17:15 〜 19:15
[ACG46-P06] プロセスベースモデルを用いた対流圏オゾンがコムギ生産量に与える影響の将来予測
キーワード：オゾン、プロセスベースドモデル、コムギ、気候変動
Tropospheric ozone, which has risen due to industrialization and population growth, is projected to increase (Paul and Lee et al., 2021). It forms when nitrogen oxides and volatile organic compounds from sources like factories, vehicles, and power plants react with ultraviolet radiation. Not only does it negatively impact human health, but it also damages plants. Under high ozone concentration, visible foliar injury, reduced growth, and premature aging (NIES). Inside plant cells, absorbed ozone generates reactive oxygen species, which are toxic. If it exceeds a certain threshold, Rubisco capacity, electron transport rate, and stomatal conductance will be reduced. Additionally, many studies show that ozone severely reduces crop yields as a result. Given that global population is expected to reach 10 billion (United Nations, WPP 2024) and food demand may rise by 1.2–1.3 times (Michiel et al., 2021), it is important to accurately assess ozone’s impact on food production and develop effective countermeasures. To estimate these effects, in this research, we used process-based model which is called MATCRO (Masutomi et al., 2016), originally developed for rice. A former senior member of my laboratory had developed the MATCRO model for wheat, and I incorporated the effects of ozone into it.
While statistical models predict yields directly from past data, process-based models estimate yields by simulating the physiological processes of crops. For input data, it uses climate data such as temperature, precipitation, as well as fertilization data. Then, it calculates glucose production in the photosynthesis module and allocates to various plant parts like panicles and leaves. To incorporate the ozone effect, I choose Anetgsto mech model (Pande et al., 2024) for MATCRO-Wheat. This model establishes a relationship between photosynthesis (Anet) and gas stomatal conductance (gsto). Unlike direct ambient ozone concentration-based approaches, this model is flux-based model, and it can consider plant stomata and affects physiological processes. This model represents the effects of ozone by incorporating terms that express both short-term and long-term impacts of ozone on under RuBP limited photosynthesis. The short-term effects mainly focus on stomatal conductance and flux thresholds. Long-term impacts are integrated crop’s developmental stage related valuables and ozone uptake.
The results revealed that ozone significantly reduces wheat yields, with global losses estimated at 4.6 million tons under SSP1-2.6 and 6.4 million tons under SSP3-7.0, representing a global yield reduction of approximately 1% to 5%. Additionally, the geographical impact varies, with China, Eastern Europe, and North America experiencing higher yield losses, with some areas reaching nearly a 10% reduction, while the Southern Hemisphere remains relatively unaffected. A correlation was also observed between POD6 (cumulative ozone uptake) and yield reduction, validating the model’s ability to capture ozone stress on wheat. However, some regions exhibited high POD6 values with minimal yield loss. Regarding biomass, the results indicated that both aboveground growth and root development were affected by ozone exposure, leading to growth suppression. To improve the accuracy of ozone impact estimation, the model parameters should be adjusted to better reflect the specific physiological characteristics of wheat.
While statistical models predict yields directly from past data, process-based models estimate yields by simulating the physiological processes of crops. For input data, it uses climate data such as temperature, precipitation, as well as fertilization data. Then, it calculates glucose production in the photosynthesis module and allocates to various plant parts like panicles and leaves. To incorporate the ozone effect, I choose Anetgsto mech model (Pande et al., 2024) for MATCRO-Wheat. This model establishes a relationship between photosynthesis (Anet) and gas stomatal conductance (gsto). Unlike direct ambient ozone concentration-based approaches, this model is flux-based model, and it can consider plant stomata and affects physiological processes. This model represents the effects of ozone by incorporating terms that express both short-term and long-term impacts of ozone on under RuBP limited photosynthesis. The short-term effects mainly focus on stomatal conductance and flux thresholds. Long-term impacts are integrated crop’s developmental stage related valuables and ozone uptake.
The results revealed that ozone significantly reduces wheat yields, with global losses estimated at 4.6 million tons under SSP1-2.6 and 6.4 million tons under SSP3-7.0, representing a global yield reduction of approximately 1% to 5%. Additionally, the geographical impact varies, with China, Eastern Europe, and North America experiencing higher yield losses, with some areas reaching nearly a 10% reduction, while the Southern Hemisphere remains relatively unaffected. A correlation was also observed between POD6 (cumulative ozone uptake) and yield reduction, validating the model’s ability to capture ozone stress on wheat. However, some regions exhibited high POD6 values with minimal yield loss. Regarding biomass, the results indicated that both aboveground growth and root development were affected by ozone exposure, leading to growth suppression. To improve the accuracy of ozone impact estimation, the model parameters should be adjusted to better reflect the specific physiological characteristics of wheat.