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Article

Smallholder Farmers’ Climate Change Adaptation Strategies and Their Effect on Household Food Security: Evidence from KwaZulu-Natal, South Africa

by
Mbongeni Maziya
1,*,
Lungani Mvelase
2 and
Mbuyazwe Michael Dlamini
3
1
Institute for Rural Development, University of Venda, Thohoyandou 0950, South Africa
2
SA Canegrowers Association, Mtubatuba 3935, South Africa
3
Agricultural Education and Extension, Faculty of Agriculture, University of eSwatini, Luyengo P.O. Box M205, Eswatini
*
Author to whom correspondence should be addressed.
Agriculture 2024, 14(10), 1729; https://doi.org/10.3390/agriculture14101729
Submission received: 19 August 2024 / Revised: 7 September 2024 / Accepted: 26 September 2024 / Published: 1 October 2024
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
Browse Figure
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Abstract

:
Climate change poses a significant risk to the long-term viability of smallholder agriculture in developing countries. Climate change has a direct effect on agricultural output, ultimately impacting the food security of smallholder farmers. However, the link between climate change and food security in developing countries is underexplored. This article investigates the effect of climate change adaptation strategies on household food security. A survey was conducted among 400 smallholder farmers in the uMkhanyakude district of KwaZulu-Natal using a stratified random sampling procedure. Survey data were analysed using descriptive statistics. The findings indicate that factors such as access to credit, access to government funding, and participation in agricultural groups play an important role in supporting adaptation to climate change. Adaptation to climate change is associated with reduced levels of food insecurity. These results underscore the important role of climate change adaptation in enhancing household food security. The study recommends that programmes that target smallholder farmers should focus on enhancing the adaptive capacity of smallholder farmers.

1. Introduction

There is a general consensus in the literature that climate change and variability have negatively affected the livelihoods of farming households [1,2,3]. Climate change and variability are a threat to socioeconomic development and affect all sectors of the economy [4,5]. Climate change is expected to hinder global progress towards achieving the 2030 Agenda for Sustainable Development, particularly goals focused on eliminating poverty and hunger (SDG 1 and SDG 2). Climate change is associated with economic losses from disasters and loss of income in sectors such as agriculture and tourism [6]. The Intergovernmental Panel on Climate Change (IPCC) projected an increase in climate-related events, including drought, floods, heatwaves, and variations in rainfall; such events will amplify economic losses from climate change [6]. It is widely accepted that economically and geographically, developing countries, especially those in the sub-Saharan region, have been disproportionately affected due to their inherent vulnerability and difficulties adapting to climate change [7]. South Africa is considered a water-scarce country, with an annual precipitation of 450 mm, which is below the global average of 860 mm per annum [8]. The country has experienced extreme weather events in the previous years. For example, between 2014 and 2016, South Africa experienced the worst drought in years, resulting in the designation of certain regions as disaster areas [8]. As a result, farmers were negatively affected by the drought and the low agricultural output translated to low farm earnings and high food prices [9]. Smallholder farmers’ predicament is further exacerbated by inadequate access to land, high poverty rates, low levels of education and limited financial resources [9].
The agricultural sector in sub-Saharan Africa is inexplicably linked to household food security. Food security remains a significant development challenge in the region due to the high prevalence of malnutrition [10]. It is estimated that approximately 226 million people in Africa are starving, and the majority are located in the south of the Sahara [10]. Most of the impoverished population in sub-Saharan Africa resides in rural areas and depends on farming for their livelihood. The agricultural sector provides direct and indirect employment to approximately 70% of the total population in the region, and agriculture is an important source of food and income. Crop and livestock production predominantly relies on rainfed systems, which are highly susceptible to climate change and variability. Climate models for Southern Africa project increased aridity and more frequent climatic events, such as drought and floods [11]. Climate change is anticipated to reduce agricultural yields by approximately 1%, whereas yields need to increase by around 14% per decade to accommodate population growth in sub-Saharan Africa [6]. Due to climate change, smallholder farmers will suffer complex, localised impacts because of their limited adaptive capacity [12].
Adaptation plays a key role in mitigating the risk of climate change on farming operations [3,13]. Smallholder farmers can adopt different types of adaptation strategies, which are dependent on the level of farmers’ perception and availability of resources. According to [14], farm-level responses may include crop management practices (i.e., shifting planting dates, adopting new crop cultivars), livestock management practices, and land use management practices (i.e., planting trees, irrigation and water harvesting, soil and water conservation practices, tillage practices).
In South Africa, very few studies have examined the effect of climate change adaptation on household food security [9,12,15]. The focus in most studies broadly has been on climate change perceptions and adaptation [16,17,18]. The link between climate change adaptation strategies and household food security in South Africa is inadequately explored and unclear. Therefore, this study examines the effect of climate change adaptation strategies on household food security by answering the following research questions: 1. What are the factors affecting the adoption of climate change adaptation strategies? 2. How does climate change adaptation affect household food security? This paper is divided into three sections. The next section discusses the materials and methods, followed by the results and discussion. The last section provides conclusions and policy recommendations.

