Geoderma 237–238 (2015) 159–167

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Organic matter dynamics in an intensive dairy production system on aDutch Spodosol

J. Verloop a,⁎, G.J. Hilhorst b, A.A. Pronk a, L.B. Šebek b, H. van Keulen d,1, B.H. Janssen c, M.K. Van Ittersum c

a Plant Research International, Wageningen, The Netherlandsb Wageningen UR Livestock Research, Lelystad, The Netherlandsc Plant Production Systems, Wageningen University, The Netherlandsd Wageningen, The Netherlands

⁎ Corresponding author at: PRI, PO Box 616, 6700 AP, WE-mail address: koos.verloop@wur.nl (J. Verloop).

1 Deceased.

http://dx.doi.org/10.1016/j.geoderma.2014.09.0030016-7061/© 2014 Elsevier B.V. All rights reserved.

a b s t r a c t

a r t i c l e i n f o

Article history:Received 21 August 2013Received in revised form 22 August 2014Accepted 1 September 2014Available online xxxx

Keywords:C sequestrationCrop rotationMaizeGrasslandDecompositionManure digestion

In many studies, possibilities are being explored to adjust farmmanagement to increase or maintain soil organicmatter (SOM) contents in agricultural soils. Some options may be conflicting with efficient nutrient (N andP) management, i.e. management aiming at maximum conversion of imported nutrients into exported products(milk andmeat in the case of dairy farms). This study explored long term effects of efficient nutrientmanagementon SOM dynamics on an experimental dairy farm with three types of land use: permanent grassland, a 3 yeargrass–3 year arable crop rotation (ROTI), and a 3 year grass–5 year arable crop rotation (ROTII). The arablephase in the crop rotations consisted predominantly of maize. The experimental farm, called ‘DeMarke’, is locat-ed on a Spodosol with an Anthropic Epipedon in the Netherlands. The study consisted of: i) trend analyses basedon data ofmeasured SOMmass percentage (SOM%) from1989–2010, and ii) simulations of long term (50 years)SOMdynamics for fourmanagement alternatives. Three alternativeswere related tomanure digestion: no diges-tion; ‘mild anaerobic digestion’ (degrading 35% of organic matter) and; ‘strong digestion’ (degrading 70% of or-ganic matter). The fourth management alternative was similar to the first (no digestion) but differed in that nocatch cropwas grown aftermaize. The trend analyses showed that SOM%of the 0–0.2m layerwas approximatelystable under permanent grassland. In ROTI, SOM % decreased on average by 0.04 y−1 and in ROTII by 0.03 y−1.The decline did not slow down over time. SOM decline was more severe on plots with relatively high initialSOM %. Decomposition was described using a mono-component model with a time dependent relative decom-position rate. Decomposition rates in the rotations with arable crops were not higher than those for permanentgrassland indicating that tillage did not affect decomposition rate and that SOMdynamicswere dominated by thequantity and quality of the substrate input. Simulations indicated that in the long term, decline of SOM must beexpected both under arable crop–grassland rotations and under permanent grassland, even in the case of perma-nent grassland receiving undigestedmanure. Our results further indicate that strongmanure digestion puts pres-sure on future SOM levels suggesting a trade-off with bio-energy production, and that the contribution of a catchcrop to long term SOM is marginal.

© 2014 Elsevier B.V. All rights reserved.

1. Introduction

There is a general concern about decreasing soil organic matter(SOM) contents in agricultural soils. In soils low in SOM, the retentioncapacity for water and plant nutrients is relatively low, thus restrictingtheir availability to crops and possibly resulting in drought stress andlow crop yields (Bell and Van Keulen, 1995; Mayr and Jarvis, 1999;Vereecken et al., 1989). These soils are susceptible to nitrate leachingto ground and surface water (Boumans et al., 2001, 2005; Wösten andVan Der Zee, 1993). In addition, decomposition of SOM contributes to

ageningen, The Netherlands.

atmospheric CO2 emissions, whereas sequestration of carbon (C) insoils mitigates emissions of greenhouse gasses (Lal, 2011; Lal andKimble, 1998). For these reasons, management strategies have been ex-plored for agricultural production systems to sequester C (Freibaueret al., 2004; Hopkins and Del Prado, 2007; Paustian et al., 2000) or, atleast, to maintain SOM contents.

According to Reijneveld et al. (2009), in the Netherlands SOMundermaize and grassland—the two main crops on dairy farms—increasedslightly during the period 1984–2004. This suggests that SOM contentscan at least be maintained with the current dairy farming practices inthe Netherlands. However, another study, covering the same regionand period, found that there was a substantial risk of SOM decline inparticular on sandy soils that are continuously used for maize cropping(Hanegraaf et al., 2009). Several other studies indicate that arable

Table 1Farm plan of experimental dairy farm ‘De Marke’ (ha) and crop sequencea in the tworotations.

