Effects of crop production practices on soil characteristics and metabolic diversity of microbial communities under winter wheat. (2024)

Introduction

More attention should be paid to the introducing into broadagricultural practice of management systems that meet the principles ofprotection of both the soil and the agroecosystem, which would allowtoday's agriculture to fulfil requirements related to protection ofsoil quality (Doran et al. 1996). Conventional production systemsmaximise yields through intensive soil fertilisation and the use ofpesticides to protect plants. Such practices often lead to deteriorationof biological and physicochemical soil properties. In recent years,there has been increasing interest in and development of organicpractices. The organic production system is one alternative toconventional agriculture (Marinari et al. 2006; Kus and Jonczyk 2008; dePonti et al. 2012), and stimulates the microbial activity and diversityat or near the soil surface (Gajda et al. 2016).

The activities in soil of various enzymes, mostly of microbialorigin, determine the decomposition of soil organic matter (SOM) andnutrient cycling (Marinari et al. 2006). The SOM is a key factoraffecting the soil directly by supplying nutrients or indirectlymodifying its physical, chemical and microbiological properties and soimproving soil environment quality for microorganisms and plant growth(Liebig and Doran 1999). Particulate organic matter (POM) is a type ofSOM fraction, and has been demonstrated as a useful tool in the processof orientation of research on SOM (Sequeira et al. 2011). The POM is theSOM fraction of size in the range 0.053-2 mm (Cambardella and Elliott1992)--it is regarded as the most labile component of SOM and isimportant for chemical and microbial transformation processes of carbon(C) in soil (Baldock and Skjemstad 2000). The POM is considered to bethe intermediate state between fresh, readily-decomposable organic(usually plant) remains and the more decomposed and stable SOM (humus).

Healthy ecosystems are usually characterised by high speciesdiversity, and the Biolog EcoPlate[TM] method is often used to analysefunctional diversity in soil microbial communities (Furtak et al. 2017;Li et al. 2017). EcoPlate allows testing for several substrates andenables evaluations of metabolic potential diversity of microbialpopulations in different environmental samples. The substrates on theEcoPlate are naturally occurring in the soil environment and some areproducts of exudates of plant roots (Preston-Mafham el al. 2002). Thismethod provides many results on metabolism of microorganisms thatcorrelate with other soil quality indicators (Gafezka et al. 2017).

The influence of agricultural techniques on the diversity of soilmicroorganisms is well known (Liebig and Doran 1999; Wolinska et al.2017). The objective of the present study was to evaluate the effects ofconventional and organic crop production systems on chosen soilcharacteristics: chemical (SOM and POM contents) and microbiological(dehydrogenase activity (DHA) and metabolic microbial diversity usingEcoPlate).

Material and methods

Field experiment and soil sampling

This study was conducted during 2015-2017 in a long-term fieldexperiment established in 1994 (area of each field ~1 ha) and locatedpredominantly on Haplic Luvisol soil at the Experimental Station ofInstitute of Soil Science and Plant Cultivation State Research Institute(IUNG - PIB) in Osiny (Lublin Voivodeship; 51[degrees]28' N,22[degrees]30' E), Poland. Winter wheat (Triticum aestivum L.) cv.Jantarka was grown in two different crop production systems: organic(ORG) and conventional (CON). The ORG included a crop rotation ofpotato-spring barley-grass/clover mixture (1st year) and grass/ clovermixture-winter wheat (2nd year), with grass/clover compost (30 t[ha.sup.-1]) applied under potato. No mineral fertilisers and plantprotection chemicals were used. Weed control was mainly by mechanicaltreatments. The CON system was based on mouldboard ploughing invertingsoil to 20 cm depth and crop rotation of winter rape-winter wheat-springbarley, and mineral fertilisation and plant protection chemicals.Organic fertilisation was limited to winter rape and winter wheat strawspread on the field surface after the fall harvest and ploughed into thesoil. In the CON system, crops were grown according to the high-inputrecommendations generally used in Poland. The tillage treatment for eachfield always remained the same. Crops in this experiment were grown indifferent crop production systems on non-replicated fields. Moreinformation about the field experiment can be found in Kus and Jonczyk(2008). The weather conditions in the experimental fields are shown inTable 1. Each year, soil samples for analysis were collected throughoutthe growing season, at ear formation (55-56 BBCH) and at harvest. Therepresentative soil samples (~1500 g each) were taken from the in-rowplanting area from the depths of arable layer (0-5, 5-10 and 15-20 cm)and sub-arable layer (30-35 cm) randomly across each trial andtransferred to the laboratory in coolers. Plant roots were carefullyremoved from soil before the analysis procedure.