2. Materials and Methods

uMkhanyakude District Municipality is located in the northern part of KwaZulu-Natal (KZN) Province, South Africa (32.014489; −27.622242) [19]. The district shares its eastern boundary with the Indian Ocean, while to the north, it borders Mozambique, and to the northwest, it is adjacent to the Kingdom of Eswatini. The district is bordered in the south and west by King Cetshwayo and Zululand districts, respectively. Figure 1 illustrates the location of uMkhanyakude district within the KZN Province and South Africa. The uMkhanyakude District comprises five local municipalities: Jozini, uMhlabuyalingana, Hlabisa, Mtubatuba, and Big Five False Bay. uMkhanyakude is predominantly rural, with Jozini and Mtubatuba as its main towns. uMkhanyakude has a surface area of 12,818 km2 and a population of approximately 625,846 [19]. In terms of geographical size, uMkhanyakude is the second-largest district in KZN.

2.1. Sampling

Out of the 11 district municipalities in KZN, uMkhanyakude was purposively selected for this study. uMkhanyakude is one of the most impoverished municipalities in the province and has been significantly affected by climate-induced changes [21]. These arid conditions have significantly constrained agricultural production in the district. [22] provides guidelines for determining appropriate sample sizes based on population size, margin of error, and confidence levels. For populations of 10,000, 100,000, and 500,000, the respective sample sizes are 370, 383, and 388, assuming a margin of error of 5% and a confidence level of 95%. Following these guidelines, a sample size of 400 households was sufficient for this study. The study employed a multi-stage random sampling procedure to select farming households. In the initial stage, 50% of the wards within each local municipality (LM) were randomly selected. Farming households were randomly chosen from these selected wards in the subsequent stage. Jozini LM comprises 20 wards, while uMhlabuyalingana LM consists of 18 wards. Data collection was carried out in both Jozini and uMhlabuyalingana local municipalities. Jozini LM has a population of 198,215 and 44,584 households, whereas uMhlabuyalingana LM has a population of 172,077 and 39,614 households [23].

