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Trade-offs in soil fertility management on arable farms
Jules F.F.P. Bos a,⁎,1, Hein F.M. ten Berge a, Jan Verhagen a, Martin K. van Ittersum b
a Wageningen Plant Research, Agrosystems research, P.O. Box 16, NL-6700 AA Wageningen, The Netherlandsb Plant Production Systems, Wageningen University & Research, P.O. Box 430, NL-6700 AK Wageningen, The Netherlands
Please cite this article as: Bos, J.F.F.P., et al., Tr10.1016/j.agsy.2016.09.013
a b s t r a c t
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Article history:Received 22 November 2015Received in revised form 11 September 2016Accepted 17 September 2016Available online xxxx
Crop production and soil fertility management implies a multitude of decisions and activities on crop choice, ro-tation design and nutrient management. In practice, the choices to bemade and the resulting outcomes are sub-ject to a wide range of objectives and constraints. Objectives are economic as well as environmental, for instancesequestering carbon in agricultural soils or reducing nitrogen losses. Constraints originate from biophysical andinstitutional conditions that may restrict the possibilities for choosing crops or using specific cultivation and fer-tilization practices. To explore the consequences ofmanagement interventions to increase the supply of organic Cto the soil on income and N losses, we developed the linear programming model NutMatch. The novelty of themodel is the coherent description ofmutual interdependencies amongst a broad rangeof sustainability indicatorsrelated to soil fertilitymanagement in arable cropping, enabling the quantification of synergies and trade-offs be-tween objectives. NutMatchwas applied to four different crop rotations subjected to four fertiliser strategies dif-fering in the use of the organic fertilisers cattle slurry, pig slurry or compost, next to mineral fertiliser. Eachcombination of rotation and fertiliser strategy contributed differently tofinancial return, N emissions and organicmatter inputs into the soil.Our model calculations show that, at the rotational level, crop residues, cattle slurry and compost each substan-tially contributed to SOC accumulation (range 200-450 kg C ha-1 yr-1), while contributions of pig slurry and covercrops were small (20-50 kg C ha-1 yr-1). The use of compost and pig slurry resulted in increases of 0.61-0.73 and3.15-3.38 kg N2O-N per 100 kg extra SOC accumulated, respectively, with the other fertilizers taking an interme-diate position. From a GHG emission perspective, themaximum acceptable increase is 0.75 kg N2O-N per 100 kgextra SOC accumulated,whichwas onlymet by compost. Doubling thewinterwheat area combinedwith the cul-tivation of cover crops to increase SOC accumulation resulted in a net GHG emission benefit, but was associatedwith a financial trade-off of 2.30-3.30 euro per kg SOC gained.Our model calculations suggest that trade-offs between C inputs and emissions of greenhouse gases (notablyN2O) or other pollutants (NO3, NH3) can be substantial. Due to the many data from a large variety of sources in-corporated in the model, the trade-offs are uncertain. Our model-based explorations provide insight in soil car-bon sequestration options and their limitations vis-a-vis other objectives.
The amount and quality of soil organic carbon (SOC) is often used asindicator of soil quality and productivity (Amundson et al., 2015;Powlson et al., 2011a). At the global scale, agricultural soils constitutea large C pool in the form of soil organic matter, and there is thusscope for large amounts of C to be lost or gained from soils as a conse-quence of farming practices (Smith, 2012). Management of arableland through repeated disturbance has turned many arable soils into Csources (Lal et al., 2007), contributing to climate change. Increased
. Bos).ox 925, NL-3700 AX Zeist, The
ade-offs in soil fertilitymanag
awareness of climate change and concerns about soil quality declinehave led to increased emphasis on sequestering C in the soil: increasingSOC content is often seen as a desirable objective. Strategies to increaseSOC content in crop rotations include cover crop cultivation (Poeplau &Don, 2015), nutrient and crop residue management (Lehtinen et al.,2014; Blair et al., 2006), application of manures and composts(Triberti et al., 2008) and no- or minimum-till farming (e.g. Powlsonet al., 2014), with the latter a much debated option. While there aremany advantages to increasing soil C stocks, there are a number of is-sues associated with soil C sequestration which make it a risky climatechangemitigation option (Smith, 2012; Powlson et al., 2011b). These is-sues include the finiteness of the amount of C that can be stored in thesoil, the reversibility of C sequestration, and a number of ‘leakage’ andpollution swapping issues. Despite these limitations, soil C sequestra-tion may have a role in reducing the short term atmospheric CO2
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concentration, thus buying time to develop longer termemission reduc-tion solutions across all sectors of the economy (Smith, 2012).
Besides of CO2, agricultural soils are also a source of nitrous oxide(N2O) (Reay et al., 2012). Nitrous oxide is emitted largely duringmicrobially governed transformation processes of soil-N, derived fromcrop residues and the application of inorganic and organic fertilizers.In developed, high-input agriculture, theN taken up by crops is typicallyno more than 60 per cent of that applied (Lassaletta et al., 2014; Janzenet al., 2003). The remainder is lost in various forms, withmajor environ-mental impacts such as high nitrate levels in drinking water aquifersand eutrophication of surface waters. Reducing N input is an importantstrategy in ameliorating the effect of arable crop production on N2Oemission and water quality (Hillier et al., 2009), but may have a penaltyin terms of (economic) productivity.
Crop production and related soil management implies amultitude ofdecisions and activities on soil tillage, crop choice, rotation design, nutri-ent supply, water supply and crop protection. Within each of thesemanagement categories, many options are usually available to farmers,and the choices to be made and the resulting outcomes are subject to awide range of economic and environmental objectives and constraints(Hengsdijk & van Ittersum, 2002; Groot et al., 2012). Finding ways tomaintain farm profitability while reducing undesirable emissions ormaintaining carbon stocks is complicated by interactions and feedbacksamong agricultural practices. For example, the addition of organicmate-rials to the soil, such as animal manures and composts, potentially in-creases SOC content, and increased yields resulting from fertiliserapplication can result in increased crop residue additions to the soil or-ganicmatter pool (Blair et al., 2006). However, large additions ofminer-al and organic fertilisers to the soil may enhance nitrogen losses towater and atmosphere or result in phosphorus saturation of agriculturalsoils. These and other examples illustrate the existence of conflicts ortrade-offs between objectives of soil management (Powlson et al.,2011a). Given the complexity of interactions and conflicts, the selectionofmanagement options that result in amaximization of the net benefitsfrom agriculture is no easy task.
