Drivers of Multiple Cropping-Systems as Adaptive Strategy to Climate Change in Central-Benin (West Africa)
Bibliographic record
Abstract
Climate change is currently one of the most important global environmental issues that negatively affect agriculture in Sub-Saharan Africa. This importance has resulted in a great interest to understand both the farmers’ perception of and adaptation to observed climate change. A good number of previous studies did explicitly focus on several adaptation strategies. Nevertheless, a better understanding of the socio-economic drivers underlying specific adaptive measures to climate change is crucial to inform specific adaptation components that will fall into a wider adaptation plan. In this respect, the present study focuses on the use of multiple cropping systems consisting of growing two or more crops on the same field either at the same time or one after another as climate change adaptation strategy. Accordingly, this paper examines different strategies commonly used to intensify agricultural production in tropical agriculture. These include crop rotation and association in the center of Benin. Data were collected in central Benin through interviews with 80 farmers selected by using a multistage random sampling technique. Data analysis was carried-out by using descriptive statistics and a Probit regression. The results showed that the major drivers of multiple cropping systems as adaptive strategy to climate change include contacts with extension services, education level, and farm size. Major constraints to the use of multiple cropping systems are gender, adult literacy, perception of adaptation to climate change, experience with climate change impacts, and farmer location. Policy options should include, among others, production of information related to impacts of climate change and their dissemination through formal services such as extension services; identification of potential ways to greatly improve returns on extra agricultural activities, and investigating on the effects of past adoption strategies on the different cropping systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".