sustainability Article Differential Impacts of Cropland Expansion on Soil Biological Indicators in Two Ecological Zones Dora Neina 1,* and Eunice Agyarko-Mintah 2 1 Department of Soil Science, School of Agriculture, College of Basic and Applied Sciences, University of Ghana, Legon, Accra P.O. Box LG 245, Ghana 2 Biotechnology and Nuclear Agricultural Research Institute, Ghana Atomic Energy Commission, Legon, Accra P.O. Box LG 80, Ghana; adjomint@yahoo.com * Correspondence: dneina@ug.edu.gh or dneina@gmail.com Abstract: Agricultural expansion in Sub-Saharan Africa is characterized by different farm ages in smallholder communities. This study investigated changes in microbial indices broadly (i) at the reconnaissance survey level in four agro-ecological zones and (ii) in different farms at the forest (Dompem) and forest–savanna transition (Adansam) zones, as influenced by the duration of cultiva- tion. Soils from one-year (first cultivation of cleared forest/fallow), three-year, five-year, and ten-year farms were analyzed for basic soil properties, active or labile carbon (POXC), basal respiration (BR), microbial biomass (Cmic) using permanganate oxidizable C, alkali trap, and chloroform fumigation incubation. In both study levels, POXC content was <1% of soil organic carbon (SOC) in all zones, higher in the wet agro-ecological zones, and positively correlated with SOC (r = 0.70, 0.81; p < 0.01, p < 0.001). Dompem SOC and BR declined by 1–23% and 6–25% (p < 0.001), respectively, in the first three years; Cmic (p = 0.002) and %Cmic/SOC (p = 0.610) decreased from three-year farms onwards. Conversely, the Adansam SOC, BR, Cmic, and %Cmic/SOC rather had irregular trends. The microbial indices were influenced by exchangeable acidity, the sum of exchangeable bases, and effective cation exchangeable capacity negatively or positively, followed by SOC, pedogenic compounds, particularly dithionite-citrate iron (Fed), oxalate iron (Feox), and lastly, soil pH. Therefore, understanding the degree, direction, and changing aspects of these drivers of soil ecosystem services is necessary for sustainable soil management practices in different agro-ecological zones. Citation: Neina, D.; Agyarko-Mintah, E. Differential Impacts of Cropland Keywords: basal respiration; farm types; labile carbon; metabolic quotient; microbial biomass Expansion on Soil Biological Indicators in Two Ecological Zones. Sustainability 2023, 15, 8138. https:// doi.org/10.3390/su15108138 1. Introduction Academic Editors: Lidong Huang Cropland expansion occurs within a loop of drivers [1] and impacts [2–4]. Most of the and Renying Li impacts have been widely known and are mostly associated with booms and busts [5,6] Received: 15 March 2023 as well as trade-offs, such as low, long-term real income levels per capita [5], the loss of Revised: 5 May 2023 biodiversity, and reduced carbon stocks [6–8]. The trade-offs are attributed to negative Accepted: 15 May 2023 impacts resulting from a decline in the delivery of ecosystem services, although synergies Published: 17 May 2023 have also been found [3,9,10]. Other negative impacts of cropland expansion range from changes in land quality [11] to nutrient losses with decreased agricultural productivity and soil ecosystem services [12,13], mostly caused by huge nutrient exports and a reduced capacity for recycling [14–16] caused by above and belowground carbon losses [15–18]. Copyright: © 2023 by the authors. Interestingly, the nature of the impacts presents diverse spatial variability [9] depending on Licensee MDPI, Basel, Switzerland. the region under consideration [9,17] and the spatial pattern of conversion defined by time This