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A B S T R A C THeterotrophic respiration (Rh), microbial processing of soil organic matter to carbon dioxide (CO 2 ), is a major, yet highly uncertain, carbon (C) flux from terrestrial systems to the atmosphere. Temperature sensitivity of Rh is often represented with a simple Q 10 function in ecosystem models and earth system models (ESMs), sometimes accompanied by an empirical soil moisture modifier. More explicit representation of the effects of soil moisture, substrate supply, and their interactions with temperature has been proposed as a way to disentangle the confounding factors of apparent temperature sensitivity of Rh and improve the performance of ecosystem models and ESMs. The objective of this work was to insert into an ecosystem model a more mechanistic, but still parsimonious, model of environmental factors controlling Rh and evaluate the model performance in terms of soil and ecosystem respiration. The Dual Arrhenius and Michaelis-Menten (DAMM) model simulates Rh using Michaelis-Menten, Arrhenius, and diffusion functions. Soil moisture affects Rh and its apparent temperature sensitivity in DAMM by regulating the diffusion of oxygen, soluble C substrates, and extracellular enzymes to the enzymatic reaction site. Here, we merged the DAMM soil flux model with a parsimonious ecosystem flux model, FöBAAR (Forest Biomass, Assimilation, Allocation and Respiration). We used high-frequency soil flux data from automated soil chambers and landscape-scale ecosystem fluxes from eddy covariance towers at two AmeriFlux sites (Harvard Forest, MA and Howland Forest, ME) in the northeastern USA to estimate parameters, validate the merged model, and to quantify the uncertainties in a multiple constraints approach. The optimized DAMM-FöBAAR model better captured the seasonal and inter-annual dynamics of soil respiration (Soil R) compared to the FöBAAR-only model for the Harvard Forest, where higher frequency and duration of drying events significantly regulate substrate supply to heterotrophs. However, DAMM-FöBAAR showed improvement over FöBAAR-only at the boreal transition Howland Forest only in unusually dry years. The frequency of synopticscale dry periods is lower at Howland, resulting in only brief water limitation of Rh in some years. At both sites, the declining trend of soil R during drying events was captured by the DAMM-FöBAAR model; however, model performance was also contingent on site conditions, climate, and the temporal scale of interest. While the DAMM functions require a few more parameters than a simple Q 10 function, we have demonstrated that they can be included in an ecosystem model and reduce the model-data mismatch. Moreover, the mechanistic structure of the soil moisture effects using DAMM functions should be more generalizable than the wide variety of empirical functions that are commonly used, and these DAMM functions could be readily incorporated into other ecosystem models and ESMs.

Production and consumption of nitrous oxide (N 2 O), methane (CH 4 ), and carbon dioxide (CO 2 ) are affected by complex interactions of temperature, moisture, and substrate supply, which are further complicated by spatial heterogeneity of the soil matrix. This microsite heterogeneity is often invoked to explain non-normal distributions of greenhouse gas (GHG) fluxes, also known as hot spots and hot moments.To advance numerical simulation of these belowground processes, we expanded the Dual Arrhenius and Michaelis-Menten model, to apply it consistently for all three GHGs with respect to the biophysical processes of production, consumption, and diffusion within the soil, including the contrasting effects of oxygen (O 2 ) as substrate or inhibitor for each process. High-frequency chamber-based measurements of all three GHGs at the Howland Forest (ME, USA) were used to parameterize the model using a multiple constraint approach. The area under a soil chamber is partitioned according to a bivariate log-normal probability distribution function (PDF) of carbon and water content across a range of microsites, which leads to a PDF of heterotrophic respiration and O 2 consumption among microsites. Linking microsite consumption of O 2 with a diffusion model generates a broad range of microsite concentrations of O 2 , which then determines the PDF of microsites that produce or consume CH 4 and N 2 O, such that a range of microsites occurs with both positive and negative signs for net CH 4 and N 2 O flux. Results demonstrate that it is numerically feasible for microsites of N 2 O reduction and CH 4 oxidation to co-occur under a single chamber, thus explaining occasional measurement of simultaneous uptake of both gases. Simultaneous simulation of all three GHGs in a parsimonious modeling framework is challenging, but it increases confidence that agreement between simulations and measurements is based on skillful numerical representation of processes across a heterogeneous environment.

