Abstract
Purpose
Climate change and variability present different challenges to the livelihoods of forest-dependent communities. This paper aims to determine climate variability/change and its effects on the livelihoods of the Buyangu community, which depends on Kakamega tropical rain forest in Kenya.
Design/methodology/approach
Rainfall and temperature trends were analysed using Mann–Kendall tests and Sen’s slope estimator. The effects of climate variability on the community were determined using household survey questionnaires, focus group discussions and in-depth interviews with key stakeholders.
Findings
Temperature trend analyses represent statistically significant trends for the period of 1980-2015. Results reveal a warming trend for both mean annual maximum temperatures and mean annual minimum temperatures by 0.04°C/year and 0.02°C/year, respectively. Moreover, analysis of annual precipitation (1923-2015) indicated an increase of 0.068 mm/year; however, the mean monthly rainfall showed a decreasing trend. As a result, crop production and livestock rearing are negatively affected. Although there is a high level of awareness of climate variability and its related effects on livelihoods, a majority of the Buyangu community still do not understand the influence of climate change on forests and the provision of forest products. Lack of knowledge on this subject will consequently limit adaptation responses.
Originality/value
This research fulfills the need to study climate variability and its effects on the livelihoods of forest-dependent communities. The study calls for all-round stakeholder participation of local and national players in formulating coherent adaptation strategies that will enhance the resilience of forest-dependent communities to a changing climate.
Keywords
Citation
Saalu, F.N., Oriaso, S. and Gyampoh, B. (2020), "Effects of a changing climate on livelihoods of forest dependent communities: Evidence from Buyangu community proximal to Kakamega tropical rain forest in Kenya", International Journal of Climate Change Strategies and Management, Vol. 12 No. 1, pp. 1-21. https://doi.org/10.1108/IJCCSM-01-2018-0002
Publisher
:Emerald Publishing Limited
Copyright © 2019, Faith Nyangute Saalu, Silas Oriaso and Benjamin Gyampoh.
License
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Africa is one of the continents most vulnerable to the effects of climate change and variability, owing to its high exposure and low adaptive capacity. This is aggravated by multiple stress factors that are non-climatic, such as high poverty levels, population pressures and overexploitation (Intergovernmental Panel on Climate Change (IPCC), 2014). It is been widely projected that as global warming persists, climatic conditions will become more unpredictable.
In sub-Saharan Africa (SSA), climate change projections manifest a warming trend with changes in precipitation patterns (Serdeczny et al., 2015). The severity of climatic events is increasingly becoming a challenge not only to humanity but also to the existing natural systems (Intergovernmental Panel on Climate Change (IPCC), 2012). The main sectors in this region – agriculture, forestry, health and transport – forming the backbone of the national economy are the hardest hit. Currently, millions of people across SSA depend on forest products and services for their daily income and well-being. However, not all incomes of forest-based communities are obtained from forest products alone. Often, part of the income is also gathered from agricultural activities and other livelihood options (Adhikari et al., 2004).
In recent years, Kenya’s forests have been depleted at an alarming rate with the current forest cover estimated at 7.4 per cent of total land area, which is below the recommended global minimum of 10 per cent (FAO, 2010). The low forest cover in the country is largely attributed to deforestation, induced by excessive human activities, such as illegal logging, unsustainable charcoal production and clearing of forests for farming and settlement (Lambrechts et al., 2005; Ongong’a and Sweta, 2014), resulting in forest fragmentation. Kakamega Forest, the only tropical rainforest in Kenya, is no exception as nearly half of it has been lost in the past 38 years due to human activity, leaving only about 230 km2 of it standing [Kakamega Forest Ecosystem Management Plan (KFEMP), 2012]. While the Government of Kenya (GOK) has made enormous efforts to protect this forest from human interference, it is difficult to save it from the impact of climate change.
Of particular concern are the livelihoods of the Buyangu community, who live in close proximity to Kakamega Forest and who draw on forest resources to support their livelihoods. Added to the low adaptive capacities of rural populations, climate change impacts on forests will exacerbate the vulnerability of any forest-dependent community (Davidson et al., 2004; Okali, 2011). Past studies conducted in Kakamega County have identified and emphasised the impacts of climate change on smallholding farmers (Barasa et al., 2015; Ochenje et al., 2016; Mulinya, 2017). However, little is known and documented on climate variability and related impacts on the communities that depend on Kakamega Forest.
Sadly, most climate change studies have either ignored or devalued climate variability, probably due to uncertainties with regard to expected future changes in rainfall and temperature variability (Ramirez-Villegas et al., 2013; Thornton et al., 2014). For this reason, the effects of climate variability on forest-dependent communities are still poorly understood (Williamson et al., 2005; Turpie and Visser, 2013). Understanding climate variability trends is critical in mitigating adverse effects on the livelihoods of forest-dependent communities.
