Cited by 292Genetic and phenotypic traits in livestockGenetics and Plant BreedingGenetic Mapping and Diversity in Plants and Animals

The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.

Genomic prediction of maize yield across European environmental conditions

Open access

Millet, Emilie J., Kruijer, Willem, Coupel-Ledru, Aude, Alvarez Prado, Santiago, Cabrera-Bosquet, Llorenç, Lacube, Sébastien, Charcosset, Alain, Welcker, Claude, van Eeuwijk, Fred, Tardieu, François·2019·Nature Genetics

DOI 10.1038/s41588-019-0414-y

Open this paper in CoChat — free account

Abstract

The development of germplasm adapted to changing climate is required to ensure food security 1,2. Genomic prediction is a powerful tool to evaluate many genotypes but performs poorly in contrasting environmental scenarios 3-7 (genotype × environment interaction), in spite of promising results for flowering time 8. New avenues are opened by the development of sensor networks for environmental characterization in thousands of fields 9,10. We present a new strategy for germplasm evaluation under genotype × environment interaction. Yield was dissected in grain weight and number and genotype × environment interaction in these components was modeled as genotypic sensitivity to environmental drivers. Environments were characterized using genotype-specific indices computed from sensor data in each field and the progression of phenology calibrated for each genotype on a phenotyping platform. A whole-genome regression approach for the genotypic sensitivities led to accurate prediction of yield under genotype × environment interaction in a wide range of environmental scenarios, outperforming a benchmark approach.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1038/s41588-019-0414-y

Cites

46 of 46 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

ggplot2
Wickham, H. (2016) · Use R! · doi:10.1007/978-3-319-24277-4 · Cited by 34,873
The B73 Maize Genome: Complexity, Diversity, and Dynamics
Schnable, P. S., Ware, D., Fulton, R. S., Stein, J. C., Wei, F., Pasternak, S., Liang, C., Zhang, J., Fulton, L., Graves, T., et al. (2009) · Science · doi:10.1126/science.1178534 · Cited by 4,096
Climate and the efficiency of crop production in Britain
Monteith, J. L. (1977) · Philosophical Transactions of the Royal Society of London. B, Biological Sciences · doi:10.1098/rstb.1977.0140 · Cited by 3,484
Breeding Technologies to Increase Crop Production in a Changing World
Tester, M. A., & Langridge, P. (2010) · Science · doi:10.1126/science.1183700 · Cited by 2,335
Climate Impacts on Agriculture: Implications for Crop Production
Hatfield, J. L., Boote, K. J., Kimball, B. A., Ziska, L. H., Izaurralde, R. C., Ort, D., Thomson, A. M., & Wolfe, D. W. (2011) · Agronomy Journal · doi:10.2134/agronj2010.0303 · Cited by 1,733
Genome-Wide Regression and Prediction with the BGLR Statistical Package
Pérez‐Rodríguez, P., & Campos, G. D. L. (2014) · Genetics · doi:10.1534/genetics.114.164442 · Cited by 1,632
A reaction norm model for genomic selection using high-dimensional genomic and environmental data
Jarquín, D., Crossa, J., Lacaze, X., Du Cheyron, P., Daucourt, J., Lorgeou, J., Piraux, F., Guerreiro, L., Pérez, P., Calus, M., et al. (2014) · Theoretical and Applied Genetics · doi:10.1007/s00122-013-2243-1 · Cited by 687
Rice yields in tropical/subtropical Asia exhibit large but opposing sensitivities to minimum and maximum temperatures
Welch, J. R., Vincent, J. R., Auffhammer, M., Moya, P., Dobermann, A., & Dawe, D. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1001222107 · Cited by 677
Genomic Prediction of Breeding Values when Modeling Genotype × Environment Interaction using Pedigree and Dense Molecular Markers
Burgueño, J. A., Campos, G. D. L., WEIGEL, K. A., & Crossa, J. L. (2012) · Crop Science · doi:10.2135/cropsci2011.06.0299 · Cited by 624
A Large Maize (Zea mays L.) SNP Genotyping Array: Development and Germplasm Genotyping, and Genetic Mapping to Compare with the B73 Reference Genome
Ganal, M. W., Durstewitz, G., Polley, A., Berard, A., Buckler, E. S., Charcosset, A., Clarke, J. D., Graner, E.-M., Hansen, M., Joets, J., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0028334 · Cited by 604
Self-citation

