The adaptive and coping strategies of pastoralists to climate change in Baringo, Laikipia and Nyeri Counties of Kenya

Margaret Syomiti, E Maranga¹, G Obwoyere¹, G Getachew², H Dana³, M Beatrice, D Wamae and J Duyu

Kenya Agricultural and Livestock Research Organization, PO Box 30148-00100, Nairobi, Kenya
syomitimargaret@yahoo.com
¹ Egerton University, P. O Box 536-Njoro, Kenya
² MARIL-Ethiopia, P.O Box 90112, Addis Ababa-Ethiopia
³ Colorado State University, Campus Delivery 1683, Fort Collins, CO 80523-1683

Abstract

Production of pasture and fodder grasses is low in the Kenya’s rangelands as a result of unreliable and low rainfall regimes. A sustainable livelihood in the region is threatened by climate change due to frequent droughts and erratic rainfall. Technologies aimed at increasing rural communities’ resilience are necessary to support their capacity to adapt and respond to new hazards. In view of this, a baseline survey was conducted to identify and document local climate change adaptation and coping mechanisms in livestock feeds and feeding systems among the livestock keepers in Baringo, Laikipia and Nyeri counties of Kenya.

Results obtained from the study showed that over 60% of respondents in Laikipia did not attend school, as compared to literacy levels in other study counties of Nyeri and Baringo (45 and 39% respectively). Lack of feeds was cited as the most important livestock keeping constraints in Nyeri county (61%), whereas lack of water was mentioned by most of the respondents in Laikipia and Baringo counties (50 and 38% respectively). Migration to search for greener pastures and safety was the main coping strategy to drought and floods in Baringo (97%) and Laikipia (64%). In Nyeri however, mobility as a coping strategy was almost non-existence (99%). Water harvesting was most important coping strategy (67%) in Nyeri county. With respect to adoption of improved climate change adaptation strategies, Nyeri county recorded the highest number of adopters, followed by Baringo and Laikipia counties (459>174>128 respectively). Off-farm purchase of feed supplements as a coping strategy was highly recorded in Nyeri county (61%). However, in Laikipia and Baringo counties, feed supplementation was hardly used, with a mention of 44 and 43% respectively. The study shows majority of the respondents from Baringo county were seeking for agricultural information from various sources namely; newspapers (100%), NGOs (100%), television (80%), chiefs’ ‘baraza’(80%) and a small number (45%) from village elders. Higher percentage of respondents in Nyeri county received agricultural information through the extension (100%), while in Laikipia county, village elders were the main source of agricultural information (42%). It is concluded that livestock as mainstay in Laikipia and Baringo counties is more vulnerable to climate change than in Nyeri. However, although efficient agricultural information channels were used in Nyeri county, the high recorded cases of purchasing feeds off-farm is a challenge in adapting to climate change for a sustainable livelihood in this region.

Key words: drought resilience, feeds and feeding, livestock

Introduction

Over 80% of Kenya’s landmass is classified as Arid and semi-arid lands (MoA 2007), and prone to drought and other natural disasters. A sustainable livelihood in the region is threatened by climate change due to frequent droughts and erratic rainfall. The region is a home to approximately 70% of Kenya’s livestock population estimated at 60 million kept under extensive production systems. The livestock sector is the major enterprise in the ASALs and contributes about 40% of the agricultural Gross Domestic Product (GDP) and 10% of Kenya’s total GDP (Kenya Agricultural Research Institute (KARI 2004). The vulnerability of pastoralists in this area is escalating due to recurrent natural disasters, coupled with the increasing population growth and declining carrying capacity of the land. Production of pasture and fodder grasses is low as a result of erratic and low rainfall regimes. In the ASALs, the livestock sector employs approximately 90% of the 7 million people and contributes about 95% of the family income (GoK 2003). Therefore, the government under Vision 2030 (GoK 2008; GoK 2007) recognizes the potential of arid lands and the livestock sub-sector as an important driver for economic growth. The challenge facing the ASALs ecosystem is how to enhance communities’ resilience whose livelihoods depend entirely on climate-sensitive resources (KARI 2000). Generally, developing countries are investing on several adaptation activities to address impacts of climate change in the agricultural sector. Technologies aimed at increasing rural communities’ resilience are necessary to support their capacity to adapt and respond to new hazards. Innovative options are available to mitigate emissions and adaptations in the livestock sector such as efficiency in feed utilization and introduction and promotion of alternative drought-resistant forages, among others. The current study was carried out to characterize local climate change adaptation mechanisms in livestock feeds and feeding systems among the pastoral and agro-pastoral communities in selected ASAL regions for further development of community-based adaptation strategies and policy.

