Results and discussion

Main authors: Mart Ros, Gerard Velthof, Oene Oenema, Meindert Commelin, Susanne Klages, Linda Tendler, Jenny Rowbottom, Isobel Wright, Donnacha Doody, Luke Farrow, Birgitte Hansen, Morten Graversgaard, Irene Asta, Andrej Jamsek, Katarina Kresnik, Matjaz Glavan, Jean-François Vernoux, Nicolas Surdyk, Christophoros Christophoridis, Kate Smith, Irina Calciu, Sonja Schimmelpfennig, Hyojin Kim, Piet Groenendijk.
FAIRWAYiS Editor: Jane Brandt
Source document: »Ros, M. et al. 2020. Identification of most promising measures and practices: 2. Reduction nitrate transport from agricultural land to groundwater and surface waters by management practices. FAIRWAY Project Deliverable 4.3, 72 pp

 

Contents table
1. Review of existing meta-analyses  
2. Meta-analysis
3. Case studies

1. Review of existing meta-analyses

Results from reviews and meta-analyses that assessed the effects of different measures on NO3 losses and soil NO3 concentrations were summarized and are organized below. The amount of literature available greatly differed for the various practices: several reviews on cover crops and biochar have been published, but for other measures (e.g. adaptations in soil drainage or irrigation management) hardly any (quantitative) reviews were found. Most reviews did not include a cost-benefit analysis, but in two cases (both for nitrification inhibitors) they were reported.

Overall, assessment of these literature reviews showed that most included measures were effective to some extent at reducing the risk of NO3 losses to water bodies. There is overwhelming evidence that the use of non-legume cover crops is an efficient practice, with reductions in NO3 leaching from 35% to 98%. The effect does however diminish when legumes are used. Besides cover crops, the use of (nitrification) inhibitors is also effective. In particular for dicyandiamide (DCD), a lot of studies reported a reduction in NO3 leaching. For biochar, the effect differs from none at all, to considerable reductions in NO3 leaching. The success of biochar applications seem to depend on soil and environmental conditions, as well as the nature of the biochar used. For changes in tillage systems (switching from conventional to no-till), the reviewed study did not show a significant reduction in NO3 losses. Rather, for losses through leaching a significant increase was even reported. Switching to organic farming often includes no-till practices and did seem to reduce NO3 losses. However, there was considerable variation in the results, and when losses were expressed per unit of produce, losses were often increased due to lower yields by organically managed farms.

Table 2.3 gives a summary on the studies and effects of the various measures in the reviews. Below, reviews on the various measures will be discussed per measure.

Table 2.3: Summary of the measures and main effects found in the literature search.

Study  Measure Response variable Observ. Overall effect1 Comments
Basche et al. 2014 Cover crops NO3 leaching 11 -98%  
Borchard et al. 2019 Biochar NO3 leaching 688 N.S. Long-term studies show greater effect. No effect in grasslands.
Cai and Akiyama 2017 Inhibitors NO3 leaching 45  -46% DCD application
Daryanto et al. 2017 No-till NO3 leaching 180 +13% NO3 concentrations in leaching samples were similar.
Daryanto et al. 2017 No-till  NO3 runoff 61 N.S.  NO3 concentrations in runoff increased significantly.
Liu et al. 2018 Biochar N leaching 156 -26%  
Liu et al. 2018 Biochar Soil NO3 concentration 350 -12%  
Mayer et al. 2007 Buffers N leaching/runoff 89 -68%  
Mondelaers et al. 2009 Organic farming NO3 leaching ? -32% Considerable variation
Nguyen et al. 2017 Biochar Soil NO3 concentration 862   -11%
Qiao et al. 2015 Inhibitors NO3 leaching 102 -47% Cost-benefit analysis: DCD - $162.70 ha-1 y-1
Quemada et al. 2013 Fertilizer management NO3 leaching 106 -40%  
Quemada et al. 2013 Cover crops NO3 leaching 59 -35% Includes both legume and non-legume crops
Quemada et al. 2013 Improved irrigation NO3 leaching 82 -58% Average of several practices
Thapa et al. 2018 Cover crops NO3 leaching 216 -56% Non-legume crops only
Tonitto et al. 2006 Cover crops NO3 leaching ? -70% Non-legume crops only
Tonitto et al. 2006 Cover crops NO3 leaching ? -40% Legume crops only
Tuomisto et al. 2012 Organic farming N leaching 48 -31% But an increase in N leaching per unit of produce.
Valkama et al. 2015 Cover crops N leaching 27 -50% Non-legume crops only
Valkama et al. 2015 Cover crops Soil N concentration 29 -35% Non-legume crops only
Yang et al. 2016 Inhibitors NO3 leaching 298 -55% Both DCD and DMPP Cost-benefit analysis: DCD - $109.49 ha-1 y-1 DMPP - $15.67 ha-1 y-1

1 Negative numbers indicate a reduction in NO3 losses/concentrations 2 N.S. Not significant

Cover crops

Basche et al. (2014) did a meta-analysis that focuses on the effect of cover crops on nitrous oxide (N2O) emissions, but also includes a few studies with data on NO3 leaching (3 studies, 11 data points; Figure 2.2). They only included studies in which the cover crop was not harvested. The authors argue that, while cover crops may not reduce (and sometimes even increase) N2O emissions from agricultural fields, the N that is prevented from leaching as NO3 represents a reduction of subsequent N2O emissions from leachate once it has been transported outside field boundaries. Therefore, reducing NO3 leaching to ground and surface waters may also benefit N2O emissions from agricultural sources.

