SIAC program report

Photo: S. Kilungu (CCAFS)
SIAC Program Report
16th
August 2016

of 42
SIAC Program Report: Summary of Progress To-Date
Prepared by SPIA for the SIAC External Evaluation
SIAC, Strengthening Impact Assessment in the CGIAR, is a four-year program of work led by SPIA that aims to
broaden the coverage and widen the range of impact measures of CGIAR research. The program started in
2013 and was motivated by a strong and growing demand across the development community for high-quality
ex post impact assessment to support evidence base decision making in the CGIAR. Expanding the evidence
base, including data collection across the full range of CGIAR research types, was deemed essential in
underpinning and sustaining investments in the CGIAR in the post-reform period. Expanding the inventory of
impact assessment of CGIAR research is one component of that effort; the other relates to enhancing the
credibility and rigor of these studies through greater independence, transparency and external review. Ex post
impact assessment is important in the reformed CGIAR in terms of assessing the strength of the linkages
between agricultural research and the System-Level Objectives (SLOs) of poverty reduction, food security,
improvements in nutrition and health, and sustainable natural resource management. SIAC activities aimed at
developing new methodological tools and collecting the needed data for impact assessment will allow the
CGIAR to extend its work across a broader range of research activities and impact pathways.
The key objectives of the SIAC program are as follows:
 Objective 1 (Methods): Develop, pilot and verify innovate methods for collection and assembly of
diffusion data;
 Objective 2 (Outcomes): Institutionalize the collection of diffusion data needed to conduct critical
CGIAR impact evaluations;
 Objective 3 (Impacts): Assess the full range of impacts from CGIAR research;
 Objective 4 (Building a community of practice): Support the development of communities of practice
for ex post impact assessment within the CGIAR and between the CGIAR and the development
community more broadly.
A major feature of the work being carried out in this program, and most particularly for the activities under
Objective 3, is the sponsorship through a competitive grant-making process of studies of economic, social, and
environmental impacts. All else being equal, employing external and independent researchers ensures a higher
level of objectivity and credibility, compared with relying on CGIAR centers to carry out these studies.
Nevertheless, CGIAR centers and researchers often have key knowledge and, in the majority of cases, are
involved to a varying extent (depending on capacity) in many of the studies.
The program is now in its fourth year but is likely to extend another six months to allow time to bring to
completion a number of studies (see Annex 1 for list of funded studies and timetable). The four-year budget
for this program of work is approximately US$ 12 million1
. This report summarizes the progress to-date and is
organized around the Objectives spelled out in the SIAC program of work.
1 Funding for SIAC for the period 2013 – 2016 comes from three donors:
- USD 5,238,799 from the Bill and Melinda Gates Foundation (BMGF);
- USD 4,453,057 from CGIAR Window 1 and
- USD 1,923,568 from the CGIAR ISPC.

of 42
Underpinning this objective is the development of a robust set of methods for routinely tracking adoption of
CGIAR-related technologies in a cost-effective manner. Such information is a prerequisite for achieving the
highest quality assessment of outcomes and impacts. A set of activities are designed to test innovative ways of
assessing the adoption of improved varieties of crops, livestock and fish technologies, and agronomic and
natural resource management interventions, with the goal of eventually embedding protocols derived on these
tests into large-scale surveys carried out by other institutions outside the CGIAR, such as the World Bank’s
Living Standards Measurement Survey – Integrated Surveys of Agriculture (LSMS-ISA). There are four main
areas of activity here which are managed by Michigan State University.
Activity 1.1. Advance methodologies for tracking the uptake and adoption of improved varieties
The objective of this Activity is to pilot test and validate alternate approaches to collect variety-specific
adoption data against the gold standard benchmark (DNA fingerprinting) to determine which
method/approach is the most cost-effective (i.e., which method provides a given level of accuracy at the least
cost). The idea is to come up with ‘lessons learned’ and recommendations on methods / approaches that can
be used in scaling up the collection and assembly of diffusion data on improved varieties. The following crop-
by-country combinations were targeted:
1. Cassava in Ghana;
2. Maize in Uganda
3. Beans in Zambia
Two further crop-by-country combinations were added to the SIAC portfolio
by SPIA at the start of 2015, as part of the collaboration with LSMS-ISA:
4. Cassava in Malawi
5. Sweet potato in Ethiopia
1. Cassava in Ghana:
This study tests the effectiveness of the following four household-based methods of tracking varietal adoption
for cassava against the benchmark of DNA analysis of cassava leaf samples.
A. Elicitation from farmers by asking him/her the: 1) names of varieties planted and some basic
questions for each variety planted; and 2) type of variety (improved vs. local)
B. Farmer elicitation on varietal characteristics by showing a series of photographs (or actual plants).
This information will be later used by the analyst to identify varieties based on morphological
characteristic data.
C. A trained enumerator recording observations on varietal characteristics by visiting the field and
sharing their opinion on what the variety is by: 1) name and 2) by type (based on observations). The
information collected will be also used by the analyst to identify varieties based on morphological
characteristic data.
D. Enumerator taking photos of the plant in the field for latter identification by experts (i.e., breeders)
The field work for this study is jointly supported by SIAC and the RTB CRP and conducted in partnership with
IITA, Crops Research Institute (CRI)-Ghana, and Agriculture Innovation Consulting (AIC) Ghana. Field work was
completed in late fall 2013. All the samples collected from the farmers’ fields and the 40 genotypes included
in the reference library were sent to IITA by the Ghanaian partners in January 2014. DNA extraction work for
almost 1000 samples was completed by IITA and all the samples were shipped to Cornell for Genotyping by
Sequencing (GBS). Data from the GBS analysis were submitted by Cornell to IITA in July 2014. But due to some
issues on limited library materials to classify all the farmer samples, more analysis was required that included
cassava genotypes from IITA’s collection. The data from this updated analysis were made available to IITA in
December 2014.
A presentation summarizing the main results of this case study along with the results of the bean study (case
OBJECTIVE 1: Develop, pilot and verify innovative methods for collection and assembly of
diffusion data (METHODS)

of 42
study 3, below) were presented at ICRISAT in June 2015 (upon their invitation during a visit by M. Maredia), at
the AAEA meetings in July 2015, and at the International Conference of Agricultural Economists (ICAE) in August
2015. A research paper summarizing the DNA fingerprinting methodology and results of this study was
published in BMC Genetics (v. 16:115 DOI 10.1186/s12863-015-0273-1).
The proportion of farmer collected samples classified as released/improved varieties using different methods
ranged from 1% for method A1, to 15% for method D. The implication of this is that there could be a large
variation in the estimates of adoption of improved varieties based on which method is used to derive the
estimate. Additionally, several interesting findings about the materials included in the reference library have
emerged from this case study. The most interesting results were that: 1) some released varieties included in
the reference library were genetically identical (e.g., IFAD and UCC); 2) many released varieties were hybrids
or admixtures; and 3) Library accessions representing both ‘released varieties’ and ‘landraces’ fall under the
same varietal cluster groups. This last finding especially posed a challenge for varietal identification. The
problem it created is: How to classify farmer samples that fall in these three variety cluster groups? Should
they be classified as ‘improved/released’ varieties or local/landrace varieties?
To address this dilemma, the analysis of effectiveness of methods (A to D) against the benchmark of DNA
fingerprinting is done under two scenarios / assumptions: Liberal scenario: which assumes that all the farmer
samples that fall in a variety cluster in which there is at least one released variety are essentially improved
varieties. Under this assumption, 31% of farmer samples are classified as improved materials; and Conservative
scenario which assumes the opposite (i.e., farmers samples that match the DNA results of a variety group in
which there are both released varieties and landraces, the variety group is considered not-improved). Under
this scenario only 4% of farmer samples are classified as improved materials.
In both the liberal and conservative scenario, when the results of methods A - D are compared with DNA
analyses, the results indicate that: 1) a large number of farmers are mistakenly identifying varieties as improved
varieties when it was not or identifying a variety as traditional when it was in fact improved; and b) the methods
of varietal identification that relied on ‘experts’ were better than the farmers’ elicitation, but still way off from
the truth established by the DNA fingerprinting method. At least at the variety level, the results of this study
has clearly demonstrated the unreliability of both farmer and expert elicitation based methods of varietal
identification.
2. Maize in Uganda:
As part of the planned DTMA (Drought Tolerant Maize in Africa) adoption survey by CIMMYT in three districts
in Eastern Province of Uganda, MSU had designed and implemented modules and protocols to test the
effectiveness of the household-based methods of tracking varietal adoption for maize similar to those used for
the cassava in Ghana experiment. Field data were collected in June 2014 and leaf tissues from 416 maize fields
across 34 villages were collected for DNA analysis. The National Crops Research and Resource Institute
(NaCRRI) of NARO served as the ‘technical’ partner for DNA analysis through their ongoing project with the
University of Ghana. Due to delays in transferring the leaf tissues from the field to the lab and to the large
amount of compacted leaf material in the tubes, virtually all the samples were lost due to mold development.
Due to the delays and difficulties experienced during this project, LGC Genomics offered to repeat the work
for this project for free of charge before June 2015 (for 34 sample plates x 146 assays). An alternative was
found with SPIA to piggy back on a planned LSMS experiment on maize in Uganda in 2015, and management
of the study was transferred over from MSU to SPIA in March 2015 (see below). Regarding the survey data
received from CIMMYT, the survey results indicate that, in general, farmers do not have a clear or accurate
idea about what types of maize varieties they are growing and very few (6%) were able to show the bags in
which the seed planted was obtained (most share or purchase seed with neighboring farmers).
Since March 2015, under SPIA, the context for the study has now shifted to a large methods experiment run
by the World Bank LSMS-ISA team and UBOS on estimating maize productivity – the Methodological
Experiment on Measuring Maize Productivity, Varieties, and Soil Fertility (MAPS). The following three methods
for varietal identification were embedded in the design of the experiment:

