Assessing climate change adaptation needs in the 
agricultural sector 
Experiences from the CGIAR Research Program on Climate Change, Agriculture and 
Food Security 
Chase Sova 
FEBRUARY 2017 
Key messages 
 In the agricultural sector there is an 
especially urgent need to develop and 
disseminate adaptation prioritization tools 
given the prominence of the sector in INDCs to 
the Paris Climate Agreement. 
 Adaptation practitioners in the sector are 
increasingly considering a more holistic view 
of adaptation planning that, from early in the 
prioritization process, takes in to account food 
security considerations and mitigation co-
benefits.  
 There are several growing sources of data on 
agricultural adaptation, including: Climate-
Smart Agriculture (CSA) Country Profiles, CSA 
Compendium, and evidence from Climate-Smart 
Villages. 
 The CSA Prioritization Framework (CSA-PF) 
was designed to help countries prioritize 
adaptation interventions in the agricultural 
and water sectors, drawing on known practices 
to develop adaptation portfolios that can be 
scaled out. 
 Cost-benefit analysis (CBA) alone should not 
serve as a proxy for prioritization. It is critical 
that CBA analyses are complemented by 
qualitative assessments of barriers to adoption 
and an assessment of environmental and social 
impacts of adaptation strategies. 
 Prioritization frameworks are only as good as 
the data entered into them. National research 
institutions must plan long-term experiments or 
data collection schemes to measure the impacts 
of adaptation interventions on farming systems 
and to provide future inputs into adaptation 
prioritization. 
 
1. Defining adaptation needs 
As noted in the IPCC’s 5th Assessment report, climate 
change adaptation needs are “the gap between what 
might happen as the climate changes and what we would 
desire to happen.” Adaptation needs can be derived from 
a variety of factors including the nature of the climate 
impacts experienced and projected; an assessment of the 
geographical areas and users that adaptation 
interventions should be prioritized for; the 
criteria/indicators used to evaluate and prioritize options; 
and the timeframe for adaptation, among other 
considerations.  
While adaptation needs can refer to the underlying socio-
economic conditions or hazards affecting a system, for 
the purpose of this brief “needs” refer to practices, 
services, policies or a range of best-bet adaptation 
interventions that can be scaled out and used to attract 
investment and funding and that are determined through 
prioritization methodologies.  
Figure 1. National Adaptation Planning Meeting for 
Kenya’s Agriculture Sector. Photoo: S. Kilungu (CCAFS). 
 
 C C A F S  I N F O  N O T E  2  
 
  
 
Decisions regarding the most appropriate adaptation 
strategies in a given country will necessarily result in 
tradeoffs across levels of operation, beneficiaries, and 
even sectors. Securing sufficient funding to assess 
options and act on priorities is a challenge for low and 
middle income countries developing countries, as well as 
assembling the necessary information regarding the likely 
economic, social and environmental impacts of competing 
adaptation strategies. The synergies or tradeoffs that can 
occur in a portfolio of adaptation options comprising a 
comprehensive policy is also difficult to assess. 
Box 1. Select resources for adaptation needs 
assessment and planning 
CSA Country Profiles: Country briefs that include 
relevant agriculture sector context, analysis of potential 
climate-smart practices, institutional and policy 
background, and overview of CSA finance opportunities to 
help decision makers identify adaptation opportunities and 
prioritize actions of high interest. Briefs are currently 
available for countries in Latin America (Argentina, 
Colombia, Costa Rica, El Salvador, Grenada, Mexico, 
Nicaragua, Peru, Uruguay), Africa (Kenya, Rwanda, 
Senegal), Asia (Sri Lanka) and Europe (Moldova). 
Additional profiles are being completed for Ethiopia, 
Ghana, Mali, Niger, and Uganda. Link: 
https://ccafs.cgiar.org/publications/csa-country-profiles 
CSA Compendium: A searchable web-based database of 
published scientific literature to date on the outcomes of 
CSA practices globally. Thousands of promising practices 
identified as potentially climate-smart are organized into 
five general themes: agronomy, agroforestry, livestock and 
aquaculture, postharvest management, and energy 
systems and can be searched by region. Link: 
https://cgspace.cgiar.org/rest/bitstreams/67313/retrieve  
Climate-Smart Villages (CSV):  Real-life laboratories, 
founded on the principles of participatory action research, 
that aim to generate greater context-specific evidence on 
the effectiveness of CSA practices, technologies, services 
and institutional arrangements. CSVs facilitate the co-
development of scaling mechanisms towards landscapes, 
subnational, and national levels for CSA practices. CSVs 
have been established to identify practical steps that 
smallholder farmers and other stakeholders from local to 
sub-national level can take to adapt their agricultural 
practices to secure dependable food supplies and 
livelihoods, generating CSA-related outcomes. Since their 
inception, 36 pilot CSV sites have been established in 20 
countries, and efforts are underway to scale up to 2,000 
sites. Link:  
https://ccafs.cgiar.org/publications/climate-smart-villages-
ar4d-approach-scale-climate-smart-agriculture  
 
