Uniform rate seeding (URS) density may result in under- or over-optimal plant population that negatively affects crop growth and yield. Site-specific seeding (SSS) is one of the solutions to manage in-field soil variation by optimizing the input seed rate to match soil fertility. This study has evaluated the agronomic and economic response of maize to SSS compared to the URS using a data fusion scheme. Two fields of 5.5 ha and 10 ha in Belgium and France, respectively, were scanned using an on-line visible and near-infrared spectroscopy sensor to measure soil pH, organic carbon (OC), phosphorus (P), potassium (K), magnesium (Mg), calcium (Ca), sodium (Na), moisture content (MC), and cation exchange capacity (CEC). Crop normalized difference vegetation indices (NDVI) were retrieved from several Sentinel-2 images. Measured crop yield, retrieved NDVI and on-line measured soil attributes were then fussed using k-means clustering algorithm to delineate management zone (MZ) maps, whose classes were ranked based on their fertility level and crop yield. A parallel strips experiment was overlaid upon the MZ map, to allow comparing the performance of SSS against URS. Two SSS treatments were implemented in the strip experiment, e.g., the “Kings” approach that recommended the highest seeding density for the highest fertile MZ class and vice versa, and the “Robin Hood” approach followed the opposite principle. Results revealed that SSS treatments increased maize grain yield by 0.25–0.70 Mg ha⁻¹ and thus improved gross margin by 26.7–92.67 € ha⁻¹, compared to the URS. Besides, the SSS-Kings treatment outperformed the URS and SSS-Robin Hood in both fields, whereas the SSS-Robin Hood outperformed the URS treatment only in one field. Soil OC, MC, Mg, and pH revealed a positive correlation each with grain yield in the SSS-Kings treatment. The SSS-Kings treatment is therefore recommended to manage in-field soil variation, which can result in optimizing input seed rates for increasing maize productivity and profitability.

均匀速率播种 (URS) 密度可能会导致植物种群的优化不足或过度优化，从而对作物生长和产量产生负面影响。定点播种 (SSS) 是通过优化输入播种量以匹配土壤肥力来管理田间土壤变化的解决方案之一。本研究使用数据融合方案评估了与 URS 相比，玉米对 SSS 的农艺和经济反应。分别使用在线可见光和近红外光谱传感器扫描比利时和法国的两个 5.5 公顷和 10 公顷土地，以测量土壤 pH 值、有机碳 (OC)、磷 (P)、钾 (K)、镁 (Mg)、钙 (Ca)、钠 (Na)、水分含量 (MC) 和阳离子交换容量 (CEC)。从几个 Sentinel-2 图像中检索作物归一化差异植被指数 (NDVI)。测量的作物产量，用于描绘管理区 (MZ) 地图的k 均值聚类算法，其类别根据其肥力水平和作物产量进行排名。在 MZ 图上叠加了一个平行条带实验，以比较 SSS 与 URS 的性能。在条状试验中实施了两种 SSS 处理，例如，“Kings”方法建议最高可育 MZ 等级的播种密度最高，反之亦然，“Robin Hood”方法遵循相反的原则。结果表明，SSS 处理使玉米产量提高了 0.25-0.70 Mg ha ^{-1 ，从而提高了 26.7-92.67 € ha -1}的毛利率，与 URS 相比。此外，SSS-Kings 处理在两个领域都优于 URS 和 SSS-Robin Hood，而 SSS-Robin Hood 仅在一个领域优于 URS 处理。在 SSS-Kings 处理中，土壤 OC、MC、Mg 和 pH 值均与粮食产量呈正相关。因此，建议使用 SSS-Kings 处理来管理田间土壤变化，从而优化输入种子率，从而提高玉米生产力和盈利能力。