Against this backdrop, this study sought to determine the impacts of a changing climate on the Buyangu community’s sources of livelihoods and its dependence on Kakamega tropical rain forest. The findings will not only enrich our understanding of climate variability and its related impacts on such communities but also will provide insights to researchers and policymakers on practical initiatives needed to enhance resilience at the local scale.
2. Literature review
The interaction between climate change and forest-dependent communities cannot be underestimated. Climate change will interfere with entire livelihoods of the rural poor, producing both negative and positive effects: negative externalities often being more pronounced (Somorin, 2010). Past studies have demonstrated how forested areas and their surroundings have experienced general reductions in precipitation and increases in temperature over time (Stern, 2007; Boon and Ahenkan, 2011). These changes profoundly affect the overall health of the ecosystem, reducing the supply of forest materials such as foods, fuelwood, medicinal herbs and other non-timber forest products (NTFPs). In due course, this will impose additional stress on the livelihoods of mostly poor, forest-dependent people.
Perhaps, the most severe is the impacts of weather extremes on NTFPs, which are becoming scarcer by the day (Easterling et al., 2007). Changing climate patterns cause biodiversity loss, as forest plant species become extinct, leading to further ecological destabilisation and an alteration of community livelihoods in the affected areas (Dube et al., 2016). However, Seppälä et al. (2009) notes that climate change effects on NTFPs are still not clear, as there is still uncertainty regarding the ecological effects. Needless to say, NTFPs thrive on the delicate balance of natural factors.
On the same perspective, weather extremes are well-known to have grave impacts on agricultural farming. As rural livelihoods become more precarious, rain-fed agricultural and livestock systems will bear the brunt of climate extremes, adding to the vulnerability of forest-dependent communities who form a significant portion of poor rural farmers (Ofoegbu et al., 2016). Specifically, reduced crop yields compromise food security, affecting the health conditions of vulnerable groups including the elderly, women and children (Altieri and Nicholls, 2017). Moreover, while grazing animals inside forests is a widespread practice among forest-adjacent communities, biodiversity loss means inadequate pastures and this adds more psychological pressures on livestock farmers, who will be forced to look for alternative animal feeds.
Also, as climate changes, populations living within fragmented forests stand a higher risk of contracting zoonotic infectious diseases such as yellow fever, dengue, malaria and sleeping sickness, arising out of increased contact with vectors at the forest edges (Graczyk, 2002). Furthermore, when temperatures rise to a critical level, forest fires are expected to occur more frequently, causing various respiratory diseases associated with smoke inhalation, such as asthma and bronchitis (Molyneux, 2003).
Last but not least, communities closely entwined with forests tend to regard them with much spirituality and respect, believing that powerful spirits are resident within (Laird, 2004). Indeed, most African forests have been used as gathering points where community leaders meet with their people to discuss crucial issues or conduct important ceremonies, such as circumcision. A lot of the ceremonies are conducted under sacred trees such as Maesopsis eminii (Mutere) and Brachystegia spiciformis (Musasa) in Western Kenya. The cultural and spiritual values associated with forests and trees underline the importance of considering the social dimension of climate change on the livelihoods of forest communities. It must, therefore, be emphasised that climate change will induce fundamental changes to the cultural values of forest-dependent communities (Seppälä et al., 2009).
3. Research methodology
This section presents the methodology of the study, describing the characteristics of the study area, sampling procedure, the main techniques used for data collection and data analysis.
3.1 Description of the study area
The study was carried out in Buyangu village, near Kakamega tropical rain forest in Kakamega County (Figure 1). The forest is located in Western Province in Kenya at 0.29°N 34.86°E. The choice of the study site was informed by its high human population with higher demands on forest products (Muller and Mburu, 2009) but whose accessibility is prohibited by Kenya Wildlife Service (KWS), a state corporation whose mandate is to enhance conservation of wildlife and its resources, including protecting forests from human interference. The study targeted households living within 6 km from the forest margin, as they are the largest consumers of the forest’s resources, despite the visibility of KWS officers (Mbuvi et al., 2009). The area receives bimodal rains of about 2,000 mm annually and temperatures range from 18°C to 29°C. January-March are the hottest months while April-July receive the most rainfall (GOK, 2013). Forest resources, rain-fed agriculture and livestock are key to the livelihoods of the majority of Buyangu dwellers.
3.2 Research design
The study undertook a transdisciplinary approach, which emphasises the collaboration and engagement of all stakeholders, including the local communities, in addressing complex societal challenges such as climate change and its effects (Balsiger, 2004; Ramadier, 2004). A mixed research method was used, using both qualitative and quantitative techniques to understand the research problem.
