3.1. Farmers’ Knowledge and Perceptions of Climate Change and Variability
Through FGD and questionnaire interviews, farmers from both study sites generally concurred that in the 1960’s, 1970’s and 1980’s when they settled in the study area, rainfalls were more regular and predictable in seasons. Rainfall seasons were distinct, but currently, rains have become more unpredictable. In questionnaire interviews, about 88% of farmers value ‘good’ climate at the time of settlement and about 89% of the farmers in both sub-locations value current climate as ‘bad’ (
Table 1). Farmers’ constantly stressed declining agricultural production due to unpredictable, sometimes incessant rains on the one hand, as well as low rainfall, coupled with high temperatures on the other hand, and the occurrence of extreme climatic events including hailstorms, frost and persistent droughts. The farmers consent from both sub locations that the previous climates at time of settlement were ‘good’ compared to the current climate reported to be ‘bad’, is consistent with climate data showing increasing trend to droughts from the Laikipia Air Base station (
Figure 2). Rainfalls are valued as decreased, while temperature and wind are valued as increased. Discussants recognize long and short season rains based on the rainfall amounts and duration. According to the interviewees, the long rains occur between March and June, while the short rains occur between September and December. Farmers explained that rainfalls have reduced in both quantity (amounts per rainfall) and quality (ability of the rains to sustain the crops for a reasonable period to crops maturity) in comparison to the time they settled in the area. Changes in rainfall amount and patterns, affects soil erosion rates and soil moisture, both of which are significant for crop yields [
68]. In addition, increasing temperatures make it difficult for the crops to grow with little rains, while increased wind puts crops at risk of being blown away or destroyed. These findings show the ability of farmers to value their climate as either ‘good’, ‘constant’, ‘bad’ or ‘very bad’, which farmers are able to define subjectively is an indicator of their in-depth local knowledge and perceptions (see explanations below
Table 1).
Table 1.
Farmers’ perception of climates of Umande and Muhonia Sub Locations (n = 106 for Umande sub location and n = 100 for Muhonia sub location; total n = 206).
Table 1.
Farmers’ perception of climates of Umande and Muhonia Sub Locations (n = 106 for Umande sub location and n = 100 for Muhonia sub location; total n = 206).
Variable | | Umande sub location (% of n) | Muhonia sub location (% of n) | Both sub locations (% of total n) |
---|
Current perception of climate | good | 7.5 | 4 | 5.8 |
| bad | 84.0 | 95 | 89.3 |
| very bad | 6.6 | 0 | 3.4 |
| constant | 1.9 | 1 | 1.5 |
Perception at settlement time | good | 86.8 | 89 | 87.9 |
| bad | 11.3 | 10 | 10.7 |
| very bad | 0.9 | 0 | 0.5 |
| constant | 0.9 | 1 | 1.0 |
Rainfall | increased | 3.8 | 2 | 2.9 |
| decreased | 92.5 | 97 | 94.7 |
| constant | 3.8 | 1 | 2.4 |
Temperature | increased | 97.2 | 95 | 96.1 |
| decreased | 0.0 | 3 | 1.5 |
| constant | 2.8 | 5 | 3.9 |
Wind | increased | 95.3 | 84 | 89.8 |
| decreased | 2.8 | 12 | 7.3 |
| constant | 0.9 | 4 | 2.4 |
| unsure | 0.9 | 0 | 0.5 |
Sun’s heat | increased | 98.1 | 92 | 95.1 |
| decreased | 1.9 | 7 | 4.4 |
| constant | 0.0 | 0 | 0.0 |
Frequency of droughts | increased | 96.2 | 95 | 95.6 |
| decreased | 1.9 | 4 | 2.9 |
| constant | 1.9 | 0 | 1.0 |
| unsure | 0 | 1 | 0.5 |
Frequency of drying rivers | increased | 98.1 | 95 | 96.6 |
| decreased | 1.9 | 1 | 1.5 |
| constant | 0.0 | 3 | 1.5 |
| unsure | 0 | 3 | 1.5 |
Incidence of crop diseases | increased | 89.6 | 95 | 94.3 |
| decreased | 6.6 | 3.8 | 10.4 |
| constant | 0.9 | 0 | 0.9 |
| unsure | 2.8 | 0.9 | 3.8 |
incidence of animal disease | increased | 54.7 | 76 | 65.0 |
| decreased | 20.8 | 19 | 19.9 |
| constant | 17.0 | 4 | 10.7 |
| unsure | 7.5 | 1 | 4.4 |
frequency of hunger | increased | 97.2 | 98 | 97.6 |
| decreased | 1.9 | 2 | 1.9 |
| constant | 0.9 | 0 | 0.5 |
incidence of human diseases | increased | 75.5 | 75 | 75.2 |
| decreased | 10.4 | 10 | 10.2 |
| constant | 11.3 | 13 | 12.1 |
| unsure | 1.9 | 2 | 2.4 |
Figure 2.
