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Review Article | Open Access

Crop/Plant Modeling Supports Plant Breeding: Ⅰ. Optimization of Environmental Factors in Accelerating Crop Growth and Development for Speed Breeding

Yi Yu1,Qin Cheng2,Fei Wang1Yulei Zhu1Xiaoguang Shang3Ashley Jones4Haohua He2( )Youhong Song1,5( )
Anhui Agricultural University, School of Agronomy, Hefei, Anhui Province 230036, China
Jiangxi Agricultural University, School of Agricultural Sciences, Nanchang, Jiangxi Province 330045, China
State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, Jiangsu Province 210095, China
The Australian National University, Research School of Biology, Canberra, ACT 2601, Australia
The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, Brisbane, QLD, Australia

†These authors contributed equally to this work.

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Abstract

The environmental conditions in customered speed breeding practice are, to some extent, empirical and, thus, can be further optimized. Crop and plant models have been developed as powerful tools in predicting growth and development under various environments for extensive crop species. To improve speed breeding, crop models can be used to predict the phenotypes resulted from genotype by environment by management at the population level, while plant models can be used to examine 3-dimensional plant architectural development by microenvironments at the organ level. By justifying the simulations via numerous virtual trials using models in testing genotype × environment × management, an optimized combination of environmental factors in achieving desired plant phenotypes can be quickly determined. Artificial intelligence in assisting for optimization is also discussed. We admit that the appropriate modifications on modeling algorithms or adding new modules may be necessary in optimizing speed breeding for specific uses. Overall, this review demonstrates that crop and plant models are promising tools in providing the optimized combinations of environment factors in advancing crop growth and development for speed breeding.

References

1

Li Y, Ma XL, Wang TY, Li YX, Liu C, Liu ZZ, Sun BC, Shi YS, Song YC, Carlone M, et al. Increasing maize productivity in China by planting hybrids with germplasm that responds favorably to higher planting densities. Crop Sci. 2011;51(6):2391–2400.

2

Kastner T, Rivas MJI, Koch W, Nonhebel S. Global changes in diets and the consequences for land requirements for food. Proc Natl Acad Sci U S A. 2012;109(18):6868–6872.

3

Beltran-Pena A, Rosa L, D’Odorico P. Global food self-sufficiency in the 21st century under sustainable intensification of agriculture. Environ Res Lett. 2020;15(9):095004.

4

Lobell DB, Schlenker W, Costa-Roberts J. Climate trends and global crop production since 1980. Science. 2011;333(6042):616–620.

5

Xie W, Xiong W, Pan J, Ali T, Cui Q, Guan DB, Meng J, Mueller ND, Lin E, Davis SJ. Decreases in global beer supply due to extreme drought and heat. Nat Plants. 2018;4(11):964.

6

Challinor AJ, Watson J, Lobell DB, Howden SM, Smith DR, Chhetri N. A meta-analysis of crop yield under climate change and adaptation. Nat Clim Chang. 2014;4(4):287–291.

7

Liu B, Martre P, Ewert F, Porter JR, Challinor AJ, Mueller C, Ruane AC, Waha K, Thorburn PJ, Aggarwal PK, et al. Global wheat production with 1.5 and 2.0 degrees C above pre-industrial warming. Glob Chang Biol. 2019;25(4):1428–1444.

8

Watson A, Ghosh S, Williams MJ, Cuddy WS, Simmonds J, Rey MD, Hatta MAM, Hinchliffe A, Steed A, Reynolds D, et al. Speed breeding is a powerful tool to accelerate crop research and breeding. Nat Plants. 2018;4(1):23–29.

9

Bugbee B, Koerner G. Yield comparisons and unique characteristics of the dwarf wheat cultivar ‘USU-Apogee’. Adv Space Res. 1997;20(10):1891–1894.

10

Hickey LT, Dieters MJ, DeLacy IH, Kravchuk OY, Mares DJ, Banks PM. Grain dormancy in fixed lines of white-grained wheat (Triticum aestivum L.) grown under controlled environmental conditions. Euphytica. 2009;168(3):303–310.

11

Alahmad S, Dinglasan E, Leung KM, Riaz A, Derbal N, Voss-Fels KP, Able JA, Bassi FM, Christopher J, Hickey LT. Speed breeding for multiple quantitative traits in durum wheat. Plant Methods. 2018;14:36.

12

Rana MM, Takamatsu T, Baslam M, Kaneko K, Itoh K, Harada N, Sugiyama T, Ohnishi T, Kinoshita T, Takagi H, et al. Salt tolerance improvement in Rice through efficient SNP marker-assisted selection coupled with speed-breeding. Int J Mol Sci. 2019;20(10):2585.

