The Impact of Climate Variability on the Livelihoods of Smallholder Farmers in an Agricultural Village in the Wider Belfast Area, Mpumalanga Province, South Africa

Abstract

The purpose of this study was to investigate the impact of climate change on smallholder farmers in South Africa, particularly focusing on the relationship between agriculture and weather patterns. Understanding this connection is crucial for helping farmers adapt to changing climate conditions and improve their resilience and sustainability. This research analyses 33 years of climate data (1990–2023) from the Belfast weather station to identify long-term climate trends, seasonal shifts, and the frequency of extreme weather events. Statistical analysis, including the Mann–Kendall test, revealed significant changes in temperature, rainfall, and the intensity of extreme weather events, indicating that climate change is already affecting the region. Specifically, the research highlighted significant damage to agricultural infrastructure, such as greenhouses, due to climate-related wind events. This study emphasises the importance of using digital technologies to monitor weather patterns in real-time, aiding in decision-making, and enhancing agricultural efficiency. Additionally, it calls for further research into the social impacts of climate variability, including its effects on community cohesion, migration, and access to social services among smallholder farmers. These findings provide a foundation for developing effective interventions to support the resilience of smallholder farming communities in the face of climate change. Future studies need to consider how climate variability affects farmers’ abilities to access markets, both in terms of transport and product quality.

1. Introduction

The global agricultural system faces significant pressure due to climate change [1,2,3,4,5]. This may compromise the ability of the agricultural system to meet the needs of the growing population and ensure food security. At a global level, Africa is considered as the leading region that is most vulnerable to climate change [6].

Africa, as noted by multiple scholars, remains one of the most vulnerable continents to climate change [7,8,9]. The impact of climate change on food security and agricultural productivity are significant. Extreme weather events, such as windstorms, have already affected millions of people across the continent [8]. Hailstorms, another severe weather phenomenon, further aggravate the situation by introducing secondary effects, including chemical and water contamination, and destruction of agricultural infrastructure. If climate change is not managed effectively, it is expected to have a consequential negative impact on smallholder farmers, which could pose a serious threat to food security [10,11].

In South Africa, agriculture is an essential sector of the economy; yet it is under threat from climate change and variability [7,12,13]. The agricultural sector is vital in South Africa because it contributes approximately 2.53% to the country’s Gross Domestic Product [14]. In addition, the agriculture sector contributes substantially to economic growth, by means of food production and job creation, and thereby, it can play a considerable role in reducing poverty.

South Africa is extremely sensitive to the effects of climate change due to its reliance on rainfed agricultural output, which is particularly susceptible to climatic and weather fluctuations [12,15,16]. Agriculture is threatened by rising temperatures, which are expected to persist in both the short- and long-term. The detrimental impact of rising temperatures on agri-food systems is exacerbated in most parts of South Africa by irregular and extremely variable rainfall [12,17,18]. At the end of the 21st century, it is anticipated that mean rainfall in South Africa will decline by 6 to 20% [19]. Furthermore, in the next few decades, household food security will be under threat because of droughts and will be exacerbated by climate change, which is anticipated to have an impact on the severity and spread of crop diseases in South Africa [19]. These hazards are more severe in smallholder rainfed farming systems of South Africa [20,21]. Smallholder farmers are particularly vulnerable due to limited adaptive capacity to climate change.

In South Africa, Mpumalanga Province, in general, is the warmest province and receives rainfall of about 500 mm or less, resulting in poor moisture availability for crops, low crop yields, and amplified vulnerability of the smallholder farmers [22]. Furthermore, the lack of comprehensive strategies and preparedness plans to address climate-related emergencies has been highlighted as a critical gap in the province’s response to climate change [23]. However, near the Drakensberg escarpment and 210 km east of Pretoria, the wider Belfast area is also one of the coldest places in South Africa, with very strong windy conditions. Livelihood capital among farmers is affected by the negative impacts of climate change. Accordingly, in the rural areas of Mpumalanga, the dependency of smallholder farmers on agriculture as a form of livelihood has resulted in poor commercial viability of these small farms, aggravating high levels of poverty. Weather events and climate change impacts hugely on the lives and livelihoods of millions of poor people [24].

As a result, timely information on the essential meteorological elements that affect rainfed farming productivity in the region is essential for improving decision-making and agricultural risk management at regional and farm levels [25]. This is also necessary for fine tuning research agendas, extension messages, and agricultural policy formulation. Additionally, the use of current temperature, wind, and rainfall trends is, therefore, crucial for designing adaptation and mitigation strategies in the study area. Adaptation strategies can enhance farmers’ abilities to withstand climate change impacts in agricultural production [26,27]. These adaptive strategies are designed to reduce climate-related risks and enhance crop production [28]. Further research is needed to fully understand how maximum and minimum temperatures, as well as rainfall patterns, have changed over time. There are limited studies that investigate trends and changes in the range of temperatures, wind, and rainfall in the study area. Similar studies on the area have been conducted by Netshakhuma [23], focusing on the impact of climate change on South Africa’s Mpumalanga Provincial Archives (MPA) and related records management activities. Additionally, Tagwi and Khoza (2024) [29] investigated the socioeconomic determinants of modern climate change adaptation among small-scale vegetable farmers in the region.

This study is important for the following reasons. Firstly, understanding the extreme minimum and maximum trends can help researchers predict how these changes might affect the growth of crops, health ecosystems, and survival of crops. Secondly, climate change impacts can help inform adaptation strategies and policy decisions, such as developing more resilient infrastructure or changing agricultural practices. A better understanding of the parameters can also provide a deeper awareness and comprehension of the impact of climate change on plants and animals. For example, earlier spring arrival or longer growing seasons may occur if the minimum temperature increases. This study will add invaluable awareness and understanding when we close this knowledge gap.

Mupangwa et al. (2023) [12] have contributed much to the body of current literature on the discourses of climate change. However, a major limitation of their studies is that it only focused on the trends of temperatures in Southern Africa. Therefore, there is a need to include rainfall and windy conditions because they both affect farming productivity, which in turn affects the livelihoods of the smallholder farmers.

Similar studies by Omotoso (2023) [15] and Rusere et al. (2023) [16] concentrated on temperatures and rainfall within a Sub-Saharan Africa region, indicating the need for further exploration in other areas. These studies reveal that the effects of climate change in the region are already substantial and demonstrate variations in average temperature and rainfall, along with the severity of extreme weather events. This current study aims to bridge these gaps by synthesising a case study enhancing adaptive planning, translating knowledge into future policy, revitalising vulnerability research, and ensuring consistent application of concepts and methods in adaptation studies. Due to limited access to such information, smallholder farmers are unable to develop suitable long-term coping and adaptation strategies [30]. Smallholder farmers may use climate data to improve their resilience to unfavourable weather conditions. Additionally, access to this data can help smallholder farmers improve their productivity and income, despite the changing climate.

