Tag Archives: UNL

The University of Nebraska–Lincoln is part of a new Lincoln-based ecosystem selected to join a global movement devoted to dramatically improving how students learn.

 The Lincoln STEM Ecosystem was announced April 3 as one of the latest to join the STEM Learning Ecosystems Community of Practice.

 STEM Learning Ecosystems build meaningful regional connections among educators, business and industry partners, and after-school and summer programs to prepare students for the opportunities and challenges of the future. Each ecosystem connects to counterparts from across the country and world, enabling the exchange of best practices, information and resource-sharing.

 Admittance into the STEM Learning Ecosystems Community of Practice is highly competitive, with just 85 total ecosystems across the globe. The College of Agricultural Sciences and Natural Resources and College of Engineering are both represented within the Lincoln STEM Ecosystem.

 “Addressing the complex challenges society will face in the future will require the combined efforts of great minds from different backgrounds,” said Tiffany Heng-Moss, a founding member for the ecosystem. “Through partnerships with those within the Lincoln STEM Ecosystem, we can identify ways that we, as a community, can foster positive STEM interactions for youth in Lincoln and beyond.”

 Sally Wei, education and outreach coordinator in the College of Engineering, is also a founding member.

 Forming STEM ecosystems was listed as the No. 1 priority for STEM education in a December 2018 report by the Federal Office of Science and Technology Policy.

 “The ecosystems that we selected now have pulled together diverse partners who no longer accept the status quo in education; they want to see all students access high-quality STEM education that will prepare them for life and work in the next century,” said Jan Morrison, president and founding partner of TIES, the organization that operates the STEM Learning Ecosystems Community of Practice.

 James Blake, a K-12 science curriculum specialist for Lincoln Public Schools and co-director of the Lincoln STEM Ecosystem, said Lincoln is making great strides to offer recommended rich, meaningful STEM education and experiences to youth in the community.

 “As a recognized STEM Learning Ecosystem, we can tailor quality STEM learning opportunities to our specific needs in Lincoln while leveraging the experiences of similar alliances across the world,” Blake said.

 Joining Blake in directing and shaping the ecosystem is Bryan Seck, director of workforce development for the Lincoln Partnership for Economic Development.

 Early plans for Lincoln are to host a formal kickoff, a “STEMixer.” This annual event will add partners and keep those interested in supporting and building a STEM ecosystem at the table. The group aims to make Lincoln a leader in STEM workforce competitiveness in Nebraska and the United States.

 In addition to Heng-Moss and Wei, founding members of the new Lincoln STEM Ecosystem include: Dan Hohensee, director, The Career Academy; Tiffany Mousel, community outreach specialist, Lincoln Electric System; Patricia Wonch Hill, interim director, methodology and evaluation research core facility, University of Nebraska; Anna Wishart, senator, Nebraska Legislature; Nola Derby-Bennett, Community Learning Centers director, Lincoln Public Schools; Jeff Cole, network lead, Beyond School Bells; Tracy Bohaboj, team leader, engineering, Duncan Aviation; Jessilyn Vraspir, continuous improvement, Lincoln Public Schools; and Reeves Cleve, principal architect, BVH Architecture.

 To learn more about the Lincoln STEM Ecosystem, visit http://lnkse.org or follow the group on Twitter at @lnkse.

 As part of ongoing efforts to support those affected by recent flooding, Nebraska Extension county offices across the state have moisture meters available for homeowners to borrow to monitor the moisture content of flooded materials.

 

It can take weeks or months to dry a house to the point where repairs can be made. It’s common for homeowners to discover large amounts of mold in walls months after a flood because they didn’t wait for the structure to dry before making repairs. The moisture level of structures cannot be determined by appearance or time spent drying, so a calibrated meter is recommended to measure moisture levels before rebuilding.

 

“It’s important to wait until wood and other materials dry out before attempting to repair a flood-damaged home,” said Dave Varner, associate dean with Nebraska Extension. “Renovating too soon could trap moisture, leading to rotting and promoting the growth of mold.”

 

One-hundred-fifty moisture meters have been distributed to extension offices throughout Nebraska and more are on the way. Homeowners wanting to borrow a meter are encouraged to contact their county office. Instructions for using the meter will be provided upon checkout.