13

Hickey LT, German SE, Pereyra SA, Diaz JE, Ziems LA, Fowler RA, Platz GJ, Franckowiak JD, Dieters MJ. Speed breeding for multiple disease resistance in barley. Euphytica. 2017;213(3):2.

14

Samineni S, Sen M, Sajja SB, Gaur PM. Rapid generation advance (RGA) in chickpea to produce up to seven generations per year and enable speed breeding. Crop J. 2020;8(1):164–169.

15

Collard BCY, Mackill DJ. Marker-assisted selection: An approach for precision plant breeding in the twenty-first century. Philoso Trans Royal Soc B-Biol Sci. 2008;363(1491):557–572.

16
Bentley AR, Jensen EF, Mackay IJ, Hönicka H, Fladung M, Hori K, Yano M, Mullet JE, Armstead IP, Hayes C, etal. Flowering time. In: Kole C, editor. Genomics and breeding for climate-resilient crops: Vol. 2 target traits. Berlin, Heidelberg: Springer; 2013. p. 1–66.
17

Hickey LT, Hafeez AN, Robinson H, Jackson SA, Leal-Bertioli SCM, Tester M, Gao CX, Godwin ID, Hayes BJ, Wulff BBH. Breeding crops to feed 10 billion. Nat Biotechnol. 2019;37(7):744–754.

18

Velez-Ramirez AI, van Ieperen W, Vreugdenhil D, Millenaar FF. Plants under continuous light. Trends Plant Sci. 2011;16(6):310–318.

19

Muller B, Martre P. Plant and crop simulation models: Powerful tools to link physiology, genetics, and phenomics. J Exp Bot. 2019;70(9):2339–2344.

20

Perez RPA, Dauzat J, Pallas B, Lamour J, Verley P, Caliman JP, Costes E, Faivre R. Designing oil palm architectural ideotypes for optimal light interception and carbon assimilation through a sensitivity analysis of leaf traits. Ann Bot. 2018;121(5):909–926.

21

Saddique Q, Cai HJ, Xu JT, Ajaz A, He JQ, Yu Q, Wang YF, Chen H, Khan MI, Liu DL, et al. Analyzing adaptation strategies for maize production under future climate change in Guanzhong plain, China. Mitig Adapt Strateg Glob Chang. 2020;25(8):1523–1543.

22

Fodor N, Challinor A, Droutsas I, Ramirez-Villegas J, Zabel F, Koehler AK, Foyer CH. Integrating plant science and crop modeling: Assessment of the impact of climate change on soybean and maize production. Plant Cell Physiol. 2017;58(11):1833–1847.

23

Ewert F, Rotter RP, Bindi M, Webber H, Trnka M, Kersebaum KC, Olesen JE, van Ittersum MK, Janssen S, Rivington M, et al. Crop modelling for integrated assessment of risk to food production from climate change. Environ Model Softw. 2015;72:287–303.

24

Priesack E, Gayler S, Hartmann HP. The impact of crop growth sub-model choice on simulated water and nitrogen balances. Nutr Cycl Agroecosyst. 2006;75(1-3):1–13.

25

Jalota SK, Kaur H, Ray SS, Tripathi R, Vashisht BB, Bal SK. Mitigating future climate change effects by shifting planting dates of crops in rice-wheat cropping system. Reg Environ Chang. 2012;12(4):913–922.

26

Zhang YL, Wu ZY, Singh VP, Su Q, He H, Yin H, Zhang YX, Wang F. Simulation of crop water demand and consumption considering irrigation effects based on coupled hydrology-crop growth model. J Adv Model Earth Syst. 2021;13(11):2360.

27

Yin XG, Kersebaum KC, Beaudoin N, Constantin J, Chen F, Louarn G, Manevski K, Hoffmann M, Kollas C, Armas-Herrera CM, et al. Uncertainties in simulating N uptake, net N mineralization, soil mineral N and N leaching in European crop rotations using process-based models. Field Crop Res. 2020;255:107863.

28

Constantin J, Beaudoin N, Launay M, Duval J, Mary B. Long-term nitrogen dynamics in various catch crop scenarios: Test and simulations with STICS model in a temperate climate. Agric Ecosyst Environ. 2012;147:36–46.

29

Yin XY. Improving ecophysiological simulation models to predict the impact of elevated atmospheric CO2 concentration on crop productivity. Ann Bot. 2013;112(3):465–475.

30

Kollas C, Kersebaum KC, Nendel C, Manevski K, Muller C, Palosuo T, Armas-Herrera CM, Beaudoin N, Bindi M, Charfeddine M, et al. Crop rotation modelling-A European model intercomparison. Eur J Agron. 2015;70(2):98–111.

