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Literature

Recent Literature coming from HeDWIC (and a few key older references) – updated February, 2023

Abu-Zaitoun, S. Y., Chandrasekhar, K., Assili, S., Shtaya, M. J., Jamous, R. M., Mallah, O. B., et al. (2018). Unlocking the Genetic Diversity within A Middle-East Panel of Durum Wheat Landraces for Adaptation to Semi-arid Climate. Agronomy, 8(10). https://doi.org/10.3390/agronomy8100233

Alvarado, G., Rodríguez, F. M., Pacheco, A., Burgueño, J., Crossa, J., Vargas, M., et al. (2020). META-R: A software to analyze data from multi-environment plant breeding trials. Crop Journal, 8(5). https://doi.org/10.1016/j.cj.2020.03.010

Asseng, S., Ewert, F., Martre, P., Rötter, R. P., Lobell, D. B., Cammarano, D., et al. (2015). Rising temperatures reduce global wheat production. Nature Climate Change, 5, 143–147. https://doi.org/10.1038/nclimate2470

Asseng, Senthold, Cammarano, D., Basso, B., Chung, U., Alderman, P. D., Sonder, K., et al. (2017). Hot spots of wheat yield decline with rising temperatures. Global Change Biology. https://doi.org/10.1111/gcb.13530

Bhati, P. K., Juliana, P., Singh, R. P., Joshi, A. K., Vishwakarma, M. K., Poland, J., et al. (2022). Dissecting the Genetic Architecture of Phenology Affecting Adaptation of Spring Bread Wheat Genotypes to the Major Wheat-Producing Zones in India. Frontiers in Plant Science, 13(July), 1–13. https://doi.org/10.3389/fpls.2022.920682

Bhavani, S., Singh, R. P., Hodson, D. P., Huerta-Espino, J., & Randhawa, M. S. (2022). Wheat Rusts: Current Status, Prospects of Genetic Control and Integrated Approaches to Enhance Resistance Durability. In Matthew P. Reynolds & H.-J. Braun (Eds.), Wheat Improvement: Food Security in a Changing Climate (p. 629). Cham, Switzerland: Springer Cham. https://doi.org/10.1007/978-3-030-90673-3_8

Braun, H. J., Atlin, G., & Payne, T. (2010). Multi-location testing as a tool to identify plant response to global climate change. In Matthew P Reynolds (Ed.), Climate change and crop production (pp. 115–138). Wallingford: CABI. https://doi.org/10.1079/9781845936334.0115

Caamal-Pat, D., Pérez-Rodríguez, P., Crossa, J., Velasco-Cruz, C., Pérez-Elizalde, S., & Vázquez-Peña, M. (2021). lme4GS: An R-Package for Genomic Selection. Frontiers in Genetics, 12. https://doi.org/10.3389/fgene.2021.680569

Camarillo-Castillo, F., Huggins, T. D., Mondal, S., Reynolds, M. P., Tilley, M., & Hays, D. B. (2021). High-resolution spectral information enables phenotyping of leaf epicuticular wax in wheat. Plant Methods, 17(1), 1–17. https://doi.org/10.1186/s13007-021-00759-w

Cerón-Rojas, J. J., & Crossa, J. (2022). The statistical theory of linear selection indices from phenotypic to genomic selection. Crop Science, 62(2), 537–563. https://doi.org/10.1002/csc2.20676

Coast, O., Shah, S., Ivakov, A., Gaju, O., Wilson, P. B., Posch, B. C., et al. (2019). Predicting dark respiration rates of wheat leaves from hyperspectral reflectance. Plant Cell and Environment, 42, 2133–2150. https://doi.org/10.1111/pce.13544

Comastri, A., Janni, M., Simmonds, J., Uauy, C., Pignone, D., Nguyen, H. T., & Marmiroli, N. (2018). Heat in Wheat: Exploit Reverse Genetic Techniques to Discover New Alleles Within the Triticum durum sHsp26 Family. Frontiers in Plant Science, 9(September), 1–16. https://doi.org/10.3389/fpls.2018.01337

