Report about new farming productivity for the GTEM-C

To quantify the fresh new architectural changes in the farming trading network, we set-up a collection based on the matchmaking ranging from uploading and exporting regions since seized in their covariance matrix

The modern variety of GTEM-C spends the newest GTAP nine.step one databases. We disaggregate the nation for the fourteen autonomous financial regions coupled by the agricultural exchange. Nations out-of higher economic proportions and distinctive line of institutional structures was modelled alone from inside the GTEM-C, in addition to remaining portion of the business is aggregated towards the places in respect to help you geographical proximity and climate similarity. Into the GTEM-C for every single part has actually an agent house. Brand new 14 places utilized in this research are: Brazil (BR); Asia (CN); Eastern Asia (EA); European countries (EU); Asia (IN); Latin The united states (LA); Middle eastern countries and you can Northern Africa (ME); The united states (NA); Oceania (OC); Russia and you can neighbour nations (RU); South China (SA); South east Asia (SE); Sub-Saharan Africa (SS) therefore the Us (US) (Select Secondary Recommendations Dining table A2). The local aggregation used in this study greet me to run more than two hundred simulations (the brand new combos away from GGCMs, ESMs and you can RCPs), utilising the powerful computing establishment within CSIRO in approximately a good month. An elevated disaggregation might have been too computationally high priced. Here, i concentrate on the change out-of four biggest harvest: grain, grain, coarse grain, and you will oilseeds that form from the sixty% of human calorie intake (Zhao ainsi que al., 2017); however, the fresh new database used in GTEM-C is the reason 57 merchandise that we aggregated for the 16 groups (Pick Secondary Suggestions Dining 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.

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 gay hookup Worcester corresponds to the RCP2.6’s CO₂ emissions trajectory.

Analytical characterisation of trading system

We represent the spectrum of the eigenvalues of this covariance matrix as the elements, s_ij 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.