2.1.1. Data Collection

Quantitative data were collected between November and December 2020 by administering a questionnaire. The survey instrument was designed to collect data on farmers’ characteristics, including demographics, crop production, household assets, livestock ownership, access to support services and farmer training, land ownership, food security, perceptions of climate change, and adaptation strategies. The study focused on smallholder farmers engaged in crop and livestock production. The predominant crops in the area include maize, legumes, sweet potatoes, and cassava, with maize being the principal staple crop in the district. Enumerators conducted visits to the sampled households and interviewed the household heads.
Food security, defined as the capacity of households and individuals to acquire sufficient food, is a fundamental aspect of well-being. However, its measurement presents significant challenges. Various indicators are employed to assess the food security status of households. Some of the most widely used tools for food security assessment include the Food Consumption Score (FCS), the Household Dietary Diversity Score (HDDS), the Household Coping Strategy Index (HCSI), the Household Hunger Scale (HHS), and the Household Food Insecurity Access Scale (HFIAS). There is no food security proxy that can capture all the various aspects of food security. An effective indicator for assessing food security should be valid, reliable, and comparable across different temporal and spatial contexts while encompassing multiple dimensions of the concept [9].
Data on food security were collected using the HFIAS (Appendix A). The HFIAS represents a self-reported metric for assessing food insecurity, formulated through a methodology developed by the United States Agency for International Development (USAID) in the Food and Nutrition Technical Assistance (FANTA) Project. Like other experience-based metrics, the HFIAS is derived from a concise questionnaire intended to capture the behavioural and psychological dimensions of food insecurity [9]. This includes actions such as reducing the number of meals or compromising food quality due to resource constraints. The HFIAS is particularly distinctive in its ability to assess the physical and psychological aspects of food insecurity, which can adversely affect health and well-being. A limitation of the HFIAS is that it does not measure the quantity and quality of food consumed by households. Intra-household disparities in individual food insecurity experiences may also exist. Individual perceptions and conditions of the respondent can influence the overall assessment of the food security situation in the household.
In this study, food security levels were determined by creating an HFIAS score indicator [24]. This score represents a continuous measure of the degree of food insecurity related to access within the past four weeks. It was calculated for each household by summing the coded frequencies for responses to the nine questions addressing household-level food access [24]. Each question carries a maximum score of three, resulting in a possible cumulative range from 0 to 27 when summing responses across all questions. Higher scores indicate increased levels of food insecurity experienced by the household, whereas lower scores reflect greater food security. The HFIAS tool is designed such that a household is placed in one of the four categories: food secure, mildly food insecure, moderately food insecure and severely food insecure. A food-secure household does not experience any conditions associated with food insecurity. A mildly food insecure household occasionally or frequently worries about not having enough food and may be unable to consume preferred foods, leading to a more monotonous diet than desired, or may resort to consuming undesirable foods. Moderately food-insecure households compromise on food quality more frequently by consuming a monotonous diet or, at times, undesirable foods. They sometimes, however rarely, start cutting back on quantity by reducing the size or number of meals, although they do not experience any of the three main severe conditions. A severely food insecure household frequently resorts to reducing meal size or the number of meals and/or experiences any of the three most severe conditions: depleting their food supply, going to bed hungry, or enduring an entire day and night without eating.

2.1.2. Data Analysis

Descriptive analysis was performed using STATA (version 17) to identify means, proportions and correlations between variables of interest.

3. Results

3.1. Descriptive Statistics

Table 1 presents the descriptive statistics for all smallholder farmers in the sample. For the purposes of this study, farmers who implemented adaptation strategies are referred to as adapters, and those who did not adapt are referred to as non-adapters. The average age of smallholder farmers in the uMkhanyakude district was 45 years. Climate change adapters were younger than non-adapters, i.e., the average age for adapters was 44 years, while the average age for non-adapters was 47 years. Refs. [9,25] also found that adapters were younger than non-adapters in Limpopo, Free State, KwaZulu-Natal, and Northwest Provinces of South Africa. The results also show that the majority of smallholder farmers in the sample did not progress beyond primary school. The average schooling years for adapters is 7.4 years, while non-adapters have an average of 5.1 years of formal education. [26] also found adapters to be better educated than non-adapters. The average tropical livestock unit for adapters was 11.18, while the average for non-adaptors was 4.5. This implies that adapters include livestock to diversify their farming operations to mitigate climate risk. [27] also found similar results in Ethiopia.
Table 2 compares the categorical variables across adapters and non-adapters of climate change adaptation strategies in the uMkhanyakude district of KwaZulu-Natal. Most adapters and non-adapters in the area were females. The findings contradict findings by most studies in the literature; males are mostly adapters when compared to their female counterparts [26,27,28]. This is because female household heads are less likely to meet the investment demands of climate change adaptation strategies since they mostly have limited access and control to productive and financial resources than their male counterparts [29,30]. The contradicting outcome in this study might be because women dominate smallholder farming in South Africa, with males mostly involved in off-farm jobs in towns or cities [31,32].
Several studies have recognised the key role that the government can play in smallholder farmers’ climate change adaptation strategies [33,34]. Government grants can be seen as one of the strategies smallholder farmers can use to promote climate change adaptation strategies as they can improve their ability to implement climate change adaptation strategies. In the uMkhanyakude district, however, there is a minor difference between adaptors and non-adaptors who received government grants; this can be explained by the fact that there is very little involvement of government in South Africa on smallholder farmers [35].
The results indicate that a small proportion of smallholder farmers were members of farmers’ associations, i.e., less than 40% for both adapters and non-adapters. Although the membership in farm associations is low, more adapters are members (38.6%) relative to non-adapters (28.7%). African studies, including [9,33], also found that adapters were active members of farm organisations/associations. This implies that farmers’ associations are an important information-sharing platform for smallholder farmers.
Climate change adapters had better access to credit (60%) relative to non-adapters (36.5%). [25,27] also found similar results in South Africa. Farmers can use the credit to purchase farm inputs to reduce climate risk. Therefore, access to credit enables smallholder farmers to adapt to climate change. About 21% of adapters and 13.9% of the non-adapters received extension services. Although the larger proportion of adopters (21%) in the uMkhanyakude district received extension services relative to non-adapters (13.9%), smallholder farmers have inadequate access to extension services. Several studies in South Africa have found similar results [25,27].