3.2.1 Sampling technique and sample size.
Simple stratified random sampling was used to categorise households in Buyangu. This technique was considered supreme as the target population is heterogeneous and not widely spread out geographically. Past studies have used simple random sampling in selecting households living adjacent to forests (Balama et al., 2016), which in most cases results into underrepresentation or overrepresentation of some elements within the groups. According to Alvi (2016), simple random sampling works better in a homogenous population where an exhaustive list of elements is available. Relative to simple random sampling, selection of units using a stratified procedure improves representation of particular strata (groups) within the population. Thus, households were divided into three strata according to their distance from the forest edge: 0-2 km, 2-4 km and 4-6 km. Efforts were made to map out both male- and female-headed households in each stratum during the reconnaissance survey. All female-headed households identified in the three strata were purposively sampled to ensure adequacy in female gender sample representation. However, male-headed households were selected for a household survey through simple random sampling. A considerable sample of 203 was arrived at through Yamane (1967) formula.
3.2.2 Data collection process.
A variety of data collection methods were incorporated to collect both quantitative and qualitative data from both primary and secondary sources. Primary data was gathered through household survey questionnaires, focus group discussions (FGDs), key informant interviews (KII), desk research and observations. Generally, the questionnaire administered to household heads captured the socio-economic activities in Buyangu, knowledge of climate variability and its effects on the main sources of livelihoods. Two separate FGDs were organised (men and women), with an average of eight participants in each. Both the FGDs created a forum for interaction and discussions on climate variability and how it impacts the community’s livelihoods. More insights were gathered from key informants: forest officers (KWS), agricultural officers, the area chief and village elders in Buyangu.
Past meteorological data on rainfall and temperature parameters was sourced from Kenya Meteorological Department (Sichirai station) located at 0.28°N 34.75°. Data included average monthly rainfall for the years between 1923 and 2015, as well as minimum and maximum temperatures covering 1980-2015 to support analysis. Similarly, average crop yields data for common crops in Buyangu for the period 1985 to 2015 were compiled from different annual reports provided by the Ministry of Agriculture, Livestock and Fisheries in Kakamega County.
3.2.3 Data analysis.
Analysis combined both qualitative and quantitative approaches. The Mann–Kendall (MK) test, a non-parametric test, was considered the most ideal to determine rainfall and temperatures trends over time. This test was applied at 95 per cent confidence levels and used as described by Sneyers (1990). Positive (+ve) values from the results indicated an increase over time while negative (−ve) values point to a decreasing trend. MK detects non-linear trends but is limited in showing the magnitudes of significant trends (Babar and Ramesh, 2013). Thus, Sen’s slope estimator, also a non-parametric test, was used to detect magnitudes of climatic trends for both rainfall and temperatures.
Predictions of yield changes in response to changes in climate variables were assessed through multiple regression, which is considered relatively accurate (Lobell and Field, 2007; Boubacar, 2010). Further, an analysis was carried out on common crops cultivated in the region (dependent variables) and climate variables (annual mean temperature and annual mean rainfall), which were considered explanatory variables. Additional quantitative analyses were derived through SPSS 16 and Microsoft Excel. Qualitative data obtained from FGDs and KII were analysed thematically.
4. Results and discussion
4.1 Analysis of rainfall and temperature variability in Kakamega
4.1.1 Mean monthly rainfall variability.
Observations of the behaviour of mean monthly rainfall for the period 1923-2015 indicate a rainy season in the months of April (255 mm) and May (257 mm) (Figure 2). The precipitation then slightly subsides in June before picking up again in July with August registering higher amounts (222 mm). December, January and February recorded the least amount of rainfall at 91 , 68 and 92 mm, respectively. Generally, the March-April-May (MAM) peaks were stronger than the October-November-December (OND) peaks. According to Nicholson (2017), MAM constitutes the long rainy season, while OND covers the short rain season in most parts of equatorial Eastern Africa. This coincides with crossing the inter tropical convergence zone along the equator in south-north, before taking the north-south migration (Camberlin and Philippon, 2002).
MK tests for mean monthly rainfall (1923-2015) indicate positive trends in January, March, April, September, October, November and December (Table I).
However, increase of rainfall both in October and November were declared significant at α = 0.01. The remaining calendar months displayed trends that were not significant, with the exception of July, which was significant at 95 per cent level. Sen’s slope ranged between - 0.543 and 0.761 for the same climatological period. Results on the magnitude of change are largely negative, with the month of May indicating a decline in rainfall by 0.543 mm/year. On the same note, the magnitude of change was highly positive in November implying an increased precipitation by 0.0761 mm/year.