Then, soil samples were weighed and 15 g of subsamples were driedat 105[degrees]C for 24 h to determine soil water content. Next, soilsamples were sieved through a 2-mm sieve and stored at 4[degrees]C untilanalysis. Some soil chemical characteristics are contained in Table 2.

SOM and POM contents

The SOM content was measured with the[K.sub.2][Cr.sub.2][O.sub.7]-[H.sub.2]S[O.sub.4] wet oxidation Tiurinmethod. For POM analysis, representative air-dry soil samples weredispersed with 100mL of sodium hexametaphosphate (5 g [L.sup.-1]) andshaken for 18 h on a reciprocal shaker at ~ 180 oscillations[min.sup.-1]. Next, the dispersed soil was passed through 500- and53-[micro]m sieves. Particles retained on the 53-[micro]m sievecorresponded to the POM fraction. The quantities of the POM fractionwere measured according to Cambardella and Elliott (1992), in a modifiedmethod, in which POM content was estimated with loss-on-ignition (LOI)procedure (Schulte and Hopkins 1996), as detailed by Gajda et al.(2001). For calculations of the percentage of POM in total SOM, the SOMcontent was assessed using the LOI procedure.

DHA

The DHA was evaluated spectrophotometrically by the reduction oftriphenyl tetrazolium chloride to triphenyl formazan (TPF) (PolishStandard PN-EN ISO 23753-1, 2011). The DHA values were calculated basedon oven-dry (105[degrees]C) weight of soil.

Metabolic diversity of microbial communities

The Biolog EcoPlate (Biolog Inc., Hayward, CA, USA) method was usedto evaluate diversity in metabolic activity of soil microorganisms. Eachplate contained 31 different sole C sources and water (as a blanksample) in three replicates. The C sources distributed in 96 wells oneach plate were subdivided into five groups of substrates: carbohydrates(CH), carboxylic acids (CXA), amino acids (AA), amines and amides (AMAD)and polymers (PLM) (Weber and Legge 2009). The utilisation rate of eachgroup was measured by reduction of a redox indicator dye tetrazoliumviolet that changes from colourless to purple. One gram of each freshsoil sample was suspended in 99 mL of sterile water and shaken for 20min. Next, the suspensions were incubated at 4[degrees]C for 30 min(Weber and Legge 2009; Gajda et al. 2017). After this, solutions werefiltered (Bag Filter, Interscience, Paris, France). Then, 120 [micro]Lof the sample was applied to each well on EcoPlate and incubated at25[degrees]C for 120 h. The absorbance at 590 nm was measured every 24 hwith a plate reader Biolog MicroStation[TM] (Biolog Inc.).

Statistical analyses

Statistical analyses were performed using Statistica ver. 10.0software (Stat. Soft Inc., Tulsa, OK, USA) and ANOVA. The significantdifferences within the data were calculated according to post-hocTukey's HSD (honest significant differences) and post-hocFisher's l.s.d. (least significant difference) tests at P< 0.05.Pearson's correlation coefficients were calculated to showrelationships between soil properties and winter wheat yield (WWY) atP< 0.01 and P <0.05.

Based on the data ([OD.sub.590]) obtained from EcoPlate after 120 hof incubation, Shannon's diversity (H') index was calculated(Hill et al. 2003) and a heat map created (Stat. Soft Inc.). Averagewell colour development (AWCD) was measured as a mean of opticaldensities from 31 wells as given in Garland and Millis (1991).

Results

SOM and POM contents

The ORG system favoured increase in SOM content (17.0 g [kg.sup.-1]of soil), in particular at the top soil (0-5 cm) compared with CON (14.3g [kg.sup.-1] of soil) (Fig. 1). In the sub-arable layer (30-35 cm) aninverse image of the SOM content was observed in relation to upper soillayers (0-5, 5-10 and 15-20 cm). The SOM content in sub-arable soil was1.5 times higher in CON compared with ORG system (5.7 g [kg.sup.-1] ofsoil).

