Bringing genetics and biochemistry to crop modelling, and vice versa

Adachi, 2013, The mesophyll anatomy enhancing CO2 diffusion is a key trait for improving rice photosynthesis, J. Exp. Bot., 64, 1061, 10.1093/jxb/ers382

Aggarwal, 1997, Simulating genotypic strategies for increasing rice yield potential in irrigated: tropical environments, Field Crops Res., 51, 5, 10.1016/S0378-4290(96)01044-1

Barbour, 2016, Genetic control of mesophyll conductance in common wheat, New Phytol., 209, 461, 10.1111/nph.13628

Bogard, 2014, Predictions of heading date in bread wheat (Triticum aestivum L.) using QTL-based parameters of an ecophysiological model, J. Exp. Bot., 65, 5849, 10.1093/jxb/eru328

Boote, 1994, Modeling genetic yield potential, 533

Boote, 2013, Putting mechanisms into crop production models, Plant Cell Environ., 36, 1658, 10.1111/pce.12119

Boote, 2016, Crop modeling approaches for predicting phenotype of grain legumes with linkage to genetic information, 163

Bortesi, 2015, The CRISPR/Cas9 system for plant genome editing and beyond, Biotechnol. Adv., 33, 41, 10.1016/j.biotechadv.2014.12.006

Brown, 2013, Integration of molecular and physiological models to explain time of anthesis in wheat, Ann. Bot., 112, 1683, 10.1093/aob/mct224

Chapman, 2003, Evaluating plant breeding strategies by simulating gene action and dryland environment effects, Agron. J., 95, 99, 10.2134/agronj2003.0099

Chew, 2014, Multiscale digital Arabidopsis predicts individual organ and whole-organism growth, Proc. Natl. Acad. Sci. U. S. A. (PLUS), 111, E4127, 10.1073/pnas.1410238111

Chew, 2017, Multi-scale modelling to synergise plant systems biology and crop science, Field Crops Res., 202, 77, 10.1016/j.fcr.2016.02.012

de Wit, 1959, Potential photosynthesis of crop surfaces, Netherlands J. Agric. Sci., 7, 141, 10.18174/njas.v7i2.17686

de Wit, 1965

Dingkuhn, 2015, Improving yield potential of tropical rice: achieved levels and perspectives through improved ideotypes, Field Crops Res., 182, 43, 10.1016/j.fcr.2015.05.025

Dingkuhn, 2017, Crop-model assisted phenomics and genome-wide association study for climate adaptation of indica rice. 1. Phenology, J. Exp. Bot., 68, 4369, 10.1093/jxb/erx249

Dingkuhn, 2017, Crop-model assisted phenomics and genome-wide association study for climate adaptation of indica rice. 2. Thermal stress and spikelet sterility, J. Exp. Bot., 68, 4389, 10.1093/jxb/erx250

Driever, 2014, Natural variation in photosynthetic capacity, growth, and yield in 64 field-grown wheat genotypes, J. Exp. Bot., 65, 4959, 10.1093/jxb/eru253

Duncan, 1967, A model for simulating photosynthesis in plant communities, Hilgardia, 4, 181, 10.3733/hilg.v38n04p181

Farquhar, 1980, A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species, Planta, 149, 78, 10.1007/BF00386231

Fischer, 2014

Gu, 2012, Using chromosome introgression lines to map quantitative trait loci for photosynthesis parameters in rice (Oryza sativa L.) leaves under drought and well-watered field conditions, J. Exp. Bot., 63, 455, 10.1093/jxb/err292

Gu, 2012, Physiological basis of genetic variation in leaf photosynthesis among rice (Oryza sativa L.) introgression lines under drought and well-watered conditions, J. Exp. Bot., 63, 5137, 10.1093/jxb/ers170

Gu, 2014, Can exploiting natural genetic variation in leaf photosynthesis contribute to increasing rice productivity? A simulation analysis, Plant Cell Environ., 37, 22, 10.1111/pce.12173

Gu, 2014, Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved rice (Oryza sativa L.) yields under drought stress, Ann. Bot., 114, 499, 10.1093/aob/mcu127

Hammer, 2004, On systems thinking, systems biology, and the in silico plants, Plant Physiol., 134, 909, 10.1104/pp.103.034827

Hammer, 2006, Models for navigating biological complexity in breeding improved crop plants, Trends Plant Sci., 11, 587, 10.1016/j.tplants.2006.10.006

Hammer, 2010, Adapting APSIM to model the physiology and genetics of complex adaptive traits in field crops, J. Exp. Bot., 61, 2185, 10.1093/jxb/erq095

