A Comparative Study of Agroecological Intensification Across Diverse European Agricultural Systems to Assess Soil Structure and Carbon Dynamics
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This research was funded by the European Union Horizon 2020 research and innovation program via the AGROECOseqC project, grant number 862695.
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Citation: Doyeni, M.O.; Kadziene, G.; Pranaitiene, S.; Slepetiene, A.; Skersiene, A.; Shamshitov, A.; Trinchera, A.; Warren Raffa, D.; Testani, E.; Fontaine, S.; et al. A Comparative Study of Agroecological Intensification Across Diverse European Agricultural Systems to Assess Soil Structure and Carbon Dynamics. Agronomy 2024,14, 3024. https://doi.org/10.3390/ agronomy14123024 Academic Editor: Joji Muramoto Received: 31 October 2024 Revised: 12 December 2024 Accepted: 13 December 2024 Published: 18 December 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Article A Comparative Study of Agroecological Intensification Across Diverse European Agricultural Systems to Assess Soil Structure and Carbon Dynamics Modupe Olufemi Doyeni 1,* , Grazina Kadziene 1,* , Simona Pranaitiene 1, Alvyra Slepetiene 1, Aida Skersiene 1, Arman Shamshitov 1, Alessandra Trinchera 2, Dylan Warren Raffa 2, Elena Testani 2, Sebastien Fontaine 3, Antonio Rodriguez-Hernandez 3,4, Jim Rasmussen 5, Sara Sánchez-Moreno 6, Marjoleine Hanegraaf 7, Akin Un 8, Simon Sail 9and Skaidre Suproniene 1 1Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry (LAMMC), Instituto al. 1, Kedainiai District, LT-58344 Akademija, Lithuania; [email protected] (S.P.); [email protected] (A.S.); [email protected] (A.S.); [email protected] (A.S.); skaidre.supr[email protected] (S.S.) 2Council for Agricultural Research and Economics (CREA), Via della Navicella 2, 00184 Rome, Italy; [email protected].it (A.T.); [email protected] (D.W.R.); [email protected] (E.T.) 3French National Institute for Agriculture, Food, and Environment (INRAE), UniversitéClermont Auververgne, VetAgro Sup, UREP, 5 chemin de Beaulieu, 63000 Clermont-Ferrand, France; [email protected] (S.F.); [email protected] (A.R.-H.) 4URP3F, INRAE, Le Chêne-RD 150, CS 80006, 86600 Lusignan, France 5Department of Agroecology, Aarhus University (AU), Viborg, Blichers Allé20, 8830 Tjele, Denmark; [email protected] 6National Institute for Agricultural and Food Research and Technology, Spanish National Research Council (INIA-CSIC), Crta. Coruña km 7.5, 28040 Madrid, Spain; [email protected] 7Stichting Wageningen Research (WR), Droevendaalsesteeg 4, 6708PB Wageningen, The Netherlands; [email protected] 8GAP Agricultural Research Institute, General Directorate of Agricultural Research and Policies (TAGEM), Republic of Turkiye Ministry of Agriculture and Forestry, Pasabagi st., Recep Tayyip Erdo˘gan Blvd., No. 106, 7563040 Haliliye, Türkiye; [email protected] 9The Walloon Agricultural Research Centre (CRAW), rue de Liroux 9, 5030 Gembloux, Belgium; [email protected] *Correspondence: [email protected] (M.O.D.); [email protected] (G.K.) Abstract: Continuous agricultural activities lead to soil organic carbon (SOC) depletion, and agroecological intensification practices (i.e., reduced soil disturbance and crop diversification) have been suggested as strategies to increase SOC storage. The study aims to assess the effect of agroecological intensification levels (lower (T1) and highest (T2)) on the soil C pool and aggregate stability and validate the correlation between different variables compared to the control (lowest/none (T3), where agroecological intensification was not applied. The C-stock, soil microbial biomass carbon (SMB-C), SOC, water extractable organic carbon (WEOC) in bulk soil, fine and coarse soil aggregates, and water-stable aggregates (WSA) were measured during maximum nutrient uptake in plants under diversified agroecological practices across different environmental conditions (core sites: Italy (CS1), France (CS2), Denmark (CS4), Spain (CS5), Netherlands (CS6), Lithuania (CS7), Turkey (CS8), and Belgium (CS9)). The soil aggregate stability varied among the CSs and treatments. At sites CS7 and CS9, WSA was higher in T1 and T2 compared to the control; a similar trend was observed at other sites, except CS1. SMB-C differed among the core sites, with the lowest value obtained in CS5 (52.3 µ g g −1 ) and the highest in CS6 (455.1 µ g g −1 ). The highest average contents of SOC and WEOC were obtained in bulk soil at CS2 (3.1 % and 0.3 g kg −1 respectively). Positive and statistically significant (p< 0.001) correlations were detected among all variables tested with SOC in bulk soil and WSA. This study demonstrates the significance of agroecological practices in improving soil carbon stock and optimizing plant–soil–microbe interactions. Agronomy 2024,14, 3024. https://doi.org/10.3390/agronomy14123024 https://www.mdpi.com/journal/agronomy
