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Ferrise, R., Toscano, P., Pasqui, M., Moriondo, M., Primicerio, J., Semenov, M. A., et al. (2015). Monthly-to-seasonal predictions of durum wheat yield over the Mediterranean Basin. Clim. Res., 65, 7–21.
Abstract: Uncertainty in weather conditions for the forthcoming growing season influences farmers’ decisions, based on their experience of the past climate, regarding the reduction of agricultural risk. Early within-season predictions of grain yield can represent a great opportunity for farmers to improve their management decisions and potentially increase yield and reduce potential risk. This study assessed 3 methods of within-season predictions of durum wheat yield at 10 sites across the Mediterranean Basin. To assess the value of within-season predictions, the model SiriusQuality2 was used to calculate wheat yields over a 9 yr period. Initially, the model was run with observed daily weather to obtain the reference yields. Then, yield predictions were calculated at a monthly time step, starting from 6 mo before harvest, by feeding the model with observed weather from the beginning of the growing season until a specific date and then with synthetic weather constructed using the 3 methods, historical, analogue or empirical, until the end of the growing season. The results showed that it is possible to predict durum wheat yield over the Mediterranean Basin with an accuracy of normalized root means squared error of <20%, from 5 to 6 mo earlier for the historical and empirical methods and 3 mo earlier for the analogue method. Overall, the historical method performed better than the others. Nonetheless, the analogue and empirical methods provided better estimations for low-yielding and high-yielding years, thus indicating great potential to provide more accurate predictions for years that deviate from average conditions.
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Dalgaard, T. (2015). Models for regional scale farming system evaluation of climate change mitigation options and environmental impact assessment (Vol. 5).
Abstract: The aim of the present paper is to exemplify and discuss the importance of farm scale modeling in relation to The EU Joint Programming Initiative (JPI-FACCE) knowledge hub on Agriculture, Food Security and Climate Change project.In particular, livestock production systems include complex interactions, with non-linear relationships between input factors, production, emissions, local climate as well as natural resources (e.g. soil types, rotational land versus permanent grasslands etc.). Moreover, management options pursued by the different types of farmers and other relevant decision makers are important to integrate. Consequently, results of regional scale impact assessments depend on the farming systems model approach, the approach to upscale results, and the inclusion of the relevant stakeholders and decision makers at the scales considered.Different farming systems models are reviewed, including the existing dynamic and static biophysical models. Finally, procedures for upscaling and validity testing of synthesized model results at regional scales are presented. Based on a discussion of these procedures, recommendations for hot-spot analyses in farming systems with regard to integrated climate change adaptation and mitigation for a sustainable food production are synthesized, and the potentials for integration of recommended policies and farm management options into overarching models in order to assess their impact on the regional to global scales are discussed. No Label
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Pardo, G., Yañez-Ruiz, D., Martin-Garcia, I., Arco, A., Moral, R., & del Prado, A. (2015). Modelling the impact on greenhouse gas emissions of using underutilized feed resources in dairy goat systems. Advances in Animal Biosciences, 6(01), 40–42.
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Wu, L., Whitmore, A. P., & Bellocchi, G. (2015). Modelling the impact of environmental changes on grassland systems with SPACSYS. Advances in Animal Biosciences, 6(01), 37–39.
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Özkan, S., Østergaard, S., & Strøm, T. (2015). Modelling The Impact Of Diseases On Greenhouse Gas Emissions In Dairy Cows..
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