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Rational of coffee agroforestry to contribute to adaptation and food security in the context of climate change
Mono-crop agriculture is both a victim of climate change and an emitter of GHG. Regarding sustainability of agriculture in the future, Iglesias et al., (2011) raised three challenging questions linked to adaptation: How can agriculture deal with an uncertain future? How do local vulnerabilities and global disparities respond to this uncertain future? How do we prioritize adaptation to best address the risks resulting from climate change? In short, these three questions addresses the need for adaptation, the regional and local disparities of climate change, which leads to differences in adaptation, and the way adaptation strategies has to be prioritize. Adaptation to climate change in agriculture in general and perennial cash crops in particular and its relationship with food security should also depend on the activities of research and innovation (Ollat & Touzard, 2005; Touzard, 2012). Boyer et al (2016), finds climate change is affecting French Vineyards both the agronomic and product quality, where innovations to adapt are prioritized.
At the core of the Africa’s food security and poverty debate, there is always the role of agriculture in ensuring food security, and how it is challenged by climate change. A consensus emerging is that a new approach to development must of necessity focus on sustainable food and environmental security. This should be on how to move from high input-high emission agriculture to low input-low emission agriculture as a pre-requisite for food security and climate change adaptation and mitigation strategy. Scholars such as Angeon et al. (2014); Simane et al. (2016); Altieri, (1995) argues the need to adopt efficient farming systems to different localities and recommends a fundamental shift towards agroecology as an approach to boost food production and improve the situation of the poor. In such cases, the agroforestry system gets credit to maintain the environment, while providing food security and social values. In developing countries, where economies, and livelihoods depend largely on ecosystem services, the multi-functionality of agroforestry is higher, and it has to be taken in to account for communities’ resiliency (Vignola et al., 2009).
Studies for instance, highlighted agroforestry systems use to mitigate and adapt climate change (Lasco et al., 2014; Luedeling et al., 2014; Lin, 2007), conserve resources use and facilitate low input practices (Carsan et al., 2014a), source of socio-economic and livelihood sources, such as source of food and feed (Mbow et al., 2014).
Large portion of food is grown in tropical agroforestry systems, where climate favors for productivity (Slingo et al., 2005). Coffee as a perennial crop, most of the time grown in agroforestry systems of the tropics, where mixed crop-livestock is assumed as one of the sustainable production, and resilient to climate (Alary et al., 2016). In Kenya, for instance, farmers integrate food crops and livestock with coffee in different proportion and mixing typologies. Coffee agroforestry, in this regard, contributes to food security in two ways. 1) Increased income from coffee, which is internationally traded at the international market and improve farmers’ purchasing power of food crops. And 2) increased domestic production of food crops. In Kenya, the coffee sector is one of the key pillars of the country’s economy, and employment means mainly of the rural poor (Gov. Kenya, 2007). Purchasing power of food by coffee farmers is always affected by the quality and quantity of production per year as well as the market value of coffee at the local and international market (Carsan et al., 2014b). Regarding to self-production and consumption, on the other hand, trees inside and outside coffee plots contribute to household food security (Mbow et al., 2014; Cerdán et al., 2012). Improved systems, such as intercropping with legumes reduce reliance on fertilizer by 50%, which further maximizes purchasing power of additional food (Carsan et al., 2014a). In the mixed crop-livestock agroforestry system, livestock also contributes a large share for household income and integrated farms are more resilient than monoculture (Iraizoz et al., 2011; Seo, 2010; Bell & Moore, 2012).
Institutional Innovation needs in the agricultural sector to adapt to climate change
System of innovation in the agricultural sector is the systemic interactional processes that generate and hybridize different forms of knowledge (scientific, tacit, and local know-how) produced by multiple actors to solve multifaceted environmental and social problems of agriculture (Temple et al., 2016; Bardaji et al., 2009). In relation to the applications of the concept and scope of system of innovation in the agricultural sector, two approaches are contrasted (Touzard et al., 2015). The first approach takes in to account a macro level analysis such as national system of innovation (NSI), which analyses the institutions or the regional system of innovation (RIS), analyses innovation at a regional level but common to all sectors. The second classification tends to the sectoral system of innovation (SSI), which is intended to analyze innovation of institutions, and networks promote the production of new knowledge in a specific sector (Malerba, 2002; Cooke and Morgan, 1994). This two approaches are however, needed to be coined by a system of construction and interaction. This deals how the interaction between different actors and institutions of different sectors is constructed (Carlsson, 2012).
