Plant-soil synchrony in nutrient cycles: Learning from ecosystems to design sustainable agrosystems.

Redesigning agrosystems to include more ecological regulations can help feed a growing human population, preserve soils for future productivity, limit dependency on synthetic fertilizers, and reduce agriculture contribution to global changes such as eutrophication and warming. However, guidelines for redesigning cropping systems from natural systems to make them more sustainable remain limited. Synthetizing the knowledge on biogeochemical cycles in natural ecosystems, we outline four ecological systems that synchronize the supply of soluble nutrients by soil biota with the fluctuating nutrient demand of plants. This synchrony limits deficiencies and excesses of soluble nutrients, which usually penalize both production and regulating services of agrosystems such as nutrient retention and soil carbon storage. In the ecological systems outlined, synchrony emerges from plant-soil and plant-plant interactions, eco-physiological processes, soil physicochemical processes, and the dynamics of various nutrient reservoirs, including soil organic matter, soil minerals, atmosphere, and a common market. We discuss the relative importance of these ecological systems in regulating nutrient cycles depending on the pedoclimatic context and on the functional diversity of plants and microbes. We offer ideas about how these systems could be stimulated within agrosystems to improve their sustainability. A review of the latest advances in agronomy shows that some of the practices suggested to promote synchrony (e.g., reduced tillage, rotation with perennial plant cover, crop diversification) have already been tested and shown to be effective in reducing nutrient losses, fertilizer use, and N2 O emissions and/or improving biomass production and soil carbon storage. Our framework also highlights new management strategies and defines the conditions for the success of these nature-based practices allowing for site-specific modifications. This new synthetized knowledge should help practitioners to improve the long-term productivity of agrosystems while reducing the negative impact of agriculture on the environment and the climate.

Challenges of accounting nitrous oxide emissions from agricultural crop residues.

Crop residues are important inputs of carbon (C) and nitrogen (N) to soils and thus directly and indirectly affect nitrous oxide (N2 O) emissions. As the current inventory methodology considers N inputs by crop residues as the sole determining factor for N2 O emissions, it fails to consider other underlying factors and processes. There is compelling evidence that emissions vary greatly between residues with different biochemical and physical characteristics, with the concentrations of mineralizable N and decomposable C in the residue biomass both enhancing the soil N2 O production potential. High concentrations of these components are associated with immature residues (e.g., cover crops, grass, legumes, and vegetables) as opposed to mature residues (e.g., straw). A more accurate estimation of the short-term (months) effects of the crop residues on N2 O could involve distinguishing mature and immature crop residues with distinctly different emission factors. The medium-term (years) and long-term (decades) effects relate to the effects of residue management on soil N fertility and soil physical and chemical properties, considering that these are affected by local climatic and soil conditions as well as land use and management. More targeted mitigation efforts for N2 O emissions, after addition of crop residues to the soil, are urgently needed and require an improved methodology for emission accounting. This work needs to be underpinned by research to (1) develop and validate N2 O emission factors for mature and immature crop residues, (2) assess emissions from belowground residues of terminated crops, (3) improve activity data on management of different residue types, in particular immature residues, and (4) evaluate long-term effects of residue addition on N2 O emissions.

How Modelers Model: the Overlooked Social and Human Dimensions in Model Intercomparison Studies.

There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler's experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in "trial-and-error" calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler's assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details.

A review and meta-analysis of mitigation measures for nitrous oxide emissions from crop residues.

Crop residues are of crucial importance to maintain or even increase soil carbon stocks and fertility, and thereby to address the global challenge of climate change mitigation. However, crop residues can also potentially stimulate emissions of the greenhouse gas nitrous oxide (N2O) from soils. A better understanding of how to mitigate N2O emissions due to crop residue management while promoting positive effects on soil carbon is needed to reconcile the opposing effects of crop residues on the greenhouse gas balance of agroecosystems. Here, we combine a literature review and a meta-analysis to identify and assess measures for mitigating N2O emissions due to crop residue application to agricultural fields. Our study shows that crop residue removal, shallow incorporation, incorporation of residues with C:N ratio > 30 and avoiding incorporation of residues from crops terminated at an immature physiological stage, are measures leading to significantly lower N2O emissions. Other practices such as incorporation timing and interactions with fertilisers are less conclusive. Several of the evaluated N2O mitigation measures implied negative side-effects on yield, soil organic carbon storage, nitrate leaching and/or ammonia volatilization. We identified additional strategies with potential to reduce crop residue N2O emissions without strong negative side-effects, which require further research. These are: a) treatment of crop residues before field application, e.g., conversion of residues into biochar or anaerobic digestate, b) co-application with nitrification inhibitors or N-immobilizing materials such as compost with a high C:N ratio, paper waste or sawdust, and c) use of residues obtained from crop mixtures. Our study provides a scientific basis to be developed over the coming years on how to increase the sustainability of agroecosystems though adequate crop residue management.

