Advances in crop phenotyping and multi-environment trials

Abstract

Efficient evaluation of crop phenotypes is a prerequisite for breeding, cultivar adoption, genomics and phenomics study. Plant genotyping is developing rapidly through the use of high-throughput sequencing techniques, while plant phenotyping has lagged far behind and it has become the rate-limiting factor in genetics, large-scale breeding and development of new cultivars. In this paper, we consider crop phenotyping technology under three categories. The first is high-throughput phenotyping techniques in controlled environments such as greenhouses or specifically designed platforms. The second is a phenotypic strengthening test in semi-controlled environments, especially for traits that are difficult to be tested in multi-environment trials (MET), such as lodging, drought and disease resistance. The third is MET in uncontrolled environments, in which crop plants are managed according to farmer’s cultural practices. Research and application of these phenotyping techniques are reviewed and methods for MET improvement proposed.

Graphical abstract

Keywords

crop breeding / genotyping / phenotyping / genotype-environment interaction / cultivar regional test

References

[1] Yu G H , Fang X J . Concept of phenomics and its development in plant science . Molecular Plant Breeding , 2009 , 7 ( 4 ): 639 − 645

[2] Thakur A K , Rath S , Patil D U , Kumar A . Effects on rice plant morphology and physiology of water and associated management practices of the system of rice intensification and their implications for crop performance . Paddy and Water Environment , 2011 , 9 ( 1 ): 13 − 24

[3] Pask A , Joshi A K , Manès Y , Sharma I , Chatrath R , Singh G P , Sohu V S , Mavi G S , Sakuru V S P , Kalappanavar I K , Mishra V K , Arun B , Mujahid M Y , Hussain M , Gautam N R , Barma N C D , Hakim A , Hoppitt W , Trethowan R , Reynolds M P . A wheat phenotyping network to incorporate physiological traits for climate change in South Asia . Field Crops Research , 2014 , 168 : 156 − 167

[4] Klassen S P , Villa J , Adamchuk V , Serraj R . Soil mapping for improved phenotyping of drought resistance in lowland rice fields . Field Crops Research , 2014 , 167 : 112 − 118

[5] Jha U C , Bohra A , Singh N P . Heat stress in crop plants: its nature, impacts and integrated breeding strategies to improve heat tolerance . Plant Breeding , 2014 , 133 ( 6 ): 679 − 701

[6] Nogales A , Muñoz-Sanhueza L , Hansen L D , Arnholdt-Schmitt B . Phenotyping carrot ( Daucus carota L.) for yield-determining temperature response by calorespirometry . Planta , 2015 , 241 ( 2 ): 525 − 538

[7] Junker A , Muraya M M , Weigelt-Fischer K , Arana-Ceballos F , Klukas C , Melchinger A E , Meyer RC , Riewe D , Altmann T . Optimizing experimental procedures for quantitative evaluation of crop plant performance in high throughput phenotyping systems . Frontiers in Plant Science , 2015 , 5 : 1 − 21

[8] Mutka A M , Bart R S . Image-based phenotyping of plant disease symptoms . Frontiers in Plant Science Plant , 2015 , 5 : 1 − 8

[9] Cockram J , Jones H , Norris C , O’Sullivan D M . Evaluation of diagnostic molecular markers for DUS phenotypic assessment in the cereal crop, barley ( Hordeum vulgare ssp. vulgare L.) . Theoretical and Applied Genetics , 2012 , 125 ( 8 ): 1735 − 1749

[10] Bucksch A , Burridge J , York L M , Das A , Nord E , Weitz J S , Lynch J P . Image-based high-throughput field phenotyping of crop roots . Plant Physiology , 2014 : 1 − 49

[11] Seiffert U , Bollenbeck F , Mock H-P , Matros A . Clustering of crop phenotypes by means of hyperspectral signatures using artificial neural networks . Hyperspectral Image and Signal Processing: Evolution in Remote sensing , 2010 : 1 − 4

