000			02950nab a22003377a 4500
999			_c60383 _d60375
001			60383
003			MX-TxCIM
005			20231017232835.0
008			190430s2019 ne |||p|op||| 00| 0 eng d
022			_a0168-9452
024	8		_ahttps://doi.org/10.1016/j.plantsci.2019.01.007
040			_aMX-TxCIM
041			_aeng
100	1		_99206 _aMir, R.
245	1	0	_aHigh-throughput phenotyping for crop improvement in the genomics era
260			_aNetherlands : _bElsevier, _c2019.
500			_aPeer review
520			_aTremendous progress has been made with continually expanding genomics technologies to unravel and understand crop genomes. However, the impact of genomics data on crop improvement is still far from satisfactory, in large part due to a lack of effective phenotypic data; our capacity to collect useful high quality phenotypic data lags behind the current capacity to generate high-throughput genomics data. Thus, the research bottleneck in plant sciences is shifting from genotyping to phenotyping. This article review the current status of efforts made in the last decade to systematically collect phenotypic data to alleviate this ‘phenomics bottlenecks’ by recording trait data through sophisticated non-invasive imaging, spectroscopy, image analysis, robotics, high-performance computing facilities and phenomics databases. These modern phenomics platforms and tools aim to record data on traits like plant development, architecture, plant photosynthesis, growth or biomass productivity, on hundreds to thousands of plants in a single day, as a phenomics revolution. It is believed that this revolution will provide plant scientists with the knowledge and tools necessary for unlocking information coded in plant genomes. Efforts have been also made to present the advances made in the last 10 years in phenomics platforms and their use in generating phenotypic data on different traits in several major crops including rice, wheat, barley, and maize. The article also highlights the need for phenomics databases and phenotypic data sharing for crop improvement. The phenomics data generated has been used to identify genes/QTL through QTL mapping, association mapping and genome-wide association studies (GWAS) for genomics-assisted breeding (GAB) for crop improvement.
546			_aText in English
650		7	_2AGROVOC _91132 _aGenomics
650		7	_2AGROVOC _93634 _aPhenotypes
650		7	_2AGROVOC _91059 _aCrop improvement
650		7	_2AGROVOC _91853 _aQuantitative Trait Loci
700	1		_aReynolds, M.P. _gGlobal Wheat Program _8INT1511 _9831
700	1		_aPinto Espinosa, F. _8I1707012 _gFormerly Global Wheat Program _94431
700	1		_99207 _aKhan, M.A.
700	1		_99208 _aBhat, M.
773	0		_dNetherlands : Elsevier, 2019. _gv. 282, p. 60-72 _tPlant Science _w u444702 _x0168-9452
856	4		_uhttp://libcatalog.cimmyt.org/download/cis/60383.pdf _yAccess only for CIMMYT Staff
942			_2ddc _cJA _n0