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 Genetics and Sustainable Agriculture Research: Mississippi State, MS
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ARS Home Southeast Area Mississippi State, Mississippi Crop Science Research Laboratory Genetics and Sustainable Agriculture Research Research





- Jenkins, Johnie
- Brooks, John
- Adeli, Ardeshir
- Deng, Dewayne
- Feng, Gary
- Huang, Yanbo
- Miles, Dana
- Tewolde, Haile
- Water Availability and Watershed Management (211)
- Soil and Air (212)
- Sustainable Agricultural Systems Research (216)

Research Project: **Location:
                            Genetics and Sustainable Agriculture Research** Project Number: 6064-21600-001-000-D
                        
                        Project Type: In-House Appropriated Start Date: Oct 1, 2023
                        
                        End Date: Sep 30, 2028 **Objective:**
                        
                        1. Develop dynamic, robust, and resilient cropping systems that integrate conservation and science-based solutions to improve short- and long-term crop production systems, increase input efficiencies, and provide adaptability for changing climate and supply chain shocks. 
1.A. Evaluate cover crop management and soil amendment effects on diverse ecosystem services in dryland crop productions.
1.B. Develop new and modern cropping systems that reduce or eliminate production inputs, increase yield and profit, and enhance environmental health. 
1.C. Developing plant science-based solutions to improve crop resilience to climate change.

2. Develop, expand, and deploy high throughput data acquisition and analytics systems and platforms for multi-faceted data streams to improve the sustainability and relevancy of agricultural production systems and ecosystem services with an emphasis on soil health, production inputs, water conservation, water quality, and greenhouse gas (GHG) emissions. 
2.A. Identify region-specific environmental health indicators for emissions, soil biology, and nutrient uptake by utilizing high throughput sequencing, infrared greenhouse gas analyzers, and unmanned remote sensing systems.
2.B. Identify secondary, unintended effects on ecosystem services from agricultural practices, utilizing high throughput data acquisition and analyses. 
2.C. Develop and evaluate a multi-sensor platform technology for within-the-canopy data collection and machine learning models for high throughput approaches to soybean, pea, and dry bean crop development.  
2.D. Soil carbon and GHG monitoring at the farm scale using unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) systems.

3. Advance engineering and computational technologies for cropping system best management practices and ecosystem services through innovations in precision agriculture, digital transformations, advanced hardware and software technologies, autonomous systems, computer vision, and artificial intelligence (AI). 
3.A. Develop and evaluate AI-enabled techniques and systems for in-field monitoring of crop growth status by multisource remote/proximal sensing and meteorological observations to provide data and information to regulate the performance of the cropping and animal production systems.
3.B. Determine the ‘essential data resolution’ needed to develop effective models to quantitatively estimate crop resiliency at the genotype, environment, management, and its interactions by utilizing statistical analytics, machine learning and artificial intelligence methods.   
3.C. To develop a variety of microwave (radio frequency - RF) sensors from small Unmanned Aircraft Systems (UAS) platforms, evaluate their use for water utilization and yield estimation in irrigated and rainfed farming, and create artificial intelligence-enabled algorithms for UAS-based precision agriculture.
3.D. Create the next-generation predictive and prescriptive tools for selection and deployment of climate-resilient cultivars adapted to the region. **Approach:**
                        
                        Big data, artificial intelligence, and machine learning are powerful tools that rely on high quality data input, particularly when working with complex, interconnected datasets. Agroecosystems, and their sustainable production and maintenance of environmental health, are known for their interconnected complexities. The role that agronomic management plays in these systems is key towards feeding an ever-growing population, thus ensuring production for decades to come, particularly with increasingly volatile weather systems. Agroecosystems in the southeastern and northern U.S. are the economic platform for a largely agriculture-based society, with a key focus on corn, wheat, soybean, cotton, and animal agriculture. To maintain ecosystem health and to extract all yield potential requires an understanding of all, or many, of the various systems at work (e.g. biology, chemistry, and physics). To maximize the potential of these systems, we must employ every relevant tool, such as fertilizers, cover crops, industrial byproducts, and the confluence of these inputs. No single agronomic plan is a fit for every region, thus this project plan aims to study disparate regional systems to develop best management plans addressing multiple regional conditions such as soil structure, weather, and availability of agronomic inputs. This “systems based” plan addresses the problems, solutions, and impacts of modern agroecosystems. We address these problems via dynamic, data-driven acquisition of large amounts of multi-faceted data streams. Farm, field, experiment station, and laboratory/greenhouse-based experiments will be employed to address these issues. The project datasets comprise biology, chemistry, physics, emissions, remote sensing via unmanned ground and aerial vehicles utilizing the latest data acquisition technologies. Big data analysis and management provide readily accessible data to the public at large, which facilitates transparency and further collaboration. This project plan brings together a large team with varied expertise to develop novel, flexible, and targeted best management practices for sustainable agricultural systems.


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