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Conventional agricultural practices that use excessive chemical fertilizers and pesticides come at a great price with respect to soil health, a key component to achieve agricultural sustainability. Organic farming could serve as an alternative agricultural system and solve the problems associated with the usage of agro‐chemicals by sustainable use of soil resources. A study was carried out to evaluate the impact of organic vs. conventional cultivations of basmati rice on soil health during Kharif (rainy) season of 2011 at Kaithal district of Haryana, India, under farmers' participatory mode. Long‐term application of organic residues in certified organic farms was found to improve physical, chemical, and biological indicators of soil health. Greater organic matter buildup as indicated by higher soil organic carbon content in organic fields was critical to increase soil aggregate stability by increasing water holding capacity and reducing bulk density. Proper supplementation of nutrients (both major and micro nutrients) through organic residue addition favored biologically available nutrients in organic systems. Further, the prevalence of organic substrates stimulated soil microorganisms to produce enzymes responsible for the conversion of unavailable nutrients to plant available forms. Most importantly, a closer look at the relationship between physicochemical and biological indicators of soil health evidenced the significance of organic matter to enzyme activities suggesting enhanced nutrient cycling in systems receiving organic amendments. Enzyme activities were very sensitive to short‐term (one growing season) effects of organic vs. conventional nutrient management. Soil chemical indicators (organic matter and nutrient contents) were also changed in the short‐term, but the response was secondary to the biochemical indicators. Taken together, this study indicates that organic farming practices foster biotic and abiotic interactions in the soil which may facilitate in moving towards a sustainable food future.

Disturbances fundamentally alter ecosystem functions, yet predicting their impacts remains a key scientific challenge. While the study of disturbances is ubiquitous across many ecological disciplines, there is no agreed-upon, cross-disciplinary foundation for discussing or quantifying the complexity of disturbances, and no consistent terminology or methodologies exist. This inconsistency presents an increasingly urgent challenge due to accelerating global change and the threat of interacting disturbances that can destabilize ecosystem responses. By harvesting the expertise of an interdisciplinary cohort of contributors spanning 42 institutions across 15 countries, we identified an essential limitation in disturbance ecology: the word ‘disturbance’ is used interchangeably to refer to both the events that cause, and the consequences of, ecological change, despite fundamental distinctions between the two meanings. In response, we developed a generalizable framework of ecosystem disturbances, providing a well-defined lexicon for understanding disturbances across perspectives and scales. The framework results from ideas that resonate across multiple scientific disciplines and provides a baseline standard to compare disturbances across fields. This framework can be supplemented by discipline-specific variables to provide maximum benefit to both inter- and intra-disciplinary research. To support future syntheses and meta-analyses of disturbance research, we also encourage researchers to be explicit in how they define disturbance drivers and impacts, and we recommend minimum reporting standards that are applicable regardless of scale. Finally, we discuss the primary factors we considered when developing a baseline framework and propose four future directions to advance our interdisciplinary understanding of disturbances and their social-ecological impacts: integrating across ecological scales, understanding disturbance interactions, establishing baselines and trajectories, and developing process-based models and ecological forecasting initiatives. Our experience through this process motivates us to encourage the wider scientific community to continue to explore new approaches for leveraging Open Science principles in generating creative and multidisciplinary ideas.

Intensive agriculture can cause a loss in biodiversity and concerns for global food security. Regenerative agriculture and integrative permaculture are keys to address food security. Digital agricultural tools can aid in attaining agricultural and environmental sustainability. Precision agriculture should be implemented as an agricultural decision support system. Artificial intelligence and machine learning can guide integrated agricultural input management.

Abstract. Recent developments in modelling soil organic carbon decomposition include the explicit incorporation of enzyme and microbial dynamics. A characteristic of these models is a positive feedback between substrate and consumers, which is absent in traditional first-order decay models. With sufficiently large substrate, this feedback allows an unconstrained growth of microbial biomass. We explore mechanisms that curb unrestricted microbial growth by including finite potential sites where enzymes can bind and by allowing microbial scavenging for enzymes. We further developed a model where enzyme synthesis is not scaled to microbial biomass but associated with a respiratory cost and microbial population adjusts enzyme production in order to optimise their growth. We then tested short-and long-term responses of these models to a step increase in temperature and find that these models differ in the long-term when shortterm responses are harmonised. We show that several mechanisms, including substrate limitation, variable production of microbial enzymes, and microbes feeding on extracellular enzymes eliminate oscillations arising from a positive feedback between microbial biomass and depolymerisation. The model where enzyme production is optimised to yield maximum microbial growth shows the strongest reduction in soil organic carbon in response to warming, and the trajectory of soil carbon largely follows that of a first-order decomposition model. Modifications to separate growth and maintenance respiration generally yield short-term differences, but results converge over time because microbial biomass approaches a quasi-equilibrium with the new conditions of carbon supply and temperature.