The most widely used approach for prescribing fertilizer nitrogen (N) recommendations in maize (Zea Mays L.) in Argentina is based on the relationship between grain yield and the available N (kg N ha⁻¹), calculated as the sum of pre-plant soil NO₃^--N at 0−60 cm depth (PPNT) plus fertilizer N (N_f). However, combining covariates related to crop N demand and soil N supply at a large national scale remains unexplored for this model. The aim of this work was to identify yield response patterns associated to yield environment (crop N demand driver) and soil texture (soil N supply driver). A database of 788 experiments (1980−2016) was gathered and analyzed combining quadratic-plateau regression models with bootstrapping to address expected values and variability on response parameters and derived quantities. The database was divided into three groups according to soil texture (fine, medium and coarse) and five groups based on the empirical distribution of maximum observed yields (from Very-Low = <8.5 Mg ha⁻¹ to Very-High = >13.1 Mg ha⁻¹) resulting in fifteen groups. The best model included both, attainable yield environment and soil texture. The yield environment mainly modified the agronomic optimum available N (AON_av), with an expected increase rate of ca. 21.4 kg N Mg attainable yield⁻¹, regardless of the soil texture. In Very-Low yield environments, AON_av was characterized by a high level of uncertainty, related to a poor fit of the N response model. To a lesser extent, soil texture modified the response curvature but not the AON_av, mainly by modifying the response rate to N (Fine > Medium > Coarse), and the N use efficiencies. Considering hypothetical PPNT levels from 40 to 120 kg N ha⁻¹, the expected agronomic efficiency (AEN_f) at the AON_av varied from 7 to 31, and 9–29 kg yield response kg fertilizer N (N_f)⁻¹, for Low and Very-High yield environments, respectively. Similarly, the expected partial factor productivity (PFPN_f) at the AON_av ranged from 62 to 158, and 55–99 kg yield kg N_f⁻¹, for the same yield environments. These results highlight the importance of combining attainable yield environment and soil texture metadata for refining N fertilizer recommendations. Acknowledging the still low N fertilizer use in Argentina, space exists to safely increasing N fertilizer rates, steering the historical soil N mining profile to a more sustainable agro-environmental scenario in the Pampas.

在阿根廷的玉米 ( Zea Mays L.) 中，最广泛使用的推荐肥料氮 (N) 的方法是基于谷物产量和可用 N (kg N ha ^-1 )之间的关系，计算为预植物土壤 NO ₃ ^- -N 在 0−60 cm 深度 (PPNT) 加上肥料 N (N _f）。然而，该模型尚未探索在全国范围内结合与作物氮需求和土壤氮供应相关的协变量。这项工作的目的是确定与产量环境（作物氮需求驱动因素）和土壤质地（土壤氮供应驱动因素）相关的产量响应模式。收集并分析了包含 788 个实验（1980-2016 年）的数据库，并结合二次平台回归模型和引导法来解决响应参数和派生数量的预期值和可变性。该数据库根据土壤质地（细、中、粗）分为三组，根据最大观察产量的经验分布（从极低 = <8.5 Mg ha ^-1到极高 = >13.1 Mg ）分为五组公顷^-1) 产生了 15 个组。最好的模型包括可达到的产量环境和土壤质地。产量环境主要改变了农艺最佳可用氮（AON _av），预计增加率为约。21.4 kg N Mg 可达到的产量^-1，无论土壤质地如何。在极低产量环境中，AON _{av 的}特征在于高度的不确定性，这与 N 响应模型的拟合不佳有关。在较小程度上，土壤质地修改了响应曲率，而不是 AON _av，主要是通过将响应率修改为 N（细 > 中 > 粗）和 N 使用效率。考虑到 40 到 120 kg N ha ^{-1 的}假设 PPNT 水平，预期农艺效率（AEN_f ) 在 AON _av变化从 7 到 31 和 9–29 kg 产量响应 kg 肥料 N (N _f ) ^-1，分别用于低产量和非常高产量环境。同样，AON _av的预期部分要素生产率 (PFP​​N _f )范围为 62 到 158，55-99 kg 产量 kg N _f ^-1，对于相同的产量环境。这些结果强调了结合可实现的产量环境和土壤质地元数据来完善氮肥建议的重要性。承认阿根廷氮肥使用量仍然很低，因此存在安全增加氮肥用量的空间，将历史土壤氮开采概况转向潘帕斯地区更可持续的农业环境情景。