Quantification and machine learning based N <sub>2</sub> O–N and CO <sub>2</sub> –C emissions predictions from a decomposing rye cover crop-Reference-Cited by-同舟云学术

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

Published:2022-11-24 Issue: Volume: Page:
ISSN:0002-1962
Container-title:Agronomy Journal
language:en
Short-container-title:Agronomy Journal

Author:

Joshi Deepak R.¹^ORCID,Clay David E.¹^ORCID,Clay Sharon A.¹^ORCID,Moriles‐Miller Janet¹,Daigh Aaron L. M.²³^ORCID,Reicks Graig¹^ORCID,Westhoff Shaina¹

Affiliation:

1. Dep. of Agronomy, Horticulture & Plant Science South Dakota State Univ. Brookings SD 57007 USA

2. Dep. of Soil Science North Dakota State Univ. Fargo ND 58105 USA

3. Univ. of Nebraska‐Lincoln Lincoln NE 68588 USA

Funder

National Science Foundation

South Dakota Agricultural Experiment Station

Publisher

Wiley

Subject

Agronomy and Crop Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/agj2.21185

Reference55 articles.

1. A review on applications of ANN and SVM for building electrical energy consumption forecasting

2. Winter cover crops effects on soil properties and sweet corn yield in semi‐arid irrigated systems

3. Denitrification, N 2 O and CO 2 fluxes in rice-wheat cropping system as affected by crop residues, fertilizer N and legume green manure

4. Soil water improvements with the long-term use of a winter rye cover crop

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