Reliable estimates of crop nitrogen (N) uptake and offtake are critical in estimating N balances, N use efficiencies and potential losses to the environment. Calculation of crop N uptake and offtake requires estimates of yield of crop product (e.g. grain or beans) and crop residues (e.g. straw or stover) and the N concentration of both components. Yields of crop products are often reasonably well known, but those of crop residues are not. While the harvest index (HI) can be used to interpolate the quantity of crop residue from available data on crop product yields, harvest indices are known to vary across locations, as do N concentrations of residues and crop products. The increasing availability of crop data and advanced statistical and machine learning methods present us with an opportunity to move towards more locally relevant estimates of crop harvest index and N concentrations using more readily available data. The aim of this study was to investigate whether improved estimates of maize crop HI and N concentrations of crop products and crop residues can be based on crop data available at the global scale, such as crop yield, fertilizer application rates and estimates of yield potential. Experiments from 1487 different locations conducted across 31 countries were used to test various prediction models. Predictions from mixed-effects models and random forest machine learning models provided reasonable levels of prediction accuracy (R² of between 0.33 and 0.68), with the random forest method having greater accuracy. Although the mixed-effects prediction models had lower prediction accuracy than random forest, they did provide better interpretability. Selection of which method to use will depend on the objective of the user. Here, the random forest and mixed-effects methods were applied to N in maize, but could equally be applied to other crops and other nutrients, if data becomes available. This will enable obtaining more locally relevant estimates of crop nutrient offtake to improve estimates of nutrient balances and nutrient use efficiency at national, regional or global levels, as part of strategies towards more sustainable nutrient management.

作物氮 (N) 吸收和吸收的可靠估算对于估算氮平衡、氮利用效率和对环境的潜在损失至关重要。作物氮吸收和吸收的计算需要估计作物产品（例如谷物或豆类）和作物残留物（例如稻草或秸秆）的产量以及这两种成分的氮浓度。农作物产品的产量通常是众所周知的，但农作物残留物的产量却不是。虽然收获指数 (HI) 可用于从作物产品产量的可用数据中插值作物残留量，但已知收获指数因地点而异，残留物和作物产品的 N 浓度也是如此。越来越多的作物数据以及先进的统计和机器学习方法为我们提供了一个机会，可以使用更容易获得的数据来估计作物收获指数和氮浓度的本地相关性。本研究的目的是调查改进的玉米作物 HI 和作物产品和作物残留物浓度估计值是否可以基于全球范围内可用的作物数据，例如作物产量、施肥量和产量潜力估计值。来自 31 个国家/地区的 1487 个不同地点的实验用于测试各种预测模型。混合效应模型和随机森林机器学习模型的预测提供了合理水平的预测准确度（R 本研究的目的是调查改进的玉米作物 HI 和作物产品和作物残留物浓度估计值是否可以基于全球范围内可用的作物数据，例如作物产量、施肥量和产量潜力估计值。来自 31 个国家/地区的 1487 个不同地点的实验用于测试各种预测模型。混合效应模型和随机森林机器学习模型的预测提供了合理水平的预测准确度（R 本研究的目的是调查改进的玉米作物 HI 和作物产品和作物残留物浓度估计值是否可以基于全球范围内可用的作物数据，例如作物产量、施肥量和产量潜力估计值。来自 31 个国家/地区的 1487 个不同地点的实验用于测试各种预测模型。混合效应模型和随机森林机器学习模型的预测提供了合理水平的预测准确度（R 来自 31 个国家/地区的 1487 个不同地点的实验用于测试各种预测模型。混合效应模型和随机森林机器学习模型的预测提供了合理水平的预测准确度（R 来自 31 个国家/地区的 1487 个不同地点的实验用于测试各种预测模型。混合效应模型和随机森林机器学习模型的预测提供了合理水平的预测准确度（R²介于 0.33 和 0.68 之间），随机森林方法具有更高的准确性。尽管混合效应预测模型的预测精度低于随机森林，但它们确实提供了更好的可解释性。选择使用哪种方法将取决于用户的目标。在这里，随机森林和混合效应方法被应用于玉米中的氮，但如果数据可用，同样可以应用于其他作物和其他营养物质。作为实现更可持续的养分管理战略的一部分，这将有助于获得更多与当地相关的作物养分摄取估算，以改进对国家、区域或全球层面养分平衡和养分利用效率的估算。