ABSTRACT
Climate change threatens the livelihoods of millions of small-scale farmers in East Africa. How farmers perceive climate change and its impacts has a strong bearing on how they adapt to the adverse impacts. This paper focused on factors that determine climate change adaptation and perceptions among small-scale farmers of Embu County. A survey was carried out across five sub-counties of Embu County where a multi-stage sampling procedure was used to select 411 households. A questionnaire was administered to each household. A total of five FGDs were generated by the use of quota sampling. The data obtained from the FGDs were thematically analyzed while that from each household was subjected to both descriptive statistics and Heckman's probit model. The results showed 96% of the respondents observed unreliable seasonal rainfall amount, distribution, and increased temperatures. For instance, 23% interviewed were aware of the long-term change in temperature while 55% were aware of a change in the amount of rainfall per season. These respondents identified crop failure and the decline in crop yields as indicators of climate change. The farmers’ perceptions were corroborated by the long-term rainfall and temperature of Mann-Kendall trends analysis, which showed a negative rainfall correlation and temperatures increased by 0.02°C for Kiambere and 0.03°C for Embu stations. Gender was significant at p<0.1 in influencing farmers' perception of climate change while education level and social networks were statistically significant at p<0.05. Furthermore, Heckman's selectivity probit model showed that the education level of the household head and access to a credit facility influenced small-scale farmers' adaptation choices. There is a need to strengthen the capacities of farmers through training, provision of extension services, and formulation of a climate advisory committee within the county government to breakdown climate change information into user-friendly.
Key words: Heckman model, climate variability.
Climate variability directly affects the agricultural system that has a predictable influence on the socio-economic system in all the regions of the world (Porter et al., 2014). For instance, extreme drought hinders the ability of farmers to rear livestock and grow food (Connolly-Boutin and Smit, 2016). This is because climate change alters the patterns of both precipitation and temperature which are the major elements in agricultural production.
According to Arimi (2014), a rise in temperature amid crop growing season leads to a water deficit that affects seedlings on the farm. Besides, superfluous rainfall threatens soil functions by causing erosion, loss of organic carbon, waterlogging, salinization, and nutrient imbalances (Montanarella et al., 2016). Furthermore, climate change causes uncertainty and risks in the decisions on the onset of farming season and losses in agricultural production as a result of fluctuating temperatures and rainfall patterns (Thornton and Herrero, 2015). For instance, a study carried out in Pannonian, Central Europe indicates an increase in water deficit affects planting time and limits vegetation physiological ability hence reducing yields (Trnka et al., 2010). Furthermore, unfavorable weather conditions render sowing impossible during spring. Climate change leads to productivity decline with a range of 3.8-5.5% (Lobell et al., 2011). This decline is a result of modifications in rainfall, temperature, soil quality, pest, and disease infestations on both crops and livestock (Connolly-Boutin and Smit, 2016).
Small-scale farmers in Africa are vulnerable to increasing temperature and constant droughts (Codjoe et al., 2011). Furthermore, in Sub-Saharan Africa (SSA), 97% of agricultural land is rain-fed and people depend on agriculture for subsistence and surplus produce for income (Blanc, 2012). It is estimated that African countries are more likely to experience losses in agriculture in ranges of 8-22% depending on the crop and the region (Schlenker and Lobell, 2010). According to Arimi (2014), African regions that face soil erosion and rely on rain-fed agriculture are likely to experience 50% losses as a result of the increasing impacts of climate change. One-third of the African population resides in drought-prone areas (Rojas et al., 2011). These populations face devastating epidemics, famine, malnutrition, and displacement of the human population Malnutrition is a result of an increase in infectious disease transmission, scarcity of clean and safe water, and inadequate food. Also, these droughts have had an impact on water levels (Mboera et al., 2011). These in turn escalate poverty and reduce the standards of living of the African population.
