Cropping system impact on soil quality determinants · 2002-12-20 · 312 AGRICULTURAL AND FOOD...

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A G R I C U L T U R A L A N D F O O D S C I E N C E I N F I N L A N D

Vol. 11 (2002): 311–328.

© Agricultural and Food Science in FinlandManuscript received March 2002

Cropping system impact on soil quality determinantsMauritz Vestberg, Sanna Kukkonen, Kaisa Saari, Marjatta Uosukainen

MTT Agrifood Research Finland, Plant Production Research, Laukaa Research and Elite Plant Station,Antinniementie 1, FIN-41330 Vihtavuori, Finland, e-mail: [email protected]

Ansa PalojärviMTT Agrifood Research Finland, Environmental Research, Soils and Environment, FIN-31600 Jokioinen, Finland

Tuomo TuovinenMTT Agrifood Research Finland, Plant Production Research, Plant Protection, FIN-31600 Jokioinen, Finland

Milja Vepsäläinen and Maarit NiemiFinnish Environment Institute, PO Box 140, FIN-00251 Helsinki, Finland

Worldwide interest in soil quality evaluation has increased rapidly throughout the past decade, prompt-ing us to evaluate the long-term impact of four cropping systems on several biological, chemical andphysical determinants of soil quality. We hypothesized that after 17 years several of the determinantswould show significant differences between conventional cereal and low input/organic rotations.Four crop rotations were imposed on a silt soil from 1982 through 1999. Rotation A was a conven-tionally managed cereal rotation that received 100% of the recommended mineral fertilizer eachyear. Rotation B was also managed conventionally from 1982 until 1993, although it received only50% of the recommended mineral fertilizer. From 1994 through 1999, rotation B was managed as anorganic rotation. Rotations C and D were low-input rotations with plant residues returned eitheruntreated (C) or composted (D) from 1982 until 1994. From 1994 through 1999, they were alsomanaged organically. Significant decreases in extractable phosphorus (P) and potassium were ob-served in rotations C and D compared with rotation A, presumably because their yearly nutrientinputs were somewhat lower. The amount of soil organic carbon (Corg), soil water holding capacity,the numbers and biomass of earthworms and the microbial biomass carbon and nitrogen were ortended to be higher in low input/organic than in conventionally managed plots. These effects may bein connection with the slightly increased levels of Corg in soil of the organic rotations. Activities oftwelve enzymes were strongly affected by sampling time (early- versus late-summer), but much lessby long-term management. Litter decomposition, numbers of soil nematodes, arbuscular mycorrhizal(AM) fungal diversity, AM spore density and AM functioning were little affected by rotation. How-ever, AM spore density correlated positively with the high amounts of extractable calcium and Pwhich were a result from excessive liming applied to some plots in 1982. The crucial question to be

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Vestberg, M. et al. Cropping system impact on soil quality determinants

answered in future is whether plant growth and yield will correlate with the changes in soil proper-ties. This question will be dealt with in a further paper using data from the same experiment.

Key words: liming, soil property, soil quality indicator, soil resilience

A number of biological soil properties havebeen reported to respond to changes in agricul-tural practices, thus having a potential to be usedas specific indicators of soil quality. These in-clude general biological properties such as or-ganic matter (Dumontet et al. 2001), soil ani-mals like earthworms (Pfiffner and Mäder 1997)and nematodes (Ferris et al. 2001), and litterdecomposition ability (Wardle et al. 1999). Sten-berg (1999) listed five different levels at whichmicroorganisms can be studied. There are the (1)individual and (2) population levels (Hill et al.2000), (3) population on the functional level in-cluding autotrophic nitrification (Stenberg et al.1998), function of arbuscular mycorrhiza (Ka-hiluoto et al. 2001) and specific soil enzymesand (4) the whole microbial community studiedas genetic or physiological diversity, or quanti-tative methods for the total community includ-ing for example microbial biomass (Ananyevaet al. 1999), basal respiration rate (Griffiths etal. 2001), nitrogen mineralization, denitrification(Stenberg et al. 1998) and general soil enzymes(Vepsäläinen et al. 2001). Finally there is the (5)ecosystem level that may be described by datafrom all the other levels.

It is not possible to use all ecosystem or soilattributes as indictors of soil quality (Karlen andAndrews 2000). Therefore, a minimum data set(MDS) consisting of selected chemical, physi-cal and biological soil properties has been sug-gested (Doran and Parkin 1996). Descriptive orqualitative indicators have also been suggested.A spade diagnosis method showing basic quali-ty aspects of soils has also been developed es-pecially for use in organic agriculture (Preuschen1987).

This study was conducted to determinechanges in soil chemical and biological proper-ties in a field with 17 years of different crop-

Introduction

Agriculture is today characterized by a high de-gree of intensity. Heavy machines are repeated-ly used during the growing season and crops areoften given high amounts of fertilizers and pes-ticides to maximize yields. To ensure a maxi-mum economic outcome, crops are often alsogrown as monoculture. Some agricultural soilscan stand such cultivation practices, but manygradually show a lower productivity due to animpaired soil quality. In recent years there hasbeen increasing awareness of soil quality to en-sure a greater sustainability of agricultural soils.Two main questions are subject to research: (1)Which soil properties most effectively determinethe soil quality and can be used as soil qualityindicators, and (2) How can soil quality be im-proved or maintained?

Soil quality has been broadly defined as thecapacity of a soil to function within ecosystemboundaries to sustain biological productivity,maintain environmental quality and promoteplant and animal health (Doran et al. 1996, Kar-len et al. 1997). Until recently, many research-ers defined soil quality mainly using chemicaland physical terms, but now it is generally ac-cepted that the concept includes physical, chem-ical and biological properties of the soil (Karlenand Andrews 2000, van Bruggen and Semenov2000). As soil is characterized by the threeabove-mentioned properties, indicators relatedto all of these factors are needed for a completegood description of soil quality (Stenberg 1999).The indicators should respond promptly and ac-curately to perturbation (Holloway and Stork1991), but they should not be influenced by nat-ural annual variations such as weather conditionsand crop development (Stenberg 1999).

