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CRP Portfolio 2017-2022
Value for Money Analysis
Frank Rijsberman September 2015
Dryland Cereals and Legumes systems
Agri-food system programs Global integrating programs
Coordinating programs / platforms
Gender
Capacity Development
Genebank S
++
Big data/ ICT
Fish agri-food systems
Forest and Agroforestry landscapes
Livestock agri-food systems
Maize agrifood systems
Rice agri-food systems
Roots, tubers and bananas systems
Wheat agri-food systems
N U TR I T I ON
&
HEA L TH
P I
M
Genetic Resources Policy
W L E
C L I
MAT E
CH A N G E
Breakdown of investments at the highest level
Agri-food system vs.
Global Integrating
SRF SLOs vs.
Crosscutting (Gender et al)
Breakdown of investments by
pre-proposal
Breakdown of investments by
SLO(*)
(*) Increased
Productivity
treated as its
own SLO and
represented
here as 0
Heatmap of pre-proposals by IDO
Second SRF Priority Framework:
2022 (and 2030) CGIAR Targets – link to SDGs
R
EDU
CED
P
OV
ERTY
100 million more farm households have adopted improved varieties, breeds or trees, and / or improved management practices
30 million people, of which 50% are women, assisted to exit poverty
IMP
RO
VED
FO
OD
A
ND
NU
TRIT
ION
SE
CU
RIT
Y FO
R
HEA
LTH
Improve the rate of yield increase for major food staples from current <1% to 1.2-1.5% per year
30 million more people, of which 50% are women, meeting minimum dietary energy requirements
150 million more people, of which 50% are women, without deficiencies in one or more of the following essential micronutrients: iron, zinc, iodine, vitamin A, folate and vitamin B12
10% reduction in women of reproductive age who are consuming less than the adequate number of food groups
IMP
RO
VED
NAT
UR
AL
RES
OU
RC
ES S
YSTE
MS
AN
D E
CO
SYST
EMS
SER
VIC
ES
5% increase in water and nutrient (inorganic, biological) use efficiency in agro-ecosystems, including through recycling and reuse
Reduce agriculturally-related greenhouse gas emissions by 0.2 Gt CO2-e yr-1 (5%) compared with business-as-usual scenario in 2022
55 million hectares (ha) degraded land area restored
2.5 million ha of forest saved from deforestation
V4M combines analysis of outcomes and budgets of a proposal.
What outcomes does a 1 Bn $ investment buy?
The analysis aims to answer five key questions:
1. What share of the CGIAR targets is a proposal aiming for?
2. Are the outcome targets consistent across summary narrative,
Performance Indicator Matrix and Flagship detailed descriptions (and
reasonable? credible?)?
3. Are outcomes budgeted by Flagship and Outcome (reasonable?
credible?)?
4. Will the portfolio deliver on the SRF (cost? distribution? overinvestment?)?
5. Are there gaps?
What the V4M analysis is NOT:
• A complex economic analysis, or a complex technical calculation
• One single number of return on investment
• An assessment of the social benefits of the projects
• A monetization of the research outcomes
• An algorithm to maximize value-for-money
We evaluate the value of each preproposal at two different levels, by
analyzing their claims for how they contribute to the SRF Targets. We
map these claims against the corresponding non-crosscutting funding.
Overall
Most preproposals map their funding to the
sub-IDOs. To get to a SLO level aggregation
from the IDO level, we count the funding
allocated to “Increased Productivity”
twice: once against “Reduced Poverty”,
and once against “Improved Food and
Nutrition Security” (See SRF tree).
At the flagship level 1 2
VA
LU
E
Most preproposals provide a summary table with their claim against the 2022 SRF Targets.
MO
NE
Y
We count the funding allocated to an
outcome towards this outcome’s result
and add it up to the flagship level.
In the Performance Indicator Matrix, we look for the individual outcomes with results that closely match the SRF Targets. We then add up these claims to the flagship level.
