Abstract
Crop modeling in support of breeders’ decisions on selection criteria can benefit from the new global focus on phenomics because it provides new information on existing genetic diversity for useful traits. This study attempted an in silico prediction of margins for genetic improvements of early vigor (biomass produced during vegetative growth) and drought resistance combined, based on virtual recombination of several traits (here syn. model parameters) within ranges of trait variation observed in a panel of diverse rice genotypes. The Ecomeristem model was parameterized by multi-parameter optimization procedures applied to observed datasets for 136 rice genotypes. The traits within the observed ranges were then recombined in silico to generate a virtual population of 9000 individuals. Simulations of real and virtual phenotypes under three water treatments, using finite water resources during stress cycles, indicated strong and similar trade-offs between constitutive vigor and drought resistance in both real and virtual, recombinant populations. A substantial margin for potential genetic improvement of vigor with unchanged drought resistance was predicted, drawing chiefly from structural growth and development traits that would increase internal demand for assimilates (larger and thicker leaves, increased leaf appearance rates). Increased vigor would not necessarily require greater photosynthetic potential per se. However, improved drought resistance with unchanged constitutive vigor would require greater water economy (increased photosynthetic potential and limited water use, therefore higher transpiration efficiency) and greater tolerance of leaf extension and gas exchange rates to drought, while tillering ability should be limited in favor of larger and thicker leaves. These results carry significant uncertainty because they predict virtual genotypes and their phenotypes, based on simple assumptions in the model (namely on gas exchange) and in genetics (free, additive trait combinability). But the approach is innovative and may eventually help developing ideotypes drawing from information of existing diversity and integrative modeling of phenotypes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Araus JL, Slafer JA, Reynolds MP, Roy C (2002) Plant breeding and drought in C3 cereals: what should we breed for? Ann Bot 89:925–940
Babu C, Shashidhar HE, Lilley JM, Thanh ND, Ray JD, Sadasivam S, Sarkarung S, O’Toole JC, Nguyen H-T (2001) Variation in root penetration ability, osmotic adjustment and dehydration tolerance among accessions of rice adapted to rainfed lowland and upland ecosystems. Plant Breed 120:233–238
Baldazzi V, Bertin N, de Jong H, Genard M (2013) Towards multiscale plant models: integrating cellular networks. Trends Plant Sci 17:728–736
Bertin N, Martre P, Génard M, Quilot B, Salon C (2010) Under what circumstances can process-based simulation models link genotype to phenotype for complex traits? Case study of fruit and grain quality traits. J Exp Bot 61:955–967
Brisson N, Gary C, Justes E, Roche R, Mary B, Ripoche D (2003) An overview of the crop model STICS. Eur J Agron 18:309–332
Chapman S, Cooper M, Podlich D, Hammer G (2003) Evaluating plant breeding strategies by simulating gene action and dryland environment effects. Agron J 95:99–113
Chenu K, Chapman SC, Tardieu F, McLean G, Welcker C, Hammer GL (2009) Simulating the yield impacts of organ-level quantitative trait loci associated with drought response in maize: a “gene-to-phenotype” modeling approach. Genetics 183:1507–1523
Cooper DW, Poldich DW, Micallef KP, Smith OS, Jensen NM, Chapman SC, Kruger NL (2002) Linking biophysical and genetics models to integrate physiology, molecular biology and plant breeding (Chapter 11). In: Kang MS (ed) Quantitative genetic, genomics and plant breeding, vol 1, CAB international. CAB Publishing, Wallingford, pp 143–166
Cooper M, Messina CD, Podlich D, Totir LR, Baumgarten A, Hausmann NJ, Wright D, Graham G (2014) Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction. Crop Pasture Sci 65:311–336
Courtois B, McLaren G, Sinha PK, Prasad K, Yadav R, Shen L (2000) Mapping QTLs associated with drought avoidance in upland rice. Mol Breed 6:55–66
Courtois B, Audebert A, Dardou A, Roques S, Ghneim- Herrera T, Droc G, Frouin J, Rouan L, Gozé E, Kilian A, Ahmadi N, Dingkuhn M (2013) Genome-wide association mapping of root traits in a japonica rice panel. PLoS One 8, e78037
