The main aim of this paper is to examine the effects of peri-urbanization on peri-urban land use change patterns, using a binary logistic regression model, in the Bosomtwe district of the Asante region, Ghana. The decision to convert from agricultural land uses to residential and commercial land uses are driven by a myriad of factors, ranging from social to economic in the Bosomtwe District. A triangulation of qualitative and quantitative design was used. Household questionnaires were proportionately administered to 270 respondents in 14 communities, on the basis of population. The data was subjected to the Pearson’s Chi-square, Nigelkerk R 2 and Cramer’s V test of strength of association. Astep-wise binary logistic regression modeling was performed using the PASW v.17. Pearson chi-square value of ? 2 = 73.546 at 26 degrees was significant at p < .000,athough the Cramer’s V test of the strength of the association was moderate at V = 0.37. The logistic regression model reported an overall significance of the model at p < .000 with ? 2 = 24.453, at 4 degrees of freedom. The confidence intervals of (CIs) of (CI: 1.358—4.517) and (CI: 1.039—11.486) for the two main predictors of the outcome, and a B(Exp) values ranging between 2.477 and 3.455 were also reported. This means the odds of respondents being more likely to change their land uses is about 66%. Increasing rate of peri-urbanization is caused by increasing demand for residential, recreational (Hotels and Guest houses) and commercial land uses at the expense of agro-forest land uses. These have some negative implications on local climate and food security. The District assembly should strictly monitor physical development in line with planning schemes.

Using Satellite Remote Sensing and Geographic Information System, this paper analyzes the land use and land cover change dynamics in the Bosomtwe District of Ghana, for 1986, 2010 thematic mapper and enhanced thematic Mapper+ (TM/ETM+) images, and 2014 Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIS) image. The three images were geo-referenced and processed for classification, using the maximum likelihood classifier algorithm. A Jeffries-Matusita’s separability check was used in confirming the degree of spectral separation acceptability of the bands used for each of the land use and land cover classes. The best Kappa hat statistic of classification accuracy was 83%. Land Use and Land Cover (LULC) transition analysis in Environmental Systems Research Institute ESRI’s ArcMap was performed. The results of the classification over the three periods showed that built up, bare land and concrete surfaces increased from 1201 in 1986 to 5454 ha in 2010. Dense forest decreased by 2253 ha over the same period and increased by 873 ha by the 2014. Low forest also decreased by 1043 ha in 2010; however, it increased by 13% in 2014. Our findings showed some of the important changes in the land use and land cover patterns in the District. After the urbanization process, coupled with farmland abandonment, between 1986 and 2010, substantial increments in urban land and clear increments in farmland coverage between 1986 and 2014were found to be the reason for vegetation cover decreases. This suggests that major changes in the socio-ecological driving forces affecting landscape dynamics have occurred in the last few decades.

Recently, peri-urbanisation has led to the transformation of the rural landscape, changing rural land uses into peri-urban land uses, under varying driving factors. This paper analyzes the dynamic transitions among identified land use and land cover (LULC) types in the Bosomtwe district of Ghana, from 1986 to 2014. An integrated approach of geo-information tools of satellite remote sensing in Earth Resource Data Analysis System (ERDAS) Imagine 13 and ArcMap 10.2 Geographic Information System (GIS), with Markov chain analytical techniques were used to examine the combined forest land cover transitions, relative to build-up, recent fallows and grasslands and projected possible factors influencing the transitions under business as usual and unusual situations. Statistical analyses of the classified Landsat TM, ETM+ and Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIS) indicated that over the period of 24 years, the Bosomtwe district has undergone a series of land use conversions with remarkable forest losses especially between 2002 and 2010. In 2010 dense forest cover was degraded to low forest by 4040 ha indicating 0.40% transition probability in the future. There was a remarkable increase of built-up/bare and concrete area with a 380% increment in the 1986–2002 transition periods. The application of the Markov futuristic land use dynamics by the years 2018 and 2028, projected from the 2014 LULC indicated a future steady decline in the area coverage of the dense forest to low forest category. This is currently being driven (as at the 2017 LULC trends), by the combined effects of increasing build up bare and concrete surface land uses as well as the expanding recent fallows and grassland. The paper concluded that the health of the ecosystem and biodiversity of the lake Bosomtwe need to be sustainably managed by the Bosomtwe district assembly.

The Offinso Municipality in the Ashanti region of Ghana as an agricultural region, is vulnerable to climate variability and climate change. A greater segment of the population is least able to buffer and reverberate from climatic stress. This paper assesses the perceptual insights of rural smallholder farmers on climate change and how it influences their adaptation practices. Using a triangulation of the quantitative with qualitative methodological approach we purposively sampled 102 farmer respondents and 15 key informants from 3 communities in the Municipality. Quantitative and qualitative data collected were analyzed using descriptive statistics technique of cross-tabulation and frequency tools in the statistical package for social sciences Version 17 and thematic content analysis respectively. The results from the respondents indicated that a myriad of factors are responsible for the anthropogenic climate change. These were the deforestation, bad farming practices, bush burning and emissions from light industrial and vehicular emissions. Also considered was the precarious effect of climate change on smallholder farmers’ crop yield, due to reduced rainfall and high temperatures, the proxy variables for climate variability and change. These adverse impacts accordingly have repercussions on their livelihood sustenance. Thus, the practicing of agroforestry as both adaptation and mitigation among smallholder farmers is essential to build local resilience, and to promote climate-smart agriculture.