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Nguyen, T.P.L.; Seddaiu, G.; Virdis, S.G.P.; Tidore, C.; Pasqui, M.; Roggero, P.P. |
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Title |
Perceiving to learn or learning to perceive? Understanding farmers’ perceptions and adaptation to climate uncertainties |
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Journal Article |
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Year |
2016 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agricultural Systems |
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Volume  |
143 |
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Pages |
205-216 |
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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 |
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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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0308-521x |
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CropM, ft_macsur |
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no |
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Call Number |
MA @ admin @ |
Serial |
4707 |
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Author |
Milford, A.B.; Le Mouel, C.; Bodirsky, B.L.; Rolinski, S. |
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Title |
Drivers of meat consumption |
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Journal Article |
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Year |
2019 |
Publication |
Appetite |
Abbreviated Journal |
Appetite |
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141 |
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Pages |
Unsp 104313 |
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Keywords |
Meat consumption; Nutrition transition; Climate change mitigation; Cross-country analysis; nutrition transition; food; sustainability; globalization; countries; future; health; income; price |
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Abstract |
Increasing global levels of meat consumption are a threat to the environment and to human health. To identify measures that may change consumption patterns towards more plant-based foods, it is necessary to improve our understanding of the causes behind the demand for meat. In this paper we use data from 137 different countries to identify and assess factors that influence meat consumption at the national level using a cross-country multivariate regression analysis. We specify either total meat or ruminant meat as the dependent variable and we consider a broad range of potential drivers of meat consumption. The combination of explanatory variables we use is new for this type of analysis. In addition, we estimate the relative importance of the different drivers. We find that income per capita followed by rate of urbanisation are the two most important drivers of total meat consumption per capita. Income per capita and natural endowment factors are major drivers of ruminant meat consumption per capita. Other drivers are Western culture, Muslim religion, female labour participation, economic and social globalisation and meat prices. The main identified drivers of meat demand are difficult to influence through direct policy intervention. Thus, acting indirectly on consumers’ preferences and consumption habits (for instance through information, education policy and increased availability of ready-made plant based products) could be of key importance for mitigating the rise of meat consumption per capita all over the world. |
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2020-02-14 |
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0195-6663 |
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TradeM, ft_macsur |
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MA @ admin @ |
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5224 |
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Zhang, W.; Liu, C.; Zheng, X.; Zhou, Z.; Cui, F.; Zhu, B.; Haas, E.; Klatt, S.; Butterbach-Bahl, K.; Kiese, R. |
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Title |
Comparison of the DNDC, LandscapeDNDC and IAP-N-GAS models for simulating nitrous oxide and nitric oxide emissions from the winter wheat–summer maize rotation system |
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Journal Article |
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Year |
2015 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agricultural Systems |
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140 |
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1-10 |
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Model ensemble; Straw incorporation; Irrigation; Fertilization; Calcareous soil; North China Plain; process-oriented model; soil organic-matter; biogeochemical model; cropping system; N2O emissions; forest soils; microbial-growth; rainfall events; calcareous soil |
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The DNDC, LandscapeDNDC and IAP-N-GAS models have been designed to simulate the carbon and nitrogen processes of terrestrial ecosystems. Until now, a comparison of these models using simultaneous observations has not been reported, although such a comparison is essential for further model development and application. This study aimed to evaluate the performance of the models, delineate the strengths and limitations of each model for simulating soil nitrous oxide (N2O) and nitric oxide (NO) emissions, and explore short-comings of these models that may require reconsideration. We conducted comparisons among the models using simultaneous observations of both gases and relevant variables from the winter wheat-summer maize rotation system at three field sites with calcareous soils. Simulations of N2O and NO emissions by