of 42
A. Asking the farmer to identify the variety.
B. Asking the farmer to answer questions related to 15 phenotypic characteristics (using a visual aid),
checked against sets of reference responses for each variety using alternative decision rules.
C. Focus group meeting with a number of experts.
These will be benchmarked against two DNA genotyping methods:
D. DNA fingerprinting using SNP markers on samples from maize leaf tissue (using the credit with LGC
Genomics under their contract with MSU/NaCCRI)
E. DNA fingerprinting using DArT method of genotypingon samples from maize grain.
Expert opinion elicitation for these two districts (Iganga and Mayuge) was also carried out prior to any field
work taking place.
Field work for the whole survey took place over three visits to a sample of 900 households (post-planting; crop-
cutting; post-harvest) over the period April 2015 – August 2015 in 5 districts in Uganda. For budget reasons,
DNA fingerprinting was possible only on a subset of 550 farms in two districts – Iganga and Mayuge.
Enumerators from UBOS were recruited and trained intensively for one month, and survey data collection was
facilitated by the use of networked tablets for real-time data management and processing. Leaf samples were
collected at the post-planting visit in April and May 2015, from within the quadrant laid down by enumerators
for subsequent crop-cutting, using leaf collection kits from LGC Genomics. Grain samples were subsequently
collected from these quadrants in the follow-up crop-cut visit in June and July 2015. SNP-based genotyping
data was received from LGC in September 2015 following analysis of the leaf samples. Grain samples were
processed (dried, ground to flour, labelled) by NACCRI in August and September 2015, and shipped to Diversity
Arrays in Australia at the end of October 2015.
Both genotyping methods (D and E) have been successfully applied and the results are currently being
compared by colleagues at Diversity Arrays and NaCCRI. Early results show that the SNP-based genotyping used
on the leaf samples was insufficiently discriminating among improved varieties – some of the varieties in the
reference library appear genetically identical when screen against a low number of SNPs (approx. 140),
whereas when a highly quantitative assay used in DArt is applied (examining more than 10,000 alleles), then
genetic distance is observed.
Before any consistency with the correct genotype (correct identification) can be established, we have to
consider whether unique identification is possible for a given method. This is possible only for 47% of farmer
responses, and 13% of responses to questions in the morphological protocol. Farmers submit “don’t know”
responses to the open question of what the variety is that they are growing in 53% of cases. In theory, a set of
11 morphological questions is sufficient to uniquely discriminate among all varieties in the reference library.
However, in practice, farmers are clearly unable to respond to the morphological questions with sufficient
accuracy to allow for unique identification. Only 60 (13%) of the response sets from farmers interviewed
correspond completely to a valid set of responses (as determined by characterization of the reference library).
How many of these 60 response sets (from method B) are consistent with the genotyped identification (as
opposed to occurring by chance from the set of multiple choice questions) is currently being investigated, as is
the consistency with the expert opinion elicitation process.
Only 2% of the sample of 477 farmers are able to correctly identify the variety when checked against the
genotype (method E). Method E results show that every farmer sampled (all 477) is growing an improved
variety of maize – an astonishing finding. Furthermore, there is deep penetration of the commercial varieties
from Western Seed company (WE varieties) and YARA seeds, whereas farmer’s expectations are largely
confined to the LONGE series released by the government NARO.
While this suggests that improved materials are reaching farmers’ fields some way or another, there is also a
deeper, less clear picture that emerges when one considers the heterogeneity of the samples. There is
considerable genetic heterogeneity within the samples used for the reference library. A good cut-off for
acceptable heterogeneity in the reference material is 15%, whereas our mean reference library heterogeneity
level is 33%, suggesting that genetic lines have not been well separated in the breeding process, or the seed
sampled for the reference library was not pure breeders’ seed. Second, there is a very low average purity level
of the field samples. Only a minority of field samples are at 80% purity or above, dropping sharply and bringing

of 42
the average purity to 63%. This means that for an average plot, 37% of the genetic material sown is not from
the primary improved variety planted in the plot, but has been introduced to the plot either deliberately
(through counterfeiting, or from a farmer choosing to combine varieties together in the same plot) or through
handling / labelling errors in the seed supply chain.
The MAPS experiment also has first-rate data on agricultural productivity, soil quality, varietal identification,
and household characteristics. We can estimate some simple models for the determinants of productivity:
once using the data that are typically collected in surveys, based on farmer testimony; and once using objective
methods for varietal identification (DNA fingerprinting), yields (crop-cuts), soil quality (soil samples taken and
analysed in laboratory). These results will help us understand more about the importance of data quality in
context – does improved data quality substantially impact on our understanding of some fundamental issues
in impact assessment? A second round of MAPS is currently in the field between June and September 2016.
SPIA is considering have the second round of crop cuts genotyped, as a follow-on from the first round.
Four academic articles are in various stages of preparation with SPIA involvement (John Ilukor and/or James
Stevenson), drawing on various elements of the MAPS experiments.
3. Beans in Zambia:
This study tests the effectiveness of the following four household-based methods of tracking varietal adoption
for common beans.
A. Elicitation from farmers by asking him/her some basic questions for each variety planted.
B. Showing the farmer seed samples representing different varieties and asking him/her to identify the
sample that matches each of the variety grown on their farms.
C. Collecting seed samples representing each variety planted by farmers for latter identification by
experts (i.e., breeders).
D. Enumerator taking photos of the seeds during the survey for latter identification by experts (i.e.,
breeders).
The accuracy of adoption estimates derived from the above four methods was evaluated against the varietal
identification established through DNA fingerprinting of seed samples collected from the farmers.
The context for this study was a bean adoption study conducted by PABRA (Pan-African Bean Research
Alliance), in collaboration with CIAT and the Zambian Agricultural Research Institute (ZARI). Seed samples and
data corresponding to four methods, similar but not identical to the cassava in Ghana study, have been
collected from 402 households that were surveyed under a PABRA study, thus allowing to leverage survey
costs.
During a visit to Zambia, the MSU team visited the local market (in Kasama) and collected some bean seed
samples from local vendors. These seeds were added to the pool of seeds for DNA analysis to check if the seeds
of varieties sold in the market as named by the ‘vendors’ match the actual variety as named and identified by
farmers. The total seed sample (both collected from the farmers and from the vendors) was about 900.
As a next step the seeds collected from the farmers’ fields and from the market were germinated by the ZARI
breeder in June 2014. A technician from CIAT-Uganda traveled in July to Zambia to help with the DNA extraction
and samples were shipped to LGC Genomics. To establish the library, ZARI included all the released varieties
plus 15 other local materials in the samples shipped to LGC.
Overall, the adoption (or more appropriately, the frequency of use by farmers) of improved variety as a group
based on farmer elicitation methods varied substantially from 15% in method A (asking farmers what type of
variety they planted—local or improved) and 72% in method B (showing farmers seed samples). The estimate
of overall adoption of improved varieties based on the two expert elicitation methods--method C (showing
breeders seed samples) and method D (showing breeders’ seed photos) were similar. The estimated adoption
(or frequency of use) was 36% based on method D and 37% based on method C.
Comparing the two methods based on farmer elicitation, results indicate that there was only 25% agreement
on the name of the variety planted between methods A and B. In the case of the two methods based on
experts’ opinion (i.e., methods C and D), there was close to 80% agreement on identifying the varieties either

of 42
by name or by type. The raw data from the SNP analysis were received from LGC Genomics Lab in May 2015.
These data were shared with CIAT researchers for interpretation and a report of the data analysis was
submitted by CIAT soon after. Based on these data and report from CIAT, MSU has completed the data analysis
to test the effectiveness of different methods of varietal identification.
Preliminary results show DNA fingerprinting benchmark of 16% adoption, with farmer elicitation method A
returning estimates of 4% adoption of improved varieties when asked for by name, and 13% when asked for
as the aggregate class (local vs improved). Method B of showing farmer seed samples resulted in an estimate
of 71% improved variety adoption, whereas showing seeds or photos to breeders resulted in adoption
estimates rather close in aggregate (though with many possible mis-matches at level of individual varieties, at
18% for showing photos and 15% for showing seeds.
A report summarizing the results of the two pilot studies in Ghana (with cassava) and Zambia (with beans) is
under preparation and will be soon published as a MSU Staff Paper. In addition, a manuscript targeted to a
peer reviewed journal is being developed and will be ready for submission by the end of August.
4. Cassava in Malawi
The context for this study is a methods experiment led by Talip Kilic of the World Bank LSMS-ISA based around
alternative approaches to estimating cassava production from households using diaries and different lengths
of recall data. SPIA, through Research Associate John Ilukor, have embedded the following varietal
identification approaches into the design of the experiment:
A. Asking the farmer to identify the variety.
B. Asking the farmer to answer questions related to phenotypic characteristics (using a visual aid),
checked against sets of reference responses for each variety using alternative decision rules.
C. Focus group meeting with a number of experts.
These are benchmarked against:
D. DNA fingerprinting using GBS on samples from cassava leaf.
Cassava leaf collection was integrated in the survey, along with a phenotypic protocol of traits, which was
implemented starting June 2015 and continued through to end of June 2016. Leaf collection for fingerprinting,
and phenotypic data for subjective identification, are complete. The reference library of varieties, and their
corresponding phenotypic attributes, was compiled by the Malawian NARS. DNA was extracted in-country by
the Chitedze laboratory for samples from 1002 farms and shipped to Diversity Arrays in Australia for
sequencing.
In terms of unique identification, virtually all (1001 or 99.9%) of the farmers submitted a response to the
question as to which variety was being grown (method A), whereas unique identification was only possible for
23% of the sample using method B, and indeed only 73% of the sample using genotyping (method D). The low
level of unique and valid responses for method B is likely a combination of error on the part of farmers but also
the fact that the number of local varieties is very long and as many as 130 local varieties were not included in
the reference library. This large number of distinct local varieties without samples held by the NARS for
inclusion in the reference library also explains the relatively low (73%) rate at which the genotyping exercise
was able to find a match between samples and a valid reference.
In terms of correct identification, farmer elicitation (method A) gives an adoption rate for improved varieties
of 19%, method B suggests a 70% adoption rate, whereas the genotyping (method D) actually shows only 10%
adoption of improved varieties.
5. Sweet potato varietal identification in Ethiopia
This experiment was the initiative of SPIA Research Associate Frederic Kosmowski, working in Ethiopia with
locally recruited enumerators and contacts through the NARS system in Ethiopia. The objective was to assess
the accuracy of three household-based methods for identifying sweet potato varieties using DNA fingerprinting
as the benchmark. The methods used were:
A. Elicitation from farmers with basic questions for the most widely planted variety;

