Predicting the nitrogen content of mediterranean forage crops: A remote sensing approach

Antonio Pulina; Davide Cammarano; Francesca Piseddu; Lisa Deiana; Alberto Sassu; Alessandro Deidda; Filippo Gambella; Giovanna Seddaiu; Pier Paolo Roggero

doi:10.1016/j.eja.2025.127518

Predicting the nitrogen content of mediterranean forage crops: A remote sensing approach

Antonio Pulina, Davide Cammarano^*, Francesca Piseddu, Lisa Deiana, Alberto Sassu, Alessandro Deidda, Filippo Gambella, Giovanna Seddaiu, Pier Paolo Roggero

^*Corresponding author af dette arbejde

Institut for Agroøkologi - Klima og Vand

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

Abstract

Remote sensing with Unpiloted Aerial Systems can provide information on the Nitrogen status of forage crops more quickly than destructive sampling techniques, which are not compliant with the need for fast and sustainable methodologies to support farmers’ decisions on livestock feeding. The study aimed to assess a remote sensing algorithm based on the Canopy Chlorophyll Content Index (CCCI) and the Canopy Nitrogen Index (CNI) to predict the canopy N content of forage crops under Mediterranean rainfed conditions. A dataset from a two-year field experiment on four forage crops, as both pure stands and mixtures, under two different mowing intensities was used to calculate CNI from plant N concentration and aboveground biomass. Multispectral data from an Unpiloted Aerial System were collected during the two-year cropping system to calculate CCCI. The N canopy content was then predicted based on the relationship between CNI and CCCI. A good agreement (RMSD = 4.72 g m⁻², d = 0.92; P < 0.001) between the predicted and observed N canopy content (g m⁻² of N) was found. The estimation of canopy N content improved under high cover of rigid ryegrass (RMSD = 5.56 g m⁻², index of agreement = 0.95) and in frequently mowed plots. Overall, the agreement between observed and predicted N content improved under the threshold of 12.4 g m⁻². The N content of different forage crops can be predicted from the remote-sensed CCCI starting from N dilution curves. The prediction accuracy is influenced by the mowing intensity and the differences in the relative abundance of species, and it is limited over a threshold of N corresponding to a high biomass level. The results can represent a basis for developing decision support tools for livestock farmers for a real-time field estimation of the forage quality in extensively managed grasslands. Further insights are needed to assess the predictive ability in relation to the relative abundance of legumes in mixtures and above the saturation threshold.

Originalsprog	Engelsk
Artikelnummer	127518
Tidsskrift	European Journal of Agronomy
Vol/bind	164
ISSN	1161-0301
DOI	https://doi.org/10.1016/j.eja.2025.127518
Status	Udgivet - mar. 2025

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title = "Predicting the nitrogen content of mediterranean forage crops: A remote sensing approach",

abstract = "Remote sensing with Unpiloted Aerial Systems can provide information on the Nitrogen status of forage crops more quickly than destructive sampling techniques, which are not compliant with the need for fast and sustainable methodologies to support farmers{\textquoteright} decisions on livestock feeding. The study aimed to assess a remote sensing algorithm based on the Canopy Chlorophyll Content Index (CCCI) and the Canopy Nitrogen Index (CNI) to predict the canopy N content of forage crops under Mediterranean rainfed conditions. A dataset from a two-year field experiment on four forage crops, as both pure stands and mixtures, under two different mowing intensities was used to calculate CNI from plant N concentration and aboveground biomass. Multispectral data from an Unpiloted Aerial System were collected during the two-year cropping system to calculate CCCI. The N canopy content was then predicted based on the relationship between CNI and CCCI. A good agreement (RMSD = 4.72 g m−2, d = 0.92; P < 0.001) between the predicted and observed N canopy content (g m−2 of N) was found. The estimation of canopy N content improved under high cover of rigid ryegrass (RMSD = 5.56 g m−2, index of agreement = 0.95) and in frequently mowed plots. Overall, the agreement between observed and predicted N content improved under the threshold of 12.4 g m−2. The N content of different forage crops can be predicted from the remote-sensed CCCI starting from N dilution curves. The prediction accuracy is influenced by the mowing intensity and the differences in the relative abundance of species, and it is limited over a threshold of N corresponding to a high biomass level. The results can represent a basis for developing decision support tools for livestock farmers for a real-time field estimation of the forage quality in extensively managed grasslands. Further insights are needed to assess the predictive ability in relation to the relative abundance of legumes in mixtures and above the saturation threshold.",

keywords = "Canopy chlorophyll content index, Canopy nitrogen index, Forage quality, Legume mixtures, Unpiloted aerial system",

author = "Antonio Pulina and Davide Cammarano and Francesca Piseddu and Lisa Deiana and Alberto Sassu and Alessandro Deidda and Filippo Gambella and Giovanna Seddaiu and Roggero, {Pier Paolo}",

