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Nguyen, T. P. L., Seddaiu, G., Virdis, S. G. P., Tidore, C., Pasqui, M., & Roggero, P. P. (2016). Perceiving to learn or learning to perceive? Understanding farmers’ perceptions and adaptation to climate uncertainties. Agricultural Systems, 143, 205–216.
Abstract: Perception not only shapes knowledge but knowledge also shapes perception. Humans adapt to the natural world through a process of learning in which they interpret their sensory impressions in order to give meaning to their environment and act accordingly. In this research, we examined how farmers’ decision making is shaped in the context of changing climate. Using empirical data (face-to-face semi-structured interviews and questionnaires) on four Mediterranean farming systems from a case study located in Oristano (Sardinia, Italy) we sought to understand farmers’ perception of climate change and their behaviors in adjustment of farming practices. We found different perceptions among farmer groups were mainly associated with the different socio-cultural and institutional settings and perceived relationships between climate factors and impacts on each farming systems. The research findings on different perceptions among farmer groups can help to understand farmers’ current choices and attitudes of adaptation for supporting the development of appropriate adaptation strategies. In addition, the knowledge of socio-cultural and economic factors that lead to biases in climate perceptions can help to integrate climate communication into adaptation research for making sense of climate impacts and responses at farm level.
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Ruget, F., Buis, S., Irfan, K., Delmotte, S., Mouret, J. - C., Ridaura, S. L., et al. (2016). Parametrization of a crop model using a regional agronomical database: rice in Camargue with STICS.. Berlin (Germany).
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Wallach, D., Thorburn, P., Asseng, S., Challinor, A. J., Ewert, F., Jones, J. W., et al. (2016). Overview paper on comprehensive framework for assessment of error and uncertainty in crop model predictions (Vol. 8).
Abstract: Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. Several ways of quantifying prediction uncertainty have been explored in the literature, but there have been no studies of how the different approaches are related to one another, and how they are related to some overall measure of prediction uncertainty. Here we show that all the different approaches can be related to two different viewpoints about the model; either the model is treated as a fixed predictor with some average error, or the model can be treated as a random variable with uncertainty in one or more of model structure, model inputs and model parameters. We discuss the differences, and show how mean squared error of prediction can be estimated in both cases. The results can be used to put uncertainty estimates into a more general framework and to relate different uncertainty estimates to one another and to overall prediction uncertainty. This should lead to a better understanding of crop model prediction uncertainty and the underlying causes of that uncertainty. This study was published as (Wallach et al. 2016)
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Abdelrahman, H. M., Olk, D. C., Dinnes, D., Ventrella, D., Miano, T., & Cocozza, C. (2016). Occurrence and abundance of carbohydrates and amino compounds in sequentially extracted labile soil organic matter fractions. Journal of Soils and Sediments, 16(10), 2375–2384.
Abstract: Purpose The study aimed to describe the carbohydrates and amino compounds content in soil, the light fraction (LF), the >53 μm particulate organic matter (POM), and the mobile humic acid (MHA) fraction and to find out whether the carbohydrates and amino compounds can be used to explain the origin of SOM fractions. Materials and methods Soil samples were collected from two agricultural fields managed under organic farming in southern Italy. The LF, the POM, and the MHA were sequentially extracted from each soil sample then characterized. Seven neutral sugars and 19 amino compounds (amino acids and amino sugars) were determined in each soil sample and its correspondent fractions. Results and discussion The MHA contained less carbohydrate than the LF or the POM but its carbohydrates, although dominated by arabinose, were relatively with larger microbial contribution as revealed by the mannose/xylose ratio. The amino compounds were generally less in the LF or the POM than in the MHA, while the fungal (aspartic and serine) and bacterial (alanine and glycine) amino acids were larger in the MHA than in the LF or the POM, underlining the microbial contribution to the MHA. Results from both sites indicated that total carbohydrates content decreased moving from the LF (younger fraction) to the MHA (older fraction), which seems to follow a decomposition continuum of organic matter in the soil-plant system. Conclusions The study showed that the MHA is a labile humified fraction of soil C due to its content of carbohydrates and concluded that the content of carbohydrates and amino compounds in the LF, the POM and the MHA can depict the nature of these fractions and their cycling pattern and response to land management.
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Baranowski, P., Krzyszczak, J., Hoffmann, H., & Sławiński, C. (2016). Multifractal properties of spatially aggregated meteorological data – a regional study.. Berlin (Germany).
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