ADAPTATION METHODS OF ARABLE CROP FARMERS TO CLIMATE VARIABILITY

ADAPTATION METHODS OF ARABLE CROP FARMERS TO CLIMATE VARIABILITY

ABSTRACT

This study examined the adaptation strategies of arable crop farmers to climate variability. The study used multistage sampling technique and primary data were collected from 360 food crop farmers (i.e. 180 respondents were randomly selected from each selected state from the savanna and the rainforest agro-ecological zones that dominates the region). The analytical techniques involved descriptive and inferential statistics. Results of the multinomial logit analysis showed that household size negatively influenced the use of multiple crop varieties, land fragmentation (i.e. multiple farm plots), multiple planting dates and crop diversification. Age of household head had an inverse relationship with the choice and use of multiple crop varieties, land fragmentation (multiple farm plots), multiple planting dates and off-farm employment. Education had a negative effect on the choice and use of multiple crop varieties and multiple planting dates. Sex had positive influence on the choice and use of multiple crop varieties, multiple planting dates and off-farm employment but average distance had a positive relationship with the choice and use of land fragmentation. Tenure security positively influenced the choice and use of crop diversification but access to credit negatively correlated with multiple crop varieties, multiple planting dates and crop diversification. The stochastic frontier analysis showed that labour, farm size and other agrochemicals are highly significant at 1% level of probability in food crop production. The computed mean technical efficiency estimate was 0.84. The technical inefficiency model showed that land fragmentation (i.e. multiple farm plots) and multiple planting dates had significant positive relationship with technical inefficiency but years of climate change awareness and social capital had significant inverse relationship with it. The stochastic frontier profit function showed that rent on farm land and price of labour were highly significant at 1% level of probability. The computed average profit efficiency of the respondents was 0.67. The profit inefficiency model revealed that off-farm employment, multiple planting dates, crop diversification and education level had significant positive relationship with profit inefficiency but land fragmentation (i.e. multiple farm plots), years of climate change awareness and social capital had negative relationship with it. The factor analysis revealed that the major constraints to climate change adaptation among the arable farmers were public, institutional and labour constraints; land, neighbourhood norms and religious beliefs constraints; high cost of inputs, technological and information constraints; farm distance, access to climate information, off-farm-job and credit constraints; and poor agricultural programmes and service delivery constraints. The study, therefore, recommends, inter alia, proactive regulatory land use systems that will make food crop farmers to participate in a more secured land ownership system should be put in place to enhance their investment in climate change adaptation strategies that has a long-term effect. Morealso, Government and non-governmental organizations should help the farmers in the area of provision and/ or facilitate the provision of input-based adaptation strategies in the study area. Again, intensive use of already proven adaptation strategies at farm-level by the farmers at their present resource technology will still make them to reduce technical and profit inefficiencies by 16% and 33% respectively, in the study area.

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