Zimmermann, A., & Britz, W. (2016). European farms’ participation in agri-environmental measures. Land Use Policy, 50, 214–228.
Abstract: Due to their diversity and voluntariness, agri-environmental measures (AEMs) are among the Common Agricultural Policy instruments that are most difficult to assess. We provide an EU-wide analysis of AEM adoption and farm’s total AEM support over total Utilised Agricultural Area using a Heckman sample selection approach and single farm data. Our analysis covers 22 Member States over the 2000-2009 period, assesses the entire portfolio of AEMs and focuses on the relationship between AEM participation and farming system. Results show that participation in AEMs is more likely in less intensive production systems, where, however, per committed hectare AEM premiums tend to be lower. Member States group into three categories: high/low intensity farming systems with low/high AEM enrollment rates, respectively, and large high diversity countries with medium AEM enrollment rates. (C) 2015 Elsevier Ltd. All rights reserved.
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Saetnan, E. R., & Kipling, R. P. (2016). Evaluating a European knowledge hub on climate change in agriculture: Are we building a better connected community. Scientometrics, 109(2), 1057–1074.
Abstract: In order to maintain food security and sustainability of production under climate change, interdisciplinary and international collaboration in research is essential. In the EU, knowledge hubs are important funding instruments for the development of an interconnected European Research Area. Here, network analysis was used to assess whether the pilot knowledge hub MACSUR has affected interdisciplinary collaboration, using co-authorship of peer reviewed articles as a measure of collaboration. The broad community of all authors identified as active in the field of agriculture and climate change was increasingly well connected over the period studied. Between knowledge hub members, changes in network parameters suggest an increase in collaborative interaction beyond that expected due to network growth, and greater than that found in the broader community. Given that interdisciplinary networks often take several years to have an impact on research outputs, these changes within the relatively new MACSUR community provide evidence that the knowledge hub structure has been effective in stimulating collaboration. However, analysis showed that knowledge hub partners were initially well-connected, suggesting that the initiative may have gathered together researchers with particular resources or inclinations towards collaborative working. Long term, consistent funding and ongoing reflection to improve networking structures may be necessary to sustain the early positive signs from MACSUR, to extend its success to a wider community of researchers, or to repeat it in less connected fields of science. Tackling complex challenges such as climate change will require research structures that can effectively support and utilise the diversity of talents beyond the already well-connected core of scientists at major research institutes. But network research shows that this core, well-connected group are vital brokers in achieving wider integration.
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Lehtonen, H. (2016). Evaluating competitiveness of clover-grass as a resilient feed production option in Finland (Vol. 9 C6 -).
Abstract: Clover-grasses address the following objectives:– Decreased input use (N-fertilization), reduced dependency ofinorganic N => reduced GHG emissions– Possibility for increased protein content of silage, reduceddependency on purchased protein feed supplement (homegrown proteins, resilience)© Natural Resources Institute Finland– Better utilisation of farmland in the context of climate changein the north: Higher T – improved N fixation– Compatible with sustainable agriculture and sustainableintensification: more output with the same inputs / the sameoutput with reduced (non-renewable) inputs• In contrast: Shifting to silage maize increases N fertilisation– Major shift from grasslands to silage maize in e.g. Denmark 1. Small cost reductions in clover-grass cultivation, or clover-grasspremiums, may or may not increase clover cultivation- Their effectiveness is uncertain and subject to prices2. N tax is effective, but is not a suitable policy action in currentfinancial situation of farms (milk crisis 2015-2016)3. However, the results suggest that a 25% higher N price lead to© Natural Resources Institute Finlandsignificantly higher clover grass area and a small reduction ínmilk output – with no cost reductions or extra premiums!4. To increase clover cultivation, price ratios should be adjusted!5. If increasing clover -grass yield, a robust increase in clovergrass areas may realise, with small benefits for farm economyand overall production – How much more clover grass yieldcould be attained at low costs? A topic for further discussionand analysis
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Zhao, G., Hoffmann, H., Yeluripati, J., Xenia, S., Nendel, C., Coucheney, E., et al. (2016). Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops. Env. Model. Softw., 80, 100–112.
Abstract: We compared the precision of simple random sampling (SimRS) and seven types of stratified random sampling (StrRS) schemes in estimating regional mean of water-limited yields for two crops (winter wheat and silage maize) that were simulated by fourteen crop models. We found that the precision gains of StrRS varied considerably across stratification methods and crop models. Precision gains for compact geographical stratification were positive, stable and consistent across crop models. Stratification with soil water holding capacity had very high precision gains for twelve models, but resulted in negative gains for two models. Increasing the sample size monotonously decreased the sampling errors for all the sampling schemes. We conclude that compact geographical stratification can modestly but consistently improve the precision in estimating regional mean yields. Using the most influential environmental variable for stratification can notably improve the sampling precision, especially when the sensitivity behavior of a crop model is known.
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Sinabell, F., Heinschink, K., & Tribl, C. (2016). Explicit cost accounting for analyses on climate change adaptation, mitigation and ecosystem service provision in agriculture. In S. Sauvage, J. M. Sánchez-Pérez, & A. E. Rizzoli (Eds.),. 8th International Congress on Environmental Modelling and Software.
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