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.
Predicting the nitrogen content of mediterranean forage crops: A remote sensing approach / Pulina, A.; Cammarano, D.; Piseddu, F.; Deiana, L.; Sassu, A.; Deidda, A.; Gambella, F.; Seddaiu, G.; Roggero, P. P.. - In: EUROPEAN JOURNAL OF AGRONOMY. - ISSN 1161-0301. - 164:(2025). [10.1016/j.eja.2025.127518]
Predicting the nitrogen content of mediterranean forage crops: A remote sensing approach
Pulina A.;Piseddu F.;Deiana L.;Sassu A.;Deidda A.;Gambella F.;Seddaiu G.;Roggero P. P.
2025-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
