Irrigation management for profitable corn production, water conservation, and ground-water quality protectionDr. Vasudha SharmaThe environmental impact of irrigated agriculture on ground and surface water resources in central Minnesota is of major concern. These areas are characterized by coarse-textured soils, high water tables that are vulnerable to contamination, and extensive agricultural crop production with highly water-sensitive crops such as corn, potatoes, and edible beans. Irrigation is essential for crop production in this region due to low water holding capacities and rapid drainage of the soils. In addition, more erratic precipitation creates severe water stress conditions during critical crop growth stages, making irrigation imperative. However, improper management of irrigation i.e., when and how much to irrigate, leads to deep percolation of water below the root zone and leaching of nitrate in the groundwater. Nitrate contamination of groundwater above the maximum contamination limit (MCL) of 10 mg N/L especially in the areas where groundwater is the source of drinking water, poses a major public health risk. In this talk, I will give a brief update on some of my research projects focusing on developing and evaluating best irrigation management practices that would enhance the crop water productivity and reduce environmental impacts. |
Improving decision making in precision nitrogen management of corn using remote sensing and machine learningDr. Yuxin MiaoImproper management of nitrogen (N) fertilizers in the cropping systems of the U.S. Midwest has resulted in significant N leaching into the Mississippi River Basin that flows to the Gulf of Mexico. The majority of the U.S. Midwest states need to develop a plan for a nutrient loss reduction strategy to decrease 45% N and phosphorous loadings into waters and the Gulf of Mexico by 2050. In Minnesota, high nitrate concentration and loads have not been significantly reduced in surface and ground waters over the last twenty years. Corn cropping systems have been identified as dominant non-point source of nitrate loads to surface and ground waters. Precision N management aims to match N supply with crop N demand in both space and time and has the potential to improve N use efficiency and reduce environmental pollution while maintaining high crop yield. Our group has been developing different precision N management strategies for corn using proximal sensing, UAV and satellite remote sensing, crop growth modeling and machine learning. For this lightning talk, I will briefly introduce our recent research using machine learning to improve proximal and remote sensing-based crop N status diagnosis and in-season site-specific N recommendations for corn. |