000			03591nab a22004697a 4500
001			G95911
003			MX-TxCIM
005			20230630174754.0
008			211110s2012 ne |||p|op||| 00| 0 eng d
022			_a0378-4290
024	8		_ahttps://doi.org/10.1016/j.fcr.2011.12.016
040			_aMX-TxCIM
041			_aeng
090			_aCIS-6566
100	1		_aWeber, V.S. _924881
245	1	0	_aPrediction of grain yield using reflectance spectra of canopy and leaves in maize plants grown under different water regimes
260			_aAmsterdam (Netherlands) : _bElsevier, _c2012.
500			_aPeer review
500			_aPeer-review: Yes - Open Access: Yes|http://science.thomsonreuters.com/cgi-bin/jrnlst/jlresults.cgi?PC=MASTER&ISSN=0378-4290
520			_aThe ability to accurately estimate grain yield using spectral reflectance measurements prior harvest could be used to reduce phenotyping time and costs. In this study, grain yield of 300 maize testcrosses grown under different water and temperature regimes in the dry season 2010 was predicted using spectral reflectance (495?1853 nm) of both leaves and canopy measured between tassel emergence until milk-grain stage. Partial least square regression (PLSR) was used for data analysis. Coefficients of determination (R2) between predicted and actual grain yield were highest for measurements conducted at anthesis and milk-grain stage, explaining at maximum 23% and 40% of the genotypic variation in grain yield after validation, respectively. PLSR models explained a higher proportion of the genetic variation in grain yield under drought stress compared to well-watered conditions. The association between predicted and actual grain yield was stronger in spectral reflectance measurements taken at the leaf level compared to canopy level. By combining the most predictive PLSR models across trials, at maximum of 40% of the variation in grain yield could be explained in each trial with a relative efficiency of selection of 0.88 and 0.68 using leaf and canopy reflectance, respectively. The most relevant wavelengths for predicting grain yield were associated with photosynthetic capacity (495?680 nm), red inflection point (680?780 nm) and plant water status (900, 970, and 1450 nm, 1150?1260 nm, and 1520?1540 nm). Additional wavelengths based on leaf (800, 1000, and 1260?1830 nm) and canopy (988?999 nm and 1430?1640 nm) reflectance of unknown physiological relevance were also identified for prediction of grain yield. Caution must be exercised before integrating our spectral reflectance approach into a breeding program because this is a pilot study based on a single location and season.
536			_aGlobal Maize Program
546			_aText in English
591			_aCIMMYT Informa No. 1779|Elsevier
594			_aINT2948
595			_aCSC
650		7	_aMaize _2AGROVOC _91173
650		7	_aCanopy _2AGROVOC _91800
650		7	_aSpectral analysis _2AGROVOC _94070
650		7	_aReflectance _2AGROVOC _95862
650		7	_aStatistical methods _2AGROVOC _92624
650		7	_aGrain _2AGROVOC _91138
650		7	_aYields _2AGROVOC _91313
650		7	_aFlowering _2AGROVOC _93729
700	1		_91436 _aAraus, J.L.
700	1		_9879 _aCairns, J.E. _gGlobal Maize Program _8INT2948
700	1		_aSánchez, C. _94725
700	1		_aMelchinger, A.E. _93373
700	1		_aOrsini, E. _924882
773	0		_tField Crops Research _gv. 128, no. 1, p. 82-90 _x0378-4290 _dAmsterdam (Netherlands) : Elsevier, 2012. _wG444314
856	4		_uhttps://hdl.handle.net/20.500.12665/873 _yAccess only for CIMMYT Staff
942			_cJA _2ddc _n0
999			_c28812 _d28812