Search

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 N 2 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., (© 2023 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

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.

The emission of nitrous oxide (N 2 O), a strong greenhouse gas, during crop residue decomposition in the soil can offset the benefits of residue recycling. The IPCC inventory considers agricultural N 2 O emissions proportional to the amount of nitrogen (N) added by residues to soils. However, N 2 O 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 N 2 O 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 N 2 O flux dynamics. The net cumulative N 2 O emitted after 60 days originated mostly from MUS (4828 ± 892 g N-N 2 O ha -1 ), SUB (2818 ± 314 g N-N 2 O ha -1 ) and RC (2567 ± 1245 g N-N 2 O ha -1 ); the other residue treatments had much lower emissions (<200 g N-N 2 O ha -1 ). For the first time N 2 O 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 N 2 O 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 N 2 O emissions and objectively weighing their other potential benefits to agriculture and the environment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

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., (© 2020 John Wiley & Sons Ltd.)

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., (Copyright © 2018. Published by Elsevier B.V.)

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., (© 2014 Society of Chemical Industry.)

Crop residues are important inputs of carbon (C) and nitrogen (N) to soils and thus directly and indirectly affect nitrous oxide (N2O) emissions. As the current inventory methodology considers N inputs by crop residues as the sole determining factor for N2O 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 N2O 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 N2O 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 N2O 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 N2O 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 N2O emissions. [ABSTRACT FROM AUTHOR]

We have read with interest an opinion paper recently published in the European Journal of Soil Science (Berthelin et al., 2022). This paper presents some interesting considerations, at least one of which is already well known to soil scientists working on soil organic carbon (SOC), that is, a large portion (80%–90%) of fresh carbon inputs to soil is subject to rapid mineralization. The short‐term mineralization kinetics of organic inputs is well‐known and accounted for in soil organic matter models. Thus, clearly, the long‐term predictions based on these models do not overlook short‐term mineralization. We point out that many agronomic practices can significantly contribute to SOC sequestration. If conducted responsibly whilst fully recognising the caveats, SOC sequestration can lead to a win‐win situation where agriculture can both contribute to the mitigation of climate change and adapt to it, whilst at the same time delivering other co‐benefits such as reduced soil erosion and enhanced biodiversity. Highlights: Rapid mineralization of organic inputs is an important factor for soil carbon sequestration.Mineralization kinetics of organic inputs are well‐known and accounted for in soil organic matter models.Many agronomic practices can contribute significantly to SOC sequestration.SOC sequestration can lead to a win‐win situation where agriculture can both contribute to the mitigation of climate change and adapt to it. [ABSTRACT FROM AUTHOR]

Abstract: 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 (N2O) 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 N2O 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 N2O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2O 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 N2O emissions. Yield‐scaled N2O emissions (N2O 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 N2O emissions at field scale is discussed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Increasing the accumulation of organic carbon (C) in soils is a crucial challenge both for soil fertility and for climate change mitigation. Heterotrophic microbial communities are key drivers of C cycling in the soil and are influenced by cultural practices, among other factors. However, whether changes in microbial communities in turn affect their C degradation functions is not well understood. Here, we studied the effects of prior soil management on the microbial taxonomic composition and activity of soils amended with wheat litter. Prior soil management was either conventional (CONV) (i.e., full inversion ploughing) or reduced tillage (RT) during a 5-year period in the same loamy soil in northern France. Soil samples taken from the top 5 cm of field plots were incubated with 13 C-labelled litter of either flowering wheat or mature wheat for 29 days at 15 °C. We measured the C-CO 2 and 13 C-CO 2 , microbial biomass C (MBC) and 13 C, and hydrolytic enzyme activities during decomposition. The initial bacterial and fungal community diversity was studied via high-throughput sequencing of ribosomal genes. The results showed that the MBC in the RT soil was initially 1.5-fold greater than that in the CONV soil; contrasting taxonomic compositions were also recorded. The soil biotic legacy impacted the degradation functions when the soils were amended with wheat litter. Compared with that in the CONV soil, the enzymatic efficiency of microorganisms in the RT soil increased by 49% and 61% in the presence of mature and flowering wheat litter, respectively. Enzyme efficiency was positively correlated with microbial litter C use efficiency (CUE) (r = 0.92, P-Value < 0.001) but negatively associated with the priming effect (PE) (r = −0.85, P-value < 0.001) across all soils and litter treatments. These findings demonstrated that the RT soil benefited both from an increase in litter C incorporated in the microbial biomass and from a reduction in soil C loss due to the PE, regardless of the quality of the decomposed litter. Our study indicated that agricultural practices such as RT, which enriches the amount of soil organic C (SOC) in the topsoil layer, can lead to positive feedback against C stabilization functions. [ABSTRACT FROM AUTHOR]

Abstract: Residue quality is a key factor governing biodegradation and the fate of C in soil. Most investigations of relationships existing between crop residue quality and soil decomposition have been based on determining the relative proportions of soluble, cellulose, hemicellulose and lignin components. However, cell wall cohesion is increased by tight interconnections between polysaccharides and lignin that involve cross-linking agents (phenolic acids). The aim of this study was to determine the role of lignin composition and phenolic acids on short- to medium-term decomposition of maize roots in soil. Sixteen maize genotypes, presenting a range of chemical characteristics related to root lignin and phenolic acids, were used. The main components were characterized by Van Soest (VS) extraction and cell wall acid hydrolysis, and the non-condensed Syringyl and Guaicyl lignin monomers, esterified phenolic acids and etherified phenolic acids were determined. Maize roots were then incubated in soil under controlled conditions (15 °C, −80 kPa moisture) for 796 days. Results showed that VS extraction over-estimated the structural hemicellulose content and that VS lignin was more recalcitrant than Klason lignin. The tremendous effect of cell wall chemical characteristics was shown by marked variations (almost two-fold differences in C mineralization), between the 16 maize roots. Decomposition was controlled by soluble residue components in the short term whereas lignin and the interconnections between cell wall polymers were important in the long-term. Notably the cell wall domain rich in non-condensed lignin and esterified phenolic acids was prone to decomposition whereas the presence of etherified ferulic acids seemed to hamper cell wall decomposition. [Copyright &y& Elsevier]