A good and sensitive parameter in monitoring changes in soilenvironment is POM, a biologically active fraction of SOM (Fig. 2). ThePOM content in top soil (0-5 cm) for ORG was 3.9 g [kg.sup.-1] air-drysoil and was 25.6% higher than under CON. In ORG, the POM contentdecreased with increased soil depth, but no such relationship wasobserved in CON. In sub-arable soil, the POM content was 76% lower, onaverage, compared with upper soil layers (0-5, 5-10 and 15-20 cm).

Compared with POM, the changes in SOM content in soil under bothcrop production systems were difficult to detect within the three-yearstudy (Fig. 3a, b). The POM content in soil under ORG measured in 2017was significantly higher (twice, on average) than in 2015, whereas inthe same soil under CON the POM increase was negligible (Fig. 3b). Thepositive effects on soil of the ORG system were also reflected inchanges in the percentage of POM in total SOM (Fig. 4). In ORG,especially in the top soil layer (0-5 cm), there was a significantly(f<0.05) greater percentage of POM in SOM (23.9%) than in CON(20.3%).

DHA

The studied crop production systems significantly determined thesoil microbial activity measured as DHA (Fig. 5). The DHA in the 0-5 cmsoil layer was 47.5% higher in ORG than CON (0.16 and 0.11 mg TPF g DW[soil.sup.-1] 24 [h.sup.-1] respectively). The DHA in the sub-arablesoil layer (30-35 cm) was significantly lower than in the arable layer(0-20 cm) for both ORG and CON systems, but the DHA decrease with depthwas more marked for ORG than CON.

Diversity and metabolic activity of soil microbial communities

The abilities of the microbial communities in soil under ORG andCON systems to utilise specific C-substrates were analysed usingEcoPlate (Figs 6-9). Optical density values at 120 h of incubation wereused to assess microbial functional diversity and for statisticalanalyses because at that time the highest activity of soilmicroorganisms was recorded.

It was observed that some C substrates were utilised differentlyand some similarly by microorganisms inhabiting soil under the two cropproduction systems. Also, the rate of C-substrate utilisation wasinfluenced by depth in the soil profile. In the ORG system, the highestutilisation rates of C sources by microbial communities were for the CHgroup (34.3%) in the top soil (0-5 cm); and in the CON system, the CXAgroup (30.8%) in the 15-20cm layer. In the other studied soil layers,the microbial communities utilised the CH and CXA groups at a similarrate, with no significant differences observed. The least used C sourcesby soil microorganisms for both crop systems was the AMAD group (5.1% inORG and 5.6% in CON, on average), but with significant differences inthe rate of utilisation between crop systems in the 0-5, 5-10 and 30-35cm layers. Also, utilisation rate of the PLM group differedsignificantly in both crop systems, particularly in the 5-10 cm layer by~2.0%. In the sub-arable layer (30-35 cm) the CXH and PLM utilisationrates showed negligible differences between the two crop systems, butthe CH, AA and AMAD groups were utilised at significantly (P <0.05)different rates between the systems (3.0% difference, on average).

The AWCD values showed differences in microbial metabolic activitybetween the two crop systems (Fig. 7). In all arable soil layers (0-5,5-10 and 15-20 cm) the AWCD values were higher in the ORG (1.64-1.67)than the CON system (1.47-1.60), but the difference (12%) was onlysignificant in the 5-10 cm layer. In the sub-arable layer (30-35 cm),the AWCD value was ~8% higher in the ORG than the CON system.

The H' values indicated the effects of studied crop productionsystems on the metabolism of soil microorganisms (Fig. 8). In general,higher values of H' characterised soil under the ORG compared withthe CON system. The highest H' values were in the ORG system forthe 5-10 cm layer (3.36, on average); the lowest H' values were insoil at 30-35 cm in CON (3.30). There were significantly (P <0.05)higher H' values for ORG compared to CON in soil at 5-10 and 30-35cm depths.

A heat map was generated for better visualisation of similaritiesand dissimilarities in C-substrate utilisation rates by soil microbialcommunities between the crop systems (Fig. 9).

WWY

In our studies, the WWY differed for the ORG and CON systems with6.38 and 8.14 t [ha.sup.-1] respectively (Table 3). The higher WWY inthe CON system may be due to relatively high yield losses as a result ofpests and diseases and phosphorus limitations in the ORG system (Table2).

Correlations among soil properties

There were strong correlations (P < 0.01) between DHA and SOM(0.931) and POM (0.969) (Table 4). There were also significant(P<0.05) positive correlations of WWY with SOM (0.783), POM (0.587),DHA (0.512) and precipitation (0.445).