Costa-Neto, G., Fritsche-Neto, R., & Crossa, J. (2021). Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials. Heredity, 126(1). https://doi.org/10.1038/s41437-020-00353-1

Costa-Neto, G., Galli, G., Carvalho, H. F., Crossa, J., & Fritsche-Neto, R. (2021). EnvRtype: A software to interplay enviromics and quantitative genomics in agriculture. G3: Genes, Genomes, Genetics, 11(4). https://doi.org/10.1093/g3journal/jkab040

Crespo-Herrera, L., Howard, R., Piepho, H. P., Pérez-Rodríguez, P., Montesinos-Lopez, O., Burgueño, J., et al. (2021). Genome-enabled prediction for sparse testing in multi-environmental wheat trials. Plant Genome, 14(3), 1–17. https://doi.org/10.1002/tpg2.20151

Crespo-Herrera, Leonardo A., Crossa, J., Vargas, M., & Braun, H.-J. (2022). Defining Target Wheat Breeding Environments. In Matthew P. Reynolds & H.-J. Braun (Eds.), Wheat Improvement: Food Security in a Changing Climate (p. 629). Cham, Switzerland: Springer Cham. https://doi.org/10.1007/978-3-030-90673-3_3

Crespo-Herrera, Leonardo Abdiel, Crossa, J., Huerta-Espino, J., Mondal, S., Velu, G., Juliana, P., et al. (2021). Target Population of Environments for Wheat Breeding in India: Definition, Prediction and Genetic Gains. Frontiers in Plant Science, 12. https://doi.org/10.3389/fpls.2021.638520

Crossa, José, Cerón-Rojas, J. J., Martini, J. W. R., Covarrubias-Pazaran, G., Alvarado, G., Toledo, F. H., & Velu, G. (2022). Theory and Practice of Phenotypic and Genomic Selection Indices. In Matthew P. Reynolds & H.-J. Braun (Eds.), Wheat Improvement: Food Security in a Changing Climate (p. 629). Cham, Switzerland: Springer Cham. https://doi.org/10.1007/978-3-030-90673-3_32

Crossa, Jose, Fritsche-Neto, R., Montesinos-Lopez, O. A., Costa-Neto, G., Dreisigacker, S., Montesinos-Lopez, A., & Bentley, A. R. (2021). The Modern Plant Breeding Triangle: Optimizing the Use of Genomics, Phenomics, and Enviromics Data. Frontiers in Plant Science, 12(April), 1–6. https://doi.org/10.3389/fpls.2021.651480

Crossa, José, Montesinos-López, O. A., Pérez-Rodríguez, P., Costa-Neto, G., Fritsche-Neto, R., Ortiz, R., et al. (2022). Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction. In N. Ahmadi & J. Bartholomé (Eds.), Genomic Prediction of Complex Traits: Methods and Protocols (p. 648). New York, NY: Humana New York, NY. https://doi.org/10.1007/978-1-0716-2205-6_9

Cuevas, J., Montesinos-López, O. A., Martini, J. W. R., Pérez-Rodríguez, P., Lillemo, M., & Crossa, J. (2020). Approximate Genome-Based Kernel Models for Large Data Sets Including Main Effects and Interactions. Frontiers in Genetics, 11. https://doi.org/10.3389/fgene.2020.567757

Dreccer, M. F., Molero, G., Rivera-Amado, C., John-Bejai, C., & Wilson, Z. (2019). Yielding to the image: How phenotyping reproductive growth can assist crop improvement and production. Plant Science, 282, 73–82. https://doi.org/10.1016/j.plantsci.2018.06.008

Du, Y., Chen, L., Wang, Y., Yang, Z., Saeed, I., Daoura, B. G., & Hu, Y.-G. (2018). The combination of dwarfing genes Rht4 and Rht8 reduced plant height, improved yield traits of rainfed bread wheat (Triticum aestivum L.). Field Crops Research, 215, 149–155. https://doi.org/10.1016/j.fcr.2017.10.015