[12] Harbinson J , Prinzenberg A E , Kruijer W , Aarts M G . High throughput screening with chlorophyll fluorescence imaging and its use in crop improvement . Current Opinion in Biotechnology , 2012 , 23 ( 2 ): 221 − 226

[13] Barabaschi D , Guerra D , Lacrima K , Laino P , Michelotti V , Urso S , Valè G , Cattivelli L . Emerging knowledge from genome sequencing of crop species . Molecular Biotechnology , 2012 , 50 ( 3 ): 250 − 266

[14] Cobb J N , Declerck G , Greenberg A , Clark R , McCouch S . Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype-phenotype relationships and its relevance to crop improvement . Theoretical and Applied Genetics , 2013 , 126 ( 4 ): 867 − 887

[15] Life Technologies . Life Technologies introduces the Benchtop Ion Proton™ Sequencer . Available at Life Technologies website on January 10, 2012

[16] BGI . 1000 plant and animal reference genomes project . Available at LDL Genomics website on April 5, 2012

[17] Monsanto . Research and development pipeline . Available at Monsanto website on January 5, 2012

[18] Pioneer . Technologies brochures & specification sheets . Available at Pioneer website on March 1, 2012

[19] Nanni L , Lumini A . A reliable method for cell phenotype image classification . Artificial Intelligence in Medicine , 2008 , 43 ( 2 ): 87 − 97

[20] Zhang L , Grift T E . A LIDAR-based crop height measurement system for Miscanthus giganteus . Computers and Electronics in Agriculture , 2012 , 85 : 70 − 76

[21] Busemeyer L , Ruckelshausen A , Möller K , Melchinger A E , Alheit K V , Maurer H P , Hahn V , Weissmann E A , Reif J C , Würschum T . Precision phenotyping of biomass accumulation in triticale reveals temporal genetic patterns of regulation . Scientific Reports , 2013 , 3 : 2442

[22] Jay S , Rabatel G , Hadoux X , Moura D , Gorretta N . In-field crop row phenotyping from 3D modeling performed using Structure from Motion . Computers and Electronics in Agriculture , 2015 , 110 : 70 − 77

[23] CropDesign . Traitmill- platform and process . Available at CropDesign website on April 16, 2015

[24] LemnaTec . Available at LemnaTec website on April 16, 2015

[25] ShangHai ZeQuan Technology Limited . Fully automated high-throughput plant 3D imaging system . Available at Zealquest website on December 8, 2009

[26] Biotechnology Research Institute of Chinese Academy of Agricultural Sciences (CAAS) . CAAS completed China’s first fully automated high-throughput 3D imaging phenomics research platform . Available at CAAS website on April 16, 2015

[27] Dang J M C , Copeland L . Imaging rice grains using atomic force microscopy . Journal of Cereal Science , 2003 , 37 ( 2 ): 165 − 170

[28] Ogawa Y , Kuensting H , Sugiyama J , Ohtani T , Liu X Q , Kokubo M , Kudoh K , Higuchi T . Structure of a rice grain represented by a new three-dimensional visualisation technique . Journal of Cereal Science , 2002 , 36 ( 1 ): l − 7

[29] Neethirajan S , Jayas D S , White N D G , Zhang H . Investigation of 3 D geometry of bulk wheat and pea pores using X-ray computed tomography images . Computers and Electronics in Agriculture , 2008 , 63 ( 2 ): 104 − 111

[30] Jayas D S , Karunakaran C , Paliwal J . Grain quality monitoring using machine vision and soft X-rays for cereal grains: 2004 international quality grains conference proceedings . Indianapolis,Indiana , 2004 : 19 − 22

[31] Yang W N . Development of automatic and digital extraction instrument for rice yield-related traits . Dissertation for the Doctoral Degree . WuHan : HuaZhong University of Science and Technology , 2011 ( in Chinese )