Discussion

In our studies, positive effects of the ORG system on SOM contentin soil were observed. Several studies have shown that SOM decreases inintensively-managed cropland soils (Liebig et al. 2004; de Ponti et al.2012; Gajda et al. 2016). Management systems that affect SOMaccumulation usually also affect the content of the labile POM fraction.Cambardella and Elliott (1992) reported that POM is preferentiallydepleted compared to SOM with increasing intensity of tillage. As areadily available source of soil nutrients, POM contributes to improvedsoil structure and is very sensitive to soil management, and thereforeis frequently used as an indicator of soil quality (Liebig et al. 2004;Gajda et al. 2016). The high POM content in soil is an indicator of soilorganic C and other nutrients stored in an intermediary pool, which areprotected from losses and available when needed. Within our three-yearstudy, the quantitative changes in POM content showed relatively earlythe direction of ongoing changes in SOM induced by crop productionsystems, while changes in SOM content were scarcely noticeable in thesame period. The positive effects of the ORG system on soil were welldescribed by changes in the percentage of POM in total SOM. Thesignificant differences in the percentage of POM in total SOM betweenthe two crop production systems were mostly in the surface soil (20.3%)in favour of ORG. Cambardella et al. (2001) reported that the share ofPOM ranged within 10-45% in soils of the Great Plains, USA. Theseresults indicated that POM is important to SOM turnover and respondsmuch faster to changes in soil caused by management than does the totalSOM. Similar effects of farming practices on the content of the labilePOM fraction in soil were reported by Marriott and Wander (2006) andGajda et al. (2016).

Many researchers have reported that crop production systems greatlyinfluence the activity of soil microbial communities, very often ismeasured in terms of DHA (Marinari et al. 2006; Karaca et al. 2011).This group of enzymes is widely used as a good indicator of changes insoil microbial activity (Wolinska et al. 2017). In our studies, the ORGsystem enhanced SOM and its labile fraction POM content which positivelyinfluences the abundance and activity of microbial communities, asreflected in higher DHA, compared with the CON system. Our results aresupported by the findings of Jarvan et al. (2014), who concluded that anorganically-managed crop rotation significantly increased the number ofsoil microbial communities and the DHA. Also, Heidari et al. (2016)showed beneficial effects of no-tillage and organic fertilisation onenzyme activities in contrast to conventionally managed soil. Theauthors of most published results have mentioned that soil microbialdiversity and abundance, and soil enzymatic activity, are significantlygreater for organic compared with conventional management (Karaca et al.2011).

Knowledge of the diversity of soil microbial communities is crucialto understanding soil function in the ecosystem (Nannipieri andBadalucco 2003). The C-substrate utilisation pattern indicated thatstudied crop production systems influenced soil microorganisms, and thatthe ORG system created a more favourable environment for soil microbialcommunities and their metabolic activity compared with CON. Liu et al.(2007) observed that functional diversity as indicated by C-substrateutilisation patterns was considerably higher in organic thanconventional management practice.

The AWCD index is very useful to characterise activity anddiversity of microbial populations (Furtak et al. 2017). Itcharacterises well the oxidative ability of microorganisms developed inthe EcoPlate system and may serve as an indicator of microbial activity(Garland and Millis 1991). The EcoPlate results expressed as AWCD valuesshowed some differences in the catabolic potential of microorganismsbetween systems of studied arable and sub-arable soil layers. That mayindicate that agrotechnical treatments (e.g. ploughing) can spreadmicroorganisms deeper into soil, in accordance with the results of Wanget al. (2007) and Gajda et al. (2016).

As is widely known, crop production systems affect crop yields bychanging the soil properties (Mader et al. 2002; Marinari et al. 2006).In this research, the ORG system resulted in a lower WWY than for theCON system, de Ponti et al. (2012), Bilsborrow et al. (2013),Svancarkova and Zak (2015) and Galazka et al. (2018) reported similaryields. An organic production system relies for its crop nutrients onnatural soil fertility, whereas a conventional system relies on externalinputs to maintain soil fertility. Crop yields are a result of theinteraction of many overlapping factors, both related to appliedagrotechnics and environment, e.g. weather conditions. Variability inmeteorological conditions is a significant challenge to crop productionand yield stability. Precipitation and temperature during the earlyvegetative and late reproductive phases of winter wheat growth explainedmuch of the yield variation in our studied crop production systems,consistent with the results of Teasdale and Cavigelli (2017).

