Studentinformatie

MSc thesis subject: Spatial analysis and visualization of triadic crop variety trials in Central America

Adapting food systems to future climates needs quick crop varietal replacement informed by climatic information. Large-N on-farm trials in which triplets of new crop varieties are being tested under different environmental conditions have proven to be a valuable information source. However, owing to methodological complexity, the experimental approach has not been used to its full potential. The objectives of this thesis topic is to enhance the analysis of data from the on-farm trials in combination with improved environmental data and/or to facilitate interpretation and decision making by improved reporting and visualization of the analysis results.

So-called large-N on-farm trials involve large groups of farmers who test and disseminate new varieties on their farms. The approach takes advantage of variation between heterogeneous farms under diverse environmental conditions to determine varietal performance under varying environmental conditions. The methods extract much more information about environmental adaptation than conventional participatory methods and allow more farmers to engage with the technology testing process. Bioversity has developed innovative methods to make this possible through “triadic comparisons of technologies” (tricot). Accordingly, farmers test three crop varieties on their farms and assign relative scores to these. The full experiment involves partitioning several varieties in triplets over many farms covering a wide range of environmental conditions. Environment-specific scores for the full set of tested varieties are obtained by paired comparisons methods, such as (extensions of) the Bradley-Terry model and the Plackett-Luce model while the environmental interactions are currently accounted for by model based recursive partitioning using environmental covariates.

Objectives:

  • Enhance analysis of data from the on-farm trials in combination with environmental data.
  • Facilitate interpretation and decision making with appropriate reporting and visualization methods.

Literature

  • van Etten, J., E. Beza, L. Calderer, K van Duijvendijk, C. Fadda, B. Fantahun, Y.G. Kidane, J. van de Gevel, A. Gupta, D.K. Mengistu, D. Kiambi, P. Mathur, L. Mercado, S. Mittra, M. Mollel, J.C. Rosas, J. Steinke, J.G. Suchini, K. Zimmerer., 2017. First experiences with a novel farmer citizen science approach: Crowdsourcing participatory variety selection through on-farm triadic comparisons of technologies (tricot). Experimental Agriculture, Online.
  • Coe, 2002. Analyzing ranking and rating data from participatory on-farm trials. In: Bellon, M.R. and Reeves, J. (eds). Quantitative analysis of data from participatory methods in plant breeding. CIMMYT, Mexico, pp. 44-65.

Requirements

  • Strong analytical skills
  • Scripting skills

Theme(s): Modelling & visualisation, Empowering & engaging communities