Based on threats of climate change on agricultural productivity in Africa, farmers need to should implement adaptation mechanisms to the effects of climate change to ensure continuous productivity (Fahad and Wang, 2018). Climate variability varies from region to region and so individual farmers may adapt to climate change in various ways based on their capability Batisani and Yarnal, 2010). According to Kawasaki and Herath (2011) individual farmers have a unique adaptation scheme that differs from large-scale government policy. The adaptation scheme can either prevent the occurrence or minimize damages. Adaptation refers to “decision-making processes and actions that ensure adjustment or coping with potential damage” (Eisenack and Stecker, 2010). Therefore, climate change adaptation is planned or unplanned actions that individuals make to shield themselves from the effects of climate change. This may involve making adjustments to the land, natural resources, and social and economic establishments (Sanga et al., 2013).
In Kenya, small-scale farmers are facing climate change which is associated with extreme weather events such as droughts, soil infertility, floods, and unreliable rainfall patterns up (Nzau, 2013). According to IPCC (2007) temperatures are expected to increase gradually to nearly 3°C by 2050 whereas Nzau (2013) reports an increase of 1.3°C for maximum temperature and 2.0°C for minimum temperature. High temperatures in South West Kenya reduce the length of crop growing periods while in highlands the interplay of high temperature and rainfall variability leads to the extension of the crops growing season (Herrero et al., 2010). In terms of rainfall, Kenya displays considerable topographic and climatic variability associated with temporal and spatial bimodal rainfall (Bryan et al., 2013). According to Nzau (2013), drought brings about water deficiency which affects crop productivity and livestock rearing. Moreover, floods of different magnitude and frequency occur in different parts of the country as a result of the change in rainfall patterns (Opere, 2013). Small-scale farmers develop many adaptation mechanisms to cope with the scathing effects of climate change. For example, shifting to mixed cropping, crop diversification and agroforestry, early maturing crops, and destocking (Hoang et al., 2014; Mwang’ombe et al., 2011). Despite the availability of these measures, food insecurity is still recorded in the eastern region of the country (Bryan et al., 2013), the reason being that adaptation to climate change is site-specific and effective mechanisms depend on many other factors such as vulnerability, socioeconomic status, and the degree of climate change (IPCC, 2014). Furthermore, adaptation may be influenced by individuals’ perceptions of the uncertainty and risks to vulnerability (Fahad and Wang, 2018). Singh et al. (2017) observe that households' ability to determine adaptation is a factor of perception of risks and change. Perception is a process in which stimulus or information is received and transformed to generate a psychological awareness (Ayal and Filho, 2017). This stimulus is formulated based on cultural background, prior experience, and socioeconomic factors. Farming is a risky adventure where farmers have to decide on what to plant, when to plant, how to plant, what input to use, and what crop, water, and soil management strategies to use to avoid massive losses (Rao et al., 2011). Determinant factors being site-specific, it is imperative to carry out this study to enhance adaptability among the small-scale farmers. It is hypothesized that demographic, socio-economic, and perception levels of small-scale farmers increase the probability of climate change adaptation.
Study area
The study was carried out in Embu County within the Kenyan highlands on the eastern foot slopes of Mount Kenya (Figure 1). The County is located on the latitude of 0° 8' and 0° 50' South and longitude 37° 3' and 37° 9' East, with an altitude ranging from 1,080 m to over 4,700 m above the sea level (Embu County Government Integrated Development Plan, 2013). Embu County with an area of about 2,818 km2 is divided into five sub-counties namely Embu North, Embu West, Embu East, Mbeere North, and Mbeere South. Embu County has soils of volcanic origin in the upper midland and higher zones near Mt Kenya which include andosols, ando-humic nitisols, and humic nitisols. In most of the lower midland zones, soils are based on metamorphic basement rocks with volcanic influence with moderate to low fertility (Jaetzold et al., 2010). The County borders Kitui County to the East, Kirinyaga County to the West, Tharaka Nthi County to the North, and Machakos County to the South. The County receives a total annual rainfall of between 1,200 and 1,500 mm in two rainy seasons, March to June which is considered as the long rainy season, and October to December as the short rainy season, although the rainfall quantity received varies with altitude. Temperatures range from a minimum of 12°C in July to a maximum of 30°C in March and September with a mean of 21°C (Kisaka et al., 2015). The difference between the minimum and maximum temperatures is due to the extensive altitudinal range of the county. However, there is localized climate in areas along the Tana River due to the presence of five dams, Masinga, Kamburu, G?taru, K?ndaruma, and K?ambere with a total population of 513,363 comprising of 254,303 males and 261,909 females as of 2009 census who occupy 2,615.2 km2 excluding 202.8 km2 which is a part of Mount Kenya forest.