Show all 46 references

Sorted by citations

Items 201–292 of 292

A conceptual framework for simultaneous optimization of integrated climate scenario data and phenology models
Tschurr, F., Kotlarski, S., Martre, P., Walter, A., & Roth, L. (2026) · Agronomy for Sustainable Development · doi:10.1007/s13593-026-01091-0 · Cited by 2
Genomic prediction for grain yield and biotic stress resistance in field pea (Pisum sativum L.)
Riaz, A., Li, Y., Pandey, B. R., Gebremedhin, A., Azizinia, S., Sudheesh, S., Lin, Z., Fanning, J., Rosewarne, G. M., Hayden, M., et al. (2026) · Frontiers in Plant Science · doi:10.3389/fpls.2026.1739804 · Cited by 1
Genome-Wide Association Studies Using Multiple Models Reveal the Genetic Basis of Plant Architecture-Related Traits in Maize
Wang, B., Wu, P.-H., Wu, R.-T., Xie, X., Ren, Z.-L., Wang, K., & Ren, J.-J. (2026) · Agronomy · doi:10.3390/agronomy16070761 · Cited by 1
Integrating environmental covariates and growth data to optimize Eucalyptus clonal allocation across a plantation landbase
Cavalheiro, R., Dantas, D., Vatsavai, R. R., Logasundar, M., Hodge, G. R., Yarramsetti, H., Bison, O., de Lima, B. M., Nascimento, L. I., Rezende, G. D. S. P., et al. (2026) · Forest Ecology and Management · doi:10.1016/j.foreco.2026.123831 · Cited by 1
From mechanistic models to breeding digital twins: Predictive plant improvement for a changing climate
Đức, N. T. (2026) · Ecological Genetics and Genomics · doi:10.1016/j.egg.2026.100504 · Cited by 1
Integrating and optimizing genomic, weather, and secondary trait data for multiclass classification
Manthena, V., Jarquín, D., & Howard, R. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2022.1032691 · Cited by 1
Maize yield and dry milling grain quality trade‐offs in Argentinean flint germplasm
Abdala, L. J., Prado, S. A., Donadio, P., & Borrás, L. (2023) · Crop Science · doi:10.1002/csc2.21142 · Cited by 1
Climate change−resilient crop production
Nawaz, T., Fahad, S., Nawaz, T., Saud, S., Rahman, T. U., Adnan, M., Khan, M. N. R., Khan, I., Hussain, Q., & Hassan, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-23707-2.00013-1 · Cited by 2
Heterotic quantitative trait loci analysis and genomic prediction of seedling biomass-related traits in maize triple testcross populations
Zhang, T., Jiang, L., Ruan, L., Qian, Y., Liang, S., Lin, F., Lü, H., Dai, H., & Zhao, H. Q. (2021) · Plant Methods · doi:10.1186/s13007-021-00785-8 · Cited by 1
Preparation and Curation of Phenotypic Datasets
Prado, S. A., Hernández, F., Achilli, A. L., & Amelong, A. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2237-7_2 · Cited by 1
Polygenic Prediction of Complex Traits with Iterative Screen Regression ModelsPreprint
Luo, M., & Gu, S. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.29.402180 · Cited by 1
Leveraging probability concepts for genotype by environment recommendationPreprint
Dias, K. O. D. G., dos Santos, J. P. R., Krause, M. D., Piepho, H., Guimarães, L. J. M., Pastina, M. M., & Garcia, A. A. F. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.21.440774 · Cited by 1
Environmental Drivers of Crop Phenology: Simultaneous Integration of Climate Scenarios and Ground Truth Data Makes the DifferencePreprint
Tschurr, F., Kotlarski, S., Walter, A., & Roth, L. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4801211 · Cited by 1
Leveraging phenotypic plasticity in seed oil content for climate-adapted breeding and productionPreprint
Guo, T., Zeng, L., Xu, H., Gou, X., Pei, H., Shao, Y. W., Cao, Y., Zhang, Z., Li, X., Yu, J., et al. (2024) · doi:10.22541/au.171411325.56371733/v1 · Cited by 1
WISER: an innovative and efficient method for correcting population structure in omics-based selection and association studiesPreprint