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ping histories. The greatest emphasis was put onbiological properties as determinants of soilquality. The study site was chosen with a prioriassumption that soil quality had developed dif-ferently in long-term conventional cereal rota-tions as compared with long-term low input/or-ganic rotations. One conventional rotation waschanged into organic after twelve years, thusenabling us to also track changes due to crop-ping history.

Material and methods

Field experimentIn 1982, a long-term field crop rotation experi-ment was established on a silt soil at LaukaaResearch and Elite Plant Station of MTT Agri-food Research Finland. The soil had a high meanpH (7.1) but its content of organic carbon (Corg)was low, 1.36%. The amounts of acid ammoni-um acetate extractable phosphorus (P), potassi-um (K), calcium (Ca) and magnesium (Mg) were30, 84, 2156 and 266 mg l–1 soil, respectively.These values represent, according to the inter-pretation of soil test results (Viljavuuspalvelu Oy2000), a good status for P, Ca and Mg, but a fairstatus for K. The experiment consisted of 36 plotsarranged in three complete blocks with a plot sizeof 50 m2 (5 m × 10 m; width × length). Eachblock included four treatments, i.e. rotationsA–D, that were repeated three times (startingpoints 1–3) by starting a rotation from years 2, 4and 5. The rotations were 6 years long.

Rotations A and B represented conventionalfarming and were fertilized using full (A) or half(B) amounts of recommended mineral fertiliz-ers (Table 1). The fertilizers (Kemira, Finland)contained varying amounts of NPK during 1982–1999 with nutrient applications of 96–102 kgnitrogen (N), 15–42 kg P and 45–85 kg K ha–1

for barley (Hordeum vulgare L.) in rotation A.The corresponding amounts for rye (Secale ce-reale L.) were 40–133 kg N, 35 kg P and 18–85 kg K ha–1. Turnip (Brassica rapa ssp. rapa

L.) was given 85–96 kg N, 30–42 kg P and 60–78 kg K ha–1. Rotations C and D were low-inputrotations resembling those used in organic agri-culture. In rotation C, crop residues were re-turned to the plot as such but in rotation D thecrop residues were composted. Rotations C andD had a leguminous crop every second year (Ta-ble 1). When red clover (Trifolium pratense L.)was cultivated as a preceding crop to rye, it wasploughed down as green manure. To compensatefor losses of P and K in the yield, rotations Cand D were also fertilized with Finnish rawphosphate and biotite (Kemira), a K fertilizer;500 kg ha–1 and 2000 kg ha–1, respectively, forthe first 6-year period. For the next 6-year peri-od the corresponding amounts were 300 kg ha–1

and 3000 kg ha–1. Raw phosphate and biotitewere added composted to rotation D. At estab-lishment of the experiment, rotations C and Dwere also given 20 t ha–1 animal manure. Theamounts of crop residues returned to the plots inrotations C and D varied greatly according tostanding crop and year being on average only553 kg and 415 kg dry weight (DW) ha–1 on tur-nip growing plots C and D, respectively. Redclover showed the greatest DW accumulationproducing on average 7710 and 6997 kg DWha–1 in rotations C and D, respectively. The nu-trient composition of returned residues (uncom-posted or composted) has not been measured forall crops making a calculation of the returnedamounts of nutrients impossible.

In 1994, rotations B, C and D were changedinto transitional stage of organic farming, whilerotation A continued as a pure cereal rotation byomitting turnip from the rotation. By 1999, allplots of rotations B–D were organic. RotationsB and C included three successive years of ley,while rotation D had two successive years of leyat the most (Table 1). Leys and rye were given15 t ha–1 composted animal manure in the yearof establishment before 1997. Thereafter theamount was doubled to 30 t ha–1. During 1994–1999 the organic rotations B–D received in ani-mal manure lower amounts of extractable N(84% lower), P (35% lower) and K (23% lower)than rotation A was given in the mineral fertilizer.

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Study of the experimental data revealed ahidden error of treatment. Already in 1982 someblock 1 plots growing barley in 1999 had re-ceived unintentionally a heavy liming treatment.This means that block 1 plots can be regarded asa liming treatment and that the intended treat-ments were mainly studied on the basis of blocks2 and 3.

SamplingSoil samples were collected in 1983, 1989, 1991and 1995 for determination of extractable nutri-ents, pH, electrical conductivity (EC) and Corg.Ten subsamples were collected from the upper-most 20 cm of each plot and pooled and mixedthoroughly to make the final sample. In 1999,soil samples were collected from plots growingbarley at rotation starting point 1 (A1, B1, C1,D1) and from rye plots at rotation starting point3 (A3, D3). There were two main sampling times;seedling stage (12 June) and harvest (9 August).Earthworms and mycorrhiza were sampled in lateSeptember. Table 2 shows soil parameters esti-mated at different times. In 1999, 20 subsam-ples were collected from each plot, pooled andmixed to make the final sample (one per plot).The samples were stored at +6°C until the nextday. Then they were delivered cooled (soil en-

zymes, nematodes), frozen (soil microbial bio-mass) or air-dried (extractable nutrients, Corg%)to laboratories involved in the study. For studiesof mycorrhiza, 10 subsamples per plot were col-lected from the uppermost 15 cm of soil. Afterpooling and mixing one sample per plot wasstored at +6°C until use.

Soil analysesExtractable nutrients were determined from acid(pH 4.65) ammonium acetate (0.5 M acetic acid,0.5 M ammonium acetate), (Vuorinen and Mäki-tie 1955). Soil pH was analysed from a soil inwater suspension, 1:2.5 (v/v). Water holding ca-pacity (WHC) of sieved soil was measured bysoaking the soil samples in water for 30 min andletting them drain for 0.5–2 h, until equilibrium.

Activities of extracellular soil enzymes weremeasured using a ZymProfilerTM as described byVepsäläinen et al. (2001). Results from blocks 2and 3 had to be omitted because refrigeration forseveral days had affected the enzyme activitiesin a random fashion. The activities of a total of12 soil enzymes were determined.

The microbial biomass in soil was estimatedby a slightly modified fumigation-extractionmethod of Vance et al. (1987) and Brookes et al.(1985). Briefly, sieved (6 mm) soil samples,

Table 1. Standing crops of rotations A–D of the Laukaa field experiment during 1982–1999.

