Food production in smallholder farming systems : developing a spatially explicit decision-model for trade opportunities

Growing pressure on land and other resources is threatening the security of supplies for agribusinesses and jeopardizes the sustainability of agricultural production. A bio-economic tool, named CONSUS, is currently being developed. The tool shall assists both agribusinesses and producers by evaluating sustainability, suitability and profitability of specific production opportunities and assessing the overall feasibility of such projects

Deconstructed and Inverted Multi-Criteria Evaluation for On-The-Fly Scenario Development and Decision-Making (Short Paper)

We propose a variation of the conventional spatial multi-criteria evaluation workflow for suitability analysis that allows efficient on-the fly scenario development for decision-making. Our approach proposes to reconstruct the conventional MCE workflow in order to exclude computationally expensive geoprocessing from the iterative scenario development. We then introduce a procedure that replaces costly iterations of spatial operations with one off-line preprocessing step followed by iterations of much less computationally expensive database queries. We illustrate our approach for deconstructed and inverted multi-criteria analysis with a case study aiming at selecting suitable sites for wind turbines in the Swiss alps

Parameter-free aggregation of value functions from multiple experts and uncertainty assessment in multi-criteria evaluation

This paper makes a threefold contribution to spatial multi-criteria evaluation (MCE): firstly by presenting a new method concerning value functions, secondly by comparing different approaches to assess the uncertainty of a MCE outcome, and thirdly by presenting a case-study on land-use change. Even though MCE is a well-known methodology in GIScience, there is a lack of practicable approaches to incorporate the potentially diverse views of multiple experts in defining and standardizing the values used to implement input criteria. We propose a new method that allows generating and aggregating non-monotonic value functions, integrating the views of multiple experts. The new approach only requires the experts to provide up to four values, making it easy to be included in questionnaires. We applied the proposed method in a case study that uses MCE to assess the potential of future loss of vineyards in a wine-growing area in Switzerland, involving 13 experts from research, consultancy, government, and practice. To assess the uncertainty of the outcome three different approaches were used: firstly, a complete Monte Carlo simulation with the bootstrapped inputs, secondly a one-factor-at-a-time variation, and thirdly bootstrapping of the 13 inputs with subsequent analytical error propagation. The complete Monte Carlo simulation has shown the most detailed distribution of the uncertainty. However, all three methods indicate a general trend of areas with lower likelihood of future cultivation to show a higher degree of relative uncertainty

From Change Detection to Change Analytics: Decomposing Multi-Temporal Pixel Evolution Vectors (Short Paper)

Change detection is a well-established process of detaining spatial and temporal changes of entities between two or more timesteps. Current advancements in digital map processing offer vast new sources of multitemporal geodata. As the temporal aspect gains complexity, the dismantling of detected changes on a pixel-based scale becomes a costly undertaking. In efforts to establish and preserve the evolution of detected changes in long time series, this paper presents a method that allows the decomposition of pixel evolution vectors into three dimensions of change, described as directed change, change variability, and change magnitude. The three dimensions of change compile to complex change analytics per individual pixels and offer a multi-faceted analysis of landscape changes on an ordinal scale. Finally, the integration of class confidence from learned uncertainty estimates illustrates the avenue to include uncertainty into the here presented change analytics, and the three dimensions of change are visualized in complex change maps

Parameter-free aggregation of value functions from multiple experts and uncertainty assessment in multi-criteria evaluation

This paper makes a threefold contribution to spatial multi-criteria evaluation (MCE): firstly by presenting a new method concerning value functions, secondly by comparing different approaches to assess the uncertainty of a MCE outcome, and thirdly by presenting a case-study on land-use change. Even though MCE is a well-known methodology in GIScience, there is a lack of practicable approaches to incorporate the potentially diverse views of multiple experts in defining and standardizing the values used to implement input criteria. We propose a new method that allows generating and aggregating non-monotonic value functions, integrating the views of multiple experts. The new approach only requires the experts to provide up to four values, making it easy to be included in questionnaires. We applied the proposed method in a case study that uses MCE to assess the potential of future loss of vineyards in a wine-growing area in Switzerland, involving 13 experts from research, consultancy, government, and practice. To assess the uncertainty of the outcome three different approaches were used: firstly, a complete Monte Carlo simulation with the bootstrapped inputs, secondly a one-factor-at-a-time variation, and thirdly bootstrapping of the 13 inputs with subsequent analytical error propagation. The complete Monte Carlo simulation has shown the most detailed distribution of the uncertainty. However, all three methods indicate a general trend of areas with lower likelihood of future cultivation to show a higher degree of relative uncertainty

