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The priming effect generated by stoichiometric decomposition and nutrient mining in cultivated tropical soils: Actors and drivers

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Abstract

The priming effect (PE) in soil, when induced by a fresh carbon supply, is believed to result from two different mechanisms, "stoichiometric decomposition" and "nutrient mining", and contributes to either long-term SOM stabilization or depletion. Understanding how to affect the balance between both mechanisms can provide valuable insight into agroecology, especially in Southern countries where organic matter management is the primary method of fertilizing cultivated plots. Therefore, the objective of this study was to investigate the bacterial actors in each mechanism and the influence of three drivers in Malagasy Ferralitic soils: the quality of the inducing substrate, the quality of the SOM, and the soil nutrient status. Three different agricultural Ferralitic soils, characterized by different levels of NH4 and PO4 availability, different SOM size fractionation profiles, and different bacterial communities, were amended with three types of ¹³C-labeled organic substrate (glucose, wheat residue, and rice residue) and the PE was measured at two incubation times (7 and 42 days). The results showed that a PE generated by stoichiometric decomposition was mainly induced by the polymerized fraction of crop residues, especially in soil enriched by decaying plant tissues, mineral N, and its associated decomposers guild such as Verrucomicrobia, α- and δ-Proteobacteria and Actinomycetes. Conversely, when this young SOM pool was reduced, the PE was mainly generated by N mining, especially when induced by soluble compounds and when N was limiting but P was available.
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%%Corresponding author: UMR 410 Eco&Sols. 2 place Viala Bt12. Montpellier Cedex 1. F-
34060. France. Fax: +33(0)4.99.61.21.19. Tel: +33(0)4.99.61.21.01. email:
laetitia.bernard@ird.fr
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)/  +/B $       1!C   &3
'//-29=433
 12      >  3   
43 49D
3555
$4$ 9$$
        12      54  E  
5$ 9 03
83 
    43            3
   5 4 $     1   &3  '//,
C53  '/&/29  D  3            
5 4 $ 4    123 5 
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1F823? #4 C
 12943 
  G3 4     
 5$
?1 3'//+29
D434$
1295
    4   
      $   H
1&3'//(2901&2 $
 G #4  4  3  
4 C$31'2 $
8  $ 48    4    
 C9D3 91'//(2
    $9 D
 =43  53
8 3  G
1&3'//)29"431&2
  G        
  4 $   1'2      
5 4$1
&3'/&)29
D584$
 =4$
     $ 96
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 C4  
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L=AG
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1D3:A29# 
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 12  4  5 4
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The experimental design was a 3 X 4 X 2 factorial design that had 3 replications with
the following treatment factors: soil type (black, red or yellow soils), substrate quality
(no amendment, glucose, rice or wheat residues) and incubation time (7 days or 42
days)90$3$?
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&--
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23 89 A 4  =5 $
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5 4 0   & 1'/&*29  G  L>A    =?
<4      I  L>A  U  F  1
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'///     &3  '//+29  L>A            "  L>A
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4$  =  3$5 4
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 1,)'42$$   9!"!"9 I
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          >8>)               X5 
  9
Glucose treatment
@   M'//
*/8
'// O*/ 5, >8>), !", I, ,
!", @AB" @B= <@A@C <@A$$ /9(-, @D$@  @EA= 8/9//&
I, @AC /9(.. <@AC= <@AC= 8/9')& @A= @EA= 8/9),*
, 8/9),( /9'.( @ED" @E$C @AB$ <@ED@ 8/9//& 8/9),*
X8>8>), <@CD" 8/9&+, @B@ @CBC @EC= <@B=@ 8/9))( <@DCB @DB=
X85, /9(&, 8/9(), 8/9)/& 8/9(,- <@AAB /9(*( 8/9&&* /9(-+ <@E@@
@   M'//
*/8
'// 8O*/ 5)' >8>))' !")' I)' )'
!")' @BED @ACE <@B <@B"D /9/.( /9'*/ 8/9/.+ /9)/+
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)' /9(/( @D@E 8/9'(& 8/9'*' /9)*- 8/9&.* /9)/+ <@D"A 
X8>8>))' /9&(( @DBA 8/9/(/ 8/9/*. @ACD <@AE" /9((* /9()) /9()-
X85)' 8/9/-& <@DA 8/9/&' /9/&) <@AE= @DE= 8/9'+. 8/9'&, 8/9'(,
Rice treatment
@   M'//
*/8
'// 8O*/ 5, >8>), !", ", ,
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!", @AB" @B= <@A@C <@A$$ /9(-, @D$@  @A$D @EA@
", @B$= @E=D <@B"D <@B$@ 8/9'/, @C=@ @A$D @A"C
, @C"$ /9)). <@C"" <@C"= 8/9'/, @A" @EA@ @A"C
X8>8>), /9'*, <@ED= 8/9(,' 8/9()/ <@BA /9'+. 8/9('* /9'-) /9'*.
X85, <@EEC /9'./ @DE= @D"C @C"C <@D=$ /9/&+ <@E@C <@ECC
@   M'//
*/8
'// 8O*/ 5)' >8>))' !")' ")' )'
!")' @BED @ACE <@B <@A$$ /9/.( /9'*/  @B"@ @E$$
")' @BC@ @E"D <@BC@ <@BC" 8/9'(- /9)+) @B"@  @C@D
)' @A$@ /9/'& <@AB <@AAE <@D" @DB @E$$ @C@D
X8>8>))' <@C"$ 8/9(&. @C=$ @C$B /9(+- <@ECB <@A$ <@CB@ <@AC=
X85)' <@DE@ /9/'/ @A@ @DBE @E$ 8/9(,+ 8/9)-) <@DBD <@AA@
Wheat treatment
@   M'//
*/8
'// 8O*/ 5, >8>), !", , ,
!", @AB" @B= <@A@C <@A$$ /9(-, @D$@ /9'*/ @DCD
, 8/9',* /9)-' /9(,- /9(*/ @C"A 8/9(,, /9'*/ /9&&+
, /9)+* @AED <@$C" 8/9)/+ /9)-( /9(/* @DCD /9&&+
X8>8>), <@CDB 8/9&,' @BA @B@E @EB@ <@CB$ 8/9))* @D@$ 8/9')(
X85, 8/9',. /9),( /9(-& /9(*( @AAD 8/9)/) /9'** @DC= /9)(+
@   M'//
*/8
'// 8O*/ 5)' >8>))' !")' )' )'
!")' @BED @ACE <@B <@A$$ /9/.( /9'*/  @A@C @C"$
)' @CDB /9&*' <@B"@ <@B@C <@ECB @D=C @A@C @C=D
)' @B$ /9)+* <@BA <@BC 8/9'*& /9))+ @C"$ @C=D
X8>8>))' @E"= /9(+( <@E@ <@EE /9&,+ 8/9&.+ <@ACE <@ABE <@ABB
X85)' <@CE" 8/9)&- @CEC @CEC /9/() 8/9&(& @EC /9(.' /9'.+
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      =4      #3      
1!&3'//(29
A 4   $ 3   # 
4 4>)P,3#
>)P    8 9 D
  $ 3 8 
4  54        #    =4
5 4  >89!#5 
4  54            $9  A  
 53
    3  4          
90   5
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A 9 0       5
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 3  5$  
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... It is widely recognized that positive PE can be driven by two distinct microbial mechanisms: stoichiometric decomposition and nutrient mining (Chen et al., 2014;Fontaine et al., 2004;Razanamalala et al., 2018;Yang et al., 2020a). The stoichiometric decomposition theory indicates that microbial decomposers of fresh OM release extra extracellular enzymes to promote the mineralization of 4 / 34 native OM. ...
... The stoichiometric decomposition theory indicates that microbial decomposers of fresh OM release extra extracellular enzymes to promote the mineralization of 4 / 34 native OM. The nutrient mining theory believes that microbial cometabolisms stimulate some microorganisms to extract nutrients from native OM to enhance their growth, thereby increasing the mineralization of native OM (Blagodatskaya and Kuzyakov, 2008;Fontaine et al., 2003;Razanamalala et al., 2018). Previous studies have suggested that in response to the input of fresh OM, stoichiometric decomposition mechanism usually governs the early stage of PE generation, while nutrient mining mechanism frequently controls the late stage of PE generation when available nutrients are depleted (Razanamalala et al., 2018;Yang et al., 2020a). ...
... The nutrient mining theory believes that microbial cometabolisms stimulate some microorganisms to extract nutrients from native OM to enhance their growth, thereby increasing the mineralization of native OM (Blagodatskaya and Kuzyakov, 2008;Fontaine et al., 2003;Razanamalala et al., 2018). Previous studies have suggested that in response to the input of fresh OM, stoichiometric decomposition mechanism usually governs the early stage of PE generation, while nutrient mining mechanism frequently controls the late stage of PE generation when available nutrients are depleted (Razanamalala et al., 2018;Yang et al., 2020a). Considering the different generation mechanisms of early and late PE, it is reasonable to hypothesize that the regulatory factors of their intensities will be distinctly different in lakes. ...
... Priming effect results from different processes involving microbial actors, their own physico-chemical determinants and targeting different compartments of organic matter (Fontaine et al., 2003). This lack of clear understanding challenges the management of organic matter in the sense that, depending on the processes involved, the PE could stimulate either humification or favor the release of stabilized organic matters characterized by longer residence times (Razanamalala et al., 2018). Using a combinatory approach in a greenhouse experiment and at the field scale, we showed that combining different biological and chemical fertilizing materials is required to fill all the nutrient deficiencies of these soils without inducing large PE. ...
... The fertilizing materials have been classified in functional groups allowing the farmer to choose the materials according to different criteria. Following Razanamalala et al. (2017), it has been proposed that a regular supply (3-4 times per cropping season unlike a traditional single supply at the start of the season) of fertilizer materials could maintain the activity of soil organisms and may limit the stimulation of the release of stabilized C by the microbial community (Razanamalala et al., 2018). With farmers, we then co-designed a field experiment testing innovative practices based on combined organic material and earthworm inoculation with earthworm-responding rice varieties (Fig. 3.D & E). ...
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Soils are now recognized as key components in the design of sustainable agricultural practices within the agroecological framework. They are the place of many ecological functions achieved by living organisms interacting with each other and which support the sustainable provision of agrosystem services. In the context of the transformation of agriculture and to improve the sustainability and resilience of family farming, it becomes urgent to promote soil ecological functions, to intensify them by appropriate practices considering the socio-economic constraints, and finally, to be able to monitor them. Here, to improve our consideration of the soil functions for a sustainable agriculture, we first rely on the ecological theories of terrestrial ecosystem functioning to better establish the concept of sustainable functions in agroecosystems. We then propose a methodological framework, called SE-CURE (for “Soil Ecology Cure”), that aims to optimize the ecological functions of the soil for a sustainable supply of ecosystem services. This framework relies on the involvement of stakeholders and is illustrated by a case study in Madagascar where the different steps of the SE-CURE approach have been applied.
... agricultural expansion have also contributed immensely to the global cumulative atmospheric CO 2 increase (Achard et al. 2002). Furthermore, previous studies using soils from Africa (Mganga and Kuzyakov 2018;Razanamalala et al. 2018;Fontaine et al. 2004) demonstrated that addition of different C compounds e.g. glucose and cellulose, accelerated native SOM mineralization. ...
... Soils were maintained at 60% WHC. This is within the range (50-70%) of other previous soil incubation studies to investigate priming effect as affected by land use and soil types in Africa (Okolo et al. 2022;Razanamalala et al. 2018;Mganga and Kuzyakov 2018). During the incubation, CO 2 concentrations inside the bottles were measured daily using gas chromatography (Hewlett Packard 6890). ...
Article
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Addition of labile carbon (C) inputs to soil can accelerate or slow down the decomposition of soil organic matter (SOM), a phenomenon known as priming effect (PE). However, the magnitude and direction of PE is often difficult to predict, consequently making its relationship with labile C inputs and nutrient availability elusive. To assess this relationship, we added ¹³ C labelled glucose (corresponding to 50% of initial soil microbial biomass C) to two soil types (Vertisol and Acrisol) with different concentrations of available N and from four land use systems (agricultural, pasture, grassland and shrubland). Parallel laboratory incubations i.e. short-term (6 days) and long-term (6 months), were set up to determine the effect of land use and soil type (N availability) on PE. Addition of labelled glucose in solution led to the retardation of SOM mineralization (negative PE) in both soil types and across all land use systems. This is attributed to preferential substrate utilization characterized by the higher mineralization of added glucose. Land use systems and soil types with higher N-availability displayed weaker negative PE, which is in line with the stoichiometric decomposition theory. In conclusion, our study demonstrate that N-availability plays a major role in determining mineralization of labile C inputs, magnitude and direction of PE in the studied dryland soils and land use systems. The fact that 15–27% of the added ¹³ C remained in the soil at the end of the 6 months incubation and PE was negative, indicates that continuous labile C inputs could contribute to C immobilization and stabilization in these semiarid soils. Moreover, ¹³ C glucose remaining in soils after 6 months in semi-natural pastures was comparable to those under natural grassland and shrubland systems especially in Acrisols. This demonstrates that incorporation and maintaining a perennial cover of native pastures has the potential to increase C sequestration in African semi-arid agricultural soils and landscapes.
... However, N-mining may not regulate the PEs through the entire incubation because negative PEs were observed in the topsoil after Day 73 (Fig. 3C). Similarly, Razanamalala et al. (2018) reported that plant insoluble compounds induced PE through stoichiometric decomposition, whereas soluble fraction compounds generate PE via Nmining. Therefore, consistent with our third hypothesis, the later-stage PE was controlled by substrate stoichiometry (C:N ratio) and the quantity of C input, which regulates the decomposition rate of the slow C pool through the shift between stoichiometric decomposition vs. Nmining mechanisms. ...
Article
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The priming effect (PE), referring to the change in the soil organic carbon (SOC) turnover induced by fresh C input, is crucial to the SOC budget. Temperate coniferous forests store a large SOC pool that can be influenced by litter C input. Root litter is a major source of belowground C input but much less studied than leaf litter. Nowadays, it is not clear how PEs are controlled by root litter input of different availability and stoichiometry in coniferous forests and what the underlying mechanisms are. We prepared soluble fraction (SF) and insoluble fraction (IF) from 13 C-depleted spruce roots using hot-water extraction and incubated these fractions with spruce forest soils (Cambisols) to measure the PE. Labile and slow C pools were simulated using a first-order parallel model, whereas soils were harvested during and after the incubation to estimate microbial utilization of substrate-C and C use efficiency (CUE). The SF had higher substrate availability, greater C:N ratio, and smaller C quantity than the IF. The PE ranged from − 0.71 ± 0.44 to 3.34 ± 1.31 mg C/g SOC. The addition of SF induced an immediate and short-lasting positive PE, whereas that of IF caused a gradual and long-lasting positive PE. The immediate PE was associated with abundant, substrate-derived labile C. This indicates that the early-stage PE was controlled by substrate availability. The long-lasting PE was accompanied by an accelerated decomposition of the slow C pool, reflecting that substrate stoichiometry (C:N ratio) and total C input control the later-stage PE by regulating the decomposition of the slow C pool. This can be further explained by a shift between stoichiometric decomposition and N-mining mechanisms of the PE due to changes in CUE, microbial utilization of substrate-C, and K-vs. r-strategists with substrate C:N ratios. Regarding net C budget, a smaller C accumulation induced by the SF addition corresponded to the higher substrate availability and C:N ratio due to the greater early-stage PE and lower CUE, respectively. We propose that in our studied soils: (1) substrate availability determines the early-stage PE through substrate-derived labile C; (2) substrate stoichiometry and total C input regulate the later-stage PE through microbial utilization of substrates; and (3) both substrate availability and stoichiometry control net C budget. Our study highlighted that the shift in PEs with incubation time was regulated by substrate availability, stoichiometry, and C input at the level of the C input in natural spruce forests.
... The NCR mean values varied between 0.1 and 0.3, indicating that the soil organic matter decomposition pathway in our systems was more fungal-than bacterial-driven (Yeates, 2003), whereas it was more explained by bacterial populations under natural fallow, Aristida sp. savanna (Razanamalala et al. 2018). Similarly, the enrichment index (EI) did not vary significantly among treatments with low values, inferior to 50%, in all treatments except the practice with the conventional farmyard manure at 6 t DM ha −1 combined with dolomite at 500 kg ha −1 . ...
Article
Upland rainfed rice cropping in the highlands of Madagascar is strongly limited by poor Ferralsol mineral fertility. There is an urgent need to identify efficient and sustainable fertilization practices that improve soil fertility without inducing pest proliferation. For that purpose, using a field trial for 2 successive years, we tested the effect of 16 fertilization practices on the abundance and taxonomic diversity of soil active nematodes, which are known to be biological indicators of soil fertility. We tested both fertilization practices traditionally used by farmers and innovative ones based on the assemblage of organic, mineral and biological (earthworms and mycorrhiza) fertilizers. We identified eight types of practices: (1) no fertilization; (2) fertilization with NPK and urea; (3) low input rates (3 t dry matter ha−1) of organic fertilizers without NPK; (4) low input rates of organic fertilizers with NPK; (5) high input rates (6 t dry matter ha−1) of organic fertilizers; (6) high input rates of organic fertilizers with mineral fertilizers; (7) high input rates of a mixture of organic fertilizers and (8) high input rates of a mixture of organic fertilizer with mineral fertilizers. After 2 years, we identified 41 soil nematode taxa. The taxonomic composition of the nematode communities revealed that Ferralsols are a stressful environment for the soil biota. The low abundance of opportunistic bacterivores indicated that the different fertilization practices did not significantly and deeply increase the amount of plant-available nutrients, and thus soil fertility. However, organic fertilizers significantly increased the abundance of omni-predators, indicating a moderately mature food web. Some practices induced worrying increases in the endoparasitic Meloidogyne and Pratylenchus, which requires monitoring of root symptoms to avoid the establishment of their populations. Monitor soil nematode communities in upland rice growing on Ferralsols could be used as agronomic indicators to promote sustainable rice production systems in Madagascar.
... Thus, the abundances of Acidobacteriae and Anaerolineae were the highest in NPK soil, which had low SOC content and DOC: Olsen P ratio. Acidobacteriae and Anaerolineae can mineralize recalcitrant organic matter (Razanamalala et al., 2018). Most Anaerolineae species can degrade carbohydrates and proteins under anaerobic conditions (Yamada et al., 2006). ...
Article
Microbial volatile organic compounds (VOCs) can suppress plant pathogens. Although fertilization strongly affects soil microbial communities, the influence of fertilization on microbial VOC-mediated suppression of pathogens has not been elucidated. Soil was sampled from a paddy field that had been subjected to the following treatments for 30 years: a no-fertilizer control, mineral fertilization (NPK), NPK combined with rice straw (NPK + S), NPK combined with chicken manure (70% NPK + 30% M). Then, within a laboratory experiment, pathogens were exposed to VOCs without physical contact to assess the impact of VOCs emitted from paddy soils on in vitro growth of the fungal rice pathogens: Pyricularia oryzae and Rhizoctonia solani. The VOCs emitted from soil reduced the mycelial biomass of P. oryzae and R. solani by 36-51% and 10-30%, respectively, compared to that of the control (no soil; no VOCs emission). Overall, the highest suppression of P. oryzae and R. solani was in the NPK and NPK + S soils, which emitted more quinones, phenols, and low alcohols than NPK + M soils. The abundances of quinones and phenols in the soil air were maximal in the NPK-fertilized soil because the low ratio of dissolved organic carbon and Olsen-P increased the population of key species such as Acidobacteriae, Anaerolineae, and Entorrhizomycetes. The abundance of alcohols was minimum in the NPK + S fertilized soil because the high SOC content decreased the population of Sordariomycetes. In conclusion, mineral fertilization affects bacterial and fungal VOC emissions, thereby suppressing the growth of R. solani and P. oryzae.
... The soil TN concentrations and microbial biomass in HN soil were all significantly higher than those in LN soil (Table S3), which might have provided more substrates and greater microbial activities for the mineralization process (Booth et al., 2005). The priming effect on GM rates after exogenous N addition might be explained by the stoichiometric decomposition hypothesis and the N-mining hypothesis (Razanamalala et al., 2018;Feng et al., 2021). This effect identified in our study was positive (Fig. 2 a), which more easily coincides with the stoichiometry hypothesis since abundant addition of N fertilizer would eliminate the N-constraint conditions and reduce the need for N mining. ...
Article
Recent 15nitrogen (N) tracing studies have shown that unlabeled N2O emissions (UNEs) following 15N fertilizer application tend to be higher than those from zero-N controls, which indicates a potential risk for elevated N2O emissions from native soil. However, researchers do not clearly understand whether these increased UNEs are derived from a priming effect or how interactions between N in fertilizer and native soil affect various sources of N2O emissions. Here, we combined 15N tracing and 15N pool dilution in an incubation study to track gross N transformations, specific sources of N2O emissions, and N2O-production pathways following N fertilization. Four treatments, including two fertilizer N levels (0 and 120 mg N kg−1) in conjunction with two native soil N levels (0.56 and 1.25 g kg−1), were established in the study. Soil gross N mineralization (GM) increased by 33.0%−98.5% (4.59−14.97 mg N kg−1) after N fertilizer addition, which indicated a positive priming effect on soil N turnover and resulted in additional native soil N-derived N2O emissions. In addition to priming GM, the pool substitution effect after 15N addition, which was not triggered by stimulated soil N turnover, also generated substrates for the UNEs. Therefore, overlooking the pool substitution effect might overestimate native soil N-derived emissions by 104%−180%. Differentiation of the specific N2O emission sources showed that both the fertilizer N- and primed native soil N-derived N2O emissions were dominated by nitrifier-mediated processes, which were governed by ammonia-oxidizing bacteria. At a specified N fertilizer addition rate, soils with higher native N levels had greater levels of fertilizer N- and native soil N-derived N2O emissions due to the higher GM and gross nitrification rates. The study reveals that fertilizer N application triggers potential N2O emissions from native soil N by stimulating the GM and nitrifier-mediated N2O-production processes.
... Instead, these studies only focused on the positive role of species richness in determining microbial diversity. Additionally, according to the stoichiometric theory of available nutrients, the degradation process of organic carbon in the original system maintained a faster decomposition rate under the influence of the stoichiometric ratio of nutrient elements when the restriction of nutrients slowed down (Razanamalala et al. 2018;Fan et al. 2019). As unstable carbon sources, algal residues are easily utilized by microbes to improve their activities, which makes up for the limitation of nutrients in the original system. ...
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Understanding the mixed decomposition processes of aquatic plant residues is crucial for evaluating the carbon cycle of lakes. However, the complex effect of species evenness, and especially the algae driving co-metabolism effect in eutrophic lakes are still far from clear. In this study, three dominant aquatic plants ( Phragmites australis , Nymphoides peltatum , and Potamogeton malaianus ) and algae from the typical eutrophic and shallow Lake Taihu, China, were selected to simulate their mixed decomposition process. The addition of algae accelerated the mass loss of cellulose, hemicellulose, and lignin of aquatic plant residues and increased the total mass loss by 2.29~6.32% in mixed decomposition. The positive co-metabolism effect, with the intensity ranging from 10% to 17%, occurred during the mixed decomposition process. In addition, the positive co-metabolism effect was also found among plant residues during mixed decomposition and the co-metabolism intensity of species evenness mixed decomposition was more than twice as high as that of non-evenness mixed decomposition. The addition of algae during the decomposition of aquatic plant residues altered the stoichiometry of available nutrients and affected the microbial decomposition activity. The abundance of decomposition bacteria, especially Bacteroidetes , was increased and the community structure also changed, as evidenced by a 71% increase in the number of bacteria phylum. As a result, these biogeochemistry processes accelerated the decomposition rates of aquatic plant residues and thus produced the positive co-metabolism effect. Therefore, the co-metabolism effects of mixed decomposition described in this study are prevalent in eutrophication lakes and have important effects on the lake carbon cycle, which need to be considered in future lake management. Graphical Abstract
Article
Background In farmland, microbes in soils are affected by exogenous carbon, nitrogen, and soil depth and are responsible for soil organic carbon (SOC) mineralization. The cherry industry has been evolving rapidly in northwest China and emerged as a new source of income for local farmers to overcome poverty. Accordingly, it is highly imperative to probe the effect of defoliation and nitrogen addition on carbon dioxide (CO 2 ) emissions and microbial communities in soils of dryland cherry orchards. Methods CO 2 emissions and microbial communities were determined in soil samples at three depths, including 0–10 cm, 10–30 cm, and 30–60 cm, from a 15-year-old rain-fed cherry orchard. The samples were respectively incubated with or without 1% defoliation under three input levels of nitrogen (0 mg kg ⁻¹ , 90 mg kg ⁻¹ , and 135 mg kg ⁻¹ ) at 25°C in the dark for 80 days. Results Defoliation and nitrogen addition affected CO 2 emissions and microbial communities and increased microbial biomass carbon (MBC), the activity of soil catalase, alkaline phosphatase, and cellulase in soils of the dryland cherry orchard. The culture with defoliation significantly promoted CO 2 emissions in soils at the three depths mainly by increasing the MBC, catalase, alkaline phosphatase, and cellulase activities, resulted in positive priming index. Nitrogen addition elevated the MBC and changed soil enzymes and reduced CO 2 emissions in soils at the three depths. Moreover, the priming index was higher in deep soils than in top and middle soils under the condition of defoliation and nitrogen addition. No significant differences were observed in the soil bacterial diversity (Chao1, Shannon, and Simpson) among all treatments. Meanwhile, the relative abundance of Proteobacteria was markedly increased and that of Acidobacteria was substantially diminished in soils at the three depths by defoliation and nitrogen addition. The results sustained that defoliation and nitrogen can regulate SOC dynamics by directly and indirectly affecting soil microbial activities and communities. As a result, the combination of defoliation return and nitrogen fertilization management is a promising strategy to increase SOC and promote soil quality in dryland cherry orchards.
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The priming effect in soil is proposed to be generated by two distinct mechanisms: ‘stoichiometric decomposition’ and/or ‘nutrient mining’ theories. Each mechanism has its own dynamics, involves its own microbial actors, and targets different soil organic matter (SOM) pools. The present study aims to evaluate how climatic parameters drive the intensity of each priming effect generation mechanism via the modification of soil microbial and physicochemical properties. Soils were sampled in the center of Madagascar, along climatic gradients designed to distinguish temperature from rainfall effects. Abiotic and biotic soil descriptors were characterized including bacterial and fungal phylogenetic composition. Potential organic matter mineralization and PE were assessed 7 and 42 days after the beginning of incubation with 13C-enriched wheat straw. Both priming mechanisms were mainly driven by the mean annual temperature but in opposite directions. The priming effect generated by stoichiometric decomposition was fostered under colder climates, because of soil enrichment in less developed organic matter, as well as in fast-growing populations. Conversely, the priming effect generated by nutrient mining was enhanced under warmer climates, probably because of the lack of competition between slow-growing populations mining SOM and fast-growing populations for the energy-rich residue entering the soil. Our study leads to hypotheses about the consequences of climate change on both PE generation mechanisms and associated consequences on soil carbon sequestration.
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The temperature sensitivity of soil organic matter (SOM) decomposition in tropical forests will influence future climate. Studies of a 3.5-kilometer elevation gradient in the Peruvian Andes, including short-term translocation experiments and the examination of the long-term adaptation of biota to local thermal and edaphic conditions, have revealed several factors that may regulate this sensitivity. Collectively this work suggests that, in the absence of a moisture constraint, the temperature sensitivity of decomposition is regulated by the chemical composition of plant debris (litter) and both the physical and chemical composition of preexisting SOM: higher temperature sensitivities are found in litter or SOM that is more chemically complex and in SOM that is less occluded within aggregates. In addition, the temperature sensitivity of SOM in tropical montane forests may be larger than previously recognized because of the presence of “cold-adapted” and nitrogen-limited microbial decomposers and the possible future alterations in plant and microbial communities associated with warming. Studies along elevation transects, such as those reviewed here, can reveal factors that will regulate the temperature sensitivity of SOM. They can also complement and guide in situ soil-warming experiments, which will be needed to understand how this vulnerability to temperature may be mediated by altered plant productivity under future climatic change.
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An optimized method, based on the coupling of two commercial kits, is described for the extraction of soil nucleic acids, with simultaneous extraction and purification of DNA and RNA following a cascade scheme and avoiding the use of harmful solvents. The protocol canmonitor the variations in the recovery yield of DNA and RNA from soils of various types.The quantitative version of the protocol was obtained by testing the starting soil quantity, the grinding parameters and the final elution volumes, in order to avoid saturation of both kits. • A first soil-crushing step in liquid nitrogen could be added for the assessment of fungal parameters. • The protocol was efficienton different tropical soils, including Andosol, while their high contents of clays, including poorly crystalline clays, and Fe and Al oxides usually make the nucleic acid extraction more difficult. • The RNA recovery yield from the previous tropical soils appeared to correlate better to soil respiration than DNA, which is positively influenced by soil clay content.