Jacquin, L. J., Guerra, W., Lewandowski, M., Patocchi, A., Rymenants, M., Durel, C.-É., Laurens, F. N. D., Aranzana, M. J., & Muranty, H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.07.637171 · Cited by 1
The Potential of Refugee Seed Systems to Promote Contemporary Adaptation in Traditional Crops: A Case Study of African Maize in New England
Kur, A. (2020) · ScholarWorks -A service of University of Vermont Libraries (University of Vermont)
Enviromics, nonlinear kernels and optimized training sets for a climate-smart genomic prediction of yield plasticity in maize
Neto, G. M. F. C. (2021) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.11.2021.tde-11102021-134352
Accounting for Epistasis in Genomic Phenotype Prediction
Vojgani, E. (2021) · eDiss (Georg-August-Universität Göttingen) · doi:10.53846/goediss-8641
Upcycling yield trial data using a weather-driven crop growth modelPreprint
Shimono, H., Abe, A., Kim, C. H., Sato, C., & Iwata, H. (2022) · Research Square · doi:10.21203/rs.3.rs-1544941/v1
Towards breeding for improved photosynthesis traits using the Arabidopsis thaliana model
van Bezouw, R. F. H. M. (2022) · doi:10.18174/571274
On the biological constraints that limit the productivity of rain-fed annual cropsPreprint
Kan, I., Tsur, Y., & Moshelion, M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.21.496884
Upcycling yield trial data using a weather-driven crop growth modelPreprint
Shimono, H., Abe, A., Kim, C. H., Sato, C., & Iwata, H. (2022) · Research Square · doi:10.21203/rs.3.rs-1544941/v2
Multi-environment genomic selection in rice elite breeding linesPreprint
Nguyễn, V. H., Morantte, R. I. Z., Lopena, V., Verdeprado, H., Murori, R., Ndayiragije, A., Katiyar, S., Islam, M. R., Juma, R. U., Galvez, H. F., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-2133066/v1
Breeding for higher yield in tomato by yield dissection and quantitative trait loci for yield components
Tsutsumi-Morita, Y. (2023) · doi:10.18174/581306
Tillering structures the genetic variability of wheat vegetative growth and its plasticity under water deficitPreprint
Leveau, S., Parent, B., Giunta, F., Luchaire, N., Cabrera‐Bosquet, L., Beauchêne, K., Jezequel, S., Motzo, R., & Martre, P. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.26.550706
Self-citation
Forecasting floral futures: leveraging genetic and microenvironmental data to improve seed provenancing under climate changePreprint
Fournier‐Level, A. (2024) · doi:10.22541/au.170669020.02416932/v1
Integrating statistical genetics, geographical information systems and envirotyping: a novel approach for predictive breeding and decision-making
Araújo, M. D. S. (2024) · doi:10.47328/ufvbbt.2024.091
Phenotypic plasticity and breeding for future climates: Case studies in Zea mays
Kusmec, A. (2021) · doi:10.31274/td-20240329-582
Mapping Heat Tolerance QTLs and Incorporating Environmental Information to Improve Genomic Prediction in Maize
McNellie, J. P. (2022) · doi:10.31274/td-20240329-490
The environmental, developmental, and genetic regulation of plant height: Insights from phenotypic plasticity and genetic cloning
Mu, Q. (2021) · doi:10.31274/td-20240329-751
Additional file 6 of The genetic mechanism of heterosis utilization in maize improvement
Xiao, Y., Jiang, S., Cheng, Q., Wang, X., Yan, J., Zhang, R., Qiao, F., Ma, C., Luo, J., Li, W., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14569979
Additional file 3 of The genetic mechanism of heterosis utilization in maize improvement
Xiao, Y., Jiang, S., Cheng, Q., Wang, X., Yan, J., Zhang, R., Qiao, F., Ma, C., Luo, J., Li, W., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14569970
Additional file 1 of Heterotic quantitative trait loci analysis and genomic prediction of seedling biomass-related traits in maize triple testcross populations
