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1. Meta‐analysis on the potential for increasing nitrogen losses from intensifying tropical agriculture

   https://doi.org/10.1111/gcb.14951  

   Huddell, Alexandra M.; Galford, Gillian L.; Tully, Katherine L.; Crowley, Cynthia; Palm, Cheryl A.; Neill, Christopher; Hickman, Jonathan E.; Menge, Duncan N. L. (January 2020, Global Change Biology)

   Abstract Fertilized temperate croplands export large amounts of reactive nitrogen (N), which degrades water and air quality and contributes to climate change. Fertilizer use is poised to increase in the tropics, where widespread food insecurity persists and increased agricultural productivity will be needed, but much less is known about the potential consequences of increased tropical N fertilizer application. We conducted a meta‐analysis of tropical field studies of nitrate leaching, nitrous oxide emissions, nitric oxide emissions, and ammonia volatilization totaling more than 1,000 observations. We found that the relationship between N inputs and losses differed little between temperate and tropical croplands, although total nitric oxide losses were higher in the tropics. Among the potential drivers we studied, the N input rate controlled all N losses, but soil texture and water inputs also controlled hydrological N losses. Irrigated systems had significantly higher losses of ammonia, and pasture agroecosystems had higher nitric oxide losses. Tripling of fertilizer N inputs to tropical croplands from 50 to 150 kg N ha⁻¹ year⁻¹would have substantial environmental implications and would lead to increases in nitrate leaching (+30%), nitrous oxide emissions (+30%), nitric oxide (+66%) emissions, and ammonia volatilization (+74%), bringing tropical agricultural nitrate, nitrous oxide, and ammonia losses in line with temperate losses and raising nitric oxide losses above them. 

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2. Ammonia-oxidizing archaea are integral to nitrogen cycling in a highly fertile agricultural soil

   https://doi.org/10.1038/s43705-021-00020-4  

   Huang, Laibin; Chakrabarti, Seemanti; Cooper, Jennifer; Perez, Ana; John, Sophia M.; Daroub, Samira H.; Martens-Habbena, Willm (December 2021, ISME Communications)

   null (Ed.)

   Abstract Nitrification is a central process in the global nitrogen cycle, carried out by a complex network of ammonia-oxidizing archaea (AOA), ammonia-oxidizing bacteria (AOB), complete ammonia-oxidizing (comammox) bacteria, and nitrite-oxidizing bacteria (NOB). Nitrification is responsible for significant nitrogen leaching and N 2 O emissions and thought to impede plant nitrogen use efficiency in agricultural systems. However, the actual contribution of each nitrifier group to net rates and N 2 O emissions remain poorly understood. We hypothesized that highly fertile agricultural soils with high organic matter mineralization rates could allow a detailed characterization of N cycling in these soils. Using a combination of molecular and activity measurements, we show that in a mixed AOA, AOB, and comammox community, AOA outnumbered low diversity assemblages of AOB and comammox 50- to 430-fold, and strongly dominated net nitrification activities with low N 2 O yields between 0.18 and 0.41 ng N 2 O–N per µg NO x –N in cropped, fallow, as well as native soil. Nitrification rates were not significantly different in plant-covered and fallow plots. Mass balance calculations indicated that plants relied heavily on nitrate, and not ammonium as primary nitrogen source in these soils. Together, these results imply AOA as integral part of the nitrogen cycle in a highly fertile agricultural soil. 

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3. Improving model capability in simulating spatiotemporal variations and flow contributions of nitrate export in tile-drained catchments

   https://doi.org/10.1016/j.watres.2023.120489  

   Cao, Peiyu; Lu, Chaoqun; Crumpton, William; Helmers, Matthew; Green, David; Stenback, Greg (October 2023, Water Research)

   It is essential to identify the dominant flow paths, hot spots and hot periods of hydrological nitrate-nitrogen (NO3-N) losses for developing nitrogen loads reduction strategies in agricultural watersheds. Coupled biogeochemical transformations and hydrological connectivity regulate the spatiotemporal dynamics of water and NO3-N export along surface and subsurface flows. However, modeling performance is usually limited by the oversimplification of natural and human-managed processes and insufficient representation of spatiotemporally varied hydrological and biogeochemical cycles in agricultural watersheds. In this study, we improved a spatially distributed process-based hydro-ecological model (DLEM-catchment) and applied the model to four tile-drained catchments with mixed agricultural management and diverse landscape in Iowa, Midwestern US. The quantitative statistics show that the improved model well reproduced the daily and monthly water discharge, NO3-N concentration and loading measured from 2015 to 2019 in all four catchments. The model estimation shows that subsurface flow (tile flow + lateral flow) dominates the discharge (70%-75%) and NO3-N loading (77%-82%) over the years. However, the contributions of tile drainage and lateral flow vary remarkably among catchments due to different tile-drained area percentages and the presence of farmed potholes (former depressional wetlands that have been drained for agricultural production). Furthermore, we found that agricultural management (e.g. tillage and fertilizer management) and catchment characteristics (e.g. soil properties, farmed potholes, and tile drainage) play important roles in predicting the spatial distributions of NO3-N leaching and loading. The simulated results reveal that the model improvements in representing water retention capacity (snow processes, soil roughness, and farmed potholes) and tile drainage improved model performance in estimating discharge and NO3-N export at a daily time step, while improvement of agricultural management mainly impacts NO3-N export prediction. This study underlines the necessity of characterizing catchment properties, agricultural management practices, flow-specific NO3-N movement, and spatial heterogeneity of NO3-N fluxes for accurately simulating water quality dynamics and predicting the impacts of agricultural conservation nutrient reduction strategies. 