Dingkuhn M, de Vries FWT P, De Datta SK, van Laar HH (1991) Concepts for a new plant type for direct seeded flooded tropical rice. In: Direct seeded flooded rice in the tropics. International Rice Research Institute, Manila, pp 17–38
Dingkuhn M, Luquet D, Quilot B, Reffye PD (2005) Environmental and genetic control of morphogenesis in crops: towards models simulating phenotypic plasticity. Aust J Agr Res 56:1–14
Dingkuhn M, Luquet D, Clément-Vidal A, Tambour L, Kim HK, Song YH (2007) Is plant growth driven by sink regulation? Implications for crop models, phenotyping approaches and ideotypes. In: Struik PC, Spiertz HJ, van Laar HH (eds) Scale and complexity in plant systems research: gene-plant-crop relations, vol 21, Wageningen UR Frontis. Springer, Wageningen, pp 157–170
Gibson K, Park JS, Nagaia Y, Hwanga SK, Chod YC, Roh KH, Lee SM, Kim DH, Choie SB, Ito H, Edwards GE, Okita TW (2011) Exploiting leaf starch synthesis as a transient sink to elevate photosynthesis, plant productivity and yields. Plant Sci 181:275–281
Gu J, Yin X, Zhang C, Wang H, Struik PC (2014) Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved yields of rice (Oryza sativa) under drought stress. Ann Bot 114:499–511
Hammer GL, Kropff MJ, Sinclair TR, Porter JR (2002) Future contributions of crop modelling – from heuristics and supporting decision making to understanding genetic regulation and aiding crop improvement. Eur J Agron 18:15–31
Hammer G, Sinclair TR, Chapman S, van Oosterom E (2004) On systems thinking, systems biology, and the in silico plant. Plant Physiol 134:909–911
Hammer GL, Chapman S, Van Oosterom E, Poldich DW (2005) Trait physiology and crop modelling as a framework to link phenotypic complexity to underlying genetic systems. Aust J Agr Res 56:947–960
Hammer G, van Oosterom E, McLean G, Chapman C, Broad I, Harland P, Muchow RC (2010) Adapting APSIM to model the physiology and genetics of complex adaptive traits in field crops. J Exp Bot 61:2185–2202
Jongdee B, Pantuwan G, Fukai S, Fischer K (2006) Improving drought tolerance in rainfed lowland rice: an example from Thailand. Agric Water Manage 80:225–240
Kamoshita A, Rodriguez R, Yamauchi A, Wade LJ (2004) Genotypic variation in response of rainfed lowland rice to prolonged drought and rewatering. Plant Prod Sci 7:406–420
Keurentjes JJB, Angenent GC, Dicke M, Martins Dos Santos VAP, Molenaar J, van der Putten WH, de Ruiter PC, Struik PC, Thomma B (2011) Redefining plant systems biology: from cell to ecosystem. Trends Plant Sci 16:183–190
Lilley JM, Ludlow MM, McCouch SR, O’Toole JC (1996) Locating QTL for osmotic adjustment and dehydration tolerance in rice. J Exp Bot 47:1427–1436
Luquet D, Dingkuhn M, Kim HK, Tambour L, Clément-Vidal A (2006) EcoMeristem, a model of morphogenesis and competition among sinks in rice. 1. Concept, validation and sensitivity analysis. Funct Plant Biol 33:309–323
Luquet D, Clément-Vidal A, This D, Fabre D, Sonderegger N, Dingkuhn M (2008) Orchestration of transpiration, growth and carbohydrate dynamics in rice during a dry-down cycle. Funct Plant Biol 35:689–704
Luquet D, Rebolledo MC and Soulié JC (2012a) Functional-structural plant modeling to support complex trait phenotyping: case of rice early vigour and drought tolerance using ecomeristem model. IEEE international symposium. 4 (PMA’12). In: Kang M, Dumont Y, Guo Y (eds) Plant growth modeling, simulation visualization and applications. IEEE, Shangai, pp. 270–277
Luquet D, Soulié JC, Rebolledo MC, Rouan L, Clément-Vidal A, Dingkuhn M (2012b) Developmental dynamics and early growth vigour in rice 2. Modelling genetic diversity using ecomeristem. J Agron Crop Sci 198:385–398
McCouch SR, McNally K, Wang W, Hamilton RS (2012) Genomics of gene banks: a case study in rice. Am J Bot 99:407–423
Namuco OS, Cairns JE, Johnson DE (2009) Investigating early vigour in upland rice (Oryza sativa L.): part I. Seedling growth and grain yield in competition with weeds. Field Crops Res 113:197–206
Nemoto K, Morita S, Baba T (1995) Shoot and root development in rice related to the phyllochron. Crop Sci 35:24–29
Pallas B, Clément-Vidal A, Rebolledo MC, Soulié JC, Luquet D (2013) Using plant growth modeling to analyze C source–sink relations under drought: inter- and intraspecific comparison. Front Plant Sci 4:437
Pantin F, Simonneau T, Rolland G, Dauzat M, Muller B (2011) Control of leaf expansion: a developmental switch from metabolics to hydraulics. Plant Physiol 156:803–815