article is an open access article after conversion and location, i.e., forest interiors, from forests edges into forests [19]. distributed under the terms and Most of the impacts of cropland expansion have been either examined at different conditions of the Creative Commons scales [9,20,21] or compartmentalized [22] and mostly focused on the aboveground natural Attribution (CC BY) license (https:// environment and its ecosystem services [13,22,23]. Research on the effects of agricultural creativecommons.org/licenses/by/ expansion is widespread [4,4,10–12,24]. However, there is a huge focus on aboveground 4.0/). Sustainability 2023, 15, 8138. https://doi.org/10.3390/su15108138 https://www.mdpi.com/journal/sustainability Sustainability 2023, 15, 8138 2 of 14 biodiversity [4,4], socio-economic impacts, policy, and governance [10], broad land is- sues [11,24], carbon storage, and climate [17,24]. Sadly, less attention has been given to the belowground environment, though there is evidence that land use drives soil micro- bial properties [7,25], land clearing affects soil, and micro-climate [26], which all strongly affect soil ecosystem services. The ecosystem services provided by soils cannot be under- estimated or overlooked since a decline in the supporting services is one of the drivers of yield gaps [27,28] and cropland expansion [10,29,30]. This is strongly manifested in Sun-Saharan Africa (SSA), where crop yields are increased marginally through increased farm size compared to intense inputs utilization per unit area in other continents [23,31]. In addition, land rotation is widely practiced as an upgraded form of shifting cultivation caused by soil fertility decline and its associated yield decline. Research shows that there is a link between aboveground and belowground biodiversity [32–36]. This implies that an effect on aboveground biodiversity also affects belowground biodiversity. This relationship occurs through C supply and the alteration of micro-habitat conditions [37]. It was found that the plant species’ effect on belowground biodiversity is below that of soil type [38]. Furthermore, there may be disconnects between the link to specific ecosystem services that vary with regional differences. For instance, Felix et al. [9] found a high spatial variation in the impacts of agricultural expansion across Europe. Narrowing onto C losses, it is estimated that about twice the amount of C lost in temperate regions is lost in the tropics. Specifically, one hectare of land cleared in the tropics releases about 120 tons of C annually but produces only 1.7 tons of crops (dry weight) compared to 63 tons of C loss with an annual crop production of 3.8 tons [8]. These have been attributed to conventional tillage and rapid decomposition in the tropics (Nunes et al., 2011), cited by Souza et al. [39]. It is worth appreciating the strong nexus between soil organic carbon (SOC) and soil biology, and the latter is the energy source of the food chain in the soil environment. Previous studies have found that converting native vegetation to cropland had no effect on total C and N within 1 m depth, but it affected C dynamics, causing soil health decline [14]. This is because these trade-offs tend to vary with the type of ecosystem cleared, soil type, and crops grown, as well as soil and crop management practices employed [8]. Earlier research on farm types under agricultural expansion in the forest zone of Ghana showed a decline in SOC with farm age, which occurred along with a decline in soil charge properties [40]. This pattern differed from each of the two agro-ecological zones studied. In a four-stage Argentine chronosequence, Tosi et