Hengsdijk & van Ittersum (2003) presented an agro-ecologicalmodelling approach, converting information on specific aims for agri-cultural systems into targeted identification and quantification of landuse systems and their management options. In the approach, processbased knowledge and empirical data regarding agronomic relationshipsare integrated and synthesised, using a variety of numerical tools, whiletaking into account available resources and prevailing land-related ob-jectives (ten Berge et al., 2000). Typically, such ‘engineered’ land usesystems are expressed in terms of inputs and outputs, including produc-tion, environmental and socio-economic characteristics. At relativelylow costs and risks, agro-ecological modelling of land use systems en-ables the systematic exploration of land use options at farmand regionalscales that are difficult to monitor otherwise. Such model-based landuse systems hence provide a framework to disentangle the complex re-lationships between agricultural production, environment and econo-my and to explicate synergies and trade-offs between different goalvariables, contributing to informed decision making with respect to fu-ture land use or research priorities. Currently, many descriptions andapplications of such model studies exist (Janssen & van Ittersum,2007), but to our knowledge nomodel is available that provides the re-quired detail in nutrient management at farm level to reveal trade-offsresulting from soil fertility management. The purpose of this paper isto show how the NutMatch model can support multi-criteria decisionmaking in nutrient and soil fertility management. To this end, themodel is deployed for ex-ante assessments of choices in soil fertilitymanagement in arable farming in the Netherlands, illustrating longterm consequences of these choices on farm income, nitrogen lossesand the build-up of soil organic matter.
In the next section we present the linear programming (LP) modelNutMatch. The novelty of this model is the coherent description of mu-tual interdependencies amongst a broad range of sustainability
Please cite this article as: Bos, J.F.F.P., et al., Trade-offs in soil fertilitymanag10.1016/j.agsy.2016.09.013
indicators related to crop production, soil fertility management, SOCcontent, N emissions and farm economics, enabling the quantificationof synergies and trade-offs between objectives. NutMatch differs frommost other modelling efforts related to soil fertility management inthat it is a static optimizationmodel that can beused for integrating sev-eral sustainability aspects within a whole farming system context. Thiscan be contrasted with dynamic, process-oriented simulation modelsused for predicting nitrogen and soil fertility dynamics at the plot orhigher scales in response to changed climate, management or land use(e.g. Ryals et al., 2015; Lugato et al., 2014; Viaud et al., 2010;Batlle-Aguilar et al., 2010), that lack the capacity to handle a range of ob-jectives simultaneously.
2. Materials and Methods
2.1. Case study
TheNutMatchmodel was applied to arable farming on sandy soils inthe Netherlands. Here, arable farming is characterized by high intensity,expressed in the adoption of crop rotations with a large share of high-value crops (potatoes, vegetables) and the use of relatively high levelsof external inputs such as pesticides and fertilisers. The use of organicandmineral fertilisers on arable farms is currently ceiled by legally bind-ing maximum nitrogen and phosphorus application standards definedat the crop level (Schröder & Neeteson, 2008). Due to the ample supplyof animal slurries in the Netherlands, suppliers pay arable farmers forusing animal slurries in crop fertilisation. Therefore, arable farmerstend to import a large part of the maximum allowable phosphorus ap-plication (28.4 kg P or 65 kg P2O5 ha-1 yr-1 in 2014) as phosphorus in an-imal slurries.
Arable farmers are concerned that restrictions on the use of organicand mineral fertilizers will in the long term reduce soil fertility, jeopar-dizing quality production and economic profits (ten Berge et al., 2010,Reijneveld et al., 2009). While no general decline in soil fertility hasbeen documented for the Netherlands as yet (Reijneveld et al., 2009),it is recognized that past management has resulted in high levels ofsoil fertility indicators such as SOC content, soil N supply and phospho-rus status. Although Nitrates Directive regulations have resulted in re-duced fertiliser inputs over time, nitrate leaching from agriculture stillposes a serious problem, with nitrate concentrations in shallow ground-water under arable farming among the highest in the country. Aboutseventy percent of arable farms on sandy soils have until now notbeen able to meet the EU target for shallow groundwater of 11.3 mgNO3-N per litre (RIVM, 2012). Since 2000, average nitrate concentra-tions on arable farms in the sandy region (covering the southeast, eastand northeast of the Netherlands, i.e. about half the agricultural areain the Netherlands), have varied from about 13.6 mg per litre to 19.2mg NO3-N per litre, with no clear trend.
2.2. Rotation and nutrient management variants
Based on the above regional context, arable cropping systems inNutMatch were described according to so-called design criteria(Hengsdijk & van Ittersum, 2003), each represented by a number of var-iants. Our design criteria were the composition of the rotation, nutrientsources used, and the level of N supply to individual crops relative totheir full N demand at economically optimal N rate (Table 1). We de-fined four crop rotations differing in the relative areas of winterwheat, ware potato, sugar beet and silage maize, and differing in theuse or not of a cover crop afterwinterwheat. The four rotations obvious-ly have different nutrient requirements, financial returns and inputs ofcrop residues into the soil, affecting SOC and soil N dynamics. Of thecrops considered, ware potato is the single most important crop infarm economic terms (see Supplementary Material). The crop withthe largest crop residue input is winter wheat, with straw assumed tobe incorporated into the soil. Cover crops after winter wheat bring
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Table 1Design criteria and their variants to characterise arable cropping systems on sandy soil inthe Netherlands.
Design Criteria Variants
Rotation Four rotation types:
1. ROT1: 25% of each of the crops winter wheat, potato, maizeand sugar beet;
2. ROT1+: as ROT1, with yellow mustard as cover crop afterwheat;
3. ROT2: 50% winter wheat, 25% of each of the crops potato andsugar beet;
4. ROT2+: as ROT2, with yellow mustard as cover crop afterwheat.
Nutrient source Four variants:
1. Mineral fertilisers only;2. Mineral fertilisers and cattle slurry;3. Mineral fertilisers and pig slurry;4. Mineral fertilisers and compost.
Relative Nsupply
Eight N rates per crop, ranging from economically optimal N rateto zero N rate.
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additional organic inputs into the soil, but their cultivation comes withextra costs (Section 2.3).
Each crop could be grown at one of eight different levels of relative Nsupply, ranging from economically optimal N rate to zero rate. N supplyis the sumof annual N inputs (expressed as fertiliser equivalents) and Nmineralised annually from organic soil pools (see Section 2.4). Nitrogenapplication rate affects crop yield and N losses to groundwater (NO3-N)and atmosphere (NH3-N and N2O-N).
