Abstract:
This study examined the effects of climate change on maize productivity in Southwestern Nigeria. A multistage sampling was utilized to select 540 respondents for the study. Both primary and secondary data were collected and analyzed using descriptive statistics, trend and growth rate function analysis, auto-regressive distributed lag (ARDL) (1979 – 2020), multinomial logistic regression (MNL), Double-hurdle model and two stage least square regression (2SLS) model. The descriptive results revealed that majority (36.8%) of the sampled respondents were between the ages of 46 and 55 and most (81.2%) of the sampled respondents were male. Similarly, 88.6% were married while their major occupation was farming.
The findings further revealed that 86.8% of the respondents had formal education while most of the maize farmers had between 11 – 15 years farming experience with farming as their major occupation. Results also revealed that farmers cultivated on rented farmland (45.0%) with farm size average of 2 hectares indicating that maize farmers were smallholders. Furthermore, it was revealed that most (94.2%) of the maize farmers were aware of climate change in the study area; only 78.6% stated that there was information on climate change. According to the farmers, the effects of climate change on crop production included (1) reduced crop production levels and (2) no production, which have been negatively affecting their livelihood in a variety of ways, including an increase in socioeconomic issues, a decrease in income, and an increase in unemployment.
In addition, the exponential growth rate results showed significant growth rates in all the variables (maize, output, temperature, amount of rainfall and humidity) except for Ondo and Oyo states where growth rates of temperature and relative humidity were negatively insignificant over the period. The time series cointegration test using ARDL model indicated a long run cointegration relationship among the variables. The result of the short-run dynamic coefficients associated with the long-run cointegration relationships indicated that Error Correction Model (ECM) was statistically significant at 1% and had values (-0.133 and -0.079635) respectively. The result showed that time had a significant impact on maize productivity and climate while climatic variables greatly influenced maize productivity both at short run and long run in the study area.
The main adaptation strategies employed by maize farmers were planting different varieties, practicing crop diversification, mixed cropping, soil conservation, use of agrochemicals and move to different site. The multinomial logistic regression model's (MNL) findings indicated that factors such as age, gender, marital status, education level, household size, major occupation, farming experience, and knowledge of climate change were statistically significant and had an impact on climate change adaptation in the study area.
The adoption intensity of climate change adaptation was studied using a double hurdle model. Most of the criteria were shown to be insignificant in determining the adoption intensity following the decision-making phase of adapting to climate change in the research area. Age, marital status, educational level, household size, land tenure, farm ownership, farm size, information on climate change, and farming experience were found to be determinants of climate change adaptation in the study area, whereas educational level, household size, major occupation, major source of income, information on climate change, climate change awareness, and farming experience were found to be determinants of climate change adaptation intensity.
The two-stage least square result revealed revenue to be endogenous and therefore was instrumented in the empirical analysis and significant exogenous variables that affect maize farmers’ productivity include revenue, climate change adaptation adoption rate, age, gender, educational level, household size, farm ownership, farm size and farming experience.
Conclusively, the study shows that climatic variables and time had a significant impact on maize productivity in the study area. It was therefore recommended that government should develop productivity-enhancing measures that include formal agricultural education, simple access to agricultural inputs, credits, and extension services. Furthermore, there should be improvement of farmers’ knowledge about the different adaptation strategies that were mentioned by the farmers in the study area.