al. [41] observed a huge effect on the functionality and biomass of soil microbial communities during the first years of cultivation. Thus, the link between the aboveground and belowground biodiversities and the high spatial variability implies that the effect varies from place to place. It was hypothesized that cropland expansion tends to exert different pressures on the aboveground and belowground ecosystems in different cropping patterns, soils and soil management regimes, and agro- ecological zones. Therefore, cropland expansion manifested as land rotation yielding farms of different ages have variable effects on microbial-related soil properties. The objectives of this study were to investigate whether the effects of agricultural expansion on microbial soil properties present the same patterns of variability across the agro-ecological zones and whether the variability has innate relationships with the various factors associated with the unique ecosystems in each agro-ecological zone. Microbial properties are indicators of soil ecosystem health, soil quality, and biodiversity, which play a huge role in the delivery of almost all the ecosystem services provided by soil [42–44]. Therefore, the significance of this study is that it can (a) provide an indication of ecosystem health, (b) yield insights into ecosystem-specific long-term soil and environmental management strategies to enhance worldwide sustainability in resource utilization and continued supply of ecosystem services, and (c) ultimately contribute to the achievements of the sustainable development goals. SuSsutastinaianbailbiitlyit y2022032,3 1,51,5 x, 8F1O3R8 PEER REVIEW 3 o3f o1f41 4 2.2 M. Mataetreirailasl sanadn dMMetehtohdosd s 2.21..1 S. tSutduyd ySiSteitse asnadn dSaSmamplpinlign g CConosnisdiedreinrign gthteh deiffdeifrfeenrecnesc eins sinpastpiaalt ivaalrviaabriilaitbyi laitnyda pnadtteprantste orfn csoonfvceorsnivoenr, sai ornec, oanr-e- connaissance survey was first conducted, after which two study sites were selected from naissance survey was first conducted, after which two study sites were selected from the the two agro-ecological zones. Consequently, the study was conducted at reconnaissance two agro-ecological zones. Consequently, the study was conducted at reconnaissance sur- survey sites in four agro-ecological zones (see Figure 1) and in more detail at farm age vey sites in four agro-ecological zones (s◦ee ′Figur′′e 1) a◦nd in more detail at farm age levels levels in the Dompem–Pepesa area (5 09 33.7 N; 2 04′29.4′′ W), located in the Tarkwa– in the Dompem–Pepesa area (5°09′33.7″ N; 2°04′29.4″ W), located in the Tarkwa◦–N′suaem Nsuaem Municipality in the forest zone, and the Adansam–Kokuma area (7 50 35.9′′ N, Mu◦nic′ipali′′ty in the forest zone, and the Adansam–Kokuma area (7°50′35.9″ N, 1°45′59.9″ 1 45 59.9 W) within the Kintampo South District in the forest–savanna transition zone of WG) hwanitahi(nlo twhee rKpianntaeml Fpigou Sroeu1t)h. ADtisetarcicht siinte t,hseix ftoyrfeasrtm–ssavoafndnifaf etrreanntsiatgioens wzoenree coofn Gsihdaenread . (lTowheesre pianncleul dFeignuerwe l1y).c Aleta eraecdhn saittiev, esivxetyg eftaartmiosn oof rdfiafflleorwen(to angeesy ewaerr),et choennstihdreeree,dfi. vTeh,easne d intcelnudyee anreswolfyc culletaivreadti onnat.iDvee vtaeiglsetoaftisoitne osre fleaclltoiown (,odnees cyreiaprt)io, tnh,eann tdhrseaem, fipvlien, gancdan tebne yfeoaurns d ofi ncuFlitgivuarteio1n.a Dndetianilsp oref vsiioteu ssesletuctdioiens, bdyesNcreipintiaonan, adnAd gsyamarpkloin-Mg icnatna hbe[ 4fo0]unandd inN Feiginuarea n1 d anAdd ionl pphre[v45io].us studies by Neina and Agyarko-Mintah [40] and Neina and Adolph [45]. FiFgiugruer e1.1 S.iSteit seesleelceticotino nscshcehmeme: eD: eDtaeitlasi losfo tfhteh seesleecleticotino ncrcitreirteiar icaacna nbeb feofuonudn dini nNNeineian aanadn Ad gAygayrakrok-o- MMinitnatha h[4[04]0 a]nadn dNNeienian aanadn dAAdodloplhp h[4[54]5. ]. 