The Oklahoma State University algorithm (OSU) is the most widespread recommendation system to calculate the optimal nitrogen (N) rate in cereal crops. This system is based on the map of a crop vegetation index and N-rich strips in the field, serving as a reference for crop vigour. Based on these inputs, yield potential and in-season crop response to N fertilisation are predicted. The system does not consider other factors influencing the spatial variability of crop growth (e.g., soil), even if the integration of additional data sources could provide more correct N recommendations by considering simultaneously the main drivers of crop growth and production variability. Therefore, the main aims of this study were to modify the OSU algorithm by merging maps of soil electrical conductivity and crop vegetation index (NDRE) to identify management zones instead of using vegetation index alone and to implement and apply the algorithm to define crop response to nitrogen, specific by management zones using a statistical approach instead of N-rich strips in the field. The proposed algorithm was calibrated for maize in northern Italy on four experimental fields in 2021. A comparison among the proposed, the original algorithm and one its previous modification (at Clemson University) was carried out in terms of spatial accuracy and levels of recommended N rates. The proposed algorithm, thanks to the integrated approach, distinguished more in detail different crop responses to N within each management zone and provided N recommendations that match the within-field variability better than the original algorithm and subsequent similar modifications. The new approach resulted in potential average N savings of about 12% compared to uniform management. We conclude that the potential of merging soil and vegetation indices and the definition of N rates with a statistical approach could improve performances and, at the same time, facilitate the adoption of the OSU algorithm. Field validation is needed to confirm the promising results shown in this work.

Site-specific nitrogen recommendations' empirical algorithm for maize crop based on the fusion of soil and vegetation maps / V. Fassa, N. Pricca, G. Cabassi, L. Bechini, M. Corti. - In: COMPUTERS AND ELECTRONICS IN AGRICULTURE. - ISSN 0168-1699. - 203:(2022 Dec), pp. 107479.1-107479.12. [10.1016/j.compag.2022.107479]

Site-specific nitrogen recommendations' empirical algorithm for maize crop based on the fusion of soil and vegetation maps

V. Fassa
Primo
;
L. Bechini
Penultimo
;
M. Corti
Ultimo
2022

Abstract

The Oklahoma State University algorithm (OSU) is the most widespread recommendation system to calculate the optimal nitrogen (N) rate in cereal crops. This system is based on the map of a crop vegetation index and N-rich strips in the field, serving as a reference for crop vigour. Based on these inputs, yield potential and in-season crop response to N fertilisation are predicted. The system does not consider other factors influencing the spatial variability of crop growth (e.g., soil), even if the integration of additional data sources could provide more correct N recommendations by considering simultaneously the main drivers of crop growth and production variability. Therefore, the main aims of this study were to modify the OSU algorithm by merging maps of soil electrical conductivity and crop vegetation index (NDRE) to identify management zones instead of using vegetation index alone and to implement and apply the algorithm to define crop response to nitrogen, specific by management zones using a statistical approach instead of N-rich strips in the field. The proposed algorithm was calibrated for maize in northern Italy on four experimental fields in 2021. A comparison among the proposed, the original algorithm and one its previous modification (at Clemson University) was carried out in terms of spatial accuracy and levels of recommended N rates. The proposed algorithm, thanks to the integrated approach, distinguished more in detail different crop responses to N within each management zone and provided N recommendations that match the within-field variability better than the original algorithm and subsequent similar modifications. The new approach resulted in potential average N savings of about 12% compared to uniform management. We conclude that the potential of merging soil and vegetation indices and the definition of N rates with a statistical approach could improve performances and, at the same time, facilitate the adoption of the OSU algorithm. Field validation is needed to confirm the promising results shown in this work.
unmanned aerial vehicle; soil electrical conductivity; NDRE; crop response to nitrogen; crop yield
Settore AGR/02 - Agronomia e Coltivazioni Erbacee
dic-2022
Article (author)
File in questo prodotto:
File Dimensione Formato  
1-s2.0-S0168169922007876-main (1).pdf

accesso riservato

Tipologia: Publisher's version/PDF
Dimensione 15.94 MB
Formato Adobe PDF
15.94 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
1-s2.0-S0168169922007876-main+(1)_compressed.pdf

accesso riservato

Tipologia: Publisher's version/PDF
Dimensione 796.28 kB
Formato Adobe PDF
796.28 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/955369
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 7
  • ???jsp.display-item.citation.isi??? 4
social impact