The document discusses the concept of climate-smart agriculture (CSA) and proposes expanding the focus to climate-smart food systems. CSA aims to improve agricultural productivity and livelihoods while adapting to climate change and reducing emissions. However, there is little empirical evidence of CSA's impacts and interventions often only address one goal. A food systems approach is needed to understand outcomes across the food chain and tradeoffs between climate goals. Case studies show how climate benefits can be lost if interventions only target agriculture and do not consider the broader food system. The authors conclude a climate-smart food systems framework is required to achieve climate goals and food security.

1. From climate-smart agriculture to climate-smart food systems?
Polly Ericksen1, Laura Cramer2, Peter Kromann3, Patrik Henriksson4, Inge Brouwer5, Roseline Remans6, Mark Constas7, Stephanie Coker8
1International Livestock Research Institute (ILRI), 2International Center for Tropical Agriculture, Climate Change, Agriculture and Food Security (CIAT-CCAFS), 3International Potato Center (CIP), 4Stockholm
Resilience Centre, 5Wageningen University & Research, 6Bioversity International, 7Cornell University, 8Tetra Tech
Rationale
Climate smart agriculture (CSA) is a popular framework to articulate how
agriculture can adapt to climate change, mitigate GHG emissions, and
sustainably improve productivity. Despite high level interest from many donors
and governments, there is little empirical evidence of the actual impact of
climate smart agriculture within its three pillars (see ‘What is CSA?’ to the right)
(Lipper et al. 2014; Dinesh et al. 2015; Rosenstock et al. 2016). Given the high
levels of interest to invest in CSA, this lack of evidence is troubling and merits
further investigation. We consider the design of existing interventions and their
intended impact pathways, while recognizing this is a still a relatively new topic.
There are reasons to doubt whether an approach based only on agricultural
activities can succeed in meeting expected outcomes: productivity is only one
component of food security; adaptive capacity requires support from institutions
and policy (Gupta et al. 2010); and the ability of agriculture to deliver the
required mitigation is questionable (Wollenberg 2017).
Discussion
Our findings suggest that the evidence gap on CSA outcomes is due to four issues:
1. The newness of the CSA concept, meaning the body of literature is still small.
2. The lack of a clear impact pathway in all of the CSA studies for how agriculture actually contributes to food
and nutrition security.
3. The lack of clear research design to demonstrate causality between a given CSA intervention and an
outcome.
4. The lack of tradeoff analysis, leading to uni-dimensional interventions that will only contribute to one of the
pillars of CSA and may have negative impacts on the other two.
We propose that a food systems approach is necessary to achieve climate smart targets/goals (Fig. 4);
otherwise many entry points along the food system are missed, and tradeoffs among outcomes are not
elucidated. The case studies demonstrate this more clearly (see box in bottom left).
Conclusion and recommendations
We conclude that for all CSA interventions in the agricultural sector a food systems framework is needed to
understand more broadly the outcomes (or lack of) and the tradeoffs (or synergies) amongst the three pillars
of CSA. Without this broader view, the impact of CSA will remain limited. We propose a draft Climate Smart
Food Systems framework to be built upon by the research community over the coming years. Using the
concept of Climate Smart Food Systems will push the research for development agenda forward, much as
the agriculture for nutrition agenda has now evolved. We suggest that a clearer focus on indicators and
outcomes is necessary, as well as research across a range of food systems (not only developing country
agriculture) to clarify the tradeoffs across objectives and throughout the broader system.
Case studies
To illustrate the need to focus on the entirety of the food system when designing climate
smart interventions, these examples show how interventions aimed only at agriculture can
“lose” climate smartness farther along the food system.
Peruvian anchoveta – The Peruvian anchoveta (Engraulis ringens) fishery
provides one of the most energy efficient animal protein sources in the world,
using only 15 kg of diesel per tonne of landed anchovies (Fréon et al. 2014). The
majority of these anchoveta are, however, turned into fishmeal and used as
animal feed across the globe (Fréon et al. 2017), often ending up in carnivorous
aquaculture systems associated with much higher GHGs (e.g. Pelletier et al.