The course of precipitation (grey line) and the Palmer Drought Severity Index (PDSI ) (bold line) and of linear PDSI (dotted line) of Laikipia Air Base Station, Kenya (Trend statistics: y = dependent parameter (PDSI or precipitation); x = independent parameter (number of month in the whole period; R = correlation coefficient; p = probability of R = 0). Source: [
69]
Figure 2.
The course of precipitation (grey line) and the Palmer Drought Severity Index (PDSI ) (bold line) and of linear PDSI (dotted line) of Laikipia Air Base Station, Kenya (Trend statistics: y = dependent parameter (PDSI or precipitation); x = independent parameter (number of month in the whole period; R = correlation coefficient; p = probability of R = 0). Source: [
69]
Our study is comparable to others in that, for example, some of the perceptions of farmers were termed as vulnerability indicators by studies of Adimo
et al. [
70] in Kenya. In Mount Kenya region, studies by Kauti [
4] identified perceptions on crop varieties as determinants of crop varieties for cultivation on farms. In both cases reported by Adimo
et al. [
70] and Kauti [
4] from Kenya, hailstorms were not identified by the authors as the case for Umande and Muhonia. Elsewhere, in Ethiopia, farmers reported similar sentiments of reduced rainfalls and changed rainfall patterns [
71]. Farmers in Southern Africa region termed perceptions of droughts, floods, reduced rainfalls as stressors [
48], although Umande and Muhonia farmers did not mention floods as a perception. Thus as farmers give value of climate perceptions, we interpret their valuing as emphasis of what climate change and variability entirely means to these farmers’ agriculture and their livelihoods in general.
Moreover, farmers reported that at present, they are experiencing shorter rain seasons than in the past and sometimes, there were only dry spells with no rains. Discussants from Umande and Muhonia also said that they used to expect the very first rains of every year in February, which were locally referred to as ‘maguna ng’ombe’, meaning ‘help for livestock’. Maguna ng’ombe’are short rains for about three days, ensured grass would grow to sustain the livestock until the rainy season started in March every year. The farmers said that maguna ng’ombe is no longer occurring since the year 2000 and identify this as a remarkable change in their climate. We interpret the lack of maguna ng’ombe as a pointer to the needed support of farmers from agricultural ministries, development agencies and NGOs interested in enhancing local agriculture through livestock production.
The majority of farmers also reported increased frequency of droughts (about 96%) and increased frequency of rivers drying out (97%). Perceptions on droughts have been reported from Kenya where the locals perceived droughts as acts of god that could not be controlled [
36,
72] and as disasters beyond their control [
73]. Umande and Muhonia farmers bear similar beliefs although they opt to undertake strategies that help them cope with the drought effects.
3.2. Effects of Climatic Variability in Smallholder Agriculture
Following droughts, increased temperatures, and erratic and unpredictable rainfalls and drying rivers, farmers from Umande and Muhonia report incidences of crop and livestock losses. These were also raised in key informant interviews and focus group discussions as highlighted by one farmer:
In 1984, there was drought, we lost our livestock and we did not harvest from our crops. Recently, we lost our crops and livestock to a major drought of 2009 (Male key informant No. 4 Umande).
