It was fitting last Friday that the NYAS held their First International Workshop on Climate Informatics at their downtown offices on a beautiful day when everyone seemed to be dodging the city in advance of Hurricane Irene. Aside from being a wonderful venue to hold a workshop — I enjoyed reading the pages of Darwin’s “Descent of Man” writings on the wall — the discussions gave me much food for thought.

As with any small conference in a single-speaker setting, the majority of talks were good, covering the range of climate data and statistical methods and applications. And as is often the case, I was more impressed with the talks that addressed topics outside of my disciplines, particularly the machine learning discussion provided by Arindam Banerjee of the University of Minnesota.

But the highlight came during the breakout sessions, which provided in-depth discussions surrounding the challenges and opportunities in applying new methods to climate data management and analysis. Topics ranged from multiple-petabyte data management issues faced by paleoclimatologists to management and manipulation of large datasets associated with global climate modeling and Earth Observation (EO) technologies.

Overall, the workshop showed that we’re seeing the early confluence of two communities: climate scientists looking for new tools and techniques are on one side, data scientists and statisticians looking for new problems to tackle are on the other.

Data poor to data rich

One of the event’s more interesting side notes came from a breakout session where we explored the transition from being a data-poor field to a data-rich field. As an applied scientist, I certainly would say that climate researchers have been blessed with more data, both spatially and temporally. While the days of stitching various datasets together to test an idea may be behind us, the main issues tend to come down to scale. Is global coverage at 4KM resolution good enough for satellite observations? Can we build a robust model with data at this scale? Do interpolation methods for precipitation and temperature work across various physiographic environments?

While more data helps alleviate some of the scientific challenges we have faced in the past, it also raises more questions. Further, each year of global observations builds the database of reanalysis data — as an example, look at the reanalysis data that’s part of the MERRA maintained at NASA’s Goddard Space Flight Center.

That said, I’ll default to the position that too much data is a good problem to have.

Path forward for the data community

The timing of this event was also useful for another reason. The upcoming Strata Summit in New York will bring together data scientists and others in the data domain to address the challenges and strategies this growing community faces. I’ll be giving a talk on new ways to collect, generate and apply atmospheric and oceanic data in a decision-making context under the rubric of atmospheric analytics. In addition to the talk, I’m eager to learn how I can better utilize the data I’m working with as well as bring back some new tools to share with my colleagues in other fields who may face similar big data challenges.

Strata Conference New York 2011, being held Sept. 22-23, covers the latest and best tools and technologies for data science — from gathering, cleaning, analyzing, and storing data to communicating data intelligence effectively.

Save 30% on registration with the code ORM30

Related:

• The quiet rise of machine learning
• Interest in renewable energy could benefit data services
• Industrial ecology and big data

Such investments from non-traditional cleantech investors are starting to receive more attention. The sustainable increase of large-scale infrastructure investments in the alternative energy sector will likely be accompanied by a rise in the demand for data-driven services that can help optimize efficiency of the related operational costs.

Enter the growing need for timely and accurate weather data. Last month I touched upon the potential for the weather services sector to contribute their expertise to the smart grid arena. Demand anticipation, efficient raw material utilization, baseload and peak usage forecasting, logistics planning — these are just a few of the many areas where atmospheric analytics can contribute to this growing global market. More frequent and more precise weather data can help utilities anticipate demand surges, and in the process reduce both unnecessary expenditures and unnecessary emissions. Such supporting weather data is not just limited to the network of government-maintained observation stations — cheap ubiquitous sensors can be placed just about anywhere, and granular data that can help make a decision more efficient translates to more streamlined raw material procurement and utilization, not to mention lower costs passed on to the consumer.

At many energy conferences of late containing the cleantech theme (look at the Green:Net event event sponsored by GigaOm), there has been a lot of talk around the benefits of “smart” meters and “smart” algorithms, which will in part be used to help transform the energy infrastructure. These tools and techniques can only truly be considered smart if they are embedded with ambient data feeds that can supply accurate data streams, which can be developed into weather-driven efficiency algorithms (largely based upon persistence). The resultant algorithms can then help to enable the energy management systems to operate in sync with their surroundings — in essence, becoming smarter.

