Agricultural nutrient loading under alternative climate, societal and manure recycling scenarios

Sci Total Environ. 2021 Aug 20:783:146871. doi: 10.1016/j.scitotenv.2021.146871. Epub 2021 Mar 31.

Abstract

This paper introduces a framework for extending global climate and socioeconomic scenarios in order to study agricultural nutrient pollution on an individual catchment scale. Our framework builds on and extends Representative Concentration Pathways (RCPs) and Shared Socioeconomic Pathways (SSPs) at the spatial and temporal scales that are relevant for the drivers of animal husbandry, manure recycling and the application of inorganic fertilisers in crop production. Our case study area is the Aura river catchment in South-West Finland, which discharges into the heavily eutrophic Baltic Sea. The Aura river catchment has intensive agriculture - both livestock and crop production. Locally adjusted and interpreted climate and socioeconomic scenarios were used as inputs to a field-level economic optimisation in order to study how farmers might react to the changing markets and climate conditions under different SSPs. The results on economically optimal fertilisation levels were then used as inputs to the spatially and temporally explicit nutrient loading model (VEMALA). Alternative manure recycling strategies that matched with SSP narratives were studied as means to reduce the phosphorus (P) overfertilisation in areas with high livestock density. According to our simulations, on average the P loads increased by 18% during 2071-2100 from the current level and the variation in P loads between scenarios was large (from -14% to +50%). By contrast, the nitrogen (N) loads had decreased on average by -9% (with variation from -20% to +3%) by the end of the current century. Phosphorus loading was most sensitive to manure recycling strategies and the speed of climate change. Nitrogen loading was less sensitive to changes in climate and socioeconomic drivers.

Keywords: Agriculture; Climate change; Manure recycling; Nutrient loading modelling; Optimal fertilisation; VEMALA.