ArticleFrontiers in plant science2023
Improving in-season wheat yield prediction using remote sensing and additional agronomic traits as predictors.
Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Establishing a knowledge structure for yield prediction in cereal crops using unmanned aerial vehicles.Frontiers in plant science · 2024Pooled it
- Wheat leaf dark respiration acclimates more strongly at night than in the day when responding to nocturnal warming.Journal of experimental botany · 2026Article
- Defining durum wheat ideotypes adapted to Mediterranean environments through remote sensing traits.Frontiers in plant science · 2023Article
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Authors and funding
8 authors.
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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
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