Effective adaptation action to climate change requires a balance between reducing vulnerabilities and managing risks. However, in most adaptation actions, risks such as greenhouse gas emissions, and those that impose negative externalities on global communities and ecosystems, are often overlooked. This article contextualises adaptation of maize stover (MS) as a dairy cattle feed among resource-poor farmers in western Kenya. In so doing, it attempts to establish the nexus between resource constraint and maladaptation to climate change. Simulation of methane emissions was carried out from secondary data and a survey of dairy cattle feeding strategies by resource-poor farmers. The level of greenhouse gas emissions in dairy feeding strategies is used as a measure and indicator of sustainability. Using disaster risk reduction principles, policymakers and community of practice in climate change action are encouraged to design and implement policies and strategies that take cognisance of poverty–maladaptation–environmental degradation nexus.
Many parts of the world have been experiencing growing urbanisation and change in dietary preferences that favour dairy production. However, a current and projected increase in levels of milk production would not be possible without expanding production and yield of crop agriculture, hence an increase in demand for land. The lack of additional available land except in parts of tropical Latin America prohibits horizontal expansion of existing modes of dairy cattle production. This necessitates search for alternative dairy feed resources (Steinfeld, Wassenaar & Jutzi
Worldwide, wild and domestic ruminants, as a result of metabolic processes (enteric fermentation), produce 15% – 25% of total methane gas emissions, 74% of which is caused by cattle (Tamminga
Ruminants produce carbon (IV) oxide and methane (CH4). However, carbon (IV) oxide (CO2) produced by ruminants, notably cattle, is of less concern because it originates entirely from newly generated biomass and does not contribute to its net rise in the atmosphere. This leaves methane as the gas of concern (Tamminga
Although there is high uncertainty in the models used to estimate social costs from emissions, the social cost of CH4 is estimated to grow 50% faster per year compared to 2.4% for carbon dioxide (Hope
The nexus approach to environmental resource management examines the inter-relatedness and interdependencies of environmental resources through the concept of trade-offs and synergies (Kurian & Meyer
By investigating risk attitude and risk management among resource-poor small-scale dairy farmers, we are able to link risk attitude and livelihood strategies in adaptation to climate to livelihoods outcomes and GHGs emission levels, hence the poverty–production risk–maladaptation–environmental degradation nexus. The main contribution of this article is thus a robust analytical framework that integrates socio-ecological interfaces in adaptation planning and nexus thinking. The framework makes it possible to analyse climate adaptation-related risks, as well as trade-offs and synergies in climate change action. It further informs policy on holistic and integrated approaches that could bridge mitigation–adaptation divide in climatic change action decision support systems, transdisciplinary and sustainable development agenda.
Research authorisation for the study was obtained from the National Council for Science and Technology (reference number: NCST/RCD/10/013/23).
Unplanned or autonomous adaptation to climate change could be a driver to degradation of land resources, ecosystems and biodiversity, with far-reaching negative impacts on food security, incomes of small-scale farmers and poverty reduction initiatives. According to Jung et al. (2012), development paths and the choices that define adaptation may affect the severity of climate impacts, not only through changes in exposure and sensitivity but also through changes in the capacities of systems to adapt. This includes local-scale disaster risk reduction (DRR) and resource management and broader social dimensions (Haddad
According to C-CIARN Agriculture (
To maintain or improve their livelihoods, farmers have to adapt to changing policy contexts and environment in which they operate (Maredia & Minde
Given that disasters are potentially embedded in implementation of socio-economic policies and interventions formulated to manage climate change, it is posited that failure to identify, quantify and treat risks embedded in dairy feeding adaptation initiatives actually enhances climate change risks and exacerbates small-scale farmers’ vulnerability to climate change and weather variability (Volenzo
Maladaptation occurs when adaptation action or investment taken to avoid or reduce climate change impacts increases vulnerability to other adverse impacts or increases the vulnerability of other systems, sectors or social groups (Adger, Arnell & Tompkins
The main objective in scaling up sustainable agriculture practices is to transform food production from a major GHG emitter to a net neutral and possibly a GHG sink (UNEP
Previous studies have postulated several relationships in the analysis of poverty–environmental degradation nexus. In one of the relationships, poverty–environmental degradation nexus is considered in a bidirectional order for policy making purposes, that is, the prioritisation of environmental management or poverty alleviation interventions. It was argued by Dasgupta et al. (
Simulated effect of supplementation on milk and methane emission risks.
