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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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... More specifically, microbial limitations for C or nutrients are great driver for soil organic C sensitivity to PE (Gaudel et al., 2021), and they could in fact explain the PE variations along a soil depth gradient. Two hypotheses have been proposed to explain such control of soil organic matter priming by soil nutrient availability: 'stoichiometric decomposition', when stoichiometric ratios of the inputs match those of the microbial demand (Chen et al., 2014), generally occurring in the first days (immediate PE) following the inputs (Razanamalala et al., 2018), and'microbial N-mining' under N-shortage conditions (Craine et al., 2007), possibly occurring after the stoichiometric decomposition PE (Razanamalala et al., 2018). Despite the important consequences for C dynamics, most studies investigating the microbial response to FOM addition in subsoil (Fontaine et al., 2007;Salomé et al., 2010;de Graaff et al., 2014;Shahzad et al., 2019) do not take into account the soil and microbial nutrient status and stoichiometry. ...
... More specifically, microbial limitations for C or nutrients are great driver for soil organic C sensitivity to PE (Gaudel et al., 2021), and they could in fact explain the PE variations along a soil depth gradient. Two hypotheses have been proposed to explain such control of soil organic matter priming by soil nutrient availability: 'stoichiometric decomposition', when stoichiometric ratios of the inputs match those of the microbial demand (Chen et al., 2014), generally occurring in the first days (immediate PE) following the inputs (Razanamalala et al., 2018), and'microbial N-mining' under N-shortage conditions (Craine et al., 2007), possibly occurring after the stoichiometric decomposition PE (Razanamalala et al., 2018). Despite the important consequences for C dynamics, most studies investigating the microbial response to FOM addition in subsoil (Fontaine et al., 2007;Salomé et al., 2010;de Graaff et al., 2014;Shahzad et al., 2019) do not take into account the soil and microbial nutrient status and stoichiometry. ...
... In addition to the lower intensity of PE in the subsoil, a lag in microbial response to C addition was found in the subsoil and has already been reported (Sanaullah et al., 2011;Sanaullah et al., 2016). Several hypotheses can explain the observed lag in the subsoil: (i) a higher proportion of microbial communities with slower growth rates, (ii) a higher proportion of dormant biomass (Maharjan et al., 2017;Joergensen and Wichern, 2018), (iii) the legacy of the C inputs (Schiedung et al., 2023) such as limited organic matter inputs and a resulting higher proportion of recalcitrant organic matter (stable C) , or (iv) a shift from a stoichiometric decomposition PE to a N-mining PE due to the induced N limitations (Razanamalala et al., 2018). These differences contributed to the negative C balance in the topsoil and a positive C balance in the subsoil when comparing PE loss and added soil organic C. When nutrients were added with C, the C balance became negative in the subsoil, as opposed to our hypothesis based on the low soil C:N and in agreement with Meyer et al. (2018). ...
... Under oligotrophic or stoichiometrically imbalanced conditions, however, the expression of the PE may be limited due to a shortage of essential nutrients. Here, the "nutrient mining theory" for priming posits that the nutrients (N, P), liberated by the decomposition of FOM (or SOM), stimulate the activity and growth of SOM-degrading microorganisms and, with that, enhance the PE (Razanamalala, Fanomezana, et al., 2018). ...
... The composition and diversity of the microbial community are important factors determining OM degradation and PEs (Razanamalala, Fanomezana, et al., 2018;. ...
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Warming and eutrophication influence carbon (C) processing in sediments, with implications for the global greenhouse‐gas budget. Temperature effects on sedimentary C loss are well understood, but the mechanism of change in turnover through priming with labile organic matter (OM) is not. Evaluating changes in the magnitude of priming as a function of warming, eutrophication, and OM stoichiometry, we incubated sediments with ¹³ C‐labeled fresh organic matter (FOM, algal/cyanobacterial) and simulated future climate scenarios (+4°C and +8°C). We investigated FOM‐induced production of CH 4 and microbial community changes. C loss was primed by up to 17% in dominantly allochthonous sediments (ranging from 5% to 17%), compared to up to 6% in autochthonous sediments (−9% to 6%), suggesting that refractory OM is more susceptible to priming. The magnitude of priming was dependent on sediment OM stoichiometry (C/N ratio), the ratio of fresh labile OM to microbial biomass (FOM/MB), and temperature. Priming was strongest at 4°C when FOM/MB was below 50%. Addition of FOM was associated with activation and growth of bacterial decomposers, including for example, Firmicutes, Bacteroidetes, or Fibrobacteres, known for their potential to degrade insoluble and complex structural biopolymers. Using sedimentary C/N > 15 as a threshold, we show that in up to 35% of global lakes, sedimentation is dominated by allochthonous rather than autochthonous material. We then provide first‐order estimates showing that, upon increase in phytoplankton biomass in these lakes, priming‐enabled degradation of recalcitrant OM will release up to 2.1 Tg C annually, which would otherwise be buried for geological times.
... 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.
... This microbial C-P colimitation is likely a generic case for ferrallitic soils, but there can be differences in microbial nutrient limitations depending on the degree of rock weathering (Raminoarison et al., 2020). Thus, even though fertilization strategies in this tropical region should imperatively meet the C and P requirements of microorganisms, it is essential not to overlook other elements, especially N, which is severely deficient and known to be related to microbial C functions (Razanamalala et al., 2018). ...
... 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. ...
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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.
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Studying green manure in several returning methods to enhance soil fertility and crop benefits is a strong foundation for cropland nutrient management. However, how different types of green manures and their variable doses affect the efficacy of applied manures, either buried or mulched, remain overlooked. The objective of this study was to optimize green manure management to enhance soil fertility and maize biomass using five types of green manures (white mustard, forest rye, fiddleneck, sufflower, and pea) in two different doses (low, 5 g per pot, and high, 10 g per pot), which were either buried or mulched before and after maize sowing. Results revealed that total carbon content increased due to green manure treatments, representing a 10% increase over control, particularly through buried w. mustard (10% increase before maize cultivation) and mulched safflower and pea (12% and 11% increase after maize cultivation over control). Dry maize aboveground biomass yields also improved across all variants, with buried mustard yielding 18.4 g·plant−1 (compared to 8.6 g·plant−1 in the control), mulched mustard yielding 16.4 g·plant−1, and buried pea yielding 17.8 g·plant−1. Green mulching generally acidified the soil (pH 5.71 compared to 6.21 in the control), except for buried fiddleneck (pH 6.39 after maize cultivation) at a high dose of manures. Carbon-mineralizing enzyme activities (dehydrogenase and β-glucosidase) were significantly increased by green manures, with buried fiddleneck showing a 22.6% and 20.6% increase over the control, and mulched fiddleneck showing a 24.5% and 22.4% increase under high doses. The study suggests that partially decomposed and mineralized mulched biomass may induce a negative priming effect on carbon-mineralizing enzymes due to a decrease in the C/N ratio of the soil. It emphasizes that the nutrient content and stoichiometry of green manures, alongside soil characteristics such as the C/N ratio, are critical factors for sustainable soil management and carbon sequestration. These findings underscore the need for careful selection and management of green manures to optimize soil health and carbon-storage outcomes.
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Afforestation is a crucial pathway for ecological restoration and has the potential to modify soil microbial community, thereby impacting the cycling and accumulation of carbon in soil across diverse patterns. However, the overall patterns of how afforestation impacts below-ground carbon cycling processes remain uncertain. In this comprehensive meta-analysis, we systematically evaluated 7045 observations from 210 studies worldwide to evaluate the influence of afforestation on microbial communities, enzyme activities, microbial functions, and associated physicochemical properties of soils. Afforestation increases microbial biomass, carbon and nitrogen hydrolase activities, and microbial respiration, but not carbon oxidase activity and nitrogen decomposition rate. Conversely, afforestation leads to a reduction in the metabolic quotient, with significant alteration of bacterial and fungal community structures and positive effects on the fungi: bacteria ratio rather than alpha and beta diversity metrics. We found a total 77 % increase in soil organic carbon (SOC) content after afforestation, which varied depending on initial SOC content before afforestation, afforestation stand age, and aridity index of afforestation sites. The modified SOC is associated with bacterial community composition along with intracellular metabolic quotient and extracellular carbon degrading enzyme activity playing a role. These findings provide insights into the pathways through which afforestation affects carbon cycling via microorganisms, thus improving our knowledge of soil carbon reservoir’s responses to afforestation under global climate change.
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The arid and semi-arid regions of northwest China play a crucial role in ensuring the national cotton production. Soil water potential (SWP)-based deficit irrigation is potentially an effective irrigation strategy in maintaining agricultural productivity in these regions. However, the impact of various SWP thresholds and nitrogen application rates on carbon balance and environmentally friendly economic benefits in cotton systems remains unclear. A two-year field experiment was conducted to investigate the effects of three SWP thresholds (W1, W2 and W3: − 30, − 20 and − 10 kPa) and three nitrogen rates (F1, F2 and F3: 200, 300 and 400 kg ha − 1) on greenhouse gas (GHG) emissions, seed cotton yield, carbon storage, and economic benefits in drip-fertigated saline cotton fields. The results showed that increasing nitrogen rate significantly increased N 2 O emission, while higher irrigation level reduced the soil's capacity to absorb CH 4. Moreover, increasing irrigation level and nitrogen rate led to higher soil CO 2 emission. The W3F3 obtained the highest seed cotton yield, ranging from 6529.6 to 6804.6 kg ha − 1. Nitrogen application increased soil organic carbon storage by 5.6-12.5 %, whereas excessive nitrogen fertilization resulted in significant losses in soil inorganic carbon, ranging from 20.7 % to 34.9 %. The W2F2 enhanced net ecosystem carbon budget accumulation by increasing the input efficiency of carbon and reducing GHG emissions, and excessive GHG emissions limited the net ecosystem carbon budget of high-fertilization treatments. Meanwhile, the increased fertilizer and environmental costs reduced net ecosystem economic benefit. Fertilizer was identified as the major contributor to the ecosystem carbon footprint, accounting for more than 22.9 %. In conclusion, the W2F2 not only obtained the optimal soil carbon sequestration and carbon balance in the system, but also generated economic profits comparable to those of the high-fertilization treatments while producing lower direct GHG emissions. These findings highlight the significance of rational drip irrigation and nitrogen fertilization in maintaining high productivity and carbon sustainability in saline cotton fields.
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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.
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Soils are the largest reservoir of global terrestrial carbon (C). Conversion from natural to agricultural ecosystems has generally resulted in a significant loss of soil organic-C (SOC, up to 50% or ~30-40tha-1) and 'restoring' this lost C is a significant global challenge. The most stable component of soil organic matter (SOM), hereafter referred to as fine fraction SOM (FF-SOM), contains not only C, hydrogen (H) and oxygen (O), but substantial amounts of nitrogen (N), phosphorus (P) and sulphur (S), in approximately constant ratios. The availability of these associated nutrients is essential for the formation of FF-SOM. Here we show, in short term (56 day) incubation experiments with 13C labelled wheaten straw added to four soils with differing clay content, that conversion of straw into "new" FF-SOM is increased by up to three-fold by augmenting the residues with supplementary nutrients. We also show that the loss of "old" pre-existing FF-SOM increased with straw addition, compared to soils with no straw addition, but that this loss was ameliorated by nutrient addition in two of our soils. This find