 
1.1 Adaptation needs in the agricultural sector 
 
In the agricultural sector, there is an especially urgent 
need for support to assess adaptation needs and devise 
suitable adaptation strategies. Over 90% of Intended 
Nationally Determined Contributions (INDCs) to the 
UNFCCC’s Paris Agreement that include adaptation 
selected agriculture as a priority sector for action 
(Richards et al. 2016), and suitable methodologies and 
tools can help translate these priorities into actions.  
Fortunately, years of agriculture research for development 
has produced useful resources for adaptation needs 
assessment and planning in the sector (Box 1). 
Increasingly, adaptation practitioners are considering a 
more holistic view of adaptation that—from early in the 
prioritization process— takes into account food security 
considerations and mitigation co-benefits that can be 
realized as a result of adaptation actions. This is true of 
Climate-Smart Agriculture (CSA), or agricultural actions 
that aim to sustainably increase productivity, incomes and 
food security, adapt and build resilience to climate 
change, and reduce greenhouse gas emissions when 
possible, in a context specific manner.  
 
2. Methodologies for assessing 
adaptation needs   
Just as there is complexity in defining adaptation needs, 
there are several types of tools and methodologies to 
support countries to assess and prioritize adaptation 
needs. These range from comprehensive step-by-step 
frameworks to more specific tools designed to support 
isolated stages of the adaptation decision making 
process. Prioritization frameworks also differ in their level 
of application, some designed for national governments 
developing comprehensive plans and others for 
community level rapid-appraisal of adaptation 
interventions, and their degree of multi-
stakeholder/community participation. 
Here, we provide a detailed overview of the Climate-
Smart Agriculture Prioritization Framework (CSA-PF), a 
flexible analytical framework, applicable across levels, for 
prioritization of adaptation/CSA options, as well as other 
prioritization tools developed under the CGIAR Climate 
Change, Agriculture, and Food Security (CCAFS) 
research program that are narrower in scope or level of 
application. We then provide examples of the application 
of these tools across CCAFS regions in Latin America, 
East and West Africa, and South and Southeast Asia.  
 