Besides crop rotation and relative N supply, different nutrientsources were used (Table 1). In one variant, nutrient supply to cropswas based on mineral fertilisers (NPK) only. Three other options com-bined mineral fertilisers with either cattle slurry, pig slurry or compost.The organic manures could be combined with mineral fertilisers in anyratio, depending on objectives and restrictions (e.g. on maximum Nemissions) imposed in NutMatch.
2.3. Modelling framework
NutMatch optimises soil fertility management in arable crop rota-tions. The model is formulated as an optimisation matrix, consisting ofrows and columns. The rows in this matrix are linear mathematicalequations representing objective functions and restrictionswith respectto crop production, supply of nutrients to crops from fertilisers and thesoil, nutrient balances, build-up of soil carbon and N emissions. The col-umns are the decision variables in these equations, representingcropping activities (unit: ha) with different variants of fertiliser use(mineral and organic) and different levels of N supply (Table 1), mineraland organic fertiliser activities (Mg yr-1) and a number of related deci-sion variables required to fully formulate the LP problem to beoptimised (see Supplementary Material for a list of all decision vari-ables). An activity is a coherent set of operationswith corresponding in-puts and outputs, resulting in, e.g., the delivery of marketable cropproducts, maintenance of soil fertility and N emissions. Each possiblemodel outcome is a ‘farm configuration’ that represents one of thefour defined rotations, fertilised according to one of the four nutrientsource variants, with each crop grown at its own level of relative N sup-ply. Each activity is characterised by a set of pre-defined coefficients thatexpress the activity’s claim on available resources (e.g. land, inputs re-quired) and its contributions to defined objective variables (e.g. income,build-up of soil carbon) and other desired or undesired outputs (e.g.crop yields, crop residue returns, N emissions). The sum of activities’claims is subject to a series of constraints, which represent the restric-tions imposed (e.g. on N emissions) and the minimum or maximum
Please cite this article as: Bos, J.F.F.P., et al., Trade-offs in soil fertilitymanag10.1016/j.agsy.2016.09.013
amount of a certain resource that can be used (e.g. maximum 170 kgN ha-1 yr-1 in animal manures, as stipulated by the Nitrates Directive).Coefficients were calculated based on various literature and secondarydata sources (see below and Supplementary Material). They should in-corporate all relevant non-linearities that are so common in agriculture.For example, the non-linear response of crop yield and N leaching to Nsupply is embedded in the respective coefficient values for each of theeight defined levels of N supply. All coefficients are quantified at the ac-tivity level and are defined prior to NutMatch optimisations. Hence,they are presumed independent of resulting farm configurations.NutMatch combines all activities and constraints with underlying coef-ficient tables in one optimisation matrix, which can then be optimisedfor one objective function. Objective functions used in this paper are:
(1) financial return (maximise), calculated as revenues from soldcrop products minus fertilisation costs and other variable costsattributed to crop cultivation,
(2) nitrate leaching to groundwater (minimise),(3) ammonia emission (minimise),(4) nitrous oxide emission (minimise),(5) build-up of soil carbon (maximise).
In our optimizationswithNutMatch, focuswasnot onmaximising orminimising single objectives in isolation. Instead, we considered two orthree objectives simultaneously, with one objective beingmaximised orminimised, with the other one(s) serving as restriction. E.g. “maximisefinancial return, while nitrate leaching to groundwater may not exceed80 kg N”. Separate optimizations were done for each of the 16 variantsof four rotations and four nutrient source variants (Table 1), whichwere hence imposed onto the model before optimization. Other thanthe rotation and nutrient source variants, the eight relative N supplyvariants were not imposed onto the model. Instead, N supply to singlecrops was an outcome of model optimizations, with the selected levelper crop and the share of N from organic fertilisers in the total N supplydepending on restrictions imposed in the model. Further details on theset up of our optimizations are given in Section 2.6. The definition of ob-jective functions and their quantification are further detailed in follow-ing sections.
NutMatch incorporates costs and revenues associated with cultiva-tion of crops and selling of crop products. To exclude economy of scaleeffects, labour costs and fixed costs of land, buildings and machinerywere not considered. Costs attributed to crop cultivation includedcosts for seeds, fertilisers, crop protection products and fuel. With theexception of fertiliser costs, these costs were assumed to be indepen-dent of soil fertility management, and hence were assigned to crops asfixed cost terms. Costs and 5-year average farm product prices werebased on default values for the sandy regions in the Netherlands(KWIN-AGV, 2012). Revenues were calculated as the product of cropyields as defined by relative N supply and product price. Costs offertilisers refer to the purchase of mineral fertilisers (calcium ammoni-um nitrate, potassium chloride and triple superphosphate) and organicfertilisers (cattle slurry, pig slurry or compost). Depending on regionaland seasonal supply and demand, prices of organic fertilisers varywide-ly across Europe.We fixedmanure prices on the basis of their NPK con-tent and application costs, with the assumption that arable farmers paya share of 75% of both application costs and the value of NPK in mineralfertiliser form (pers. comm. H. Steinmann, Univ. of Göttingen). Hence,nutrients in organic fertilisers were set to be only slightly cheaperthan in mineral fertilisers.
The nutrient (NPK) requirement at rotation level is the sum of re-quirements by selected cropping activities, given their relative N supplylevels that best match the imposed conditions. P and K requirementswere set according to Dutch fertilizer recommendations (de Haan &van Geel, 2013) and corresponded to N-defined yields. All nutrient re-quirements are to be met by the respective nutrient supplies from
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manures (short and long term), mineral fertilisers, crop residues andfrom the soil itself. Each crop could be grown at only one N supplylevel, but levels were allowed to differ between crops in the rotation.As stipulated by theNitrates Directive, N input in the formof animalma-nures could not exceed 170 kg N ha-1 yr-1. This restriction was not ap-plied to compost-N. Agronomic P surplus at rotation level wasrestricted to zero, with a small margin (for numerical purposes) of ±2.2 kg P ha-1 yr-1, which is stricter than current legislation in theNetherlands.