N2O emissions from decomposing crop residues are strongly linked to their initial soluble fraction and early C mineralization.

The emission of nitrous oxide (N2O), a strong greenhouse gas, during crop residue decomposition in the soil can offset the benefits of residue recycling. The IPCC inventory considers agricultural N2O emissions proportional to the amount of nitrogen (N) added by residues to soils. However, N2O involves several emission pathways driven directly by the form of N returned and indirectly by changes in the soil induced by decomposition. We investigated the decomposition factors related to N2O emissions under controlled conditions. Residues of sugar beet (SUB), wheat (WHT), rape seed (RAS), potato (POT), pea (PEA), mustard (MUS), red clover (RC), alfalfa (ALF), and miscanthus (MIS), varying by maturity at the time of collection, were incubated in two soils (GRI and SLU) at 15 °C with a water-filled pore space of 60%. The residues contained a wide proportion range of water-soluble components, components soluble in neutral detergent (SOL-NDS), hemicellulose, cellulose, and lignin. Their composition drastically influenced the dynamics of C mineralization and soil ammonium and nitrate and was correlated with N2O flux dynamics. The net cumulative N2O emitted after 60 days originated mostly from MUS (4828 ± 892 g N-N2O ha-1), SUB (2818 ± 314 g N-N2O ha-1) and RC (2567 ± 1245 g N-N2O ha-1); the other residue treatments had much lower emissions (<200 g N-N2O ha-1). For the first time N2O emissions could be explained only by the residue content in the SOL-NDS, according to an exponential relationship. Residues with a high SOL-NDS (>25% DM) were also non-senescent and promoted high N2O emissions (representing 1-5% of applied N), likely directly by nitrification and indirectly by denitrification in microbial hotspots. Crop residue quality appears to be valuable information for accurately predicting N2O emissions and objectively weighing their other potential benefits to agriculture and the environment.

Predicting field N2O emissions from crop residues based on their biochemical composition: A meta-analytical approach.

Crop residue incorporation is a common practice to increase or restore organic matter stocks in agricultural soils. However, this practice often increases emissions of the powerful greenhouse gas nitrous oxide (N2O). Previous meta-analyses have linked various biochemical properties of crop residues to N2O emissions, but the relationships between these properties have been overlooked, hampering our ability to predict N2O emissions from specific residues. Here we combine comprehensive databases for N2O emissions from crop residues and crop residue biochemical characteristics with a random-meta-forest approach, to develop a predictive framework of crop residue effects on N2O emissions. On average, crop residue incorporation increased soil N2O emissions by 43% compared to residue removal, however crop residues led to both increases and reductions in N2O emissions. Crop residue effects on N2O emissions were best predicted by easily degradable fractions (i.e. water soluble carbon, soluble Van Soest fraction (NDS)), structural fractions and N returned with crop residues. The relationship between these biochemical properties and N2O emissions differed widely in terms of form and direction. However, due to the strong correlations among these properties, we were able to develop a simplified classification for crop residues based on the stage of physiological maturity of the plant at which the residue was generated. This maturity criteria provided the most robust and yet simple approach to categorize crop residues according to their potential to regulate N2O emissions. Immature residues (high water soluble carbon, soluble NDS and total N concentration, low relative cellulose, hemicellulose, lignin fractions, and low C:N ratio) strongly stimulated N2O emissions, whereas mature residues with opposite characteristics had marginal effects on N2O. The most important crop types belonging to the immature residue group - cover crops, grasslands and vegetables - are important for the delivery of multiple ecosystem services. Thus, these residues should be managed properly to avoid their potentially high N2O emissions.

Dataset of biomass and chemical quality of crop residues from European areas.

