Tag: software

Kate Dreher, Data Manager at CIMMYT, presents to scientists, technicians, data management and support teams during the training on the Enterprise Breeding System (EBS) in Nairobi, Kenya. (Photo: Susan Umazi Otieno/CIMMYT)

Researchers in East Africa add the Enterprise Breeding System to their work tools

Written by Susan Otieno on June 8, 2022. Posted in News.

Scientists overseeing breeding, principal technicians and data management and support staff from the International Maize and Wheat Improvement Center (CIMMYT) learned about the Enterprise Breeding System (EBS) at a training in Nairobi, Kenya, on May 4–6, 2022. This was the first in-person training on this advanced tool held in Eastern Africa.

Kate Dreher, Data Manager at CIMMYT, was the primary trainer. Dreher sought to ensure that scientists and their teams are well equipped to confidently use the EBS for their programs, including the creation and management of trials and nurseries. During the training, participants had the opportunity to test, review and give feedback on the system.

“The EBS is an online comprehensive system that brings together different types of data, including field observations and genotypic data, to harmonize processes across all teams and enable optimized decision-making in the short term and continuous learning for the long term,” Dreher said.

She explained that the EBS is more efficient than the former approach of using the Excel-based Maize Fieldbook software, even though it managed several useful processes.

The EBS is currently available to registered breeding and support team members and data managers from CIMMYT, IITA, IRRI and AfricaRice, across all geographies where related programs are implemented. Currently, the EBS is used by programs in maize, rice and wheat crops.

A more streamlined approach

“Although teams sent germplasm and phenotypic data for centralized storage in two databases (IMIS-GMS and MaizeFinder) managed by the data management team in Mexico in the past, this required curation after the data had already been generated,” Dreher said. “The EBS will enable teams to manage their germplasm and trial nursery data directly within one system.”

The EBS stores information on germplasm and linked seed inventory items. It is also designed to house and perform analyses using phenotypic and genotypic data. Users can also capture metadata about their trials and nurseries, such as basic agronomic management information and the GPS coordinates of sites where experiments are conducted.

Yoseph Beyene, Regional Maize Breeding Coordinator for Africa and Maize Breeder for Eastern Africa at CIMMYT, observed that the training gave him firsthand information on the current capabilities and use of the live version to search germplasm and seed, and the capabilities to create nurseries and trials.

“In the AGG project, we have one primary objective which focuses on implementing improved data management, experimental designs and breeding methods to accelerate genetic gain and improved breeding efficiency. Therefore, implementing EBS is one of the top priorities for AGG project,” said Yoseph, who leads the Accelerating Genetic Gains in Maize and Wheat for Improved Livelihoods Project (AGG).

Lourine Bii, an Assistant Research Associate who recently joined CIMMYT and the only female research technician on the Global Maize program based in Kenya, also found the training useful. “The EBS is a fantastic system that enables an individual to create experiments. The system links a team, for instance a product development team, to get live updates on the various stages of creating an experiment, reducing back and forth by email.”

The system’s software development is ongoing. The development team continues to add and enhance features based on feedback from users.

A CIMMYT technician cuts a leaf sample for DNA extraction. (Photo: CIMMYT)

Breeders take quantum leap

Written by Rodrigo Ordóñez on February 19, 2021. Posted in News.

Wheat breeders from across the globe took a big step towards modernizing their molecular breeding skills at a recent workshop sponsored by the Wheat Initiative, with the CGIAR Excellence in Breeding Platform (EiB) and the International Maize and Wheat Improvement Center (CIMMYT).

The workshop focused on three open-source tools used in molecular breeding: GOBii-GDM for genomic data management, Flapjack for data visualization and breeding analysis, and Galaxy for Genomic Selection. These tools help breeders make selections more quickly and precisely, and ultimately lead to more cost effective and efficient improvement of varieties.

The Wheat Initiative — a global scientific collaboration whose goals are to create improved wheat varieties and disseminate better agronomic practices worldwide — and its Breeding Methods and Strategies expert working group had planned to host these trainings during the 2020 Borlaug Global Rust Initiative Technical Workshop in the United Kingdom. After it became obvious that in-person trainings were not possible, the course organizers — including CIMMYT molecular wheat breeder Susanne Dreisigacker and EiB Adoption Lead and former GOBii project director Elizabeth Jones — decided to come together to host online workshops.

Many of the tools will be incorporated into EiB’s Enterprise Breeding System (EBS), a new integrated data management system being developed for CGIAR breeders. Jones plans to also design training modules for these molecular breeding tools that will be accessible to anyone through the EiB Toolbox.