2. Materials and Methods

2.1. Study Area

Phumulani Agri Village (PAV) is a post-mining agroecological-based village in Belfast, Mpumalanga, South Africa (Figure 1). Phumulani village has thirty-two households and approximately 200 individuals, and is close to several surrounding communities, schools, farms, mining communities, and small businesses. Of these households, 60% were female-headed, and 40% were male-headed. Additionally, 50% were engaged in crop production, and 50% in livestock production. Belfast has a subtropical highland climate with mild summers and chilly, dry winters. According to South African weather service, in 2023 [31], the average annual precipitation was 674 mm, with most rainfall occurring mainly during summer.

The project’s objectives were to develop a sustainable agroecological-based village that generates decent jobs and income for the resettled residents and households, providing sustainable rural livelihoods. These objectives embrace an economic and social development framework integrated with environmentally conscious solutions, such as soil fertility, nutrition, green energy, and water security, and developing a model that can be replicated in similar settings. The purpose of this study was to investigate the impact of climate change on smallholder farmers in South Africa in general, particularly focusing on the relationship between agriculture and weather patterns at the Phumulani Agri Village.

The Agricultural Research Council of South Africa assisted with gathering secondary data from 1990 to 2023. The selection of the weather station was based on its long-term records of rainfall, wind, and temperature data, which allowed us to better understand weather patterns and trends at the farm level, such as:

• Maximum and minimum temperatures of the study area measured in degrees Celsius using digital thermometers.
• Rainfall data measured in mm using rain gauges.

To confirm the presence of trends in the data, we performed a Mann–Kendall (M-K) test which can determine whether the data exhibit a monotonic trend (linear or non-linear) of significance [32]. This is an appropriate test, since it is much less sensitive to outliers and skewed distributions.

In some cases, the data had missing values which were removed so that seasonal and yearly trends could be assessed.

2.2. Primary Data

Primary data were collected directly by the researcher through direct observation [33]. Qualitative methods, such as direct observation, were used to gain an understanding of the impact of climate change on the livelihoods of smallholder farmers in the study area. Field observations involved data collection through detailed observation of the site and its natural ecosystem. This collection of data was for obtaining reliable self-report data. Photographs, a powerful form of direct observation, were taken of the study area.

In the context of primary data collection, the authors focused on understanding the impact of climate change on the livelihoods of smallholder farmers within the study area. The livelihoods of smallholder farmers typically encompass a range of resources that farmers rely on to sustain themselves and their families. Direct observation allowed them to collect qualitative data by closely observing the natural ecosystem and the farming practices. This involved studying the changes in the local environment, agricultural practices, and the general conditions of the land, crops, and water resources that could be affected by climate change.

The use of photographs as part of the observation process provided visual evidence of the observed conditions, documenting both the physical landscape and farming activities. These photographs supported the data by capturing elements like the extent of greenhouse damage by wind.

The direct observation data, along with qualitative data collected through interviews and field observations, provided baseline information about the current state of farmers’ livelihoods. This included documenting farming practices and the condition of the greenhouse damage by wind. The primary data were collected in November 2023.

2.3. Secondary Data

The meteorological secondary data were collected with the assistance of the Agricultural Research Council of South Africa and analysed to assess whether there were statistical trends in the temperature and rainfall. These assessments were carried out using the standard Mann–Kendall trend test which was implemented in Python version 3.8 [33].

Secondary data were previously gathered by other investigators, or organisations [34] and enabled an analysis of trends over time. Monitoring climate data from weather stations is critical for identifying and tracking trends and patterns in extreme weather events such as floods, droughts, heat waves, and cold spells. This information is essential for understanding the current and future impacts of climate change on agriculture. Long-term climate data provide valuable historical information that can be used to understand the local and regional climatic conditions, the variability of the climate, and its impact on agricultural productivity. These data can also be used to plan for and adapt to climate change.

3. Results

3.1. Qualitative Results

The qualitative findings of this study revealed that windstorm events in the Phumulani Agri Village (PAV) had a substantial impact on the livelihoods of smallholder farmers, particularly those engaged in greenhouse farming (Figure 2). The damage and destruction of greenhouses significantly disrupted crop production, resulting in income losses for farmers who relied on these crops for their sustenance. Repairing damaged infrastructure and replanting crops proved costly, further compounding the financial burdens on the farmers. This demonstrated the vulnerability of smallholder farmers in the Belfast area to climate-related events, particularly windstorms, which affected both productivity and income.

One key factor exacerbating this vulnerability is the farmers’ limited access to consistent weather data. Reliable weather data could help farmers better understand climate patterns and trends, enabling them to adjust their agricultural practices and build long-term resilience. Improved access to weather information would also encourage community collaboration, allowing farmers to share information and develop collective strategies to manage and recover from adverse weather events.

Although some South African communities use traditional indigenous indicators to interpret weather patterns, these methods lack the precision and detail that scientific weather forecasting offers [35].

3.2. Discussion of Qualitative Results

The design and structural integrity of greenhouses are crucial in mitigating windstorm damage [36,37]. Studies emphasise the importance of constructing greenhouses to withstand various environmental stresses, with wind identified as one of the most destructive forces [37]. Lightweight plastic greenhouses, commonly used by smallholder farmers, are particularly vulnerable due to their flexibility and low stiffness [38].

The height of greenhouses also plays a significant role in reducing wind damage. Taller structures provide a larger surface area for wind dissipation, reducing the likelihood of damage [36]. Moreover, taller greenhouses are less prone to wind uplift, which can lead to structural collapse, and can enhance the energy efficiency of the structure by retaining more heat inside. Reinforcing the walls, foundations, and using stronger materials like galvanised metal bars can further increase resistance to wind [39].

Additional considerations include the location of greenhouses, as certain sites may be more prone to wind damage. Farmers need to assess the wind exposure of potential locations before constructing greenhouses. Features like a zigzag channel on the greenhouse side could disrupt wind flow, reducing the potential for damage. The zigzag pattern could also function as a wind turbine, generating power as wind passes through it.

The findings underscore the vulnerability of smallholder farmers to climate-related events and emphasise the importance of integrating modern technologies, such as advanced weather forecasting and resilient infrastructure, to mitigate the impact of such events. While indigenous knowledge systems remain valuable, they should be complemented by scientific methods to provide farmers with more precise and actionable information. Additionally, improving access to weather data and financial tools, like insurance, can help farmers better adapt to climate variability, ultimately enhancing food security and agricultural productivity.

Climate change is expected to bring more frequent and intense weather events, including windstorms. Smallholder farmers, particularly those using greenhouses, must be equipped with strategies and technologies to withstand these conditions. Collaborative adaptation planning that involves local communities, and considers their socioeconomic and cultural realities, will ensure that resilience measures are sustainable and widely adopted.