3.2. Climate Change Adaptation Strategies

Table 3 presents adaptation strategies undertaken by smallholder farmers in the uMkhanyakude district of KwaZulu-Natal. The study found 10 common adaptation strategies practised by smallholder farmers in the uMkhanyakude district. Most of the adaptation strategies focused on addressing the effects of drought because drought is more common than floods in the study area. The most widely used adaptation strategy in the study area was intercropping, i.e., 72% of the households adopted this strategy. The reason is that intercropping enables smallholder farmers to achieve stable yields from diversified crops while using fewer inputs, thus facilitating cost savings [36]. The second most widely used climate change adaptation strategy was mixed farming. Mixed farming includes a combination of crop and livestock farming. According to [37], smallholder farmers in KwaZulu-Natal often practice mixed farming of crops and livestock. [38] also found that smallholder farmers in the Vhembe District of South Africa practice mixed farming. The third most widely used adaptation strategy was soil conservation, with 66.25% of smallholder farmers adopting this strategy. This strategy includes protecting soil and improving soil fertility [9,39]. The other adaptation strategies practised by smallholder farmers in the study area include planting drought-resistant crops (63.5%), shifting planting dates (63%), planting improved crop varieties (61.5%), Crop residue management (48.25%), minimum tillage (47.75%), water harvesting (44.75%), leasing land (23%).

3.3. Farmers’ Socioeconomic Characteristics and Adaptation Strategies

The results in Table 4 show that male farmers tend to lease fallow land as a climate change adaptation strategy. This is expected because males, not females, inherit ancestral land in rural areas. In addition, studies have also found women to have limited access and rights to land, and women are often excluded from household decision-making [9,40].
Householders with larger family sizes tend to respond to climate change by using different climate change adaptation strategies: drought-resistant crops, improved crop varieties, soil conservation, shifting planting dates, water conservation, mixed farming, intercropping and crop residue management. This implies that households with larger family sizes can choose different climate change adaptation strategies because of labour availability. The results also show that farmers who received government grants were likely to adopt intercropping as a climate change adaptation strategy. The positive correlation could be attributed to farmers investing the government subsidies for agricultural activities. Table 4 also shows a positive correlation between farmers’ level of education and various climate change adaptation strategies: planting drought-resistant crops, improved crop varieties, water harvesting, mixed farming, intercropping and leasing of land. Indeed, farmers with higher levels of education have better access to information on adaptation strategies to climate change [3,41].
The results in Table 4 show that farmers’ membership in associations is positively correlated to climate change adaptation strategies, i.e., drought-resistant crops and improved crop varieties. This implies that farmer organisations play a key role in shaping climate change adaptation. The positive correlation could be attributed to farmers sharing information on climate change adaptation in farmer organisations. Tropical Livestock Units (TLU) are correlated to the adoption of climate change adaptation strategies, i.e., drought-resistant crops, improved crop varieties and mixed farming. As for mixed farming, livestock serves as an additional enterprise alongside crop farming, enabling farmers to mitigate risks associated with unfavourable climatic conditions and potential crop failures. Moreover, the positive correlation between TLU and the implementation of adaptation strategies to climate change could be due to the fact that farmers can dispose of or sell livestock and invest the proceeds in crop farming, i.e., purchasing drought-resistant crops or improved crop varieties. Studies by [42,43] also found a positive association between livestock ownership and climate change adaptation.
Our empirical findings indicate that access to credit is positively correlated to climate change adaptation. This positive correlation could be attributed to farmers using the credit to invest in climate change adaptation strategies. This finding aligns with previous studies on climate change adaptation [3,44].