Dwivedi, S. L., Reynolds, M. P., & Ortiz, R. (2021). Mitigating tradeoffs in plant breeding. iScience, 24(9). https://doi.org/10.1016/j.isci.2021.102965

Erenstein, O., Jaleta, M., Mottaleb, K. A., Sonder, K., Donovan, J., & Braun, H.-J. (2022). Global Trends in Wheat Production, Consumption and Trade. In Matthew P. Reynolds & H.-J. Braun (Eds.), Wheat Improvement: Food Security in a Changing Climate (p. 629). Cham, Switzerland: Springer Cham. https://doi.org/10.1007/978-3-030-90673-3_4

Foulkes, M. J., Molero, G., Griffiths, S., Slafer, G. A., & Reynolds, M. P. (2022). Yield Potential. In Matthew P. Reynolds & H.-J. Braun (Eds.), Wheat Improvement: Food Security in a Changing Climate (p. 629). Cham, Switzerland: Springer Cham. https://doi.org/10.1007/978-3-030-90673-3_21

Gholami, M., Wimmer, V., Sansaloni, C., Petroli, C., Hearne, S. J., Covarrubias-Pazaran, G., et al. (2021). A Comparison of the Adoption of Genomic Selection Across Different Breeding Institutions. Frontiers in Plant Science, 12(November), 1–6. https://doi.org/10.3389/fpls.2021.728567

Guzmán, C., Crossa, J., Mondal, S., Govindan, V., Huerta, J., Crespo-Herrera, L., et al. (2022). Effects of glutenins (Glu-1 and Glu-3) allelic variation on dough properties and bread-making quality of CIMMYT bread wheat breeding lines. Field Crops Research, 284(June). https://doi.org/10.1016/j.fcr.2022.108585

Hernandez-Ochoa, I. M., Asseng, S., Kassie, B. T., Xiong, W., Robertson, R., Luz Pequeno, D. N., et al. (2018). Climate change impact on Mexico wheat production. Agricultural and Forest Meteorology. https://doi.org/10.1016/j.agrformet.2018.09.008

Honsdorf, N., Mulvaney, M. J., Singh, R. P., Ammar, K., Govaerts, B., & Verhulst, N. (2022). Dataset of historic and modern bread and durum wheat cultivar performance under conventional and reduced tillage with full and reduced irrigation. Data in Brief, 43, 108439. https://doi.org/10.1016/j.dib.2022.108439

Howard, R., Jarquin, D., & Crossa, J. (2022). Overview of Genomic Prediction Methods and the Associated Assumptions on the Variance of Marker Effect, and on the Architecture of the Target Trait. In N. Ahmadi & J. Bartholomé (Eds.), Genomic Prediction of Complex Traits: Methods and Protocols (p. 648). New York, NY: Humana New York, NY. https://doi.org/10.1007/978-1-0716-2205-6_5

Hunt, J. R., Hayman, P. T., Richards, R. A., & Passioura, J. B. (2018). Opportunities to reduce heat damage in rain-fed wheat crops based on plant breeding and agronomic management. Field Crops Research, 224(January), 126–138. https://doi.org/10.1016/j.fcr.2018.05.012

Joynson, R., Molero, G., Coombes, B., Gardiner, L. J., Rivera-Amado, C., Piñera-Chávez, F. J., et al. (2021). Uncovering candidate genes involved in photosynthetic capacity using unexplored genetic variation in Spring Wheat. Plant Biotechnology Journal, 19(8). https://doi.org/10.1111/pbi.13568

Juliana, P., Govindan, V., Crespo-Herrera, L., Mondal, S., Huerta-Espino, J., Shrestha, S., et al. (2022). Genome-Wide Association Mapping Identifies Key Genomic Regions for Grain Zinc and Iron Biofortification in Bread Wheat. Frontiers in Plant Science, 13(June), 1–17. https://doi.org/10.3389/fpls.2022.903819

