  To fight climate change and accommodate the increasing population, global
crop production has to be strengthened. To achieve the "sustainable
intensification" of agriculture, transforming it from carbon emitter to carbon
sink is a priority, and understanding the environmental impact of agricultural
management practices is a fundamental prerequisite to that. At the same time,
the global agricultural landscape is deeply heterogeneous, with differences in
climate, soil, and land use inducing variations in how agricultural systems
respond to farmer actions. The "personalization" of sustainable agriculture
with the provision of locally adapted management advice is thus a necessary
condition for the efficient uplift of green metrics, and an integral
development in imminent policies. Here, we formulate personalized sustainable
agriculture as a Conditional Average Treatment Effect estimation task and use
Causal Machine Learning for tackling it. Leveraging climate data, land use
information and employing Double Machine Learning, we estimate the
heterogeneous effect of sustainable practices on the field-level Soil Organic
Carbon content in Lithuania. We thus provide a data-driven perspective for
targeting sustainable practices and effectively expanding the global carbon
sink.
