Simple random sample

Agriculture

Demography

Descriptive statistics

Deforestation (computer science)

Ecology

Neuroscience

Biology

Statistics

Agroforestry

Stratified sampling

Archaeology

Agricultural science

Economics

Computer science

Genetics

Climate change

Psychology

Mathematics

Geography

Adaptive response

Environmental resource management

Multistage sampling

Population

Sociology

Environmental science

Programming language

Perception

Adaptive capacity

Logistic regression

Socioeconomics

Cogent Social Sciences

This paper assesses the perception and adaptation strategies employed by smallholder farmers to abate the effect of climate variability. Using a mixed method cross-sectional research approach on smallholder farmers in the Bosomtwe district, 152 farmer respondents were sampled from 12 communities by a simple random sampling technique. The quantitative data gathered were subjected to the tools of binary logistic regression analysis, multiple regression analysis, contingency tables and frequencies, embedded in the Statistical Package for Social Sciences (SPSS) v. 17. Farmers' observations were consistent with meteorological data which revealed that temperature has been increasing since 1981 and rainfall pattern has been erratic. Perception of deforestation (p < 0.05) was a significant and positive predictor of climate change. A binary logistic regression analysis performed on disposition of farmers persistently practicing a particular adaptation strategy indicated that irrigation, mulch, use of agrochemicals and changing the type of crop cultivated were positive and significant at p = .027, p = .000, p = .020 and p = .035, respectively. Policy on adaptation must make room for and capitalize on local knowledge for sustainable climate action. Agroforestry should be introduced to farmers to curb such maladaptive practices of clearing of forests and help sustain forest and agriculture.

Smallholder farmers’ perceptions and adaptive response to climate variability and climate change in southern rural Ghana