Facts:
Project name: Baby Grain Passport
Budget: 9.9 Miljoner kronor
Funder: Formas
Planned project period: 2019-2024 (June)
Baby Grain Passport – Traceable and sustainable production of cereals for baby food
Last changed: 20 June 2024
Limits for heavy metals and mycotoxins are especially strict in cereals for baby food. In collaboration with Lantmännen, Agroväst Livsmedel och Dataväxt we develop tools for easier sourcing of the best grain, and for tracing selected batches from the farmers’ fields to baby food producer. We also develop agricultural decision support systems for farmers to improve the resource use efficiency of the crop production.
The project contributes to traceable, sustainable, safe and competitive production of wheat and oats for infant food in Sweden - from field to food producer. The goal is to develop the Baby Grain Passport concept and a prototype coherent information system for wheat and oats production aimed at infant food that will:
- keep heavy metal and mycotoxin concentrations well below legislated threshold values,
- minimize negative environmental and climate impact from the grain production,
- increase traceability, and facilitate streamlining, along the grain-for-infants value chain.
BGP should enable increased production and better documentation, which means that cereal for baby food can become a more attractive export product.
Publications
Articles in scientific journals with peer review
Adler, K., Persson, K., Söderström, M., Eriksson, J., & Pettersson, C. G. (2023). Digital Soil Mapping of Cadmium: Identifying Arable Land for Producing Winter Wheat with Low Concentrations of Cadmium. Agronomy, 13(2), 317. https://doi.org/10.3390/agronomy13020317
Alshihabi, O., Persson, K., & Söderström, M. (2024). Easy yield mapping for precision agriculture. Acta Agriculturae Scandinavica, Section B—Soil & Plant Science, 74(1), 2411950. https://doi.org/10.1080/09064710.2024.2411950
Marzec-Schmidt, K., Börjesson, T., Suproniene, S., Jędryczka, M., Janavičienė, S., Góral, T., ... & Piikki, K. (2021). Modelling the Effects of Weather Conditions on Cereal Grain Contamination with Deoxynivalenol in the Baltic Sea Region. Toxins, 13(11), 737. https://doi.org/10.3390/toxins13110737
Piikki, K., Söderström, M., Stadig, H., & Wolters, S. (2022). Remote sensing and on-farm experiments for determining in-season nitrogen rates in winter wheat–Options for implementation, model accuracy and remaining challenges. Field Crops Research, 289, 108742. https://doi.org/10.1016/j.fcr.2022.108742
Söderström, M., Piikki, K., & Stadig, H. (2021). Yield maps for everyone–scaling drone models for satellite-based decision support. In Precision agriculture’21 (pp. 415-421). Wageningen Academic Publishers. https://doi.org/10.3920/978-90-8686-916-9_109
Wang, X., Borjesson, T., Wetterlind, J., & van der Fels-Klerx, H. J. (2024). Prediction of deoxynivalenol contamination in spring oats in Sweden using explainable artificial intelligence. npj Science of Food, 8(1), 75. https://doi.org/10.1038/s41538-024-00310-w
Wolters, S., Söderström, M., Piikki, K., Börjesson, T., & Pettersson, C. G. (2022). Predicting grain protein concentration in winter wheat (Triticum aestivum L.) based on unpiloted aerial vehicle multispectral optical remote sensing. Acta Agriculturae Scandinavica, Section B—Soil & Plant Science, 72(1), 788-802. https://doi.org/10.1080/09064710.2022.2085165
International conferences
Alshihabi O., Persson K., Söderström M. (2023). Post-processing yield maps of winter wheat using data from satellites and combines. In: Stafford, J.V. (Ed.) Precision Agriculture '23. Wageningen Academic Publishers, Wageningen, the Netherlands, pp. 523-530. https://doi.org/10.3920/978-90-8686-947-3
Persson K., Söderström M., Stadig H., Martinsson J. (2023). Target-N: Sentinel-2 based nitrogen optimisation in Swedish cereal production. In: Stafford, J.V. (Ed.) Precision Agriculture '23. Wageningen Academic Publishers, Wageningen, the Netherlands, pp. 315-322. https://doi.org/10.3920/978-90-8686-947-3
Söderström M., Piikki K. & Stadig, H. (2021). Yield maps for everyone - scaling drone models for satellite-based decision support. In: Stafford, J.V. (Ed.) Precision Agriculture '21. Wageningen Academic Publishers, Wageningen, the Netherlands, pp. 911 - 918. https://doi.org/10.3920/978-90-8686-916-9_109
Note that numerous publications are outputs from cooperation with other projects.
Reports
Contact
Kristin Persson
Researcher
Department of soil and environment, SLU
kristin.persson@slu.se, +46 511-67222
Links
The project is one out of several where we are working within the Laboratory for intelligent agricultural decision support systems (LADS): https://www.slu.se/LADS