4.1.2 Mean annual rainfall variability.
Figure 3 below shows appreciable variations of mean annual rainfall for the period 1923-2015. Notable peaks of rainfalls were experienced between 1963 and 1983, whereas significant dips were emerged in 1988 and 2012. While coefficient of rainfall variation (R2 linear = 0.193) was low and probably insignificant, the cumulative effects of rainfall variability are expected to advance in future with less predictability.
The MK test for annual precipitation for the same period was found to increase by 0.068 mm/year (Table I), probably due to increased frequency of hailstorms in the region during previous years. Lau and Wu (2007) asserted that satellite-based studies conducted in the tropics during 1979-2003 reported an increase in the occurrence of heavy rain-related events.
4.1.3 Mean monthly temperature variability.
Maximum and minimum temperatures in Kakamega County both escalate from January to April while they are at their lowest from July to September (Figure 4). It is important to note that onset of the long rainy season is always expected towards the end of March and into the month of April. Given that April comes immediately after a dry spell (January-March), temperatures in the region would still be expected to be higher as displayed in the graph (Figure 4). February was the hottest (31°C) month during the climatological period while September was the coolest at 13.6°C. The three-month period July, August and September recorded the lowest minimum temperatures, while April had the highest minimum temperature of 15.2°C. These findings concur with observations made by Barasa et al. (2015) on temperature variations in the region. Generally, it can be deduced that temperature trends on both scales (maximum and minimum) increase/decrease almost at the same time range.
4.1.4 Mean annual temperature variability.
Figure 5 below represents the mean annual temperature trends for the period 1980-2015. There was variability in both annual mean maximum and minimum temperatures, with the highest values recorded in 2009 and 2014. The lowest mean maximum temperatures of 19.9°C, 20.4°C and 22.6°C were recorded in 1999, 1991 and 1994, respectively. Similarly, mean minimum temperatures were lowest (10.4°C) in 1999. This coincides with the lowest maximum temperatures recorded for the same year, marking 1999 as the coolest year for the entire period under examination. However, the highest minimum temperatures (15°C) occurred in 2010.
The MK test for maximum and minimum temperatures indicates positive trends, suggesting that temperatures have been on the rise for the climatological period 1980-2015 (Table II).
With regard to maximum temperatures, the rise was found to be significant at α = 0.05 for January, February, September, November and December. The magnitude of the temperature increase as represented by Sen’s slope was 0.019°C/year for minimum temperatures and 0.037°C/year for maximum temperatures. The findings indicate that global warming is being felt in the region, echoing Mulinya et al. (2016), who observed similar characteristics in temperature trends. The knowledge of the significance of temperature variations is pertinent in the understanding of climate change.
4.2 Perceptions of climate variability
The views collected from study participants as regards to climate variability were vital for comparison with meteorological data. In general, the majority of respondents were aware of climate variability and would explain their experiences using observable features, such as delayed rainfall, rising temperatures and prolonged drought spells over the past 20 years and more (Table III).
Many respondents felt that rainfall patterns were uncertain, probably due to large interannual fluctuations in rainfall quantities over the years. These perceptions reinforce our findings on the meteorological data analysis on temperatures and rainfall patterns. Simply, it can be deduced that farmers are cognizant of climate variability and its effects on livelihoods, especially on a short-term basis. Concerns about rural people’s ability to perceive climate change and variability is equally important as many farmers may take longer to understand the unusual weather patterns, which actually represent a permanent shift in climate (Maddison, 2007). In the same vein, Simelton et al. (2013) suggested that locals’ perspectives of climate variability play a significant role in addressing climate change, as farmers are likely to adapt.
4.3 Effects of climate variability on livelihood options of Buyangu community
Climate variability has affected the main sources of livelihood of Buyangu residents in several ways. The key ones are crop farming and livestock rearing.
4.3.1 Effects of climate variability on crop production.
Based on the perspectives of farmers in Buyangu, Table IV displays a myriad of negative effects on crop production caused by climate variation. Feedback provided shows that there has been a high decline in common crop productivity over the past years with maize, sugarcane and vegetables being hardest hit.
These findings are in agreement with previous studies conducted in the region, when farmers experienced a decline in crop yields during years past (Barasa et al., 2015; Mulinya et al., 2016). Buyangu is predominantly home to an Abaluhya community that relies heavily on maize for preparation of an Ugali meal. And whenever there is an increase in climate extremes, the disruption falls on maize production. For instance, one farmer told us:
We are now harvesting nearly half the yield we used to harvest some years ago from the same piece of land. While most of us are poor, we cannot afford to buy fertilizers to multiply our harvests.
Another farmer added:
We used to plant and harvest maize twice a year because even short rains were sufficient and reliable, but nowadays we plant once a year as the short rains are no longer predictable.