Agronomy 2024,14, 3024 2 of 19 Keywords: aggregate stability; agroecological intensification; microbial biomass carbon; soil carbon; water extractable organic carbon 1. Introduction The interest in soil health is increasing globally due to its active role in sustainable agriculture, enhanced biodiversity, and ecosystem services. As soil biodiversity and resilience are susceptible to anthropogenic disturbances and climate change impacts, implementing effective agricultural management and conservation strategies becomes imperative [ 1 ]. The challenges of climate change to agriculture are changing and daunting, requiring innovative solutions. Adopting sustainable agroecological practices is vital for maintaining soil productivity and resilience while preserving key functions like carbon sequestration to support sustainable agriculture and mitigate climate change [ 2 ]. Simultaneously, the interactions between plants and soil biota are crucial to regulating soil organic carbon (SOC) dynamics and overall soil health. The diverse pedoclimatic conditions across Europe, combined with varying levels of agricultural intensification, have introduced numerous drivers, such as plant diversity, rhizosphere traits, soil mesofauna, microbial functional diversity, nutrient cycles (C, N, and P), and water availability, that influence critical soil functions in different cropping systems. For example, perennial plant cover and high-input annual crops can yield similar biomass within the same pedoclimatic context [ 3 ]. However, the perennial cover has the added benefit of preserving soil organic matter (SOM) and fertility while delivering essential regulating services like water purification and carbon storage. Similarly, incorporating perennial legumes into annual crop rotations offers multiple benefits. It increases the total and labile fractions of water-extractable organic carbon (WEOC) and enhances the potential for organic matter leaching and rapid microbial transformation of dissolved organic materials in soils [ 4 ]. In some agricultural settings, high-input annual crops, prevalent in many farming systems, frequently cause soil degradation, nutrient losses, and elevated GHG emissions by disrupting natural plant–soil interactions. Agroecological farming practices have been adopted as a promising pathway to address these challenges. This will encourage the shift in agricultural intensification toward beneficial practices that enhance critical soil functions such as a shift in microbial communities and the breakdown and re-synthesis of SOM. Agroecological practices have positively impacted soil functions, especially the efficiency of plant–soil biota interactions [ 5 ]. These practices optimize the synchronization between soil nutrient supply and plant demand, reduce nutrient losses and greenhouse gas emissions (GHG), and enhance carbon sequestration [ 3 ]. The agroecological practices include but are not limited to conservation tillage practices, ecological service crops (cover crops, intercropping), diversification strategies (rotations, multi-cropping, agroforestry), and organic inputs (compost, biochar, digestate). These strategies foster beneficial microbial associations, including symbiotic relationships with rhizobia [ 6 ]. Amongst the different soil indicators considered in soil health, the description of soil aggregate stability with soil C nutrient cycling is hugely important. Soil aggregate stability is an important physical indicator of soil health, as improvements in aggregate stability are related to reduced resistance against erosion [ 7 ]. The relevance of soil aggregate stability in maintaining soil structure remains critical due to the multiple functions played by microand macroaggregates [ 8 – 10 ]. The diverse pedoclimatic conditions across Europe, combined with varying levels of agricultural intensification, have introduced numerous drivers, such as plant diversity, rhizosphere traits, soil mesofauna, microbial functional diversity, nutrient cycles (C, N, and P), and water availability, that influence critical soil functions in different cropping systems. For example, perennial plant cover and high-input annual crops can yield similar biomass within the same pedoclimatic context [ 3 ]. However, the perennial cover has the added benefit of preserving soil organic matter (SOM) and fertility while delivering essential regulating services like water purification and carbon storage. Similarly, incorporating perennial legumes into annual crop rotations has multiple benefits.