Using trends in rainfall, temperature, and wind can help smallholder farmers prepare for future climate challenges. However, the inherent limitations of indigenous indicators underscore the need for more advanced weather forecasting tools tailored to local contexts.

The results highlight the urgent need for interventions to enhance the resilience of smallholder farmers. These could include weather-indexed insurance, climate-smart agricultural practices, improved early warning systems, and increased access to credit and financial services. Importantly, these interventions must be adapted to the socioeconomic and cultural contexts of smallholder farmers in Mpumalanga.

We now turn to the secondary data to assess whether there are trends in the relevant climate data. This involves examining data for temperature and rainfall to identify any patterns or significant changes over time.

3.3. Quantitative Analysis of Maximum Temperatures

3.3.1. Maximum Temperature Patterns During the Winter Season

Figure 3 illustrates the average yearly maximum temperature (Tx) patterns for the winter months of June, July, and August over a 33-year period, from 1990 to 2023. The line graph distinctly represents each month with a different line style—solid for June, dashed for July, and dotted for August—allowing for clear differentiation of temperature trends across the winter season. The y-axis measures the average maximum temperature in degrees Celsius, ranging from 22 °C to 28 °C, while the x-axis spans the years from 1990 to 2023.

The graph shows that there is noticeable fluctuation in temperature patterns across all three months, but with a general upward trend over the 33-year period. The Mann–Kendall (M-K) test confirmed this increasing trend and indicated a significant rise in maximum temperatures during the winter months. The month of August exhibited the highest variability and the highest maximum temperature values, while June and July showed a less pronounced, but still upward, trend. Notably, there were sharp rises and occasional declines, reflecting variability in the region’s climate system. The early 21st century, in particular, showed a marked increase in temperature for all months, with several of the highest temperatures recorded toward the latter part of the dataset.

3.3.2. Discussion of Winter Maximum Temperatures

The observed upward trend in maximum winter temperatures is consistent with global studies that have documented similar warming patterns. Notably, Siabi et al. (2021) [40] found that anomalies in atmospheric circulation contributed to winter warming in the northern hemisphere, while Luo et al. (2020) [41] highlighted significant decadal oscillations alongside a warming trend in winter temperatures across East Asia. These studies reinforce the notion that winter warming is a prominent feature of global climate change, which is reflected in the increasing maximum temperatures seen in the Belfast region.

Several factors may be contributing to the rising temperatures in this region, including land-use changes, urbanisation, deforestation, and air pollution. These human-induced factors can significantly alter local climate patterns, leading to warmer winters.

For smallholder farmers, adapting to these warming trends is crucial. One potential adaptation strategy is to plant early maturing crop varieties, which can complete their life cycle before the end of the growing season. These crops are particularly advantageous as they can be harvested before the onset of drought conditions, which are becoming increasingly common due to climate variability [42]. Similarly, in Zimbabwe, farmers are encouraged to plant early maturing varieties of sorghum and millet, which are more resilient to heat and can thrive in the semi-arid conditions exacerbated by climate change [43]. This strategy helps mitigate the risk of heat stress or drought that may occur later in the season. Early maturing varieties have shorter growing periods, require less water, and can be harvested before extreme weather conditions become problematic. This not only reduces the risk of crop failure but also allows farmers to maximise yield potential while freeing up land for other uses.

Additionally, adopting soil conservation practices, such as mulching and cover cropping, can help farmers cope with the impacts of climate change. Mulching helps conserve soil moisture, prevent erosion, and improve soil health by increasing organic matter content. Cover cropping, on the other hand, protects the soil during off-seasons, reducing nutrient loss, enhancing organic matter, and suppressing weeds. These methods can enhance the resilience of smallholder farmers to climate variability, ensuring more sustainable and productive agricultural practices. Research indicates that when used as a cover crop, crimson clover can enhance soil organic matter and reduce soil erosion, making it a valuable addition to crop rotations [44].

3.3.3. Maximum Temperature Patterns During the Summer Season

The analysis of maximum temperature trends for the summer months in Belfast, South Africa, reveals a consistent upward trajectory, as confirmed by the Mann–Kendall (M-K) test (Figure 4). This upward trend suggests that temperatures have been increasing over time, aligning with broader climatic changes potentially linked to global warming. The temperature trends for December, in particular, display more pronounced variability and sharper spikes compared to January and February, suggesting that different climatic factors may be influencing December’s temperatures. This variability warrants further investigation into the regional climate phenomena that may contribute to these temperature fluctuations.

The observed increase in maximum temperatures aligns well with global climate change patterns as reported by the IPCC (2021) [24], where rising temperatures are a key indicator of ongoing environmental shifts. Despite the overall warming trend, the temperature data also show intermittent periods of decline or stabilisation, highlighting the complex nature of climate dynamics. These fluctuations may be driven by various factors, including natural climate variability, as noted in the works of Hartmann et al. (2013) [45] and Soroye et al. (2020) [46]. This complexity underscores the challenges in predicting long-term temperature trends and their impacts on local environments.

In addition to global climate drivers, the rising maximum temperatures in Belfast may be influenced by regional climate phenomena like El Niño and La Niña events. These events can temporarily deviate from the overall warming trend by impacting atmospheric circulation patterns and sea surface temperatures. Moreover, local factors such as urbanisation and land-use changes likely play a role in the observed temperature variability. The formation of urban heat islands and changes in surface albedo [47,48] can contribute to localised temperature increases, particularly in urbanised areas, further complicating the interpretation of temperature trends.

3.3.4. Discussion of Summer Maximum Temperatures

The increasing summer temperatures present significant challenges for smallholder farmers in the region. Heat stress on crops, shifts in growing seasons, and the overall unpredictability of weather patterns can significantly hinder agricultural productivity. In response, community-based adaptation strategies have proven valuable. Farmers, for example, can provide a platform for farmers to share knowledge and experiences, enabling them to adopt new techniques and technologies that address local climate challenges. These collaborative efforts foster resilience, allowing farmers to develop coping strategies that can mitigate the adverse effects of climate change on their livelihoods.

The increasing maximum temperatures in Belfast reflect both global and regional climatic changes. While global warming trends are evident, local factors and climate phenomena introduce variability that require careful consideration. Adaptive strategies such as community-based learning and farmer field schools are crucial in helping smallholder farmers navigate the challenges posed by summer warming. By sharing knowledge and innovating together, these farmers are better equipped to manage the impacts of a changing climate on their agricultural practices.

