QTL and genomic prediction accuracy for grain yield and secondary traits in a maize population under heat and heat-drought stresses

References

• Ahmad, I., B. Ahmad, K. Boote, and G. Hoogenboom. 2020. “Adaptation Strategies for Maize Production under Climate Change for semi-arid Environments.” European Journal of Agronomy 115: 126040. doi:10.1016/j.eja.2020.126040.
   Web of Science ®Google Scholar
• Almeida, G. D., D. Makumbi, C. Magorokosho, S. Nair, A. Borém, J. M. Ribaut, M. Bänziger, B. M. Prasanna, J. Crossa, and R. Babu. 2013. “QTL Mapping in Three Tropical Maize Populations Reveals a Set of Constitutive and Adaptive Genomic Regions for Drought Tolerance.” Theoretical and Applied Genetics 126 (3): 583–600. doi:10.1007/s00122-012-2003-7.
   PubMed Web of Science ®Google Scholar
• Alvarado, G., M. López, M. Vargas, A. Pacheco, F. Rodriguez, J. Burgueño, and J. Crossa. 2019. “META-R (Multi Environment Trail Analysis with R for Windows). Version 6.04.”
   Google Scholar
• Andrade, F. H., C. Vega, S. Uhart, A. Cirilo, M. Cantarero, and O. Valentinuz. 1999. “Kernel Number Determination in Maize.” Crop Science 39 (2): 453–459. doi:10.2135/cropsci1999.0011183X0039000200026x.
   Web of Science ®Google Scholar
• Araus, J. L., G. A. Slafer, C. Royo, and M. D. Serret. 2008. “Breeding for Yield Potential and Stress Adaptation in Cereals.” Critical Reviews in Plant Sciences 27 (6): 377–412. doi:10.1080/07352680802467736.
   Web of Science ®Google Scholar
• Barnabás, B., K. Jäger, and A. Fehér. 2008. “The Effect of Drought and Heat Stress on Reproductive Processes in Cereals.” Plant, Cell & Environment 31: 11–38. doi:10.1111/j.1365-3040.2007.01727.x.
   PubMed Web of Science ®Google Scholar
• Bernier, J., G. N. Atlin, R. Serraj, A. Kumar, and D. Spaner. 2008. “Breeding Upland Rice for Drought Resistance.” Journal of the Science of Food and Agriculture 88 (6): 927–939. doi:10.1002/jsfa.3153.
   Web of Science ®Google Scholar
• Beyene, Y., K. Semagn, J. Crossa, S. Mugo, G. N. Atlin, A. Tarekegne, B. Meisel, et al. 2016. “Improving Maize Grain Yield under Drought Stress and Non-stress Environments in Sub-Saharan Africa Using Marker-Assisted Recurrent Selection.” Crop Science 56 (1): 344–353. doi:10.2135/cropsci2015.02.0135.
   Web of Science ®Google Scholar
• Bolaños, J., and G. O. Edmeades. 1996. “The Importance of the anthesis-silking Interval in Breeding for Drought Tolerance in Tropical Maize.” Field Crops Research 48 (1): 65–80. doi:10.1016/0378-4290(96)00036-6.
   Web of Science ®Google Scholar
• Cairns, J. E., J. Crossa, P. H. Zaidi, P. Grudloyma, C. Sanchez, J. L. Araus, S. Thaitad, et al. 2013. “Identification of Drought, Heat, and Combined Drought and Heat Tolerant Donors in Maize.” Crop Science 53 (4): 1335–1346. doi:10.2135/cropsci2012.09.0545.
   Web of Science ®Google Scholar
• Calus, M. P., Y. de Haas, M. Pszczola, and R. F. Veerkamp. 2013. “Predicted Accuracy of and Response to Genomic Selection for New Traits in Dairy Cattle.” Animal 7 (2): 183–191. pmid:23031684. doi:10.1017/S1751731112001450.
   PubMed Web of Science ®Google Scholar
• Campos, H., M. Cooper, J. E. Habben, G. O. Edmeades, and J. R. Schussler. 2004. “Improving Drought Tolerance in Maize: A View from Industry.” Field Crops Research 90 (1): 19–34. doi:10.1016/j.fcr.2004.07.003.