2.4. Nitrogen and organic matter dynamics
N response curves for each crop (van Dijk et al., 2007) define theamount of plant available N required to reach a given target yield andcorresponding N offtake. No N response curves were used for covercrops. Instead, here we defined one yield level only, with correspondingN requirement based on standard fertiliser recommendation. Van Dijket al. (2007) based their curves on a large number of trials where yieldand N offtake were measured in response to N fertiliser rate. We con-verted responses to fertiliser-N into responses to total N supply (plantavailable N), by estimating the sum of soil N supply (mineralisation)and atmospheric N in each of their trials as the observed N offtake inunfertilised plots divided by the apparent fertiliser-N recovery (ANR)at low N rate. Crops in our calculations must respond to total N supplyrather than just fertiliser-N rate, because soil N supply (and hence sup-plemental fertiliser-N required tomeet a given target yield) evolves as afunction of farm configuration (model outcome) itself. A priori definedresponses to just fertiliser-N would therefore not be consistent with so-lutions found.While NutMatch is not a dynamic simulationmodel, timecan, therefore, not be ignored. Based on the above data set, we set initialsoil N supply rate to 100 kg N ha-1 yr-1, and considered it representativefor intensively farmed soils in the case study region.
We distinguished N mineralised from organic materials appliedwithin the first year of application (‘first year mineralisation’) from Nreleased by the accumulated older organic materials. The ‘first yearmineralisation’ from recent organic amendments was allocated to indi-vidual crops, while N mineralised from older materials was averagedover the total farm area. This approach eliminates the need to keeptrack of soil organic N pools per field, and is consistent with realitywhere crops and organic inputs usually rotate spatially over the farmarea. In other words, soil N supply was calculated at the whole-farmlevel and assumed equal for all crops in the rotation.
Model outcomes refer to a time horizon of 25 years. This means thatthe resulting farm configuration represents the optimal solution (to theset of goals and constraints imposed) after maintaining this configura-tion during 25 years. Long-termeffects of organicmanures and crop res-idues are thus accounted for. Annual soil N supply after 25 years consistsof N still released from the initial (t=0) soil organic N pool, and Nmineralised from organic manures and crop residues accumulated inthe soil over the 25-year period since t=0.
The build-up of soil carbon from organic amendments was calculat-ed according to Yang (1996), and Yang& Janssen (1997, 2000), account-ing for decomposition using specific decomposition parameters fordifferent organic materials (see Supplementary Material). This mono-component model is a simplified but practical approach to describecomplex dynamic systems. Yang’s model was deemed suitable to de-scribe soil carbon accumulation over periods up to several decades,but less so for calculations over centuries (de Willigen et al., 2008). Toassess the performance of a farm configuration in building up SOC, wedistinguish newly formed SOC (SOCnew) from SOC already present att=0 (SOCini). SOCnew is all C that remains in the soil from organicamendments added during the 25 years since t=0. During this period,SOCini continues to break down, but its amount and fate depend on pre-ceding land use history, not on farm management as composed by theoptimisation model. In presenting model outcomes, we only consider
Please cite this article as: Bos, J.F.F.P., et al., Trade-offs in soil fertilitymanag10.1016/j.agsy.2016.09.013
SOCnew, expressed as a mean formation rate across the 25 year period(kg C ha-1 yr-1).
2.5. Calculation of N losses
Annual N loss from the soil-crop systemwas calculated at rotationallevel as annual throughput of mineral N in the soil minus annual N up-take in crops and soil surface ammonia loss. Throughput is the sum of Ndeposition, mineral N in applied organic and inorganic fertilisers, andtotal Nmineralised from organic inputs and the soil organic N pool. An-nual N uptake in crop products and residueswas based on vanDijk et al.(2007). Soil surface ammonia loss was calculated via emission factors(de Haan & van Geel, 2013; Huijsmans & Hol, 2012; de Ruijter et al.,2013) that specify ammonia loss fractions from land application of or-ganic fertilisers and from crop residues.
N not taken up by crops or lost as ammonia is subject to loss process-es in the soil, notably denitrification and leaching. Leaching was calcu-lated via a fixed leaching factor: the proportion of non-ammoniacal Nloss that is actually leached as nitrate, as derived from long-term mon-itoring programmes on commercial farms in the region (Schröder etal., 2011; see Supplementary Material). We calculated nitrate concen-tration in upper groundwater (mg NO3-N l-1) from the amount of Nleached (above), assuming this N is diluted in an average precipitationexcess for this region of 346mm(Schröder et al., 2011). Direct and indi-rect (off-farm)N2O emissions from agricultural soils were quantified byemission factors (van der Hoek et al., 2007; see Supplementary Materi-al). Direct N2O emissions come from crop residues and applied fertil-izers, indirect emissions from N first lost from our case study rotationsas ammonia or nitrate. All emissions associated with imported mineraland organic fertilisers, hence occurring upstream of the rotations,were not accounted for.
2.6. Set up of model runs
In a first optimisation cycle, trade-off curves were calculated show-ing how one objective variable is restricted by another one. Trade-offcurves were calculated for each combination of four rotation and fournutrient source variants (Table 1), considering financial return vs.SOCnew, financial return versus either ammonia, nitrous oxide or nitrateloss and SOCnew versus either ammonia, nitrous oxide or nitrate loss.End points of each trade-off curve are defined by maximum values ofdesirable objective variables (financial return, SOCnew), and minimumvalues of undesirable objective variables (N losses). Intermediate pointswere calculated by maximising financial return or SOCnew, respectively,while stepwise tightening the restriction on ammonia, nitrous oxideand nitrate loss, respectively. For example, the trade-off curves of finan-cial return and N2O-N loss were calculated in a series of optimizationsmaximising financial return under the condition that N2O-N emissionper ha should not exceed 6, 5, 4 etc. kg per ha.
An objective variable can also be restricted by two other objectivevariables simultaneously. This was illustrated in a second series of opti-misations, where financial return was maximised while stepwise tight-ening restrictions onmaximumN2O-N emission andminimum SOCnew.(“maximisefinancial return,while N2O-N emission should not exceed 6,5, 4 etc. kg per ha and SOCnew should be at least 1000, 900, 800 etc. kgper ha”). Through this procedure, the so-called trade-off surface isestablished. The trade-off surface is made up of binding solutions only,meaning that each co-ordinate on the trade-off surface is optimal, be-cause none of the objective variables can be improved without sacrific-ing one of the others, hence without moving to another point on thesurface. The outer boundaries of the surface are defined by the best at-tainable values of each single objective under stepwise tightened re-strictions for the other objectives. Trade-off surfaces were alsocalculated for each of the four rotation and four nutrient source variants.
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3. Results
In this section, we focus on results for rotations ROT1 and ROT2+.Designs of ROT1+ and ROT2 represented the stepwise transition fromROT1 to ROT2+, and their results are therefore intermediate of the re-sults for the two extreme rotations.