Historical Meteorological data
Historical meteorological record of temperature (daily minimum and maximum) and rainfall (daily mean rainfall) over 35 years (1981-2014) from representative stations at the high and low altitude was retrieved from the Kenya Meteorological Department (KMD). Initially, an inventory of all the meteorological stations available in the County was done, yielding 21 stations. We then screened stations with reliable and sufficient data. Data quality was checked analyzing missing values and out of range data. Reliability of the data was set to be 95%. Meteorological stations which did not fulfill the reliability requirement or had only too short data sequences to allow trend analysis were rejected. Nine stations that represent the high, mid and lower altitude of the county were finally selected. Daily minimum and maximum temperatures were computed to get mean annual minimum, mean annual maximum and mean difference temperatures while daily rainfall measures were computed to annual rainfall measures.
Farmers’ perception of changes in climate
Farmers in the study area have been farming in the area for many years ranging between 30 to 80 years and their understanding of climate change was therefore, based on two sources i.e., external information on climate and weather change and own observation from experience and inter-generational knowledge transfer.
A total of 58.3 % of the interviewed farmers have heard about the word or expression of climate and weather change in one or another way. Among the farmers who had heard about climate and weather change, less than half of them had received the information formally from meteorological information dissemination, seminars, NGO consultations and warning systems, while others received it through personal experience sharing and interaction. The FGDs also revealed that coffee farmers appeared better informed on weather and climate changes than the food crops farmers, perhaps as a result of coffee farmers being better off in wealth and social status and hence positing a greater capacity to access information.
Apart from their access and use of official climate information, farmers described changes in climate by describing their personal experiences and observations of how it used to be in the past and how the rainfall and temperature was during the recent years. Farmers repeatedly explained about the changes in seasons, and weather patterns (rainfall and temperature patterns). Farmers’ perception was, analyzed in relation to three aspects: (1) change in overall climate, (2) change in rainfall, and (3) change in temperature. A total of nearly 87% of the farmers perceived the overall climate has changed, while 84 and 76 % perceived that either the rainfall has declined or the temperature has increased respectively. Findings from farmers’ perception identified three indicators that show climate is indeed changing and affecting their livelihood: (1) the trends observed in the pattern of rainfall, (2) trends in temperature and (3) observations on the effects of the changes such as the natural environment. In addition to these three indicators, farmers also explain the changes in climate in association to their observations of declined crops yield.
Trend analysis of temperature and rainfall
Trend analysis of the long term climate data (1980-2014) clearly shows climate is indeed changing. We detected two types of changes i.e., spatial changes between the coffee and food crops zones on one hand, and temporal changes within each zone on the other had. Mean minimum and maximum temperature (Figure 12a, 12b) shows an increasing trend over time for both the coffee and food crop zones, as
confirmed by the Mann-Kendall test and Sen’s slop estimators (Table 5). In addition, the mean inter annual variability of temperature (Figure 12c) is also increasing. The increase in temperature for the study area in general was similar with specific results from coffee and food crops zones separate results except the magnitude. We therefore, reported in this paper the results of temperature trend for the county omitting the particular results to conserve a space.
Mean annual rainfall (Figure 13) was higher in the coffee zone compared to food crops zone. For rainfall, the trend analysis indicated a decrease for the coffee zone (Figure 13a), while no clear trend was found by the Mann-Kendall trend analysis and Sen’s slop estimator (Table 5, Figure 13b) for the food crops. Besides the trend in annual rainfall, rainfall pattern is also important. To this effect, we analyzed the onset (Figure 14a), and cessation (Figure 14b) of the rainy season on a weekly basis. We considered mid-March as the “standard” onset time against which onset of a given year is compared. The results therefore, indicated a delay in onset and early offset of rainfall is becoming common. Regarding specific periods, our analysis indicates there are at least three different periods. Prior to 1991, onset was early from 1992-2001, onset was very variable and since 2002 onwards; late onset of rainfall characterizes the area (Figure 14a). A similar analysis for rainfall offsets, considering mid-June as the “standard” cessation time shows that rainfall offset time has been steadily declining over time for both the coffee and food crops zones (Figure 14b) since 1980.