2.2. Laboratory Procedures 2.22..2 L.1a.bAornataolryys iPsroofceBdausriecs Soil Properties 2.2.1. ASntaanlydsairsd olfa Bboasraict oSroyila Pnraolypseerstiwese re employed to measure basic soil properties, such as bulkStdaenndsaitryd; lpaabrotircalteosriyz ea;npaHlyisnesw wateerrea enmdpinloKyCedl; ttot amlecasruboren b(Cas),icn istoroilg perno(pNe)r,tiaensd, ssuclfhu r as( Sb)u; blka sdicencsaittiyo;n pcaorntitcelnet s;izeex;c phHan igne awbaletearc aidnidty i;na KndClp; etdotoagle cnaircbcoonm (pCo),u nidtrso, gi.en., d(Nith),i oandit e- suclifturart (eS-)b; ibcarsbico cnaattieona ncodnotexnatlsa;t e-xecxhtarancgteaabbleleA acl iadnitdy;F aen. dT phedporgoecneidcu croems pwoeurnedaslr, ei.aed.,y ddi-e- thsicornibiteed-caitnrdatree-pbiocratrebdobnyatNe eainda aonxdalAatgey-eaxrktroa-cMtaibnltea hA[l4 a0n] dan Fde.N Tehinea parnodceAdduorelpsh w[4e5re]. aTlh- e resaodilys dcoensctaribned annodc arelcpiuormtecda brbyo Nnaetien.aT ahnuds ,Athgeyatorktaol-MC icnotnathe n[4ts0]w aenrde cNoenisniad earned Atodboelpsho il [4o5r]g. aTnhiec scoairlbs ocnon(StaOinCe)d. no calcium carbonate. Thus, the total C contents were considered to be soil organic carbon (SOC). 2.2.2. Labile C, Basal Respiration, and Microbial Biomass 2.2.2. LTahbeilela Cb,i lBeaCsalc Ronestepnirtastioofnt,h aendso Milsic, rmobeiasl uBrieodmassp ermanganate oxidizable carbon (POTXhCe) ,lawbeilree Cde tceornmteinntesd oufs itnhge tshoeilsW, emileeatsaulr.e[d4 6a]–s mpoedrmifiaendgmaneattheo odxoidf iBzlaabilree tcaarlb. o[4n7 ]. (PTOhXe Cba ◦ ),s awlerrees pdieratetiromniwneads museinasgu trhede Wfroemil efrte aslh. [s4o6il]s–mstoordeidfieadt 4meCthuosdin goft hBelaIisre ermt aely. e[r47[4].8 ] Thalek balaismal ertehsopdir.aTtihoen swoailss mweearesuardedju sfrteodmt ofre5s0h% sowilast esrtohroeldd iant g4 c°aCp aucsiitnyg( WthHe ICs)eramndeyperre - [4i8n]c aulbkaatlei dmteothaolldo.w Theeq suoiliilbs rwateiroen awdjiuthstethde toa m50b%ie wntaetenrv hiroolndminegn cta.pAafctietryw (WarHd,C1)0 a0ngdo pfrseo-il inwcuebreatwede itgoh aeldloiwnt oeq1u0i0libmraLtipolna swtiicthc uthpes aanmdbipelnatc eednviinro1n.2mLengtl.a Assftjearrws awrdit,h 1a0i0r -gti gohf tsoliidl s. wGerlaes ws veiigalhsecdo nintatoin 1in0g0 1m0Lm pLla0s.2ti5c McuKpOs Hanwd eprleacpeladc eind 1in.2t hLe gjalarsssa jnadrsi nwciutbha ateird-tiinghtht eliddasr. k Galats2s8 ◦ viCalas cccoonrdtaiinngintog C10re mamLe 0r.e2t5 aMl. [K49O] Hfo rwseervee npldaacyeds. iTnh tishete jmarpse arantdu riencwuabsaatelsdo icnh tohsee n dabrekc aauts 2e8i t°iCs calcocsoertdointhge toav Cerreaagme aemr ebti eanl.t [t4e9m] pfoerra steuvreesn odfatyhse. eTchoilso gteicmalpzeoranteusr.eT owmase aalssuor e Sustainability 2023, 15, 8138 4 of 14 the microbial biomass, the chloroform fumigation extraction method of Brookes et al. [50] was first tested. However, it failed probably because of the low SOC contents, low pH of some, and uncertainties associated with the extraction efficiency of 0.5 M K2SO4 [51]. Therefore, the chloroform fumigation incubation method [52] was used. Two sets of 40 g of fresh soil adjusted to 50% WHC were weighed into plastic cups. One set was fumigated with ethanol-free chloroform, while the other set was equally placed in vacuum desiccators for 24 h. After fumigation, the fumigated set was inoculated with 1 g of fresh soil. Both sets were incubated in 1.2 L glass jars and along with 0.25 M KOH in glass vials. The jars were tightly closed and incubated for 10 days at 28 ◦C, followed by back titration after precipitation with BaCl2. 