Hammer, 2016, Molecular breeding for complex adaptive traits: how integrating crop ecophysiology and modelling can enhance efficiency, 147

Huang, 2011, Analysis of natural allelic variation in Arabidopsis using a multiparent recombinant inbred line population, Proc. Natl. Acad. Sci. U. S. A., 108, 4488, 10.1073/pnas.1100465108

Jahan, 2014, Variation in mesophyll conductance among Australian wheat genotypes, Funct. Plant Biol., 41, 568, 10.1071/FP13254

Kadam, 2017, Genetic control of plasticity in root morphology and anatomy of rice in response to water-deficit, Plant Physiol., 74, 2302, 10.1104/pp.17.00500

Koester, 2016, Has photosynthetic capacity increased with 80 years of soybean breeding? An examination of historical soybean cultivars, Plant Cell Environ., 39, 1058, 10.1111/pce.12675

Li, 2015, Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions, Glob. Change Biol., 21, 1328, 10.1111/gcb.12758

Long, 2015, Meeting the global food demand of the future by engineering crop photosynthesis and yield potential, Cell, 161, 56, 10.1016/j.cell.2015.03.019

Loomis, 1979, Explanatory models in crop physiology, Annu. Rev. Plant Physiol., 30, 339, 10.1146/annurev.pp.30.060179.002011

Luquet, 2016, Heuristic exploration of theoretical margins for improving adaptation of rice through crop-assisted phenotyping, 105

Ma, 2002, Functional mapping of quantitative trait loci underlying the character process: a theoretical framework, Genetics, 161, 1751, 10.1093/genetics/161.4.1751

Mangin, 2017, Genetic control of plasticity of oil yield for combined abiotic stresses using a joint approach of crop modeling and genome-wide association, Plant Cell Environ., 40, 2276, 10.1111/pce.12961

McCouch, 2016, Open access resources for genome-wide association mapping in rice, Nat. Commun., 7, 10532, 10.1038/ncomms10532

Messina, 2006, A gene-based model to simulate soybean development and yield responses to environment, Crop Sci., 46, 456, 10.2135/cropsci2005.04-0372

Minorsky, 2003, Achieving the in silico plant: systems biology and the future of plant biological research, Plant Physiol., 132, 404, 10.1104/pp.900076

Mitchell, 2006, Supercharging rice photosynthesis to increase yield, New Phytol., 171, 688, 10.1111/j.1469-8137.2006.01855.x

Onogi, 2016, Toward integration of genomic selection with crop modelling: the development of an integrated approach to predicting rice heading dates, Theor. Appl. Genet., 129, 805, 10.1007/s00122-016-2667-5

Parent, 2014, Can current crop models be used in the phenotyping era for predicting the genetic variability of yield of plants subjected to drought or high temperature?, J. Exp. Bot., 65, 6179, 10.1093/jxb/eru223

Penning de Vries, 1974, Products, requirements and efficiency of biosynthesis: a quantitative approach, J. Theor. Biol., 45, 339, 10.1016/0022-5193(74)90119-2

Penning de Vries, 1991, Improving yields: designing and testing VHYVs, 13

Picheny, 2017, Using numerical plant models and phenotypic correlation space to design achievable ideotypes, Plant Cell Environ., 40, 1926, 10.1111/pce.13001

Prudent, 2011, Combining ecophysiological modelling and quantitative trait locus analysis to identify key elementary processes underlying tomato fruit sugar concentration, J. Exp. Bot., 62, 907, 10.1093/jxb/erq318

Quilot, 2005, Analysing the genetic control of peach fruit quality through an ecophysiological model combined with a QTL approach, J. Exp. Bot., 56, 3083, 10.1093/jxb/eri305

Rebolledo, 2015, Phenotypic and genetic dissection of component traits for early vigour in rice using plant growth modelling, sugar content analyses and association mapping, J. Exp. Bot., 66, 5555, 10.1093/jxb/erv258

Reymond, 2003, Combining quantitative trait loci analysis and an ecophysiological model to analyze the genetic variability of the responses of maize leaf growth to temperature and water deficit, Plant Physiol., 131, 664, 10.1104/pp.013839

Shikanai, 2014, Central role of cyclic electron transport around photosystem I in the regulation of photosynthesis, Curr. Opin. Biotechnol., 26, 25, 10.1016/j.copbio.2013.08.012

Sinclair, 2016, Limited-transpiration trait for increased yield for water-limited soybean: from model to phenotype to genotype to cultivars, 129

Spindel, 2015, Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, trait population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines, PLoS Genet., 11, e1004982, 10.1371/journal.pgen.1004982

