10 August 2022
The 1000FARMS Community
You can now be part of the 1000FARMS Community of Practice and meet other members, discuss events, share your ideas, make suggestions, find solutions to technical problems and connect with the people who make 1000FARMS possible. You can also discuss features on your ClimMob or tricot wish list and share ideas for improving existing features. No technical knowledge is needed! You can join here: https://community.1000farms.net/
Meet Dean Muungani and Hugo Dorado
Dean, a Zimbabwean, recently joined Wageningen University & Research as a guest PhD under the 1000FARMS project. He has been working as a Product Manager, Grain Crops at IITA since January 2021. Previously he worked as a Senior Maize Breeder and Product Development Manager for Seed Co Limited, an African seed company with presence in more than 16 African countries.
Hugo is a data scientist with more than 10 years of experience applying machine learning and statistical methods in the analysis of G × E × M patterns on tropical crop yields. He was part of the Alliance of Bioversity International and CIAT, where he led a team of data scientists, and he recently joined Wageningen University & Research as a PhD student.
Dean and Hugo will be part of the variety performance analysis activity.
Tricot data and the variety release process
Led by Anna Muller, the informing product management activity is geared toward putting all the conditions in place to ensure that the improved on-farm trial (OFT) data is parlayed into better release decisions. This is why during the past months, Anna and her team have reviewed the existing literature on variety release in Africa, and gained an overview of important regulatory and legal documents with regard to variety release. They have produced a table with key data for the release systems in the project target countries, as well as a document with background information for understanding the political economy of variety release systems in Africa.
“By talking to five selected experts with experience in using tricot or other OFT data in the variety release process, we gained a better understanding on opportunities and challenges for tricot data in the variety release process.” – Anna Muller.
How does tricot data integrate into the decision-making process of the crop-improvement cycle? By talking to experts (breeders, social scientists, data scientists, etc.) the team started to gain a deeper understanding of how this data is, or can be, integrated into decision making, and what challenges or opportunities could arise. They came to the conclusion that trust in the data coming from the tricot trial might be an issue among decision makers, and they have decided to focus on producing supporting information to communicate the data quality strategy in tricot and ClimMob; developing training materials in collaboration with the 1000FARMS course activity; and establishing a Community of Practice for the project. In the coming months, a document that will help communicate data quality in ClimMob will be available.
Standard operating procedures: developed, submitted and agreed
Generating on-farm trial standard operating procedures (SOPs) with farmer-led yield estimation that are applicable to all CGIAR mandate crops, for both farmer and consumer testing, is at the core of the data collection activity. During the past few months, developing the SOPs for maize, groundnut, potato, sorghum, beans, cassava and cowpea has been a priority. With all of these being submitted to the various breeding teams, partners and collaborators for feedback, and with three of them – for maize, bean and cassava – being agreed upon, good progress has been made so far. A draft document has also been developed that accompanies the SOPs to serve as a reference for organizing, managing and establishing tricot-based trials. This document includes (among others) information on farmer sampling, logistics, distribution of packages and institutional requirements. The testing of the SOPs for maize and beans will begin imminently in Kenya and Tanzania, respectively. Feedback from these tests will be used to further improve the methodology for these and other grain crops.