3.3.5. Maximum Temperature Patterns During the Spring Season

The Mann–Kendall (M-K) test, applied to spring temperature data, confirmed an increasing trend in spring temperatures, which has direct consequences on environmental processes like evapotranspiration and runoff timing [49,50] (Figure 5). The observed variability in temperature influences vegetation growth across diverse biomes [51,52], indicating a broader ecological response to climatic changes. Temperature shifts during spring are reflected in altered carbon dioxide uptake and groundwater recharge [53,54]

A study by Wang et al. (2011) [55] indicates notable temperature changes in North America during spring, characterised by non-continuous fluctuations rather than a steady trend. These temperature shifts have a significant impact on phenological events such as budburst and leaf area expansion, as corroborated by Lucht et at. (2002) [56]. Furthermore, trends in phenological phases, as explored by Menzel (2000) [57], reveal valuable insights into winter and spring temperature variations, highlighting the interconnectedness between phenology and climatic changes.

3.3.6. Discussion of Spring Maximum Temperatures

The results underscore the complexity of temperature–vegetation interactions during spring, with temperature fluctuations directly influencing phenological shifts. Rebetez and Reinhard (2007) [58] and Chen and Xu (2011) [51] reported that the relationship between phenological changes and spring temperatures is vital for understanding the broader ecological impacts of climate change.

The confirmed increasing trend in spring temperatures leads to earlier budburst and changes in vegetation cover, which, in turn, affect the absorption or reflection of solar radiation, contributing to local climate alterations. This feedback loop between vegetation and climate highlights the delicate balance within ecosystems, where vegetation growth and seasonal cycles are susceptible to changing climatic conditions.

Our study’s results align with previous research, illustrating how rising temperatures during spring affect key environmental processes such as groundwater recharge and evapotranspiration. Shifts in runoff timing due to temperature increases can impact water availability, a critical issue for agricultural and ecological systems dependent on consistent hydrological patterns.

Adaptation strategies are essential to mitigate the effects of these phenological shifts and vegetation responses. Adjusting crop varieties and planting schedules to align with new climatic conditions can ensure agricultural resilience [59]. The promotion of climate-resilient crops, capable of withstanding extreme weather events, offers another pathway to increase yields even in the face of climate change [60]. Conservation efforts aimed at preserving local ecosystems can also help mitigate the risk of crop loss and ecosystem degradation.

Overall, the study results highlight the pressing need for continuous monitoring of spring temperature trends and their impacts on phenology and environmental processes. By understanding these dynamics, policymakers and agricultural extension advisors can develop more effective strategies for climate adaptation and ecosystem management.

3.3.7. Maximum Temperature Patterns During the Autumn Season

A study by Liao et al. (2023) [61] highlights that climate change in China has led to a rise in average surface temperatures and an increase in extreme weather events, adversely affecting human systems. The observed climate variability includes cycles of warmer-wetter and colder-drier periods [62]. These climatic fluctuations disproportionately impact marginalised communities, exacerbating existing socioeconomic inequalities [63].

Climate change has raised concerns about food insecurity, linking it to crop failures, reduced agricultural yields and rising food prices. Additionally, poverty is intensified by climate-related challenges that lead to lost livelihoods, increased migration, and decreased access to essential resources. Health disparities are also exacerbated, with heightened morbidity and mortality rates associated with heat-related illnesses, vector-borne diseases, and food-borne illnesses [64].

It is pertinent to note that the period after the year 2010 shows a marked increase in maximum temperatures for all three months, indicating a possible acceleration in the rate of temperature rise (Figure 6).This trend, confirmed by a Mann–Kendall (M-K) test, holds significant implications for smallholder farmers in the region, whose agricultural activities are particularly sensitive to temperature variations [65].

The changes in temperature patterns, especially the increase in maximum temperatures, pose risks for agriculture. Smallholder farmers, who often rely on traditional farming practices vulnerable to temperature fluctuations, are particularly affected [66,67]. Given these challenges, the adaptation of smallholder farmers through sustainable agricultural practices is crucial for enhancing resilience against climate change impacts.

3.3.8. Discussion of Autumn Maximum Temperatures

The results of the study area underscore the pressing reality of climate change and its adverse effects on both human systems and marginalised communities. The evident rise in average surface temperatures and extreme weather events, as discussed by Liao et al. (2023) [61], reveals an urgent need for adaptive strategies to mitigate the impacts on food security and socioeconomic stability.

The oscillation between warmer-wetter and colder-drier periods, as highlighted by Zhao (2020) [62], contributes to an increasingly unpredictable agricultural environment. The implications for food security are profound, as crop failures and reduced yields are likely to exacerbate poverty among already vulnerable populations, limiting their access to necessary resources.

The significant increase in maximum temperatures observed post-2010 further complicates the agricultural landscape, particularly for smallholder farmers who are often dependent on traditional practices. As confirmed by the Mann–Kendall test, this trend suggests a critical need for these farmers to adapt to the changing climate. The call for implementing climate-smart agricultural practices is essential, as these techniques can enhance the adaptive capacity of smallholder farmers and improve crop production efficiency in the face of climate variability.

Moreover, the promotion of appropriate mechanisation in smallholder farms can significantly contribute to sustainability and increased productivity. By addressing the challenges posed by climate change through improved practices and technologies, farmers can enhance their resilience, ensuring food security and better socioeconomic outcomes.

In conclusion, this study highlights the intricate relationship between climate change and its socioeconomic ramifications, particularly for marginalised communities and smallholder farmers. To navigate these challenges effectively, targeted support and adaptation strategies are necessary to empower vulnerable populations and foster sustainable agricultural practices in a changing climate.

3.3.9. Maximum Temperatures in Winter and Summer Seasons (Yearly Averages)

The observed trends in both winter and summer average maximum temperatures over the past three decades provide critical insights into the ongoing shifts in climate patterns (Figure 7). The gradual warming of winter seasons, as evidenced by the slightly increasing trend in winter average maximum temperatures, aligns with broader global climate change patterns. This gradual winter warming can have profound implications for ecosystems, agriculture, and energy consumption. Warmer winters may reduce the occurrence of frost, potentially altering the growing seasons for certain crops, but they may also contribute to the disruption of ecosystems that depend on cold temperatures for certain life cycles or processes.

3.3.10. Discussion of Winter and Summer Maximum Temperatures

The sharp increase in winter temperatures around 2012, followed by a significant peak in both summer and winter temperatures in 2015, suggests that these years experienced anomalous climatic conditions. A likely explanation for this anomaly is the influence of the El Niño phenomenon, which is known to cause substantial increases in global temperatures. El Niño events typically bring warmer-than-average temperatures, particularly in the tropics and subtropics, which could explain the observed spikes. The 2015 El Niño was one of the strongest on record, contributing to global temperature anomalies. This event’s impact on both winter and summer temperatures in our dataset underscores the sensitivity of seasonal temperature trends to broader climatic events.

In contrast, summer average maximum temperatures exhibit greater variability compared to the more stable winter trends. This variability is likely influenced by a combination of local weather patterns, such as heatwaves, droughts, and the changing intensity and frequency of these extreme events under a warming climate. The significant peaks observed in the mid-1990s and mid-2015s could correspond to periods of heightened climatic variability or the amplification of heat extremes during these periods. This increasing variability in summer temperatures presents challenges for various sectors, particularly agriculture, where more extreme heat during growing seasons could affect crop yields, water demand, and pest populations.