D4.3 figa02
Figure 2.2

Thapa et al. (2018)also did a meta-analysis on the effect of cover crops on NO3 leaching (28 studies, 238 observations). They reported a 56% reduction of NO3 leaching by non-legume cover crops. Mixtures of legumes and non-legumes showed a response similar to the non-legume crops and both were more effective than legumes on their own. These results were obtained when one study was omitted from the results due to high variation. Results were affected by planting date, shoot biomass, and precipitation, but the lack of statistical information in the used studies prevented a deep analysis of contributing factors.

Tonitto et al. (2006) conducted a study on cash crop yields and N retention in systems with and without cover crops. They found that non-legume cover crops decreased NO3 leaching by 70%, but there was no difference in cash crop yields between systems with and without a cover crop. Legume-based systems reduced NO3 leaching by 40% on average. There was an overall 10% yield penalty when using legumes, rather than mineral fertilizer to provide N to the cash crops, but no negative effect was observed when legumes provided more than 110 kg N ha-1. There were no differences in soil N status of conventional and green manure systems after harvest, suggesting that NO3 leaching losses were mainly reduced by avoiding bare fallow throughout the cropping rotation.

Valkama et al. (2015) studied the effects of catch crops on nitrogen (either NO3 or total N) leaching and yield of spring cereals in the Nordic countries (Denmark, Sweden, Finland, and Norway). In their meta-analysis, non-leguminous catch crops reduced N leaching by 50% (27 observations; Figure 2.3a) and soil NO3/inorganic N by 35% in fall (29 observations; Figure 2.3b). For the effect on soil N, there were differences among species used: annual ryegrass was more effective (60% reduction) than perennial ryegrass and Westerwolds ryegrass (25% reduction). Legumes, on the other hand, did not reduce soil N. Studies with non-legume catch crops also reported a slight (3%) yield reduction, whereas the ones with legume or mixed (legume + non-legume) catch crops reported increases for yield and crop N content (6%).

D4.3 figa03
Figure 2.3

Biochar

Borchard et al. (2019) investigated the effect of biochar additions on N2O emissions, soil NO3 concentrations, and NO3 leaching. Their main findings show that, overall, soil NO3 concentrations remained unaffected and the use of biochar did not significantly reduce NO3 leaching (13% reduction, not significant). However, in studies that lasted longer than 30 days (shorter studies showed an increase in NO3 losses) the effect was significant (26-32% reduction). Biochar decreased both N2O and NO3 losses in annual arable crops and horticulture, but no effect was found for grasslands or perennial crops. Besides this, addition of large additional N (> 150 kg/ha) as (mineral) fertilizer diminished the effect of biochar on NO3 leaching. Although biochar addition may suppress soil N losses as N2O emissions and NO3 leaching, there is a higher risk of NH3 volatilization when applying biochar.

Liu et al. (2018) assessed the effect of biochar additions on the soil N cycle. Aside from N leaching losses, they summarized effects on gaseous losses and soil N pools. On average, biochar reduced N leaching by 26% (22% for NH4 and 29% for NO3) and soil NO3 concentrations with 12%. NH3 volatilization, on the other hand, was increased by 19% (this effect was larger in soils with a low buffering capacity). Wood-based biochars were the most effective, whereas manure-based biochars did not seem to have a significant effect. There was no effect of pyrolysis temperature on the effect size (with biochar/no biochar), but the effectiveness in reducing N leaching increased with higher biochar application rates.

D4.3 figa04
Figure 2.4

Nguyen et al. (2017) reported a meta-analysis on the effect of biochar on soil inorganic N (56 studies, 1080 observations). They found that biochar applications reduced soil inorganic N (-11% for NH4 and -10% for NO3). Most of their studies were shorter than a year. They found plant-derived biochars and biochars pyrolyzed at lower temperatures (< 401 °C) to be more effective at reducing soil N concentrations than woody biochars. Higher biochar application rates were more effective, but application of urea alongside biochar decreased the biochar’s effect of lowering soil NO3 concentrations and even increased them compared to the control. Biochar worked best to reduce soil NO3 concentrations on neutral soils (Figure 2.4). Very acidic soils showed increased NO3 levels when biochar was applied. Generally, time between application and observation had little effect on soil NO3 concentrations. Climatic conditions may affect the effect of biochar on reducing nitrate leaching, but an assessment of climatic conditions was not included in the meta-analysis paper.