In the meantime, the tools used in the workshop are all freely available: DArTView, Flapjack (training videos), GOBii-GDM (request access through the web form or by email), and Galaxy Genomic Selection.

The first session of the workshop “Transforming Wheat Breeding Through Integrated Data Management with GOBii and Analysis in Flapjack” benefited breeders from Australia, Canada, Ethiopia, France, India, Ireland, Italy, Morocco, Pakistan, Switzerland, Tunisia, the United Kingdom and the United States.

Susanne Dreisigacker presents during one of the sessions of the workshop.

Powering data analysis around the world

The workshop series, “Transforming Wheat Breeding Through Integrated Data Management with GOBii and Analysis in Flapjack,” aimed to benefit breeders from wheat producing countries all over the world, with sessions over two different time zones spread out over three days to reduce “Zoom fatigue.” Participants joined the first session from Australia, Canada, Ethiopia, France, India, Ireland, Italy, Morocco, Pakistan, Switzerland, Tunisia, the United Kingdom and the United States.

“It was wonderful to see the diversity of participants that we were able to train through an online workshop, many of whom otherwise might not have been able to travel to the UK for the original meeting,” said Jones. “Participants were very engaged, making the workshop so rewarding.”

The workshop was guided by Teresa Saavedra, Wheat Initiative coordinator. Apart from Dreisigacker and Jones, other trainers explained specific tools and approaches. Iain Milne from the James Hutton Institute in Scotland gave more details about the Flapjack genotyping visualization tool, which includes analysis for pedigree verification, marker assisted backcrossing and forward breeding. Andrew Kowalczyk, developer at Diversity Arrays Technology, spoke about the genotyping data QC tool DArTView.

A CIMMYT technician performs one of the steps to extract DNA samples from plants. (Photo: CIMMYT)

Clay Sneller, wheat breeder at Ohio State University, contributed training materials for important molecular breeding tools. Carlos Ignacio, previously based at the International Rice Research Center (IRRI) and now working on a PhD in Genomic Selection at Ohio State University, contributed his experience as a GOBii team member and a major contributor towards the design of Flapjack tools. Star Gao, application specialist with GOBii and now a requirements analyst for the Enterprise Breeding System, also facilitated the sessions.

Gilles Charmet, research director at the France’s National Research Institute for Agriculture, Food and Environment (INRAE), introduced the sessions in the Americas/Europe time zone with welcome remarks and overview of the goals of the Wheat Initiative. Alison Bentley, director of the CIMMYT Global Wheat Program, briefed on the achievements and goals of the CIMMYT Wheat program and the Accelerating Genetic Gains in Maize and Wheat for Improved Livelihoods (AGG) project.

“This training will contribute towards us reaching our AGG goals of accelerating gains in wheat, by sharing technical knowledge, and allowing our beneficiary partners to have state-of-the-art know-how in the use of genetic and genomic data,” Bentley said.

Participant Stéphane Boury from Caussade Semences, France commented, “This was a very effective way to learn about new tools in wheat breeding.”

The sessions continue in Australasia next week, and will be introduced by Peter Langridge, chair of the Scientific Board for the Wheat Initiative, and EiB director Michael Quinn. Sanjay Kumar Singh, incoming chair of the Breeding expert working group for the Wheat Initiative, will close the event.

Members of the Enterprise Breeding System advisory committee listen to a presentation from Tom Hagen. (Photo: Alfonso Cortés/CIMMYT)

Development of the Enterprise Breeding System well underway

Written by Sam Storr on January 29, 2020. Posted in News.

Members of the Enterprise Breeding System (EBS) advisory committee met on January 17-18, 2019, to review progress on the development of a full-spectrum breeding data management software.

CGIAR plant breeders currently rely on a suite of different software projects to make use of the data that is crucial to developing better varieties. Developed under the CGIAR Excellence in Breeding Platform (EiB), the EBS aims to provide a single solution that links data across new and existing applications so that the entire breeding data workflow — from experiment creation to analytics — can be accessed from a single user-friendly dashboard.

Development of the system is well underway, with the goal of providing a “minimum viable implementation” to pilot users at the International Maize and Wheat Improvement Center (CIMMYT) and the International Rice Research Institute (IRRI) in 2020. More advanced functions, institutions and crops will be added to the EBS over the next three years.

Working between breeders and developers to ensure needs are translated into software functions, the EBS team has trained CIMMYT staff and consultants as requirements analysts, five of whom presented to members of the EBS advisory committee the meeting on progress in the five “domains” of breeding software functions.