Zhang, T., Jiang, L., Ruan, L., Qian, Y., Liang, S., Lin, F., Lü, H., Dai, H., & Zhao, H. Q. (2021) · Figshare · doi:10.6084/m9.figshare.15086195
Additional file 1 of The genetic mechanism of heterosis utilization in maize improvement
Xiao, Y., Jiang, S., Cheng, Q., Wang, X., Yan, J., Zhang, R., Qiao, F., Ma, C., Luo, J., Li, W., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14569964
Additional file 5 of The genetic mechanism of heterosis utilization in maize improvement
Xiao, Y., Jiang, S., Cheng, Q., Wang, X., Yan, J., Zhang, R., Qiao, F., Ma, C., Luo, J., Li, W., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14569976
High-dimensional multi-omics measured in controlled conditions are useful for maize platform and field trait predictionsPreprint
Baber, A., Bertrand, H.-B., Laurent, M., François, C., Laurie, C. M., Nicolas, S., Duborjal, H., Welcker, C., Tardieu, F., Mary‐Huard, T., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.30.596567
Self-citation
factReg: Multi-Environment Genomic Prediction with Penalized Factorial Regression
Kruijer, W., & van Rossum, B.-J. (2024) · doi:10.32614/cran.package.factreg
Self-citation
Genetic Basis of Adaptation
James, M. E., & Ortíz-Barrientos, D. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-15750-9.00112-9
Genomic Resources and Modern Breeding Approaches to Accelerate Genetic Gain in Minor Millets
Singh, A. K., Singh, M., Arya, L., Singh, B., & Verma, M. (2025) · Springer eBooks · doi:10.1007/978-981-96-4265-6_10
CROP SELECTION AND GENETIC ADAPTATIONS FOR ARID CONDITIONS: ENHANCING YIELD AND TOLERANCE TO WATER SCARCITY
Merugu, C. S. R., Udamala, A., Dokka, V. S., Polamuri, A., Udayana, S. K., Banavath, B., & Pandu, U. (2025) · Iterative International Publishers eBooks · doi:10.58532/nbennuracepsw4
WISER: spectral whitening of fixed effects for improved breeding value estimation in structured, unbalanced, and genotype–environment-confounded populationsPreprint
Jacquin, L. J., Guerra, W., Lewandowski, M., Patocchi, A., Rymenants, M., Durel, C.-É., Laurens, F. N. D., Aranzana, M. J., Aranzana, M. J., Mary‐Huard, T., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.17.665325
WISER: an innovative and efficient method for correcting population structure in omics-based prediction and selectionPreprint
Jacquin, L. J., Guerra, W., Lewandowski, M., Patocchi, A., Rymenants, M., Durel, C.-E., Laurens, F. N. D., Aranzana, M. J., Lozano, L., & Muranty, H. (2025) · Research Square · doi:10.21203/rs.3.rs-7091927/v1
Phenomics and Next-Generation Phenotyping to Increase Genetic Gains in Crop Breeding
Reddy, S. P. P., Kumar, N., Bi, M., Kumar, S., & Bharadwaj, C. (2025) · Springer eBooks · doi:10.1007/978-981-95-0583-8_10 · Cited by 1
Unravelling the Genetic Architecture of Field Traits through Multi-Omics Platform Data integrationPreprint
Ali, B., Nicolas, S., Blein‐Nicolas, M., Martin‐Magniette, M., Djabali, Y., Mary‐Huard, T., Charcosset, A., Moreau, L., & Rincent, R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.27.684747
Self-citation
A method for quantifying 3D variation in photosynthetic ability in maize canopies
Liu, F., Yu, G., Wu, W., Zhou, P., Struik, P. C., Yin, X., & Zhu, X. (2025) · PLANT PHYSIOLOGY · doi:10.1093/plphys/kiaf557
When does accounting for gene–environment interactions improve complex trait prediction? A case study with Drosophila lifespan
Morgante, F., & Tiezzi, F. (2025) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkaf304
Additional file 2 of Heterotic quantitative trait loci analysis and genomic prediction of seedling biomass-related traits in maize triple testcross populations
Zhang, T., Jiang, L., Ruan, L., Qian, Y., Liang, S., Lin, F., Lu, H., Dai, H., & Zhao, H. (2021) · Figshare · doi:10.6084/m9.figshare.15086198
Additional file 7 of The genetic mechanism of heterosis utilization in maize improvement
Xiao, Y., Jiang, S., Cheng, Q., Wang, X., Yan, J., Zhang, R., Qiao, F., Ma, C., Luo, J., Li, W., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14569982
Additional file 5 of Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits
Oury, V., Leroux, T., Turc, O., Chapuis, R., Palaffre, C., Tardieu, F., Prado, S. A., Welcker, C., & Lacube, S. (2022) · Figshare · doi:10.6084/m9.figshare.20400877
Self-citation
Additional file 2 of Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits
Oury, V., Leroux, T., Turc, O., Chapuis, R., Palaffre, C., Tardieu, F., Prado, S. A., Welcker, C., & Lacube, S. (2022) · Figshare · doi:10.6084/m9.figshare.20400868
Self-citation
Additional file 10 of Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits
Oury, V., Leroux, T., Turc, O., Chapuis, R., Palaffre, C., Tardieu, F., Prado, S. A., Welcker, C., & Lacube, S. (2022) · Figshare · doi:10.6084/m9.figshare.20400862
Self-citation
Additional file 6 of Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits
Oury, V., Leroux, T., Turc, O., Chapuis, R., Palaffre, C., Tardieu, F., Prado, S. A., Welcker, C., & Lacube, S. (2022) · Figshare · doi:10.6084/m9.figshare.20400880
Self-citation
Additional file 1 of Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits
Oury, V., Leroux, T., Turc, O., Chapuis, R., Palaffre, C., Tardieu, F., Prado, S. A., Welcker, C., & Lacube, S. (2022) · Figshare · doi:10.6084/m9.figshare.20400865
Self-citation
Additional file 4 of Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits
Oury, V., Leroux, T., Turc, O., Chapuis, R., Palaffre, C., Tardieu, F., Prado, S. A., Welcker, C., & Lacube, S. (2022) · Figshare · doi:10.6084/m9.figshare.20400874
Self-citation
Additional file 6 of Multi-environment Genomic Selection in Rice Elite Breeding Lines
Nguyen, V. H., Morantte, R. I. Z., Lopena, V., Verdeprado, H., Murori, R., Ndayiragije, A., Katiyar, S., Islam, M. R., Juma, R. U., Galvez, H. F., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26567258
Additional file 1 of Multi-environment Genomic Selection in Rice Elite Breeding Lines
Nguyen, V. H., Morantte, R. I. Z., Lopena, V., Verdeprado, H., Murori, R., Ndayiragije, A., Katiyar, S., Islam, M. R., Juma, R. U., Galvez, H. F., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26567243
Additional file 9 of Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits
Oury, V., Leroux, T., Turc, O., Chapuis, R., Palaffre, C., Tardieu, F., Prado, S. A., Welcker, C., & Lacube, S. (2022) · Figshare · doi:10.6084/m9.figshare.20400889
Self-citation
Additional file 3 of Multi-environment Genomic Selection in Rice Elite Breeding Lines
Nguyen, V. H., Morantte, R. I. Z., Lopena, V., Verdeprado, H., Murori, R., Ndayiragije, A., Katiyar, S., Islam, M. R., Juma, R. U., Galvez, H. F., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26567249
Additional file 3 of Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits
Oury, V., Leroux, T., Turc, O., Chapuis, R., Palaffre, C., Tardieu, F., Prado, S. A., Welcker, C., & Lacube, S. (2022) · Figshare · doi:10.6084/m9.figshare.20400871
Self-citation
Additional file 5 of Multi-environment Genomic Selection in Rice Elite Breeding Lines
Nguyen, V. H., Morantte, R. I. Z., Lopena, V., Verdeprado, H., Murori, R., Ndayiragije, A., Katiyar, S., Islam, M. R., Juma, R. U., Galvez, H. F., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26567255
Additional file 2 of The genetic mechanism of heterosis utilization in maize improvement
Xiao, Y., Jiang, S., Cheng, Q., Wang, X., Yan, J., Zhang, R., Qiao, F., Ma, C., Luo, J., Li, W., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14569967