Peng S, Khush GS (2003) Four decades of breeding for varietal improvement of irrigated lowland rice in the International Rice Research Institute. Plant Prod Sci 6:157–164
Quesnel G, Duboz R, Ramat E (2009) The virtual laboratory environment – an operational framework for multi-modelling, simulation and analysis of complex systems. Simul Model Pract Theory 17:641–653
Quilot B, Kervella J, Genard M, Lescourret F (2005) Analysing the genetic control of peach fruit quality through an ecophysiological model combined with a QTL approach. J Exp Bot 56:3083–3092
Quilot-Turion B, Ould-Sibi MM, Kadrani A, Hilgert N, Génard M, Lescourret F (2012) Optimization of parameters of the ‘Virtual Fruit’ model to design peach genotype for sustainable production systems. Eur J Agron 42:34–48
Rebolledo M-C, Dingkuhn M, Clement-Vidal A, Rouan L, Luquet D (2012a) Phenomics of rice early vigour and drought response: are sugar related and morphogenetic traits relevant? Rice 5:22
Rebolledo MC, Dingkuhn M, Péré P, McNally KL, Luquet D (2012b) Developmental dynamics and early growth vigour in rice. I. Relationship between development rate (1/phyllochron) and growth. J Agron Crop Sci 198:374–384
Rebolledo MC, Luquet D, Courtois B, Henry A, Soulié JC, Rouan L, Dingkuhn M (2013) Does early vigor occur in combination with drought tolerance and efficient water use in rice genotypes? Funct Plant Biol 40:582–594
Reymond M, Muller B, Tardieu F (2004) Dealing with the genotype x environment interaction via a modelling approach: a comparison of QTLs of maize leaf length or width with QTLs of model parameters. J Exp Bot 55:2461–2472
Richards R, Lukacs Z (2001) Seedling vigor in wheat–source of variation for genetic and agronomic improvement. Aust J Agr Res 53:41–50
Robin S, Pathan MS, Courtois B, Lafitte R, Carandang SLS, Amante M, Nguyen HT, Li Z (2003) Mapping osmotic adjustment in an advanced back-cross inbred population of rice. Theor Appl Genet 107:1288–1296
Saltelli A, Tarantola S, Chan K (1999) A quantitative, model independent method for global sensitivity analysis of model output. Technometrics 41:39–56
Sekhon S, Mebane WR Jr (2011) Genetic optimization using derivatives: the rgenoud package for R. J Stat Softw 42, 26 pp
Tardieu F, Granier C, Muller B (2011) Water deficit and growth. Co-ordinating processes without an orchestrator? Curr Opin Plant Biol 14:283–289
ter Steege MW, den Ouden FM, Lambers H, Stam P, Peeters AJM (2005) Genetic and physiological architecture of early vigor in Aegilops tauschii, the D-genome donor of hexaploid wheat. A quantitative trait loci analysis. Plant Physiol 139:1078–1094
Tisne S, Schmalenbach I, Reymond M, Dauzat M, Pervent M, Vile D, Granier C (2010) Keep on growing under drought: genetic and developmental bases of the response of rosette area using a recombinant inbred line population. Plant Cell Environ 33:1875–1887
von Caemmerer S, Quick WP, Furbank RT (2012) The development of C4 rice: current progress and future challenges. Science 336:1671–1672
Xu L, Henke M, Zhu J, Kurth W, Buck-Sorlin G (2011) A functional structural model of rice linking quantitative genetic information with morphological development and physiological processes. Ann Bot 107:817–828
Yan HP, Kang MZ, Reffye PD, Dingkuhn M (2004) A dynamic, architectural plant model simulating resource-dependent growth. Ann Bot 93:591–602
Yin X, Struik PC (2010) Modelling the crop: from system dynamics to systems biology. J Exp Bot 61:2171–2183
Yin X, Kropff M, Stam P (1999) The role of ecophysiological models in QTL analysis: the example of specific leaf area in barley. Heredity 82:412–421
Yin X, Chasalow SD, Dourleijn CJ, Stam P, Kropff MJ (2000) Coupling estimated effects of QTLs for physiological traits to a crop growth model: predicting yield variation among recombinant inbred lines in barley. Heredity 85:539–549
Yin X, Struik PC, Kropff MJ (2004) Role of crop physiology in predicting gene-to-phenotype relationships. Trends Plant Sci 9:426–432
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Luquet, D., Rebolledo, C., Rouan, L., Soulie, JC., Dingkuhn, M. (2016). Heuristic Exploration of Theoretical Margins for Improving Adaptation of Rice through Crop-Model Assisted Phenotyping. In: Yin, X., Struik, P. (eds) Crop Systems Biology. Springer, Cham. https://doi.org/10.1007/978-3-319-20562-5_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-20562-5_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20561-8
Online ISBN: 978-3-319-20562-5
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)