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Data on Nitrogen Use Efficiency in Sorghum: exploring native variability for traits under variable N-regimes

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Title Data on Nitrogen Use Efficiency in Sorghum: exploring native variability for traits under variable N-regimes
 
Identifier https://doi.org/10.21421/D2/WCAHRK
 
Creator Gupta Rajeev
Deshpande Santosh
Srikanth Bollam
Laavanya Rayaprolu
Abhishek Rathore
Roma Rani Das
Anil Kumar V
 
Publisher ICRISAT Dataverse
 
Description A set of sixty diverse Sorghum genotypes including parents of several genetic populations such as Back-Cross derived Nested Association Mapping (BCNAM) populations; bi-parental mapping populations, along with accessions from different countries such as Nigeria, USA, Lesotho, Ethiopia, and Mali were evaluated for different leaf, growth, panicle, and biological yield traits along with NUE traits in the field (under three N regimes0%, 50% and 100% of the recommended (90 kg ha-1 for two seasons (2016-17 & 2017-18)) in the black soil precision fields of ICRISAT, Patancheru, India). Different physiological, agronomical, and biological yield attributes associated with NUE were systematically recorded in three different N dosages; chlorophyll content, leaf area, leaf number, days to 50% flowering, plant height, a number of tillers, panicle number, panicle weight, fresh straw yield, dry straw yield, grain yield, test weight, harvest Index, N content in grain and straw. On the basis of the data selection under high and low nitrogen, efficient lines will be identified and contribute towards the development of nitrogen efficient sorghum cultivars.




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Subject Agricultural Sciences
Sorghum
DFF: days to fifty percent flowering-days
PH: plant height
ETNo_Ct_tllrplnt: number of tillers
StmHvYld_C_kgha: fresh stalk weight
StmDMYld_C_kgha: dry stalk weight
GW_100grnM_g
StoND_Kjdl_pct: stover nitrogen content
GLNo_Ct_lfplnt: green leaf number
GHvYld_C_kgha: grain yield
GHI_C_pct: harvest index
PcleNo_Ct_pclePlot: panicle number
PGRWT: panicle weight
 
Language English
 
Contributor Deshpande, Santosh
 
Type Phenotypic data