31

Sietz D, Conradt T, Krysanova V, Hattermann FF, Wechsung F. The crop generator: Implementing crop rotations to effectively advance eco-hydrological modelling. Agric Syst. 2021;193:103183.

32
Farooqi MQU, Nawaz G, Wani SH, Choudhary JR, Rana M, Sah RP, Afzal M, Zahra Z, Ganie SA, Razzaq A, et al. Recent developments in multi-omics and breeding strategies for abiotic stress tolerance in maize (Zea mays L.). 2022;13:965878.
33

Cortes LT, Zhang ZW, Yu JM. Status and prospects of genome-wide association studies in plants. Plant Genome. 2021;14(1):e20077.

34
Lorenz AJ, Chao S, Asoro FG, Heffner EL, Hayashi T, Iwata H, Smith KP, Sorrells ME, Jannink J-L. Genomic selection in plant breeding: knowledge and prospects. In: Sparks DL, editor. Advances in agronomy. Cambridge (MA): Academic Press; 2011. p. 77–123.
35

Scheben A, Batley J, Edwards D. Genotyping-by-sequencing approaches to characterize crop genomes: Choosing the right tool for the right application. Plant Biotechnol J. 2017;15(2):149–161.

36

Zhang Y, Massel K, Godwin ID, Gao C. Applications and potential of genome editing in crop improvement. Genome Biol. 2018;19(1):210.

37

Ahmar S, Gill RA, Jung KH, Faheem A, Qasim MU, Mubeen M, Zhou WJ. Conventional and molecular techniques from simple breeding to speed breeding in crop plants: Recent advances and future outlook. Int J Mol Sci. 2020;21(7):2590.

38

De La Fuente GN, Frei UK, Lubberstedt T. Accelerating plant breeding. Trends Plant Sci. 2013;18(12):667–672.

39

Li L, Li X, Liu YW, Liu HT. Flowering responses to light and temperature. Sci Chin Life Sci. 2016;59(4):403–408.

40

Dodd AN, Salathia N, Hall A, Kevei E, Toth R, Nagy F, Hibberd JM, Millar AJ, Webb AAR. Plant circadian clocks increase photosynthesis, growth, survival, and competitive advantage. Science. 2005;309(5734):630–633.

41

Ghosh S, Watson A, Gonzalez-Navarro OE, Ramirez-Gonzalez RH, Yanes L, Mendoza-Suarez M, Simmonds J, Wells R, Rayner T, Green P, et al. Speed breeding in growth chambers and glasshouses for crop breeding and model plant research. Nat Protoc. 2018;13(12):2944–2963.

42

Walter J, Kromdijk J. Here comes the sun: How optimization of photosynthetic light reactions can boost crop yields. J Integr Plant Biol. 2022;64(2):564–591.

43

Krieger-Liszkay A. Singlet oxygen production in photosynthesis. J Exp Bot. 2005;56(411):337–346.

44

Farquhar GD, Busch FA. Changes in the chloroplastic CO2 concentration explain much of the observed Kok effect: A model. New Phytol. 2017;214(2):570–584.

45

Li T, Yang QC. Advantages of diffuse light for horticultural production and perspectives for further research. Front Plant Sci. 2015;6:704.

46

Gu LH, Baldocchi D, Verma SB, Black TA, Vesala T, Falge EM, Dowty PR. Advantages of diffuse radiation for terrestrial ecosystem productivity. J Geophys Res-Atmos. 2002;107(D5-6):ACL 2-1–ACL 2-23.

47

Brodersen CR, Vogelmann TC, Williams WE, Gorton HL. A new paradigm in leaf-level photosynthesis: Direct and diffuse lights are not equal. Plant Cell Environ. 2008;31(1):159–164.

48

Sanchez F, Bassil E, Crane JH, Shahid MA, Vincent CI, Schaffer B. Spectral light distribution affects photosynthesis, leaf reflective indices, antioxidant activity and growth of Vanilla planifolia. Plant Physiol Biochem. 2022;182:145–153.

49

Lotscher M, Nosberger J. Branch and root formation in Trifolium repens is influenced by the light environment of unfolded leaves. Oecologia. 1997;111(4):499–504.

50

Finlayson SA, Krishnareddy SR, Kebrom TH, Casal JJ. Phytochrome regulation of branching in Arabidopsis. Plant Physiol. 2010;152(4):1914–1927.

51

Hogewoning SW, Trouwborst G, Maljaars H, Poorter H, van Ieperen W, Harbinson J. Blue light dose-responses of leaf photosynthesis, morphology, and chemical composition of Cucumis sativus grown under different combinations of red and blue light. J Exp Bot. 2010;61(11):3107–3117.