Although the yields in the ORG system varied considerably becauseof the high dependency on the weather, this system favours protection ofsoil quality as the main basis for crop production. Therefore, the ORGcan be recommended as an alternative to the CON system.

Conclusions

Greater activity of soil microbial communities in a Haplic Luvisolsoil was found under the ORG compared with CON production system. Thisactivity was measured with dehydrogenases as well as metabolic diversityrevealed by Biolog EcoPlate analysis and confirmed by AWCD and H'results. Also, higher concentrations of POM and SOM were observed insoil under the ORG, particularly in the 0-5 cm layer, compared with theCON system. The results suggest that ORG much better maintained soilfertility than the CON system, which is extremely important for the soilenvironment and sustainable land use as it avoids SOM losses and soildegradation processes and improves the microbial status of soil.Therefore, legume-based crop rotations in an organic crop productionsystem, utilising organic fertilisers generated within the system are arealistic alternative to the conventional crop production system.However, further field experiments are needed to monitor changes in thesoil environment and to confirm the qualitative and quantitativelong-term benefits of the applied crop production systems.

Conflicts of interest

The authors declare no conflicts of interest.

https://doi.org/10.1071/SR 18113

Acknowledgements

The research was conducted within the statutory activity ofIUNG-PIB (2.26 and 2.38) and the frames of Task 1.3 and 1.4 Multi-AnnualProgram IUNG-PIB (2016-2020).

References

Baldock JA, Skjemstad JO (2000) Role of the soil matrix andminerals in protecting natural organic materials against biologicalattack. Organic Geochemistry 31, 697-710.doi:10.1016/S0146-6380(00)00049-8

Bilsborrow P, Cooper J, Tetard-Jones C, Srednicka-Tober D, BaranskiM, Eyre M, Schmidt CH, Shotton P, Volakakis N, Cakmak I, Ozturk L,Leiferta C, Wilco*ckson S (2013) The effect of organic and conventionalmanagement on the yield and quality of wheat grown in a long-term fieldtrial. European Journal of Agronomy 51,71-80. doi: 10.1016/j.eja.2013.06.003

Cambardella CA, Elliott ET (1992) Particulate soil organic matter.Changes across grassland cultivation sequence. Soil Science Society ofAmerica Journal 56, 777-783. doi:10.2136/sssaj1992.03615995005600030017x

Cambardella CA, Gajda AM, Doran JW, Wienhold BJ, Kettler TA (2001)Estimation of particulate and total organic matter by weightloss-on-ignition. In 'Assessment methods for soil carbon'.(Eds R Lai, JM Kimble, RF Follett, BA Stewart) pp. 349-359. (CRC Press:Boca Raton, FL, USA)

de Ponti T, Rijk B, van Ittersum MK (2012) The crop yield gapbetween organic and conventional agriculture. Agricultural Systems 108,1-9. doi:10.1016/j.agsy.2011.12.004

Doran JW, Sarrantonio M, Liebig M (1996) Soil health andsustainability. In 'Advances in agronomy'. (Eds DL Spark) pp.1-54. (Academic Press: San Diego, USA)

Furtak K, Gawryjolek K, Gajda AM, Galjzka A (2017) Effects of maizeand winter wheat grown under different cultivation techniques onbiological activity of soil. Plant, Soil and Environment 63(10),449-454. doi:10.17221/486/2017-PSE

Gajda AM, Doran JW, Kettler TA, Wienhold BJ. Pikul JL Jr.Cambardella CA (2001) Soil quality evaluations of alternativeconventional management systems in the Great Plains. In 'Assessmentmethods for soil carbon'. (Eds R Lai, JM Kimble, RF Follett, BAStewart) pp. 381-400. (CRC Press: Boca Raton, FL, USA)

Gajda AM, Czyz EA, Dexter AR (2016) Effects of long-term use ofdifferent farming systems on some physical, chemical and microbiologicalparameters of soil quality. International Agrophysics 30, 165-172. doi:10.1515/intag-2015-0081

Gajda AM, Czyz EA, Stanek-Tarkowska J, Dexter AR, Furtak KM,Grzadziel J (2017) Effects of long-term tillage practices on the qualityof soil under winter wheat. Plant, Soil and Environment 63, 236-242.doi: 10.17221/223/2017-PSE

