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Author Cassardo, C.; Andreoli, V.
Title (up) On the Representativeness of UTOPIA Land Surface Model for Creating a Database of Surface Layer, Vegetation and Soil Variables in Piedmont Vineyards, Italy Type Journal Article
Year 2019 Publication Applied Sciences-Basel Abbreviated Journal Applied Sciences-Basel
Volume 9 Issue 18 Pages 3880
Keywords land-surface; UTOPIA; NOAH; GLDAS; micrometeorology; exchanges; processes; vineyards; cabernet-sauvignon; climate-change; wine color; temperature; parameterization; simulations; circulation; balances; moisture; sunlight
Abstract The main aim of the paper is to show how, and how many, simulations carried out using the Land Surface Model UTOPIA (University of TOrino model of land Process Interaction with Atmosphere) are representative of the micro-meteorological conditions and exchange processes at the atmosphere/biosphere interface, with a particular focus on heat and hydrologic transfers, over an area of the Piemonte (Piedmont) region, NW Italy, which is characterized by the presence of many vineyards. Another equally important aim is to understand how much the quality of the simulation outputs was influenced by the input data, whose measurements are often unavailable for long periods over country areas at an hourly basis. Three types of forcing data were used: observations from an experimental campaign carried out during the 2008, 2009, and 2010 vegetative seasons in three vineyards, and values extracted from the freely available Global Land Data Assimilation System (GLDAS, versions 2.0 and 2.1). Since GLDAS also contains the outputs of the simulations performed using the Land Surface Model NOAH, an additional intercomparison between the two models, UTOPIA and NOAH, both driven by the same GLDAS datasets, was performed. The intercomparisons were performed on the following micro-meteorological variables: net radiation, sensible and latent turbulent heat fluxes, and temperature and humidity of soil. The results of this study indicate that the methodology of employing land surface models driven by a gridded database to evaluate variables of micro-meteorological and agronomic interest in the absence of observations is suitable and gives satisfactory results, with uncertainties comparable to measurement errors, thus, allowing us to also evaluate some time trends. The comparison between GLDAS2.0 and GLDAS2.1 indicates that the latter generally produces simulation outputs more similar to the observations than the former, using both UTOPIA and NOAH models.
Address 2020-02-14
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Notes CropM, ft_macsur Approved no
Call Number MA @ admin @ Serial 5228
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Author Rolinski, S.; Weindl, I.; Heinke, J.; Bodirsky, B.L.; Biewald, A.; Lotze-Campen, H.
Title (up) Pasture harvest, carbon sequestration and feeding potentials under different grazing intensities Type Journal Article
Year 2015 Publication Advances in Animal Biosciences Abbreviated Journal Advances in Animal Biosciences
Volume 6 Issue 01 Pages 43-45
Keywords global dynamic vegetation model; LPJmL; grasslands; livestock production
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Notes CropM, LiveM, ft_macsur Approved no
Call Number MA @ admin @ Serial 4541
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Author Nguyen, T.P.L.; Seddaiu, G.; Virdis, S.G.P.; Tidore, C.; Pasqui, M.; Roggero, P.P.
Title (up) Perceiving to learn or learning to perceive? Understanding farmers’ perceptions and adaptation to climate uncertainties Type Journal Article
Year 2016 Publication Agricultural Systems Abbreviated Journal Agricultural Systems
Volume 143 Issue Pages 205-216
Keywords climate variability; socio-cognitive learning process; adaptation strategies; mediterranean agricultural systems; agricultural land-use; adaptive capacity; farming systems; variability; knowledge; risk; drought; africa; future; rain
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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Language English Summary Language Original Title
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ISSN 0308-521x ISBN Medium Article
Area Expedition Conference
Notes CropM, ft_macsur Approved no
Call Number MA @ admin @ Serial 4707
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Author Yang, H.; Dobbie, S.; Ramirez-Villegas, J.; Feng, K.; Challinor, A.J.; Chen, B.; Gao, Y.; Lee, L.; Yin, Y.; Sun, L.; Watson, J.; Koehler, A.-K.; Fan, T.; Ghosh, S.
Title (up) Potential negative consequences of geoengineering on crop production: A study of Indian groundnut Type Journal Article
Year 2016 Publication Geophysical Research Letters Abbreviated Journal Geophys. Res. Let.
Volume 43 Issue 22 Pages 11786-11795
Keywords Mangrove Tidal Creek; Land-Ocean Boundary; Carbon-Dioxide; Organic-Matter; River Estuary; European Estuaries; CO2 Fluxes; NE Coast; Water; Bay; fCO(2) (water); air-water CO2 flux; Hugli Estuary; Matla Estuary; Blue Carbon; source of CO2
Abstract Geoengineering has been proposed to stabilize global temperature, but its impacts on crop production and stability are not fully understood. A few case studies suggest that certain crops are likely to benefit from solar dimming geoengineering, yet we show that geoengineering is projected to have detrimental effects for groundnut. Using an ensemble of crop-climate model simulations, we illustrate that groundnut yields in India undergo a statistically significant decrease of up to 20% as a result of solar dimming geoengineering relative to RCP4.5. It is somewhat reassuring, however, to find that after a sustained period of 50 years of geoengineering crop yields return to the nongeoengineered values within a few years once the intervention is ceased.
Address 2017-01-20
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Language English Summary Language Original Title
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ISSN 0094-8276 ISBN Medium Article
Area Expedition Conference
Notes CropM, ft_MACSUR Approved no
Call Number MA @ admin @ Serial 4936
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Author Müller, C.; Robertson, R.D.
Title (up) Projecting future crop productivity for global economic modeling Type Journal Article
Year 2014 Publication Agricultural Economics Abbreviated Journal Agric. Econ.
Volume 45 Issue 1 Pages 37-50
Keywords climate change; crop modeling; agricultural productivity; land use; greenhouse-gas emissions; soil organic-carbon; sub-saharan africa; climate-change; elevated co2; land-use; system model; wheat yields; maize yields; agriculture
Abstract Assessments of climate change impacts on agricultural markets and land-use patterns rely on quantification of climate change impacts on the spatial patterns of land productivity. We supply a set of climate impact scenarios on agricultural land productivity derived from two climate models and two biophysical crop growth models to account for some of the uncertainty inherent in climate and impact models. Aggregation in space and time leads to information losses that can determine climate change impacts on agricultural markets and land-use patterns because often aggregation is across steep gradients from low to high impacts or from increases to decreases. The four climate change impact scenarios supplied here were designed to represent the most significant impacts (high emission scenario only, assumed ineffectiveness of carbon dioxide fertilization on agricultural yields, no adjustments in management) but are consistent with the assumption that changes in agricultural practices are covered in the economic models. Globally, production of individual crops decrease by 10-38% under these climate change scenarios, with large uncertainties in spatial patterns that are determined by both the uncertainty in climate projections and the choice of impact model. This uncertainty in climate impact on crop productivity needs to be considered by economic assessments of climate change.
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Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN 0169-5150 ISBN Medium Article
Area Expedition Conference
Notes CropM, TradeM, ft_macsur Approved no
Call Number MA @ admin @ Serial 4533
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