SPIA activities that seek to develop a robust set of methods to routinely track adoption of CGIAR technologies in a cost-effective manner. Such information is a prerequisite for high quality outcome and impact assessments. Some of these activities are an attempt to fulfil Objective 1 of SIAC.
Managed by researchers from the Michigan State University (MSU), this SIAC Objective 1 activity aims to pilot test and validate alternate approaches to collect variety-specific adoption data against a reliable benchmark (in this instance, 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. Three studies have been initiated and are at different stages of implementation: cassava in Ghana, maize in Uganda; and beans in East/Southern Africa.
This two-stage call announced in August 2013 sought to identify organizations and individuals to undertake an activity or a set of activities to building capacity within CGIAR, both at Center and CRP levels, for conducting highly credible ex post impact assessments. Over 2014 and 2015, Virginia Tech (George Norton and Jeff Alwang) will work in collaboration with CIP and CIFOR to build IA capacity, including complete four pilot IA studies.
As a part of SIAC Objective 4 to strengthen impact assessment (IA) capacity, this call for one-page proposals aimed to support a variety of IA activities by CGIAR scientists and CRP partners through a simple, fast approval process. Four proposals from CIMMYT, ILRI, IWMI and Bioversity were funded between October 2013 and March 2014, and the call is now closed.
SPIA is 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 is partnering with the World Bank Living Standards Measurement Survey (LSMS) – Integrated Surveys of Agriculture (LSA) in some of the eight LSMS-ISA Sub-Saharan Africa countries to develop, test and facilitate the introduction of new questions and data collection protocols between 2014 and 2016.
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. As part of SIAC Objective 1, we will explore new alternatives for outsourcing the collection of data on a routine basis that will allow the CG system to track the adoption of major agricultural technologies in developing countries.
The goal of this study was to assess how technical change in agriculture may have differential effects on indicators of well being, including poverty levels, hunger and food security, and nutrition. The intention was to apply a number of advances in empirical economic work over the ten preceding years to this complex technology-poverty-food security issue. Through a competitive call for proposals in 2011, a number of studies to document this linkage were funded by SPIA. The study authors, reviewers and other experts were also brought together at a July 2014 workshop to discuss methodological and data measurement challenges, and lessons for future studies. Final publications are available.
Previous estimates of natural resource management (NRM) technologies have relied either on “expert opinion” or on elicited responses from farmers in farmer-level surveys. These two methods differ in cost of data collection and the accuracy of resulting estimates of adoption. In addition to the tradeoff between accuracy and cost, the nature of NRM technologies (location specificity, defining what constitutes adoption of a package of technologies etc.) adds to the challenges of documenting adoption. A two-stage call was issued in July 2013 to identify other methodologies including remote sensing, and mobile phones to track and document NRM technology adoption at broad geographical scale and test its effectiveness against a benchmark method.
This review was commissioned by SPIA as a part of its mandate to capitalize on newly available data and methods and thereby conduct rigorous assessments on the ways in which agriculture can affect various indicators of well being, which include poverty, hunger, and food security. It aimed to (1) review and provide critical evaluation of previous empirical ex post impact assessments within the CGIAR; and (2) suggest options that could be used by the CGIAR in ex post identification of the poverty impacts of technological change and the pathways involved in these impacts.