Agronomy 2024,14, 3024 3 of 19 It increases the total and labile fractions of water-extractable organic carbon (WEOC) and enhances the potential for organic matter leaching and rapid microbial transformation of dissolved organic materials in soils [ 4 ]. In some agricultural settings, high-input annual crops, prevalent in many farming systems, frequently cause soil degradation, nutrient losses, and elevated GHG emissions by disrupting natural plant–soil interactions. To address these challenges, agroecological farming practices have been adopted to encourage the shift in agricultural intensification toward beneficial practices that enhance critical soil functions, such as a shift in microbial communities and the breakdown and re-synthesis of SOM. Agroecological practices have positively impacted soil functions, especially the efficiency of plant–soil biota interactions. These practices optimize the synchronization between soil nutrient supply and plant demand, reduce nutrient losses, and enhance carbon sequestration [ 3 , 5 ]. Additionally, these strategies foster beneficial microbial associations, including symbiotic relationships with rhizobia [ 6 ]. The agroecological practices include but are not limited to conservation tillage practices, ecological service crops (cover crops, intercropping), diversification strategies (rotations, multi-cropping, agroforestry), and organic inputs (compost, biochar, digestate). The description of soil aggregate stability with soil C nutrient cycling among the soil indicators considered in soil health is hugely important. Soil aggregate stability is an important physical indicator of soil health, as improvements in aggregate stability are related to reduced resistance against erosion [ 7 ]. The relevance of soil aggregate stability in maintaining soil structure remains critical due to the multiple functions played by microand macroaggregates [8–10]. The impact of agroecological intensification on aggregate stability cannot be overemphasized. For instance, several studies have demonstrated that intensive tillage weakens soil structure and enhances soil erosion [ 11 ]. In contrast, no-till as a form of conservative agriculture reduces soil disturbance and strengthens the soil’s physical attributes. The benefits have been well studied, with some of the benefits listed as the formation of larger aggregates [ 11 ], gradual build-up in soil organic carbon due to a slower rate of crop residue breakdown [ 12 ], and better soil productivity [ 13 , 14 ]. Additionally, managing cover crops as part of agroecological intensification levels can offer many advantages for the soil’s physical, chemical, and biological characteristics, with improved aggregation forming a crucial part [15,16]. The complex interaction between soil structure and microbiome is crucial in stabilizing SOM. It is acknowledged that soil management practices significantly affect SOC [ 17 , 18 ]. This connection is further emphasized by implementing specific management practices, such as organic material inputs (digestate, livestock manures, composts, biosolids, etc.) [19,20] . SOM content and the stability of soil aggregates, being closely linked and mutually influential, serve as significant indicators of soil quality and degradation [ 21 ]. Furthermore, increased organic matter inputs enhance microbial activity and facilitate the synthesis of organic substances that bind soil particles into aggregates but also serve as a nutrient reservoir, promoting the formation of stable aggregates [22]. Considering the interlinked multiple factors affecting soil aggregate formation and distribution, there are questions about how to measure the effects of agroecological intensification on soil carbon cycling across varied EU pedoclimatic conditions. There is a need to identify the relevant and most sensitive drivers that can describe and enhance the productivity of agroecosystems in different cropping systems concerning soil carbon dynamics and aggregate stability. Hence, it was opined that agricultural intensification could shape soil structure and carbon cycling while promoting nutrient supply-plant demand synchrony and SOC persistence in soil. It was hypothesized that reducing soil disturbance and increasing plant diversity improve soil C retainment and soil aggregate stability. Therefore, this study aimed to assess the effect of the agroecological intensification, introduced in different EU agricultural cropping systems, on soil carbon (C) pool and aggregate stability. To achieve this, we tested a gradient of agricultural practices, ranging from the most intensive (intense tillage, monocropping, mineral fertilization) to the most
