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1. Clay soil amendment suppressed microbial enzymatic activities while increasing nitrogen availability in sandy soils

   https://doi.org/10.1002/saj2.20731  

   Poudel, Pratima; Ye, Rongzhong; Parajuli, Binaya (July 2024, Soil Science Society of America Journal)

   Abstract Conservation management practices often produced positive but limited desirable outcomes in US Southeast sandy soils, likely due to their intrinsically low clay contents that constrain the soil's capacity to preserve organic carbon (C) and nutrients. In the field, we tested the effectiveness of a novel approach, that is, clay soil amendment, to improve sandy soils. In October 2017, clay‐rich soils (25% clay) were spread at 25 metric tons ha⁻¹ and tilled onto a sandy soil (1.9% clay) in the field, which was further mixed by light tillage at 0‐ to 15‐cm depth, followed by planting winter cover crop mixtures (cereal rye, crimson clover, and winter pea). The crop rotation was cotton and corn with cover crop mixtures planted in the winter fallow season. Soils (0–15 cm) were collected in August 2021 and subjected to physio‐biochemical analyses. Clay amendment increased soil clay content to 3.4%, which improved nitrogen (N) availability by 51% but inhibited the activities of C (β‐d‐cellubiosidase [CB]; β‐xylosidase [BX];N‐acetyl‐β‐glucosaminidase [NAG]) and N (leucine aminopeptidase [LAP]) cycling enzymes, resulting in up to 78% reduction in microbial respiration. A follow‐up kinetic study on BG and LAP enzymes suggested that clay addition can have different impacts on enzymes with diverse biological origins through distinct mechanisms. Clay addition can potentially improve sandy soils by stabilizing the organic inputs in soils. However, more research is required to understand its long‐term impacts making this approach practical. 

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2. Long‐term changes in soil carbon and nitrogen fractions in switchgrass, native grasses, and no‐till corn bioenergy production systems

   https://doi.org/10.1002/saj2.20575  

   Perry, Sophie; Falvo, Grant; Mosier, Samantha; Robertson, G_Philip (August 2023, Soil Science Society of America Journal)

   Abstract Cellulosic bioenergy is a primary land‐based climate mitigation strategy, with soil carbon (C) storage and nitrogen (N) conservation as important mitigation elements. Here, we present 13 years of soil C and N change under three cellulosic cropping systems: monoculture switchgrass (Panicum virgatumL.), a five native grasses polyculture, and no‐till corn (Zea maysL.). Soil C and N fractions were measured four times over 12 years. Bulk soil C in the 0–25 cm depth at the end of the study period ranged from 28.4 (± 1.4 se) Mg C ha⁻¹in no‐till corn, to 30.8 (± 1.4) Mg C ha⁻¹in switchgrass, and to 34.8 (± 1.4) Mg C ha⁻¹in native grasses. Mineral‐associated organic matter (MAOM) ranged from 60% to 90% and particulate organic matter (POM) from 10% to 40% of total soil C. Over 12 years, total C as well as both C fractions persisted under no‐till corn and switchgrass and increased under native grasses. In contrast, POM N stocks decreased 33% to 45% across systems, whereas MAOM N decreased only in no‐till corn and by less than 13%. Declining POM N stocks likely reflect pre‐establishment land use, which included alfalfa and manure in earlier rotations. Root production and large soil aggregate formation explained 69% (p < 0.001) and 36% (p = 0.024) of total soil C change, respectively, and 60% (p = 0.020) and 41% (p = 0.023) of soil N change, demonstrating the importance of belowground productivity and soil aggregates for producing and protecting soil C and conserving soil N. Differences between switchgrass and native grasses also indicate a dependence on plant diversity. Soil C and N benefits of bioenergy crops depend strongly on root productivity and pre‐establishment land use. 

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3. Baseline Spatial Variability Study at the Kellogg Biological Station, Hickory Corners, MI (1988)

   https://doi.org/10.6073/pasta/b49a09795d29e0c1149efcc068f3426e  

   Robertson, G.; Wall, Diana; Gross, Katherine; Renner, Karen (January 2019, Environmental Data Initiative)

