Stefan Dekker

Land-use change is a major driver of biodiversity loss and a key contributor to GHG emissions, making sustainable land use essential for biodiversity preservation and climate change mitigation. The impacts of land use change are location-specific, shaped by the biophysical context. Consequently, the extent and nature of these impacts are deeply influenced by the spatial configuration of land-use change. This is particularly relevant for Brazil, a global agricultural powerhouse, where agricultural expansion impacts biodiversity-rich and carbon-rich biomes. Understanding the future land-use trade-offs and synergies between agro-economic development, biodiversity preservation, and climate change mitigation is crucial to support sustainable land use in Brazil. In this study, we quantified these trade-offs and synergies for three SSP-based land-use change scenarios projected for 2050. For each scenario, we assessed the spatial variation in impacts on carbon stocks, mammal distributions, and agricultural revenues. Our results show that the agricultural economy is projected to grow at the expense of biodiversity preservation and climate change mitigation objectives, and vice versa. These trade-offs and synergies result from changes in natural vegetation and agricultural land, driven by shifting demand for agricultural products. In particular, under the SSP3-7.0 scenario, rising agricultural demand between 2015 and 2050 is projected to drive agricultural expansion into natural areas, increasing annual agricultural revenue by 36.5 billion USD2015 but reducing carbon stock by 4.5 Gt and mammal distribution areas by 3.4%. In contrast, the SSP1-1.9 scenario projects a decline in agricultural demand over the same period, driving the conversion of agricultural land to natural vegetation. This shift increases carbon stocks by 5.6 Gt and expands mammal distribution areas by 6.8%, although it would lower annual agricultural revenue by 33.4 billion USD2015. Our findings further highlight opportunities to reduce trade-offs by containing agriculture outside biodiversity-rich and carbon-rich biomes, in combination with strategic restoration of these regions.

The export of agrochemicals and their transformation products (TPs) following their application in the agricultural fields poses a threat to water quality. Future changes in climatic conditions (e.g. extreme weather events such as heavy rainfall or extended dry periods) could alter the degradation and mobility of agrochemicals. In this research, we use an integrated modelling framework to understand the impact of extreme climate events on the fate and transport of the agrochemical S-Metolachlor and two of its TPs (M-OXA, Metolachlor Oxanilic Acid and M-ESA, Metolachlor Ethyl Sulfonic Acid). This is done by coupling climate model outputs to the Zin-AgriTra agrochemical reactive transport model in four simulation scenarios. 1) Reference (2015–2018), 2) Very dry (2038–2041), 3) Very wet (2054–2057) and 4) High temperature (2096–2099) conditions of a selected RCP8.5 based regional climate scenario. The modelling framework is tested on an agricultural catchment, Wulka, in Burgenland, Austria. The model results indicate that 13–14 % of applied S-Metolachlor is retained in the soil, and around 85 % is degraded into TPs in the different scenarios. In very dry and high-temperature scenarios, degradation is higher, and hence, there is less S-Metolachlor in the soil. However, a large share of formed M-OXA and M-ESA are retained in the soil, which is transported via overland and groundwater flow, leading to a build-up effect in M-OXA and M-ESA river concentrations over the years. Though a small share of S-Metolachlor and TPs are transported to rivers, their river export is affected by the intensity and amount of rainfall. The very wet and high-temperature scenarios show higher S-Metolachlor and TP concentrations at the catchment outlet due to higher river discharge. The reference scenario shows higher river peak concentrations associated with higher overland flow caused by measured hourly rainfall compared to disaggregated daily precipitation data in the other scenarios.