the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 Mar 2026
Balancing nitrogen use efficiency, losses and soil nitrogen depletion to evaluate agri-environmental performance across spatial scales over 40 years
Abstract. Nitrogen (N) is essential for agricultural productivity, but excessive N inputs result in substantial losses to the environment. Conducting N assessments at national scales is challenging because observational data are limited, especially over long time periods. Here we compiled detailed datasets and performed high-resolution biogeochemical modelling to quantify N budgets for Switzerland's diverse agricultural ecosystems over four decades. Between the 1980s and the 2010s, N use efficiency improved from 47 % to 57 % in croplands and from 63 % to 71 % in grasslands, while losses through leaching and gas emissions decreased by 24 % in croplands and 4 % in grasslands. These improvements are closely linked to the implementation of national-scale agri-environmental policies that reduced fertilizer use in the 1990s. However, despite increased efficiency, cropland soils experienced substantial N depletion between 1995 and 2011 (−23 kg N ha-1 yr-1) in croplands. Our results demonstrate that policy reforms have improved agricultural system functioning and reduced losses, but also reveal risks associated with unbalanced soil N, underscoring the need for integrated N management for sustainable agriculture.
- Preprint
(10035 KB) - Metadata XML
-
Supplement
(4626 KB) - BibTeX
- EndNote
Status: open (until 27 Jun 2026)
- RC1: 'Comment on egusphere-2026-1287', Zimeng Wang, 18 May 2026 reply
Data sets
Modelling of nitrogen budgets of Swiss agricultural lands Jize Jiang https://doi.org/10.3929/ethz-c-000788419
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 492 | 299 | 41 | 832 | 77 | 37 | 45 |
- HTML: 492
- PDF: 299
- XML: 41
- Total: 832
- Supplement: 77
- BibTeX: 37
- EndNote: 45
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
This manuscript uses the Swiss agricultural system as a case study and applies the DayCent biogeochemical model to reconstruct nitrogen budgets for croplands and managed grasslands from 1981 to 2020 at a 1 km × 1 km resolution. The study focuses on evaluating N inputs, N outputs, nitrogen use efficiency, N losses, and soil N balance. The core contribution of the manuscript is that it integrates NUE, N losses, and soil N stock dynamics into a single framework for assessing agri-environmental performance. The authors show that Swiss agri-environmental policy reforms improved nitrogen use efficiency and reduced N losses, while also indicating that cropland soil N stocks may have experienced long-term depletion. This perspective has strong policy relevance and methodological significance.
Specific comments
The title of the manuscript is “Balancing nitrogen use efficiency, losses and soil nitrogen depletion to evaluate agri-environmental performance across spatial scales over 40 years”. Overall, the title is accurate. It captures the three core variables of the study: NUE, N losses, and soil N depletion, and it also reflects the long-term and spatial dimensions of the work. However, one aspect of the title could be further improved. The phrase “across spatial scales” is somewhat broad. The study was actually conducted at the national scale with 1 km × 1 km spatially explicit simulations, rather than comparing multiple spatial scales in a strict sense, such as field, regional, national, or continental scales. Therefore, I suggest that the authors revise the title to make it more precise.
L35: The overall logic of the Introduction is clear, but the background and rationale for the key evaluation indicators could be strengthened. NUE is an important assessment metric in this study, but the Introduction does not provide a sufficiently detailed explanation of this concept. The authors should better justify why NUE was selected as a core indicator for evaluating N balance and N losses.
L15: The term grasslands in this manuscript actually refers to managed meadows, excluding managed pastures and summer pastures. Although this is explained in the Methods and figure legends, for example, “Grasslands in Switzerland are categorised into three major types: 1) meadows, 2) pastures and 3) summer pastures. In this study, we focused on meadows, which are managed for grass production for livestock feed”, the direct use of “grasslands” in the Abstract may lead readers to assume that the study covers all Swiss grasslands. I suggest that the authors define this more clearly when the term first appears in the Abstract.
L93: “Inputs of site-specific soil properties such as soil texture (sand, silt, clay), soil pH and soil organic matter (SOM)”. The main research focus of the manuscript is “Balancing nitrogen use efficiency, losses and soil nitrogen depletion”. However, the model input data do not include any soil N-related indicators, such as TN. If soil N is generated internally by the model, I think the authors should provide a more detailed description of the relevant model processes and initialization.
L95: In the Abstract, the method is described as “high-resolution biogeochemical modelling”. However, in the specific description of Model input data, the 1 km × 1 km high-resolution dataset refers to weather data, while the site-specific soil properties were originally provided at 30 m × 30 m resolution and were then resampled to 1 km × 1 km using the conservative remapping method in Climate Data Operator (CDO). I am not fully convinced about the accuracy of this resampling approach across such a large resolution difference. The authors should provide a more detailed description and justification of this method.
L175: A similar issue applies here. “The total N inputs include organic and synthetic fertilizers, biological N fixation and atmospheric N deposition”. The authors do not clearly describe the source of the N deposition input data. In addition, it is unclear whether the BNF referred to in the manuscript represents ecosystem-level BNF, and whether it includes both plant-associated and soil-based biological nitrogen fixation.
L285: The soil N balance data for grassland in 1981–1990 in Table 2 need to be checked. According to the definition provided in the Methods:
N input=124+57+60+12=253
N output=158+35+24=217
Δsoil N=253−217=36
However, the Δsoil N value reported in the table is 25 kg N ha⁻¹ yr⁻¹. If this is a calculation error, it should be corrected.
L305: In Figures 5 and 6, total N input is expressed in kg N ha⁻¹ yr⁻¹, whereas the other variables are expressed as percentages of total N input. This design is useful, but it may also cause confusion. I suggest that the authors clearly label the units in the figures, for example, “Fertilizer N: % of total N input”. Alternatively, the percentage calculation rules should be explicitly described in the Methods, especially for Δsoil N%.
L435: The Uncertainty section identifies three sources of uncertainty: input data, model parameters and processes, and spatial application of the model. However, the discussion remains mainly qualitative. As this is a model-driven study, I suggest that the authors provide quantitative uncertainty ranges for the key conclusions. In particular, cropland soil N depletion is one of the main findings of this study, and it strongly depends on modelled N loss, especially nitrate leaching. The authors also acknowledge limitations in DayCent’s representation of water movement and NH₃ volatilization, which may affect estimates of leaching and gaseous N losses. For example, the manuscript states that “DayCent lacks a sophisticated scheme for NH3 volatilization, leading to substantially underestimated NH3 fluxes”. I think the authors should add an assessment of uncertainty in N loss and provide uncertainty ranges for soil N balance.
Overall, this manuscript is innovative, particularly in integrating NUE, N losses, and soil N balance into a unified framework for assessing agricultural nitrogen management. The use of high-resolution DayCent simulations to reveal changes in nitrogen cycling following Swiss agri-environmental policy reforms is also valuable. However, the authors need to further strengthen the methodological description and uncertainty analysis, especially regarding soil N depletion. My recommendation is minor revision.