Materials and methods

Ecology of the Study Areas

The study was carried out in three administrative counties as representatives of arid and semi-arid lands (ASALs) in Kenya, namely; Baringo, Laikipia and Nyeri. The target study sites lie within agro-ecological zones of medium to low agricultural potential where climatic conditions are unfavorable for rain-fed agriculture. A cross-sectional survey design was used in the study.

Sampling Design

A multi-stage sampling technique was used in the study. The first stage involved purposive selection of Baringo, Laikipia and Nyeri counties in Kenya as representative samples of dry land characterized by frequent droughts and erratic rainfalls. The second stage employed the same techniques (as indicated above) in selection of districts in the study regions namely; Baringo central, Laikipia North and Nyeri North. In the third stage, one division in each district was selected randomly, namely; Marigat, Mukogodo and Gataragwa divisions respectively, using the same purposive sampling technique due to reasons indicated in the first two stages of sampling. The fourth stage involved random selection of one location in each division namely; Loboi, Ilmotiok and Lamuria locations in Marigat, Mukogodo and Gataragwadivisions respectively. Loboi and Ilmotiok locations in Baringo central and Laikipia North districts practice pastoral system of livestock production with approximately 979 and 473 households respectively, while Nyeri North practice agro-pastoralism with approximately 375 households (KNBS 2010).

Sampling Frame

A current list of the households in the two locations in each district (indicated above) was obtained from government documents/reports (KNBS, 2010). Each of the names on the list was assigned a number.

Sample Size Determination

Sample size for the pastoral and agro-pastoral farmers was determined according to the procdures described by Mugenda and Mugenda (2003) using the formula below;

Where:

n = the desired sample size if the target population is greater than 10,000

Z= the standard normal deviate at the required confidence interval

P= the proportion in the target population estimated to have the desired characteristics being measured

q= 1-p

d = the level of statistical significance

In this case no pilot study has been done to establish the proportion of the population with the desired characteristics, that is households that access and use improved/advanced climate change adaptation strategies in livestock feeds and feeding such as fodder conservation, improved pasture and fodder crop varieties (drought tolerant and high yielding). In such a case Mugenda and Mugenda (2003) recommended that 50% should be used. Thus the target population who has access and use improved adaptation strategies is taken to be 0.5 and the statistical level of significance of 0.05 giving a z-value of 1.96.

Sample size for population greater than 10 000 was calculated according to Mugenda and Mugenda (2003)as follows:

In this case, the total sample size in both study sites was less than 10 000, therefore the formula below was used:

Where: n_f = the desired sample size (when the population is less than 10,000)

 n = thedesired sample size (when the population is more than 10,000)

Total sample size nf

Final sample size; Baringo County: 979/1827 * 317 = 170

Final sample size; Laikipia County: 473/1827 * 317 = 82

Final sample size; Nyeri County: 375/1827 * 317 = 65

Method of Sampling

A systematic random sampling procedure was employed. This approach was chosen because it ensures an equal probability of inclusion of each unit in the population than simple random sampling (Nassiuma and Mwangi 2004). The procedure involves drawing a sample of size n from a population consisting of N units in such a way that starting with a unit corresponding to a number r chosen at random from the numbers 1,2….,k every k^th unit is selected. The number k is taken as the nearest integer N/n and is called the random interval. The number r picked at random is called the random start.