Gatazka A, Gawryjolek K, Grzjidziel J, Frac M, Ksigzak J (2017)Microbial community diversity and the interaction of soil under maizegrowth in different cultivation techniques. Plant, Soil and Environment63(6), 264-270. doi : 10.17221/171 /2017-PSE

Galjzka A, Gawryjolek K, Gajda A, Furtak K, Ksipzniak A, Jonczyk K(2018) Assessment of the glomalins content in the soil under winterwheat in different crop production systems. Plant, Soil and Environment64, 32-37. doi: 10.17221/726/2017-PSE

Garland JL, Millis AL (1991) Classification and characterization ofheterotrophic microbial communities on the basis of patterns ofcommunity-level sole-carbon-source utilization. Applied andEnvironmental Microbiology 57, 2351-2359.

Heidari G, Mohammadi K, Sohrabi Y (2016) Responses of soilmicrobial biomass and enzyme activities to tillage and fertilizationsystems in soybean (Glycine max. L.) production. Frontiers of PlantScience 7. 1730. doi: 10.3389/fpls.2016.01730

Hill TCJ, Walsh KA, Harris JA, Moffett BF (2003) Using ecologicaldiversity measures with bacterial communities. FEMS Microbiology Ecology43, 1-11. doi:10.1111/j. 1574-6941,2003.tb01040.x

Jarvan M, Edesi L, Adamson A, Vosa T (2014) Soil microbialcommunities and dehydrogenase activity depending on farming systems.Plant, Soil and Environment 60, 459-463. doi: 10.17221 /410/2014-PSE

Karaca A, Cema CC, Turgay OC, Kizilkaya R (2011) Soil enzymes asindicator of soil quality. In 'Soil enzymology'. (Eds GShukla, A Varma) pp. 119-148. (Springer-Verlag: Berlin, Heidelberg,Germany)

Kus J, Jonczyk K (2008) Influence of organic and conventional cropproduction system on some parameters of soil fertility. Journal ofResearch and Applications in Agricultural Engineering 53, 161-165.

Li G, Kim S, Park M, Son Y (2017) Short-term effects ofexperimental warming and precipitation manipulation on soil microbialbiomass C and N, community substrate utilization patterns and communitycomposition. Pedosphere 27(4), 714-724. doi: 10.1016/S1002-0160(17)60408-9

Liebig MA, Doran JW (1999) Impact of organic production practiceson soil quality indicators. Journal of Environmental Quality 28,1601-1609. doi:10.2134/jeq1999.00472425002800050026x

Liebig MA, Tanaka DL, Wienhold BJ (2004) Tillage and croppingeffects on soil quality indicators in the northern Great Plains. Soil& Tillage Research 78, 131-141. doi: 10.1016/j.still.2004.02.002

Liu B, Tu C, Hu S, Gumpertz M, Ristaino JB (2007) Effect oforganic, sustainable, and conventional management strategies in growerfields on soil physical, chemical, and biological factors and theincidence of Southern blight. Applied Soil Ecology 37, 202-214.doi:10.1016/ j.apsoil.2007.06.007

Mader P, Fliefibach A, Dubois D, Gunst L, Fried P, Niggli U (2002)Soil fertility and biodiversity in organic farming. Science 296,1694-1697. doi:10.1126/science.l071148

Marinari S, Mancinelli R, Campiglia E, Grego S (2006) Chemical andbiological indicators of soil quality in organic and conventionalfarming systems in Italy. Ecological Indicators 6, 701-711. doi:10.1016/j.eco lind.2005.08.029

Marriott EE, Wander M (2006) Qualitative and quantitativedifferences in particulate organic matter fractions in organic andconventional farming systems. Soil Biology & Biochemistry 38,1527-1536. doi: 10.1016/j.soilbio.2005.11.009

Nannipieri P, Badalucco L (2003) Biological processes. In'Handbook of processes in the soil-plant system: modelling conceptsand applications'. (Eds DK Bembi, R Nieder) pp. 57-76. (The HaworthPress: Binghamton NY, USA)

Preston-Mafham J, Boddy L, Randerson PF (2002) Analysis ofmicrobial community functional diversity using sole-carbon-sourceutilisation profiles - a critique. FEMS Microbiology Ecology 42(1),1-14.

