Description

Datasets visualized in this webpage aims to provide trade-off assessment of restoration options for agriculture and forestry areas with productivity information. Results from this study, in combination with other tree-growth and forest regrowth related information, will provide a comprehensive overview on ecological and economic impact of restoration interventions ranging from improved management to ecological restoration. The datasets cover current practices and potential management practices of tree crops cultivation and forest productivity that can increase the benefits to people’s livelihood while contributing to climate change mitigation and biodiversity.

Projection method

Biophysical productivity of production systems involving oil palm, rubber, cacao, coffee and coconut on mineral soil were generated using the WaNuLCAS model developed by ICRAF. The WaNuLCAS model (van Noordwijk and Lusiana 1999; van Noordwijk et al., 2011) is a generic tree-crop growth model for a wide range of agroforestry systems that considers both aboveground (light) and belowground (soil, water, and nutrients). Yield and carbon sequestration calculation of the corresponding production systems in peatland were developed using a hybrid approach through adjusting results for mineral soil production system with productivity gaps identified through literature review. Finally, land suitability and statistical data analyses were conducted to estimate productivity of sago and pineapple.

Potential growth of secondary forest and tree species typology were assessed using biophysical productivity model developed by IIASA's Agriculture, Forestry, and Ecosystems Services Group. The methodology involved the integration of random forest algorithm, ground data, remote sensing products, soil properties, and literature on yield tables (more detailed methodology publication in preparation). To calibrate the model, the MODIS NPP dataset was adjusted using forest biomass and land cover maps. The model utilized ERA5-Land monthly averaged meteorological data from 2006 to 2015, with a resolution of 0.1° x 0.1°, in addition to soil properties, land cover, and elevation. This comprehensive approach allowed for the determination of spatially explicit site index values for plantations, secondary forests, and primary forests. The resulting productivity information are reflected in growth curves for both fast and slow-growing commercial species as well as native tree species. The parametrization of Chapman-Richards growth curves was conducted using data from representative tree species available in the literature. In the case of peatland areas, hydrological scenarios were provided by IIASA's EPIC model, offering insights into the specific conditions of these regions.

Spatial resolution

Productivity calculation is based on spatially explicit datasets that are processed in accordance to the Spatial Simulation Infrastructure developed for the RESTORE+ project (Skalský et al. 2022). Geographical grid of regular grid cells with spatial resolution of 5 x 5 arcmin (about 9.25 km at the equator) covering all administrative regions of Indonesia has been chosen to serve as basic spatial reference for all geographical data (inputs/outputs of the models, auxiliary and supporting data).

Cautions

The projection datasets should not be treated as forecast or predictions. Indicators should also not be taken out of the context of modelled assumptions and methodological limitations. Please refer to information the publications below for more technical details.

License

Creative Commons Attribution 4.0 International

Sources

Lusiana, Betha, Pambudi, Sidiq, Khasanah, Ni'matul, Dewi, Sonya, & Yowargana, Ping. (2023). Modelling yield and carbon sequestration of main tree crops in Indonesia. https://doi.org/10.5281/zenodo.7937135

Lusiana, Betha, Pambudi, Sidiq, Khasanah, Ni'matul, Dewi, Sonya, & Yowargana, Ping. (2023). Dataset for yield and carbon sequestration of main tree crops in Indonesia [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7937085

Krasovskiy, Andrey, Yowargana, Ping, Platov, Anton, Pambudi, Sidiq, & Rahayu, Subekti. (2023). Modelling potential growth of forest restoration options in Indonesia [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8117522

Description

Datasets visualized in this webpage aims to provide trade-off assessment of restoration options for agriculture and forestry areas with productivity information. Results from this study, in combination with other tree-growth and forest regrowth related information, will provide a comprehensive overview on ecological and economic impact of restoration interventions ranging from improved management to ecological restoration. The datasets cover current practices and potential management practices of tree crops cultivation and forest productivity that can increase the benefits to people’s livelihood while contributing to climate change mitigation and biodiversity.