Out of the total population in this County, 83% live in rural areas where agriculture is prominent (Embu County Government Integrated Development Plan, 2013). The presence of favorable temperature and rainfall allows the small-scale farmers of the study area to practice rain-fed agriculture. In the study area agriculture supports 70.1% of the population and 87.9% of the households are directly involved in farming activities. These agricultural activities take about 80% of the total area of Embu County (Embu County Government Integrated Development Plan, 2013). Arable land is used for both crop and livestock production. In crop production, the county has three categories; food, industrial, and horticulture crops. The food crops include maize, sorghum, pearl millet, beans, cowpeas, green grams, sweet potatoes, cassava, and Irish potatoes while the industrial crops are cotton, coffee, tea, and macadamia (Embu County Government Integrated Development Plan, 2013). Besides, horticultural crops are mangoes, bananas, passion fruits, avocadoes, kales, tomatoes, carrots, butternuts, and watermelons. Furthermore, livestock types are cattle, sheep, goats, chickens, rabbits, donkeys, beehives, and pigs.
Data collection
This survey was conducted in March and April 2018. An exploratory study was done in the County to help enhance an understanding of perception and adaptation of climate change. A total of 411 respondents were arrived at by the use of a multi-stage sampling procedure. Purposive sampling was the first stage where five sub-counties were selected within Embu County. This was to ensure equitable distribution of the questionnaires and unbiased responses from the households. The second stage involves the stratified sampling of administrative divisions within the sub-counties to form the sub strata. This was to arrive at sampling units with proportional sample sizes for each division. A simple random sampling technique was the third stage that involved selecting respondents from each division (Table 1).
The questionnaires were coded and entered into a digital survey tool and tablets for data collection. This enabled standardization of responses, quality control, quick retrieval, and analysis. These questionnaires captured data on demographics, socioeconomic characteristics, agricultural practices, perception of climate change, and adaptation options. Local field enumerators assisted in collecting the required data after a vigorous four days of training and piloting before the start of the interviews. The objective was to reduce biases and errors in data collection. The enumerators were selected from each sub-county to ensure they are familiar with the local environment and the native language. This survey was conducted in March and April 2018.
Quota sampling was used to generate Focus Group Discussion (FGDs) participants which comprised of 8-12 smallholder farmers. Every sub-county age cohort was used to generate the sample size. The age cohorts were divided into 6 groups with an interval of 10years. In every group, 2 smallholder farmers were randomly chosen to participate in the focus group discussion. Long-term rainfall and temperature data (1976-2016) relevant to this study were obtained from the Kenya Meteorological Department (KMD). The data collected was for Embu station with 40 years’ data (1976-2016) which was a representative of three sub-counties namely Embu East, Embu West, and Embu North. Kiambere station had records for 13years (2003-2016) that represented Mbeere North and Mbeere South Sub-counties.
Data analysis
Heckman’s model is a two-stage process that is used to analyze the determinants of climate change adaptation and perceptions as proposed by Maddison (2006). The first stage involves small-scale farmers' ability to perceive or not to perceive changes in temperature or rainfall amount, intensity, or duration of a season, while the second stage is whether the households adapted to climatic change immediately they experienced climate change or otherwise.
According to Heckman (1976)probit model for sample, selection accepts that there are underlying relationship that exists with a latent equation as shown below;
yxj =xjβ + u1j, (1)
Only the binary outcome is observed given by probit model as
yjprobit = (yxj>0), (2)
If j is observed in the selection equation, then the dependent variable is detected
yjselect = (zjδ + u2j >0), (3)
u1~N (0, 1)
u2 ~ N (0, 1)
corr (u1, u2) =p,
Where x represents a k-victor of regressors which includes explanatory variables with different factors assumed to sway adaptation mechanisms, z is an m vector of repressors that include explanatory variables with different factors assumed to affect perception, u1 and u2 are error terms. The first stage in Heckman's model is therefore represented in Equation 3 which denotes the perception of the household towards climate change. Equation 1 gives the outcome model in the second stage which shows whether the small-scale farmers adapted to climate change and is restricted on stage one which represents the perception of climate change. Rainfall and temperature data from KMD (1976-2016) were subjected to the Mann-Kendall trend test by the use of XLSTAT version 2020 to give a graphical representation of the variation of time and standard precipitation Index.