3.1. Trade-off curves
3.1.1. Financial return versus SOCnewMaximum financial return varied from 970 to 1400 euro per ha
(Fig. 1). Maximum values for SOCnew ranged from 325 to 880 kg C perha per year (Fig. 1), with rotation and nutrient source having majoreffects. Compared to using mineral fertilisers only, the use of cattleslurry and compost in both rotations added approximately 240 and390 kg SOCnew per ha per year, respectively, while pig slurry addedonly 26 kg per ha per year (Fig. 1). Given that the use of organicfertilisers was limited by restrictions on P surplus (Section 2.3), consid-erable differences in the contribution of organic fertilisers to soil Cinputs are explained by differences in their organic matter contentsper kg P (Table 5 in Supplementary Material). Using different nutrientsources within rotation types did not result in drastic changes in finan-cial return, so that organic fertilisers were largely interchangeable froma financial perspective.
Changes in design of ROT2+ compared to ROT1 added about 160 kgSOCnew per ha per year, but reduced financial return by about 340 europer ha, irrespective of nutrient source (Fig. 1). The extra build-up of soil-C was mainly due to the increased winter wheat area, while the covercrops only had a modest effect (see Section 3.2). Income foregone inROT2+ was 2.30-3.30 euro per kg SOCnew gained.
When either financial return or SOCnew is maximised, high N levelsbased on maximum use of organic fertilisers were selected. However,the solutions for maximum financial return and maximum SOCnew
slightly differed in selected N levels. When maximizing SOCnew, allcrops were supplied N at the highest defined rate, as this resulted inthe highest carbon returns to the soil via crop residues.Whenmaximiz-ingfinancial return, winterwheat and sugar beetwere not suppliedN atthe highest defined rate, hence carbon returned to the soil in crop resi-dues was slightly below maximum values.
3.1.2. Financial return versus nitrogen lossesWhen financial return was at (near-)maximum values, N losses
were also high (Fig. 2, Table 2). N losses were higher when organic
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SO
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Mineral fertilisers only
ROT2+
ROT1
Fig. 1. Trade-off curves of financial return and SOCnew in ROT1 and ROT2+ under fournutrient source variants. End points of each curve correspond with maximum financialreturn and maximum SOCnew, respectively.
Please cite this article as: Bos, J.F.F.P., et al., Trade-offs in soil fertilitymanag10.1016/j.agsy.2016.09.013
fertilisers were used. Higher nitrate leaching was caused by increasedN mineralisation outside the growing season, while higher emissionsof nitrous oxide and ammoniawere caused by higher N emission factorsassumed for organic fertilisers (SupplementaryMaterial). When nitrateleaching is not constrained, the annual soil N supply is highest whencompost was used and lowest when only mineral fertilizers were used(Table 2), whichwasmirrored in, respectively, lowest and highest avail-able N required from fertilisers.
Nitrate concentrations atmaximumfinancial return ranged between18 and 21 mg NO3-N per litre (Fig. 2a, Table 2). Restricting nitrateleaching initially had only a moderate effect on financial return. This isexplained by a rather weak response of yield to reduced N in theupper end of the response curves and because it initially sufficed to re-duce N levels of economically low-yielding crops only. When furthertightening the restriction on nitrate leaching, the response of yield to re-duced N becomes steeper and N application rates in high-yielding cropsalso had to be reduced.
If the norm of the Nitrates Directive was to be met (11.3 mg NO3-Nper litre), N levels were strongly reduced, resulting in reduced yieldsand reduced financial returns (Fig. 2a, Table 2). Reductions in N avail-able to crops from fertilisers ranged from 22% to 55%, depending on ro-tation and nutrient source.Meeting theNitrates Directive norm reducedfinancial return by 100-150 euro per ha in ROT1 and by 200-300 europer ha in ROT2+ (Table 2), or 15 and 30 euro per mg NO3-N reduced,respectively. Relative income loss in ROT2+ (20-25%) was higherthan in ROT1 (10%), because in NutMatch only one N level was definedfor cover crops, implying that N supply to these crops could not be re-duced. Meeting the 11.3 mg standard in ROT2+ therefore requiredextra reductions of N supply to financially rewarding crops.
By substituting mineral fertilisers for animal slurries, ammoniaemissions could be reduced without dramatically affecting financialreturns (Fig. 2b). Hence, when restricting ammonia loss to 1 kg NH3-Nper ha in ROT1 and 2.5 kg NH3-N in ROT2+, fertilisation was entirelybased on mineral fertilisers, so that remaining ammonia emission wasfrom crop residues only. In ROT1, ammonia emission could be furtherreduced than in ROT2+, as in ROT1 ammonia emitting crop residuesfrom cover crops were absent. When unrestricted, ammonia emissionreached particularly high values in ROT1when cattle slurrywas applied.This is explained by the spring application of cattle slurry in winterwheat using shallow injection, a technique that is associated withhigher ammonia emission than standard injection (SupplementaryMaterial).
Nitrous oxide emissions at maximum financial return ranged from3.1 to 5.8 kg N2O-N per ha per year (Fig. 2c). Compared to usingmineralfertiliser only, the use of cattle slurry, pig slurry and compost in ROT1 in-creased nitrous oxide emissions by 2.4, 1.3 and 1.0 kg N2O-N per ha peryear, respectively. Similar to nitrate leaching and for similar reasons,restricting nitrous oxide emission initially had only a moderate effecton financial yield. When restrictions became tighter, the use of organicfertilisers was strongly reduced, explained by their fourfold higheremission factor than that of mineral N fertiliser. Further restricting ni-trous oxide emission, organic fertilisers were not applied anymore, sothat differences between the nutrient source variants disappeared.
3.1.3. SOCnew versus N losses(Near-)maximum values for SOCnew were attained when crops are
grown at high N levels and when organic fertilisers were used at maxi-mum levels. High SOCnew values were hence associated with high Nlosses (Fig. 3). The levelling off of the curves can be explained by ahigher proportion of applied N that is lost, due to a diminishingN recov-ery by crops with increasing N input. Relationships between SOCnew onthe one hand and NH3 and N2O emission on the other are more or lesslinear. Nitrate concentration in groundwater, however, could be consid-erably reduced without affecting SOCnew. When maximizing SOCnew
while tightening the restriction on nitrate concentration, the use of or-ganic fertilisers remained high at first, while mineral N input was
ement on arable farms, Agricultural Systems (2016), http://dx.doi.org/
Fig. 2. a-c: Trade-off curves of N losses and financial return in ROT1 and ROT2+ under four nutrient source variants. End points of each curve correspond with minimum N loss andmaximum financial return, respectively.
