A predictive model of wheat grain yield based on canopy reflectance indices and the theoretical definition of yield potential

A - Papers appearing in refereed journals

Pennacchi, J. P., Virlet, N., Barbosa, J. P. R. A. D., Parry, M. A. J., Feuerhelm, D., Hawkesford, M. J. and Carmo-Silva, E. 2022. A predictive model of wheat grain yield based on canopy reflectance indices and the theoretical definition of yield potential. Theoretical and Experimental Plant Physiology. https://doi.org/10.1007/s40626-022-00263-z

AuthorsPennacchi, J. P., Virlet, N., Barbosa, J. P. R. A. D., Parry, M. A. J., Feuerhelm, D., Hawkesford, M. J. and Carmo-Silva, E.
Abstract

Predicting crop yields through simple methods would be helpful for crop breeding programs and could be deployed at farm level to achieve accurate crop management practices. This research proposes a new method for predicting wheat grain yields throughout the crop growth cycle based on canopy cover (CC) and reflectance indices, named Yieldp Model. The model was evaluated by comparing grain yields with the outputs of the proposed model using phenotypic data collected for a wheat population grown under field conditions for the 2015 and 2016 seasons. Accumulated radiation (RAD), Normalized Difference Vegetation Index (NDVI), Photochemical Reflectance Index (PRI), Water Index (WI), Harvest Index (HI) and CC indices were the components of the model. We found that the biomass accumulation predicted by the model was responsive throughout the crop cycle and the grain yield predicted was correlated to measured grain yield. The model was able to early predict grain yield based on biomass accumulated at anthesis. Evaluation of the model components enabled an improved understanding of the main factors limiting yield formation throughout the crop cycle. The proposed Yieldp Model explores a new concept of yield modelling and can be the starting point for the development of cheap and robust, on-farm, yield prediction during the crop cycle.

KeywordsCrop breeding; Early yield prediction; Mathematical modelling; On-farm yield; Remote sensing; Triticum aestivum
Year of Publication2022
JournalTheoretical and Experimental Plant Physiology
Digital Object Identifier (DOI)https://doi.org/10.1007/s40626-022-00263-z
Open accessPublished as non-open access
FunderBiotechnology and Biological Sciences Research Council
Funder project or codeDesigning Future Wheat - WP1 - Increased efficiency and sustainability
[20:20 Wheat] Maximising yield potential of wheat
Output statusPublished
Publication dates
Online09 Nov 2022
Publication process dates
Accepted27 Sep 2022
PublisherBrazilian Soc Plant Physiology
ISSN2197-0025

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