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
Robles-Zazueta, C. A., Molero, G., Pinto, F., Foulkes, M. J., Reynolds, M. P., & Murchie, E. H. (2021). Field based remote sensing models predict radiation use efficiency in wheat. Journal of Experimental Botany. https://doi.org/10.1093/jxb/erab115
Robles-Zazueta, C. A., Pinto, F., Molero, G., Foulkes, M. J., Reynolds, M. P., & Murchie, E. H. (2022). Prediction of Photosynthetic, Biophysical, and Biochemical Traits in Wheat Canopies to Reduce the Phenotyping Bottleneck. Frontiers in Plant Science, 13. https://doi.org/10.3389/fpls.2022.828451
Roitsch, T., Cabrera-Bosquet, L., Fournier, A., Ghamkhar, K., Jiménez-Berni, J., Pinto, F., & Ober, E. S. (2019). Review: New sensors and data-driven approaches—A path to next generation phenomics. Plant Science, 282, 2–10. https://doi.org/10.1016/j.plantsci.2019.01.011
Roncallo, P. F., Larsen, A. O., Achilli, A. L., Pierre, C. Saint, Gallo, C. A., Dreisigacker, S., & Echenique, V. (2021). Linkage disequilibrium patterns, population structure and diversity analysis in a worldwide durum wheat collection including Argentinian genotypes. BMC Genomics, 22(1). https://doi.org/10.1186/s12864-021-07519-z
Rutkoski, J. E., Krause, M. R., & Sorrells, M. E. (2022a). Breeding Methods: Population Improvement and Selection Methods. 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_6
Rutkoski, J. E., Krause, M. R., & Sorrells, M. E. (2022b). Breeding Methods: Line Development. 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_5
Sehgal, D., Mondal, S., Crespo-Herrera, L., Velu, G., Juliana, P., Huerta-Espino, J., et al. (2020). Haplotype-Based, Genome-Wide Association Study Reveals Stable Genomic Regions for Grain Yield in CIMMYT Spring Bread Wheat. Frontiers in Genetics, 11. https://doi.org/10.3389/fgene.2020.589490
Sharma, R., Crossa, J., Ataei, N., Lodin, R., Joshi, A. K., Vargas, M., et al. (2022). Plant breeding increases spring wheat yield potential in Afghanistan. Crop Science, 62(1), 167–177. https://doi.org/10.1002/csc2.20653
Sierra-Gonzalez, A., Molero, G., Rivera-Amado, C., Babar, M. A., Reynolds, M. P., & Foulkes, M. J. (2021). Exploring genetic diversity for grain partitioning traits to enhance yield in a high biomass spring wheat panel. Field Crops Research, 260. https://doi.org/10.1016/j.fcr.2020.107979
Silva-Pérez, V., Furbank, R. T., Condon, A. G., & Evans, J. R. (2017). Biochemical model of C3 photosynthesis applied to wheat at different temperatures. Plant Cell and Environment, 40, 1552–1564. https://doi.org/10.1111/pce.12953
Silva-Perez, V., Molero, G., Serbin, S. P., Condon, A. G., Reynolds, M. P., Furbank, R. T., & Evans, J. R. (2018). Hyperspectral reflectance as a tool to measure biochemical and physiological traits in wheat. Journal of Experimental Botany, 69(3), 483–496. https://doi.org/10.1093/jxb/erx421
Singh, R. P., Juliana, P., Huerta-Espino, J., Velu, G., Crespo-Herrera, L. A., Mondal, S., et al. (2022). Achieving Genetic Gains in Practice. 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_7
Singh, S., Jighly, A., Sehgal, D., Burgueño, J., Joukhader, R., Singh, S. K., et al. (2021). Direct introgression of untapped diversity into elite wheat lines. Nature Food, 2, 819–827. https://doi.org/10.1038/s43016-021-00380-z