In FGD in both sub locations, farmers concurred that they had experienced severe droughts in several years, although, most farmers recalled year 2009 as a year of most severe droughts that culminated in losses of livestock and crops. Loss of livestock and crops was translated as food shortage and decrease in crop yields by the locals, hence their perception of increased hunger (
Table 1). Farmers recognize losses associated to droughts through comparison of numbers of livestock owned before droughts and livestock numbers after droughts. The majority of farmers lost livestock (57.8%), with Muhonia farmers losing more livestock in numbers in year 2009 than Umande in the same year (
Table 2). Based on farmers’ estimates, the total livestock lost in year 2009 stands at 442 herds in Umande and 1,169 from Muhonia. Muhonia farmers were hit harder by the drought in the direct comparison with Umande (see
Table 2). The losses in crops and livestock noted by farmers in 2009 could mean that farmers were not fully prepared with appropriate strategies to moderate the damage caused by the drought.
Table 2.
Livestock losses in Umande and Muhonia sub locations of Laikipia, Kenya (n = 106 for Umande sub location and n = 100 for Muhonia sub location; Total n = 206).
Table 2.
Livestock losses in Umande and Muhonia sub locations of Laikipia, Kenya (n = 106 for Umande sub location and n = 100 for Muhonia sub location; Total n = 206).
Variable | | Year | Umande n = 106% | Muhonia n = 100% | Total n = 206 (%) |
---|
% of farmers with livestock | 59.4 | 71 | 65.0 |
No of farmers with livestock losses | 1976-2000 | 8.5 | 1 | 4.9 |
| | 2007 | 0.9 | 0 | 1.0 |
| | 2008 | 1.9 | 0 | 0.5 |
| | 2009 | 46.2 | 70 | 57.8 |
| | 2010 | 1.9 | 0 | 1.0 |
Total No. of livestock lost in 2009 | 442 | 1169 | 1611 |
In literature, droughts are identified as a potential risk and source of losses in agricultural production [
8,
74,
75,
76,
77,
78]. An increase in the frequency of droughts in the region leads to decreased agricultural production [
8,
16,
68,
75,
79]. Farmers can lose all their livestock and crops from a single drought. Affected farmers are likely to fall into poverty [
80]. Drought losses in Umande and Muhonia concur with findings from Northern Kenya [
81] and Mount Kenya [
70], where drought accounted for high losses in cattle population. Climate change and variability increases farmers’ vulnerability as they lose their natural assets (crops and livestock) upon which their livelihoods depend. The foregoing picture of increasing climatic variability in the two sampled sub locations is consistent with descriptions in the literature on climate change and variability in Kenya and Africa in general [
8,
13]. Therefore, issues resulting from droughts such as livestock and crop losses need to be addressed in order to enhance adaptive capacities of the vulnerable farmers. Nonetheless, apart from climate related factors, there are other factors that discourage adaptive capacity among farmers in Laikipia such as economic instability, trade liberalization, conflicts, poor governance, diseases, limited access to climate and agricultural information and poor institutional and legal frameworks [
21] which may require more extensive research than was possible under the remit of this study.
Perceptions of high incidence of animal diseases in Muhonia stand at 76% and 54% in Umande. Examples of diseases associated with climate variability reported in FGD, key informant interviews and questionnaires included heart water, east coast fever (
ngai), anaplasmosis (
ndigana) and pneumonia (
mahuri) (italicized names are diseases and or pests names in Kikuyu). Other diseases and pests mentioned included blindness, babesiosis, worms (
njoka) and lumpy disease. Similarly, smallholders perceive increased incidences of crop diseases at about 90% in Umande and 95% in Muhonia. Crop diseases mentioned include blight and leaf rust. Crop pests mentioned include spider mite, aphids, millipedes and
muthingiriri (tiny black ants). The respondents reported that disease prevalence had increased in comparison to the time they settled as a result of increasing temperatures and droughts. These farmers’ knowledge compares with the PDSI drought indicator, showing increasing temperatures and less precipitation (see
Figure 2). In addition, the farmers’ sentiments can be supported by the IPCC reports, citing the increase and burden of some livestock and crops diseases due to increasing temperatures and decreasing water availability caused by climate change [
16]. Warmer temperatures may accelerate development rates of some insect species, resulting in a shorter time span between generations [
82]. On the contrary, some respondents from both sites in one FGD and some key informants, reported that disease prevalence was a result of increasing numbers of livestock, which was not the case when they settled, when farmers had one or two herds of cattle. Some farmers reasoned that livestock was better adapted to droughts than crops and that there was the possibility of relocating with livestock to other areas when there were droughts. These farmers were able to save some livestock from the droughts. However, some key informants and discussants shared their own experiences where they lost all their livestock, despite relocating to forested areas where grass was assumed abundant. These results confirms that migrating of livestock has proven successful for some but not for all. With many authors predicting a worsening climate in future in terms of frequent droughts and dry spells, inconsistent rains among others [
10,
16,
24,
83], Umande and Muhonia farmers are likely to experience tougher challenges from climate change and variability, if their adaptive capacities to the current trend of occurrences are not abated.
