Climate change is a potent factor in agriculture production, and the sustainability of food security in sub-Saharan Africa (SSA). While many studies have emphasised modelling climate change impacts on crop yield and adaptation strategies on regional scale, there is still limited understanding of smallholder farmers response to climate change and the measures to combat food security in local communities. This thesis explores a mixed methodological approach to understand the impact of climate variability on maize production for the principal maize-producing communities in the rainforest and savannah agroclimatic zones of Cross River State, Nigeria. This was aimed to expand the knowledge of local adaptation to climate change impact on maize crop production. Four research questions were set to guide the study. Observed climate and maize yield data were collected from the Nigerian Meteorological Agency (NIMET) and Cross River Agricultural Development Project (ADP) in the zones from 1990-2016. The DSSAT model was calibrated with existing site-specific soil, weather, and crop data obtained from the field experiments conducted by the CRADP, and the farmers Focus Group Interview (FGI). The measured data on grains yield, days to anthesis, days to physiological maturity, leaf area index, and harvest index data for the growing seasons experiment between 1990 and 2016 were used to validate the model efficacy to simulate these parameters. The results of r-square above 0.6, and the d-index statistics greater than 0.9 for the evaluated parameters in these agroclimatic zones indicates the model ability to simulate the observed and simulated yield adequately. While the Normalized Root Mean Square Error was less than 10% and the agreement index closer to unity also indicates excellent prediction of the model capacity.
The model was applied to test the sensitivity of maize yield response to changes in rainfall using the Environmental Modification Unit (Emu) in DSSAT. This reveals that rainfall has a strong positive correlation with grain yield, and a significant confidence level at (0.05) in the zones. A seasonal analysis for changes in planting date also reveals that the month of April has the highest mean grain yield of 6557kg/ha in the rainforest, while the savannah was 5942kg/ha. Hence, planting Obasuper 2 maize in the month of April was found to be the best time in the year for the zones.
Secondly, the thesis adopted a participatory survey approach to quantify farmers’ response to the factors influencing maize yield and adaptation to climate variability in the zone. A five-point Likert scale questionnaire of 35 items was designed, and 68 maize farmers were systematically sampled from communities in each zone. A Varimax orthogonal rotation scheme was adopted and factors loading with Eigenvalues greater than one was extracted for a-Factor solution analysis. Factors scores obtained were run in a multiple regression model. The model results were significant at the 0.05 level with R2 of 0.73 in the rainforest and R2 of 0.88 in the savannah. The finding revealed that those crucial factors influencing yield in the zones were climate factor, socioeconomic factor (income), and farm size and fertilizer application.
Thirdly, the thesis explores data from eight focus group discussions conducted in the selected communities of the zones to understand farmers’ responses to climate variability and the coping strategies adopted. Their responses were analyzed in NVIVO software. The findings revealed that climate variability was evident with increased levels of rainfall, heatwaves, and widespread insect infestation, which has not been known in the zones. In response to these impacts, farmers change planting dates, adopt an early maturity cultivar, diversify to other crops like cassava and use a native plant called ‘dogoyaro’ to combat insects. Finally, a synthesis of the approaches was employed to explore the nexus between the quantitative modelling and participatory approaches which showed that changes in planting dates to mid-April as noted by the local farmers, also produced a good yield in the model. These approaches provide a holistic understanding and direction for future sustainable interventions of the impacts of climate change on the rainfed maize growers in the agroclimatic zones.