2.3. Statistical Analysis For the statistical analysis, 10 to 11 farm replicates per farm type were obtained from Adansam, whereas 9 to 10 farm replicates were obtained from Dompem. The data were assessed for their conformity to the analysis of variance (ANOVA) before subjecting them to one-way ANOVA, followed by means separation, where necessary, using a Tukey HSD 5% significance level. Where possible, non-normal data were square-root, log, or ln transformed before further analysis. Where data were not normally distributed, Kruskal- Wallis [53] and Mann–Whitney U tests were used for analysis [54]. In addition to ANOVA, Pearson and Spearman correlation analyses [54] were run to determine the effects of the farm types on measured properties and relationships between basic soil properties and the microbial indices. The statistical analyses were conducted using SPSS version 20 (IBM® SPSS® Statistics, New York, NY, USA), whereas the graphs were produced using Sigma Plot 13 (Systat Software Inc., San Jose, CA, USA). 3. Results 3.1. Basic Soil Properties Previous studies on the same sites and soils have already presented data on the basic properties of the reconnaissance sites in the various ecological zones [45] and two study sites [40], where more details can be found. The soils are sandier in the northern half and less sandy in the southern part of the country. The SOC contents of the reconnaissance survey sites varied widely, showing significant differences (p = 0.014). The semi-deciduous forest zone had about twice the SOC contents of the other zones, with the least content found in the forest–savanna transition zone (Table 1). Generally, the soils of the two selected study sites were acidic, although the Adansam soils were slightly acidic, whereas the Dompem soils had very strong acidity with a mean difference of 1.9 pH units. The Dompem soils are fine-textured soils while the Adansam soils are slightly coarse-textured (Table 2). The Dompem soils had about twice the SOC contents of the Adansam soils, showing no significant differences (p > 0.05) among the farm types. However, the first-year cultivation (freshly cleared forests or fallows) of both study sites had 6.7 to 11.7 g kg−1 and 1.3 to 1.9 g kg−1 more SOC than the older farms in the Dompem and Adansam soils, respectively. Whereas the SOC of the Dompem farms did not differ (p = 0.050) from each other, those of Adansam differed significantly (p = 0.008) (Table 2). Table 1. SOC and microbial indices of the reconnaissance survey sites in the various ecological zones of Ghana (N = 3/4). 1 SOC POXC POXC/SOC CO2-C Cmic Cmic/SOC qCO2 Ecological Zone mg kg−1 % µg g−1 % Forest–savanna transition (Adansam) 5.86 a 46.71 a 0.82 a 25.60 a 70.11 a 1.31 0.40 a Semi-deciduous forest (Sefwi-Ahokwa) 17.03 b 73.72 b 0.44 b 55.12 b 52.65 a 0.33 1.09 b South Guinea savanna (Lito) 8.90 a 33.08 a 0.38 b 27.79 a 145.25 b 1.75 0.18 a Sustainability 2023, 15, 8138 5 of 14 Table 1. Cont. 1 SOC POXC POXC/SOC CO2-C Cmic Cmic/SOC qCO2 Ecological Zone mg kg−1 % µg g−1 % North Guinea savanna (Wallembelle) 10.81 a 27.96 a 0.27 b 28.81 a 115.46 c 1.24 0.25 a CV (%) 51 44 59 46 47 66 84 p-Value 0.014 0.001 0.020 0.017 0.007 0.126 0.002 1 Data overlap with part of C data published in Neina and Agyarko-Mintah [40] because only farms with specific SOC contents were used for the incubation in this study. Therefore, the mean values reported here differ slightly. Data in columns followed by different letters depict significant differences at p-value < 0.05. The reconnaissance survey data of Dompem (Forest zone) were excluded here. Table 2. Soil pH, SOC, POXC, and its fraction in total C and textural classes of the Adansam (N = 10/11 (SE)) and Dompem (N = 9/10 (SE)) soils. 