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Article

Effectiveness of Voluntary Nutrient Management Measures to Reduce Nitrate Leaching on Dairy Farms Using Soil N Surplus as an Indicator

by
J. Verloop
1,*,
C. van den Brink
2 and
J. Gielen
3
1
Agrosystems Research, Wageningen Plant Research, Wageningen University & Research, 6700 AA Wageningen, The Netherlands
2
Province of Drenthe, PB 122, 9400 AC Assen, The Netherlands
3
Countus, PB 10055, 8000 GB Zwolle, The Netherlands
*
Author to whom correspondence should be addressed.
Water 2025, 17(3), 455; https://doi.org/10.3390/w17030455
Submission received: 3 January 2025 / Revised: 31 January 2025 / Accepted: 4 February 2025 / Published: 6 February 2025
(This article belongs to the Topic Human Impact on Groundwater Environment, 2nd Edition)
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Abstract

:
A pilot study with 18 dairy farms in recharge areas of five vulnerable drinking water abstractions in the Dutch province of Overijssel aimed to reduce nitrate leaching risks to the upper meter of groundwater through improved farm management. The pilot employed a voluntary, mutual gain approach, promoting measures that enhanced both nutrient efficiency and groundwater quality. Over the research period (2011–2017), nitrogen surpluses on the soil balance declined significantly from 153 to 96 kg N per ha per year, achieving the target of 100 kg N per ha per year. Despite this decline, average nitrate concentrations in the upper meter of groundwater fluctuated annually, showing no significant reduction in grassland but a noticeable decrease in maize. Economic evaluation showed that relative fodder profitability (RFP) increased over time, suggesting positive financial effects of implemented measures, as acknowledged by participating farmers. However, the adoption of measures perceived as complex or less financially rewarding remained limited, highlighting the challenges of relying solely on voluntary implementation. The absence of farm-specific feedback on nitrate leaching emerged as a critical limitation, emphasizing the need for additional monitoring tools, such as residual soil nitrogen assessments, to provide actionable insights at the farm or field level. These findings underscore the potential for further reducing nitrate leaching through enhanced feedback systems, precise execution of measures, and collaborative efforts integrating farmer expertise and scientific knowledge.

1. Introduction

Nitrogen (N) and phosphorus (P) leaching as well as runoff from agricultural lands into groundwater and surface water remain a persistent problem within the European Union (EU) [1,2]. In the Netherlands, an inherent unbalance between nutrient inputs to farmland and their removal through harvested crops significantly contributes to nutrient leaching [3]. During the last decades of the twentieth century, elevated N and P surpluses were common, particularly on animal production farms [4,5,6].
Initiatives focusing on education and extension, research, and regulation of nutrient management have been implemented since the early 1990s [6,7]. Since the early 1990s, the Netherlands has established upper limits to the application of N and P on farmland [7,8]. For total P, the inputs must align with the withdrawal of harvested crops, while a threshold of 170 kg N per ha according to the EU nitrate directive has been set for animal N. Until 2023, dairy farms comprising at least 80% grassland could apply for a derogation of this limit, allowing them to apply 250 to 230 kg N per ha, depending on the soil type and region of the farm. On farms where manure N and P exceed the permissible limits, excessive manure must be exported from the farm [7,8]. These policy interventions, supported by research, consultancy, and education, have yielded notable improvements in nutrient management on dairy farms and have led to reduced nutrient surpluses and diminished emissions of nutrients into water systems over time [3].
However, concerns persisted regarding the risks of nitrate leaching from dairy farms in vulnerable drinking water abstraction sites. These risks could not adequately be mitigated by national regulations and extension efforts, particularly for drinking water abstraction sites in the vulnerable sandy areas in The Netherlands [9,10]. Consequently, both national and regional governments acted between 1993 and 2000 by implementing land use restrictions within designated groundwater protection zones [11]. Despite these measures, it became evident that the specific groundwater protection policies fell short of adequately shielding groundwater and water abstraction sites. This was especially true in the sandy areas of the Netherlands, where excessive concentrations of nitrates continued to be found in abstracted groundwater [12].
In response, in 2010, national and regional stakeholders collaborated on an action program [13] aimed at mitigating the risks associated with farming on sandy soils for water abstraction sites. This collaborative effort involved engagement from all stakeholders working collectively to design and implement appropriate measures to enhance the protection of drinking water abstractions, aligned with the EU-WFD [14,15]. A key principle of the action program was that its implementation should not lead to additional costs for the agricultural sector [16].
The Province of Overijssel, located in the eastern Netherlands, was the first regional government to develop an action program addressing nitrate leaching in drinking water catchments. A regional risk assessment identified dairy farms in vulnerable groundwater abstraction areas—characterized by sandy unconfined aquifers—as major contributors to groundwater contamination risks [17]. In response, a pilot project was launched in 2011 to encourage voluntary reductions in nitrate leaching among dairy farmers. The initiative followed a mutual gains approach [18], aligning environmental objectives with farmers’ financial interests. To explore this approach, the pilot tested an implementation process following a structured, cyclical framework for farming system development, incorporating goal setting, design, implementation, monitoring, and evaluation, with soil N surplus serving as the major indicator of nitrate leaching.
This paper presents and discusses the outcomes of this approach, evaluating soil N surpluses, economic farm performance, and nitrate leaching to groundwater. The analysis is guided by the following research questions:
  • Do farmers adopt management changes when informed about financially viable measures that reduce nitrate leaching?
  • To what extent do these changes lead to lower soil N surpluses and reduced nitrate concentrations in groundwater?
  • What are the financial implications of improved nutrient management aimed at nitrate leaching reduction?

2. Materials and Methods

2.1. Site Characteristics

The groundwater abstractions in the Province of Overijssel (52.4° N, 6.5° E) are situated in regions with sandy unconfined aquifers of Pleistocene origin. Frequently, clay layers are interspersed within the Pleistocene sand layers, creating a hydrogeological system divided into two or more aquifers [19]. This geological configuration contributes to a heterogeneous regional geohydrological situation, leading to a wide range of travel times of groundwater flowing toward the abstraction wells. Infiltration of rainfall is by far the major contributor to the recharge, with typical recharge rates averaging 300 mm/year. The water company Vitens operates 20 locations in the Province of Overijssel where drinking water is produced from groundwater. Five of these locations were selected for the pilot because of the vulnerability of the abstraction site and threat by agricultural land use: Archemerberg, Herikerberg/Goor, Hoge Hexel, and Wierden, in which Herikerberg/Goor is treated as one pilot because these abstraction sites share one groundwater protection area. Typical travel times are in the order of decades, with a small travel time distribution.