Average area, ha

Permanent grassland 11Rotation I 23Rotation II 21Crop sequence rotation I 3 years grassland → 3 years arable crops

1993–1996 G,G,G → Fb,M,M1996–2000 G,G,G → M,M,M2000–2003 G,G,G → M,M,Tr2003–2010 G,G,G → M,M,B

Crop sequence rotation II 3 years grassland → 5 years arable crops1993–1996 G,G,G → Fb,M,M,M,M1996–2000 G,G,G → M,M,M,M,M2000–2003 G,G,G → M,M,M,M,Tr2003–2010 G,G,G → M,M,M,M,B

a G: grass (mixture of Lolium perenne L. and Trifolium repens L.), Fb: fodder beet (Betavulgaris L.), M: maize (Zea mays L.), Tr: triticale (× Triticosecale), B: spring barley(Hordeum vulgare L.).

160 J. Verloop et al. / Geoderma 237–238 (2015) 159–167

cropping is associated with a decline in SOM (e.g. Tyson et al., 1990;Vertès and Mary, 2007). This decline is attributed to tillage in arablefields (Bronick and Lal, 2005; Kern and Johnson, 1993; Salinas-Garciaet al., 1997; Vellinga et al., 2004) and to the relatively low organic mat-ter inputs through the residues of arable crops (Bell et al., 2012;Bolinder et al., 2002; Neal et al., 2013). Besides crop residues, the useof organic fertilizers also has a significant impact on SOM dynamics.Many studies have found that inputs of organic fertilizer contribute tomaintenance of SOM contents (Liu et al., 2003; Min et al., 2003;Sommerfeld and Chang, 1985; Van Eekeren et al., 2009). Hence, withindairy farms, land use, tillage, crop residuemanagement and fertilizationmay all affect SOM dynamics. These ‘soil-bound’ aspects of farm man-agement are related to the whole farm and its various interacting com-ponents: animals, manure, soil and crops (Aarts et al., 1992; Mölleret al., 2008). Therefore, there is a need for more specific insights intothe effects of soil and crop management on SOM dynamics in a whole-farm context. This is particularly the case for dairy farms in theNetherlands where farmers are increasingly adopting measures thatcontribute to a maximum conversion of imported nutrients into theexported milk and meat products (so called efficient nutrient manage-ment, Oenema, 2013). Strategies implemented to enhance nutrientuse efficiency on dairy farms include a larger share of maize on thefarmland, crop rotation including frequent soil tillage, growing catchcrops after maize and anaerobic digestion of farm slurry (Möller,2009). These strategies could affect SOM dynamics.

The objective of this paper is to report on the impacts of efficient nu-trient management strategies on SOM dynamics under permanentgrassland and two grass–arable crop rotations. The following questionsare addressed:

1. What were the dynamics of SOM in the upper topsoil (0–0.2 m), thelower topsoil (0.2–0.4 m) and the subsoil (0.4–0.6 m), in the periodfrom 1989 to 2010?

2. How did the soil characteristics, land use and associated soil–cropmanagement affect the SOM dynamics?

3. What are the expected levels of SOM in the long term, if current farmmanagement practices continue?

4. What changes in management would be necessary to maintain SOMlevels or to enhance C sequestration in the soil?

This study was based on data from different parcels of experimentaldairy farm ‘DeMarke’where effects of farmmanagement onN and P useefficiency were explored at farm scale (Aarts et al., 2000a,b). We con-ducted trend analyses of the SOMmass percentage (SOM %) for the re-search period (1989–2010). A mono-component model proposed byYang and Janssen (2000) was parameterized for the farm and wasused to estimate long term dynamics of SOM concentrations corre-sponding to management strategies with different inputs of OM.

2. Materials and Methods

2.1. Experimental Farm ‘De Marke’

2.1.1. Site Characteristics‘DeMarke’ is situated in the east of the Netherlands (52° 2′ 23″N; 6°

20′ 53″ E) on a Spodosol with an Anthropic Epipedon (Soil Survey Staff,2010). The area comprising the farmwas reclaimed from heather at thebeginning of the 20th century (Kleinsman et al., 1973). From 1950 to1989, parts of the farm received high doses of cattle slurry, equivalentto estimated manure OM rates of up to 6.1 Mg ha−1 y−1. Other partsof the farm received lower doses of manure (Verloop and Van Keulen,2007). During the establishment of the experimental farm in 1989, theupper 0.25 m of the whole farm was homogenized by plowing.