Juliana, P., He, X., Marza, F., Islam, R., Anwar, B., Poland, J., et al. (2022). Genomic Selection for Wheat Blast in a Diversity Panel, Breeding Panel and Full-Sibs Panel. Frontiers in Plant Science, 12(January), 1–18. https://doi.org/10.3389/fpls.2021.745379

Juliana, P., He, X., Poland, J., Roy, K. K., Malaker, P. K., Mishra, V. K., et al. (2022). Genomic selection for spot blotch in bread wheat breeding panels, full-sibs and half-sibs and index-based selection for spot blotch, heading and plant height. Theoretical and Applied Genetics, 135(6), 1965–1983. https://doi.org/10.1007/s00122-022-04087-y

Juliana, P., He, X., Poland, J., Shrestha, S., Joshi, A. K., Huerta-Espino, J., et al. (2022). Genome-Wide Association Mapping Indicates Quantitative Genetic Control of Spot Blotch Resistance in Bread Wheat and the Favorable Effects of Some Spot Blotch Loci on Grain Yield. Frontiers in Plant Science, 13(March), 1–18. https://doi.org/10.3389/fpls.2022.835095

Juliana, P., Singh, R. P., Poland, J., Shrestha, S., Huerta-Espino, J., Govindan, V., et al. (2021). Elucidating the genetics of grain yield and stress-resilience in bread wheat using a large-scale genome-wide association mapping study with 55,568 lines. Scientific Reports, 11(1). https://doi.org/10.1038/s41598-021-84308-4

Laddomada, B., Blanco, A., Mita, G., D’amico, L., Singh, R. P., Ammar, K., et al. (2021). Drought and heat stress impacts on phenolic acids accumulation in durum wheat cultivars. Foods, 10(9). https://doi.org/10.3390/foods10092142

Langridge, P., & Reynolds, M. (2021). Breeding for drought and heat tolerance in wheat. Theoretical and Applied Genetics, 134(6), 1753–1769. https://doi.org/10.1007/s00122-021-03795-1

Li, C., Li, L., Reynolds, M., Wang, J., Chang, X., Mao, X., & Jing, R. (2021). Recognizing the hidden half in wheat: root system attributes associated with drought tolerance. Journal of Experimental Botany, erab124. https://doi.org/10.1093/jxb/erab124

Li, L., Peng, Z., Mao, X., Wang, J., Chang, X., Reynolds, M., & Jing, R. (2019). Genome-wide association study reveals genomic regions controlling root and shoot traits at late growth stages in wheat. Annals of Botany, 1–14. https://doi.org/10.1093/aob/mcz041

Li, Y., Tao, F., Hao, Y., Tong, J., Xiao, Y., Zhang, H., et al. (2023). Linking genetic markers with an eco-physiological model to pyramid favourable alleles and design wheat ideotypes. Plant, Cell & Environment, 46(3), 780–795. https://doi.org/10.1111/pce.14518

Liu, C., Pinto, F., Cossani, C. M., Sukumaran, S., & Reynolds, M. P. (2019). Spectral reflectance indices as proxies for yield potential and heat stress tolerance in spring wheat: Heritability estimates and marker-trait associations. Frontiers of Agricultural Science and Engineering, 6(3). https://doi.org/10.15302/J-FASE-2019269

Liu, C., Sukumaran, S., Claverie, E., Sansaloni, C., Dreisigacker, S., & Reynolds, M. (2019). Genetic dissection of heat and drought stress QTLs in phenology-controlled synthetic-derived recombinant inbred lines in spring wheat. Molecular Breeding, 39, 1–18. https://doi.org/10.1007/s11032-019-0938-y

Lopez-Cruz, M., Dreisigacker, S., Crespo-Herrera, L., Bentley, A. R., Singh, R., Poland, J., et al. (2022). Sparse kernel models provide optimization of training set design for genomic prediction in multiyear wheat breeding data. Plant Genome, (July), 1–15. https://doi.org/10.1002/tpg2.20254

Lyra, D. H., Griffiths, C. A., Watson, A., Joynson, R., Molero, G., Igna, A.-A., et al. (2021). Gene-based mapping of trehalose biosynthetic pathway genes reveals association with source- and sink-related yield traits in a spring wheat panel. Food and Energy Security. https://doi.org/10.1002/fes3.292

