African Journal of
Environmental Science and Technology

  • Abbreviation: Afr. J. Environ. Sci. Technol.
  • Language: English
  • ISSN: 1996-0786
  • DOI: 10.5897/AJEST
  • Start Year: 2007
  • Published Articles: 1047

Article in Press

Assessment of Farmers Adaptive Capacity to Climate Variability in the Federal Capital Territory (FCT), Abuja, Nigeria

Dada Ibilewa, Samaila K. Ishaya and Joshua I. Magaji, Mustapha Aliyu

  •  Received: 11 April 2021
  •  Accepted: 17 June 2021
The study assessed the adaptive capacity of crop farmers in the six Area Councils of FCT using Geoinformatics. Socio-economic indicators were used to map the adaptive capacity of FCT farmers to climate variability from 1981-2017. The arable crops considered are: yam, beans and maize. The selected climatic variables based on their importance to crop production are: rainfall, temperature, relative humidity and potential evapotranspiration. Four (4) farming communities (ten [10] farming households in each community) were selected from each Area Council using a systematic sampling technique making a total of 24 farm settlements in all. A total of 240 questionnaires were administered. Time series analysis was carried out on the datasets using Microsoft Office Excel to present them over time. The ability of farmers to adapt to climate variability was assessed based on five factors that have a direct influence on crop production which are: financial, human, natural, physical and social capital. The indicator scores were summarized, normalized and weighed for all the Area Councils using the same software. The weight was assigned through the Analytic Hierarchy Process (AHP). This was used to determine the Adaptive Capacity Index (ACI) which was used to produce the Adaptive Capacity Map. The mean adaptive capacity of farmers in FCT shows that, croplands in Abaji registered the highest adaptive capacity (0.7494) followed by croplands in Kuje (0.6608). Moderate adaptation was recorded in croplands in Bwari (0.5507) while low adaptations were documented in AMAC (0.3873) and Kwali (0.2676). Lowest adaptation was revealed in Gwagwalada (0.0691). The implication of this is that Abaji and Kuje will adjust to climate variability by using their assets and do not need external assistance while AMAC, Kwali and Gwagwalada will require expert support to recover from the impact of climate variability. Bwari will require some level of external assistance to overcome the climate variability. AMAC, Kwali, Gwagwalada and Bwari will have low yields without the required external assistance. Abaji and Kuje will use their assets to recover from climate variability and restore their crop yields. Irrigation infrastructures should be subsidized for farmers. Crop farmers were also advised to diversify into off-farm activities. The study demonstrated the potentials of Geoinformatics in climate variability studies through the production of adaptive capacity maps.

Keywords: Adaptation, Croplands, Geoinformatics, Socio-economic, Questionnaires.