Before establishment of ‘DeMarke’ in 1989 soil characteristics of thesite were determined by sampling (3 points per ha) to a depth of 2 to3.5 m (Dekkers, 1992). The soil was characterized by an anthropogeniclayer extending from the surface to 0.25–0.5 m, with an average SOM %

of 4.8% and a bulk density of 1300 kg m−3. A deep layer of yellow sand,very low in SOM and hardly penetrable by roots, was found below thisanthropogenic layer (Aarts et al., 2000c). Clay (soil particles b 2 μm)was not found in the soil profile. Silt mass percentage was on average12%, ranging from 8 to 20. Base saturation was low and pH tended todrop below 5, but is kept between 5 and 6 through liming. Soil moisturecontent is usually at field capacity in spring (0.18 g per g oven dry soil)and decreases to 0.12 in mid-summer, or even to 0.10–0.05 in dry sum-mers. These summer moisture contents correspond with pF values of1.8 to greater than 4.2. Thewater table is 1 to 3m below the soil surface.

In the experimental period (1989–2010), precipitation (mean annu-al 792 mm) was on average evenly distributed over the year, and aver-age daily temperature fluctuated from 2.3 °C in December to 17.3 °C inJuly.

2.1.2. Integrated Systems Research at ‘De Marke’The experimental farm was established to investigate the possibili-

ties to produce 12,000 kg ha−1 of milk annually without violating theDutch and European environmental standards with respect to nitrateleaching, ammonia emission, phosphorus leaching and phosphorusaccumulation in soil (Aarts et al., 2000a; Verloop et al., 2010). Longterm EU and national environmental standards were set as boundaryconditions for system design. In accordance with the EU Nitrate Direc-tive (EC, 1991) the maximum permitted nitrate concentration in theupper meter of groundwater was set to 50 mg l−1, corresponding to34 kg N ha−1 leaching per year. The maximum permissible N surplusin the soil, i.e. the sum of inputs of N to the farmland minus the outputsof N with harvested crops, was 79 kg ha−1. The maximum permissibleammonia-N emission from feces and urine was 30 kg ha−1 and themax-imum permissible N surplus at farm scale was set to 128 kg ha−1 y−1

(Aarts et al., 2000b). The maximum P concentration in groundwaterwas set to the Dutch surface water threshold value (0.15 mg l−1), corre-sponding to an annual soil P surplus of 0.45 kg ha−1. Acceptable N and Pemissions must be realized without exporting slurry in order to preventshifting of problems derived from intensive farming (Schröder et al.,2003).

On the experimental farm, the effects of management alternativeson nutrient flows for the entire farm and for the sub-systems herd, ma-nure, soil, crop and feeds, were studied. This was based on a relativelynew research approach called ‘prototyping’ that consists of a stepwiseprocedure of goal setting, designing, implementation, monitoring andevaluation (Vereijken, 1992, 1997). In this research, the experimentalsetup differs from the conventional experimental researchwith replicat-ed control and treatment plots. Rather, the stepwise procedure requiresan extensive monitoring program within the farm, with multiple obser-vation units (parcels, plots, boreholes).

161J. Verloop et al. / Geoderma 237–238 (2015) 159–167

2.1.3. Farm LayoutThe farm area (55 ha) is divided between permanent grassland

(11 ha) and two crop rotations: ROTI and ROTII (23 ha and 21 ha, respec-tively) (Table 1). The farm layoutwas implemented immediately after es-tablishment in 1989. Monitoring began in 1993 when the layout hadbeen finalized. The rotation schemes of the arable fields were modifiedslightly over the course of time (Verloop et al., 2006). These changesare outlined in Table 1. On maize land, Italian ryegrass (Lolium perenneL. ssp. multiflorum) was sown as a catch crop between the rows in Juneto create a soil cover directly after the harvest of maize in September.Catch crops were plowed-in in the first week of March. Temporal andpermanent grassland consisted of a mixture of perennial ryegrass(L. perenne L.) andwhite clover (Trifolium repens L.). Permanent grasslandwas re-sown approximately once every six years. To prepare the newseed bed, the grass sod was destroyed and plowed to a depth of 0.25 m.A total of 31 ha of grassland was available for grazing by 80 milkingcows and, on average, 55 young stocks. During the arable phase of ROTIand ROTII, the land was plowed in spring to a depth of 0.25 m.

2.1.4. Fertilizer and Manure ManagementFertilization aimed to meet crop requirements as well as to comply

with environmental standards (Aarts et al., 1992). The levels of fertilizerNwere 250 kgha−1 for grass and 150 kg ha−1 formaize. Fertilizer Pwasapplied at a rate corresponding with P equilibrium fertilization (Aartset al., 2000a; Verloop et al., 2010). The input rates of N and P throughmanure products (slurry, liquid and solid fraction) were tuned to thetarget N and P fertilization schemes.