6 J.F.F.P. Bos et al. / Agricultural Systems xxx (2016) xxx–xxx
reduced. This strategy slightly affected carbon input via crop residues,but left the more significant supply of carbon via organic fertilisers un-affected. However, when nitrate concentration was restricted to thenorm set in the Nitrates Directive, the use of organic fertilisers had tobe reduced, minimizing N loss due to N mineralisation outside thegrowing season. Hence, the use of cattle slurry was reduced by morethan half in ROT1 and even further in ROT2+. The use of pig slurrywas not reduced as strongly, which is attributable to its high contentof readily availablemineral N. Due to the severely constrained use of or-ganic fertilisers, maximum SOCnew values were considerably reducedwhen nitrate loss was restricted.
Expressing SOCnew and N2O emission both in CO2-equivalents (1 kgSOCnew = 3.7 kg CO2-equivalents, 1 kg N2O-N = 487 kg CO2-equiva-lents) it can be calculated, from a GHG emission perspective, that themaximum acceptable increase in N2O-N emission is 0.75 kg per 100kg extra SOCnew. This break-even line is shown in Fig. 3c, with sectionsof trade-off curves below the line indicating a net increase in emissionof CO2-equivalents and sections of trade-off curves above the line anet decrease. Only the compost nutrient variant curves were entirelyabove the break-even line, i.e. SOCnew more than compensated N2Oemission in all cases. All other curves were below the break-even linewhen approaching higher values of SOCnew. Based on the slopes oftrade-off curves in Fig. 3c, increases in N2O-N emission ranged from0.61 to 3.38 kg per 100 kg extra SOCnew (Table 3), with compostresulting in the lowest increase and pig slurry in the highest.
Please cite this article as: Bos, J.F.F.P., et al., Trade-offs in soil fertilitymanag10.1016/j.agsy.2016.09.013
3.2. Net GHG emission as affected by rotation and nutrient source variants
Differences in SOCnew and N2O emissions between rotation and nu-trient source variants can be used to quantify net GHG emission effectsof changing from one variant to another. For example, the net GHGemission effect of introducing fertilised cover crops on half of the culti-vated area can be quantified by comparing SOCnew and N2O emissionbetweenROT2 and ROT2+.With compost as organic fertiliser andmax-imizing financial return, the additional SOCnew due to cover crops inROT2+ was 167 kg CO2-equivalents per ha per year (Table 4). Thisamount, however, did not compensate for additional N2O emission inROT2+ (239 kg CO2-equivalents per ha per year), so that there was anet increase of 71 kg CO2-equivalents per ha per year due to the cultiva-tion of cover crops in ROT2+. Using other organic fertilisers, a similarresult was found. The net GHG emission effect of doubling the winterwheat area was quantified by comparing SOCnew and N2O emission be-tween ROT1 and ROT2. With again compost as organic fertiliser, addi-tional SOCnew and N2O emission in ROT2 were 416 and 36 kg CO2-equivalents per ha per year, respectively (Table 4), hence a net decreasein GHG emission of 380 kg CO2-equivalents per ha per year. Comparingresults for ROT1 and ROT2+, the effect of both interventions combinedwas a net decrease in GHG emission of 310 kg CO2-equivalents per haper year.
Optimizations with andwithout the application of organic fertilisersallow quantification of the net GHG emission effect resulting from the
ement on arable farms, Agricultural Systems (2016), http://dx.doi.org/
Table 2NPK supply frommineral and organic fertilisers, N losses, NO3-N concentration in groundwater, soil N supply, financial return and SOCnew in ROT1 and ROT2+ under four nutrient sourcevariants, maximising financial return with and without the restriction that the Nitrates Directive norm (11.3 mg NO3-N per litre) has to be met. All data pertain to an average ha in therotation and are expressed in kg N/P/K/C ha-1 yr-1, except nitrate-N concentration (mg NO3-N l-1) and financial return (Euro ha-1 yr-1).
7J.F.F.P. Bos et al. / Agricultural Systems xxx (2016) xxx–xxx
use of organic fertilisers. For ROT1, onlywhen using compost, additionalSOCnew outweighed additional N2O emissions (Table 4).
3.3. Trade-off surfaces
Trade-off surfaces of ROT1 and ROT2+ and nutrient source variantsinvolving compost and cattle slurry are given in Fig. 4, with x,y-coordi-nates connected by labelled iso-financial return lines. Trade-off surfacesof the nutrient source variants involving pig slurry and mineralfertilisers only were too compressed to plot in a readable way (Supple-mentaryMaterial) and are hence not shown.Note that the trade-off sur-faces differ from the trade-off curves in Figs. 2c and 3c in that in thesurfaces the objective variable ‘financial return’ is restricted by twoother objective variables (N2O-N emission and SOCnew) simultaneouslyinstead of one (Section 2.6). The surfaces show that SOCnew and finan-cial return were highest when restrictions on N2O emission were le-nient. Tightening the restriction on N2O emission reduced bothSOCnew and financial return. The shapes of the surfaces exemplify thatreducing N2O emission on the one hand and adding carbon to the soiland generating income on the other are conflicting goals. Only thetrade-off surfaces involving compost were almost entirely above thebreak-even line, i.e. SOCnew outweighed N2O emission in all caseswhen expressed in CO2-equivalents, indicating a net decrease in GHGemission. Trade-off surfaces of the other nutrient source variants werepartly above and partly below the break-even line, the latter indicatinga net increase in GHG emissions.
4. Discussion
4.1. Trade-off between SOC accumulation, N losses and GHG emissions
In the introduction section of this paper, we briefly highlighted anumber of limitations related to soil C sequestration, including
Please cite this article as: Bos, J.F.F.P., et al., Trade-offs in soil fertilitymanag10.1016/j.agsy.2016.09.013
saturation of the carbon sink, the reversibility of C sequestration and‘leakage’ issues (Smith, 2012). The results of our study suggest that, inaddition, trade-offs between C inputs and N losses can be substantial,and in situations where increases in soil C are associated with increasesin emissions of greenhouse gases (notably N2O) or other pollutants(NO3, NH3), these trade-offs should be made explicit. Although thesetrade-offs have been widely addressed in other scientific publicationsin a qualitative way as well, there are very few papers that do so in aquantitative manner. In quantitative terms, the most important N losspathway in our calculations is through leaching, with about 70 kgNO3-N per ha lost when maximizing income and 40 when the NitratesDirective norm has to be met (Table 2). Compared to leaching loss,losses of N2O and NH3 are relatively small (b 6 kg N per ha in all cases).
