Climate Change Awareness and Joint Decision to Adopt Agroforestry and Conservation Agriculture Practices in Zambia
Bibliographic record
Abstract
Climate change is one of the biggest challenges that small holder farmers face in Sub-Saharan Africa, including Zambia. In response to this, various interventions such as Agroforestry (AF) and Conservation Agriculture (CA) have been promoted within the country so as to enhance uptake among farmers and ultimately mitigate climate change. However, the adoption rates of these technologies has been low. To understand the adoption process several adoption studies have focused on the effects of socio-economic and other institutional factors on adoption of the technologies. The direct link between small holder farmer’s climate change awareness and the uptake of both AF and CA is an area that has received less attention among these studies. This paper estimates the effect of climate change awareness on the adoption of AF and CA, and establishes whether the adoption of the two technologies is jointly determined in Zambia. Data used emanated from the Indaba Agricultural Policy Research Institute (IAPRI) and University of Zambia (UNZA) Climate Change and Land Use Project that implemented a supplemental climate change survey in 2013 on Rural Agricultural Livelihood Survey (RALS 2012) panel sample of 1,231 households in six districts of Zambia. Results showed that the majority of farmers (77.2 %) were aware of climate change issues and their consequences on agriculture production and the environment. It was also observed that holding other factors constant, a farmer aware of climate change and its consequences was 6% more likely to adopt both AF and CA compared to those not aware. The decision to adopt AF and CA was found to be jointly determined by farmers. In addition to climate change awareness, the other factors affecting the joint decision to adopt the two technologies included gender, farmer group membership of the household head as well as ownership of radio sets and rippers. There is need therefore to deliberately increase climate change awareness among smallholder farmers and promote the simultaneous uptake of both AF and CA through the mass media and the provision of an environment that increases accessibility to tools that ease up the uptake of these technologies.
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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 | 0.002 |
| 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 | 0.001 |
| 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".