Determinants of farmers’ adaptation to climate change
In this study Hausman test
(Hausman and McFadden, 1984) was used to check for the validity of the independence of irrelevant alternatives (IIA) assumption and the study failed to reject the null hypothesis (Table 1). As to
Roco et al., (2014) the difference between the alternatives, which is distributed as chi-square with degrees of freedom equal to the rows in restricted model if IIA is true. Significant c2 values indicate violation of the assumption that the difference between the alternatives is not equal to zero
(Sofoluwe et al., 2011).
The result of multinomial logit model shows that family size as in
(Enete et al., 2015) farm size
(Fadina and Barjolle, 2018) climate change awareness and farm experience (
Gadédjisso-Tossou, 2015;
Yong, 2017) were variables significantly increasing the probability of adoption of multiple crop type adaptation measure. A unit increase in family size, farm size,
(Fadina and Barjolle, 2018) climate change awareness and farm experience would increase the probability of adoption of multiple crop type adaptation measure by 0.072, 0.0416, 0.0868 and 0.0344 respectively (Table 2 and 3).
Family size, year of education as in
(Adeoti et al., 2016; Gadédjisso-Tossou, 2015), farm size, extension service
(Kim et al., 2012; Vijayasarathy and Ashok, 2015), climate change awareness (
Gadédjisso-Tossou, 2015;
Obayelu et al., 2014) and nonfarm income were significant factors that determine the probability of adoption of planting improved crop varieties measure to change in climate (Table 2). A unit increase in family size, year of education, farm size, extension, climate change awareness and nonfarm income would increase the choice of planting improved crop varieties by 0.0104, 0.022, 0.0384, 0.014, 0.0174 and 0.0042 respectively (Table 3).
Adjusting planting dates as an adaptation strategy helps farms to adjust the timing of agricultural activity. Year of education as reported by
(Adeoti et al., 2016; Obayelu et al., 2014), extension services (
Gadédjisso-Tossou, 2015;
Kim et al., 2012; Obayelu et al., 2014; Tanko and Muhsinat, 2014), climate change awareness (
Gadédjisso-Tossou, 2015;
Obayelu et al., 2014; Swai, 2017) and farming experience and
(Adeoti et al., 2016; Gadédjisso-Tossou, 2015;
Kim et al., 2012; Yong, 2017) were found factors that would increases the probability that farmers will chose adjusting planting dates adaptation measures. A year increase of education, climate change awareness and farming experience and a unit increase in extension contact would increase the choice of adjusting planting dates as a climate change adaption strategy by 0.0264, 0.037, 0.094 and 0.063 respectively (Table 2 and 3).
The analysis of land fragmentation climate change adaptation strategy show that family size
(Enete et al., 2015) and farm size
(Adeoti et al., 2016) were a positive and significant factor that determine the probability of adoption of land fragmentation measure and the marginal effect shows that a unit increase in family size and farm size would increase the adoption of land fragmentation adaptation strategy to climate change by 0.0448 and 0.0308 respectively. Distance to farmland was found negatively significant factor in adoption of land fragmentation measure to change in climate. Thus a unit increase in farmland would decrease the adoption of the land fragmentation strategy for climate change (Table 2 and 3).
Irrigation practices were one of the adaptation strategy farmers adopted in the study area. The result shows that family size, farm size and nonfarm income were identified as significant factors that determine the probability of choice of irrigation practice as a climate change adaptation strategy (Table 2). A unit increase in family size, farm size and nonfarm income would increase the adoption of irrigation practices by 0.027, 0.0492 and 0.071 respectively (Table 3).
Constraints to climate change adaptation
The varimax-rotated factor analysis of major factors that constrain food crop farmers in adapting to change in climate in the study area is presented in Table 4. The data indicate that four (4) factors were identified as constraints to adaption to the changing climate. To select the principal factors explaining the data, the Kaiser criterion was employed. These factors are institution and government, technology and inputs, information and knowledge and belief, culture, participation. After rotation, the first factor reported 18.32 percent, the second factor reported for 14.73 per cent, the third factor reported for 11.3 per cent and the fourth factor reported for 6.94 per cent of the variance in the 28 constraining components.