2.2. Participating Pilot Farms

All dairy farmers with farmland in the recharge areas of one of the five abstraction sites were invited to participate in the project. In meetings with farmers, the urge to improve groundwater quality, the project organization, and the effort asked from the participants were addressed. Eighteen of the several hundred invited farmers decided to join the pilot for the entire period from 2011 to 2017.
The characteristics of participating farms are presented in Table 1. Each farm had a minimum of 80% grassland, which is a prerequisite to applying for a derogation of the EU Nitrate directive [20]. All participants received a derogation allowing them to apply 250 kg N per ha from manure, exceeding the standard of 170 kg per ha. Since 2014, the application limit has been restricted to 230 kg N per ha. The mean N excretion (337 kg N per ha) exceeded the amount permissible on farmland in 2014 according to the application limits (230 kg N per ha). Consequently, an average of 107 kg per ha of organic N needed to be exported from the farm. This was accomplished through the export of slurry from the farms, with an equivalent of, on average, 1619 m3, ranging across farms from 561 to 3053 m3. Thus, slurry export occurred on all farms in the pilot.
On grassland, manure was consistently applied by sod injection, typically between early March and the end of August. Furthermore, on 16 out of the 18 farms, organic N and P were excreted by livestock grazing on the pastures. On arable land, manure was applied in April or May, either by injection or broadcast application, followed by immediate incorporation through ploughing. The use of catch crops after maize harvest was standard practice, and some farmers leased parts of their land to arable farmers for the cultivation of potatoes or flower bulbs.

2.3. Farm Development

On each farm, a cyclic prototyping procedure was applied [6,21,22] involving the following steps:
  • Framing the problem and setting targets: The effect of dairy farm management on nitrate leaching was framed by identifying aspects of farm management that are associated with risks of nitrate leaching and indicating maximum thresholds for the soil N surplus.
  • Analysis of the farm performance: Farm performance was assessed based on the gap between current and maximum acceptable soil N surpluses. The farm management was analyzed, with a focus on the cropping plan, the fertilization plan, as well as soil, crop, and grazing management, using the Annual Nutrient Cycle Assessment (ANCA) tool [23,24].
  • Design of alternative farm management: Improved farm design options were explored to reduce N surpluses and to alleviate nitrate leaching by adjusting management practices with high risks for nitrate leaching. The measures that were proposed of which expected benefits exceeded costs resulting in a net expected financial gain. Agreed-upon measures were documented in a farm management plan.
  • Implementation in practice: The agreed-upon measures were implemented by the farmers and monitored by the ANCA.
  • Evaluation: The performance of adjusted farm management was evaluated on a farm and crop scale using the monitoring data retrieved from ANCA and with a focus on the farmer’s experiences with practical implementation, the development of the soil N surplus, and economic effects.
Farm visits were conducted twice a year, with the first visit planned before the growing season to discuss management strategies and planned actions. The second visit occurred at the end of the growing season to evaluate experiences and results. Moreover, two annual meetings were organized in clusters of 6–10 participants to discuss technical issues such as farm nutrient management or grazing management. Each year, an overall project meeting was organized to evaluate the environmental and economic trends of the participating farms and to assess the progress of the entire project.

2.4. Target Setting

The overarching project goal for the drinking water catchments was to reduce the nitrate concentration in the upper meter of the groundwater to 50 mg per L or below [20]. This groundwater target was translated into a maximum acceptable soil N surplus of 100 kg per ha. This specific target was derived from modeled relationships between soil surplus and nitrate leaching [8] as well as empirical research conducted on similar vulnerable sandy soils at the experimental farm De Marke [6] and on commercial farms in the Cows & Opportunities project [25]. Farmers who already maintained N surpluses lower than 100 kg N per ha were challenged to sustain these low levels.
The focus on farm development extended beyond the reduction of the annual soil N surplus; specific activities were considered to mitigate high risks for nitrate leaching. For instance, practices such as grazing cattle in autumn or intensive grazing on small parcels were addressed [26,27].

2.5. Monitoring and Data Collection

2.5.1. Groundwater Quality

The monitoring program was specifically designed to assess the impact of the strategies implemented in the pilot, both at the catchment level and the overall pilot level, rather than to evaluate the performance of individual farms or to dissect the effectiveness of specific measures.
Each year, during the winter period (November to January), the upper meter of groundwater was sampled and analyzed for nitrate concentrations. A stratified monitoring design was applied to extrapolate these measurements to the entire catchment area, accounting for various strata. The sampling procedure followed the framework of the Minerals Policy Monitoring Program [28,29]. In this study, 16 monitoring points per farm, as applied in the Minerals Policy Monitoring Program, were adopted as a starting point. This number was chosen as a practical balance between achieving the required precision and managing available resources. In agricultural areas, stratification was based on soil type (5 classes), groundwater table (3 classes), and land use (2 categories), resulting in 30 unique combinations [30]. In natural areas, stratification was based on soil type (6 classes), groundwater table (8 classes), and nature type (3 categories), yielding 11 distinct combinations with varying areal extent [31]. Sampling points were distributed proportionally to the surface area of each stratum, and their locations were determined through randomized selection. The sampling density was approximately 0.31 per hectare on agricultural lands and 0.03 per hectare in nature areas (Table 2). Monitoring in natural areas within the catchments was conducted exclusively in 2014 as part of the broader monitoring program, due to resource constraints and because trends in natural areas were not the primary focus of the research.
The primary aim of the monitoring program was to observe trends in nitrate concentrations across grassland and arable land. The variability within and among strata was captured in the setup of the monitoring to balance statistical precision with practical feasibility, enabling consistent and representative monitoring over time.
The upper meter of the groundwater, occurring within three meters of the surface, was sampled. The upper 0.3 m of soil was drilled using a 0.10 m in diameter auger. A PVC ground sleeve was inserted into the borehole to prevent contamination of the sample with topsoil. Drilling continued with a 0.07 m auger up to 0.8 m below the groundwater table. Water samples, obtained using a well screen and a suction pump, were directly filtered over a 0.45 pm filter, acidified, and stored at 4 °C prior to chemical analysis [28].
If the phreatic groundwater level exceeded 3 m below the surface, a soil moisture sample was taken following the procedures described in [32]. The upper 0.3 m of soil was drilled with an auger of 0.10 m in diameter. A PVC ground sleeve was placed into the borehole to prevent contamination with topsoil. Drilling continued with an auger of 0.07 m in diameter until 1.5 m depth. Thereafter, the soil layer between 1.5 and 3.0 m was sampled in steps of 0.1 m with the same auger. A clean core was retrieved from each 0.1 m by removing the soil from the flanks and the top and bottom of the core with a knife. Each of the 15 soil cores per borehole was divided over two 800 mL glass containers, which each were placed on a weighing scale.
Glass containers with mixed soil samples were removed from the refrigerator and stored at room temperature in the dark for at least 12 h before processing. Each sample was split into two subsamples of equal weight by filling two centrifuge apparatuses. Centrifugation was performed at 25 °C (according to internal procedure AC-W-016) and after centrifugation, the collected soil moisture of the two subsamples was mixed and filtered with a 0.45-μm polyethersulfone (PES) syringe filter (Dispolab) using a polypropylene (PP) syringe.
The centrifugation process followed the method described by [33], using a Sorval RC6+ centrifuge with a fixed angle (23°) SLA-3000 rotor with six 500-mL positions, entirely made from Delrin (polyoxymethyleen) [34]. The soil sample compartment had an inner diameter of 0.053 m and a length of 0.115 m, and the cup for collecting soil moisture had the same inner diameter and a length of 0.015 m. A Sartorius FT-2-205-58058 filter paper was placed on the perforated base of the soil sample compartment.