   Web of Science ®Google Scholar
• Cao, S., A. Loladze, Y. Yuan, Y. Wu, A. Zhang, J. Chen, G. Huestis, et al. 2017. “Genome-Wide Analysis of Tar Spot Complex Resistance in Maize Using Genotyping-by-Sequencing SNPs and Whole-Genome Prediction.” The Plant Genome 10 (2): 1–14. doi:10.3835/plantgenome2016.10.0099.
   Web of Science ®Google Scholar
• Cerrudo, D., S. Cao, Y. Yuan, C. Martinez, E. A. Suarez, R. Babu, X. Zhang, and S. Trachsel. 2018. “Genomic Selection Outperforms Marker Assisted Selection for Grain Yield and Physiological Traits in a Maize Doubled Haploid Population across Water Treatments.” Frontiers in Plant Science 9: 1–12. doi:10.3389/fpls.2018.00366.
   PubMed Web of Science ®Google Scholar
• Cerrudo, D., L. González Pérez, J. A. Mendoza Lugo, and S. Trachsel. 2017. “Stay-Green and Associated Vegetative Indices to Breed Maize Adapted to Heat and Combined Heat-Drought Stresses.” Remote Sensing 9 (3): 235. doi:10.3390/rs9030235.
   Web of Science ®Google Scholar
• Cooper, M., I. H. DeLacy, and K. E. Basford. 1996. “Relationships among analytical methods used to analyze genotypic adaptation in multi-environment trials.” In Plant adaptation and crop improvement, edited by M. Cooper and G. L. Hammer, 193–224. Wallingford, UK: CAB Int.
   Google Scholar
• Cossani, C. M., J. Pietragalla, and M. P. Reynolds. ”Canopy temperature and plant water relations traits.” In Physiological Breeding I: Interdisciplinary Approaches to Improve Crop Adaptation, edited by M. P. Reynolds, A. Pask, D. M. Mullan, M. P. Reynolds, A. Pask, and D. M. Mullan 58–66. CIMMYT: Texcoco. 2012.
   Google Scholar
• Crafts-Brandner, S. J., and M. E. Salvucci. 2002. “Sensitivity of Photosynthesis in a C4 Plant, Maize, to Heat Stress.” Plant Physiology 129 (4): 1773–1780. doi:10.1104/pp.002170.
   PubMed Web of Science ®Google Scholar
• Eberhart, S. A., and W. A. Russell. 1966. “Stability Parameters for Comparing Varieties 1.” Crop Science 6 (1): 36–40. cropsci1966.0011183X000600010011x. doi:10.2135/cropsci1966.0011183X000600010011x.
   Web of Science ®Google Scholar
• Eyshi Rezaei, E., H. Webber, T. Gaiser, J. Naab, and F. Ewert. 2015. “Heat Stress in Cereals: Mechanisms and Modelling.” European Journal of Agronomy 64: 98–113. doi:10.1016/j.eja.2014.10.003.
   Web of Science ®Google Scholar
• Fischer, R. A., and G. O. Edmeades. 2010. “Breeding and Cereal Yield Progress.” Crop Science 50: 85–98. doi:10.2135/cropsci2009.10.0564.
   Web of Science ®Google Scholar
• Frascaroli, E., M. A. Canè, P. Landi, G. Pea, L. Gianfranceschi, M. Villa, M. Morgante, and P. ME. 2007. “Classical Genetic and Quantitative Trait Loci Analyses of Heterosis in a Maize Hybrid between Two Elite Inbred Lines.” Genetics 176 (1): 625–644. doi:10.1534/genetics.106.064493.
   PubMed Web of Science ®Google Scholar
• Frascaroli, E., M. A. Canè, P. ME, G. Pea, M. Morgante, and P. Landi. 2009. “QTL Detection in Maize Testcross Progenies as Affected by Related and Unrelated Testers.” Theoretical and Applied Genetics 118 (5): 993–1004. doi:10.1007/s00122-008-0956-3.
   PubMed Web of Science ®Google Scholar
• Frey, F. P., T. Presterl, P. Lecoq, A. Orlik, and B. Stich. 2016. “First Steps to Understand Heat Tolerance of Temperate Maize at Adult Stage: Identification of QTL across Multiple Environments with Connected Segregating Populations.” Theoretical and Applied Genetics 129 (5): 945–961. doi:10.1007/s00122-016-2674-6.
