Feedback and Unintended Policy Consequences

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Today has been a day to reflect on the way things actually work and the perverse outcomes that sometimes obtain when we do what seems logical…meaning, of course, that unintended consequences are everywhere.
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Over the past 20 years or so, perhaps more, environmental advocacy groups (Environmental Defense, Sierra Club, etc.) have trained their guns on industrial agriculture, especially industrial ANIMAL agriculture, arguing that the larger our “animal factories” (their term, not mine!) become, the greater risk they pose to the environment. The logical conclusion they reach is that the livestock and poultry industries need to be regulated more and more heavily.

It should be obvious to anyone who thinks about it for even a moment that an increase in regulatory intensity nearly always results in significantly increased costs of regulatory compliance. Those may include the professional labor costs of defending against newly enabled litigation, or the capital and operating costs of additional pollution-control technologies, or the administrative costs of generating more piles of paperwork to be filed and copied and shipped, or all three and many more.

It’s also obvious that increased labor, capital, operating, and administrative costs are all nothing more or less than increased costs of producing a marketable product. The “animal factory” now costs more to run per unit of livestock produced than it did prior to the increased regulatory intensity. Profit margin shrinks.

Naturally, the producer wants to stay in business. It’s pretty well understood that if a business’ profit margin is shrinking, one way to reverse that is to get larger, to increase capacity. That is what we call “economies of scale,” and it is one of the main reasons (no doubt) that Tortilleria Lupita now occupies at least four facilities in Amarillo instead of just one. And McDonald’s. And Chick-Fil-A. Why did we go from the old, brown Wal-Marts to the big, blue, Super Wal-Marts? Economies of scale are at least one of the main reasons. We spread fixed costs over more units of production, and the decline in profit margin can be reversed.
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Wait, though! Wasn’t the big, bad “animal factory” the problem we set out to solve in the first place? And yet: what we’ve just done is describe an interesting perversity that lurks in “the way things actually work.” We set out to turn the screws on large “animal factories,” hoping – vainly, as it turns out – to incentivize a return to mom-and-pop agriculture, happy cows on green pasture, free-range chickens, and all the rest. But what we actually DID was incentivize further concentration of animal units in these “animal factories.”

A reinforcing feedback loop helps to drive the intensification of the livestock and poultry industries.

A reinforcing feedback loop helps to drive the intensification of the livestock and poultry industries.

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So what’s the answer? First, let’s get a couple of things straight. We can afford a juicy steak (or, for you vegans, a fresh, juicy strawberry in the dead of winter) precisely because we have industrialized agriculture. Have you tried growing your own strawberries around here in January? And you may have noticed that if you want a Kobe filet, you’d better be independently wealthy. For the rest of us, that corn-fed NY strip from a Kansas or Texas feedyard may not be an everyday meal, but you probably don’t have to take out a mortgage to enjoy one every once in a while. Prime Kobe beeves may be flown across the Pacific on 747s, but a choice, English-cross steer only has to be rolled a few miles on a semi. Be thankful.

Second, these are not the only dynamics at play. I’ve just isolated this particular dynamic to show how it works. In truth, lots of other things are going on at the same time this reinforcing loop is executing.

Third, we can probably conclude that, all other things being equal, a large-scale producer is more able to afford (i. e., absorb the costs of without going bankrupt) a government-mandated increase in environmental protection than the small-scale producer. That’s the way it is. The selection pressures are mostly in favor of the larger, more robust, industrial model…

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Mostly. But that’s a discussion for another day.

Sustainability Conversation?

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In my work as an environmental researcher and Extension specialist, I spend a lot of time with people who work on “sustainability issues.”  I suppose my work is “sustainability related,” too.  And hardly a week goes by in the popular media without someone making a bold claim that this enterprise or that enterprise is “unsustainable.”  (Usually, it’s meat consumption in some form or other; more often than not, beef consumption is the bête noir.)

But I don’t have a good fix on what I mean when I use the word “sustainable” or any of its cognates.  So I want to get educated.  And I’d like to end up with a common understanding of “sustainability” that is general enough to apply to human enterprises of all kinds but concrete enough to imply enterprise-specific ways of measuring it.  How do I know when a human enterprise is sustainable or when it’s not?

