September 25, 2024 by Marianne Stein, University of Illinois College of Agricultural, Consumer and Environmental Sciences (ACES)
Collected at: https://phys.org/news/2024-09-climate-smart-grazing-weather-mitigates.html
Livestock production is an important component of U.S. agriculture, with global demand for meat and dairy expected to double in the coming decades. This increase will lead to intensified grazing on U.S. grasslands, potentially exacerbating water quality degradation from livestock waste runoff into waterways.
A new study from the University of Illinois Urbana-Champaign examines the combined influence of grazing and climate on the outflow of nitrogen from pastures into water resources under different grazing schemes. The researchers conclude that climate conditions could mitigate the effects of grazing on water quality, and that producers should consider weather when making decisions about stocking rates and grazing continuity.
“The main goal of this research is to identify factors affecting the transport of nitrogen into our water bodies and determine the right combination of stocking rate, grazing duration, and precipitation to maximize production while minimizing nitrogen transport,” said corresponding author Maria Chu, an associate professor in the Department of Agricultural and Biological Engineering (ABE), part of the College of Agricultural, Consumer and Environmental Sciences and The Grainger College of Engineering at the U. of I.
The researchers developed a modeling framework that simulated nitrogen transport from livestock grazing under different climate conditions. They evaluated the model using data from the USDA-ARS Oklahoma and Central Plains Agricultural Research Center in El Reno, Oklahoma. They collected data on land use, soil moisture, precipitation, temperature, and evapotranspiration, as well as water quality in the area.
The framework featured seven different grazing schemes, including continuous and intermittent grazing implemented at low, recommended, and high stocking rates. The scenarios also included varying precipitation conditions at the time of grazing, from low to heavy rainfall events. The researchers estimated total nitrogen concentration in the overland flow for each scenario.
“Our results suggest the impact of grazing on nitrogen loss cannot be generalized. It is not always true that more cattle in the field leads to greater nutrient loss. That depends on the prevailing weather conditions in the pasture during grazing,” said Jeric Sadsad, a doctoral student in ABE and lead author of the paper.
While factors like stocking rate, grazing duration, and grazing frequency are critical, their influence on nutrient flows can be minimized if management decisions are aligned with the prevailing climatic and hydrologic conditions in the pasture, Sadsad noted.
“In the future, there will be an increasing demand for livestock production due to increasing population. Expected increases in heavy rainfall and other extreme climate events will also affect the transport of nitrogen into water bodies,” he said. “One application of our research is to implement flexible or adaptive grazing schemes that incorporate weather forecasting into the decision-making process. For example, if there is substantial rainfall, we should reduce the number of animal units that are allowed to graze in the area during that time to reduce nutrient runoff.”
The researchers recommend a strategy that matches grazing activities with prevailing weather patterns to increase livestock production while promoting environmental sustainability in pasture management.
“Management tools—such as the model that we developed—can help livestock producers achieve a sustainable balance, finding the optimal window where they can implement practices that maximize productivity while minimizing the environmental footprint,” Chu concluded.
The findings are published in the journal Ecological Modelling.
More information: Jeric S. Sadsad et al, Transport of nitrogen in grassed watersheds accounting for the combined influence of grazing and climate, Ecological Modelling (2024). DOI: 10.1016/j.ecolmodel.2024.110827
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