Inhibitors

Cai and Akiyama (2017) reviewed the effect of inhibitors and biochar on N2O and NO3 losses in urine patches on grasslands. Studies originated predominantly from temperate areas (UK and New Zealand). They reported a decrease of 46% in NO3 losses when the nitrification inhibitor DCD was applied. When used in combination with the urease inhibitor n-butyl thiophosphoric triamide (NBPT) NO3 losses were reduced by 42%. Effectiveness increased with higher doses of DCD. There was no significant difference between coated and liquid forms of DCD and study type or duration did not affect the results. Although there was no difference between the effects of DCD and those of DCD+NBPT, the authors state that if NH3 large losses are expected, a combination of DCD and NBPT would be the more logical option.

Qiao et al. (2015) collected 62 field studies of nitrogen enriched studies to summarize the effect of nitrification inhibitors on the nitrogen cycle. They found that the use of inhibitors decreased NO3 leaching by 47%. Besides this, N2O emissions were decreased by 44%, NO emissions by 24%, but NH3 emissions were increased by 20%. They also conducted cost-benefit analysis and calculated that applications of nitrification inhibitors could increase the revenue of a maize farm by $162.70 ha-1 y-1 which would correspond to a 8.95% in financial gain (Table 2.4).

Yang et al. (2016) investigated the effect of dicyandiamide (DCD) and 3,4-dimethypyrazole phosphate (DMPP) on soil nitrogen transformations and plant productivity. They found that both nitrification inhibitors were equally effective at altering soil N transformations. Both inhibitors increased soil NH4 content (DCD: 25.3% and DMPP: 41.1%) and decreased NO3 content (DCD: 17.0% and DMPP: 20.7%). DCD and DMPP were equally effective at reducing NO3 leaching (~55%, n=298), but NH4 leaching was increased for DMPP, but decreased for DCD. Also, in neutral soils or when urea was applied, DMPP seemed more effective than DCD. Total N leached did not differ between the two inhibitors. For plant production, DCD was more effective than DMPP in increasing yields. These authors also conducted a cost-benefit analysis and concluded applying fertilizer N in combination with DCD had a benefit of $109.49 ha-1 y-1, whereas for DMPP this was only $15.67 ha-1 y-1 (Table 2.5). The authors do note that DCD has a higher toxicity to plants and human health than DMPP (although toxicity for both products is relatively low) and that this may change the cost-benefit analysis over a longer time.

Table 2.4: Cost-benefit analysis for a maize farm applying nitrification inhibitors (NI) with fertilizer rate of 125 kg N/ha/yr. For change in N loss under NI, positive values indicate that NI increases N losses, and negative ones indicate N reduces N loss. For the monetary response, the positive numbers indicate the amount of the economic benefit, whereas the negative ones indicate the amount of the economic cost. From Qiao et al. (2015).

D4.3 taba04

Table 2.5: Cost-benefit analysis of nitrification inhibitor (NI) application in a maize farm with fertilizer N rate of 125 kg N/ha/yr. For change in N loss under NI, positive values indicate that NI increases N losses, and negative ones indicate N reduces N loss. For the monetary response, the positive numbers indicate the amount of the economic benefit, whereas the negative ones indicate the amount of the economic cost. From Yang et al. (2016)

D4.3 taba05

No tillage systems

Daryanto et al. (2017) compared no-till systems to conventional tillage systems in their NO¬3 losses through leaching and runoff processes for several field crops. They compared both NO3 load and concentrations (Figure 2.5). No-till provided no overall reduction in either concentration or load than conventional tillage systems. No-till systems had higher NO3 concentrations in runoff, but due to lower runoff volumes the load was similar. Leaching NO3 losses were significantly higher in no-till systems. Soil drainage characteristics (texture, artificial drainage) are likely to play an important role in the effects of no-till on NO3 losses. Fertilizer type (organic vs. inorganic vs. no fertilizer) had no effect on the NO3 concentrations in runoff and leaching samples.

D4.3 figa05
Figure 2.5

Vegetative buffers

Mayer et al. (2007) reviewed the effect of riparian buffers on nitrogen concentrations in streams and tried to link the effects of buffers to the buffer width (45 studies; 89 observations). Overall, buffers were very effective at removing N from streams (67.5%). Buffers were particularly effective at removing subsurface N. They found a wide variation in effectiveness and a small part could be explained by buffer width. Buffers > 50 m were more effective at removing N than were those <25 m. This was particularly true for horizontally transferred N removal, but not for vertical transferred N removal. No effect of buffer vegetation was observed, but buffers with herbaceous or herbaceous/forest vegetation became increasingly effective as they got wider.

Organic farming

Tuomisto et al. (2012) studied the effect of organic farming on the environment. Their main conclusion is that, while organic farming may have environmental benefits and may reduce N leaching per land unit, this is not necessarily true per unit of produce. This is a result from both the lower inputs and outputs of organic farming. Over 48 observations, they found a 31% reduction of N leaching in organic systems when expressed per land unit, but a 49% increase of leaching losses per unit of product. The lower leaching losses (and lower yields) were likely a result of reduced N inputs in organic farming systems. In addition to the effect on N leaching, Tuomisto et al. (2012) show a reduction in N2O emissions (per land unit) and an increase in soil organic matter content. Losses of ammonia and P were not significantly different between conventional and organic systems.