Rotation 1982–1993 1994–1999

Description Crops in rotation Description Crops in rotation

A Conventional, full barley – barley – rye – Conventional barley – barley – rye –mineral fertilization oat – turnip – oat oat – barley – barley

B Conventional, half barley – barley – rye – Organic rotation 1 barley – ley – ley –mineral fertilization oats – turnip – oat ley – rye – pea+oat

C Low input, plant barley – red clover – Organic rotation 1 barley – ley – ley –residues returned rye – pea+oat – turnip – ley – rye – pea+oatuntreated pea+oat

D Low input, plant Barley – red clover – Organic rotation 2 barley – ley – ley –residues returned rye – pea+oat – turnip – rye – oat – leycomposted pea+ oat

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moisture content adjusted to 40–60% WHC, werefumigated for 24 h with ethanol-free chloroform(Merck 102444). Immediately after the treatmentthe fumigated and respective control sampleswere extracted with 0.5 M K2SO4. For microbialbiomass carbon calculations, total organic car-bon was determined from the extracts using aShimadzu TOC-5050 analyzer. Microbial bio-mass N calculations were based on the total Nvalues analyzed by the Kjeldahl method. K-fac-tors 0.45 and 0.54 for microbial biomass carbon(Wu et al. 1990), and N (Brookes et al. 1985),respectively, were used. The results are ex-pressed on an oven-dry basis (105°C over night).

For nematode extraction, soil samples werestored in plastic bags at +3 to +5°C. Subsam-ples of 500 ml of mixed and homogenized sam-ples were taken for analysis. The soil was firstmixed carefully in water and then passed througha 0.14-mm sieve, and the remaining soil was dis-posed of. The rinsed soil was passed through a0.06-mm sieve and then twice through a 0.042-mm sieve. After sieving-decanting the rinsedwater with soil and nematodes from the last rins-ings was poured on the screen of a Baermannfunnel coated with a double milk bolting clothand the funnel was filled with water to reach thelower surface of the sample. A glass tube wasattached to the bottom. Escaping nematodes werecollected during 3–5 days. The nematodes werecounted on a glass grid plate under a stereomi-croscope. Samples of the nematodes were pre-pared for microscopic identification.

Earthworms were sampled from two random-ly chosen areas per plot. Both electrical methodand hand sorting was used for sampling. For theelectrical extraction, an apparatus build accord-ing to Thielemann (1987) was used. Eight elec-trical rods were submerged into the soil 30 cmdeep at regular intervals forming a cylinder witha volume of 0.038 cm3 and a surface area of0.126 cm2. Electric power of 30 and 60 voltswas conducted to the soil for a total of 16 min-utes. Earthworms emerging were picked and pre-served in 70% ethanol. Immediately after thisthe soil cylinder outlined by the rods was re-moved to a depth of 20 cm. The sample was

spread onto a white plastic sheet and earthwormswere picked by handsorting and preserved in70% ethanol. They were identified and classi-fied into adults, subadults and juveniles. Thefresh (preserved) weight was measured with theirgut contents.

The effectiveness of indigenous arbuscularmycorrhizal fungal (AMF) populations was es-timated in a bioassay using flax (Linum usitatis-simum L.) as a test plant (Kahiluoto et al. 2000).In order to create a non-mycorrhizal control,benomyl was mixed with soil at a rate of 20 mgl–1 soil–1. The relative mycorrhizal effectiveness(RME), i.e. the mycorrhizal contribution to thegrowth of the mycorrhizal plant, was defined bythe following formula: RME (%) = [(Ymyc+ –Ymyc–) / (Ymyc+)] × 100 where Ymyc+ and Ymyc– arethe dry weights of the mycorrhizal treatment andcontrol with inhibited AM function, respective-ly. AM spores were extracted by wet sieving anddecanting (Gerdemann and Nicolson 1963) fol-lowed by centrifugation in water and in 50%sucrose solution (Walker et al. 1982). Sieves of500 µm and 50 µm were used for wet sieving.After centrifugation the spores were transferredinto a dish of water for examination under a dis-secting microscope. Thereafter the spores werecounted and classified.

Litter decomposition in soil was studied us-ing the mesh bag method (Berg et al. 1987). Air-dry clover leaves (0.5 g without petioles) weresealed into polyester bags (mesh size 0.2 × 0.2mm, bag size 5 × 5 cm) that were placed in thesoil in upright position, the top 5 cm below thesoil surface and 2–3 cm apart. Two sets of bagswith two bags in each set were buried in eachexperimental plot. One bag in each set was dugup after 30 days and the remaining bags after 60days. The bags were gently washed and dried at60°C overnight. The dry weight of the remain-ing clover leaves was measured.

Statistical analysesRepeated measurements of response variables(chemical properties and Corg) were made from

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each plot for several years, starting in 1983 andending in 1999 (Table 2). The repeated mea-surements from the same plot were correlatedand the correlation was taken into account in thestatistical models. Proper covariance structurefor the repeated measurements was chosen. Un-structured proved useful for pH, Mg, Corg, P andEC. Compound symmetry proved useful for Ca

and K. The response variables were analysed ac-cording to the following mixed model:

Yijk = µ + bi + Tj + eij + Pk + fik + TPjk + gijk

where is the overall mean, b is the random blockeffect, T and P are the fixed effect of treatmentand year, TP is the two-factor interaction of fixed

Table 2. Chemical and biological soil properties determined from the experimental area during 1983–1999. Enzyme activ-ities and ATP were calculated per soil loss on ignition (organic matter) and enzyme activities as µmol product (MUF = 4-methylumbelliferone or AMC = 7-amino-4-methylcoumarine) g–1 soil (loi) 3 h–1; xylanase as µmol glucose g–1 soil (loi)16 h–1.

