Accurate assessment of grass nitrogen status based on multispectral data from two optical sensors and the critical nitrogen dilution curve

Shaohui Zhang*, Poul Erik Lærke, Mathias Neumann Andersen, Kiril Manevski

*Corresponding author af dette arbejde

Publikation: Bidrag til bog/antologi/rapport/proceedingKonferenceabstrakt i proceedingsForskningpeer review

56 Downloads (Pure)

Abstract

The critical nitrogen dilution curve (CNDC) is efficient for diagnosing plant nitrogen (N) deficiency. For perennial plants, including grasses (family Poaceae), deriving CNDC is not straightforward due to in-season modulations by cuts and fertilization. Gislum et al. (2009) developed CNDC for grass species for seed production in Denmark and suggested improvements since it was not possible to calculate a % critical N concentration at biomass lower than 2 Mg ha-1. Optical sensors deployed on the field can partly answer the question of how much and when to fertilize, e.g., the Yara’s N tester (Yara International ASA, Oslo, Norway) that allows assessing plant N requirement and real-time variable-rate spread of fertilizer. Moreover, multispectral data from unmanned aerial vehicle (UAV) with their ultra-high spatial and temporal resolution can also be used to estimate crop N status (Peng et al., 2021), though high accuracy is difficult to achieve due to the dynamics and the transformation of N. Accurate N management requires establishment of a solid ‘link’ between the remote sensing data and the crop N status. Parametric regression involving models of linear or nonlinear nature has often been used for describing this link (Peng et al., 2021). Peng et al. (2021) investigated how well potato N status can be described by RS data obtained from ground, air- and spaceborne sensors using parametric and non-parametric, i.e., machine learning regression. Few previous studies have used RS to calculate the N status of grasses and predict their N requirements. The objective of this study is to estimate precisely the amount of N fertilizer needed for optimal plant growth according to N requirement and the CNDC by two optical sensors (Yara N sensor and UAV-mounted) based on machine learning method (random forest and alike). Two-year field experiment has been initiated with grass (Lolium perenne) in 2022 and 2023 (established in September 2021) on a sandy loam soil in Denmark, with four nitrogen rates (N0: 0 kg ha-1, N1: 75 kg ha-1, N2: 300 kg ha-1 and N3: 450 kg ha-1). We present initial results of the canopy multispectral reflectance and CNDC developed for the grass and provide novel insight for improving N diagnosis and management of grass in Denmark and Europe.
OriginalsprogEngelsk
TitelProceedings of the XXI International Nitrogen Workshop. Halving nitrogen waste by 2030
RedaktørerL. Lassaletta, A. Sanz-Cobeña, C. Pinsard, S. Garde
Antal sider1
ForlagCEPADE-Universidad Politecnica de Madrid
Publikationsdatonov. 2022
Sider201
ISBN (Elektronisk)78-84-122114-6-7
StatusUdgivet - nov. 2022
BegivenhedThe XXI International Nitrogen Workshop. Halving nitrogen waste by 2030. - Madrid, Spanien
Varighed: 24 okt. 202228 okt. 2022
https://nworkshop.org/

Konference

KonferenceThe XXI International Nitrogen Workshop. Halving nitrogen waste by 2030.
Land/OmrådeSpanien
ByMadrid
Periode24/10/202228/10/2022
Internetadresse

Fingeraftryk

Dyk ned i forskningsemnerne om 'Accurate assessment of grass nitrogen status based on multispectral data from two optical sensors and the critical nitrogen dilution curve'. Sammen danner de et unikt fingeraftryk.

Citationsformater