Global change of land use systems : IMAGE: a new land allocation module

The Integrated Model to Assess the Global Environment (IMAGE) aims at assessing the state of the environment taking into account the effects of human activities. Although human population often makes use of a land area to satisfy various needs, most of the current global land use datasets and models use a classification based on dominant land use/cover types disregarding the diversity and intensity of human activities. In this working document we investigate if the simulation of land use change and the IMAGE outcomes can be improved by using a classification based on land use systems. An expert based cluster analysis was used to identify and map land use systems. The analysis accounted for population density, accessibility, land use / cover types and livestock and provided a new insight on human interactions with the environment. Then, a conceptual framework was developed and implemented to simulate land use systems changes based on local conditions and demand for agricultural products and accounting for land management changes

Assessing spatial uncertainties of land allocation using the scenario approach and sensitivity analysis

The paper assess uncertainty of future spatial allocation of agricultural land in Europe. To assess the possible future development of agricultural production and land for the period 2000 – 2030, two contrasting scenarios are constructed. The scenarios storylines lead to different measurable assumptions concerning scenario specific drivers (variables) and parameters. Many of them are estimations and thus include a certain level of uncertainty regarding their true values. This leads to uncertainty of the scenario outcomes. In this study we use sensitivity analysis to estimate the uncertainty of agricultural land use.spatial uncertainty, scenario approach, sensitivity analysis., Agribusiness, Agricultural and Food Policy, Community/Rural/Urban Development, Food Consumption/Nutrition/Food Safety, Labor and Human Capital,

Effects of canola, wheat and faba bean on yield and nitrogen use efficiency in potato production systems

Non-Peer Reviewe

Un modèle intégré pour explorer les trajectoires d’utilisation de l’espace

International audienceDynamic spatial models are important tools for the study of complex systems like environmental systems. This paper presents an integrated model that has been designed to explore land use trajectories in a small region around Maroua, located in the far north of Cameroon. The model simulates competition between land use types taking into account a set of biophysical, socio-demographic and geo-economics driving factors. The model includes three modules. The dynamic simulation module combines results of the spatial analysis and prediction modules. Simulation results for each scenario can help to identify where changes occur. The model developed constitutes an efficient knowledge support system for exploratory research and land use planning.Les modèles spatiaux dynamiques sont des outils de très grande importance pour l'étude des systèmes complexes comme les systèmes environnementaux. De plus, une approche intégrée est indispensable lorsqu'on veut avoir une compréhension plus complète du comportement de ces systèmes. Cet article décrit les bases d'un modèle intégré développé pour explorer les trajectoires d'utilisation de l'espace dans la région autour de Maroua, à l'Extrême Nord du Cameroun. Le modèle simule la compétition entre différentes catégories d'utilisation de l'espace en prenant en compte l'influence d'un ensemble de facteurs biophysiques, sociodémographiques et géoéconomiques. On distingue trois principaux modules. Le module de simulation dynamique combine les résultats des modules d'analyse spatiale et de prédiction. La calibration et la validation du modèle ont été effectuées pour la période entre 1987 et 1999, et la simulation des changements entre 1999 et 2010. Trois scénarios ont été formulés en s'appuyant sur l'analyse des tendances observées et les hypothèses de transition du système d'utilisation de l'espace. Les principales dynamiques observées concernent le développement de la culture maraîchère et l'extension de la culture du sorgho de contre saison qui induisent une compétition plus importante et des conflits. Les résultats de simulation pour chaque scénario permettent d'identifier des zones prioritaires pour toute intervention allant dans le sens de l'intensification ou d'une gestion intégrée et plus durable de l'espace. Le modèle développé constitue ainsi un outil de recherche exploratoire et un support de connaissances utilisable pour la planification de l'utilisation de l'espace. Une utilisation est envisageable pour initier toute concertation ou négociation entre les acteurs concernés par la gestion de l'espace

Cultural landscapes and behavioral transformations:An agent-based model for the simulation and discussion of alternative landscape futures in East Lesvos, Greece

Agricultural intensification and abandonment have been identified as two of the more prominent and polarizing drivers of landscape change in Europe. These transitions may induce deterioration in landscape functioning and character, particularly in cultural landscapes demonstrative of evolving human-environment dynamics that have sustained environmental benefits through time. Cultural and behavioral motives are important root influences to such landscape transitions, yet efforts to address landscape degradation are often hampered by a failure to account for the heterogeneous decision-making nature of its agents of change and the inherent complexity of socio-ecological systems. Novel techniques are required to further disentangle responses to multi-level drivers and discuss alternative landscape development trajectories. Agent-based models constructed by means of participatory approaches present increasingly applied tools in this context. This study sought to capture and model the future perspectives emerging from presently occurring farming discourses in the region of Gera (Lesvos, Greece), characterized by persistent abandonment of its traditionally managed olive plantations. We constructed an agent-based model iteratively in collaboration with the local farming community and experts in landscape research. Empirical findings informed the model through the construction of a farmer typology, revealing a heavy reliance of the farming community upon sectorial profitability, prevalent cultural farming motives and emerging landscape initiatives. The model examined the de-coupled role of agricultural profitability and landscapes initiatives in shaping the behavior of land managers, mapping alternative landscape futures over a period of 25 years. Model results illustrate both increased profitability and action by landscape initiatives are required to reverse abandonment trends within the simulated time frame. The hypothesized ability of landscape initiatives to maintain and promote a cultural drive amongst adhering farmers is crucial for securing behavioral transformations towards professionalism. This study confirmed agent-based modelling to be intuitively received by stakeholders who significantly contributed to model structure refinement and the rejection of a status quo scenario

Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling: a case study

The objective of this study is to compare the abilities of logistic, auto-logistic and artificial neural network (ANN) models for quantifying the relationships between land uses and their drivers. In addition, the application of the results obtained by the three techniques is tested in a dynamic land-use change model (CLUE-s) for the Paochiao watershed region in Taiwan. Relative operating characteristic curves (ROCs), kappa statistics, multiple resolution validation and landscape metrics were used to assess the ability of the three techniques in estimating the relationship between driving factors and land use and its subsequent application in land-use change models. The validation results illustrate that for this case study ANNs constitute a powerful alternative for the use of logistic regression in empirical modeling of spatial land-use change processes. ANNs provide in this case a better fit between driving factors and land-use pattern. In addition, auto-logistic regression performs better than logistic regression and nearly as well as ANNs. Auto-logistic regression and ANNs are considered especially useful when the performance of more conventional models is not satisfactory or the underlying data relationships are unknown. The results indicate that an evaluation of alternative techniques to specify relationships between driving factors and land use can improve the performance of land-use change models

Lake surface temperature [in “State of the Climate in 2017”]

Observed lake surface water temperature anomalies in 2017 are placed in the context of the recent warming observed in global surface air temperature by collating long-term in situ lake surface temperature observations from some of the world’s best-studied lakes and a satellite-derived global lake surface water temperature dataset. The period 1996–2015, 20 years for which satellite-derived lake temperatures are available, is used as the base period for all lake temperature anomaly calculations