129
approaches in crop improvement. The approach was designed to overcome a number of specific challenges in participatory crop improvement, including the need for
scaling, cost reduction, data standardisation, and taking into account heterogeneity
in environments and farmer preferences.
The tricot approach is especially suited for climate adaptation. Combining the
resulting geo-referenced variety evaluation data with environmental data and climatic data, the approach distinguishes different responses of crop varieties to seasonal climatic conditions. The data can then be translated into concrete variety
recommendations that reflect current farm conditions, stabilise yields, and track
climate change over time. We illustrate the approach with an example, using simulated (yet realistic) data.
11.2 Analyzing Data from On-Farm Trials Using the Tricot
Approach
We performed a series of simple simulations to illustrate the methods and results of
the tricot approach. In our simulation we use realistic data that mimic the data collected in a number of countries, including India, Nicaragua, Honduras and Ethiopia.
Farmers provide feedback based on different traits. These traits are selected together
with farmers and technical personnel in focus groups. The traits can include yield,
pest and disease resistance, phenological characteristics, plant vigour and more.
In the trials, each farmer receives three different varieties and is trained on how
to set up the experiment in terms of plot layout and management. They plant the
seeds, and, as the crop grows, they rank the varieties for each of the traits. Farmers
do not know the names of the varieties in their set, which are randomly allocated to
them from a larger portfolio, generally at least 10–20 per trial, at times previously
selected from a much larger set—up to 400 in a recent application in Ethiopia
(Mancini et al. 2017). Farmers are asked to fill out the forms during the season
based on the traits they are evaluating. At the end of the season, farmers complete
the forms by providing their assessments of productivity and the quality of the final
product, as well as an overall performance judgment (Steinke and Van Etten 2016;
Van Etten et al. 2016). The entire process is supported by the digital platform
ClimMob (http://climmob.net/), which takes the user through a structured process
of trial design, electronic data collection, analysis and automatic reporting.
The overall performance of crop varieties was analysed using Plackett-Luce
trees, using R software (Hothorn and Zeileis 2015; Zeileis et al. 2008; Turner et al.
2018). Publicly available soil and climate data can be linked to the trial dataset by
using location data (latitude and longitude) and the planting date of each farm. The
Plackett-Luce model can use these data to distinguish between groups of environments with different patterns of variety performance.
We simulated two examples. In each, 500 farmers ranked a set of 3 varieties
taken from a set of 20 varieties. Varieties are assigned in a randomised and balanced
11 Generating Farm-Validated Variety Recommendations for Climate Adaptation
approaches in crop improvement. The approach was designed to overcome a number of specific challenges in participatory crop improvement, including the need for
scaling, cost reduction, data standardisation, and taking into account heterogeneity
in environments and farmer preferences.
The tricot approach is especially suited for climate adaptation. Combining the
resulting geo-referenced variety evaluation data with environmental data and climatic data, the approach distinguishes different responses of crop varieties to seasonal climatic conditions. The data can then be translated into concrete variety
recommendations that reflect current farm conditions, stabilise yields, and track
climate change over time. We illustrate the approach with an example, using simulated (yet realistic) data.
11.2 Analyzing Data from On-Farm Trials Using the Tricot
Approach
We performed a series of simple simulations to illustrate the methods and results of
the tricot approach. In our simulation we use realistic data that mimic the data collected in a number of countries, including India, Nicaragua, Honduras and Ethiopia.
Farmers provide feedback based on different traits. These traits are selected together
with farmers and technical personnel in focus groups. The traits can include yield,
pest and disease resistance, phenological characteristics, plant vigour and more.
In the trials, each farmer receives three different varieties and is trained on how
to set up the experiment in terms of plot layout and management. They plant the
seeds, and, as the crop grows, they rank the varieties for each of the traits. Farmers
do not know the names of the varieties in their set, which are randomly allocated to
them from a larger portfolio, generally at least 10–20 per trial, at times previously
selected from a much larger set—up to 400 in a recent application in Ethiopia
(Mancini et al. 2017). Farmers are asked to fill out the forms during the season
based on the traits they are evaluating. At the end of the season, farmers complete
the forms by providing their assessments of productivity and the quality of the final
product, as well as an overall performance judgment (Steinke and Van Etten 2016;
Van Etten et al. 2016). The entire process is supported by the digital platform
ClimMob (http://climmob.net/), which takes the user through a structured process
of trial design, electronic data collection, analysis and automatic reporting.
The overall performance of crop varieties was analysed using Plackett-Luce
trees, using R software (Hothorn and Zeileis 2015; Zeileis et al. 2008; Turner et al.
2018). Publicly available soil and climate data can be linked to the trial dataset by
using location data (latitude and longitude) and the planting date of each farm. The
Plackett-Luce model can use these data to distinguish between groups of environments with different patterns of variety performance.
We simulated two examples. In each, 500 farmers ranked a set of 3 varieties
taken from a set of 20 varieties. Varieties are assigned in a randomised and balanced
11 Generating Farm-Validated Variety Recommendations for Climate Adaptation