Under factor 1 lack of training on adaptation by extension personnel (0.671), lack/inadequate credit facilities (0.642), lack government policies in empowering food crop farmers (0.573), lack extension programs directed to CCA strategies (0.549), lack of awareness about NGO’s program on adaptation to climate change (0.498), Government unresponsiveness to climate risk management (0.435) and poor agricultural extension services (0.427) were variables that leaded high. Similarly (
Ifeanyi-Obi and Issa, 2013;
Otitoju and Enete, 2016;
Ozor et al., 2010) found that the poor agricultural extension service delivery and the incapability of extension workers to capacitate farmers on climate change were identified as a factor that constraints to adaption to the changing climate.
The government can play a great role by making development policies that will empower farmers, climate risk management and strategy in coping with the changing climate. Availability of credit facilities will provide the farmers financial support to adopt climate change adaptation strategy. Since banks look for collateral the appreciation of small and microfinance could be a source of finance to the farmers. The extension program in the area was intended to help farmers in solving farming-related problems. The constraint regarding the extension program was that there is no specific program or training regarding climate change and coping mechanism. Thus, improvement in credit facility and extension program would help farmers to make the right decision regarding the changing climate.
Under factor 2 variables that loaded high were poor access to improved crop varieties (0.792), high cost of using new technologies (0.642), lack of adequate technical knowledge about new technology (0.633), poor water harvesting technology (0.563), lack of access to irrigation facility (0.532), high cost of improved crop varieties (0.496), non-availability to fertilizer and farm input (0.473), lack of functional irrigation scheme (0.471), high cost of irrigation facility 0.466, high cost of fertilizer and farm input, (0.465). These results are consistent with findings by
(Guodaar and Asante, 2018) that the high cost of fertilizer and farm input; (
Ifeanyi-Obi and Issa, 2013;
Ozor et al., 2010) high cost of irrigation facility was identified as a factor that hinders farmers from using adaptation to changing climate.
It is clear that innovation plays an important role in agricultural transformation and climate adaptation planning. In the study area, farmers identified constraints related to technology and inputs. Crop varieties that can adapt to changing climate would help farmers to increase their productivity but they maintained the problem of availability and high cost of improved varieties as a constraint. Fertilizer and other farm inputs that can help farmers to adapt to the changing climate were also identified as a constraint both in availability and cost-wise. Technology regarding irrigation and water harvest was also another constraint. The respondents identified the cost of technology and knowhow was hindering them to adapt to changing climate. Government and concerned bodies can influence constraints of technology and inputs through the development of research and institutions.
Under factor 3 the main constraints were inadequate knowledge in coping climate variability (0.682), early warning systems (0.631), poor climate adaptation measures information (0.547), lack of adequate information sources on new technology (0.493), unavailability of access to climate or weather information (0.491) and inadequate knowledge on climate variability (0.428) were factor loaded higher.
Information and knowledge gap was also identified as one factor hindering the farmers to adapt to change in climate. Availability of information regarding climate variability and early warning would help farmers to adjust their activity to reduce the risk of climate change. But in the study area, the responders have identified poor climate information, early warning and knowledge of climate variability as a constraint to the adaptation to climate change. Knowledge of climate change adaptation measures and the coping mechanism would help farmers to adapt to the changing climate. But in the study area farmers identified that they would have adjusted their farming to the changing climate if they would knew coping mechanisms. Thus providing them information regarding climate change and knowledge transfer on coping mechanisms would help them to reduce the climate risk.
Variables loaded high under factor 4 were involvement in off-farm activities (0.633), traditional practices on the commencement farming seasons (0.587), multiple domestic responsibilities of farmers (0.531), norms, customs, culture and traditional belief (0.492) and religious belief/holidays/festivals of the farming households (0.475). These results confirm the findings of
(Otitoju and Enete, 2016) that neighbourhood norms, customs, culture and traditional beliefs against adaptation and religious belief of the farming household identified as a factor that hinders farmers from using adaptation strategy. Norms, customs, culture and tradition and religion are variables that influence the activity/action of the human being. In the study area, these variables were identified as a factor that constraints the adaptation to changing climate. The other factors are the multiple domestic responsibilities by farmers and participation in a nonfarm activity to generate income.