Martini, J. W. R., Gao, N., & Crossa, J. (2022). Incorporating Omics Data in Genomic Prediction. In N. Ahmadi & J. Bartholomé (Eds.), Genomic Prediction of Complex Traits: Methods and Protocols (p. 648). New York, NY: Humana New York, NY. https://doi.org/10.1007/978-1-0716-2205-6_12

Martini, J. W. R., Molnar, T. L., Crossa, J., Hearne, S. J., & Pixley, K. V. (2021). Opportunities and Challenges of Predictive Approaches for Harnessing the Potential of Genetic Resources. Frontiers in Plant Science, 12. https://doi.org/10.3389/fpls.2021.674036

Mathews, K. L., & Crossa, J. (2022). Experimental Design for Plant Improvement. In Matthew P. Reynolds & H.-J. Braun (Eds.), Wheat Improvement: Food Security in a Changing Climate (p. 629). Cham, Switzerland: Springer Cham. https://doi.org/10.1007/978-3-030-90673-3_13

Molero, G., Coombes, B., Joynson, R., Pinto, F., Piñera-Chávez, F. J., Rivera-Amado, C., et al. (2023). Exotic alleles contribute to heat tolerance in wheat under field conditions. Communications Biology, 6(1). https://doi.org/10.1038/s42003-022-04325-5

Molero, G., Joynson, R., Pinera‐Chavez, F. J., Gardiner, L., Rivera‐Amado, C., Hall, A., & Reynolds, M. P. (2019). Elucidating the genetic basis of biomass accumulation and radiation use efficiency in spring wheat and its role in yield potential. Plant Biotechnology Journal, 17(7), 1276–1288. https://doi.org/10.1111/pbi.13052

Montesinos-López, A., Gutierrez-Pulido, H., Montesinos-López, O. A., & Crossa, J. (2020). Maximum a posteriori threshold genomic prediction model for ordinal traits. G3: Genes, Genomes, Genetics, 10(11). https://doi.org/10.1534/g3.120.401733

Montesinos-López, A., Montesinos-López, O. A., Montesinos-López, J. C., Flores-Cortes, C. A., de la Rosa, R., & Crossa, J. (2021). A guide for kernel generalized regression methods for genomic-enabled prediction. Heredity. https://doi.org/10.1038/s41437-021-00412-1

Montesinos-López, A., Runcie, D. E., Ibba, M. I., Pérez-Rodríguez, P., Montesinos-López, O. A., Crespo, L. A., et al. (2021). Multi-trait genomic-enabled prediction enhances accuracy in multi-year wheat breeding trials. G3 Genes|Genomes|Genetics, 11(10). https://doi.org/10.1093/g3journal/jkab270

Montesinos-López, Osval A., Montesinos-López, A., Bernal Sandoval, D. A., Mosqueda-Gonzalez, B. A., Valenzo-Jiménez, M. A., & Crossa, J. (2022). Multi-trait genome prediction of new environments with partial least squares. Frontiers in Genetics, 13(September), 1–15. https://doi.org/10.3389/fgene.2022.966775

Montesinos-López, Osval A., Montesinos-López, A., Cano-Paez, B., Hernández-Suárez, C. M., Santana-Mancilla, P. C., & Crossa, J. (2022). A Comparison of Three Machine Learning Methods for Multivariate Genomic Prediction Using the Sparse Kernels Method (SKM) Library. Genes, 13(8). https://doi.org/10.3390/genes13081494

Montesinos-López, Osval Antonio, Montesinos-López, A., Acosta, R., Varshney, R. K., Bentley, A., & Crossa, J. (2022). Using an incomplete block design to allocate lines to environments improves sparse genome-based prediction in plant breeding. Plant Genome, 15(1), 1–24. https://doi.org/10.1002/tpg2.20194