Schulte EE, Hopkins BG (1996) Estimation of organic matter byweight loss-on-ignition. In 'Soil organic matter: analysis andinterpretation". (Eds FR Magdoff et al.) pp. 21-31. (SSSA: Madison,WI, USA)

Sequeira CH, Alley MM, Jones BP (2011) Evaluation of potentiallylabile soil organic carbon and nitrogen fractionation procedures. SoilBiology & Biochemistry 43, 438-444. doi:10.1016/j.soilbio.2010.11.014

Svancarkova M, Zak S (2015) The grain quality of winter wheat inorganic and conventional farming. Acta Fytotechnica et Zootechnica 18,22-24. doi: 10.15414/afz.2015.18.si.22-24

Teasdale JR. Cavigelli MA (2017) Meteorological fluctuations definelongterm crop yield patterns in conventional and organic productionsystems. Scientific Reports 7, 688. doi: 10.1038/s41598-017-00775-8

Wang GH, Jin J, Chen XL, Liu JD, Liu XB, Herbert SJ (2007) Biomassand catabolic diversity of microbial communities with long-termrestoration, bare follow and cropping history in Chinese Mollisols.Plant, Soil and Environment 53, 177-185. doi:10.17221/2313-PSE

Weber KP, Legge RL (2009) One-dimensional metric for trackingbacterial community divergence using sole carbon source utilizationpatterns. Journal of Microbiological Methods 79. 55-61.doi:10.1016/j.mimet. 2009.07.020

Wolinska A, Gorniak D, Zielenkiewicz U, Goryluk-Salmonowicz A,Kuzniar A, Stepniewska Z, Blaszczyk M (2017) Microbial biodiversity inarable soils affected by agricultural practices. InternationalAgrophysics 31, 259-271. doi: 10.1515/intag-2016-0040

Handling Editor: Anne Naeth

Anna M. Gajda (iD) (A,D), Ewa A. Czyz (B), Karo Una Furtak (iD)(A), and Krzysztof Johczyk (C)

(A) Department of Agriculture Microbiology, Institute of SoilScience and Plant Cultivation - State Research Institute, CzartoryskichStreet 8, 24-100 Putawy, Poland.

(B) Department of Soil Science, Environmental Chemistry, andHydrology, Faculty of Biology and Agriculture, Rzeszow University,Zelwerowicza Street 8 B, 35-601 Rzeszow, Poland.

(C) Department of Systems and Economics Crop Production, Instituteof Soil Science and Plant Cultivation - State Research Institute,Czartoryskich Street 8, 24-100 Putawy, Poland.

(D) Corresponding author. Email: [emailprotected]

Caption: Fig. 1. Mean values (24 replicates) of soil organic matter(SOM) under different crop production systems at different depths foryears 2015-2017. Values with different letters are significantlydifferent (Tukey's HSD test, P <0.05, n = 24). Vertical barsrepresent the standard error (SE). ORG, organic; CON, conventional.

Caption: Fig. 2. Mean values (24 replicates) of particulate organicmatter (POM) content in soil under different crop production systems atdifferent depths for years 2015-2017. Values with different letters aresignificantly different (Tukey's HSD test, P <0.05, n = 24).Vertical bars represent SE. ORG, organic; CON, conventional; ADS,air-dry soil.

Caption: Fig. 3. Soil organic matter (SOM) (a) and particulateorganic matter (POM) (b) contents under different crop productionsystems at different depths for years 2015-2017. Values with differentletters are significantly different (Tukey's HSD test, P <0.05,n = 24). Vertical bars represent SE. ORG, organic; CON, conventional.

Caption: Fig. 4. Particulate organic matter (POM) expressed as apercentage of soil organic matter (SOM) under different crop productionsystems at different depths for years 2015-2017. Values with differentletters are significantly different (Tukey's HSD test, P< 0.05,n = 24). Vertical bars represent SE. ORG, organic; CON, conventional.

Caption: Fig. 5. Mean values (24 replicates) of soil dehydrogenaseactivity (DHA) under different crop production systems at cropproduction systems for years 2015-2017. Values with different lettersare significantly different (Tukey's HSD test, P<0.05, n = 24).Vertical bars represent SE. ORG, organic; CON, conventional; DW, dryweight of soil.

Caption: Fig. 6. Percentage of utilisation of particular carbonsubstrates group (PCSG) by microbial communities influenced by cropproduction systems based on 120 h of incubation; CH, carbohydrates; CXA,carboxylic acids; AA, amino acids; AMAD, amines and amides; PLM,polymers; ORG, organic; CON, conventional.

