Track genomics, neuroscience and molecular biology.
Search papers, labs, and topics across Lattice.
This study evaluates nitrogen recommendations for winter wheat using a profit-based approach rather than traditional prediction accuracy, revealing that machine learning models fail to identify the most profitable nitrogen rates within acceptable farm tolerances. By analyzing 892 yield response curves from long-term UK experiments and adjusting for varying nitrogen to grain price ratios, the research demonstrates that existing machine learning models underperform compared to standard agricultural advice. A simple correction step applied post-modeling significantly reduces profit losses, indicating that machine learning should complement rather than replace established practices in agricultural decision-making.
Machine learning models for nitrogen recommendations in winter wheat fail to outperform standard advice, but a simple correction can cut profit losses by 43%.
Nitrogen rates for winter wheat are set before the season, under unknown prices and weather. The standard UK advice does not respond to prices, yet recent price swings moved the most profitable rate by tens of kilograms per hectare. Machine learning is often proposed as the fix. However, it is usually judged on prediction accuracy, and accurate prediction does not by itself make the recommended rate more profitable. Our insight is to score nitrogen advice directly by the profit it forgoes on measured yield response curves. We build a test bench on 892 such curves from two long running UK experiments, and sweep the nitrogen to grain price ratio to cover all price scenarios. On this bench, machine learning fails as a predictor. No model recovers the best rate within farm tolerance, and the benchmark noise shows none can. At normal prices, every model also loses to the standard advice on profit. The gain sits elsewhere. A simple correction step applied after the model cuts profit losses by a quarter, while better models and extra features give no gain. The same frozen correction cuts losses by 43% at the second site without any retraining. A hybrid of standard advice plus a damped correction removes bias and trims rare large losses. The same price sweep also prices emission cuts, at a cost comparable to current carbon prices. Machine learning therefore pays as a profit scored correction to standard advice, not as its replacement.