Sharing bits and bytes

Rosemary Shresthra introduced experiment creation, where users can quickly select the type of experiment they wish to run and automatically set up all the steps needed to complete it in the EBS.

Kate Dreher took the attendees through field implementation, where it is possible to map fields in the system and connect them to a range of plot data collection tools developed by external projects.

Ricardo León outlined the germplasm management component of the system, where the seed inventory is kept, and new entries made after trials are harvested to go on to the next stage.

Pedro Medeiros explained how an analytics request manager will allow EBS users to push their data to different analytics tools that support decision-making and, ultimately, their ability to deliver better varieties that meet farmers’ needs.

Finally, Star Gao, a breeding informatics specialist for the Genomic and Open-Source Breeding Informatics Initiative (GOBii), showed how users will be able to request phytosanitary, genotypic and quality analysis of samples from their trials through the EBS system. The system will provide an overview of the status of all samples submitted for analysis with different service providers, in addition to the ability to connect with various databases.

“We can do all this because all information in the EBS is treated the same way, from experiment creation through implementation,” said EBS coordinator Tom Hagen in summary.

The EBS advisory group, which includes user representatives from CIMMYT and IRRI breeding teams alongside EiB staff, ended the day by discussing and prioritizing new functions that could be added to the EBS over the next three years.

Behind the scenes, different applications are replicated in a single software solution, the Enterprise Breeding System. (Photo: CIMMYT)

First steps taken to unify breeding software

Written by Sam Storr on November 21, 2019. Posted in News.

Participants of the EBS DevOps Hackathon stand for a group photo at CIMMYT's global headquarters in Texcoco, Mexico. (Photo: Eleusis Llanderal Arango/CIMMYT) — Participants of the EBS DevOps Hackathon stand for a group photo at CIMMYT’s global headquarters in Texcoco, Mexico. (Photo: Eleusis Llanderal Arango/CIMMYT)

From October 21 to November 1, 2019, software developers and administrators from several breeding software projects met at the global headquarters of the International Maize and Wheat Improvement Center (CIMMYT) in Mexico to work on delivering an integrated solution to crop breeders.

Efforts to improve crop breeding for lower- and middle-income countries involves delivering better varieties to farmers faster and for less cost. These efforts rely on a mastery of data and technology throughout the breeding process.

To realize this potential, the CGIAR Excellence in Breeding Platform (EiB) is developing an Enterprise Breeding System (EBS) as a single solution for breeders. EBS will integrate the disparate software projects developed by different institutions over the years. This will free breeders from the onerous task of managing their data through different apps and allow them to rapidly optimize their breeding schemes based on sound data and advanced analytics.

“None of us can do everything,” said Tom Hagen, CIMMYT-EiB breeding software product manager, “so what breeding programs are experiencing is in fact fragmented IT. How do we come together as IT experts to create a system through our collective efforts?”

For the EBS to succeed, it is essential that the system is both low-cost and easy to deploy. “The cost of the operating environment is absolutely key,” said Jens Riis-Jacobson, international systems and IT director at CIMMYT. “We are trying to serve developing country institutions that have very little hard currency to pay for breeding program operations.”

Stacked software

During the hackathon, twelve experts from software projects across CGIAR and public sector institutions used a technology called Docker to automatically stack the latest versions of their applications into a single configuration file. This file can be loaded into any operating environment in less than four minutes — whether it be a laptop, local server or in the cloud. Quickly loading the complete system into a cloud environment means EBS can eventually be available as a one-click, Software-as-a-Service solution. This means that institutions will not need sophisticated IT infrastructure or support staff to maintain the software.

“If everything goes as planned, the end users won’t know that we exist,” said Peter Selby, coordinator of the Breeding API (BrAPI) project, an online collective working on a common language for breeding applications to communicate with each other. Updates to individual apps will be automatically loaded, tested and pushed out to users.

As well as the benefits to breeders, this automated deployment pipeline should also result in better software. “We have too little time for development because we spend too much time in deployment and testing,” said Riis-Jacobson.

A cross-institution DevOps culture

Though important technical obstacles were overcome, the cultural aspect was perhaps the most significant outcome of the hackathon. The participants found that they shared the same goals, language and were able to define the common operating environment for their apps to work together in.

“It’s really important to keep the collaboration open,” said Roy Petrie, DevOps engineer at the Genomic and Open-Source Breeding Informatics Initiative (GOBii) based at the Boyce Thompson Institute, Cornell University. “Having a communications platform was the first thing.”

