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Causality and Explainability for Trustworthy Integrated Pest Management
Pesticides serve as a common tool in agricultural pest control but significantly contribute to the climate crisis.
Understanding the impacts of crop diversification in the context of climate change: a machine learning approach
The concept of sustainable intensification in agriculture necessitates the implementation of management practices that prioritize sustainability without compromising productivity.
Evaluating Digital Agriculture Recommendations with Causal Inference
To this end, we propose an observational causal inference framework for the empirical evaluation of the impact of digital tools on target farm performance indicators (e. g., yield in this case).
Evaluating Digital Tools for Sustainable Agriculture using Causal Inference
To this end, we propose an observational causal inference framework for the empirical evaluation of the impact of digital tools on target farm performance indicators.
Personalizing Sustainable Agriculture with Causal Machine Learning
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.
Towards assessing agricultural land suitability with causal machine learning
Understanding the suitability of agricultural land for applying specific management practices is of great importance for sustainable and resilient agriculture against climate change.