of 42
B. Farmer elicitation on five sweet potato phenotypic attributes by showing a visual-aid protocol
C. Enumerator recording observations on five sweet potato phenotypic attributes using a visual-
aid protocol and visiting the field
D. DNA fingerprinting using GBS on samples from sweet potato leaves (as the benchmark).
Data were collected in early 2015 from 259 plots in Ethiopia. Leaf samples were taken, DNA was extracted by
ILRI in Addis, and plates for sequencing shipped to Diversity Arrays in November 2015. The reference library
was collected and has been complemented by sequencing accessions from the CIP genebank. Initial analysis
shows the following results:
 Method D (DNA analysis) finds that 63% of the farmers are cultivating improved varieties and 37%
are using Local varieties;
 Method A produces almost identical results in the aggregate: across all responses 64% of the cultivars
are self-identified as improved, 35% as local and 1% unknown.
 However, 30% of the actual (DNA-based) improved varieties were identified by farmers as Local and,
of the 85 actual Local varieties in the sample, slightly more than half were wrongly identified as
improved.
 Variety names (for both improved and Local) given by farmers delivered inconsistent and uncertain
varietal identities and only 4% of the farmers were able to correctly identify the registered names of
specific improved varieties.
 Visual-aid protocols employed in methods B and C were more accurate than method A, but still far
below the adoption estimates given by the DNA fingerprinting method.
Results here suggest that estimating the adoption of improved varieties of sweet potato in this area of Ethiopia
with methods based on farmer self-reports is not reliable and indicate a need for wider use of DNA
fingerprinting, which is likely to become the gold standard for crop varietal identification across many crops
and countries, subject to further testing.

of 42
Table 1 - Comparison of methods for estimating % adoption of improved varieties in SIAC program (DATA COLLECTED – SUMMARY BY SEPT 2016)
Focus (Study team – SIAC
activity)
Sample size Corresponding
expert opinion
estimate
Farmer:
Asking
for
variety
name
Farmer:
Asking
“Improved
or local”?
Farmer:
Showing
range of
reference
seeds
Farmer:
Series of
questions
on
phenotypic
attributes
Experts: Visit
field and
identify by
specific
variety name
Experts:
Visit field
and identify
“Improved
or local”
Experts:
Enumerator
takes photo
and experts
identify
Experts: Seeds
collected from
farms and
shown to
experts
DNA
Reference
Cassava, Ghana (MSU / IITA –
Activity 1.1)
914
(all methods)
36%
(DIIVA, 2009)
1% 6% 2% 5% 15% 4% – 31%
Beans, Zambia (MSU / CIAT –
Activity 1.1)
Between 736
and 855
(varies)
9.5%
(DIIVA, 2009)
4% 13% 71% 18% 15% 16%
Maize, Uganda – Leaf (SPIA /
Diversity Arrays – Activity 1.1)
550 How to
handle
don’t
knows?
46% Didn’t
uniquely
identify..
Pending
from
Andrzej
Maize, Uganda – Grain (SPIA
/ LGC – Activity 1.1)
550 Ditto 46% Ditto 100%
Sweet potato, Ethiopia (SPIA
/ Diversity Arrays – Activity
1.1)
259 4% 64% Ditto 63%
Cassava, Malawi (SPIA /
Diversity Arrays – Activity 1.1)
1,200 61%
(DIIVA, 2009)
How to
handle
don’t
knows?
19% 70% 10%
Wheat, Bihar
(MSU / CIMMYT / ICRISAT –
Activity 2.1)
3,278
Lentil, Bihar
(MSU / CIMMYT / ICRISAT –
Activity 2.1)
1,003
Cassava, Vietnam
(MSU / CIAT – Activity 2.1)
1,000
Rice, Indonesia
(MSU / IRRI – Activity 2.1)
810
Cassava, Nigeria (IITA –
Activity 3.1)
GIFT Tilapia, Philippines
(World Fish – Activity 3.1)

of 42
Activity 1.2. Develop protocols for tracking diffusion of natural resource management technologies
A call for pilot projects under this activity was issued by MSU in July 2013 and two studies were commissioned:
1. Innovative use of mobile phone based applications in tracking adoption of Natural Resource
Management Technologies in India (CIMMYT), and
2. Hyperspectral signature analysis: a proof of concept for tracking adoption of crop management practices
in Gazipur, Bangladesh (IRRI)
The final technical reports for the two competitively selected pilot studies funded under this Activity were
received by MSU in April 2015 (from CIMMYT) and June 2015 (from IRRI). The reports were reviewed by SPIA
and MSU, and comments of this review were shared with the authors. SPIA is publishing an impact brief on the
CIMMYT study to highlight the results and lessons learned on the application of an Integrated Voice Response
System to track the adoption of resource management technologies and farming practices.
Overall, the pilot study on the mobile phone based Interactive Voice Response System (IVRS) method was
inconclusive about the validity of results collected through the IVRS model compared to paper based survey
results. But this study has helped identify several key lessons that can guide future applications of such
methods. Some of these insights/lessons gained from this pilot are:
 Monitoring the adoption trends is a dynamic and time consuming process. The use of IVRS can help
reduce some of the time and money costs.
 The IVRS technology used to collect data on adoption resolves the issue of scalability as it can be
inclusive of all types of locations with mobile penetration, and does not have bias towards the type
of phone handsets and service providers thus increasing the reachability especially in rural
environments.
 The application of the IVRS based survey is neutral to any of the agricultural or natural resource
management technologies and practices. The set of survey questions and the question sequence can
be easily adapted on any technology and its adoption and can be customized in the system.
 Farmers do appreciate the limited time that they have to spend with IVRS as compared to the long
paper surveys done conventionally. However, the use of this method requires basic literacy and
understanding of the use of mobile phones.
 The technology is simple, customizable, and scalable. But it is suitable only for short surveys (i.e.,
focused on only one technology at a time).
 The project had envisioned the risk on validity of the data collected through IVRS if the farmers are
not able to understand the purpose of this survey and disconnect the phone. Thus it is important to
run pilots and awareness creation on how to respond to the IVRS based survey.
For the IRRI study, the research team was unable to obtain hyperspectral imagery for the test site (as initially
proposed) and resorted to using alternates (Landsat 8 and MODIS). SPIA has a number of concerns regarding
the study, in particular whether this is the right type of remote sensing imagery that should be used. The
lessons from these two pilots have significantly informed our strategy for Activity 2.2 implementation, which
was the intention when the SIAC program was designed. Indeed, a contract to Nong Lam University and UC
Santa Cruz has been awarded under that activity to apply alternative remote sensing approaches to assessing
AWD adoption in Vietnam. Regarding cell-phone surveys, we have understood more about the biases from
phone surveys and constraints regarding assembling a sample frame, and regarding remote sensing, through
having this work externally reviewed, we have understood more about the heterogeneity of remote sensing
approaches and the strengths and limitations of different methods.

of 42
3. Measuring crop residue cover in Ethiopia
This pilot study that was added in 2015, but is directly managed under SPIA. The experiment was the initiative
of SPIA Research Associate Frederic Kosmowski, working in Ethiopia with locally recruited enumerators and
contacts through the NARS system in Ethiopia. The study is testing six alternative methods of crop residue
coverage measurement among a sample of rural households in Ethiopia. These methods are compared against
a benchmark, the line-transect method. The alternative methods compared against the line-transect include:
A. Interviewee (respondent) estimation;
B. Enumerator estimation visiting the field;
C. Interviewee with visual-aid without visiting the field;
D. Enumerator with visual-aid visiting the field;
E. Field picture collected with a drone and analyzed with image-processing methods
F. Satellite picture of the field analyzed with remote sensing methods.
The survey experiment was implemented in five enumeration areas located in the sub-humid areas of East and
West Shewa zones. In each enumeration area, 12 panel households from the Ethiopian Socio-Economic Survey
(ESS) were interviewed. In addition, 28 households were randomly selected to participate in the experiment.
Data collection took place in late 2015 and early 2016. Low-cost drones (Phantom 2+) were used to capture
aerial pictures of the surveyed fields. Close supervision ensured collection of high quality data and resulted in
a total sample of 197 households and 314 fields observed. After survey completion, two archived full scenes
of Landsat 8 Thematic Mapper satellite imagery were acquired from the United States Geological Survey’s
Earth Explorer imagery search and delivery website. These images match the dates (late 2015 and early 2016)
associated with each field location. A Normalized Difference Tillage Index was calculated for each field. The
experiment delivered the following results:
 Survey-based methods (A, B, C, D) tend to underestimate field residue cover.
 In continuous analysis, suing boxplot and scatterplots, correlations with the line-transect benchmark
ranged from -0.25 (for Method E) to 0.76 (for Method D). The best, i.e., closest to the benchmark,
estimates came from the two visual-aid protocols.
 In categorical analysis (>30% cover or not), visual-aid protocols (C and D) and the remote sensing
method (F) perform equally well with greater than 80% accuracy.
 Among survey-based methods, the strongest correlates of measurement errors are total farm size,
field size, distance and slope.
 Results deliver a ranking of measurement options and suggest a wider use of visual-aid protocols
among survey practitioners and researchers for accuracy, cost and ease of implementation. Remote
sensing provides a good measure only in terms of categorical analysis but is much harder to
implement for statistical institutes.
Activity 1.3. New institutional approaches to collecting technology diffusion data
Most diffusion surveys in the past have depended on CGIAR research teams, either working on their own or
working in collaboration with national programs and statistical services to generate the data. In many
countries, there are private market research firms as well as private survey firms engaged in carrying out
household surveys for academic purposes. A call for proposals was issued by MSU with a focus on doing a case
study in India. The call was issued in February 2015, and applied to either for-profit or non-profit entities with
the relevant capacity.
A total of six proposals were received and after review carried by MSU, proposals received from two private
sector firms based in New Delhi (Synergy Technofin and Creative Agri-Solutions Private Limited-CASPL) and one
firm based in Chennai (Nathan Economic Consulting India Private Limited) were recommended to SPIA for
funding. After receiving an approval from SPIA, MSU established Letters of Agreement with the three firms to
undertake the pilot studies to test the innovative approaches. The scope of these pilots is outlined below:
1. Led by Synergy; Haryana (Karnal) and Bihar (Vaishali); Technologies: Zero till, direct seeded rice, LLL;
Wheat-rice based farming systems
2. Led by CASPL; Haryana (Karnal) and Punjab (Ludhiana); Technologies: Zero till, direct seeded rice, LLL;