2022 SRF TARGETS
Reduced Poverty Improved Food & Nutrition Security for Health Improved Natural Resource Systems & Ecosystem Services
Total non cross
cutting $ as a %
distributn
VALUE MONEY VALUE MONEY VALUE MONEY
100M HH adopt
varieties /
practices
30M people
exit poverty
% weight in this
SLO ($ M(*))
Yield inc. From
<1% to 1.2% to 1.5%/yr
30M people w/ min
diet energy
150M people
w/o micronutrdefcienc
y
10% less women
w insufficient food groups
% weight in this
SLO ($ M(*))
5% inc. in water
&nutrient use
efficiency
Reduce Ag GHG
by 0.2 Gt CO2/yr
55M ha restored
2.5M ha of forest
saved
% weight in this
SLO ($ M(*))
LiveStck2
12%
Dryld 20 40 60% (515) 1.5% 3 110 30% (265) 27 10% (89) 11%
RTB 8 20 50% (336) x 5M 40% (317) 2.5 10% (85) 8%
Maize1 8%
Rice 22 17 50% (473) 1.5% 30 40% (333) 5% 5% 10% (116) 11%
AgroFrst 100 25 35% (257) x 25 50 20% (170) 0.2 – 0.6 30 3 -9.6 45% (319) 11%
Wheat1 6%
Fish 5 2.5 40% (158) 5 1M 40% (172) 15% 20% (89) 6%
A4N 35 10% (66) 100 10% 90% (532) 0 10%
PIM 29.9 8.9 60% (271) 35% (154) 22.7 5% (25) 5%
Ccafs 12.5 3.75 50% (129) 25% (75) 0.2 (8%) 2 25% (68) 4%
WLE2 8%
Notes: (*) Dollar figures only include investments in non cross-cutting issues. “Increased Productivity” counted twice
Comments: 1) Only 2030 targets were provided, but they are written in alignment with the SRF 2) Targets by flagship only, not aggregated at the CRP level.
Color legend Green = good coherence of the contributions to SRF Targets between overall preproposal’s and individual outcomes’ claims Yellow= medium coherence of the contributions to SRF Targets between overall preproposal’s and individual outcomes’ claims Red = poor coherence of the contributions to SRF Targets between overall preproposal’s and individual outcomes’ claims
Overall V4M Assessment by pre-proposals
AG
RI-
FOO
D
GLB
L IN
TEG
RA
TIN
G
2022 Targets in SRF
Reduced Poverty Improved Food & Nutrition Security for Health Improved Natural Resource Systems & Ecosystem Services
100M HH adopt
varieties / practices
30M people (50% women) exit poverty
Yield inc. From <1% to
1.2% to 1.5%/yr
30M people (50%
women)w/ min diet energy
150M people (50% women)
w/o micronutrient deficiencies
10% reduction in women w/
less than adequate food
groups
5% inc. in water and
nutrient use efficiency
Reduce Ag greenhouse gas by 0.2 Gt CO2-e yr-1
(50%)
55M ha restored
2.5M ha of forest saved
Total Invt.
amnt
CCAFS Value Money Value Money Value Money Value Money Value Money Value Money Value Money Value Money Value Money Value Money $
FS1 11.5 45 45
FS2 8 25 25
FS3 10.8 30 0.2 50 2 18 98
FS4 x1 45 45
Total 30.3 100 x 45 0.2 50 2 18 213
Comments: 1) Not expressed in terms of SRF target Number of organisations and institutions in selected countries/states adapting plans and directing investment towards climate- and nutrition-smart food systems to optimise consumption of diverse nutrient-rich foods
Reduced Poverty $ 99M
Increased productivity 30M
Improved food and nutrition security 45M Improved natural resource and eco systems 68M
V4M at the flagship level for Climate Change, Agriculture and Food Security
Moneyby IDO
242
Heat map of CCAFS Flagships by sub-IDO
2022 SRF Targets
Reduced Poverty Improved Food & Nutrition Security for Health Improved Natural Resource Systems & Ecosystem Services
100M HH adopt
varieties / practices
30M people exit poverty
Yield inc. From <1% to
1.2% to 1.5%/yr
30M people w/ min diet
energy
150M people w/o
micronutrdefciency
10% less women w
insufficient food groups
5% inc. in water
&nutrient use efficiency
Reduce Ag GHG by 0.2 Gt
CO2/yr
55M ha restored
2.5M ha of forest saved
Total Invst. Amnt
RTB Value Money Value Money Value Money Value Money Value Money Value Money Value Money Value Money Value Money Value Money $
FS1 x1 31 31
FS2 2.5 315 202 31 10%3 47 x8 33 142