Maize has been observed to be highly sensitive not only to water deficiencies but also temperature variability (Slingo et al., 2005; Lobell et al., 2011). Multiple reasons can be assigned to declining crop yields, and lack of farming accessories (e.g. fertiliser) for smallholding farmers cannot be underestimated. The authors argue that because Buyangu is a community accustomed to high levels of poverty, the fall in crop yields may be a result of other combined factors of which climate variability cannot be overruled.
4.3.2 Trend of common crop yields.
Multiple regression analysis was undertaken to establish relationships between productivity of common crops (Kgs per hectare) and climate variables as shown in Table V.
Yield trends of common crops based on regression coefficient over time shows that the effect of rainfall variability was statistically significant on sugarcane and cabbages (p < 0.05) resulting into 93.2 and 77 per cent decline in yields, respectively. However, trends of yields for other crops, including maize, displayed no significant changes as regards variations in temperature and rainfall over time. Authors explain that drops in maize harvests as attested by almost 62 per cent of the respondents (Table IV) are probably due to delays in the onset of rains, but farmers strive to improve yields thereafter by using top dressing fertilisers and compost manure.
4.3.3 Nature of livestock production in Buyangu.
Livestock keeping is a mainstay of most rural households in Kenya and contributes significantly to their livelihoods. In Buyangu, many households rear livestock, such as cattle, pigs, goats and chickens (or some combination of these), mainly to generate income and for subsistence. According to the Kakamega County Integrated Plan (2018-2022), chicken rearing is predominant with 63 per cent of the households keeping them while 24 per cent are engaged in cattle rearing. Another 6, 5 and 2 per cent of the population rear sheep, goats and pigs, respectively (Figure 6).
This is also a replica of the Buyangu community, where chicken farming is commonest followed by cattle keeping. While 66 per cent of cattle farmers in Buyangu kept 1-3 animals, a significant 27 per cent of farmers did not rear cattle (Table VI).
This outcome resonates with Peters et al. (2012), who established that 76 per cent of small scale farmers in Kakamega County could not sustain large herds due to high poverty rates and small land acreage. As depicted in Table VII, this community is land-constrained with an average land size of 1-3 acres per household.
However, more analysis indicated that indigenous cattle breeds were preferred by farmers (Figure 7), mainly because of their ability to withstand harsh weather conditions, require less feed and resist disease. However, a section of farmers (39 per cent) still opt for cross-breeds for higher milk and meat production.
4.3.4 Adverse effects of climate variability on livestock production.
4.3.4.1 Herd size.
The study revealed that a majority of the respondents (87 per cent) had reduced their herd size by selling part of their livestock, due to the unavailability of pasture on their farms arising out of prolonged drought spells (Figure 8). It is important to note that seasonal variations in weather patterns affect pasture growth, resulting in decreased forage production.
Prolonged dry spells force farmers to better manage their livestock to minimise losses. In Buyangu, a large group of farmers are reducing their herd size to a manageable size of one or two cows (Table VI) as a way of reducing vulnerability to climate change. Lesnoff et al. (2012) emphasise that livestock farmers with a cattle-dominated herd structure will have difficulties in coping with shortages of animal feed during dry episodes. Most households in this region have to make do with low incomes that cannot sustain purchasing sufficient cattle feed. It is worthwhile mentioning that despite the Buyangu community being a farming group, cattle rearing is slowly being abandoned, while chicken rearing, which demands less feed, is being preferred. This demonstrates livelihood transitions, which predispose the community to future uncertainties, especially with the advent of climate change, bearing in mind that cattle rearing has been one of the major contributors to rural households’ income.
4.3.4.2 Milk production.
The majority (37 per cent) of the respondents who reared dairy cattle had their animals produce an average of 1-3 L of milk per day, while 28 per cent had their dairy animals producing no milk at all (Table VIII).
The low or zero milk output in dairy cattle is mostly attributed to insufficient animal forage, due to prolonged dry spells. Wanjala and Njehia (2014) also reported that milk production for one animal per day is relatively low in Western Kenya. As much as the respondents in the study considered milk production from their cattle sufficient for their own domestic consumption, optimum milk production is yet to be realised. Kenya’s dairy sector experiences milk shortages during times of drought, thereby inflating prices of dairy products in the market, especially in the urban areas. Njarui et al. (2011) maintains that pasture and water are the largest inputs for dairy farming and their inadequacy impacts negatively on milk supply.
Consequentially, scarcity of green pasture in Buyangu arising from climate variability has forced farmers to feed livestock with crop residues, mostly gathered from sugarcane leaves (bagasse) and maize stalks. This resonates with Mutibvu et al. (2012) who made similar revelations of livestock being fed crop residues, especially during dry seasons. Participants in the FGDs attested to the fact that feeding animals with crop residues is not as productive as it results in low milk output. Where farmers have been forced to either purchase or seek alternative animal feed, many have chosen to abandon animal husbandry altogether.
