| 000 | 02964nab a22003857a 4500 | ||
|---|---|---|---|
| 999 |
_c60391 _d60383 |
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| 001 | 60391 | ||
| 003 | MX-TxCIM | ||
| 005 | 20240919021026.0 | ||
| 008 | 190506s2019 sz |||p|op||| 00| 0 eng d | ||
| 015 | _a0 | ||
| 022 | _a1664-462X (Online) | ||
| 024 | _ahttps://doi.org/10.3389/fpls.2019.00552 | ||
| 040 | _aMX-TxCIM | ||
| 041 | 0 | _aeng | |
| 100 | 1 |
_aLoladze, A. _gFormerly Global Wheat Program _8INT3176 _9896 |
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| 245 | 1 | 0 | _aApplication of remote sensing for phenotyping tar spot complex resistance in maize |
| 260 |
_aSwitzerland : _bFrontiers, _c2019. |
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| 500 | _aPeer review | ||
| 500 | _aOpen Access | ||
| 520 | _aTar spot complex (TSC), caused by at least two fungal pathogens, Phyllachora maydis and Monographella maydis, is one of the major foliar diseases of maize in Central and South America. P. maydis was also detected in the United States of America in 2015 and since then the pathogen has spread in the maize growing regions of the country. Although remote sensing (RS) techniques are increasingly being used for plant phenotyping, they have not been applied to phenotyping TSC resistance in maize. In this study, several multispectral vegetation indices (VIs) and thermal imaging of maize plots under disease pressure and disease-free conditions were tested using an unmanned aerial vehicle (UAV) over two crop seasons. A strong relationship between grain yield, a vegetative index (MCARI2), and canopy temperature was observed under disease pressure. A strong relationship was also observed between the area under the disease progress curve of TSC and three vegetative indices (RDVI, MCARI1, and MCARI2). In addition, we demonstrated that TSC could cause up to 58% yield loss in the most susceptible maize hybrids. Our results suggest that the RS techniques tested in this study could be used for high throughput phenotyping of TSC resistance and potentially for other foliar diseases of maize. This may help reduce the cost and time required for the development of improved maize germplasm. Challenges and opportunities in the use of RS technologies for disease resistance phenotyping are discussed. | ||
| 526 | _aMCRP | ||
| 546 | _aText in English | ||
| 650 | 0 |
_aFungal diseases _gAGROVOC _91539 |
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| 650 | 7 |
_aMaize _gAGROVOC _2 _91173 |
|
| 650 | 7 |
_2AGROVOC _95669 _aDisease control |
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| 650 | 7 |
_aDisease resistance _gAGROVOC _2 _91077 |
|
| 700 | 1 |
_9782 _aRodrigues, F. _gFormerly Sustainable Intensification Program _8I1705451 |
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| 700 | 1 |
_8I1706676 _91999 _aToledo, F.H. _gGenetic Resources Program |
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| 700 | 1 |
_8INT3035 _9884 _aSan Vicente, F.M. _gGlobal Maize Program |
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| 700 | 1 |
_9946 _aGerard, B. _gFormerly Sustainable Intensification Program _8INT3372 |
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| 700 | 1 |
_aPrasanna, B.M. _gGlobal Maize Program _8INT3057 _9887 |
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| 773 |
_dSwitzerland : Frontiers, 2019. _gv. 10, art. 552 _tFrontiers in Plant Science _wu56875 _x1664-462X |
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| 856 | 4 |
_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/20159 |
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| 942 |
_2ddc _cJA _n0 |
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