Caption: Fig. 7. Average well colour development (AWCD) ofmetabolised substrates on EcoPlate based on data after 120 h incubation.Values with different letters are significantly different (Fisher'sl.s.d. test, P<0.05, n = 3). Vertical bars represent SE. ORG,organic; CON, conventional.

Caption: Fig. 8. Mean values of Shannon's diversity (H')index based on date after 120 h incubation for EcoPlate. Values withdifferent letters are significantly different (Fisher's l.s.d.test, P <0.05, n = 3). Vertical bars represent SE. ORG, organic; CON,conventional.

Caption: Fig. 9. Heat maps for the carbon utilisation patterns ofthe substrates on EcoPlate based on the average absorbance values after120 h incubation. ORG, organic; CON, conventional; soil depths: 1, 0-5cm; 2, 5-10 cm; 3, 15-20 cm; 4, 30-35 cm.

Table 1. Monthly mean air temperature and total precipitationin the growing season of winter wheat at Osiny ExperimentalStation (2015-2017) Temperature ([degrees]C)Month 1950-2013 2014/2015 2015/2016 2016/2017Sept. 13.3 14.7 15.3 15.6Oct. 8.4 10.0 7.0 7.7Nov. 3.1 4.7 5.2 3.2Dec. -0.9 0.6 4.0 0.8Jan. -2.9 0.6 -3.4 -4.6Feb. -2.0 0.9 3.7 -1.2Mar. 1.9 5.2 4.3 6.0Apr. 8.1 8.2 9.6 7.6May 13.8 13.6 15.5 13.6Jun. 17.1 16.8 19.8 18.1Jul. 18.6 19.8 20.0 18.6Aug. 17.8 22.4 18.7 19.6 Precipitation (mm)Month 1950-2013 2014/2015 2015/2016 2016/2017Sept. 53.2 15.5 126.0 21.0Oct. 39.3 19.0 30.0 100.0Nov. 39.2 30.4 47.0 45.0Dec. 38.5 55.3 25.0 65.0Jan. 29.0 49.7 33.0 30.2Feb. 28.4 8.1 65.0 44.1Mar. 28.1 49.0 53.0 31.9Apr. 42.0 29.4 38.0 65.1May 55.0 108.7 72.0 61.7Jun. 71.0 29.2 28.0 30.9Jul. 78.2 52.1 87.0 108.5Aug. 67.3 4.3 42.0 96.3Table 2. Properties of the Haplic Luvisol soil(0-20cm layer) in 2015-2017Mean values with different letters are significantlydifferent (Tukey's HSD test, P< 0.05, n = 3). SOM,soil organic matter; P, phosphorus; K, potassium;Mg, magnesium; ORG, organic; CON, conventionalCrop production SOMsystem (g [kg.sup.-1] soil) [H.sub.2]OORG 16.5 (a) 6.6 (a)CON 14.3 (b) 6.7 (a)Crop productionsystem KCI [P.sub.Egner]ORG 5.7 (a) 153.9 (b)CON 6.0 (a) 204.1 (a)Crop production [K.sub.Egner] [Mg.sub.system (mg [kg.sup.-1] soil) Schachtschabel]ORG 156.3 (b) 134.3 (a)CON 230.1 (a) 79.5 (b)Table 3. Effects of organic and conventional crop productionsystems on winter wheat yield (average for 2015-2017)Mean values with different letters are significantly different(Tukey's HSD test, P<0.05, n = 3). ORG, organic;CON, conventional Number ofCrop production Yield ears per Thousand-grainsystem (t [ha.sup.-1]) [m.sup.2] weight (g)ORG 6.38 (b) 415.0 (b) 45.8 (b)CON 8.15 (a) 497.0 (a) 48.2 (a)Table 4. Pearson's correlation coefficients among selectedsoil properties, precipitation and winter wheat yieldDHA, dehydrogenase activity; PCSG, particular carbonsubstrate group utilised by microbial communities;SOM, soil organic matter; POM, particulate organicmatter; PCP, precipitation; WWY, winter wheat yield;** P < 0.01; * P < 0.05Related Correlation Level of significanceparameter coefficientsDHA SOM 0.931 ** High degree positive POM 0.969 ** High degree positive PCSG 0.650 * Significant positive PCP 0.418 Non-significantPCSG SOM 0.706 * Significant positive POM 0.832 * Significant positiveWWY DHA 0.512 * Significant positive SOM 0.783 * Significant positive POM 0.587 * Significant positive PCSG 0.246 Non-significant PCP 0.445 * Significant positive