2022 Fiscal Year Annual Research Report
Developing an agricultural production and land-use change model and examining the land-use scenarios under mitigation policies
Publicly Offered Research
| Project Area | Digital biosphere: integrated biospheric science for mitigating global environment change |
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| Project/Area Number |
22H05735
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| Research Institution | Japan International Research Center for Agricultural Sciences |
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Principal Investigator |
Wu Wenchao 国立研究開発法人国際農林水産業研究センター, 社会科学領域, 任期付研究員 (50868693)
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| Project Period (FY) |
2022-06-16 – 2024-03-31
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| Keywords | land use / agriculture / climate change / climate mitigation |
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| Outline of Annual Research Achievements |
In this fiscal year, I have first developed the modelling framework for model development. Based on literature review and preliminary trial, it was divided into two steps, i.e. spatial sownscaling and LULC downscaling. Multinominal logit model and cross entropy method will be employed for the modeling.
In addition, I have collected and verified two types of spatial datasets that are required for developing the high resolution agricultural production and land-use change model.
The first type of datasets is the biophysical data, which includes historical land use data, crop distribution data, temperature and precipitation data, slope data, elevation data, soil type data. The second type of datasets is the socioeconomic data, which includes GDP per capita and population density data.
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| Current Status of Research Progress |
Current Status of Research Progress
3: Progress in research has been slightly delayed.
Reason
Since I was not able to find a research assistant with enough ability to assist the literature survey, data collection and processing as early as possible, I have to do these tasks alone by myself. Therefore, the research process was slightly delayed.
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| Strategy for Future Research Activity |
In the next fiscal year, the data processing should be acclerated. After that, the statistical land use and land cover change model will be estimated. Then, the cross entrophy method will be used for the spatial downscaling.
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