52

Tan TT, Li SL, Fan YF, Wang ZL, Raza MA, Shafiq I, Wang BB, Wu XL, Yong TW, Wang XC, et al. Far-red light: A regulator of plant morphology and photosynthetic capacity. Crop J. 2022;10(2):300–309.

53

Lund JB, Blom TJ, Aaslyng JM. End-of-day lighting with different red/far-red ratios using lightemitting diodes affects plant growth of chrysanthemum x morifolium ramat. ‘Coral charm’. HortScience. 2007;42(7):1609–1611.

54

Hitz T, Hartung J, Graeff-Honninger S, Munz S. Morphological response of soybean (Glycine max (L.) Merr.) cultivars to light intensity and red to far-red ratio. Agronomy-Basel. 2019;9(8):9080428.

55

Zhen SY, Bugbee B. Far-red photons have equivalent efficiency to traditional photosynthetic photons: Implications for redefining photosynthetically active radiation. Plant Cell Environ. 2020;43(5):1259–1272.

56

Marin FR, Ribeiro RV, Marchiori PER. How can crop modeling and plant physiology help to understand the plant responses to climate change? A case study with sugarcane. Theor Exper Plant Physiol. 2014;26(1):49–63.

57

Jahne F, Hahn V, Wurschum T, Leiser WL. Speed breeding short-day crops by LED-controlled light schemes. Theor Appl Genet. 2020;133(8):2335–2342.

58

Wang MZ, Wei H, Jeong BR. Lighting direction affects leaf morphology, stomatal characteristics, and physiology of head lettuce (Lactuca sativa L.). Int J Mol Sci. 2021;22(6):3157.

59

Yang J, Jeong BR. Side lighting enhances Morphophysiology by inducing more branching and flowering in chrysanthemum grown in controlled environment. Int J Mol Sci. 2021;22(21):12019.

60

Joshi J, Zhang G, Shen SQ, Supaibulwatana K, Watanabe CKA, Yamori W. A combination of downward lighting and supplemental upward lighting improves plant growth in a closed plant factory with artificial lighting. HortScience. 2017;52(6):831–U132.

61

Yang J, Song J, Jeong BR. Lighting from top and side enhances photosynthesis and plant performance by improving light usage efficiency. Int J Mol Sci. 2022;23(5):2448.

62

Yang J, Song J, Jeong BR. Side lighting enhances Morphophysiology and runner formation by upregulating photosynthesis in strawberry grown in controlled environment. Agronomy-Basel. 2022;12(1):12019.

63

Tenhunen JD, Weber JA, Yocum CS, Gates DM. Development of a photosynthesis model with an emphasis on ecological applications: Ⅱ. Analysis of a data set describing theP M surface. Oecologia. 1976;26(2):101–119.

64

Ziska LH. Growth temperature can alter the temperature dependent stimulation of photosynthesis by elevated carbon dioxide in Albutilon theophrasti. Physiol Plant. 2001;111(3):322–328.

65

Marchiori PER, Machado EC, Ribeiro RV. Photosynthetic limitations imposed by self-shading in field-grown sugarcane varieties. Field Crop Res. 2014;155:30–37.

66

Theobald M, Mitchell RA, Parry MA, Lawlor DW. Estimating the excess investment in ribulose-1,5-bisphosphate carboxylase/oxygenase in leaves of spring wheat grown under elevated CO2. Plant Physiol. 1998;118(3):945–955.

67

Bassi D, Menossi M, Mattiello L. Nitrogen supply influences photosynthesis establishment along the sugarcane leaf. Sci Rep. 2018;8(1):2327.

68

Holzworth DP, Huth NI, Devoil PG, Zurcher EJ, Herrmann NI, McLean G, Chenu K, van Oosterom EJ, Snow V, Murphy C, et al. APSIM - Evolution towards a new generation of agricultural systems simulation. Environ Model Softw. 2014;62:327–350.

69

Keating BA, Carberry PS, Hammer GL, Probert ME, Robertson MJ, Holzworth D, Huth NI, Hargreaves JNG, Meinke H, Hochman Z, et al. An overview of APSIM, a model designed for farming systems simulation. Eur J Agron. 2003;18(3-4):267–288.

70

Hanan J. Virtual plants—Integrating architectural and physiological models. Environ Model Softw. 1997;12(1):35–42.

71

Sievanen R, Nikinmaa E, Nygren P, Ozier-Lafontaine H, Perttunen J, Hakula H. Components of functional-structural tree models. Ann For Sci. 2000;57(5-6):399–412.

