Livelihood Vulnerability from Drought among Smallholder Livestock Farmers in South Africa
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Study Design
2.3. Sampling Procedure
2.4. Data Analysis
2.4.1. Livelihood Vulnerability Index
2.4.2. Livelihood Vulnerability Index-Intergovernmental Panel on Climate Change
3. Results
3.1. Social and Economic Characteristics of Livestock Farmers
3.2. Livelihood Vulnerability Index
3.3. Livelihood Vulnerability Index-Intergovernmental Panel on Climate Change
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Abbass, K.; Zeeshan, M.; Huaming, Q.; Muntasir, S.; Mahmood, H. A review of the global climate change impacts, adaptation, and sustainable mitigation measures. Environ. Sci. Pollut. Res. 2022, 29, 42539–42559. [Google Scholar] [CrossRef]
- Lottering, S.; Mafongoya, P.; Lottering, R. Drought and its impacts on small-scale farmers in sub-Saharan Africa: A review. S. Afr. Geogr. J. 2021, 103, 319–341. [Google Scholar] [CrossRef]
- Nhemachena, C.; Nhamo, L.; Matchaya, G.; Nhemachena, C.R.; Muchara, B.; Karuaihe, S.T.; Mpandeli, S. Climate Change Impacts on Water and Agriculture Sectors in Southern Africa: Threats and opportunities for sustainable development. Water 2020, 12, 2673. [Google Scholar] [CrossRef]
- Bahta, Y.T.; Myeki, V.A. Adaptation, coping strategies and resilience of agricultural drought in South Africa: Implication for the sustainability of livestock sector. Heliyon 2021, 7, e08280. [Google Scholar] [CrossRef]
- Ogundeji, A.A. Adaptation to Climate Change and Impact on Smallholder Farmers’ Food Security in South Africa. Agriculture 2022, 12, 589. [Google Scholar] [CrossRef]
- Thao, N.T.T.; Khoi, D.N.; Xuan, T.T.; Tychon, B. Assessment of Livelihood Vulnerability to Drought: A Case Study in Dak Nong Province, Vietnam. Int. J. Disaster Risk Sci. 2019, 10, 604–615. [Google Scholar] [CrossRef]
- Mugandani, R.; Muziri, T.; Murewi, C.T.F.; Mugadza, A.; Chitata, T.; Sungirai, M.; Zirebwa, F.S.; Manhondo, P.; Mupfiga, E.T.; Nyamutowa, C.; et al. Mapping and Managing Livelihoods Vulnerability to Drought: A Case Study of Chivi District in Zimbabwe. Climate 2022, 10, 189. [Google Scholar] [CrossRef]
- Daniell, J.; Wenzel, F.; Schaefer, A. The economic costs of natural disasters globally from 1900–2015: Historical and normalised floods, storms, earthquakes, volcanoes, bushfires, drought and other disasters. In Geophysical Research Abstracts; Geophysical Insititute & Center for Disaster Management and Risk Reduction Technology, Karlsruhe Institute of Technology: Karlsruhe, Germany; Melbourne, VIC, Australia, 2016; p. 2016. [Google Scholar]
- Maltou, R.; Bahta, Y.T. Factors influencing the resilience of smallholder livestock farmers to agricultural drought in South Africa: Implication for adaptive capabilities. Disaster Risk Stud. 2019, 11, 4102. [Google Scholar] [CrossRef]
- Vetter, S. Effect of drought on communal livestock farmers in KwaZulu-Natal, South Africa. Afr. J. Range Forage Sci. 2020, 37, 93–106. [Google Scholar] [CrossRef]
- Bogale, G.A.; Erena, Z.B. Drought vulnerability and impacts of climate change on livestock production and productivity in different agro-Ecological zones of Ethiopia. J. Appl. Anim. Res. 2022, 50, 471–489. [Google Scholar] [CrossRef]
- Bahta, Y.T.; Myeki, V.A. The Impact of Agricultural Drought on Smallholder Livestock Farmers: Empirical Evidence Insights from Northern Cape, South Africa. Agriculture 2022, 12, 442. [Google Scholar] [CrossRef]
- Danso-abbeam, G.; Ogundeji, A.A.; Ojo, T.O.; Okolie, C.C. Livelihood Impact and Risk Management of Drought: The Experiences of Farming Households in the Free State Province, South Africa. Free. State. Nat. Hazards 2021. Preprint. [Google Scholar]