Overall, the observed warming trends and variability in seasonal maximum temperatures support the notion that climate change is impacting both winter and summer temperatures, with increasing temperatures and greater variability posing significant risks for ecosystems and human activities. The data also highlight the importance of understanding seasonal differences in climate change impacts, as winter and summer seasons are experiencing changes in both the magnitude and variability of temperature trends. Further analysis of the underlying drivers of these patterns, such as atmospheric circulation changes or feedback mechanisms, would enhance our understanding of these temperature trends and improve future climate projections.

3.3.11. Maximum Temperatures in Spring and Autumn Seasons (Yearly Averages)

The analysis of temperature trends from 1990 to just beyond 2020 reveals several key findings. The average maximum temperatures during both spring and autumn show noticeable variability, fluctuating significantly year-to-year, yet a clear long-term warming trend is present. For spring temperatures, a notable peak is observed around the year 2000, whereas this peak is absent in autumn, indicating a divergence in seasonal temperature patterns during this period. Despite these fluctuations, both seasons display an overall increase in temperatures over the three decades.

The data suggest that temperatures in both seasons have followed a general upward trajectory, though they have done so with significant inter-annual variability. This is evident from the peaks and troughs in the temperature patterns across the years, particularly in the spring season. After 2015, both spring and autumn seasons exhibit a series of high peaks, indicating that more frequent periods of extreme heat have occurred in recent years. This pattern supports the hypothesis that climate change is intensifying temperature extremes, particularly during key agricultural seasons.

3.3.12. Discussion of Spring and Autumn Maximum Temperatures

The findings from the temperature trend analysis have important implications for smallholder farmers in the Belfast area (Figure 8). The increasing average maximum temperatures, especially the sharp peaks observed after 2015, suggest a heightened risk of heat stress on crops. This aligns with global climate projections, which anticipate more frequent warm seasons due to climate change, potentially exacerbating the vulnerability of agricultural systems in the region. As Zhang (2023) [68] discusses, global warming has direct consequences for agricultural productivity by affecting surface temperatures, which can impair crop development and reduce yields.

One of the most notable observations from the analysis is the divergence in seasonal temperature patterns, particularly around the year 2000, when spring temperatures experienced a sharp increase not mirrored in autumn. This divergence may indicate that different crops or farming systems could be affected differently depending on their growing seasons, further emphasising the need for season-specific climate adaptation strategies.

The fluctuations in temperature patterns are also of concern. While the overall trend points towards warming, the year-to-year variability introduces a level of uncertainty that could complicate farming practices. Farmers rely on relatively predictable seasonal cycles to plan planting and harvesting activities, but the observed volatility in temperature patterns may require them to adopt more flexible and adaptive farming techniques. For example, adjusting planting schedules, selecting more heat-tolerant crop varieties, or implementing water conservation measures could be necessary to mitigate the adverse impacts of increased temperature variability.

Moreover, the series of high temperature peaks following 2015 suggests that future seasons may experience even more frequent extreme heat events. This trend aligns with broader climate projections, which predict more frequent and intense warm periods in many parts of the world, including South Africa. The impacts of these temperature increases are likely to be felt most acutely by smallholder farmers, who have limited resources and may struggle to adapt to rapidly changing climate conditions.

In conclusion, this study’s findings highlight the need for targeted adaptation strategies to help smallholder farmers cope with rising temperatures and increased temperature variability. By understanding the specific seasonal impacts and adopting climate-resilient practices, farmers can better mitigate the risks posed by a warming climate.

3.4. Quantitative Analysis of Minimum Temperatures

3.4.1. Minimum Temperature Patterns During the Winter Season

The most pronounced short-term temperature variations occur between 1990 and 2020, suggesting potential cyclical patterns due to natural climatic variability or unspecified external factors. Notably, June and July temperatures are consistently closer compared to August, which shows a wider range of variability. This variability impacts daily planning and agricultural activities. In agriculture, temperature fluctuations can affect crop growth and harvesting schedules, potentially causing stress or reduced yields. Farmers might need to adopt adaptive practices like adjusting irrigation schedules and using protective coverings. A Mann–Kendall (M-K) test indicated no significant trend in the data.

3.4.2. Discussion of Winter Minimum Temperatures

The analysis of temperature data revealed that temperatures in June and July were consistently similar, exhibiting minimal variability, while August demonstrated a wider range of temperature fluctuations. This variability can significantly impact agricultural planning and activities, as fluctuations in temperature can affect crop growth, harvesting schedules, and overall yields, potentially leading to crop stress. To address these challenges, farmers may need to implement adaptive practices, such as adjusting irrigation schedules to account for increased evaporation rates during hotter days and utilising protective coverings to shield crops from extreme temperature changes.

3.4.3. Minimum Temperature Patterns During the Summer Season

Analysis of the minimum temperatures in the Belfast area over a 30-year period reveals significant fluctuations during December, January, and February. Despite these variations, the Mann–Kendall (M-K) test confirms that there is no clear long-term upward or downward trend in temperatures. January temperatures predominantly range from 19.0 °C to 20.5 °C, displaying notable peaks and troughs. February follows a similar pattern, also remaining within the 19.0 °C to 20.5 °C range. December’s minimum temperatures are slightly lower, averaging between 18.5 °C and 20.0 °C. Interestingly, instances of simultaneous peaks and troughs across all three months suggest a common climatic influence on summer temperatures. The implications of rising minimum temperatures for agricultural productivity are highlighted in the work of Neelima and Kumar (2023) [69], which underscores the impact of temperature fluctuations on crop yields.

3.4.4. Discussion of Summer Minimum Temperatures

The observed fluctuations in minimum temperatures throughout December, January, and February underscore the dynamic climatic conditions affecting the Belfast area. The lack of a significant long-term trend, despite pronounced oscillations, suggests that while the climate is stable on a macro scale, it remains variable at a micro level, impacting local agricultural practices. As noted by Neelima and Kumar (2023) [69], even small changes in minimum temperatures can have profound effects on crop yields, particularly for sensitive crops like groundnuts. This fluctuation pattern calls for adaptive strategies among farmers to mitigate potential adverse effects on productivity. Understanding the climatic influences on these temperature patterns is essential for developing effective agricultural management practices and enhancing resilience to climate variability in the region.