This dataset presents the chemical characteristics of plant biomass and crop residues from agrosystems in European areas (carbon and nitrogen contents and biochemical composition). These data have been collected from the scientific literature. The specific data and their origins are presented. The mean values from these data are also provided by major production type (main crops, forage and pasture crops, green manure and cover crops, vegetable crops and energy crops), species and litter type. These data were collected as part of the framework of the European project ResidueGas (ERA-GAS, 2017-2021), which aims to improve the estimation of greenhouse gas emissions associated with crop residues.

Ensemble modelling, uncertainty and robust predictions of organic carbon in long-term bare-fallow soils.

Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate-change studies. It is imperative to increase confidence in long-term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process-based C models by comparing simulations to experimental data from seven long-term bare-fallow (vegetation-free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi-year simulation periods (from 28 to 80 years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge-based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin-up initialization of SOC. Changes in the multi-model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (±15.5) Mg C/ha compared to the observed mean of 36.0 (±19.7) Mg C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5 ± 16.7 Mg C/ha) and Spe (36.8 ± 19.8 Mg C/ha) provided only marginal gains in accuracy, but modellers would need to apply more knowledge and a greater calibration effort than in Gen, thereby limiting the wider applicability of models.

Multiscale modeling of microbial degradation of outer tissues of fiber-crop stems during the dew retting process.

Dew retting of fiber crops, such as hemp or flax, in the field after harvest promotes the microbial biodegradation of the tissues surrounding cellulosic fibers, which helps preserve the quality of fibers during their extraction and valorization for industry. This bioprocess is currently the bottleneck for plant fiber valorization because it is empirically managed and its controlling factors have not been properly quantified. A novel multiscale model representing tissue and polymer biodegradation was developed to simulate microbial growth on the stem during retting. The model was evaluated against experimental hemp retting data. It consistently simulated the mass loss of eight plant polymers belonging to two tissues of the stem outer layer, i.e., parenchyma and fiber bundles. Microbial growth was modeled by Monod equations and modulated by the functions of temperature and moisture. This work provides a tool for gaining more insights into microorganism behavior during retting under local climate conditions.

The use of biogeochemical models to evaluate mitigation of greenhouse gas emissions from managed grasslands.

Simulation models quantify the impacts on carbon (C) and nitrogen (N) cycling in grassland systems caused by changes in management practices. To support agricultural policies, it is however important to contrast the responses of alternative models, which can differ greatly in their treatment of key processes and in their response to management. We applied eight biogeochemical models at five grassland sites (in France, New Zealand, Switzerland, United Kingdom and United States) to compare the sensitivity of modelled C and N fluxes to changes in the density of grazing animals (from 100% to 50% of the original livestock densities), also in combination with decreasing N fertilization levels (reduced to zero from the initial levels). Simulated multi-model median values indicated that input reduction would lead to an increase in the C sink strength (negative net ecosystem C exchange) in intensive grazing systems: -64 ±â€¯74 g C m-2 yr-1 (animal density reduction) and -81 ±â€¯74 g C m-2 yr-1 (N and animal density reduction), against the baseline of -30.5 ±â€¯69.5 g C m-2 yr-1 (LSU [livestock units] ≥ 0.76 ha-1 yr-1). Simulations also indicated a strong effect of N fertilizer reduction on N fluxes, e.g. N2O-N emissions decreased from 0.34 ±â€¯0.22 (baseline) to 0.1 ±â€¯0.05 g N m-2 yr-1 (no N fertilization). Simulated decline in grazing intensity had only limited impact on the N balance. The simulated pattern of enteric methane emissions was dominated by high model-to-model variability. The reduction in simulated offtake (animal intake + cut biomass) led to a doubling in net primary production per animal (increased by 11.6 ±â€¯8.1 t C LSU-1 yr-1 across sites). The highest N2O-N intensities (N2O-N/offtake) were simulated at mown and extensively grazed arid sites. We show the possibility of using grassland models to determine sound mitigation practices while quantifying the uncertainties associated with the simulated outputs.

Mulch of plant residues at the soil surface impact the leaching and persistence of pesticides: A modelling study from soil columns.