the three models agreed well with annual observations, but not with daily observations. All models failed to correctly simulate soil moisture, which could explain some of the incorrect daily fluxes of N2O and NO, especially for intensive fluxes during the growing season. Multi-model ensembles are promising approaches to better simulate daily gas emissions. IAP-N-GAS underestimated the priming effect of straw incorporation on N2O and NO emissions, but better results were obtained with DNDC95 and LandscapeDNDC. LandscapeDNDC and IAP-N-GAS need to improve the simulation of irrigation water allocation and residue decomposition processes, respectively, and together to distinguish different irrigation methods as DNDC95 does. All three models overestimated the emissions of the nitrogenous gases for high nitrogen fertilizer (>430 kg N ha(-1) yr(-1)) addition treatments, and therefore, future research should focus more on the simulation of the limitation of soil dissolvable organic carbon on denitrification in calcareous soils. |
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0308-521x |
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CropM, ft_macsur |
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MA @ admin @ |
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4685 |
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Wallach, D.; Mearns, L.O.; Ruane, A.C.; Rötter, R.P.; Asseng, S. |
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Title |
Lessons from climate modeling on the design and use of ensembles for crop modeling |
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Journal Article |
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Year |
2016 |
Publication |
Climatic Change |
Abbreviated Journal |
Clim. Change |
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139 |
Issue |
3-4 |
Pages |
551-564 |
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Keywords |
change projections; elevated CO2; uncertainty; wheat; water; soil; simulations; yield; rice; 21st-century; Model ensembles; Crop models; Climate models; Model weighting; Super ensembles |
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Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor. |
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2017-01-06 |
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0165-0009 |
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CropM, ft_MACSUR |
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MA @ admin @ |
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4933 |
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Author |
Vitti, C.; Stellacci, A.M.; Leogrande, R.; Mastrangelo, M.; Cazzato, E.; Ventrella, D. |
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Title |
Assessment of organic carbon in soils: a comparison between the Springer–Klee wet digestion and the dry combustion methods in Mediterranean soils (Southern Italy) |
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Journal Article |
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Year |
2016 |
Publication |
Catena |
Abbreviated Journal |
Catena |
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137 |
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113-119 |
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• Comparison of two methods for soil organic C quantification is presented. • Springer–Klee wet digestion and dry combustion with automated analyser were compared. • Soil samples were collected from three different sites in a Southern Italy area. • Recoveries close to one were observed for whole dataset and for data grouped per site. • The strong agreement between the methods would enable direct comparison of results. Abstract Soil organic carbon (SOC) is the largest carbon pool in the terrestrial biosphere and it is among the most important factors responsible for conservation of soil quality. Automated dry combustion techniques are gradually replacing traditional quantification methods based on wet digestion chemistry. Critical comparison of different methods is fundamental to reevaluate archives of SOC data and accurately assess and model long-term carbon stock variation and should be performed for different soil types and management conditions. Two analytical methods, the Springer–Klee wet digestion and the dry combustion using an automated analyser, were compared for soils typical of a Mediterranean environment in Southern Italy. Soil samples were collected from three sites, at two depths. Soils were fine textured (from clay–loam to clay) with total carbonate ranging from 6.6 to 16.7 g 100 g− 1. SOC content varied from 6.92 to 28.86 g kg− 1 (as average of the two methods), with values and ranges typical of Southern Europe. On average, Springer–Klee method gave slightly higher values and showed greater data variability. This behaviour, in agreement with other studies, can be attributed to the reaction of K2Cr2O7 with other soil constituents and to analytical constraints. Our results suggest high consistency between Springer–Klee and dry combustion techniques and show recoveries close to one both for the whole dataset and for data grouped per experimental site or soil depth. Linear regression equations between the two methods were slightly affected by different soil types (P = 0.0621). The best fitting of the relationship was a linear regression passing through the origin for the whole dataset (Radj2 = 0.965; RPD = 3.41). The strong overall agreement observed between the two methods would enable the direct comparison of new data set with those already existing in Southern Italy for soils with similar characteristics. |
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0341-8162 |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4989 |
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