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Chandra Madramootoo, Director

Dr. Chandra Madramootoo, P.Eng., is a Distinguished James Ã山ǿ¼é Professor in the Department of Bioresource Engineering at Ã山ǿ¼é, and Director of the Ã山ǿ¼é Water Innovation Lab. With growing water scarcity and concerns about climate change, new technologies are being developed to conserve limited freshwater supplies and increase crop water productivity. Irrigation systems and techniques are being developed which can apply precise amounts of water while taking spatial field properties, crop type, and growth stage into account. Innovative technologies to predict crop water requirements and the impacts of various water management practices on greenhouse gas emissions are also being investigated. Water table management systems are being designed and field tested to reduce non-point source pollution and algal blooms/cyanobacterial contamination of rivers and lakes. 

☎ 514 398 7834

✉ chandra.madramootoo [at] mcgill.ca

 Shirley Mongeau, Administrative Coordinator

☎ 514 398 7833

✉ shirley.mongeau [at] mcgill.ca

Naresh Arumugagounder, Ph.D. Candidate

In the global urge of water conservation, new innovative methods involving water use efficiency are essential. Being a critical source of freshwater usage, agricultural industry is working on ways to increase the efficiency of the water use. An innovation in crop water stress studies is to combine satellite observatory images of ETc and soil moisture with scaled daily climatic data to generate real time prescription maps for site-specific irrigation which is applicable to thousands of hectares within irrigation/water management districts. Linking this data to a crop growth model is a powerful method of optimizing limited water supplies for maximum crop yield. The main objective of this study is to predict the irrigation water requirement of large-scale field crops using remotely sensed images using data fusion and GIS. In this research, we aim to fuse the several covariates extracted from the remotely sensed images together to generate yield prescription maps processed using the GIS. Covariate points extracted from the remotely sensed images are processed using these coupled ML models and the extracted data points from the model is processed using the GIS software to generate prescription maps.

✉ naresh.arumuga [at] mail.mcgill.ca

Calista Brown, M.Sc. Candidate

Corn is a major crop in North America, with production in Quebec on over 360,000 ha of corn being intensively cultivated. This cereal crop requires 180-220 kg/ha of nitrogen to be applied in order to optimize yield, resulting in 65-80 thousand tonnes of nitrogen being applied to corn lands.  This addition of nitrogen has major environmental impacts, including the contribution to global warming via the emission of nitrogen oxide and water pollution, caused by fertilizer runoff and leaching. In order to explore more sustainable methods of corn production my research will aid in the development of a nitrogen index that includes losses from nitrous oxide emissions, nitrogen uptake by the plant, nitrogen transformations in the soil, and nitrate fluxes at tile drainage outlets. The N index can be used by farmers to select fertilizer management practices that are economically and environmentally sustainable. The research is being conducted on six corn fields under both inorganic and organic fertilization practices around Saint-Hyacinthe, Quebec. The DRAINMOD-N submodel will be used to simulate nitrate fluxes at tile drainage outlets and to determine the impact of different fertilizer management and soil type on nitrate levels in subsurface drainage. This information will be used in the derivation of the nitrogen index. 

✉ calista.brown [at] mail.mcgill.ca

Aidan De Sena, Ph.D. Candidate

Microbial hotspots occupy almost every niche found in nature, from thermal vents on the sea floor to your own gut. Due to favorable growing conditions, the activity of the rich, diverse microbiome in these niches is orders of magnitude greater than its surroundings. Such hotspots are common in agricultural soils because the cultivation of crops creates a conducive environment around plant roots for the microbiome, known as the rhizosphere. Here, exudates from plant roots as well as nitrogen (N) fertilizer provide energy-rich substrates and nutrients for rhizosphere microorganisms. However, while microbes in these rhizosphere hotspots produce essential plant nutrients, their activity also generates potent greenhouse gases (GHGs) like carbon dioxide and nitrous oxide, via microbial respiration of the root exudates and reactive N forms. Despite only occupying ~1% of the soil volume, hotspots like these produce 10-100 times more GHGs than bulk soil, accounting for up to 36% of agriculturally-derived GHGs and contributing to global climate change. The GHGs released by rhizosphere hotspots depends on the microorganisms actively metabolizing root exudates and reactive N. Thus, we must identify the key microbes that produce GHGs before we can manage soil hotspots to control emissions. The objective of my research is to understand CO2 and N2O emissions from rhizosphere hotspots of ryegrass by resolving the effect of root exudates and urea, a common N fertilizer, in stimulating specific microorganisms to produce GHGs in this unique niche.