of 42
Wheat-rice based farming systems
3. Led by Nathan; AP (Anantapur and Kurnool); Technologies: 15 soil conservation measures promoted by
ICRISAT; Groundnut farming system
To validate the estimates of technology adoption to be obtained from the three pilot studies, MSU has
conducted representative surveys in the 5 study districts by using a more ‘traditional’ approach. This approach
consists of working with a survey firm in India (identified from a process of issuing expressions of interest) to
help with the logistics of doing data collection. The questionnaire and sampling design was developed by MSU
with little involvement of the contracted survey firm. But the survey firm provided enumerators (hired
specifically for this survey), organized training for the enumerators (with one MSU PhD student actively
participating in the training of enumerators and making sure all the field activities are planned as per the survey
design), took charge of programming the survey questionnaire as a CAPI survey, provided logistical support to
the field staff, and did data quality checks, data verification, and submitted the data set to MSU.
The main idea behind this pilot was to see if agricultural technology adoption data collection can be outsourced
to survey firms at lower cost and with no loss in quality. In particular, the approach tested the use of locally
based enumerators equipped with tablets. It was envisioned that this would reduce cost by minimizing
enumerator travel costs.
The results on cost are mixed. The 4,000 household validation survey, conducted by MSU and used as a
comparison for this approach, cost 22 USD per household. The cost for the three survey firms ranged from12
USD to 43 USD. There are several reasons, however, to believe that the local enumerator approach may still
ultimately be more cost effective. One is that each of the survey firms that participated in the pilot study
developed their own Android based data collection application with this budget. While the application may
require tweaking on a survey by survey basis, that amount will be significantly lower than the development
cost, reducing pre household data collection costs. The second reason is that the scale of the validation survey
(five districts rather than two per survey firm) allowed for data collection and management cost efficiencies
that could have lowered the average cost. Additionally, validation survey deployed less than half the number
of enumerators per household, reducing enumerator training costs per household. Finally, the local
enumerator approach is likely to be more cost effective for reoccurring data collection such as for annual
technology adoption. After the first round of data collection, the cost per household via the local enumerator
approach will likely drop due to reduced travel cost and from the survey site.
The primary issue with the implementation of the local enumerator approach was that android application
development, testing and refinement took longer than anticipated. And because this was the first survey using
the newly developed application for each of the implementers, the applications were not as polished as they
may be for subsequent data collection. This means that built in validations and logic checks are not as accurate
as they may be for subsequent rounds of data collection. This had a negative effect on data quality as data still
required extensive cleaning. This includes for errors such as impossibly high values that are easily avoidable
with a more refined app. A more refined application will also reduce the impact of enumerator ability on data
quality. This is especially important as local, rural enumerators may have less experience and education than
more urban-based conventional enumerators. However in this study at least, results on key enumerator
characteristics are mixed. The comparison of enumerator characteristics across the approaches piloted suggest
that at least in India it is possible to find people in rural areas with as good or better qualifications as
conventional enumerators. India, however may be relatively unique in this respect. In less developed countries
it will be more difficult to find qualified, rural based enumerators. This then makes it even more important to
use well developed data collection applications that reduce the effect of enumerator characteristics on data
quality.
All the data collection for this pilot study have been completed and received by MSU. Data analysis to compare
the estimates of adoption of focused technologies using the two approaches (local enumerator vs. validation
survey) is currently ongoing..
Activity 1.4. Develop and disseminate best practices for collecting diffusion data
The idea with this activity is to take stock of activities, results and lessons learned from activities 1.1, 1.2 and

of 42
1.3, in order to generate guidance for the CGIAR system more broadly. This activity will be organized in the
form of a workshop and earlier discussions with SPIA and PIM (F. Place) has resulted in the decision to organize
this workshop in Boston post-AAEA meetings (August 3-4, 2016). This workshop will bring together
results/learning from SIAC Objectives 1 and 2, and other private and public sector partners involved in finding
innovative ways to collect technology adoption in developing countries. The main objectives of this workshop
are to:
1. Take a stock of current and innovative methods for measuring adoption of agricultural technologies
2. Share and discuss results and insights from pilot studies and experiments conducted to establish proof
of concepts to harness the potential of new methods for tracking adoption of agricultural practices
and other types of technologies
3. Further the discussion on scaling up proven methods for measuring technology adoption
We are expecting more than 40 participants to attend this workshop. Information about the workshop,
including the draft agenda and the participant list, is available from the SPIA website. A document summarizing
the main outcomes of the discussion of this workshop will be developed as a deliverable of this Activity.

of 42
The objective here is to compile and make available the best information on outcomes that are at least
plausibly attributable to CGIAR research outputs, and on a large-scale. This is where a key bench-marking
function for the CRPs is most obviously fulfilled by this program. Large gaps in existing adoption databases for
genetic improvement technologies (activity 2.1), natural resource management technologies (activity 2.2) and
policy-oriented research (activity 2.3) will be filled for priority regions. In addition, under activity 2.4, the World
Bank Living Standards Measurement Study-Integrated Surveys of Agriculture (LSMS-ISA) team and SPIA and
Centers are working together with NARS partners and statistical agencies to see how some of these processes
can best be integrated into existing surveys to reduce cost and increase frequency of data collection. MSU is
exploring similar objectives in Zambia and Mozambique and in dialogue with Indian counterparts for a similar
objective.
Activity 2.1. Organize the collection of crop germplasm improvement research related direct
outcomes
Under the SIAC project objective 2, this Activity (2.1) has expanded on the DIIVA and TRIVSA projects that have
come to a closure, and focus on the collection of varietal diffusion data in South and Southeast Asia.
MSU is leading a process for which varietal release and varietal adoption data are collected for 62 crop x
country combinations (CCCs) (which increases to 130 if we count individual states within India and regions
within China as equivalent to countries – they all have their own data collection efforts) using expert opinion
elicitation methods. Towards the planning of Activity 2.1, a two day inception meeting with Center and NARS
partners was held in Bangkok on January 15-16, 2014 for a total of 35 participants. Based on the discussion
and input from resource persons and participants, a guideline document on the methodology for collecting
varietal release and varietal adoption data using expert elicitation methodology was finalized by MSU and
shared with all the Centers and NARS partners. Subsequent to the inception meeting, each participating Center
prepared a budget and workplan, upon which MSU established sub-contracts with the centers to collect
varietal release and adoption data (using expert elicitation method) for 130 CCCs (see Table 2).
For 3 CCCs (all legume crops), MSU will work directly with NARS to collect the information. These include
chickpea in Pakistan, and Lentil in Bangladesh and Nepal. For the former two CCCs, MSU has identified and
contracted local NARS partners (NARC in Pakistan and BARI in Bangladesh) to collect the information and
develop the two datasets by mid-2015. The NARS partners in Nepal were also contacted for their assistance in
completing this Activity for lentil crop. But they have not been able to give their commitment to complete this
task. MSU is working with the ICARDA researcher based in India to find an appropriate partner to collect this
data for Nepal.
For the work contracted to CGIAR Centers, activities have progressed as per the plan. Towards the
implementation of this Activity, CIMMYT organized a training workshop in August 2015 in Nepal for the NARS
coordinators. Sushil Pandey and M. Maredia participated as trainer and resource person at this workshop. A
similar training workshop was planned by IRRI in September 2014 in Laos, by ICRISAT in October 2014 in India,
and by CIP in China in February 2015. Sushil participated in all these training workshops as a trainer and
resource person. M. Maredia and T. Kelley participated in the India workshop in October 2014. CIAT has
identified a regional economist to lead this Activity working closely with the NARS coordinators in each CCC.
Since the last report, Centers have made significant progress in completing the data collection for their
targeted numbers of CCCs. Table 3 provides a summary progress report on work accomplished and still pending
towards completing data collection for the two databases (varietal release and varietal adoption). Overall, data
collection to compile the two databases has been completed for all but 3 CCCs. This represents an overall
achievement of 98% of the targeted numbers of CCCs. Three centers have completed the data collection for
100% of their CCCs, and have also submitted the technical reports and the two databases. The LOA for ICRISAT,
CIP and IRRI has been extended till end of July or August and they plan to submit the datasets for the remaining
CCCs over the next month. According to the last progress report received from CIP, they will have completed
all EE workshops by August 9.
OBJECTIVE 2: Institutionalize the collection of the diffusion data (OUTCOMES)

of 42
Table 2: Final list of CCCs as per the workplan submitted by Centers (July 2014) – amended in September 2014
Country Rice Maize Wheat Barley Sorghum
Ground-
nut
Chick-
pea
Pigeon
pea
Lentil Cassava Potato
Sweet
potato
ALL
Afghanistan 1 1
Bangladesh 1 1 1 1 1 5
Bhutan 0
Cambodia 1 1 1 3
China (provinces
listed below)
8 8 6 2 1 12 9 46
India (states listed
below)
4 8 6 4 4 2 6 3 37
Indonesia 1 1 1 1 1 1 6
Iran ** 0 0 0 0 0 0
Laos 1 1 2
Malaysia 1 1
Myanmar 1 1 1 1 1 5
Mongolia 0
Nepal 1 1 1 1 1 5
Pakistan 1 1 1 1 1 5
Papua New Guinea 1 1
Philippines 1 1 1 1 4
Thailand 1 1 1 3
Vietnam 1 1 1 1 1 1 6
Total 21 25 17 5 0 5 3 1 7 10 23 18 130
Lead center IRRI CIMMYT ICRISAT CIAT CIP/RTB
Commitment from
lead center
21 39 15 10 41 127
Gap * 3 0 3
*MSU will work directly with national programs or consultants to get information for the two data base for these 3 CCCs.
** Due to US Government’s restrictions on ‘working’ with Iran, five CCCs have been removed from MSU’s workplan and LOA.

