Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany.
University of Sine Saloum EL-HÂDJ IBRAHIMA NIASS, Kaolack, Senegal.
Sci Rep. 2024 Oct 5;14(1):23172. doi: 10.1038/s41598-024-74309-4.
High-yielding traits can potentially improve yield performance under climate change. However, data for these traits are limited to specific field sites. Despite this limitation, field-scale calibrated crop models for high-yielding traits are being applied over large scales using gridded weather and soil datasets. This study investigates the implications of this practice. The SIMPLACE modeling platform was applied using field, 1 km, 25 km, and 50 km input data resolution and sources, with 1881 combinations of three traits [radiation use efficiency (RUE), light extinction coefficient (K), and fruiting efficiency (FE)] for the period 2001-2010 across Germany. Simulations at the grid level were aggregated to the administrative units, enabling the quantification of the aggregation effect. The simulated yield increased by between 1.4 and 3.1 t ha with a maximum RUE trait value, compared to a control cultivar. No significant yield improvement (< 0.4 t ha) was observed with increases in K and FE alone. Utilizing field-scale input data showed the greatest yield improvement per unit increment in RUE. Resolution of water related inputs (soil characteristics and precipitation) had a notably higher impact on simulated yield than of temperature. However, it did not alter the effects of high-yielding traits on yield. Simulated yields were only slightly affected by data aggregation for the different trait combinations. Warm-dry conditions diminished the benefits of high-yielding traits, suggesting that benefits from high-yielding traits depend on environments. The current findings emphasize the critical role of input data resolution and source in quantifying a large-scale impact of high-yielding traits.
高产生性状有潜力提高气候变化下的产量表现。然而，这些性状的数据仅限于特定的田间地点。尽管存在这种局限性，但高产生性状的田间校准作物模型仍在使用网格化的天气和土壤数据集在大范围内应用。本研究探讨了这种做法的意义。使用 SIMPLACE 建模平台，采用田间、1km、25km 和 50km 的输入数据分辨率和来源，对德国 2001-2010 年期间的三个性状（辐射利用效率（RUE）、光衰减系数（K）和结实效率（FE））的 1881 种组合进行了模拟。网格水平的模拟结果被汇总到行政区，从而能够量化汇总效应。与对照品种相比，具有最大 RUE 性状值的模拟产量增加了 1.4 至 3.1 吨/公顷。单独增加 K 和 FE 对产量没有明显的改善（<0.4 吨/公顷）。利用田间尺度的输入数据，每增加一个单位的 RUE 都能带来最大的产量提高。与温度相比，与水相关的输入（土壤特性和降水）的分辨率对模拟产量的影响明显更大。然而，它并没有改变高产生性状对产量的影响。不同性状组合的数据汇总对模拟产量的影响很小。暖干条件削弱了高产生性状的好处，这表明高产生性状的好处取决于环境。目前的研究结果强调了输入数据分辨率和来源在量化高产生性状的大规模影响方面的关键作用。
