英国研究显示，机器学习作为标准氮肥建议的校正工具可提升利润，而非直接替代。
研究基于892条英国冬小麦产量响应曲线，评估机器学习模型在氮肥推荐中的表现。在正常价格下，所有机器学习模型利润均低于标准建议。简单校正步骤可减少25%的利润损失，而更优模型和额外特征无增益。混合标准建议与阻尼校正可消除偏差并减少罕见的大额损失。
Profit based evaluation of machine learning for nitrogen recommendations in winter wheat
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.