✉ aidan.desena [at] mail.mcgill.ca

Mfon Essien, Ph.D. Candidate

The Agricultural Greenhouse Gas emissions Project aims to link the impacts of climate change to the development of water adaptation strategies for Eastern Canada. The focus of my research is to identify and assess beneficial water management practices that have the potential to mitigate GHG emissions in Quebec. If all the co-benefits from water management systems are taken into account, there is usually a far stronger case for climate action. I will be conducting a financial and life cycle analysis at the farm level and will conduct rigorous economic modeling and multi-criteria analysis to evaluate the economic and environmental impact of water management technologies at the regional level. Performing a robust biophysical and socio-economic evaluation of BMPs constitutes a crucial part of developing scientifically informed policy solutions. However, it does not ensure the success of adoption of improved practices and technologies by agricultural producers. With this concern in mind, my research aims to have an active involvement of farmers in the research process. Using a knowledge co-production pathway that will enable the research community to work more closely with the farmers to develop solutions which are better designed.

✉ mfon.essien [at] mail.mcgill.ca

Sushree Sangita Dash, Ph.D. Candidate

Methane (CH4), a potent GHG with 28-30 times more global warming potential than carbon dioxide (CO2), contributes to 16% of total emissions, with agriculture responsible for a significant 40% of global CH4 emissions. Cattle alone contribute 77% of these emissions. Alberta is home to the largest share of Canada's beef farms, accounting for 36.8%, surpassing all other provinces. My research employs Unmanned Aerial Vehicles (UAVs) in tandem with atmospheric dispersion modeling to estimate CH4 concentrations in intensive cattle feedlots across Southern Alberta. Additionally, I am using machine learning (ML) and deep learning (DL) techniques to accurately estimate CH4 emission rates, incorporating various biophysical factors from the feedlots. This research is conducted in collaboration with scientists from Agriculture and Agri-food Canada (AAFC) in Lethbridge. This work is expected to contribute to the development of improved tools for monitoring, reporting, and verification (MRV) for effective CH4 emission management. Ultimately, my research aims to make a significant contribution to the global methane reduction pledge and Sustainable Development Goal 13 (Climate Action).

✉ sushree.dash [at] mail.mcgill.ca

Meaghan Kilmartin, M.Sc. Candidate

Wetlands are important ecosystems that store carbon, filter and store water, mitigate flooding, recycle nutrients, and support biodiversity. Unfortunately, wetlands suffer major losses and degradation due to anthropogenic causes such as urban expansion, agriculture, and climate change. My research is part of a larger study to develop and evaluate technical solutions to water use management in Lanoraie, QC to meet agricultural irrigation requirements without degrading the hydrological functions of the surrounding wetland-complex in future climate change scenarios. The agricultural producers of the region rely on the wetlands as a water source to irrigate their crops. The aim of my research is to propose irrigation water supply scenarios that will support the conservation of the wetland ecosystem. Irrigation requirements will be simulated using the Aquacrop model to evaluate the water deficits and surpluses for the average year, 30-year dry return period, and for future climate scenarios. The agricultural production area will be analyzed using GIS software and mapped into a network of irrigation water supply units. Water sources, installations, and types of water supply will help define the basin’s water management plan.

✉ meaghan.kilmartin [at] mail.mcgill.ca