Manure and urine excreted indoor were collected as mixed slurryand stored in a covered silo until application to grassland and arableland. Manure management was adjusted in 2004 and in 2009. From2004 onwards, slurry was anaerobically digested before application. In2009 and 2010, 30% of the digested slurry was separated into a liquidand solid fraction. On grassland, manure was applied by injection andapplication started in mid-March. On maize land, manure was applied

Fig. 1. Plan of the farming system at ‘De Marke’, including its par

in May by injection. A rotational grazing system was applied, in whichthe herd grazed for 5–7 days on individual grassland parcels, afterwhich it was moved to the next parcel.

2.2. Data Collection

‘DeMarke’ consists of 30 parcels of 1–3 ha each. Each parcelwas sub-divided into two or three plots of 0.5–1 ha, resulting in a total of 51 plots(Fig. 1). For each of the 30 parcels, relevantmanagement data, i.e. timingand method of manure application, harvesting/cutting, grazing, re-sowing, plowing, nutrient flows and yields were collected (Aarts et al.,1992). Organicmassflows of harvested cropmaterial and applied slurrywere measured by weighing and the related nutrient flows were quan-tified on the basis of chemical analyses of representative samples (theprocedure is described in detail in Verloop et al., 2006). OM from cropresidues comprising roots, stubble, harvest losses and grazing losses,was estimated as a fraction of dry matter yields (harvest) or as fixedvalues (Table 2) according to Ten Berge et al. (2000) and Whitehead(1986) and supported by data from Van Dijk et al. (1996). Residues ofcatch crops were assumed to be equal to the total biomass becausethe crops were not harvested but plowed in.

Samples of the upper topsoil (0–0.2 m)were taken from each of the51 plots, in 1989 and annually from 1994 to 2010 (Table 3). The lowertopsoil (0.2–0.4m) and the subsoil (0.4–0.6 m)were sampled in select-ed plots only (details are specified in Table 3). SOM%was determined inall samples as loss on ignition (NEN 5754, 2005). From 1993 to 2010,depth and pH of groundwater were measured annually as described inVerloop et al. (2010).

2.3. Analysis

2.3.1. SOM DynamicsIn 1989, the initial SOM % varied strongly, and seemed to depend on

land use and management from the preceding period of use. This

cels; inset: illustration of the sub-division of parcels in plots.

Table 2Estimates of organic matter input to soil with crop residues (kg ha−1).

Crops Estimate of cropresiduea

Remarks

Grass no grazing 1.3 × harvest Roots, stubbles and harvest lossGrass grazed 1.4 × harvest Roots, stubbles and grazing lossBarley, Triticale 0.25 × harvest Roots and stubbles. Straw is removed

from the fieldsMaize 2050 Roots and stubblesFodder beet 0.25 × harvest Roots and stubbles. Leaves are removed

from the fieldsCatch crops* Total biomass

productionTotal plant (Italian ryegrass) plowed-in,not harvested

a Units of harvested and total biomass production: kg dry matter ha−1.

162 J. Verloop et al. / Geoderma 237–238 (2015) 159–167

variation allowed for analysis of the effect of initial SOM % on the dy-namics of SOM % during the research period. SOM changes were ana-lyzed for each plot by regression of SOM % versus time. We fit the datato linear and power functions based on the expectation that the changeof SOM%would level off in the long term.Next, the relationship betweenestimates of the initial status and estimates of the rate of change of SOM%was analyzed by linear regression. This procedurewas followed for theupper topsoil (51 plots), the lower topsoil (26 plots) and the subsoil. Forthe two latter soil layers, data was only available for the period up to2004 (Table 3).

2.3.2. Organic Matter DecompositionSOMdynamics are the result of annual OM inputs andOMoutputs in

the form of CO2 produced by microbial decomposition. We consideredSOM to consist of: i) a portion remaining from SOM present at thestart of the experimental period (OSOM), ii) a portion originating fromrepetitive OM inputs bymanure products (slurry, its separation productsand grazing excreta, MANSOM), iii) a portion originating from inputs ofresidues of the main crops (MCSOM) and, iv) a portion originatingfrom the catch crops (CCSOM). Total SOM was calculated for each yearas the sum of OSOM, MANSOM, MCSOM and CCSOM.

It is generally assumed that decomposition follows first order kinet-ics. The quantity of a substrate remaining at time t can be calculated by:

Yt ¼ Y0 exp −Ktð Þ ð1Þ

where

K average relative decomposition rate between time 0 and time tY0, Yt the amount of substrate (SOM) at time 0 and t, respectively.

It has been found that K is not a constant (Yang and Janssen, 2000,2002) but changes over time as:

K ¼ R9 � f � tð Þ1−S ð2Þ

where:

Table 3Collection of soil data at experimental farm ‘De Marke’ from 1993–2010.