 
 C C A F S  I N F O  N O T E  3  
 
  
2.1 CSA Prioritization Framework 
 
The CSA-PF, developed by CCAFS and the International 
Center for Tropical Agriculture (CIAT), was designed to 
assist decision makers in identifying priority best-bet CSA 
investment portfolios that achieve gains in food security, 
farmers’ resilience to climate change, and low-emissions 
development of the agriculture sector. The framework 
stakeholder driven and is divided into four phases: (i) 
Scoping and initial assessment of CSA options; (ii) 
Identification of top CSA options (workshop); (iii) 
Calculation of cost and benefits of top CSA options; and 
(iv) portfolio development and evaluation of barriers 
(workshop). While the framework was developed for the 
agricultural sector, it has potential utility to related sectors, 
especially water and land use.  
 Phase 1. Initial assessment of CSA options 
The objective of the first phase of the CSA-PF is to 
identify key stakeholders and define the scope of the 
study (e.g. determining the production systems, agro-
ecological zones, nature of climate change to be 
addressed, vulnerable areas, types of farmers targeted, 
transformative actions needed). Drawing on resources 
like the CSA Country Profiles, CSA Compendium, 
Climate-Smart Villages work, local expertise and 
knowledge and other data sources, a preliminary 
extensive list of relevant CSA practices (adaptation 
strategies) is created. Indicators for assessing the 
practices are then selected by stakeholders based on the 
outcomes they desire from interventions and practices are 
assessed and ranked according to these various metrics. 
(For a list of indicator tools see: 
https://csa.guide/csa/monitoring-evaluation-and-learning 
or 
https://cgspace.cgiar.org/rest/bitstreams/83141/retrieve) 
Phase 2. Identification of top CSA options 
The second phase of the CSA-PF seeks to narrow the 
initial long list of CSA options. This is done by engaging a 
broad group of agriculture development stakeholders in a 
workshop where participants analyze and discuss the 
indicator analyses of the long list of practices. The relative 
weighting given to each of the three pillars of CSA 
(adaptation, mitigation and productivity) can be shifted to 
modify rankings of practices for discussion among 
participants regarding prioritization criteria. This group will 
analyze the expected impacts that different land use 
practices/development trajectories will have on the CSA 
goals, as well as the scalability, feasibility and potential 
beneficiaries of each practice. At the end, a short list of 
high-interest practices (usually 10 or less) are selected for 
further analysis. 
Phase 3. Calculation of cost and benefits of top CSA 
options 
During the third phase of the CSA-PF the costs and 
benefits, and potential externalities, of each CSA option 
on the short list is assessed. There are numerous 
methods that can be used to conduct cost-benefits 
analysis (CBA), which need to tailored to the funders and 
implementers aims regarding use of the results from the 
prioritization process (Box 2).  
Figure 2. CCAFS-CIAT Climate-Smart Agriculture Prioritization Framework 
 C C A F S  I N F O  N O T E  4  
 