- Nordstrom, A.; Cotton, C. Impact of a Severe Drought on Education: More Schooling but Less Learning; Department of Economics, Queen’s University: Kingston, LO, Canada, 2020; pp. 1–31. [Google Scholar]
- Myeki, V.A.; Bahta, Y.T. Determinants of smallholder livestock farmers’ household resilience to food insecurity in South Africa. Climate 2021, 9, 117. [Google Scholar] [CrossRef]
- Kayastha, R.B.; Lee, W.K.; Shrestha, N.; Wang, S.W. Assessing the Livelihood Vulnerability of Nomads to Changing Climate in the Third Pole Region of Nepal. Land 2023, 12, 1105. [Google Scholar] [CrossRef]
- Maru, H.; Haileslassie, A.; Zeleke, T.; Esayas, B. Analysis of smallholders’ livelihood vulnerability to drought across agroecology and farm typology in the upper awash sub-basin, Ethiopia. Sustainability 2021, 13, 9764. [Google Scholar] [CrossRef]
- Tofu, D.A.; Haile, F.; Tolossa, T. Livelihood vulnerability and socio-economic determinants of households to climate change-induced recurrent drought in Ethiopia. GeoJournal 2023, 88, 5043–5067. [Google Scholar] [CrossRef]
- Bahta, Y.T.; Lombard, W.A. Nexus between Social Vulnerability and Resilience to Agricultural Drought amongst South African Smallholder Livestock Households. Atmosphere 2023, 14, 900. [Google Scholar] [CrossRef]
- RSA. Frances Baard District, Northern Cape: Profile and Analysis, District Development Model. 2020. Available online: https://municipalities.co.za/map/134/frances-baard-district-municipality (accessed on 25 April 2024).
- uMoya-NILU Consulting (Pty) Ltd. Air Quality Management Plan for the Northern Cape; Air Quality Baseline Assessment Report for Northern Cape Department of Environment and Nature Conservation, Report No. uMN072-2017; uMoya-NILU Consulting (Pty) Ltd.: Kimberley, South Africa, 2017. [Google Scholar]
- Melketo, T.; Schmidt, M.; Bonatti, M.; Sieber, S.; Müller, K.; Lana, M. Determinants of pastoral household resilience to food insecurity in Afar region, northeast Ethiopia. J. Arid. Environ. 2021, 188, 104454. [Google Scholar] [CrossRef]
- Stats, S.A. (Statistics South Africa), Community Survey 2016: Provincial Profile Northern Cape; Report No. 03-01-14; Statistics: Pretoria, South Africa, 2016. [Google Scholar]
- Northern Cape Department of Agriculture, Forestry, and Fisheries (NDAFF). Beneficiaries of an Agricultural Drought Relief Program; NDAFF: Kimberly, South Africa, 2020. [Google Scholar]
- Cochran, W.G. Sampling Techniques; John Wiley & Sons: Hoboken, NJ, USA, 1977. [Google Scholar]
- Bartlett, J.E.; Kotrlik, J.; Higgins, C. Organizational research: Determining appropriate sample size in survey research appropriate sample size in survey research. Inf. Technol. Learn. Perform. J. 2001, 19, 43–50. [Google Scholar]
- Hahn, M.B.; Riederer, A.M.; Foster, S.O. The Livelihood Vulnerability Index: A pragmatic approach to assessing risks from climate variability and change-A case study in Mozambique. Glob. Environ. Chang. 2009, 19, 74–88. [Google Scholar] [CrossRef]
- UNDP. Human Development Report 2007/2008—Fighting Climate Change: Human Solidarity in a Divided World [Internet]; Palgrave Macmillan: New York, NY, USA, 2007. [Google Scholar]
- Mekuria, W.; Mekonnen, K. Determinants of crop-livestock diversification in the mixed farming systems: Evidence from central highlands of Ethiopia. Agric. Food Secur. 2018, 7, 1–15. [Google Scholar] [CrossRef]
- Rehima, M.; Belay, K.; Dawit, A.; Rashid, S. Factors affecting farmers’ crops diversification: Evidence from SNNPR, Ethiopia. Int. J. Agric. Sci. 2013, 3, 558–565. [Google Scholar]
- IPCC. Climate Change 2001: Impacts, Adaptation & Vulnerability [Internet]. Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom. 2001. Available online: http://www.ipcc.ch/ipccreports/tar/wg2/index.htm (accessed on 12 June 2024).