2.5.2. Farm Management and Farm Performance

At the start of the project, the initial status (management and structure) of each participating farm was assessed, and its development was monitored annually. Data retrieved from the farms were thoroughly checked for consistency, reliability, and completeness. Following this evaluation, the data from two farms were excluded from the analysis of soil N surpluses and financial results due to insufficient quality. Consequently, these aspects were analyzed for 16 farms instead of the original 18. The ANCA [23] was used to register, analyze, and evaluate farm management practices and to calculate the soil N surplus. To support the evaluation of N surpluses, the pilot results were compared to a benchmark derived from as many representative farms as possible from the Dutch FADN data [35].
The analysis of nutrient management was based on a set of farm and management characteristics, encompassing nutrient inputs, crop yields, livestock parameters, feed composition, farm structure, land use, and nutrient use efficiency. Nutrient inputs were quantified for grassland and maize land, including N and P from organic manure, synthetic fertilizers, grazing, biological N fixation by legumes, and atmospheric deposition. Crop yields were assessed in terms of dry matter production as well as N and P uptake. Livestock parameters included milk production and nutrient excretion by the herd. Feed composition was analyzed with an emphasis on the ratio of crude protein (CP) to net energy for lactation (NEL). Farm structure was characterized by herd size, including young stock, and total agricultural area. Finally, nutrient use efficiency was evaluated across different farm subsystems, including the herd, manure storage, soil, and crop production.

2.6. Fodder Profitability as Indicator of Mutual Gain

To assess the financial profitability of implemented measures, the Forage Profitability (FP) was used as an indicator. The FP is calculated annually for each farm according to the following set of calculation rules:
FP = Milk sales + Meat sales − Feed Costs,
where:
Milk sales (€) = Milk Produced by the farm (kg) × Milk Price (€ per kg)
Meat sales (€) = Σ (animals exported (no.))i × (prize (€ per head)) i − (animals imported (no.))j × (prize ((€ per head))j
i,j = milking cows, young stock < 1 yr, young stock > 1 jr
Forage costs (€) = Σ (Cost of forage ((€ per kg))i × (Forage consumed by herd (kg))i
i = Forage component in the total ration
Actual milk prices depend on the fat and protein content of the milk and regional and global market effects. As these price fluctuations are irrelevant to the assessment of the financial profitability of implemented measures, a standardized valuation of milk delivered was derived from the mean milk price of a regional dairy company over (2016, 2017, and 2018). For farms where cows graze for a minimum of 720 h a year, the standardized milk price was adjusted to account for supplements provided by dairy companies. This adjustment reflects the additional benefits granted to farms meeting the specified grazing criteria. The prices of animals exported are different from those of animals imported. Standard prices per head were used as reported in KWIN [36].
The calculation of forage costs for each farm involves summing the amounts of each component of the ration offered to the herd (kg) and multiplying it by the price of each component per kg. Adjustments are made for some farmers who export young stock to specialized breeders. Since these exported animals do not contribute to forage costs on the farm, the net costs of exporting young stock and subsequently importing them again as heifers are taken into account to correct this potential bias in the calculation.
Data on milk produced (kg), net changes in herd size (no. of heads), consumption of ration components (kg), and grazing hours were retrieved from the ANCA annually for each farm. ANCA also allowed the assessment of the consumption of the various roughage components, i.e., the amount of silage grass offered to the herd in the stable, fresh grass offered in the stable after mowing, the uptake of fresh grass during grazing, and the amount of maize silage. We assumed that the production of forage was balanced with the forage requirements of the herd, i.e., no value was attributed to production exceeding the requirements of the herd. The costs of concentrates and other ration components purchased (€ per kg) were derived from the farm registration, and the costs of roughage components (€ per kg) were based on the standards of KWIN [36]. The FP (€) was then corrected for farm size by dividing it by the area of the farmland dedicated for roughage production (€ per ha). This indicator applies only to farms specializing in milk production.
The FP shows a distinct relationship with production intensity. This relationship was established using data from 4608 farms on sandy soils, obtained from the Centrale Database KringloopWijzer (CDKLW), managed by ZuivelNL [37]. This dataset served as a benchmark for the study and was accessed under specific agreements that restrict public dissemination of the raw data. The Relative Fodder Profitability (RFP) was defined as the difference between the fodder profitability of a pilot farm and a benchmark at an equal production intensity. This allows for a comparison of farms with different production intensities, providing a measure of how well a farm performs in terms of FP compared to the benchmark at an equivalent production intensity (Figure 1). Thus, farm 1 was compared with the benchmark at a production intensity of 15,000 kg milk per ha, i.e., 3443 € per ha, and farm 2 at a production intensity of 20,000 kg milk per ha, i.e., 4000 € per ha, implying that farm 1 performs better in terms of RFP than farm 2, whereas the FP of farm 2 is higher than that of farm 1.
The RFP was calculated annually for each pilot farm from 2012 to 2017, generating a time series for each farm. Linear regression was applied to the RFP values over time, producing 16 regression coefficients for each farm. These regression coefficients were analyzed to determine whether RFP increased during the study period, serving as a basis for evaluating the financial profitability of the implemented measures.
To test the overall trend, we compiled the regression coefficients and conducted a Student’s t-test to determine whether their mean was significantly greater than zero. A positive and statistically significant result would indicate an increase in RFP over time, supporting the hypothesis that the implemented measures positively influenced financial profitability.