As short-range demand anticipation models are largely based on a set of standard assumptions, there will be limited human involvement once a system is constructed (as long as good input data is available), and this will fit in nicely with the functionality associated with automated demand response systems. WeatherTrends360 provides examples of granular hourly weather data feeds and displays that can be embedded into such systems (see chart below). While the cornerstone of applied modeling (garbage in = garbage out) is implicit, it should be noted that a smart algorithm will only be as good as the data upon which it has been trained.

As the shift toward increasing the share that renewables make up in the total energy-generating matrix gathers momentum, the need for data services providing temperature, wind, and solar analytics will strengthen. Look for innovative ways in which the weather industry, including both data providers and forecasters, can generate new sources of returns in this space, as the industry evolves.

This statement, the title of an essay by evolutionary biologist T.G. Dobzhansky, was published in “American Biology Teacher” in 1973. It properly asserts that evolution is the cornerstone of any meaningful dialogue in the biological sciences, stressing the importance of ecological theory in understanding biological system behavior.

We can extend this ecological theme of interconnectedness to modern financial and commercial activity, where we can just as easily state: “Nothing in economics makes sense, except in the light of ecology.” Large-scale events that have disrupted energy, agricultural and material supply chains in recent months underscore the importance of viewing the world through a spatial lens.

With each passing decade, as new manufacturing and production origins have come online, the barriers to entry that for many years had prevented the functioning of a true global economy have slowly been dissolving. No industry or country can now be considered immune to the financial fallout stemming from supply disruptions. While globalization has most certainly provided much of the world with many benefits, from cheap, reliable energy, food and telecommunications, to an ever-expanding universe of choices to enrich our lives culturally and materially, the increasing number of choices is accompanied by a more fragile economy, susceptible to perturbations.

So while we continue to enjoy the aforementioned benefits of a connected economy, we need to recognize and appreciate the potential magnitude of the underlying risks. The global raw material supply chain is now spread out very thinly. So much that when even the undocumented news of a potential threat to the supply of particular materials sends ripples through the markets, affecting in near real time both the price of the material in question and the equity valuations of those in the exposed industries.

Image from Gary Horvitz

Darwin meets the Fed

As such, perhaps it is now time to take a more serious ecological or geographical approach to risk as it pertains to global commerce. Nassim Taleb repeatedly argues that the financial system needs to robustify. Using nature as a metaphor, Taleb maintains that mother nature is redundant, and the financial system should employ similar measures of redundancy to avoid blowing up. Multiple layers of protection are necessary to protect against shocks. While this redundancy can limit the swings to the upside (much to the chagrin of many fund managers ), it also spreads out the risk when the negative events inevitably arise, minimizing the disastrous consequences.

Theoretical biologist (and former managing director at Deutsche Bank) George Sugihara also calls for ecological principles to be included in more comprehensive risk management and mitigation strategies. Drawing from Sugihara’s own research into fishery management and collapse, he suggests that the early identification of tipping points, rather than trying to model irrational investor behavior, may serve as a more effective means to alert economists to potentially significant market corrections. We may even go as far back applying some of the early ideas of Darwin, by taking into account the influence of selective pressures, and the subsequent nonlinear nature of the evoked response.

Natural capital

These are just a few of the many instances where modern economic analysis and the goal of financial stability can benefit from approaches grounded in the natural sciences. This is a theme that has certainly been around for a long time, but I don’t feel that it has ever been taken seriously by those outside of academia. For risk managers striving to protect either natural or financial capital (or both), the need to appreciate not only the connections within a system, but more importantly their underlying geographical opportunities and constraints, is the first step in robustifying markets. Therefore, instead of trying to export theories of what prudent investors would or should do according to standard economic theory and structuring positions to capitalize on these assumptions (efficient markets are actually a fallacy), when a market-moving event commences, a robust strategy grounded in ecological principles will survive, and over the long term have a better chance at providing a healthy return.

