000			03453nab a22004097a 4500
999			_c59342 _d59334
001			59342
003			MX-TxCIM
005			20220920151105.0
008			180315s2018 sz |||p|op||| 00| 0 eng d
024	8		_ahttps://doi.org/10.3390/rs10020349
040			_aMX-TxCIM
041			_aeng
100	1		_96248 _aGracia-Romero, A.
245	1	0	_aPhenotyping conservation agriculture management effects on ground and aerial remote sensing assessments of maize hybrids performance in Zimbabwe _h[Electronic Resource]
260			_aBasel, Switzerland : _b MDPI, _c2018.
500			_aPeer review
500			_aOpen Access
520			_aIn the coming decades, Sub-Saharan Africa (SSA) faces challenges to sustainably increase food production while keeping pace with continued population growth. Conservation agriculture (CA) has been proposed to enhance soil health and productivity to respond to this situation. Maize is the main staple food in SSA. To increase maize yields, the selection of suitable genotypes and management practices for CA conditions has been explored using remote sensing tools. They may play a fundamental role towards overcoming the traditional limitations of data collection and processing in large scale phenotyping studies. We present the result of a study in which Red-Green-Blue (RGB) and multispectral indexes were evaluated for assessing maize performance under conventional ploughing (CP) and CA practices. Eight hybrids under different planting densities and tillage practices were tested. The measurements were conducted on seedlings at ground level (0.8 m) and from an unmanned aerial vehicle (UAV) platform (30 m), causing a platform proximity effect on the images resolution that did not have any negative impact on the performance of the indexes. Most of the calculated indexes (Green Area (GA) and Normalized Difference Vegetation Index (NDVI)) were significantly affected by tillage conditions increasing their values from CP to CA. Indexes derived from the RGB-images related to canopy greenness performed better at assessing yield differences, potentially due to the greater resolution of the RGB compared with the multispectral data, although this performance was more precise for CP than CA. The correlations of the multispectral indexes with yield were improved by applying a soil-mask derived from a NDVI threshold with the aim of corresponding pixels with vegetation. The results of this study highlight the applicability of remote sensing approaches based on RGB images to the assessment of crop performance and hybrid choice.
526			_aMCRP _bFP2
546			_aText in English
591			_bCIMMYT Informa : 2012 (May 3, 2018)
650		7	_aFood security _gAGROVOC _2 _91118
650		7	_91151 _aHybrids _2AGROVOC
650		7	_aMaize _gAGROVOC _2 _91173
650		7	_91986 _aRemote sensing _2AGROVOC
650		7	_98627 _aMultispectral Imagery _2AGROVOC
650		7	_92619 _aConservation agriculture _2AGROVOC
651		7	_94496 _aZimbabwe _gAGROVOC
700	1		_91438 _aVergara, O.
700	1		_aThierfelder, C. _gSustainable Intensification Program _gSustainable Agrifood Systems _8INT2939 _9877
700	1		_9879 _aCairns, J.E. _gGlobal Maize Program _8INT2948
700	1		_96249 _aKefauver, S.C.
700	1		_91436 _aAraus, J.L.
773	0		_gv. 10, no. 2, art. 349 _tRemote Sensing _wu57403 _x2072-4292
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/19468
942			_2ddc _cJA _n0