2009; Henriksson et al. 2015). Meanwhile, 14.6% of Peruvian infants are stunted
(FAO 2018). A combination of overexploitation and a strong influence of El Niño
results in highly variable landings from year to year (Fréon et al. 2014). Although
the Peruvian anchoveta fishery initially appears climate-smart, the inefficient
utilization of this nutritious food source has inhibited its potential to mitigate
GHGs as the resource flows into less climate-smart value chains and fails to
improve local Peruvian food security.
Increasing dairy productivity with improved feed – The Kenyan dairy sector
is growing in response to increased demand, but individual productivity per cow
remains low and methane emissions per unit of milk produced are relatively
high. Higher quality feed is more digestible and improves milk production,
resulting in lower methane emissions intensities (Knapp et al. 2014). Feed
quality improvements can come through adding grain or higher quality forage, or
by adding commercially-produced concentrates. However, growing additional
grain requires significant land conversion, which results in increased GHG
emissions (Brandt et al. 2018). If transport emissions are considered, importing
concentrates may do the same (e.g., in Vietnam feed is imported from Brazil).
Drought tolerant maize – Maize meal is the staple food for many people living
in East and Southern Africa. Maize is a risky crop in many of these
environments, as they are semi-arid and prone to droughts every few years.
Progress has been made in breeding more drought tolerant maize (Fisher et al.
2015), offsetting the risk of crop losses due to drought. However, diets high in
starchy maize meal are not very nutritious as they are low in diversity, proteins
and micronutrients. Hence this CSA intervention does nothing to improve
nutrition, and it may only delay adaptation to longer term climate change if even
drought tolerant maize cannot be produced in highly variable climates.
What is climate smart agriculture (CSA)?
Adapted from https://csa.guide
As defined by the Food and Agriculture Organisation of the United Nations (FAO), climate
smart agriculture (CSA) is an approach that has three objectives or pillars (see below). The key
characteristics of CSA are that it aims to address climate change while integrating multiple
goals and managing trade-offs. There are multiple entry points, and there is no set portfolio of
practices. What is considered CSA is highly context-specific, and what is CSA in one area will
not necessarily qualify as CSA in another context.
References cited
Brandt P, Herold M, Rufino M. 2018. The contribution of sectoral climate change mitigation options to national targets: a quantitative assessment of dairy production in Kenya. Environ Res Lett 13(3):034016.
Constas M, Coker S, Ericksen P. 2017. Climate smart agriculture, food security, and adaptive capacity: quality of evidence briefing. Report prepared for the International Livestock Resilience Research Institute. Nairobi,
Kenya: unpublished manuscript.
Dinesh D, Frid-Nielsen S, Norman J, et al. Is Climate-Smart Agriculture effective? A review of selected cases. CCAFS Working Paper no. 129. Copenhagen, Denmark: CGIAR Research Programme on Climate Change,
Agriculture and Food Security (CCAFS).
FAO. 2018. FAOSTAT database collections. Rome: Food and Agriculture Organisation of the United Nations.
Fisher M, Abate T, Lunduka RW, et al. 2015. Drought tolerant maize for farmer adaptation to drought in sub-Saharan Africa: Determinants of adoption in eastern and southern Africa. Clim Change 133(2):283–299.
Fréon P, Avadí A, Vinatea Chavez RA, Iriarte Ahón F. 2014. Life cycle assessment of the Peruvian industrial anchoveta fleet: Boundary setting in life cycle inventory analyses of complex and plural means of production. Int J
Life Cycle Assess 19:1068–1086.
Fréon P, Durand H, Avadí A, et al.. 2017. Life cycle assessment of three Peruvian fishmeal plants: Toward a cleaner production. J Clean Prod 145:50–63.
Gupta J, Termeer C, Klostermann J, et al. 2010. The adaptive capacity wheel: a method to assess the inherent characteristics of institutions to enable the adaptive capacity of society. Environ Sci and Policy 13(6):459–471.
Henriksson PJ, Rico A, Zhang W, et al. 2015. Comparison of Asian Aquaculture Products by Use of Statistically Supported Life Cycle Assessment. Environ Sci Technol 49:14176–14183.