Ration | DMI gKg−1 | CP (%) | ME (%) | Prod (L) | Kes/Kg ration | Kes/Kg/L | CH4 MjKg−1 | CH4 MjKg−1/L |
---|---|---|---|---|---|---|---|---|
Ms | 910.0 | 4.0 | 2.00 | 3 | 5.00 | 0.60 | 26.10 | 8.70 |
Ms+L | 782.6 | 8.2 | 2.10 | 7 | 5.60 | 1.25 | 18.07 | 2.58 |
Ms+u | 890.0 | 6.6 | 2.00 | 6 | 5.45 | 0.91 | 25.73 | 4.30 |
Ms+CSC+U | 872.0 | 14.1 | 1.58 | 10 | 19.00 | 1.90 | 24.04 | 2.40 |
Ms+CSC+U+M | 857.8 | 16.6 | 2.31 | 17 | 24.20 | 1.45 | 23.60 | 1.39 |
Napier | 675.0 | 6.5 | 7.50 | 5 | 20.00 | 4.00 | 17.90 | 3.58 |
Napier+L | 596.0 | 12.0 | 6.50 | 10 | 21.50 | 2.15 | 16.80 | 1.70 |
Napier+CSC | 700.0 | 13.8 | 3.86 | 12 | 26.00 | 2.17 | 17.60 | 1.47 |
Napier+CSC+M | 635.0 | 14.3 | 11.70 | 15 | 29.80 | 1.99 | 17.10 | 1.14 |
Ms+Napier | 793.0 | 5.3 | 4.80 | 5 | 12.50 | 2.50 | 18.09 | 3.62 |
Ms+Napier+CSC | 795.0 | 14.9 | 4.15 | 16 | 23.75 | 1.48 | 18.08 | 1.13 |
Ms+Napier+CSC+M | 786.0 | 16.9 | 4.00 | 18 | 28.00 | 1.56 | 18.07 | 1.00 |
Note: Urea (CP of 265, Loosli and McDonald, 1968) given at maximum of 10 gKg−1 of ration. Ms/Napier ration in ratio of 1:1 while, CSC and legume fodder and molasses do not exceed 30% and 20% of the ration, respectively.
CSC, Cotton seed cake; M, molasses; Ms, maize stover; U, urea; L, legume fodder; ME, metabolisable energy Mcal/Kg; CP, crude protein; DMI, potential dry matter intake; Prod, production based on critical thresholds and literature data.
Volenzo (
Modelling and simulation can be used in the assessment of poverty–production risks–maladaptation to climate change nexus. As simulated quantitative models offer a range of scenarios, they can be utilised in an iterative way to develop scenarios that project impacts within a coupled human–environment system (Turner et al.
Vulnerabilities to climate change risks, various policy responses and economic actions (adaptation) at household and national levels, as well as resource base can be used to simulate outcomes of adaptive action. In dairy cattle production, energy and protein supplementation are a production risk management strategy. The analogy is applied in this article to assess the effect of MS supplementation on emission of methane.
From the above analysis, the authors suggest an analytical framework (
Conceptual framework.
The introductory and the analytical frameworks provide evidence on poverty–maladaptation–environmental degradation nexus. Therefore, this article focuses on specific issues that are critical to sustainability and climate change mitigation and adaptation and argues that integrating the risk component provides the basis for comprehensive policy analysis and response, risk assessment and mainstreaming of sustainability concerns into agricultural sector adaptation to climate change. Using the level of methane emission from different dairy feeding strategies, issues that impinge on poverty–maladaptation–environmental degradation nexus are examined.
Changes in climate and their effects have serious threats on the stability and productivity of the agricultural sector (FAO
The changing climatic conditions, particularly rainfall and temperature patterns, portend adverse impacts on Kenya’s socio-economic sectors, with current projections indicating that such impacts will worsen in the future if significant reductions in the anthropogenic GHGs emissions that are responsible for climate change are not made (GOK
About 40% of dairy cattle in Kenya are found in semi-intensive farming systems. This feature, coupled with information that such production systems have the highest maize densities, indicates a high potential for benefits from maize–livestock integration (Thorne et al.
