In a changing climate, sustainable agriculture is essential for food security and environmental health. However, it is challenging to understand the complex interactions among its biophysical, social, and economic components. Predictive machine learning (ML), with its capacity to learn from data, is leveraged in sustainable agriculture for applications like yield prediction and weather forecasting. Nevertheless, it cannot explain causal mechanisms and remains descriptive rather than prescriptive. To address this gap, we propose causal ML, which merges ML's data processing with causality's ability to reason about change. This facilitates quantifying intervention impacts for evidence-based decision-making and enhances predictive model robustness. We showcase causal ML through eight diverse applications that benefit stakeholders across the agri-food chain, including farmers, policymakers, and researchers.

Accurate, detailed, and timely crop type mapping is a very valuable information for the institutions in order to create more accurate policies according to the needs of the citizens. In the last decade, the amount of available data dramatically increased, whether it can come from Remote Sensing (using Copernicus Sentinel-2 data) or directly from the farmers (providing in-situ crop information throughout the years and information on crop rotation). Nevertheless, the majority of the studies are restricted to the use of one modality (Remote Sensing data or crop rotation) and never fuse the Earth Observation data with domain knowledge like crop rotations. Moreover, when they use Earth Observation data they are mainly restrained to one year of data, not taking into account the past years. In this context, we propose to tackle a land use and crop type classification task using three data types, by using a Hierarchical Deep Learning algorithm modeling the crop rotations like a language model, the satellite signals like a speech signal and using the crop distribution as additional context vector. We obtained very promising results compared to classical approaches with significant performances, increasing the Accuracy by 5.1 points in a 28-class setting (.948), and the micro-F1 by 9.6 points in a 10-class setting (.887) using only a set of crop of interests selected by an expert. We finally proposed a data-augmentation technique to allow the model to classify the crop before the end of the season, which works surprisingly well in a multimodal setting.

The Common Agricultural Policy (CAP) is responsible for allocating agricultural subsidies in the European Union, which nears 50 billion euros each year [36]. Consequently, monitoring the crop types for subsidy allocation represents a significant challenge for payment agencies, which have encouraged the development of automated crop classification tools based on machine learning [24]. In particular, The Sentinels for Common Agricultural Policy (Sen4CAP) project [19] aims to provide EU member states with algorithmic solutions and best practice studies on crop monitoring based on satellite data from the Sentinel constellation [10]. Despite the inherent difficulty of differentiating between the complex growth patterns of plants, this task is made possible by the nearly limitless access to data and annotations. Indeed, Sentinel-2 offers multi-spectral observations at a high revisit time of five days on average, which are particularly appropriate for characterizing the complex spectral and temporal characteristics of crop phenology.