1 pH SOC POXC POXC/SOC Farm Type Water g kg−1 mg kg−1 % Dompem Year one (forest) 4.3 (0.16) 28.49 (5.60) 64.82 (6.90) 0.26 (0.00) a Year one (fallow) 4.3 (0.15) 21.81 (1.81) 67.03 (4.02) 0.32 (0.00) b Three years 4.4 (0.14) 21.43 (3.70) 53.91 (7.21) 0.26 (0.00) a Five years 4.5 (0.13) 16.82 (1.16) 65.06 (5.22) 0.39 (0.00) b Ten years 4.5 (0.13) 16.89 (2.45) 52.01 (3.82) 0.33 (0.00) b p-value - 0.086 0.175 0.001 Adansam Year one (forest/fallow) 6.3 (0.08) 12.23 (0.63) a 47.27 (2.85) 0.39 (0.00) Three years 6.2 (0.15) 10.29 (0.70) a 38.03 (4.93) 0.36 (0.00) Five years 6.4 (0.13) 8.79 (0.69) b 36.61 (2.36) 0.43 (0.00) Ten years 6.3 (0.17) 10.91 (0.73) a 38.85 (2.55) 0.37 (0.00) p-value - 0.008 0.100 0.482 1 Part of the data was published in Neina and Agyarko-Mintah [40] because only farms with specific SOC contents were used for the incubation in this study. Therefore, the mean values reported here differ slightly. Data in columns followed by different letters depict significant differences at p-value < 0.05. Data in parentheses represent the standard error of means. 3.2. Labile C, Basal Respiration, and Microbial Biomass In both stages of the study, the POXC content was higher in the wet agro-ecological zones. For the reconnaissance survey sites, POXC ranged from 28 to 74 mg kg−1. The semi-deciduous forest zone had the highest content, which was about 27 to 45 mg kg−1 more POXC than the other zones (Table 1). The lowest POXC content was found in the Northern Guinea savanna zone. The POXC fraction of SOC was <1% in all zones ranging from 0.27 to 0.82%, with the highest occurring in the forest–savanna transition zone. In the main study of the two selected sites, the Dompem soils had 20 mg kg−1 more POXC content with significant differences (p = 0.001) among the farm types but showed no particular trend. The fractions of POXC in the SOC were generally <1%, not even up to 0.5%. The Adansam soils had higher fractions of POXC in their SOC than the Dompem soils (Table 2). POXC correlated positively with the SOC of both the Adansam soils (r = 0.70, p < 0.01) and the Dompem soils (r = 0.81, p < 0.001) (Figure 2). Again, the wet agro-ecological zones had the highest basal respiration. Among the reconnaissance survey sites, the semi-deciduous forest zone had the highest amount of basal respiration, which was 1.9 to 2-fold that of the other zones (Table 1). The order was semi-deciduous forest > Northern Guinea savanna > Southern Guinea savanna > forest– savanna transition zones. Of the two study sites, Dompem soils had 22.8 µg g−1 more basal respiration than the Adansam soils, showing significant differences (p < 0.002) among the farm types. An increasing trend was observed from the three-year-old farms toward the ten-year-old farms (Figure 3). The Adansam soils rather showed more irregularity, with significant differences (p < 0.001) among the farm types. Sustainability 2023, 15, x FOR PEER REVIEW 6 of 14 Sustainability 2023, 15, x FOR PEER REVIEW 6 of 14 Sustainability 2023, 15, 8138 6 of 14 Figure 2. Pearson correlation between SOC and POXC for the Adansam (left) and Spearman for the Dompem (right) soils. Again, the wet agro-ecological zones had the highest basal respiration. Among the reconnaissance survey sites, the semi-deciduous forest zone had the highest amount of basal respiration, which was 1.9 to 2-fold that of the other zones (Table 1). The order was semi-deciduous forest > Northern Guinea savanna > Southern Guinea savanna > forest– savanna transition zones. Of the two study sites, Dompem