Table of contents :
1. INTRODUCTION
1.1. OVERALL CHALLENGES OF CLIMATE CHANGE
1.2. IMPACTS OF CLIMATE CHANGE
1.2.1. Impacts of climate change in tropical countries
1.3. THE NEED FOR ADAPTATION AND MITIGATION
1.4. AGRICULTURE IN THE CLIMATE CHANGE CONTEXT
1.5. RATIONAL OF COFFEE AGROFORESTRY TO CONTRIBUTE TO ADAPTATION AND FOOD SECURITY IN THE CONTEXT OF CLIMATE CHANGE
1.6. PROBLEM DESCRIPTION
1.7. THEORETICAL AND CONCEPTUAL FRAMEWORK
1.7.1. Disciplinary base: innovation studies and the application of innovation economics in climate change adaptation
1.7.2. Adaptation gaps and adaptive capacity of farmers
1.7.3. Institutional Innovation needs in the agricultural sector to adapt to climate change
1.8. STRUCTURE OF THE THESIS
1.9. PUBLICATIONS
2. METHODOLOGY OF THE STUDY
2.1. AREA OF THE STUDY
2.1.1. Geographical location and climate of the study area
2.1.2. Socio-economic context
2.1.3. Farming systems and types of enterprises
2.1.3.1. Agriculture
2.1.3.2. Agroforestry Diversity and Practice
2.1.4. Institutional setups
2.1.4.1. Meteorological stations
2.1.4.2. Extension, advisory services and microfinance
2.2. FRAMEWORK, DATA AND METHODS
2.2.1. Framework
2.2.2. Sampling design and data
2.2.3. Analytical methods
2.2.3.1. Descriptive analysis
2.2.3.2. Model analysis
3. FARMERS’ PERCEPTIONS OF CLIMATE CHANGE AND HISTORICAL DATA: LINKING EVIDENCE TO SUPPORT ADAPTATION POLICY IN CENTRAL KENYA
ABSTRACT
RESUME
RESUMEN
3.1. INTRODUCTION
3.2. DATA AND METHODS
3.2.1. Profile of the study area
3.2.2. Data sources
Survey data
Historical Meteorological data
3.2.3. Analytical methods
Analysis of farmers’ perception of climate change
Analysis of historical meteorological record
3.3. RESULTS
3.3.1. Farmers’ perception of changes in climate
Changes in rainfall
Changes in temperature
Perception on the effects of the changes
3.3.2. Trend analysis of temperature and rainfall
3.3.3. Complementarity of farmers’ perception and historical data of climate change
3.4. DISCUSSION AND CONCLUSIONS
4. CURRENT CHOICES AND FUTURE NEEDS: FARMERS RESPONDING TO PRESSURES AND THEIR PERCEPTION OF CLIMATE CHANGE IN CENTRAL KENYA 78
ABSTRACT
RESUME
RESUMEN
4.1. INTRODUCTION
4.2. THE MURANG’A CASE STUDY IN CENTRAL KENYA
4.3. METHODS
4.3.1. Framework and rationale for model development
4.3.2. Data collection
4.3.2.1. Sample
4.3.2.2. Questionnaire
4.3.3. Choice model
4.3.4. Description of explanatory variables
4.4. RESULTS
4.4.1. Defining farming systems
4.4.2. Defining farmers’ choices
Crop management choices
Livestock management choices
Livelihood options
4.4.3. Current economic pressure on the farm; implications of the choices on household income
4.4.4. Determinants of adaptation choices
4.5. DISCUSSION AND CONCLUSIONS
5. THE ROLE OF SYSTEM OF INNOVATION TO BRING NEW FRONTIER OF ADAPTATION TO CLIMATE CHANGE: EVIDENCE FROM THE KENYAN COFFEE AND DAIRY SECTOR
ABSTRACT
RESUME
RESUMEN
5.1. INTRODUCTION
5.2. SECTORAL SYSTEMS OF INNOVATION APPROACH; INNOVATION TO ADAPT TO CLIMATE
CHANGE
5.3. DATA AND METHODS
5.3.1. The climate change context and case description
5.3.2. Methodological approach
Data sources
5.4. RESULTS
5.4.1. Technological and institutional innovation: A case from the coffee sector of Central Kenya
Developing disease resistant coffee varieties
5.4.2. Innovation in the dairy sector: An example from the dairy sector in Central Kenya
Technological innovation
Institutional Innovation in the dairy sector
5.4.3. Comparison of the contributions of actors in the coffee and dairy sectors
5.5. DISCUSSION
6. RESULTS, DISCUSSIONS AND CONCLUSIONS: A SYNTHESIS
6.1. INTRODUCTION
6.2. MAIN FINDINGS
6.3. THEORETICAL CONTRIBUTION
6.3.1. Farm production and farming system economics in the context of climate change adaptation
6.3.2. Approaches and dimensions of innovation: towards a new domain of economics of adaptation
6.3.3. Institutional infrastructure: need towards adaptation process
6.4. METHODOLOGICAL IMPLICATION
6.4.1. Analysis of climate change knowledge: triangulation of information and methods
6.4.2. Sectoral analysis of adaptation to climate change
6.5. CONTRIBUTION TO LEARNING, INNOVATION AND RESEARCH PROCESS
6.5.1. Climate change knowledge: farmers and scientists perspective
6.5.2. Advancement in the innovation process to adapt to climate change
6.5.3. Research process in climate change adaptation and farmers adaptive capacity
6.6. IMPLICATION FOR POLICY DEVELOPMENT
6.6.1. Farming system based decisions
6.6.2. Research on system of innovation for climate change adaptation
6.6.3. Input to policy on international agreements and negotiations
6.7. FUTURE RESEARCH
7. REFERENCES
