Abstract
Alfalfa (Medicago sativa L.) produce relatively high yields of high-quality forage and contributes to carbon sequestration. Still, its winter survival is often lower than that of grasses, reducing plant density and field productivity. Winterkill is influenced by environmental factors (e.g., snow cover, temperature fluctuations, hardening period) and management practices (e.g., cutting time, fertilization, drainage). To assess these factors and improve persistence, this study developed a quantitative assessment tool for Canadian forage producers. A numerical simulation framework integrated soil, weather, and field management variables to evaluate yield variability and winterkill risk. Data were collected from 225 farms across Nova Scotia, Quebec, Ontario, and Manitoba using a randomized hierarchical sampling design. Soil samples were analyzed in commercial laboratories, and stem density was measured each spring and fall over three years. Descriptive statistics linking stem characteristics with soil and topographic features, weather conditions, and management practices, including soil nutrient levels, revealed a decline in mean stem counts from 49 in spring 2021 to 37 in spring 2023. To illustrate performance, weighted scores and persistence analyses were used to define model parameters across three distinct scenarios: optimal, average, and worst-case. The risk-assessment tool offers decision support to Canadian forage growers, enhancing productivity through informed management, species selection, and soil recommendations.
Data availability
The data supporting the findings of this study are available from Mon Système Fourrager; however, restrictions apply to their availability, as they were used under license for the current research and are not publicly accessible. Data are, however, available from the authors upon reasonable request and with permission of Mon Système Fourrager.
Abbreviations
- Al:
Aluminum
- BS:
Base saturation
- B:
Bore
- BD:
Bulk density
- Ca:
Calcium
- CEC:
Cation exchange capacity
- CRAAQ:
Centre de référence en agriculture et agroalimentaire du Québec
- Cu:
Copper
- DSS:
Decision support system
- GDD:
Growing degree days
- HRDEM:
High-resolution digital elevation model
- ISP:
Integral suspension pressure
- Fe:
Iron
- LiDAR:
Light detection and ranging
- Mg:
Magnesium
- Mn:
Manganese
- Max:
Maximum value
- Min:
Minimum value
- NDVI:
Normalized difference vegetation index
- OMAFRA:
Ontario Ministry of Agriculture, Food and Rural Affairs
- P:
Phosphorous
- K:
Potassium
- IRDA:
Research and Development Institute for the Agri-environment
- K + Mg+Ca:
Saturation of Ca, K, Mg
- Na:
Sodium
- SOM:
Soil organic matter
- Std.:
Standard deviation
- TWI:
Topographic wetness index
- Zn:
Zinc
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Acknowledgements
The authors give special thanks to the Canadian Forage and Grassland Association (CFGA) and Agriculture and Agri-Food Canada and Institut de recherche et de développement en agroenvironnement (IRDA) Quebec for data support and cooperation. We are grateful to Dr. Gilles Bélanger and Dr. Dan Undersander for their valuable suggestions and contributions to the project. Additionally, we would like to express our appreciation to Felipe Hoffmann Silva Karp and other graduate students for their assistance with data processing at various stages.
Funding
This research was supported in part through the Mitacs Accelerate program with support from the Canadian Forage Grassland Association (Fredericton, NB, Canada), Agriculture and Agri-Food Canada (AAFC), and Ministère de l’Agriculture, des Pêcheries et de l’Alimentation (MAPAQ).
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MS and VIA conceived and designed the study. MS, VIA, and ML developed an analytical framework and methodology. MS, VIA, ML, and HE performed data analysis and investigations. MS, VIA, ML, and KC created visualizations and contributed to the interpretation of the results. MS drafted the initial manuscript. MS, VIA, PS, ML, and KC reviewed, revised, and approved the final version of the manuscript for submission.
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Saifuzzaman, M., Adamchuk, V.I., Leduc, M. et al. A probabilistic approach to predicting alfalfa’s winter survival using local conditions, weather and management factors.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-37585-w
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DOI: https://doi.org/10.1038/s41598-026-37585-w
Keywords
- Alfalfa
- Environmental factors
- Field management
- Persistency
- Probability model
- Risk assessment
Source: Ecology - nature.com
