Using the Positive Mathematical Programming Method to Calibrate Linear Programming Models

In agricultural economics, several calibration and aggregation approaches have evolved in mathematical programming models. This article combines in a linear programming model features of the Positive Mathematical Programming method with an aggregation approach that is constrained to the production possibility set spanned by a convex combination of observed production activities. The combination is obtained by using a variable separation technique that approximates a non-linear objective function. Therefore, linear programming models can be exactly calibrated to observed production activities. The aggregation of production activities in homogenous production response units assumes that farmers in a region are treated such as they respond in the same way. Both methodologies are embedded in economic reasoning and provide a robust framework to solve large-scale linear programming models in reasonable time

Co-evaluating and -designing a Sustainable Agriculture Matrix for Austria in an international context

Agricultural ecosystems provide essential services mainly through food, feed, fiber and consequently income but they also contribute cultural, supporting and regulating services. In turn, farming can adversely affect ecosystem services, especially those from natural ecosystems, if farming practices are unsustainable. Recently, a Sustainable Agriculture Matrix (SAM; https://doi.org/10.1016/j.oneear.2021.08.015) of indicators across environmental, economic, and social dimensions has been developed by an international research team to coherently quantify the sustainability of countries’ farming systems globally. The focus was on indicators that can be tracked over time and relate to performance to facilitate analyzes of synergies and trade-offs. At present, this indicator system is being co-evaluated with stakeholders in ten countries within an international consortium including Austria, to elicit stakeholders’ appraisal of the framework’s applicability in their specific geographical and socioeconomic context and eventually co-design a revised matrix based on stakeholders’ requirements. A first workshop has shown that most indicators from the environmental dimension are useful for stakeholders in the Austrian context, but some need further refinements. Biodiversity, for example, is only considered via land cover change whereas threats to (agro-)biodiversity in Austria and the EU foremost occur in-situ. The economic dimension is ranking second in its usefulness for Austrian stakeholders with few indicators such as food loss being of little relevance. The indicators presently included in the social dimension are least relevant as they cover aspects such as land rights, undernourishment, and rural poverty, which do not pose major issues in Austria and more broadly the EU. General concerns of stakeholders are the directionality of indicator ratings and their scope which is in part considered too narrow. E.g., high government expenditure for agriculture is considered positive in the matrix regardless of its purpose and may cause dependencies. Human nutrition is only included via undernourishment and soil nutrient status solely as surplus, whereas in both cases also the other extreme may be adverse. Accordingly, a bell-shaped indicator and rating would be favored in such cases. A general requirement was expressed for an additional context dimension. Governance arrangements and the overall socioeconomic situation are so far deliberately not included due to the focus on performance in the existing SAM. Yet, indicators describing such framework conditions can be essential to interpret synergies and trade-offs and the effectiveness of policy measures aiming at achieving SDGs. Beyond the evaluation of existing indicators, the stakeholder process yielded comprehensive suggestions for additional indicators, covering biodiversity, research and education, self-sufficiency, as well as various aspects of resilience and stability. Overall, the co-evaluation with stakeholders highlights that only few globally defined indicators are readily applicable in a regional context where consideration of local conditions and specifics is vital. The proposed revisions are now being matched with available data across geographic scales to revise the matrix and perform further analyses on trade-offs and synergies. This will also include further context information to facilitate the evaluation of policies, ultimately allowing for improved policy-making to attain agricultural sustainability. Results will be further co-evaluated iteratively with stakeholders to eventually produce a globally applicable indicator system

Rural Development Programme measures on cultivated land in Europe to mitigate greenhouse gas emissions – regional ‘hotspots’ and priority measures

© 2016 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.Agriculture is a significant source of GHG emissions, contributing 10% of total emissions within the EU-28. Emissions from European agriculture have been reduced, albeit at the expense of crop yield and the risk of production displacement (the transfer of production, and associated emissions, to land outside of Europe). This article assesses the impact on GHG emissions of selected European Rural Development Program measures, representative of a diversity of management strategies implemented on cultivated land, within nine European Member States. Climatic zone and underlying spatial environmental variables were accounted for using a novel technique, “Regional Variation Categories,” developed with European-scale GIS data sets. Production displacement is assessed with two benchmarks: (1) compared with existing crop production and (2) relative to a “minimum requirement” land management scenario, where an emissions reduction of less than this does not constitute mitigation. Most measures reduce emissions relative to the baseline crop scenario; however, many do not reduce emissions beyond the “minimum requirement,” this being limited to measures such as catch crops and within-field grass areas to prevent soil erosion. The selection and targeting of measures to maximize agricultural GHG mitigation on cultivated land within Europe is discussed...Peer reviewedFinal Published versio

Technische Zusammenfassung

Die Technische Zusammenfassung des APCC-Sonderberichts ″Landnutzung und Klimawandel in Österreich″ umfasst die Kernbotschaften der Kapitel 1–9. In ihr sind die Hauptaussagen zu den sozioökonomischen und klimatischen Treibern der Landnutzungsänderungen, zu den Auswirkungen von Landnutzung und -bewirtschaftung auf den Klimawandel, zu Minderungs- und Anpassungsoptionen im Kontext nachhaltiger Entwicklungsziele sowie zu Synergien, Zielkonflikten und Umsetzungsbarrieren von Klimamaßnahmen enthalten