Perception of temperature and rainfall
The small-scale farmers of Embu county drew most of their livelihoods from subsistence farming. The farming experience of these farmers ranged from 1 to >60 years. Agriculture is the major source of livelihood which is under threat due to the effects of climate change and variability. The farmers identified climate change indicators as crop failure, the decline in crop yields, the disappearance of crop variety, outbreak of crop pest and diseases, the outbreak of livestock pest and diseases, insufficient and poor quality pasture, low milk and meat production, and death of livestock as the major constraints to farm incomes. The majority (96%) of these farmers indicated that they had observed unreliable seasonal rainfall amount, distribution, and increased temperatures. For instance, 23% interviewed were aware of the long-term change in temperature while 55% were aware of a change in the amount of rainfall per season. There were frequencies of prolonged dry spells and a general delay in on-set of rains and abrupt end of the seasons (Table 2).
Analysis of temperature and rainfall data
The small-scale farmers’ perception was compared to the meteorological data from the two stations in Embu County. The results indicated a statistically significant trend (P< 0.05) in both minimum and maximum temperatures during the 40years period (Table 3). In the Embu station, the minimum temperature has risen by 0.014°C, (y=0.0135x -24.936) whereas the maximum temperature has increased by 0.032°C, (y=0.0318x – 38.806). On the Kiambere station, an increase in trend was also recorded in both minimum and maximum temperature (0.02°C) fluctuating between the lowest being 16°C and highest of 30°C. This scenario is repeatedly seen in various parts of the country with an increase in temperature ranging from 0.2 to 1.3°C depending on the regions in Kenya (Kotir, 2011; Nzau, 2013).
Figure 2 shows a significant increase in temperature in 1978, 1988, 1990, 1997, 2002, 2006, and 2013 for the Embu station. Increased drought incidences were a common view in the study area for both the individual farmers and focus groups. Furthermore, focus group discussions identified 1979-1980, 1983-1984, 1999-2001, 2004-2005, and 2013-2014 recording worst memories of extreme temperatures. The respondents were concerned about the high variability and seasonal changes that stalled their ability to predict and plan farming activities on time. Analysis of rainfall data showed a mean rainfall amount of 3553 mm with an SD 81.57 and a var6653.96 (y = -10.0171x +376.65) in the Embu station while Kiambere station recorded 1257 mm with y=13.541x + 435.67.
These results show that there was a slight decline in the amount of rainfall with 10.02 mm per year for 40 years in Embu station while 13.54 mm in Kiambere station for 13 years (2003-2016). SPI representation in Figure 2 showed a rainfall variability pattern with drought events being witnessed between 1983 and 1987, 1991 and 1993, 1999 and 2001, 2004 and 2005, 2007 and 2011, 2013 and 2014, and 2016 in Embu station. These timescales reflect the possible impact of unreliable rainfall that affected both crops and livestock production. Small-scale farmers can relate to this dryness because of precipitation anomalies which directly influence the soil moisture conditions on the farm. According to Kisaka et al. (2015)rainfall patterns have become unreliable with a short rainy season shifting from mid-October to late October and early November. This shift becomes worrisome to the small-scale farmers who find it problematic to time their farming activities for instance planting crops.
Factors influencing the perception of climate change by households
Explanatory variables in the Heckman model
Heckman model makes use of independent and dependent variables (Table 4). The independent variables are assumed to affect small-scale farmers' perception of climate change and show the extent they are adapted based on their perception. The findings of the selection model analysis show the factors that influence small-scale farmers' perception of climate change in the study area in Table 5.