note = "Publisher Copyright: {\textcopyright} 2025 The Authors",

year = "2025",

month = mar,

doi = "10.1016/j.eja.2025.127518",

language = "English",

volume = "164",

journal = "European Journal of Agronomy",

issn = "1161-0301",

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TY - JOUR

T1 - Predicting the nitrogen content of mediterranean forage crops

T2 - A remote sensing approach

AU - Pulina, Antonio

AU - Cammarano, Davide

AU - Piseddu, Francesca

AU - Deiana, Lisa

AU - Sassu, Alberto

AU - Deidda, Alessandro

AU - Gambella, Filippo

AU - Seddaiu, Giovanna

AU - Roggero, Pier Paolo

PY - 2025/3

Y1 - 2025/3

N2 - Remote sensing with Unpiloted Aerial Systems can provide information on the Nitrogen status of forage crops more quickly than destructive sampling techniques, which are not compliant with the need for fast and sustainable methodologies to support farmers’ decisions on livestock feeding. The study aimed to assess a remote sensing algorithm based on the Canopy Chlorophyll Content Index (CCCI) and the Canopy Nitrogen Index (CNI) to predict the canopy N content of forage crops under Mediterranean rainfed conditions. A dataset from a two-year field experiment on four forage crops, as both pure stands and mixtures, under two different mowing intensities was used to calculate CNI from plant N concentration and aboveground biomass. Multispectral data from an Unpiloted Aerial System were collected during the two-year cropping system to calculate CCCI. The N canopy content was then predicted based on the relationship between CNI and CCCI. A good agreement (RMSD = 4.72 g m−2, d = 0.92; P < 0.001) between the predicted and observed N canopy content (g m−2 of N) was found. The estimation of canopy N content improved under high cover of rigid ryegrass (RMSD = 5.56 g m−2, index of agreement = 0.95) and in frequently mowed plots. Overall, the agreement between observed and predicted N content improved under the threshold of 12.4 g m−2. The N content of different forage crops can be predicted from the remote-sensed CCCI starting from N dilution curves. The prediction accuracy is influenced by the mowing intensity and the differences in the relative abundance of species, and it is limited over a threshold of N corresponding to a high biomass level. The results can represent a basis for developing decision support tools for livestock farmers for a real-time field estimation of the forage quality in extensively managed grasslands. Further insights are needed to assess the predictive ability in relation to the relative abundance of legumes in mixtures and above the saturation threshold.

AB - Remote sensing with Unpiloted Aerial Systems can provide information on the Nitrogen status of forage crops more quickly than destructive sampling techniques, which are not compliant with the need for fast and sustainable methodologies to support farmers’ decisions on livestock feeding. The study aimed to assess a remote sensing algorithm based on the Canopy Chlorophyll Content Index (CCCI) and the Canopy Nitrogen Index (CNI) to predict the canopy N content of forage crops under Mediterranean rainfed conditions. A dataset from a two-year field experiment on four forage crops, as both pure stands and mixtures, under two different mowing intensities was used to calculate CNI from plant N concentration and aboveground biomass. Multispectral data from an Unpiloted Aerial System were collected during the two-year cropping system to calculate CCCI. The N canopy content was then predicted based on the relationship between CNI and CCCI. A good agreement (RMSD = 4.72 g m−2, d = 0.92; P < 0.001) between the predicted and observed N canopy content (g m−2 of N) was found. The estimation of canopy N content improved under high cover of rigid ryegrass (RMSD = 5.56 g m−2, index of agreement = 0.95) and in frequently mowed plots. Overall, the agreement between observed and predicted N content improved under the threshold of 12.4 g m−2. The N content of different forage crops can be predicted from the remote-sensed CCCI starting from N dilution curves. The prediction accuracy is influenced by the mowing intensity and the differences in the relative abundance of species, and it is limited over a threshold of N corresponding to a high biomass level. The results can represent a basis for developing decision support tools for livestock farmers for a real-time field estimation of the forage quality in extensively managed grasslands. Further insights are needed to assess the predictive ability in relation to the relative abundance of legumes in mixtures and above the saturation threshold.

KW - Canopy chlorophyll content index

KW - Canopy nitrogen index

KW - Forage quality

KW - Legume mixtures

KW - Unpiloted aerial system

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U2 - 10.1016/j.eja.2025.127518

DO - 10.1016/j.eja.2025.127518

M3 - Journal article

AN - SCOPUS:85215418532

SN - 1161-0301

VL - 164

JO - European Journal of Agronomy

JF - European Journal of Agronomy

M1 - 127518

ER -

Predicting the nitrogen content of mediterranean forage crops: A remote sensing approach

Abstract

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