Montesinos-López, Osval Antonio, Montesinos-López, A., Hernandez-Suarez, C. M., Barrón-López, J. A., & Crossa, J. (2021). Deep-learning power and perspectives for genomic selection. Plant Genome. https://doi.org/10.1002/tpg2.20122

Montesinos-López, Osval Antonio, Montesinos-López, A., Mosqueda-Gonzalez, B. A., Montesinos-López, J. C., & Crossa, J. (2022). Accounting for Correlation Between Traits in Genomic Prediction. In N. Ahmadi & J. Bartholomé (Eds.), Genomic Prediction of Complex Traits: Methods and Protocols (p. 648). New York, NY: Humana New York, NY. https://doi.org/10.1007/978-1-0716-2205-6_10

Montesinos-López, Osval Antonio, Montesinos-López, A., Mosqueda-Gonzalez, B. A., Montesinos-López, J. C., Crossa, J., Ramirez, N. L., et al. (2021). A zero altered Poisson random forest model for genomic-enabled prediction. G3: Genes, Genomes, Genetics, 11(2). https://doi.org/10.1093/g3journal/jkaa057

Montesinos-López, Osval Antonio, Montesinos-López, A., Pérez-Rodríguez, P., Barrón-López, J. A., Martini, J. W. R., Fajardo-Flores, S. B., et al. (2021). A review of deep learning applications for genomic selection. BMC Genomics. https://doi.org/10.1186/s12864-020-07319-x

Montesinos-López, Osval Antonio, Montesinos-López, J. C., Montesinos-López, A., Ramírez-Alcaraz, J. M., Poland, J., Singh, R., et al. (2022). Bayesian multitrait kernel methods improve multienvironment genome-based prediction. G3: Genes, Genomes, Genetics, 12(2). https://doi.org/10.1093/g3journal/jkab406

Montesinos-López, Osval Antonio, Montesinos-López, J. C., Singh, P., Lozano-Ramirez, N., Barrón-López, A., Montesinos-López, A., & Crossa, J. (2020). A multivariate poisson deep learning model for genomic prediction of count data. G3: Genes, Genomes, Genetics, 10(11). https://doi.org/10.1534/g3.120.401631

Montesinos López, O. A., Montesinos López, A., & Crossa, J. (2022). Multivariate Statistical Machine Learning Methods for Genomic Prediction. Multivariate Statistical Machine Learning Methods for Genomic Prediction. https://doi.org/10.1007/978-3-030-89010-0

Pequeno, D. N. L., Hernandez-Ochoa, I. M., Reynolds, M. P., Sonder, K., Molero-Milan, A., Robertson, R., et al. (2021). Climate impact and adaptation to heat and drought stress of regional and global wheat production. Environmental Research Letters, 16, 054070. https://doi.org/10.1088/1748-9326/abd970

Phuke, R. M., He, X., Juliana, P., Kabir, M. R., Roy, K. K., Marza, F., et al. (2022). Identification of Genomic Regions and Sources for Wheat Blast Resistance through GWAS in Indian Wheat Genotypes. Genes, 13(4). https://doi.org/10.3390/genes13040596

Puhl, L. E., Crossa, J., Munilla, S., Pérez-Rodríguez, P., & Cantet, R. J. C. (2021). Additive genetic variance and covariance between relatives in synthetic wheat crosses with variable parental ploidy levels. Genetics, 217(2). https://doi.org/10.1093/genetics/iyaa048

Quintero, A., Molero, G., Reynolds, M. P., & Calderini, D. F. (2018). Trade-off between grain weight and grain number in wheat depends on GxE interaction: A case study of an elite CIMMYT panel (CIMCOG). European Journal of Agronomy, 92. https://doi.org/10.1016/j.eja.2017.09.007

Reynolds, M., Atkin, O. K., Bennett, M., Cooper, M., Dodd, I. C., Foulkes, M. J., et al. (2021). Addressing Research Bottlenecks to Crop Productivity. Trends in Plant Science, 26(6), 607–630. https://doi.org/10.1016/j.tplants.2021.03.011

