My research is in the area of information technology for precision conservation. The natural environment is highly spatially and temporally variable, and being able to determine what land management strategies will be the most suitable in a particular location requires that we be able to characterize the variable factors at an appropriate scale, and synthesize data with respect to their influence on hydrology. My research program includes design of sensing and monitoring systems and utilization of such data in modeling and decision support systems.
My work is motivated in part by my belief that environmental conservation and agricultural production are not by definition opposing forces. My research program is aimed at using robust and smart technology and analysis to maintain or enhance environmental quality (particularly with respect to hydrology) while enabling a safe and sustainable food production system.
Key Environmental Science Publications
Upadhyay, P., L.O.S Pruski, A.L. Kaleita, and M.L. Soupir, 2018. Evaluation of AnnAGNPS for simulating the inundation of farmed closed depressions. Agricultural Water Management. 204: 38-46 (doi:10.1016/j.agwat.2018.03.037).
Zimmerman, B.A., and A.L. Kaleita, 2017. Dissolved constituents in agricultural drainage water. Transactions of the ASABE. 60(3): 847-859. DOI: 10.13031/trans.12051
Zimmerman, B.A., and A.L. Kaleita, 2017. Electrical conductivity of agricultural drainage water in Iowa. Applied Engineering in Agriculture. 33(3): 369-378. DOI: 10.13031/aea.12040
Kaleita, A., L.R. Schott, S.K. Hargreaves and K. Hofmockel, 2017. Differences in soil biological activity by terrain types at the sub-field scale in central Iowa US. PLoS ONE 12(7): e0180596. DOI: 10.1371/journal.pone.0180596.
Gali, R. K., M.L. Soupir, A.L. Kaleita, and P. Daggupati. 2015. Identifying potential locations for grassed waterways using terrain attributes and precision conservation technologies. Transactions of the ASABE 58(5): 1231-1239. DOI: 10.13031/trans.58.10995
Vesali, F, M. Omid., A. Kaleita, and H. Mobli. 2015. An android app to estimate chlorophyll content of corn leaves based on contact imaging. Computers and Electronics in Agriculture 116: 211-220. DOI: 10.1016/j.compag.2015.06.012
Van Arkel, Z., and A.L. Kaleita. 2014. Identifying sampling locations for field-scale soil moisture estimation using K-Means clustering; Water Resources Research 50(8): 7050-7057. DOI: 10.1002/2013WR015015