  
Box 2. Approaches to Cost-Benefit Analysis of 
adaptation strategies 
While there are excel and web-based tools designed by 
CCAFS for cost-benefit analysis (CBA) associated with 
the CSA-PF, there are many ways to undertake CBA.  In 
fact, over the last decade cost analyses of adaptation 
have been widespread in climate change literature. 
Adaptation costing efforts vary widely in their scope and 
level of application. Generally speaking, we can 
differentiate between the following CBA support tools: (1) 
global analyses of investment and financial flows and 
integrated assessments models that seek to determine 
the global cost of adaptation and the impacts on the 
global economy of action/inaction; and (2) national level 
analyses that include the costing exercises associated 
with National Adaptation Programmes of Action (NAPA) 
and National Adaptation Plans (NAP) and the use of more 
sophisticated tools like computable general equilibrium 
analyses (also applicable at global levels), among others. 
For a summary of global and national CBA/costing 
approaches see: https://ciat.cgiar.org/wp-
content/uploads/2013/01/policy_brief5_climate_change.p
df 
More recently, site-specific methodologies for cost-benefit 
analysis have been developed. In the agricultural sector, 
this means undertaking CBA at the farm and community 
level. This bottom-up approach allows for more detailed, 
high-resolution assessments of cost-effectiveness and of 
scaling these interventions up and out. Here are three 
such examples: 
a) In Guatemala, CCAFS researchers applied 
‘probabilistic cost-benefit analysis’, or CBA that does not 
rely solely on a single average of return but rather a range 
of potential values. This approach more appropriately 
takes in to consideration the diverse interests present in a 
community and can help to assuage the value-laden 
assumptions common to most CBA analyses. 
Probabilistic CBA adopts an internal rate of return (IRR) 
approach that does not require specific definition of 
capital costs, only of returns on investment in the form net 
present value (i.e. a representation of the benefits over 
the lifetime of the intervention). An intervention is 
considered profitable when the IRR is higher than the 
discount rate used to determine net present value.  Link: 
http://www.sciencedirect.com/science/article/pii/S0308521
X16301160 
b) In India, CCAFS applied a “willingness-to-pay 
approach” with farmers in state of Rajasthan across 
diverse rainfall zones. “Farmers' were organized into a 
group of 5-6 for discussion on [21] CSA technologies and 
then asked to score each technology from 0 to 3 scale (0 
= no preferences, 1 = low preference, 2 = medium 
preference, and 3 = high preference)”. In a second phase, 
“for only those technologies that were highly preferred by 
the farmers in the scoring exercise, the study team 
conducted a bidding exercise using pseudo money”.   
Bidding exercises/scenarios in this way can effectively 
measure financial burden and identify reluctance to invest 
in the technologies. Link: 
http://www.sciencedirect.com/science/article/pii/S0308521
X1630645X 
c) Further highlighting the value of farm-level CBA 
analyses, in Kenya, CCAFS researchers applied an 
approach known as Participatory Social Return on 
Investment (PSROI).  Social Return on Investment 
(SROI) is a CBA strategy designed to go beyond 
economic returns alone to measure the social and 
environmental impacts of an intervention. Application of  
‘participatory’ SROI (i.e. SROI built in to a wider 
participatory process of adaptation prioritization) with 
farmers in Western Kenya determined that there was an 
approximate 70% reduction in the community valuation of 
intercropping when compared with expert –led desk-
based valuations. This difference was attributed to a lack 
of knowledge about the intervention, misconception about 
the potential costs and benefits, and the risk-averse 
nature of the farmers. Link: 
http://link.springer.com/article/10.1007/s11027-014-9600-
5; or https://ccafs.cgiar.org/publications/participatory-
social-return-investment-psroi#.WEQ_3GWrw80 
 
 
Phase 4. Portfolio development and evaluation of 
barriers 
 
The final phase of the CSA- PF brings stakeholders 
together again in a workshop/forum to review the 
analyses resulting from previous phases. This is done 
using a visualization tool demonstrating the tradeoffs and 
synergies of individual practices and packages of 
practices. The perceived constraints and barriers to 
adoption from the perspective of different stakeholder 
groups is also explored. Portfolios of practices are 
developed by stakeholders and can be accompanied by 
action plans. This phase recognizes that prioritization of 
CSA interventions extends beyond indicator assessments 
of expected outcomes and CBAs alone. The portfolios of 
options and suggested best practices with the greatest 
prospects of achieving desired outcomes as defined by 
stakeholders, such as maximized synergies, minimized 
negative externalities, or potential for uptake at scale, are 
selected by stakeholders for national, regional and/or 
local implementation. 
 
 
 