 

Access to moisture meters is just one of the many ways that Nebraska Extension is helping Nebraskans recover from the flood. For more information and flood-related resources for individuals and families, homeowners, businesses, and farmers and ranchers, visit https://flood.unl.edu.

Livestock producers face a recurring challenge: watching animal behavior for signs of illness or injury.

 

An interdisciplinary team from the University of Nebraska–Lincoln has developed precision technology to help producers continuously monitor animals and use the resulting data to improve animal well-being.

 

The team includes Nebraska electrical and computer engineers Lance C. Pérez, Eric Psota and Mateusz Mittek, and animal scientists Ty Schmidt and Benny Mote, who developed the technology system using video footage of pigs.

 

The system processes video footage from livestock facilities — day and night — and applies machine learning, which uses statistical algorithms to help computer systems improve without being explicitly programmed. It identifies individual pigs and provides data about their daily activities, such as eating, drinking and movement.

 

Based on this data, the system can also estimate how much each pig weighs and how fast it is growing.

 

“Our system provides a pattern of typical behavior,” said Psota, research assistant professor of electrical and computer engineering. “When an animal deviates from that pattern, then it may be an indicator that something’s wrong. It makes it easier to spot problems before they get too big to fix.”

 

The team created their system using deep learning networks, a form of machine learning with millions of coefficients and parameters. To identify pigs from all angles, the networks processed images large and small, rotated, skewed and otherwise transformed. The team uses ear tags to help with identification but aims to rely on unique physical characteristics such as ear shape, saving producers the added work of tagging.

 

Although the system has been developed to identify pigs, its algorithms can be used for other livestock, such as cattle, horses, goats and sheep.

 

“We want to make a tool that is available to the livestock producers,” said Schmidt, associate professor of animal science. “In a competitive agricultural market with rising costs, producers are looking for solutions that streamline operations while enhancing the health and well-being of their animals.”

 

The team is pursuing further development with the help of NUtech Ventures, the university’s technology commercialization affiliate. NUtech has patented the technology and is exploring industry investment.

 

“NUtech provides a valuable service and opens us up to conversations with people outside the university,” Schmidt said. “We’re now looking for industry collaboration to help us advance this system.”

 

DETECTING ILLNESS, DECIPHERING TRAITS

 

The team recently received $675,000 from the National Association of Pork Producers to fund two studies. In collaboration with Kansas State University, the first study will explore the technology’s ability to predict illness. The team plans to collect data from both healthy and immune-compromised pigs, training the system to distinguish early symptoms.

 

The second study will explore the lifespan of sows — female pigs of reproductive age — and traits that may be associated with longevity. The Nebraska team’s technology will track sows over time and identify changes in movement, gait patterns and physical activity — data that may yield links between genetic background and longevity. It’s a connection that hasn’t been measured because there hasn’t previously been technology to do it, Schmidt said.

 

“Could we make more informed management decisions — identifying optimal genetic lines that are healthier, more efficient or less aggressive?” Schmidt said. “Can we identify a sick pig, days ahead of when symptoms are visible to the producer? In both of these studies, we’re looking to push the boundaries of what we’ve already created.”

LINCOLN — This article originally appeared in Cornhusker Economics.

Cattle producers base major decisions such as calving season on expected economic outcomes which include productivity, input costs, personal capability, individual preferences, markets, and so on. Calving systems are generally categorized into three seasons, spring, summer and fall. Many producers select the spring calving season to maximize the weaned calf weight at the end of the summer grass season. Unfortunately, this increases feeding costs since extra nutrition is needed in the spring, prior to pastures being opened. Also due to the widespread adoption of this early spring calving, the mass marketing in late fall often results in depressed calf prices relative to the rest of the year. Given these observations, efforts to reduce costs and increase prices have prompted more detailed investigations and research focused on moving the calving season to later in the year, such as May or June. While moving the calving season may decrease labor costs and alter market timing, it also changes the matchup between available pasture nutrition and the cow’s needed nutrition level, i.e. during lactation and the breeding season. Choosing the best calving time depends on the producers’ ability to understand their own operation by recognizing both the biological and economic impacts on their production and profitability.