Mondelaers et al. (2009) did a meta-analysis on the differences in environmental impacts between organic and conventional farming. They assessed NO3 losses among other parameters. Their analysis showed that NO3 leaching was 32% lower in organic farming systems. However, the variability between studies was considerable.

Other

Quemada et al. (2013) conducted a meta-analysis (44 studies, 279 observations) on the effects of water and fertilizer management, cover crops, and fertilizer technology on NO3 leaching and crop yield. They found that proper water application management can reduce NO3 leaching by up to 80% without lowering crop yields. Improving fertilizer management reduced leaching by 40% (Figure 2.6), and the best results were obtained if fertilization occurred at the recommended rate. Cover crops reduced NO3 leaching by 50% compared to fallow land, but only if the cover crops were not leguminous. Legume cover crops had no significant effect.

D4.3 figa06
Figure 2.6

Wang et al. (2019) used meta-analysis (86 studies, 324 observations) to construct a model to describe the emission factor for NO3 leaching from N fertilizer additions. They show that NO3 leaching from N additions do not remain constant (as a set fraction of the added N), but increase with higher N additions according to a quadratic relationship. Their conclusion is that the emission factor for NO3 leaching set by the IPCC (30% of N input) overestimates NO3 leaching.

Zhou and Butterbach-Bahl (2014) conducted a meta-analysis on the link between NO3 leaching and crop yields in maize and wheat cropping systems. They showed that maize systems saw NO3 losses that were about two times higher than those in wheat systems. Due to higher maize yields however, yield-scaled NO3 losses were comparable between the two systems. They further conclude that NO3 losses can be reduced by fertilizing close to the optimal N rate, as NO3 leaching increased with N application rate.

2. Meta-analysis

In the meta-analysis we included 53 studies and 278 observations that compared a variety of measures to reduce NO3 losses (Table 2.6). Because of a lack of studies and the absence of a solid, uniform type of pairwise comparisons (treatment group vs. control group), it was impossible to incorporate studies covering measures like implementation of balanced N fertilization, adaptations of N application timing or rate, restricted grazing, changes in crop rotations, and mulching (see Table 2.1). It was also necessary to combine studies with different indicators, and so the effect on N or NO3 concentrations in soil and water is assessed jointly with reported results on NO3 flux from soil (field) to water. This implies a significant generalization of the data and the results should thus be viewed with some caution.

Table 2.6: Comprehensive list of studies included in the meta-analysis, including the NO3 indicator and the type of measure described in the studies.

Study Indicator Measure type Study Indicator Measure type
Adams and Jan, 2006 NO3 flux Cover crops Martinez and Guiraud, 1990 NO3 concentration Cover crops
Asing et al., 2008 NO3 concentration Inhibitor Mehdi and Madramootoo, 1999    NO3 concentration Tillage
Askegaard et al., 2006 NO3 flux Cover crops Menneer et al., 2008   NO3 concentration Inhibitor
Benham et al., 2007 NO3 flux Tillage Monaghan et al., 2009   NO3 concentration Inhibitor
Besnard, 2004 NO3 flux + NO3 concentration Cover crops Nicholson et al., 2016   NO3 flux Application method
Besnard and Kerveillant, 2006 NO3 flux Cover crops O’Connor et al., 2016  NO3 flux Inhibitor 
Bock et al., 2015 NO3 concentration Biochar Parkin et al., 2016   NO3 concentration  Cover crops
Bonaiti and Borin, 2010 NO3 flux + NO3 concentration Controlled drainage Pisani et al., 2017  NO3 concentration Tillage 
Bosch et al., 2015 NO3 flux + NO3 concentration Tillage Premrov et al., 2014   NO3 flux Cover crops + Tillage
Dennis et al., 2010 NO3 flux Inhibitor Ritter et al., 1998   NO3 content  Cover crops + Tillage 
Di and Cameron, 2012 NO3 concentration Inhibitor Saarnio et al., 2018 N content Biochar 
Drury et al., 2009 NO3 flux + NO3 concentration Controlled drainage Sanz-Cobena et al., 2012  NO3 concentration   Inhibitor
Dunn et al., 2011 NO3 concentration Vegetative buffer Schipper and Vojvodić-Vuković, 2000  NO3 concentration  Vegetative buffer 
Eykelbosh et al., 2015 NO3 concentration Biochar Schmidt and Clark, 2012  NO3 concentration  Vegetative buffer 
Francis et al., 1995 NO3 flux + N content Cover crops Shepherd, 2006  NO3 flux  Cover crops 
García-González et al., 2018 N content Cover crops Shepherd et al., 2017  NO3 concentration  Inhibitor
Gordon et al., 2011 ? Tillage Smith et al., 2002  N concentration    Inhibitor
Goss et al., 1993 NO3 flux Tillage Stolzenburg, 2010  NO3 flux + NO3 concentration  Cover crops 
Guardia et al., 2018 N content Inhibitor Tauchnitz et al., 2018  NO3 concentration   Inhibitor
Hill et al., 2015 NO3 flux Biochar + Inhibitor Thorman et al., 2016  NO3 flux  Application method 
Huang et al., 2015 NO3 flux Tillage Ventura et al., 2013  NO3 flux   Biochar
Jabro et al., 2016 NO3 concentration Tillage Vos et al., 1994  NO3 concentration  Cover crops 
Johnson and Smith, 1996 NO3 flux Tillage Welten et al., 2014  NO3 concentration   Inhibitor
Jouni et al., 2018 ? Controlled drainage Wesström and Messing, 2007  NO3 flux  Controlled drainage 
Kaspar et al., 2012 NO3 flux Cover crops Yamulki and Misselbrook, 2016  NO3 flux  Application method 
Krueger et al., 2011 NO3 concentration Cover crops Zaman and Blennerhassett, 2010  NO3 concentration  Inhibitor
Macdonald et al., 2005 NO3 flux Cover crops      