3.3. Overview of Climate from 1975 to 2010 of Laikipia District of Kenya and Relationship with Smallholder Perceptions
At Laikipia weather station, the PDSI shows a significant linear trend towards more dryness during the past 35 years, however, with distinct variations between years (
Figure 2). For example, the periods 2000 to 2003 show as low as −5 (extreme drought) that slightly improves to −3 (tending to extreme droughts) in 2003. In 2008, the PDSI shows moderate to extreme drought conditions over the Laikipia region, with the drought conditions getting worse at PDSI of −4 in year 2010. The results agree with the general trends in the global model predictions of variable climate in some parts of Africa [
14,
16,
84]. Studies by Herrero
et al. [
8] reported that Kenya in general would get wetter as a result of climate change and few places would receive decreased rainfalls. Muhonia and Umande constitute the few areas with reducing rainfalls. The farmers’ observations match with data from Laikipia Air Base station. For example, farmers mentioned that the years 1984 and 2009 (see statement of key informant No. 4, Umande) were characterized by severe droughts matches with
Figure 2 where PDSI of 1984 and 2009 stand at −2.5 (tending to extreme droughts). The drought years identified by farmers match with drought years highlighted in studies from Kenya by Herrero
et al. [
8]. However, extreme drought years such as 2001 (PDSI −4.8), 1986 (PDSI −4.0) and 1981 (PDSI −3.7), although mentioned in questionnaire and FGD, were not emphasized as major drought years according to interviewees. These extreme drought years, however, could constitute the interpretation of ‘very bad’ climates by farmers where persistent droughts of 2–4 years were reported (see explanations below
Table 1). We deduce that interviewees are keen to emphasize years they lost their assets badly to droughts. However, we acknowledge this as potential area for further research and debate. The PDSI drought indicator shows a stronger decreasing trend than precipitation (no significant decreasing linear trend over the whole period), due to the increasing temperatures (significant linear trend over the whole period, not shown), which leads to higher evapotranspiration and therefore increasing drought conditions.
The KMD often gives reports on expected long and short rains through the daily newspapers and radio for farmers to be alert on rains or droughts of the season [
85]. However, these reports often exclude some key critical details of occurrences in seasons such as
maguna ng’ombe, expected prevalent diseases that are considered important by farmers. We argue that farmers’ knowledge and perceptions give in-depth explanations of rainfalls, temperatures, droughts and diseases that often lack in the meteorological reports given by the KMD. Farmers can easily follow the KMD reports if they are written in an understandable way suited for farmers’ [
13]. Therefore, the acceptability of KMD reports by farmers can be improved if they include local knowledge as part and parcel of these reports. These results provide evidence for increasing risk of droughts over Laikipia. Increase in frequency of droughts will add further pressure on smallholder agriculture and lead to greater losses [
15].
3.4. Coping and Adapting to Climate Change and Variability
Faced with unpredictable climatic variables, farmers from Umande and Muhonia adopt different responses to cope. Much of this response is reactive, in the sense that it is triggered by past or current events (e.g., drought occurrences) but it is also anticipatory in the sense that it is based on some assessment of conditions in the future (e.g., rainfall occurrences). Adaptations may already be practiced before droughts while others are activated as drought evolves [
67]. Such adaptive changes at household farming level have been argued by Darnhofer
et al. [
86] as mostly undertaken due to uncertainties faced by farmers.