In the future, this could mean that teams synchronize their development timeline to consistently release updates with new versions of the EBS, suggested Franjel Consolacion, systems admin at CIMMYT.

“They are the next generation,” remarked Hagen. “This is the first time that this has happened in CGIAR informatics and it validated a key aspect of our strategy: that we can work together to assemble parts of a system and then deploy it as needed to different institutions.”

By early 2020, selected CIMMYT and International Rice Research Institute (IRRI) breeding teams will have access to a “minimal viable implementation” of the EBS, in which they can conduct all basic breeding tasks through a simple user interface. More functionality, breeding programs and crops from other institutions including national agricultural research programs will be added in phases over three years.

Farmer Gudeye Leta harvests his local variety maize in Dalecho village, Gudeya Bila district, Ethiopia. (Photo: Peter Lowe/CIMMYT)

Reducing high yield gaps with decision-support apps

Written by Emma Orchardson on January 15, 2019. Posted in Features.

Ethiopia is Africa’s third largest producer of maize, after Nigeria and South Africa. Although the country produces around 6.5 million tons annually, the national average maize yield is relatively low at 3.5 tons compared to the attainable yield of 8.5 tons. This high yield gap — the difference between attainable and actual yields — can be attributed to a number of factors, including crop varieties used, farm management practices, and plant density.

The Taking Maize Agronomy to Scale (TAMASA) project aims to narrow maize yield gaps in Ethiopia, Nigeria and Tanzania through the development and scaling out of decision-support tools, which provide site-specific recommendations based on information held in crop and soil databases collected from each country. These help farmers to make decisions based on more accurate variety and fertilizer recommendations, and can contribute to improving maize production and productivity.

One such tool is Nutrient Expert, a free, interactive computer-based application. It can rapidly provide nutrient recommendations for individual farmers’ fields in the absence of soil-testing data. The tool was developed by the International Plant Nutrition Institute in collaboration with the International Maize and Wheat Improvement Center (CIMMYT), the International Institute of Tropical Agriculture (IITA), and research and extension service providers.

In Ethiopia, regional fertilizer recommendations are widely used, but soil fertility management practices can vary greatly from village to village and even between individual farmers. This can make it difficult for farmers or extension agents to receive accurate information tailored specifically to their needs. Nutrient Expert fills this gap by incorporating information on available fertilizer blends and giving customized recommendations for individual fields or larger areas, using information on current farmer practices, field history and local conditions. It can also provide advice on improved crop management practices such as planting density and weeding, thereby helping farmers to maximize net returns on their investment in fertilizer.

Data calibration was based on the results of 700 multi-location nutrient omission trials conducted in major maize production areas in Ethiopia, Nigeria and Tanzania. These trials were designed as a diagnostic tool to establish which macro-nutrients are limiting maize growth and yield, and determine other possible constraints.

In Ethiopia, CIMMYT scientists working for the TAMASA project conducted nutrient omission trials on 88 farmer fields in Jimma, Bako and the Central Rift Valley in 2015 to produce a version of Nutrient Expert suitable for the country. Researchers trialed the app on six maize-belt districts in Oromia the following year, in which Nutrient Expert recommendations were compared with soil-test based and regional ones.

Researchers found that though the app recommended lower amounts of phosphorus and potassium fertilizer, overall maize yields were comparable to those in other test sites. In Ethiopia, this reduction in the use of NPK fertilizer resulted in an investment saving of roughly 80 dollars per hectare.

Results from Nutrient Expert trials in Ethiopia, Nigeria and Tanzania showed improved yields, fertilizer-use efficiency and increased profits, and the app has since been successfully adapted for use in developing fertilizer recommendations that address a wide variety of soil and climatic conditions in each of the target countries.

The World Bank’s 2016 Digital Dividends report states that we are currently “in the midst of the greatest information and communications revolution in human history.” This shifting digital landscape has significant implications for the ways in which stakeholders in the agricultural sector generate, access and use data. Amidst Africa’s burgeoning technology scene, CIMMYT’s TAMASA project demonstrates the transformative power of harnessing ICTs for agricultural development.

Learn more about different versions of Nutrient Expert and download the free software here.

TAMASA is a five-year project (2014-2019) funded by the Bill & Melinda Gates Foundation, seeking to improve productivity and profitability for small-scale maize farmers in Ethiopia, Nigeria and Tanzania. Read more about the project here.

View of the interface of the Maize-Seed-Area app on mobile phones and tablets. (Photo: CIMMYT)

Are advisory apps a solution for collecting Big Data?