4.3.4.3 Livestock market value.
A majority of the farmers (65 per cent) deduced that climatic variation directly impacts on pasture, greatly influences livestock body weight and ultimately determines the market prices for livestock (Figure 8). High temperatures and long dry spells deplete pastures, resulting in reduced fodder availability, reduced livestock productivity, and lower market returns due to animals’ poor health (Lagat and Nyagena, 2016). Moreover, an increase in vector-borne diseases, such as pneumonia and foot and mouth, were mostly attributed to excessive rainfall. This sentiment was echoed during FGDs, where participants explained their inability to manage climate-related diseases affecting livestock. According to Devendra et al. (2000), livestock diseases are a major setback to livestock production and marketing in the Tropics. Düvel and Stephanus (2000) assert that animal health issues are a barrier to livestock trading, due to reduced animal productivity and increased morbidity.
In summation, climate variability impacts on livestock have interfered with households’ incomes, as many people who relied on the net returns from domestic herds find that they cannot meet their household needs. Cattle are more sensitive to climate variability and their vulnerability increases as the climate gets drier and warmer (Lunde and Lindtjørn, 2013).
4.3.5 Forest accessibility and resource use across gender groups.
To gauge forest accessibility based on the households’ proximity to forest, the respondents estimated their household distances from the forest edge. The study revealed that almost 63 per cent of the households were located within 3 km from the forest’s edge (Table IX).
These households were frequent consumers of the forest resources, exerting pressures on the natural base. A similar observation was made by Kisaka and Sitati (2014), who found that households’ proximity to the forest is a major contributor to the depletion of forest materials, especially along the forest edges. Mujawamariya and Karimov (2014) also observed that people living closer to a forest had a higher dependency on forest resources than those located farther away.
4.3.5.1 Women and forest resource use.
In exploring gender differences in the use of forest resources, the study established that the majority of women (70 per cent) obtain firewood from the forest (Figure 9).The main factor behind the large percentage of women harvesting firewood from the forest is their societal gender role of ensuring a constant supply of cooking fuel for their families.
Although women seem to commercialise forest products less often than men, FGDs revealed that selling firewood is a thriving business in Buyangu. The reduction in crop yields affected by changing weather patterns has now forced women to diversify their sources of income by selling firewood gathered from the forest and the cash remittances used to purchase maize from the markets. This further increases women’s dependence on forests to meet food security needs as many lack alternative options for generating income. Additionally, the task of collecting firewood for women in Buyangu is a nightmare as many are not brave enough to access the forest amid the presence of KWS officers. This situation is further aggravated by the compelling evidence of climate change affecting forest ecosystems and influencing availability of resources through periodic variations of temperatures and precipitation. FGDs held with men and women further revealed that there is actual disappearance of tree species best known for fuel wood and medicinal purposes. Difficulties in accessing fuel sources and other forest materials are indirect impacts of climate change posing considerable challenges to the livelihoods of women in rural set-ups (Brown et al., 2014).
4.3.5.2 Men and forest resource use.
Generally, men are reported to collect forest products for commercial purposes (Cavendish, 2000; Shackelton and Shackelton, 2000). This was in contrast to men in Buyangu as we observed that they gather forest products – animal fodder – mainly for domestic purposes (Figure 10).
Customarily in the Abaluhya community, searching for animal pastures is a male dominated activity that starts from an early age. Male household heads will always ensure Napier grass and other animal feeds are planted on farms. This only works with households owning larger pieces of land, which can support both cultivation of crop foods and animal feed. However, the intrusion of men into the forest in search of animal fodder supports current observations by the community on the decline of on-farm pastures because of climate change and variability.
4.3.6 Understanding factors that influence supply of forest resources.
The study sought to establish opinions of the Buyangu community as regards to supply of forest resources over the past years. Figure 11 illustrates that most of the respondents observed a decline in forest resources, except for 39 per cent of the respondents who reported the contrary.
The reduction in forest resources was largely attributed to overharvesting, while the increase was linked to the presence of KWS officers protecting the forest heritage. This corresponds with Hermans-Neumann et al. (2016), whose study also associates the increase of forest resources with forest management. However, with respect to a decrease in supply of forest resources, all respondents unanimously named overharvesting as a presumable cause, while the influence of climate variability on forest resources was not underscored. These revelations demonstrate high levels of ignorance amongst forest-based communities who do not regard extreme climatic events to be a significant threat to the forest ecosystems, thereby affecting their livelihoods.
