ArticleMethods in molecular biology (Clifton, N.J.)2022
Integration of Crop Growth Models and Genomic Prediction.
Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 16 citations in OpenAlex.
- Bridging tradition and innovation: a review of computer simulations in plant breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- KineticGP: A computational framework for genomic prediction of leaf photosynthetic traits.Plant communications · 2026Article
- Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Modeling soybean growth: A mixed model approach.PLoS computational biology · 2024Article
- Linking genetic markers and crop model parameters using neural networks to enhance genomic prediction of integrative traits.Frontiers in plant science · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Crop growth models (CGMs) consist of multiple equations that represent physiological processes of plants and simulate crop growth dynamically given environmental inputs. Because parameters of CGMs are often genotype-specific, gene effects can be related to environmental inputs through CGMs. Thus, CGMs are attractive tools for predicting genotype by environment (G×E) interactions. This chapter reviews CGMs, genetic analyses using these models, and the status of studies that integrate genomic prediction with CGMs. Examples of CGM analyses are also provided.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.