| Records |
Author  |
Andreoli, V.; Cassardo, C.; Iacona, L.T.; Spanna, F. |
| Title |
Description and Preliminary Simulations with the Italian Vineyard Integrated Numerical Model for Estimating Physiological Values (IVINE) |
Type |
Journal Article |
| Year |
2019 |
Publication |
Agronomy |
Abbreviated Journal |
Agronomy |
| Volume |
9 |
Issue |
2 |
Pages |
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| Keywords |
viticulture; crop model; phenology; physiological processes; climate; micrometeorology; microclimate; climate change; water status; balance model; crop; phenology; growth; STICS; implementation; carbon; yield |
| Abstract |
The numerical crop growth model Italian Vineyard Integrated Numerical model for Estimating physiological values (IVINE) was developed in order to evaluate environmental forcing effects on vine growth. The IVINE model simulates vine growth processes with parameterizations, allowing the understanding of plant conditions at a vineyard scale. It requires a set of meteorology data and soil water status as boundary conditions. The primary model outputs are main phenological stages, leaf development, yield, and sugar concentration. The model requires setting some variety information depending on the cultivar: At present, IVINE is optimized for Vitis vinifera L. Nebbiolo, a variety grown mostly in the Piedmont region (northwestern Italy). In order to evaluate the model accuracy, IVINE was validated using experimental observations gathered in Piedmontese vineyards, showing performances similar or slightly better than those of other widely used crop models. The results of a sensitivity analysis performed to highlight the effects of the variations of air temperature and soil water potential input variables on IVINE outputs showed that most phenological stages anticipated with increasing temperatures, while berry sugar content saturated at about 25.5 °Bx. Long-term (60 years, in the period 1950–2009) simulations performed over a Piedmontese subregion showed statistically significant variations of most IVINE output variables, with larger time trend slopes referring to the most recent 30-year period (1980–2009), thus confirming that ongoing climate change started influencing Piedmontese vineyards in 1980. |
| Address |
2019-02-21 |
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2073-4395 |
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article |
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| Notes |
CropM, ft_macsur |
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no |
| Call Number |
MA @ admin @ |
Serial |
5218 |
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| Author |
Bassu, S.; Brisson, N.; Durand, J.-L.; Boote, K.; Lizaso, J.; Jones, J.W.; Rosenzweig, C.; Ruane, A.C.; Adam, M.; Baron, C.; Basso, B.; Biernath, C.; Boogaard, H.; Conijn, S.; Corbeels, M.; Deryng, D.; De Sanctis, G.; Gayler, S.; Grassini, P.; Hatfield, J.; Hoek, S.; Izaurralde, C.; Jongschaap, R.; Kemanian, A.R.; Kersebaum, K.C.; Kim, S.-H.; Kumar, N.S.; Makowski, D.; Müller, C.; Nendel, C.; Priesack, E.; Pravia, M.V.; Sau, F.; Shcherbak, I.; Tao, F.; Teixeira, E.; Timlin, D.; Waha, K. |
| Title |
How do various maize crop models vary in their responses to climate change factors |
Type |
Journal Article |
| Year |
2014 |
Publication |
Global Change Biology |
Abbreviated Journal |
Glob. Chang. Biol. |
| Volume |
20 |
Issue |
7 |
Pages |
2301-2320 |
| Keywords |
Carbon Dioxide/metabolism; *Climate Change; Crops, Agricultural/growth & development/metabolism; Geography; Models, Biological; Temperature; Water/*metabolism; Zea mays/*growth & development/*metabolism; AgMIP; [Co2]; climate; maize; model intercomparison; simulation; uncertainty |
| Abstract |
Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 μmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information. |
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English |
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1354-1013 |
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CropM |
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no |
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MA @ admin @ |
Serial |
4510 |
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| Author |
Bennetzen, E.H.; Smith, P.; Porter, J.R. |
| Title |
Decoupling of greenhouse gas emissions from global agricultural production: 1970-2050 |
Type |
Journal Article |
| Year |
2016 |
Publication |
Global Change Biology |
Abbreviated Journal |
Glob. Chang. Biol. |
| Volume |
22 |
Issue |
2 |
Pages |
763-781 |
| Keywords |
climate change; energy use; global agriculture; greenhouse gas emissions; land use; mitigation; sustainable intensification |
| Abstract |
Since 1970 global agricultural production has more than doubled; contributing ~1/4 of total anthropogenic greenhouse gas (GHG) burden in 2010. Food production must increase to feed our growing demands, but to address climate change, GHG emissions must decrease. Using an identity approach, we estimate and analyse past trends in GHG emission intensities from global agricultural production and land-use change and project potential future emissions. The novel Kaya-Porter identity framework deconstructs the entity of emissions from a mix of multiple sources of GHGs into attributable elements allowing not only a combined analysis of the total level of all emissions jointly with emissions per unit area and emissions per unit product. It also allows us to examine how a change in emissions from a given source contributes to the change in total emissions over time. We show that agricultural production and GHGs have been steadily decoupled over recent decades. Emissions peaked in 1991 at ~12 Pg CO2 -eq. yr(-1) and have not exceeded this since. Since 1970 GHG emissions per unit product have declined by 39% and 44% for crop- and livestock-production, respectively. Except for the energy-use component of farming, emissions from all sources have increased less than agricultural production. Our projected business-as-usual range suggests that emissions may be further decoupled by 20-55% giving absolute agricultural emissions of 8.2-14.5 Pg CO2 -eq. yr(-1) by 2050, significantly lower than many previous estimates that do not allow for decoupling. Beyond this, several additional costcompetitive mitigation measures could reduce emissions further. However, agricultural GHG emissions can only be reduced to a certain level and a simultaneous focus on other parts of the food-system is necessary to increase food security whilst reducing emissions. The identity approach presented here could be used as a methodological framework for more holistic food systems analysis. |
