Climate smart agriculture and global food-crop production

In order to simulate no-till, the default setting for conventional tillage in DSSAT was removed and a seed planting stick and a deep fertilizer injection were used as planting and fer- tilizer application methods to minimize soil disturbance (more details in Supplementary Dis- cussion 3 in S1 File). Six countries (Argentina, Australia, Brazil, New Zealand, Paraguay, and Uruguay) where no-till has already been widely adopted were excluded from the simulations [51,52] because we assume that the potential for expansion in these six countries is low. In other places, especially North America where no till is already present but not widely adopted, we assume that it is not utilized and therefore our analysis may overestimate the impacts of using this practice. Integrated soil fertility management was implemented by simulating the application of organic amendment, in addition to the inorganic fertilizer applications already defined in the BAU scenario. The site-specific organic manure application quantity was based on Potter et al. (2010; http://www.earthstat.org) [53] and was used monthly during the fallow period (after harvesting–before planting). The rate of inorganic fertilizer application is the same as in the BAU scenario; however, the application scheduling is optimized based on the growth stage of each crop to minimize nitrogen stress during flowering and grain filling. For rice production, enhanced nitrogen use efficiency and alternate wetting and drying (AWD) were simulated. Nitrogen use efficiency was simulated by focusing on enhanced plant

uptake of nitrogen fertilizer under both rainfed and irrigated conditions. This was done by turning on in DSSAT the option for nitrogen fertilizer application based on deep placement of urea supergranules at a depth of 10 cm beneath the surface soil. Simulation of AWD was only applied to irrigated rice and was based on a recent study by the International Fund for Agricul- tural Development-IFPRI Partnership Program that identified alternative agricultural mitiga- tion options for rice production using the DeNitrification-DeComposition model [DNDC; 54]. We employed the same modeling approach of DNDC, which assumes that: i) rice paddy is initially flooded to 10 cm, ii) water level is reduced at rate of -0.5 cm/day to -5cm, and iii) then re-flooded at rate of 0.5 cm/day till to 10 cm. 2.3 Calculation of GHG emissions DSSAT was used to calculate per-hectare GHG emissions which were then multiplied by the harvested areas projected by the IMPACT model to compute total GHG emissions. Specifi- cally, temporal changes in soil carbon stock were simulated in the CENTURY soil organic mat- ter (SOM) module embedded into DSSAT [55]. Direct N2O emissions were simulated by modifying DSSAT’s source codes to model denitrification processes. The modifications ensure that our estimates of direct N2O emissions are comparable to those calculated using the 2006 Intergovernmental Panel on Climate Change (IPCC) emission factors [56], where 1% of N additions from mineral fertilizers, from organic amendments and from crop residues, 1% of N

mineralized from soil organic matter, and ~0.7% of N from residue inputs are converted into N2O emissions. For flooded rice soils, we used the IPCC default emission factor of 0.3% of applied N. Meth- ane (CH4) emissions were calculated by combining DSSAT-simulated rice biomass with IPCC Tier 1 method’s emission coefficients proposed by Yan et al. 2009 [57]. Parameters of the Tier 1 method include baseline emission factor (1.3), scaling factors for continuous flooding (1) and multiple drainage (0.52), simulating effect of rice straw (0.59), and conversion factor of farmyard manure (0.14). These were combined with the simulated outputs of rice yields and straws, days in growing season, soil organic carbon content, and the input data of manure application rate. Finally, all GHG emissions were converted into tons of CO2 e using global warming potential for 100-yr time horizon of each GHG (Supplementary Table S4 in S1 File). PLOS ONE Climate smart agriculture and global food-crop production PLOS ONE | https://doi.org/10.1371/journal.pone.0231764 April 29, 2020 5 / 19