW can lead to between 20 and 25 percent yield losses in cereals, particularly maize. Therefore, the factors influencing smallholder farmers’ choice(s) of management practices should be a source of worry and thus extensively pursued. Ironically, the extant literature largely focuses on documenting FAW management practices and estimating damage caused, neglecting what drives farmers’ choices of selecting a combination(s) of FAW management practices. To this end, this study addresses two research questions. First, what factors influence the adoption of FAW management practices in southwestern Ghana? Second, what factors influence the choice of adoption intensity of FAW management practices? The study relied on cross-sectional data involving 290 smallholder maize farmers using multivariate probit and Poisson regressions. The empirics show that 24 percent of farmers do not adopt any management practices. About 45 percent of maize farmers use a single management practice to combat FAW, with an estimated 28 percent using two control methods and only 3 percent using three management practices (i.e., pesticides, handpicking and frequent weeding). The choice of adoption intensity of specific management practice(s) is positively influenced by sex, education, farm size, membership of farmer-based organizations (FBOs) and access to agricultural extension and advisory services. The study highlights a take-home message that demographic characteristics and membership in social networks influence the choice and intensity of FAW management practices. It is therefore prudent for the Ministry of Food and Agriculture (MoFA) and specific Department of Agriculture (DoA) at various sub-national levels to intensify and sustain agricultural extension and advisory services through useful conduits such as agricultural extension agents (AEAs), peer farmers and FBOs to ensure the sustainable management of the FAW in Ghana and by implication, countries in Africa.
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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".