A couple of ideas to begin.  First, of course, the famous Brundtland Report, Our Common Future, defined “sustainable development” as, “the kind of development that meets the needs of the present without compromising the ability of future generations to meet their own needs.”  Fine, as far as it goes; it’s general enough, but not concrete enough.

Second, a question or two.  Is “sustainable” an adjective that can even apply to a particular enterprise (as in, “a sustainable seawater-desalination plant”)?  Does it, rather, apply to a political/administrative unit, like a city, a state, or a nation?  Or does it only make sense when applied to the earth as a whole?

Third, folks generally accept that “sustainability” has at least three dimensions – environmental, social, and economic – and that those three dimensions are on par with one another.  That is, we shouldn’t privilege one dimension over the other two.

The floor is open.  I’ll be posting on this from time to time, with no particular deadline.

#sustainability #Brundtland

La Niña Watch

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That sound you hear is the death rattle of El Niño, but he’s not going down without a fight.  Welcome rains across the state of Texas – and some rains not so welcome, now that many of the reservoirs are full – suggest that El Niño, the oceanic phenomenon that seems to drive a lot of North America’s weather patterns, is not done with us yet.

Still, the climatologists at NOAA are fairly certain we’ll transition to a full-blown La Niña by fall 2016.

What that portends is just an educated guess, but our wettest years in the southern High Plains tend to be associated with El Niño episodes and the driest with the La Niña episodes.  Pacific oceanic temperature trends, wind anomalies, and outgoing radiation data are all consistent with a developing La Niña.  The summer of 2016 may keep us wetter than average, but summer 2017 may be setting up a new drought cycle for the Texas Panhandle.  We’ll see!

NCBA Environmental Stewardship Award Program

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Do you raise cattle as your life’s vocation?

Are you a dedicated steward of your land, cattle, air, water, and community?

Are you a dedicated servant-leader of your fellow cattle producers (and agricultural producers generally)?

If the answer to all three of those questions is “yes,” then maybe it’s time you thought about applying to the prestigious National Cattlemen’s Beef Association’s Environmental Stewardship Award Program!

Here is the NCBA-ESAP Application 2016.  The 2016 Regional winners have already been selected, but it’s never too early to start thinking about a 2017 application.  In fact, I’d recommend it!  It’s not a trivial process.  An award of ESAP’s prestige takes some doing.  The 2016 application package will give you a good idea of what to expect.

I’ve sat on the NCBA-ESAP Selection Committee for 20 years as a representative of the land-grant university system.  The quality of the applications has increased dramatically over the years…but more importantly, the degree of attention cattle producers devote to being stewards of their land, cattle, water, air, and communities seems to be growing, as well.  And that’s the point:  raising the national profile of conscientious cattle producers,

If you raise cattle in Texas, New Mexico, or Colorado, and if you’d be interested in applying to ESAP, I’d be happy to help you.  We’ll talk candidly about what it takes to be a Regional winner, and we’ll look at your operation, your track record, and your involvement in the public square on behalf of the beef industry.  And if we go forward together, I’ll have to recuse myself from Selection Committee deliberations next year in the Region in which you apply.  It’ll be hard work, but if you have what it takes to be a Regional winner, it’ll be well worth the effort!

Contact me at b-auvermann@tamu.edu or (806)670-8081, and let’s get the conversation started.

 

Stormzilla and the Value of Panhandle Groundwater

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The Weather Channel is estimating that the storm that caused last week’s epic flooding in southeast Texas generated 6.5 TRILLION gallons of water.  Most of that water ended up in the Gulf of Mexico, where it’s probably doing some good.  So that water wasn’t entirely wasted.  But what if?

What if we had a reservoir and pipeline system that allowed us to pump that water up the hill to the Texas Panhandle, where we could inject it into the Ogallala Aquifer as artificial recharge?  How much Panhandle crop production would that water irrigate?  And what would the water be worth?  Let’s do a thought experiment with some back-of-the-envelope calculations.