Based on modelling, Ryals et al. (2015) quantified the effects of dif-ferent compost amendment scenarios to grassland on both soil C stor-age and GHG emissions. They found that increased GHG emissions,particularly direct soil N2O emissions and indirect N2O emissionsthrough NO3-N leaching, partially offset C sequestration benefits ofcompost additions. However, all modelled scenarios resulted in a netGHG sink in the soil that persisted for several decades, indicating thatcompost additions to grassland have potential to contribute to climatechange mitigation. In our study, we found a similar result for compostadditions to arable land. Based on a literature review and modelling,Conant et al. (2005) quantified the effects of a broader set of grasslandmanagement options on C sequestration and GHG emissions. Their re-sults showed that changes in soil C and N stocks due to changed grass-land management were tightly linked, i.e. in most cases either bothincreased or both decreased. The study further demonstrated thatevenwhen improved grasslandmanagement practices result in consid-erable rates of C and N sequestration, changes in N2O fluxes can offset asubstantial portion of C sequestration gains.We are not aware of similarstudies quantifying trade-offs betweenmaximizing C sequestration andminimizing N2O and other N emissions in an arable context. The lack of
ement on arable farms, Agricultural Systems (2016), http://dx.doi.org/
ROT1 cattle slurry plus min. fert.ROT1 pig slurry plus min. fert.ROT1 compost plus min. fert.ROT1 min. fert. onlyROT2+ cattle slurry plus min. fert.ROT2+ pig slurry plus minf. fert.ROT2+ compost plus min. fert.ROT2+ min. fert. only
11.3 mg NO3-N
0
200
400
600
800
1000
0 1 2 3 4 5 6 7 8
SO
Cne
w (
kg h
a-1 y
r-1)
N2O-N emission (kg ha-1yr-1)
ROT1 cattle slurry plus min. fert.
ROT1 pig slurry plus min. fert.
ROT1 compost plus min. fert.
ROT1 min. fert. only
ROT2+ cattle slurry plus min. fert.
ROT2+ pig slurry plus min. fert.
ROT2+ compost plus min. fert.
ROT2+ min. fert. only
break even line
Fig. 3. a-c: Trade-off curves of N losses and SOCnew in ROT1 and ROT2+ under four nutrient source variants. End points of each curve correspond with minimum N loss and maximumSOCnew, respectively. The dotted line in curve (c) is, from a GHG emission perspective, the break-even line between the build-up of soil C and N2O-N emission, with sections of trade-off curves below the line indicating a net increase in emission of CO2-equivalents and sections of trade-off curves above the line a net decrease.
8 J.F.F.P. Bos et al. / Agricultural Systems xxx (2016) xxx–xxx
more quantitative studies in this field is probably the result of majorknowledge gaps. According to Paustian et al. (2016) the implementa-tion of soil-based GHG mitigation activities is still at an early stage,with accurately quantifying reductions and emissions remaining a sub-stantial challenge. Referring to N2O emissions, these authors concludethat high temporal and spatial variability make predictions of changesin N2O fluxes in response tomanagement surprisingly difficult. Particu-larly lacking are empirical data for multi-intervention strategies thatmay interact in unexpected ways.
There currently exists much policy interest in enhancing carbon se-questration in agricultural soils for climate change mitigation and cropproduction purposes (e.g. the “4‰ Initiative”, http://newsroom.unfccc.int/media/408539/4-per-1000-initiative.pdf). Oneimportant lesson to be learned from our study is that this may be acounterproductive strategy formitigation purposes, verymuch depend-ing on the way in which this enhanced carbon sequestration is accom-plished. Based on our results, effective strategies in crop rotations are
Table 3Increase in N2O-N emission per 100 kg extra SOCnew for each nutrient source variant inROT1 and ROT2+ (kg N2O-N per 100 kg SOCnew).
Please cite this article as: Bos, J.F.F.P., et al., Trade-offs in soil fertilitymanag10.1016/j.agsy.2016.09.013
the use of compost in fertilisation and increasing the area of crops thatreturn a large residue to the soil.
Apart from the above questions concerning the net benefits in termsof GHG emissions, there remain good reasons for adding organic mate-rials to soils, for instance to maintain organic matter levels in the soiland for nutrient recycling purposes (Powlson et al., 2011b). This is pos-sibly to result in improved soil functioning and more efficient crop pro-duction in terms of input use per unit output, hence potentially yieldingindirect climate benefits. A key question then is what is the origin ofthese organic materials and how much GHG emissions have occurredto produce these materials. Whilst the application of animal slurriesand other organic fertilisers on arable farms results in substantial sav-ings in mineral fertiliser imports on these farms, the production ofthese organic fertilisers also generated GHG emissions and a range ofother environmental impacts. Savings in mineral fertilisers on arablefarms through imported organic fertilisers are thus based on pollutionswapping. A more complete account of climate effects of managementinterventions on arable farms can be obtained in NutMatch by factoringin upstreamGHGemissions of all external inputs andGHGemissions as-sociated with fuel use required for crop cultivation, including covercrops. In such an approach, linear programming is combined with lifecycle analysis (LCA) frameworks. A modelling approach combining op-timization and LCA has been developed by Glithero et al. (2012) to eval-uate biofuel feedstock production at farm level.
Doubling the winter wheat area combined with the cultivation ofcover crops to increase SOC accumulation resulted in a financial trade-off of 2.30-3.30 euro per kg SOCnew gained. This is a much higher pricethan the price currently prevailing in the European carbon emission
ement on arable farms, Agricultural Systems (2016), http://dx.doi.org/
Table 4Additional SOCnew and N2O emissions resulting from soil fertility management interventions when maximizing financial return compared to a reference.
kg C ha-1 yr-1 kg CO2-eqv ha-1 yr-1 kg N2O-N ha-1 yr-1 kg CO2-eqv ha-1 yr-1 kg CO2-eqv ha-1 yr-1
Rotation designROT1+: Cover crop on 25% of area ROT1 23 84 0.30 144 +60ROT2+: Cover crop on 50% of area ROT2 46 167 0.49 239 +71ROT2: Doubling of winter wheat area ROT1 114 416 0.07 36 -380
Nutrient sourceROT1: Cattle slurry Mineral fertilisers only 211 773 2.43 1185 +412ROT1: Pig slurry Mineral fertilisers only 25 93 1.33 650 +556ROT1: Compost Mineral fertilisers only 317 1159 0.92 446 -713
9J.F.F.P. Bos et al. / Agricultural Systems xxx (2016) xxx–xxx
trading system (about 0.01 euro kg-1 C), suggesting that this manage-ment intervention is far from competitive. Using different nutrientsources within rotation types had little effect on financial return, sothat, within rotation types, there was no trade-off between gains inSOCnew and financial return (Fig. 1). The lack of a trade-off is the conse-quence of the method used to calculate prices of organic fertilisers(Section 2.3), which were slightly cheaper than mineral fertilisers. Inorder to be competitivewithmineral fertilisers,market prices of organic
ROT1 Cattle slurry plus mineral fertiliser
ROT1 Compost plus mineral fertiliser
Fig. 4. Trade-off surfaces of ROT1 (left) and ROT2+ (right) under nutrient source variants invoyellow colours represent a decrease in financial return (Euro ha-1 yr-1) while gradually tighteninGHG emission perspective, the break-even line indicating the minimum value of SOCnew requfigure legend, the reader is referred to the web version of this article.)