Agronomy 2024,14, 3024 4 of 19 disruptive ones (agroforestry, introduction of ecosystem service plants) in a network of long-term experiment sites representative of several pedoclimatic EU regions. 2. Materials and Methods 2.1. Experimental Design and Treatments The experiment was carried out in 8 experimental fields/core sites across Europe—6 long-term experiments (LTE) and 2 recently established (Figure 1). The selected sites were representative of two crossed gradients of agroecological intensification: (i) a local-scale gradient and (ii) a European-scale gradient built in the frame of this study (Figure 1). Agronomy2024,14,30244of22 toidentifytherelevantandmostsensitivedriversthatcandescribeandenhancethe productivityofagroecosystemsindifferentcroppingsystemsconcerningsoilcarbondynamicsandaggregatestability.Hence,itwasopinedthatagriculturalintensificationcould shapesoilstructureandcarboncyclingwhilepromotingnutrientsupply-plantdemand synchronyandSOCpersistenceinsoil.Itwashypothesizedthatreducingsoildisturbance andincreasingplantdiversityimprovesoilCretainmentandsoilaggregatestability. Therefore,thisstudyaimedtoassesstheeffectoftheagroecologicalintensification,introducedindifferentEUagriculturalcroppingsystems,onsoilcarbon(C)poolandaggregatestability.Toachievethis,wetestedagradientofagriculturalpractices,rangingfrom themostintensive(intensetillage,monocropping,mineralfertilization)tothemostdisruptiveones(agroforestry,introductionofecosystemserviceplants)inanetworkoflongtermexperimentsitesrepresentativeofseveralpedoclimaticEUregions. 2.MaterialsandMethods 2.1.ExperimentalDesignandTreatments Theexperimentwascarriedoutin8experimentalfields/coresitesacrossEurope—6 long-termexperiments(LTE)and2recentlyestablished(Figure1).Theselectedsiteswere representativeoftwocrossedgradientsofagroecologicalintensification:(i)alocal-scale gradientand(ii)aEuropean-scalegradientbuiltintheframeofthisstudy(Figure1). Figure1.Selectedsitesarerepresentedas(i)local-scaleand(ii)European-scalegradientsofagroecologicalintensification.SeeTable1forthefulldescriptionoftheexperimentalcoresites. Severalcriteriawereconsideredforselection:(i)availabilityoflong-termexperimentssites—LTEs;(ii)introductionofcropdiversity(covercrops,intercropping,agroforestry);(iii)reductionofsoildisturbance(reducedtillage,no-tillage);(iv)organicinputs (plantresidues,animalmanure,composts,bioinoculants).Eachsampledsiteincludesa loweragroecologicalintensificationlevel(T1),thehighestagroecologicalintensification (T2),andthelowest/noagroecologicalintensification“control”site(T3),whereagroecologicalintensificationwasnotapplied(tillage,monocropping,noservicecrop Figure 1. Selected sites are represented as (i) local-scale and (ii) European-scale gradients of agroecological intensification. See Table 1for the full description of the experimental core sites. Several criteria were considered for selection: (i) availability of long-term experiments sites—LTEs; (ii) introduction of crop diversity (cover crops, intercropping, agroforestry); (iii) reduction of soil disturbance (reduced tillage, no-tillage); (iv) organic inputs (plant residues, animal manure, composts, bioinoculants). Each sampled site includes a lower agroecological intensification level (T1), the highest agroecological intensification (T2), and the lowest/no agroecological intensification “control” site (T3), where agroecological intensification was not applied (tillage, monocropping, no service crop introduction, no organic inputs), and applied in four field replicates (Table 1), allowing the effect of site-scale ecological intensification on ecosystem variables to be tested. To limit the immediate effect of management practices on soil sampling, samples were predominantly collected at different time intervals, representing the peak of green biomass (maximum nutrient uptake when soil functions as a C source) at the respective core sites. The soil samples were taken at a 0–20 cm depth range for soil aggregate stability, soil organic carbon (SOC), water-extractable organic carbon (WEOC), and soil microbial biomass C (SMBC). Macroaggregates are essential in organic matter stabilization, C accumulation, sequestration, and macroaggregate formation. Hence, in this study, we focused on the characterization of SOC and WEOC in coarse >1.0 mm (1.0–4.0 mm) and fine (0.25–1.0 mm) soil macroaggregates under different site conditions and agroecological intensification at the European-scale gradients. In all the eight core sites, three treatments (T1, T2, T3) × 4 blocks × 1 replicate/block = 96 soil samples. Each sample was made as a composite sample from 4 sub-samples collected within each plot. To ensure standardization and appropriate quality control measures, soil samples collected at a specified time in any core sites were immediately dispatched after following the standard protocol for each
Agronomy 2024,14, 3024 5 of 19 soil analysis to the receiving laboratory for further analysis. Additionally, for individual analysis, such as soil aggregate analysis, the sampling was carried out when the soil was of adequate moisture (normally moist—moisture close to field capacity) at sampling. If the soil is too wet or too dry, the soil structure can be damaged, and it would influence the results of the analysis. For SMB-C, freshly collected field samples were analyzed immediately. Table 1. Treatment classifications in the experimental core sites. Core Site Experimental Location Experimental Plot Size pH Soil Texture Treatments YFE Sampling Time Code Fertilization Rate CS1 (ITALY) Italy, Rome, 41◦79′89′′ N 12◦57′21′′ E6.9 Sandy clay loam INC—compost, no-tillage, spontaneous cover T1 Municipal waste compost 3% N: 4909 kg ha−1, locally distributed 132 m2ICC—compost, tillage, mixed cover crops 2017 April 2023 T2 Municipal waste compost 3% N: 4909 kg ha−1 locally distributed BAU—tillage, organic fertilizer, no CC (control) T3 Commercial organic fertilizer 5% N: 3030 kg ha−1, locally distributed CS2 (FRANCE) France, Clermont Ferrand, 45◦46′27.03” N 3◦8′31.02” E 6.1 Sandy clay loam GLN—grasses, legumes—new system T1 None 490.9 cm2 (Mesocosms of 25 cm diameter). WGLN— spring wheat, grasses, legumes—new system 2016 May 2023 T2 None WN—spring wheat—new system (control) T3 None CS4 (DENMARK) Denmark, Foulum, 56◦30′N, 9◦34′E altitude 45 m a.s.l. 5.8 Sandy loam LP + TR— Lolium perenne and white clover New Experiment T1 N 75, P 24, K 303 kg ha−1y−1 18 m2MS6—Sixspecies mixture July 2022 T2 N 75, P 24, K 303 kg ha−1y−1 LP—Lolium perenne (control) T3 N 75, P 24, K 303 kg ha−1y−1 CS5 (SPAIN) Spain, Alcalá de Henares, 40◦52′34′′ N 3◦12′42′′ W 250 m2 Loam No tillage, monocrop T1 NPK 15:15:15 at 200 kg ha−1at sowing and 200 kg ha−1of ammonium nitrate 27% in spring. 