3.4.5. Minimum Temperature Patterns During the Spring Season

The analysis of minimum temperatures from September to November between 1990 and 2020 indicates a slight decreasing trend, as confirmed by the Mann–Kendall (M-K) test. Each month’s temperature data exhibit variability, marked by distinct peaks and troughs, reflecting annual fluctuations that may stem from natural variability or other climatic factors. Notably, November temperatures are consistently the highest among the three months, remaining above 15 °C since the early 1990s. October shows a similar pattern, albeit with slightly lower temperatures. September, the first month of spring, records the lowest minimum temperatures, displaying more pronounced fluctuations and a notable increase after the mid-2000s. Additionally, the temperature range for October and November appears to be increasing, indicating greater variability in minimum temperatures over time.

3.4.6. Discussion of Spring Minimum Temperatures

The observed slight decreasing trend in minimum temperatures for September, October, and November over the past three decades highlights the need for further investigation into the factors influencing this climate behaviour. While the Mann–Kendall test confirms this trend, the significant variability observed within each month suggests that fluctuations are influenced by both natural climatic cycles and potentially anthropogenic factors. The consistency of November temperatures above 15 °C indicates a shift in seasonal patterns, which may affect local agricultural practices by altering planting and harvesting schedules. Furthermore, the increasing range of temperature fluctuations in October and November signals greater climatic instability, potentially complicating weather predictability. This unpredictability is particularly crucial for agricultural planning and management, necessitating adaptive strategies to cope with the evolving climate conditions. Continued monitoring and research are essential to understand these trends better and develop effective mitigation and adaptation strategies for agriculture in the region.

3.4.7. Minimum Temperature Patterns During the Autumn Season

The analysis of minimum temperatures during the autumn months over the observed period shows no clear shift or significant trend. The Mann–Kendall (M-K) test confirms that the temperatures do not exhibit a significant long-term upward or downward movement. The data reveal that minimum temperatures oscillate within a certain range without significant deviation, suggesting stability in the temperature patterns for autumn.

3.4.8. Discussion of Autumn Minimum Temperatures

The absence of a clear trend in minimum temperatures during the autumn months indicates climate stability, which is a positive outcome for agriculture in the region. Stable temperatures reduce the risk of crop stress caused by unexpected temperature fluctuations, allowing farmers to maintain consistent growth and harvesting schedules. However, despite the stability, farmers should still consider adaptive practices to prepare for potential short-term variability. Adjusting irrigation schedules, using protective coverings, and selecting climate-resilient crop varieties can help safeguard yields. As noted by Acevedo et al. (2020) [59], cultivating climate-resilient varieties can increase agricultural resilience by providing stable yields under adverse conditions, reducing vulnerability to climate change impacts. By diversifying crops and incorporating resilient varieties, farmers can mitigate risks associated with temperature variability and ensure sustainable agricultural productivity.

Understanding these temperature dynamics is crucial for assessing climate change impacts on ecosystems and environmental processes. During the autumn season, the average minimum temperatures fluctuate a lot, however, they stay within the same range of values. This variability in transitional seasons can lead to unpredictable weather patterns that influence plant growth and productivity. Springtime temperature fluctuations may delay or hasten the onset of growing seasons, while unpredictable autumn temperatures can disrupt harvests.

Moreover, understanding these seasonal temperature trends is essential for managing water resources, as fluctuating temperatures directly impact evaporation rates and soil moisture retention. Climate change adaptation strategies, particularly in agriculture and natural resource management, rely heavily on knowledge of how temperature patterns are shifting and how these shifts are influencing ecosystems. By studying these dynamics, policymakers and stakeholders can better design mitigation and adaptation plans to protect ecosystems, ensure food security, and build resilience against the growing risks posed by climate change.

3.5. Quantitative Analysis of Rainfall

3.5.1. Average Minimum Rainfall Patterns During Winter Season

From the analysis of rainfall data spanning June, July, and August between 1990 and 2020, a clear pattern of fluctuation emerges. These months exhibit considerable variability in year-to-year rainfall levels, characterised by notable peaks and troughs. For instance, June shows a pronounced spike in the early 1990s, followed by a decline toward the mid-1990s. However, after 2015, a discernible long-term increase in rainfall is observed across all three months. This uptick in rainfall is particularly significant as it marks a deviation from the historical variability, suggesting a possible shift in the region’s winter rainfall patterns.

The Mann–Kendall (M-K) test was applied to assess the trend’s significance. The test results confirm a statistically significant increasing trend in winter rainfall over the last few years, particularly post-2015. While inter-annual variation remains high, the long-term trend points towards wetter winters, underscoring the need for further investigation into the underlying drivers potentially linked to climate change.

3.5.2. Discussion of Winter Minimum Rainfall

The increasing winter rainfall trend observed in the latter years of the study is consistent with predictions of changing precipitation patterns due to global climate change. The significant variability noted in the early 1990s, particularly the dramatic peak in June rainfall, reflects short-term climatic fluctuations that may have been influenced by regional weather systems or anomalies such as El Niño events. The subsequent decline and long-term fluctuations before 2015 indicate the inherent variability in the region’s winter rainfall.

The more recent increase in rainfall post-2015 could indicate a shift in winter weather dynamics, possibly driven by broader changes in atmospheric circulation patterns. The results from the Mann–Kendall test, which confirms the upward trend, suggest that the region may be experiencing more frequent or intense winter rainfall events. This has implications for both water resource management and agricultural planning, as the region could face challenges related to increased rainfall variability and flood risks.

Understanding these long-term fluctuations is critical for anticipating future rainfall patterns and their impacts. Climate models often predict changes in rainfall intensity and distribution, which could have far-reaching consequences for ecosystems, infrastructure, and livelihoods. Therefore, tracking these trends is essential not only for understanding the present impacts of climate change but also for future preparedness. Climate change adaptation strategies, particularly for water management and agricultural systems, must be informed by such analyses to ensure resilience in the face of these evolving patterns.

3.5.3. Average Minimum Rainfall Patterns During the Summer Season

The rainfall analysis for the summer months of December, January, and February from 1990 to 2020 reveals considerable inter-annual variability. Throughout this period, rainfall patterns exhibit clear fluctuations, with pronounced peaks and troughs. For instance, January shows notable sharp peaks around the years 2000 and 2006, representing periods of significantly higher rainfall. However, in the later years, particularly between 2015 and 2020, a relative decline in rainfall is observed for all three months, indicating a drying trend during the summer season.

The application of the Mann–Kendall (M-K) test to the data highlights a statistically significant decreasing trend in rainfall over the last decade. This suggests that the region is experiencing less consistent and reduced summer rainfall, posing potential risks for agricultural activities reliant on these months for water supply. The decreasing trend, combined with year-to-year variability, underscores the growing uncertainty in rainfall patterns, which may be attributed to broader climatic shifts.

3.5.4. Discussion of Summer Minimum Rainfall

The variability and declining trend in summer rainfall over the last decade have profound implications for farming systems, particularly for smallholder farmers who depend on consistent rainfall for crop production. The peaks in rainfall observed in the early 2000s, followed by a marked reduction between 2015 and 2020, suggest that the region’s summer rainfall patterns are becoming more erratic, complicating agricultural planning. Farmers may face increased challenges in predicting water availability, making it harder to sustain crop yields.