Property 1999

Abbreviation 1983 1989 1991 1995 Jun Aug Sep

Water holding capacity (ml g–1 dw soil) WHC xpH pH x x x x xElectrical conductivity (10–4 S cm–1) EC x x x x xExtractable phosphorus (mg l–1 dry soil) P x x x x xExtractable calcium (mg l–1 dry soil) Ca x x x x xExtractable potassium (mg l– dry soil) K x x x x xExtractable magnesium (mg l–1 dry soil) Mg x x x x xEnzymes

Acid phosphatase PMEacid x xAlkaline phosphatase PMEalkaline x x

α-Glucosidase α-Gluc x xβ-Glucosidase β-Gluc x xArylsulphatase ArylS x xβ-Xylosidase β-Xyl x xChitinase Chitin x xAlanine aminopeptidase Alanine AP x xLeusine aminopeptidase Leucine AP x xLysine-alanine AP Lys Ala AP x xPhosphodiesterase PDE x xXylanase Xyl x x

Adenosine triphosphate (nmol g –1 (loi) soil) ATP x xSoil organic carbon (%) Corg x x xMicrobial biomass N (µg Nmic g–1 dw soil) Nmic x xMicrobial biomass C (µg Cmic g–1 dw soil) Cmic x xNumber of nematodes (no. 500 ml–1 soil) NEMnumber xNumber of earthworms (no. m–2 soil) EWnumber xEarthworm fresh weight (g m–2 soil) EWFW xNumber of AM spores (no. 100 ml–1 soil) AM spores xNumber of AM species (no.) AM species xRelative mycorrhizal effectivenss, fresh weight

(RME-%) RMEFW xRelative mycorrhizal effectivenss, dry weight

(RME-%) RMEDW xLitter decomposition (% decomposed) LD x

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effects and e, f and g are random error terms.Residual errors (gijk) were correlated with the co-variance structure defined above. The modelswere fitted by using the residual maximum like-lihood (REML) estimation method. The degreesof freedom were approximated through the meth-od introduced by Kenward and Roger (1997).Accordances of the data with the distributionalassumptions of the models were checked bygraphic plots. The residuals were checked fornormality using box plot (Yandell 1997). In ad-dition, the residuals were plotted against the fit-ted values. The MIXED of the SAS/STAT soft-ware was used in the analyses (Littell et al.1996).

Soil biological and chemical properties meas-ured from the experiment in 1999 were subject-ed to cluster analysis that was carried out withZymProfilerTM software applying standardizationusing averages for each variable, squared euclu-dian distance as dissimilarity measure and UPG-MA (group mean) as the clustering method. Re-sults of 1999 were also tested using analysis ofvariance and Statistix for Windows software(Analytical Software, Thallahassee, USA). Be-cause of the variation between blocks observed

in the cluster analysis the impact of blocks wastested in addition to treatment in the two-wayanalysis of variance. Enzyme activities weremeasured from both spring and autumn samplesof block 1 and were thus analysed using two-way analysis of variance without replication.Only measurement replicates were available andthe results must be regarded as tentative. Due tothe assumed impact of cultivated plant, rye andbarley plots were analysed separately for eachsoil property.

Results

Chemical properties 1983–1999After one year already, but especially after sev-en or more years, extractable P and K had de-creased significantly in rotations C and D (lowinput/organic) as compared with the convention-al rotation A, but in rotation B (low mineral fer-tilization/organic) the contents of these nutrientsdid not differ from the values measured in A.

Table 3. Amounts of extractable P and K in different cropping systems (rotations A–D, see Table 1) in 1983–1999. Analy-ses were made from starting point 1 of rotations.

Soil property and year Rotation

A B C D

Extractable P, mg l–1 dry soil1983 46 42 031* 0341989 33 38 021* 023*1991 30 31 019 ** 023 *1995 33 35 018 0231999 31 33 018*** 021**

Extractable K, mg l–1 dry soil1983 80 77 072 * 0751989 97 94 081 *** 073 ***1991 92 85 073 *** 077 ***1995 86 82 068 *** 070 ***1999 1080 1260 105 102

Means in rows followed by *, ** or *** differ from the value in rotation A at P < 0.05, P < 0.01 or P < 0.001 (tested by theESTIMATE statement of PROC MIXED), respectively.

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Tab

le 4

. Bio

logi

cal,

chem

ical

and

phy

sica

l cha

ract

eris

tics

of s

oil s

ampl

es in

spr

ing.

In th

e cl

uste

r ana

lysi

s st

anda

rdis

ed d

ata,

squ

are

eucl

idia

n di

stan

ce a

nd U

PGM

A w

ere

used

. The

low

er f

ract

ile is

indi

cate

d w

ith it

alic

s an

d th

e up

per

frac

tile

with

bol

d. T

reat

men

ts a

s in

Tab

le 1

.

Tre

atm

ent

Plan

tB

lock

Clu

ster

PC

aK

Mg

pHE

CSo

il C

LD

WH

C%

DW

Nm

icC

mic

AT

P

AB

arle

y1

239

2582

108

228

7.2

0.8

1.8

760 .

5778

3722

31 .

9A

Bar

ley

21

2518

6710

924

16.

60.

71.

873

0.63

7827

215

2.0

AB

arle

y3

129

1717

109

230

6.8

0 .6

1 .8

760 .

6279

2620

93.

0A

Rye

11

2719

8511

124

96.

70.

52.

072

0 .61

7622

297

2.7

AR

ye2

131

1873

111

240

6.8

0.6

1 .7

710 .

6179

3022

92.

3A

Rye

31

2419

4311

025

66.

50 .

52.

072

0.63

7727

275

1 .9

BB

arle

y1

246

5972

120

241

7.5

1.1

2.0

720.

6278

3728

71 .

1B

Bar

ley

21

2719

3513

826

96.

60.

92.

076

0.66

7733

310

2.5

BB

arle

y3

124

1896

118

246

6.7

0.5

1 .9

690.

6678

3628

52.

7C

Bar

ley

11

2522

5211

023

57.

00.

71.

974

0.64

7838

284

2.9

CB

arle

y2

113

1872

112

248

6 .5

0.7

2.1

740 .

6778

2726

13.

0C

Bar

ley

31

1518

1109

521

76.

50.

62.

172

0.67

7829

279

2.8

DB

arle

y1

231

4166

105

208

7.4

0.9

1 .9

720.

6379

3228

22.

4D

Bar

ley

21

1419

0910

324

36.

50.

72.

273

0.66

7737

279

3.5

DB

arle

y3

117

1863

097

235

6.6

0.7

2.0

580 .

