Leaving Ricardo Energy & Environment after almost 15 years was a tough decision. I worked with a great team and I was involved with many fascinating and impactful projects relating to sustainable agriculture and specifically on monitoring and measuring greenhouse gas emissions.
The decision was driven by an increasing realisation that we can do so much more with information and data gathered on farm to measure the impacts of our agricultural systems, and importantly, develop strategies to reduce impacts and enhance profitability. My working assumption was that integrating data science, farming systems and sustainability expertise could improve our understanding of the situation through:
1. Baseline analysis
2. Strategy development
3. Scenario planning
All of these are desperately needed if we are to meet the climate-related targets, at the government, industry and business levels that have been set.
Industry and government commitments
In the past year we have seen more commitments to meet the climate crisis than in the last decade. New ‘net zero’ targets are being declared daily with Scope 3 emissions (those outside the direct control of the organisation making the commitment) being a focal point in food companies’ supply chains. Arguably these are the most challenging emissions to address due to the biological processes involved with production. These commitments build on the NFU 2040 Net Zero ambition which was announced by Minette Batters at the 2019 Oxford Farming Conference.
They also run in parallel with government net zero targets: Westminster and the Welsh government have a 2050 target, and the Scottish government is aiming for 2045.
Improving production efficiency is a key element of meeting targets as it will result in reductions in emissions intensity (CO2e/Kg product). Note that efficiency should not be confused with intensity and that efficiencies can be found in all production systems. What is important is being able to inform management decisions with the appropriate data – and that applies to every farming system. The other side of the net zero balance is creating removals of carbon dioxide. Again, creating a baseline of carbon stock in soil and above ground woody biomass (trees, hedges etc) is essential and the data that feeds this must be accurate and gathered in an appropriate way.
This table summarises some food companies and retailers’ commitments to reduce their own (Scope 1) or their farming suppliers’ (Scope 3) emissions.
Source: Map of Ag research
Experience with Map of Ag
I am very pleased that my reasoning for joining Map of Ag has proven well placed. There is so much value in improving the methods for data collection, organisation, and use. Specifically, to GHG emissions, I knew that the Map of Ag approaches for collecting data could reduce administrative burdens on farmers by minimising duplication in data collection, but the most valuable benefit that I have found has been in improving the accuracy of the data collected, which has considerably improved the level of analysis and recommendations that are made as a result.
With every conference, webinar or farmer group meeting I have been involved in the past few months, the same questions about what ‘tool’ should be used arise in the Q&A and Chat functions of Zoom and Teams. Farmers are looking for appropriate means to measure the baseline. I have some experience of greenhouse gas (GHG) emissions measurement through farm scale ‘tools’ and national inventories and I have found that while the methodological approaches meet international standards, and generally provide a useful indicator for emissions, they do not allow us to detect emissions reductions because of changes in management practices.
This is not a UK-specific issue. I presented on the challenges of Monitoring, Reporting and Verification (MRV) at COP 24 in Katowice in 2018 as part of the Global Research Alliance initiative. I described the challenges in accounting for the effect of GHG mitigation activities both within inventories and on farms and the inability to detect changes in emission due to good practice adoption. This resonated with delegates from all parts of the globe, and there is a common thread relating to the lack of granular activity data to inform emissions measurement. The collection of high-resolution activity data is the key to developing appropriate measurement and monitoring programmes for GHG emissions.
What have we achieved?
My initial goal in joining Map of Ag was to establish approaches for data collection and integration so that we could populate GHG emissions models for priority product areas; beef and milk in the first instance. I wanted to ensure we were making the best use of data available to avoid duplication of effort, and to streamline data collection for the benefit of farmers and supply chain customers.
We have achieved far more than I had expected in the first six months – the reason for this is that the foundations were well and truly established. The work that has been done by colleagues over the last few years has established many of the data collection mechanisms required for key data sources. These include animal movements, health and fertility information, feed data and detailed production information. The ability to access this activity data has meant the addition of GHG calculations has been relatively straightforward and importantly, working with a brilliant data science team has given us the ability to undertake analytics that have until now been impossible in existing tools.
While we have established the GHG emissions calculation models for beef and dairy enterprises, we are very open to collaboration and working with other GHG calculation tools that meet the required standards to populate activity data. This will be a focus of our work in 2021.
Planning mitigation activities: I have bored many people with my mantra for GHG emissions measurement and reduction on farm: I say, WHAT THE F? – focus on Feed, Fertility, Fertiliser and Fuel. With the granularity in activity data, we can set the baseline and really start to understand the impact of management improvements in these areas, and then develop plans and key performance indicators (KPIs) for production improvements. With high-resolution activity data, we can strategically plan the emissions reduction.
In the not-too-distant future, the idea of a specific tool for GHG emissions measurement will be surpassed. Integrated approaches will inform and improve efficiencies in nutrient use, herd health and fertility and feed. The driver for this will be platforms that integrate data and allow this type of detailed analytics that offer value to the farmer and through the supply chain. It is an area of great opportunity but rife with complexity. I am looking forward to working with a very talented group of people to overcome the challenges and offer a data solution for agricultural production that helps address the sustainability challenges today and well into the future.
Head of Sustainability