First, the sheer volume of water:

6.5 trillion gallons = 870 billion cubic feet
870 billion cubic feet = 20 million acre-feet

Now, we’ve got to choose an irrigated crop typical of the Panhandle.  Panhandle corn production requires about 24″ of water per year.  Let’s be optimistic and say that we get half of that in the form of rainfall.  Then we’d have to provide 12″ of irrigation per year.  So we could support a total of 20 million acres of corn for one year, 10 million acres for two years, 5 million acres for 4 years, or 1 million acres for 20 years.  Roughly speaking, the Panhandle irrigates 1.5 million acres of corn each year. We could support that entire acreage with 12″/yr of irrigation for 13 years with only the water from that one storm!

Assume $2.50 per bushel and a yield of 200 bushels per acre, and that stormwater would support corn revenues of $10 billion, or $750 million per year gross revenue to Panhandle farmers.  That’s gross revenue of about $0.0015 per gallon of stormwater pumped from the Houston area.

As a point of reference, we pay maybe $0.25 per gallon of drinking water when we buy it retail from the kiosk on 34th Avenue. By that strictly economic comparison, drinking water is worth 162 times what that same water is worth irrigating corn!

What’s more, we’ve extended the life of our aquifer by 13 years.  (Corn is the biggest irrigation water-user in the Panhandle.  Cotton, sorghum, and wheat all benefit from irrigation, but they can be grown without it, albeit at much lower yields.)

Now, let’s consider the pumping costs to do this.  Assume that the infrastructure is already in place.  (It’s not.)

We need to pump 6.5 trillion gallons from roughly sea level to an elevation of 3,600 feet in Amarillo.  We need to get it completely done in two weeks so that the Houston reservoirs are emptied in time for the next big storm.  We’ll need pumps capable of 3.25 trillion gallons per week, or 322 million gallons per minute (3.22E+08 gpm).  That is roughly 25% more water than turning the Mississippi River back upstream (in New Orleans, the Mississippi averages a flow rate of 270 million gallons per minute).

We’ll need a lot of booster pumps along the way that are capable of pumping at the same rate, but we assumed that infrastructure is already in place.

The pumping lift is 3,600 feet, but there will be friction in the 700 miles of pipeline, so we have to add that friction cost into our calculations.  To minimize the friction cost, let’s assume that we can build a pipeline that is smooth enough to keep the average water velocity in the pipeline to 5 feet per second or less.  That pipeline has a diameter of 430 feet!  That’s quite a public-works project.  But it helps us keep our pumping costs down.  Let’s add an 11% premium on the pumping lift, which gives us a total lift of about 4,000 feet.

The amount of power in the flowing water is given by (4,000 feet x 3.22E+08 gpm)/3,960 = 3.25E+08 horsepower.  Our pumps are 75% efficient, and the electric motors are 85% efficient, so we need about 500 million horsepower of electricity to run the system.  We’re going to be running them for 2 weeks or 336 hours, so that’s 1.68E+11 horsepower-hours or 1.26E+11 kilowatt-hours.  Let’s assume that industrial electricity costs about $0.08 per kilowatt-hour.  We’d spend $10 billion on the energy alone!

You can’t afford it, and we don’t have the power plants to support it anyway.  And check this out:  all of the GROSS REVENUES associated with producing corn on that water would be swallowed up in paying the energy bill to pump that water up to the Panhandle…to say nothing of the infrastructure cost, which we’ve neglected thus far.

  • So let’s be a bit more realistic (!), and let’s spread the pumping over a year instead of getting it done in two weeks.  Last time Houston flooded like this was in…2015!  That reduces the pumping rate to 12.4 million gallons per minute and the pipeline diameter to 84 feet.  The total pumping cost is not going to be that much different, but it will be spread over 52 weeks instead of 2 weeks.  And the infrastructure will cost a lot less.
  • Just for grins, let’s use 13.3 years as the time horizon instead of one year.  Recall, we’ll be able to irrigate all 1.5 million acres of corn every year for 13.3 years with just one storm of this size.  The pumping rate is now only 932,300 gallons per minute, and the pipeline diameter is only 23 feet.  We’re making progress, but we’re still paying $10 billion to pump all of that water up the hill.