6179

3625

82.

8D

Rye

11

2320

7410

626

16.

60.

52.

172

0.64

7639

313

1 .8

DR

ye2

115

1725

087

245

6.7

0.5

1.8

730 .

6278

3424

04.

1D

Rye

31

1518

6209

626

26 .

50.

72.

071

0.67

7728

261

1.7

EC

= E

lect

rica

l con

duct

ivity

(10–4

S c

m–1

), L

D =

Litt

er d

ecom

posi

tion

(% d

ecom

pose

d), W

HC

= W

ater

hol

ding

cap

acity

(ml g

–1 d

w s

oil)

, %D

W =

% s

oil d

ry w

eigh

t, C

org

= S

oil

orga

nic

carb

on (

%),

Cm

ic =

Mic

robi

al b

iom

ass

C

(µg

Cm

ic g

–1 d

w s

oil)

, N

mic

= M

icro

bial

bio

mas

s N

(µg

Nm

ic g

–1 d

w s

oil)

, A

TP

= A

deno

sine

tri

phos

phat

e(n

mol

g–1

(lo

i) s

oil)

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Vol. 11 (2002): 311–328.

The amount of extractable P decreased signifi-cantly in all rotations (on average 0.6–0.9 mgl–1 year–1) from 1983 to 1999 (F4,32 = 9.36, P <0.01). In spite of the decrease, soil P status waswell above a deficiency level in all rotations evenat the end of the experimental period, represent-ing a good status in rotations A and B and a sat-isfactory status in rotations C and D (Viljavuus-palvelu Oy 2000). Year affected significantlyalso the amount of extractable K (F4,32 = 155.69,P < 0.001), but in an opposite direction (Table3). Almost all results of K showed a fair soil Kstatus throughout the experiment (Viljavuuspal-velu Oy 2000). Thus, the soils were substantial-ly poorer in K than they were in P. The long-term cropping system had no significant effecton soil pH (mean value 6.8 in 1999), EC (meanvalue 0.7 in 1999) or amounts of extractable Ca(mean value 2487 mg l–1 soil in 1999) and Mg(mean value 237 mg l–1 soil in 1999).

Cluster analysis of the 1999 early summerresults revealed that in the barley growing plots

of block 1, extractable P, Ca, pH and EC tendedto be higher than elsewhere. In plots of blocks 2and 3 in rotations C and D, P, Ca, K and pH werelower and WHC was higher (Table 4).

Treatment affected significantly the amountsof extractable P in the barley plots (P = 0.0014)but not in the rye plots (Table 5). P concentra-tions tended to be lower in the organically thanin the conventionally cultivated plots but, dueto the significant (P = 0.0004) variation betweenblocks (Table 5), the differences were not con-sistent. P concentrations in early summer wereat higher level in the barley plots of rotation Abut also in block 1 of rotation D. It was highestin rotation B of block 1 (Table 4).

The amount of extractable Ca was signifi-cantly affected by block but not by treatment(Table 5). Ca concentrations were highest inblock 1 of the barley growing plots irrespectiveof treatment (Table 4).

K concentration was significantly affected bytreatment in the barley plots in the analysis of

Table 5. Statistically significant sources of variation for chemical, physical and biological variables (general analysis ofvariance).

Variable Test cover Significant source df F Pof variation

Early summerP barley treatment 3, 6 20.79 0.0014

block 2, 6 37.35 0.0004Ca barley block 2, 6 05.17 0.0495K barley treatment 3, 6 08.92 0.0125Mg barley block 2, 6 05.86 0.0388Mg rye block 1, 2 20.67 0.0461Corg rye block 1, 2 31.00 0.0313pH barley block 2, 6 45.94 0.0002pH rye block 1, 2 23.75 0.0404

Early and late summerNmic barley treatment 03, 12 04.34 0.0274Nmic rye treatment 1, 8 08.42 0.0198Cmic barley treatment 03, 12 19.01 < 0.0001

block 02, 12 04.04 0.0366Cmic rye block 07.34 0.0191ATP barley treatment 03, 12 05.22 0.0354

block 02, 12 10.59 0.0009

Corg = Soil organic carbon (%), Nmic = Microbial biomass N (µg Nmic g–1 dw soil),Cmic = Microbial biomass C (µg Cmic g1 dw soil), ATP = Adenosine triphosphate (nmol g–1 (loi) soil)

320



Tab

le 6

. T

he i

mpa

ct o

f tr

eatm

ent

on s

oil

qual

ity i

n th

e au

tum

n. T

he l

ower

fra

ctile

s is

ind

iact

ed w

ith i

talic

s an

d th

e up

per

frac

tile

with

bol

d. I

n th

e cl

uste

r an

alys

isst

anda

rdis

ed d

ata,

squ

are

eucl

idia

n di

stan

ce a

nd U

PGM

A w

ere

used

. T

reat

men

ts a

s in

Tab

le 1

.

Tre

atm

ent

Plan

tB

lock

Clu

ster

%D

WN

mic

Cm

icC

mic/N

mic

AT

PE

Wnu

mbe

rE

WFW

NE

Mnu

mbe

rA

Msp

ores

AM

spec

ies

RM

EFW

RM

ED

W

AB

arle

y1

378

20.4

212

10.4

2.1

048

2214

012

45

5.6

0.9

AB

arle

y2

177

29.2

237

08.1

2.4

090

4428

607

04

9.4

8.5

AB

arle

y3

379

15.2

213

14.0

3.4

048

0253

604

63

4.9

14.0

AR

ye1

377

19.2

233

12.2

2.6

024

0341

411

06

–11.

2–1

0.5

AR

ye2

378

23.9

216

09.1

2.8

008

0306

55

–11.

4–8

.5A

Rye

33

7723

.421

409

.14.

503

204

560

090

4–2

.23.

0B

Bar

ley

12

7838

.124

606

.52.

204

308

210

470

51.

0–4

.1B

Bar

ley

21

7829

.632

611

.02.

410

418

244

093

611

.74.

4B

Bar

ley

32

7956

.431

305

.64.

308

426

085

69.

310

.5C

Bar

ley

13

7828

.927

109

.43.

