| Records |
| Author |
Köchy, M.; Banse, M. |
Title  |
Food security — is climate important at all? |
Type |
Conference Article |
| Year |
2013 |
Publication |
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| Volume |
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Issue |
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Pages |
165-172 |
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Conference |
Impacts World 2013, International Conference on Climate Change Effects, Potsdam, Germany, 2013-05-27 to 2013-05-30 |
| Notes |
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Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
2554 |
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| Author |
Müller, C.; Elliott, J.; Levermann, A. |
| Title |
Food security: Fertilizing hidden hunger |
Type |
Journal Article |
| Year |
2014 |
Publication |
Nature Climate Change |
Abbreviated Journal |
Nat. Clim. Change |
| Volume |
4 |
Issue |
7 |
Pages |
540-541 |
| Keywords |
elevated CO2; human-nutrition; climate-change; carbon; face |
| Abstract |
Atmospheric CO2 fertilization may go some way to compensating the negative impact of climatic changes on crop yields, but it comes at the expense of a deterioration of the current nutritional value of food. |
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| Language |
English |
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Series Issue |
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Edition |
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| ISSN |
1758-678x 1758-6798 |
ISBN |
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Medium |
Editorial Material |
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Expedition |
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| Notes |
CropM |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4537 |
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| Author |
Murat, M.; Malinowska, I.; Gos, M.; Krzyszczak, J. |
| Title |
Forecasting daily meteorological time series using ARIMA and regression models |
Type |
Journal Article |
| Year |
2018 |
Publication |
International Agrophysics |
Abbreviated Journal |
Int. Agrophys. |
| Volume |
32 |
Issue |
2 |
Pages |
253-264 |
| Keywords |
regression models; forecast; time series; meteorological quantities; Response Surfaces; Extreme Heat; Wheat; Climate |
| Abstract |
The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt-Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts. |
| Address |
2018-06-14 |
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English |
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Series Issue |
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Edition |
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| ISSN |
0236-8722 |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
5202 |
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| Author |
Dietrich, J.P.; Schmitz, C.; Lotze-Campen, H.; Popp, A.; Muller, C. |
| Title |
Forecasting technological change in agriculture-An endogenous implementation in a global, and use model |
Type |
Journal Article |
| Year |
2014 |
Publication |
Technological Forecasting and Social Change |
Abbreviated Journal |
Technological Forecasting and Social Change |
| Volume |
81 |
Issue |
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Pages |
236-249 |
| Keywords |
Technological change; Land use; Agricultural productivity; Land use; intensity; Research and development; land-use; research expenditures; productivity growth; impact; deforestation; forest; yield; Business & Economics; Public Administration |
| Abstract |
Technological change in agriculture plays a decisive role for meeting future demands for agricultural goods. However, up to now, agricultural sector models and models on land use change have used technological change as an exogenous input due to various information and data deficiencies. This paper provides a first attempt towards an endogenous implementation based on a measure of agricultural land use intensity. We relate this measure to empirical data on investments in technological change. Our estimated yield elasticity with respect to research investments is 029 and production costs per area increase linearly with an increasing yield level. Implemented in the global land use model MAgPIE (”Model of Agricultural Production and its Impact on the Environment”) this approach provides estimates of future yield growth. Highest future yield increases are required in Sub-Saharan Africa, the Middle East and South Asia. Our validation with FAO data for the period 1995-2005 indicates that the model behavior is in line with observations. By comparing two scenarios on forest conservation we show that protecting sensitive forest areas in the future is possible but requires substantial investments into technological change. (C) 2013 Elsevier Inc. All rights reserved. |
| Address |
2016-10-31 |
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| Language |
English |
Summary Language |
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Edition |
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| ISSN |
0040-1625 |
ISBN |
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Article |
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| Notes |
CropM |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4789 |
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| Author |
Dietrich, J.P.; Schmitz, C.; Lotze-Campen, H.; Popp, A.; Müller, C. |
| Title |
Forecasting technological change in agriculture—An endogenous implementation in a global land use model |
Type |
Journal Article |
| Year |
2014 |
Publication |
Technological Forecasting and Social Change |
Abbreviated Journal |
Technological Forecasting and Social Change |
| Volume |
81 |
Issue |
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Pages |
236-249 |
| Keywords |
Technological change; Land use; Agricultural productivity; Land use intensity; Research and development; land-use; research expenditures; productivity growth; impact; deforestation; forest; yield; Business & Economics; Public Administration |
| Abstract |
► Endogenous technological change in an economic land use model ► Estimation of yield elasticity with respect to investments in technological change ► Projections of future agricultural productivity rates ► Validation with observed data and historic trends ► Trade-off between required technological change and forest protection objectives Technological change in agriculture plays a decisive role for meeting future demands for agricultural goods. However, up to now, agricultural sector models and models on land use change have used technological change as an exogenous input due to various information and data deficiencies. This paper provides a first attempt towards an endogenous implementation based on a measure of agricultural land use intensity. We relate this measure to empirical data on investments in technological change. Our estimated yield elasticity with respect to research investments is 0.29 and production costs per area increase linearly with an increasing yield level. Implemented in the global land use model MAgPIE (“Model of Agricultural Production and its Impact on the Environment”) this approach provides estimates of future yield growth. Highest future yield increases are required in Sub-Saharan Africa, the Middle East and South Asia. Our validation with FAO data for the period 1995–2005 indicates that the model behavior is in line with observations. By comparing two scenarios on forest conservation we show that protecting sensitive forest areas in the future is possible but requires substantial investments into technological change. |
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English |
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0040-1625 |
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| Notes |
CropM |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4518 |
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