3. Results

3.1. Farm Management and Soil N Surplus

The implementation of measures aimed at reducing the N surplus and nitrate leaching has brought about significant changes in farm management practices in the pilot. Table 3 provides an overview of the adoption of measures in the pilot, expressing the extent to which measures were (i) already incorporated (I) in the farms at the project’s commencement, (ii) introduced during the pilot (N), and (iii) elevated to a higher level (O) during the project. The results in Table 3 were corrected to account for situations where certain measures were not applicable on specific farms. For instance, on farms where cattle are not allowed into the fields but are housed all year, the measure of lower grazing intensity is not relevant. Similarly, in case undersowing of Italian Ryegrass in maize is applied, the early harvest of maize directly followed by sowing of catch crops is not relevant.
At the start of the pilot, 19 of the 20 proposed measures were already in place at one or more of the pilot farms, 16 measures were optimized during the pilot, and 17 measures were newly introduced at one or more farms (note: I + O can exceed 1). The average fraction of adopted measures increased from 0.4 to 0.6. Initially, the highest implementation rate was observed for the reduction of the crude protein content in the ration compared to the content of the net energy lactation (CP/NEL), while the supply of manure in the rows where maize seeds are placed had the lowest implementation rate. The latter was considered complex and posed risks related to potential soil compaction due to the use of heavy manure application machinery after seeding.
Rotational grazing of cattle, involving allowing cattle in parcels for grazing for a short period before moving them to a new parcel, was already practiced by many participants by the project’s commencement but underwent optimization on several farms. This also applied to fine-tuning fertilization per parcel to meet crop requirements as precisely as possible.
Figure 2 presents the dynamic trends in the soil N surplus for pilot farms in comparison to FADN farms. The N surplus of both groups of farmers did not differ significantly in 2012 and 2013. However, a notable shift occurred from 2014 onward, with the N surplus on the pilot farms being significantly lower than that of FADN farms (p < 0.05). Throughout the project duration, the N surplus of the pilot farms showed a significant (p < 0.05) decline from 153 to 96 kg/ha, showcasing a decline rate of 11.8 kg N per year (p < 0.004). Over the entire research period, 60% of the pilot farmers exceeded the N surplus of 100 kg/ha. This fraction showed a significant decline from 80% in 2012 and 90% in 2013 to just 10%. It then stabilized at 57% in 2015 and 56% in 2016, followed by a slight decrease to 0.44% by the end of the study. In contrast, the N surplus of FADN farms showed annual fluctuations without a clear trend. The dynamic trends of N surpluses also show some similarities for the two groups of farmers, e.g., relatively low values in 2014 and higher values in 2015. This may be attributed to the effects of annual fluctuations in weather conditions.
Figure 3 provides a more detailed analysis of the trends in soil N surplus, focusing on the main crops on the farms: grassland and maize. A distinct decline over time is observed in the N surplus for grassland (p < 0.05), whereas no clear trend is observed for maize. Additionally, the N surplus in maize is consistently lower than that for grassland throughout the research period.

3.2. Nitrate Concentration Upper Groundwater

Measured Nitrate Concentrations

The nitrate concentrations measured in the nitrate monitoring network are listed in Table 4. The nitrate concentrations in grassland remained relatively stable, with annual fluctuations, including higher values in 2017. In contrast, arable land, particularly maize land, underwent a significant decline over the 5-year period until 2016 (p < 0.001). However, similar to grassland, there was an increase in nitrate concentrations in 2017. The decline in arable land aligns with the focus on measures for arable land, especially maize land. The decline in the overall concentration of nitrate in the agricultural area from 90 mg NO3 per L in 2011 to 75 mg NO3 per L in 2015 can mainly be attributed to the decline in arable land. The measured concentration in natural areas in the province of Overijssel is, on average, 31 mg NO3 per L, with concentrations exceeding 50 mg/L in natural areas with low groundwater tables.

3.3. The Fodder Profitability

The overall average RFP was -281 € per ha, indicating that, on average, the FP of the farms in the project was lower than that of the reference farms. However, during the research period, the mean RFP across all farms increased significantly over time, with an average annual growth of 45 € per ha per year (p < 0.05). Notably, the rate of the annual change varied significantly among individual farms, ranging from −80 € to 211 € per ha per year.

4. Discussion

4.1. Changes in Soil N Surpluses

The results reveal a notable decline in soil N surplus, decreasing from 153 to 96 kg per ha per year. From 2014 to 2017, 57% of the farms reduced the surplus below the target of 100 kg N per ha per year, compared to only 12% in 2012 and 2013. This decline indicates that changes in farm practices were effective in reducing N surpluses on the soil balance. The reduction in soil N surplus was particularly pronounced in grassland, while no substantial change was observed in maize land.
In grassland, the reduced N surplus was primarily driven by a gradual decrease in artificial fertilizer N inputs, from approximately 260 to 225 kg N per ha over the research period, alongside an increase in the N uptake through harvest, which rose from 250 to about 300 kg N per ha. The modification in fertilizer use aligns with strategies promoted within the pilot, encouraging the application of fertilizer N only under conditions that favor high N utilization by the crops. The increased N uptake through mowing and grazing may be attributed to improved grassland management and grazing strategies.
Farm registration data show that the number of measures implemented on maize land was comparable to that on grassland (Table 3). This suggests that the absence of a decline in the soil N surplus in maize is unlikely to result from insufficient efforts to adjust crop management in maize. Over half of the pilot farmers ceased applying both organic and artificial fertilizer N in maize following a grassland phase in crop rotation, relying on the natural release of N from the ploughed grassland sod. Additionally, N fertilization in maize was reduced by adjusting for the fertilization value of N released from catch crops incorporated into the soil. However, two aspects obscured the impact of these adjustments on the soil N surplus. First, some farmers supplied compost on maize land to improve soil resilience to drought and nitrate leaching. Although composts have a low content of directly available N, the N inputs with composts are fully accounted for in the calculation of the soil N balance. Second, efforts to enhance catch crops led to increased N uptake from catch crops grown after the harvest of maize. However, the N absorbed by the catch crops hardly contributes to N uptake by crops registered in ANCA and consequently does not directly influence the soil N surplus.