While the risk manager may not be able to prevent the initial shock to the market, he or she may be able to better anticipate the potential effects, and in the process, properly construct a hedge to avoid the consequences. On paper, the job of the modern financial risk manager is to preserve capital, striving to maintain positive returns while minimizing drawdown. In practice however, the risk manager is as much a speculator as the head trader. To compound the increased risk for a collapse, most fund managers are watching their radar for the same potential risks. When an event surfaces and catches the market by surprise, (ie., Australia floods, Argentina drought, MENA civil unrest, increased volatility in oil prices, Japan earthquake, etc.) almost everyone loses. Will an approach that is partially resistant to such shocks limit the upside, curbing the potential bonus of a fund manager? Absolutely. But it will also ensure that the fund manager is still around to enjoy the capital they are paid to preserve and grow.

Only a truly diverse portfolio can be considered stable or robust. As in large scale-agriculture, monoculture may work for a while, but eventually, diversity wins.

Related:

• Industrial ecology and big data

The primary theme of the conference centered around tools and themes relating to investments in the environmental infrastructure of the next 10-50 years. Like any conference with a green theme, the typical high-level topics were covered. Discussions were woven around ESG reporting requirements, market outlooks for renewable credits, equity driven recommendations around who is/is not taking the lead, and long term sustainable investment themes.

Despite all of the positive discussions, I felt somewhat empty after leaving the final session of the day. I should be clear that this is not through a lack of qualified speakers or interesting discussion topics. Quite the contrary — for the most part, the speaker lineup was far better than what has been the standard self-promotional speakers that typify green conferences.

The problem was that there was not much new to discuss in what can be a viewed as a great market opportunity. I have been working at the intersection of applied technology, finance and environmental commodity markets for more than 15 years. The piece that bothers me is that the action items, for the most part, are the same as those that were ubiquitous when I came out of graduate school (ie., define “sustainability”) in 1996.

The bright spot: data

However, it became clear through the course of the day that there is a bright spot for a niche that has not been exploited to any meaningful degree in this community: the application and analysis of new and existing forms of data.

It was only a couple of decades ago that most manufacturing companies reported only what was required to satisfy local, state and federal pollution permits and/or regulations. The environmental management divisions were usually part of health and safety groups (many still are) and they were largely considered to be internal watchdogs, making sure that operations continued with minimal permit violations.

As cost pressures on raw materials and operations increased, innovative companies started to look at their own wastestreams for alternative uses of material not integral to the primary activity of the facility. As a result, new markets were spawned out of incremental improvements in operational procedures, centered around efficiency. Fast forward to 2011 and we now see job titles such as “chief sustainability officer” and “director of global environmental strategy.” Instead of being a liability, cleaner operational procedures essentially evolved into a strategic profit center, right alongside the “core” business units.

The fact is that most companies are now tracking their inputs and byproducts very carefully, whether they are doing so as a direct means to reduce pollution, as a cost-savings measure, or simply for PR to satisfy investors. The net result is that there has been an exponential increase in efficiency/environmental data available for primary stakeholders and investors alike. The challenge, and what I see as the opportunity, is how this data can be turned into something that creates value beyond the obligatory satisfaction of regulatory requirements.

All of the panels at the FT event presented cogent arguments for being proactive, and how their financial performance correlates to their various activities (I was particularly impressed by the discussion by the Bombardier exec). But I do want to see what comes next. What I really wanted to learn was how these massive datastreams, possibly analyzed in a different way, can create new markets. It is nice to say that we reduced pollutant-X by a measure of 20% year on year. It’s even better to say that we turned 15% of pollutant-X into a profit center.

Some interesting facts and partial truths came out of the various talks of the day, many of which stressed that new ways of doing business are needed to keep up with seemingly insatiable demand for raw materials. For example, in one early talk, a panelist noted that the US has only one metro population center with greater than 5 million people. By contrast, China has 51.

