Integration of bio-physical and economic models to analyze management intensity and landscape structure effects at farm and landscape level

Schönhart, Martin; Schauppenlehner, Thomas; Schmid, Erwin; Muhar, Andreas

doi:10.1016/j.agsy.2010.03.014

Cited by 56 publications

(19 citation statements)

References 77 publications

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“…Also, when modeling seed exchange networks, there is a trade-off between the coverage of features deemed to influence a certain system and the ease with which a model can be run and understood (Levins 1966;Matthewson 2011;Orzack 2012). Models are particularly useful when baseline data are lacking, when rare events play an important role or where the available data span a period which is too short to allow the perception of a temporal trend (Schönhart et al 2011;Jensen et al 2012;Savary et al 2012). These situations are common for seed exchange networks.…”

Section: Simulation Modelsmentioning

confidence: 99%

Seed exchange networks for agrobiodiversity conservation. A review

Pautasso

Aistara²,

Barnaud

et al. 2012

Agron. Sustain. Dev.

206

149

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The circulation of seed among farmers is central to agrobiodiversity conservation and dynamics. Agrobiodiversity, the diversity of agricultural systems from genes to varieties and crop species, from farming methods to landscape composition, is part of humanity's cultural heritage. Whereas agrobiodiversity conservation has received much attention from researchers and policy makers over the last decades, the methods available to study the role of seed exchange networks in preserving crop biodiversity have only recently begun to be considered. In this overview, we present key concepts, methods, and challenges to better understand seed exchange networks so as to improve the chances that traditional crop varieties (landraces) will be preserved and used sustainably around the world. The available literature suggests that there is insufficient knowledge about the social, cultural, and methodological dimensions of environmental change, including how seed exchange networks will cope with changes in climates, socio-economic factors, and family structures that have supported seed exchange systems to date. Methods available to study the role of seed exchange networks in the preservation and adaptation of crop specific and genetic diversity range from meta-analysis to modelling, from participatory approaches to the development of bio-indicators, from genetic to biogeographical studies, from anthropological and ethnographic research to the use of network theory. We advocate a diversity of approaches, so as to foster the creation of robust and policy-relevant knowledge. Open challenges in the study of the role of seed exchange networks in biodiversity conservation include the development of methods to (i) enhance farmers' participation to decision-making in agro-ecosystems, (ii) integrate ex situ and in situ approaches, (iii) achieve interdisciplinary research collaboration between social and natural scientists, and (iv) use network analysis as a conceptual framework to bridge boundaries among researchers, farmers and policy makers, as well as other stakeholders. (Résumé d'auteur

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Section: Simulation Modelsmentioning

confidence: 99%

Seed exchange networks for agrobiodiversity conservation. A review

Pautasso

Aistara²,

Barnaud

et al. 2012

Agron. Sustain. Dev.

206

149

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“…Balkovič at al. 2011;Schönhart et al 2011;Schneider et al 2011) by using statistical and survey data. However, the calibration of models becomes infeasible and model validation is often restricted to comparisons between simulated and statistical crop yields.…”

Section: Biophysical Impacts Of Climate Changementioning

confidence: 99%

Modeling climate change and biophysical impacts of crop production in the Austrian Marchfeld Region

et al. 2011

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Climate change affects major biophysical processes in agricultural crop production (e.g. evaporation of plants and soils, nutrient cycles, and growth of plants). This analysis aims to assess some of these effects by simulating regional climate projections that are integrated in the biophysical process model EPIC (Environmental Policy Integrated Climate). Statistical climate models have been developed for six weather parameters based on daily weather records of a weather station in the Austrian Marchfeld region from 1975 to 2006. These models have been used to estimate daily weather parameters for the period 2007-2038. The resulting projections have been compared to climate scenarios provided from the TYNDALL Centre for Climate Change Research, which are based on General Circulation Models (GCMs). The comparison indicates some differences, namely a smaller temperature increase and a higher precipitation amount in the TYNDALL data. Both climate datasets have been used to simulate impacts of climate change on crop yields, topsoil organic carbon content, and nitrate leaching with EPIC and thus to perform a sensitivity analysis of EPIC. Yield impacts have been assessed for four simulated crops, i.e. 6.2 t/ha for winter wheat for statistical climate projections compared to 5.7 t/ha for TYNDALL scenarios, 10.6 t/ha for corn compared to 10.5 t/ha, 3.9 t/ha for sunflower compared to 3.7 t/ha, and 4.5 t/ha for spring barley compared to 4.3 t/ha-all values as an average over the period 2007-2038. Smaller differences have been simulated for topsoil Climatic Change (2012) 111:641-664 organic carbon content i.e. 55.1 t/ha for the statistical climate projections compared to 55.3 t/ha for the TYNDALL scenarios and nitrate leaching i.e. 7.1 kg/ha compared to 11.1 kg/ha. All crop yields as well as topsoil organic carbon content and nitrate leaching show highest sensitivity to temperature and solar radiation.

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“…Much progress has been made in IAM and many models have become available [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36] also for application to agriculture [38][39][40]. However, most models are targeted at specific issues, e.g., assessment of land-use change [42], nitrogen emissions and leaching [41], or food production [8], or at specific scales ranging from farming systems to global assessments.…”

Section: Integrated Assessment and Modeling (Iam)mentioning

confidence: 99%

“…Agricultural policies subsidizing production and market prices have been reformed toward instruments that are increasingly decoupled from production but coupled to ecological services and environmental quality standards. Schönhart et al [30] presented an integrated modeling framework that combines a spatially explicit economic farm model, a biophysical process simulation model, and a crop rotation model.…”

Section: Integrated Assessment and Modeling (Iam)mentioning

confidence: 99%

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Integrated Assessment and Modeling of Agricultural Mechanization in Potato Production of Iran by Artificial Neural Networks

2015

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Mechanization is a concept and cannot be measured directly, so we used appropriate indicators in order to determine mechanization status for the first time in this field of study. In this paper, based on energy and power availability, four indicators, namely machinery energy ratio, mechanization index, productivity level of consumed power, and mechanization level, were selected for assessing agricultural mechanization in potato production of Iran using integrated assessment and modeling (IAM) to provide insight into the potential impacts of policy changes. To do an IAM in agricultural mechanization, we used pervasive analysis using more than 90 features in sample farms. This IAM is the first generalized model in agricultural mechanization. The main purpose of this study is presenting and showing capability of ANNs to model agricultural mechanization status and indicating best ANN model. Finally, a two hidden layer model with these features showed best performance: generalized feed-forward network with Levenberg Marquart learning rule and Bias Axon transfer function with 4-10 neurons in two hidden layers which have 27 input items for modeling four outputs. In this study, ANN models were introduced and applied to help IAM investigation which integrating production factors to have better knowledge about agricultural system of potato production in a wide region.

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Integration of bio-physical and economic models to analyze management intensity and landscape structure effects at farm and landscape level

Cited by 56 publications

References 77 publications

Seed exchange networks for agrobiodiversity conservation. A review

Seed exchange networks for agrobiodiversity conservation. A review

Modeling climate change and biophysical impacts of crop production in the Austrian Marchfeld Region

Integrated Assessment and Modeling of Agricultural Mechanization in Potato Production of Iran by Artificial Neural Networks

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