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This study was funded by the projects AGL2015-65351-R, PID2019-109089RB-C31 and TED2021-131606B-C21 of the Spanish Ministry of Economy and Competitiveness. AG-R was funded by a Margarita Salas post-doctoral contract from the Spanish Ministry of Universities affiliated to the Research Vice-Rector of the University of Barcelona. VRRY was funded by a pre-doctoral contract from the Spanish Ministry of Economy and Competitiveness (PRE2020-092369). The funders had no role in the study design, data collection and analysis, decision to publish, or manuscript preparation.

Analysis of institutional authors

Gracia-Romero, AAuthorRufo, RAuthorGomez-Candon, DAuthorSoriano, JmAuthorBellvert, JAuthorYannam, VrrAuthorGulino, DAuthorLopes, MsCorresponding Author

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May 19, 2023
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Improving in-season wheat yield prediction using remote sensing and additional agronomic traits as predictors

Publicated to:Frontiers In Plant Science. 14 1063983- - 2023-04-03 14(), DOI: 10.3389/fpls.2023.1063983

Authors: Gracia-Romero, A; Rufo, R; Gómez-Candón, D; Soriano, JM; Bellvert, J; Yannam, VRR; Gulino, D; Lopes, MS

Affiliations

Cultius Extensius Sostenibles. IRTA Investigación y Tecnología Agroalimentarias - Author
Inst Food & Agr Res & Technol IRTA, Efficient Use Water Agr Program, Lleida, Spain - Author
Inst Food & Agr Res & Technol IRTA, Field Crops Program, Lleida, Spain - Author
Producció Vegetal. IRTA Investigación y Tecnología Agroalimentarias - Author
Protecció Vegetal Sostenible . IRTA Investigación y Tecnología Agroalimentarias - Author
Ús eficient de l'aigua. IRTA Investigación y Tecnología Agroalimentarias - Author
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Abstract

The development of accurate grain yield (GY) multivariate models using normalized difference vegetation index (NDVI) assessments obtained from aerial vehicles and additional agronomic traits is a promising option to assist, or even substitute, laborious agronomic in-field evaluations for wheat variety trials. This study proposed improved GY prediction models for wheat experimental trials. Calibration models were developed using all possible combinations of aerial NDVI, plant height, phenology, and ear density from experimental trials of three crop seasons. First, models were developed using 20, 50 and 100 plots in training sets and GY predictions were only moderately improved by increasing the size of the training set. Then, the best models predicting GY were defined in terms of the lowest Bayesian information criterion (BIC) and the inclusion of days to heading, ear density or plant height together with NDVI in most cases were better (lower BIC) than NDVI alone. This was particularly evident when NDVI saturates (with yields above 8 t ha(-1)) with models including NDVI and days to heading providing a 50% increase in the prediction accuracy and a 10% decrease in the root mean square error. These results showed an improvement of NDVI prediction models by the addition of other agronomic traits. Moreover, NDVI and additional agronomic traits were unreliable predictors of grain yield in wheat landraces and conventional yield quantification methods must be used in this case. Saturation and underestimation of productivity may be explained by differences in other yield components that NDVI alone cannot detect (e.g. differences in grain size and number).

Keywords

grain yieldndviphenologyplant heightprediction modelsuavCanopyGrain yieldGrain-yieldLandracesNdviNumberPerformancePhenologyPlant heightPrediction modelsUavWheat

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Frontiers In Plant Science due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2023, it was in position 44/265, thus managing to position itself as a Q1 (Primer Cuartil), in the category Plant Sciences.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.87. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Field Citation Ratio (FCR) from Dimensions: 6.81 (source consulted: Dimensions Oct 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-10-24, the following number of citations:

  • WoS: 9
  • Europe PMC: 1
  • Google Scholar: 7

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-10-24:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 28.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 28 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 7.05.
  • The number of mentions on the social network X (formerly Twitter): 11 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: http://hdl.handle.net/20.500.12327/2342

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (GRACIA ROMERO, ADRIAN) and Last Author (DA SILVA LOPES, MARTA).

the author responsible for correspondence tasks has been DA SILVA LOPES, MARTA.