But there were also times where some fact checking was probably needed, evidenced by the statement from another panelist who described the sustainability initiatives at most Indian companies as top of mind to everyone from CEO to clerk. Or, that China has emerged as the world’s cleantech leader as a result of the desire to develop industries around cleaner sources of energy extraction and production. While the benefits can not be discounted, we should be clear that in both cases the market is the driving mechanism.

I was also pleased to see a session devoted to what I feel will be the environmental wildcard of the century. Regardless of what may or may not emerge on the climate side, the limiting factor for all global corporations, whether they be in energy, material extraction, or agriculture, will be water.

Getting back to data, the sustainability focus of a manufacturing company stands to benefit from the current developments in the fledgling world of the smart grid. Industrial ecology is, at heart, a perfect application of applied data science. If what the associated sectors have been moving toward comes to fruition, in theory, a facility can be expected to manage every ounce of material (and byproduct) from the moment it crosses the gate on the inbound side, to where it leaves the facility as product, emission or effluent. New economic uses for previously discarded material will also be realized, as one man’s waste is another’s treasure. One need to look no farther than bagasse in Brazil for a simple example.

When it comes to establishing a meaningful dialogue around the creation and utility of data, the use of proper metrics will certainly arise as an issue, as will the creation of a meaningful baseline. When a panelist described her percentage reduction of a certain byproduct without referencing normal usage at a facility, this does not mean very much out of context. A weather analogy is appropriate here: Regions of interior Australia have been reported over the last few months to have received 500% more precipitation than the five-year average. However, +500% over 0.1 mm is still nothing.

What I am really looking forward to is next year’s FT conference, with at least a session or two that has data at its core.

The Southern Oscillation index (SOI), a measure of El Nino/La Nina strength and duration, was strongly positive over the last half of the year, and in fact this may be the strongest La Nina that we have seen since the 1973/74 event. The figure below highlights the intensification of the La Nina over the course of the year. The numbers on the perimeter show the day of the year, and the SOI is represented by the solid line; we can see that at the start of 2010, the SOI was in negative phase (-10.1, -14.5 & -10.6 for Jan, February, and Mar 2010 respectively), but then a strong shift occurred during the El Nino – La Nina transition, and the year finished at nearly +27. Further, as the map above shows, the current equatorial Pacific Ocean Sea Surface Temperature (SST) anomalies are still negative, and while some La Nina indicators seem to be approaching a peak and then a return to neutral phase, the current event is still certainly not over.

Statistically, there are certain types of patterns that we can associate with a given La Nina or El Nino year. However, there is no typical event where all expected seasonal weather outcomes manifest themselves, so using this approach, or relying on analog years when attempting to identify potential seasonal impacts, can be dangerous. Disclaimer notwithstanding, there are some general relationships that tend to hold up that are highlighted in this map from NOAA’s National Climatic Data Center. Some of seasonal relationships did verify, and also led to some of the food stories that are getting an increased amount of attention by analysts and traders at the start of the year. A few examples include: dryness in Brazil & Argentina (wheat), increased precpitation and flooding in eastern Australia (sugarcane), and a wetter summer pattern across the Indian subcontinent (sugarcane, pulses).

In recent months, we have seen agriculture commodity prices exhibit a sharp rise in everything from coffee to natural rubber, and these higher prices have started to impact the margins of the producers, who more often than not, pass this along to the consumer. In an interesting recent article in the Financial Times (paid registration required), the author noted the following price changes (in %) of agriculture and energy commodities since January 1, 2010:

Remember that weather, particularly excess precipitation, will also affect the production cycle in the natural resources sector. As such, while impacts to agricultural interests are usually the first associated with an acute or extended adverse weather pattern, the energy and mineral sectors have weather exposure also. Further, with saturated fields and subsurface conditions present across much of the Queensland mining territories, there will not be a quick return to a normal production schedule after the heavy rains subside. With the potential financial implications (and opportunities) abound as the mining sector deals with the effects of this pattern, we are seeing this concern reflected by the types of inquiries received within our own customer base at Weather Trends. Most of our clients in the commodity sector aim to act upon the relationship between weather and commodities in the “traditional” sectors of energy and agriculture, but in recent months we have seen a marked increase in inquiries for forecasts and models relating the pattern to the broader natural resource extraction industries.