008

013

332

137

42.

4–1

4.4

CB

arle

y2

177

34.7

329

09.5

2.8

143

4012

812

17

8.1

–0.3

CB

arle

y3

177

32.5

268

08.2

4.6

137

6704

63

1.9

0.5

DB

arle

y1

279

36.1

273

07.6

2.5

129

1852

229

07

6.3

0.1

DB

arle

y2

176

35.8

323

09.0

2.7

146

4514

804

05

6.6

–3.5

DB

arle

y3

279

41.3

276

06.7

3.8

179

3924

204

86

9.1

3.1

DR

ye1

177

40.6

305

07.5

3.1

100

2111

604

95

–13.

5–1

8.2

DR

ye2

178

27.2

239

08.8

3.4

119

8105

94

19.5

12.3

DR

ye3

178

29.5

273

09.3

2.5

133

2623

606

84

21.4

–4.8

%D

W =

% s

oil d

ry w

eigh

t, N

mic =

Mic

robi

al b

iom

ass

N (

µg N

mic g

–1 d

w s

oil)

, Cm

ic =

Mic

robi

al b

iom

ass

C (

µg C

mic g

–1 d

w s

oil)

, Cor

g = S

oil o

rgan

ic c

arbo

n (%

),A

TP

= A

deno

sine

trip

hosp

hate

(nm

ol g

–1 (

loi)

soi

l), E

Wnu

mbe

r = N

umbe

r of

ear

thw

orm

s (n

r m

–2 s

oil)

, EW

FW =

Ear

thw

orm

fre

sh w

eigh

t (g

m–2

soi

l), N

EM

num

ber =

Num

ber

of n

emat

odes

(nr

500

ml–1

soi

l), A

Msp

ores

= N

umbe

r of A

M s

pore

s (n

r 100

ml–1

soi

l), A

msp

ecie

s = N

umbe

r of A

M s

peci

es (n

r), R

ME

FW =

Rel

ativ

e m

ycor

rhiz

al e

ffec

tiven

ss,

fres

h w

eigh

t (R

ME

-%),

RM

ED

W =

Rel

ativ

e m

ycor

rhiz

al e

ffec

tiven

ss, d

ry w

eigh

t (R

ME

-%)

321


Vol. 11 (2002): 311–328.

variance (Table 5). Organically cultivated plotsand blocks 2 and 3 tended to have lower K con-centrations than the other plots.

Mg concentrations were high in the rye plots.Nevertheless, the ranges of Ca, K and Mg con-centrations were narrow. Soil pH tended to behigh in block 1 in both the barley and rye plots(Table 4). No significant differences were ob-served for EC or WHC between plots.

Soil biological quality in spring andautumn 1999

Cluster analysis performed with the early sum-mer data show high Corg in blocks 2 and 3 of ro-tations C and D. Plots in rotation A tended tohave low microbial biomass N (Table 4).

The cluster analysis of soil quality variablesin autumn (Table 6) reveals a cluster of rye grow-ing plots in rotation A with low quantities ofearthworms, high numbers of nematodes and lowmicrobial biomass carbon and N. High quanti-ties of earthworms and low numbers of nema-todes tend to be present in organically cultivat-ed rotations C and D. A positive impact of lim-ing (barley growing block 1) was observed forAM spore numbers.

Enzyme activities were measured only for theliming treatment (block 1). Due to the assumedimpact of cultivated plant, rye and barley plotswere analysed separately. The impact of seasonwas observed for chitinase, β-xylosidase and leu-

cine aminopeptidase activities, but no significantimpact was observed due to treatment. Chitinaseand β-xylosidase activities were high in earlysummer, but aminopeptidase activies increasedtowards autumn. (Fig. 1, Table 7). Arylsulphataseactivity tended to depend on both the season andtreatment. Especially rye growing plots yieldedhigh activities in early summer.

The effects of long-term cropping systems onvarious biological soil properties other than en-zymes are shown in Fig. 2, in Table 5 showingstatistically significant differences and in Tables4 and 6 showing results from all the blocks andindicating clusters. No differences due to treat-ment were observed for litter decomposition, butCorg tended to be higher in the organic rotationsB–D than in the conventional rotation A. Rota-tion A, especially rye growing plots (A3), yield-ed lower numbers and biomass of earthwormsthan the organic rotations C and D, but the limedplots of block 1 had lower numbers than the othertwo blocks in rotation C and D. Despite the or-ganic management during 1994–1999, rotationB, especially the limed block 1, showed also lowbiomass and numbers of earthworms, thus be-having like the conventional rotation. ATP con-tent was low in block 1 and high in block 3,which hampers comparisons between treatments.High numbers of nematodes were present in allthe chemically fertilized rye growing plots butalso in the barley growing block 3. Microbialbiomass carbon and nitrogen tended to be highin plots of rotation C and D but low in the plots

Table 7. Statistically significant sources of variation for adenosine triphosphate and enzyme activitities for barley plots.

Variable Significant source df F Pof variation

β-Xylosidase season 1, 3 24.89 0.0155Chitinase season 1, 3 47.42 0.0063Leucine AP season 1, 3 77.15 0.0031β-Glucosidase/Biomass N treatment 3, 3 10.19 0.0441β-Xylosidase/Biomass N season 1, 3 21.72 0.0186Chitinase/Biomass N season 1, 3 21.11 0.0194Leucine AP/Biomass N season 1, 3 14.20 0.0327Lycine-alanine AP/Biomass N season 1, 3 10.33 0.0488

322



of rotation A in both the barley and rye growingplots.

Diversity of mycorrhiza (number of AM spe-cies and spore types identified), ranging fromthree to seven species per sample, did not corre-late with treatment, plant or block. The highestmean species numbers were found in the organ-

ic rotations D1 and B1 (6.0 and 5.7 species, re-spectively), while the lowest numbers werefound in rotations A1 and D3 (4.0 and 4.3 spe-cies, respectively). Spores belonging to the ge-nus Glomus, especially G. mosseae and G. geo-sporum, were dominating. Sporadically, alsospores of the genera Acaulospora and Scutel-

Fig. 1. Soil enzyme activities measured in different cropping systems (see Table 1) in early and late summer of 1999 fromblock 1. Enzyme activities were calculated per soil loss on ignition (organic matter) as µmol product (MUF = 4-methylum-belliferone or AMC = 7-amino-4-methylcoumarine) g–1 soil (loi) 3 h–1; xylanase as µmol glucose g–1 soil (loi) 16 h–1. Verticallines indicate standard deviation of measurement replicates.