In the analysis gender of the household head, social networks, and education level of the tertiary, secondary, and upper primary were found to be significantly influencing household's perception towards climate change. Male-headed households (P<0.05) were more likely to perceive climatic change than female-headed households (Table 5). This is because male-headed households had a better chance to attain information and new technology as compared to their counterparts (Ndambiri et al., 2013). According to Bryan et al. (2013), training and capacity building is associated with a better perception of climate change. This would benefit the female-headed households within the county towards perception and adaptation to climate change. About the education level of the respondents, the study established a likelihood of farmers with tertiary, secondary, and upper primary education levels as more likely to perceive climate change than the less educated farmers (Table 5). This is because more educated farmers are more likely to be exposed to more information and have a better appreciation of climate change. Ofuoku (2011) observed a likelihood increase in appreciation of climate change with an increased number of years in school among the farmers. Further, Ndambiri et al. (2013) noted that higher education exposed farmers to more information on climate change.
Furthermore, social networks which are informal mechanisms in the study area to acquire and pass climate-related information among farmers was significant (Table 5). This implies that small-scale farmers are more likely to be influenced to perceive climate change by the existence of social interaction. According to Katungi (2006), early adopters slowly circulate information of new technology through sparse social networks that enable perception. Besides, Kristiansen (2004)argues that social networks strengthen individuals' attitudes and bring a commitment to work hard to reduce the risks.
Determinants of households' adaptation to climate change
The results of the outcome model presented in Table 5 show the factors influencing adaptation. The explanatory variables such as age, secondary school education levels credit availability, extension services, size of land under cultivation, and distance to the market centers were found to be significant (P<0.05). Concerning age, the findings showed household heads between the age of 31 and 70 years were influencers of adaptation to changing climate. This indicates that almost all age groups were active in minimizing the climate change effects on the agricultural fields. According to Ajuang et al. (2016), middle-aged farmers are likely to adapt to changes. The education level was categorized into five groups which included no formal education, lower primary, upper primary, secondary and tertiary education (Table 5). The head of households with the secondary educational level was found to be a statistically significant variable in adaptation to climate change. This implies that household heads with more than 10 years of schooling are in a better position to comprehend any information on adaptation to climate change. Meaning that for a better resilience of the small-scale farmers in the study area there is a need to strengthen the education sector (Opiyo et al. 2016). The findings showed that access to credit was statically significant in influencing adaptation to climate change (Table 5). This implies that ease of access to credit facilities by the small-scale farmers in the study area is likely to influence investment in strategies to mitigate impacts of climate change such as the use of drought-tolerant seeds and the adoption of climate-smart technologies. Opiyo et al. (2016)observe that access to credit facilities enables farmers to capitalize on the creation of inputs for adaptation. Besides, access to cash enables households to diversify their livelihood which is a form of adaptation. According to Hassan and Nhemachena (2012), households with more financial resources can use all available information to adapt to climate change. Distance to the market center was found to be significantly influencing households' adaptation to climate change (Table 5). This implies that an increase in distance to the market center negatively influences the adaptation. This is because access to the market centers provides an avenue for the farmers to purchase inputs and sell their produce thus earning income for farm diversification. Farmers with easy access to the market are motivated to purchase certified seeds, fertilizers, and irrigation equipment (Belay et al., 2017). Access to extension services was another explanatory variable that was significant to adaptation (Table 5). This implies that access to extension services leads to improved and better adaption to climate change. This is because extension services are important as a source of information for small-scale farmers in the study area on farming activities and climate-related information. Extension education motivates and increases the likelihood of farmers implementing an adaptation mechanism (Belay et al., 2017). The size of land under cultivation was also considered and the results showed that it is statistically significant to adaptation (Table 5). This implies that as the size of land increases there is a probability of farmers adapting to climate change. This is because land size increases the probability of mixed farming which translates to diversification (Mugi-Ngenga et al., 2016).