Knapp JR, Laur GL, Vadas PA, et al. 2014. Invited review: Enteric methane in dairy cattle production: quantifying the opportunities and impact of reducing emissions J Dairy Sci 97(6):3231–61.
Lipper L, Thornton P, Campbell BM, et al. 2014. Climate-smart agriculture for food security. Nature Clim Change 4:1068–1072.
Pelletier N, Tyedmers P, Sonesson U, et al. 2009. Not All Salmon Are Created Equal: Life Cycle Assessment (LCA) of Global Salmon Farming Systems. Environ Sci Technol 43:8730–6.
Rosenstock TS, Lamanna C, Chesterman S, et al. 2016. The scientific basis of climate-smart agriculture: A systematic review protocol. CCAFS Working Paper no. 138. Copenhagen, Denmark: CGIAR Research Program on
Climate Change, Agriculture and Food Security (CCAFS).
Wollenberg E. 2017. The mitigation pillar of Climate-Smart Agriculture (CSA): targets and options. Agriculture for Development 30:19-22.
Structured review findings
Our analysis of studies that were categorized as impact studies revealed the
following features of contemporary research within CSA:
• Narrow band of impact studies – Among those studies that were identified as
impact studies, the topical foci were narrow (primarily on crops) and
geographic coverage was limited (mainly from sub-Saharan Africa and Asia).
• Limited focus on outcomes – Reports of food security outcomes are limited to
agricultural production with some attention paid to stability. Adaptive capacity
rarely measured or reported. Little attention to mitigation outcomes.
• Relatively weak causal inference – The strengths of causal inferences varied,
mainly as a function of limitations found in methodological features of some
studies.
• Reliance on simulation exercises – The reliance on simulation to run empirical
studies allowed for the application of interesting analytical approaches but the
lack of truly contextualized studies raises questions about the external validity
of those studies.
14
Africa
1
North
America
3
Central and
South America
9
Asia
Fig. 2: Geographic coverage of CSA
impact studies
Inclusion criteria:
• Published between 2010 and 2017
• Empirical studies
• Global geographic location
• Includes outcomes related to the three
dimensions of CSA: agricultural productivity,
adaptive capacity and resilience, and
greenhouse gas emissions.
• Interventions or practices that are defined as
adhering to CSA principles
• English language
• Journal articles or organizational grey literature
Laura Cramer, CCAFS
P.O. Box 30709, Nairobi, Kenya 00100
E-mail: l.cramer@cgiar.org
Phone: +254 715 687 380
https://ccafs.cgiar.org/
Sustainable
increases in
productivity and
income
Reduction in
agriculture’s
contribution to
climate change
Strengthened
resilience to
climate change
and variability
National food security and development goals
Fig. 3: Three pillars of CSA
We undertook a systematic review
(Constas et al. 2017) of empirical
literature linking climate smart
agriculture to outcomes related to
food security, adaptive capacity,
and/or mitigation published between
2012 and 2017. This only included
the results of CSA impact studies,
and 26 articles met the inclusion
criteria.
Food
security
Fig. 4: Windows of gains and losses within the food system
toward achievement of climate smart objectives
•Improved
food storage
•Value
addition in
processing
•Local
markets
•Diversified
outlets
•Promotion of
healthy diets
•Changing
demand
•Food waste
•Pathogens
•Processed to
animal feed
•Cold chain
inefficiencies
•Food miles
•Packaging
•Unhealthy
diets
Climate
smart
agriculture
potential
Food
availability
Adaptive
capacity
Mitigation
Opportunities for accumulation of
climate smartness
Production
Storage and
processing
Transport,
marketing
and retail
Consumption
and
utilization
Risks for loss of climate smartness
Focus of most CSA
interventions
Acknowledgements
This work was implemented as part of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), which is carried out with support from CGIAR Fund Donors and through bilateral funding agreements.
For details please visit https://ccafs.cgiar.org/donors. The views expressed in this document cannot be taken to reflect the official opinions of these organisations.
CSA has risen to
importance through
promotion by FAO, the
World Bank, CCAFS,
and other international
agricultural research
and development
groups. Many
countries, especially
those in Africa, have
adopted it as an
organizing principle in
their national climate
change and agriculture
policies.