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English |
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1354-1013 |
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CropM, ftnotmacsur |
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no |
| Call Number |
MA @ admin @ |
Serial |
4706 |
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| Author |
Bennetzen, E.H.; Smith, P.; Porter, J.R. |
| Title |
Agricultural production and greenhouse gas emissions from world regions—The major trends over 40 years |
Type |
Journal Article |
| Year |
2016 |
Publication |
Global Environmental Change |
Abbreviated Journal |
Glob. Environ. Change |
| Volume |
37 |
Issue |
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Pages |
43-55 |
| Keywords |
Agriculture; Greenhouse gas intensity; Climate change; Kaya-Porter; identity; Decoupling emissions; Kaya-identity; land-use change; carbon-dioxide emissions; sustainable intensification; livestock production; forest transitions; global agriculture; crop; production; food security; deforestation; mitigation |
| Abstract |
Since 1970, global agricultural production has more than doubled with agriculture and land-use change now responsible for similar to 1/4 of greenhouse gas emissions from human activities. Yet, while greenhouse gas (GHG) emissions per unit of agricultural product have been reduced at a global level, trends in world regions have been quantified less thoroughly. The KPI (Kaya-Porter Identity) is a novel framework for analysing trends in agricultural production and land-use change and related GHG emissions. We apply this to assess trends and differences in nine world regions over the period 1970-2007. We use a deconstructed analysis of emissions from the mix of multiple sources, and show how each is changing in terms of absolute emissions on a per area and per produced unit basis, and how the change of emissions from each source contributes to the change in total emissions over time. The doubling of global agricultural production has mainly been delivered by developing and transitional countries, and this has been mirrored by increased GHG emissions. The decoupling of emissions from production shows vast regional differences. Our estimates show that emissions per unit crop (as kg CO2-equivalents per Giga Joule crop product), in Oceania, have been reduced by 94% from 1093 to 69; in Central & South America by 57% from 849 to 362; in sub-Saharan Africa by 27% from 421 to 309, and in Europe by 56% from 86 to 38. Emissions per unit livestock (as kg CO2-eq. GJ(-1) livestock product) have reduced; in sub-Saharan Africa by 24% from 6001 to 4580; in Central & South America by 61% from 3742 to 1448; in Central & Eastern Asia by 82% from 3,205 to 591, and; in North America by 28% from 878 to 632. In general, intensive and industrialised systems show the lowest emissions per unit of agricultural production. (C) 2016 Elsevier Ltd. All rights reserved. |
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0959-3780 |
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CropM, ftnotmacsur |
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no |
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MA @ admin @ |
Serial |
4758 |
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| Author |
Bindi, M.; Palosuo, T.; Trnka, M.; Semenov, M.A. |
| Title |
Modelling climate change impacts on crop production for food security INTRODUCTION |
Type |
Journal Article |
| Year |
2015 |
Publication |
Climate Research |
Abbreviated Journal |
Clim. Res. |
| Volume |
65 |
Issue |
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Pages |
3-5 |
| Keywords |
Crop production; Climate change impact and adaptation assessments; Upscaling; Model ensembles |
| Abstract |
Process-based crop models that synthesise the latest scientific understanding of biophysical processes are currently the primary scientific tools available to assess potential impacts of climate change on crop production. Important obstacles are still present, however, and must be overcome for improving crop modelling application in integrated assessments of risk, of sustainability and of crop-production resilience in the face of climate change (e.g. uncertainty analysis, model integration, etc.). The research networks MACSUR and AGMIP organised the CropM International Symposium and Workshop in Oslo, on 10-12 February 2014, and present this CR Special, discussing the state-of-the-art-as well as future perspectives-of crop modelling applications in climate change risk assessment, including the challenges of integrated assessments for the agricultural sector. |
| Address |
2016-10-31 |
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English |
Summary Language |
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0936-577x |
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Editorial Material |
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CropM, ftnotmacsur |
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no |
| Call Number |
MA @ admin @ |
Serial |
4785 |
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