This study was implemented to assess the determinants of climate change adaptation and perceptions among small-scale farmers in Embu County, Eastern Kenya. The climatic data records from the weather station within the County obtained from the Kenya Meteorological Department (KMD) were in line with the farmers' perception of temperature and rainfall data. The views of the majority of the respondents and FGDs were closely similar to those obtained from those of increasing temperatures and rainfall variability data obtained from KMD. This showed that the small-scale farmers in the study area can be used as reliable key informants concerning climate change. The findings indicated that male-headed households were more likely to perceive climate change because they had a better chance to obtain information and new technology. Given that female-headed households were the majority in the study area, there was a need to empower the females through training and capacity building to increase the perception of climate change. Besides, there was a need to invest in the education sector to improve the empowerment of males and females to ameliorate their perception of climatic changes and adapt to them. Social networks were paramount in the circulation of information on adaptation mechanisms which gradually improves the farmers' perception levels towards climate change. Furthermore, households with access to extension services were likely to adapt to climatic changes. There was a need therefore to invest in extension agents to provide information and knowledge related to climate change. Also, policies on the minimum land size under cultivation, access to credits, and markets for both livestock and crop production were likely to enhance climate adaptation strategies among the small-scale farmers in the study area. Furthermore, the County government needed to incorporate a climate advisory committee to assist the farmers in the abridgment of the climate change information into user-friendly. Besides, there is a need to assimilate extension services among the small-scale farmers and a continuous follow-up carried out to improve the perceptions that lead to adaptation of climate change. However, a wider scope study is needed to look at both small and large-scale farmers’ perceptions and adaptation to climate change to ensure large-scale policy formulation within the country.
The authors have not declared any conflict of interests.
The authors appreciate the farmers, the experts in Embu County who responded to this study. Appreciation goes to the Embu County Agricultural and Extension teams for the support to access county-specific information and to access the study sites. Appreciation also goes to Dismas Ongoro, the lead field technicians, and the enumerators who supported with fieldwork. Special thanks go to Mercy Mutua for the support in coding the questionnaires and setting up the digital data collection framework using ODK. Further appreciation goes to Kevin Onyango for support with data analysis. This study was independently financed by the first author as part of her Ph.D. study. There has been no financial assistance from any organization.
REFERENCES
|
Ajuang CO, Abuom PO, Bosire EK, Dida GO, Anyona DN (2016). Determinants of climate change awareness level in upper Nyakach Division, Kisumu County, Kenya. SpringerPlus 5(1).
Crossref
|
|
|
|
Arimi K (2014). Determinants of climate change adaptation strategies used by rice farmers in Southwestern, Nigeria. Journal of Agriculture and Rural Development in the Tropics and Subtropics 115(2):91-99.
|
|
|
|
|
Ayal DY, Filho W (2017). Farmers' perceptions of climate variability and its adverse impacts on crop and livestock production in Ethiopia. Journal of Arid Environments 140(0140-1963):20-28.
Crossref
|
|
|
|
|
Batisani N, Yarnal B (2010). Rainfall variability and trends in semi-arid Botswana: implications for climate change adaptation policy. Applied Geography 30(4):483-489.
Crossref
|
|
|
|
|
Belay A, Recha JW, Woldeamanuel T, Morton JF (2017). Smallholder farmers' adaptation to climate change and determinants of their adaptation decisions in the Central Rift Valley of Ethiopia. Agriculture and Food Security 6(1):1-13.
Crossref
|
|
|
|
|
Blanc É (2012). The impact of climate change on crop yields in Sub-Saharan Africa. American Journal of Climate Change 1(1):1-13.
Crossref
|
|
|
|
|
Bryan E, Ringler C, Okoba B, Roncoli C, Silvestri S, Herrero M (2013). Adapting agriculture to climate change in Kenya: household strategies and determinants. Journal of Environmental Management 114:26-35.
Crossref
|
|
|
|
|
Codjoe S, Atidoh LK, Burkett V (2011). Gender and occupational perspectives on adaptation to climate extremes in the Afram Plain of Ghana Gender and occupational perspectives on adaptation to climate extremes in the Afram Plains of Ghana. June 2014.
Crossref
|
|
|
|
|
Connolly-Boutin L, Smit B (2016). Climate change, food security livelihoods in sub-Saharan Africa. Regional Environmental Change 16(2):385-399.
Crossref
|
|
|
|
|
Eisenack K., Stecker R (2010). An action theory of adaptation to climate change.
|
|
|
|
|
Embu County Government integrated Development plan CI (2013). Republic of Kenya Embu County Government Theme: A prosperous and united county.
|
|
|
|
|
Fahad S, Wang J (2018). Farmers' risk perception, vulnerability, and adaptation to climate change in rural Pakistan. Land Use Policy 79:301-309.
