Theses and Dissertations

Advisor

Shanmugam, Shankar

Committee Member

Feng, Gary

Committee Member

White, Joshua

Committee Member

Bheemanahalli, Raju

Committee Member

Kingery, William

Date of Degree

8-7-2025

Original embargo terms

Embargo 1 year

Document Type

Dissertation - Open Access

Major

Plant and Soil Sciences (Agronomy)

Degree Name

Doctor of Philosophy (Ph.D.)

College

College of Agriculture and Life Sciences

Department

Department of Plant and Soil Sciences

Abstract

Cover cropping (CC) is a widely adopted strategy to improve soil health and microbial diversity in agroecosystems. However, its influence on microbial community dynamics and ecosystem functions remains context-dependent, particularly under variable nitrogen (N) inputs. This dissertation integrates a three-year field study (2021-2024) in corn-based systems with a meta-analysis of 473 soil samples from diverse Mississippi cropping systems to evaluate CC effects on soil microbial structure, function, and ecosystem multifunctionality. In the field study, soil bacterial and fungal communities were profiled using high-throughput amplicon sequencing (16S rRNA for bacteria, ITS2 for fungi). Seven CC treatments ryegrass, balansa, radish, red clover, CC-mix1 (oats + radish), CC-mix2 (ryegrass + radish + red clover), and a no-cover control were tested under two N regimes (0 and 100 lbs N ac-1) at two sites (Starkville and Newton, MS). Cover cropping significantly influenced microbial richness, enzymatic activities (β-glucosidase, β-glucosaminidase), and soil health indicators (active carbon, glomalin). However, microbial improvements did not consistently enhance corn yield, possibly due to N immobilization, moisture competition, and environmental variation. Microbial network analysis showed higher co-occurrence complexity and robustness under 100 lbs N ac⁻¹, while 0 N plots exhibited lower vulnerability. The meta-analysis reanalyzed amplicon datasets from five independent studies. CC significantly influenced microbial community structure (PERMANOVA, p < 0.001), increased bacterial and fungal richness by 16%, and shifted assembly processes toward greater stochasticity. Random Forest models classified samples with high accuracy (AUC = 1 for bacteria, 0.87 for fungi) and identified CC-enriched indicator taxa such as Bradyrhizobium and Phomatospora. CC favored copiotrophic phyla (e.g., Proteobacteria), likely due to enhanced organic inputs and microhabitat complexity. Collectively, these findings demonstrate that CC alters microbial communities and functions, improving soil multifunctionality and network stability. However, agronomic benefits depend on synchronizing CC residue decomposition with crop nutrient demands and tailoring CC strategies to local conditions. This work provides empirical and computational insights to optimize CC use for sustainable intensification in corn-based systems.

Sponsorship (Optional)

Mississippi Corn Promotion Board

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