Projection method

Biophysical productivity of production systems involving oil palm, rubber, cacao, coffee and coconut on mineral soil were generated using the WaNuLCAS model developed by ICRAF. The WaNuLCAS model (van Noordwijk and Lusiana 1999; van Noordwijk et al., 2011) is a generic tree-crop growth model for a wide range of agroforestry systems that considers both aboveground (light) and belowground (soil, water, and nutrients). Yield and carbon sequestration calculation of the corresponding production systems in peatland were developed using a hybrid approach through adjusting results for mineral soil production system with productivity gaps identified through literature review. Finally, land suitability and statistical data analyses were conducted to estimate productivity of sago and pineapple.

Spatial resolution

Productivity calculation is based on spatially explicit datasets that are processed in accordance to the Spatial Simulation Infrastructure developed for the RESTORE+ project (Skalský et al. 2022). Geographical grid of regular grid cells with spatial resolution of 5 x 5 arcmin (about 9.25 km at the equator) covering all administrative regions of Indonesia has been chosen to serve as basic spatial reference for all geographical data (inputs/outputs of the models, auxiliary and supporting data).

Cautions

The projection datasets should not be treated as forecast or predictions. Indicators should also not be taken out of the context of modelled assumptions and methodological limitations. Please refer to information the publications below for more technical details.

License

Creative Commons Attribution 4.0 International

Citations

Lusiana, Betha, Pambudi, Sidiq, Khasanah, Ni'matul, Dewi, Sonya, & Yowargana, Ping. (2023). Modelling yield and carbon sequestration of main tree crops in Indonesia. https://doi.org/10.5281/zenodo.7937135

Lusiana, Betha, Pambudi, Sidiq, Khasanah, Ni'matul, Dewi, Sonya, & Yowargana, Ping. (2023). Dataset for yield and carbon sequestration of main tree crops in Indonesia [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7937085

See publication Download dataset

Description

Datasets visualized in this webpage aims to provide trade-off assessment of restoration options for agriculture and forestry areas with productivity information. Results from this study, in combination with other tree-growth and forest regrowth related information, will provide a comprehensive overview on ecological and economic impact of restoration interventions ranging from improved management to ecological restoration. The datasets cover current practices and potential management practices of tree crops cultivation and forest productivity that can increase the benefits to people’s livelihood while contributing to climate change mitigation and biodiversity.

Projection method

Potential growth of secondary forest and tree species typology were assessed using biophysical productivity model developed by IIASA's Agriculture, Forestry, and Ecosystems Services Group. The methodology involved the integration of random forest algorithm, ground data, remote sensing products, soil properties, and literature on yield tables (more detailed methodology publication in preparation). To calibrate the model, the MODIS NPP dataset was adjusted using forest biomass and land cover maps. The model utilized ERA5-Land monthly averaged meteorological data from 2006 to 2015, with a resolution of 0.1° x 0.1°, in addition to soil properties, land cover, and elevation. This comprehensive approach allowed for the determination of spatially explicit site index values for plantations, secondary forests, and primary forests. The resulting productivity information are reflected in growth curves for both fast and slow-growing commercial species as well as native tree species. The parametrization of Chapman-Richards growth curves was conducted using data from representative tree species available in the literature. In the case of peatland areas, hydrological scenarios were provided by IIASA's EPIC model, offering insights into the specific conditions of these regions.

Spatial resolution

Productivity calculation is based on spatially explicit datasets that are processed in accordance to the Spatial Simulation Infrastructure developed for the RESTORE+ project (Skalský et al. 2022). Geographical grid of regular grid cells with spatial resolution of 5 x 5 arcmin (about 9.25 km at the equator) covering all administrative regions of Indonesia has been chosen to serve as basic spatial reference for all geographical data (inputs/outputs of the models, auxiliary and supporting data).

Cautions

The projection datasets should not be treated as forecast or predictions. Indicators should also not be taken out of the context of modelled assumptions and methodological limitations. Please refer to information the publications below for more technical details.

License

Creative Commons Attribution 4.0 International

Citation

Krasovskiy, Andrey, Yowargana, Ping, Platov, Anton, Pambudi, Sidiq, & Rahayu, Subekti. (2023). Modelling potential growth of forest restoration options in Indonesia [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8117522

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