Crossref
|
|
|
|
|
Hassan R, Nhemachena C (2012). Determinants of African farmers' strategies for adapting to climate change: Multinomial choice analysis. Relational Responsibility: Resources for Sustainable Dialogue 2(1):83-104.
|
|
|
|
|
Heckman JJ (1976). The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models. Annals of Economic and Social Measurement 5(4):475-492.
|
|
|
|
|
Herrero MT, Ringler C, Steeg JVD, Thornton PK., Zhu T, Bryan E, Notenbaert AMO (2010). Climate variability and climate change and their impacts on Kenya's agricultural sector.
|
|
|
|
|
Hoang MH, Namirembe S, van Noordwijk M, Catacutan D, Öborn I, Perez-Teran AS, Nguyen HQ, Dumas-Johansen MK. (2014). Farmer portfolios, strategic diversity management and climate-change adaptation - implications for policy in Vietnam and Kenya. Climate and Development 6(3):216-225.
Crossref
|
|
|
|
|
Intergovernmental Panel on Climate Change (IPCC) (2014). Summary for policymakers. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. In: C. Field, D. Barros, K.J. Dokken, M. Mach, T. Matrandrea, M. Bilir, K. Chatterjee, Y. Ebi, R. Estrada, GRC, B. Girma, KES, LAN, S. MarcCracken, P. Mastrandrea, L. White (Eds.), Encyclopedia of Atmospheric Sciences: Second Edition (pp. 90-94). Camridge University Press.
|
|
|
|
|
Intergovernmental Panel on Climate Change (IPCC) (2007). Fourth Assessment Report. 27th Session of the Intergovernmental Panel on Climate Change.
Crossref
|
|
|
|
|
Jaetzold R, Schmidt H, Hornetz B, Shisanya C (2010). Farm Management Handbook of Kenya, Vol. II, Natural Conditions and Farm Management Information. PART II/B Central Kenya, Subparts B1 and B2 Central and Rift Valley Province Middle and Ministry of Agriculture, Kenya, in Cooperation with the German Agency for Technical Cooperation (GTZ), Nairobi.
|
|
|
|
|
Katungi EM (2006). Gender, social capital and information exchange in rural Uganda. IFPRI and Melinda Smale, IFPRI (International Food Policy Research Institute) CAPRi Working Paper No. 59, University of Pretoria pp. 2-243.
|
|
|
|
|
Kawasaki J, Herath S (2011). Impact assessment of climate change on rice production in Khon Kaen province, Thailand. Journal of ISSAAS 17(2):14-28.
|
|
|
|
|
Kisaka MO, Mucheru-Muna M, Ngetich FK, Mugwe JN, Mugendi D, Mairura F (2015). Rainfall variability, drought characterization, and efficacy of rainfall data reconstruction: case of Eastern Kenya. Advances in Meteorology.
Crossref
|
|
|
|
|
Kotir JH (2011). Climate change and variability in Sub-Saharan Africa: A review of current and future trends and impacts on agriculture and food security. Environment, Development and Sustainability 13(3):587-605.
Crossref
|
|
|
|
|
Kristiansen S (2004). Social networks and business success the role of subcultures in an African context. American Journal of Economics and Sociology 63(5):1149-1171.
Crossref
|
|
|
|
|
Lobell DB, Schlenker W, Costa-Roberts J (2011). Climate trends and global crop production since 1980. Science 333(6042):616-620.
Crossref
|
|
|
|
|
Maddison D (2006). The perception of and adaptation to climate change in Africa (CEEPA Discussion Paper No. 10). Centre for Environmental Economics and Policy in Africa. University of Pretoria, South Africa.
Crossref
|
|
|
|
|
Mboera LEG, Mayala BK, Kweka EJ, Mazigo HD (2011). Impact of climate change on human health and health systems in Tanzania: A review. Tanzania Journal of Health Research 13(5):1-23.