Instead of devoting the entire gross revenue of corn over that period to pumping costs, we’re going to have to come up with a more valuable use for Stormzilla water.  If it were drinking water instead, the water would be worth $1.6 trillion at retail.  That’s enough water to supply Amarilloans with 100 gallons per day for nearly 1,000 years, or to supply 9.5 billion people (the earth’s projected population in 2050) with 98 gallons per day for a whole week.

Yes, that was a BIG storm.

 

In Defense of Skeptics

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With the relentless media-academia assault on so-called “climate-change deniers” – a defamatory, misleading term if ever there was one – I am always and ever reflecting on why I continue to cast my lot with the skeptics. And I nearly always end up back here, pondering the graph below.

I’ve been preparing to teach an ultra-short course on Stella [TM] modeling at a conference in North Carolina next month. (Stella is a simulation platform for complex systems, a simple way to simulate a system’s evolution over time.) In preparation for that workshop, I decided to put together a Stella model of the “Lorenz system,” a highly simplified version of a system of equations that Dr. Edward Lorenz derived to describe atmospheric dynamics back in the early 1960s. With a bunch of simplifying assumptions, the system reduces to an ensemble of three short equations that are easily solved by a computer.

Two simulations of the same Lorenz system, with only a 0.001% difference between the initial values of X.

Two simulations of the same Lorenz system, with only a 0.001% difference between the initial values of X.

The computers they had then were extremely crude, of course, and quite slow by modern standards. A simulation my Mac can handle in 20 seconds used to take many days. Lorenz would run his simulations in stages; he’d run a simulation for a long work day, print out the appropriate data, and start a new simulation the next day, starting at the point he had stopped the simulation on the previous day. Perfectly logical, right? Except he noticed something odd.

The computer he was using actually retained numbers at pretty high precision. Let’s say the internal storage held the number 3.208945992. When he printed his data at the end of each day, his output format only extended to four or five decimals, rounding the number accordingly: 3.20895, let’s say. So the next day, when starting the simulation, he would enter 3.20895, which the computer interpreted as 3.20895000, a difference from the number the computer had actually held at the end of the previous day’s run. The difference between the two, in this case, is 0.000004008, which is an error of about 0.00001%. No big deal, right?

When he noticed he was inserting these tiny, tiny errors into the simulation, he was interested in how much of an effect they had. So he ran one simulation continuously for a period of days, never stopping the computer. He then repeated the simulation from exactly the same starting point, but he stopped and started the simulation daily as I described earlier. And what he found was, some might argue, the birth of modern “chaos theory.”

The two simulations tracked along very, very closely for several days. After a few days, some small divergences appeared, but they didn’t seem to amount to much. But then…the two simulations went completely in different directions.

I’ve done a similar thing with my Stella model of the Lorenz system this morning. The system tracks the evolution of three variables, X, Y, and Z. The system requires that the initial values of all three variables be specified. In my first simulation, I set X=1.00000, Y=1.00000, and Z=1.00000 at the outset. In my second simulation, I kept the exact same values for Y and Z, but I changed X to 1.00001. Then I ran each simulation exactly the same way.

Just as Lorenz found, the first half of the simulation yielded no detectable difference between the two scenarios. In fact, you can’t even see the blue line because it’s directly behind the yellow line. But look what happens after that! The two graphs diverge a little, then a bit more, and then they go in completely different directions!

Now you’re asking: so what?

A Stella embodiment of the simplified Lorenz system.

A Stella embodiment of the simplified Lorenz system.

Keep in mind that much climate science depends on numerical models much more complicated, with many more variables, than the Lorenz system. Climate models involve averaging some kinds of data over very large areas, say, 1 km x 1 km squares of land or sea area. Even if we had 100 weather stations in that square, there is no way to be sure that the average value of the variable X computed from the measurements of X at each weather station would represent the “true” average value of X over the grid. In fact, the very notion of a “true” average value of X is itself kinda funny; each of the weather stations’ sensors is slightly different, each generates its own errors (by definition), and we can’t measure X at each point in the grid, especially with only 100 weather stations. So an error of .00001% is not hard to imagine, is it?

But look what happened when Lorenz made such an error in specifying his initial conditions for a particular daily run!