Overall, the data was spread widely for the different measures assessed. Figure 2.7 shows the average and 95% confidence intervals for the effect size (ln(R)) of the different measures. The results from the meta-analysis show that implementation of a vegetative buffer, the use of cover crops, and application of (nitrification) inhibitors lead to a significant decrease in NO3 losses (95% confidence interval not overlapping 0). For the other analyzed measures (tillage, controlled drainage, biochar, and changes in application method), no significant average effect was recorded in the compiled database. Moreover, although some of the measures had a significant effect on NO3 losses, including ‘measure type’ as an explanatory variable in the meta-analysis model did not significantly improve it. This indicates that the variation of the effect explained by the different measures is limited.

D4.3 figa07
Figure 2.7

For the measures for which enough studies and observations were available, we assessed the effect of the measure more closely. Figure 2.8 shows the effect of the individual cover crops on NO3 losses (18 studies, 84 observations). Lupins, grass, barley, oat, mustard, and rye were particularly effective in reducing losses. For turnips and wheat the effect was not significant. In our database, we did not observe the large difference in effectiveness between legume and non-legume crops, as observed in other literature reviews. It should be noted, however, that the number of studies included in the current analysis was limited. Moreover, there was no significant effect of including ‘cover crop type’ as an explanatory variable in the model.

When examining changes in tillage practice (11 studies, 47 observations), we did see a significant improvement when the type of tillage was considered (p=0.0011, Fig. 2.9). Whereas studies that reported the effect of reduced tillage and no-till did not significantly affect NO3 losses, there was one study (Gordon et al., 2011) that used row shaper and basin tillage that did not match with the tillage forms included in the other studies and was therefore kept separate. No-till and reduced tillage had no effect on NO3 losses however, and this is in line with the results reported in previous meta-analyses.

For research on the use of nitrification inhibitors (14 studies, 67 observations) we were able to distinguish between DCD used alone, or in combination with a urease inhibitor. The analysis shows that by itself, DCD significantly reduced NO3 losses, but the studies in which it was used in combination with a urease inhibitor showed no significant reduction (Figure 2.10). Including the differences between these two groups significantly improved the statistical model (p=0.0224). Overall, the effectivity of DCD as a measure is in line with the results found in previous meta-analyses.

D4.3 figa08
Figure 2.8 D4.3 figa09
Figure 2.9 D4.3 figa10
Figure 2.10

3. Case studies

The results of the questionnaires sent out to the FAIRWAY case studies were collected and aggregated in a table (see »Annex 2). From 9 different case studies, 34 different measures were recorded. They were then aggregated by measure type and the average/overall scores for effectivity, cost, applicability, and adoptability were assessed from the individual records and comments.

In general, there was a wide variety of measures described (Table 2.7). Optimizing the rate and timing of fertilizer and manure applications were measures that were applicable throughout (almost) all of the case studies. With a highly rated effectivity, applicability and adoptability, as well as a relatively low cost these are measures that can be taken easily an may yield quick results. Nevertheless, storage space, weather conditions and labour demand may be limiting factors for implementation of these measures. Additionally, reducing application rates or balancing N fertilization may result in yield losses and potential to implement this may depend on the characteristics of the farm.

Table 2.7: Overview of the measure types applied and studied within the FAIRWAY case studies, with indications on effectivity, cost, applicability and adoptability.

