Abstract
Canopy temperature based regression models were developed for the prediction of grain yield of wheat. During the reproductive phase, all the canopy temperature based indices viz., canopy temperature (Tc), average canopy minus air temperature (Tc-Ta) and summation stress degree days (SDD) showed a negative and significant relationship explaining 71-87% variation in grain yield. These findings signify an ample scope of mid day canopy temperature as a possible tool for monitoring plant water status and grain yield prediction.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by. Read at the source ↗
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Claims 2
Claim · model-written summary
Canopy temperature based indices showed a negative and significant relationship with wheat grain yield during the reproductive phase, explaining 71-87% of its variation.
- Taxon
- wheat
Triticum aestivume2 - Organ / stage
- reproductive phasee1
- Outcome
- grain yielde1
- Direction
- negativee1
- Value
- 71-87%e1
- Qualifier
- canopy temperature (Tc), average canopy minus air temperature (Tc-Ta) and summation stress degree days (SDD)e1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.

Verbatim
e1“During the reproductive phase, all the canopy temperature based indices viz., canopy temperature (Tc), average canopy minus air temperature (Tc-Ta) and summation stress degree days (SDD) showed a negative and significant relationship explaining 71-87% variation in grain yield”
e2“prediction of grain yield of wheat”
Claim · model-written summary
Mid-day canopy temperature has ample scope as a possible tool for monitoring wheat plant water status and predicting grain yield.
- Taxon
- wheat
Triticum aestivume2 - Outcome
- monitoring plant water status and grain yield predictione3
Organisms pictured · Representative pictures of the matched species, not figures from the paper.

Verbatim
e2“prediction of grain yield of wheat”
e3“These findings signify an ample scope of mid day canopy temperature as a possible tool for monitoring plant water status and grain yield prediction”