8.0 No tillage, rotation 1994 May 2023 T2 NPK 15:15:15 at 200 kg ha−1at sowing and 200 kg ha−1of ammonium nitrate 27% in spring. Minimum tillage, monocrop (control) T3 NPK 15:15:15 at 200 kg ha−1at sowing and 200 kg ha−1of ammonium nitrate 27% in spring. CS6 (NETHERLANDS) Netherlands, Wageningen, 51◦59′41.9′′ N, 5◦39′17.5′′ E VO—Vetch + oat T1 None 50 m25.3 Loamy sand VOR—Vetch + oat + radish 2016 April 2023 T2 None F—Fallow (control) T3 None
Agronomy 2024,14, 3024 6 of 19 Table 1. Cont. Core Site Experimental Location Experimental Plot Size pH Soil Texture Treatments YFE Sampling Time Code Fertilization Rate CS7 (LITHUANIA) Lithuania, Akademija, Kedainiai distr., 55◦39′72′′ N 23◦86′11′′ E 6.7 Loam NT—No tillage without cover crops October 2022 T1 N–219; P–72; K–136; S–140 45 m2NT + CC—No tillage + cover crops 2013 T2 N–219; P–72; K–136; S–140 T— Conventional tillage without cover crops (control) T3 N–219; P–72; K–136; S–140 CS8 (TURKEY) Turkey, Sanliurfa; 36 ◦ 53 ′ 12.72 ′′ N– 38◦55′15.04′′ E 7.7 Clay NFC—No fungi inoculum with cover crop New Experiment September 2023 T1 109.4 kg ha −1 yr −1 57.6 m2FC—Fungi inoculum with cover crop T2 109.4 kg ha −1 yr −1 NFNC—No fungi inoculum without cover crop (control) T3 109.4 kg ha −1 yr −1 CS9 (BELGIUM) Belgium, Gembloux, Latitude = 50.5606556, Longitude = 4.7264556, Altitude = 170 m 6.8 Silt loam SBWB-FYM— Sugar beet, wheat, barley + farmyard manure 1959 April 2023 T1 Organic fertilization: 10000 kg ha−1 yr−1 (0.6% N—0.4% P—0.8% K) 48 m2 SBWB-CR— SBWB + crop res. restitution and cover crop T2 Mineral fertilization: N–143; P–0; K–0 SBWB—SBWB + residue exportation (control) T3 Mineral fertilization: N—63; P–0; K–0 T1 = lower agroecological intensification, T2 = highest agroecological intensification, T3 = lowest/no agroecological intensification (control), YFE—year of field experiment establishment. 2.2. Soil Sampling and Preparation for the Aggregate Stability Analysis The analysis procedure for the soil aggregate stability was carried out according to the method described in [ 23 ]. Soil sampling was carried out at the maximum crop demand and when the soil was sufficiently moist. Undisturbed soil monoliths were collected by flat shovel, approximately 5 cm × 15 cm (thickness and wideness) from a 0 to 20 cm depth. Immediately after the collection, each soil monolith was gently broken into ~1 cm 3 size soil clods (aggregates) by removing large stones, roots, straw, etc., and left at room temperature for approximately 1 month to air dry before the sieving procedure. 2.2.1. Dry Sieving Process Air-dried soil samples (200 g) were weighed and sieved by the Retsch AS200 basic (Retsch GmbH, Haan, Germany) sieve shaker with a set of 8000, 5600, 4000, 2000, 1000, 500, and 250 µ m mesh sizes. The sieving procedure proceeded for 2 min at the shaking amplitude of 60 rpm. The soil samples from the 1 mm sieve were analyzed for the wet sieving procedure. The aggregates from dry sieving were also used to maintain the SOC and WEOC content analysis with fine macroaggregates (0.25–1.0 mm fractions: from 0.5 mm and 0.25 mm sieves) and coarse macroaggregates (>1.0 mm (1.0–4.0 mm) fractions: from 2.0 mm and 1.0 mm sieves). 2.2.2. Wet Sieving Procedure by Ejkelkamp Apparatus A 4 g sample of each 1.0–2.0 mm soil fraction (from 1 mm sieve) sample was weighed, placed on numbered 0.25 mm sieves, moistened with distilled water by hand fog-sprayer, and allowed to become adequately wet for about 15 min. After that, the samples with sieves were placed in the Ejkelkamp wet sieving apparatus (Eijkelkamp Soil & Water, Zevenaar, The Netherlands) apparatus, with numbered holes, and the cylinder was placed
Agronomy 2024,14, 3024 7 of 19 below the sieves according to numbers. A measurement of 100 mL of distilled water was added to each cylinder, the sieves with samples were immersed down to the cylinders with distilled water, and the apparatus was turned on for 3 min. at intervals for the sieving procedure. The non-stable aggregates are separated by this procedure and remain in the cylinders with distilled water. The sieves with water-stable soil aggregates were placed on the side of the apparatus hole and allowed to drain. Furthermore, an alkalic solution of 2 g sodium hexametaphosphate (NaPO 3 ) 6 /1 L distilled water was prepared to separate the water-stable aggregates. New, numbered cylinders filled with 100 mL prepared solution were placed below the sieves according to the above-mentioned procedure and the sieving process for about 0.5–3 h, depending on the soil type. The sieving procedure lasts until there is no soil left, leaving only garbage and pebbles. After the sieving, all the cylinders are oven-dried at 110 ◦ C for about 17–24 h (including an extra sample to control 100 mL alkalic solution without the soil). Before weighing, the cylinders are placed in an exicator or left in an oven to cool down. WSA was calculated with Equation (1). WSA(%)=(WSA(g)–AC(g)) (NSA(g)+WSA(g)–AC(g)) ∗100 (1) where WSA refers to water stable aggregates (g), NSA indicates non-stable aggregates (g), and AC is the alkalic control (g). 