Breeding planet-friendly cattle
Agriculture produces a large share of greenhouse gas emissions, with the methane produced by cattle the worst offender. A canny use of data and genetics could help create a new generation of green cows.
When it comes to climate change, we hear a lot about carbon dioxide, or CO2, but not so much about CH4 - methane.
However, with estimates that methane from livestock amounts to 14.5 per cent of all global greenhouse gas emissions, the issue is moving rapidly up the agenda - especially after the UN Intergovernmental Panel on Climate Change report was published in August 2021.
The report said methane accounted for about 30 per cent of global warming since the pre-industrial era and that the growth in atmospheric concentration of methane had accelerated in recent years, largely driven by emissions from the fossil fuels and agriculture sectors, the latter dominated by livestock.
Methane emissions from agriculture have increased gradually since 1990 due to an increase in global total livestock numbers. So what can be done?
The holy grail of research in this area is to marry environmental and economic benefits - simultaneously reducing methane emissions and improving cattle feed efficiency.
This is the goal of Alireza Ehsani, a Marie Curie Fellow whose research work is supported by the European Union Horizon 2020 fund, the Data-Driven Innovation initiative in Edinburgh and Norway-based global cattle breeding association Geno.
Less feed, less methane, more milk
Ehsani believes it will be possible to use wide-ranging data - and genetics - to breed cattle that produce less methane and utilize feed more efficiently - eating less, emitting less and producing more milk.
His partnership with Geno, a large breeding association for Norwegian Red cattle, means the research will cover half a million animals, giving it both scale and industry credibility.
Covid-19 restrictions have limited Ehsani’s ability to travel to Norway, but he has weekly meetings with Geno to share ideas and findings, challenges, and new information.
One challenge is that methane is a relatively new focus for cattle breeders’ research.
“Methane emissions were not high up in the breeding goals, but when research says 14 per cent of the global warming and greenhouse gas production is coming from livestock, it is becoming more important,” Ehsani says.
“If the animals eat less and produce more, that is feed efficiency. This aspect is very important because feeding cattle is about 50 per cent of the entire cost of keeping a herd. And we are seeing a link between feed efficiency and producing less methane. Because the cattle are consuming less food, they are ruminating less - which means less methane emissions.”
More than 90 per cent of the methane from cows is produced by microbes in the rumen – the largest stomach compartment in cattle - which comes out mainly from the mouth. Some microbes produce less methane than others and there is also variation in the animals themselves, making this a fruitful area of study.
Ehsani’s three-year research project, which began in April 2020, is split into three broad areas: the collection and assessment of data from Geno; modelling and simulation using a wide range of variables to find out what combination produces the most efficient cattle in terms of reduced methane emissions and greater feed efficiency; and deploy the proposed data system in the breeding programme to breed future generations of more resource-efficient cattle.
This area of study is growing, but Ehsani – who is based at the University’s Roslin Institute - says his collaboration with Geno stands out because of its scale.
“There are lots of studies looking at information from 500 or 1,000 cattle, but that’s not large enough to make a significant difference in the real world of dairy cattle breeding,” he says. “Having a partner like Geno allows me to simulate very large numbers of a real population of Norwegian reds, using the age of the cows, number in each herd, and their performance - across the whole breeding cycle. In my simulation, there will be about 500,000 animals every year and I am running simulations for multiple years.”
Data plays a key role in the project, especially in getting detailed and accurate measurements of feed intake.
“Feed represents more than 50 per cent of the whole cost in dairy farming, but feed intake is very difficult and costly to measure in cattle,” says Ehsani. “If we want to select the best animals, we must measure one by one how much feed is consumed and how much milk is produced from this feed.”
Ehsani says technology helps, by supporting the collection of more accurate and relevant data.
“Automatic feeders can measure the feed intake of an animal - so technology helps us and reduces the cost of measurements. And now we have access to the genomes of those animals, we can combine that with the feed intake to make the selection of the best animals more accurate. Furthermore, we can then deploy associations between feed intake and genome information to all animals with genome information, without measuring feed intake in them.
“In all aspects, my project uses data-driven innovations, from collecting data by robots to statistical modelling, and analyzing and simulating the data on the computer.”
Ehsani says there has been scepticism that such research can deliver both reduced methane emissions and improved feed efficiency. But he believes the scale of the trial in Geno’s real-world population can win over the doubters.
“The very important thing to win trust in both the statistical modelling and animal breeding is the number of observations you have. If you collect a huge number of observations, like in this study, the results will be useful.”
Ehsani says this kind of research has been helped massively by the advances in genetics.
“We can now genotype animals, so based on the genome, we can select the very best individual - for feed efficiency and methane reduction - and then mate them together to produce good offspring,” he says.
“The important thing about genetics is that it is permanent and cumulative. If you do something with genetics, you do it forever. If you do something with the feed, you must do it every day.
“Because my work is going to be applied in a real breeding programme, we must be cautious in every bit of it. We are doing it in the best way, because when we apply this in the real breeding programme, we can make mistakes - and we have to avoid this.”
Ehsani says the research can also help show policy-makers the value of working on animal genetics to help with global warming. As well as benefiting cattle breeding, he adds, the research could be used in other animals.
“It can easily be applied in any kind of animal because we are simulating a real-world population and applying a huge number of different scenarios. So, you can make small changes and apply it to other species.”
Håvard Melbo Tajet, Chief Technology & Innovation Officer for Geno, says: “Geno already records methane in Norwegian Red dairy cattle, and is in the process of establishing recordings of accurate feed intake. We are very interested to see the relationship between feed efficiency and climate footprint and hope to develop the Norwegian Red population further for improved sustainability, combining reduced feed cost with lower methane emissions.
“Feed recording equipment is expensive and the collaboration with The University of Edinburgh allows us to optimise the return on investment, using advanced simulation techniques. These techniques offer the opportunity to optimise all types of breeding activities to achieve the highest possible genetic gain.
“We are very pleased with the collaboration with the research team at the University of Edinburgh and the close relationship between Alireza Ehsani and one of Geno’s own young talents. This collaborative project is a major contributor to Geno’s strategy, where sustainability and profitability come together to further develop the Norwegian Red.”
Picture credit: close-up cow - JazzLove/Getty; Norwegian reds - Alireza Ehsani; cattle feed - Korneeva_Kristina/Getty; back end of cow - Digital Vision/Getty
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 801215.