Please cite this article as: Bos, J.F.F.P., et al., Trade-offs in soil fertilitymanag10.1016/j.agsy.2016.09.013
fertilisers may in practice be considerably lower than the prices weused, especially in regions with high animal densities. In such regions,animal slurries are consideredwastes, and their pricesmay even beneg-ative, i.e. arable farmers are paid if they use slurries to fertilise theircrops. Compared to animal slurries, compost is generally the more ex-pensive fertiliser. If slurries are to be replaced by compost to increasesoil-C build-up, the resulting compost-based SOC gainwill be associatedwith a financial trade-off.
ROT2+ Compost plus mineral fertiliser
ROT2+ Cattle slurry plus mineral fertiliser
lving cattle slurry and compost as determined by maximisation of financial return. Red tog restrictions onmaximumN2O emission andminimumSOCnew. The dotted line is, from aired to compensate for N2O emission. (For interpretation of the references to color in this
ement on arable farms, Agricultural Systems (2016), http://dx.doi.org/
10 J.F.F.P. Bos et al. / Agricultural Systems xxx (2016) xxx–xxx
4.2. Model evaluation
Modelling, in principle, allows a transparent and consistent evalua-tion of a large number and wide diversity of farming systems. Themodelling approach used in this study integrates and synthesises alarge number of data from very different sources and as such contrib-utes to bridging the gap between basic and applied sciences and inte-grating the bio-physical and socio-economic sciences. However, owingto the very different data sources, it is hardly possible to validate themodel. Therefore, rather than making predictions, the main purpose ofthe model was to explicate synergies and trade-offs, contributing to in-formed decision making and setting the research agenda (van Ittersumet al., 1998).
N2O emissions were quantified on the basis of simple emission fac-tors for different sources of N, assuming a linear increase with increas-ing N application of each source. In our optimizations, emissionsranged from 6.8 to 0.9 kg N2O-N per ha per year, of which 4.6 and 0.4kg were direct emissions. Emissions of similar magnitude have beenmeasured in experiments (Bell et al., 2015; Ball et al., 2014), but withlarge variations between sites and years. Results of some experimentssuggest that N2O emissions increase exponentially with increasing Napplication rates (e.g. Hoben et al., 2011), while other experimentsfound a less than linear increase (Bell et al., 2015). Yet, other experi-mental work suggests that the use of cattle slurry, compost and otherorganic amendments is, at least in some years, associated with in-creased N2O emissions compared to a control based on mineralfertilisers (Ball et al., 2014; Jones et al., 2007). For crop residues, Chenet al. (2013) conclude that N2O emissions are at least similar if not great-er than those ofmineral fertilisers, suggesting that they could play a rolebeyond their N content in N2O production. Enhanced N2O productionfrom organic amendments could be due to their stimulating effect onmicrobial respiration, thus enhancing oxygen depletion and promotingdenitrification in anaerobic conditions. This would justify the greateremission factors used for organic amendments in our study. Currentlyour scientific understanding of N2O emissions is insufficient to quantifyN2O emissions as depending on N source, N level, soil characteristicsand local climatic and weather conditions with more precision(Paustian et al., 2016).
Similar to N2O emissions, model calculations on soil C build-up arealso uncertain. De Willigen et al. (2008) showed that different modelsused to predict the effects of management interventions on organicmatter contents result in widely differing outcomes. Differences arisefrom different conceptual approaches followed in these models andfrom differences in time scales considered (from decades to centuries).DeWilligen et al. (2008) considered themodel we used suitable for cal-culations about expected changes in the short term up to 25 years.
5. Conclusions
The bio-economic farm model NutMatch was successfully used toexplore the effects of rotation design and the use of inorganic fertiliserand different types of organic fertilizers on SOC accumulation, N lossesand financial returns for an arable farming system in the Netherlands.At rotation level, crop residues, cattle slurry and compost each substan-tially contribute to SOC accumulation (range 200-450 kg C ha-1 yr-1),while contributions of pig slurry and cover crops are small (20-50 kgC ha-1 yr-1). The use of compost and pig slurry resulted in increases of0.61-0.73 and 3.15-3.38 kg N2O-N per 100 kg extra SOC accumulated,respectively, with the other fertilizers taking an intermediate position.The maximum permissible increase from a GHG emission perspectiveis 0.75 kg N2O-N per 100 kg extra SOC accumulated, which was onlymet by compost. From a greenhouse gas emission perspective, soil-Cgained via the use of animal slurries was hence entirely offset by in-creased N2O emissions. Doubling the winter wheat area combinedwith the cultivation of cover crops to increase SOC accumulation result-ed in a net GHG emission benefit, but was associated with a financial
Please cite this article as: Bos, J.F.F.P., et al., Trade-offs in soil fertilitymanag10.1016/j.agsy.2016.09.013
trade-off of 2.30-3.30 euro per kg SOCnew gained. This is a much higherprice than the price currently prevailing in the European carbon emis-sion trading system. Trade-offs between C inputs and emissions ofgreenhouse gases (notably N2O) or other pollutants (NO3, NH3) can besubstantial. Identifying these trade-offs is relevant for decision makers,but unfortunately there is still a lack of scientific understanding to accu-rately quantify them in carbon sequestration studies in agriculture.
Acknowledgements
This publication was financially supported by the Dutch Ministry ofEconomic Affairs (KB14 and PPS Sustainable Soil Management TKI-AF-12054A) and by the European Commission under the CATCH-C project(Grant Agreement N° 289782) within the 7th Framework Programmefor Research, Technological Development and Demonstration. Its con-tent does not represent the official position of the EC and is entirelythe responsibility of the authors.
Appendix A. Supplementary data
Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.agsy.2016.09.013.
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