Climate smart agriculture and global food-crop production

2.4 Scenarios 2.4.1 Business as usual scenario. BAU scenario reflects the use of current practices and technologies throughout the 2010–2050 period and assumes that agriculture is developing under climate change conditions. BAU and all the alternative scenarios use the population and income growth assumptions that underly the middle of the road Shared Socioeconomic Path- way 2 (SSP2; [37,38]) from the IPCC Assessment Report 5 (AR5; [15,24,58]). Under the SSP2 narrative, global population will reach over 9 billion people by 2050 at an average annual growth rate of 0.6% and per capita GDP grows at just below 2% per year. The climate models used in both DSSAT and IMPACT under a Representative Concentration Pathway 8.5 (RCP 8.5; [59]) represent two possible future states of the climate. One, the GFDL-ESM2M (GFDL; [60]), is drier and cooler than the other, the HadGEM2-ES (HadGEM; [61]) (Supplementary Figs 4 and 5 and Supplementary Table S5 in S1 File). Given the high uncertainty around the overall effects of carbon fertilization on crop productivity [62,63] we cannot make conclusive statements about how increased concentrations of CO2 in the atmosphere will impact produc- tion (see a detailed treatment of the issue in Supplementary Discussion S5 in S1 File). There- fore, our modeling approach assumes no additional effects on yields from atmospheric carbon. 2.4.2 Climate smart agriculture scenarios. These scenarios were constructed by assum- ing that farmers who are currently using a particular set of practices to grow maize, wheat, and

rice are offered a portfolio of alternatives from which to choose (i.e. the four CSA practices considered). We first explored two scenarios with the objective to shed light on the largest pos- sible effects of adopting CSA practices. The scenarios are based on two basic adoption rules implemented at the grid-cell level. The first (Rule 1) requires that CSA practices generate a yield gain compared to BAU in order to be adopted. The second (Rule 2) requires that an alter- native practice generates higher yields and a decrease in emission intensity (i.e. the quantity of CO2 e per unit of product). If none of the alternatives increases yields, it is assumed that farm- ers retain their current practices. Given our objective, both scenarios assume that when a CSA practice is chosen all farmers in a given area adopt it beginning from the first year (i.e. 100% of the area in which Rule 1 or Rule 2 are satisfied it is assumed to adopt the CSA practices). Admittedly, the chosen adoption rules overestimate the role that yield gains play in farmers’ decisions. We do recognize that there are other benefits that might motivate adoption. How- ever, at this time there are no widely applied financial mechanisms that disincentivize the use of current agricultural practices or that promote CSA [64]. Furthermore, non-monetary bene- fits vary greatly with local conditions and with farmers’ idiosyncrasies which cannot be repre- sented in a global modeling exercise. More importantly, it is unlikely that countries would

support the wide uptake of practices that reduce yields, given the importance that increased productivity holds for food security. We therefore think that, albeit with limitations, the assumption that adoption requires a yield increase is a pragmatic way to move forward in global analysis such as the one we are undertaking. We did however test the possible effects of production costs and other barriers to adoption through two additional scenarios. It is well known that production costs and other factors (e.g. farmers’ access to markets and to credit, the characteristics of a particular technology, the qual- ity of extension services, attitudes towards risk and risk exposure) affect the adoption of new practices and technologies [65–68]. Based on these notions, a third scenario was implemented which uses adoption Rule 1 together with the adoption rates used by Rosegrant et al. [9]. These implicitly include multiple costs related to adoption and were obtained through surveys and interviews with experts. These rates are more realistic than the 100% adoption assumed in the previous scenarios (Supplementary Discussion S6 with Supplementary Table S6 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 6 / 19