of 42
Table 3. Data collection status by Centers, as of the end of July 2016.
Total
mandated
CCCs
Data collection in mandated CCCs
Center Completed
To be
completed
Percentage
completed
Report
submitted
Databases
submitted
CIMMYT 40 40 0 100% Yes 40
CIAT 10 10 0 100% Yes 10
IRRIa 21 21 1 100% Yes 21
ICRISAT b 15 15 0 100% No 11
CIP 41 39 2 95% No 18
MSUc 3 2 0 67% Yes 2
Total 130 127 3 98% 102
a For Indonesia, the NARS collaborator plans to implement the EE method to collect adoption data for Upland rice. This will be in
addition to the adoption estimates obtained for Lowland rice in Indonesia using the seed sales data.
b ICRISAT has indicated they will share the outputs of 2 additional CCCs (groundnuts in China) for which they are collecting the data
using similar methodology. Similarly, CIP may submit data from Bhutan for potato, in addition to the 41 CCCs in their workplan
c Adoption data for one CCC -- Lentils in Nepal will not be completed due to non-response from the local NARS partner identified by
ICARDA last year.
MSU, in consultation with SPIA had identified the four CCCs for doing validations of adoption estimates to be
derived using expert elicitation method or secondary data sources. Two methods are being used for
validation—estimating adoption using representative farmer surveys and DNA fingerprinting on all or a sub-
set of seed samples. The four CCCs identified for validation of Activity 2.1 are:
 Wheat in Bihar (state level)
 Lentil in Bihar (state level)
 Cassava in Vietnam (country level)
 Rice in Lampung Province, Indonesia (province level)
Field work (data and sample collection) for all these CCCs has been completed. Samples have been finally
received by the labs (ICRISAT lab for wheat and lentils; IRRI lab for rice; and CIAT lab for cassava) and genotyping
analysis is currently undergoing. No DNA fingerprinting results have yet been received for these validation
CCCs.
As part of the audit requirement, and to collect opinion and assessment of experts on the elicitation
methodology used in Activity 2.1, MSU has designed a survey (see Annex 2), which is being sent to all the EE
workshop participants. This email and online survey is being facilitated by each Center focal point with the help
of their national coordinating partners. The results of this survey will help assess the approach used to collect
crop varietal adoption data across different CCCs, and to get some additional information on the status of the
seed system for specific crops. Hopefully, the feedback from this survey will help us improve this methodology
of collecting adoption data in future studies.
Activity 2.2. Organize the collection of natural resource management (NRM) research outcomes
This was initially part of the Michigan State University sub-grant but it was agreed in Jan 2014 that SPIA would
manage this part of the program. Following a delayed start after this work was transferred back to SPIA, a call
for Expressions of Interest was finally issued in October 2015, for case-studies focused on the following
priorities NRM practice – country combinations:

of 42
Table 4 – Priority NRM practice-country combinations for call for EoIs issued October 2015
PRIORITY NRM PRACTICES PRIORITY COUNTRIES
AGROFORESTRY (PARTICULARLY
“FERTILIZER TREES”, LEGUMINOUS
FODDER SHRUBS)
Kenya, Zambia, Zimbabwe, Rwanda
ALTERNATE WETTING AND DRYING (AWD)
IN RICE PRODUCTION SYSTEMS
China, Vietnam, Philippines, Indonesia, Myanmar, Bangladesh
CONSERVATION AGRICULTURE IN MAIZE-
BASED SYSTEMS
Zambia, Zimbabwe, Mozambique, India, Pakistan, Nepal, Bangladesh,
Kyrgyzstan, Uzbekistan, Tajikistan, Turkmenistan, Kazakhstan, Iraq, Mexico
COCOA INTEGRATED CROP AND PEST
MANAGEMENT (ICPM)
Cameroon, Cote d’Ivoire, Ghana, Liberia, Nigeria
MICRO-DOSING OF FERTILIZER IN MAIZE-
BASED SYSTEMS
Kenya, Zimbabwe, Mozambique
INTEGRATED SOIL FERTILITY
MANAGEMENT
Kenya, Rwanda, Burundi, DRC
From this call, 62 expressions of interest were received, and these were scored and review by SPIA in November
2015. Proponents from 18 expressions of interest as well as a number of resource people and SPIA secretariat
members were invited to participate in a workshop in Rome in December 2015 comprising: discussions of the
nature of the priority practices; the existing data infrastructure in place in the relevant countries that can serve
as the basis for generating adoption estimates; prospects for remote sensing; and group work clustered around
the six practices. The overall objective of the workshop was to try and broker collaborations across interested
parties to ensure we got a strong set of full proposals.
Following the workshop, SPIA issued an invitation to the workshop participants specifying a set of 9 work
packages that full proposals should be targeted towards. Proponents were invited to outline “core” and
“upgraded” budget options for their proposals, with sets of activities to match. In February 2016, the 12 full
proposals received (together covering a total of 25 of our practice-country combinations) were externally
reviewed by a five-member expert panel, and a recommendation for funding proposals was put to the PSC for
discussion and decision on 17th March 2016. Work by the proposal teams takes place throughout the
remainder of 2016 and run to mid-2017. Hence, this is one of the activities that has made the no-cost extension
to mid-2017 necessary. The following contracts are now in place for work being carried out between April
2016 and June 2017.
Table 5 – Funded NRM practice-country combinations with institutions and methodological approaches
TEAM NRM PRACTICE COUNTRIES METHOD(S)
MUTENJE ET AL, CIMMYT /
ICRISAT
Conservation agriculture Mozambique,
Zambia
Panel methods
HOLDEN ET AL, NMBU Conservation agriculture Malawi Panel methods, lead farmers
and followers
ARSLAN ET AL, FAO (EPIC
TEAM)
Conservation agriculture and
Agroforestry
Zambia, Malawi Coordination across all other
CA projects, analysis of
secondary data
MAVZIMAVI ET AL, ICRISAT
/ UIUC
Conservation agriculture and
micro-dosing
Niger,
Zimbabwe
New surveys

of 42
BUTLER ET AL, IFMR / U
MICH
Conservation agriculture India - Bihar,
UP, Haryana,
Punjab
New sur vey + remote sensing
SONDER ET AL, CIMMYT Conservation agriculture Mexico Remote sensing
LOVELL ET AL, NONG LAM
UNIVERSITY / UC SANTA
CRUZ
Alternate Wetting and Drying Vietnam Remote sensing
VAGEN ET AL, ICRAF Fertilizer trees and fodder shrubs Zambia Remote sensing + HH survey
NKONYA ET AL, IFPRI /
GEOPOLL
Integrated soil fertility
management
Zambia, Kenya,
Rwanda
Panel (Zamiba, Kenya) + SMS
survey (Rwanda)
Paul Vlek has been appointed as a senior consultant to help guide this set of studies over the period of
implementation, working with the SPIA team - James Stevenson and Nuri Niyazi in particular. A results
workshop will be organized in May / June 2017.
Related to the documentation of NRM outcomes, James Stevenson represented SPIA in a workshop held in
Cairns, Australia in June 2015, on assessing the effectiveness of landscape level interventions. The consensus
in the group was that there is too little attention paid to demonstrating whether, and under what
circumstances, a landscape scale approach is beneficial and will bring about impact. A paper reflecting these
ideas, led by Jeff Sayer, is under review at the journal Ecology and Society.
Activity 2.3. Organize the collection of policy-oriented research outcomes
This activity focuses on another under-assessed area of CGIAR research – policy oriented research, in particular,
identifying intermediate outcomes of CGIAR research that bear on macro level policies and practices plausibly
linked to Center outputs. Work under this Activity attempts to document several categories of policy research
related to:
 Agricultural and relevant macro, trade and nutrition/health policies, all of which can have a large
impact on economic incentives in agriculture, as well as modulating the poverty and nutrition impacts
of some new technologies Management practices/protocols/agreements adopted at national or
international levels
 Levels and types of investments in agricultural research, roads, markets and other infrastructure
 Expansion of training and institutional capacity (e.g., through farmer field schools)
 Major international conferences / workshops around a highly relevant theme, e.g., IFPRI’s 2020 Vision
conferences
Activity 2.3, therefore, focuses on outcomes of CGIAR policy-oriented research (POR) that have influenced
significant policy changes related to agriculture, food and nutrition at the regional, national or global level. The
aim is to compile and make available to CGIAR stakeholders the best available information on outcomes that
are, at least plausibly, attributable to CGIAR policy research outputs. Ultimately, the objective is to build an
inventory of CGIAR policy-oriented research outcome claims that have been externally vetted and passed
minimum plausibility test, as a basis for selecting more in depth case studies of influence and impact could
(through Objective 3 type activity).
In the first phase completed in 2014, consultant Mitch Renkow drew on earlier CGIAR PMS data files from 2006
through 2010 to compile a list of 93 outcome statements that credibly describe significant achievements of
‘deriving from Center POR outputs’. For each POR outcome, information is provided on the constraint or
problem that was addressed, the key research outputs underpinning the outcome, a description of the specific
POR outcome itself, what supporting evidence exists, and the region or country in which the outcome took
place. Sixty-one of these were assessed as Category I “strong” cases – ones that satisfied certain specific
criteria. In addition to the 61 strong outcomes, there were 32 other outcome statements that were deemed
to have significant potential but required further documentation to be considered plausible cases of influence.