- Mainali, J.; Pricope, N.G. Mapping the need for adaptation: Assessing drought vulnerability using the livelihood vulnerability index approach in a mid-hill region of Nepal. Clim. Dev. 2019, 11, 607–622. [Google Scholar] [CrossRef]
- Stats, S.A. Census of Commercial Agriculture, 2017: Northern Cape Financial and Production Statistics, Statistics: Pretoria, South Africa, 2020; Volume 10.
- Ntali, Y.M.; Lyimo, J.G. Community livelihood vulnerability to drought in semi-arid areas of northern Cameroon. Discov. Sustain. 2022, 3, 22. [Google Scholar] [CrossRef]
- Rakgase, M.; Norris, D. Factors that Influence Choice of Drought Coping Strategies in Limpopo Province, South Africa. J. Hum. Ecol. 2014, 47, 111–116. [Google Scholar] [CrossRef]
- Sujakhu, N.M.; Ranjitkar, S.; He, J.; Schmidt-Vogt, D.; Su, Y.; Xu, J. Assessing the livelihood vulnerability of rural indigenous households to climate changes in Central Nepal, Himalaya. Sustainability 2019, 11, 2977. [Google Scholar] [CrossRef]
- Obrien, L.V.; Berry, H.L.; Coleman, C.; Hanigan, I.C. Drought as a mental health exposure. Environ. Res. 2014, 131, 181–187. [Google Scholar] [CrossRef] [PubMed]
- Stain, H.J.; Kelly, B.; Carr, V.J.; Lewin, T.J.; Fitzgerald, M.; Fragar, L. The psychological impact of chronic environmental adversity: Responding to prolonged drought. Soc. Sci. Med. 2011, 73, 1593–1599. [Google Scholar]
- Devkota, N.; Phuyal, R.K. Adoption Practice of Climate Change Adaptation Options among Nepalese Rice Farmers: Role of Information and Communication Technologies (ICTs). Am. J. Clim. Chang. 2018, 7, 135–152. [Google Scholar] [CrossRef]
- Sattar, R.S.; Wang, S.; Tahir, M.N.; Caldwell, C. Assessment of smallholder farmer’s vulnerability due to climate change in arid areas of Pakistan. Appl. Ecol. Environ. Res. 2017, 15, 291–312. [Google Scholar] [CrossRef]
- Awazi, N.P.; Tchamba, M.N.; Temgoua, L.F.; Avana, M.L.T. Appraisal of smallholder farmers’ vulnerability to climatic variations and changes in the Western Highlands of Cameroon. Sci. Afr. 2020, 10, e00637. [Google Scholar] [CrossRef]
- Bahta, Y.T. Social vulnerability to agricultural drought: Insights from Northern Cape, South Africa. Sci. Afr. 2022, 17, e01324. [Google Scholar] [CrossRef]
Freq | % | Mean | Median | St.dev | Min | Max | ||
---|---|---|---|---|---|---|---|---|
Age (years) | 21–30 | 13 | 5.99 | 51.66 | 52 | 14.16 | 21 | 85 |
31–40 | 42 | 19.35 | ||||||
41–50 | 42 | 19.35 | ||||||
51–60 | 59 | 27.19 | ||||||
61+ | 61 | 28.11 | ||||||
Sex | Female | 61 | 28.1 | 0.72 | 0.45 | 0 | 1 | |
Male | 156 | 71.9 | ||||||
Education | Primary | 118 | 54.38 | 8.01 | 9 | 4.31 | 0 | 16 |
Secondary | 91 | 41.94 | ||||||
Tertiary | 8 | 3.68 | ||||||
Household size | 1–10 | 204 | 94 | 4.97 | 5 | 2.88 | 1 | 25 |
11–25 | 13 | 6 | ||||||
Farming experience (years) | 0.5–20 | 196 | 90.32 | 10.92 | 9 | 8.86 | 0.5 | 60 |
21–60 | 21 | 9.68 | ||||||
Livestock holding | Cattle | 14.72 | 7 | 22.60 | 0 | 200 | ||
Sheep | 13.97 | 9 | 19.64 | 0 | 121 | |||
Goats | 17.03 | 10 | 22.50 | 0 | 198 | |||
Chickens | 40.26 | 10 | 140.36 | 0 | 1000 | |||
Pigs | 3.48 | 0 | 7.95 | 0 | 52 | |||
Land size (ha) | 477.04 | 1 | 1338.38 | 0 | 9400 |
LVI Components | Subcomponents | Subcomponent Value for All the LMs | Index (sd) | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Unit | Maximum Value | Minimum Value | Overall Index Value | Phokwane | Dikgatlong | Sol Plaatje | Magareng | |||
