Salecker, Jan

[["dc.bibliographiccitation.artnumber","103944"],["dc.bibliographiccitation.journal","Journal of Economic Dynamics and Control"],["dc.bibliographiccitation.volume","117"],["dc.contributor.author","Kopp, Thomas"],["dc.contributor.author","Salecker, Jan"],["dc.date.accessioned","2020-12-08T08:09:28Z"],["dc.date.available","2020-12-08T08:09:28Z"],["dc.date.issued","2020"],["dc.description.abstract","Marketing channel choices in agricultural trade networks affect the networks\\’ overall performance and influence rural livelihoods. This study identifies key determinants of these choices among natural rubber traders in Indonesia to evaluate four policy scenarios and their potential effects on rural incomes. Since traders\\’ marketing decisions are based on past interactions, resulting trade networks are formed in recursive processes and can be understood as complex adaptive systems. Due to inherent endogeneity in these systems, process-based approaches such as agent-based modelling (ABM) can be effective in understanding them. Using a self-gathered primary dataset from Jambi Province, Indonesia, we implement and parameterise an ABM to simulate the formation of the rubber trading network and analyse the effects on rural livelihoods of four hypothetical policy scenarios: improved micro-credit availability, increased access to education, better infrastructure and transportation capacity, and market information availability. The model is calibrated through a genetic algorithm which maximises the similarity between the simulated network and the actual network observed in the data. Results indicate that sellers\\’ decisions on a buyer are primarily determined by debt obligations and past peer-interactions. The most influential sellers have a similar level of formal education as their peers and live in close physical proximity. Results of the policy scenario analysis suggests that policies aimed at reducing sellers\\’ dependence on credit from buyers and increasing education are the most effective policies for improving value chains and reducing poverty in the region under consideration."],["dc.identifier.doi","10.1016/j.jedc.2020.103944"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/69458"],["dc.language.iso","en"],["dc.relation.issn","0165-1889"],["dc.relation.orgunit","Abteilung Ökosystemmodellierung"],["dc.subject.gro","Agent-based modelling"],["dc.subject.gro","Complex adaptive systems"],["dc.subject.gro","Networks"],["dc.subject.gro","Rubber"],["dc.subject.gro","Indonesia"],["dc.subject.gro","Agricultural trade"],["dc.title","How traders influence their neighbours: Modelling social evolutionary processes and peer effects in agricultural trade networks"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e0222949"],["dc.bibliographiccitation.issue","9"],["dc.bibliographiccitation.journal","PLoS One"],["dc.bibliographiccitation.volume","14"],["dc.contributor.author","Salecker, Jan"],["dc.contributor.author","Dislich, Claudia"],["dc.contributor.author","Wiegand, Kerstin"],["dc.contributor.author","Meyer, Katrin M."],["dc.contributor.author","Pe Er, Guy"],["dc.date.accessioned","2020-12-08T07:41:51Z"],["dc.date.available","2020-12-08T07:41:51Z"],["dc.date.issued","2019"],["dc.description.abstract","Spatially-explicit simulation models are commonly used to study complex ecological and socio-economic research questions. Often these models depend on detailed input data, such as initial land-cover maps to set up model simulations. Here we present the landscape generator EFFortS-LGraf that provides artificially-generated land-use maps of agricultural landscapes shaped by small-scale farms. EFForTS-LGraf is a process-based landscape generator that explicitly incorporates the human dimension of land-use change. The model generates roads and villages that consist of smallholder farming households. These smallholders use different establishment strategies to create fields in their close vicinity. Crop types are distributed to these fields based on crop fractions and specialization levels. EFForTS-LGraf model parameters such as household area or field size frequency distributions can be derived from household surveys or geospatial data. This can be an advantage over the abstract parameters of neutral landscape generators. We tested the model using oil palm and rubber farming in Indonesia as a case study and validated the artificially-generated maps against classified satellite images. Our results show that EFForTS-LGraf is able to generate realistic land-cover maps with properties that lie within the boundaries of landscapes from classified satellite images. An applied simulation experiment on landscape-level effects of increasing household area and crop specialization revealed that larger households with higher specialization levels led to spatially more homogeneous and less scattered crop type distributions and reduced edge area proportion. Thus, EFForTS-LGraf can be applied both to generate maps as inputs for simulation modelling and as a stand-alone tool for specific landscape-scale analyses in the context of ecological-economic studies of smallholder farming systems."],["dc.identifier.doi","10.1371/journal.pone.0222949"],["dc.identifier.pmid","31560726"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16510"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/69449"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation","SFB 990: Ökologische und sozioökonomische Funktionen tropischer Tieflandregenwald-Transformationssysteme (Sumatra, Indonesien)"],["dc.relation","SFB 990 | B | B10: Landschaftsbezogene Bewertung der ökologischen und sozioökonomischen Funktionen von Regenwald- Transformationssystemen in Sumatra (Indonesien)"],["dc.relation.eissn","1932-6203"],["dc.relation.orgunit","Abteilung Ökosystemmodellierung"],["dc.relation.orgunit","Zentrum für Biodiversität und Nachhaltige Landnutzung"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.gro","landscape generator"],["dc.subject.gro","agent-based model"],["dc.subject.gro","ABM"],["dc.subject.gro","NetLogo"],["dc.subject.gro","process-based"],["dc.subject.gro","Indonesia"],["dc.subject.gro","sfb990_journalarticles"],["dc.title","EFForTS-LGraf: A landscape generator for creating smallholder-driven land-use mosaics"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]