   PubMed Web of Science ®Google Scholar
• Gates, D. M. 1968. “Transpiration and Leaf Temperature.” Annual Review of Plant Physiology 19 (1): 211–238. doi:10.1146/annurev.pp.19.060168.001235.
   Google Scholar
• Gholipoor, M., S. Choudhary, T. R. Sinclair, C. D. Messina, and M. Cooper. 2013. “Transpiration Response of Maize Hybrids to Atmospheric Vapour Pressure Deficit.” Journal of Agronomy and Crop Science 199 (3): 155–160. doi:10.1111/jac.12010.
   Web of Science ®Google Scholar
• Gitelson, A., A. Viña, T. J. Arkebauer, D. C. Rundquist, G. Keydan, and B. Leavitt. 2003. “Remote Estimation of Leaf Area Index and Green Leaf Biomass in Maize Canopies.” Geophysical Research Letters 30 (5): 1248. doi:10.1029/2002GL016450.
   Web of Science ®Google Scholar
• Glaubitz, J. C., T. M. Casstevens, F. Lu, J. Harriman, R. J. Elshire, Q. Sun, and E. S. Buckler. 2014. “TASSEL-GBS: A High Capacity Genotyping by Sequencing Analysis Pipeline.” Plos One 9: e90346.
   PubMed Web of Science ®Google Scholar
• Guyot, G., F. Baret, and D. J. Major. 1988. “High Spectral Resolution: Determination of Spectral Shifts between the Red and the near Infrared.” The International Archives of Photogrammetry and Remote Sensing and Spatial Information Sciences 27 (11): 750–760.
   Google Scholar
• Hao, Z., X. Li, X. Liu, C. Xie, M. Li, D. Zhang, and S. Zhang. 2010. “Meta-analysis of Constitutive and Adaptive QTL for Drought Tolerance in Maize.” Euphytica 174 (2): 165–177. doi:10.1007/s10681-009-0091-5.
   Web of Science ®Google Scholar
• Heffner, E. L., A. J. Lorenz, J. L. Jannink, and M. E. Sorrells. 2010. “Plant Breeding with Genomic Selection: Gain per Unit Time and Cost.” Crop Science 50 (5): 1681–1690. doi:10.2135/cropsci2009.11.0662.
   Web of Science ®Google Scholar
• Josè-Estanyol, M., and P. Puigdomènech. 1998. “Developmental and Hormonal Regulation of Genes Coding for Proline-Rich Proteins in Female Inflorescences and Kernels of Maize1.” Plant Physiology 116 (2): 485–494. doi:10.1104/pp.116.2.485.
   PubMed Web of Science ®Google Scholar
• Kelcey, J., and A. Lucieer. 2012. “Sensor Correction of a 6-Band Multispectral Imaging Sensor for UAV Remote Sensing.” Remote Sensing 4 (5): 1462–1493. doi:10.3390/rs4051462.
   Web of Science ®Google Scholar
• Liu, X., Y. Yuan, C. Martinez, R. Babu, E. A. Suarez, X. Zhang, N. Neiff, and S. Trachsel. 2020. “Identification of QTL for Early Vigor and Leaf Senescence across Two Tropical Maize Doubled Haploid Populations under Nitrogen Deficient Conditions.” Euphytica 216 (3): 42. doi:10.1007/s10681-020-2577-0.
   Web of Science ®Google Scholar
• Lobell, D. B., M. Bänziger, C. Magorokosho, and B. Vivek. 2011. “Nonlinear Heat Effects on African Maize as Evidenced by Historical Yield Trials.” Nature Climate Change 1 (1): 42–45. doi:10.1038/nclimate1043.
   Web of Science ®Google Scholar
• Lobell, D. B., and M. B. Burke. 2010. “On the Use of Statistical Models to Predict Crop Yield Responses to Climate Change.” Agricultural and Forest Meteorology 150 (11): 1443–1452. doi:10.1016/j.agrformet.2010.07.008.
   Web of Science ®Google Scholar
• Messmer, R., Y. Fracheboud, M. Bänziger, P. Stamp, and J. M. Ribaut. 2011. “Drought Stress and Tropical Maize: QTLs for Leaf Greenness, Plant Senescence, and Root Capacitance.” Field Crops Research 124 (1): 93–103. doi:10.1016/j.fcr.2011.06.010.