The study was conducted in Bungoma and Kakamega counties located between longitude 34°25’E and 35°10’E and latitude 0°1’N and 0°15’ (Jaetzold et al. 2005). The area is characterised by commercial sugarcane farming as well as maize production at subsistence and commercial levels as major economic activities (KNBS
Map of study area (GIS generated).
The western Kenya maize–sugar belt is under increased population pressure (Jaetzold et al. 2005). Diminishing land sizes and seasonality in production of the feeds makes it difficult to bridge protein and energy gaps in dairy cattle feeding (Ongadi et al.
Climate change refers to the change in the state of climate whether due to natural variability or as a result of human activity that can be identified by changes in the mean and/or the variability of its properties, and that persist for extended period, typically decades or longer (IPCC
Climate change and weather variability are among the biggest challenges to human development as they present a combination of risks that negatively impact human health, global food security, economic development and the natural environment on which much of the human livelihoods depends (Zakarya et al.
Maintaining and improving livelihoods is one of the farmers’ objectives. These require that farmers adapt to changing policy contexts and environment in which they operate. In this context, adaptation is an active decision-making process framed by risk attitude and perception. The risk attitude and perception is influenced both by farmer characteristics and external factors (Maredia & Minde
Coping capacity defines the ability of people, organisations and systems to face and manage adverse conditions, emergencies and disasters using available skills and resources (UNISDR
Reducing farmers’ vulnerability in terms of exposure to risks associated with climate change increases their propensity to engage in more productive economic activities (Siegel
The challenging task in planning adaptation activities is finding ways to combine different measures in a meaningful way to avoid maladaptation. The most attractive adaptation measures are those that offer benefits in the near future and reduce vulnerabilities in the long-term (Mimura et al.
Disaster risk reduction is the development and application of policies, strategies and practices designed to minimise vulnerabilities and impacts of disasters through a combination of technical measures to reduce physical hazards and enhance social and economic capacity to adapt (UNISDR
Livelihood refers to activities done by a farmer for earning a living. A livelihood is sustainable when it can cope with and recover from stresses and shocks (such as droughts) while maintaining or enhancing its capacities and assets and at the same time not undermining the natural resource base at local and global levels in the short and long-term (Chambers & Conway
Overall efficiency, resilience, adaptive capacity and mitigation potential of production systems can be inferred to and improved through its various components, such as soil nutrient management (FAO
The above analysis suggests that the cost of feeds and feeding is important in farmers’ decision-making process and choice of risk management strategies. Exploring this linkage is particularly critical with the rising need for inclusion of climate-related targets in SDGs and a more climate-oriented set of indicators as parts of systems for sustainable development and environmental quality.
Definitions of sustainability vary across sectors, but the common theme is to change the way resources are exploited, and how hazards are managed so that adverse impacts downstream or, for subsequent generations, are reduced. Some of the sustainable development indicators pertain to climate change variables, such as level of GHG emissions (Kates, Parris & Leiserowitz
The use of MS, different supplementation regimes and their effect on methane emissions can be used in exploring and extending sustainability concept to emission and mitigation of GHGs. In dairy production, energy and protein supplementation reduces methane emissions at herd level (Mills et al.
The dairy sub-sector accounts for about 7% of Kenya’s GDP and 17% of agricultural gross domestic product, in addition to supplying domestic requirements for meat and dairy products (ASDS
About 40% of dairy cattle in Kenya are found in semi-intensive farming systems (ILRI
Risk is a combination of the probability of occurrence of an event such as drought and resultant negative consequences such as reduced revenues (Mimura et al.
Dairy production operations occur in an environment of intertwined risks. Risk refers to any factor that could lower profits or increase expenses, adversely impacting the economic performance of the dairy enterprise (Bailey
Risk management depends on endowment (Satya
Mean values of household socio-economic characteristics in Kakamega and Bungoma counties.