To cope with decreasing rainfalls, farmers juggle with mixing long cycle crops and short cycle crops on their farms in both sub locations reflected in the following narrative:
We plant maize, beans and potatoes, we mix both long and short season crops, because of the rains, it rains sometimes and sometimes it doesn’t, when we have long rains, we harvest the long season crops and short season crops with little rains (male participant in FGD 1 Muhonia).
Discussants know which crop varieties they cultivate based on rain seasons. The long season signifies more rains and thus, crops that take long to mature are cultivated, the short seasons signifies less rain and crops that take a short time to mature are cultivated. However, discussants point the mixing of long and short cycle varieties to ensure some harvests as highlighted in the following informant’s quote:
In 1980, we used to cultivate 614 maize, now we still plant 614 because we saw it yields highly, the variety also waits for the rains and you get some little harvests with little rains, 513 maize does not survive for long, it’s affected by mbaa (mbaa means frost in Kikuyu) (key informant No. 4 Muhonia).
Maize variety 614 was cultivated by farmers at the time of settlement. Currently, the variety is still cultivated in addition of other varieties, which is embraced by all farmers because it is reported to work for them. Farmers can tell the difference between the responses of various maize varieties they cultivate through previous random observation of their crops seasonally. For example, maize variety 614 is considered a long season variety because it takes about six to nine months to mature, while maize variety 513 takes four to five months to mature. Farmers are able to tell the difference in terms of survival and tolerance of these varieties to the lack of rains and
mbaa (frosts). Interestingly, farmers prefer maize variety 614. Farmers argue that the variety 614 takes long to mature and therefore, there is higher probability of rainfall within the 9 months, which can salvage the crop and guarantee the farmers some little harvest. In contrast, to the maize variety 513 that may not survive beyond 5 months with lack of rains and susceptibility to frosts attacks. Many studies have shown that farmers have mixed long and short season crops systematically over years to cope with climatic variability [
87,
88,
89]. Umande and Muhonia farmers confirm other studies that showed farmers prefer long cycle and short cycle varieties for a variety of reasons such as superior taste and high yields, tolerance to the drought conditions [
4,
87]. We argue that Umande and Muhonia prefer both long and short cycle crops to enable them to manage uncertainty of variable weather in terms of rainfalls, droughts and frosts. Crop breeders can thus work together with farmers to determine crop varieties that suit smallholders’ perceptions in order to reduce uncertainty of variable weather at local levels.
When droughts occur within Umande and Muhonia, farmers use a number of coping strategies identified from FGD and key informant interviews (see
Table 3).
Table 3.
Coping and adaptation strategies to local perceptions of climate change and variability from Umande and Muhonia sub locations of Laikipia, Kenya.
Table 3.
Coping and adaptation strategies to local perceptions of climate change and variability from Umande and Muhonia sub locations of Laikipia, Kenya.
Perception of climate change/variability variable | Perceived effects on humans, livestock, crops | Range of responsesRapid (coping) and longer-term (adaptation) |
---|
Crops | Livestock |
---|
Decreasing Rainfall, | Humans: hunger, food insecurity, loss of livelihoods Livestock: Lack of fodder, livestock deaths crops: loss of crops, loss of seeds
| Use early maturing crop varieties e.g. 511, 513; DHO4, DHO2 ) use late maturing crop varieties such as 614, 628, 611 | crop residues used as livestock feeds grass growing for sale during droughts livestock watering from water pans |
unpredictable rainfalls, |
breaks in rainy seasons |
early rains |
late rains | early planting late planting |
planting whenever a spell of rains is determined) |
continuous planting |
rain harvesting into manually dug water pans |
irrigation from water pans seed preservation using local innovative techniques e.g. wood ash and use of expired batteries making shallow basins around every crop |
Increasing temperatures | Humans: hunger, food insecurity, loss of livelihoods Livestock: drying of pasture and grass leads to lack of fodder, death of livestock crops: loss of crops, loss of seeds
| Mulching to reduce loss of water from soils | Shifting from crop production to livestock keeping |
Irrigation from water pans |
Increasing wind | | Intercropping maize, beans, potatoes; growing castor oil plant (