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4. Reparameterizing Litter Decomposition Using a Simplified Monte Carlo Method Improves Litter Decay Simulated by a Microbial Model and Alters Bioenergy Soil Carbon Estimates

   https://doi.org/10.1029/2023JG007625  

   Juice, S M; Ridgeway, J R; Hartman, M D; Parton, W J; Berardi, D M; Sulman, B N; Allen, K E; Brzostek, E R (March 2024, Journal of Geophysical Research: Biogeosciences)

   Litter decomposition determines soil organic matter (SOM) formation and plant‐available nutrient cycles. Therefore, accurate model representation of litter decomposition is critical to improving soil carbon (C) projections of bioenergy feedstocks. Soil C models that simulate microbial physiology (i.e., microbial models) are new to bioenergy agriculture, and their parameterization is often based on small datasets or manual calibration to reach benchmarks. Here, we reparameterized litter decomposition in a microbial soil C model (CORPSE ‐ Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment) using the continental‐scale Long‐term Inter‐site Decomposition Experiment Team (LIDET) dataset which documents decomposition across a range of litter qualities over a decade. We conducted a simplified Monte Carlo simulation that constrained parameter values to reduce computational costs. The LIDET‐derived parameters improved modeled C and nitrogen (N) remaining, decomposition rates, and litter mean residence times as compared to Baseline parameters. We applied the LIDET litter decomposition parameters to a microbial bioenergy model (Fixation and Uptake of Nitrogen – Bioenergy Carbon, Rhizosphere, Organisms, and Protection) to examine soil C estimates generated by Baseline and LIDET parameters. LIDET parameters increased estimated soil C in bioenergy feedstocks, with even greater increases under elevated plant inputs (i.e., by increasing residue, N fertilization). This was due to the integrated effects of plant litter quantity, quality, and agricultural practices (tillage, fertilization). Collectively, we developed a simple framework for using large‐scale datasets to inform the parameterization of microbial models that impacts projections of soil C for bioenergy feedstocks. 

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5. Simulating measurable ecosystem carbon and nitrogen dynamics with the mechanistically defined MEMS 2.0 model

   https://doi.org/10.5194/bg-18-3147-2021  

   Zhang, Yao; Lavallee, Jocelyn M.; Robertson, Andy D.; Even, Rebecca; Ogle, Stephen M.; Paustian, Keith; Cotrufo, M. Francesca (January 2021, Biogeosciences)

   null (Ed.)

   Abstract. For decades, predominant soil biogeochemical models have used conceptual soil organic matter (SOM) pools and only simulated them to a shallow depthin soil. Efforts to overcome these limitations have prompted the development of the new generation SOM models, including MEMS 1.0, which representsmeasurable biophysical SOM fractions, over the entire root zone, and embodies recent understanding of the processes that govern SOM dynamics. Herewe present the result of continued development of the MEMS model, version 2.0. MEMS 2.0 is a full ecosystem model with modules simulating plantgrowth with above- and belowground inputs, soil water and temperature by layer, decomposition of plant inputs and SOM, and mineralization andimmobilization of nitrogen (N). The model simulates two commonly measured SOM pools – particulate and mineral-associated organic matter (POM andMAOM, respectively). We present results of calibration and validation of the model with several grassland sites in the US. MEMS 2.0 generallycaptured the soil carbon (C) stocks (R2 of 0.89 and 0.6 for calibration and validation, respectively) and their distributions between POM andMAOM throughout the entire soil profile. The simulated soil N matches measurements but with lower accuracy (R2 of 0.73 and 0.31 for calibrationand validation of total N in SOM, respectively) than for soil C. Simulated soil water and temperature were compared with measurements, and theaccuracy is comparable to the other commonly used models. The seasonal variation in gross primary production (GPP; R2 = 0.83), ecosystemrespiration (ER; R2 = 0.89), net ecosystem exchange (NEE; R2 = 0.67), and evapotranspiration (ET; R2 = 0.71) was wellcaptured by the model. We will further develop the model to represent forest and agricultural systems and improve it to incorporate newunderstanding of SOM decomposition. 

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