Multi-criteria aggregation for sensistive parcel-based census data

Modern urban planning considers various socio-economic issues and hence requires fine-grained, potentially sensitive spatial data, raising the disclosure dilemma: Whilst it is imperative to aggregate fine-grained sensitive records large enough to avoid disclosure, aggregation blurs the information, potentially hindering insights about spatially explicit relationships. MASC is an aggregation algorithm for sensitive data, balancing five aggregation criteria

Decentralized Monitoring of Moving Objects in a Transportation Network Augmented with Checkpoints

This paper examines efficient and decentralized monitoring of objects moving in a transportation network. Previous work in moving object monitoring has focused primarily on centralized information systems, like moving object databases and geographic information systems. In contrast, in this paper monitoring is in-network, requiring no centralized control and allowing for substantial spatial constraints to the movement of information. The transportation network is assumed to be augmented with fixed checkpoints that can detect passing mobile objects. This assumption is motivated by many practical applications, from traffic management in vehicle ad hoc networks to habitat monitoring by tracking animal movements. In this context, this paper proposes and evaluates a family of efficient decentralized algorithms for capturing, storing and querying the movements of objects. The algorithms differ in the restrictions they make on the communication and sensing constraints to the mobile nodes and the fixed checkpoints. The performance of the algorithms is evaluated and compared with respect to their scalability (in terms of communication and space complexity), and their latency (the time between when a movement event occurs, and when all interested nodes are updated with records about that event). The conclusions identify three key principles for efficient decentralized monitoring of objects moving past checkpoints: structuring computation around neighboring checkpoints; taking advantage of mobility diffusion and separating the generation and querying of movement informatio

Deferred decentralized movement pattern mining for geosensor networks

This paper presents an algorithm for decentralized (in-network) data mining of the movement pattern flock amongst mobile geosensor nodes. The algorithm DDIG (Deferred Decentralized Information Grazing) allows roaming sensor nodes to 'graze' over time more information than they could access through their spatially limited perception range alone. The algorithm requires an intrinsic temporal deferral for pattern mining, as sensor nodes must be enabled to collect, memorize, exchange, and integrate their own and their neighbors' most current movement history before reasoning about patterns. A first set of experiments with trajectories of simulated agents showed that the algorithm accuracy increases with growing deferral. A second set of experiments with trajectories of actual tracked livestock reveals some of the shortcomings of the conceptual flocking model underlying DDIG in the context of a smart farming application. Finally, the experiments underline the general conclusion that decentralization in spatial computing can result in imperfect, yet useful knowledge

Expected global suitability of coffee, cashew and avocado due to climate change

Coffee, cashew and avocado are of high socio-economic importance in many tropical smallholder farming systems around the globe. As plantation crops with a long lifespan, their cultivation requires long-term planning. The evaluation of climate change impacts on their biophysical suitability is therefore essential for developing adaptation measures and selecting appropriate varieties or crops. In this study, we modelled the current and future suitability of coffee arabica, cashew and avocado on a global scale based on climatic and soil requirements of the three crops. We used climate outputs of 14 global circulation models based on three emission scenarios to model the future (2050) climate change impacts on the crops both globally and in the main producing countries. For all three crops, climatic factors, mainly long dry seasons, mean temperatures (high and low), low minimum temperatures and annual precipitation (high and low), were more restrictive for the global extent of suitable growing regions than land and soil parameters, which were primarily low soil pH, unfavourable soil texture and steep slopes. We found shifts in suitable growing regions due to climate change with both regions of future expansion and contraction for all crops investigated. Coffee proved to be most vulnerable, with negative climate impacts dominating in all main producing regions. For both cashew and avocado, areas suitable for cultivation are expected to expand globally while in most main producing countries, the areas of highest suitability may decrease. The study reveals that climate change adaptation will be necessary in most major producing regions of all three crops. At high latitudes and high altitudes, however, they may all profit from increasing minimum temperatures. The study presents the first global assessment of climate change impacts on cashew and avocado suitability