Integrated Cropping Systems Management
Innovation in holistic assessment of crop responses to environmental factors
An integrated approach to managing cropping systems
A cropping system refers to the crop or combination of crops and the growing environment over a particular production area. The Integrated Cropping Systems Management research program assesses crop responses to environmental factors from a holistic point of view. It examines how changes in system components or environmental conditions impact the behavior of the system and designs the optimal strategies for effective management of the system.
Faculty
Yubin Yang, Ph.D.
Assistant Professor, Cropping Systems Management
Five areas of research focus
1
Quantify plant responses to environmental factors (climate, soil, water, nutrients, and pests).
2
Integrate high-throughput phenotyping and machine learning technologies to assess plant responses to environment variations and management inputs.
3
Understand key plant and soil processes that govern crop growth and development.
4
Develop cropping-system models that capture and integrate genotype, environment, and management interactions at multiple spatial scales (local, regional, national, and global).
5
Deliver systems-based decision-support tools to improve crop management, productivity, economic viability, and environmental sustainability.
Research Projects
Projects of the Integrated Cropping Systems Management program span rice, energycane, and biomass sorghum production systems.
Geo-referenced Databases
The geo-referenced databases include World Climatic Data, Cropland Data, and Soil Data. They are part of our integrated Agriculture Information Management System or iAIMS. They are developed to support large scale spatial simulations, integrated analysis, and decision support systems. They have been the foundation of many of our projects and our cropping systems applications. The climatic database is based on data from NOAA and other sources. It contains up-to-date weather data for most of the countries in the world, featuring more than 20,000 stations worldwide. The cropland database is based on the Cropland Data Layers from NASS, which has data from 1997 to 2023. We customized the database so that our applications can access information on individual crops up to individual fields. The soil database is based on the NRCS’ SSURGO soil database. We customized the database so that our applications can access soil information at any geographic location in the US.
Rice Post-Harvest Grain Management
Control grain quality and pests through low volume aeration and integrated pest management. Use low volume ambient air to cool the grain mass to reduce or suppress pests. The research was conducted through collaboration with scientists in ag engineering and entomology. We divided a rice storage bin into multiple thin layers and concentric rings. We then simulated grain the temperature and moisture dynamics in each layer. Based on the grain temperature and moisture, we then simulated population dynamics of the storage pests in each layer and estimate the number of fumigations needed to control the pests while maintaining grain quality.
Cropping systems modeling and decision-support systems for sustainable agriculture
Assess rice crop response to temperature and CO2 under current and future climate; design rice ideotypes for different climatic conditions; model genotype × environment interactions through integration of genomics, phenomics, machine learning, and crop modeling; Model- and AI-driven decision-support systems for crop production and management.
Sustainable Bioenergy Production Systems
Assess biomass production potential, economic viability, environment sustainability, and site selection under energycane and biomass sorghum production in the southeastern US.
UAV and High-throughput Phenotyping
High-throughput phenotyping through machine learning to assess crop phenotypic traits and monitor crop status (nutrients, water, and pests)