Ricinus communis) (locally referred to as mbariki) around the farm/plot | |
Increasing frequency of droughts | Humans: hunger, food insecurity, loss of livelihoods Livestock: Lack of fodder, death of livestock crops: loss of crops, loss of seeds
| Use early maturing crop varieties e.g. 511, 513 DHO4, DHO2 ) use late maturing crop varieties such as 614, 628, 611 | Migrating with livestock to forests Sale of livestock Buy feed for livestock |
use of certified seeds |
conservation agriculture |
planting in shallow trenches mulching |
relocating to river banks to access river water for irrigation cultivation of commercial horticultural crops (tomatoes, peas, cabbages) |
Increasing frosts | Humans: frostbites crops: frostbites
| Planting
Ricinus communi around the farm/plot Planting frost resistant crops e.g. 614 | |
Increase incidence of crop pests, diseases, animal pests and diseases | Humans: sickness Livestock: poor livestock health, low production, death of livestock crops: poor yields, loss of crops
| Seek agricultural extension services, use local knowledge such as wood ash to destroy pests | Seek veterinary services, use of local knowledge to treat diseases (hot rod to burn swollen lymph nodes) |
Most farmers preferred multiple options, which were used in combination at the same time. The most practiced adaptation on most farms was the diversification and use of various crop varieties. Diversification has been identified as a potential farm-level adaptation to climatic variability [
90,
91,
92]. Studies done by Gajanana and Sharma (1990) in Tumkur district, Karnataka, India, quoted in Speranza [
92] found that many crops were cultivated simultaneously by farmers in Karnataka. However, limitations associated with cultivating crops simultaneously are low yield as a result of limited time, labor and capital. Many crops limit the scope for diversification of more crop enterprises [
92]. Farmers in Umande and Muhonia cultivate many crop varieties simultaneously, thus practicing crop diversification, increasing the number of crops varieties to reduce the susceptibility of agriculture to micro-climatic events that might result in crop failure [
90,
91,
92,
93]. For farmers in Umande and Muhonia, crop diversification does, to an extent, guarantee small harvests; however, there are years in which farmers report total crop losses. Adaptation policies that target such farmers should be worked out consultatively with farmers to ensure that feasible farmer adaptations are promoted and supported in policies.
The cultivation of short cycle crops and long cycle crop varieties shows the tendency of farmers to take advantage of the different maturing times of crops, to strengthen their resilience to impacts associated with variable unpredictable rainfalls and drier conditions, in order to increase chances of harvesting a crop during the drier and wetter seasons. These findings agree with the case of Kenyan farmers from Makueni [
46,
94] and Namibian farmers who cultivated early maturing crops to cope with drier conditions [
38,
47].
From FGD, key informants and questionnaire interviewees coupled with participant observation confirmed that farmers from both sub locations have various preferences for particular crop varieties, although other crop varieties were grown at a smaller scale on farms. We argue that farmers maybe are trying out experiential learning on their farms to determine which varieties will suit them. Planners, breeders and farmers can use this experimentation attitude of farmers to develop crop varieties that incorporate local needs and knowledge of farmers in order to make such varieties easily attractive to farmers.
Crop and animal diseases were managed by access to agricultural extension services and veterinary services in combination with local knowledge, where finances were limited. For example, farmers from both sub-locations sought veterinary services to treat livestock diseases e.g., east coast fever (ECF); however, some farmers used a hot rod to burn the swollen nodes on the animal before seeking veterinary services. Some farmers from Muhonia explained their use of local knowledge to treat livestock as a result of their long distance from the veterinary services in comparison to Umande. As a result, Muhonia farmers considered lack of veterinary services as an impediment to their adapting. We interpret this to mean that, while farmers use their local knowledge to cope and adapt, they also appreciate the need for other external sources of knowledge (e.g., veterinary) that would integrate with their knowledge to enhance their adaptive capacities.
Table 4 shows additional coping/adaptation strategies of farmers and statistical significance in comparison to their drought perceptions.
