| Records |
| Author |
Sandor, R.; Ehrhardt, F.; Grace, P.; Recous, S.; Smith, P.; Snow, V.; Soussana, J.-F.; Basso, B.; Bhatia, A.; Brilli, L.; Doltra, J.; Dorich, C.D.; Doro, L.; Fitton, N.; Grant, B.; Harrison, M.T.; Kirschbaum, M.U.F.; Klumpp, K.; Laville, P.; Leonard, J.; Martin, R.; Massad, R.-S.; Moore, A.; Myrgiotis, V.; Pattey, E.; Rolinski, S.; Sharp, J.; Skiba, U.; Smith, W.; Wu, L.; Zhang, Q.; Bellocchi, G. |
| Title |
Ensemble modelling of carbon fluxes in grasslands and croplands |
Type |
Journal Article |
| Year |
2020 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
| Volume |
252 |
Issue |
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Pages |
107791 |
| Keywords |
C fluxes; croplands; grasslands; multi-model ensemble; multi-model; median (mmm); soil organic-carbon; greenhouse-gas emissions; climate-change impacts; crop model; data aggregation; use efficiency; n2o emissions; maize; yield; wheat; productivity |
| Abstract |
Croplands and grasslands are agricultural systems that contribute to land–atmosphere exchanges of carbon (C). We evaluated and compared gross primary production (GPP), ecosystem respiration (RECO), net ecosystem exchange (NEE) of CO2, and two derived outputs – C use efficiency (CUE=-NEE/GPP) and C emission intensity (IntC= -NEE/Offtake [grazed or harvested biomass]). The outputs came from 23 models (11 crop-specific, eight grassland-specific, and four models covering both systems) at three cropping sites over several rotations with spring and winter cereals, soybean and rapeseed in Canada, France and India, and two temperate permanent grasslands in France and the United Kingdom. The models were run independently over multi-year simulation periods in five stages (S), either blind with no calibration and initialization data (S1), using historical management and climate for initialization (S2), calibrated against plant data (S3), plant and soil data together (S4), or with the addition of C and N fluxes (S5). Here, we provide a framework to address methodological uncertainties and contextualize results. Most of the models overestimated or underestimated the C fluxes observed during the growing seasons (or the whole years for grasslands), with substantial differences between models. For each simulated variable, changes in the multi-model median (MMM) from S1 to S5 was used as a descriptor of the ensemble performance. Overall, the greatest improvements (MMM approaching the mean of observations) were achieved at S3 or higher calibration stages. For instance, grassland GPP MMM was equal to 1632 g C m−2 yr-1 (S5) while the observed mean was equal to 1763 m-2 yr-1 (average for two sites). Nash-Sutcliffe modelling efficiency coefficients indicated that MMM outperformed individual models in 92.3 % of cases. Our study suggests a cautious use of large-scale, multi-model ensembles to estimate C fluxes in agricultural sites if some site-specific plant and soil observations are available for model calibration. The further development of crop/grassland ensemble modelling will hinge upon the interpretation of results in light of the way models represent the processes underlying C fluxes in complex agricultural systems (grassland and crop rotations including fallow periods). |
| Address |
2020-06-08 |
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LiveM |
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| Call Number |
MA @ admin @ |
Serial |
5230 |
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| Author |
Dono, G.; Cortignani, R.; Doro, L.; Giraldo, L.; Ledda, L.; Pasqui, M.; Roggero, P.P. |
| Title |
An integrated assessment of the impacts of changing climate variability on agricultural productivity and profitability in an irrigated Mediterranean catchment |
Type |
Journal Article |
| Year |
2013 |
Publication |
Water Resource Management |
Abbreviated Journal |
Water Resource Manage. |
| Volume |
27 |
Issue |
10 |
Pages |
3607-3622 |
| Keywords |
discrete stochastic programming; climate change variability; adaptation to climate change; net evapotranspiration and irrigation requirements; water availability; epic crops model; economic impact of climate change; precipitation; uncertainty; region; series; yield; model; scale; wheat; gis |
| Abstract |
Climate change is likely to have a profound effect on many agricultural variables, although the extent of its influence will vary over the course of the annual farm management cycle. Consequently, the effect of different and interconnected physical, technical and economic factors must be modeled in order to estimate the effects of climate change on agricultural productivity. Such modeling commonly makes use of indicators that summarize the among environmental factors that are considered when farmers plan their activities. This study uses net evapotranspiration (ETN), estimated using EPIC, as a proxy index for the physical factors considered by farmers when managing irrigation. Recent trends suggest that the probability distribution function of ETN may continue to change in the near future due to changes in the irrigation needs of crops. Also, water availability may continue to vary due to changes in the rainfall regime. The impacts of the uncertainties related to these changes on costs are evaluated using a Discrete Stochastic Programming model representing an irrigable Mediterranean area where limited water is supplied from a reservoir. In this context, adaptation to climate change can be best supported by improvements to the collective irrigation systems, rather than by measures aimed at individual farms such as those contained within the rural development policy. |
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English |
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0920-4741 |
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TradeM |
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no |
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MA @ admin @ |
Serial |
4487 |
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| Author |
Gutierrez, L.; Piras, F.; Roggero, P.P. |
| Title |
A global vector autoregression model for the analysis of wheat export prices |
Type |
Journal Article |
| Year |
2015 |
Publication |
American Journal of Agricultural Economics |
Abbreviated Journal |
American Journal of Agricultural Economics |
| Volume |
97 |
Issue |
5 |
Pages |
1494-1511 |
| Keywords |
Global dynamic models; price analysis; wheat market; lagged dependent-variables; commodity-markets; error-correction; food-prices; unit-root; regressors; tests; cointegration; dynamics; time |
| Abstract |
Food commodity price fluctuations have an important impact on poverty and food insecurity across the world. Conventional models have not provided a complete picture of recent price spikes in agricultural commodity markets, and there is an urgent need for appropriate policy responses. Perhaps new approaches are needed to better understand international spill-overs, the feedback between the real and the financial sectors, as well as the link between food and energy prices. In this article, we present the results from a new worldwide dynamic model that provides the short and long-run impulse responses of the international wheat price to various real and financial shocks. |
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English |
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0002-9092 1467-8276 |
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TradeM, ft_macsur |
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no |
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MA @ admin @ |
Serial |
4658 |
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