Technology, Digitalization, and DatabasesOne of the most severe challenges to achieving objectives is the scalability of regenerative agriculture. Digitalization and the adoption of technological solutions are essential to accelerating scalability; here we are confronted by another challenge: agriculture and food systems face a technological gap. And with the required digital shift today, this sector experiences a "gap within the gap."
If we speak specifically about Latin America, the problem is intensified by the lack of updated and reliable databases. As a solution to this, technology service providers must create high-quality, traceable, integrable, and profitable data ecosystems.
CarbonSpace offers an effective solution by providing high-quality, verified, and direct carbon flux data. We are taking a step forward by moving away from assumptions or average values in data modeling and helping companies accurately quantify their major efforts to transition to regenerative agriculture. Bad data leads to bad accounting, which undermines sustainable efforts and can lead to miscommunication or inadvertent greenwashing.
Read more: What Makes CarbonSpace Data Different?Food supply chains benefit from CarbonSpace’s MRV tool by accessing primary carbon data, reducing uncertainties, including carbon stock changes in natural carbon pools, and assessing the impact of sustainable land management, among other use cases. Companies not only benefit from measuring their economic and operational efforts on regenerative investment but also facilitate corporate narrative claims, setting baseline targets, and carbon accounting reporting toward major standards like ISO-14000 and the GHG Protocol, while also supporting life cycle assessments (LCAs) and carbon insetting and offsetting projects.
Companies like CarbonSpace can facilitate data transformation in Latin America, paving the way for greater visibility and support for regenerative practices and nature-based solutions. These regions are diverse and varied, and only with deep stores of data can personalized interventions be developed, measured, maintained, validated, and reported on.