Measure type Country1 Target2 Effectivity3 Cost4 Applicability5 Adoptability6 Notes
Changes in cropping system or crop rotation NL, SI GW/SW/NUE ++ € ++ ++ May improve soil health/quality, decrease chance of diseases.
Changes in fertilization timing  NL, DK, GR, RO, SI GW/SW   +++  €  +++   +++   e.g. no manure spreading in the fall or splitting fertilizer applications. Expenses may increase if it demands more labor or requires additional manure storage space.
Changes in application method DE, DK GW ++ € ++ ++ Effectivity may depend on the farm; may decrease other N losses such as greenhouse gases.
Changes in application dose (reduced input, balanced fertilization, or optimal fertilization) NO, PT, DE, DK, GR, SI GW/SW/NUE ++ € +++ +++ May require soil testing. May be mandatory.
Cover crops DK, GR, RO, SI GW/SW +++ €€ ++ ++ May increase soil OM content. Cost varies based on farm type. Less applicable/adoptable in Slovenia.
Reduced tillage NO SW ++ €€ +++ ++ May prevent soil erosion.
Buffer strips (either between crops and waterways, or between rows of crops) NL, FR, GR, RO, SI GW/SW ++ €€ ++ + May contribute to landscape diversity, but decrease crop yields. Implementation costs differ per country.
Grassed waterways NO SW +++ €€€€ + + May reduce erosion and contribute to landscape diversity. Reduces the amount of cropland
Farm-scale nutrient management tools DE NUE * € +++ +++ Farmers may be obliged to use these tools.
Outreach and information events DE NUE * € ++ ++ Effectivity depends on farm type and farmer knowledge.
Other GR GW/SW ? ? ? ? Grassland and grazing management; improved fertilizer storage; no data available yet.

1 Abbreviations for the various participating countries: NL Netherlands; SI Slovenia; DK Denmark; GR Greece; RO Romania; DE Germany; FR France; NO Norway; PT Portugal
2 Target of the measure: GW groundwater; SW surface water; NUE nitrogen use efficiency
3 Effectivity is evaluated as Low (+, 5-10% load reduction), Moderate (++, 10-25% load reduction), High (+++, >25% load reduction), Variable (*), or Unknown (?).
4 Implication costs are evaluated as Low (€, < €10/ha), Moderate (€€, 10-50/ha), High (€€€, €50-100/ha), Very high (€€€€, > €100/ha), or Unknown (?).
5 Applicability is evaluated as No (+, on < 25% of the land), Partly (++, on 25-75% of the land), Yes (+++, on > 75% of the land), or Unknown (?).
6 Adoptability is evaluated as No (+, in < 25% of the cases), Partly (++, in 25-75% of the cases), Yes (+++, in > 75% of the cases), or Unknown (?).

 From the questionnaire results there was no clear distinction between the type of measures adopted in the different parts of the continent (»Annex 2). There were a few measures that were reported by just one or two case studies, but that does not directly imply that these measures are not used elsewhere. From the Greek case study, data on effectivity, cost, applicability, and adoptability was missing, as the case study had not been running for very long.

As reflected in the literature review and meta-analysis, the effectiveness of cover crops was rated as high. While it may not be the cheapest measure to implement, four out of eight case studies mentioned this measure. Buffer strips between crops and water ways (or between rows of crops) was also a frequently reported measure, but the effectivity was evaluated slightly lower and so was the adoptability. Compared to the literature review and the meta-analysis, there were several measures that were absent in the questionnaire results. Implementation of biochar and nitrification inhibitors was not reported by the experts. Measures on drainage or irrigation management were not reported either. The Norwegian case study reported a positive effect of reduced tillage, which in addition to decreasing nutrient transports to surface water, decreases erosion.

Another difference between the measures included in the literature review and meta-analysis on one hand, and the response from the case study questionnaires on the other is that the measures from the latter seemed to focus more on the farm-scale. Measures on outreach, information sharing, whole-farm assessments and large-scale N input reductions were reported. Although the effect of management decisions at this level is more difficult to quantify than field-ready measures such as cover crops, buffer zones, or inhibitors, they are relatively cheap to organize and may prove beneficial for reducing other N losses and increasing N use efficiency across the entire farm.

 

Notes:

For full references to papers quoted in this article see »References

Download the full report for Annex 2

Identification of most promising measures and practices

 

Materials and methods

Main authors: Mart Ros, Gerard Velthof, Oene Oenema, Meindert Commelin, Susanne Klages, Linda Tendler, Jenny Rowbottom, Isobel Wright, Donnacha Doody, Luke Farrow, Birgitte Hansen, Morten Graversgaard, Irene Asta, Andrej Jamsek, Katarina Kresnik, Matjaz Glavan, Jean-François Vernoux, Nicolas Surdyk, Christophoros Christophoridis, Kate Smith, Irina Calciu, Sonja Schimmelpfennig, Hyojin Kim, Piet Groenendijk.
FAIRWAYiS Editor: Jane Brandt
Source document: »Ros, M. et al. 2020. Identification of most promising measures and practices: 2. Reduction nitrate transport from agricultural land to groundwater and surface waters by management practices. FAIRWAY Project Deliverable 4.3, 72 pp

 

Contents table
1. Literature review
2. Meta-analysis  
3. Case studies

1. Literature review

A systematic search was performed through online databases, and a local/expert based search was done throughout Europe. The aim of the local search was to find high quality studies which are not easily accessible through online databases, but which contain valuable data. The criteria used for this search were:

  1. well documented (peer reviewed or reports),
  2. the article/report should provide the results of one or more experiments to decrease NO3 leaching to groundwater/surface waters,
  3. the article/report should present quantitative data of results and statistics to enable a meta-analysis. For the online systematic search online databases were used; CAB-Abstract/Ovid and Web of Science.