About 80% of Muhonia farmers and 68% of Umande depend on relief food. Although farmers mentioned relief food as coping strategy, farmers confirmed that the relief food was unreliable as it occurred spontaneously during very drought years based on what the government interpreted as “very dry” period of the year. In addition, there were special rules attached to who is entitled to the relief food. Provision of relief food, often delivered by the government in collaboration with World Food Programme (WFP) could be an indication that farmers from Muhonia tend to be worse hit by droughts in comparison to Umande. Reliance on relief food could be an indicator of increasing vulnerability during droughts. 84% of Muhonia farmers sell livestock, whereas in Umande only 56% do so. This difference could be due to higher livestock stocking rates in Muhonia than in Umande. Sale of tree products occurs in Umande at 4.7%. Umande farmers’ ability to sell tree products such as charcoal and firewood could be attributed to their proximity to the administrative Nanyuki town in comparison to Muhonia, which is distant from the town. Accessibility to administrative and shopping centers expose communities to more opportunities for non-farm activities [
4]. The finding on sale of tree products by Umande farmers agrees with studies from central Kenya [
4] and Makueni district of Kenya [
92], where the sale of tree products was considered as adaptation particularly during drought periods.
Table 4.
Coping/adaptation strategies to total crop failure in drought years in Umande and Muhonia sub locations of Laikipia, Kenya (n = 106 for Umande sub location and n = 100 for Muhonia sub location; Total n = 206).
Table 4.
Coping/adaptation strategies to total crop failure in drought years in Umande and Muhonia sub locations of Laikipia, Kenya (n = 106 for Umande sub location and n = 100 for Muhonia sub location; Total n = 206).
Strategy | Umande Sub location n=106 (%) | Muhonia Sub location n=100 (%) | Overall n=206 (%) | Pearson chi-square Value | df | Asymp. Sig. (2-sided) |
---|
Purchased new inputs for next season | 12.3 | 1.9 | 7.3 | 8.030(b) | 1 | 0.005 * |
Received government relief food | 67.9 | 80.2 | 76.2 | 8.329(b) | 1 | 0.004 * |
Sale of tree products (charcoal, wood, timber) | 4.7 | 0.0 | 2.4 | 0.078(b) | 1 | 0.780 |
Migration/sought employment elsewhere | 39.6 | 35.8 | 38.8 | 6.434(b) | 1 | 0.011 * |
Borrowed from neighbors and relatives | 0.0 | 1.9 | 1.0 | 0.371(b) | 1 | 0.542 |
Sold our livestock (large and small) | 56.6 | 84.0 | 72.3 | 15.462(b) | 1 | 0.000 * |
There is a statistical relationship between some coping/adaptations strategies used by farmers and their drought perceptions (
Table 4). For example, purchase of new inputs such as planting seeds and fertilizer were related to drought perceptions (0.005) where the confidence interval stands at 95% or 0.05, hence (0.005 < 0.05) is significant. In contrast to sale of wood (0.780 > 0.05); and borrowing from neighbors (0.542 > 0.05); these that are not related to drought perceptions are insignificant. Borrowing from farmers, for example, confirms farmers’ reports that they depend on their neighbors in one way or the other all through the year, with rains or with droughts. Similar analysis have been used by Eriksen
et al. [
67] to show relationships between droughts and adaptations of farmers in Tanzania and Kenya.
The subject of adaptations and adaptive capacity of farmers is subject to various debates. For example, acknowledging that different regions and areas exhibit varying degrees of temporal variability [
13,
14], means that different areas may require different appropriate adaptations befitting the locals. Eignenauer [
33] argues that scientists and planners often place emphasis on the extrapolation from one set of coping and adaptation practices, rather than taking lessons from the variability and diversity of farmers’ coping and adaptation practices. Policies and development oriented programs stand to succeed in enhancing adaptive capacity of the vulnerable farmers if they support and implement adaptation strategies that are currently being practiced by farmers [
68]. The local knowledge of Umande and Muhonia farmers is fundamental to enhancing the adaptive capacities of these vulnerable farmers, who show differences in preferences for adapting. Ideas seen in the context of existing practices of farmers can be easily accepted and embraced by farmers [
13]. Since farmers use their perceptions to make decisions on coping and adapting, through local knowledge, policy makers can understand what smallholders perceive and adapt to, before they design policies on how best to prepare and respond to climatic variability. Adaptation policies that integrate farmers’ knowledge will improve smallholders’ agriculture through promotion of farmer friendly adaptations.