Fertilization and soil management machine learning based sustainable agronomic prescriptions for durum wheat in Italy
Author:
Funder
Horizon 2020 Framework Programme
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11119-024-10153-w.pdf
Reference87 articles.
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2. Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., & Hegewisch, K. C. (2018). TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific Data, 5(1), 170191. https://doi.org/10.1038/sdata.2017.191
3. Adamchuk, V. I., Hummel, J. W., Morgan, M. T., & Upadhyaya, S. K. (2004). On-the-go soil sensors for precision agriculture. Computers and Electronics in Agriculture, 44(1), 71–91. https://doi.org/10.1016/j.compag.2004.03.002
4. Aklilu Tesfaye, A., & Gessesse Awoke, B. (2021). Evaluation of the saturation property of vegetation indices derived from sentinel-2 in mixed crop-forest ecosystem. Spatial Information Research, 29(1), 109–121. https://doi.org/10.1007/s41324-020-00339-5
5. Argento, F., Anken, T., Abt, F., Vogelsanger, E., Walter, A., & Liebisch, F. (2021). Site-specific nitrogen management in winter wheat supported by low-altitude remote sensing and soil data. Precision Agriculture, 22(2), 364–386. https://doi.org/10.1007/s11119-020-09733-3
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