Harnessing the Power of The Endemic Soil Microbiome for Developing Tailored Crop Management



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This dissertation explored the potential of conservation agriculture to improve soil health, enhance microbial biodiversity, and promote sustainable farming practices in semi-arid regions. The dissertation consists of several chapters that investigate the impact of conservation agriculture on soil bacterial and rhizobial diversity in cotton fields in the Southern High Plains (SHP). Chapter I provides an overview of the potential benefits of conservation agriculture practices; focusing on the effects of no-tillage and cover crop systems on soil health, microbial communities, and crop yield in semi-arid regions. The chapter also highlights the benefits of incorporating Plant Growth Promoting Rhizobacteria (PGPR) into a crop and soil management system. Chapter II focuses on assessing the components of soil bacterial community and rhizobial diversity in long-term experimental cotton fields in Lamesa, Texas. By using next-generation amplicon sequencing, this study compared conventional tillage monoculture systems with winter fallow (CT) and no-tillage with mixed cover crop (M-NT) systems. The results demonstrated that the M-NT system promoted higher rhizobial diversity and richness, with the abundance of the order Rhizobiales consistently higher in M-NT fields. The study suggested that incorporating legumes into cover-crops may positively impact the indigenous rhizobial community. In Chapter III, the dissertation examined the relationships between edaphic and microbial soil health indicators and soil management systems in producer-managed cotton fields across multiple seasons located on the Southern High Plains in Hale County, near Petersburg, Texas. Using machine learning algorithms, the study identified key predictors of soil management type, including soil organic matter, gravimetric water content, and nutrient levels. The analysis revealed that the conventional tillage field differed from the no-tillage fields in terms of various soil health parameters, and that the bacterial community composition was significantly influenced by soil management practices and sampling time. The research highlights the potential of bacterial community data as predictors of soil health responses. Chapter IV discusses the implications and potential explanations derived from the overall research project, which included both experimental and producer-managed cotton production systems. The findings suggest that adopting conservation agriculture practices, such as no-tillage and mixed cover crop systems, coupled with the incorporation of indigenous rhizobia, can lead to improved soil health and crop yield in semi-arid regions. Furthermore, the research introduced a novel approach to assessing soil health through the application of machine learning techniques. In conclusion, this dissertation provided valuable insights into the benefits of conservation agriculture in semi-arid regions, offering strategies to enhance soil health, microbial biodiversity, and agricultural sustainability. The findings demonstrate the importance of adopting conservation practices and highlight the complex interactions between soil health parameters, microbial communities, and climate impacts. The research also showcases the potential of utilizing machine learning techniques for evaluating soil health.

Embargo status: Restricted until 09/2025. To request the author grant access, click on the PDF link to the left.



Rhizobia, rpoB amplicon sequencing, Tillage, Soil Management, Machine Learning