Skip to main content
ARS Home » Southeast Area » Mississippi State, Mississippi » Crop Science Research Laboratory » Genetics and Sustainable Agriculture Research » Research » Research Project #443825

Research Project: Dynamic, Data-Driven, Sustainable, and Resilient Crop Production Systems for the U.S.

Location: Genetics and Sustainable Agriculture Research

Project Number: 6064-21660-001-045-S
Project Type: Non-Assistance Cooperative Agreement

Start Date: Jun 1, 2023
End Date: May 31, 2028

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 stocks. 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 GHG emissions. 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.

Proper termination method optimizes cover crop benefits in agroecosystems. Hypothesis: Cover crop termination methods will reduce evaporative soil moisture loss, and enhance residue decomposition rate, increase available soil N, and improve crop performance. Screen crops for efficient root systems. Development of cutting-edge sensing/imaging technologies and plant science solutions to mitigate climate change challenges. Hypothesis: Crop performance and yield potential will increase by growing stress-tolerant genotypes under extreme weather conditions. Growing deep-rooted cover crop(s) followed by row crop(s) can add more atmospheric CO2 to the soil and make cash crops more capable of foraging nutrients and water under unfavorable climatic conditions. Identify traits associated with higher yield under climate resilient cropping system. Detection of stress symptoms has been a significant bottleneck in ‘climate-smart’ agriculture. Advancements in sensing and imaging now enable screening diverse cultivars for stress resilience and identifying traits associated with higher yield under new management systems. The precision phenotyping methods will be streamlined to identify varieties with high-yield and nutrient-rich seeds (nutrient and quality) in addition to physiology, canopy cover, leaf area index, crop growth, yield, seed quality, soil and leaf water content, and tissue nutrients data. Linking GHG, UAS, and soil biology (fungi and bacteria). Goal: Soil biological and environmental health indicators will be identified through field and greenhouse-based experiments whereby natural and treatment “stress” will be quantified and qualified from experiments in Objective 1 to ascertain novel soil health indicators. Early diagnosis of nutrient deficiencies in row crops using UAS hyperspectral imaging. Goal: Use UAS hyperspectral imaging to accurately estimate crop canopy nutrient contents. Developing a small UAV and UGV system and associated model to monitor GHG fluxes. Goal: Develop and deploy an UAV and UGV system with sensors to collect GHG fluxes and meteorological data from small plot experiments. Large-scale soil carbon sensing and monitoring using a collaborative UAV - UGV system. Goal: We propose to use an unmanned ground vehicle (UGV), a Clearpath Husky robot, equipped with a visible and near-infrared (Vis-NIR) spectroscopy sensor to carry out soil carbon measurements. 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. Goal: We posit that exploring the RF spectrum for remote sensing from drones is unprecedented because no existing small UAS instrument is capable of remote sensing subfield scale soil moisture.