Drought management largely depends on the availability of timely, accurate and integrated information about its characteristics. Concurrently, biostimulants could represent a sustainable measure to foster the resilience of cropping systems under water-limited conditions. Nevertheless, scientific recognition of the potential of biostimulants has not grown as fast as the interest from industry: therefore, there is an urgent need to investigate biostimulant action. In recent decades, remote sensing has been successfully applied to crop growth and stress monitoring. The use of radiative transfer models, often rooted in artificial intelligence, to estimate plant traits from remotely sensed data can be considered the link between the generalisation and the spatialisation of data, bringing field phenotyping and remote sensing closer together. In this framework, the present study was designed as a factorial combination of irrigation treatment (3 levels) and biostimulant treatment (3 levels) and conducted on processing tomato in open field during the summer of 2020. PROSAIL inversion was carried out to retrieve three major biophysical traits (LAI, LCC, CCC). The parameters dynamics during the season were investigated through GAM modelling. The validation of the model was carried out with a positive outcome in terms of accuracy. The PROSAIL inversion enabled the efficient retrieval of LAI and LCC at rates comparable to those in literature, while performing worse than literature findings only for CCC, probably due to the characteristics of tomato canopy. At the same time, the effect of irrigation was detected both for yield and quality data and detected through the GAM modelisation of the parameters. However, no biostimulant effect could be detected. The internal variability per plot of the retrieved biophysical traits was high. This, jointly with the uncertainty surrounding biostimulant testing and the magnitude of biostimulant effects, corroborated by the absence of results regarding biostimulant effect on yield and DM, lead to hypothesise that the bottleneck was linked to the biostimulant effect itself.

Antonucci, G., Impollonia, G., Croci, M., Potenza, E., Marcone, A., Amaducci, S., Evaluating biostimulants via high-throughput field phenotyping: Biophysical traits retrieval through PROSAIL inversion, <<SMART AGRICULTURAL TECHNOLOGY>>, 2023; 3 (N/A): 100067-100080. [doi:10.1016/j.atech.2022.100067] [https://hdl.handle.net/10807/230879]

Evaluating biostimulants via high-throughput field phenotyping: Biophysical traits retrieval through PROSAIL inversion

Antonucci, Giulia;Impollonia, Giorgio;Croci, Michele;Potenza, Eleonora;Marcone, Andrea;Amaducci, Stefano
2023

Abstract

Drought management largely depends on the availability of timely, accurate and integrated information about its characteristics. Concurrently, biostimulants could represent a sustainable measure to foster the resilience of cropping systems under water-limited conditions. Nevertheless, scientific recognition of the potential of biostimulants has not grown as fast as the interest from industry: therefore, there is an urgent need to investigate biostimulant action. In recent decades, remote sensing has been successfully applied to crop growth and stress monitoring. The use of radiative transfer models, often rooted in artificial intelligence, to estimate plant traits from remotely sensed data can be considered the link between the generalisation and the spatialisation of data, bringing field phenotyping and remote sensing closer together. In this framework, the present study was designed as a factorial combination of irrigation treatment (3 levels) and biostimulant treatment (3 levels) and conducted on processing tomato in open field during the summer of 2020. PROSAIL inversion was carried out to retrieve three major biophysical traits (LAI, LCC, CCC). The parameters dynamics during the season were investigated through GAM modelling. The validation of the model was carried out with a positive outcome in terms of accuracy. The PROSAIL inversion enabled the efficient retrieval of LAI and LCC at rates comparable to those in literature, while performing worse than literature findings only for CCC, probably due to the characteristics of tomato canopy. At the same time, the effect of irrigation was detected both for yield and quality data and detected through the GAM modelisation of the parameters. However, no biostimulant effect could be detected. The internal variability per plot of the retrieved biophysical traits was high. This, jointly with the uncertainty surrounding biostimulant testing and the magnitude of biostimulant effects, corroborated by the absence of results regarding biostimulant effect on yield and DM, lead to hypothesise that the bottleneck was linked to the biostimulant effect itself.
2023
Inglese
Antonucci, G., Impollonia, G., Croci, M., Potenza, E., Marcone, A., Amaducci, S., Evaluating biostimulants via high-throughput field phenotyping: Biophysical traits retrieval through PROSAIL inversion, <<SMART AGRICULTURAL TECHNOLOGY>>, 2023; 3 (N/A): 100067-100080. [doi:10.1016/j.atech.2022.100067] [https://hdl.handle.net/10807/230879]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/230879
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