Estimation of crop nutrient demand, seed nutrient removal, and nutrient use efficiency (yield to nutrient uptake ratio) are crucial for pursuing both balanced nutrition and more sustainable farming systems. However, the estimation of the nutrient requirements as the nutrient uptake per unit of seed yields is impaired in many situations due to the narrow variation of the dataset used to obtain these values or by the overgeneralization of considering a constant value for the nutrient demand at varying yield levels. Past studies focused on other crops and using linear models for estimation of the nutrient requirements, but not yet for soybeans (Glycine max L.). The aims of this research study were to: (i) quantify nitrogen (N), phosphorus (P), potassium (K), and sulfur (S) requirements in soybean and (ii) compare linear and non-linear (spherical) models in their relationship between plant and seed nutrient content all relative to seed yield at varying probabilities utilizing quantile regression. A large dataset from different studies conducted between 2009–2018 period, including data of seed yield, total biomass at physiological maturity, and N, P, K, and S uptake. Soybean seed yield ranged from 955 to 6525 kg ha⁻¹, aboveground biomass from 1990 to 15,814 kg ha⁻¹, and harvest index from 0.16 to 0.57. On average, nutrient uptake was 261 kg N ha⁻¹, 25 kg P ha⁻¹, 133 kg K ha⁻¹, and 16 kg S ha⁻¹ (N:P:K:S ratio = 17:1.6:8.5:1), while nutrient content in seeds averaged 191 kg N ha⁻¹, 17 kg P ha⁻¹, 54 kg K ha⁻¹, and 9 kg S ha⁻¹ (N:P:K:S ratio = 21:1.8:5.8:1). The spherical model described better than the linear model the relationship between plant nutrient uptake or nutrient content in seeds with seed yield in soybean, and thus, nutrient requirements per unit of yield decreased as seed yield increased. A relationship between nutrient internal efficiency and seed yield for the different percentiles as determined by the non-linear quantile regression offered probabilistic values for estimating nutrient uptake in soybean, providing useful information for obtaining more reliable estimates of nutrient balances at the system-level.

估算作物养分需求，种子养分去除和养分利用效率（产量与养分吸收比）对于追求均衡营养和更可持续的耕作制度至关重要。但是，由于用于获取这些值的数据集的狭窄变化，或者由于考虑了变化时对养分需求的恒定值而过分归纳，在许多情况下，以每单位种子产量的养分吸收量来估算养分需求会受到影响。产量水平。过去的研究集中在其他农作物上，并使用线性模型估算养分需求，但尚未用于大豆（甘氨酸L.）。这项研究的目的是：（i）量化大豆中氮（N），磷（P），钾（K）和硫（S）的需求，以及（ii）比较线性和非线性（球形）模型利用分位数回归，在不同概率下植物和种子养分含量之间的关系都与种子产量有关。在2009年至2018年期间进行的大量不同研究得出的大型数据集，包括种子产量，生理成熟时的总生物量以及N，P，K和S吸收的数据。大豆种子产量为955至6525 kg ha ^-1，地上生物量为1990至15,814 kg ha ^-1，收获指数为0.16至0.57。平均而言，养分吸收为261千克氮公顷^-1 25公斤P公顷^-1，133公斤ķ公顷^-1，和16公斤小号公顷^-1（N：P：K：S比= 17：1.6：8.5：1），而在种子养分含量平均191千克Ñ公顷^-1，17公斤P公顷^-1，54 kg K ha ^-1和9 kg S ha ^-1（N：P：K：S比率= 21：1.8：5.8：1）。球形模型比线性模型更好地描述了植物养分吸收或种子中养分含量与大豆种子产量之间的关系，因此，单位产量的养分需求随着种子产量的增加而降低。非线性分位数回归确定的不同百分位数的养分内部效率与种子产量之间的关系为估算大豆中的养分吸收提供了概率值，为在系统级获得更可靠的养分平衡估算提供了有用的信息。