Sociodemographic profile | Dependency ratio; | Ratio | 4.41 | 12.5 | 1 | 0.297 | 0.342 | 0.269 | 0.213 | 0.312 |
Percent of female-headed HHs; | Percent | 28.5 | 100 | 0 | 0.285 | 0.434 | 0.159 | 0.250 | 0.261 | |
Percent of HHs where the head did not attend school; | Percent | 12.0 | 100 | 0 | 0.120 | 0.167 | 0.111 | 0.000 | 0.087 | |
Average age of HH heads; | Years | 51.66 | 85 | 21 | 0.479 | 0.453 | 0.500 | 0.531 | 0.469 | |
Education level of HH head; | Years | 8.01 | 16 | 0 | 0.501 | 0.438 | 0.500 | 0.500 | 0.563 | |
Average age of female HH heads; | Years | 50 | 79 | 23 | 0.482 | 0.482 | 0.482 | 0.571 | 0.411 | |
Average HH size. | 4.97 | 25 | 1 | 0.165 | 0.208 | 0.125 | 0.125 | 0.208 | ||
Md | 0.333 | 0.361 | 0.307 | 0.313 | 0.330 | |||||
Livelihood strategies | Percentage of HHs with family members working outside the community; | Percent | 69 | 100 | 0 | 0.690 | 0.531 | 0.862 | 0.632 | 0.652 |
Percent of households dependent solely on agriculture as a source of income; | Percent | 78.3 | 100 | 0 | 0.783 | 0.738 | 0.822 | 0.800 | 0.783 | |
Average Agricultural Livelihood Diversification Index (range: 0.20–1) = (1/(number of occupation + 1)). | ALDI | 0.4343 | 0.5 | 0.25 | 0.737 | 0.680 | 0.800 | 0.800 | 0.720 | |
Md | 0.737 | 0.650 | 0.828 | 0.744 | 0.718 | |||||
Health | Percentage of households in which a family member had to miss school or work due to drought; | Percent | 17.1 | 100 | 0 | 0.171 | 0.214 | 0.178 | 0.050 | 0.087 |
Percentage of households that experienced anxiety and depression due to drought; | Percent | 27.6 | 100 | 0 | 0.276 | 0.226 | 0.344 | 0.150 | 0.304 | |
Percent of HHs that experienced depression due to livestock death; | Percent | 23.5 | 100 | 0 | 0.235 | 0.262 | 0.289 | 0.100 | 0.043 | |
Percent of HHs with poor health conditions because of drought. | Percent | 19.8 | 100 | 0 | 0.198 | 0.250 | 0.233 | 0.000 | 0.043 | |
Md | 0.220 | 0.238 | 0.261 | 0.075 | 0.119 | |||||
Social Networks | Percentage of HHs that have not gone to government or non-governmental organisations for assistance; | Percent | 77.6 | 100 | 0 | 0.776 | 0.805 | 0.744 | 1.000 | 0.556 |
Percentage of HHs using the internet; | Percent | 47 | 100 | 0 | 0.470 | 0.452 | 0.467 | 0.600 | 0.435 | |
Percentage of HHs that participated in the village assistance activities | Percent | 11.4 | 100 | 0 | 0.114 | 0.114 | 0.114 | 0.114 | 0.114 | |
Percentage of HHs owning a mobile phone; | Percent | 59.5 | 100 | 0 | 0.595 | 0.560 | 0.596 | 0.650 | 0.682 | |
Percentage of HHs without radios; | Percent | 34.8 | 100 | 0 | 0.348 | 0.296 | 0.395 | 0.300 | 0.391 | |
Percentage of people who have not borrowed or lent money in the past month. | Percent | 91.2 | 100 | 0 | 0.912 | 0.940 | 0.867 | 1.000 | 0.913 | |
Md | 0.536 | 0.528 | 0.531 | 0.611 | 0.515 | |||||
Food | Percentage of households in which the food consumption pattern decreased due to drought; | Percent | 62.2 | 100 | 0 | 0.622 | 0.667 | 0.633 | 0.800 | 0.261 |
Percentage of households in which choice of food preferences was affected because of drought; | Percent | 55.3 | 100 | 0 | 0.553 | 0.536 | 0.567 | 0.800 | 0.348 | |
Average Livestock Diversification Index (calculated by Margalef Diversification Index). | Livestock DI | 0.4295 | 1 | 0 | 0.430 | 0.420 | 0.440 | 0.510 | 0.370 | |
Md | 0.535 | 0.541 | 0.547 | 0.703 | 0.326 | |||||