Some of the previous economic comparisons of June versus March calving systems have shown that June-born calves weaned in April are more profitable than March-born calves weaned and sold in November (Griffin et al., 2012). These results were due to the available forage during peak periods of production, less harvested feed (hay) needed, size of calf produced, the availability of inexpensive corn residue pasture and the early season calf price premiums. In the western part of Nebraska and other locations, there are producers who have chosen to use a May calving system over a June or March system. These decisions can be made for many reasons, i.e. workload, available labor, size or scale of the operation, or a desire to calve during a warmer part of the year. However, there is still the question of how this choice affects profitability. A head-to-head net return comparison of March versus May calving systems was done via an electronic simulation using relevant historical information and physical and economic factors associated with costs and revenues.

The March versus May comparison utilized three years of data collected at the University of Nebraska-Lincoln Gudmundsen Sandhills Laboratory in Whitman, Nebraska. The biological results for both March and May calving systems were assigned economic factors for nine years (2005-2013). The Nebraska Calving Systems Model (NCSM) was used to capture, match and process all of the relevant prices, costs and relationships for this period. The NSCM was developed by Matt Stockton and others to be used in several of the earlier comparisons of June versus March calving systems. The model accounts for labor, cull and replacement cow costs, breeding costs, productivity differences, feed costs, summer range, winter range and supplementation for both May and March calving systems. In both systems, market prices were average prices including the price differences based on calf weight at the time of sale (price slide), differences by year, and the seasonal price differences based on the time in which the calves were sold. Death and labor costs were similar for both systems, the only difference was the timing of labor inputs.

The nine-year average market price received for March-born steers was $162.84/cwt and $148.21/cwt for 4 cwt and 5 cwt steers, respectively. The 4 cwt May-born steers averaged higher at $170.84/cwt, no May 5 cwt steers were produced. This price difference shows that the May-born calves sold for a higher average price per pound than the March calves. This difference is due to seasonal price variation. However, the seasonal price advantage and the price slide difference for May-born calves was overcome by the sheer pounds of production achieved by the heavier March-produced calves. The March-born weaned calves sold on average for $669.19/hd, while May-born weaned calves averaged $617.68/hd. Cow replacement costs for the March systems averaged $185.22/hd whereas the May-born averaged $297.87/hd, an overall average difference of $112.65/hd giving the March system the advantage in cow replacement costs (Figure 1). This was due to both higher replacement rates and the increased cost per head due to seasonal prices for May cows. Average feed costs for the March system were $395.27/pair whereas the May average was $321.40/pair. The final result for the nine years was that March cows annually averaged $25.23/hd profit while the May cows averaged a negative $65.77/hd. However, due to the cyclical nature of the cattle markets over the nine-year period, there were individual years when the May system had higher net returns than those of the March system.

This article demonstrates the importance of considering all the factors that alter a calving system’s profitability. Three key factors that altered the economic ranking of the two systems are changes in 1) productivity, 2) production costs and 3) market prices. Productivity changes included lower pregnancy rates (Figure 2) for the May-calving system due to a declining plane of nutrition related to pasture maturity during the breeding season. May cows also weaned calves with lighter body weights. May-calving cow’s production costs had mixed effects. May-calving cows had significantly lower feed costs than March-calving cows, but higher replacement costs.

The higher replacement costs were largely due to increased culling rates (lower pregnancy rates), and higher seasonal market values during the replacement period. These results illustrate the value of understanding long- and short-term costs when making a seasonal decision. The market price component has a dual effect with seasonal and price slide effects. If the May-calving cow replacement costs were similar to those of the March-calving cows, the May-calving system would be more profitable than the March-calving system by more than $22/hd. This profit advantage comes from the feed costs savings, seasonally higher calf values and the price slide. Understanding market trends may be just as important as any production trait. When considering a change, producers should substitute their individual expected productivity and price expectations as they estimate profitability differences. Performance and timing drive the outcome. Any change that increases productivity more than its costs, while maintaining price, will have a positive effect on profit and vice versa.

References:

Griffin, W.A., Stalker, L.A., Stockton, M.C., Adams, D.C., Funston, R.N., Klofenstein, T.J. (2012) Calving date and wintering system effects on cow and calf performance II: Economic Analysis

Nebraska Calving Systems Model

Interviews with the authors of BeefWatch newsletter articles become available throughout the month of publication and are accessible at https://go.unl.edu/podcast.

Listen to a discussion of the content in this article on this episode of the BeefWatch podcast. You can subscribe to new episodes in iTunes or paste http://feeds.feedburner.com/unlbeefwatch into your podcast app.