4.2. Changes in Nitrate Leaching

Despite the decline in the soil N surpluses, the mean nitrate leaching for agricultural areas showed annual fluctuations around a relatively constant level. At the crop level, a reduction in nitrate concentration was observed in arable land, but not in grassland, even though the decline in N surpluses was more pronounced in grassland. The lack of a clear relationship between nitrate concentration and N surplus on a crop scale may be caused by biases in the accounting of N surpluses as discussed before. The discrepancy between the observed decline in N surplus on farms and the lack of a corresponding response in nitrate concentrations warrants further analysis. One possible explanation is a spatial mismatch between the monitoring of soil N surpluses, conducted at the farm scale, and nitrate concentrations, assessed using strata selected within the pilot farms. However, this explanation is inconsistent with the monitoring program’s design, which specifically aimed to evaluate the overall impact of pilot farm development on nitrate leaching. Consequently, alternative explanations need to be explored.
The absence of a response of nitrate on the reduction of soil N surpluses on farms appears to contradict the findings of previous studies [38,39], which identified a distinct relationship between farm N surplus on the soil balance and nitrate concentrations for dairy farms on sandy soil. However, both studies [38,39] acknowledged that this relationship is influenced by additional factors beyond soil N surplus alone. Variability in nitrate leaching can result from multiple factors, including the proportion of the N surplus present as mineral N [38], annual weather variability [40], differences in nitrogen travel time to groundwater, the presence of organic material or reactive minerals (e.g., pyrite) in sandy soils, a weaker leaching response at lower N surplus levels, and the mineralization of previously stored soil nitrogen [41,42].
While this study does not allow for definitive conclusions on which factors most significantly obscure the effects of declining N surplus on nitrate leaching, some insights can be drawn. A comparison of nitrate concentration trends with those observed in the manure policy monitoring program—representative of common farm management practices [3]—reveals no anomalies linked to weather-related factors, such as extreme rainfall or high mineralization rates of stored nitrogen [43]. Several explanations are possible. The decline in nitrate concentrations in maize likely had a limited overall impact due to maize’s relatively modest share of agricultural land. Moreover, most of the N surplus reduction occurred in grassland, which is less prone to nitrate leaching than maize and therefore less responsive to surplus reductions. Additionally, farm practices such as autumn grazing strongly influence nitrate leaching but are only weakly linked to N surplus levels. Another consideration is that the reduction in soil N surplus achieved in the pilot may not have been substantial enough to elicit a response of nitrate leaching to groundwater. Further reductions might be necessary to achieve measurable improvements in nitrate leaching outcomes.

4.3. Financial Effects

The RFP showed a positive trend over time during the implementation and optimization of measures, indicating that the financial effects aligned with the objective of proposing mutually beneficial solutions. This trend also suggests that the farm guides’ expectations regarding the financial effects of recommended measures were generally accurate. Although the observed correlation between RFP improvements and the implementation of measures does not conclusively establish a causal relationship—because annual effects of weather could also influence RFP—the evaluation of measures with farmers supports the notion that measures likely have had a positive effect on RFP.
The absolute negative value of the FP may be attributed to limitations in the representativeness of the benchmark. Specifically, the benchmark did not distinguish between farms on dry sandy soils with lower crop yields on the one hand and farms on normal or wet sandy soils with generally higher crop yields on the other hand.
It is important to note that the RFP only expresses the benefits resulting from fodder production and additional investments related to the implementation of measures are not included in this calculation. However, measures requiring substantial investments, such as increasing the capacity of the manure storage, were implemented only occasionally and not as a result of instructions from farm guides. This indicates that the starting point of mutual gain was respected, as investment costs of measures were low and FRP increased.

4.4. Advancing Farm Management to Reduce Nitrate Leaching: Challenges and Pathways

The introduction of the soil N surplus as a target and indicator provided a feedback system for evaluating farm management in terms of its potential to mitigate nitrate leaching. Unlike nitrate leaching, which is not typically monitored in practical dairy farming, N surpluses and N application rates align closely with the regular management practices and strategies of commercial farmers. By incorporating soil N surplus as a focal point, the pilot project enhanced farmers’ understanding of the rationale behind proposed measures and significantly enhanced awareness of how daily farming practices impact water systems.
Despite progress, participants were hesitant to adopt measures perceived as complex, requiring significant investments, or offering limited financial returns. While these measures could substantially reduce nitrate leaching, this reluctance raises questions about the feasibility of achieving nitrate targets within a framework of voluntary farmer autonomy. Results from De Marke suggest that the nitrate target can be achieved in farm structures like the pilot farms while maintaining economically viable management practices [6]. This achievement may be explained by the high commitment of those responsible for farm management on De Marke to the careful and precise execution of measures, a factor critical to success that may not universally apply across pilot farms. In addition, differences in effectiveness in reducing nitrate leaching may be caused by the absence of farm-specific feedback systems in the pilot project: nitrate monitoring in the pilot focused on regional dynamics rather than individual farm performance. Conversely, De Marke monitors nitrate concentrations at the field level, providing actionable insights that drive continuous improvement of management.
To address these challenges, introducing farm and parcel-specific feedback systems could significantly enhance management practices. Structural monitoring of residual mineral N in fields in autumn offers a promising field-specific indicator of nitrate leaching risk [44]. Additionally, the active involvement of farmers and researchers is crucial. Collaboration must integrate farmers’ practical knowledge, researchers’ expertise [45,46,47], and the genuine interest of consultants and researchers in the unique characteristics and needs of individual farms [18].

5. Conclusions

This pilot study assessed the effectiveness of a mutual gain approach in promoting voluntary measures to reduce nitrate leaching. The approach, which aimed to improve farmers’ nutrient management while maintaining financial viability, led to significant changes in farm practices and the adoption of measures. Farmers acknowledged and appreciated the financial benefits, as reflected in increased fodder profitability. Soil N surplus levels declined significantly and were lower than national averages, suggesting improvements attributable to the pilot measures. However, despite these reductions, nitrate concentrations in groundwater fluctuated annually without a clear downward trend. Notably, nitrate leaching decreased in maize fields, even without a decline in soil N surplus, whereas grassland showed a distinct reduction in soil N surplus but no corresponding decrease in nitrate leaching. No clear explanation was found for the weak relationship between soil N surplus reduction and nitrate leaching response.
  • More precise evaluation tools are needed to assess the combined effectiveness of measures at the farm level. Beyond soil N surplus monitoring, farmers lacked site-specific feedback on nitrate leaching. Implementing tailored feedback systems could enhance decision-making and further reduce nitrate losses.
  • While the mutual gain approach encourages farmer participation, it may limit the adoption of highly effective nitrate reduction measures that offer little financial benefit.

Author Contributions

Conceptualization, J.V. and C.v.d.B.; investigation, J.V. and J.G.; writing and formal analysis, J.V. All authors have read and agreed to the published version of the manuscript.

Funding

The analysis and preparation of this paper received no external funding. However, the research was based on data from a project funded by the Province of Overijssel and Vitens, who supported the dissemination of the results in this publication.

Data Availability Statement

The data supporting the findings of this study are not publicly available due to agreements with the funding organization and confidentiality concerns regarding private farmer information. Interested researchers may contact the corresponding author for inquiries regarding data access, subject to applicable restrictions.

Acknowledgments

The authors express gratitude to Sander van Lienden (Province of Overijssel) and Janneke Paalhaar (Vitens) for their valuable comments during the execution of the pilot Overijssel. Appreciation is extended to Dick Brus (Wageningen Environmental Research) for designing the nitrate monitoring program and to Wageningen Economic Research for providing data from FADN, facilitating the creation of a benchmark for N surpluses on dairy farms. The authors also acknowledge ZuivelNL for providing data from the Central Database KLW (CDKLW) to enable the calculation of RFP.