3.4. Effect of Climate Change on Food Security

The results in Table 5 indicate that adapters were more food secure relative to non-adapters (28.7% and 24.9%, respectively). The results also show that adapters were more mildly food insecure compared to non-adapters (41.1% and 33%, respectively) and significant differences were also noted between the two groups. According to the T-test results, non-adapters were more likely to experience severe food insecurity compared to adapters (3.5% and 1.4%, respectively). In general, the results of the T-test analysis suggest that climate change adapters tend to experience low levels of food insecurity compared to non-adapters. This implies that through adaptation, farmers can maintain or increase production, resulting in improved food and nutrition security. These findings corroborate related studies conducted in South Africa [9,12].
The correlations between climate change adaptation strategies and measures of food security were also investigated among smallholder farming households. The food security measures include food quality, quantity, and anxiety about food supply. The results in Table 6 show that, in general, climate change adaptation strategies are negatively associated with the HFIAS score. This implies that climate change adaptation decreases household food insecurity. Lower levels of food insecurity are associated with carrying out several adaptation strategies: drought-resistant crops, improved crop varieties, soil conservation, changing planting dates, water harvesting, mixed farming, intercropping, minimum tillage and crop residue management. This implies that adaptation strategies improve crop and livestock production, improving food security outcomes. Moreover, the use of drought-resistant crops was associated with reduced anxiety about the quantity of food. Adaptation strategies such as soil conservation, mixed farming and minimum tillage were associated with reduced anxiety about food supply.

4. Conclusions

Climate change and variability adversely affect agricultural production and pose a major threat to household food security. This study examined the climate change adaptation strategies employed by smallholder farmers and their effect on household food security. This study has established a nexus between climate change adaptation and household food security in the uMkhanyakude District of KwaZulu-Natal, South Africa. Descriptive statistics revealed that farmers adopt various adaptation strategies to mitigate the negative effects of climate change. Factors such as education level, participation in farmer organisations, household size, tropical livestock units (TLU), access to credit, and government support through grants or subsidies play an important role in facilitating climate change adaptation. Furthermore, the adoption of climate adaptation strategies is associated with lower levels of household food insecurity, suggesting that such strategies contribute to improved food security. Based on these findings, the study recommends that extension services should prioritise programmes that promote climate change adaptation. In addition, extension services should focus on training farmers in adaptation strategies and the delivery materials should be cognizant of their low levels of formal education.

Author Contributions

Conceptualization, M.M.; methodology, M.M.; software, M.M.; validation, L.M. and M.M.D.; formal analysis, M.M., L.M. and M.M.D.; writing—original draft preparation, M.M., L.M. and M.M.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study data collection tools were approved by the ethics committee of the University of the Free State (protocol reference number: UFS-HSD2020/0632/2107). Informed consent was obtained from all farmers who participated in the study.

Data Availability Statement

Data are unavailable due to ethical restrictions.

