Determinants of climate smart agriculture technology adoption in the Norther Province of Zambia

Abstract

Our world, as we know it, is changing faster than what scientific evidence has thus far predicted. Globally, we see an increased occurrence of indeterminate and unpredictable climatic events changing the daily livelihood of people across the planet. Particularly, such impacts include the frequent occurrences of droughts, the increased incidences of pests and diseases in farmer fields (such as the fall army worm in Zambia), the reduced annual rainfall and shrinking freshwater supplies, the increased number of forest wild fires, and the reduction of farmers’ yields. This calls for the need to adapt and build resilience. To support the adaptation and resilience agenda, various global initiatives have been undertaken and include the Intergovernmental Panel on Climate Change (IPCC), the Kyoto Protocol, the Sustainable Development Goals (SDGs), and the Paris Agreement. Despite these global efforts, climate change impacts are still severe for developing countries like Zambia, experienced through erratic weather conditions leading to droughts and floods. This affects rural households more severely, where 70% of the Zambian population rely on agriculture (IAPRI, 2016). Between 1960 and 2003, Zambia’s average temperature rose by 1.3 degrees Celsius and rainfall decreased by 2.3 % each decade (Norimitsu, 2016). To counter these adverse effects, policies were formulated at national level to guide the national agenda on climate change, which includes the National Policy on Climate Change (NPCC) and the National Adaptation Program of Action (NAPA). These policy initiatives have explicitly identified environmentally friendly agricultural and natural resource management practices, which include: (1) improved agronomic practices, (2) tillage and residual management, (3) agroforestry, and (4) increased participation of women, youth and children in climate change programmes, among others, as the main tools for improving smallholder productivity and building resilience strategies. These measures have shown to suit the Climate Smart Agriculture (CSA) framework developed by the Food and Agricultural Organization (FAO) of the United Nations (UN), which is governed by three clear objectives. The CSA objectives include: (i) sustainably increasing agriculture productivity and incomes; (ii) adapting to climate change; and (iii) reducing greenhouse gas emissions. A good volume of literature exists that has assessed the determinants and intensity of the adoption of conservation agricultural technology. However, few studies have examined the uptake of single technologies within the conservation agriculture package, and the low adoption rates of the entire conservation package confirms that farmers have a tendency to selectively pick technologies in the package. As a result of the selective picking of technologies, factors influencing the adoption of individual agricultural technologies and the interrelatedness of the adopted technologies, i.e. whether adopting one particular technology influences the decision to adopt another climate smart technology within a household, has remained subtle. Further, evidence on the impact of the demographic diversity of age is elusive in the CSA framework with regard to the adoption of crop rotation and an efficient stove design as individual technologies. In addition to determining factors influencing the adoption decision of crop rotation as an adaptation strategy and the efficient stove as a mitigation strategy to climate change, we test and analyse whom between the young and old farmer is most likely to adopt the efficient stove and/or the crop rotation technologies by testing hypotheses and observing the effect of the age variable. The reason for including the age variable is not only to assess the demographic impact, but also to guide the Zambian policymakers who are promoting youth participation in technology adoption. We further investigate the role of other demographic variables, such as family size, income and gender, in assessing their roles in the adoption decision. In addition to the econometric analyses, we use independent t-tests and tests of association to examine the statistical differences that exist amongst the respondents as they pertain to the adoption of the CSA technologies, i.e. the efficient cooking stove and crop rotation technologies. This study makes use of survey data collected by the International Fund for Agricultural Development IFAD1 as part of their Smallholder Productivity Promotional Program (S3P). The data is cross-sectional in nature, consisting of a total of 182 smallholder farm households from the Northern Province of Zambia. They used random sampling techniques, based on a sampling frame provided by the Zambian Central Statistical Office (CSO). The first stage involved identifying the Primary Sampling Unit (PSU) and randomly selected Standard Enumeration Areas (SEAs) within the PSU in which the farm households belonged. The data was captured by administering survey questionnaires to the selected respondents. Further, Key Informant Interviews (KII) and Focused Group Discussions (FGDs) were held to enrich and verify the data collected. The model used in this study is the Recursive Bivariate Probit Model (RBPM), which checks for potential biases, such as non-randomness and self-selection. This was necessary, given the nature of the survey that captured data in an area where development programmes are promoted. Overall, the study revealed that, of the CSA technologies practised in the Northern Province of Zambia, crop rotation and the efficient cooking stove design were the most adopted technologies, followed by minimum tillage and residual retention. In this study, we focused on crop rotation and the efficient stove for analyses for the reason that higher rates of adoption are an indication of technology suitability and acceptance. The findings show that a greater number (55%) of the respondents indicated that they were aware of climate change and its consequences, and have since adopted measures to mitigate and build resilience. The study also identified variables found to have significant effects on influencing adoption decisions, such as various human and social capital characteristics; the wealth status of the respondent households; group formation as part of social capital, extension and awareness variables; and location and crops grown. Remarkably, the effect of age on the two technologies under investigation, i.e. the efficient cook stove and crop rotation, was mixed. For instance, the older farmers located in Mungwi and Kasama Districts were more likely to adopt the efficient stove, compared than those in Mbala District were, whereas no significant age effects were found on the crop rotation technology. We also show that those respondents who are exposed to the technologies through demonstration trials are less likely to adopt the technologies, indicating a reluctance to switch to the CSA technologies being promoted, i.e. crop rotation and the efficient stove. In terms of gender, the results show that women-headed households have statistically lower levels of income and smaller household sizes than their male counterparts do, and this can have profound effects on accessing and adopting the CSA technologies.