Texas Daily Ag Market News Summary

Feeder cattle auctions mixed; futures down.

Formula trades lower; Beef prices up.

Cotton prices down.

Grains and soybeans mixed.

Milk futures steady.

Crude oil down; Natural gas up.

Stock markets up.

Cattle:

Texas feeder cattle auctions were mixed, with instances of steady to $5 higher and $1 to $3 lower. January Feeder cattle futures were down 10 cents, closing at $147.65 per hundredweight (cwt). The Texas fed cattle cash trade was not active today. December Fed cattle futures were higher, gaining $1.07 to close at $119.97 per cwt. Wholesale boxed beef values were up, with Choice grade gaining $1.28 to close at $203.15 per cwt and Select grade gaining $1.76 to close at $185.01 per cwt. Estimated cattle harvest for the week totaled 119,000, down 1,000 from last week’s total and up 6,000 from last year’s total. Year-to-date harvest is up 5.31%. 

Cotton:

Cotton prices were down, closing at 73.25 cents per pound and March cotton futures losing 0.72 cents to close at 75.20 cents per pound. 

Corn and Grain Sorghum:

Corn prices were down a penny, closing at $3.56 per bushel. December corn futures were down, losing a penny to close at $3.47 per bushel. Grain sorghum was down a penny to close at $5.61 per cwt.

Wheat:

Wheat was up, gaining a penny to close at $3.61 per bushel. March wheat futures were up a penny, closing at $4.19 per bushel.

Milk:

Milk prices were steady, with December Class III milk closing at $15.50 per cwt.

Stock Markets and Crude Oil:

Stock markets were up, with all three major indexes showing gains. January Crude oil futures were down 14 cents to close at $57.16 per barrel.

Daily Market News Summary Data

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From Agri-pulse:

Lab robots crucial for end-to-end food safety data systems

Next generation sequencing is beginning to replace traditional DNA methods in food safety testing. As this trend continues, sequencing will no longer be the time intensive process it once was. Laboratories will be limited by how quickly they can prepare samples, not how quickly they can sequence them. Automation will, therefore, play an increasingly critical role in the evolution of laboratory processes.

Critical stages of the NGS (next generation sequencing) workflow are already being automated by a variety of hardware and software innovations. Robotic solutions, for example, play an important role in addressing the substantial bottlenecks created by humans preparing lab samples.

In the next two to three years, automation of a different kind will help us to fully leverage the precision and speed of NGS technologies. The effective automation of bioinformatic workflows will dramatically increase our ability to analyze enormous bodies of data and identify macro-level trends across large volumes of data.

By leveraging NGS technologies and the technologies that automate NGS workflows — from library preparation to sequencing, analysis, and interpretation — the food industry at large will finally have the kinds of tools and information it needs to proactively identify threats and prevent outbreaks from occurring.

Sequencing secrets
Next generation sequencing is on its own an automated DNA sequencing technology. The technology has revolutionized the study of genomics and is quickly making inroads in food safety applications. NGS has distinct advantages over traditional sampling methods like Polymerase Chain Reaction (PCR) or antigen-based methods.

First, NGS generates and analyzes millions of sequences per run, allowing researchers to sequence, re-sequence and compare data at a rate previously not possible. Second, NGS testing is universal, while PCR testing methods are targeted. With PCR you have to know what you expect to find in order to test for it. What’s more, each target requires a separate testing run. This is costly and doesn’t scale.

By contrast, a single NGS test exposes the precise ingredients and all potential threats — both expected — in any given sample. In the not too distant future, the cost and speed of NGS will meet and then surpass legacy technologies.

Adopting NGS technologies, however, doesn’t necessarily mean you’ve solved a number of bottlenecks in workflow. Robotics is playing a key role in automating critical stages of the NGS workflow.

Challenges to food analysis automation
Preparing sample materials for food testing workflows requires a coordinated series of molecular biology reactions. Library prep can take up to 60 percent of a lab technician’s time and directly impacts the quality of resulting analyses. This process, known as sample preparation or library preparation, can be performed manually or automatically and can vary according to application and throughput.