 
 C C A F S  I N F O  N O T E  5  
 
  
2.2 CSA-PF Case Studies 
Guatemala: In Guatemala, the CSA-PF was implemented 
in collaboration with the Ministry of Agriculture, Livestock, 
and Food (MAGA). In phase one, 24 potential CSA 
strategies were identified associated with maize and bean 
systems (most prominent cropping system) in five sub-
national regions. Following a phase two workshop with 42 
stakeholders from the region, CBAs were conducted on 
eight practices across three categories: agroforestry, 
agronomy and water resource management. Probabilistic 
CBA was used, with data collected through a survey of 
200 farms and secondary literature. Social and 
environmental externalities were also considered and 
analyzed, including impacts of biodiversity, carbon 
sequestration and labor/employment. All CSA strategies, 
with the exception of one, were profitable over the lifetime 
of the interventions and contributed to improved 
biodiversity. The results of this analysis were explored 
with stakeholders in the phase 4 workshop and three 
portfolios of were developed by different types of actors 
(producers, government, research) demonstrating various 
criteria for addressing various tradeoffs between the CSA 
goals (productivity, adaptation, mitigation) and other 
priority outcomes for different sectoral actors. This 
example demonstrates how CSA-PF can be adopted by 
governments and integrated in to existing planning 
processes. Link: 
http://www.sciencedirect.com/science/article/pii/S0308521
X16301160 
Mali: In Mali, the CSA-PF was implemented by a non-
governmental organization, the Malian Association of 
Awareness to Sustainable Development (AMEDD), with 
the support of the Agency of Environment and 
Sustainable Development (AEDD), in direct collaboration 
with government actors and donors from the beginning. In 
the phase one assessment, analysts identified 24 CSA 
strategies applicable across three regions of the country 
and assessed them based on 11 CSA indicators selected 
by stakeholders. In the second phase workshop, 9-10 
practices were selected for each of the three regions, 
such as the fixation of dunes in the Sahelian region, 
sorghum-cowpea association for the Sudano-sahelian 
region, and contour fields for the southern region were 
identified. Eight interventions associated with the 
Sudanian zone were selected for CBA analysis over a 
five-year life cycle, focusing on impacts on the main crops 
found in the diversified farming systems (maize, millet, 
sorghum). Like in Guatemala, social and environmental 
externalities were considered, in this case carbon 
sequestration, gender, and social conflicts related to land 
access. In phase 4, two portfolios of priority CSA activities 
were developed, the first focusing on farm-level activities 
(e.g. sorghum and cowpea integration) and the second on 
a landscape level initiatives (e.g. development of rice 
cultivation valleys). Portfolios have been presented to the 
Malian parliament and integrated into ongoing 
programming. This example demonstrates the cross-level 
utility of the CSA-PF and potential integration with existing 
development initiatives through fostering stakeholder 
ownership. Link: 
https://cgspace.cgiar.org/rest/bitstreams/71151/retrieve 
Vietnam: In Vietnam, the CSA-PF served as the model 
for an alternative framework for the rapid appraisal of 
climate-smart practices. In its application, a baseline 
assessment of adaptation needs and potential options 
was conducted in 2014 in My Loi village. The 13 original 
practices were reduced to 10 based on their “climate-
smartness” through phase 1 assessment through 
consultations with male and female villagers, local leaders 
and experts, field visits, and cost-benefit analysis using a 
net-present value approach.  The top ten interventions 
were presented to the broader community in “CSA Fair” 
where 200 community members participated.  
Intervention posters were posted on the walls of an event 
hall and community members, following technical 
presentations on each practice, voted for the interventions 
they thought were most applicable to them. This included 
home gardening, intercropping, agroforestry, and 
livestock (pig) raising.  Both, “CBA and the prioritization”, 
the authors note, “clearly show that women and men both 
want trees, but women preferred fruit trees and home 
garden development while men were more interested in 
forestry development”.  This example demonstrates how 
the CSA-PF framework can be modified to suit local 
conditions, providing not prescriptive steps, but a broad 
guiding framework. Link: 
https://cgspace.cgiar.org/rest/bitstreams/78307/retrieve 
2.3 Other Prioritization frameworks/tools for 
adaptation needs assessment 
The CSA-PF is the result of learning from best practices 
in the area of adaptation prioritization, yet it is not the only 
CCAFS framework available to low and middle income 
countries. Other toolkits rely more heavily on agricultural 
modeling, or highlight specific aspects of adaption 
planning, like gender, and tend to be focused centrally on 
communities. Here, a range of alternative tools are 
provided: 
CSA Prioritization (CSAP) toolkit: This approach to 
adaptation prioritization requires a detailed location-
specific database on soil, crop varieties, cropping area, 
agronomic practices, irrigation and historical weather 
information along with socio-economic data.  Future crop 
yields, water-use and emissions are then forecasted 
under different climate-scenarios using crop-modeling 
techniques. The approach identifies priorities for 
investment in: (i) crops best suited to delivering target 
growth under impacts of climate change on yields; (ii) 
technologies to deliver targeted increases in productivity, 
based on potential yield increases and the efficient use of 
resources; and (iii) locations for priority investment given 
 C C A F S  I N F O  N O T E  6  
 