Conflicts of Interest

Author Lungani Mvelase is employed by SA Canegrowers Association. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Appendix A. Household Food Insecurity Access Scale

No.QuestionResponse OptionsCode
1In the past four weeks, did you worry that your household would not have enough food?0 = No (skip to Q2)
1 = Yes
1 AHow often did this happen?1 = Rarely (once or twice in the past four weeks)
2 = Sometimes (three to ten times in the past four weeks)
3 = Often (more than ten times in the past four weeks)
2In the past four weeks, were you or any household member not able to eat the kinds of foods you preferred because of a lack of resources?0 = No (skip to Q3)
1 = Yes
2 AHow often did this happen?1 = Rarely (once or twice in the past four weeks)
2 = Sometimes (three to ten times in the past four weeks)
3 = Often (more than ten times in the past four weeks)
3In the past four weeks, did you or any household member have to eat a limited variety of foods (fewer kinds of food on the plate) due to a lack of resources?0 = No (skip to Q4)
1 = Yes
3 AHow often did this happen?1 = Rarely (once or twice in the past four weeks)
2 = Sometimes (three to ten times in the past four weeks)
3 = Often (more than ten times in the past four weeks)
4In the past four weeks, did you or any household member have to eat some foods that you really did not want to eat because of a lack of resources to obtain other types of food?0 = No (skip to Q5)
1 = Yes
4 AHow often did this happen?1 = Rarely (once or twice in the past four weeks)
2 = Sometimes (three to ten times in the past four weeks)
3 = Often (more than ten times in the past four weeks)
5In the past four weeks, did you or any household member have to eat a smaller meal than you felt you needed because there was not enough food?0 = No (skip to Q6)
1 = Yes
5 AHow often did this happen?1 = Rarely (once or twice in the past four weeks)
2 = Sometimes (three to ten times in the past four weeks)
3 = Often (more than ten times in the past four weeks)
6In the past four weeks, did you or any other household member have to eat fewer meals in a day because there was not enough food?0 = No (skip to Q7)
1 = Yes
6 AHow often did this happen?1 = Rarely (once or twice in the past four weeks)
2 = Sometimes (three to ten times in the past four weeks)
3 = Often (more than ten times in the past four weeks)
7In the past four weeks, was there ever no food to eat of any kind in your household because of a lack of resources to get food?0 = No (skip to Q8)
1 = Yes
7 AHow often did this happen?1 = Rarely (once or twice in the past four weeks)
2 = Sometimes (three to ten times in the past four weeks)
3 = Often (more than ten times in the past four weeks)
8In the past four weeks, did you or any household member go to sleep at night hungry because there was not enough food?0 = No (skip to Q9)
1 = Yes
8 AHow often did this happen?1 = Rarely (once or twice in the past four weeks)
2 = Sometimes (three to ten times in the past four weeks)
3 = Often (more than ten times in the past four weeks)
9In the past four weeks, did you or any household member go a whole day and night without eating anything because there was not enough food?0 = No
1 = Yes
9 AHow often did this happen?1 = Rarely (once or twice in the past four weeks)
2 = Sometimes (three to ten times in the past four weeks)
3 = Often (more than ten times in the past four weeks)