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Vol. 11 (2002): 311–328.

lospora were detected. The unintended limingtreatment of some plots of block 1 had caused astrong increase of spores belonging to the genusGlomus. The AMF spore numbers correlatedpositively with the amounts of soil Ca (r = 0.98),soil P (r = 0.69), pH (r = 0.83) and EC (r = 0.76).In the RME test the overall impact of the indig-

enous AM fungal populations on flax growth wassmall, although a slight positive effect of AMFwas observed in barley plots of rotation A. Therewere, however, great variation in RME valuesbetween blocks with block 1 (both barley andrye plots) yielding lower values than the otherblocks (Fig. 2, Table 6).

Fig. 1. continues.

324



Fig. 2. Soil biological properties measured in different cropping systems (see Table 1) in early and late summer of 1999.Means of three blocks except for numbers of AM spores which are means of block 2 and 3. Vertical lines indicate standarddeviation of means.

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Vol. 11 (2002): 311–328.

Discussion

Soil biological and chemical properties werestudied in a field experiment during and after 17years of different cropping systems. Although theimpact of cropping system on soil properties wasseldom statistically significant, several soil prop-erties had changed or showed an indication ofchange in response to a long-term cropping his-tory. These were extractable P and K, WHC, Corg,soil microbial biomass carbon and N and numberof earthworms. These results are only partly inagreement with an earlier Finnish study wherelong-term organic farming was shown to de-crease soil extractable P levels and to increasemicrobial biomass N (Palojärvi et al. 2000). Thereason for the differences between these twostudies may be found in the Corg levels. The soilin the Laukaa experiment had a very low initialCorg content which was increased in the low in-put/organic rotations. Although this increase ofCorg was very small it may have influenced thesoil properties listed above. The big differencesin the number of earthworms cannot be explainedby the small increase in Corg. The organicallymanaged plots may have provided occasionallymore and better food for the earthworms thanthe conventionally managed plots. This organicmaterial was however easily decomposed and didnot cause big increase in Corg. The lower levelsof P and K in organically managed plots as com-pared with conventionally managed plots wasprobably due to a lower P and K fertilizationduring the last five years in those plots. Soil en-zyme activities were mainly affected by sam-pling time, and the impact of treatment on en-zyme activities must be evaluated, paying simul-taneously attention to crop, plant and season. Along-term effect of liming was also detected asincreased concentrations of Ca and P, higher pHand EC, and increases in AM spore numbers.

The results were characterized by a high de-gree of variation which influenced the results ofstatistical tests. The soils of different plots weresubject to a long-term influence of crop rotationand fertilizer management, crop rotation effect.

The influence of cropping system on soil prop-erties was the main objective of this study. How-ever, soil properties were also subject to the ef-fect of the standing crop (barley or rye in thiscase) or of the preceding crop the year before.There were also very great variation betweenblocks for some characteristics. Furthermore,season (time of sampling) had a distinct effect,especially on soil enzyme activities.

Replicate enzyme activity measurementsyielded sufficiently small variation. Enzyme ac-tivities differed distinctly in the barley and ryegrowing plots but management practice did notclearly affect enzyme activities in the analysisof variance where season was a more importantfactor. This lack of impact may be due to theinsensitive statistical analysis without replicates.On the other hand, the impact of liming possi-bly masked the impact of rotation. Only the block1 plots which had received unintentionally limein 1982 were analysed for enzyme activities. Inthe cluster analysis the slight differences aresummed up if they occurred simultaneously. Thissensitivity of cluster analysis is evident with thepresent set of data because of the rare signifi-cant impact of treatment in the analysis of vari-ance, whereas cluster analysis showed differenc-es related to treatment (Vepsäläinen et al. 2001).Vepsäläinen et al. (2001) made a tentative ob-servation with this set of data that the plots thathad earlier received composted plant materialand the plots receiving mineral fertilisers be-longed to separate clusters. However, it is aninteresting finding that may be confirmed withthe new data that will be available from this ex-periment later on.

Despite five years of organic managementduring 1994–1999 in rotation B, soil P and Kand the amounts and biomass of earthwormswere still closer to the levels prevailing in theconventional rotation A than in the organic rota-tions that had run over a longer period. This isan example of slow soil resilience after a bigchange of management. The above-mentionedvariation between blocks of the experimentalarea was at least partly due to an unintentionallygiven high amount of lime in certain plots of

326



block 1 in 1982. This liming treatment was stilldetected after 17 years as increased levels of Caand P, higher pH and EC and higher numbers ofspores of AM fungi. The liming treatment cantherefore be regarded as another example of slowsoil resilience.

This study can be seen as one of the first at-tempts to find properties of importance for de-termination of soil quality in Finnish agriculture.However, reliable indicators of soil quality canbe chosen only after certain criteria have beenmet. Several authors (Brookes 1993, Doran andParkin 1996) list such criteria and they are sum-marized by Stenberg (1999) into five essentialpoints: (1) indicators should integrate physical,chemical and biological properties and process-es and represent soil properties or functions thatare more difficult to measure directly, (2) theecological relevance and natural variation of in-dicators should be well understood, (3) indica-tors should be sensitive to long-term variationin management and climate, but resistant toshort-term fluctuations due to weather changeor crop development, (4) it should be possibleto measure indicators accurately and preciselyacross a wide range of soil types and soil condi-tions and (5) indicators should be simple to as-sess and cost efficient to allow for a large numberof analyses to be made.

It can be concluded that long-term field ex-periments as the one used by us or others (e.g.Svensson 2002) are valuable tools for studying

changes in soil quality. In our study, the crop-ping system affected several soil properties in-cluding both chemical and biological ones. Thesesoil characteristics have a potential to be chosenas soil quality indicators. However, biologicalproperties often also showed great variation inresponse to standing crop and season. In furtherstudies, the reasons for this variation need to bestudied. Search for indicators less sensitive toprevailing environmental conditions should alsobe emphasized. The crucial question to be an-swered in future is whether plant growth andyield will correlate with the changes in soil prop-erties. This question will be dealt with in a fur-ther paper using data from the same experiment.

