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Report about the newest agricultural returns from inside the GTEM-C

Report about the newest agricultural returns from inside the GTEM-C
In order to quantify brand new architectural alterations in the latest farming trade circle, we establish a directory according to research by the matchmaking between posting and you can exporting places given that seized within their covariance matrix

The modern version of GTEM-C uses the latest GTAP nine.1 databases. I disaggregate the country into the fourteen independent monetary nations coupled from the agricultural exchange. Countries from highest financial size and collection of organization formations are modelled by themselves within the GTEM-C, additionally the rest of the world is actually aggregated towards countries according so you can geographical distance and you will climate resemblance. For the GTEM-C for each and every part provides a real estate agent family. The latest fourteen places utilized in this study was: Brazil (BR); Asia (CN); East China (EA); European countries (EU); India (IN); Latin America (LA); Middle eastern countries and you will Northern Africa (ME); United states (NA); Oceania (OC); Russia and you may neighbor places (RU); Southern Asia (SA); South-east China (SE); Sub-Saharan Africa (SS) and also the Us (US) (Look for Second Recommendations Dining table A2). The area aggregation utilized in this study greeting me to focus on over two hundred simulations (the combos away from GGCMs, ESMs and you may RCPs), utilising the high performing calculating business at the CSIRO within a beneficial few days. A greater disaggregation would-have-been as well computationally pricey. Here, we concentrate on the trading of four significant plants: wheat, grain, rough grain, and oilseeds you to definitely make-up on the 60% of the people calories (Zhao et al., 2017); not, new databases found in GTEM-C is the reason 57 merchandise that we aggregated into sixteen sectors (Get a hold of Supplementary Recommendations Table A3).

The RCP8.5 emission scenario was used to calibrate GTEM-C’s business as usual case, as current CO2 emissions are tracking above RCP8.5 levels. A carbon price was endogenously calculated to force the model to match the lower RCP4.5 emissions trajectory. This ensured internal consistency between emissions scenarios and energy production (Cai and Arora, 2015). Climate change affects agricultural productivity, which leads to variations in agricultural outputs. Given the global demand for agricultural commodities, the market adjusts to balance the supply and demand for these commodities. This is achieved within GTEM-C by internal variations in prices of agricultural products, which determine the position and competitiveness of each region’s agricultural sector within the global market, thus shaping the patterns of global agricultural trade.

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We use the AgMIP (Rosenzweig et al., 2014; Elliott et al., 2015) dataset to modify agricultural productivities in GTEM-C. The AgMIP database comprises simulations of projected agricultural production based on a combination of GGCM, ESMs and emission scenarios. Here we perturb GTEM-C agricultural production of coarse grains, oilseeds, rice and wheat (the full list of sector modelled in GTEM-C can be seen in Supplementary Information Table A3). The crop yield projections for these four commodities were obtained from seven AgMIP GGCMs accessed in ( EPIC, GEPIC, pDSSAT, LPJml, LPJ-GUESS, IMAGE-LEITAP and PEGASUS. The crop yield projections of the selected commodities are based on five ESMs: HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, GFDL-ESM2M and NorESM1-M (see Table 1 in Villoria et al., 2016). Our scenarios are based on two RCP trajectories, 4.5 and 8.5 and the very optimistic carbon mitigation scenario, RCP2.6 (van Vuuren et al., 2011) was not included in our study for two reasons: first, the AgMIP database contains a limited number of simulations for the four analysed commodities for RCP2.6 compare to RCPs 4.5 and 8.5. Second, it would be necessary to include into GTEM-C a negative carbon emissions technology in order to achieve the first Shared Socio-economic Pathway that corresponds to the RCP2.6’s CO2 emissions trajectory.

Analytical characterisation of one’s exchange community

We represent the spectrum of the eigenvalues of this covariance matrix as the elements, sij of a diagonal 14 ? 14 matrix, where we have modelled 14 importing and exporting regions in our simulations. It is natural to interpret a rapidly converging spectrum as indicative of a trade network dominated by just a few importers and exporters while a flat spectrum of eigenvalues implies a network with many more equal actors. We capture this difference by the Shannon entropy of the eigenvalue spectrum and define the structural trade index as S. A smaller value of S represents a centralised network structure, where export/import flows are dominated by just few regions; larger values of S indicate a more distributed trading structure, where export/import flows are more uniformly distributed between all regions.

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