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

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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 yieldsthroughout 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.

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Fig. 1

Adapted from Pennacchi et al. (2018a). (Color figure online)

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Acknowledgements

This research was partly supported by the Rothamsted Research Strategic Programs 20:20 Wheat® (BBSRC BB/J00426X/1) and Designing Future Wheat (Biotechnology and Biological Sciences Research Council, BBSRC BB/P016855/1). JPP was funded by the Brazilian CNPq (National Coucil of Research) through the Science without Borders Program for the PhD degree (246221/2012-7). ECS and MAJP acknowledge financial support from the Lancaster Environment Centre. The authors are thankful to the many Rothamsted colleagues and visitors who helped with data collection during field campaign days and sample processing post-harvest. In particular, we would like to thank Mr Andrew Riche for the technical help and Mr Chris Hall for sharing his expertise with sample handling post-harvest. The authors also acknowledge the Rothamsted Farm staff, the Computational and Analytical Sciences department, Rothamsted Research for meteorological data from the e-RA database, which is supported by the UK BBSRC (LTE-NCG BBS/E/C/000J0300) and the Lawes Agricultural Trust.

Funding

This research was partially funded by the BBSRC (Biotechnology and Biological Sciences Research Council), award numbers J00426X/1 and BB/P016855/1, granted to Malcolm Hawkesford.

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Authors and Affiliations

  1. Department of Biology, Federal University of Lavras, Lavras, Brazil

    João Paulo Pennacchi & João Paulo Rodrigues Alves Delfino Barbosa

  2. Sustainable Soils and Crops, Rothamsted Research, Harpenden, UK

    Nicolas Virlet & Malcolm Hawkesford

  3. Lancaster Environment Centre, Lancaster University, Lancaster, UK

    Martin A. J. Parry & Elizabete Carmo-Silva

  4. Syngenta AG, Cambridge, UK

    David Feuerhelm

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  1. João Paulo Pennacchi
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Contributions

JPP, MAJP, DF and ECS designed research. JPP and ECS performed research. JPP and NV analysed data. JPP, NV, JPRADB, SPL, MAJP, DF and ECS wrote the paper.

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Correspondence to João Paulo Pennacchi.

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Pennacchi, J.P., Virlet, N., Barbosa, J.P.R.A.D. et al. A predictive model of wheat grain yield based on canopy reflectance indices and theoretical definition of yield potential. Theor. Exp. Plant Physiol. 34, 537–550 (2022). https://doi.org/10.1007/s40626-022-00263-z

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