SIAC mid-term workshops are an attempt to stock take funded studies, and through discussions provide feedback on analysis approach and preliminary results. The 30th July workshop focuses on the seven (7) studies funded under SIAC 3.1 – these are a rather diverse set of studies, some quite macro in nature, that assess the adoption and impact of a number of technologies that have apparently spread widely. Description of these studies (including the CGIAR innovation under study), early results and snapshot of discussions follow.
C88, for instance, which is a late blight resistant variety has been claimed by CIP as one of its most successful varieties. Considering the extension efforts in China to promote potatoes – the study focuses on Yunnan province which accounts for 10% of the Chinese potato production – and, expert estimate that 33% of potato varieties in China can be traced to CIP germplasm, this study carefully examines the adoption (including through DNA fingerprinting), the determinants of adoption, and consumer/producer surplus through household and community surveys. Data from another SIAC activity suggests that C88 is an important crop in the (main) early spring season (around 16% of all cultivated varieties, 400K ha), and a significant winter crop variety (around 50%, 60K ha). So, what is the story of C88 as revealed by this study (so far)?
The focus of DNA fingerprinting (leaf or tuber samples, SSR marker) was not to identify the range of potato varieties – it was to confirm that the potatoes grown by households that self-identified the variety as C88 was indeed C88. 137 of the 141 fresh samples were confirmed to be C88 suggesting that C88 self-identification by farmers is not an issue. What we don’t know yet is the varietal identity of potatoes in households that do not self-report C88 – are they growing C88 and are we underestimating C88 diffusion in Yunnan? What are the varieties that C88 has not replaced or have that replaced C88 following dis-adoption? There are also questions about the dynamics of adoption over time: for instance, farmers recycle seeds and seed degradation could be an issue. While preliminary analysis suggests that current disease pressure and adoption is related, farmers who value blight resistance are less likely to continue growing C88 over time – plausibly suggesting that farmers are constantly looking for resistant varieties and dis-adopt C88 over time as seed degeneration occurs. Seed degeneration might also account for up to 25% of yield loss. Location is also found to be critical for adoption: farmers close to urban areas are likely to have grown C88 at some point in the past, but much less likely to grow it now. There are also some interesting issues raised by value chain providers – chip manufacturers prefer C88 because of its quality, but are forced to source other varieties from other provinces because high quality C88 potatoes are not available.
What’s next? The researchers will model economic surplus – look at both fresh and processed market (exports). The impact analysis will need to consider village fixed efforts considering the adoption varies much more between villages than within.
The other direct crop adoption related story is that of cassava in Nigeria – this is an adoption and impact study based on data from 2500 randomly selected households across four (4) regions of Nigeria, with DNA fingerprinting again used to validate self-reported varietal identification. Cassava happens to be a crop for which phenotypic identification is not reliable – that is a clear lesson from SIAC work. Looking at relatively downstream impact indicators like yield is also complicated because of the nature of production/consumption – it is a continuously harvested crop and is inter-cropped. Farmers also grow multiple varieties of cassava in the same plot. (You might be interested in an experiment run as a part of LSMS-ISA in Nigeria where they compare various approaches to measuring yield – including, one year recall, two visits and 6-month recall, diaries maintained by households).
Preliminary results suggest that farmers are over-estimating improved varietal adoption: 40% versus the 20% (IV) or 30% (IV+landrace selections) estimate arrived at from two approaches to DNA fingerprinting. An interesting story here is that of slightly selected landraces – the fact that farmers seem to prefer this over improved varieties. While not directly comparable, data from the DIIVA project (expert estimates) suggested that around 46% of the cassava area in Nigeria was improved varieties (compared to 19% in 1998 and around 3 million hectares in 1998). As an aside, data on plot area estimates – self-reported, and then validated through GPS readings – shows a surprising lack of correlation: farmers systematically tend to overestimate small plot sizes, and underestimate large plots. There may be multiple explanation of this divergence. For instance, this may simply be down to measurement – whether surveyors are matching plots self-reported and GPS correctly, are GPS meters are unable to calibrate correctly for very small plots etc.? Any other ideas on what else could explain this?
The study will continue to look into the effect of adoption on yields, incomes and poverty as well as arrive at an estimate of improved cassava varieties on poverty reduction in Nigeria.
Watch this space for more discussions and results from other studies in the workshop.