Impact assessment of delineated management zones and variable nitrogen application rates in potato production using artificial intelligence techniques

Prince Edward Island (PEI) potato production represents 23% of the total production in Canada, contributes 6.6% in the local provincial economy, therefore maintaining higher potato yield is the main goal of local farmers. This can be achieved through proper management practices including nutrients supply such as nitrogen. Nitrogen (N) is the one of the most limiting nutrient in potato (Solanum tuberosum L.) growth and plays a significant role in yield attributing factors such as plant emergence, plant height and weight of tuber per plant. However, uniform application of N fertilizer without considering spatial variability in terms of soil physicochemical properties and other environmental factors, leads to overuse of N fertilizer. The excess N supply leads to premature leaf senescence, low starch content, reduction in tuber yield and promotes nitrate loss through leaching, causing environmental pollution. To our knowledge, few studies have been carried out to reduce within field spatial variability using combined approach of integrating soil proximal sensor data with previous years tuber yield along with the artificial intelligence techniques to monitor in season N dynamics and tuber yield. Therefore the goal is to explore the optimization of N application in potato production to improve yield and minimizing N loss.

Faculty Supervisor:

Antoine Karam

Student:

Partner:

Walloon Agricultural Research Centre

Discipline:

Earth science

Sector:

Agriculture and Food; Artificial Intelligence; Environmental Science and Technology

University:

Université Laval

Program: