17:15 〜 19:15
[ACG46-P03] Development of process-based potato growth model,
MATCRO-Potato for global yield estimation
キーワード：気候変動、プロセスベース作物生長モデル、パラメタリゼーション、検証、バレイショ
Climate change has significant impacts on crop productivity worldwide (IPCC, 2022). Rising temperatures can lead to lower yields due to earlier phenology. Furthermore, global population growth makes it difficult to meet the increasing demand for food on limited agricultural land area. Crop models are effective tools for projecting these impacts and supporting decision-making in agricultural managements. Process-based crop growth models have been used to understand the effects of climate change on crop production, focusing on plant physiology and growth.
Potato (Solanum tuberosum L.) ranks as the fourth most important food crop worldwide, after maize, wheat, and rice (FAOSTAT, 2022). It is cultivated in over 100 countries, spanning tropical to temperate regions and from lowlands to highlands (Haverkort, 1990), providing nutrition for over a billion people. Despite its crucial role in global food security, fewer comprehensive studies have been conducted regarding crop modelling for potato compared to other major cereal crops. Additionally, many of the major potato models have adopted the radiation use efficiency (RUE) approach, which simplifies physiological processes related to light and CO2 but does not aim to represent the photosynthetic mechanism itself.
Masutomi et al. (2016) developed a process-based model called “MATCRO-Rice”, which simulates rice yield by calculating daily crop growth while considering photosynthesis. To date, MATCRO has been extended and adapted for wheat, maize, and soybean, but not for potato. Therefore, the objective of this study is to develop a new potato growth model, MATCRO-Potato.
To develop the model, several parameters were adjusted for potato. First, we calculated development stages (DVS) and Growing Degree Days (GDD) using the phenology model given by Masutomi et al. (2016). With the calculated phenological index, we calculated the ratio of glucose partitioned to each organ (leaf, stem, root, and tuber), the ratio of dead leaves, specific leaf weight during the growing period. Moreover, we also adjusted the parameters related to RuBisCO-limited photosynthetic rate. The data for parameterization, including biomass and leaf area index (LAI), were observed from 2019 to 2021 at the Memuro Experiment Station of Hokkaido Agricultural Research Center (42°53´N, 143°05´E; 94 m above sea level) in eastern Hokkaido, Northeast Japan (Shimoda et al., 2023).
After parameterization, we conducted validation at five sites. One site in Japan was described in the preceding paragraph, while the other four sites (Bolivia, Burundi, Denmark, and the United States) were obtained from common datasets by Fleisher et al (2017), based on Agricultural Model Intercomparison and Improvement Project (AgMIP) (Rosenzweig et al., 2013).
We are currently working on yield simulation at the site scale using meteorological observational data at each location and conducting validation at five sites by comparing simulated LAI, biomass, and yield with observational values. Moreover, global yield simulation is planned to with the spatial resolution of 0.5 x 0.5 degrees and the simulation period from 2000 to 2005. These simulations will be validated with FAOSTAT yield data for the top 10 countries in potato production.
Potato (Solanum tuberosum L.) ranks as the fourth most important food crop worldwide, after maize, wheat, and rice (FAOSTAT, 2022). It is cultivated in over 100 countries, spanning tropical to temperate regions and from lowlands to highlands (Haverkort, 1990), providing nutrition for over a billion people. Despite its crucial role in global food security, fewer comprehensive studies have been conducted regarding crop modelling for potato compared to other major cereal crops. Additionally, many of the major potato models have adopted the radiation use efficiency (RUE) approach, which simplifies physiological processes related to light and CO2 but does not aim to represent the photosynthetic mechanism itself.
Masutomi et al. (2016) developed a process-based model called “MATCRO-Rice”, which simulates rice yield by calculating daily crop growth while considering photosynthesis. To date, MATCRO has been extended and adapted for wheat, maize, and soybean, but not for potato. Therefore, the objective of this study is to develop a new potato growth model, MATCRO-Potato.
To develop the model, several parameters were adjusted for potato. First, we calculated development stages (DVS) and Growing Degree Days (GDD) using the phenology model given by Masutomi et al. (2016). With the calculated phenological index, we calculated the ratio of glucose partitioned to each organ (leaf, stem, root, and tuber), the ratio of dead leaves, specific leaf weight during the growing period. Moreover, we also adjusted the parameters related to RuBisCO-limited photosynthetic rate. The data for parameterization, including biomass and leaf area index (LAI), were observed from 2019 to 2021 at the Memuro Experiment Station of Hokkaido Agricultural Research Center (42°53´N, 143°05´E; 94 m above sea level) in eastern Hokkaido, Northeast Japan (Shimoda et al., 2023).
After parameterization, we conducted validation at five sites. One site in Japan was described in the preceding paragraph, while the other four sites (Bolivia, Burundi, Denmark, and the United States) were obtained from common datasets by Fleisher et al (2017), based on Agricultural Model Intercomparison and Improvement Project (AgMIP) (Rosenzweig et al., 2013).
We are currently working on yield simulation at the site scale using meteorological observational data at each location and conducting validation at five sites by comparing simulated LAI, biomass, and yield with observational values. Moreover, global yield simulation is planned to with the spatial resolution of 0.5 x 0.5 degrees and the simulation period from 2000 to 2005. These simulations will be validated with FAOSTAT yield data for the top 10 countries in potato production.