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Author Twardy, S.; Kopacz, M.
Title Comparison of concentrations and loads of macronutrients brought with precipitation and leaching from the soil profile Type Journal Article
Year 2014 Publication Polish Journal of Environmental Studies Abbreviated Journal Pol. J. Environ. Stud.
Volume (down) 23 Issue 3a Pages 132-136
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Notes CropM Approved no
Call Number MA @ admin @ Serial 4640
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Author Kopacz, M.; Twardy, S.
Title Spatial modeling as a tool supporting the management of catchment area of retention reservoir Type Journal Article
Year 2014 Publication Polish Journal of Environmental Studies Abbreviated Journal Pol. J. Environ. Stud.
Volume (down) 23 Issue 3a Pages 53-57
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Notes CropM, LiveM Approved no
Call Number MA @ admin @ Serial 4628
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Author Fleisher, D.H.; Condori, B.; Quiroz, R.; Alva, A.; Asseng, S.; Barreda, C.; Bindi, M.; Boote, K.J.; Ferrise, R.; Franke, A.C.; Govindakrishnan, P.M.; Harahagazwe, D.; Hoogenboom, G.; Naresh Kumar, S.; Merante, P.; Nendel, C.; Olesen, J.E.; Parker, P.S.; Raes, D.; Raymundo, R.; Ruane, A.C.; Stockle, C.; Supit, I.; Vanuytrecht, E.; Wolf, J.; Woli, P.
Title A potato model intercomparison across varying climates and productivity levels Type Journal Article
Year 2017 Publication Global Change Biology Abbreviated Journal Glob. Chang. Biol.
Volume (down) 23 Issue 3 Pages 1258-1281
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Abstract A potato crop multimodel assessment was conducted to quantify variation among models and evaluate responses to climate change. Nine modeling groups simulated agronomic and climatic responses at low-input (Chinoli, Bolivia and Gisozi, Burundi)- and high-input (Jyndevad, Denmark and Washington, United States) management sites. Two calibration stages were explored, partial (P1), where experimental dry matter data were not provided, and full (P2). The median model ensemble response outperformed any single model in terms of replicating observed yield across all locations. Uncertainty in simulated yield decreased from 38% to 20% between P1 and P2. Model uncertainty increased with interannual variability, and predictions for all agronomic variables were significantly different from one model to another (P < 0.001). Uncertainty averaged 15% higher for low- vs. high-input sites, with larger differences observed for evapotranspiration (ET), nitrogen uptake, and water use efficiency as compared to dry matter. A minimum of five partial, or three full, calibrated models was required for an ensemble approach to keep variability below that of common field variation. Model variation was not influenced by change in carbon dioxide (C), but increased as much as 41% and 23% for yield and ET, respectively, as temperature (T) or rainfall (W) moved away from historical levels. Increases in T accounted for the highest amount of uncertainty, suggesting that methods and parameters for T sensitivity represent a considerable unknown among models. Using median model ensemble values, yield increased on average 6% per 100-ppm C, declined 4.6% per °C, and declined 2% for every 10% decrease in rainfall (for nonirrigated sites). Differences in predictions due to model representation of light utilization were significant (P < 0.01). These are the first reported results quantifying uncertainty for tuber/root crops and suggest modeling assessments of climate change impact on potato may be improved using an ensemble approach.
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ISSN 1354-1013 ISBN Medium article
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Notes CropM, ftnotmacsur Approved no
Call Number MA @ admin @ Serial 4968
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Author Mitter, H.; Sinabell, F.; Schmid, E.
Title Impacts of climate and policy change on Austrian protein crop supply balances Type Conference Article
Year 2015 Publication Jahrbuch der ÖGA Abbreviated Journal
Volume (down) 23 Issue Pages 131-140
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Area Expedition Conference 23. ÖGA Jahrestagung gemeinsam mit der 41. SGA-Jahrestagung “Grenzen der Qualitätsstrategie im Agrarsektor”, 2013-09-12 to 2013-09-14, Zürich
Notes TradeM Approved no
Call Number MA @ admin @ Serial 5030
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Author Below, T.B.; Mutabazi, K.D.; Kirschke, D.; Franke, C.; Sieber, S.; Siebert, R.; Tscherning, K.
Title Can farmers’ adaptation to climate change be explained by socio-economic household-level variables Type Journal Article
Year 2012 Publication Global Environmental Change Abbreviated Journal Glob. Environ. Change
Volume (down) 22 Issue 1 Pages 223-235
Keywords Sub-Saharan Africa; Tanzania; Adaptive capacity; Index; Vulnerability; Adaptation; adaptive capacity; environmental-change; south-africa; vulnerability; variability; resilience; tanzania; framework; drought; policy
Abstract A better understanding of processes that shape farmers’ adaptation to climate change is critical to identify vulnerable entities and to develop well-targeted adaptation policies. However, it is currently poorly understood what determines farmers’ adaptation and how to measure it. In this study, we develop an activity-based adaptation index (AAI) and explore the relationship between socioeconomic variables and farmers’ adaptation behavior by means of an explanatory factor analysis and a multiple linear regression model using latent variables. The model was tested in six villages situated in two administrative wards in the Morogoro region of Tanzania. The Mlali ward represents a system of relatively high agricultural potential, whereas the Gairo ward represents a system of low agricultural potential. A household survey, a rapid rural appraisal and, a stakeholder workshop were used for data collection. The data were analyzed using factor analysis, multiple linear regression, descriptive statistical methods and qualitative content analysis. The empirical results are discussed in the context of theoretical concepts of adaptation and the sustainable livelihood approach. We found that public investment in rural infrastructure, in the availability and technically efficient use of inputs, in a good education system that provides equal chances for women, and in the strengthening of social capital, agricultural extension and, microcredit services are the best means of improving the adaptation of the farmers from the six villages in Gairo and Mlali. We conclude that the newly developed AAI is a simple but promising way to capture the complexity of adaptation processes that addresses a number of shortcomings of previous index studies.
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ISSN 0959-3780 ISBN Medium Article
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Notes TradeM Approved no
Call Number MA @ admin @ Serial 4467
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