Later that year, he published a research paper in which he concluded,

Two states differing by imperceptible amounts may eventually evolve into two considerably different states … If, then, there is any error whatever in observing the present state—and in any real system such errors seem inevitable—an acceptable prediction of an instantaneous state in the distant future may well be impossible….In view of the inevitable inaccuracy and incompleteness of weather observations, precise very-long-range forecasting would seem to be nonexistent.

In other words, there’s a limit to how accurate our atmospheric models can be. We can be fairly accurate in the short run, but long-range forecasting is doomed.

BTW, don’t bother with the “weather is not climate” thing. I know that. But both involve atmospheric dynamics, and if we can’t simulate weather with great confidence, why would we expect to simulate climate with any better confidence?

Does that mean we shouldn’t do it? Of course not. But we ought to be humble about the accuracy of any long-range projections and forecasts of complex, dynamic systems like the earth’s atmosphere.

Dairies and Air Pollution

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Last week, at the invitation of Dr. Ellen Jordan (Extension Dairy Specialist, Dallas), I made a short presentation at the annual Dairy Outreach Program Area (DOPA) workshop in Stephenville.  Dairy producers in the Central Texas (Erath, Comanche, Johnson, Bosque, and Hamilton Counties) and East Texas (Hopkins, Rains, and Wood Counties) DOPAs  are required under their state water-quality permits to obtain a certain number of continuing-education units each year.  Because these CEU programs are a regulatory requirement, they’re usually well attended, and this year’s was no exception:  somewhere around 45 dairy producers assembled at the Texas A&M AgriLife Research and Extension Center at Stephenville for a chicken-fried steak lunch and four hours of CEU-eligible instruction.

My remarks centered on air emissions from open-lot dairies, including dust, greenhouse gases, and ammonia.  Here are the highlights:

  • Ammonia emissions matter because:
    1. Ammonia can combine with other atmospheric gases (mainly sulfates and nitrates) to form very small particles that stay airborne a long time and contribute to stresses on the human respiratory system.
    2. Ammonia emissions represent an effectively unrecoverable loss of expensive nitrogen, for which dairy producers pay quite a lot when they buy alfalfa hay or crude-protein supplements.
    3. The human nose often recognizes ammonia’s characteristic “note,” and the general public therefore associates ammonia emissions with unpleasant odors.
  • By far, most of the ammonia emitted by open-lot dairies comes from the open lots instead of the lagoon(s).
  • Controlling ammonia emissions is, first and foremost, a matter of optimizing the level of crude protein fed to the herd.  Protein intake that exceeds the animal’s maintenance and milk-production requirements is eventually excreted onto corral surfaces, typically as urea, and is rapidly lost to the atmosphere as gaseous ammonia.
  • After feed optimization, the next most important practice to reduce ammonia emissions is to intercept manure-borne nitrogen before it accumulates on the corral surfaces and undergoes the wetting-drying cycles that drive so much of the emissions process.  Scraping manure from feed alleys instead of flushing it into a lagoon allows a dairy producer to capture the manure nitrogen and use it beneficially as an organic fertilizer on grass or row crops.
  • Methane, a potent greenhouse gas, comes primarily from the anaerobic lagoons on a dairy.  Again, intercepting manure before it goes into a lagoon allows a producer to reduce methane emissions and build soil organic matter via land application of manure.
  • Lots of dust-related research over the past five or ten years has centered on bioaerosols and their implications for both occupational and public health.  Dairy producers should pay close attention to this body of research; it’s not going away any time soon.

Here is a link to my slide deck:  Auvermann slides 20160407 .

I’m deeply indebted to Dr. Rick Todd of USDA-ARS at Bushland, TX, for providing me with much of the ammonia- and methane-related research on which I based those portions of my remarks.

Here is a link to the Texas Dairy Matters web site, a site hosted by the Texas A&M AgriLife Extension Service’s dairy program.

Clean Water Act and Livestock Producers

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Short post today.  Over at the Texas Agriculture Law Blog last week, Tiffany Dowell-Lashmet posted a couple of articles that will be of interest to livestock producers in the southern High Plains.

In this post, the question is whether or not winter grazing of cropland residues is an activity that requires a federal NPDES permit under the federal Clean Water Act.