Crossref
|
|
|
|
|
Montanarella L, Pennock DJ, McKenzie N, Badraoui M, Chude V, Baptista I, Mamo T, Yemefack M, Aulakh MS, Yagi K, Hong SY, Vijarnsorn P, Zhang GL, Arrouays D, Black H, Krasilnikov P, Sobocká J, Alegre J, Henriquez CR, Vargas R(2016). World's soils are under threat. Soil 2(1):79-82.
Crossref
|
|
|
|
|
Mugi-Ngenga EW, Mucheru-Muna MW, Mugwe JN, Ngetich FK, Mairura FS, Mugendi DN (2016). Household's socio-economic factors influencing the level of adaptation to climate variability in the dry zones of Eastern Kenya. Journal of Rural Studies 43:49-60.
Crossref
|
|
|
|
|
Mwang'ombe A, Ekaya W, Muiru W, Wasonga V, Mnene W, Mongare P, Chege S (2011). Livelihoods under climate variability and change: An analysis of the adaptive capacity of rural poor of water scarcity in Kenya's drylands. Journal of Environmental Science and Technology 4(4):403-410.
Crossref
|
|
|
|
|
Ndambiri HK, Ritho CN, Mbogoh SG (2013). An Evaluation of Farmers' Perceptions of and Adaptation to the Effects of Climate Change in Kenya. International Journal of Food and Agricultural Economics 1(1):75:96.
|
|
|
|
|
Nzau V (2013). Mainstreaming climate change resilience into development planning in Kenya.
View
|
|
|
|
|
Ofuoku AU (2011). Rural Farmers' Perception of Climate Change in Central Agricultural Zone of Delta State, Nigeria. Indonesian Journal of Agricultural Science 12(2):63.
Crossref
|
|
|
|
|
Opere A (2013). Floods in Kenya. In Developments in Earth Surface Processes (1st edition) Elsevier BV 16(1965).
Crossref
|
|
|
|
|
Opiyo F, Wasonga OV, Nyangito, MM, Mureithi SM, Obando J, Munang R (2016). Determinants of perceptions of climate change and adaptation among Turkana pastoralists in northwestern Kenya. Climate and Development 8(2):179-189.
Crossref
|
|
|
|
|
Porter JJ, Dessai S, Tompkins EL (2014). What do we know about UK household adaptation to climate change?? A systematic review pp. 371-379.
Crossref
|
|
|
|
|
Rao KPC, Ndegwa WG, Kizito K, Oyoo A (2011). Climate Variability and Change: Farmer Perceptions and Understanding of Intra-Seasonal Variability in Rainfall and Associated Risk in Semi-Arid Kenya. Experimental Agriculture 47(2):267-291.
Crossref
|
|
|
|
|
Rojas O, Vrieling A., Rembold F (2011). Assessing drought probability for agricultural areas in Africa with coarse resolution remote sensing imagery. Remote Sensing of Environment 115(2):343-352.
Crossref
|
|
|
|
|
Sanga GJ, Moshi AB, Hella JP (2013). Small Scale Farmers ' Adaptat ion to Climate Change Effects in Pangani River Basin and Pemba?: Challenges and Opportunities 2(3):169-194.
|
|
|
|
|
Schlenker W, Lobell DB (2010). Robust negative impacts of climate change on African agriculture. Environmental Research Letters 5(1).
Crossref
|
|
|
|
|
Singh RK., Zander KK, Kumar S, Singh A, Sheoran P, Kumar A, Hussain SM, Riba T, Rallen O, Lego YJ, Padung E, Garnett ST (2017). Perceptions of climate variability and livelihood adaptations relating to gender and wealth among the Adi community of the Eastern Indian Himalayas. Applied Geography 86:41-52.
Crossref
|
|
|
|
|
Thornton PK, Herrero M (2015). Adapting to climate change in the mixed crop and livestock farming systems in sub-Saharan Africa. Nature Climate Change 5(9):830-836.
Crossref
|
|
|
|
|
Trnka M, Eitzinger J, Dubrovský M, Semerádová D, Št?pánek P, Hlavinka P, Balek J, Skalák P, Farda A, Formayer H, Žalud Z (2010). Is rainfed crop production in central Europe at risk? Using a regional climate model to produce high resolution agroclimatic information for decision makers. Journal of Agricultural Science 148(6):639-656.
Crossref
|
|