FS3 x6 18 x6 18 1.7 40 76
FS4 3.52 15 2M4 18 33
FS5 x9 18 52 25 x9 18 1.2M7 15 0.5 21 97
FS6 0
Total 2.5+ 67 28.5 71 x 114 x 33 3.2M 33 2.2 61 379
Comments: 1) Amount of yield increase not quantified 2) Stated in terms of outcome increase, not poverty reduction 3) Target provided but not in terms that align with SRF At least 5,000,000 HH increased their annual RTB yield by at least 10% 4) Target expressed in terms of diversity index increase for 2M HH 5) Budget amount shared with an outcome on post-harvest loss reduction
6) Investments not described in terms of SRF targets In areas affected by pests and diseases, yield restored to previous infection conditions by 1,800,000 farmer HH, of which at least 25% are female headed households 7) Target include small children and expressed as # of HH 8) Is this a play in micro-nutrient? Annual production of RTB nutrient-rich varieties increased by 5-10% in target countries
Reduced Poverty $ 85M
Increased productivity 251M
Improved food and nutrition security 66M Improved natural resource and eco systems 85M
V4M at the flagship level for Roots, Tubers and Bananas
Moneyby IDO
487
9) A play in both yield increase and adoption? Whole-farm productivity increased by 60% for at least 600,000 HH
Heat map of RTB Flagships by sub-IDO
Preproposal Name
Total amount for non-
crosscutting (USD, M)
PIM amount linked to SRF
targets (USD, M)
PIM as a % of Total
Agr
i-fo
od
Pro
gram
s Maize 435 n/a n/a
Wheat 324 n/a n/a
Dryld 634 500 79%
RTB 487 379 78%
Fish 376 285 76%
LiveStck 673 230 34%
Rice 616 202 33%
Frst 642 69 11%
Glo
bal
In
tegr
atin
g P
rogr
ams PIM 317 284 90%
Ccafs 242 213 88%
WLE 435 206 47%
A4N 599 211 35%
Coherence between high level goals and outcome level goals (PIM), as measured by % of total non-crosscutting investments in the PIM that could be linked to a SRF target
Color assignment based on
“PIM as % of total”
This color code will be used
in the next slide
Green = good coherence of
the contributions to SRF
Targets between overall
preproposal’s and individual
outcomes’ claims
Yellow= some coherence of
the contributions to SRF
Targets between overall
preproposal’s and individual
outcomes’ claims
Red = poor coherence of the
contributions to SRF Targets
between overall
preproposal’s and individual
outcomes’ claims
Limitations of the V4M Analysis
• Value analysis: Not many “outcomes” are expressed in terms of the SRF targets. And when they are, they use inconsistent language making it hard to map to the SLOs.
• Money analysis: Inconsistencies on the level of budgeting in the PIM, by outcomes or by sub-IDOs.
• Heat maps: does not capture that not all sub-IDOs are created equal. Some receive higher level of priority from donors or from experts.
• Crosscutting investments: we don’t currently have a methodology for assessing the V4M of those.
• Some investments contribute to multiple targets (“Increased Productivity”, targets for IDOs or Sub-IDOs, “Reduction in GHG” in two places).
• We don’t have a way to produce heat maps along the value chain (upstream vs. downstream).
• Large volume of outcomes (315), flagships (71) and a framework for collecting value information that is too loose.
• The money numbers in the PIM are often not credible and clearly not backed up by detailed CoA budgeting.
Conclusions
• Priority setting:
1. Set targets linked to SDGs
2. Allocate funding to CRPs & platforms that are rated
“A” by ISPC and achieve the targets
• V4M analysis shows pre-proposals aim to deliver SRF
targets for 2022, by and large, if rated A and demonstrate
outcomes and budgets credibly in full prop.
• Many questions on consistency and credibility in
outcomes and budgeting – particularly in PIM-for full prop
• Reco: clearly separate outcomes from milestones
Consortium of International Agricultural Research
Centers