[32] Bi K . Study on some parameter measurement system for rice phenotype . Dissertation for the Doctoral Degree . WuHan : HuaZhong University of Science and Technology , 2009 ( in Chinese )

[33] Yang W , Guo Z , Huang C , Duan L , Chen G , Jiang N , Fang W , Feng H , Xie W , Lian X , Wang G , Luo Q , Zhang Q , Liu Q , Xiong L . Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice . Nature Communications , 2014 , 5 : 5087

[34] Technical Regulations for Crop cultivar Regional Testing-Maize [S] . China’s Ministry of Agriculture, NY/T 1209–2006

[35] Technical Regulations for Crop cultivar Regional Testing-Rice [S] . China’s Ministry of Agriculture, NY/T 1300–2007

[36] Technical Regulations for Crop cultivar Regional Testing-Wheat [S] . China’s Ministry of Agriculture, NY/T 1301–2007

[37] Pioneer . Pioneer Research to Develop Drought-Tolerant Corn Hybrids . Available at Pioneer website on April 16, 2015

[38] Spectrum Seed Company (SSC) . Spectrum managed stress environments . Available at SSC website on April 16, 2015

[39] Troyer A F . Phenotypic selection and evaluation of maize inbreds for adaptedness . Plant Breeding Reviews , 2007 , 29 ( 28 ): 101 − 123

[40] Piepho H P . Methods for comparing the yield stability of cropping system-a review . Journal Agronomy & Crop Science , 1998 , 180 ( 4 ): 193 − 213

[41] Piepho H P , McCulloch C E . Transformations in mixed models: application to risk analysis for a multienvironment trial . Journal of Agricultural Biological & Environmental Statistics , 2004 , 9 ( 2 ): 123 − 137

[42] Kempton R A , Fox P N . Statistical methods for plant cultivar evaluation . London : Chapman and Hall , 1997

[43] Zhang Q Y , Kong F L , Yang F X . Optimizing the experimental design of regional crop trial by nonlinear program . Scientia Agricultura Sinica , 2000 , 33 ( 4 ): 25 − 30 ( in Chinese )

[44] Kong F L , Zhang Q Y , Ge Z N , Yang F X , Zhang D G , Liu W X . Expriment precision and cultivar comparison precision in regional crop trials . Scientia Agricultura Sinica , 2001 , 34 ( 3 ): 266 − 271 ( in Chinese )

[45] Zhang Q Y , Kong F L , Liao Q , Yang F X , Wang L . Evaluation system and methods for regional crop trials . System Sciences and Comprehensive Studies in Agriculure , 2000 , 16 ( 2 ): 81 − 86

[46] Liu Z , Li S M , Yang J Y , Yang Y , Mi C Q , Wang H , Zhang X D , Zhu D H . Method of test environments selection for corn lodging resistance . Transactions of the CSAE , 2010 , 26 ( 10 ): 167 − 171

[47] Liu Z , Guo J , Li S M , Yang Y , Zhang X D , Chen B , Zhao Z , Yang J Y , Zhu D H . Verification and quantitative expression on southern corn leaf blight . Transactions of the CSAE , 2011 , 27 ( 11 ): 160 − 163

[48] Mo H D . Agricultural experiment statistics. Shanghai: Shanghai Science and Technology Publishing House , 1992

[49] Wang F T , Guo R L . Analysis of agricultural experiment design and statistics . Beijing : The Rural Books Publishing House , 1993

[50] Fasoulas A C . Rating cultivars and trials in applied plant breeding . Euphytica , 1983 , 32 ( 3 ): 939 − 943

[51] Zhu J , Xu Y H , Lai M G . Analysis methods for unbalanced data from regional trial of crop cultivar: analysis for single trait . Journal of Zhejiang Agricultural University , 1993 , 19 ( 1 ): 7 − 13 ( in Chinese )

[52] Zhao F C , Duan D F , Tan H P , Wang G Y . Analysis of yield character and stability for unbalanced data in sweet corn regional trial . Journal of Maize Sciences , 2007 , 15 ( Z1 ): 142 − 145 ( in Chinese )

