Reducing uncertainty and productive risk increase the resilience of agricultural systems and food security of a region. While crop productivity strongly depends on weather conditions during the growing season, El Niño Southern Oscillation (ENSO) phenomenon is the main driver of global interannual climate variability and affects crop production in many regions. The ENSO, Pacific Decadal Oscillation (PDO) and Indian Ocean Dipole (IOD) impact on corn and soybean yields between 1973 and 2017 was compared in Córdoba Province, Argentina, looking for an early signal to support agricultural activity. The analysis suggests that not only ENSO signal (SOI and SST El Niño 3.4) but also PDO anomaly (PDOA) link better than IOD with productivity for both crops, as well as the Southern Oscillation Index of August and September (SOI_as) is a proper yield predictor. An inverse relationship was determined between SOI_as and yields anomaly, more spread territorially for soybeans since the linear functions are significant (p < 0.05) in 9 of the 12 administrative areas and, with a slightly lower significance (p < 0.1), in the remaining too. On the other hand, the low R² values indicate little capacity to explain the yield interannual variability from the SOI_as signal that for corn, has a departmental range between 2 % and 19 % and, for soybeans, slightly higher between 7 % and 24 %. While the mean difference test of yield anomaly between different ENSO phases (El Niño, La Niña and Neutral) supports the more general indifference of both extreme phases with the neutral cases, it reaches a significant character in 8 departments when the cold phase is compared with the warm. The seasonal rainfall regime has also a reduced capacity to explain productive variability. However, this information is useful to realize that both the wettest (El Niño) and the driest years (La Niña) fail to differentiate from those with normal rainfall. This confusion was verified by a combinatory frequency analysis of rainfall and productivity data related to the ENSO phases. Because ENSO allows anticipating the rainfall condition and the productive potential of the main rain-fed crops in Córdoba, Argentina, it constitutes appropriate information to reduce uncertainty and the risk level in agricultural production. Therefore, although the ENSO predictive capacity is limited and more knowledge about the influence of other climatic variability sources like PDO is needed, its incorporation into a climate monitoring protocol must conforms to the basis of an early warning system for a sustainable agriculture in the region.

减少不确定性和生产风险可以提高农业系统的复原力和区域的粮食安全。虽然作物生产力在很大程度上取决于生长季节的天气条件，但厄尔尼诺南方涛动 (ENSO) 现象是全球年际气候变化的主要驱动因素，并影响许多地区的作物生产。在阿根廷科尔多瓦省比较了 1973 年至 2017 年间 ENSO、太平洋年代际振荡 (PDO) 和印度洋偶极子 (IOD) 对玉米和大豆产量的影响，以寻找支持农业活动的早期信号。分析表明，不仅 ENSO 信号（SOI 和 SST El Niño 3.4）而且 PDO 异常（PDOA）与两种作物的生产力以及 8 月和 9 月的南方涛动指数（SOI _as) 是一个合适的收益率预测器。在SOI之间确定的反比关系_为并产生异常，更扩展地域大豆由于线性函数是显著（P <0.05）在12个行政区域9和，具有稍低意义（p <0.1），在也剩下。另一方面，低 R ²值表明从 SOI 解释产量年际变化的能力很小，_如表明玉米的部门范围在 2% 到 19% 之间，而大豆的部门范围在 7% 到 24% 之间略高。ENSO 不同阶段（厄尔尼诺、拉尼娜和中性）产量异常的均值差异检验支持了两个极端阶段与中性情况更普遍的无差异性，但与冷期相比，它在 8 个部门达到显着特征。温暖。季节性降雨情况也降低了解释生产力变化的能力。然而，该信息有助于认识到最湿润的年份（厄尔尼诺现象）和最干旱的年份（拉尼娜现象）都无法与正常降雨的年份区分开来。通过对与 ENSO 阶段相关的降雨量和生产力数据进行组合频率分析，证实了这种混淆。由于 ENSO 允许预测阿根廷科尔多瓦主要雨养作物的降雨条件和生产潜力，因此它构成了减少农业生产不确定性和风险水平的适当信息。因此，虽然 ENSO 的预测能力有限，需要更多关于其他气候变率源（如 PDO）的影响的知识，但将其纳入气候监测协议必须符合该地区可持续农业预警系统的基础。