of 42
Of the latter 32 outcomes, 17 were judged to require additional evidence linking the outcome to specific Center
outputs, e.g., the existing outcome statement provided insufficient information to make a compelling case that
the policy outcome could be reasonably attributed to the Center. Fifteen additional statements described
outcomes that look promising, but either were at an early stage, e.g., described early outcomes emanating
from pilot projects, or were simply not described well enough to make a strong case for being a POR outcome
– but, again, appear to have good potential to generate meaningful policy outcomes. The categorization draws
on: original Science Council commissioned external reviewers’ evaluations and the consultant’s own
judgement about the strength of evidence/logic.
Phase 2, also led by Renkow, focused on updating the 2006-2010 database, primarily by searching the websites,
annual reports and other relevant documents published by Centers and CRPs between 2011 and 2014, and
applying a similar set of criteria to potential cases of POR influence/impact. This resulted in an updated (2011-
2014) inventory of plausible case study outcomes. Typically, though with a few exceptions like IFPRI, there
were much fewer cases to report over this latter period and with much less information to substantiate the
stories, presumably due to the lack of any strong incentive to produce evidence of outcomes – compared to
earlier years. A follow-up activity, but not as yet undertaken in SIAC Phase 1, entails offering Centers the
opportunity to verify earlier submitted information or provide updated information to substantiate or modify
earlier claims in the phase 1 & 2 inventories. That activity will take place in the Fall 2016, with the possibility
of initiating an external validation process of POR outcome claims assembled under Phases 1 and 2. The latter
may also feature in a SIAC Phase 2.
Two other Activity 2.3 related outputs are worth noting here:
 IFPRI, the PIM CRP and SPIA recently co-sponsored a Workshop on Best Practice Methods for Assessing
the Impact of Policy Oriented Research at IFPRI HQ in Washington DC. The workshop brought together
more than 40 people, including evaluation experts from within CGIAR, the academic community,
donors, and developing country policymakers. The workshop format was designed to foster the
expression of different perspectives on the current state and prospects of impact assessment of POR.
One of the workshop’s objectives was to seek agreement on realistic expectations for what can and
cannot be achieved in evaluating the impact of different types of policy research, and how best to
undertake the work. Key findings of the workshop can be found on the IFPRI website:
https://www.ifpri.org/publication/workshop-best-practice-methods-assessing-impact-policy-
oriented-research-summary-and
 Renkow authored and presented a paper on ‘assessing the impact of policy-oriented research in the
CGIAR: methodological challenges and reasonable expectations’ at the International Conference on
Impacts of Agricultural Research – Towards an Approach of Societal Values (French National Institute
for Agricultural Research INRA, Paris, November 3-4, 2015). The paper offers a critical assessment of
efforts by the CGIAR and kindred national agricultural research institutions to evaluate the welfare
impacts of policy-oriented research conducted under their auspices.
Activity 2.4. Long-term institutionalization of collection of adoption data
SPIA’s long-term vision in achieving this objective is to involve a broader and more diverse set of national
institutional partners in the collection of adoption data so as to systematize the collection of nationally
representative data (on a regular basis) in the most cost-effective way possible. MSU is working in India,
Mozambique and Zambia to explore the integration of technology adoption data into existing surveys. On a
parallel track, SPIA is working with the World Bank Living Standards Measurement Study – Integrated Surveys
of Agriculture (LSMS-ISA) team through two researchers – Frederic Kosmowski and John Ilukor.
1. India (MSU):
The initial efforts (meetings and discussions) focused on ICAR to leverage existing data or future data collection
efforts (cost of cultivation data) for the purpose of tracking and monitoring the adoption of improved varietal
technologies (and any other technologies, if data are available) by farmers on a regular basis. While there was
some initial interest, subsequent interactions suggested that ICAR did not have institutionalized data collection
mechanism in place to integrate this data, and a better target for such efforts might be the Ministry of

of 42
Agriculture or National Sample Survey Organization (NSSO) or to try and work at the state level (in 1-2 states)
and see if the Department of Agriculture in a given state is open to this idea of institutionalizing the collection
of technology adoption data at least on a pilot stage.
Since the SIAC update in February 2015, Mywish Maredia traveled to Odisha, India, in May 2015 for a day, and
visited the Department of Economics and Statistics for the State of Odisha to find out more about the types of
agricultural data being collected at a state level. From this visit and the desk review of questionnaires used to
collect different types of data through surveys that are routinely conducted (such as the crop cut experimental
data, input surveys, agriculture census surveys and NSSO surveys), the emerging conclusion is that India is a
data rich country. There is an impressive amount of data being routinely collected (many at representative
scale), and all these efforts are already institutionalized within the government system. However, despite these
efforts, the fact remains that it is not easy to get an overall representative picture and trend of the adoption
of different types of agricultural technologies that are generated by the Indian research system (and the
collaborating CGIAR centers) due to a number of reasons, including government confidentiality laws.
Due to the characteristics of the way data are collected, processed and reported in India, there is limited utility
of these data for tracking technology adoption at a representative scale. There is certainly room for
improvements in this data system, but a local institution or a research center needs to champion this cause.
The goal would be to make some changes in the institutionalized data collection system so that the data
collected using public resources can serve the research and monitoring needs of the agricultural research
communities. MSU has initiated a conversation along these lines with the National Institute of Agricultural
Economics and Policy Research (NIAP/ICAR), and will continue to pursue these efforts: NIAP/ICAP Director has
written to the Secretary of Agriculture to make household unit level data available to researchers, and intends
to approach the Chairman of the Statistical Commission. However, to date, we have not been able to make
any meaningful progress towards our objective with this work. The reason being that the Director of NIAP with
whom MSU had initiated the discussions has left NIAP and according to the new Director the chances of
influencing any change in the current data collection efforts is highly unlikely in the short time frame of the
SIAC project.
2. Mozambique (MSU):
MSU has liaised with the Directorate of Economics and Statistics (DEST) within the Ministry of Agriculture and
Food Security (MINAG) that is responsible for producing official agricultural statistics. The Integrated
Agricultural Survey (IAI) is a routine data collection effort – representative at the provincial level – and done
every 1-3 years. Last year, MSU reviewed the IAI survey instruments and provided feedback on integrating
some technology specific questions in different sections of the survey. However, DEST was unable to
incorporate all the suggestions as it was planning to conduct only a “light” round of IAI last year. They have
also expressed interest in testing new methods of tracking adoption of varietal technology, especially using
DNA fingerprinting, but no concrete plans emerged on implementing this method due to resource constraints.
No other activities or plans for institutionalizing data collection were discussed or planned in Mozambique.
3. Zambia (MSU):
MSU reviewed the Crop Forecast Surveys (CFS) that is conducted annually by the Ministry of Agriculture &
Livestock and Central Statistical Office. This survey is representative of small and medium scale holdings at the
country level. Suggestions for modifications and addition of a one page section on the adoption of conservation
technology were made to the CFS coordinator – this was pilot tested in February 2015, but was not
implemented in the March-April round of CFS due to time constraints (increased survey length and time).
However, the team has agreed to integrate a page of questions in the second follow-up round (post-harvest
season in September-October 2015). During a recent visit to Zambia (on another project), MSU (M. Maredia)
visited the Ministry of Agriculture and Livestock to get an update on this activity. All data collection has been
completed and currently undergoing data entry and cleaning. Once the data are cleared by the Central
Statistical Office, it will be shared with MSU and broader research community to assess the adoption of
conservation technologies at the national level. This is an example of a successful outcome from this process
of engagement with country statistics agencies.

of 42
4. Ethiopia (SPIA and World Bank LSMS-ISA):
The third wave (2015/16) of the Ethiopia Socioeconomic Survey (ESS) presents an opportunity for integrating
a number of questions related to the adoption of CGIAR-related agricultural technologies. The ESS is a
nationally representative survey of 4,000 households, and is managed by Central Statistics Agency (CSA) via a
network of some 300 resident enumerators.
SPIA were able to incorporate additional adoption-related questions into the ESS for the following
technologies: Orange-fleshed sweet potato; Awassa variety sweet potato; Crop rotation in previous three
years; Treadle pump; Motorised pump; Desi / Kabuli type of chickpea; Weather index insurance; Broad-bed
maker; Improved livestock feed module. Data collection is complete, but we can expect to have access to the
data in September 2016 – ahead of the formal release in 2017.
5. Uganda (SPIA and World Bank LSMS-ISA):
The Annual Agricultural Survey (AAS) is a new survey funded by the Ugandan government and implemented
by the Ugandan Bureau of Statistics (UBoS). The survey instruments were pre-tested in the second season of
2015 and the main survey will start in September / October 2016. SPIA were able to incorporate questions into
the AAS for the following technologies: bean varieties; cassava varieties; maize varieties; sweet potato
varieties; sorghum varieties; agroforestry; livestock; conservation agriculture. John Ilukor has played an
important role in testing the questionnaires, and SIAC funds paid for 20 tablet computers for UBoS to use in
the survey.
In Uganda, the fourth wave of the Integrated Household Survey (the true LSMS-ISA panel survey) has been
delayed, but a second round of the maize experiment (MAPS, described in Activity 1,1) has is currently in the
field.
6. Malawi (SPIA and World Bank LSMS-ISA):
In Malawi, the Integrated Household Survey 4 (LSMS-ISA panel survey) is taking place in 2016. Training began
in February 2016, and fieldwork started in late March 2016. John Ilukor and James Stevenson, with input from
the FAO EPIC team, have introduced questions on a number of NRM practices into the survey instrument,
relating to inter-cropping, crop residue management, agroforestry, crop rotation. John Ilukor helped in training
enumerators with the Malawian National Statistics Office.

of 42
While work under Objectives 1 and 2 paves the way for future ex post impact assessment studies, Objective 3
activities are focused on carrying out a number of impact assessments of CGIAR research and development
initiatives along the entire chain of causation - from research investments to the System-Level Outcomes. Since
this causal chain is long and complex, SPIA is approaching it from a number of different perspectives: case
studies that focus on measuring the impact of CGIAR research on health and nutrition (activity 3.0); long-term
large-scale studies of impact for major areas of CGIAR investment (activity 3.1); sets of micro-scale impact
studies using experimental methods (activity 3.2) to provide evidence on the impact of CGIAR research-derived
technologies to adopting households; studies of a number of under-evaluated areas of research, e.g. irrigation
and water management; livestock, agroforestry and biodiversity (activity 3.3); a system-level meta-analysis of
ex post IA of CGIAR research (activity 3.4).
Activity 3.0. Assessing the impacts of agricultural research on nutrition and health
Evidence of causal linkages between agricultural research and effects on health and nutrition is anecdotal at
best, and yet the demand for such evidence has never been stronger. This activity is motivated by the need to
broaden and deepen the evidence base regarding the potential for agriculture research and development to
leverage health and nutrition benefits, and to improve our understanding of the multiple pathways linking
those two variables. The intention is to complement, not to duplicate, on-going work in the A4NH and other
CGIAR Research Programs, and giving priority to areas that until now are relatively “under-evaluated.” This
prominently includes activities related to measuring the impact of research-derived interventions that
plausibly impact on nutrition and health.
A competitive call for case studies was issued in July 2013. Led by Erwin Bulte at Wageningen University, an
external review team identified an interesting portfolio of studies with different methods and focal
technologies. An inception workshop for the five funded studies was held in July 2014 and since late 2014, we
have had the five studies running as follows:
1. Adoption of high iron bean varieties in Rwanda (CIAT, Harvest Plus, Virginia Tech, Rwanda Agric Board)
The study is assessing the adoption and nutrition impacts of High Iron Bean (HIB) delivery efforts. This involves
verifying the adoption of HIB varieties in Rwanda and then comparing bean consumption and iron intakes of
adopters to those of non-adopters of HIB varieties. Two cross-sectional surveys of bean growers in Rwanda are
planned in order to collect adoption and in depth socioeconomic and nutrition data from a sample of randomly
selected 91 communities and 1104 bean growing farmers in Rwanda. The impact of HIB delivery interventions
on nutritional outcomes, i.e. bean consumption and iron intake, will be assessed using two methods:
propensity score matching and an instrumental variable approach. Cost-effectiveness of HIB delivery
interventions will be calculated by comparing costs of delivery of HIB to the health benefits of the intervention
(measured in terms of DALYs saved).
Progress: A progress report received from the team in December 2015 demonstrated that the project has
overcome some logistical difficulties and is progressing well. Erwin Bulte has been providing ongoing support
to the team to try and ensure they identify a good instrumental variable for their analysis, and that the follow-
up survey rounds in 2016 include dietary diversity and food security modules. Household and community
surveys were completed in 2015, and DNA fingerprinting will take place during 2016, with sampling from 120
communities taking place in January 2016. Survey preparation and implementation has taken a long time,
including a long delay for a permit from the Rwandan government to allow blood sampling.
FINAL REPORT EXPECTED IN DECEMBER 2016
2. Shortening the hungry season through NERICA in Sierra Leone (IPA, MIT, Sierra Leone Agr Res Inst)
This study investigates the impact of early maturing NERICA rice on consumption and nutrition outcomes of
farming communities in Sierra Leone. Most agricultural communities in Africa experience large seasonal
variations in the price of crops. High prices and low stocks of staple crops prior to the new harvest create a
OBJECTIVE 3: Assessing the full range of impacts from CGIAR research (IMPACTS)

