Whole Farm Research

Manitoba Crop Alliance’s Whole Farm Research program supports whole-farm, cross-commodity research. Whole Farm Research is not crop-specific and addresses complex research needs of diverse cropping systems across Manitoba. This multi-disciplinary approach leads to innovative solutions that benefit Manitoba producers now and into the future.

For more information on the Whole Farm Research program, please contact:

Madison Kostal

Research Program Manager, Special Crops
Manitoba Crop Alliance

Examples of priorities identified by our farmer-led Whole Farm Research Committee include:

  • Crop rotation innovation – nutrient use efficiencies, cover crops, intercrops, harvest management
  • Soil health
  • Economic analyses of management changes
  • Climate resilience – extremes of moisture
  • Pest management – weeds, disease and insects

To learn more about current and completed Whole Farm projects, click here.

We are now accepting Letters of Intent (LOI) for Whole Farm Research projects to begin in 2025. LOIs will be accepted until Friday, March 15, 2024.


On-farm research is scientific research that is conducted on real, working farms to:

  • involve farmers in the scientific method, in collaboration with research specialists.
  • conduct on-farm research tests of new practices or products over a wide range of farming environments, to guide management decisions.
  • ensure that protocols are simple and practical to implement, because farming runs on tight deadlines during the growing season.
  • determine whether a practice is good or bad by assessment of economic and/or environmental parameters.

Benefits to MB Producers

On-farm research benefits farmers by demonstrating how products or practices behave on their own farm, on their own land and with their own equipment. The question of whether research results apply to their soil type or environment is answered immediately.

On-Farm research also benefits the entire industry. By involving farmers in the scientific method, we can draw results and conclusions from a wider range of environments. The amount of data produced adds up quickly and can be used to make inferences and predictions that are relevant and robust over a wide, geographical region.