2.3. Soil Microbial Biomass Carbon Determination The fumigation-extraction method was used to determine the soil’s microbial biomass carbon from the soil samples (sieved with 2 mm mesh) that were collected from all the core sites at a depth of 0–20 cm [ 24 ]. A 20 g sample of the sieved soil was measured and fumigated by exposing the soil to the alcohol-free chloroform (CHCl 3 ) vapor in a sealed vacuum desiccator for 24 h. The fumigated soil was evacuated repeatedly in a clean, empty desiccator until the odor of chloroform (CHCl 3 ) was no longer detected and then further extracted with 0.5 M K 2 SO 4 (soil (20 g): K 2 SO 4 (80 mL) in a ratio 1:4) for 30 min by oscillating shaking at 200 rpm and then filtered through a Whatman No. 42 filter paper. The same procedure was applied to sieved soil samples (20 g) without exposure to alcohol-free chloroform to obtain unfumigated soil extracts. Organic carbon content in the extracted samples was subsequently determined using the dichromate digestion method. Two mL of potassium dichromate (K 2 Cr 2 O 7 (66.7 mM) and 15 mL of the digestion mixture (2:1 conc. H 2 SO 4 :H 3 PO 4 (v/v) was added to 8 mL of extract in a 250 mL conical flask. The mixture was gently refluxed for 30 min, cooled, and diluted with 20 mL distilled water. The excess K 2 Cr 2 O 7 was measured by back titration with ferrous ammonium sulfate solution (40.0 mM) using a 1.10-phenanthroline-ferrous sulfate complex [Fe(C 12 H 8 N 2 ) 3 ]SO 4 (25 mM) solution as an indicator. SMB-C was calculated from the differences in extractable organic carbon between the fumigated and non-fumigated soil samples with a conversion factor (KEC) of 0.38 [24]. 2.4. SOC, WEOC, and C-Stock Concentrations in Fine Macroaggregates (0.25–1.0 mm) and Coarse Macroaggregates (>1.0 mm) of Soil The SOC content in bulk soil and soil aggregate fractions was analyzed according to the Nikitin-modified Tyurin dichromate oxidation method using wet combustion at 160 ◦ C. SOC measurement was performed using an automatic spectrophotometer Carry 50 at a wavelength of 590 nm, with glucose as a standard [ 25 ]. The WEOC determination was performed according to the methodology guided by SKALAR, using C 8 H 5 KO 4 as a standard. The soil prepared for the chemical analyses was dispensed with distilled water at a ratio of 1:5, and the extract was prepared by shaking, centrifugating for 15 min at 4500 rpm, and filtration. After that, the automated measurement procedure was performed based on the IR detection method following UV-catalyzed persulfate oxidation under a nitrogen environment (SKALAR, The Netherlands). Each soil sample was analyzed in triplicate after dry sieving with a 1 mm mesh size sieve, and the mean value was calculated.
Agronomy 2024,14, 3024 8 of 19 For calculating the C-stock, the soil bulk density was determined by estimating the Soil volume weight (SVW). The sieved soil (<2 mm) was used to measure the SVW and then calculated as the ratio of the oven-dried (105 ◦ C) soil mass to a known volume. A coarser fraction (stones, gravels) was weighed to derive bulk density. 2.5. Statistical Analysis The observed data were statistically processed using R Studio 4.3.2 software [ 26 ]. The Shapiro–Wilk test for normality and Levene’s test for homogeneity of variance were applied to each indicator separately for each core site. Based on these tests, indicators meeting both assumptions in a given CS were analyzed using ANOVA, followed by Tukey’s HSD for post hoc comparisons. For indicators in CSs where normality or homogeneity assumptions were not met, Kruskal–Wallis tests were used, followed by Dunn’s test for post hoc analysis. Pearson’s correlation analysis was used to analyze the relationship between the C-stock, SMB-C, WSA, SOC, and WEOC data. 3. Results 3.1. Water-Stable Aggregates The data distribution in the core sites was closely related to the European-scale gradient of agroecological intensification. The percentage of water-stable aggregates varied across the experimental sites and treatments (Figure 2). The lowest WSA value was determined at CS5 (Spain), while higher WSA values were observed at CS2 (France), CS1 (Italy), and CS4 (Denmark), respectively. Significant differences (p< 0.05) were found within the CS7 (Lithuania) and CS9 (Belgium) treatments, with the lowest WSA values in the control group in both treatments. In contrast, the WSA value at the CS1 (Italy, organically managed system) site was greater in the control treatment, where minimum tillage (0–10 cm) was applied, compared to T1 and T2 (no-tillage). Overall, significant differences were found between the level of agroecological intensification and the control at the following core sites: CS7 (Lithuania)—T1 = T2 > T3; CS9 (Belgium)—T2 and T3; and CS1 (Italy)—T1 = T2 < T3. Agronomy2024,14,30249of22 posthocanalysis.Pearson’scorrelationanalysiswasusedtoanalyzetherelationshipbetweentheC-stock,SMB-C,WSA,SOC,andWEOCdata. 