Project number: 26.13.20.04.03_SMAFER

SMAFER: Sensing for smart fertilisation

The project “Sensing for Smart Fertilization”, or SMAFER, explores the potential of remote sensing and in-field sensors for improving the nitrogen (N) fertilization practice of arable crops. Advances in remote sensing using drones or satellites allow real-time monitoring of crop N status, providing actionable insights for site-specific fertilization. In-field soil nitrate sensors provide real-time indication of the current nitrogen availability to plants, with the potential to improve the accuracy of nutrient recommendations. Other sensors can be used to measure the current soil enzymatic activity, which is related to N release dynamics in the soil. In the future, a combination of such technologies with data-driven decision models will enable precision N fertilizer application, reducing environmental impact while ensuring high productivity. The SMAFER project contributes to the development of the necessary knowhow and scientific basis, by making extensive use of Agroscope’s long-term fertilization experiments and lysimeter facilities.

Last Name, First Name Location
Argento Francesco Reckenholz
Gerber Simone Tänikon
Hiltbrunner Jürg Reckenholz
Latsch Annett Jana Tänikon
Simmler Michael Tänikon
Turek Maria Eliza Reckenholz
Wittwer Raphaël Reckenholz

Lozano-Fondon C., Lorenzetti R., Barbetti R., Metzger K., Buttafuoco G., Özge Pinar M., Madenoglu S., Sanden T., Gholizadeh A., Stenberg B., Fantappiè M., van Egmond F., Liebisch F., Lopez Nunez R., Knadel M. and others
Accuracies and costs of prediction and mapping soil properties using proximal sensors: A systematic review.
Computers and Electronics in Agriculture, 243, 2026, Article 111378.

Žydelis R., Weihermüller L., Gomes L. C., Møller A. B., Castaldi F., Volungevičius J., Kavaliauskas A., Koganti T., Wetterlind J., Cinkaya İ., Borůvka L., van Egmond F., Higgins S., Liebisch F., Povilaitis V. and others
Comparison of soil property predictions in Lithuanian croplands using UAV, satellite, EMI data and machine learning.
Computers and Electronics in Agriculture, 244, 2026, Article 111543.