  
an existing surplus of productive capacity. Link: 
https://cgspace.cgiar.org/rest/bitstreams/38402/retrieve 
CSA Rapid Appraisal (CSA-RA) tool: “The Climate-
Smart Agriculture Rapid Appraisal (CSARA) provides an 
assessment of key barriers to and opportunities for CSA 
adoption across landscapes by collecting gender-
disaggregated data, perceptions of climate variability, and 
resource and labor allocation, as well as economic 
assessments at the household level. This approach 
combines participatory workshops, expert interviews, 
household/farmer interviews, and farm transect walks to 
gather and capture the realities and challenges facing 
diverse farming communities”.  CSA-RA was piloted in 
Tanzania and Northern Uganda. Link: 
https://dataverse.harvard.edu/dataset.xhtml?persistentId=
doi:10.7910/DVN/28703 
“TargetCSA” Framework: TargetCSA is a national-level 
CSA prioritization tool that integrates stakeholder/expert 
opinion and quantitative data on vulnerability and CSA 
options to produce a portfolio of spatially-explicit CSA 
options.  The method uses a workshop to identify 
vulnerability indicators and CSA practices and a survey to 
conduct a pair-wise comparison of those options (i.e. 
assigning numerical weights) that are then analyzed in a 
computerized optimization model to produce a ‘majority 
consensus’ that most closely reflects stakeholder 
preferences, or other scenarios.  These preferences are 
then coupled with spatial data (.e.g. annual precipitation, 
literacy, soil organic matter etc.) to produce mapped 
indices demonstrating the highest areas of CSA potential. 
This approach is documented in Kenya’s agricultural 
sector. Link: 
http://www.sciencedirect.com/science/article/pii/S0308521
X1530069X 
“Generic” Framework: Notenbaert et al. (2017) offer a 
generic framework for CSA prioritization applicable for 
diverse operational levels and users.  It follows four steps 
(1) Diagnosis and identification of potential options, (2) 
characterization of options, (3) identification of the 
recommendation domains (i.e. spatial mapping) and out-
scaling potential of the options, and (4) ex-ante impact 
assessment of the alternative options. The framework is 
intended to be iterative and non-linear, and employ 
varying degrees of qualitative and quantitative data 
including expert opinion and spatially explicit data. The 
approach was documented in Tanzania’s livestock sector. 
Link: 
http://www.sciencedirect.com/science/article/pii/S0308521
X16301962 
Minimum Data Approach: Shikuku et al. (2017) combine 
a livestock model with a Trade-Off Analysis Model for 
Multi-Dimensional Impact Assessment (TOA-MD) to 
identify the potential rates of adoption for CSA strategy 
variations.  In the case of rural Tanzania, farmers were 
divided in to strata, or groups, pertaining to local or 
improved cow ownership. Adoption of improved breeds 
and improved feeding strategies were determined by the 
TOA-MD model, producing economic, environmental and 
social impact indicators for adopters and non-adopters. 
Based on adoption rates, income, food security poverty 
and GHG emissions were then calculated. This ‘minimum 
data approach’ utilizes survey data, expert consultations, 
and secondary data as inputs in to the livestock and 
economic models. Link: 
http://www.sciencedirect.com/science/article/pii/S0308521
X16302189 
 
