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Prediction of Wheat Yield Using Spectral Reflectance Indices Under Different Tillage, Residue and Nitrogen Management Practices


Affiliations
1 Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi 110 012, India
2 National Institute of Research on Jute and Allied Fibre Technology, Kolkata 700 040, India
3 Centre for Environment Sciences and Climate Resilient Agriculture, Indian Agricultural Research Institute, New Delhi 110 012, India
4 Division of Soil Science and Agricultural Chemistry, Indian Agricultural Research Institute, New Delhi 110 012, India
 

Effect of tillage, residue mulch and nitrogen manage-ment on canopy spectral reflectance indices and their potential to predict the grain and biomass yield of wheat in advance were studied in a field experiment conducted at the Indian Agricultural Research Institute, New Delhi during 2016–17 and 2017–18. The canopy reflectance was measured using a hand-held ASD FieldSpec spectroradiometer at booting, milking and dough stage of wheat. Then 38 hyperspectral structural indices were recorded using the spectral reflectance data and correlated with wheat yield. It was observed that correlation of these indices with wheat grain and biomass yield was maximum for the booting stage. Among the 38 indices recorded at the booting stage, 13 showed significantly higher correlation with grain yield and 10 indices with biomass yield of wheat (r³0.8). Regression models were developed between grain and biomass yield of wheat with these identified spectral indices recorded at booting stage for 2016–17. Validation of these regression models during 2017–18 showed that normalized difference red edge index (NDREI)-based model performed best for grain and biomass prediction. It could account for maximum 76.4% and 84.3% variation in the observed grain and biomass yield of wheat with ischolar_main mean square error of 37.8% and 50.5% of the corresponding mean values respectively. Thus the regression models based on NDREI recorded at booting stage can be successfully used for the prediction of grain and biomass yield of wheat in advance.

Keywords

Canopy Reflectance, Regression Models, Spectral Indices, Wheat, Yield Prediction.
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  • Prediction of Wheat Yield Using Spectral Reflectance Indices Under Different Tillage, Residue and Nitrogen Management Practices

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Authors

Sujan Adak
Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi 110 012, India
K. K. Bandyopadhyay
Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi 110 012, India
R. N. Sahoo
Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi 110 012, India
N. Mridha
National Institute of Research on Jute and Allied Fibre Technology, Kolkata 700 040, India
M. Shrivastava
Centre for Environment Sciences and Climate Resilient Agriculture, Indian Agricultural Research Institute, New Delhi 110 012, India
T. J. Purakayastha
Division of Soil Science and Agricultural Chemistry, Indian Agricultural Research Institute, New Delhi 110 012, India

Abstract


Effect of tillage, residue mulch and nitrogen manage-ment on canopy spectral reflectance indices and their potential to predict the grain and biomass yield of wheat in advance were studied in a field experiment conducted at the Indian Agricultural Research Institute, New Delhi during 2016–17 and 2017–18. The canopy reflectance was measured using a hand-held ASD FieldSpec spectroradiometer at booting, milking and dough stage of wheat. Then 38 hyperspectral structural indices were recorded using the spectral reflectance data and correlated with wheat yield. It was observed that correlation of these indices with wheat grain and biomass yield was maximum for the booting stage. Among the 38 indices recorded at the booting stage, 13 showed significantly higher correlation with grain yield and 10 indices with biomass yield of wheat (r³0.8). Regression models were developed between grain and biomass yield of wheat with these identified spectral indices recorded at booting stage for 2016–17. Validation of these regression models during 2017–18 showed that normalized difference red edge index (NDREI)-based model performed best for grain and biomass prediction. It could account for maximum 76.4% and 84.3% variation in the observed grain and biomass yield of wheat with ischolar_main mean square error of 37.8% and 50.5% of the corresponding mean values respectively. Thus the regression models based on NDREI recorded at booting stage can be successfully used for the prediction of grain and biomass yield of wheat in advance.

Keywords


Canopy Reflectance, Regression Models, Spectral Indices, Wheat, Yield Prediction.

References





DOI: https://doi.org/10.18520/cs%2Fv121%2Fi3%2F402-413