For Laikipia County k = 473/82 = 5

For Nyeri County k = 375/65 = 5

For Baringo County k = 979/170 = 5

To get a random start number between 1 and 5 was randomly picked from a container. In this study the number 4 was picked and from the list obtained every 5 ^th number (HH) was selected from the list until a total of 82, 65 and 170 households was obtained from the list for Laikipia, Nyeri and Baringo counties respectively.

Sampling Unit

The units of measurement was pastoral and agro-pastoral farmers. A pre-tested structured questionnaire was developed to elicit responses from interviewees to provide the data required in this study. Questionnaire pre-testing was done at Nyala dairy cooperative in Nyandarua County with a sample size of 15 dairy farmers, determined according to procedures described by Mugenda and Mugenda (2003) for sample size less than 10, 000.

Data Analysis

Raw data was coded and input into the computer for analysis. The statistical package for social sciences (SPSS) version 20.0 was used for data analysis. Qualitative data or non numerical data was used in describing various aspects in drawing conclusions and recommendation.

Results and discussion

Demographic characteristics of the respondents

The gender profile of the respondents in the study area is summarises in Table 1 below. It was confirmed from the study that majority of respondents were male, constituting an average of 55% overall. This can best be explained by the fact that men are the main decision makers in most farming household contests (Rota and Sperandini 2010). According to Bitende et al (2001), women are usually under men, with regards to extorting authority over family resources, and in most cases they are grouped together with children, with little or no voice with regards to family affairs. Similar observation has been reported that in livestock keeping communities there is strong ethnic background biased against women (Chenyambuga et al 2014).

Figure 1. Age distribution

Overall, majority of the respondents were below 45 years (Figure 1. However, the study revealed an advanced age of the respondents in Baringo and Nyeri than that recorded in Laikipia county. This can be attributed to high reported illiteracy levels in Laikipia county (Figure 2). This implied that most youths who were un-educated engaged in other economic activities such as livestock keeping, unlike those in Baringo and Nyeri counties, who could get alternative jobs in towns based on their education level.

Figure 2. Literacy level of the respondents

The study confirmed that over 60% of Laikipia respondents did not attend school, a factor that may have contributed to greater percentage of youths being involved in livestock keeping in this county. Most respondents in Nyeri and Baringo went up to upper primary. Majority of the respondents were married (Figure3), while majority of the respondents’ households were male headed (Figure 4). In Laikipia however a small percent were headed by children.

Figure 3. Marital status of respondents

Figure 4. Household type

Main challenges in livestock production and coping strategies

Constraints to livestock production in the study area are illustrated in Figure 5. Lack of feeds was the main livestock constraints in Nyeri county, whereas in Laikipia and Baringo, lack of water was mentioned by most of the respondents. This observation was expected in Nyeri county due small land holdings of approximately 0.2 to 0.4 ha per household (Staal et al 2001). These small land holdings are planted with high-valued cash crops such as coffee and horticultural crops, at the expense of fodder crops which are mainly purchased off-farm.

Figure 5. Main constraints in livestock production

Local climate change adaptation strategies

Migration was cited by most respondents in Baringo and Laikipia as the main coping strategy to droughts in search of greener pastures and safe places for their livestock (Table 2). In Nyeri however, mobility as a coping strategy was almost non-existent. The low mobility in Nyeri can be attributed to land tenure system, with individual land ownership with title deeds, unlike in Laikipia and Baringo where land ownership is communal (Syomiti, personal communication). With this kind of land ownership, mobility with livestock is without boundaries, unlike in the individual ownerships where there is limited mobility and much confinement. Long distance walking was also an important coping strategy to drought in Nyeri. This is in agreement with report by Njoroge (personal communication) who indicated that farmers in Nyeri North were walking long distances with their animals to the slopes of Mt. Kenya, abadares and Mau in Central Kenya to seek for pastures.

Household mobility by gender

Mobility was almost non existence (Figure 6) in Nyeri, unlike in Laikipia where higher mobility for men and animals was mention. Majority of the residents in Baringo were not mobile. Women and children were hardly mobile, a factor which exposes this group of household members vulnerable to climate change risks. Mobility not only exposes a household member to greater life opportunities such as food and security from attackers but can ensure social interactions and knowledge exchange (Willard 2010).