   Web of Science ®Google Scholar
• Messmer, R., Y. Fracheboud, M. Bänziger, M. Vargas, P. Stamp, and J. M. Ribaut. 2009. “Drought Stress and Tropical Maize: QTL-by-environment Interactions and Stability of QTLs across Environments for Yield Components and Secondary Traits.” Theoretical and Applied Genetics 119 (5): 913–930. doi:10.1007/s00122-009-1099-x.
   PubMed Web of Science ®Google Scholar
• Mistele, B., R. Gutser, and U. Schmidhalter (2004). “Validation of field-scaled Spectral Measurements of the Nitrogen Status in Winter Wheat.” In “Proceedings of the 7th International Conference on Precision Agriculture and other precision resources management”. (Ed. D. J. Mulla) pp. 1187–1195. Minneapolis, Minnesota, USA. CD ROOM.
   Google Scholar
• Mittler, R. 2006. “Abiotic Stress, the Field Environment and Stress Combination.” Trends in Plant Science 11 (1): 15–19. doi:10.1016/j.tplants.2005.11.002.
   PubMed Web of Science ®Google Scholar
• Monneveux, P., C. Sanchez, D. Beck, and G. O. Edmeades. 2006. “Drought Tolerance Improvement in Tropical Maize Source Populations: Evidence of Progress.” Crop Science 46 (1): 180–191. doi:10.2135/cropsci2005.04-0034.
   Web of Science ®Google Scholar
• Nakaya, A., and S. N. Isobe. 2012. “Will Genomic Selection Be a Practical Method for Plant Breeding?” Annals of Botany 110 (6): 1303–1316. doi:10.1093/aob/mcs109.
   PubMed Web of Science ®Google Scholar
• Neiff, N., T. Dhliwayo, E. A. Suarez, J. Burgueño, and S. Trachsel. 2015. “Using an Airborne Platform to Measure Canopy Temperature and NDVI under Heat Stress in Maize.” Journal of Crop Improvement 29 (6): 669–690. doi:10.1080/15427528.2015.1073643.
   Web of Science ®Google Scholar
• Neiff, N., E. Ploschuk, O. Valentinuz, and F. Andrade. 2019. “Physiological Responses and post-stress Recovery in field-grown Maize Exposed to High Temperatures at Flowering.” Australian Journal of Crop Science 13 (12): 2053–2061. https://doi.org/10.21475/ajcs.19.13.12.p2070.
   Google Scholar
• Neiff, N., S. Trachsel, O. Valentinuz, C. Balbi, and F. Andrade. 2016. “High Temperatures around Flowering in Maize: Effects on Photosynthesis and Grain Yield in Three Genotypes.” Crop Science 56 (5): 2702–2712. doi:10.2135/cropsci2015.12.0755.
   Web of Science ®Google Scholar
• Otegui, M. E., F. H. Andrade, and E. E. Suero. 1995. “Growth, Water Use, and Kernel Abortion of Maize Subjected to Drought at Silking.” Field Crops Research 40 (2): 87–94. doi:10.1016/0378-4290(94)00093-R.
   Web of Science ®Google Scholar
• Otegui, M. E., and R. Bonhomme. 1998. “Grain Yield Components in Maize I: Ear Growth and Kernel Set.” Field Crops Research 56 (3): 247–256. doi:10.1016/S0378-4290(97)00093-2.
   Web of Science ®Google Scholar
• Pacheco, Á., M. Vargas, G. Alvarado, F. Rodríguez, J. Crossa, and J. Burgueño (2018). “GEA-R: Genotype X Environment Analysis with R for Windows [software] Version 4.1.”
   Google Scholar
• Panigada, C., M. Rossini, M. Meroni, C. Cilia, L. Busetto, S. Amaducci, M. Boschetti, et al. 2014. “Fluorescence, PRI and Canopy Temperature for Water Stress Detection in Cereal Crops.” International Journal of Applied Earth Observation and Geoinformation 30:167–178. doi:10.1016/j.jag.2014.02.002.
   Web of Science ®Google Scholar
• Patterson, H. D., E. R. Williams, and E. R. Hunter. 1978. “Block Designs for Variety Trials.” The Journal of Agricultural Science 90 (2): 395–400. doi:10.1017/S0021859600055507.
   Google Scholar
• Peng, T., X. Sun, and R. H. Mumm. 2014. “Optimized Breeding Strategies for Multiple Trait Integration: II. Process Efficiency in Event Pyramiding and Trait Fixation.” Molecular Breeding 33 (1): 105–115. doi:10.1007/s11032-013-9937-6.
