Assessment of Triticum aestivum Yield Losses in Punjab and Haryana Using 5 Years of In-situ Ozone Measurements and the DO3SE Model
Abstract
Modelling of leaf-level stomatal conductance (gsto) with the help of observed or modelled meteorological parameters and environmental response functions has been introduced as a new way to conduct ozone (O3) risk assessment and calculate Triticum aestivum yield loss based on the absorbed O3 phytotoxic dose (POD). In this study we present environmental response functions of nine triticum aestivum cultivars grown as irrigated winter wheat in the state of Punjab, in the North West Indo Gangetic Plain during winter 2016-17 and 2017-18. The cultivars PBW550, HD2687, HD2967, RAJ3765, WH1105, GW322, C306 and DBW88 were directly obtained from breeders, while local farmers cultivars obtained from a seed shop were grown for comparison. We subsequently use meteorological observations and ozone measurements obtained at the Central Atmospheric Chemistry facility of IISER Mohali in Punjab, India between November 2011 and April 2018 to estimate triticum aestivum relative yield losses and crop production losses. Relative yield losses based on the POD6 metrics agree well with yield losses calculated based on the AOT40 metrics while using an India-specific relationship introduced by Sinha et al. 2015 and ranged from 25 to 40% for wheat for a "normal" sowing time window. Late sowing typically not only results into high thermal stress during sensitive growth stages, but also in high ozone exposure. Optimizing the sowing window keeping in mind the environmental response functions of the sown cultivar has the potential to significantly reduce the ozone dose for a typical year. Significant difference between cultivars in terms of the thermal sum required to reach the flag leaf stage and flowering stage can be exploited to minimize ozone related yield losses when sowing in early November is not feasible due to practical constraints.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFMGC11I1013K
- Keywords:
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- 0402 Agricultural systems;
- BIOGEOSCIENCESDE: 0430 Computational methods and data processing;
- BIOGEOSCIENCESDE: 1926 Geospatial;
- INFORMATICSDE: 1942 Machine learning;
- INFORMATICS