Spitters, 1989, A simple and universal crop growth simulator: SUCROS87, 147

Stam, 1998, Crop physiology, QTL analysis and plant breeding, 429

Tardieu, 2003, Virtual plants: modelling as a tool for the genomics of tolerance to water deficit, Trends Plant Sci., 8, 9, 10.1016/S1360-1385(02)00008-0

Technow, 2015, Integrating crop growth models with whole genome prediction through approximate Bayesian computation, PLoS One, 10.1371/journal.pone.0130855

Uptmoor, 2017, Combining genome-wide prediction and a phenology model to simulate heading date in spring barley, Field Crops Res., 202, 84, 10.1016/j.fcr.2016.08.006

van Eeuwijk, 2001, Some vocabulary and grammar for the analysis of multi-environment trials, as applied to the analysis of FBB and PPB trials, Euphytica, 122, 477, 10.1023/A:1017591407285

van Oijen, 2004, Simple equations for dynamic models of the effects of CO2 and O3 on light use efficiency and growth of crops, Ecol. Modell., 179, 39, 10.1016/j.ecolmodel.2004.05.002

Wang, 2002, Development of a generic crop model temperate in the cropping system model APSIM, Eur. J. Agron., 18, 121, 10.1016/S1161-0301(02)00100-4

Welch, 2003, Genetic neural network model of flowering time control in Arabidopsis thaliana, Agron. J., 95, 71, 10.2134/agronj2003.0071

White, 1996, Simulating effects of genes for physiological traits in a process-oriented crop model, Agron. J., 88, 416, 10.2134/agronj1996.00021962008800030009x

White, 2008, Simulation-based analysis of effects of Vrn and Ppd loci on flowering in wheat, Crop Sci., 48, 678, 10.2135/cropsci2007.06.0318

Yin, 2007, Crop systems biology: an approach to connect functional genomics with crop modelling, 61

Yin, 2008, Applying modelling experiences from the past to shape crop systems biology: the need to converge crop physiology and functional genomics, New Phytol., 179, 629, 10.1111/j.1469-8137.2008.02424.x

Yin, 2010, Modelling the crop: from system dynamics to systems biology, J. Exp. Bot., 61, 2171, 10.1093/jxb/erp375

Yin, 2012, Mathematical review of the energy transduction stoichiometries of C4 leaf photosynthesis under limiting light, Plant Cell Environ., 35, 1299, 10.1111/j.1365-3040.2012.02490.x

Yin, 2015, Constraints to the potential efficiency of converting solar radiation into phytoenergy in annual crops: from leaf biochemistry to canopy physiology and crop ecology, J. Exp. Bot., 66, 6535, 10.1093/jxb/erv371

2016, 233

Yin, 2017, Can increased leaf photosynthesis be converted into higher crop mass production? A simulation study for rice using the crop model GECROS, J. Exp. Bot., 68, 2345, 10.1093/jxb/erx085

Yin, 2017, Simple generalisation of a mesophyll resistance model for various intracellular arrangements of chloroplasts and mitochondria in C3 leaves, Photosyn. Res, 132, 211, 10.1007/s11120-017-0340-8

Yin, 2005, 155

Yin, 1999, The role of ecophysiological models in QTL analysis: the example of specific leaf area in barley, Heredity, 82, 415, 10.1038/sj.hdy.6885030

Yin, 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, 10.1046/j.1365-2540.2000.00790.x

Yin, 2004, Role of crop physiology in predicting gene-phenotype relations, Trends Plant Sci., 9, 426, 10.1016/j.tplants.2004.07.007

Yin, 2004, Extension of a biochemical model for the generalized stoichiometry of electron transport limited C3 photosynthesis, Plant Cell Environ., 27, 1211, 10.1111/j.1365-3040.2004.01224.x

Yin, 2006, Mathematical review of literature to assess alternative electron transports and interphotosystem excitation partitioning of steady-state C3 photosynthesis under limiting light, Plant Cell Environ., 29, 1771, 10.1111/j.1365-3040.2006.01554.x

Yin, 2016, Modelling QTL-trait-crop relationships: past experiences and future prospects, 193

Zheng, 2013, Quantification of the effects of VRN1 and Ppd-D1 to predict spring wheat (Triticum aestivum) heading time across diverse environment, J. Exp. Bot., 64, 3747, 10.1093/jxb/ert209

Zhu, 2016, Plants in silico: why, why now and what? −an integrative platform for plant systems biology research, Plant Cell Environ., 39, 1049, 10.1111/pce.12673