Data Soil layer (m) Meth

Texture, SOM, structure of soil profile 0–3.5 m Soil sSOM, pH, N, P, K 0–0.2 m Comp

0.2–0.4 m Comp(aver

0.4–0.6 m Comp(aver

Depth of groundwater table 0.8 m below groundwater table 3 bor

t time (years)R9 average decomposition rate between t = 0 and t = 1

(dimension: t1 − S) at a temperature of 9 °CS aging factor (dimensionless)f temperature correction factor (dimensionless); f = 0 for

T b −1, f = 0.1 (T + 1) for −1 b T b 9, f = 2(T − 9) / T for9 b T b 27, where T (°C) is the average temperature duringthe period considered (Yang, 1996).

After substitution of Eq. (2) into Eq. (1) the quantity of a substrateremaining at time t can be calculated as:

Yt ¼ Y0 � exp −R � f � tð Þ1−Sh i

: ð3Þ

The product of (f ∗ t), denoted by decomposition time (Dt), was cal-culated to account for effects of temperature on decomposition. Thedaily values of Dtwere added to provide the total annual decompositiontime for the average weather year, which was 1.09 mineralization yearper calendar year.

Eq. (3) describes the amount of SOM remaining after one applicationof a source of organic matter (manure, roots, etc.). Hence, the totalamount of organic matter remaining at time (n + 1) after n annual ap-plications of, e.g. manure (MANSOM) is calculated as:

MANSOM ¼ Y1 þ Y2 þ Y3 þ…þ Y nþ1ð Þ: ð4Þ

The values of R9 and S are characteristics of the organicmaterial con-sidered (Yang and Janssen, 2000), but theymay differ for a given organ-ic material in soils with a different texture, moisture regime and pH.

For each plot, R9 and Swere estimatedwith the following procedure.The sum of OSOM,MANSOM,MCSOM and CCSOM, predictedwith stan-dard values of R9 and S (Yang and Janssen, 1997), was compared withthe observed value of SOM, and the difference between predicted andobserved values was added across the whole research period. Next, weimproved the fit of predicted and observed SOM for each plot throughan iterative procedure with the Solver function of Excel. The values ofR9 and S that gave the lowest cumulative error were considered thebest fit.

We assumed that OM inputs and OSOM were distributed homoge-neously over the 0–0.25 m soil layer due to tillage. Hence, the SOM %in the top 0.25 m of the soil was the same as in the 0–0.2 m layer.To convert SOM % into kg per ha, the percentage was multiplied bythe soil mass per ha (volume multiplied by soil bulk density). Soilbulk density was corrected for SOM %, using ρ (kg m−3) = 1.728–0.271 ∗ ((0.45 ∗ SOM)0.5) according to Whitmore et al. (1992).

Regression analysis was conducted to explore the effects of land use,the associated management, the soil characteristics (Hassink, 1997),groundwater depth and pH on R9 and S. The depth to the groundwatertable was seen as a proxy for the time that the soil remains wet enoughto support microbial decomposition of SOM (Leirós et al., 1999; Reyet al., 2002).

od of sampling Years

urvey based on 306 sampling points 1989osite sample of 40 soil cores per plot 1993–2010osite sample of 40 soil cores per plot. Each year 6–36age 16) plots were selected at random.

1991–2004

osite sample of 40 soil cores per plot. Each year 1–6age 3) plots were selected at random.

1991, 1998, 2000, 2001,2002, 2004

eholes per plot, annually, in spring. 1993–2010

163J. Verloop et al. / Geoderma 237–238 (2015) 159–167

2.3.3. Simulated Management AlternativesThe mono-component decomposition model was used to simulate

SOM dynamics in the upper 0.25 m of the soil over a period of 50 years.Four management alternatives were simulated. The simulations wereapplied to two divergent land uses, permanent grassland and ROTII. Inmanagement alternative A (Table 4), manure is not treated. In manage-ment alternative B, farm slurry is treated with ‘mild anaerobic diges-tion,’ degrading 35% of the organic matter. In management alternativeC, farm slurry is treated with ‘strong anaerobic digestion,’ degrading70% of the organic matter (Zeeman, 1994). Management alternative Dis similar to management A with the exception that no catch crops aregrown after maize. Management alternatives A and B were imple-mented between 1993–2003, and between 2004–2010, respectively.Management alternatives C and D have not been implemented onthe farm and are therefore hypothetical.

3. Results

3.1. Initial SOM Status and OM Inputs

At farm scale, the average initial SOM % in the upper topsoil was 4.8(ranging from 3.0 to 6.4, but slightly skewed — Fig. 2). High and lowvalues occurred in all land uses, and the distribution in permanentgrassland (3.1–6.2; average 4.6) was not significantly different fromthat in ROTI (3.0–6.0; average 4.9) and ROTII (3.6–6.4; average 4.6;Fig. 2). At farm scale, in the lower topsoil, the average initial SOM %was 3.9 (ranging from1.9 to 5.9). Again, the SOM%was not significantlydifferent for permanent grassland, ROTI and ROTII (Table 5). Similarly,in the subsoil the average SOM % was 2.4 and was not significantly dif-ferent for permanent grassland, ROTI and ROTII.

