When soil heterogeneity meets seedling emergence: the interdisciplinary challenge of adaptive sowing strategies
Abstract
Background and aims Achieving uniform crop establishment under heterogeneous soil conditions remains a major limitation in precision agriculture. We argue that this limitation arises less from the lack of sensing technologies than from the persistent disconnection between soil heterogeneity, seedling emergence requirements, and adjustment of machine settings. Current strategies largely fail to account for soil heterogeneity, relying on static sowing settings or management zones that do not capture fine-scale spatial differences. Methods This opinion paper is based on evidence from 109 studies and uses maize (Zea mays L.) as a reference system to examine the role of proximal sensing technologies in real-time soil characterization. We propose that precision sowing requires an integrated framework linking seedling emergence, soil properties, and machine control through three aspects: soil-seed-water interactions during emergence, proximal sensing, and predictive modeling for dynamic adjustment of sowing depth, downforce, and furrow closing force. Results We highlight key mechanisms of soil-seedwater interactions affecting seedling emergence and assess proximal sensing technologies based on their relevance and feasibility. Building on this analysis, we propose a conceptual framework showing how sensor-derived data can support dynamic adjustment of sowing parameters. Conclusion We propose that the next step in precision sowing is the development of integrated decision-making frameworks that connect soil characterization, crop establishment processes, and machine control. Such frameworks could enable adaptive sowing strategies capable of responding to seedbed-scale heterogeneity through real-time adjustment of machine settings. Achieving this objective will require advances in field validation, sensor integration, predictive modeling, and the translation of soil measurements into actionable agronomic recommendations.
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BibTeX
@article{veiga2026when,
title = {{When soil heterogeneity meets seedling emergence: the interdisciplinary challenge of adaptive sowing strategies}},
author = {Alicia Veiga and Michel-Pierre Faucon and David Houben and Arnaud Dujany and Hamilton de Araujo and Ludovic Tetard and Carolina Ugarte},
journal = {Plant and Soil},
year = {2026},
month = sep,
doi = {10.1007/s11104-026-09102-5},
url = {https://hal.science/hal-05757287v1},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - When soil heterogeneity meets seedling emergence: the interdisciplinary challenge of adaptive sowing strategies
AU - Alicia Veiga
AU - Michel-Pierre Faucon
AU - David Houben
AU - Arnaud Dujany
AU - Hamilton de Araujo
AU - Ludovic Tetard
AU - Carolina Ugarte
PY - 2026
DA - 2026/09/19
JO - Plant and Soil
DO - 10.1007/s11104-026-09102-5
UR - https://hal.science/hal-05757287v1
AB - Background and aims Achieving uniform crop establishment under heterogeneous soil conditions remains a major limitation in precision agriculture. We argue that this limitation arises less from the lack of sensing technologies than from the persistent disconnection between soil heterogeneity, seedling emergence requirements, and adjustment of machine settings. Current strategies largely fail to account for soil heterogeneity, relying on static sowing settings or management zones that do not capture fine-scale spatial differences. Methods This opinion paper is based on evidence from 109 studies and uses maize (Zea mays L.) as a reference system to examine the role of proximal sensing technologies in real-time soil characterization. We propose that precision sowing requires an integrated framework linking seedling emergence, soil properties, and machine control through three aspects: soil-seed-water interactions during emergence, proximal sensing, and predictive modeling for dynamic adjustment of sowing depth, downforce, and furrow closing force. Results We highlight key mechanisms of soil-seedwater interactions affecting seedling emergence and assess proximal sensing technologies based on their relevance and feasibility. Building on this analysis, we propose a conceptual framework showing how sensor-derived data can support dynamic adjustment of sowing parameters. Conclusion We propose that the next step in precision sowing is the development of integrated decision-making frameworks that connect soil characterization, crop establishment processes, and machine control. Such frameworks could enable adaptive sowing strategies capable of responding to seedbed-scale heterogeneity through real-time adjustment of machine settings. Achieving this objective will require advances in field validation, sensor integration, predictive modeling, and the translation of soil measurements into actionable agronomic recommendations.
SN - 0032-079X
SN - 1573-5036
ER -