000			03739nab a22003857a 4500
999			_c59150 _d59142
001			59150
003			MX-TxCIM
005			20220205062617.0
008			180131s2017 sz |||p|op||| 00| 0 eng d
024	8		_ahttps://doi.org/10.3389/fpls.2017.02004
040			_aMX-TxCIM
041			_aeng
100	1		_96248 _aGracia-Romero, A.
245	1	0	_aComparative performance of ground vs. aerially assessed RGB and multispectral indices for early-growth evaluation of maize performance under phosphorus fertilization
260			_aSwitzerland : _b Frontiers, _c2017.
500			_aPeer review
500			_aOpen Access
520			_aLow soil fertility is one of the factors most limiting agricultural production, with phosphorus deficiency being among the main factors, particularly in developing countries. To deal with such environmental constraints, remote sensing measurements can be used to rapidly assess crop performance and to phenotype a large number of plots in a rapid and cost-effective way. We evaluated the performance of a set of remote sensing indices derived from Red-Green-Blue (RGB) images and multispectral (visible and infrared) data as phenotypic traits and crop monitoring tools for early assessment of maize performance under phosphorus fertilization. Thus, a set of 26 maize hybrids grown under field conditions in Zimbabwe was assayed under contrasting phosphorus fertilization conditions. Remote sensing measurements were conducted in seedlings at two different levels: at the ground and from an aerial platform. Within a particular phosphorus level, some of the RGB indices strongly correlated with grain yield. In general, RGB indices assessed at both ground and aerial levels correlated in a comparable way with grain yield except for indices a* and u*, which correlated better when assessed at the aerial level than at ground level and Greener Area (GGA) which had the opposite correlation. The Normalized Difference Vegetation Index (NDVI) evaluated at ground level with an active sensor also correlated better with grain yield than the NDVI derived from the multispectral camera mounted in the aerial platform. Other multispectral indices like the Soil Adjusted Vegetation Index (SAVI) performed very similarly to NDVI assessed at the aerial level but overall, they correlated in a weaker manner with grain yield than the best RGB indices. This study clearly illustrates the advantage of RGB-derived indices over the more costly and time-consuming multispectral indices. Moreover, the indices best correlated with GY were in general those best correlated with leaf phosphorous content. However, these correlations were clearly weaker than against grain yield and only under low phosphorous conditions. This work reinforces the effectiveness of canopy remote sensing for plant phenotyping and crop management of maize under different phosphorus nutrient conditions and suggests that the RGB indices are the best option.
526			_aMCRP _bFP3
546			_aText in English
591			_bCIMMYT Informa : 2008 (March 1, 2018)
650		7	_95313 _aPhosphate fertilizers _2AGROVOC
650		7	_aMaize _gAGROVOC _2 _91173
650		7	_95543 _aAgricultural production _2AGROVOC
650		7	_91952 _aSoil fertility _2AGROVOC
700	1		_96249 _aKefauver, S.C.
700	1		_91438 _aVergara, O.
700	1		_9779 _aZaman-Allah, M. _gGlobal Maize Program _8I1705364
700	1		_aPrasanna, B.M. _gGlobal Maize Program _8INT3057 _9887
700	1		_9879 _aCairns, J.E. _gGlobal Maize Program _8INT2948
700	1		_91436 _aAraus, J.L.
773	0		_gv. 8, art. 2004 _tFrontiers in Plant Science _wu56875 _x1664-462X
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/19227
942			_2ddc _cJA _n0