Acknowledgements. This study was financed by the Minis-try of Agriculture and Forestry, by a special grant from MTTAgrifood Research Finland and the National TechnologyAgency of Finland as a project of the Biodiversity ResearchProgramme of the Academy of Finland. The field experi-ment was carried out at the Laukaa Research and Elite PlantStation of MTT Agrifood Research Finland where somelaboratory analyses were also carried out. Other analyseswere carried out in the laboratories of MTT in Jokionen(Plant Protection and Soils and Environment) and the Finn-ish Environment Institute in Helsinki. We wish to thank thestaff of all the laboratories for doing these analyses. We areparticularly grateful to Mauri Räkköläinen at Laukaa forsorting out research results during the long period from 1982to 1999 and to Christian Eriksson at MTT in Jokioinen forhelp with the statistical analyses. We also wish to thankSevastiana Ruusamo, M.A., for valuable revision of theEnglish manuscript.

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SELOSTUS

Viljelyjärjestelmän vaikutus maan kasvukuntoa määrääviin tekijöihinMauritz Vestberg, Sanna Kukkonen, Kaisa Saari, Marjatta Uosukainen, Ansa Palojärvi,

Tuomo Tuovinen, Milja Vepsäläinen ja Maarit NiemiMTT (Maa- ja elintarviketalouden tutkimuskeskus) ja Suomen ympäristökeskus

Viljelyjärjestelmän vaikutusta maan kasvukunnonbiologisiin ja kemiallisiin tekijöihin tutkittiin MTT:nLaukaan tutkimus- ja valiotaimiaseman pitkäaikaises-sa kenttäkokeessa. Tutkimuksessa verrattiin neljänviljelykierron vaikutuksia maaperäominaisuuksiin.Tavanomaisessa viljelykierrossa (A) viljeltiin lähin-nä viljakasveja (ohra-ohra-ruis-kaura-nauris-kauravuoteen 1993, jonka jälkeen ohra-ohra-ruis-kaura-ohra-ohra), jotka lannoitettiin suositusten mukaises-ti. Kierto A jatkui tavanomaisena viljanviljelynä pe-rustamisvuodesta 1982 vuoteen 1999 asti. Kierto Boli vuoteen 1993 asti muuten A:n kaltainen, mutta sitälannoitettiin 50 % vähemmän. Kierrot C ja D olivatvuoteen 1993 asti omavaraisia kiertoja, joten niidentyppilannoitus oli peräisin kiertoihin sisältyvistä pal-kokasveista (ohra-puna-apila-ruis-hernekaura-nauris-hernekaura). Lisäksi näille kierroille annettiin karjan-lantaa (vain kokeen alussa), biotiittia ja raakafosfaat-tia korvaamaan sadon mukana poistunutta kaliumiaja fosforia. Kierrosta C kaikki palautukset kynnettiinkäsittelemättöminä maahan, kun taas D-kierrosta pa-lautukset kompostoitiin. Vuonna 1994 kierrot B ja Cmuutettiin luonnonmukaisen viljelyn siirtymävaihe-kierroksi 1 (ohra-nurmi-nurmi-nurmi-ruis-hernekau-ra) ja kierto D luonnonmukaisen viljelyn siirtymävai-hekierroksi 2 (ohra-nurmi-nurmi-ruis-kaura-nurmi).Koealueelle tehtiin viljavuusanalyysit vuosina 1983,1989, 1991, 1995 ja 1999. Lisäksi vuonna 1999 mää-ritettiin erilaisia biologisia ominaisuuksia koealueenviljaa kasvavista ruuduista.

Kiertojen C ja D maaperässä oli vähemmän liu-koista fosforia ja kaliumia kuin A-kierron maaperäs-sä. Viljelyjärjestelmä ei sen sijaan vaikuttanut maanpH- tai johtolukuun eikä maan kalsiumin ja magne-siumin määriin. Jotkut biologiset ominaisuudet oli-vat reagoineet viljelyjärjestelmään ja toiset eivät.Maan mikrobien typen ja hiilen määrä sekä lierojenlukumäärä ja biomassa olivat luomukierroissa suu-rempia kuin tavanomaisessa kierrossa. Myös maanorgaanisen hiilen määrä ja vedenpidätyskapasiteettiolivat yleensä suurempia luomu- kuin tavanomais-kierroissa. Sen sijaan vuodenaika vaikutti enemmänkahdentoista maaperäentsyymin aktiivisuuksiin. An-keroisten määrä, orgaanisen aineksen hajoamisno-peus, mykorritsan määrä ja toiminta eivät juuri muut-tuneet viljelyjärjestelmän muuttuessa.

Kenttäkokeen perustamisvaiheessa vuonna 1982oli pienelle alueelle tapahtunut kalkitusvirhe, jokavielä 17 vuotta myöhemmin näkyi kohonneina kal-sium-, fosfori-, pH- ja johtolukuina alueella. Maa”muistaa” äärikäsittelynsä pitkään. Ylimääräinen kal-kitus ei kuitenkaan heijastunut maaperän biologisiinominaisuuksiin paitsi mykorritsaitiöiden määrään,joka kasvoi yllä mainittujen kemiallisten ominaisuuk-sien kohotessa. Toinen esimerkki maan muistista onB-kierto, jonka fosforin ja kaliumin sekä lierojenmäärät vuonna 1999 olivat lähempänä A-kierron lu-kuja kuin C- ja D-kierron lukuja. Tässä tutkimuksessayritettiin määrittää keskeisiä biologisia maaperäomi-naisuuksia, joita voitaisiin tulevaisuudessa ottaa käyt-töön pelto- ja puutarhamaan kasvukunnon mittareina.

Cropping system impact on soil quality determinants · 2002-12-20 · 312 AGRICULTURAL AND FOOD...

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Transcript of Cropping system impact on soil quality determinants · 2002-12-20 · 312 AGRICULTURAL AND FOOD...