Reducing phosphorus losses from agricultural land to surface water.

Phosphorus (P) enrichment of water impairs its quality by stimulating algal growth and eutrophication, affecting an estimated 1.7 billion people. Remediation costs are substantial, estimated at $1 billion annually in Europe and $2.4 billion in the USA. Agricultural intensification over the past 50 years has increased P use brought into the system from mined fertiliser sources. This has enriched soil P concentrations and loss to surface waters via pathways such as surface runoff and subsurface flow, which are influenced by precipitation, slope, and farming practices. Effective mitigation of losses involves managing P sources, mobilisation, and transport/delivery mechanisms. The cost-effectiveness of mitigation actions can be improved if they are targeted to critical source areas (CSAs), which are small zones that disproportionately contribute to P loss. While targeting CSAs works well in areas with variable topography, flatter landscapes require managing legacy sources, such as enriched soil P to prevent P losses.

Difficulties in using land use pressure and soil quality indicators to predict water quality.

Intensive agriculture can impair river water quality. Soil quality monitoring has been used to measure the effect of land use intensification on water quality at a point and field scales but not at the catchment scale. Other farm scale land use pressures, like stocking rate and the value of land, which relate to land use intensity are now publicly available, nationally. We therefore tested whether point scale soil quality measures, together with newly available farm scale land use pressures (land valuation and stocking rate) and existing catchment and climatic characteristics could help predict the behaviour of water quality data across 192 catchments in New Zealand. We used a generalised additive model to make predictions of the change in nitrogen fractions (r2 = 0.65-0.71), phosphorus fractions (r2 = 0.51-0.70), clarity and turbidity (r2 = 0.42-0.46), and E. coli (r2 = 0.35) over 15 years. The state and trend of water quality was strongly related to a refined farm scale land use classification, and to catchment and climatic characteristics (e.g. slope, elevation, and rainfall). Relationships with point scale soil quality measures and the land use pressures were weak. The weak relationship with land use pressures may be caused by using a single snapshot in time (2022), which cannot account for lag times in water quality response but leaves room for additional temporal data to improve predictive power. The weak relationship to soil quality measures was probably caused by limited data points (n = 667 sites) that were unrepresentative of land use, and areas of catchment subject to processes like runoff or leaching. While national soil quality measures might be useful for evaluating environmental risk at the field or farm scale, without a large increase in sampling, they were not relevant at the catchment scale. Additional analyses should be performed to determine how many samples would be needed to detect a change using an environmentally focused soil test that can guide water quality management.

A global dataset on phosphorus in agricultural soils.

Numerous drivers such as farming practices, erosion, land-use change, and soil biogeochemical background, determine the global spatial distribution of phosphorus (P) in agricultural soils. Here, we revised an approach published earlier (called here GPASOIL-v0), in which several global datasets describing these drivers were combined with a process model for soil P dynamics to reconstruct the past and current distribution of P in cropland and grassland soils. The objective of the present update, called GPASOIL-v1, is to incorporate recent advances in process understanding about soil inorganic P dynamics, in datasets to describe the different drivers, and in regional soil P measurements for benchmarking. We trace the impact of the update on the reconstructed soil P. After the update we estimate a global averaged inorganic labile P of 187 kgP ha-1 for cropland and 91 kgP ha-1 for grassland in 2018 for the top 0-0.3 m soil layer, but these values are sensitive to the mineralization rates chosen for the organic P pools. Uncertainty in the driver estimates lead to coefficients of variation of 0.22 and 0.54 for cropland and grassland, respectively. This work makes the methods for simulating the agricultural soil P maps more transparent and reproducible than previous estimates, and increases the confidence in the new estimates, while the evaluation against regional dataset still suggests rooms for further improvement.