LSMS-ISA, 2014-2016


SPIA is endeavoring trying to support the CGIAR in collecting data at a nationally representative scale that can be used to assess adoption and impact of CGIAR innovations. To this end, SPIA has been partnering with the World Bank Living Standards Measurement Survey (LSMS) – Integrated Surveys of Agriculture (ISA) in some of the eight LSMS-ISA Sub-Saharan Africa (SSA) countries during the past two years. The eight LSMS-ISA countries are: Burkina Faso, Ethiopia, Malawi, Mali, Niger, Nigeria, Tanzania, and Uganda.

Frédéric Kosmowski and John Ilukor (SPIA Research Associates ) have been testing alternative data collection approaches and improved survey protocols for specific CGIAR-related agricultural technologies along two strands of work. 

Terms of Reference for Research Associates (PDF)

  • Under SIAC Activity 1.1, advancing methodologies for tracking the uptake and adoption of improved varieties, the joint LSMS/SPIA effort has been pilot-testing and validating alternate approaches to collecting variety-specific adoption data against the gold-standard benchmark (DNA fingerprinting of crop varieties tender (PDF) with the aim of determining which method/approach is the most cost-effective.
  • Under SIAC Activity 2.4, long-term institutionalization of collection of adoption data, SPIA, the LSMS-ISA team and Centers have been working together with NARS partners and statistical agencies to see how existing surveys (such as the LSMS-ISA household surveys) can be leveraged to reduce cost and increase frequency of relevant data collection.

Methods for tracking varietal adoption

Maize in Uganda

Since March 2015, SPIA has been conducting an experiment to benchmark farmer elicitation (on names and phenotypical characteristics of varieties planted) and expert elicitation against a combination of two DNA genotyping methods (SNP markers on leaf samples and DArT genotyping on grain samples). Data and sample collection for this study has taken advantage of ‘piggy-backing’ on a large methods experiment by LSMS and the Uganda Bureau of Statistics (UBOS) – the Methodological Experiment on Measuring Maize Productivity, Varieties, and Soil Fertility (MAPS). The focus of MAPS is on exploring yield estimation methodologies (specifically the effect of sub-plot number and size on yield estimates), while also collecting objective information on soil fertility and maize variety.

As the MAPS experiment also has first-rate data on agricultural productivity, soil quality and household characteristics, we will attempt to estimate simple models for the determinants of productivity using survey data and results obtained from DNA fingerprinting, crop-cuts, and soil sample analysis. This will enable us to understand more about the importance of data quality in the context of impact assessment. A second round of MAPS was in the field between June and October 2016. SPIA has decided to have the second round of crop cuts genotyped, as a follow-on from the first round. The objective is to (1) match varieties in field with the reference library and (2) assess the quality of maize seed market in Uganda

Cassava in Mali

Similar to the work on maize in Uganda, SPIA has been conducting a second methods experiment to benchmark farmer elicitation (on names and phenotypical characteristics of cassava varieties planted) and expert elicitation against DNA genotyping (DArT genotyping of leave samples). Again, data and sample collection has been undertaken in tandem with a large study by LSMS and the Mali National statistical Office (NSO) – the Methodological Experiment on Cassava Variety Identification Productivity Survey (CVIP). As with MAPS in Uganda, CVIP compares yield estimation methodologies and collects objective information on cassava variety. Simple model estimations for the determinants of productivity will also be endeavored here, using data from the survey, crop-cuts and DNA fingerprinting results.

Sweet potato in Ethiopia

This experiment was conducted by the SPIA team Ethiopia, working with locally recruited enumerators and contacts through the NARS system. The objective was to assess the accuracy of the following three household-based methods for identifying sweet potato varieties using DNA fingerprinting as the benchmark (GBS on leaf samples): farmer elicitation of names and of phenotypic attributes of sweet varieties planted, and enumerator observations on phenotypic attributes of crop varieties in farmers’ fields.

Data was collected in early 2015 from 259 plots in Ethiopia. Leaf samples were taken, DNA was extracted by ILRI in Addis Ababa, 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 gene bank.

Institutionalization of collection of adoption data 


The third wave (2015/16) of the Ethiopia Socioeconomic Survey (ESS) presented an opportunity for integrating a number of questions related to the adoption of CGIAR-related agricultural technologies. The ESS is an LSMS-ISA supported nationally representative survey of 4,000 households, and is managed by Central Statistics Agency (CSA) via a network of some 300 resident enumerators.

SPIA was 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; and Improved livestock feed module. Wave 3 of the ESS was been completed over 2016 and we already have access to some of the data ahead of the formal release in January 2017.


The Annual Agricultural Survey (AAS) is a new survey funded by the Ugandan government and implemented by the Ugandan Bureau of Statistics. It is a nationally representative survey covering all the 10 agro-ecological zones, sampling some 7,200 agricultural households. The survey instruments were pre-tested in the second season of 2015/2016 and the main survey is to be conducted in 2016/2017. The household list was drawn up in October/November 2016 and the teams will start with field surveys in January 2017. SPIA was able to incorporate questions into the AAS for the following technologies: bean varieties; cassava varieties; maize varieties; sweet potato varieties; sorghum varieties; agroforestry; livestock; and conservation agriculture.

In Uganda, the fourth wave of the Integrated Household Survey (the true LSMS-ISA panel survey) has been delayed and is currently expected to be launched around March 2017. Relevant questions on agricultural technology will be incorporated into this survey.


In Malawi, the Integrated Household Survey 4 (IHS4) is in the field over late 2016 and will end in April 2017. The IHS4 is a new, LSMS-ISA supported, nationally representative survey of some 12,200 households. SPIA, with input from the FAO Economics and Policy Innovations for Climate-Smart Agriculture (EPIC) team, have introduced questions on a number of natural resource management (NRM) practices in the survey instrument, relating to inter-cropping, cover crops and crop residue management, agroforestry, crop rotation, animal husbandry etc., in addition to crop varietal identification. SPIA helped in training enumerators with the Malawian National Statistics Office. Training began in February 2016, and fieldwork started in late March 2016.



  • Kosmowski, et al. 2016. Varietal identification in household surveys: results from an experiment using DNA fingerprinting of sweet potato leaves in southern Ethiopia. Policy Research working paper; no. WPS 7812. Washington, D.C.: World Bank Group. External link to PDF.
  • Kosmowski, et al. 2016. On the ground or in the air? A methodological experiment on crop residue cover measurement in Ethiopia. Policy Research working paper; no. WPS 7813. Washington, D.C.: World Bank Group. External link to PDF