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Regenerative Alberta

Living Lab

  • Writer's pictureBarb Sheldon

Cutting Edge Project Has Farmers and Ranchers RALLying for Soil in Alberta

More than 100 innovative Alberta farmers and ranchers are rooting around and learning more about the many soil-based benefits their farms are providing to society, thanks to the Regenerative Alberta Living Lab (RALL).


Funded generously by Agriculture and Agri-Food Canada (AAFC), RALL is being led by the Food Water Wellness Foundation (FWWF) and Alberta Conservation Association (ACA) with Alberta farmers and ranchers and producer and research organizations. The RALL project is targeting Beneficial Management Practices (BMPs) (i.e. regenerative tools and practices) and aims to quantify and validate the qualitative benefits and outcomes on ranches and farm fields.


The key is engaging farmers and ranchers at every step in the project, so producers will move to adopt practices that increase resiliency and soil regeneration on the over 50 million acres of agricultural land in Alberta by increasing carbon storage, reducing fertilizer use, and enhancing ecosystem services through BMPs and new agricultural practices.


The RALL will use producer input, soil sampling, geospatial information and analyses, and machine learning as a living laboratory to measure the impacts of on-farm innovation on soil health, productivity, resilience and the producer’s bottom line. The RALL will develop landscape level soil maps (i.e. heat maps) to quantify carbon storage (see Figure 1); other soil chemical, physical, and biological soil health parameters; and the environmental benefits of soil-focused regenerative agriculture.


Participating producers will provide legal land descriptions or polygons in Google Earth to identify areas of interest (i.e. fields, pastures, or rangeland) on their farm or ranch for FWWF to create a sampling plan for each location.


A preliminary soil map will be generated from data collected for the pilot study as well as analysis of over 60 layers of geospatial data. Geospatial data is any type of data with a geographic component or location information and can be derived from satellite imagery, remote sensing, GPS, etc.


Figure 1. Soil map showing carbon stocks (tonnes/hectare) at 0-30, 30-60, and 60-100 cm soil depths for the entire agricultural zone in the province of Alberta.


DEEP DIVE: Life in the Living Lab: Pedometrics and Sampling


Each sampling plan is rooted in pedometrics which is the study of soil as part of the natural environment and uses quantitative mathematical and statistical methods to understand the organization or classification of soils based on their distinguishing characteristics. In RALL, pedometrics will accurately predict responses of a soil-plant ecosystem to soil management and generate maps of soil properties for on-farm decision-making. The maps illustrate how soil properties and processes vary over space and time.


After the sampling points are cleared by utilities and approved of by the producer, the FWWF Soil Sampling Team uses GPS units to go to the sampling points in the field identified in the sampling plan. The team uses a skid-steer mounted vibrating hydraulic sampler to a 1-meter depth where possible (Figure 2). By using a skid-steer, the Sampling Team can reach sampling points even on steep slopes or far from a road with minimum disturbance. In addition to collecting the soil samples, the Team records air temperature, soil temperature at four depths of the soil core, core length and dimensions for accurate calculation of bulk density and takes a minimum of six photographs at each sampling point including all four directions around the point, the point itself, and the soil core for future assessment of plant composition and management parameters. Soil cores are then wrapped to maintain integrity and transported to the FWWF laboratory near Olds, Alberta.


Figure 2. Skid-steer and sampling probe used to collect 1-meter deep soil samples at each sampling point.


What types of measurements are made in the laboratory?


Wrapped soil cores are stored in a cold room maintained at 4°C until they can be separated into different depth fractions – 0-15, 15-30, 30-60, and 60-100 cm – and analyzed for several soil health parameters (Figure 5). Before cores are separated into the four fractions, any plant material on the surface is removed and the length of the core is measured as well as how deep the roots go, the depths and colours of the soil horizons, and temperatures for each fraction. The core is separated into up to four depth fractions and subsamples are removed to measure oven dry weight at 105°C and for DNA extraction. The sample for DNA extraction is stored at -20°C until DNA is extracted by Dr. Monika Gorzelak’s Agriculture and Agri-Food Canada laboratory in Lethbridge, AB with soil bacteria and fungal populations identified and quantified. Gravel pieces larger than ~5 mm and significant roots weighing more than 1 g are removed from each soil fraction with the wet and dry weights of gravel and roots measured along with gravel volume. Subsamples from each depth fraction are then air-dried at 40°C and then analyzed for texture (i.e. sand, silt, and clay content), the percentage of stable 1-2 mm aggregates, pH, EC (i.e. electrical conductivity), total and organic carbon, and macro-and micro-nutrient content.


All this data will be combined with the geospatial data used to generate the field maps and additional geospatial data to create predictive soil maps and estimate carbon, nitrogen, clay

content, soil aggregate stability, microbial communities, etc. across farms, ranches and the entire agricultural zone of Alberta with a resolution of 10-25 meters. This will allow participating farmers and ranchers to better understand regenerative management practices and tools precisely and accurately with the measurable soil health parameters such as soil organic matter, nitrogen or other plant nutrient concentrations, soil porosity and structure. This will help farmers and ranchers mitigate risks, reduce production costs by precisely and efficiently using inputs, and potentially increase productivity while reducing insurance indemnity payments and fiscal losses. This data also will help to create carbon crediting, GHG emission reduction, and ecosystem services programs based on measurable data and measured results.

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