Additional file 4 of The genetic mechanism of heterosis utilization in maize improvement
Xiao, Y., Jiang, S., Cheng, Q., Wang, X., Yan, J., Zhang, R., Qiao, F., Ma, C., Luo, J., Li, W., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14569973
Additional file 1 of Enhancing winter wheat prediction with genomics, phenomics and environmental data
Montesinos-López, O. A., Herr, A. W., Crossa, J. L., Montesinos‐López, A., & Carter, A. H. (2024) · Figshare · doi:10.6084/m9.figshare.26728417
Additional file 8 of Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits
Oury, V., Leroux, T., Turc, O., Chapuis, R., Palaffre, C., Tardieu, F., Prado, S. A., Welcker, C., & Lacube, S. (2022) · Figshare · doi:10.6084/m9.figshare.20400886
Self-citation
Additional file 4 of Multi-environment Genomic Selection in Rice Elite Breeding Lines
Nguyen, V. H., Morantte, R. I. Z., Lopena, V., Verdeprado, H., Murori, R., Ndayiragije, A., Katiyar, S., Islam, M. R., Juma, R. U., Galvez, H. F., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26567252
Additional file 7 of Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits
Oury, V., Leroux, T., Turc, O., Chapuis, R., Palaffre, C., Tardieu, F., Prado, S. A., Welcker, C., & Lacube, S. (2022) · Figshare · doi:10.6084/m9.figshare.20400883
Self-citation
Additional file 2 of Multi-environment Genomic Selection in Rice Elite Breeding Lines
Nguyen, V. H., Morantte, R. I. Z., Lopena, V., Verdeprado, H., Murori, R., Ndayiragije, A., Katiyar, S., Islam, M. R., Juma, R. U., Galvez, H. F., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26567246
Additional file 1 of A successive time-to-event model of phyllochron dynamics for hypothesis testing: application to the analysis of genetic and environmental effects in maize
Plancade, S., Marchadier, E., Huet, S., Ressayre, A., Noûs, C., & Dillmann, C. (2024) · Figshare · doi:10.6084/m9.figshare.26595242
Accelerating Sugarcane Breeding with Machine Learning: A Concurrent Multi-trait Predictive Approach
Fan, H., Chao, J., Liu, X., Ma, P., Zhu, G., Li, M., Yan, R., LI, X., Jing, Y., Li, F., et al. (2026) · Sugar Tech · doi:10.1007/s12355-025-01709-9
Does Modern Plant Breeding Techniques Increase Crop Stability Performance?
Ćeran, M., Đorđević, V., Miladinović, J., Miladinović, D., Trkulja, D., Radanović, A., & Kondić‐Špika, A. (2026) · Springer eBooks · doi:10.1007/978-981-95-5664-9_5
Impact of Biotic Stresses Influence on Wheat G × E Interactions and Yield Performance
Bhati, P. K., Govindan, V., Vishwakarma, M. K., & Joshi, A. K. (2026) · Springer eBooks · doi:10.1007/978-981-95-5664-9_7
Robust Random Forests for Genomic Prediction: Challenges and RemediesPreprint
Lourenço, V., Ogutu, J. O., & Piepho, H. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.30.715203
Variable Genetic Architecture Limits Cross‐Range Genomic Prediction of Functional Traits in Invasive Common Ragweed ( Ambrosia artemisiifolia )
Putra, A. R., Hodgins, K. A., & Fournier‐Level, A. (2026) · Evolutionary Applications · doi:10.1111/eva.70232 · Cited by 1
Combining Association and Haplotype Studies Towards the Improvement of Fruit Quality in Tomato
Zhao, J. (2019) · theses.fr (ABES) · doi:10.70675/998b4925z8633z4ddcz9bc0z4fb6ad557530
Genetic determinants of tomato response to abiotic stresses
Diouf, I. (2019) · theses.fr (ABES) · doi:10.70675/21bd9983zee82z42a6zb0bbz9e9c390caa39
Intégration de la modélisation écophysiologique et génétique pour la définition et l'estimation des valeurs génétiques d'idéotypes variétaux - application au cas de la croissance en biomasse chez le sorgho
Larue, F. (2021) · theses.fr (ABES) · doi:10.70675/34ec2143z3295z4b97z85aaz3b6a5f06a3a6
