Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Adaptive Cropping Systems Laboratory » Research » Research Project #445297
Research Project: Sustainable and Resilient Crop Production Systems Based on the Quantification and Modeling of Genetic, Environment, and Management Factors

Location: Adaptive Cropping Systems Laboratory

Publications (Clicking on the reprint icon Reprint Icon will take you to the publication reprint.)

Process-based vegetative growth model for cereal rye winter cover crop using object-oriented programming and linked-list data structure Reprint Icon - (Peer Reviewed Journal)
Wang, Z., Timlin, D.J., Thapa, R., Fleisher, D.H., Beegum, S., Han, E., Schomberg, H.H., Mirsky, S.B., Sun, W., Reddy, V., Horton, R., Tully, K. 2025. RYESIM: Process-based vegetative growth model for cereal rye winter cover crop using object-oriented programming and linked-list data structure.. Agriculture, Ecosystems and Environment. 231(2025):109964. https://doi.org/10.1016/j.compag.2025.109964.

USDA-ARS adaptive cropping systems laboratory crop and soil models - (Book / Chapter)

Hyperspectral-based high-throughput phenotyping to assess water use efficiency in cotton Reprint Icon - (Peer Reviewed Journal)
Beegum, S., Hassan, M., Ramamoorthy, P., Bheemanahalli, R., Reddy, K.N., Reddy, V., Reddy, K. 2024. Hyperspectral-based high-throughput phenotyping to assess water use efficiency in cotton. Journal of Agriculture. 14(7):1054. https://doi.org/10.3390/agriculture14071054.

Planting for perfection: How to maximize cotton quality with the right planting dates in the face of climate change Reprint Icon - (Peer Reviewed Journal)
Beegum, S., Raja Reddy, K., Ambinakudige, S., Reddy, V. 2024. Planting for perfection: How to maximize cotton quality with the right planting dates in the face of climate change. Field Crops Research. 315. Article e109483. https://doi.org/10.1016/j.fcr.2024.109483.

Cotton yield prediction: A machine learning approach with field and synthetic data Reprint Icon - (Peer Reviewed Journal)
Mitra, A., Beegum, S., Fleisher, D.H., Reddy, V., Sun, W., Ray, C., Timlin, D.J., Malakar, A. 2024. Cotton yield prediction: A machine learning approach with field and synthetic data. IEEE Access. (12):101273-101288. https://doi.org/10.1109/access.2024.3418139.