The statement that “what gets measured gets managed,” which is often attributed to Peter Drucker, has applied to a broad range of problems that humanity has faced. The existential problem of global warming and the urge to decarbonize the largest emitting industries, including agriculture, has shone the light on the need to evolve the methods that we use to understand the impact that agriculture has on the environment.
I have previously shared my thoughts about the importance of making agriculture more sustainable. In order to lower greenhouse emissions from agriculture, we need to promote and incentivize transition away from conventional agricultural practices such as deep cultivation of the land, lack of crop diversity and use of synthetic fertilizers.
Until recently, companies working to lower their emissions were using some general ways to account for those emissions, attributing them to a particular type of commodity sourced from a certain area (for example, soybeans grown in Brazil). To generate an emission factor of soybeans in Brazil, a static set of parameters that reflect emissions from an average farm production would have been used.
These emissions would be calculated on a ton of CO2e/ton of ingredient basis and would be static (independent of weather conditions or yields) and non-location-specific (the differences in management between neighboring farms, or even those in different parts of the country, wouldn’t be captured with this static emission factor accounting method).
While the development of a standard framework for accounting for the emissions from sourced ingredients was a necessary step toward industry-wide approach standardization, this approach does not lend itself to measure improvement, since it is not granular enough and does not follow the changes of practices implemented on farms.
What previously was not possible due to the lack of data and visibility is becoming possible today thanks to a range of technologies that make monitoring agricultural practices and measuring their environmental outcomes possible on a global scale. These technologies are not only fascinating but are key to enabling the next generation of climate-smart agriculture.
So what are these new tools that enable leading agricultural producers and food manufacturers to gain visibility into the state and the impact of the global food production? These technologies include:
• satellite imagery.
• big data.
• impact models.
Satellite imagery enables us to map crop production areas, track plant growth stages, identify the use (or the lack thereof) of climate-smart production practices and even predict yields. Satellite imagery plays an important role in the monitoring of food production systems, which enables us to “see” the key aspects of agriculture production and manage not only the emissions but also the risk that comes with disruptions in production due to climate change or supply-chain blockages. Satellite imagery, however, is best at reporting the “symptoms” of the processes that shape the land rather than its causes or outcomes. Hence, it is not a holy grail of data for agriculture but a part of the solution.
Satellite imagery gets combined with big data covering other “inputs” into the crop production process, such as weather, soil maps and information about inputs like fertilization recorded by the farm equipment.
All this information is fed into impact models. Impact models such as crop and soil simulators run in the cloud, consuming terabytes of imagery and other data and producing estimates of the environmental impacts of past and current agricultural practices, as well as modeling the impact of future practices. Modeling the possible outcomes of alternative future production scenarios, those that would include the adoption of climate-smart practices, is not only a way to estimate our potential to reduce emissions but also a way to understand the improved resiliency that these practices would add to an agricultural system.
Technology leaders have a few options for getting started with these technology solutions in their organizations. They will need to choose between investing in the in-house development of the solution or accessing it via a third-party provider.
Owning the solution comes with the benefit of having in-house development. Companies with the essential time and resources to implement accordingly will find this to be a worthwhile investment. However, it is worth noting that this can be expensive and come with additional challenges, such as needing to find ways to retrain existing staff or stay competitive in an area of expertise not native to the company’s core business. Establishing a new department or technology group, hiring and training talent and establishing an operating cadence takes time, which leads to long lead times to first outcomes from these investments.
When considering a third-party provider, use your team’s time to survey the market, meet with the leading solution providers and perform an evaluation with the goal of running one or several projects to test out how they can achieve business results with the help of the partner solutions. This approach will help you not only to explore technology solutions that can transform your business but also build the capabilities of your team and business as a whole to take advantage of the benefits these new technologies can offer.
With the appropriate investment into the adoption of climate-smart practices, agricultural production systems will gain greater resilience which is vital in times of changing climate and disrupted food supply.
Agriculture, like many industries, is seeking to decarbonize rapidly and at scale. This is where technologies such as satellite imagery, big data and impact models are uniquely positioned to rapidly accelerate investments into the measurement and monitoring of global food systems, which in turn will lead to the more targeted implementation of programs, improving the resiliency of global food systems.