Productivity and distribution of forests could be affected by both changes in climatic (temperature and precipitation) and non-climatic factors (population pressure, overharvesting, etc.). Both these factors are expected to alter forest ecosystems and the supply of forest resources will no longer be business as usual (Fisher et al., 2010). As much as past studies have shown that future distribution of forest biomass in tropical forests will be determined by climate variables, mostly temperature and rainfall (Lin et al., 2010; Song and Zeng, 2017); the impact of human-induced climate change on forests cannot be underestimated. Unless forest-dependent communities realise that there have been changes in forest ecosystems arising out of anthropogenic activities, they cannot be expected to take up appropriate measures to adapt.
5. Conclusions
This paper has demonstrated climate variability and its considerable effects on the livelihoods of the Buyangu forest-dependent community, both directly and indirectly. The community explained how locals have been experiencing delayed onset of rains, long dry spells and increasing temperatures over the past few years, which have resulted in reduced common crops yields from recurrent seasons. Yields of sugarcane, maize and vegetables are adversely affected by the current climate trend in Buyangu. Similarly, reduction in rainfall and increasing temperatures have suppressed animal pastures, compromising milk output, herd sizes and livestock market values.
The study findings on the effects of climate variability and the supply of forest resources provide valuable insights into the community’s lack of knowledge on the subject. A majority of the respondents suggested that the decline in forest resources was more influenced by the socioeconomic pressures of overharvesting rather than the manifestation of climate change and variability. The community’s ignorance of climate change directly impacting on forest resources demonstrates the likelihood of higher vulnerabilities and the need to prioritise adaptation strategies. Understanding the factors that underscore the vulnerability of forest-dependent communities to climate change and variability is essential in developing initiatives that will foster resilience.
This study, thus, concludes that there is need for integration of the various stakeholders at both local and national levels in addressing the impacts of climate change and variability on forest-dependent communities. Specifically, local governments need to identify adaptive measures for such communities and estimate costs of implementing them. For instance, agroforestry has the capacity to improve agricultural yields while mitigating environmental damage. It is necessary for the government to support such initiatives and encourage community participation through provision of agroforestry species and other farm inputs. The authors further recommend that government programmes working towards maximisation of crop yields should first consider food-crops such as maize and vegetables, which are an important staple food for many communities in Kenya. It would be important for the government agencies to ensure farmers gain access to good quality maize seeds varieties that are affordable and resistant to harsh climatic conditions.
Because forest-dependent communities are smallholder farmers, non-governmental organisations and the private sector should also collaborate in empowering such communities by providing support in climate-smart agriculture initiatives that will increase agricultural productivity and household incomes. Smallholding farmers need to be encouraged to practice conservation agriculture, crop rotation, integrated crop-livestock management, the use of improved pasture, use of energy-saving cooking stoves and cultivation of short-term drought tolerant crops. Local communities must not be left behind in the decision making processes to formulate more successful adaptation strategies that address their actual concerns.
Figures
Rainfall MK test
| Test methods | Jan | Feb | Mar | Apr | May | June | July | Aug | Sep | Oct | Nov | Dec | Annual |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MK test (Z) | 1 | −1.77+ | 1.88+ | 0.3 | −1.78+ | −1.23 | −2.2* | −0.77 | 0.93 | 2.79** | 3.04** | 1.15 | 0.97 |