of 42
“hungry season” when households reduce food intake with potentially important health and productivity
impacts. All else equal, the worse the base level of nutrition, the more damaging a prolonged reduction in food
intake is likely to be. In the previous phase of this project, high yielding rice (NERICA-3 and ROK16), of which
NERICA rice is also early maturing, were allocated to four treatment arms with varying subsidies. Endline results
from 2013 showed that NERICA treatment households harvested up to 5 weeks earlier and purchased less
imported rice. A survey will be administered to a subset of this sample to estimate the impact of early NERICA-
3 rice harvest on consumption and health at different points in the year.
Progress: This project has been granted a one-year no-cost extension owing to disruption caused by the Ebola
outbreak in the country in 2014. The final report is now expected at end of December 2016. Early results show
that children in households in that received NERICA (either for free or at 50% or 100% of market price) and
agronomic training on how to grow it, see positive effects using anthropometric measures that persist up to
the beginning of the next hungry season. The coefficients for the same measures for the group that did not
receive training but did have access to NERICA at the same fractions of market price are positive, but not
statistically significant and much smaller than those on the treated and trained group at the end of the hungry
season. Previous studies by the same authors have shown that NERICA is susceptible to crop failure when not
grown under correct agronomic conditions, and these findings would suggest that farmer training may be a
necessary condition for achieving certain development outcomes with NERICA.
3. Crop diversification for food and nutrition security in Malawi and Ethiopia (CIMMYT, Lilongwe University,
Georg-August-University of Gottingen, Ethiopian Institute for Agricultural Research)
Crop diversification (CD) is advocated as an essential component for making agricultural systems more
sustainable and remunerative. However, the role of CD on food and nutrition security for smallholders in SSA
has not been rigorously examined. In principle, CD involves cultivation of more than one crop and/or variety
belonging to the same or different species in space and time; to achieve higher spatial and temporal
biodiversity on the farm. The objective of this project is to assess the ex-post impact of CD options in
combination with improved maize varieties on food and nutrition under different social and market conditions.
It aims to estimate the impacts of various types of CD (e.g., legume-maize rotations & legume-maize
intercropping) on nutrition indicators such as calorie and protein consumption, food diversity, micronutrient
consumption (especially iron, zinc, and vitamin A), and childhood anthropometrics. These objectives will be
achieved through the analysis of panel data collected in 2010 and 2014 from 4,000 farm households in Ethiopia
(2,300), and data for Malawi (1,700 households) from 39 and 16 districts, respectively.
Progress: The draft report received in July 2016 for Ethiopia that has evidence that the joint adoption of crop
diversification and modern varieties has higher impacts on per capita calorie, protein and iron consumption
and diet diversity as well as child stunting than adopting each practice in isolation. This result was not
particularly unexpected but there was previously little empirical evidence to support claims that inter-cropping
could have this range of impacts. The results also suggest that adoption of combination of CD and modern
seeds has higher impacts than adopting each practice in isolation. In the case of Malawi, farm species diversity
is positively associated with dietary diversity but the effects are small. Access to markets (for buying food,
selling produce and chemical fertilizers) was shown to be more important for nutrition quality than diverse
farm production.
FINAL REPORTS ARE EXPECTED END OF JULY 2016
4. Looking beyond income: impact of dairy hubs on nutrition in Tanzania (ILRI, Emory U., Tanzania NARS)
This study aims to assess the relationship between farmers’ participation in dairy business hubs and human
nutrition in Tanzania in the context of the dairy value chain More Milk in Tanzania (MoreMilkiT) project, linked
to the CRP Livestock and Fish. ILRI and its partners are working in two regions on designing and pilot testing
dairy business hubs as a mechanism to increase smallholders’ dairy productivity and income. While the existing
project will monitor annually changes in farmers’ uptake of technologies, milk productivity and dairy income,
a plan to monitor change in household livelihood indicators like human nutrition is lacking. Still, prior studies
have shown that an increase in milk production and dairy income at the farm level need not translate into
increased consumption of milk and overall better nutrition for dairy farmers. The proposed study aims

of 42
therefore to complement the MoreMilkiT project and to assess change in nutrition linked with changes in
productivity and dairy income brought about by farmers’ use of the dairy business hubs
Progress: The progress report received in January 2016 showed that this study has moved in a different
direction than expected. The team are attempting to explain variation in nutritional status of household
members for a sample of 373 households. SPIA sent detailed comments on the progress report to the research
team led by Isabelle Baltenweck, to which they responded. The explanatory variable of interest is participation
in a dairy hub, of which the researchers are hoping to identify the causal effect using a combination of
endogenous switching regression (SPIA has some concerns that there could be identification from functional
form) and an instrumental variable (which has yet to be determined from a number of potential candidates).
The outcomes measures for the comparison are dietary diversity for women and children, and total household
food expenditure. Econometric analysis is currently in progress, with a draft of the final report expected in later
in 2016.
FINAL REPORT EXPECTED IN SEPTEMBER 2016
5. Nutritional impacts of irrigated horticulture in Senegal (Columbia., George Washington U., MDG Center)
Evidence is beginning to emerge about the pathways through which intensification of irrigated horticultural
production can affect nutritional outcomes, but most of this evidence is focused on small-scale, home garden
type interventions, rather than larger-scale, commercial and technologically advanced production. This study
will provide new, experimental evidence on the nutrition and food security impacts of an ambitious irrigation
expansion initiative in the Western Sahel, and on the pathways through which these impacts occur. It leverages
a large, funded, randomized controlled trial that evaluates the impact of the PAPSEN-TIPA project in Senegal
which works with groups of smallholder farmers, mostly women. PAPSEN-TIPA disseminates improved
horticultural technologies and equipment based on past and recent agricultural research, including
adaptations of drip irrigation technologies co-developed by ICRISAT and complementary vegetable seeds and
cultivation practices. Previous agricultural studies predict the interventions will generate large impacts on
horticultural and cereal production, women’s income, labor and time use (associated with water delivery to
plots). These are all important potential pathways by which nutrition outcomes can be affected, and the survey
data will shed new light on their relative importance. The broad research question addressed by this study
is: when do agricultural productivity interventions also result in nutritional improvements, particularly for
children? Specifically for this case study, the key questions are “Does the intervention improve diets? On its
own, or only when coupled with nutritional communication (via mobile phones)?” “What are the effective
mechanisms at work, i.e., income, diverse production, time use, etc.?”
Progress: This project got off to a slow start and got underway only in early 2015, but the December 2015
progress report showed that the project was back on track. In June-July 2015 the survey instrument was piloted
in four villages in Senegal. The pilot data indicated that overall, dietary diversity was low among infants and
young children as well as their mothers. All the study villages were visited in September-October 2015 to
ascertain a list of all the households in order to allow the study team to randomly select households that had
a woman with a child between 6-23 months at baseline. The pre-baseline survey was thus essential in order to
allow efficient random sampling of the target population. During the pilot data collection, six focus groups
were conducted to help inform the development of the nutrition education intervention. The topics discussed
in the focus groups included food production, food procurement, infant and young child feeding practices,
seasonal variation and sources of nutrition information. The focus groups have helped identify some potential
barriers and enablers for the nutrition education intervention and will be combined with findings from the
baseline data collection to identify the key infant and young child feeding messages that need to be targeted
in the nutrition education intervention. Baseline survey results related to horticulture cultivated areas, income,
food consumption, food security, anemia, women empowerment, women time use, and diet diversity for both
control and treated are now available.
UPDATE EXPECTED AT THE END OF JUNE 2017
Activity 3.1. Long-term / large-scale impact assessment studies
The basic idea behind this work is to generate studies that credibly document the impacts of successful CGIAR