3.Results 3.1.Water‐StableAggregates ThedatadistributioninthecoresiteswascloselyrelatedtotheEuropean-scalegradientofagroecologicalintensification.Thepercentageofwater-stableaggregatesvaried acrosstheexperimentalsitesandtreatments(Figure2).ThelowestWSAvaluewasdeterminedatCS5(Spain),whilehigherWSAvalueswereobservedatCS2(France),CS1(Italy),andCS4(Denmark),respectively.Significantdifferences(p<0.05)werefoundwithin theCS7(Lithuania)andCS9(Belgium)treatments,withthelowestWSAvaluesinthecontrol groupinbothtreatments.Incontrast,theWSAvalueattheCS1(Italy,organicallymanaged system)sitewasgreaterinthecontroltreatment,whereminimumtillage(0–10cm)wasapplied,comparedtoT1andT2(no-tillage).Overall,significantdifferenceswerefoundbetween thelevelofagroecologicalintensificationandthecontrolatthefollowingcoresites:CS7(Lithuania)—T1andT3;CS9(Belgium)—T2andT3;andCS1(Italy)—T2andT3. Figure2.Amountofwaterstableaggregates(WSA)(%)within0.25–1.0mmsoilfractionsunder diversifiedagroecologicalpracticesanddifferentenvironmentalconditionsintestedsites.Thesites arearrangedaccordingtoEuropean-scalegradientsofecologicalintensification(Figure1).Eachbox plotrepresentsthedistributionoffourreplicates,showingthemedian,interquartilerange,anddata range(whiskers).Thecompactlettersshownaboveeachboxplotindicatesignificantdifferences betweentreatmentsineachcoresite(p<0.05)basedonTukey’sHSDtestorDunn’stest. 3.2.SoilCarbonStock ThehighestamountofC-stockinbulksoil(onaverage49.6tha −1 )wasobservedat CS1(Italy),followedbyC-stockvaluesinCS4(Denmark)andCS6(Netherlands)(Figure 3).ThelowestC-stock(onaverage27.3tha −1 )wasfoundatsiteCS7(Lithuania).Additionally,lowcarbonstocksof27.7tha −1 ,31.3tha −1 ,and31.1tha −1 werefoundinCS5(Spain), CS8(Turkey),andCS9(Belgium),respectively.Significantdifferences(p<0.05)were foundamongthetreatmentsinCS2(T1andT2)andCS9(T2andT3). Figure 2. Amount of water stable aggregates (WSA) (%) within 0.25–1.0 mm soil fractions under diversified agroecological practices and different environmental conditions in tested sites. The sites are arranged according to European-scale gradients of ecological intensification (Figure 1). Each box plot represents the distribution of four replicates, showing the median, interquartile range, and data range (whiskers). The compact letters shown above each box plot indicate significant differences between treatments in each core site (p< 0.05) based on Tukey’s HSD test or Dunn’s test.
Agronomy 2024,14, 3024 9 of 19 3.2. Soil Carbon Stock The highest amount of C-stock in bulk soil (on average 49.6 t ha −1 ) was observed at CS1 (Italy), followed by C-stock values in CS4 (Denmark) and CS6 (Netherlands) (Figure 3). The lowest C-stock (on average 27.3 t ha −1 ) was found at site CS7 (Lithuania). Additionally, low carbon stocks of 27.7 t ha −1 , 31.3 t ha −1 , and 31.1 t ha −1 were found in CS5 (Spain), CS8 (Turkey), and CS9 (Belgium), respectively. Significant differences (p< 0.05) were found among the treatments in CS2 (T1 = T3 < T2) and CS9 (T1 = T2 > T3). Agronomy2024,14,302410of22 Figure3.TheC-stock(tha −1 )inbulksoil,underdiversifiedagroecologicalpracticesanddifferent environmentalconditionsatexperimentalsites.ThesitesarearrangedaccordingtoEuropean-scale gradientsofecologicalintensification(Figure1).Eachboxplotrepresentsthedistributionoffour replicates,showingthemedian,interquartilerange,anddatarange(whiskers).Thecompactletters shownaboveeachboxplotindicatesignificantdifferencesbetweentreatmentsineachcoresite(p< 0.05)basedonTukey’sHSDtestorDunn’stest. 3.3.SoilMicrobialBiomassCarbon TheSMB-C,asshowninFigure4,showedhigherSMB-CvaluesinCS6(Netherlands),CS2(France),andCS1(Italy).ThelowestSMB-Cmeanvalue(52.3µgg −1 )was foundinCS5(Spain)underT3whilethehighestSMB-Cvaluereached455.1µgg −1 as observedinCS6underT2.Thestudydemonstratedtheimpactofdiversifiedsystems, especiallywhenincorporatingcovercropsandexternalorganicmatter(farmmanure, cropresidue)onsoilmicrobialbiomass.Thelevelsofagroecologicalintensificationon SMB-Cwereevident,astreatmentsinCS4T1(Denmark)andCS9T2(Belgium)showed statisticallysignificantdifferences(p<0.05)comparedtoothertreatmentswithintheir respectivecoresites.ForCS4,anaverageSMB-Cvalue(144.8µgg −1 )intheT1treatments withLoliumperenneandwhiteclover.ForCS9,themeanSMB-Cvaluewas174.4µgg −1 in theT2treatment,havingcroprotationofsugarbeet,wheat,andbarleyincorporatedwith croprestitutionandcovercrop. Figure 3. The C-stock (t ha −1 ) in bulk soil, under diversified agroecological practices and different environmental conditions at experimental sites. The sites are arranged according to European-scale gradients of ecological intensification (Figure 1). Each box plot represents the distribution of four replicates, showing the median, interquartile range, and data range (whiskers). The compact letters shown above each box plot indicate significant differences between treatments in each core site (p< 0.05) based on Tukey’s HSD test or Dunn’s test. 3.3. Soil Microbial Biomass Carbon The SMB-C, as shown in Figure 4, showed higher SMB-C values in CS6 (Netherlands), CS2 (France), and CS1 (Italy). The lowest SMB-C mean value (52.3 µ g g −1 ) was found in CS5 (Spain) under T3 while the highest SMB-C value reached 455.1 µ g g −1 as observed in CS6 under T2. The study demonstrated the impact of diversified systems, especially when incorporating cover crops and external organic matter (farm manure, crop residue) on soil microbial biomass. The levels of agroecological intensification on SMB-C were evident, as treatments in CS4 T1 (Denmark) and CS9 T2 (Belgium) showed statistically significant differences (p< 0.05) compared to other treatments within their respective core sites. For CS4, the highest average SMB-C value (259.6 µ g g −1 ) was found in the T2 treatment with a six-species mixture. For CS9, the highest mean SMB-C value was 174.4 µ g g −1 in the T2 treatment, having crop rotation of sugar beet, wheat, and barley incorporated with crop restitution and cover crop.