Written by Rodrigo Ordóñez on October 2, 2018. Posted in Blogs.

Big Data is transforming the way scientists conduct agricultural research and helping smallholder farmers receive useful information in real time. Experts and partners of the CGIAR Platform for Big Data in Agriculture are meeting on October 3-5, 2018, in Nairobi, Kenya, to share their views on how to harness this data revolution for greater food and nutrition security.

Jordan Chamberlin, Spatial Economist at CIMMYT, will give his insights on best practices on electronic data capture on October 4, 2018.

NAIROBI (Kenya) — Agronomic researchers face several challenges and limitations related to data. To provide accurate predictions and useful advice to smallholder farmers, scientists need to collect many types of on-farm data; for example, field size, area devoted to each crop, inputs used, agronomic practices followed, incidence of pests and diseases, and yield.

These pieces of data are expensive to obtain by traditional survey methods, such as sending out enumerators to ask farmers a long list of questions. Available data is often restricted to a particular geographical area and may not capture key factors of production variability, like local soil characteristics, fertilizer timing or crop rotations.

As a result, such datasets cannot deliver yield predictions at scale, one of the main expectations of Big Data. Digital advisory apps may be part of the solution, as they use crowdsourcing to routinize data collection on key agronomic variables.

The Taking Maize Agronomy to Scale in Africa (TAMASA) project has been researching the use of mobile apps to provide site-specific agronomic advice to farmers through agro-dealers, extension workers and other service providers.

At CIMMYT, one of the research questions we were interested in was “Why are plant population densities in farmers fields usually well below recommended rates?” From surveys and yield estimates based on crop-cut samples at harvest in Ethiopia, Nigeria and Tanzania, we observed that yields were correlated with plant density.

What was making some farmers not use enough seeds for their fields? One possible reason could be that farmers may not know the size of their maize field. In other cases, farmers and agro-dealers may not know how many seeds are in one packet, as companies rarely indicate it and the weight of each seed variety is different. Or perhaps farmers may not know what plant population density is best to use. Seed packets sometimes suggest a sowing rate but this advice is rather generic and assumes that farmers apply recommended fertilizer rates. However, farmers’ field conditions differ, as does their capacity to invest in expensive fertilizers.

To help farmers overcome these challenges, we developed a simple app, Maize-Seed-Area. It enables farmers, agro-dealers and extension workers to measure the size of a maize field and to identify its key characteristics. Then, using that data, the app can generate advice on plant spacing and density, calculate how much seed to buy, and provide information on seed varieties available at markets nearby.

Maize-Seed-Area is developed using the Open Data Kit (ODK) format, which allows to collect data offline and to submit it when internet connection becomes available. In this case, the app is also used to deliver information to the end users.

Advisory apps usually require some input data from farmers, so advice can be tailored to their particular circumstances. For example, they might need to provide data on the slope of their field, previous crops or fertilizer use. Some additional information may be collected through the app, such as previous seed variety use. All this data entered by the user, which should be kept to a minimum, is routinely captured by the app and retrieved later.

Hello, Big Data!

As the app user community grows, datasets on farmer practices and outcomes grow as well. In this case, we can observe trends in real time, for instance on the popularity of different maize varieties.

In a pilot in western Kenya, in collaboration with Precision Agriculture for Development (PAD), some 100 agro-dealers and extension workers used the app to give advice to about 2,900 farmers. Most of the advice was on the amount of seed to buy for a given area and on the characteristics of different varieties.

Data showed that the previous year farmers grew a wide range of varieties, but that three of them were dominant: DK8031, Duma43 and WH505.

Preferred variety of maize for sample farmers in western Kenya (Bungoma, Busia, Kakamega and Siaya counties), February-March 2018.

A phone survey among some 300 of the farmers who received advice found that most of them anticipated to do things differently in the future, ranging from asking for advice again (37 percent), growing a different maize variety (31 percent), buying a different quantity of seed (19 percent), using different plant spacing (18 percent) or using more fertilizer (16 percent).

Most of the agro-dealers and extension workers have kept the app for future use.

The dataset was collected in a short period of time, just two months, and was available as soon as app users got online.

The Maize-Seed-Area pilot shows that advisory apps, when used widely, are a major source of new Big Data on agronomic practices and farmer preferences. They also help to make data collection easier and cheaper.

TAMASA is supported by the Bill and Melinda Gates Foundation and is implemented by the International Maize and Wheat Improvement Center (CIMMYT), the International Institute of Tropical Agriculture (IITA), the International Plant Nutrition Institute (IPNI) and Africa Soil Information Service (AfSIS).