TY - BOOK

T1 - Uncertainties in Agricultural Impact Assessments of Climate Change

AU - Montesino San Martin, Manuel

PY - 2014

Y1 - 2014

N2 - Future food security will be challenged by the likely increase in demand, changes in consumption patterns and the effects of climate change. Framing food availability requires adequate agricultural production planning. Decision-making can benefit from improved understanding of the uncertainties involved. The aim of the study is to identify and quantify the sources of this uncertainty and explore their interactions and influence on precision and accuracy of agricultural estimates, with emphasis on modeling of wheat. Wheat is the most widely grown cereal worldwide and Europe one of its major producers. Results demonstrated the complex interaction between the level of knowledge and complexity of crop models, the availability of data and the projection targets. Interactions lead us to believe that uncertainty may be more robustly reduced by improved datasets than using more complex models. International model inter-comparison projects (as AgMIP or MACSUR) may represent an ideal framework to conduct a deeper analysis of this issue. Ignorance about future wheat varieties and phenological development features are a significant source of uncertainty in the future agricultural socio-economic context for adaptation to climate change (and a significant aspect for the design of the Representative Agricultural Pathways).

AB - Future food security will be challenged by the likely increase in demand, changes in consumption patterns and the effects of climate change. Framing food availability requires adequate agricultural production planning. Decision-making can benefit from improved understanding of the uncertainties involved. The aim of the study is to identify and quantify the sources of this uncertainty and explore their interactions and influence on precision and accuracy of agricultural estimates, with emphasis on modeling of wheat. Wheat is the most widely grown cereal worldwide and Europe one of its major producers. Results demonstrated the complex interaction between the level of knowledge and complexity of crop models, the availability of data and the projection targets. Interactions lead us to believe that uncertainty may be more robustly reduced by improved datasets than using more complex models. International model inter-comparison projects (as AgMIP or MACSUR) may represent an ideal framework to conduct a deeper analysis of this issue. Ignorance about future wheat varieties and phenological development features are a significant source of uncertainty in the future agricultural socio-economic context for adaptation to climate change (and a significant aspect for the design of the Representative Agricultural Pathways).

UR - https://soeg.kb.dk/permalink/45KBDK_KGL/fbp0ps/alma99122112755605763

M3 - Ph.D. thesis

BT - Uncertainties in Agricultural Impact Assessments of Climate Change

PB - Department of Plant and Environmental Sciences, Faculty of Science, University of Copenhagen

ER -