  Learning latent representations has aided operational decision-making in
several disciplines. Its advantages include uncovering hidden interactions in
data and automating procedures which were performed manually in the past.
Representation learning is also being adopted by earth and environmental
sciences. However, there are still subfields that depend on manual feature
engineering based on expert knowledge and the use of algorithms which do not
utilize the latent space. Relying on those techniques can inhibit operational
decision-making since they impose data constraints and inhibit automation. In
this work, we adopt a case study for nitrogen response rate prediction and
examine if representation learning can be used for operational use. We compare
a Multilayer Perceptron, an Autoencoder, and a dual-head Autoencoder with a
reference Random Forest model for nitrogen response rate prediction. To bring
the predictions closer to an operational setting we assume absence of future
weather data, and we are evaluating the models using error metrics and a
domain-derived error threshold. The results show that learning latent
representations can provide operational nitrogen response rate predictions by
offering performance equal and sometimes better than the reference model.