of 42
research adopted at scale and over the long term using best available methods. Estimating the direct and
indirect impacts from widely adopted technologies and policies is of special relevance to CGIAR donors and
other stakeholders, particularly in a climate of high accountability and expectation of linkages between
agricultural research investments and socially desirable outcomes.
While experimental and quasi-experimental approaches potentially have much to offer in terms of rigorous
estimation of causal effects during early stages of adoption and at limited scales within producer populations,
other methods, often less quantitative and seemingly less rigorous but more comprehensive, are needed to
estimate impact over longer time periods and larger spatial scales. In addition to measuring the effects on crop
yields and total farm income (or nutritional improvements) of adopters, estimating the impact of widespread
technological change requires consideration of effects on other groups. Widespread technological change
often generates significant partial and general equilibrium effects on farm product prices and farm production
resources, especially labor, but potentially land and other inputs that in turn have significant impacts on
poverty, nutrition and other welfare measures affecting adopting farmers as well as other populations. Indeed,
in many cases, it is believed these widespread indirect effects dwarf direct impacts in the adopting regions.
The usual impact studies, which estimate producer and consumer surplus, take the first step of including
effects on consumers of the product whose production efficiency has improved, and such studies undoubtedly
have shortcomings that should be addressed. But in addition, they often do not in any way consider the indirect
effects on farm input markets or on markets of production complements or substitutes. To what extent it is
possible to demonstrate direct and indirect causal linkages from CGIAR-related technologies in these fairly
complex pathways remains to be seen, but this is the goal of this activity.
In early September 2014, SPIA issued a call for expressions of interest to fund studies that seek to measure the
impacts of widely-adopted CGIAR research related innovations. Seven studies were funded out of the 12 full
proposals received (8 impact + 4 adoption studies) in January 2015. An inception workshop for the set of
studies selected was held in July 2015 at IFPRI, DC. The workshop had two objectives: (1) to provide specific
feedback on technical and operational aspects of the funded studies, and (2) to provide an opportunity for
participants to exchange views on the operational and data-related aspects of long-term, large-scale studies
of CGIAR research impact studies – for instance, lessons from DNA fingerprinting work to estimate varietal
diffusion; reflections on using micro data for macro analysis; and challenges in sampling and extrapolation for
such studies.
The seven funded studies are as follows:
1. Adoption and diffusion of C88 potato variety in China: Spatial variability of productivity gains and cost
savings and value chain development (CIP, Virginia Tech, and Yunnan Normal Univ)
Potato variety Cooperation 88 (C88) is among CIP’s biggest single varietal successes to date. In 2010, C88 was
estimated to be grown on about 400,000 ha, with economic benefits estimated at US $350 million annually.
But there are questions about the validity of adoption estimates for the variety that were based on non-
structured expert elicitation methods. The objectives of the study are to:
A. Obtain rigorous estimates of the adoption of potato varieties in Yunnan province
B. Analyze the impact of C88 by comparing yields and costs relative to the varieties it replaced, and,
C. Estimate market-level impacts of C88 by examining benefits along the potato value chain.
Progress: Household and community surveys, DNA fingerprinting exercises and interviews with various actors
along the potato value chain (mainly potato chip producers) to understand qualitatively the value chain, have
all been completed. Economic analysis is on-going. A surprising result (thus far) is the observation of some dis-
adoption and degeneration of C88, which means that the variety’s impact has likely plateaued in Yunnan. The
project is on track and over the next four months the study team will be finalizing estimates of the economic
impacts of adoption of C88 in Yunnan province and completing their qualitative assessment of the value chain
and writing up results.
FINAL REPORT EXPECTED IN NOVEMBER 2016

of 42
2. Estimating improved Tilapia adoption using DNA fingerprinting: Philippines and Bangladesh (WorldFish)
The study is being undertaken to update (and improve on) estimates of adoption in the Philippines and
Bangladesh of an improved tilapia strain (GIFT) developed by WorldFish by using innovative tracking of
fingerling diffusion. Specifically, it aims to characterize nucleus populations of key GIFT and non-GIFT tilapia
strains using genome-wide SNP genotyping approaches, and to validate (or otherwise) recently completed
field-based adoption estimates.
Progress: Successful collection of tissue samples from nucleus populations of all the major tilapia strains
identified during the inception meetings has been achieved. The strains sampled are: GIFT-Malaysia, GIFT-
Philippines, FaST, GET-ExCEL, Chitralada and BEST, which account for the majority of tilapia production in
Philippines and Bangladesh. Genomic marker development has identified over 13,000 SNP markers, which will
be used to characterize the nucleus populations and for testing the genetic origin of hatchery samples.
Currently, hatchery tissue collections, and the compilation of government production records are underway
and expected to be completed soon. The project is on track.
3. Adoption of improved lentil varieties in Bangladesh: Comparison between expert estimates, nationally
representative farm household survey and DNA fingerprinting (ICARDA and Virginia Tech)
The overall objective is to document current lentil variety adoption levels and identify the determinants of
adoption and trait preferences of lentil farmers in western Bangladesh. In particular, the study compares lentil
varietal adoption estimates obtained by expert opinion with those obtained by household surveys and
compares those with more reliable estimates generated from DNA fingerprinting.
Progress: Data collection, digitization and cleaning for 1000 households across 10 districts in western
Bangladesh completed. Village level data for 52 sample villages has been collected and is being digitized, and
samples have been taken and analysis is underway for DNA fingerprinting exercise. The project is on track. An
early result shows the total area under lentil in these 10 districts alone is estimated to be well over 250,000 ha
of which about 80% is under improved varieties (subject to DNA validation), whereas official statistics had the
total lentil area (in 2010/11) at 70,000 ha and was expected to have grown by 10% each year to reach 100,000
ha for the same period.
4. A systematic and global assessment of the impact of CG technologies on poverty (IFPRI and World Bank)
The study will provide a systematic and global assessment of the overall impact of CGIAR research on growth,
poverty, food security and environmental indicators by combining large-scale global multi-sectoral dynamic
computable general equilibrium and households modelling using an outmatched dataset of household surveys
covering 80 percent of global poor. The framework is supported by researchers used to using such tools and
data to capture the full payoff of CGIAR research both in terms of macroeconomic and microeconomic, direct
and indirect effects.
Progress: The primary focus to-date has been on assessment of productivity implications. Three
complementary but highly different approaches have been/are being used: (i) a review of the evidence
available in the existing literature; (ii) back-casting approach to assess productivity growth in key commodities,
and; (iii) a Delphi approach to elicit opinions from experts—informed by evidence from 1 and 2. Progress is
underway on all three approaches. The study leaders have adapted an enhanced MIRAGRODEP model to be
able to perform the back-casting exercise at the macroeconomic and sectoral level. MIRAGRODEP is a recursive
dynamic multi-region, multi-sector model CGE model. International economic linkages are captured through
international trade in goods and foreign direct investment. The project is on track.
FINAL REPORT EXPECTED IN JANUARY 2017
5. Using global agricultural, health and demographic datasets to identify the impacts of CGIAR’s modern
seed varieties since 1960s (UC San Diego and George Washington University)
The objective of this study is to undertake a comprehensive global assessment of the economic, demographic

of 42
and health impacts of MV releases by integrating hundreds of spatially precise Demographic and Health Survey
(DHS) samples from around the world with detailed information on the timing of MV releases and high-
resolution geospatial data on crop distribution. The study proposes to estimate the impacts on the following
outcomes:
 Agriculture: Yields, area planted, and use of agronomic inputs (fertilizers, irrigation)
 Demography: Birth rates, sex ratios, infant and child mortality
 Nutrition and Food Security: Anthropometrics (birth weight, weight-for-height and height-for-age);
proxies of nutritional status (blood haemoglobin, night blindness as a proxy of Vitamin A availability);
direct indicators of food intake (available across a range of food groups)
 Economic Indicators: Wealth proxies (including asset indices), school enrolment and attainment, and
use of health care. Night light data (post 1992) used as spatial indicator of economic productivity.
Progress: After harmonization and integration of various data sets, initial econometric analysis of MV diffusion
effects on agricultural (yields) and demographic and health outcomes in 18,000 rural villages across 37
developing countries was completed. A report is being prepared summarizing all of the analysis using Evenson-
Gollin MV diffusion data and DHS outcomes, and a research paper analysing the impact of MV diffusion on
infant mortality (the most robust empirical result) is also being prepared. The use of GAEZ data to exploit
biophysical conditions for improved empirical identification of MV diffusion effects is being explored. A
country-level case study of impact of rice MVs in Cambodia is also planned.
6. Measuring the impact of IFPRI’s research on Strengthening Food Policy through Intra-Household Analysis
on the behavior of international NGOs (TANGO)
The overall objective of this study is to assess the extent to which findings of the IFPRI research program on
Strengthening Food Policy through Intra-Household Analysis have been widely adopted at policy level and, in
particular, evaluate the extent to which research findings and policy guidance on use of intra-household gender
analysis have been operationalized in smallholder agriculture programs, projects and components funded by
OECD-DAC members and implemented by INGOs.
Progress: Three main tasks identified in the proposal relate to: the critical junctures analysis; gender policy
analysis of DAC members; and agricultural project documents review for 4 selected least-developed countries.
At its launch workshop in October 2015, the study team agreed on a detailed work plan for implementing these
tasks and decided to hold a second workshop in February 2016 to review results, approve the mid-term
progress report, and plan the implementation of Phase Two. The first task included a bibliographical review, a
citation search and a documentary network analysis of the women in agriculture literature since 1994, as well
as semi-structured interviews with key contributors to the IFPRI research program. These are completed. The
second task involved a number of discrete activities including: an Inventory of gender policy documents of DAC
donors; a key word search; qualitative analysis of gender documents; structured Interviews, and identification
of pathways through which research results pass from originator to donor. Most of these are completed. The
third task involves assembling documentation for projects retained for in-depth study but has met with little
initial success (due to limited response of gender experts for phone interviews in the DAC donor HQ to facilitate
access to this documentation). This task will now be shifted to the collection and analysis of project documents
in Phase 2. Phase 1 is therefore completed (or soon will be) and the team is now moving on to Phase 2, the in-
country fieldwork survey and analysis.
FINAL REPORT EXPECTED IN AUGUST 2016
7. Assessing the impacts of improved cassava varieties in Nigeria (IITA)
The study aims to document the extent of adoption of improved varieties of cassava in Nigeria – as a group
and for individual varieties, identify the determinants of uptake and spread of these improved varieties, and
estimate the causal effect of adoption of improved cassava varieties on crop yields, incomes, food security,
and poverty. It also intends to investigate heterogeneity effects focusing on gender differentials in adoption of
improved cassava varieties in Nigeria.

SIAC program report

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Was ist angesagt?

Was ist angesagt? (20)

Andere mochten auch

Andere mochten auch (17)

Ähnlich wie SIAC program report

Ähnlich wie SIAC program report (20)

Mehr von Independent Science and Partnership Council of the CGIAR

Mehr von Independent Science and Partnership Council of the CGIAR (20)

Kürzlich hochgeladen

Kürzlich hochgeladen (20)

SIAC program report