Conflicts of Interest

Author C. van den Brink was employed by the company Province of Drenthe. Author J. Gielen was employed by the company Countus. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. European Environment Agency. Nitrate in Groundwater in Europe. 12 December 2024. Available online: https://www.eea.europa.eu/en/analysis/indicators/nitrate-in-groundwater-8th-eap (accessed on 15 March 2018).
  2. Sutton, M.; Howard, C.; Erisman, J.; Billen, G.; Bleeker, A.; Grennfelt, P.; van Grisven, H.; Grizzetti, B. The European Nitrogen Assessment; Cambridge University Press: Cambridge, UK, 2011. [Google Scholar]
  3. Fraters, B.; Hooijboer, A.; Vrijhoef, A.; Claessens, J.; Kotte, M.; Rijs, G.; Denneman, A.I.M.; van Bruggen, C.; Daatselaar, C.; Begeman, H. Agricultural Practice and Waterquality in The Netherlands: Status (2012–2014) and Trend (1992–2014): Monitoring Results for Nitrates Directive Reporting; National Institute for Public Health and the Environment: Bilthoven, The Netherlands, 2016. [Google Scholar]
  4. Aarts, H.; Biewinga, E.; Van Keulen, H. Dairy farming systems based on efficient nutrient management. Neth. J. Agric. Sci. 1992, 40, 285–299. [Google Scholar] [CrossRef]
  5. De Walle, F.B.; Sevenster, J. Agriculture and the Environment: Minerals, Manure and Measures; Kluwer Academic Publishers: Dordrecht, The Netherlands, 1998; Volume 7. [Google Scholar]
  6. Verloop, J. Limits of Effective Nutrient Management in Dairy Farming: Analyses of Experimental farm De Marke; Wageningen University and Research: Wageningen, The Netherlands, 2013. [Google Scholar]
  7. Schröder, J.; Neeteson, J. Nutrient management regulations in The Netherlands. Geoderma 2008, 144, 418–425. [Google Scholar] [CrossRef]
  8. Schröder, J.; Aarts, H.; Van Middelkoop, J.; Schils, R.; Velthof, G.; Fraters, B.; Willems, W. Permissible manure and fertilizer use in dairy farming systems on sandy soils in The Netherlands to comply with the Nitrates Directive target. Eur. J. Agron. 2007, 27, 102–114. [Google Scholar] [CrossRef]
  9. Freriks, A.; Keessen, A.; Korsse, D.; Van Rijswick, H.; Bastmeijer, K. As Far as the Own Instruments Reach: A Study on the Position of the Provincie of North-Brabant and the North-Brabant Water Authorities in the Realization of the Water Framework Objectives, with Special Attention to the New Dutch Environmental Act; Utrecht University, University of Tilburg: Utrecht, The Netherlands, 2016. (In Dutch) [Google Scholar]
  10. Wuijts, S.; Beekman, J.; van der Wal, B.; Suykens, C.; Driessen, P.P.J.; Van Rijswick, H.F.M.W. An ecological perspective on a river’s rights: A recipe for more effective water quality governance? Water Int. 2019, 44, 647–666. [Google Scholar] [CrossRef]
  11. Haverkate, H.; Waterwingebieden, C.B. Richtlijnen en Aanbevelingen voor de Bescherming van Waterwingebieden: Rapport van de Commissie Bescherming Waterwingebieden [voorz.: H. Haverkate]; Vereniging van Exploitanten van Waterleidingbedrijven: Rijswijk, The Netherlands, 1980. [Google Scholar]
  12. Wuijts, S.; Bogte, J.; Dik, H.; Verweij, W.; Van der Aa, N. Eindevaluatie Gebiedsdossiers Drinkwaterwinningen [Review of Drinking Water Protection Files]; RIVM Report; RIVM: Bilthoven, The Netherlands, 2014; (In Dutch, abstract in English). [Google Scholar]
  13. Van den Brink, C.; Hoogendoorn, M.; Verloop, J.; De Vries, A.; Leendertse, P.C. Effectiveness of Voluntary Measures to Reduce Agricultural Impact on Groundwater as a Source for Drinking Water: Lessons Learned from Cases in the Dutch Provinces Overijssel and Noord-Brabant. Water 2021, 13, 3278. [Google Scholar] [CrossRef]
  14. Van den Brink, C.; Wuijts, S. Towards an effective protection of groundwater resources: Putting policy into practice with the drinking water protection file. Water Policy 2016, 18, 635–653. [Google Scholar] [CrossRef]
  15. EU. Directive 2000/60/EC of the European Parliament and of the Council establishing a framework for the Community action in the field of water policy. Off. J. Eur. Communities 2000, 327, 1–72. [Google Scholar]
  16. Van der Vlies. 27 625 Water Policy, Amendment Van der Vlies No. 92; Parliamentary Papers: The Hague, The Netherands, 2002. [Google Scholar]
  17. Ten Heggeler, M.; Van den Brink, C.; Van Essen, J. Gebiedsdossiers voor kwetsbare drinkwaterwinningen in Overijssel [Groundwater protection files for vulnerable groundwater abstractions in Overijssel, the Netherlands]. H2O 2010, 6, 12–14. (In Dutch) [Google Scholar]
  18. Fisher, R.; Ury, W.; Patton, B. Getting to Yes: Negotiating Agreement Without Giving, 2nd ed.; Houghton Mifflin: Boston, MA, USA, 1991. [Google Scholar]
  19. Cramer, W.; Verweij, W.; Wuijts, S.; Zijp, M. Nitrate and the status of groundwater bodies. The Dutch experience. In Proceedings of the 3rd European Groundwater Conference, Madrid, Spain, 20–21 May 2010. [Google Scholar]
  20. Council of the European Communities. Council Directive 91/676/EEC of 12 December 1991 concerning the protection of waters against pollution caused by nitrates from agricultural sources. Off. J. Eur. Communities 1991, L 375, 1–8. [Google Scholar]
  21. Vereijken, P. A methodical way of prototyping integrated and ecological arable farming systems (I/EAFS) in interaction with pilot farms. In Developments in Crop Science; van Ittersum, M.K., van de Geijn, S.C., Eds.; Elsevier: Amsterdam, The Netherlands, 1997; pp. 293–308. [Google Scholar]
  22. Aarts, H.; Habekotté, B.; Van Keulen, H. Nitrogen (N) management in the ‘De Marke’dairy farming system. Nutr. Cycl. Agroecosystems 2000, 56, 231–240. [Google Scholar] [CrossRef]
  23. Dijk, W.V.; Boer, J.A.D.; Haan, M.H.A.D.; Mostert, P.; Oenema, J.; Verloop, J. Calculation Rules of the Annual Nutrient Cycling Assessment (ANCA) 2020: Background of BEX, BEA, BEN, BEP and BEC: Update of ANCA 2019 Version; Wageningen Livestock Research: Wageningen, The Netherlands, 2022. [Google Scholar]