The significant downward trend confirmed by the Mann–Kendall test is indicative of broader climate change impacts, particularly the shifting distribution of rainfall, which is expected to increase the risk of droughts and water shortages in this region. This unpredictability places smallholder farmers in a precarious position, as they may struggle to adapt to the changing climatic conditions without adequate resources or knowledge.

Effective adaptation strategies, as noted by Bismark et al. (2021) [70], are critical in helping farmers cope with these changes. Aligning farmers’ perceptions with scientific insights, promoting the adoption of sustainable agricultural practices, and improving access to reliable climate data can empower smallholder farmers to make informed decisions. These strategies would enhance their resilience to erratic rainfall patterns and enable better management of water resources, ultimately contributing to the sustainability of their farming systems and livelihoods amidst the ongoing climate challenges.

3.5.5. Average Minimum Rainfall Patterns During the Spring Season

The analysis of rainfall patterns for September, October, and November between 1990 and 2020 reveals significant variability in precipitation levels, reflecting the inconsistency of rainfall during the fall season. September and October exhibit similar trends, often peaking and troughing in tandem, suggesting a potential climatic linkage between these two months. Peaks in rainfall are observed in the mid-1990s and early 2000s, with October showing particularly sharp increases. In contrast, November tends to have lower rainfall levels overall, following a similar but less pronounced pattern, making it the driest of the three months.

From 2010 onwards, all three months experience a noticeable decline in rainfall, with November showing a particularly sharp decrease. This trend is supported by the results of the Mann–Kendall (M-K) test, which confirms a significant downward trend in rainfall for the period. The decline, especially in November, may signal shifts in regional climatic conditions, potentially linked to larger global climate change patterns. This decreasing trend in rainfall during the spring season raises concerns, particularly for smallholder farmers who rely on consistent water availability for their crops.

3.5.6. Discussion of Spring Minimum Rainfall

The substantial variability in rainfall observed for September, October, and November over the past three decades highlights the complex nature of climate patterns in the region. While September and October display similar rainfall trends, peaking and troughing almost in unison, the reduced precipitation in November suggests a shift towards a drier spring season. This discrepancy is particularly important for agricultural planning, as it affects water availability during the critical planting and early growth stages for many crops.

The declining rainfall trend observed from 2010 onwards, especially in November, could be indicative of changing climate conditions, with potentially severe implications for smallholder farmers. The significant downward trend confirmed by the Mann–Kendall test suggests that these months are becoming drier, a trend that, if it continues, may exacerbate water shortages during the spring growing season. This is particularly concerning given that November, being the driest month, is already susceptible to reduced water availability.

Smallholder farmers in the region will likely need to adopt climate change adaptation strategies to cope with the increasing variability and decline in rainfall. Water conservation techniques, improved access to weather forecasts, and the promotion of drought-resistant crops may help farmers mitigate the adverse impacts of drier springs. Additionally, aligning agricultural practices with climate data could lead to better decision-making, ensuring that farmers can optimise planting times and resource management in response to the evolving rainfall patterns.

3.5.7. Average Minimum Rainfall Patterns During the Autumn Season

The analysis of rainfall trends over the autumn months (March, April, and May) in Belfast, South Africa, from 1990 to 2020, reveals notable variability and trends in precipitation patterns. March consistently emerged as the wettest month compared to April and May, experiencing the highest peaks in rainfall. For example, the years 2003 and 2013 saw rainfall levels in March exceed 7 mm, reflecting particularly wet conditions. The variability in March rainfall patterns is underscored by fluctuations over 1- to 2-year periods, with no distinct long-term upward or downward trends, indicating the influence of short-term climatic anomalies.

In contrast, April and May showed generally lower rainfall than March, with April being more variable but also recording years with near-zero rainfall, signifying dry conditions. The frequency of such dry periods appears to increase toward the end of the study period. This suggests potential shifts in the climate system, which could have implications for agriculture and water resource management in the region. The rainfall trends in May closely followed those of April but with consistently lower values, further emphasising the relatively dry nature of these months.

A key finding is the significant decrease in rainfall during the autumn months in the last nine years of the study period. This was confirmed through the Mann–Kendall test, which identified a statistically significant declining trend in rainfall across March, April, and May. This decreasing trend raises concerns about the potential long-term impact on seasonal rainfall patterns, which could exacerbate drought conditions in the region.

3.5.8. Discussion of Autumn Minimum Rainfall

The observed variability in March rainfall aligns with the inherent variability of South African climate systems, influenced by larger global climate phenomena such as El Niño-Southern Oscillation (ENSO) events, which can bring wetter or drier conditions depending on the phase. The peaks in rainfall in years like 2003 and 2013 might be attributed to such events, while the overall fluctuations emphasise the complexity of local weather patterns.

The drying trend in April, especially with more frequent years of near-zero rainfall toward the end of the study period, is more concerning. Given that agriculture in regions like Belfast is highly dependent on seasonal rainfall, this trend could signal future challenges in terms of water availability for crops, potentially leading to shifts in planting schedules or the adoption of drought-tolerant crops. The consistent decrease in rainfall, particularly over the last nine years, suggests the possibility of long-term climatic changes that could influence the viability of traditional farming systems.

Moreover, the statistically significant results from the Mann–Kendall test validate the decreasing trend in rainfall, providing a quantifiable measure of this decline. If this trend continues, it could have far-reaching consequences for not only agriculture, but also water resources, ecosystem services, and the resilience of local communities in adapting to a potentially drier climate. Consequently, it is critical to consider adaptive strategies such as improved water management, climate-smart agriculture, and early warning systems to mitigate the impacts of reduced rainfall during the autumn months in this region.

3.5.9. Average Maximum Rainfall Patterns During the Winter and Summer Season

An analysis of seasonal average rainfall in the Belfast area over the period from 1990 to 2020 reveals distinct trends in both summer and winter precipitation. Summer rainfall, which traditionally accounts for the majority of the region’s annual precipitation, exhibits considerable variability, with peaks occurring in the early and mid-1990s, early 2000s, and around 2010. These peaks do not follow a regular pattern but instead appear sporadically, likely influenced by short-term climatic events. However, despite the fluctuations, a long-term trend spanning 1 to 3 years shows an overall decrease in summer rainfall, with the last nine years converging toward approximately 1 mm per season. This declining trend was confirmed by the Mann–Kendall test, which indicates a statistically significant reduction in summer rainfall over time.

In contrast, the analysis of winter rainfall shows consistently low values over the entire period, though a visible increasing trend is observed in recent years. While winter historically receives little precipitation, this trend suggests that the average winter rainfall has been rising, with values approaching 1 mm per season in the past nine years. Interestingly, this increase in winter rainfall coincides with the decline in summer rainfall, leading to a convergence in the average rainfall for both seasons.