Reynolds, M., Borrell, A., Braun, H., Edmeades, G., Flavell, R., Gwyn, J., et al. (2019). Translational Research for Climate Resilient, Higher Yielding Crops. Crop Breeding, Genetics and Genomics, 1, e190016. https://doi.org/10.20900/cbgg20190016

Reynolds, M., & Langridge, P. (2016). Physiological breeding. Current Opinion in Plant Biology, 31, 162–171. https://doi.org/10.1016/j.pbi.2016.04.005

Reynolds, M. P., Braun, H. J., Cavalieri, A. J., Chapotin, S., Davies, W. J., Ellul, P., et al. (2017). Improving global integration of crop research. Science, 357(6349), 359–360. https://doi.org/10.1126/science.aam8559

Reynolds, M., Pinto, F., Acevedo, L., Pinera‐Chavez, F. J., & Rivera-Amado, C. (2022). The evolution of trait selection in breeding: from seeing to remote sensing. In A. Walter (Ed.), Advances in plant phenotyping for more sustainable crop production (p. 404). Sawston, UK: Burleigh Dodds Science Publishing Limited. https://doi.org/10.19103/AS.2022.0102.02

Reynolds, Matthew P., & Braun, H.-J. (2022a). Wheat Improvement. In Matthew P. Reynolds & H.-J. Braun (Eds.), Wheat Improvement: Food Security in a Changing Climate (p. 629). Cham, Switzerland: Springer Cham. https://doi.org/10.1007/978-3-030-90673-3_1

Reynolds, Matthew P., & Braun, H.-J. (Eds.). (2022b). Wheat Improvement: Food Security in a Changing Climate. Springer Cham. https://doi.org/10.1007/978-3-030-90673-3

Reynolds, Matthew P., Braun, H.-J., Flavell, R. B., Gwyn, J. J., Langridge, P., Rosichan, J. L., et al. (2022). Translational Research Networks. In Matthew P. Reynolds & H.-J. Braun (Eds.), Wheat Improvement: Food Security in a Changing Climate (p. 629). Cham, Switzerland: Springer Cham. https://doi.org/10.1007/978-3-030-90673-3_26

Reynolds, Matthew P., & Lewis, J. M. (2022). The Future of Climate Resilience in Wheat. SocArXiv. https://doi.org/10.31235/osf.io/hvd4e

Reynolds, Matthew P., Lewis, J. M., Ammar, K., Basnet, B. R., Crespo-Herrera, L., Crossa, J., et al. (2021). Harnessing translational research in wheat for climate resilience. Journal of Experimental Botany, 72(14), 5134–5157. https://doi.org/10.1093/jxb/erab256

Reynolds, Matthew P., Pask, A. J. D., Hoppitt, W. J. E., Sonder, K., Sukumaran, S., Molero, G., et al. (2017). Strategic crossing of biomass and harvest index—source and sink—achieves genetic gains in wheat. Euphytica, 213, 257. https://doi.org/10.1007/s10681-017-2040-z

Reynolds, Matthew P., Pask, A. J. D., Hoppitt, W. J. E., Sonder, K., Sukumaran, S., Molero, G., et al. (2018). Correction to: Strategic crossing of biomass and harvest index—source and sink—achieves genetic gains in wheat (Euphytica, (2017), 213, 257, 10.1007/s10681-017-2040-z). Euphytica, 214(1). https://doi.org/10.1007/s10681-017-2086-y

Rivera-Amado, C., Molero, G., Trujillo-Negrellos, E., Reynolds, M., & Foulkes, J. (2020). Estimating organ contribution to grain filling and potential for source upregulation in wheat cultivars with a contrasting source-sink balance. Agronomy, 10, 1527. https://doi.org/10.3390/agronomy10101527

Rivera‐Amado, C., Trujillo-Negrellos, E., Molero, G., Reynolds, M. P., Sylvester-Bradley, R., & Foulkes, M. J. (2019). Optimizing dry-matter partitioning for increased spike growth, grain number and harvest index in spring wheat. Field Crops Research, 240, 154–167. https://doi.org/10.1016/j.fcr.2019.04.016

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