  24. De Vries, M.; Van Dijk, W.; De Boer, J.; De Haan, M.H.A.; Oenema, J.; Verloop, J.; Lagerwerf, L.A. Calculation Rules of the Annual Nutrient Cycling Assessment (ANCA); Background Information About Farm-Specific Excretion Parameters (Update of ANCA Report 2018); Wageningen University: Wageningen, The Netherlands, 2020. [Google Scholar] [CrossRef]
  25. Oenema, J. Transitions in nutrient management on commercial pilot farms in The Netherlands. In C.T. de Wit Graduate School for Production Ecology and Resource Conservation (PE&RC); Wageningen University and Research Centre: Wageningen, The Netherlands, 2013; p. 200. [Google Scholar]
  26. Broeke, M.J.D.H.-T.; De Groot, W.J.M.; Dijkstra, J.P. Impact of excreted nitrogen by grazing cattle on nitrate leaching. Soil Use Manag. 1996, 12, 190–198. [Google Scholar] [CrossRef]
  27. Verloop, J.; Boumans, L.; Van Keulen, H.; Oenema, J.; Hilhorst, G.; Aarts, H.; Sebek, L. Reducing nitrate leaching to groundwater in an intensive dairy farming system. Nutr. Cycl. Agroecosyst. 2006, 74, 59–74. [Google Scholar] [CrossRef]
  28. Fraters, D.; Boumans, L.J.; van Drecht, G.; de Haan, T.; and De Hoop, W. Nitrogen monitoring in groundwater in the sandy regions of the Netherlands. Environ. Pollut. 1998, 102, 479–485. [Google Scholar] [CrossRef]
  29. Fraters, B.; Boumans, L.J.M. De opzet van het Landelijk Meetnet Effecten Mestbeleid voor 2004 en Daarna; RIVM Rapport 680100001; RIVM: Bilthoven, The Netherlands, 2005. [Google Scholar]
  30. Roelsma, J.D.; Heidema, N.; van den Brink, C. Nitraatmonitoring Kwetsbare Drinkwaterwinningen in Overijssel. Meetrondes 2011 en 2012. [Nitrate Monitoring in Vulnerable Catchments Areas in the Provence of Overijssel: 2011 and 2012]; RoyalHaskoningDHV: Groningen, The Netherlands, 2013. (In Dutch) [Google Scholar]
  31. Brus, D.J.; and de Gruijer, J.J. Design-based generalized least squares estimation of status and trend of soil properties from monitoring data. Geoderma 2011, 164, 172–180. [Google Scholar] [CrossRef]
  32. Fraters, D.; Boom, G.J.; Boumans, L.J.; de Weerd, H.; Wolters, M. Extraction of soil solution by drainage centrifugation—Effects of centrifugal force and time of centrifugation on soil moisture recovery and solute concentration in soil moisture of loess subsoils. Environ. Monit. Assess. 2017, 189, 83. [Google Scholar] [CrossRef]
  33. Aitken, R.; Outhwaite, R. A modified centrifuge apparatus for extracting soil solution. Commun. Soil Sci. Plant Anal. 1987, 18, 1041–1047. [Google Scholar] [CrossRef]
  34. Giesler, R.; Lundström, U. Soil solution chemistry: Effects of bulking soil samples. Soil Sci. Soc. Am. J. 1993, 57, 1283–1288. [Google Scholar] [CrossRef]
  35. van Duijnen, R.; van Leeuwen, T.; Hoogeveen, M. Minerals Policy Monitoring Programme Report 2015–2018: Methods and Procedures; RIVM-Rapport; RIVM: Bilthoven, The Netherlands, 2021. [Google Scholar]
  36. Blanken, K.; de Buisonje, F.; Evers, A.; Ouweltjes, W.; Verkaik, J.; Vermeij, I.; Wemmenhove, H. KWIN 2019–2020: Kwantitatieve Informatie Veehouderij; Wageningen Livestock Research: Wageningen, The Netherlands, 2019. [Google Scholar]
  37. ZuivelNL. Centrale Database KringloopWijzer (CDKLW). Available online: https://mijnkringloopwijzer.nl (accessed on 15 June 2021).
  38. Wick, K.; Heumesser, C.; Schmid, E. Groundwater nitrate contamination: Factors and indicators. J. Environ. Manag. 2012, 111, 178–186. [Google Scholar] [CrossRef] [PubMed]
  39. Klages, S.; Heidecke, C.; Osterburg, B.; Bailey, J.; Calciu, I.; Casey, C.; Dalgaard, T.; Frick, H.; Glavan, M.; D’Haene, K.; et al. Nitrogen Surplus—A Unified Indicator for Water Pollution in Europe? Water 2020, 12, 1197. [Google Scholar] [CrossRef]
  40. Rozemeijer, J.; Van Der Velde, Y. Temporal variability in groundwater and surface water quality in humid agricultural catchments; driving processes and consequences for regional water quality monitoring. Fund. Appl. Limnol 2014, 184, 195–209. [Google Scholar] [CrossRef]
  41. Schröder, J.J.; Jansen, A.G.; Hilhorst, G.J. Long-term nitrogen supply from cattle slurry. Soil Use Manag. 2005, 21, 196–204. [Google Scholar] [CrossRef]
  42. Ten Berge, H.F.M.; Burgers, S.L.G.E.; Schro, J.J.; Hofstad, E.J. Partial balance regression models for Nmin. In A Review of Potential Indicators for Nitrate Loss From Cropping and Farming Systems in The Netherlands; Plant Research International: Wageningen, The Netherlands, 2002. [Google Scholar]
  43. Fraters, B.; Hooijboer, A.; Vrijhoef, A.; Plette, A.; van Duijnhoven, N.; Rozemeijer, J.; Gosseling, M.; Daatselaar, C.; Roskam, J.; Begeman, H. Landbouwpraktijk en Waterkwaliteit in Nederland; Toestand (2016–2019) en Trend (1992–2019): De Nitraatrapportage 2020 met de Resultaten van de Monitoring van de effecten van de EU Nitraatrichtlijn Actieprogramma’s; Rijksinstituut voor Volksgezondheid en Milieu: Bilthoven, The Netherlands, 2020. (In Dutch) [Google Scholar]
  44. Noij, G.-J.; Berge, H.T. Rapportage Project Nitraatwijzer Fase I; Stichting Wageningen Research, Wageningen Plant Research, Business unit Agrosysteemkunde: Wageningen, The Netherlands, 2019. [Google Scholar]
  45. Oenema, J.; Koskamp, G.; Galama, P. Guiding commercial pilot farms to bridge the gap between experimental and commercial dairy farms; the project ‘Cows & Opportunities’. NJAS Wagening. J. Life Sci. 2001, 49, 277–296. [Google Scholar]
  46. Zeweld, W.; Van Huylenbroeck, G.; Tesfay, G.; Speelman, S. Smallholders farmers’ behavioural intentions towards sustainable agricultural practices. J. Environ. Manag. 2016, 187, 71–81. [Google Scholar] [CrossRef]
  47. Rose, D.C.; Sutherland, W.J.; Barnes, A.P.; Borthwick, F.; Foulkes, C.; Hall, C.; Moorby, J.M.; Nicholas-Davies, P.; Twining, S.; Dicks, L.V. Integrated farm management for sustainable agriculture: Lessons for knowledge exchange and policy. Land Use Policy 2018, 81, 834–842. [Google Scholar] [CrossRef]