| Sen’s slope (Q) | 0.181 | −0.38 | 0.596 | 0.104 | −0.543 | −0.265 | −0.518 | −0.234 | 0.262 | 0.619 | 0.761 | 0.237 | 0.068 |
**Trend at α = 0.01;
*trend at α = 0.05;
+trend at α = 0.1
Mann–Kendall test and sen’s slope for max and min temperatures
| Z test | Sen's slope | |||
|---|---|---|---|---|
| Variable | Max temp | Min temp | Max temp | Min temp |
| January | 2.32* | 1.38 | 0.054 | 0.016 |
| February | 2.11* | 0.79 | 0.058 | 0.009 |
| March | 1.91+ | 0.93 | 0.047 | 0.005 |
| April | 3.39*** | 0.35 | 0.066 | 0.003 |
| May | 4.92*** | 0.78 | 0.049 | 0.007 |
| June | 4.02*** | 2.22 | 0.039 | 0.016 |
| July | 4.43*** | 3.56 | 0.059 | 0.024 |
| August | 3.26** | 2.87 | 0.033 | 0.024 |
| September | 1.99* | 2.93 | 0.018 | 0.029 |
| October | 1.17 | 2.85** | 0.014 | 0.025 |
| November | 2.19* | 1.81+ | 0.027 | 0.019 |
| December | 1.96* | 1.59 | 0.032 | 0.015 |
| Annual | 4.78*** | 3.06** | 0.037 | 0.019 |
***Trend at α = 0.001;
**trend at α = 0.01;
*trend at α = 0.05;
+trend at α = 0
Perceptions of climate variability in the past 10-20 years
| Perceptions of respondents | Frequency | (%) |
|---|---|---|
| Weather changes experienced | ||
| Delayed rainfall | 91 | 44.7 |
| Long dry spells | 74 | 36.5 |
| Heavy rainfall (hailstorms) | 9 | 4.7 |
| Rising temperatures | 29 | 14.1 |
| Total | 203 | 100 |
| Rainfall trends | ||
| Increasing | 17 | 8.2 |
| Decreasing | 153 | 75.3 |
| Unpredictable | 33 | 16.5 |
| Total | 203 | 100 |
| Temperature trends | ||
| Increasing | 193 | 95.3 |
| Decreasing total | 10 | 4.7 |
| 203 | 100 | |
Effects of climate variability on crop production
| Effects on crops | (%) |
|---|---|
| Impacts of climate variability on crop production | |
| Reduced yields of common crops | 52 |
| Floods on farms | 32 |
| Increased pests and disease attacks | 16 |
| Total | 100 |
| The degree of impact of climate variability on crop yields | |
| High | 58 |
| Moderate | 33 |
| Insignificant | 9 |
| Total | 100 |
| Most affected crops | |
| Maize | 62 |
| Sugarcane | 34 |
| Vegetables | 4 |
| Total | 100 |
Effect of climate variables on yield of major crops in Buyangu
| Intercept | Mean annual temp | Mean annual rainfall | ||||||
|---|---|---|---|---|---|---|---|---|
| Crops | Coefficient | p-value | Coefficient | p-value | Coefficient | p-value | R2 | Sig F |
| Maize | −9,018.2 | 0.739 | 520.6 | 0.715 | −1.9 | 0.928 | 0.318 | 0.682 |
| Beans | −5,490.4 | 0.651 | 304.9 | 0.632 | −3.2 | 0.740 | 0.199 | 0.801 |
| Indigenous Veg. | −283,582.6 | 0.410 | 15,804.5 | 0.386 | −207.2 | 0.450 | 0.394 | 0.606 |
| Cabbage | −89,714.3 | 0.465 | 5,661.8 | 0.393 | −147.0 | 0.212 | 0.770 | 0.230 |
| Kales | 106,527.0 | 0.869 | −5,815.1 | 0.863 | 171.9 | 0.745 | 0.149 | 0.851 |
| Tomato | 53,525.3 | 0.821 | −3,094.3 | 0.803 | 121.3 | 0.547 | 0.483 | 0.517 |
| S. potato | −166,644.7 | 0.656 | 8,393.3 | 0.667 | −16.7 | 0.955 | 0.457 | 0.543 |
| Cassava | −9,020.1 | 0.969 | 619.3 | 0.959 | 44.5 | 0.815 | 0.311 | 0.689 |
| Sugarcane | −897,268.7 | 0.123 | 53,605.9 | 0.098 | −1439.1 | 0.036 | 0.932 | 0.034 |
Herd size
| No. of cows | Frequency | (%) |
|---|---|---|
| 0 | 54 | 26.5 |
| 1 | 60 | 29.4 |
| 2 | 50 | 24.7 |
| 3 | 24 | 11.8 |
| 4 | 10 | 4.7 |
| 5 | 2 | 1.2 |
| 6 | 2 | 1.2 |
| 7 | 1 | 0.5 |
| Total | 203 | 100 |
Land sizes
| Land size | Frequency | (%) |
|---|---|---|
| < 1 acre | 72 | 35.3 |
| 1-3 acres | 111 | 54.7 |
| 4-5 acres | 13 | 6.5 |
| 6-10 acres | 5 | 2.4 |
| >10 acres | 2 | 1.2 |
| Total | 203 | 100 |
Milk production
| Litres of milk/per day | Frequency | (%) |
|---|---|---|
| 0 | 57 | 28.1 |
| 1-3 | 76 | 37.4 |
| 4-6 | 45 | 22.2 |
| 7-10 | 18 | 8.9 |
| >10 | 7 | 3.4 |
| Total | 203 | 100 |
Access to forest
| Distance from forest | Frequency | (%) |
|---|---|---|
| <1 km | 55 | 27.0 |
| 1-3 km | 72 | 35.6 |
| 4-6 km | 76 | 37.4 |
| Total | 203 | 100 |
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Acknowledgements
Data for this study was collected by the lead author for her Masters studies. The fieldwork activities and publication of this paper was made possible by research funding received from African Forest Forum (AFF). Special thanks goes to the reviewers for their insightful comments from which this paper emanates.