In this weekly roundup, the second summary asks whether or not land-applied livestock manure may be considered a “hazardous waste” under the federal Resource Conservation and Recovery Act (RCRA).

 

 

 

 

 

What is a “Nuisance?”

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CAVEAT:  This post is intended to be informative, but it in no way represents legal opinions or legal advice.  If either of those is what you’re after, seek the counsel of a competent attorney.

The term, “nuisance,” has a long and distinguished history in common law.  Generally speaking, an activity creates a nuisance when it substantially and unreasonably interferes with the right of another party to enjoy his or her property.  Normally, a nuisance activity does not involve physically trespassing on another’s property, but attributes or results of the activity may cross the property line.  Examples of such attributes and results include some forms of air pollution (odor, smoke, dust), noise, and light, but those are not the only categories of nuisance available to a plaintiff.

Here’s an example of an ongoing nuisance scenario from Great Britain, involving early-morning noise from a church.

Here’s an example of a successful nuisance lawsuit against an agribusiness in Iowa, which alleged a substantial and unreasonable interference from air pollution (dust, insects) and noise.

Nuisance law is typically enforced on a case-by-case basis because the right to enjoy one’s property is held to be a fundamental property right that may be waived by easement or covenant between neighbors, unlike some (for example) air-quality standards that are enforceable by a party, such as a regulatory agency, that is not directly affected by the condition.  Even if a person is complying with every applicable law and regulation, that person may still be subject to a nuisance lawsuit.

In some legal theories, a nuisance claim is subject to an evaluation of “temporal priority.”  That is, if person A has been engaged in a certain activity on her property for many years, and person B just recently moved onto a neighboring property, person B may not prevail in his nuisance lawsuit because he essentially moved into pre-existing conditions created by the long-standing activity of person A.  In the case of agricultural property, all fifty states have codified that theory in the form of “right-to-farm” laws.

By contrast, some states have created formal procedures for evaluating nuisance claims.  Here’s a fairly typical example from the State of Washington.  In Texas, the Texas Commission on Environmental Quality has guidelines specifically for alleged nuisance odors.

Agricultural producers should not take nuisance claims lightly.  As the Iowa example showed, a successful nuisance lawsuit can involve many thousands of dollars, including both compensation for harm and the cost of hiring attorneys and experts to defend against the suit.  In fact, expert testimony in agricultural nuisance cases has become somewhat of a cottage industry.

In fact, it’s best for agricultural producers to be pro-active by getting to know one’s neighbors, staying in touch with them regularly, notifying them in advance of certain operations (e. g., manure spreading) that could otherwise create a nuisance, and taking reasonable steps to reduce the duration and/or severity of conditions that would interfere with neighbors’ enjoyment of their property.  And remember this:  get familiar with the details of your state’s right-to-farm law; its provisions will serve as the basis for a legal defense if your operation is sued.  The scope and requirements of every state’s right-to-farm law are different!  Here is an excellent introduction to Texas’ right-to-farm statute written by Tiffany Dowell-Lashmet, Extension law specialist at the Texas A&M AgriLife Research and Extension Center in Amarillo.

As with most neighbor-relations questions, the best answer is usually found in the Golden Rule:  “As ye would have others do unto you, do ye also unto them.”

Extending the Life of the Ogallala Aquifer

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We are pleased to announce a new, USDA-funded project devoted to extending the life of the Ogallala Aquifer in the southern High Plains.  This project, led by Colorado State University and involving scientists and engineers from Texas, New Mexico, Oklahoma, Kansas, and Nebraska, centers on improving irrigation technology and management to reduce agriculture’s water consumption, thereby extending the aquifer’s useful life.  Unlike the northern half, the southern half of the Ogallala Aquifer receives hardly any recharge from rainfall or surface water, so the only currently practical way of extending the aquifer’s life is to reduce withdrawals.

Here is USDA’s press release about the Water for Agriculture Program.

Here’s the Water for Agriculture Proposal in case you’d like to read it.

Our inaugural meeting is 31 March – 01 April 2016 at the Texas A&M AgriLife Research and Extension Center in Amarillo.  We’ll be posting updates as the project gets underway.