4   SEEDWORLD.COM/CANADA   NOVEMBER 2026
FOR YEARS,  Canadian pulse breeders 
have watched genomic selection revolu­
tionize crops such as corn, soybeans and 
canola while wondering when the tech­
nology would become practical for peas, 
lentils and other smaller-acreage crops.
The answer, it turns out, isn't simply 
better science, but better collaboration.
A new national initiative involv­
ing Agriculture and Agri-Food Canada 
(AAFC), the Global Institute for Food 
Security (GIFS), the University of 
Saskatchewan's Crop Development 
Centre (CDC), private seed companies, 
Saskatchewan Pulse Growers (SPG) 
and Protein Industries Canada (PIC) is 
building a genomic selection platform 
designed to accelerate pea breeding. 
On its own, that’s an important 
development.
But viewed alongside two other 
recent announcements — a major expan­
sion of GIFS’ genomics capacity and a 
new producer-supported lentil breeding 
partnership at the CDC — it begins to 
look like something much larger.
Together, they suggest Canada isn't 
just adopting another breeding technol­
ogy, but building the ecosystem needed 
to support the next generation of pulse 
breeding.
Beyond Better Genetics
Genomic selection uses DNA mark­
ers to predict which breeding lines are 
most likely to possess desirable traits 
long before years of field testing are 
CANADA’S PULSE BREEDING FUTURE 
DEPENDS ON MORE THAN BETTER GENETICS
Three major partnerships point to a bigger shift: Canada is rebuilding the infrastructure behind 
pulse breeding. By Marc Zienkiewicz, Senior Editor
complete. The technology allows 
breeders to make selection decisions 
earlier, shortening breeding cycles 
while improving the odds of finding 
superior varieties. 
The concept isn’t new. What’s new 
is making it work economically for 
pulse crops.
Building reliable prediction 
models requires enormous amounts of 
genomic and field-performance data 
collected across years and environ­
ments. Large global crops have gener­
ated those datasets through decades of 
investment. Pulse crops haven’t.
“Genomic prediction takes years to 
build,” says AAFC’s national research 
director Francois Eudes. “It requires 
massive amounts of data. Working on 
a single breeding program with limited 
replicated sites makes it very difficult 
to achieve the quality or volume of 
datasets required to have a very good 
predictive model.”
That’s why the project brings 
together organizations that would nor­
mally operate independently. Rather 
than each attempting to build its own 
prediction system, collaborators are 
pooling data and expertise to create 
a shared platform before competing 
through their own breeding programs. 
“The opportunity to bring those 
new tools that we use elsewhere into 
a crop like peas is a great opportunity 
for us to maintain the competitiveness 
of the crop,” Eudes says. 
Building Infrastructure, 
Not Just Varieties
The project is as much about infra­
structure as it is about genetics. Private 
companies see immediate value because 
genomic prediction has become too 
expensive for individual organizations to 
build alone.
“For us to try and do this work on 
our own probably just wouldn’t be fea­
sible,” says DL Seeds General Manager 
Chris Anderson. “Just the upfront invest­
ment alone means doing it ourselves 
would be difficult, to say the least.”
That philosophy is extending beyond 
the genomic selection project itself.
GIFS recently announced a 
$1.63-million investment from 
PrairiesCan to install an Illumina 
NovaSeq X Plus sequencing platform, 
significantly expanding its genomic 
sequencing capacity. Rather than serving 
a single breeding program, the platform 
is intended to support researchers and 
breeding organizations across Western 
Canada by producing genomic informa­
tion faster and at greater scale.
Seen together, the two initiatives 
point toward a different approach to 
agricultural innovation. Instead of 
investing exclusively in individual breed­
ing programs, governments and indus­
try are increasingly investing in shared 
infrastructure that allows many breeding 
organizations to innovate more quickly.
The predictive models, sequencing 
capacity and datasets become common 

View this content as a flipbook by clicking here.