Comparison of Leaf-Level Stomatal Conductance for Nine Irrigated Winter Wheat in the NW-Indo Gangetic Plain - a Tool to Guide Farmer's Choices in Ozone and Climate Change Affected Environments
Abstract
Measurements of leaf-level stomatal conductance (gsto) are central to the ozone (O3) risk assessment and the calculation of Triticum aestivum yield loss based on the absorbed O3 phytotoxic dose (POD).
In this study we present measurements and a comparative analysis of gsto field measurements from nine Triticum aestivum cultivars grown as irrigated winter wheat in the state of Punjab, in the NW-IGP 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. The gsto measurements in combination with phenology observations on the plants are used to derive environmental response functions for the parameters light, temperature, soil moisture, water vapour pressure deficit, plant phenology and time of the day for all nine Triticum aestivum cultivars. The response functions thus obtained can be used for two purposes. Firstly, we use them for revising the gsto model parameterization of the DO3SE model in order to precisely model the ozone related crop yield losses using the POD6 exposure-response functions for each of the cultivars for both growing seasons. We find POD6 values that vary from 7-11 mmol m-2 projected leaf area and calculate relative yield losses ranging from 25%-40%. Secondly, the same environmental response functions have also more immediate uses in identifying a given cultivars potential to cope well with certain climate change or air pollution related stressors, such as heat waves and droughts or its potential to fare well in years affected by prolonged wintertime fog in the NW-IGP.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFMGC11I1012S
- Keywords:
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- 0402 Agricultural systems;
- BIOGEOSCIENCESDE: 0430 Computational methods and data processing;
- BIOGEOSCIENCESDE: 1926 Geospatial;
- INFORMATICSDE: 1942 Machine learning;
- INFORMATICS