Household characteristic | Maize zone ( |
Sugarcane zone ( |
Mean for both ( |
2-tailed χ2 |
---|---|---|---|---|
Off-farm income | 35 000 | 25 000 | 30 000 ± 5000 | 0.0500 |
Crop income (Kes)/Yr | 75 000 | 50 000 | 62 500 ± 12 500 | 0.0546 |
Dairy income (Kes)/Yr | 45 000 | 17 500 | 31 500 ± 5450 | 0.0010 |
% using biogas | 2 | 5 | 3.5 ± 0.5 | 0.0515 |
%Grain supplementation | 20 | 5 | 22.5 ± 1.2 | 0.0010 |
%Energy supplementation | 15 | 5 | 10 ± 1.1 | 0.0010 |
%Protein supplementation | 8 | 5 | 6.5 ± 1.5 | 0.7340 |
Livestock Unit (LU) | 4.45 | 2.85 | 3.65 ± 0.05 | 0.0510 |
Acreage Napier (acres) | 0.1 | 0.2 | 0.15 ± 0.1 | 0.0745 |
Land size (acres) | 3.5 | 2.5 | 3 ± 1.2 | 0.05330 |
Milk production/day/cow (Litres) | 4.2 | 1.8 | 3 ± 1.5 | 0.05120 |
% SCT use | 15 | 78 | 46.5 ± 5.3 | 0.0010 |
% Stover use | 95 | 65 | 80 ± 2.5 | 0.0010 |
, Significant difference at 0.05 and 0.01 level of significance.
, Significant difference at 0.05 and 0.01 level of significance.
A farmer may perceive a technology as high risk if it requires investing a higher proportion of his or her limited resources (such as cash for subsistence small-scale farming or improving knowledge and management skills of a farmer) or foregoing a practice that is culturally valued (such as well adapted but low external input-dependent local cattle breeds) in the current system (Maredia & Minde
In the above analysis, accumulated capital allows for purchase and use of external inputs, such as dairy feed supplements. This can significantly impact poverty outcomes and environmental degradation, as external inputs such as energy and protein supplements in dairy production significantly influence methane emission risks that impact environmental sustainability. Higher productivity of the dairy herd from increased use of external inputs not only reduces poverty but also reduces methane emission per unit litre of milk produced.
The authors have used methane emission levels in the adaptation of dairy feeding strategies as a measure of sustainable development. It is apparent that mitigating GHG emission risks is one of the pillars of sustainable adaptation to climate change in dairy production. In this study, price risks are intricately associated with maladaptation to climate change among resource-poor farmers. While anecdotal evidence points to pollution and/or environmental degradation caused by the use of external inputs in crop agriculture, considerations of GHG emission levels in the use of MS suggest otherwise. Increased levels of energy and protein supplementation mitigate GHG, with positive impacts on environmental and financial sustainability. The counterfactual seems to suggest that any policy on adaptation and poverty–environmental degradation nexus has to be resource specific. From the analysis, it is clear that climate change can be a driver of disaster risks when economic vulnerabilities that reduce access to inputs and resources that mitigate GHGs are prevalent. Tackling the underlying disaster risk drivers, such as cognitive failure, poverty and poor natural resource management, is thus critical to risk reduction.
Using MS as a specific feed resource in price and production risk contexts, this study identified potential pathways on poverty–production risks–maladaptation to climate change–Environmental degradation nexus. Simulation of methane emissions from small-scale farmers’ dairy cattle feeding adaptation strategies suggests that low or non-existent supplementation in MS-based rations is associated with higher-than-average methane emissions. The findings of the study underscore the centrality of hazard vulnerability risk assessment and multisectorial planning in the design of sustainable adaptation frameworks. Accordingly, adaptation frameworks should pay close attention to socio-economic issues, social organisations and institutions as the basis for risk-informed policies in general and for the assessment, prioritisation, monitoring and evaluation of climate change adaptation actions for the agricultural sector in particular.
The authors thank the University of Venda for sponsoring the postdoctoral studies and the National Commission for Science, Technology and Innovation (Nacosti) providing funds for field work at the Masinde Muliro University of Science and Technology.
The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.
T.E.V. was the originator of research idea and wrote the manuscript, J.O.O. revised the manuscript and advised on corrections to be made and J.O. made conceptual contributions about the disaster reduction pillar.
Data sharing is not applicable to this article as no new data were created or analysed in this study.
The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of any affiliated agency of the authors.