134
11.4 Contribution of the Tricot Approach
Our simulation exercises show that the tricot approach is statistically robust and
allows us to identify the varieties or portfolios of varieties that are preferred by
farmers in different environments. Each farm constitutes a mini-experiment in
which most of the conditions are not constant. The tricot approach does not try to
eliminate the variability between farmers’ management practices, soil types, seasons and preferences, but rather makes statistical use of such information to provide
recommendations that work in each place and are robust to climate risk.
The approach can also determine if varieties perform differently under different
environmental conditions. This has the potential to significantly contribute to the
improvement of seed systems by allowing the delivery of varieties based on seasonal climate forecasts or on prevailing conditions in different environments. When
working in a complex topography such as those found in Ethiopia, one can expect
important differences in conditions among villages, depending on altitude, rainfall
and other factors. The tricot approach can help to deliver the best seeds based on the
actual climatic conditions of a particular village.
The tricot approach also can cover a higher number of varieties than usual onfarm testing approaches. It can engage with a larger community of farmers than a
conventional participatory variety selection (PVS), and the larger number of farmers provides considerable statistical power, resulting in more data points. In addition, the tricot approach could be combined with genomic data to increase the
predictive power of the model (Jean-Luc Jannink, personal communication).
Lastly, even without determining absolute levels of yield or other variables, the
tricot approach can deliver variety recommendations for risk-reducing portfolios,
which adds another tool for climate adaptation. In the literature, limited applications
of crop variety portfolio design can be found, mainly for well-endowed production
environments in the US and Mexico (e.g., Nalley and Barkley 2010, among others).
Our simulation shows that it is possible in principle to generate crop variety portfolio recommendations for marginal environments through participatory trials at
scale.
11.5 Implications for Development
Under the current agricultural model, climate change will cause a reduction of
yields for many crops in many parts of Africa. Farmers in these environments need
accelerated seed-based innovation to cope with climate change. It seems logical,
therefore, to diversify in ways that will enhance productivity at any given locality by
quickly delivering varieties that are tested by the farmers. Such an approach will
significantly increase the adoption rate. As Ceccarelli (2015) has argued, success of
plant breeding should be measured based on the technologies that are adopted by
the farmers and not by the number of released varieties.
C. Fadda and J. Etten
11.4 Contribution of the Tricot Approach
Our simulation exercises show that the tricot approach is statistically robust and
allows us to identify the varieties or portfolios of varieties that are preferred by
farmers in different environments. Each farm constitutes a mini-experiment in
which most of the conditions are not constant. The tricot approach does not try to
eliminate the variability between farmers’ management practices, soil types, seasons and preferences, but rather makes statistical use of such information to provide
recommendations that work in each place and are robust to climate risk.
The approach can also determine if varieties perform differently under different
environmental conditions. This has the potential to significantly contribute to the
improvement of seed systems by allowing the delivery of varieties based on seasonal climate forecasts or on prevailing conditions in different environments. When
working in a complex topography such as those found in Ethiopia, one can expect
important differences in conditions among villages, depending on altitude, rainfall
and other factors. The tricot approach can help to deliver the best seeds based on the
actual climatic conditions of a particular village.
The tricot approach also can cover a higher number of varieties than usual onfarm testing approaches. It can engage with a larger community of farmers than a
conventional participatory variety selection (PVS), and the larger number of farmers provides considerable statistical power, resulting in more data points. In addition, the tricot approach could be combined with genomic data to increase the
predictive power of the model (Jean-Luc Jannink, personal communication).
Lastly, even without determining absolute levels of yield or other variables, the
tricot approach can deliver variety recommendations for risk-reducing portfolios,
which adds another tool for climate adaptation. In the literature, limited applications
of crop variety portfolio design can be found, mainly for well-endowed production
environments in the US and Mexico (e.g., Nalley and Barkley 2010, among others).
Our simulation shows that it is possible in principle to generate crop variety portfolio recommendations for marginal environments through participatory trials at
scale.
11.5 Implications for Development
Under the current agricultural model, climate change will cause a reduction of
yields for many crops in many parts of Africa. Farmers in these environments need
accelerated seed-based innovation to cope with climate change. It seems logical,
therefore, to diversify in ways that will enhance productivity at any given locality by
quickly delivering varieties that are tested by the farmers. Such an approach will
significantly increase the adoption rate. As Ceccarelli (2015) has argued, success of
plant breeding should be measured based on the technologies that are adopted by
the farmers and not by the number of released varieties.
C. Fadda and J. Etten