The average annual input of organic matter between 1993 and 2010was 13.3 Mg ha−1 in the permanent grassland, 11.0 Mg ha−1 in ROTIand 11.1 Mg ha−1 in ROTII (Fig. 3). Catch crops contributed 18 and15% of the OM inputs in ROTI and ROTII, respectively. At farm scale,the estimated OM input amounted to 11.5 Mg ha−1, fluctuating from9.4 to 13.5 Mg ha−1 (Fig. 3).

3.2. SOM Dynamics in the Upper Topsoil and in Deeper Layers

In the upper topsoil, the SOM % at farm scale declined by 0.03 y−1

over the research period. In the permanent grassland, the SOM % didnot change significantly. However, in ROTI and ROTII SOM % decreasedsignificantly over the research period (Table 5). Similar percentages ofobserved variation were described by the linear (R2 = 0.23) and powerfunctions (R2 = 0.22).

In the lower topsoil, SOM % also decreased by 0.03 y−1 at farm scale.SOM % tended to increase in permanent grassland and tended to

Table 4Manure digestion and organicmatter (OM) input ratesa (Mg ha−1) applied to permanentgrassland (PG) and a 3 year grass–5 year arable crop rotation (ROTII) according to fourmanagement alternatives.

Management alternative A B C D

Manure digestion No Mild Strong No

OM input rates in PGManure 2.9 2.5 0.7b 2.9Main cropc 10.8 10.8 10.8 10.8

OM input rates in ROTIIManure 2.5 2.0 0.63 2.5Main cropc 5.8 5.8 5.8 5.8Catch crop after maize 2.1 2.1 2.1 –

a Input rates were averaged across all years.b Low values are caused by the low level of residual OM after strong digestion.c OM originating from main crops (perennial ryegrass in permanent grassland and ar-

able crops (mainly maize) in ROTII).

decrease in ROTI and ROTII. These trends were not significant atP b 0.05 (Table 5). In the subsoil, SOM % decreased by 0.07 y−1 atfarm scale.

Fig. 4a presents the relationship between annual change of the SOM%(ΔSOM/Δt) of each plot and its initial SOMstatus for the upper topsoil. Foreach land use,ΔSOM/Δt decreased with increasing initial SOM%. Togeth-er, the initial SOM % and the land use account for 53% of the variation inΔSOM/Δt. The effects of initial SOM % and land use were both significant(P b 0.05). In the lower topsoil, under ROTI and ROTII, the effect of the ini-tial SOM % onΔSOM/Δt was similar to that observed in the 0–0.2 m layer(Fig. 4b), although the relationship was weaker. The initial SOM %accounted for 18% of the observed variation in the rate of decline. In per-manent grassland, in the subsoil, no significant correlation was observedbetween ΔSOM/Δt and the initial SOM %. There was a weak correlationbetween the ΔSOM/Δt in the upper topsoil and lower topsoil (PG:R2 = 18%; ROTI, ROTII: R2 = 22%) (not shown).

3.3. Organic Matter Decomposition Rates

The estimates of R9 and S for the various substrates were not signif-icantly different for permanent grassland and the crop rotations ROTIand ROTII (Table 6). For each plot, the SOM dynamics during 17 yearswere simulated with the plot specific estimates of R9 and S and com-pared with observed SOM dynamics. The simulated SOM dynamicswere in agreement with the observations. R2 values were 0.76 for thepermanent grassland, 0.99 for ROTI and 0.81 for ROTII and there wereonly three 3 outliers from a total of 51 plots. Regression analysis showedno significant relations between R9 and S and the soil characteristics in-cluding initial SOM, pH, silt content and depth to the groundwater table.

3.4. Long Term Dynamics

Fig. 5a presents the dynamics of SOM and OSOM, simulated for per-manent grassland and ROTII. Initial SOM%was set at 4. According to thesimulations, SOM % declines gradually under both land uses (Fig. 5a).Dynamics of OSOM were practically the same under permanent grass-land and ROTII and were, therefore, represented with the same curve.SOM % under permanent grassland increased slightly until year 12 afterwhich it declined. It became lower than the initial value after year 26.SOM%under ROTII declined immediately. The difference in SOMdynam-ics between permanent grassland and ROTII was caused by MANSOM,MCSOMandCCSOM. Fig. 5b shows the simulated impact ofmanagementalternatives on the SOM dynamics. The effects of mild manure digestionand of catch crops are rather small (compare management alternative Awith B and A with D, respectively). The effects of strong manure diges-tion are significant (compare management alternative A with C).