72

Godin C, Sinoquet H. Functional-structural plant modelling. New Phytol. 2005;166(3):705–708.

73

Sarlikioti V, de Visser PHB, Marcelis LFM. Exploring the spatial distribution of light interception and photosynthesis of canopies by means of a functional-structural plant model. Ann Bot. 2011;107(5):875–883.

74

Sarlikioti V, de Visser PHB, Buck-Sorlin GH, Marcelis LFM. How plant architecture affects light absorption and photosynthesis in tomato: Towards an ideotype for plant architecture using a functional-structural plant model. Ann Bot. 2011;108(6):1065–1073.

75

Bouman B, Keulen HV, Laar H, Rabbinge R. The ‘School of de Wit’ crop growth simulation models: A pedigree and historical overview. Agricul Syst. 1996;52(2-3):171–198.

76

Marcelis LFM, Heuvelink E, Goudriaan J. Modelling biomass production and yield of horticultural crops: A review. Horticulture. 1998;74(1-2):83–111.

77

Vos J, Evers JB, Buck-Sorlin GH, Andrieu B, Chelle M, de Visser PHB. Functional-structural plant modelling: A new versatile tool in crop science. J Exp Bot. 2010;61(8):2101–2115.

78

de Visser PHB, Buck-Sorlin GH, van der Heijden G. Optimizing illumination in the greenhouse using a 3D model of tomato and a ray tracer. Front Plant Sci. 2014;5:48.

79

Henke M, Buck-Sorlin GH. Using a full spectral raytracer for calculation light microclimate in functional-structural plant modelling. Comput Inform. 2017;36(6):1492–1522.

80

Dieleman JA, De Visser PHB, Meinen E, Grit JG, Dueck TA. Integrating morphological and physiological responses of tomato plants to light quality to the crop level by 3D modeling. Front Plant Sci. 2019;10:839.

81

Asseng S, Guarin JR, Raman M, Monje O, Kiss G, Despommier DD, Meggers FM, Gauthier PPG. Wheat yield potential in controlled-environment vertical farms. Proc Natl Acad Sci U S A. 2020;117(32):19131–19135.

82

Chauhan YS, Ryan M, Chandra S, Sadras VO. Accounting for soil moisture improves prediction of flowering time in chickpea and wheat. Sci Rep. 2019;9(1):7510.

83

Vazquez-Cruz MA, Guzman-Cruz R, Lopez-Cruz IL, Cornejo-Perez O, Torres-Pacheco I, Guevara-Gonzalez RG. Global sensitivity analysis by means of EFAST and Sobol’ methods and calibration of reduced state-variable TOMGRO model using genetic algorithms. Comput Electron Agric. 2014;100:1–12.

84

Lin DY, Wei RH, Xu LH. An integrated yield prediction model for greenhouse tomato. Agronomy-Basel. 2019;9(12).

85

Dilla A, Smethurst PJ, Barry K, Parsons D, Denboba M. Potential of the APSIM model to simulate impacts of shading on maize productivity. Agrofor Syst. 2018;92(6):1699–1709.

86

Akinseye FM, Ajegbe HA, Kamara AY, Adefisan EA, Whitbread AM. Understanding the response of sorghum cultivars to nitrogen applications in the semi-arid Nigeria using the agricultural production systems simulator. J Plant Nutr. 2020;43(6):834–850.

87

Araya A, Kisekka I, Prasad PVV, Holman J, Foster AJ, Lollato R. Assessing wheat yield, biomass, and water productivity responses to growth stage based irrigation water allocation. Trans ASABE. 2017;60(1):107–121.

88

Chen C, Wang E, Yu Q. Modeling wheat and maize productivity as affected by climate variation and irrigation supply in North China Plain. Agroclimatology. 2010;102(3):1037–1049.

89

O’Leary GJ, Christy B, Nuttall J, Huth N, Cammarano D, Stöckle C, Basso B, Shcherbak I, Fitzgerald G, Luo Q, et al. Response of wheat growth, grain yield and water use to elevated CO2 under a free-air CO2 enrichment (FACE) experiment and modelling in a semi-arid environment. Glob Chang Biol. 2015;21(7):2670–2686.

90

Mohanty M, Sinha NK, Hati KM, Reddy KS, Chaudhary RS. Elevated temperature and carbon dioxide concentration effects on wheat productivity in Madhya Pradesh: A simulation study. J Agrometeorol. 2015;17(2):185–189.

91

Hammer GL, Dong Z, McLean G, Doherty A, Messina C, Schussler J, Zinselmeier C, Paszkiewicz S, Cooper M. Can changes in canopy and/or root system architecture explain historical maize yield trends in the U.S Corn Belt? Crop Sci. 2009;49(1):299–312.

