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1. Data For: Diversifying and Perennializing Plants in Agroecosystems Alters Retention of New C and N from Crop

   https://doi.org/10.25380/iastate.21259206.v1  

   McDaniel, Marshall; Bird, Jeffrey A.; Pett-Ridge, Jennifer; Marin-Spiotta, Erika; Schmidt, Tom M.; Grandy, A. Stuart (January 2022, Iowa State University)

   These data are soil, CO2 efflux, dissolved organic carbon leaching, and various other measures from a mesocosm experiment performed in long-term (12-years) crop diversity experiment near Hickory Corners, MI, United States.Briefly, we tracked dual-labelled (13C and 15N), isotopically enriched wheat (Triticum aestivum</em>) residue in situ</em> for two years as it decomposed in three agroecosystems: maize-soybean rotation (CS), maize-soybean-wheat plus red clover and cereal rye cover crops (CSW2), and spring fallow management with regeneration of natural grassland species (7-10 species; SF). We measured losses of wheat residue (Cwheat and Nwheat) in leached soil solution and greenhouse gas fluxes, as well as how much was recovered in microbial biomass and bulk soil at 5-cm increments down to 20 cm.</p> COLLECTION INFORMATION:</h2> Time period(s):</strong> 2011 to 2013</li>Location(s):</strong> Hickory Corners, MI, United States</li>Long-term Experiment:</strong> Cropping Biodiversity Gradient Experiment</li>Further Site Information: </strong>https://lter.kbs.msu.edu/research/long-term-experiments/biodiversity-gradient/</li></ul> 

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2. Machine learning reveals dynamic controls of soil nitrous oxide (N2O) emissions from diverse long-term cropping systems

   https://doi.org/10.5061/dryad.9cnp5hqv1  

   Dhaliwal, Jashanjeet; Panday, Dinesh; Robertson, Philp; Saha, Debasish (October 2024, Dryad)

   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 LTER/LTAR site to better understand 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 to 42% of daily N2O flux variability in 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 temperature (>23 °C) as the most influential variables; in the No-till system, climate variables, precipitation, and temperature were important variables. In low input and organic systems, where red clover (Trifolium repens L.; before corn) and cereal rye (Secale cereale L.; before soybean) cover crops were integrated, nitrate was the predominant variable, followed by precipitation and 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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3. Double-Cropped Winter Camelina with and without Added Nitrogen: Effects on Productivity and Soil Available Nitrogen

   https://doi.org/10.3390/agriculture12091477  

   Gregg, Stephen; Coulter, Jeffrey A.; Strock, Jeffrey S.; Liu, Ronghao; Garcia y Garcia, Axel (September 2022, Agriculture)

   Double cropping winter camelina (Camelina sativa (L.) Crantz) with maize (Zea mays L.) and soybean (Glycine max L. (Merr.)) is a diversification strategy in northern regions. Winter camelina is reported to have low nutrient requirements, but its nitrogen (N) needs are not well understood. Studies on winter camelina without (Study 1) and with (Study 2) N fertilization were used to compare growth, seed yield and quality, and effects on soil N. Study 1 was conducted from 2015 to 2017 at one location and Study 2 was conducted from 2018 to 2020 at two locations. Grain yield was as much as six times higher in Study 2 compared with Study 1; averaged across treatments, winter camelina yielded 1157 kg ha−1 in Study 2 and 556 kg ha−1 without N. Oil and protein content ranged from 26.4 to 27.2% and 19.4 to 27.1%, respectively, in Study 1 and from 31.7 to 35.9% and 14.9 to 20.8%, respectively, in Study 2. N fertilizer increased winter camelina biomass and grain yield and soil N when double cropped with maize and soybean. Our study indicates that grain yield of winter camelina respond positively to N fertilization in a northern location. The drawback of this is the increase in residual soil N, which suggests the need for further research to balance agronomic practices with environmental outcomes. 

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4. Integrated weed management with reduced herbicides in a no‐till dairy rotation

   https://doi.org/10.1002/agj2.20757  

   Summers, Haleigh; Karsten, Heather D.; Curran, William; Malcolm, Glenna M. (June 2021, Agronomy Journal)

   null (Ed.)

   With over 65% of agronomic crops under no-till in Pennsylvania, herbicides are relied on for weed management. To lessen the environmental impact and selection pressure for herbicide resistance, we conducted a nine-year experiment to test herbicide reduction practices in a dairy crop rotation at Rock Springs, PA. The rotation included soybean (Glycine max L.) – corn (Zea mays L.) - 3-year alfalfa (Medicago sativa L.) - canola (Brassica napus L.). The following practices were used to reduce herbicide inputs: i. banding residual herbicides over corn and soybean rows and using high-residue inter-row cultivation; ii. seeding a small grain companion crop with alfalfa; iii. plowing once in six years to terminate the perennial forage. These practices were compared with standard herbicide-based weed management (SH) in continuous no-till. We hypothesized: i. There would be more weed biomass in the reduced herbicide treatment (RH), ii. leading to more weeds in RH over time, but iii. the added weed pressure would not affect yield iv. or differences in net return. We sampled weed biomass in soybean, corn, and the first two forage years. In corn and soybean, weed biomass was often greater in RH than SH and increased over the years in the RH treatments. In the forage, weed biomass did not always differ between treatments. Yield and differences in net return were similar in most crops and years. Results suggest that weed management with reduced herbicide inputs supplemented with an integrated approach can be effective but may lead to more weeds over time. 

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5. 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. 

   more » « less