3. Challenges and Opportunities in 
adaptation prioritization   
When derived at through participatory processes and 
considered holistically alongside food security, 
productivity and mitigation co-benefits, countries can build 
robust portfolios of agriculture adaptation actions that are 
simultaneously relevant to those most vulnerable and 
attractive to donors.   
The frameworks presented in this brief offer prioritization 
approaches that have been tested across continents and 
in a variety of unique field-level settings. They are not 
intended to be prescriptive, but rather to provide general 
guidelines for important considerations in the adaptation 
prioritization process. Implementation of these 
prioritization frameworks to-date has surfaced several 
important challenges and considerations: 
First, a common challenge across all prioritization 
framework pilots is presenting CSA options with 
sufficient resolution to be instructional but with 
ample flexibility to accommodate local realties— CSA 
options are not, after all, ‘climate-smart’ in every setting.  
Therefore, for phase 3 ‘cost benefit analysis’ to be 
contextually appropriate, phase 2 ‘Identification of top 
CSA options’ must also allow for modification of promising 
strategies with respect to local opportunities and barriers 
to implementation. The continued use of these 
prioritization tools will ensure that the Convention’s 
technology mechanism prioritizes the most appropriate 
technologies for specific contexts.  
Second, regarding the identification of indicators for 
measuring adaptation outcomes, in some cases, 
indicators are too costly and time-consuming for 
rapid field assessments.  Key findings from CCAFS 
Programming and Indicator Tool indicate that mitigation 
co-benefits are seldom measured at field level. The most 
common indicators tend to be related to productivity— 
especially yields and farm income. Furthermore, there are 
very few indicators to address specific adaptation 
measures such as seed varieties or crop insurance. Also 
lacking are financial indicators on adoption of CSA 
 C C A F S  I N F O  N O T E  7  
 
