The impact of historical land-use change on the simulated global yield of wheat, maize, and rice

Considering land-use change in dynamic global vegetation models has improved the simulation of vegetation state and carbon cycling since the legacies of past land-use changes persist in soil carbon and nitrogen concentrations for several years. However, the influence of land-use and management history on crop yields is not well explored in large-scale modelling. This study assessed the effect of land-use changes on global crop yield estimates with LPJ-GUESS for wheat, maize and rice and their interaction with climatic and management drivers. A total of 56 global simulations were performed by combining three different factors: (1) Four global land-use setups before the crop simulation: cropland, natural vegetation, conversion of natural vegetation to pasture before conversion to cropland, and a historical land-use change reconstruction; (2) The individual contributions of five drivers, atmospheric CO2, precipitation, radiation, temperature, and fertilisation; (3) Two different climate forcing datasets, CRU-NCEP and AgMERRA to assess the relative size of uncertainty from two different climate models. Yields simulated based on both climate-forcing datasets showed a similar relative response in trends and interannual variability. Simulations with previous land-use of natural vegetation and pastures caused higher soil nitrogen and carbon pools, increasing yields at the beginning of the simulations. Similarly, fertilisation was the main driver impacting the trend in yields and interannual variability. Simulations in which land was assumed to have always been cropland had similar trends to those using the land-use change database. All the land-use setups tended to converge over time to give similar yields, but convergence could take several decades. The main results highlight the critical role of historical land-use in simulating crop yield at subregional to local scales, particularly in locations with low fertilisation. At global to regional scales, the assumption of previous cropland cover simplifies simulations without affecting accuracy significantly.