Query criteria used:

  • (nitrate and (leaching or drain* or "surface water" or groundwater or "ground water" or runof*) and (mitigat* or measure) and (effect* or reduct* or decreas*) and(treatment or "field trial" or experiment))

Other options involved excluding of the key “model*” and including the key word “agricult*”. The final search yielded 496 results

  • (nitrate and (leaching or drain* or "surface water" or groundwater or "ground water" or runof*) and (mitigat* or measure) and (agricult* or farm* or crop* or field*) and (effect* or reduct* or decreas*) and (treatment or "field trial" or experiment) not (model*))

In addition, university and institute libraries were examined in Member States of the European Union, because a significant fraction of the research on measures to reduce NO3 leaching and surface runoff has been conducted before the 1990s and 2000s when it was still common to publish the results in reports and documents. These reports and documents quite often have not been digitalized and made available to the international scientific audience and as such are not traced by the search machines of Google Scholar and Scopus.

To extend the literature study, we searched Google Scholar for additional review papers and meta-analyses, using the search query:

  • (“Nitrate” OR “Nitrogen”) AND (“Mitigation” OR “Measure”) AND (“Meta-analysis” OR “Review”) AND “Agriculture”.

Data and results of reviewed reports and articles were categorized according to Table 2.1 and collected in Excel spreadsheets in a uniform manner. The Excel spreadsheets were subsequently transferred to a database for meta-analysis.

Table 2.1: Categories of measures to reduce nitrate losses.

Nr Name of the measure Characterization of the measures
1 Nitrogen fertilization; balanced nitrogen fertilization (dose of application) Matching nitrogen input to the average nitrogen demand of the crop is termed balanced nitrogen fertilization. This measure includes terms like “reduction in fertilization”, nutrient management planning, and more drastic measures such as withholding nitrogen fertilizer inputs. Typically, this measure has been studied in nitrogen fertilizer trials. This measure includes also the combined use of synthetic fertilizers, animal manures, organic fertilizers, bio-based fertilizers, composts, etc.
2 Precision nitrogen fertilization (optimization in space and time) Precision nitrogen fertilization builds on balanced fertilization, and includes “variable rate fertilization” and “split applications”. This includes measures like a ban on fertilization in winter, on sloping land, on frozen land, etc.
3 Enhanced efficiency nitrogen fertilizers Enhanced efficiency fertilizers include various types of nitrogen fertilizers, with or without nitrification inhibitors, urease inhibitors, special coatings (slow-release fertilizers).
4 Changes in crop types and/or crop rotations Changes in crop types and rotation (without much change in nitrogen fertilization input) may change the nitrogen output with harvested crop and thereby nitrogen leaching. This measure includes a change to high-yielding crop varieties, and energy crops.
5 Cover crops Cover crops or catch crops or green manures are grown after the harvest of the main crops, and serve to mop up residual mineral nitrogen from the soil and/or to improve soil quality. These crops may be sown in between the main crops (relay cropping) or after the harvest the main crop.
6 Mulching Mulching refers to the covering of the soil with crop mulch or with plastic mulch, mainly to reduce evaporation, modify soil surface temperature, and suppress weed growth. Due to changes in crop yield and soil water flow and utilization, leaching may be suppressed.
7 Restricted grazing Restricted grazing includes zero grazing, spring-season grazing only, and siesta-grazing. This measure refers to a decrease in the animal-grazing hours per year relative to year-round grazing or day-and-night grazing during the growing season.
8 Buffer strips Buffer strips refer to the strips of land along water courses. These strips have adjusted management (fertilization, crops, tillage) and thereby minimize the leaching and overland flow to surface waters. The width and management of the strip are critical
9 Riparian zone Riparian zones refer to wetland areas along water courses which intercept and scavenge nutrients from leaching and overland flow pathways before entering the water courses. It includes constructed wetlands. Special vegetation and management may increase the scavenging of nutrients and thereby the pollution of the surface waters
10 Irrigation This measure includes sprinkler irrigation, drip irrigation, furrow irrigation, flood irrigation, and fertigation. Irrigation may both increase or decrease leaching, depending on irrigation practice, crop type, soil type and weather conditions.