4. Discussion

4.1. SOM Dynamics From 1989–2010

The overall decline in SOM content on ‘De Marke’ at farm level wasthe result of the SOM decline under ROTI and ROTII. Clearly, arablecropping resulted in a decrease in SOM content, as has been observedbefore (Gregorich et al., 2001; Johnston, 1986; Tyson et al., 1990; Vertèsand Mary, 2007). Incorporation of grassland into the crop rotation con-tributes to themaintenance of SOM(Reeves, 1997). However, apparently,at ‘De Marke’, systematic crop rotation was not sufficient to stabilize theSOM level. The fact that SOM levels are stable under much of the perma-nent grassland on ‘De Marke,’ indicates that equilibrium between inputsand decomposition can be reached. Stable or even declining SOM levelsin grassland have also been reported in other studies (Freisinger et al.,2007; Hoogerkamp, 1973; Lettens et al., 2005; Reijneveld et al., 2009).

In most plots, the decrease in total SOM % did not slow down overthe period of this study. This contrasts with the commonly reportedphenomenon that the change of SOM % tends to level off in the long

a

c

b

d

b

d

Fig. 2. Distribution of initial (1989) SOM % (layer 0–0.2 m); a: permanent grassland (10 plots), b: ROTI (30 plots), c: ROTII (11 plots), d: entire farm (51 plots).

164 J. Verloop et al. / Geoderma 237–238 (2015) 159–167

term (Yang, 1996). The understanding behind this phenomenon isthat components that are most susceptible to decomposition aredecomposed first. This results in an increase in the share of more persis-tent components of SOM. In agricultural systems, such as ‘De Marke’,this pattern is masked by the fact that new OM is added continuously.Hence, at any moment during thewhole research period, themeasuredSOM% does not represent the remainder of initial SOM, but amixture offreshmaterial added recently and the remainder of initial SOM(OSOM).

Table 5Initial (1989) SOM mass percentage and linear regression coefficient of SOM over time(ΔSOM/Δt) in the 0–0.2 m layer and the 0.2–0.4 m layer for permanent grassland (PG), 3 -

year grass–3 year arable rotation (ROTI) and 3 year grass–5 year arable rotation (ROTII)on experimental dairy production system ‘De Marke’.

n Initial⁎ Time (ΔSOM/Δt)

Mean⁎ 95% confidence interval

Lower Upper

Layer 0–0.2 m⁎⁎ 51PG 9 4.6a 0.00a −0.025 0.055ROTI 29 4.9a −0.04b −0.045 −0.029ROTII 13 4.6a −0.03b −0.043 −0.018Layer 0.2–0.4 m⁎⁎ 26PG 6 4.3a 0.01a −0.047 0.073ROTI 13 3.6a −0.03a −0.072 0.088ROTII 7 3.7a −0.03a −0.090 0.021

⁎ Values of different land uses per soil layer with the same letter are not statisticallydifferent at the 5% error level.⁎⁎ Layer 0–0.2 m refers to data for 1998–2010; layer 0.2–0.4 m refers to data for 1991 to2004.

The change in SOM over time in the upper topsoil and lower topsoilshowed the same trend. This could be expected as part of this lower top-soil belongs to the homogeneous plow layer (0–0.25 m). Consequently,a decrease in SOM in the plow layer affects the SOM% in the entire lowertopsoil. The results for the subsoil indicate that the rate of decomposi-tion of SOM, mainly consisting of material that was incorporated inthe soil at the time the land was reclaimed from heather, exceeds therate of organic matter inputs. This is a concern as it may affect rootingin the subsoil. Incorporation of deep rooting crops such as alfalfa (Bellet al., 2012) could increase OM inputs to the subsoil.

4.2. Decomposition Rates as Affected by Plot Characteristics

The wide ranges of R9 and S values within individual plots (Table 6)suggest that plot characteristics strongly affected the decompositionprocess. However, the calculation of decomposition rates relies, in part,on assumptions of the OM inputs with crop residues. It must be kept inmind that the coefficients used (Table 2) may not be representative foreach plot in each year.

The R9 and S values, and consequently the decomposition rates,were not significantly different between the grassland and crop rota-tions (Table 6). This suggests that tillage does not accelerate decompo-sition at ‘De Marke’. This result contrasts with what has been found inother studies (Strebel et al., 1988; Studdert and Echeverría, 2000).

4.3. Simulated SOM Dynamics

The simulated SOM dynamics under permanent grassland (Fig. 5)during the first 20 years are in agreement with the measured SOM %

a

c

b

d

b

d

Fig. 3. OM inputs (103 kg ha−1) to soil; a: permanent grassland, b: ROTI, c: ROTII, d: the entire farm (weighted averages across grassland and rotations).

