Document Type

Thesis - Open Access

Award Date


Degree Name

Master of Science (MS)

Department / School

Animal Science

First Advisor

Hector M. Menendez III


Beef Cattle, Cover Crops, Dry Matter Intake, Enteric Emissions, Modeling, Soil Carbon


Regenerative agriculture is a pressing matter for the dairy industry to address cropland sustainability and carbon sequestration. One regenerative management practice that has been identified to help with row crop sustainability for key metrics like soil organic carbon (SOC) is complex covers. When producers use complex covers one of the main challenges is that it takes time to detect a change in SOC. However, simulation models are a tool that can be used to help determine if a regenerative practice is a strategy that gives the best results (i.e., increased SOC) while aligning with long-term production goals. Therefore, our objectives were to 1) modify the DAYCENT model to simulate soil carbon and flux with complex cover practices and 2) simulate different conventional and regenerative cropping scenarios on United States dairy farms in Wisconsin and Michigan to assess differences in soil carbon (C). The Soil Carbon CareTaker model used parameters from DAYCENT that were modified to estimate SOC with different complex cover and tillage practices over 30 years for dairy fields (n = 12) within Michigan. The calibrated model was shown to lack precision (R2 = 0.07) but was highly accurate [mean bias = -0.26 (MB)]. We simulated a base case for each field along with four different scenarios: no-till (NoTill), 30 years of continuous corn (CornOnly), cover crops with tillage (CC), and cover crops with no-till (CC NoTill). The Michigan dairy fields were split into three different regions: west (n = 4), central (n = 2), and east (n = 6). Within these regions, we observed an average least percent soil C change from the base case of -14% (west), -12% (central), and -15% (east) from the CornOnly scenario, while the greatest average percent change from the base for each region was 350% (west), 361% (central), and 278% (east) for the CC NoTill scenario. Thus, the Soil Carbon CareTaker model can be used as a tool for producers to assess regenerative management strategies that will enhance C sequestration, meet sustainability goals, and provide cost-effective regenerative dairy products to meet shifting consumer demands. Another goal for sustainable agriculture is assessing range cattle dry matter intake (DMI). DMI is an essential component to determining nutrient supply and for evaluating grazing management. Not only is DMI a major concern for cattle management, but it is also a key component regarding the rising pressure to assess the impact of enteric gas emissions from cattle on the environment. Since DMI and enteric emissions are directly correlated, this provides a potential to leverage enteric emissions to predict DMI. Obtaining data for beef cattle DMI and enteric emissions on forage-based diets similar to extensive rangelands is needed to develop an equation capable of predicting DMI for grazing cattle. Therefore, our objectives were to: 1) measure CH4, CO2, and O2 emissions, and DMI of dry beef cows and 2) use these data to develop a mathematical model capable of predicting grazing DMI. The predictive equation or precision system model (PSM) was developed using data from two feeding trials that were conducted using technology to measure enteric emissions (GreenFeed™), daily DMI (SmartFeed Pro™), and front-end body weights (SmartScale™). This study was conducted in western South Dakota during the winter of 2022. Two feeding trials used non-lactating beef cows (n = 7) receiving low (6% CP) or moderate (15% CP) quality grass hay using a 14-day adaptation period and a 14-day data collection period. Average CH4 (g/day), CO2 (g/day), and O2 (g/day) were 265 + 8.78, 7,953 + 228.83, 5,690 + 1,488.19, for the low and 215 + 13.63, 6,863 + 393.79, 5,244 + 328.32 for the moderate treatments, respectively. The PSM was evaluated for accuracy [mean bias (MB)] and precision (R2). Initial models were less than desirable for individual DMI with a range of R2 of 0.01- 0.36 for single and multiple linear regression. Using herd-level data and a 3-day smoothing, the CH4 model produced the best results with an R2 and MB of 0.91 and -255.00, respectively. A major limitation was poor GreenFeed™ use rates resulting in a limited sample size to compare with individual daily DMI data. Advances in DMI estimates for grazing cattle will have the potential to enhance stocking rate estimates, supplementation, and individual animal efficiency, leading to lower cost, optimized resources, and enhanced environmental sustainability.


South Dakota State University



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