   Dataset Abstract A spatial variability study conducted across the LTER Main Site area (45 ha) at KBS prior to dividing the site into 1-ha experimental plots. During the 1988 growing season a stratified unaligned sampling scheme was used to collect 400-600 geo-referenced samples across the site (uniformly planted to a single variety of soybeans) for: geomorphological characteristics (microtopography, soil horizon depths, bulk density, texture); soil chemical characteristics (pH, NO3, NH4, total C, total N, moisture, inorganic P, trace metals); soil biological characteristics (N mineralization potentials, microbial biomass C, microbial biomass N, fungal/bacterial ratios, nematodes and other soil invertebrates; seed bank size); plant weed species abundance, weed biomass at peak standing crop); and insect characteristics (major pest and predator species). Most soil samples were taken before crop emergence, plant phenology samples were taken throughout the growing season, biomass samples were taken at physiological maturity, and insect samples were taken continuously. Dried soil and plant samples are archived for potential future analysis. original data source http://lter.kbs.msu.edu/datasets/6 

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4. Machine learning reveals dynamic controls of soil nitrous oxide emissions from diverse long‐term cropping systems

   https://doi.org/10.1002/jeq2.20637  

   Dhaliwal, Jashanjeet Kaur; Panday, Dinesh; Robertson, G Philip; Saha, Debasish (October 2024, Journal of Environmental Quality)

   Abstract Soil nitrous oxide (N₂O) emissions exhibit high variability in intensively managed cropping systems, which challenges our ability to understand their complex interactions with controlling factors. We leveraged 17 years (2003–2019) of measurements at the Kellogg Biological Station Long‐Term Ecological Research (LTER)/Long‐Term Agroecosystem Research (LTAR) site to better understand the controls of N₂O emissions in four corn–soybean–winter wheat rotations employing conventional, no‐till, reduced input, and biologically based/organic inputs. We used a random forest machine learning model to predict daily N₂O fluxes, trained separately for each system with 70% of observations, using variables such as crop species, daily air temperature, cumulative 2‐day precipitation, water‐filled pore space, and soil nitrate and ammonium concentrations. The model explained 29%–42% of daily N₂O flux variability in the test data, with greater predictability for the corn phase in each system. The long‐term rotations showed different controlling factors and threshold conditions influencing N₂O emissions. In the conventional system, the model identified ammonium (>15 kg N ha⁻¹) and daily air temperature (>23°C) as the most influential variables; in the no‐till system, climate variables such as precipitation and air temperature were important variables. In low‐input and organic systems, where red clover (Trifolium repensL.; before corn) and cereal rye (Secale cerealeL.; before soybean) cover crops were integrated, nitrate was the predominant predictor of N₂O emissions, followed by precipitation and air temperature. In low‐input and biologically based systems, red clover residues increased soil nitrogen availability to influence N₂O emissions. Long‐term data facilitated machine learning for predicting N₂O emissions in response to differential controls and threshold responses to management, environmental, and biogeochemical drivers. 

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5. Quantification and machine learning based N ₂ O–N and CO ₂ –C emissions predictions from a decomposing rye cover crop

   https://doi.org/10.1002/agj2.21185  

   Joshi, Deepak_R; Clay, David_E; Clay, Sharon_A; Moriles‐Miller, Janet; Daigh, Aaron_L_M; Reicks, Graig; Westhoff, Shaina (November 2022, Agronomy Journal)

   Abstract Cover crops improve soil health and reduce the risk of soil erosion. However, their impact on the carbon dioxide equivalence (CO_2e) is unknown. Therefore, the objective of this 2‐yr study was to quantify the effect of cover crop‐induced differences in soil moisture, temperature, organic C, and microorganisms on CO_2e, and to develop machine learning algorithms that predict daily N₂O–N and CO₂–C emissions. The prediction models tested were multiple linear regression, partial least square regression, support vector machine, random forest (RF), and artificial neural network. Models’ performance was accessed using R², RMSE and mean of absolute value of error. Rye (Secale cerealeL.) was dormant seeded in mid‐October, and in the following spring it was terminated at corn's (Zea maysL.) V4 growth stage. Soil temperature, moisture, and N₂O–N and CO₂–C emissions were measured near continuously from soil thaw to harvest in 2019 and 2020. Prior to termination, the cover crop decreased N₂O–N emissions by 34% (p = .05), and over the entire season, N₂O–N emissions from cover crop and no cover crop treatments were similar (p = .71). Based on N₂O–N and CO₂–C emissions over the entire season and the estimated fixed cover crop‐C remaining in the soil, the partial CO_2ewere −1,061 and 496 kg CO_2eha^–1in the cover crop and no cover crop treatments, respectively. The RF algorithm explained more of the daily N₂O–N (73%) and CO₂–C (85%) emissions variability during validation than the other models. Across models, the most important variables were temperature and the amount of cover crop‐C added to the soil. 

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