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(Table 5) and with data in the literature (e.g. Reijneveld et al., 2009).However, based on the intersection of the regression line with theX-axis in Fig. 4a, it might be concluded that in permanent grasslandSOM%will converge to ca. 4.8%.Whereas, the simulations indicate a de-cline to lower levels (Fig. 5). This difference can be explained by the factthat Fig. 4a does not account for the expected increase in decompositionrate that is caused by changing substrate quality. The model takes intoaccount that part of the original, slowly decomposing soil organic mat-ter (OSOM) is gradually replaced by substrates that decompose faster(MANSOM,MCSOM and CCSOM) resulting in an increasing decomposi-tion rate of total SOM.

a

Fig. 4. Themean annual change of SOM% (ΔSOM/Δt) plotted against the initial SOMmass %, fo1989–2010); b: layer 0.2–0.4 m (period 1989–2004). Each point represents a plot. Note that th

The small impact on SOM dynamics of catch crops in rotations com-pared to fallow in thewinter period (alternative D— Fig. 5) is due to thecombined effect of relatively fast decomposition of CCSOM and the lowinput rates of OM through catch crops. The relatively low impact ofmildanaerobic manure digestion (alternative B — Fig. 5) compared tountreated manure (alternative A) is caused by the fact that mild diges-tion does not strongly decrease OM inputs to the soil (Table 4). The re-sults for mild digestion are in agreement with conclusions of Thomsenet al. (2013). The relatively high impact of strong manure digestion (al-ternative C— Fig. 5) is associatedwithmuch lower OM inputs to the soil(Table 4).

bb

r permanent grassland (PG), and crop rotations (ROTI and ROTII); a: layer 0–0.2 m (periode scales of the axes are different for a and b.

Table 6Estimates of first-year decomposition rates (R9) and aging factor (S) of old soil organic matter (OSOM), manure, roots of main crops, and plowed-in catch crops; means and standarddeviations (between brackets) for permanent grassland, ROTI and ROTII.

Permanent grassland ROTI ROTII

R9 S R9 S R9 S

OSOM 0.05 (0.02) 0.44 (0.16) 0.06 (0.02) 0.44 (0.15) 0.06 (0.03) 0.40 (0.23)Manure 0.58 (0.21) 0.47 (0.17) 0.65 (0.24) 0.47 (0.16) 0.58 (0.27) 0.42 (0.25)Main crop 1.06 (0.39) 0.63 (0.22) 1.18 (0.43) 0.63 (0.22) 1.07 (0.49) 0.57 (0.33)Catch crop – – 1.49 (0.55) 0.61 (0.21) 1.35 (0.62) 0.55 (0.32)

a b

Fig. 5. Simulated long term dynamics in the upper topsoil in relation to land use andmanagement alternatives; a: old soil organic matter (OSOM) and SOM in permanent grassland (PG)and in ROTII undermanagement alternative A; b: SOM in PG and ROTII undermanagement alternatives A, B, C and (for ROTII) D. Note that the scales of the Y-axis are different for a and b.

166 J. Verloop et al. / Geoderma 237–238 (2015) 159–167

4.4. Implications for Future System Development

The simulations presented in Fig. 5 suggest that a gradual decrease ofSOM is to be expected in the future. The decrease will proceed fasterunder arable crop rotations than under permanent grassland. In addi-tion, the decrease will proceed faster when the applied manure hasbeen strongly digested. We found no indications that, in this system,the plowing frequency affects SOM dynamics. For SOMmaintenance itis necessary to increase OM inputs and to take into account the draw-backs of strong anaerobic digestion.

One of the options to increase SOM is to convert crop rotations topermanent grassland (eg. Freibauer et al., 2004; Vellinga et al., 2004).However, this change would have serious drawbacks for the N useefficiency of the farming system. First, it is generally believed that to op-timize efficiency of resources, feeds for the cattle should be produced onthe farm as much as possible (Aarts et al., 1992; Verloop et al., 2010). Afarming system with only grassland has a lower total crop energy pro-duction than a system with integrated maize and grass cultivationsuch that an increase of imported feed would be needed to supplysufficient energy to the cattle. Second, a grassland based system couldresult in an excess of protein in the roughage, thus impairing N useefficiency of the herd (Bannink et al., 1999). Third, N utilization efficien-cy of maize is higher than that of grassland (Aarts et al., 2002) and alarger share of grassland would strongly increase N surpluses in thesoil (Verloop et al., 2006). Hence, it does not seem feasible to producemilk at an intensity of 12,000 kg ha−1 within the current environmentalboundary conditions for N and P, without a substantial share ofmaize inthe cropping system. Consequently, for the soil in our study, a certainextent of SOM decline must be accepted as an unavoidable natural pro-cess. This urges one to explore possibilities to sustain efficient crop pro-duction at lower SOM levels, as was done by Aarts et al. (2000c).

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