Crop residues left on the soil surface as mulch greatly influence the fate of pesticides in conservation agricultural practices because most of the applied pesticide is intercepted by mulch before passing to the soil. Modelling of pesticide losses from wash-off and leaching will greatly improve our understanding of the environmental consequences of pesticides in these systems. The PASTIS model, which simulates water transfer, mulch decomposition, and pesticide dynamics, was adapted in this new version to model the interactions between pesticides and mulch in order to simulate the impact of mulch on pesticide dynamic. Parameters of mulch dynamics and pesticide degradation and retention processes were estimated using independent incubation experiments. The PASTIS model was tested with experimental laboratory data that were obtained from two pesticides (Glyphosate and s-metolachlor) applied to soil columns where mulch composed of maize and dolichos was placed at the soil surface impacted by two rain intensities (a high and infrequent intensity and a light and frequent intensity). Simulations indicated good agreement between simulated and experimental values. After 1 day, 45-46% of the pesticides leached from the mulch and 54-55% remained in the mulch for both pesticides and both rain intensities. During the experiment, pesticide wash-off was greater for the high and infrequent rain (56-57%) compare to light and frequent rain (39-45%) for both pesticides. A smaller amount of S-metolachlor washed off with the light and frequent rain intensity (39%) than glyphosate (45%) because of its lower desorption rate from mulch residues. Glyphosate was more degraded (37-45%) than s-metolachlor (17-37%), which agrees with preliminary incubation experiments that were used for parameter estimation. A sensitivity analysis indicated that the saturation index of mulch at which pesticides started their diffusion in the rainwater and the time of the first rainfall were the two parameters that influenced the most output variables of our model. This study suggests that the PASTIS model developed for pesticide dissipation in mulch is a useful tool to evaluate the potential risk of pesticide leaching to the groundwater in conservation agriculture systems.

Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2 O emissions.

Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N2 O) emissions for wheat, maize, rice and temperate grasslands. Using a multi-stage modelling protocol, from blind simulations (stage 1) to partial (stages 2-4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N2 O emissions. Results showed that across sites and crop/grassland types, 23%-40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N2 O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2 O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2-4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N2 O emissions. Yield-scaled N2 O emissions (N2 O emissions divided by crop yields) were ranked accurately by three-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N2 O emissions at field scale is discussed.

Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes.

Biogeochemical simulation models are important tools for describing and quantifying the contribution of agricultural systems to C sequestration and GHG source/sink status. The abundance of simulation tools developed over recent decades, however, creates a difficulty because predictions from different models show large variability. Discrepancies between the conclusions of different modelling studies are often ascribed to differences in the physical and biogeochemical processes incorporated in equations of C and N cycles and their interactions. Here we review the literature to determine the state-of-the-art in modelling agricultural (crop and grassland) systems. In order to carry out this study, we selected the range of biogeochemical models used by the CN-MIP consortium of FACCE-JPI (http://www.faccejpi.com): APSIM, CERES-EGC, DayCent, DNDC, DSSAT, EPIC, PaSim, RothC and STICS. In our analysis, these models were assessed for the quality and comprehensiveness of underlying processes related to pedo-climatic conditions and management practices, but also with respect to time and space of application, and for their accuracy in multiple contexts. Overall, it emerged that there is a possible impact of ill-defined pedo-climatic conditions in the unsatisfactory performance of the models (46.2%), followed by limitations in the algorithms simulating the effects of management practices (33.1%). The multiplicity of scales in both time and space is a fundamental feature, which explains the remaining weaknesses (i.e. 20.7%). Innovative aspects have been identified for future development of C and N models. They include the explicit representation of soil microbial biomass to drive soil organic matter turnover, the effect of N shortage on SOM decomposition, the improvements related to the production and consumption of gases and an adequate simulations of gas transport in soil. On these bases, the assessment of trends and gaps in the modelling approaches currently employed to represent biogeochemical cycles in crop and grassland systems appears an essential step for future research.

Enzymatic Strategies and Carbon Use Efficiency of a Litter-Decomposing Fungus Grown on Maize Leaves, Stems, and Roots.