92

Hammer GL, McLean G, Chapman S, Zheng BY, Doherty A, Harrison MT, van Oosterom E, Jordan D. Crop design for specific adaptation in variable dryland production environments. Crop Pasture Sci. 2014;65(7):614–626.

93
Wen WL, Guo XY, Li BJ, Wang CY, Wang YJ, Yu ZT, Wu S, Fan JC, Gu SH, Lu XJ. Estimating canopy gap fraction and diffuse light interception in 3D maize canopy using hierarchical hemispheres. Agric For Meteorol. 2019;276–277:107594.
94

Zhang Y, Yang J, van Haaften M, Li L, Lu S, Wen W, Zheng X, Pan J, Qian T. Interactions between diffuse light and cucumber (Cucumis sativus L.) canopy structure, simulations of light interception in virtual canopies. Agronomy. 2022;12(3):602.

95

Hitz T, Graeff-Hönninger S, Munz S. Modelling of soybean (Glycine max (L.) Merr.) response to blue light intensity in controlled environments. Plant Basel. 2020;9(12):1757.

96

Kalaitzoglou P, van Ieperen W, Harbinson J, van der Meer M, Martinakos S, Weerheim K, Nicole CCS, Marcelis LFM. Effects of continuous or end-of-day far-red light on tomato plant growth, morphology, light absorption, and fruit. Production. 2019;10:322.

97

Katzin D, van Henten EJ, van Mourik S. Process-based greenhouse climate models: Genealogy, current status, and future directions. Agric Syst. 2022;198:103388.

98

Tahery D, Roshandel R, Avami A. An integrated dynamic model for evaluating the influence of ground to air heat transfer system on heating, cooling and CO2 supply in greenhouses: Considering crop transpiration. Renew Energy. 2021;173:42–56.

99
Hao X, Jia J, Chu X, Tao S, Gao W, Wang M. Greenhouse crop model: Methods, trends and future perspectives. 2020;8(9):386–398.
100

Vanthoor BHE, Stanghellini C, van Henten EJ, de Visser PHB. A methodology for model-based greenhouse design: Part 1, a greenhouse climate model for a broad range of designs and climates. Biosyst Eng. 2011;110(4):363–377.

101

Katzin D, van Mourik S, Kempkes F, van Henten EJ. GreenLight - An open source model for greenhouses with supplemental lighting: Evaluation of heat requirements under LED and HPS lamps. Biosyst Eng. 2020;194:61–81.

102

Kaneko T, Nomura K, Yasutake D, Iwao T, Okayasu T, Ozaki Y, Mori M, Hirota T, Kitano M. A canopy photosynthesis model based on a highly generalizable artificial neural network incorporated with a mechanistic understanding of single-leaf photosynthesis. Agric For Meteorol. 2022;323:109036.

103

Chen QY, Li LY, Chong C, Wang XN. AI-enhanced soil management and smart farming. Soil Use Manag. 2022;38(1):7–13.

104

Gill M, Anderson R, Hu HF, Bennamoun M, Petereit J, Valliyodan B, Nguyen HT, Batley J, Bayer PE, Edwards D. Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction. BMC Plant Biol. 2022;22(1):1.

105

Mobini SH, Lulsdorf M, Warkentin TD, Vandenberg A. Plant growth regulators improve invitro flowering and rapid generation advancement in lentil and faba bean. Develop Biol-Plant. 2015;51(1):71–79.

106

Croser JS, Pazos-Navarro M, Bennett RG, Tschirren S, Edwards K, Erskine W, Creasy R, Ribalta FM. Time to flowering of temperate pulses invivo and generation turnover invivo-in vitro of narrow-leaf lupin accelerated by low red to far-red ratio and high intensity in the far-red region (vol 127, pg 591, 2016). Plant Cell Tissue Org Cult. 2016;127(3):601–601.

107

O’Connor DJ, Wright GC, Dieters MJ, George DL, Hunter MN, Tatnell JR, Fleischfresser DB. Development and application of speed breeding technologies in a commercial peanut breeding program. Peanut Sci. 2013;40(2):107–114.

108

Stetter MG, Zeitler L, Steinhaus A, Kroener K, Biljecki M, Schmid KJ. Crossing methods and cultivation conditions for rapid production of segregating populations in three grain Amaranth species. Front Plant Sci. 2016;7:816.

109

Xia L, Robock A, Cole J, Curry CL, Ji D, Jones A, Kravitz B, Moore JC, Muri H, Niemeier U, et al. Solar radiation management impacts on agriculture in China: A case study in the geoengineering model Intercomparison project (GeoMIP). J Geophys Res-Atmos. 2014;119(14):8695–8711.

