Details
| Original language | English |
|---|---|
| Pages (from-to) | 30-44 |
| Number of pages | 15 |
| Journal | Ecological indicators |
| Volume | 100 |
| Early online date | 26 Apr 2018 |
| Publication status | Published - May 2019 |
Abstract
The complex interactions between environmental and anthropogenic components have significantly influenced rice cultivation. The clear understanding of these interactions is important to (i) optimize rice provisioning ecosystem service (ES) supply, (ii) minimize negative impacts on other ES and (iii) choose suitable strategies for sustainable agriculture. Impacts of environmental and anthropogenic components on rice provisioning ES supply largely depend on site selection and farming practices. The demand for rice can be determined by the size of the population and imports/exports of rice products. Rice provisioning ES supply and demand need to be balanced if the goal is an import-independent and sustainable agriculture. As a decision support tool, Bayesian Belief Networks (BBN) are used for quantifying various ES supply types, demands as well as their budgets. The BBN network presented in this study was developed through interviews, expert knowledge, geographical information systems and statistical models. The results show that the capacity of rice provision can be optimized through site selection and farming practice. The results can help to reduce crop failures and to choose suitable areas for the use of new practices and technologies. Moreover, the presented BBN has been used to forecast future patterns of rice provision through effective or ineffective options of the environmental and human-derived components in eight scenarios. Thereby, the BBN turns out to be a promising decision support tool for agricultural managers in predicting probabilities of success in scenarios of agricultural planning.
Keywords
- Agriculture, Ecosystem service budget, Ecosystem service demand, Ecosystem service supply, Scenario, Socio-ecological system
ASJC Scopus subject areas
- Decision Sciences(all)
- General Decision Sciences
- Agricultural and Biological Sciences(all)
- Ecology, Evolution, Behavior and Systematics
- Environmental Science(all)
- Ecology
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In: Ecological indicators, Vol. 100, 05.2019, p. 30-44.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A Bayesian Belief Network
T2 - Based approach to link ecosystem functions with rice provisioning ecosystem services
AU - Dang, Kinh Bac
AU - Windhorst, Wilhelm
AU - Burkhard, Benjamin Felix
AU - Müller, Felix
N1 - Funding Information: The study was embedded in the LEGATO (Land-use intensity and Ecological EnGineering – Assessment Tools for risks and Opportunities in annual crop based production systems) project, funded by the German Ministry of Research and Education within their funding measure Sustainable Land Management; Funding No. 01LL0917 . This study was co-financed by the Vietnamese Government Scholarship (911). Furthermore, we would like to thank all our LEGATO colleagues and especially Associate Professor Isoda Yuzuru from the Graduate School of Science at Tohoku University in Japan and all our colleagues in the Institute for Natural Resource Conservation, Department of Ecosystem Management at Kiel University , Germany. The authors would like to thank Mrs. Angie Faust for language corrections of the manuscript.
PY - 2019/5
Y1 - 2019/5
N2 - The complex interactions between environmental and anthropogenic components have significantly influenced rice cultivation. The clear understanding of these interactions is important to (i) optimize rice provisioning ecosystem service (ES) supply, (ii) minimize negative impacts on other ES and (iii) choose suitable strategies for sustainable agriculture. Impacts of environmental and anthropogenic components on rice provisioning ES supply largely depend on site selection and farming practices. The demand for rice can be determined by the size of the population and imports/exports of rice products. Rice provisioning ES supply and demand need to be balanced if the goal is an import-independent and sustainable agriculture. As a decision support tool, Bayesian Belief Networks (BBN) are used for quantifying various ES supply types, demands as well as their budgets. The BBN network presented in this study was developed through interviews, expert knowledge, geographical information systems and statistical models. The results show that the capacity of rice provision can be optimized through site selection and farming practice. The results can help to reduce crop failures and to choose suitable areas for the use of new practices and technologies. Moreover, the presented BBN has been used to forecast future patterns of rice provision through effective or ineffective options of the environmental and human-derived components in eight scenarios. Thereby, the BBN turns out to be a promising decision support tool for agricultural managers in predicting probabilities of success in scenarios of agricultural planning.
AB - The complex interactions between environmental and anthropogenic components have significantly influenced rice cultivation. The clear understanding of these interactions is important to (i) optimize rice provisioning ecosystem service (ES) supply, (ii) minimize negative impacts on other ES and (iii) choose suitable strategies for sustainable agriculture. Impacts of environmental and anthropogenic components on rice provisioning ES supply largely depend on site selection and farming practices. The demand for rice can be determined by the size of the population and imports/exports of rice products. Rice provisioning ES supply and demand need to be balanced if the goal is an import-independent and sustainable agriculture. As a decision support tool, Bayesian Belief Networks (BBN) are used for quantifying various ES supply types, demands as well as their budgets. The BBN network presented in this study was developed through interviews, expert knowledge, geographical information systems and statistical models. The results show that the capacity of rice provision can be optimized through site selection and farming practice. The results can help to reduce crop failures and to choose suitable areas for the use of new practices and technologies. Moreover, the presented BBN has been used to forecast future patterns of rice provision through effective or ineffective options of the environmental and human-derived components in eight scenarios. Thereby, the BBN turns out to be a promising decision support tool for agricultural managers in predicting probabilities of success in scenarios of agricultural planning.
KW - Agriculture
KW - Ecosystem service budget
KW - Ecosystem service demand
KW - Ecosystem service supply
KW - Scenario
KW - Socio-ecological system
UR - http://www.scopus.com/inward/record.url?scp=85046167933&partnerID=8YFLogxK
U2 - 10.1016/j.ecolind.2018.04.055
DO - 10.1016/j.ecolind.2018.04.055
M3 - Article
AN - SCOPUS:85046167933
VL - 100
SP - 30
EP - 44
JO - Ecological indicators
JF - Ecological indicators
SN - 1470-160X
ER -