   PubMed Web of Science ®Google Scholar
• Pérez, P., and G. De Los Campos. 2014. “Genome-wide Regression and Prediction with the BGLR Statistical Package.” Genetics 198 (2): 483–495. doi:10.1534/genetics.114.164442.
   PubMed Web of Science ®Google Scholar
• Porter, J. R., L. Xie, A. J. Challinor, K. Cochrane, S. M. Howden, M. M. Iqbal, D. B. Lobell, and M. I. Travasso. 2014. “Chapter 7: Food Security and Food Production Systems. In Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects”. In Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by Field, C. and Barros, V., 485–533. UK: Cambridge University Press.
   Google Scholar
• Rattalino Edreira, J. I., and M. E. Otegui. 2012. “Heat Stress in Temperate and Tropical Maize Hybrids: Differences in Crop Growth, Biomass Partitioning and Reserves Use.” Field Crops Research 130: 87–98. doi:10.1016/j.fcr.2012.02.009.
   Web of Science ®Google Scholar
• Rattalino Edreira, J. I., and M. E. Otegui. 2013. “Heat Stress in Temperate and Tropical Maize Hybrids: A Novel Approach for Assessing Sources of Kernel Loss in Field Conditions.” Field Crops Research 142: 58–67. doi:10.1016/j.fcr.2012.11.009.
   Web of Science ®Google Scholar
• Ribaut, J.-M., and M. Ragot. 2006. “Marker-assisted Selection to Improve Drought Adaptation in Maize: The Backcross Approach, Perspectives, Limitations, and Alternatives.” Journal of Experimental Botany 58 (2): 351–360. doi:10.1093/jxb/erl214.
   PubMed Web of Science ®Google Scholar
• Rizhsky, L., H. Liang, and R. Mittler. 2002. “The Combined Effect of Drought Stress and Heat Shock on Gene Expression in Tobacco.” Plant Physiology 130 (3): 1143–1151. doi:10.1104/pp.006858.
   PubMed Web of Science ®Google Scholar
• Rizhsky, L., H. Liang, J. Shuman, V. Shulaev, S. Davletova, and R. Mittler. 2004. “When Defense Pathways Collide. The Response of Arabidopsis to a Combination of Drought and Heat Stress.” Plant Physiology 134 (4): 1683–1696. doi:10.1104/pp.103.033431.
   PubMed Web of Science ®Google Scholar
• Rong, J., F. A. Feltus, V. N. Waghmare, G. J. Pierce, P. W. Chee, X. Draye, Y. Saranga, et al. 2007. “Meta-analysis of Polyploid Cotton QTL Shows Unequal Contributions of Subgenomes to a Complex Network of Genes and Gene Clusters Implicated in Lint Fiber Development.” Genetics 176 (4): 2577–2588. doi:10.1534/genetics.107.074518.
   PubMed Web of Science ®Google Scholar
• Rosenzweig, C., J. Elliott, D. Deryng, A. C. Ruane, C. Müller, A. Arneth, K. J. Boote, et al. (2014). “Assessing Agricultural Risks of Climate Change in the 21st Century in a Global Gridded Crop Model Intercomparison.” Proceedings of the National Academy of Sciences 111, 3268–3273. doi:10.1073/pnas.122246311.
   Google Scholar
• Rossini, M., F. Fava, S. Cogliati, M. Meroni, A. Marchesi, C. Panigada, C. Giardino, et al. 2013. “Assessing Canopy PRI from Airborne Imagery to Map Water Stress in Maize.” ISPRS Journal of Photogrammetry and Remote Sensing 86: 168–177. doi:10.1016/j.isprsjprs.2013.10.002.
   Web of Science ®Google Scholar
• Schlenker, W., and M. J. Roberts (2009). “Nonlinear Temperature Effects Indicate Severe Damages to U.S. Crop Yields under Climate Change.” Proceedings of the National Academy of Sciences. Vol. 106, 15594–15598. https://doi.org/10.1073/pnas.0906865106.
   Google Scholar
• Schussler, J. R., and M. E. Westgate. 1995. “Assimilate Flux Determines Kernel Set at Low Water Potential in Maize.” Crop Science 35 (4): 1074–1080. doi:10.2135/cropsci1995.0011183X003500040026x.