In clinical and pharmaceutical settings, for example, robotic systems automate numerous operations, including liquid handling, creating efficiencies and helping to generate the highest quality data possible.

Adopting existing robotics systems for food testing is not as straightforward as it might seem, though. In doing so, the food testing industry has had to overcome unique challenges. In contrast to the materials being analyzed in clinical and pharmaceutical settings, food comes in a wide variety of forms: environmental samples, packaged foods, dairy, meat, and produce in solid, liquid, powdered, frozen, cooked, raw and concentrated forms. All of these require different methods of preparation before they can be analyzed.

Another challenge inherent to food analysis is that the compounds in natural products are variably distributed throughout unprepared samples. Complex food items further complicate this issue, as dozens of ingredients can be heterogeneously distributed throughout any given product.

Cryogenic mills or grinders blend samples at low temperatures and are often used to prepare samples of fruits and vegetables being analyzed for volatile pesticides. These mills and grinders produce homogenous samples of small sizes that can be held in a relatively small volumes of solution. This is important because it increases the efficiency of liquid-handling applications, those most commonly automated in today’s food testing labs.

Robotic liquid handling
Having long been deployed in clinical settings, robotic liquid handlers are a mature and reliable technology. The same handler a food lab has used to prepare samples for PCR can be deployed to prepare samples for NGS.

Robotic liquid handlers are XYZ robotic systems that dispense selected quantities of samples, reagent, or other solutions into the appropriate containers for any given application. Some examples on the market include: BioRad’s iQ-Check Prep System, a robotics platform that performs DNA extraction and PCR plate set-up, as well as Illumina’s automated solutions that are specific to NGS sample prep.

Food safety’s next frontier
Beyond robotics, another target for automation in the near term time is in artificial intelligence and machine learning to amplify our existing bioinformatic workflows. NGS provides an enormous amount of data, much of which goes unused in food safety applications today. The next revolution in food safety is in automating data-science operations that can leverage this data.

Automating bioinformatic workflows will dramatically increase our ability to analyze enormous bodies of data and identify macro-level trends. Imagine the insights we could gain when we combine trillions of genomic data points from each phase in the food safety testing process — from routine pathogen testing to environmental monitoring to serotyping.

The first step is to unify all of the various tests performed at each stage of the food safety workflow into a single, universal test. Just having the all this data in a single place represents enormous opportunity.

We could begin to query the data for questions you’d typically ask of your food safety systems:

• What is the risk profile of a given sample?
• Does it contain harmful pathogens?
• What strains are present?
• Are the pathogens from a supplier or the environment in which it was manufactured?

A single algorithm could reveal such information in a single test run.

This is just the first layer of automation — which on its own we expect to drive down the costs of testing, increase scale, and reduce errors that emerge in today’s data silos.

Where it gets interesting is when you add in a secondary layer of automation in the form of machine learning and artificial intelligence. That’s how our industry will truly get our data to work harder for us. Imagine what’s possible when we can automate insights that are impossible to identify today without a team of bioinformaticians working with reams of data over long periods of time?

With the addition of machine learning, we can conduct analyses across bioinformatics runs. We can associate genomic data with data gathered across the supply chain. This will dramatically enhance our ability to identify emerging pathogen threats, locate problematic suppliers, build rapid response consumer-alert systems, model for long-term threats of climate change, and even draw conclusions about consumer preferences.

What’s next?
The holy grail of food safety is to have end-to-end systems in place for gathering supply chain and sequencing data, which can then be appended to and stored on immutable decentralized block chains.

We’re not too far off from that. Recent advances in IoT are making it easier than ever before to gather data at each stage of the supply chain, from farm to table.

Block chain technology is already being tested for food industry use cases in large pilots with top global brands. We’re even starting to see the first miniaturized sequencers becoming commercially available, which is the first step toward in-line sequencing.

The essential technological components exist. In order to build the end-to-end system of the food safety future, we will have to fuse these components and their operation together. That is an enormous technological, infrastructural, and cultural challenge. 

I’m confident we’ll get there. The opportunities are too great to imagine otherwise.