[53] Jin W L , Bai Q Y . Validity of appreciation of crop yield capability with the rank analysis method by cultivar rerional trials . Chinese Journal of Oil Crop Sciences , 2004 , 26 ( 3 ): 18 − 23 ( in Chinese )

[54] Jin W L , Bai Q Y . The analysis based on ranks of crop cultivars in regional trials . Acta Agronomica Sinica , 1999 , 25 ( 5 ): 632 − 638 ( in Chinese )

[55] Jin W L . The rank analysis model of evaluating crop cultivars yield stability in regional trials . Acta Agronomica Sinica , 2000 , 26 ( 6 ): 925 − 930 ( in Chinese )

[56] Jin W L , Bai Q Y . The rank analysis method of non-balance data in mid-long run cultivar comparative trials of rolling way . Acta Agronomica Sinica , 2001 , 27 ( 6 ): 946 − 952 ( in Chinese )

[57] Wang Y J . The evaluation method of new corn cultivar by using rank analysis . Inner Mongolia Agricultural Science and Technology , 2008 , ( 1 ): 31 − 33 ( in Chinese )

[58] Liu H Y , Zhao Y Z . Evaluation of ssesame yield potential in the national trail by using rank analysis . Chinese Journal of Oil Crop Sciences , 2008 , 30 ( 1 ): 51 − 55 ( in Chinese )

[59] An Y W , Gao X N , Ge W D . Application of AMMI method in the analysis of spring wheat regional trial date . Journal of Anhui Agricultural Sciences , 2006 , 34 ( 17 ): 4199 − 4200,4203 ( in Chinese )

[60] Zhang Q Y , Kong F L . Comparison of statistical models for regional crop trial analysis . Scintia Agricultura Sinica , 2002 , 35 ( 4 ): 365 − 371 ( in Chinese )

[61] Guo R L . Fuzzy sets in agricuture . Zhengzhou : He’nan Science and Technology Press , 1991

[62] Wang R Z , Zhao J . Applcation of fuzzy assessment method in the evaluation of rice cultivars . Modern Agriculture Science and Technology , 2007 , ( 10 ): 112

[63] Guo R L . Crop grey breeding science . Beijing : China Agriculture Science and Technology Press , 1995

[64] Zhou C , Liu XL , Shen JH , Zhang XW . The Application of computer in the association analysis in grey system theory . Seed , 2000 , ( 4 ): 42 − 44

[65] Guo R L . The test of connection trend for the similarity-difference analysis and its application in regional test of wheat cultivar . Journal of Triticeae Crops , 2004 , 24 ( 1 ): 63 − 65

[66] Guo R L , Zhang J Z , Zhang A Q . Multidimensional matter element analysis method of crop cultivar . Mathematics in Practice and Theory , 2006 , 36 ( 1 ): 115 − 121

[67] Zhao K Q . Set pair analysis and its preliminary application . Hangzhou : Zhejiang Science and Technology Press , 2000

[68] Guo R L , Yang C. L , Guan L , Hou J H , Wang K , Song Z J , He M L , Jia H Q . Study on identical and different analysis method of wheat cultivar regional test . Journal of Triticeae Crops , 2001 , 21 ( 3 ): 60 − 63

[69] Zhou X R , Kong X L . The comprehensive evaluation method of maize regional test cultivars with the DTOPSIS . Journal of Maize Sciences , 2005 , ( Z1 ): 32 − 33,36

[70] Gauch H G Jr. Statistical analysis of yield trials by AMMI and GGE . Crop Science , 2006 , 46 ( 4 ): 1488 − 1500

[71] Yan W K , Kang M S , Ma B L , Woods S L , Cornelius P L . GGE biplot vs. AMMI analysis of genotype-by-environment data . Crop Science , 2007 , 47 ( 2 ): 643 − 653