   Web of Science ®Google Scholar
• Seetharam, K., P. H. Kuchanur, K. B. Koirala, M. Prasad Tripathi, A. Patil, V. Sudarsanam, R. R. Das, et al. 2021. “Genomic Regions Associated with Heat Stress Tolerance in Tropical Maize (Zea Mays L.)”. Scientific Reports Vol. 11 (1). Nature Publishing Group: 13730. doi:10.1038/s41598-021-93061-7.
   Google Scholar
• Shank, K. J., S. Pei, I. Brglez, F. B. Wendy, E. D. Ralph, and S. B. Rebecca. 2001. “Induction of Lipid Metabolic Enzymes during the Endoplasmic Reticulum Stress Response in Plants.” Plant Physiology 126 (1): 267–277. doi:10.1104/pp.126.1.267.
   PubMed Web of Science ®Google Scholar
• Shawn, P. S., S. Aditya, E. M. Brenden, C. K. Clayton, and A. T. Philip. 2014. “Spectroscopic Determination of Leaf Morphological and Biochemical Traits for Northern Temperate and Boreal Tree Species.” Ecological Applications 24 (7): 1651–1669. doi:10.1890/13-2110.1.
   Web of Science ®Google Scholar
• Shepherd, T., and D. W. Griffiths. 2006. “The Effects of Stress on Plant Cuticular Waxes.” New Phytologist 171 (3): 469–499. doi:10.1111/j.1469-8137.2006.01826.x.
   PubMed Web of Science ®Google Scholar
• Szalma, S. J., B. M. Hostert, J. R. LeDeaux, C. W. Stuber, and J. B. Holland. 2007. “QTL Mapping with near-isogenic Lines in Maize.” Theoretical and Applied Genetics 114 (7): 1211–1228. doi:10.1007/s00122-007-0512-6.
   PubMed Web of Science ®Google Scholar
• Tagu, D., N. Walker, L. Ruiz-Avila, S. Burgess, J. A. Martìnez-Izquierdo, J. Leguay, P. Netter, and P. Puigdomènech. 1992. “Regulation of the Maize HRGP Gene Expression by Ethylene and Wounding. MRNA Accumulation and Qualitative Expression Analysis of the Promoter by Microprojectile Bombardment.” Plant Molecular Biology 20 (3): 529–538. doi:10.1007/BF00040611.
   PubMed Web of Science ®Google Scholar
• Thenkabail, P. S., R. B. Smith, and E. De Pauw. 2000. “Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics.” Remote Sensing of Environment 71 (2): 158–182. doi:10.1016/S0034-4257(99)00067-X.
   Web of Science ®Google Scholar
• Trachsel, S., J. Burgueño, E. A. Suarez, F. M. Vicente, C. S. Rodriguez, and T. Dhliwayo. 2017. “Interrelations among Early Vigor, Flowering Time, Physiological Maturity, and Grain Yield in Tropical Maize (Zea Mays L.) under Multiple Abiotic Stresses.” Crop Science 57 (1): 229–242. doi:10.2135/cropsci2016.06.0562.
   Web of Science ®Google Scholar
• Trachsel, S., T. Dhliwayo, L. Gonzalez Perez, J. A. Mendoza Lugo, and M. Trachsel. 2019. “Estimation of Physiological Genomic Estimated Breeding Values (PGEBV) Combining Full Hyperspectral and Marker Data across Environments for Grain Yield under Combined Heat and Drought Stress in Tropical Maize (Zea Mays L.).” Plos one 14 (3): e0212200. doi:10.1371/journal.pone.0212200.
   PubMed Web of Science ®Google Scholar
• Trachsel, S., M. Leyva, M. Lopez, E. A. Suarez, A. Mendoza, N. G. Montiel, M. S. Macias, J. Burgueño, and F. San Vicente. 2016a. “Identification of Tropical Maize Germplasm with Tolerance to Drought, Nitrogen Deficiency, and Combined Heat and Drought Stresses.” Crop Science 56 (6): 3031–3045. doi:10.2135/cropsci2016.03.0182.
   Web of Science ®Google Scholar
• Trachsel, S., R. Messmer, P. Stamp, and A. Hund. 2009. “Mapping of QTLs for Lateral and Axile Root Growth of Tropical Maize.” Theoretical and Applied Genetics 119 (8): 1413–1424. doi:10.1007/s00122-009-1144-9.