110

Affholder F, Scopel E, Neto JM, Capillon A. Diagnosis of the productivity gap using a crop model. Methodology and case study of small-scale maize production in Central Brazil. Agronomie. 2003;23(4):305–325.

111

Wang Y, Liu S, Shi H. Comparison of climate change impacts on the growth of C3 and C4 crops in China. Eco Inform. 2023;74:101968.

112

Wang N, Wang E, Wang J, Zhang J, Zheng B, Huang Y, Tan M. Modelling maize phenology, biomass growth and yield under contrasting temperature conditions. Agric For Meteorol. 2018;250-251:319–329.

113

Monestiez P, Courault D, Allard D, Ruget F. Spatial interpolation of air temperature using environmental context: Application to a crop model. Environ Ecol Stat. 2001;8(4):297–309.

114

Ahmadi SH, Ghorra MRR, Sepaskhah AR. Parameterizing the AquaCrop model for potato growth modeling in a semi-arid region. Field Crop Res. 2022;288(12):108680.

115

Sun H, Zhang X, Liu X, Liu X, Shao L, Chen S, Wang J, Dong X. Impact of different cropping systems and irrigation schedules on evapotranspiration, grain yield and groundwater level in the North China plain. Agric Water Manag. 2019;211(8):202–209.

116

Tavakoli AR, Moghadam MM, Sepaskhah AR. Evaluation of the AquaCrop model for barley production under deficit irrigation and rainfed condition in Iran. Agric Water Manag. 2015;161:136–146.

117

He D, Wang E, Wang J, Lilley J, Luo Z, Pan X, Pan Z, Yang N. Uncertainty in canola phenology modelling induced by cultivar parameterization and its impact on simulated yield. Agric For Meteorol. 2017;232:163–175.

118

Akinseye FM, Adam M, Agele SO, Hoffmann MP, Traore PCS, Whitbread AM. Assessing crop model improvements through comparison of sorghum (sorghum bicolor L. moench) simulation models: A case study of west African varieties. Field Crop Res. 2017;201:19–31.

119

Folliard A, Traore PCS, Vaksmann M, Kouressy M. Modeling of sorghum response to photoperiod: A threshold-hyperbolic approach. Field Crop Res. 2004;89(1):59–70.

120

de Wit A, Boogaard H, Fumagalli D, Janssen S, Knapen R, van Kraalingen D, Supit I, van der Wijngaart R, van Diepen K. 25 years of the WOFOST cropping systems model. Agric Syst. 2019;168:154–167.

121

Zhang Y, Lam SK, Li P, Zong Y, Zhang D, Shi X, Hao X, Wang J. Early-maturing cultivar of winter wheat is more adaptable to elevated CO2 and rising temperature in the eastern loess plateau. Agric For Meteorol. 2023;332:109356.

122

Li L, Vuichard N, Viovy N, Ciais P, Wang T, Ceschia E, Jans W, Wattenbach M, Beziat P, Gruenwald T, et al. Importance of crop varieties and management practices: Evaluation of a process-based model for simulating CO2 and H2O fluxes at five European maize (Zea mays L.) sites. Biogeosciences. 2011;8(6):1721–1736.

123

Kourat T, Smadhi D, Mouhouche B, Gourari N, Mostofa Amin MG, Bryant CR. Assessment of future climate change impact on rainfed wheat yield in the semi-arid eastern high plain of Algeria using a crop model. Nat Hazards. 2021;107(3):2175–2203.

124

Connolly RD, Bell M, Huth N, Freebairn DM, Thomas G. Simulating infiltration and the water balance in cropping systems with APSIM-SWIM. Aust J Soil Res. 2002;40(2):221–242.

125

Fry J, Guber AK, Ladoni M, Munoz JD, Kravchenko AN. The effect of up-scaling soil properties and model parameters on predictive accuracy of DSSAT crop simulation model under variable weather conditions. Geoderma. 2017;287:105–115.

126

Kalumba M, Bamps B, Nyambe I, Dondeyne S, Van Orshoven J. Development and functional evaluation of pedotransfer functions for soil hydraulic properties for the Zambezi River basin. Eur J Soil Sci. 2021;72(4):1559–1574.

127

MacCarthy DS, Akponikpe PBI, Narh S, Tegbe R. Modeling the effect of seasonal climate variability on the efficiency of mineral fertilization on maize in the coastal savannah of Ghana. Nutr Cycl Agroecosyst. 2015;102(1):45–64.

