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Agricultural pesticide land budget and river discharge to oceans


Pesticides are ubiquitous environmental pollutants negatively affecting ecosystem and human health1,2. About 3 Tg of pesticides are used annually in agriculture to protect crops3. How much of these pesticides remain on land and reach the aquifer or the ocean is uncertain. Monitoring their environmental fate is challenging, and a detailed picture of their mobility in time and space is largely missing4. Here, we develop a process-based model accounting for the hydrology and biogeochemistry of the 92 most used agricultural pesticide active substances to assess their pathways through the principal catchments of the world and draw a near-present picture of the global land and river budgets, including discharge to oceans. Of the 0.94 Tg net annual pesticide input in 2015 used in this study, 82% is biologically degraded, 10% remains as residue in soil and 7.2% leaches below the root zone. Rivers receive 0.73 Gg of pesticides from their drainage at a rate of 10 to more than 100 kg yr−1 km−1. By contrast to their fate in soil, only 1.1% of pesticides entering rivers are degraded along streams, exceeding safety levels (concentrations >1 μg l1) in more than 13,000 km of river length, with 0.71 Gg of pesticide active ingredients released to oceans every year. Herbicides represent the prevalent pesticide residue on both land (72%) and river outlets (62%).

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Fig. 1: Pesticide budget in the catchments and rivers.
Fig. 2: Pesticide mobility within catchments and discharge to oceans.

Data availability

We have included the land and river budgets of individual PAS as Supplementary Data. All material is available in the Figshare repository ( along with details available in the technical documentation. Any specific additional data used to conduct analyses and build figures in this work can be requested from the authors. Source data are provided with this paper.

Code availability

The BRTSim v.5.0b model is publicly accessible at, with User Manual and Technical Guide, samples and basic scripts in @Matlab for model outputs and figure compilation. Codes used to preprocess inputs and postprocess outputs are not distributed because they are associated to a highly sparce data structure and computation workflow, which is dependent on the workstation operating system and the developers’ design of data management and manipulation comprising about 3.4 terabytes of data in compressed format. For simplicity, we distribute the model for one grid cell as an example and scripts that allow to compile simple figures. However, we have compiled selected outputs in NetCDF files to distribute the 30-yr 5-d time sequences of: total pesticide application rate (; residue in the RZ (; direct runoff generation and overland routing (; pesticide concentration ( and discharge ( in river reaches; and an animation in .MP4 format of the above quantities. Additional specific data layers may be requested from the authors.


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We thank A. Porporato for advice on the hydrological modelling and general overview of the manuscript structure and writing and A. McBratney for advice on soil quality and general editing of the manuscript. The authors acknowledge the use of the National Computational Infrastructure which is supported by the Australian Government and accessed through the Sydney Informatics Hub HPC Allocation Scheme, which in turn is supported by the Office of the Deputy Vice-Chancellor (Research) and through the NCMAS Allocation Scheme granted to F.M. (NCMAS-2021-78). The views expressed in this publication are those of the author(s) and do not necessarily reflect the views or policies of the FAO.

Author information

Authors and Affiliations



F.M. conceptualized the main research subject and developed the modelling methods. F.H.M.T. curated all datasets for pre- and postmodelling analyses. F.N.T. provided contextual information on several aspects of datasets used for modelling and analyses. F.M., F.H.M.T. and F.N.T. equally contributed to data interpretation and writing of the manuscript.

Corresponding author

Correspondence to Federico Maggi.

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The authors declare no competing interests.

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Nature thanks Stefan Reichenberger, Matthias Gassmann and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Schematics of the assessment framework.

Process accounting in: (a) direct runoff and overland flow, (b) land surface; (c) belowground; and (d) river reaches.

Extended Data Fig. 2 Mass balance at the catchment land.

(a) Time sequence of PAS budget at the catchment surface and root zone (RZ, 0 to 100 cm) expressing losses (blue) and residue (red) as a percent of the cumulative PAS input mass, with an annual input of 0.94 Tg. (b) Contributions to losses from the catchment surface and RZ. (c) Contributions to residue in RZ. Percentages in parentheses refer to the end of the 30-year simulation, while the percent variability is expressed as one standard deviation (68% CI) across all PAS (see tables of the land and river budges of individual PAS distributed in ”Data and code availability”). The light grey shaded area represents the time frame of near-steady state where the relative contributions have an annual rate of change smaller than 5% relative to the previous year.

Source data

Extended Data Fig. 3 Mass balance within rivers.

Time sequence of PAS budget in rivers expressing losses (blue) and residue (red) as a percent of the cumulative PAS yield from land into rivers at an average annual mass of 0.73 Gg. Percentages in parentheses refer to the end of the 30-year simulation, while the percent variability is expressed as one standard deviation (68% CI) across all PAS (see tables of the land and river budges of individual PAS distributed in ”Data and code availability”). The grey shaded area represents the time frame of near-steady state where all represented variables have an annual rate of change smaller than 5% relative to the previous year.

Source data

Extended Data Fig. 4 Pesticide land budget within catchments.

Geographic distribution of: (a) to (d) contributions of PAS losses from land; and (e) to (h) contributions of PAS residue in the root zone (0 to 100 cm). All contributions are expressed as percent of the 30-year cumulative PAS input mass at an annual rate of 0.94 Tg.

Source data

Extended Data Fig. 5 Pesticide concentration in rivers.

Geographic location of monitoring stations of PAS concentrations in rivers in (a) Canada and the USA, (b) the EU, and (c) Australia. Locations may represent multiple stations when in close proximity (see Supplementary Table S4 for station aggregation and station-specific data density). (d) modelled versus observed average concentrations of PAS at locations in the USA, Canada, the EU, and Australia. Dark coloured markers above the detection concentration highlight observations exceeding the Regulatory Threshold Limits (RTLs) in Schulz at et., 2021 (reported in Supplementary Table S1).

Source data

Extended Data Fig. 6 PAS and Regulatory Threshold Limits (RTL).

River reaches in which the estimated concentration of individual PAS exceeded RTL values at least once during the simulated 30 years. The RTLs for aquatic invertebrates, aquatic plants, and fish from Schulz at et., (2021) are reported in Supplementary Table S1.

Source data

Extended Data Table 1 Pesticide land budget in the catchments of North America
Extended Data Table 2 PAS concentration across multiple rivers and countries
Extended Data Table 3 Analysis of principal and confounding contributions to PAS residue in soil (RES) and discharge to oceans (DIS)

Supplementary information

Source data

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Maggi, F., Tang, F.H.M. & Tubiello, F.N. Agricultural pesticide land budget and river discharge to oceans. Nature 620, 1013–1017 (2023).

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