   PubMed Web of Science ®Google Scholar
• Trachsel, S., D. Sun, F. M. San Vicente, H. Zheng, G. N. Atlin, E. A. Suarez, R. Babu, X. Zhang, and M. Li. 2016b. “Identification of QTL for Early Vigor and stay-green Conferring Tolerance to Drought in Two Connected Advanced Backcross Populations in Tropical Maize (Zea Mays L.).” PLoS ONE 11 (3): e0149636. doi:10.1371/journal.pone.0149636.
   PubMed Web of Science ®Google Scholar
• Tuberosa, R., and S. Salvi. 2009. “QTL for Agronomic Traits in Maize Production.” In Handbook of Maize: Its Biology”, edited by J. L. Bennetzen and S. C. Hake, 501–541. New York: Springer.
   Google Scholar
• Tuberosa, R., S. Salvi, M. C. Sanguineti, P. Landi, M. Maccaferri, and S. Conti. 2002. “Mapping QTLs Regulating Morpho Physiological Traits and Yield: Case Studies, Shortcomings and Perspectives in Drought Stressed Maize.” Annals of Botany 89 (7): 941–963. doi:10.1093/aob/mcf134.
   PubMedGoogle Scholar
• Tucker, C. J. 1979. “Red and Photographic Infrared Linear Combinations for Monitoring Vegetation.” Remote Sensing of Environment 8 (2): 127–150. doi:10.1016/0034-4257(79)90013-0.
   Web of Science ®Google Scholar
• Wang, J., H. Li, L. Zhang, and L. Meng. 2014. Users’ Manual of QTL IciMapping. The Quantitative Genetics Group. Beijing: Institute of Crop Science, Chinese Academy of Agricultural Sciences (CAAS.
   Google Scholar
• White, J. W., P. Andrade-Sanchez, M. A. Gore, K. F. Bronson, T. A. Coffelt, M. M. Conley, K. A. Feldmann, et al. 2012. “Field-based Phenomics for Plant Genetics Research.” Field Crops Research 133:101–112. doi:10.1016/j.fcr.2012.04.003.
   Web of Science ®Google Scholar
• Winterhalter, L., B. Mistele, S. Jampatong, and U. Schmidhalter. 2011. “High Throughput Phenotyping of Canopy Water Mass and Canopy Temperature in well-watered and Drought Stressed Tropical Maize Hybrids in the Vegetative Stage.” European Journal of Agronomy 35 (1): 22–32. doi:10.1016/j.eja.2011.03.004.
   Web of Science ®Google Scholar
• Woodhouse, M. R., E. K. Cannon, J. L. Portwood, L. C. Harper, J. M. Gardiner, M. L. Schaeffer, and C. M. Andorf. 2021. “A pan-genomic Approach to Genome Databases Using Maize as A Model System.” BMC Plant Biology 21 (1): 385. doi:10.1186/s12870-021-03173-5.
   PubMed Web of Science ®Google Scholar
• Zhang, X., P. Pérez-Rodríguez, K. Semagn, Y. Beyene, R. Babu, M. A. López-Cruz, F. S. Vicente, et al. 2015. “Genomic Prediction in Biparental Tropical Maize Populations in water-stressed and well-watered Environments Using low-density and GBS SNPs.” Heredity 114 (3): 291–299. doi:10.1038/hdy.2014.99.
   PubMed Web of Science ®Google Scholar
• Zhang, A., H. Wang, Y. Beyene, K. Semagn, Y. Liu, S. Cao, Z. Cui, et al. 2017. “Effect of Trait Heritability, Training Population Size and Marker Density on Genomic Prediction Accuracy Estimation in 22 bi-parental Tropical Maize Populations.” Frontiers in Plant Science 8: 1–12. doi:10.3389/fpls.2017.01916.
   PubMed Web of Science ®Google Scholar
• Zia, S., G. Romano, W. Spreer, C. Sanchez, J. Cairns, J. L. Araus, and J. Müller. 2013. “Infrared Thermal Imaging as a Rapid Tool for Identifying water-stress Tolerant Maize Genotypes of Different Phenology.” Journal of Agronomy and Crop Science 199 (2): 75–84. doi:10.1111/j.1439-037X.2012.00537.x.
   Web of Science ®Google Scholar