soils had 22.8 µg g−1 more basal respiration than the Adansam soils, showing significant differences (p < 0.002) among the farm types. An increasing trend was observed from the three-year-old farms toward the ten-year-old farms (Figure 3). The Adansam soils rather showed more irregularity, with signFiifigucaren2t .dPieffaerrseoncceosr r(epl a Northern Guinea savanna > Southern Guinea savanna > forest– savanna transition zones. Of the two study sites, Dompem soils had 22.8 µg g−1 more basal respiration than the Adansam soils, showing significant differences (p < 0.002) among the farm types. An increasing trend was observed from the three-year-old farms toward the ten-year-old farms (Figure 3). The Adansam soils rather showed more irregularity, with significant differences (p < 0.001) among the farm types. Figure 3. Basal respiration (p < 0.001) and Cmic (p = 0.109; p = 0.002) of the farm types from FiguArdea 3n. sBaamsa(ll reefst)piarnadtioDno (mp

e1 %rec(oTnanbaleis1s)a.nFcoer stuhrevetwy ositseitse fso, ltlhoewDed the mic ompem samseo iplsahttaedrnm aos rteheC Cmici nantdhe wrearneg emoofst0l.y7 >to1%0. 9(T%abcloem 1p).a Freodr tthoe0 t.4wtoo s0imic .t7e%s, tihnet hDeoAmdpaenmsa m Figsuoriels 3(. FBiagsualr ere4sp).irWatihoinl e(pt h< e0.%00C1) an/dS COmCic (po f= t0h.1e0A9; dp a=n 0s.a00m2) soofi tlhsed fiafrfmer etydp(eps f=romic 0m.0 A02d)aanmsamon g (leffat)r mandty Dpeosmapnedms (hroigwhet)d. Baanrsi nocfr tehaes isnagmter ecnodlorf rfoomllotwheedt hbrye ed-iyffeearern-ot lldettfearrsm dse,piticdt isdignnoifitcdaniftf er diff(per1% (Table 1). For the two sites, the Dompem Sustainability 2023, 15, x FOR PEER REVIEW 7 of 14 S ustainability 2023, 15, x FOR PEER REVIEW 7 of 14 soils had more Cmic in the range of 0.7 to 0.9% compared to 0.4 to 0.7% in the Adansam ssooiillss (hFaidg umreo 4re). CWmhici lien tthhee % raCnge/S oOf C0 .o7f ttoh e0 .A9%da ncompmic sam asroeilds dtoi ff0e.4re tdo (0p. 7=% 0. 0in02 t)h aem Aodnagn fsaarmm stoyiplse s( Faingdu rseh 4o)w. Wedh ailne tinhcer %eaCsimnicg/S tOreCn dof f trhoem A tdhaen tsharmee -syoeilasr d-oifflde rfaedrm (ps ,= i t0 .d0i0d2 )n aomt doinffge rfa (rpm < t0y.0p5e)s iann tdh seh Dowomedp eamn isnocirlesa bsuintg d tercerneda sferdom fr othme tthhree fea-lyleoawr- foalrdm fas rtmows, airt dd itdh en otet nd-iyffeearr (opl d< 0fa.0r5m) si.n T thhee mDeotmabpoelmic qsuooiltsi ebnutt ( qdCecOre2)a rsaendg ferdo mfro tmhe 0 f.a1l8l otow 1 f.0a9rm fosr taolwl tahred s tthued yte snit-eyse a(Tr aobllde f1a ramnds. F Tihgue rme e5t)a abnodli cw qauso htiigenhte r(q iCn Oth2)e rsaenmgie-dd efrcoidmu o0u.1s8 ftoor e1s.0t 9o ffo trh ea lrl etchoen sntuaidsysa snitcees s(uTravbeley 1s iatensd a Fnidg uinr et h5e) aDnodm wpaesm h sigohilesr. Tinh eth tere snedm wi-dase ciridreugouulas rf oart etshte o tfw thoe s irteecso, nbnuat itshsea nfacrem su tryvpeeys Sustainability 2023, 15, 8138 sointe tsh aen Dd oinm tpheem D soomilps edmiff seorields. sTighne itfirecanndt lwya (sp i=rr 0e.g0u27la)r f raot mth ee atwcho o stihteesr, (bFuigt uthree f5a)r. m ty7poefs1 4 on the Dompem soils differed significantly (p = 0.027) from each other (Figure 5). FFigiguurere 44. .TThhee frfaractcitoionn oof fmmicicrroobbiaial lbbioiommaassss iinn tthhee SSOCC ((%CCmmici/cC/)C o)fo sfosiolsil sfrformom didffifefreernent tfaframrm tytyppese s Foifg Audrea n4s. aThe fractioof Adansamm (p(p == 0.00.0 n2 )o (fl emfti)c aro002) (left) n b and i aDlo bmiopmemas s( pi n= 0th.6e1 S0O) (Cri g(%htC).m Bica/Crs) foofl lsoowils from different farm types d Dompem (p = 0.610) (right). Bars followeded byb ydidffifefreernent ltelttetetresr sddepepicitc t osifg Andifiacnasnatm d (ipff e=r 0e.n0c0e2s) (alte pft-)v aanlude D