Soil microorganisms can control the soil cycles of carbon (C), and depending on their C-use efficiency (CUE), these microorganisms either contribute to C stabilization in soil or produce CO2 when decomposing organic matter. However, little is known regarding the enzyme investment of microbial decomposers and the effects on their CUE. Our objective was to elucidate the strategies of litter-decomposing fungi as a function of litter quality. Fungal biosynthesis and respiration were accounted for by quantifying the investment in enzyme synthesis and enzyme efficiency. The basidiomycete Phanerochaete chrysosporium was grown on the leaves, stems, and roots of maize over 126 days in controlled conditions. We periodically measured the fungal biomass, enzyme activity, and chemical composition of the remaining litter and continuously measured the evolved C-CO2. The CUE observed for the maize litter was highest in the leaves (0.63), intermediate in the roots (0.40), and lowest in the stems (0.38). However, the enzyme efficiency and investment in enzyme synthesis did not follow the same pattern. The amount of litter C decomposed per mole of C-acquiring hydrolase activity was 354 µg C in the leaves, 246 µg C in the roots, and 1541 µg C in the stems (enzyme efficiency: stems > leaves > roots). The fungus exhibited the highest investment in C-acquiring enzyme when grown on the roots and produced 40-80% less enzyme activity when grown on the stems and leaves (investment in enzymes: roots > leaves > stems). The CUE was dependent on the initial availability and replenishment of the soluble substrate fraction with the degradation products. The production of these compounds was either limited because of the low enzyme efficiency, which occurred in the roots, or because of the low investments in enzyme synthesis, which occurred in the stems. Fungal biosynthesis relied on the ability of the fungus to invest in enzyme synthesis and the efficient interactions between the enzymes and the substrate. The investment decreased when N was limited, whereas the efficiency of the C-acquiring enzymes was primarily explained by the hemicellulose content and its embedment in recalcitrant lignin linkages. Our results are crucial for modeling microbial allocation strategies.

Effect of rainfall regimes and mulch decomposition on the dissipation and leaching of S-metolachlor and glyphosate: a soil column experiment.

BACKGROUND: Interception by plant residues is a major process affecting pesticide persistence and leaching in conservation agriculture. Dissipation and leaching of S-metolachlor and glyphosate was studied in repacked soil columns covered with a mulch of maize and lablab residues. The columns were submitted to two contrasting simulated rainfall regimes: one with light but frequent rain (LF) and one with less frequent but more intense rain (HI). In both treatments, columns received the same amount of rainwater by the end of the experiment. RESULTS: Decomposing crop residues on the soil surface retained more than 50% of the applied amount of pesticide. S-metolachlor dissipation in mulch residues was faster under the LF rainfall regime. This was attributed to more humid surface conditions, under which mulch decomposition was also faster. The formation of metabolites of both molecules was higher under the LF rainfall regime. However, leaching of S-metolachlor and its metabolites to deeper soil layers was greater under the HI rainfall regime, whereas they accumulated in the surface layer under the LF rainfall regime. Glyphosate remained in the surface soil layer because of its strong adsorption capacity, whereas aminomethylphosphonic acid leached down in small amounts without any difference between the two rainfall regimes. CONCLUSION: The impact of mulch residues on herbicide dissipation was strongly dependent on molecule type and rainfall regime. © 2014 Society of Chemical Industry.

Interacting microbe and litter quality controls on litter decomposition: a modeling analysis.

The decomposition of plant litter in soil is a dynamic process during which substrate chemistry and microbial controls interact. We more clearly quantify these controls with a revised version of the Guild-based Decomposition Model (GDM) in which we used a reverse Michaelis-Menten approach to simulate short-term (112 days) decomposition of roots from four genotypes of Zea mays that differed primarily in lignin chemistry. A co-metabolic relationship between the degradation of lignin and holocellulose (cellulose+hemicellulose) fractions of litter showed that the reduction in decay rate with increasing lignin concentration (LCI) was related to the level of arabinan substitutions in arabinoxylan chains (i.e., arabinan to xylan or A∶X ratio) and the extent to which hemicellulose chains are cross-linked with lignin in plant cell walls. This pattern was consistent between genotypes and during progressive decomposition within each genotype. Moreover, decay rates were controlled by these cross-linkages from the start of decomposition. We also discovered it necessary to divide the Van Soest soluble (labile) fraction of litter C into two pools: one that rapidly decomposed and a second that was more persistent. Simulated microbial production was consistent with recent studies suggesting that more rapidly decomposing materials can generate greater amounts of potentially recalcitrant microbial products despite the rapid loss of litter mass. Sensitivity analyses failed to identify any model parameter that consistently explained a large proportion of model variation, suggesting that feedback controls between litter quality and microbial activity in the reverse Michaelis-Menten approach resulted in stable model behavior. Model extrapolations to an independent set of data, derived from the decomposition of 12 different genotypes of maize roots, averaged within <3% of observed respiration rates and total CO2 efflux over 112 days.