Représentation sémantique et calcul de formules mathématiques pour l'exploitation de données phénomiques végétales
Vargas-Rojas, F. (2023) · doi:10.70675/bbb9d9cbz9186z4f07z9318z4741db819544
Environment-aware genomic prediction enhances the transferability of polygenic resistance to ash dieback in Fraxinus excelsiorPreprint
Meger, J., Ulaszewski, B., & Burczyk, J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.21.719819
Genetic dissection of grain yield and correlated proxy traits under suboptimal conditions
Lin, Y.-C., Urbany, C., Shlykova, A., Hölker, A. C., Ouzunova, M., Presterl, T., Pook, T., Mayer, M., Urzinger, S., & Schön, C.-C. (2026) · Theoretical and Applied Genetics · doi:10.1007/s00122-026-05364-w
Genomic Prediction Models for Trees
Hamdani, A., Boutagayout, A., Kouighat, M., Ouahzizi, B., & Adiba, A. (2026) · CABI eBooks · doi:10.1079/9781836991328.0009
A Dominant-Negative Pleiotropic QTL from Elite Maize Inbred Line Zheng58 Underpins Ideal Plant Architecture for High-Density Maize Breeding
Zhang, H., Yin, T., Jin, X., Liu, Y., Wang, P., Zhang, X., Chen, S., Zhang, H., & Wu, X. (2026) · Agronomy · doi:10.3390/agronomy16141325
Tailoring AI and ML models for genotype-by-environment prediction leveraging environmental covariates: A European rye example
Eckhoff, W. M., Parat, F., Mujica, G. B., Flamm, C., Bustos‐Korts, D., & Piepho, H. (2026) · Theoretical and Applied Genetics · doi:10.1007/s00122-026-05280-z
Current Challenges in Cereal Breeding
Igartua, E., Prats, E., Atienza, S. G., Revilla, P., Ciudad, F. J., Giraldo, P., Casas, A. M., & Castillo, A. M. (2026) · Methods in molecular biology · doi:10.1007/978-1-0716-5245-9_1
When Does Reaction Norm GWAS Discover Plasticity? The Residual Channel in Environmental Index-Based Genetic DissectionPreprint
Jenkins, S., & Graef, G. L. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.27.741005
GE-BiFormer: bidirectional cross-attention integration of genomic and Enviromic data for genotype-by-environment prediction in maize
Zhou, S., Fang, W., Gao, R., Long, X., Chen, L., Hu, X., & Zhao, T. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag437
AMMI and GGE biplot analysis for yield performance and stability assessment of Rice (Oryza sativa L.) hybrids under the multi-environmental trials (METs)Preprint
Prasad, K. R., Senguttuvel, P., Krishna, K. V. R., Deepika, K., Sreenivas, A., Nagaraju, P., Kumar, S. S., & Rao, L. V. S. (2026) · Research Square · doi:10.21203/rs.3.rs-10075848/v1
AI integration with omics and CRISPR to decipher complex traits in plants
Talakayala, A., Mediga, K. R., Naik, S. R., & Mallikarjuna, G. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44079-3.00015-2
Predictive Artificial Intelligence for Climate-Resilient Crop Breeding: Integrating Genomics, Phenomics, and Climate Modeling
Amir, E., & Mazhar, S. (2026) · Scientific Inquiry and Review · doi:10.32350/sir.101.02
Learning Environment-Associated Marker Rankings with Attention-Based Graph Neural NetworksPreprint
Morshedian, A., Pasculescu, O., & Domaratzki, M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.21.753280
Rethinking phenotypic evaluation in plant genetic resources: Environmental mismatch as a hidden source of bias
Khazaeı, H., & Ortiz, R. (2026) · Plant Genetic Resources · doi:10.1017/s1479262126100823
From metabolomes to phenotypes: predictive metabolomics across scales
Pétriacq, P., Prigent, S., Bernard, A., Frioux, C., & Gibon, Y. (2026) · Journal of Experimental Botany · doi:10.1093/jxb/erag395
Integrating Abiotic Stress Adaptation and Disease Resistance in Climate-Resilient Crops: Pangenomics, Genomic Selection, and AI-Driven Breeding
Hwang, Y., Ryu, G., Lee, M., So, Y., Yu, J., & Mun, B. (2026) · Plants · doi:10.3390/plants15203171

