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A methodology that uses geographical information systems (GIS)-integrated multiple data sources can create land use and natural resources maps that are especially useful in river basin modelling. The methodology relied on satellite imagery from two different times during two different crop seasons. Video digitization of map data yielded elevation, drainage network and other thematic and topographic data that were integrated by the GIS. The data was then combined to create maps that classify the land according to land use.

To meet rising global food demands, existing agricultural management strategies will need to be transformed to mitigate the negative impacts of climate change on crop yields. Climate change which includes elevated CO 2 , temperature increase, and change in precipitation variability give rise to uncertainties for predicting crop yields. We used a multilayer canopy-root-soil model (MLCan) to (i) explore the adverse impacts of climate change on corn yields, and (ii) investigate three irrigation scheduling methods to improve the yield. To estimate crop yields, we implemented crop growth processes in MLCan. This model was applied to an experimental farm located in Urbana, Illinois, USA and was validated using aboveground carbon and leaf area index measurements. A weather generator was used to develop forcings corresponding to future climate scenarios. Climate change scenarios were considered with ambient and elevated CO 2 concentration, 1oC to 3oC temperature increases, and precipitation changes. The 2oC and 3oC temperature change reduces the crop yields up to ~38%. The simulation results showed that a 30% decrease in precipitation could reduce the mean yields of up to ~10%. The three irrigation scheduling methods were applied on dry years as adaptation strategies, which were decided based on water balance and two plant attributes of canopy temperature-based crop water stress index and leaf water potential. The water balance approach was designed to reflect an existing irrigation scheduling method, which was found to be not efficient and required more irrigation to improve crop yields under future climate conditions. We found that the leaf water potential method was more effective and efficient to improve crop yields under climate change among the three irrigation methods considered in this study. • Studied the role elevated CO 2 , temperature change, and precipitation change and variability on corn yields. • Three alternative irrigation scheduling methods were compared under climate change. • Leaf water potential-based irrigation scheduling method is most efficient to mitigate the impacts of climate change. [ABSTRACT FROM AUTHOR]