- Department of Agricultural and Biological Engineering
Dr. Joel Paz is an Associate Professor in the Department of Agricultural and Biological Engineering. His research focuses on water quality, water resources, geospatial techniques, crop modeling, and impacts of climate change. He has worked extensively in the areas of environmental quality, water management and sustainable agriculture, food-energy-water nexus, climate education, artificial intelligence and decision support tools for risk management, and agricultural applications of climate and weather information through projects funded by USDA, DOE, NASA, NOAA, NSF, and commodity groups. He uses models and big data analysis to help stakeholders improve agricultural productivity, reduce water quality impacts, and conserve soil and water resources.
Guzmán, S.M., J.O. Paz, M.L.M. Tagert, and A.E. Mercer. 2019. Evaluation of seasonally classified inputs for the prediction of daily groundwater levels: NARX networks vs. support vector machines. Env. Modeling and Assessment. https://doi.org/10.1007/s10666-018-9639-x
Guzmán, S., J.O. Paz, M.L.M. Tagert, and J.W. Pote. 2018. An Integrated SVR and Crop Model to Estimate Daily Groundwater Level. Agricultural Systems 159:248-259. https://doi.org/10.1016/j.agsy.2017.01.017
Guzmán, S., J.O. Paz, and M.L.M. Tagert. 2017. The use of NARX neural networks to forecast daily groundwater levels. Water Resources Mgt 31(5):1591-1603. https://doi.org/10.1007/s11269-017-1598-5
Kisekka, I., K. DeJonge, L. Ma, J.O. Paz, and K. Mankin-Douglas. 2017. Crop modeling applications in agricultural water management. Trans. ASABE 60(6):1959-1964. https://doi.org/10.13031/trans.12693
Woli, P. and J.O. Paz. 2015. Crop management effects on the energy and carbon balances of maize stover-based ethanol production. Energies 2015, 8(1), 278-303; https://doi.org/10.3390/en8010278.
Radhakrishnan, S., J.O. Paz, F. Yu, S. Eksioglu, and D.L. Grebner. 2013. Assessment of potential capacity increases at combined heat and power facilities based on available corn stover and forest logging residue. Energies (6): 4418-4428. https://doi.org/10.3390/en6094418
Woli, P. and J.O. Paz. 2013. Biomass yield and utilization rate effects on the sustainability and environment-friendliness of maize stover- and switchgrass-based ethanol production. International Journal of Environment and Bioenergy 7(1): 28-42.
Paz, J.O., P. Woli, A. Garcia y Garcia and G. Hoogenboom. 2012. Cotton yields as influenced by ENSO at different planting dates and spatial aggregation levels. Agricultural Systems 111 (Sept 2012):45-52. https://doi.org/10.1016/j.agsy.2012.05.004
Paz, J.O., C. W. Fraisse, L.U. Hatch, A. Garcia y Garcia, L.C. Guerra, O. Uryasev, J.G. Bellow, J.W. Jones, and G. Hoogenboom. 2007. Development of an ENSO-based irrigation decision support tool for peanut production in the Southeastern US. Comp. and Electronics in Agriculture 55(1):28-35. https://doi.org/10.1016/j.compag.2006.11.003
ONGOING RELATED RESEARCH
A big part of Dr. Paz’s research program involves the use of simulation models, geospatial techniques, and artificial intelligence (AI). He has been successful in integrating support vector regression (SVR) and a crop model to assess the impacts of different irrigation management scenarios on daily groundwater levels in the Mississippi River Valley Alluvial Aquifer. A crop model can be used to examine crop yield variability. AI which encompasses a broad area of machine learning, includes SVR and artificial neural network. A similar approach of utilizing AI, GIS, transportation data, yield estimates, market prices, and other data can be applied to analyze and mitigate the impact of the Covid-19 pandemic on crop production and supply chain.