2. Meta-analysis

We used the R-package ‘metafor’ to conduct the meta-analysis (Viechtbauer, 2010). The goal of a meta-analysis is to combine all quantitative data from the collected studies and draw an overall conclusion on the effectiveness of a specific measure. In the reviewed studies the effect of a treatment was shown with different values and units. For a meta-analysis these different designs, units and approaches have to be normalized so they can be compared. To be able to compare effect sizes between studies all data was recalculated to the response ratio (R):

D4.3 eq01 [1]

Where (XT ) ̅ represents the means of the treatment group and (XC ) ̅ the means of the control (Borenstein et al., 2009). For each study the mean, standard deviation and sample size was recorded. The distribution of R cannot be assumed to be normal, so the values for R were log-transformed before statistical analyses using the natural logarithm (Borenstein et al., 2009; Hedges et al., 1999). Commonly, the variance of each pairwise observation is calculated to weigh the individual observations. Records with a smaller error margin are then assigned a heavier weight when the average and confidence intervals are calculated However, as a large part of the observations in our database were missing a measure of variance, we performed an unweighted analysis of the data.

A random effects model was used to assess the effect of the different measures on combined observations of NO3 fluxes to drainage or surface water and NO3 concentrations in soil and water. Study was included in the model as a random factor, to account for different studies contributing a different amount of data points to the database. The resulting means per measure were presented. A 95% confidence interval (CI) was calculated, and the effectiveness of a measure is considered significant when there is no overlap with a response effect of 0%, indicating ‘no effect’.

3. Case studies

Expert knowledge from nine FAIRWAY case studies (Figure 2.1) located across Europe was used to assess the effectivity, cost-effectiveness of measures, as well as the willingness to adopt them. These case studies are investigating measures to minimize pollution of ground- and surface drinking water resources by nitrates.

D4.3 figa01
Figure 2.1

Questionnaires were sent out and experts were asked

  1. which measures were applicable in the region of their case study, and
  2. to evaluate the measures in terms of effectiveness, cost, and applicability (»Annex 2).

Table 2.2 gives a summary of the questions asked and the information that was provided. All experts are in close contact with land managers who apply the measures.

Table 2.2: Format for the description of measures used in the case study areas.

Name of the measure Explain the measure in one sentence
Description Brief characterization of the measure in maximal three sentences; what is (are) the action(s) of the land manager/farmer/citizen
Mode of action

Brief description of the mechanism(s) of the measure in maximal three sentences, addressing the following possible mechanisms:

  • Reduction / substitution of contaminant input
  • Modification of pollution pathway
  • Re-design of the system
Expected effectiveness

Decrease of pollution (concentration or load); select one answer out of five options:

  • High: >25% decrease in concentration/load
  • Moderate: 10-25% decrease in concentration/load
  • Low: 5-10% decrease in concentration/load
  • Insignificant: <5% decrease in concentration/load
  • Unknown
Expected implementation cost

Economic cost, in euro per ha of utilized agricultural land; select one answer out of five options:

  • Low: < 10 euro per ha
  • Moderate: 10-50 euro per ha
  • High: 50-100 euro per ha
  • Very high: >100 euro per ha
  • Unknown
Underpinning of the measure 

Is the measure well examined, as shown by various reports; select one answer out of four options:

  • Yes (> 5 reports)
  • Partly (1-5 reports)
  • No (≤ 1 report)
  • Unknown
Applicability of the measure

Is the measure widely applicable; select one answer out of four options:

  • Yes (on more than 75% of the agricultural land)
  • Partly (on 25-75% of the agricultural land)
  • No (on <25% of the agricultural land)
  • Unknown
Adoptability of the measure

Do the land managers/farmers/citizen adopt the measure easily; select one answer out of four options:

  • Yes (more than 75% of the addressees)
  • Partly (on 25-75% of the addressees)
  • No (on <25% of the addressees)
  • Unknown
Other benefits

Does the measure contribute to beneficial side-effects; select one or more answers out of four options:

  • Yes, decreases energy costs
  • Yes, decreases greenhouse gas emissions
  • Yes, decreases ammonia emissions
  • Yes, contributes to landscape diversity
  • No
  • Unknown
  • Other: please specify
Disadvantages (other than implementation costs and labour)

Does the measure contribute to negative side-effects: select one or more answers out of four options:

  • Yes, decreases crop yield
  • Yes, decreases crop quality
  • Yes, decreases soil quality and biodiversity
  • Yes, contributes to (more) pest and diseases
  • No
  • Unknown
References Provide up to three key literature references

 

Notes:

For full references to papers quoted in this article see »References

Download the full report for Annex 2

Identification of most promising measures and practices

 

References

Main authors: Mart Ros, Gerard Velthof, Oene Oenema, Meindert Commelin, Susanne Klages, Linda Tendler, Jenny Rowbottom, Isobel Wright, Donnacha Doody, Luke Farrow, Birgitte Hansen, Morten Graversgaard, Irene Asta, Andrej Jamsek, Katarina Kresnik, Matjaz Glavan, Jean-François Vernoux, Nicolas Surdyk, Christophoros Christophoridis, Kate Smith, Irina Calciu, Sonja Schimmelpfennig, Hyojin Kim, Piet Groenendijk
FAIRWAYiS Editor: Jane Brandt
Source document: »Ros, M. et al. 2020. Identification of most promising measures and practices: 2. Reduction nitrate transport from agricultural land to groundwater and surface waters by management practices. FAIRWAY Project Deliverable 4.3, 72 pp

 

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