128

Aluoch SO, Li Z, Li X, Hu C, Mburu DM, Yang J, Xu Q, Yang Y, Su H. Effect of mineral N fertilizer and organic input on maize yield and soil water content for assessing optimal N and irrigation rates in Central Kenya. Field Crop Res. 2022;277:108420.

129

Singh AK, Madramootoo CA, Goyal MK, Smith DL. Corn yield simulation using the STICS model under varying nitrogen management and climate-change scenarios. J Irrig Drain Eng. 2014;140(4):1.

130

Rahimikhoob H, Sohrabi T, Delshad M. Simulating crop response to nitrogen-deficiency stress using the critical nitrogen concentration concept and the AquaCrop semi-quantitative approach. Sci Hortic. 2021;285(4):110194.

131

Sun X, Li Y, Heinen M, Ritzema H, Hellegers P, van Dam J. Fertigation strategies to improve water and nitrogen use efficiency in surface irrigation system in the North China plain. Agriculture-Basel. 2023;13(1):17.

132

Boudhina N, Masmoudi MM, Alaya I, Jacob F, Ben MN. Use of AquaCrop model for estimating crop evapotranspiration and biomass production in hilly topography. Arab J Geosci. 2019;12(8):9.

133

Dewenam LEF, Er-Raki S, Ezzahar J, Chehbouni A. Performance evaluation of the WOFOST model for estimating evapotranspiration, soil water content, grain yield and Total above-ground biomass of winter wheat in Tensift Al Haouz (Morocco):Application to yield gap estimation. Agronomy-Basel. 2021;11(12):2480.

134

Sarkar S, Gaydon DS, Brahmachari K, Poulton PL, Chaki AK, Ray K, Ghosh A, Nanda MK, Mainuddin M. Testing APSIM in a complex saline coastal cropping environment. Environ Model Softw. 2022;147.

135

Liu D, Mishra AK, Yu ZB. Evaluation of hydroclimatic variables for maize yield estimation using crop model and remotely sensed data assimilation. Stoch Env Res Risk A. 2019;33(7):1283–1295.

136

Li Y, Feng Q, Li D, Li M, Ning H, Han Q, Hamani AKM, Gao Y, Sun J. Water-salt thresholds of cotton (Gossypium hirsutum L.) under film drip irrigation in arid saline-alkali area. Agric-Basel. 2022;12(11):1769.

137

Kroes JG, Supit I. Impact analysis of drought, water excess and salinity on grass production in the Netherlands using historical and future climate data. Agric Ecosyst Environ. 2011;144(1):370–381.

138

Saddique Q, Zou Y, Ajaz A, Ji J, Xu J, Azmat M, Rahman MHU, He J, Cai H. Analyzing the performance and application of CERES-wheat and APSIM in the Guanzhong plain. Chin Trans Asabe. 2020;63(6):1879–1893.

139

Shelia V, Simunek J, Boote K, Hoogenbooom G. Coupling DSSAT and HYDRUS-1D for simulations of soil water dynamics in the soil-plant-atmosphere system. J Hydrol Hydromech. 2018;66(2):232–245.

140

Crepeau M, Jego G, Morissette R, Pattey E, Morrison MJ. Predictions of soybean harvest index evolution and evapotranspiration using STICS crop model. Agron J. 2021;113(2):20765.

141

Govindarajan S, Ambujam NK, Karunakaran K. Estimation of paddy water productivity (WP) using hydrological model: An experimental study. Paddy Water Environ. 2008;6(3):327–339.

142

Xu F, Wang B, He C, Liu DL, Feng P, Yao N, Zhang R, Xu S, Xue J, Feng H, et al. Optimizing sowing date and planting density can mitigate the impacts of future Climate on maize yield: A case study in the Guanzhong plain of China. Agronomy-Basel. 2021;11(8):1452.

143

Nafi E, Webber H, Danso I, Naab JB, Frei M, Gaiser T. Can reduced tillage buffer the future climate warming effects on maize yield in different soil types of West Africa? Soil Tillage Res. 2021;205:104767.

Plant Phenomics
Article number: 0099
Cite this article:
Yu Y, Cheng Q, Wang F, et al. Crop/Plant Modeling Supports Plant Breeding: Ⅰ. Optimization of Environmental Factors in Accelerating Crop Growth and Development for Speed Breeding. Plant Phenomics, 2023, 5: 0099. https://doi.org/10.34133/plantphenomics.0099

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Received: 04 May 2023
Accepted: 07 September 2023
Published: 09 October 2023
© 2023 Yi Yu et al. Exclusive licensee Nanjing Agricultural University. No claim to original U.S. Government Works.

Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0).

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