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Agriculture 14 01729 g001
Figure 1. Location of the uMkhanyakude district municipality. Source: [20].
Figure 1. Location of the uMkhanyakude district municipality. Source: [20].
Agriculture 14 01729 g001
Table 1. Comparison of continuous variables between adopters and non-adopters.
Table 1. Comparison of continuous variables between adopters and non-adopters.
Variable DescriptionPooled Sample (N = 400) Adapt (N = 285) Not Adapt (N = 115) T-Test
MeanStd. DevMeanStd. DevMeanStd. Dev
Age (years)4514.3244.1213.844715.321.948 *
Household size in numbers838373−4.076 ***
Education level in years74.747.44.586.425.1−1.93 *
Tropical Livestock Units9.314.8511.1814.844.59.4−4.47 ***
Note: *** and * means significant at 1% and 10% levels, respectively.
Table 2. Comparisons of categorical variables between adapters and non-adapters.
Table 2. Comparisons of categorical variables between adapters and non-adapters.
Variable DescriptionCategoriesAdapt (%) (N = 285)Not-Adapt (%) (N = 115)X2 Test
Gender 0 = female74.7466.093.058 *
1 = male25.2633.91
Government grant1 = yes87860.0617
0 = no1314
Farmer’s association1 = yes38.628.73.497 *
0 = no61.471.3
Access to credit1 = yes6036.518.143 ***
0 = no4063.5
Extension visits1 = yes2113.93.171
0 = no7986.1
Note: *** and * means significant at 1% and 10% levels, respectively.
Table 3. Adaptation strategies undertaken by smallholder farmers.
Table 3. Adaptation strategies undertaken by smallholder farmers.
Adaptation StrategyProportion (%)p-Value
Drought resistant crops63.50.000 ***
Improved crop varieties61.50.000 ***
Soil conservation66.250.000 ***
Shifting planting dates630.000 ***
Water harvesting44.750.000 ***
Mixed farming67.250.000 ***
Intercropping720.000 ***
Minimum tillage47.750.000 ***
Lease land 230.000 ***
Crop residue management48.250.000 ***
Note: *** means significant at 1% level.
Table 4. Correlation between adaptation strategies and household socio-economic characteristics.
Table 4. Correlation between adaptation strategies and household socio-economic characteristics.
Adaptation MethodAgeGenderHousehold SizeGovernment GrantEducationFarming AssociationTropical Livestock UnitsAccess to CreditExtension Visits
Drought-resistant crops−0.0749 (0.1346)−0.0172 (0.7313)0.1795 *** (0.0003)−0.0359 (0.4738)0.1248 ** (0.0125)0.0996 ** (0.0464)0.2045 *** (0.0000)0.2367 *** (0.0000)0.0567 (0.2582)
Improved crop varieties−0.0684 (0.1723)−0.0260 (0.6043)0.1801 *** (0.000)0.0393 (0.4328)0.1387 *** (0.0054)0.1292 *** (0.0097)0.2669 ***
(0.0000)		0.1854 *** (0.0002)−0.0418 (0.4044)
Soil conservation−0.0682 (0.1733)−0.0182 (0.7174)0.1758 *** (0.0004)0.0018 (0.9721)0.0762 (0.1280)0.1353 *** (0.0067)0.2371 *** (0.0000)0.1684 *** (0.0007)−0.0554 (0.2690)
Shifting planting dates−0.0284 (0.5710)−0.0686 (0.1710)0.1340 *** (0.0073)0.0823 (0.1002)0.0273 (0.5857)0.0747 (0.1361)0.1782 *** (0.0003)0.1433 *** (0.0041)0.0522 (0.2974)
Water harvesting−0.0301 (0.5478)0.0486 (0.3324)0.1812 *** (0.0003)0.0255 (0.6115)0.0880 * (0.0787)0.2519 *** (0.0000)0.2693 *** (0.0000)0.1782 *** (0.0003)−0.0193 (0.7007)
Mixed farming−0.0572 (0.2537)0.0280 (0.5768)0.1487 *** (0.0029)0.0258 (0.6068)0.1314 *** (0.0085)0.1426 *** (0.0043)0.2723 *** (0.0000)0.1896 *** (0.0001)−0.0588 (0.2404)
Intercropping−0.0696 (0.1648)−0.0736 (0.1416)0.2249 *** (0.0000)0.0847 * (0.0905)0.1178 ** (0.0184)0.0934 * (0.0620)0.2348 *** (0.0000)0.2192 *** (0.0000)−0.0644 (0.1986)