[72] Bradley J P , Knittle K H , Troyer A F . Statistical methods in seed corn product selection . Journal of Production Agriculture , 1988 , 1 ( 1 ): 34 − 38

[73] Eberhart S A , Russell W A . Stability parameters for comparing cultivars . Crop Science , 1966 , 6 ( 1 ): 36 − 40

[74] Eskridge K M , Johnson B E . Expected utility maximization and selection of stable plant cultivars . Theoretical and Applied Genetics , 1991 , 81 ( 6 ): 825 − 832

[75] Eskridge K M , Mumm R F . Choosing plant cultivars based on the probability of outperforming a check . Theoretical and Applied Genetics , 1992 , 84 ( 3−4 ): 494 − 500

[76] Eskridge K M , Smith O S , Byrne P F . Comparing test cultivars using reliability functions of test-check differences from on-farm trials . Theoretical and Applied Genetics , 1993 , 87 ( 1−2 ): 60 − 64

[77] Gauch H G Jr, Zobel R W . Accuracy and selection success in yield trial analyses . Theoretical and Applied Genetics , 1989 , 77 ( 4 ): 473 − 481

[78] Gauch H G Jr. Full and reduced models for yield trials . Theoretical and Applied Genetics , 1990 , 80 ( 2 ): 153 − 160

[79] China Corn Blog . Available at Chinamaize Blog on April 16, 2015

[80] Liu S . The debate about commercial breeding . Available at Sina Blog on February 12, 2012

[81] Sun S X . Discussion on the national crop regional test problem . Journal of Crop , 1996 , ( 5 ): 14 − 15

[82] Zhang Q Y , Kong F L , Yang F X . Evaluation of the precision of regional crop trials in China . Journal of China Agricultural University , 2001 , 6 ( 1 ): 43 − 50

[83] Ming D X . Analysis of field test and statistical , 2nd ed . Beijing : Science Press , 2008

[84] Nanjing Agricultural University . Analysis of field test and statistical , 2nd ed . Beijing : Agricultural Press , 1988

[85] Yang G H , Tang S W , Ji G D , Sun S X . Change and development of check in maize cultivars plot trial at state-level . Journal of Marine Science , 2010 , 18 ( 4 ): 49 − 51

[86] Liu Z , Li S M , Zhang X D . Environmental sample size estimation based on cultivar means estimation and means comparison for multi-environment trial . 2nd International Conference on Agro-Geoinformatics , 2013 : 460 − 465

[87] Liu Z , Yang J Y , Li S M , Wang H , Li L , Zhang X D , Zhu D H . Optimal method of transforming observables into relative values for multi-environment trials in maize . Transactions of the CSAE , 2011 , 27 ( 7 ): 205 − 209

[88] Liu Z . Wang H , Yang J Y , Li S M , Ma Q , Li L , Zhang X D , Zhu D H . Analytical graphics for multi-environment trials of breed selection . Transactions of the CSAE , 2011 , 27 ( 10 ): 142 − 147

[89] Liu Z , Li S M , Mi C Q , Wang H , Ma Q , Li L , Zhang X D , Zhu D H . A spatial-temporal diffusion study of maize cultivar in china using a new two-dimensional representation . Journal of Food Agriculture and Environment , 2012 , 10 ( 1 ): 331 − 337

Acknowledgements

This work is supported by the National Natural Science Foundation (Spatial Distribution of Multi-environment Trial Stations for Maize Cultivar, 41301075), and the National Science-technology Support Plan Projects (Research and Demonstration of North China Corn Commercialized Breeding Technique, 2014BAD01B01) and Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture. We appreciate language assistance and suggestions from Hongshuo Wang at the Ohio State University.

Compliance with ethics guidelines

Zhe Liu, Qin Ma, Dong An, Lin Li, Xiaodong Zhang, Dehai Zhu and Shaoming Li declare that they have no conflict of interest or financial conflicts to disclose.

This article is a review and does not contain any studies with human or animal subjects performed by any of the authors.

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag Berlin Heidelberg