Subbarao, G. V., Kishii, M., Bozal-Leorri, A., Ortiz-Monasterio, I., Gao, X., Ibba, M. I., et al. (2021). Enlisting wild grass genes to combat nitrification in wheat farming: A nature-based solution. Proceedings of the National Academy of Sciences of the United States of America, 118(35). https://doi.org/10.1073/pnas.2106595118
Sukumaran, S., Rebetzke, G., Mackay, I., Bentley, A. R., & Reynolds, M. P. (2022). Pre-breeding Strategies. 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_25
Sukumaran, S., Reynolds, M. P., & Sansaloni, C. (2018). Genome-Wide Association Analyses Identify QTL Hotspots for Yield and Component Traits in Durum Wheat Grown under Yield Potential, Drought, and Heat Stress Environments. Frontiers in Plant Science, 9(February), 1–16. https://doi.org/10.3389/fpls.2018.00081
Valluru, R., Reynolds, M. P., Davies, W. J., & Sukumaran, S. (2017). Phenotypic and genome-wide association analysis of spike ethylene in diverse wheat genotypes under heat stress. New Phytologist, 214(1), 271–283. https://doi.org/10.1111/nph.14367
Velu, G., Michaux, K. D., & Pfeiffer, W. H. (2022). Nutritionally Enhanced Wheat for Food and Nutrition Security. 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_12
Vikram, P., Franco, J., Burgueño, J., Li, H., Sehgal, D., Saint-Pierre, C., et al. (2020). Strategic use of Iranian bread wheat landrace accessions for genetic improvement: Core set formulation and validation. Plant Breeding, 140(1), 87–99. https://doi.org/10.1111/pbr.12885
Villar-Hernández, B. de J., Pérez-Elizalde, S., Martini, J. W. R., Toledo, F., Perez-Rodriguez, P., Krause, M., et al. (2021). Application of multi-trait Bayesian decision theory for parental genomic selection. G3 (Bethesda, Md.), 11(2). https://doi.org/10.1093/g3journal/jkab012
Volpato, L., Pinto, F., González-Pérez, L., Thompson, I. G., Borém, A., Reynolds, M., et al. (2021). High Throughput Field Phenotyping for Plant Height Using UAV-Based RGB Imagery in Wheat Breeding Lines: Feasibility and Validation. Frontiers in Plant Science, 12(February). https://doi.org/10.3389/fpls.2021.591587
Webber, H., White, J. W., Kimball, B. A., Ewert, F., Asseng, S., Eyshi Rezaei, E., et al. (2018). Physical robustness of canopy temperature models for crop heat stress simulation across environments and production conditions. Field Crops Research. https://doi.org/10.1016/j.fcr.2017.11.005
Xiong, W., Reynolds, M. P., Crossa, J., Schulthess, U., Sonder, K., Montes, C., et al. (2021). Increased ranking change in wheat breeding under climate change. Nature Plants, 7(9). https://doi.org/10.1038/s41477-021-00988-w
Xiong, W., Reynolds, M. P., & Xu, Y. (2022). Climate Change Challenges Plant Breeding. Current Opinion in Plant Biology, 102308. https://doi.org/10.1016/j.pbi.2022.102308
Xu, J., Lowe, C., Hernandez-Leon, S. G., Dreisigacker, S., Reynolds, M. P., Valenzuela-Soto, E. M., et al. (2022). The Effects of Brief Heat During Early Booting on Reproductive, Developmental, and Chlorophyll Physiological Performance in Common Wheat (Triticum aestivum L.). Frontiers in Plant Science, 13(May), 1–13. https://doi.org/10.3389/fpls.2022.886541
Zhang, Y., Wang, J., Li, Y., Zhang, Z., Yang, L., Wang, M., et al. (2022). Wheat TaSnRK2.10 phosphorylates TaERD15 and TaENO1 and confers drought tolerance when overexpressed in rice. Plant Physiology, 191(2), 1344–1364. https://doi.org/10.1093/plphys/kiac523