Minimum tillage0.0616 (0.2191)−0.0112 (0.8232)−0.0120 (0.8115)0.0045 (0.9279)−0.0029 (0.9532)0.0702 (0.1614)0.0370 (0.4611)−0.0071 (0.8875)−0.0297 (0.5533)
Lease land−0.0527 (0.2932)0.1522 *** (0.0023)0.0656 (0.1905)0.0209 (0.6776)0.1464 *** (0.0033)0.1005 ** (0.0171)0.2335 *** (0.0000)0.1192 ** (0.0171)−0.0127 (0.7994)
Crop residue management0.0231 (0.6451)−0.0062 (0.9012)0.0970 * (0.0526)0.0675 (0.1780)0.0718 (0.1515)0.0835 * (0.0952)0.1141 ** (0.0225)0.1226 ** (0.0141)−0.0215 (0.6675)
Note: The number in brackets indicates p-vales. ***, **, and * means significant at 1%, 5%, and 10% levels, respectively.
Table 5. Effect of climate change adaptation on food security.
Table 5. Effect of climate change adaptation on food security.
Variable
Description		Pooled Sample (N = 400) Adapt (N = 285) Not Adapt (N = 115) T-Test
MeanStd. DevMeanStd. DevMeanStd. Dev
Food secure0.260.4390.2870.4330.2490.4540.7794
Mildly food secure0.3880.4880.4110.4930.330.472−1.489 *
Moderately food secure0.3330.4720.3480.450.3260.4780.412
Severely food insecure0.020.140.0140.1180.0350.1841.3411 *
Note: * means significant at 10% level.
Table 6. Correlation between adaptation strategies and measures of food security.
Table 6. Correlation between adaptation strategies and measures of food security.
Adaptation StrategyHFIAS ScoreQuality of FoodQuantity of FoodAnxiety about Food Supply
Drought resistant crops−0.0123 (0.8061)−0.1980 *** (0.0001)−0.1704 *** (0.0006)−0.0772 (0.1234)
Improved crop varieties−0.0698 (0.1635)−0.1548 *** (0.0019)−0.0741 (0.1390)−0.0705 (0.1596)
Soil conservation−0.0470 (0.3484)−0.1553 *** (0.0018)−0.0798 (0.1109)−0.1246 ** (0.0126)
Changing planting dates−0.0888 * (0.0762)−0.1100 ** (0.0278)−0.0197 (0.6948)−0.0385 (0.4430)
Water harvesting−0.0406 (0.4183)−0.1098 ** (0.0281)−0.0589 (0.2400)−0.0567 (0.2575)
Mixed farming−0.0721 (0.1498)−0.1396 *** (0.0052)−0.0502 (0.3163)−0.0906 (0.0704) *
Intercropping−0.0493 (0.3258)−0.1074 ** (0.0317)−0.0444 (0.3754)−0.0700 (0.1623)
Lease land−0.0417 (0.4054)−0.0408 (0.4152)−0.0705 (0.1594)−0.0356 (0.4772)
Minimum tillage−0.1445 ***
(0.0038)		−0.0923 * (0.0651)−0.0690 (0.1686)−0.1008 (0.0439) **
Crop residue management−0.1161 **
(0.0202)		−0.1665 *** (0.0008)−0.0443 (0.3768)−0.0665 (0.1846)
Note: The number in brackets indicates p-vales. ***, **, and * means significant at 1%, 5%, and 10% levels, respectively.
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Maziya, M.; Mvelase, L.; Dlamini, M.M. Smallholder Farmers’ Climate Change Adaptation Strategies and Their Effect on Household Food Security: Evidence from KwaZulu-Natal, South Africa. Agriculture 2024, 14, 1729. https://doi.org/10.3390/agriculture14101729

AMA Style

Maziya M, Mvelase L, Dlamini MM. Smallholder Farmers’ Climate Change Adaptation Strategies and Their Effect on Household Food Security: Evidence from KwaZulu-Natal, South Africa. Agriculture. 2024; 14(10):1729. https://doi.org/10.3390/agriculture14101729

Chicago/Turabian Style

Maziya, Mbongeni, Lungani Mvelase, and Mbuyazwe Michael Dlamini. 2024. "Smallholder Farmers’ Climate Change Adaptation Strategies and Their Effect on Household Food Security: Evidence from KwaZulu-Natal, South Africa" Agriculture 14, no. 10: 1729. https://doi.org/10.3390/agriculture14101729

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