  
technologies and practices, and indicators lacked the 
ability to show a change over time, or to measure specific 
changes in low/lean season.   
Third, cost benefit analysis alone should not serve as 
a proxy for prioritization. It is critical that CBA analyses 
are complemented by qualitative assessments of barriers 
to adaptation adoption and an assessment of 
environmental and social impacts. For example, as 
demonstrated by CCAFS evidence in India, CBA can 
overestimate farmers’ willingness to pay for costly up-
front adaptation investments. Meanwhile, in Kenya, it was 
demonstrated that CBA desk studies may overestimate 
adaptation benefits compared to community level 
assessments. In all cases, practitioners should aim for the 
utmost transparency regarding the assumptions made in 
CBA calculations.  
Ultimately, as demonstrated in Vietnam, CBA analyses 
are challenged by the fact that practices may be new to 
farmers or the particular geography and the costs and 
benefits are not known; and, second, that many CSA 
options involve integrated farming systems at the 
landscape scale where indirect competition and 
complementary effects may be misjudged. Analysis of 
trade-offs at the national level can aid decisions on best 
bets for agricultural investment under climate change. For 
example, de Pinto et al (2016) provided an analysis of 
trade-offs between profitability and emissions reductions 
for oil palm expansion, forest conservation and pasture 
management that informed the Intended Nationally 
Determined Contribution (INDC) submitted by Colombia. 
Link: 
http://www.sciencedirect.com/science/article/pii/S0305750
X16304041  
Finally, prioritization frameworks are only as good as 
the data available to them.  National research 
institutions must plan long-term experiments or data 
collection schemes to measure the impacts, economic, 
social and environmental impacts of CSA/adaptation 
practices on farming systems and to provide future inputs 
in to adaptation prioritization, as these tools evolve and 
improve in sophistication. Robust monitoring and 
evaluation must also be put in place following the 
application of prioritization frameworks to capture data on 
implementation that can also inform future work.  
For each of these prioritization challenges to be 
remedied, funding, capacity and technological gaps need 
to be addressed.  It is important that any local 
prioritization process gives consideration to the 
broader policy and economic landscape framing the 
local context. Demand-driven policies that are 
mainstreamed in to existing development planning 
processes are important. The Framework Convention’s 
finance mechanism must include support for robust 
adaptation needs assessments to properly mainstream 
these efforts.  
Further Reading 
 Campbell BM, Vermeulen SJ, Aggarwal PK, Corner-
Dolloff C, Girvetz E, Loboguerrero AM, Ramirez-
Villegas J, Rosenstock T, Sebastian L, Thornton PK, 
Wollenberg E. 2016. Reducing risks to food security 
from climate change. Global Food Security 11:34-43. 
http://dx.doi.org/10.1016/j.gfs.2016.06.002   
 CCAFS. 2016. Climate-Smart Agriculture 
Prioritization Framework. 
https://ccafs.cgiar.org/climate-smart-agriculture-
prioritization-framework#.WJH-RmW6Dkw 
 CCAFS. 2016. CSA Guide: Climate-Smart Agriculture 
101 https://csa.guide/#main-index 
 Corner-Dolloff C, Nowak AC, Lizarazo M, Parker L, 
Trinh MV, Nghia TD. 2016. Multi-stakeholder 
prioritization approach for climate-smart agriculture 
planning and investment in Vietnam. In: Percy E. 
Sajise, Maria Celeste H. Cadiz, Rosario B. Bantayan, 
(Eds.). 2016. Learning and Coping with Change: 
Case Stories of Climate Change Adaptation in 
Southeast Asia. Philippines: SEARCA. 
https://cgspace.cgiar.org/rest/bitstreams/90610/retriev
e  
 Richards M, Bruun TB, Campbell B, Gregersen LE, 
Huyer S, Kuntze V, Madsen STN, Oldvig MB, 
Vasileiou I. 2016. How countries plan to address 
agricultural adaptation and mitigation: An analysis of 
Intended Nationally Determined Contributions. 
CCAFS dataset version 1.2. Copenhagen, Denmark: 
CGIAR Research Program on Climate Change, 
Agriculture and Food Security (CCAFS). 
https://ccafs.cgiar.org/publications/how-countries-
plan-address-agricultural-adaptation-and-mitigation-
analysis-intended  
 Rosenstock TS, Lamanna C, Chesterman S, Bell P, 
Arslan A, Richards M, Rioux J, Akinleye AO, 
Champalle C, Cheng Z, Corner-Dolloff C, Dohn J, 
English W, Eyrich AS, Girvetz EH, Kerr A, Lizarazo 
M, Madalinska A, McFatridge S, Morris KS, Namoi N, 
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This info note is based on the submission from the 
Consortium of International Agricultural Research 
Centers and International Centre for Tropical 
Agriculture, to the UNFCCC Adaptation Committee 
(AC), in response to the AC’s tenth session call for 
technical information on adaptation needs 
assessment. 
Dr. Chase Sova (chase.sova@gmail.com) is an 
independent consultant specializing in agricultural 
climate change adaptation policy and global food 
security. He is based in Washington, D.C.  
For more information on CCAFS work on adaptation 
needs assessments in the agricultural sector, please 
contact Dr. Osana Bonilla-Findji, Science Officer, 
CCAFS Flagship on Climate-Smart Agricultural 
Practices (O.Bonilla@cgiar.org), or visit 
https://ccafs.cgiar.org/themes/climate-smart-
agricultural-practices.   
This work was implemented as part of the CGIAR 
Research Program on Climate Change, Agriculture 
and Food Security (CCAFS), with contributions from 
various CGIAR centres and partners including the 
International Maize and Wheat Improvement Center 
(CIMMYT), International Food Policy Research 
Institute (IFPRI), International Livestock Research 
Institute (ILRI), Center for International Forestry 
Research (CIFOR), CIRAD (French Agricultural 
Research Centre for International Development), the 
Malian Association of Awareness to Sustainable 
Development (AMEDD), Commonwealth Scientific 
and Industrial Research Organisation (CSIRO), 
Wageningen University and Research Centre, 
Oregon State University, Center for Agriculture and 
Biosciences International (CABI), The Tropical 
Agricultural Research and Higher Education Center 
(CATIE), Guatemala Ministry of Agriculture, 
Livestock and Food.  
Our work 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 organizations. 
 
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