Determinants of Adaptation Measures on Climate Change. A Case of Small-Scale Maize Farmers in the North-West Province of South Africa

Oduniyi, Oluwaseun Samuel

doi:10.15666/aeer/1705_1126111272

Cited by 19 publications

(17 citation statements)

References 13 publications

(15 reference statements)

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“…The accumulation of experience possessed by a farmer determines the awareness of climate change, besides age also plays a significant role with respect to the farmer’s experience, or otherwise. Equally, according to Oduniyi (2018), Ndambiri et al (2012) and Deressa et al (2008), age was found to be statistically significant to climate change awareness. Ajuang et al (2016) also assert that age significantly affects climate change awareness.…”

Section: Resultsmentioning

confidence: 87%

“…Ironically, just as agricultural production contributes to climate change, yet it is also affected by it. In Sub-Saharan countries, South Africa inclusive, the impact of climate change, as reported by Oduniyi (2018), has reflected on financial capital, and which is attributed to low profit and low cooperative finance. According to Grain South Africa (GSA, 2015), maize industry is one of the largest supply of food production that yields about 25 to 33 per cent of the country total gross agricultural production, and this form the staple food for the majority.…”

Section: Introductionmentioning

confidence: 99%

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Adoption of agroforestry practices and climate change mitigation strategies in North West province of South Africa

Oduniyi

Tekana

2019

IJCCSM

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Purpose It is globally accepted that climate change is presently the greatest threat to the sustainability of human livelihood and biodiversity. Most farmers in the study area are highly aware of climate change and its consequences on the farming system; however, mitigation strategies are clearly lacking. Among the mitigation, mechanism to reduce the threat is achieved by increasing the amount of carbon sinks and reducing greenhouse gas emission through the adoption of agroforestry practices. The purpose of this study is to determine if awareness on climate change leads to the adoption of agroforestry practices, and to examine the determinants. Design/methodology/approach A total number of 117 questionnaires were administered to the farmers in the district using stratified random sampling technique. Data were captured and analysed using STATA and XLSTAT software. Descriptive statistics and Heckprobit sample selection model were used to determine the objectives of the study. Findings The result established that climate change awareness does not lead to the adoption of agroforestry in the study area in which information source and member’s association were statistically significant at (p < 0.1) and (p < 0.05), respectively, and determine the adoption of agroforestry practices, while farming experience (p < 0.1), age (p < 0.05), extension visit (p < 0.05) and education (p < 0.1), were the determining factors that influence the awareness of climate change in the study area. Practical implications Regular number of extensions visit, information and training on agroforestry should be provided to the farmers in the study area. Social implications Farmers’ association should be strengthened among the rural farmers. Originality/value The causal effect or relationship of climate change awareness on mitigation through the practice of agroforestry in South Africa, especially in the study area, has not been measured. This research set a pace in the area of climate change awareness leading to mitigation strategies through the use of agroforestry practices as an option to be used in the rural farming area of South Africa.

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Section: Resultsmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Adoption of agroforestry practices and climate change mitigation strategies in North West province of South Africa

Oduniyi

Tekana

2019

IJCCSM

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“…The reason for this result is that the knowledge and experience of the landowners determine the adoption of natural resources management and the level of adaptive capacity. This was supported by [23], who reported that landownership is likely to influence adoption if the innovation requires investments tied to the land. Figure 2 shows the predictive margin graph of adaptive capacity used by pasture-based livestock farmers in the study area in the face of climate-related events.…”

Section: Resultsmentioning

confidence: 81%

“…Eigenvectors and eigenvalues exist in pairs and every eigenvector has a corresponding eigenvalue. An eigenvector is a direction while an eigenvalue is a number that shows how much variance there is in the data in that direction [23].…”

Section: Empirical Modelmentioning

confidence: 99%

Sustainability of Livestock Farming in South Africa. Outlook on Production Constraints, Climate-Related Events, and Upshot on Adaptive Capacity

2020

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The practice of pasture-based livestock farming systems in South Africa is susceptible to climate-related events, low production output, income fluctuation, and by extension poor adaptive capacity. Understanding the importance and gravity of sustainable livestock farming through adaptive capacity has been identified as a tool to cope in the face of the climate-related event which extends to production output. It is to this end, that the study explored the adaptive capacity and the socioeconomic determinants that influence this capacity used by the pasture-based livestock farmers in the study area. Random sampling technique was used to select a sample of 277 pasture-based livestock farmers in the study area from and their responses concerning on demography, farm-based characteristics, production constraints and adaptive capacity were used. Data were analyzed, in which the descriptive statistics, composite scores, and the extended ordered probit model were used to establish the results. The findings revealed the adaptive capacity score of low, moderate, and high to be 40.1%, 43.7%, and 16.2% respectively. Correspondingly, the model estimate revealed the significant factors that affect the adaptive capacity to include: the use of labor (p < 0.05), other sources of income (p < 0.05). Conversely, the age of the farmers (p < 0.05) and landowners (p < 0.01) was found significant but had a negative relationship to adaptive capacity. By implication, the study concluded that there is a need for fruitions of policies that support farmers’ socioeconomic behavior to engage more in adaptive capacity and to improve the Sustainable Development Goals of the united nation as well as vision 2030 of the National Development Plan.

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“…A list of small-scale maize farmers in the municipalities was obtained from the Department of Agriculture, Forestry, and Fisheries (DAFF). The Raosoft sample-size calculator was used to determine the sample size from the population of maize farmers in the study area [16]. This sample-size calculator takes into account the margin of error, confidence level, and response distribution.…”

Section: Population Sampling Procedure and Sample Sizementioning

confidence: 99%

The Impact of Sustainable Land Management Practices on Household Welfare and Determinants among Smallholder Maize Farmers in South Africa

Oduniyi¹,

Tekana²

2021

Land

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This study investigated the impact of Sustainable Land Management Practices (SLMP) on the smallholder maize farmer’s welfare in the Gert Sibande District in the Mpumalanga Province of South Africa. Farmers’ welfare is paramount to agricultural development and rural vitalisation, especially in sub-Saharan Africa. The aim of the study is to identify the factors that influence the adoption of SLMP and to assess its impact on the net farm income. A multivariate-probit (MVP) model was used to analyse the determinants of SLMP adopted and an efficient endogenous switching regression model (ESRM) was used to estimate the impact of SLMP on the net farm income of the smallholder maize farmers. The MVP results show that household socio-economic characteristics and institutional factors statistically influenced the choice of SLMP. Subsequently, the pair-wise correlation matrix of the MVP model revealed complementarities among all SLMP implemented by the farmers. Similarly, the ESRM treatment effect indicated that the average net farm income of farmers who adopted SLMP were significantly higher than that of the group who did not. Consequently, the study recommended support policies on farmers’ demography, farm-based characteristics, and institutional factors to improve the welfare of the farmers and promote rural vitalisation.

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Determinants of Adaptation Measures on Climate Change. A Case of Small-Scale Maize Farmers in the North-West Province of South Africa

Cited by 19 publications

References 13 publications

Adoption of agroforestry practices and climate change mitigation strategies in North West province of South Africa

Adoption of agroforestry practices and climate change mitigation strategies in North West province of South Africa

Sustainability of Livestock Farming in South Africa. Outlook on Production Constraints, Climate-Related Events, and Upshot on Adaptive Capacity

The Impact of Sustainable Land Management Practices on Household Welfare and Determinants among Smallholder Maize Farmers in South Africa

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