ArticleFrontiers in plant science2023
Enhancing estimation of cover crop biomass using field-based high-throughput phenotyping and machine learning models.
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 2 papers.
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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
2 citing papers in PubMed.
- Data fusion of EnMAP and sentinel-2 for high-resolution soil fertility assessment in wheat cultivation of central Khuzestan plain.Scientific reports · 2026Article
- Comparative Evaluation of Allometric, Machine Learning, and Ensemble Approaches for Modeling Dynamic Structure-Fresh Weight Relationships in Sweet Pepper.Plants (Basel, Switzerland) · 2026Article
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Authors and funding
6 authors.
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Abstract
Incorporating cover crops into cropping systems offers numerous potential benefits, including the reduction of soil erosion, suppression of weeds, decreased nitrogen requirements for subsequent crops, and increased carbon sequestration. The aboveground biomass (AGB) of cover crops strongly influences their performance in delivering these benefits. Despite the significance of AGB, a comprehensive field-based high-throughput phenotyping study to quantify AGB of multiple cover crops in the U.S. Midwest has not been found. This study presents a two-year field experiment carried out in Eastern Nebraska, USA, to estimate AGB of five different cover crop species [canola (
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