Accurate nitrous oxide emission predictions improved with hybrid neural network
Enhanced Agriculture-informed Neural Network by Domain Knowledge
Machine Learning
Summary
Predicting nitrous oxide emissions from farms is hard because many factors like soil, climate, and farming methods interact in complex ways, and data is scarce. The authors created a new kind of neural network, called KAINN, which uses both data and knowledge about soil and fertilizer behavior to make better predictions. Their tests showed it predicts emissions more accurately and consistently than models that only use data. This approach helps make predictions easier to understand and more reliable across different environments.
What this means in practice
- •For environmental modelers: Improve accuracy and reliability of agricultural greenhouse gas emission models using hybrid neural networks with domain knowledge.
- •For precision agriculture technology developers: Design farm management tools that estimate nitrous oxide emissions more accurately by integrating mechanistic soil knowledge with neural networks.$Commercial implications: Enables creation of advanced farm software that predicts emissions to optimize fertilizer use and meet environmental standards.
Authors
Ci Lin, Futong Li, Rose Chong-Wu, Tet Yeap, Iluju Kiringa
Abstract
Accurate prediction of nitrous oxide (N2O) emissions from agriculture is important for assessing environmental impacts and supporting sustainable farming. However, prediction remains difficult because N2O emissions result from complex interactions among soil properties, climate, biochemical processes, and management practices, while high-quality observations are limited. Deep learning models can capture nonlinear relationships but often lack physical interpretability and may generalize poorly across environmental conditions. We propose the Knowledge-enhanced Agriculture-informed Neural Network (KAINN), a hybrid neural-mechanistic framework that extends the Agriculture-informed Neural Network by incorporating domain knowledge about fertilizer diffusion, soil respiration, and water-filled porosity. We evaluate KAINN using CNN, LSTM, and Transformer architectures across multiple growing seasons and input-feature configurations. The results show that KAINN generally provides lower root mean square error and mean absolute error and higher R-squared values than purely data-driven models and the original AINN. Analysis of the learned interfaces also shows smoother and more physically consistent parameter trajectories with reduced uncertainty. These findings demonstrate that incorporating environmental knowledge into neural networks can improve the reliability, interpretability, and generalization of agricultural N2O-emission predictions.