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.

为了实现气候变化和人口增长的双重挑战，需要加强全球农作物生产并将农业从碳排放转变为碳汇，了解农业管理实践的环境影响是必要的前提。在此基础上，个性化的可持续农业是提高绿色指标的有效途径和即将出台的政策中的一个重要发展方向。本文将个性化可持续农业定义为条件平均处理效应的估计任务，并使用因果机器学习来处理。利用气候数据、土地利用信息和双重机器学习，我们估计了立陶宛的田间土壤有机碳含量在可持续农业实践方面的异质性影响，为实现可持续农业的目标提供了数据驱动的视角并扩大了全球碳汇。