Agronomy 2024,14, 3024 16 of 19 strengthen the connections between WEOC, SMB-C, and SOC [ 63 ]. This is demonstrated by the higher ratios of SMQ, and WEOC:SOC, which provide key insights into microbial efficiency and the availability of readily decomposable carbon. A higher SMQ suggests more efficient microbial carbon turnover, with optimal values indicating healthier, resilient soils that support efficient carbon cycling [ 64 ]. Notwithstanding, our study, in alignment with Ren et al. [ 65 ] showed that the scale at which intensification levels and farming practices impacted these relationships was mostly limited compared to the impacts of climate and soil factors. The positive correlations among the carbon pools (SMB-C, SOC, WEOC, and WSA) underscore the strong connection between these intensification levels and soil carbon processes through soil aggregate stability. This implies that soil aggregation is vital for SOC storage and preservation, as it serves as a barrier between decomposers and SOC; however, this structure remains susceptible to management practices [ 66 ]. These findings further reinforce previous research indicating that the physical (WSA), chemical (WEOC), and microbiological (MBC) organic carbon pools are more sensitive to tillage disturbance than the total SOC [60]. 5. Conclusions This study emphasizes the complex relationship between different agroecological intensification levels and SOM stabilization across various environmental conditions. Agroecological intensification practices, such as cover cropping, reduced tillage, crop diversification, and organic matter inputs, are vital for improving soil carbon dynamics and aggregate stability, with WEOC and SMB-C serving as critical indicators of soil health. The differences in the results obtained across the studied site locations showed that soil carbon dynamics responded to the influence of environmental factors and management practices. Although variations in SOC, WEOC, and SMB-C were noted across the experimental sites, the strongest correlations were between SOC and WEOC, especially within the various soil aggregates. Furthermore, WSA is strongly and statistically correlated with SOC in bulk soil, fine aggregates (0.25–1.0 mm), and SMB-C, which highlights its importance in preserving soil structure and organic carbon pools. There is thus a strong argument pinpointing that practices promoting all these labile carbon pools significantly influence soil structure and carbon sequestration, enhance soil microbial activity, and, in effect, boost the sustenance of long-term soil health and resilience to climate variability. However, the impacts and roles of specific factors such as climate and soil types cannot be downplayed, as variations in environmental factors and management practices play a crucial role in shaping soil carbon dynamics and aggregate stability. Hence, consistent and concerted efforts must be implemented in sustainable agricultural strategies to ensure long-term soil health and carbon stability, particularly in ecological areas susceptible to climate change. Author Contributions: Conceptualization, M.O.D., G.K., A.S. (Alvyra Slepetiene) and S.S. (Skaidre Suproniene); methodology, G.K., A.T. and S.F.; software A.S. (Arman Shamshitov); validation, G.K., S.S. (Skaidre Suproniene), A.S. (Alvyra Slepetiene), A.S. (Arman Shamshitov) and D.W.R.; formal analysis, S.P. and A.S. (Aida Skersiene); investigation, G.K., A.S. (Alvyra Slepetiene), A.S. (Aida Skersiene) and M.O.D.; resources G.K., E.T., A.R.-H., J.R., S.S.-M., M.H., A.U. and S.S. (Simon Sail); data curation, G.K. and S.S. (Skaidre Suproniene); writing—original draft preparation, M.O.D.; writing—review and editing, G.K., A.S. (Alvyra Slepetiene), A.S. (Aida Skersiene), A.S. (Arman Shamshitov), A.T., D.W.R., A.R.-H. and S.S. (Skaidre Suproniene); visualization, M.O.D., A.S. (Arman Shamshitov), A.T., G.K. and S.S. (Skaidre Suproniene); supervision, S.S. (Skaidre Suproniene); project administration, A.T. and S.F.; funding acquisition, A.T. and S.F. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the European Union Horizon 2020 research and innovation program via the AGROECOseqC project, grant number 862695. Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.
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