Water 17 00455 g001
Figure 1. Schematized relation between Fodder Profit (FP) and production intensity (PI); black line: hypothetical benchmark, black dot: farm 1 and black square: farm 2, further explanation cf. text.
Figure 1. Schematized relation between Fodder Profit (FP) and production intensity (PI); black line: hypothetical benchmark, black dot: farm 1 and black square: farm 2, further explanation cf. text.
Water 17 00455 g001
Water 17 00455 g002
Figure 2. Dynamics of the N surplus on the whole farm soil balance, open squares: average of all pilot farms (n = 16; mean sd 50, range 39–66) and black dots: FADN (n = 97; mean sd 76, range 59–101) farms per year.
Figure 2. Dynamics of the N surplus on the whole farm soil balance, open squares: average of all pilot farms (n = 16; mean sd 50, range 39–66) and black dots: FADN (n = 97; mean sd 76, range 59–101) farms per year.
Water 17 00455 g002
Water 17 00455 g003
Figure 3. Dynamics of the N surplus on the whole farm soil balance, means of all farms (n = 16; grassland: mean sd = 59, range 50–73); maize: mean sd 67, range 36–97).
Figure 3. Dynamics of the N surplus on the whole farm soil balance, means of all farms (n = 16; grassland: mean sd = 59, range 50–73); maize: mean sd 67, range 36–97).
Water 17 00455 g003
Table 1. Characteristics of the 18 farms participating in the pilot Overijssel (data year 2014).
Table 1. Characteristics of the 18 farms participating in the pilot Overijssel (data year 2014).
MeanSdMinMax
Land use
Agricultural Area (ha)47143575
Grassland (%)8237790
Maize land (%)1832310
Other arable land (%)0000
Animal management
Milking cows (#)1022866155
Young stock (# per 10 milking cows)6.83.00.512.2
Milk production per cow (kg per cow)793475764789093
Milk production intensity (kg per ha)17,507412910,90728,952
Grazing intensity (h per cow per yr)81354101840
Manure management
Excretion manure N (kg per ha)33756252508
Excretion manure P (kg per ha)1001881159
Export of slurry (m3)16197686162899
Table 2. Overview of the distribution of groundwater samples over land in the pilot.
Table 2. Overview of the distribution of groundwater samples over land in the pilot.
Land UseAreaNo of Samples
Grassland553140
Arable land12568
Nature179950
Table 3. Adoption of measures in the pilot; the fraction of all participating farms where measures were initially incorporated in management (I), incorporated measures were optimized (O), and measures were new (N).
Table 3. Adoption of measures in the pilot; the fraction of all participating farms where measures were initially incorporated in management (I), incorporated measures were optimized (O), and measures were new (N).
MeasureION
Feed and animal management
Reduce CP/NEL in ration0.80.40.1
Lower grazing intensity (1)0.70.20.1
Reduce grazing in autumn (1) 0.20.10.2
Rotational grazing strategy (1)0.80.60.1
Grassland and maize management
Crop rotation with alternating grassland and maize0.40.10.2
Overseeding grassland to improve sod quality0.30.30.0
Harrowing grassland0.40.20.1
Undersowing of Italian Ryegrass in maize0.50.10.1
Early maize harvest followed by immediate sowing of catch crops (2)0.60.30.2
Catch crop with high N uptake capacity0.60.30.1
Manure management
Organic manure addition tuned to manure N, P content 0.30.10.3
Large manure storage facility0.400.1
Fertilization tuned to crop requirements for each parcel0.50.40.1
Reduced fertilizer supply in the vicinity of trees and on headlands0.40.00.1
Reduced/no fertilization on maize grown after grass (3)0.40.30.2
Manure supply of maize in sowing rows 0.00.00.2
Soil management
Resolve soil compaction0.40.20.1
Enhance soil organic matter content0.30.00.1
Reduce the weight of machinery 0.30.10.0
Delayed machine traffic on wet soils in spring0.40.20.0
Note: (1) Fraction of farms in the pilot that apply grazing. (2) Fraction of farmers that do not apply undersowing of Italian Ryegrass in maize. (3) Fraction of farmers with crop rotation with alternating grassland and maize.
Table 4. Measured nitrate concentrations in the upper meter of groundwater for different land uses (mg NO3 per L); weighed means over the entire area involved in the BVDO project and standard deviations (between brackets).
Table 4. Measured nitrate concentrations in the upper meter of groundwater for different land uses (mg NO3 per L); weighed means over the entire area involved in the BVDO project and standard deviations (between brackets).
Land Use2011201220132014201520162017
Grassland (140) 174 (6)69 (6)93 (10)79 (6)78 (6)-98 (9)
Arable land (68) 1134 (9)110 (7)92 (14)97 (8)69 (6)-91 (8)
Agricultural area (208) 190 (5)80 (5)92 (8)84 (5)75 (5)--
Nature (50) 1---31 (5)---
Note: 1 Number of measurements.
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Verloop, J.; van den Brink, C.; Gielen, J. Effectiveness of Voluntary Nutrient Management Measures to Reduce Nitrate Leaching on Dairy Farms Using Soil N Surplus as an Indicator. Water 2025, 17, 455. https://doi.org/10.3390/w17030455

AMA Style

Verloop J, van den Brink C, Gielen J. Effectiveness of Voluntary Nutrient Management Measures to Reduce Nitrate Leaching on Dairy Farms Using Soil N Surplus as an Indicator. Water. 2025; 17(3):455. https://doi.org/10.3390/w17030455

Chicago/Turabian Style

Verloop, J., C. van den Brink, and J. Gielen. 2025. "Effectiveness of Voluntary Nutrient Management Measures to Reduce Nitrate Leaching on Dairy Farms Using Soil N Surplus as an Indicator" Water 17, no. 3: 455. https://doi.org/10.3390/w17030455

APA Style

Verloop, J., van den Brink, C., & Gielen, J. (2025). Effectiveness of Voluntary Nutrient Management Measures to Reduce Nitrate Leaching on Dairy Farms Using Soil N Surplus as an Indicator. Water, 17(3), 455. https://doi.org/10.3390/w17030455

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