Figure 6. Household mobility by gender

The adoption level of selected improved climate change adaptation strategies in relation to livestock feeds and feeding is illustrated in Table 3. The study revealed that Laikipia county was ranked the last with respect to adoption of improved potential climate change adaptation technologies, followed by Baringo and Nyeri. This can be attributed to the efficient sources of agricultural information such as extension services which were highly reported in Nyeri county than in the other study counties (Figure 8). For instant village elders were the main sources of agricultural information as in the case of Laikipia county, which may not be a reliable source of scientific information.

Coping with low quality of feed resources

Figure 7. Mechanisms for coping with poor quality feeds during drought periods

 Off-farm purchase of livestock feed supplements was highly cited in Nyeri county. However, majority of respondents in Laikipia and Baringo counties were not supplementing their livestock. Collection and conserving locally available supplements such as Prosopis juliflora pods were not preferred by the livestock keeping communities in Baringo due to allegations that they were up-rooting their livestock teeth, which later die of starvation. Acacia tortilis pods were not conserved at all, but were left to rot after the animals had their fill (Syomiti, personal observation). This can be scientifically backed by the fact that the outer portion of prosopis pods is high in soluble sugars (Kyuma 2013), while the seeds which are more nutritious with high crude protein levels ranging from 20-30% pass undigested through the gut and feces, making it unavailable to the livestock. The sugar in the pods sticks in the teeth causing them to decay. Upon defaecation, the un-digested seeds are scarified and are then readily triggered to germinate more prolifically, thereby exacerbating the invasiveness of the prosopis weed species. Previous findings by Tewari et al (2000), showed that elimination of the invasive Prosopis spp. by deforestation is not economical. Seed harvesting coupled with value-addition and/or processing for animal feeding can reduce the rapid regeneration and colonization of P. Juliflora, while improving the household incomes of vulnerable groups in pastoral areas (Kyuma 2013).

Sources and access of agricultural information

The study showed that majority of the respondents from Baringo county were seeking agricultural information from various sources namely; newspapers, NGOs, television, chiefs’ ‘baraza’and a small percentage from village elders (Figure 8). Higher percentage of respondents in Nyeri county were reported to receive agricultural information through the extension, while in Laikipia county, village elders were the main source of agricultural information. This fact can be supported by the high illiteracy levels reported in Laikipia county (Figure 2).

Figure 8. Major sources of agricultural information

Access to agricultural information was high in Baringo and least in Laikipia with a mention of over 60% of resondents not accessing the information (Figure 9). Higher information access in Baringo could be associated with the Perkerra irrigation scheme, where there is more focus from research and private institutions with more agricultural activities implemented in the scheme. Village elders were the main sources of agricultural information in Laikipia (45%), an indicator of an innefficient communication channel, implying that these pastoral farmers were not well informed with regards to agriculture. This can be related to the low adoption of improved climate change adaptation strategies (Table 3).

Figure 9. Agricultural information access by respondents

Figure 10. Membership to a social agricultural group

Nyeri County had highest number of respondents’ enrollment to social groups, followed by Laikipia and least in Baringo. Membership to a social group enables the members to build networks and share useful information such as agricultural and/or weather related information. Failure of members to join social groups denies them the exposure to the outside world, thus denying them chance to learn from others. These findings are in agreement with Zendera (2010), who found out that information exchange was vital as an adaptation strategy to weather variability and climate change.

Conclusions

• Based on the findings of this study, it can be concluded that livestock farming in Laikipia was highly vulnerable to climate change risks, followed by Baringo and least in Nyeri counties.
  
  
• With regards to gender equality, women and children were more vulnerable than men and youth boys.

Acknowledgement

This research work was supported by the Feed the Future Innovation Lab for Livestock, of Colorado State University who provided funds for this study. Other special thanks to Egerton University for logistical and technical support.

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Received 22 March 2015; Accepted 15 November 2015; Published 1 December 2015

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