Water | Percentage of HHs using natural water sources; | Percent | 19.87 | 100 | 0 | 0.199 | 0.199 | 0.199 | 0.199 | 0.199 |
Percent of HHs with water storage; | Percent | 22.7 | 100 | 0 | 0.227 | 0.227 | 0.227 | 0.227 | 0.227 | |
Percent of HHs having enough drinking water for livestock. | Percent | 18 | 100 | 0 | 0.180 | 0.083 | 0.300 | 0.050 | 0.174 | |
Md | 0.202 | 0.170 | 0.242 | 0.159 | 0.200 | |||||
Natural disasters and climate variability | Mean standard deviation of monthly average minimum daily temperature (1992–2020); | °C | 5.9738 | 6.7882 | 5.0361 | 0.535 | 0.517 | 0.517 | 0.517 | 0.517 |
The mean standard deviation of monthly average maximum daily temperature (2010–2020); | °C | 5.2000 | 6.0971 | 4.2349 | 0.518 | 0.514 | 0.514 | 0.514 | 0.514 | |
Mean standard deviation of monthly average precipitation (2010–2020); | mm | 40.1921 | 71.3491 | 17.6709 | 0.420 | 0.472 | 0.472 | 0.472 | 0.472 | |
Percentage of HHs suffering any loss (agriculture or livestock) to natural disasters, including death, in the last five years; | Percent | 67.7 | 100 | 0 | 0.677 | 0.679 | 0.711 | 0.600 | 0.609 | |
Average number of livestock lost due to climate and weather-related events; | LU | 21.69 | 303 | 3 | 0.062 | 0.030 | 0.097 | 0.047 | 0.057 | |
Percentage of HHs that experienced high environmental impact due to drought; | Percent | 56.2 | 100 | 0 | 0.562 | 0.619 | 0.511 | 0.500 | 0.609 | |
Percentage of HHs that experienced high financial impact due to drought; | Percent | 62.2 | 100 | 0 | 0.622 | 0.655 | 0.600 | 0.550 | 0.652 | |
Percentage of HHs that experienced high personal impact due to drought. | Percent | 59.4 | 100 | 0 | 0.594 | 0.619 | 0.611 | 0.550 | 0.478 | |
Md | 0.499 | 0.513 | 0.504 | 0.469 | 0.489 | |||||
LVI | 0.436 | 0.436 | 0.449 | 0.433 | 0.398 |
Subcomponents | Major Component Values | Number of Subcomponents per Major Component | |||||
---|---|---|---|---|---|---|---|
Phokwane | Dikgatlong | Sol Plaatje | Magareng | General | |||
Adaptive capacity | Sociodemographic profile | 0.361 | 0.307 | 0.313 | 0.330 | 0.333 | 7 |
Livelihood strategies | 0.650 | 0.828 | 0.744 | 0.718 | 0.737 | 3 | |
Social networks | 0.528 | 0.531 | 0.611 | 0.515 | 0.536 | 6 | |
Sensitivity | Health | 0.238 | 0.261 | 0.075 | 0.119 | 0.220 | 4 |
Food | 0.541 | 0.547 | 0.703 | 0.326 | 0.535 | 3 | |
Water | 0.170 | 0.242 | 0.159 | 0.200 | 0.202 | 3 | |
Exposure | Natural disasters and climate variability | 0.513 | 0.504 | 0.469 | 0.489 | 0.499 | 8 |
Contributing factor values | Adapd | 0.477 | 0.488 | 0.505 | 0.472 | 0.485 | |
Sensd | 0.308 | 0.341 | 0.289 | 0.206 | 0.309 | ||
Expd | 0.513 | 0.504 | 0.469 | 0.489 | 0.499 | ||
LVI-IPCC Value | 0.011 | 0.005 | −0.011 | 0.003 | 0.004 |
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Bahta, Y.T.; Nyaki, S.A. Livelihood Vulnerability from Drought among Smallholder Livestock Farmers in South Africa. Hydrology 2024, 11, 137. https://doi.org/10.3390/hydrology11090137
Bahta YT, Nyaki SA. Livelihood Vulnerability from Drought among Smallholder Livestock Farmers in South Africa. Hydrology. 2024; 11(9):137. https://doi.org/10.3390/hydrology11090137
Chicago/Turabian StyleBahta, Yonas T., and Stephen Aniseth Nyaki. 2024. "Livelihood Vulnerability from Drought among Smallholder Livestock Farmers in South Africa" Hydrology 11, no. 9: 137. https://doi.org/10.3390/hydrology11090137