Understanding Farmers’ Response to Climate Variability in Nigeria: A Multinomial Logit Approach

Abstract

In Nigeria, farming still depends largely on nature and therefore, vulnerable to climate variability. Climate variability affects production in a number of ways such as uncertainties with the onset of the farming season, flooding and drought in some areas .Farmers have responded by using adaptation strategies. The determinants of farmers' use of these strategies have not been adequately ascertained. In this study, farmers 'response to climate variability was examined. Primary and secondary data were used. A multi-stage sampling procedure was adopted in the collection of the primary data using structured questionnaires. Four vegetation zones out of seven where farming is mainly carried out were selected for the study. Using a list of farmers from the Agricultural Development Project, 470 farmers were randomly selected based on probability proportionate to the number of farmers in each vegetation zone. They were administered structured questionnaire. Data collected included socio-economic characteristics of the farmers, adaptation strategies they used and constraints associated with them. Data on climate were collected from Nigeria Meteorological Station, Oshodi. Data were analyzed using descriptive statistics, and multinomial logit models.

Smallholder adoption of technology: Evidence from the context of climate smart agriculture in South Africa

“…Accordingly, farmers who were aware of the Hybrids and improved OPVs and thought they gave benefits such as immediate benefits, increased yield and improving households" food security were likely to use them relative to the traditional seeds. These results concur with research that found that if farmers believe that DTSVs were responsible for high yield and enhanced food security, they are most likely to adopt them to improve their livelihood (Diale, 2011;Ikheloa et al, 2013;Senyolo et al, 2018).…”

“…While access to government support (i.e. formal extension services) usually influence adoption of technologies positively (Diale, 2011;Ikheloa et al, 2013), this was not the case in our study. Our data shows negative but insignificant relationship between government support and the use of DTSVs.…”

“…Almost all of them 99.5% (195) noted they have experienced it. Previous studies indicated the relationship between technology adoption (also in response to climate change) and access to land (Diale, 2011;Ikheloa et al, 2013). For instance, in previous research a positive relationship between farm size and technology adoption was expected but negative statistical significance was observed (Mulaudzi and Oyekale, 2015).…”

“…Therefore, the dependent variable was specified as the seed variety that farmers adopted/used, as follows: 1= "Hybrids and Hybrids with OPVs or traditional seeds"; 2= "OPVs and OPVs with traditional seeds"; and 3= "traditional seeds". The independent variables were derived from the 5 factors produced by the factor analysis, together with other socio-economic variables based on the literature (Ikheloa et al, 2013;Mugi-Ngenga et al, 2016;Mulaudzi and Oyekale, 2015;Senyolo et al, 2018;Ubisi et al, 2017).…”

Effets De La Variabilite Climatique Sur Le Rendement De Quelques Cultures Vivrieres Dans Le Nord-Est Du Benin

“…Cette forte variabilité aussi bien temporelle, spatiale que quantitative des précipitations rend les systèmes de production agricole plus vulnérables et constitue une contrainte majeure aux objectifs d'autosuffisance alimentaire que se sont fixés les pays de l'Afrique de l'Ouest (Agbossou, 2010). La variabilité climatique affecte la production de plusieurs façons par les incertitudes avec le début de la saison agricole, les inondations et la sécheresse dans certaines zones (Ikheloa et al, 2013). L'évolution des conditions climatiques a de graves répercussions négatives sur la production agricoles, la sécurité alimentaire et les ressources naturelles (Agbongiarhuoyi et al, 2013).…”