3.5.10. Discussion of Winter and Summer Maximum Rainfall

The findings highlight important shifts in seasonal rainfall patterns, with summer rainfall showing a marked downward trend and winter rainfall increasing (Figure 9). The sporadic peaks in summer rainfall during the 1990s and early 2000s likely reflect the influence of large-scale climate systems such as the El Niño-Southern Oscillation (ENSO), which can bring increased rainfall during El Niño years. However, the overall decrease in summer rainfall in recent years is concerning, as it points to a drying climate that could have considerable implications for water resources and agriculture.

The convergence of summer and winter rainfall toward a similar range is particularly noteworthy. Summer is typically the main rainy season in this region, and the declining trend suggests that traditional summer rainfall patterns may be shifting. As the Mann–Kendall test confirms, this is not a random fluctuation but a significant long-term change. If this trend continues, it could result in increased water scarcity during the crucial growing season, putting pressure on water-dependent sectors like agriculture and increasing the vulnerability of local ecosystems.

At the same time, the increase in winter rainfall, while potentially beneficial, may not be sufficient to offset the loss of summer precipitation. Winter rains are typically less intense and spread over a longer period, making them less useful for replenishing water resources or sustaining agricultural productivity. Moreover, the shift toward a drier summer and wetter winter could disrupt traditional farming practices and require the development of new water management strategies to ensure sufficient supply during the growing season.

Overall, these changes in rainfall patterns highlight the need for proactive adaptation measures. Water management systems will need to be optimised to capture and store rainfall during the increasingly dry summers and make use of the rising winter precipitation. Additionally, agricultural practices may need to shift toward more drought-tolerant crops or alternative farming methods to cope with the drying climate. The increasing variability and changing distribution of rainfall underscore the urgency of climate adaptation strategies to sustain livelihoods and ecosystems in the region.

3.5.11. Average Maximum Rainfall Patterns During the Spring and Autumn Season

The rainfall trends depicted in Figure 10 illustrate significant variability in both spring and autumn average rainfall over the period from 1990 to 2020. Spring rainfall exhibits a cyclical pattern, with notable peaks and troughs throughout the decades. A prominent peak is observed around the year 2000, where spring rainfall reached unusually high levels. This peak is followed by considerable fluctuations, reflecting the inherent variability of the region’s climate. Similarly, autumn rainfall shows substantial variability, although it does not exhibit peaks as pronounced as those in the spring season. Instead, autumn rainfall fluctuates more subtly but steadily declines in the years following 2010. Both seasons demonstrate a downward trend in rainfall leading up to the 2020s, with autumn experiencing a more consistent decrease than spring.

This declining trend in seasonal rainfall, especially after 2010, poses significant concerns for water resources, agriculture, and ecosystems, particularly for smallholder farmers who depend on reliable seasonal rain for their livelihoods. Rainfed agriculture is highly vulnerable to shifts in rainfall patterns, as decreased rainfall can directly impact crop yields and water availability. The noticeable reduction in rainfall in both spring and autumn seasons could exacerbate water scarcity and increase the risk of drought, particularly in regions where water resources are already limited.

3.5.12. Discussion of Spring and Autumn Maximum Rainfall

The variability and downward trends observed in spring and autumn rainfall patterns align with broader climatic shifts affecting Southern Africa. The pronounced peak in spring rainfall around 2000 may have been influenced by large-scale climatic events such as the El Niño-Southern Oscillation (ENSO), which can cause sporadic increases in rainfall during specific years. However, the overall declining trend from 2010 onwards suggests more persistent changes in the climate system, with potentially far-reaching implications.

Smallholder farmers are particularly vulnerable to these changes due to their reliance on rainfed agriculture and limited access to adaptive resources. As noted by Naazie et al. (2023) [71], such farmers face heightened risks from climatic shocks and stressors, with fewer resources available to mitigate these impacts. This emphasises the need for targeted climate adaptation policies that are tailored to the unique socioeconomic, environmental, and climatic challenges faced by smallholder farmers.

Harvey et al. (2018) [72] highlights the importance of designing adaptation measures that are affordable, accessible, and practical for farmers operating under diverse socioeconomic conditions. Given the different levels of income, education, and access to resources among smallholder farmers, a one-size-fits-all approach to climate adaptation is unlikely to be effective. Instead, policies must account for the specific needs and capacities of farmers in various regions to ensure that they can adopt climate-resilient practices that are both feasible and sustainable. By utilising government assistance and focusing on innovative approaches, smallholder farmers can more effectively manage the effects of climate variability [73].

The decline in spring and autumn rainfall observed in the data underscores the importance of developing robust adaptation strategies to help smallholder farmers cope with the evolving climate. Improved water management, crop diversification, and access to early warning systems could be vital in enabling farmers to mitigate the risks associated with reduced and variable rainfall. Without such interventions, the resilience of these farmers and the ecosystems they rely on may be severely compromised in the face of a drying climate.

4. Conclusions and Recommendations

The evaluation of climate variability in drought-prone areas like Belfast, Mpumalanga Province, is critical for the following reasons: (1) understanding climate variability is vital for planning, and (2) implementing climate-sensitive development projects in the region. This ensures that resources are allocated in a manner that is resilient to climate extremes and variability. The Belfast area presents a diverse array of farming opportunities alongside climate change challenges that farmers need to navigate. The results of this study indicate that changes in temperature and precipitation patterns in the Belfast area are likely to have a significant impact on the livelihoods of smallholder crop farmers. The increase in the maximum temperatures across the seasons means that we are experiencing more frequent and intense heatwaves, which can lead to various environmental and societal challenges such as higher energy demands for cooling, increased risk of wildfires, and adverse health effects on vulnerable populations. Additionally, it may result in changes to ecosystems and agricultural patterns, affecting food security and biodiversity. Furthermore, the increase in the rainfall in winter and a decrease in the rainfall during the other seasons can lead to shifts in regional climate patterns, affecting water availability, agricultural yields, and overall ecosystem stability.

Given the vulnerability of the Mpumalanga Province to climate variability and change, these results highlight the need for adaptation strategies that help farmers and other stakeholders to prepare for, and respond to, potential water resource disruptions. Enhancing the adaptive capacity of smallholder farmers through the adoption of climate-smart agricultural techniques can help mitigate the adverse effects of changing climatic conditions on crop production efficiency. Furthermore, promoting the use of appropriate mechanisation in smallholder farms can contribute to sustainability and increased productivity, addressing some of the challenges posed by climate change. Overall, understanding the implications of climate change on agriculture, and empowering smallholder farmers with the necessary knowledge and tools to adapt to these changes, is essential for ensuring food security and livelihood sustainability in South Africa. Future studies need to consider how climate variability affects farmers’ abilities to access markets, both in terms of transport and product quality.