Authors: Millet, E. J., Kruijer, W., Coupel-Ledru, A., Alvarez Prado, S., Cabrera-Bosquet, L., Lacube, S., Charcosset, A., Welcker, C., van Eeuwijk, F., & Tardieu, F.
Affiliations: Institut Agro Montpellier (FR)
Wageningen University & Research (NL)
Université Paris-Sud (FR)
Corresponding author: Tardieu, François
ORCID: Millet, Emilie J.
Kruijer, Willem
Coupel-Ledru, Aude
Alvarez Prado, Santiago
Cabrera-Bosquet, Llorenç
Lacube, Sébastien
Charcosset, Alain
Welcker, Claude
van Eeuwijk, Fred
Tardieu, François
Venue: Nature Genetics
Publisher: Springer Science and Business Media LLC
Published: 2019-05-20
Volume: 51
Issue: 6
Pages: 952-956
ISSN: 1061-4036 (ISSN-L)
1546-1718
DOI: 10.1038/s41588-019-0414-y
OpenAlex: W2945720445
Cited by: 280
References: 46
Citations by year: 2026: 30 · 2025: 38 · 2024: 42 · 2023: 43 · 2022: 49 · 2021: 49 · 2020: 25 · 2019: 4
FWCI: 19.90
Type: journal article
Language: en
Open access: green
research.wur.nl
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Nature Genetics (publishedVersion)
PubMed (publishedVersion)
Socio-Environmental Systems Modeling (acceptedVersion)
HAL (submittedVersion)
Explore Bristol Research (submittedVersion)
CONICET Digital (CONICET) (submittedVersion, cc-by-nc-sa)
Data Archiving and Networked Services (DANS) (submittedVersion)
Funders: Agence Nationale de la Recherche (ANR-11-INBS-0012)
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
Life Sciences › Agricultural and Biological Sciences › Plant Science › Genetics and Plant Breeding
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
Keywords: maize, grain yield prediction, gene-environment interaction, environmental drivers, sensor networks, phenotyping platforms, germplasm evaluation, climate change adaptation
MeSH terms: Agriculture, Edible Grain, Zea mays, Environment, Europe, Geography, Phenotype, Genome, Plant, Genomics, Genetic Association Studies, Gene-Environment Interaction

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Genomic prediction of maize yield across European environmental conditions

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks