Modelling the long-term carbon storage potential from recalcitrant matter inputs in tropical arable croplands

doi:10.21203/rs.3.rs-3086337/v1

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Modelling the long-term carbon storage potential from recalcitrant matter inputs in tropical arable croplands

https://doi.org/10.21203/rs.3.rs-3086337/v1

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The urgency to achieve climate neutrality and limit global warming requires a transition to low fossil carbon use. Crop residues, an abundant source of renewable carbon, remain underutilized, among others due to soil conservation practices. Soil organic carbon (SOC) plays a crucial role in tropical croplands by supporting soil health, nutrient availability, and biogeochemical cycles. The incorporation of exogenous organic matter (EOM) amendments has the potential to enhance carbon storage and fertility. This study conducted in Ecuador, a biodiversity hotspot, aims to identify SOC stock vulnerabilities, estimate SOC storage potential and changes in CO₂ fluxes in tropical cropping systems resulting from changes in crop residue harvest for use within the bioeconomy, when a subsequent recalcitrant EOM application is involved. A spatially-explicit modeling framework representing the agricultural area into 15,782 agricultural pedoclimatic units was employed to assess the potential for SOC storage and to quantify resulting CO₂ emission changes in tropical cropping systems. Four scenarios were analyzed, all implying the conversion of crop residues into bioeconomy products as well as recalcitrant EOMs. The RothC soil model, adapted to incorporate additional carbon pools for labile (C_L) and recalcitrant (C_R) fractions, as well as the priming effect, was utilized alongside high-resolution data to evaluate SOC storage potential for each scenario. Baseline SOC stocks ranged from 7.43 to 235 t C ha^− 1, with an average of 61.76 t C ha^− 1. At the national level, the business-as-usual (BAU) scenario, i.e. crop residues removal, projected a potential 4% increase in SOC stocks by 2040 and a 7% increase by 2070. However, SOC stocks decreased in 79% of the study area. The simulations demonstrated the potential to supply 113 PJ biomass for the bioeconomy without incurring SOC losses in the pyrolysis and gasification scenarios. Harvesting residual biomass with co-product return led to a 19–39% reduction in CO₂ emissions over 50 years, depending on the scenario. Sensitivity analyses revealed the priming effect as a particularly sensitive parameter for the results.

Renewable Resources

Environmental Chemistry

Ecological Modeling

Soil organic carbon

exogenous organic matter

RothC

crop residues

carbon sink

bioeconomy

priming effect

biochar

digestate

gaschar

hydrochar

The soil model RothC was adapted to simulate the long term SOC dynamics of recalcitrant exogenous organic matter inputs
The adapted RothC was used to estimate the SOC storage potential of croplands amended with recalcitrant EOMs in tropical lands
The changes in CO₂ emissions was studied for various scenarios involving the harvest of crop residues and return of recalcitrant EOMs
The baseline of current cropping systems of Ecuador was developed with high spatial resolution.

The ambitious goal of climate neutrality by 2050 [1, 2] set to commit to the Paris Agreement [3] target to limit global warming well below 2°C calls for actions ensuring a transition toward low fossil carbon (C) use [4]. Biomass, particularly crop residues, constitutes one of the largest streams supplying renewable C, with an estimated production of 5 billion metric tons in 2013 [5]. However, this potential is not fully exploited as crop residues are usually left on the agricultural fields to conserve the soil organic carbon (SOC) budget [6, 7]. Typically, a precautionary principle is applied, suggesting a crop-dependent threshold ranging from 15–60% [8] to avoid SOC losses related to the harvest of crop residues.

Soil organic carbon is especially important for soil health and functioning in tropical croplands due to their high soil biodiversity and fertility, as well as year-round intensive agricultural practices that promote soil degradation and carbon losses [9]. Tropical soils can store large amounts of carbon, and the incorporation of organic matter through the use of cover crops and the application of organic amendments can help maintain soil health and fertility [10, 11].

In addition, soil organic carbon plays a crucial role in supporting biogeochemical cycles and plant nutrition in tropical croplands [12, 13]. Soil organic matter can enhance the mineralization and availability of nutrients such as nitrogen, phosphorus, and sulfur, which are often limiting factors in tropical soils [14, 15]. Moreover, soil organic matter can also improve soil structure, water-holding capacity, and infiltration, leading to increased soil productivity and crop yields in tropical croplands [9, 16].

Given the high potential for soil carbon sequestration especially in tropical croplands, there is a need for effective strategies to increase soil organic carbon stocks and mitigate carbon losses. This can be achieved through the implementation of sustainable agricultural practices, which aims to minimize soil disturbance and maintain soil cover with the use of cover crops and mulching [14]. Furthermore, the use of organic amendments such as compost and manure can also promote soil carbon sequestration and improve soil health in tropical croplands [13, 17]. Overall, the maintenance and enhancement of soil organic carbon are crucial for the sustainable management of tropical croplands and the provision of ecosystem services such as carbon sequestration, nutrient cycling, and food security.

In this context, agricultural soils can behave either as a source or sink of C in response to stress induced by farming management practices [10]. Various studies have addressed diverse agricultural practices to induce SOC sequestration, including the insertion of cover crops [11] and grasslands [18, 19] in crop rotations and the recycling of organic resources [20] as exogenous organic matter (EOM) to soils. EOM can be defined as any byproduct material of biological origin derived from industries, livestock breeding, or municipal wastes, that can be applied as a soil amendment to promote C storage, enhance soil fertility, and reduce soil degradation [21, 22].

The potential of various EOMs (e.g., manure, compost, sewage sludge) to improve agriculture and offset environmental impacts has been studied by various authors [21–24], with results depending on the specific pedoclimatic characteristics associated with the soil and the composition of the EOM, itself determined by the original feedstock and production conditions. Nevertheless, the application of EOMs to soils is highlighted as an attractive strategy for building up SOC stocks while recycling material otherwise considered waste. Moreover, previous studies have revealed that the actual harvesting potential of crop residues to supply bioeconomy services could increase if the coproduct of the process returns to the soil as an EOM [8, 25, 26].

Due to the high costs associated with SOC measurements, soil models able to simulate the soil response to EOM application are a valuable tool for developing soil C sequestration policies [27]. Previous studies have investigated the inclusion of EOMS with varying degrees of recalcitrance, such as biochar and digestate, in soil models, including RothC [26, 28–30], Century [31], APSIM [32], EPIC [33], CTOOL [25], AMG [34], and CANDY [35]. Notably, Andrade Diaz et al. [8] have included five different EOMs simultaneously in AMG and applied the model to French croplands. However, SOC modeling including recalcitrant EOMs has been predominantly studied for temperate regions and research is needed to expand their application to other biomes.

In fact, according to IPCC [36] tropics can potentially sequestrate 1.1–1.6 Pg C yr^− 1, which represents approximately 70% of the global potential carbon sequestration from 1995 to 2050. On the other hand, the favorable pedoclimatic conditions, wide crop biodiversity, and high soil fertility allow for year-round intensive agricultural practices that promote soil degradation and carbon losses in tropical croplands [12]. Tropical croplands are thus key for supplying renewable carbon to the bioeconomy if C sequestration strategies can be implemented.

This study aims to estimate at a high-spatial-resolution the i) SOC storage potential of tropical cropping systems under various scenarios entailing the harvest of crop residues followed by the application of recalcitrant EOM, and iii) the CO₂ emissions mitigation potential of these scenarios. We modeled the SOC dynamics of Ecuadorian croplands (0–30 cm), with and without harvesting crop residues to supply various bioeconomy pathways, allowing for the return of recalcitrant EOMs. Albeit Ecuador is a well-known biodiversity hotspot, vulnerabilities regarding the maintenance of SOC stocks have been identified [37, 38] and were thus selected as an illustrative case of tropical lands.

2.1 Modelling framework

We used a spatially-explicit approach to assess the SOC storage potential in tropical lands, under four mitigation scenarios involving the conversion of crop residues into stabilized EOMs as compared to a business-as-usual (BAU) reference scenario where crop residues remain on soils. The study was based on the framework developed by Andrade et al. [8] and applied with a high-spatial resolution for the Ecuadorian croplands as a representative case. A near-term (2020–2040) and long-term (2020–2070) timeframe were considered for all the scenarios. The BAU scenario reflects the current cropping systems of Ecuador and assumes that crop residues (100%) are left on soils as a source of C. As opposed, each mitigation scenario considers that all the technically harvestable crop residues are exported to supply the bioeconomy and then partially returned as a stabilized EOM rich in recalcitrant carbon. The BAU is then a benchmark for contrasting the mitigation scenarios. The EOMs considered here are biochar produced under gasification and fast pyrolysis, hydrochar from hydrothermal liquefaction (HTL), and digestate from anaerobic digestion (AD). To avoid confusion between the mitigation scenarios, biochar is referred as pyrochar and gaschar for the pyrolysis and gasification processes, respectively.

The framework entails four consecutive stages (Fig. 1) detailed in the subsequent sections: i) first, we clustered the country according to its pedoclimatic characteristics and define the area of study, ii) then, we described the baseline cropping systems and quantify the spatially explicit potential of crop residues in Ecuador, iii) thereafter, the RothC [39] soil model was adapted and applied with high resolution to determine the SOC storage potential of each mitigation scenario (compared to the BAU), and iv) we finally performed a sensitivity analysis to evaluate the influence of the key parameters driving the soil model.

2.2 Spatially explicit cropping systems

The wide pedoclimatic diversity of Ecuadorian croplands was represented by the so-called agricultural pedoclimatic units (APCU, [8]), which are unique combinations of soil characteristics, meteorological variables, and crop rotations in agricultural lands. The creation of the APCUs is thoroughly detailed in the supplementary information (SI)1, while the original and processed data are transparently documented in “a companion data paper [47]”, inhere we succinctly explore the essential concepts to build the APCUs. The APCUs were built upon the contribution of the Ministry of Agriculture of Ecuador (MAG) for the global Soil Organic Carbon Sequestration (SOC) map [37, 38], by intersecting the national SOC stock map at 0–30 cm [41], the climate layers extracted from Terraclimate [40] and the national land use map for the period 2009–2015 [42]. The average data reported for the period 2016–2019 on the National Continuous Agricultural Production and Surface Survey (ESPAC; [48]) was used to select the study area based on crop yields and surface. The selected representative crops include 10 different annual, semi perennial and perennial species (i.e., banana, barley, cocoa, coffee, maize, rice, plantain, oil palm, sugarcane, and wheat) (Table S1), representing 95% of the national production and over 90% of the national croplands surface [47]. The APCUs included in this study where only those dedicated to the selected crops, with at least 10 ha of surface, and for which the Ecuadorian SOC sequestration map presented successfully modelled SOC stocks for 2040 under a BAU development [38]. This selection yielded 15,782 APCUs covering 1.19 Mha, which represents 52% of the total Ecuadorian croplands (further detailed in SI1) in 2009–2015.

The potential residual biomass contributing to the soil C inputs in the BAU scenario was determined based on the average yield extracted from the data reported on the historical ESPAC for the period 2002–2019. The above- and belowground C inputs per APCU were calculated by multiplying the main product yield (t ha^− 1) per crop by the residue-to-product (RPR; [49]) and root-to-shoot (RtS, [50]) ratios and the corresponding C content (see Andrade et al. [47]), respectively. The C inputs in the mitigation scenarios were determined as in the BAU, with the premise that all the technically harvestable fraction (Table S1) was mobilized to supply the bioeconomy and partially returned to the soils as a recalcitrant EOM. The detailed procedure to determine the potential crop residues and C inputs in the Ecuadorian cropping systems is detailed in SI1 and in the associated data paper.

2.3 Mitigation scenarios

The mitigation scenarios consider the application of four different EOMs to offset SOC stock losses and climate change. The EOM-SOC dynamics are directly affected by the quantity of C in the EOM and its recalcitrance to degradation when input in soils. The recalcitrance represents the fraction of carbon in the EOM that is more resistant to mineralization [51] and is represented by the carbon recalcitrance (C_R) coefficient. The more easily degradable fraction of carbon is referred to as labile (C_L), and is expressed as 1 – C_R. The amount of carbon input through each EOM is affected by the carbon conversion (C_C) coefficient [52]. Both C_C and C_R are specific for each EOM and are governed by the process conditions and the feedstock material composition.

The EOMs included in this study are pyrochar produced under fast pyrolysis conditions, gaschar obtained as a coproduct of gasification, hydrochar produced from hydrothermal liquefaction (HTL), and digestate resulting from anaerobic digestion (AD). The process conditions assumed for the production of each of the EOMs are described in SI1. While thermochemical processes (i.e., pyrolysis, gasification, and HTL) are conducted under extreme pressure and temperature conditions that favour the appearance of aromatic carbon related to high C_R values [53], biochemical processes, such as AD, do not change the original structure of the C contained in the raw biomass [54]. The C_R and C_C values used were retrieved from the study by Andrade et al. [52]; the former taken directly as presented in the original study, whereas the latter included the expansion of the database to include the tropical crop residues assessed in this study (Table 1).

Due to their recalcitrant nature, EOMs used as soil amendments can change the normal rate of SOC mineralization. This effect is commonly referred to as priming effect (PE) and is considered to be positive (+ PE) if the mineralization rates observed in the BAU are accelerated by the input of the EOM, and negative (-PE) if the rate is slower [55]. Due to the tremendous role played by the mineralization rate on SOC dynamics [30], the PE was considered a key parameter to assess the EOM-SOC dynamics in this study (Table 1).

Table 1

Mitigation scenarios and inner characteristics of the carbon structure of the EOMs included in this study. ^a
EOM	Production conditions	C_C		C_R ^b	C_L	PE^c	k_L ^b (y^− 1)	k_R ^b (y^− 1)
EOM	Production conditions	Cereal straw ^b	Fibres and leaves ^c	C_R ^b	C_L	PE^c	k_L ^b (y^− 1)	k_R ^b (y^− 1)
Pyrochar	350°C – 700°C, seconds to 2 hours, DM > 90%	0.44	0.51	0.95	0.05	0.286	118.00	0.003
Gaschar	600°C – 1200°C, DM > 90%	0.20	0.31	0.95	0.05	0.449	69.67	0.007
Hydrochar ^d	180°C – 400°C, catalyst, DM < 20%	0.31	0.31	0.89	0.11	0.803	27.64	0.099
Digestate	Mesophilic: 30°C – 50°C, 1–3 months, DM < 35%	0.36	0.69	0.68	0.32	0.803	199.96	0.815

C_C:,Carbon conversion C_R:Carbon recalcitrant fraction, C_L: carbon labile fraction (1-C_R), PE: Priming effect, k_L: mineralization rate of the labile fraaction, k_R: mineralization rate of the recalcitrant fraction. ^a Parameters considered for the main simulations of this study, considered as the default average parameters. PE is 1 for the main simulations and the PE reported in here is only considered for the sensitivity analysis (section 2.5). ^b Value used as in Andrade Díaz et al. (2023). ^c Average value from 110 data records reported in Andrade et al. [56] to include lignin rich crop residues. PE values of pyrochar and gaschar were defined according to the global meta-analysis by [57]. PE for pyrochar was used as the percent change of the organic carbon content in the soil observed by the application of biochar produced at medium temperature (350–600°C) and the value observed for high temperatures (> 600°C) was used as proxy for gaschar. The determination of PE as presented in here is further detailed in section 2.4.4 and SI1. PE for digestate was calculated from a meta-analysis (unpublished) including 79 data records contrasting the SOC changes observed after the application of digestate with a control situation where no digestate is applied. ^d No data regarding the hydrochar represented in this study was found, therefore C_C and C_R defined in Andrade Díaz et al. (2023) for cereal residues were used as a proxy. Since HTL processes tolerate wet feedstocks as in AD processes, and the recalcitrance lifetime of hydrochar was found to be on the decadal scale as opposed to the hundreds found for other biochar (pyrochar and gaschar) [53, 58], the PE defined for digestate was used as a proxy for hydrochar.

2.4 SOC modeling

2.4.1 Soil model RothC Overview

The Rothamsted Carbon model (RothC) 26.3 [39] is a widely used multicompartment model for predicting soil carbon turnover. It was initially developed to simulate the SOC dynamics in croplands and later expanded to forests and grasslands. RothC has been tested and well-validated under different soils and climates worldwide, including tropical regions [59–62].

The model operates in a monthly timestep and is structured to allocate the carbon inputs in five different soil compartments (Fig. 2a). The decomposable plant material (DPM), resistant plant material (RPM), microbial biomass (BIO), and Humified organic matter (HUM) compartments are considered active and mineralize the C inputs according to specific decay rates (k) in each pool, following first-order kinetics. The inert organic matter (IOM) pool is a small compartment resistant to decomposition. The k values for the active pools are constants in decreasing order: k_DPM= 10 y^− 1, k_RPM= 0.30 y^− 1, k_BIO= 0.66 y^− 1, k_HUM= 0.02 y^− 1, with a mean residence time (MRT) of 1 k^− 1.

The carbon inputs are split between DPM and RPM according to the DPM: RPM ratio of the material. The default DPM: RPM ratio for croplands and grasslands is 1.44 (i.e., the material contains 59% DPM and 41% RPM). A portion of the carbon in each compartment is mineralized as CO2 and the remaining fraction is allotted in the BIO (46%) and HUM (54%) pools. The soil clay content determines the proportion of emitted CO₂.

2.4.2 Adapted RothC for the mitigation scenarios

We adapted RothC to include two extra pools to allocate the labile (C_L) and recalcitrant (C_R) carbon fractions of the EOMs under study (Fig. 2b). The model, hereon RothC-Bioeconomy, is built upon the study of Pulcher et al. [30] for including biochar in RothC. The C_L and C_R coefficients (Table 1) distribute the EOM’s C between the decomposable exogenous organic matter (DEOM) and the recalcitrant exogenous organic matter (REOM) pools, respectively. RothC-Bioeconomy assumes that the EOM is partly mineralized into CO₂ and the remaining C is transferred to the BIO and HUM pools. The carbon mineralization in the DEOM and REOM pools is defined by their respective mineralization rates (k_L and k_R) and is affected by the priming effect of the EOM. The size (C_L and C_R) and mineralization rates (k_L and k_R) allotted to the DEOM and REOM pools are EOM-dependent and were used as reported in the study of Andrade et al. [52], as further detailed in SI1.

The pyrochar PE was retrieved from the global meta-analysis by Chagas et al. [57], where the mean effect on SOC stocks determined for the application of biochar produced at pyrolysis temperatures ranging from 350°C to 600°C was used to represent the pyrochar PE in this study. Due to lack of information regarding the PE of gaschar, the SOC change determined by Chagas et al. [57] for pyrolysis temperatures above 600°C was used as a proxy for gaschar PE. A meta-study (unpublished) was performed to contrast the SOC stocks attained under digestate application against a control situation with no digestate inputs and it was used to derive the PE of digestate. The data used to determine the digestate PE is openly reported in Andrade et al. [56]. If no PE is considered by the input of the EOM, then PE is set as 1. If the EOM input involves a PE on the mineralization rates, then the PE value is summed or subtracted from 1, for a positive and negative PE, respectively. The details on implementing the RothC-Bioeconomy parameters (i.e., C_C, C_R, k_L, k_R, and PE) are further explored in SI1.

2.4.3 Input data for RothC-Bioeco

RothC requires minimal and relatively easily obtainable climate, soil, and management data. The climate data comprises monthly rainfall (mm), monthly evapotranspiration (mm), and average monthly air temperature (°C). The temperature and precipitation data for the period 2020–2070, were obtained from the Sub-secretary of Climate Change of Ecuador [44], for the RCP4.5 climate trajectory (Representative Concentration Pathway; [63], downscaled by the ensemble model IPSL-CM5A-MR/MIROC-ESM/GISS-E2-R/CSIRO-Mk3-6-0. The evapotranspiration was calculated using the Thornthwaite method [61, 64] within the Standardized Precipitation Evapotranspiration Index (SPEI package SPEI v1.7, updated 2019) package [46, 65]. The preparation of the spatially-explicit meteorological data is detailed in [47]. Soil data includes clay content (%) at simulation depth, initial (0–30 cm) SOC stocks (t C ha^− 1), and initial SOC allocation among the different pools. Data regarding SOC stocks and allocation across the soil pools, as well as the clay content (%), were retrieved from the contribution of MAG to the Global SOC sequestration potential map [37, 38] at a 1km resolution. The bulk density was retrieved from Soilgrids [43].

The management data comprise information on whether the soil is covered or bare, an estimate of the decomposability of the plant material (DPM:RPM ratio), and the monthly input of carbon (t C ha^− 1) from the crop residues, farmyard manure, and other EOMs. The DPM:RPM ratio used was 1.44 as suggested by default in RothC for cropping systems [39]. The monthly above- and belowground C inputs were calculated based on the average (years 2022 to 2019) production data reported in the ESPAC [45] for the sow/harvest schedule of the crops included in this study (Fig S1), as further detailed in SI1.

2.4.5 SOC storage potential calculation

The SOC storage potential of any mitigation scenario, compared to the BAU, is determined as the SOC change observed at the end of the simulations in each scenario (mitigation vs BAU). The SOC stocks change is calculated in each APCU using Eq. (1) [8] and scaled at national level as the weighted average according to the APCU surface (Eq. (2)).

$${{\varDelta SOC }_{bio-BAU}}_{i}\left(\text{%}\right)={\left(\frac{{SOC}_{tbio}-{SOC}_{tBAU}}{{SOC}_{tBAU}}\right)}_{i}\text{*}100$$

$$\stackrel{\sim}{{\varDelta SOC }_{bio-BAU}}\left(\text{%}\right)={{\varDelta SOC }_{bio-BAU}}_{i}\text{*}{w}_{i}$$

where ΔSOC_bio-BAUi (%) is the SOC stocks change percentage observed by implementing a mitigation scenario, relative to the BAU, in the APCU i. SOC_tbio is the SOC stock at time t for the mitigation scenario and SOC_tBAU is the SOC stock at time t for the BAU scenario. Here, t corresponds to December at the years 2040 and 2070 for the near-term and long-term simulations, respectively. Scenarios representing a potential SOC storage, as compared to the BAU, return positive ΔSOC_bio-BAU values, while scenarios with a SOC losing potential return a negative ΔSOC_bio-BAU. $\stackrel{\sim}{{\varDelta SOC }_{bio-BAU}}\left(\text{%}\right)$ is the weighted average SOC change observed per scenario at a national scale, considering a pondering coefficient w, which represents the contribution of the surface of APCU i to the total surface simulated.

The net SOC storage potential of the mitigation scenarios, defined as the total Mt C gained or loss (mitigation vs BAU) at a national level (only APCUs studied) is calculated using Eq. (3):

$$Total national \varDelta SOCnet= {\sum }_{i}{{\left({SOC}_{bio}-{SOC}_{BAU}\right)}_{i}\text{*} {S}_{APCU}}_{i}$$

Total national ΔSOC net (in Mt C) is the sum of the difference between the SOC stocks in the mitigation and the BAU scenarios at the end year (2040 or 2070) per APCU, times the APCU surface (S_APCU). The total croplands surface of Ecuador is 2.19 Mha but after the selection criteria described in section 2.2, only 1.19 Mha were included in the study (see SI).

The SOC stocks change in time, for a given scenario (mitigation or BAU) is calculated as the SOC observed at the final year minus the initial SOC stocks (Eq. (4)).

$${\varDelta SOC}_{{0-\text{t}}_{i,x}}\left(\text{%}\right)=\frac{{SOC}_{t,i,x}-{SOC}_{0}}{{SOC}_{0}}\text{*}100$$

where ΔSOC_0-t,i,x (%) is the percent change of SOC stocks from the initial conditions (year 2020) until the end of the simulation (near-term: 2040, long-term: 2070); SOC₀ corresponds to the initial SOC stocks and SOC_t,I,x to the final SOC stocks at time t, APCU i, and scenario x.

RothC calculates the SOC storage has a direct effect on the CO₂ flux from soil, which are accounted as the difference of the CO₂ outflows [39] in the mitigation scenario vs the BAU (Eq. 5).

$${\varDelta CO}_{2}= {CO}_{2\_bio}-{CO}_{2\_BAU}$$

where ΔCO₂ (t C ha^-1 y^-1) is the amount of CO₂-C emitted or avoided by each mitigation scenario at the end of the simulation, for CO_{2_bio} representing the emissions in the mitigation scenario and CO_{2_BAU} the emissions in the BAU.

2.5 Sensitivity analysis

The carbon inputs, EOM’s recalcitrance, and priming effect parameters included in the adapted RothC-Bioeconomy can vary in function of the process performance [52]. Moreover, knowledge gaps are still open regarding the PE of the studied EOMs thus entailing important differences on PE values across the available literature [57, 66].

To account for the sensitivity of RothC-Bioeconomy to these variations, a sensitivity analysis (SA) was performed to evaluate the SOC stocks evolution observed by varying the carbon inputs and recalcitrance to the range of values observed in the reported data compilation [67]. The carbon inputs are represented by C_C, whereas the carbon recalcitrance is represented by the combination of C_R, C_L, k_R, k_L, and PE. Three levels of C_C where considered (low, min, and high) in combination with two levels of recalcitrance, namely minimum and maximum. The values used for each parameter and combination of parameters assessed is detailed in Table S3. We performed an extra SA to account for the single effect of PE for the pyrochar scenario as a representative of high negative PE (i.e., 71.4% slower mineralization rates, [57]. To this end, we ran a simulation for which all the parameters were as default (Table 1) but instead of using a PE of 1, the PE was set to be the one presented in Table 1.

3.1 SOC evolution in the BAU scenario

The SOC stocks in Ecuadorian croplands (i.e., only the PCUs included in this study), at the baseline (2020), range from 7.43 to 235 t C ha^− 1 (average 61.76 t C ha^− 1), which represents 70.2 Mt C for the 1.19 Mha inhere studied (SI). At the national level, the BAU results show a potential increase of the national SOC stock by a mean of 4% and 7% in the short- (2040) and long-term (2070), respectively (Table 2). Despite this expected average increase, SOC stocks are, by 2040, shown to decrease in 79% of the simulated areas (up to 65% loss), representing a loss of 0.984 MtC (Fig S3). However, this loss stabilizes to 0.77 MtC (up to -90%) by 2070, due to a slight recovery between 2040–2070.The SOC lost in Ecuadorian croplands induces total cumulated emission of 304.51 Mt CO₂-C at the end of the simulations. The spatial explicitness of the study revealed the influence of the cropping systems diversity on the SOC dynamics. The highest SOC sequestration is observed in the areas with presence of sugarcane, followed by areas dedicated to banana, while the highest SOC loss is observed in the areas dedicated to cereal crops (i.e., rice and maize) (Fig S2).

3.2 Exogenous organic matter linked to SOC dynamics

The potential SOC storage (Table 2), as well as the area affected (Figs. 3 and 4), differed greatly among the mitigation scenarios, with national SOC changes ranging from − 7 (AD) to 17.70 (Pyrolysis) Mt C in 2040 and from − 10.5 to 44.7 Mt C in 2070. Pyrochar shows the highest SOC storage, with a potential increase by a mean of 64% (max 231%) compared to the BAU, while the greatest SOC loss is observed in the AD scenario with a mean change of -15.3% (min − 31.8%).

Pyrochar shows increased SOC stocks in all (100%) the simulated areas for the two timeframes studied. However, a 3-fold increase of SOC sequestering potential is observed between 2040 and 2070, with SOC changes ranging from 0.5 to 69% in 2040 and from 2 to 231% in 2070.

Albeit gaschar predicted additional SOC stocks of 1.24 Mt C ($\varDelta {SOC}_{EOMvsBAU}$ of 1.5%) at a national scale, approximately 8.58% of the simulated area could decrease SOC stocks (up to -12.3%) in the near term (2040). The SOC loss observed, could be compensated by sustained gaschar inputs, attaining additional 12.03 Mt C ($\varDelta {SOC}_{EOMvsBAU}$ of 15%) by 2070, with no SOC losses observed in any PCU. This difference reveals a 10-fold increase on SOC storage in 30 years of gaschar inputs. Moreover, while 60% of the simulated area predicted a maximum SOC increase of 2% by 2040, approximately 54% of the areas show a maximum increase of 16.5% by 2070.

The SOC storage response to the exchange of crop residues by EOMs produced under mild process conditions (i.e., hydrochar and digestate) is highly spatially heterogeneous (Figs. 3 and 4) and is greatly influenced by the time extension of the simulations. For instance, at the near-term simulations (2040), the HTL scenario shows SOC stocks changes ranging from − 14.8–13.1% (mean of -4.6%), which results in a SOC loss of 3.52 Mt C at a national scale. For the same period, the AD scenario shows a cumulated loss of 7.08 Mt C compared to BAU, with SOC variations across the PCUs ranging from − 29.1–3% (mean of -10%). If the simulations are extended until 2070, the long-term SOC stocks are predicted to loss 6.43 and 10.5 Mt C under the HTL and AD scenarios, respectively. These losses represent a mean SOC change of -9.1% and − 15.3% for each scenario. We must note that the SOC loss observed for both scenarios and both timeframes represent 99.9% of the simulated area, this due to positive SOC storage observed in only 1 single PCU covering 19.4 ha.

3.3 CO₂ emissions and key spatial observations

All the EOM scenarios represent a significative mitigation potential for CO₂ emissions, compared to the BAU scenario at the near- and long-term timeframes (Table 2). The pyrolysis and gasification scenarios represent a 40% decrease of CO₂-C emissions, with net reductions of 125 and 126 Mt C, respectively at the year 2070. Despite the negative effect on SOC stocks shown by the AD and HTL scenarios, the CO₂ emissions are reduced by 32% and 20%, which represents national CO₂-C emissions redcution of 99.2 and 61.65 Mt C in each scenario.

The highest SOC storage potential achieved by the conversion of crop residues into recalcitrant EOM is observed in the Northeast and Central West regions, for the pyrolysis and gasification scenarios. In particular, the provinces of Imbabura, Orellana, Guayas, Manabí, El Oro, and Loja show the greatest SOC stocks increase across the simulated area. On the opposite, these areas showed the most marked SOC stock losses for the HTL and AD scenarios (Fig. 3, 4).

3.4 Sensitivity analysis

The complete results of the sensitivity analysis are available in the data repository associated to this manuscript [68]. Various combinations of carbon input (C_C), recalcitrant fraction (C_R), mineralization rates (k_R and k_L), and PE were tested according to Table S4. Across the different parametric configurations, the greatest effect was related to the priming effect. A one-at-a-time test for the pyrolysis scenario, allowed comparing the SOC stock evolution under the average expected PE of pyrochar (-0.714) against the default scenario of this study where the PE was set to zero (Fig. 5). This allowed to separate the PE from the other set of parameters that could have been tested. The SOC stocks were doubled in the pyrochar scenario The scenario revealed that if we consider the PE, pyrochar SOC storage potential is increased, representing 2.54-fold the potential SOC storage observed with no PE by the year 2040, with an expected decrease to 1.8-fold if the timeframe is extended to 2070.

Table 2

Soil organic carbon storage potential and CO₂ emissions avoided of the mitigation scenarios, relative to the BAU, for the near-term (2040) and long-term (2070) simulations, at a national level. Results shown as total MtC and weighted average percentage for all the APCUs. ^a
Scenario
	Total national SOC change ^c				Average national SOC change ^d				Total national SOC change ^c				Average national SOC change ^d
	Total	Min ^e	Max ^e	CO₂^f	Mean	Min ^e	Max ^e	CO₂ ^f	Total	Min ^e	Max ^e	CO₂ ^f	Mean	Min ^e	Max ^e	CO₂ ^f
	Mt C				%				Mt C				%
BAU ^b	-1.0	-0.2	1.1	125.8	3.7	-65.4	738.9	-	-0.8	-0.3	1.7	304.5	6.7	-89.6	1167.6	-
Pyrolysis	14.7	2.8E-6	0.4	-47.0	21.1	0.5	68.5	-34.3	44.7	1.3E-5	1.3	-125.3	64.3	2.2	231.5	-39.4
Gasification	1.2	-1.4E-2	0.1	-47.0	1.5	-12.3	25.6	-34.3	12.0	1.6E-6	0.5	-126.2	15.3	0.2	90.3	-39.6
HTL	-3.5	1.2E-4	-0.1	-38.7	-4.6	-14.8	13.1	-28.4	-6.4	1.7E-4	-0.2	-99.2	-9.1	-21.2	20.6	-31.2
AD	-7.1	-0.2	2.8E-5	-21.85	-10.0	-29.1	3.0	-16.0	-10.5	-0.3	5E-05	-61.7	-15.3	-31.8	6.2	-19.2
Pyrolysis_PE^g	36.4	6.55E-05	1.1	-	54.1	14.8	154.6	-	79.8	9.5E-5	2.5	-	120.9	27.5	404.5	-

SOC: Soil organic carbon, BAU: Business as usual, HTL: Hydrothermal liquefaction, AD: anaerobic digestion, APCU: Agricultural pedoclimatic units. ^a Results are presented with a maximum of three significant digits to ensure tractability. ^b BAU scenario represents the change between SOC at the year of study (2040 or 2070) vs initial SOC at 2020 under the BAU conditions. ^c Sum of all APCUs. ^d Average SOC change for an APCU at national scale, considering the APCU surface to obtain the weighted average. ^e Minimum and maximum result observed across all the APCUs modeled. ^f Avoided C-CO₂ emissions. The C used in the main product delivered by the mitigation scenario has been deducted from the avoided C-CO₂ emissions to prevent overestimation of the mitigation potential of each scenario. ^g One at a time sensitivity analysis performed for the pyrolysis scenario, considering same parameters as pyrolysis with the difference of using a negative PE of 0.714. CO2 emissions avoided were not accounted for this scenario.

4.1 Carbon storage potential in Ecuador

Agricultural practices in Ecuador are highly intensive in frequency and rely primarily on monoculture systems. This intensity is mainly related to the great favourable pedoclimatic conditions found in tropical regions, where one harvest season can last as fast as two months and a new season starts immediately (SI). These, however pose great pressure to soils and the current cropping systems are projected to lose SOC stocks by 0.77 Mt C in the near term. Our observations follow the same trends as the potential SOC sequestration map contribution to the global database [38], which reports projected SOC losses of 0.6 MtC for the same period (2040). The insight regarding SOC storage in the BAU scenario, for 20 and 50 years, is explored in SI1 and the results are contrasted to those of FAO [37]. Temperature has a direct relationship with OM mineralization as well as high humidity, because high and humid environment can stimulate microbial enzymatic activity [69]. However, the relative nature of our results, comparing the bioeconomy vs the BAU scenario, neutralizes all the functions accounted equally in both developments and is related to main structural differences across the scenarios. As expected pyrochar has a weighted average SOC storage potential of 21% in the 2040 scenario, (Fig. 4a) which increases by 3-fold in the 2070 projections (Fig. 5a). This potential is attributed both to the recalcitrant nature and carbon content of pyrochar, which represents 50% of the C in the raw biomass for the typical lignin reach residues found in Ecuador (Table 1). Besides SOC storage, pyrochar can contribute to soils health by increasing the water holding capacity, aggregates stability and nutrient retention in the micropores (avoids leakage of nutrients) [70–73]. Although gaschar is as well composed of 95% recalcitrant carbon, the process efficiency yields less amounts in the gaschar and therefore in the soil C flow balance. Accordingly, the reduced C inputs have a greater negative effect in the near term, which is neutralized in the long-term. The SOC losses observed in the near-term gaschar application which are offset in the long-term assessment, highlights the importance of the temporal scope of SOC modeling. Overall, pyrochar and gaschar can be completely harvested with expected increase of SOC stocks by 2070, thus supplying extra 113 PJ for the bioeconomy.

Both hydrochar and digestate are less recalcitrant than the pyrochar and gaschar. The recalcitrant fractions are 0.89 and 68, each corresponding to a MRT of ca. 11 years and 1.2 years, for the hydrochar and digestate respectively. The lower recalcitrant and carbon inputs considered in these scenarios affect the overall soil C balance negatively. Andrade Diaz et al., showed that hydrochar could potentially prevent SOC losses in 87% of French croplands (10% considered not included) if all the technically harvestable crop residues are collected, while digestate would have the same effect on 50% of the surface. The expected positive effect on the French case is mainly related to lower temperatures, which are recognized to avoid microbial stimulation.

4.2 Implications of the priming effect

The approximate 2-fold increased on SOC storage potential observed with the PE is attributed to the lower rates of mineralization exerted by the interaction of biochar with the biochemical functions of soils. Wang et al [51]., demonstrated that biochar application improves the soils moisture retention and decrease the N bioavailability on soils. The combination of these effects resulted in improved structures on the microbial communities and induced carbon utilisation regulation for the microorganisms. The PE still poses a conundrum on soil sciences with results observed ranging from positive to negative effects. However, the average biochar PE observed [57] is on the negative side, attributed to the better carbon utilisation induced by the presence of biochar. The input of recalcitrant matter to soils, leads to a better distribution and usage of carbon reserves, and thus the lower mineralization rates observed, which are often assorted with microbial respiration, do not necessarily denote less microbial activity. Whereas PE has been relatively well studied for pyrochar, the PE exerted by the other coproducts remains a knowledge gap. For instance, digestate, which is often rich in N, poses an opposed effect to that observed with biochar, by providing extra N. However, research in this area is still necessary to understand the extent of the PE and the inclusion of microbial pools on SOC models is promoted [74] as the new step for more realistic ready to go conversion pathways.

4.3 Strengths and limitations of the study

Despite Ecuador’s economy being highly sustained by the agriculture sector [75], there is sparse information that systematically documents the spatially explicit farming management practices in the country (e.g., crop rotations, amount and frequency of irrigation, depth of tillage, among others). Moreover, neither the projected future evapotranspiration rates nor the spatially-explicit biomass disponible in the country have been previously documented. In this study, we present the baseline crop residue potential in Ecuador for ten crops deemed as key for the bioeconomy (see section 2), which expands and updates the Bioenergy Atlas of Ecuador [76] with high spatial resolution. Also, we deliver the spatially explicit evapotranspiration (mm) in a monthly timestep until the year 2070 according to the RCP4.5 (see companion data paper).

Besides the construction and conveyance of the baseline data used (see companion data paper) for the specific case of Ecuador, this study draws key insights for each mitigation scenario investigated, to support science-based decisions regarding the potential of crop residues in tropical croplands. For instance, the policies endeavoured by the Ecuadorian government to attain the goals of circular economy [77], which rely on the research and development of bioenergy [78] and biomaterials can be supported by the RothC-Bioeconomy tool presented in here.

However, it is worth noting, that data remains scarce and highly variable for both the baseline preparation and the soil model adaptation. Therefore, the conclusions to retain rely on the general trends observed and the sensitivity of the model rather than the absolute values foreseen.

Limitations encountered on the baseline preparation regard the lack of information on crop rotations and residues management. Despite information on crop rotations has been reported at parcel levels in Ecuador [79], this is not compatible to be scaled at the national level and fine granularity considered in the study. Consequently, the assumption of no crop rotations along the year may influence the overall C inputs. Moreover, the C inputs and exports are directly dependent on the RPR, RtS, and crop residue composition, which is highly variable across the literature [7, 49, 80, 81]. [49] denoted a high variability on the potential biomass production depending on the equation used to estimate the RPR. This calls for attention on the overall net value of crop residues potential reported in here (see companion data paper). Another limitation is the knowledge on the current use of the crop residues, which can be delivered to animals or used for the bioeconomy. In fact, the BAU scenario considers that all the residues remain on fields while in reality grey literature reports that farmers burn crop residues open-to comply with the intense harvesting cycles in Ecuador. This consideration may create unbalances on the amount of C inputs in real life vs the simulated scenarios because the ashes remaining from open burning are nearly C depleted [82] while the studied scenarios consider that all (BAU) or a part (mitigation) of the C remains on soils.

Another source of uncertainty is related to the size and recalcitrance of the EOMs, based on C_C and C_R, respectively. Sparse literature reports the C balance for the EOMs studied (i.e., biochar, digestate, hydrochar, gaschar) using tropical crops (e.g., coffee, cocoa, banana, among others) as feedstocks or their C recalcitrance, for processes fed with the tropical crops considered. While the C_C and C_R parameters used for cereal-based crop residues stem from an exhaustive review comprising over 600 data records [52], scarce data provides useful information to derive the same parameters for tropical crop residues. The PE, which plays a key role on the SOC evolution in the mitigation scenarios is still hardly understood by soil scientists, and the values observed are in extreme ranges. For instance, Zimmerman et al., 2019, reported that the application of biochar prepared from sugarcane increased the SOC mineralization in 91%, while the global metanalysis of Moura found an average decrease by 71.4%. More research regarding the microbial response to the recalcitrant EOMs is needed, which could provide science-based data to stablish the microbial degradation processes in the soil model [83].

The high temperatures, extreme moisture variation (high rain and high evapotranspiration), and intensive agricultural practices promote the loss of SOC stocks in tropical croplands. Our results show that soil application of recalcitrant EOMs as a replacement of harvested crop residues could aid to maintain the carbon balance and even promote higher SOC sequestration than if crop residues remained on soils, while providing renewable carbon for the bioeconomy.

The simulated APCUs, representing 52% of croplands in Ecuador, can deliver extra 113 PJ biomass from crop residues to supply the bioeconomy goals in the country, with no SOC losses expected, for the pyrolysis and gasification scenarios, compared to the BAU. In fact, SOC is expected to double in 8% of the cropland surface in the pyrolysis scenario in 50 years. The recalcitrance of digestate and hydrochar EOMs produced in the AD and HTL scenarios, respectively, does not compensate for the lesser C inputs returned, triggering SOC losses in all the studied APCUs.

Moreover, harvesting residual biomass with return of the co-products brings GHG mitigation in comparison to leaving the residues on land, translating into 19–39% reduction of CO₂ emissions over 50 years, depending on the bioeconomy pathway (C supplied to the bioeconomy already accounted as extra source of emissions).

Acronym	Description
AD	Anaerobic Digestion
AMG	Model simulating soil C at annual time steps
APCU	Agricultural Pedoclimatic Units
APSIM	Agricultural Production Systems Simulator
BAU	Business as Usual
BIO	Microbial biomass
C	Carbon
CANDY	Carbon and Nitrogen Dynamics model
C_C	Carbon Conversion
Century	Century Model
C_L	Carbon Labile fraction
CO₂	Carbon dioxide
CO_{2_BAU}	CO₂-C Emissions in the BAU Scenario
CO_{2_bio}	CO₂-C Emissions in the Mitigation Scenario
C_R	Carbon Recalcitrance fraction
CTOOL	Cropping System TOOL
DEOM	Decomposable Exogenous Organic Matter
DM	Dry Matter
DPM	Decomposable Plant Material
EOM	Exogenous Organic Matter
EPIC	Environmental Policy Integrated Climate model
ESPAC	National Continuous Agricultural Production and Surface Survey of Ecuador
HTL	Hydrothermal Liquefaction
HUM	Humified organic matter
IEA	International Energy Agency
IOM	Inert Organic Matter
IPCC	Intergovernmental Panel on Climate Change
ISRIC	International Soil Reference and Information Centre
k	Decay Rate
k_L	Mineralization Rate of the Labile Fraction
k_Lab	Mineralization rate of the labile fraction
k_R	Mineralization Rate of the Recalcitrant Fraction
k_Rec	Mineralization rate of the recalcitrant fraction
MAG	Ministry of Agriculture of Ecuador
MRT	Mean Residence Time
Mt C	Megatonnes of Carbon
PCU	Primary Crop Unit
PE	Priming Effect
Pg	Petagram (1 billion metric tons)
Pyrochar	Pyrolysis biochar
RCP	Representative Concentration Pathway
REOM	Recalcitrant Exogenous Organic Matter
RothC	Rothamsted Carbon Model
RPM	Resistant Plant Material
RPR	Residue to Product Ratio
RtS	Root to Shoot Ratio
SA	Sensitivity Analysis
S_APCU	Surface area of Agricultural Production and Consumption Unit
SI	Supplementary Information
SOC	Soil Organic Carbon
SOC_{_BAU}	SOC stocks in the BAU scenario
SOC_{_bio}	SOC stocks in the mitigation scenario
SOC₀	Initial SOC stocks
SOCS	Soil Organic Carbon Sequestration
SOC_t,i,x	Final SOC stocks at time t, APCU i, and scenario x
SOC_tBAU	SOC stock at time t for the BAU scenario
SOC_tbio	SOC stock at time t for the mitigation scenario
SPEI	Standardized Precipitation Evapotranspiration Index
t	Time
w	Pondering Coefficient
ΔCO₂	Difference in CO₂ emissions between mitigation and BAU scenarios
ΔCO₂ (t C ha^-1 y^-1)	Amount of CO₂-C emitted or avoided by each mitigation scenario at the end of the simulation
ΔSOC_0-t,i,x (%)	Percent change of SOC stocks from initial conditions to the end of the simulation
ΔSOC_bio-BAU (%)	Weighted average SOC change observed per scenario at a national scale
ΔSO_Cbio-BAUi(%)	SOC stocks change percentage observed by implementing a mitigation scenario, relative to the BAU, in the APCU i
ΔSOC_net	Net change in SOC stocks at the national level

Data availability

The data that support the findings of this study are openly available in “TBI - Toulouse Biotechnology Institute - T21018” at https://doi.org/10.48531/JBRU.CALMIP/VLKG8V [56] (input data) and https://doi.org/10.48531/JBRU.CALMIP/54SOW9 [68] (output data).

Acknowledgements

This work was carried out within the framework of the research project Cambioscop (https://cambioscop.cnrs.fr), partly financed by the French National Research Agency, Programme Investissement d’Avenir (ANR-17-MGPA-0006), and Region Occitanie (18015981). C. Andrade was additionally funded by the French Embassy in Ecuador under the Project “Fonds de Solidarité pour Projets Innovants” (FSPI) and the French Ministry for Europe and Foreign Affairs and by a partial Scholarship from the Universidad Técnica de Manabí, Ecuador (UTM).

The authors would like to thank Wilmer Jimenez and the Ministry of Agriculture of Ecuador for providing the backup data used to produce the Ecuadorian National Report for the Global Soil Organic Carbon Sequestration Potential Map, the Subsecretary of Climate Change of Ecuador for providing the climate projections for Ecuador, and Carlos Robles for helping with the climate data extraction.

Author contributions

Christhel Andrade Díaz: Conceptualization, Methodology, Data curation, Formal analysis, visualization, Investigation, Writing - original draft, Writing - review & editing. Enrico Balugani: Formal analysis, methodology. Ezequiel Zamora-Ledezma: Conceptualization, methodology, Writing - review & editing, Funding acquisition, and Supervision. Lorie Hamelin: Conceptualization, Methodology, Writing - review & editing, Funding acquisition, Project administration, Resources, and Supervision.

The authors declare no conflict of interest.

IEA. Net Zero by 2050 – A roadmap for the Global Energy Sector [Internet]. IEA; 2021 [cited 2023 May 3]. Available from: https://www.iea.org/reports/net-zero-by-2050
Energy & Climate Intelligence Unit. Net zero emissions race. 2022 Scorecard [Internet]. Net Zero Scorecard. 2022 [cited 2022 Feb 27]. Available from: https://eciu.net/netzerotracker
UNFCCC. Paris Agreement to the United Nations Framework Convention on Climate Change. Dec 12, 2015.
Araújo OQF, de Medeiros JL. How is the transition away from fossil fuels doing, and how will the low-carbon future unfold? Clean Techn Environ Policy. 2021 Jul 1;23(5):1385–8.
Cherubin MR, Oliveira DM da S, Feigl BJ, Pimentel LG, Lisboa IP, Gmach MR, et al. Crop residue harvest for bioenergy production and its implications on soil functioning and plant growth: A review. Sci agric (Piracicaba, Braz). 2018 May;75(3):255–72.
Blanco-Canqui H. Crop Residue Removal for Bioenergy Reduces Soil Carbon Pools: How Can We Offset Carbon Losses? Bioenerg Res. 2013;14.
Scarlat N, Fahl F, Lugato E, Monforti-Ferrario F, Dallemand JF. Integrated and spatially explicit assessment of sustainable crop residues potential in Europe. Biomass and Bioenergy. 2019 Mar 1;122:257–69.
Andrade Díaz C, Clivot H, Albers A, Zamora-Ledezma E, Hamelin L. The crop residue conundrum: Maintaining long-term soil organic carbon stocks while reinforcing the bioeconomy, compatible endeavors? Applied Energy. 2023 Jan 1;329:120192.
Lal R. Soil carbon sequestration to mitigate climate change. Geoderma. 2004 Nov 1;123(1):1–22.
Lal R. World crop residues production and implications of its use as a biofuel. Environment International. 2005 May 1;31(4):575–84.
Poeplau C, Don A. Carbon sequestration in agricultural soils via cultivation of cover crops – A meta-analysis. Agriculture, Ecosystems & Environment. 2015 Feb 1;200:33–41.
Francaviglia R, Almagro M, Vicente-Vicente JL. Conservation Agriculture and Soil Organic Carbon: Principles, Processes, Practices and Policy Options. Soil Systems. 2023 Mar;7(1):17.
Rolando JL, Turin C, Ramírez DA, Mares V, Monerris J, Quiroz R. Key ecosystem services and ecological intensification of agriculture in the tropical high-Andean Puna as affected by land-use and climate changes. Agriculture, Ecosystems & Environment. 2017 Jan 2;236:221–33.
Henry M, Tittonell P, Manlay RJ, Bernoux M, Albrecht A, Vanlauwe B. Biodiversity, carbon stocks and sequestration potential in aboveground biomass in smallholder farming systems of western Kenya. Agriculture, Ecosystems & Environment. 2009 Jan 1;129(1):238–52.
Sainju UM, Singh BP, Whitehead WF. Long-term effects of tillage, cover crops, and nitrogen fertilization on organic carbon and nitrogen concentrations in sandy loam soils in Georgia, USA. Soil and Tillage Research. 2002 Jan 1;63(3):167–79.
Bolliger A, Magid J, Amado JCT, Skóra Neto F, Ribeiro M de F dos S, Calegari A, et al. Taking Stock of the Brazilian “Zero‐Till Revolution”: A Review of Landmark Research and Farmers’ Practice. In: Advances in Agronomy [Internet]. Academic Press; 2006 [cited 2023 May 9]. p. 47–110. Available from: https://www.sciencedirect.com/science/article/pii/S0065211306910025
Lessmann M, Ros GH, Young MD, de Vries W. Global variation in soil carbon sequestration potential through improved cropland management. Global Change Biology. 2022;28(3):1162–77.
Johnston AE, Poulton PR, Coleman K, Macdonald AJ, White RP. Changes in soil organic matter over 70 years in continuous arable and ley–arable rotations on a sandy loam soil in England. European Journal of Soil Science. 2017;68(3):305–16.
Lemaire G, Gastal F, Franzluebbers A, Chabbi A. Grassland–Cropping Rotations: An Avenue for Agricultural Diversification to Reconcile High Production with Environmental Quality. Environmental Management. 2015 Nov 1;56(5):1065–77.
Launay C, Constantin J, Chlebowski F, Houot S, Graux A, Klumpp K, et al. Estimating the carbon storage potential and greenhouse gas emissions of French arable cropland using high‐resolution modeling. Glob Change Biol. 2021 Apr;27(8):1645–61.
Moinard V, Levavasseur F, Houot S. Current and potential recycling of exogenous organic matter as fertilizers and amendments in a French peri-urban territory. Resources, Conservation and Recycling. 2021 Jun 1;169:105523.
Mondini C, Cayuela ML, Sinicco T, Fornasier F, Galvez A, Sánchez-Monedero MA. Soil C Storage Potential of Exogenous Organic Matter at Regional Level (Italy) Under Climate Change Simulated by RothC Model Modified for Amended Soils. Front Environ Sci. 2018 Nov 29;6:144.
Le Noë J, Billen G, Garnier J. How the structure of agro-food systems shapes nitrogen, phosphorus, and carbon fluxes: The generalized representation of agro-food system applied at the regional scale in France. Science of The Total Environment. 2017 May 15;586:42–55.
Nowak B, Nesme T, David C, Pellerin S. To what extent does organic farming rely on nutrient inflows from conventional farming? Environ Res Lett. 2013 Dec;8(4):044045.
Hansen JH, Hamelin L, Taghizadeh-Toosi A, Olesen JE, Wenzel H. Agricultural residues bioenergy potential that sustain soil carbon depends on energy conversion pathways. 2020;12.
Woolf D, Lehmann J. Modelling the long-term response to positive and negative priming of soil organic carbon by black carbon. Biogeochemistry. 2012;13.
Karhu K, Gärdenäs AI, Heikkinen J, Vanhala P, Tuomi M, Liski J. Impacts of organic amendments on carbon stocks of an agricultural soil — Comparison of model-simulations to measurements. Geoderma. 2012 Nov 1;189–190:606–16.
Lefebvre D, Williams A, Meersmans J, Kirk GJD, Sohi S, Goglio P, et al. Modelling the potential for soil carbon sequestration using biochar from sugarcane residues in Brazil. Sci Rep. 2020 Dec;10(1):19479.
Mondini C, Cayuela ML, Sinicco T, Fornasier F, Galvez A, Sánchez-Monedero MA. Modification of the RothC model to simulate soil C mineralization of exogenous organic matter [Internet]. Biogeochemistry: Soils; 2017 Jan [cited 2020 Jul 3]. Available from: https://www.biogeosciences-discuss.net/bg-2016-551/bg-2016-551.pdf
Pulcher R, Balugani E, Ventura M, Greggio N, Marazza D. Inclusion of biochar in a C dynamics model based on observations from an 8-year field experiment. SOIL. 2022 Mar 17;8(1):199–211.
Dil M, Oelbermann M. Chapter 13. Evaluating the long-term effects of pre-conditioned biochar on soil organic carbon in two southern Ontario soils using the century model. In: Oelbermann M, editor. Sustainable agroecosystems in climate change mitigation [Internet]. The Netherlands: Wageningen Academic Publishers; 2014 [cited 2022 Mar 2]. p. 249–68. Available from: https://www.wageningenacademic.com/doi/10.3920/978-90-8686-788-2_13
Archontoulis SV, Huber I, Miguez FE, Thorburn PJ, Rogovska N, Laird DA. A model for mechanistic and system assessments of biochar effects on soils and crops and trade‐offs. GCB Bioenergy. 2016 Nov;8(6):1028–45.
Lychuk TE, Izaurralde RC, Hill RL, McGill WB, Williams JR. Biochar as a global change adaptation: predicting biochar impacts on crop productivity and soil quality for a tropical soil with the Environmental Policy Integrated Climate (EPIC) model. Mitig Adapt Strateg Glob Change. 2015 Dec;20(8):1437–58.
Levavasseur F, Mary B, Christensen BT, Duparque A, Ferchaud F, Kätterer T, et al. The simple AMG model accurately simulates organic carbon storage in soils after repeated application of exogenous organic matter. Nutrient Cycling in Agroecosystems. 2020;15.
Witing F, Prays N, O’Keeffe S, Gründling R, Gebel M, Kurzer HJ, et al. Biogas production and changes in soil carbon input - A regional analysis. Geoderma. 2018 Jun;320:105–14.
AR4 Climate Change 2007: Synthesis Report — IPCC [Internet]. [cited 2023 May 17]. Available from: https://www.ipcc.ch/report/ar4/syr/
FAO. Global Soil Organic Carbon Sequestration Potential Map – GSOCseq v.1.1 : Technical report [Internet]. Rome, Italy: FAO; 2022 [cited 2023 Jan 13]. 179 p. Available from: https://www.fao.org/documents/card/en/c/cb9002en
Jiménez W, Sánchez D, Ruiz V, Manzano D, Armas D, Jiménez L, et al. Ecuador: SOil Organic Carbon Sequestration Potential National Map. National Report. Version 1.0. Global Soil Partnership. Ministry of Agriculture and Livestock of Ecuador.; 2021. Report No.: 1.0.
Coleman K, Jenkinson DS. RothC-26.3 - A Model for the turnover of carbon in soil. In: Powlson DS, Smith P, Smith JU, editors. Evaluation of Soil Organic Matter Models [Internet]. Berlin, Heidelberg: Springer Berlin Heidelberg; 1996 [cited 2021 Apr 15]. p. 237–46. Available from: http://link.springer.com/10.1007/978-3-642-61094-3_17
Abatzoglou JT, Dobrowski SZ, Parks SA, Hegewisch KC. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci Data. 2018 Jan 9;5(1):170191.
MAG, MAATE, FAO, GSP. Mapeo digital de Carbono orgánico en los suelos del Ecuador. [Internet]. Quito, Ecuador; 2021. Report No.: Segunda Edición. Available from: sipa.agricultura.gob.ec
MAG, IEE, SENPLADES. Mapa de cobertura y uso de la tierra del Ecuador continental, 2009- 2015, escala 1: 25.000. Versión editada por el Ministerio de Agricultura y Ganadería 2020. Ecuador; 2015.
Hengl T, Jesus JM de, Heuvelink GBM, Gonzalez MR, Kilibarda M, Blagotić A, et al. SoilGrids250m: Global gridded soil information based on machine learning. PLOS ONE. 2017 Feb 16;12(2):e0169748.
Armenta Porras GE, Villa Cedeño JL, Jácome P. Proyecciones Climáticas de Precipitación y Tmperatura para Ecuador, bajo distintos escenarios de cambio climático. Ecuador: Subsecretaría de Cambio Climático del Ecuador; 2016.
MAG. Sistema de Información Pública Agropecuaria [Internet]. Sistemaa de Información Pública Agropecuaria: Estadísticas. 2023. Available from: http://sipa.agricultura.gob.ec/index.php/sipa-estadisticas/estadisticas-productivas
Beguería S, Vicente-Serrano SM, Reig F, Latorre B. Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. International Journal of Climatology. 2014;34(10):3001–23.
Andrade Díaz C, Zamora-Ledezma E, Hamelin L. Dataset for defining the spatially explicit baseline of cropping systems in Ecuadorian croplands and estimating the crop residues potential. 2023.
INEC. Encuesta de Superficie y Producción Agropecuaria Continua [Internet]. Ecuador: INEC, SENPLADES; 2021. Available from: https://anda.inec.gob.ec/anda/index.php/catalog/912#page=accesspolicy&tab=study-desc
Karan SK, Hamelin L. Crop residues may be a key feedstock to bioeconomy but how reliable are current estimation methods? Resources, Conservation and Recycling. 2021 Jan;164:105211.
Rogers HH, Prior SA, Runion GB, Mitchell RJ. Root to shoot ratio of crops as influenced by CO2. Plant Soil. 1995 Jul 1;187(2):229–48.
Wang J, Xiong Z, Kuzyakov Y. Biochar stability in soil: meta‐analysis of decomposition and priming effects. GCB Bioenergy. 2016 May;8(3):512–23.
Andrade C, Albers A, Zamora-Ledezma E, Hamelin L. A review on the interplay between bioeconomy and soil organic carbon stocks maintenance. PREPRINT (version 2) available at Research Square [Internet]. 2022 Mar 16 [cited 2022 Mar 16]; Available from: https://www.researchsquare.com
Han L, Sun K, Yang Y, Xia X, Li F, Yang Z, et al. Biochar’s stability and effect on the content, composition and turnover of soil organic carbon. Geoderma. 2020 Apr;364:114184.
Möller K. Effects of anaerobic digestion on soil carbon and nitrogen turnover, N emissions, and soil biological activity. A review. Agron Sustain Dev. 2015 Jul;35(3):1021–41.
Bernard L, Basile-Doelsch I, Derrien D, Fanin N, Fontaine S, Guenet B, et al. Advancing the mechanistic understanding of the priming effect on soil organic matter mineralisation. Functional Ecology. 2022;36(6):1355–77.
Andrade Díaz C, Zamora-Ledezma E, Hamelin L. Database: Dataset for defining the spatially explicit baseline of cropping systems in Ecuadorian croplands and estimating the crop residues potential. Dataverse - TBI - Toulouse Biotechnology Institute - T21018. 2023 May;
Chagas JKM, Figueiredo CC de, Ramos MLG. Biochar increases soil carbon pools: Evidence from a global meta-analysis. Journal of Environmental Management. 2022 Mar 1;305:114403.
Lehmann J, Cowie A, Masiello CA, Kammann C, Woolf D, Amonette JE, et al. Biochar in climate change mitigation. Nat Geosci. 2021 Dec;14(12):883–92.
Fantin V, Buscaroli A, Buttol P, Novelli E, Soldati C, Zannoni D, et al. The RothC Model to Complement Life Cycle Analyses: A Case Study of an Italian Olive Grove. Sustainability. 2022 Jan 5;14(1):569.
Gottschalk P, Smith JU, Wattenbach M, Bellarby J, Stehfest E, Arnell N, et al. How will organic carbon stocks in mineral soils evolve under future climate? Global projections using RothC for a range of climate change scenarios. 2012;21.
Morais TG, Teixeira RFM, Domingos T. Detailed global modelling of soil organic carbon in cropland, grassland and forest soils. PLOS ONE. 2019;27.
Nemo, Klumpp K, Coleman K, Dondini M, Goulding K, Hastings A, et al. Soil Organic Carbon (SOC) Equilibrium and Model Initialisation Methods: an Application to the Rothamsted Carbon (RothC) Model. Environ Model Assess. 2017 Jun 1;22(3):215–29.
Chen D, Rojas M, Samset BH, Cobb K, Diongue-Niang A, Edwards P, et al. Framing, Context, and Methods. In: Climate Change 2021: The Physical Science Basis Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. in Press. IPCC; 2021.
Thornthwaite CW. An Approach toward a Rational Classification of Climate. Geographical Review. 1948;38(1):55–94.
Vicente-Serrano SM, Beguería S, López-Moreno JI. A Multi-scalar drought index sensitive to global warming: The Standardized Precipitation Evapotranspiration Index – SPEI. Journal of Climate. 2010;23:1696–718.
Zimmerman AR, Ouyang L. Priming of pyrogenic C (biochar) mineralization by dissolved organic matter and vice versa. Soil Biology and Biochemistry. 2019 Mar 1;130:105–12.
Andrade C, Albers A, Zamora-Ledezma E, Hamelin L. Database to determine the Carbon recalcitrance and carbon conversion rate to bioeconomy coproducts [Internet]. Dataverse. TBI - Toulouse Biotechnology Institute - T21018; 2022. Available from: https://dataverse.callisto.calmip.univ-toulouse.fr/dataset.xhtml?persistentId=doi:10.48531/JBRU.CALMIP/WYWKIQ
Andrade Díaz C. Dataset for Modelling the long-term carbon storage potential from recalcitrant matter inputs in tropical arable croplands. TBI - Dataverse; 2023.
Bai X, Huang Y, Ren W, Coyne M, Jacinthe P, Tao B, et al. Responses of soil carbon sequestration to climate‐smart agriculture practices: A meta‐analysis. Glob Change Biol. 2019 Aug;25(8):2591–606.
Ippolito JA, Cui L, Kammann C, Wrage-Mönnig N, Estavillo JM, Fuertes-Mendizabal T, et al. Feedstock choice, pyrolysis temperature and type influence biochar characteristics: a comprehensive meta-data analysis review. Biochar. 2020 Dec;2(4):421–38.
Zhang Q, Xiao J, Xue J, Zhang L. Quantifying the Effects of Biochar Application on Greenhouse Gas Emissions from Agricultural Soils: A Global Meta-Analysis. Sustainability. 2020 Apr 23;12(8):3436.
Ndede EO, Kurebito S, Idowu O, Tokunari T, Jindo K. The Potential of Biochar to Enhance the Water Retention Properties of Sandy Agricultural Soils. Agronomy. 2022 Jan 25;12(2):311.
Glaser B, Wiedner K, Seelig S, Schmidt HP, Gerber H. Biochar organic fertilizers from natural resources as substitute for mineral fertilizers. Agron Sustain Dev. 2015 Apr;35(2):667–78.
Woolf D, Lehmann J. Microbial models with minimal mineral protection can explain long-term soil organic carbon persistence. Sci Rep. 2019 Dec;9(1):6522.
Orejuela-Escobar LM, Landázuri AC, Goodell B. Second generation biorefining in Ecuador: Circular bioeconomy, zero waste technology, environment and sustainable development: The nexus. Journal of Bioresources and Bioproducts. 2021 May 1;6(2):83–107.
MEER, MCPEC, INP. Atlas Bioenergético del Ecuador. Ecuador: Ministry of Electricity and Renewable Energy; 2014.
Ministerio de Produccion; comercio exterior, invversiones y pesca. Libro Blanco de Economia circular de Ecuador. Ecuador; 2021.
Posso F, Siguencia J, Narváez R. Residual biomass-based hydrogen production: Potential and possible uses in Ecuador. International Journal of Hydrogen Energy. 2020 May 11;45(26):13717–25.
Nguema A, Norton GW, Alwang J, Taylor DB, Barrera V, Bertelsen M. FARM-LEVEL ECONOMIC IMPACTS OF CONSERVATION AGRICULTURE IN ECUADOR. Experimental Agriculture. 2013 Jan;49(1):134–47.
Bentsen NS, Felby C, Thorsen BJ. Agricultural residue production and potentials for energy and materials services. Progress in Energy and Combustion Science. 2014 Feb 1;40:59–73.
Fischer G, Prieler S, van Velthuizen H, Berndes G, Faaij A, Londo M, et al. Biofuel production potentials in Europe: Sustainable use of cultivated land and pastures, Part II: Land use scenarios. Biomass and Bioenergy. 2010 Feb;34(2):173–87.
Andrade CA, Zambrano-Intriago LA, Oliveira NS, Vieira JS, Quiroz-Fernández LS, Rodríguez-Díaz JM. Adsorption Behavior and Mechanism of Oxytetracycline on Rice Husk Ash: Kinetics, Equilibrium, and Thermodynamics of the Process. Water Air Soil Pollut. 2020 Mar 2;231(3):103.
Lehmann J, Hansel CM, Kaiser C, Kleber M, Maher K, Manzoni S, et al. Persistence of soil organic carbon caused by functional complexity. Nat Geosci. 2020 Aug;13(8):529–34.

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Modelling the long-term carbon storage potential from recalcitrant matter inputs in tropical arable croplands

Status:

Version 1

Abstract

Figures

Highlights

1 Introduction

2 Methods

2.1 Modelling framework

2.2 Spatially explicit cropping systems

2.3 Mitigation scenarios

2.4 SOC modeling

2.4.1 Soil model RothC Overview

2.4.2 Adapted RothC for the mitigation scenarios

2.4.3 Input data for RothC-Bioeco

2.4.5 SOC storage potential calculation

2.5 Sensitivity analysis

3 Results

3.1 SOC evolution in the BAU scenario

3.2 Exogenous organic matter linked to SOC dynamics

3.3 CO₂ emissions and key spatial observations

3.4 Sensitivity analysis

4 Discussion

4.1 Carbon storage potential in Ecuador

4.2 Implications of the priming effect

4.3 Strengths and limitations of the study

5 Conclusions

List of abbreviations

Declarations

Data availability

Acknowledgements

Author contributions

References

Supplementary Files

Status:

Version 1

Modelling the long-term carbon storage potential from recalcitrant matter inputs in tropical arable croplands

Status:

Version 1

Abstract

Figures

Highlights

1 Introduction

2 Methods

2.1 Modelling framework

2.2 Spatially explicit cropping systems

2.3 Mitigation scenarios

2.4 SOC modeling

2.4.1 Soil model RothC Overview

2.4.2 Adapted RothC for the mitigation scenarios

2.4.3 Input data for RothC-Bioeco

2.4.5 SOC storage potential calculation

2.5 Sensitivity analysis

3 Results

3.1 SOC evolution in the BAU scenario

3.2 Exogenous organic matter linked to SOC dynamics

3.3 CO2 emissions and key spatial observations

3.4 Sensitivity analysis

4 Discussion

4.1 Carbon storage potential in Ecuador

4.2 Implications of the priming effect

4.3 Strengths and limitations of the study

5 Conclusions

List of abbreviations

Declarations

Data availability

Acknowledgements

Author contributions

References

Supplementary Files

Status:

Version 1

3.3 CO₂ emissions and key spatial observations