In a recent statement by the UN Food and Agriculture Organization, officials hinted at the potential for high prices to serve as a catalyst for food riots. As recently as 2007/08, riots broke out in three dozen countries across Asia, Africa and Latin America as a response to a strong and sustained spike in prices for staples including wheat, rice and oilseeds. However, for many food categories, the relative prices in this current regime have not equaled the highs seen three years ago. Also, while the negative impacts seem to get the most attention, growers in many origins are seeing better production and yields than they were during the season leading up to the riots.

So at the beginning of 2011, with our forecast in place, we are now developing and refining our commodity supply expectations for the year. Will the La Nina pattern remain in place to affect spring plantings and emergence for North America? If the pattern shifts back toward El Nino later in the year, what can we expect for Australia/AsiaPac? And how might a transition effect the onset of the Indian Monsoon? Finally, what do all of these factors mean for commodity prices in 2011? These are only a few of the many questions that we are attempting to quantify and provide to those with a weather risk to their operations or their portfolio.

For those who will be attending the American Meteorological Society meeting in Seattle in a couple of weeks, I will be discussing some of these topics in greater detail.

Following a Weather Trends post (Wheat Weather) the other day regarding the devastating flooding across eastern Australia, we encourage readers to follow the Standard Precipitation Index (SPI) to monitor conditions in the region through January. The map below depicts the Dec2010 SPI, and it will be worth noting the changes when the Jan2011 index is released.

( map courtesy of IRI/LDEO )

Index values >2 correlate with extremely wet conditions for the particular region. Droughts often are broken with an extreme pattern in the opposite direction, and while the short term ramifications to the agricultural sector in eastern Australia will see a negative impact in the current crop year, this pattern is likely to have a benefit for the following year(s) as areas that have been moisture deficient will have the ability to recharge soil moisture and groundwater.

The primary beneficiary will first become apparent for end users in India, but the gains will extend beyond the borders to virtually any country/entity with a financial or social exposure to raw material prices.

Science progresses through the open exchange of information, and while on one hand the applied benefits of scientific research and the resultant applications can be commercialized and therefore protected, the open nature of collaborative agreements such as the India/U.S. item discussed can also be a great benefit to a much larger segment of society.

Many public and private weather groups have developed and refined techniques to develop monthly and seasonal weather forecasts. Forecasting, and in particular long-range forecasting, often relies on a blend of common physical/fluid dynamic principles coupled with a variety of closely guarded mathematical approaches. As such, while the basic scientific principles underlying the development of a forecast may be generally the same regardless of the source of the forecast, each public or private forecasting group puts their own spin on the forecast, where they try to separate themselves from their competition.

However, there is strength in numbers. So from the point of view of the end user, a forecast can gain a higher confidence if it is in more agreement with other reputable forecasts.

For many years, the Indian Met Department (IMD) was the only source for the agricultural sector when it came time to develop plans around the annual monsoon. For growers, these decisions include seed variety, plant/harvest dates, quantity of pesticides, etc., which carry a heavy financial burden, and oftentimes the decisions are made based on one (IMD) forecast. In recent years, the IMD has had a less than spectacular record in their seasonal rainfall forecast during the monsoon, so this serves as a time where other approaches can and should be considered.

The result of a potential collaboration via partnering with NOAA or other non-Indian weather groups can only enhance the IMD’s own forecasting process. By exchanging some methods, the IMD can learn about where the strengths and weaknesses lie within their own methodology. In the long term this is a very successful methodology.

The result will then be a better forecast for not only the IMD’s primary customers (the agribiz community), but others who also have an exposure to fluctuation in prices of important raw materials sourced from India. Further, better forecasting and monitoring techniques that are jointly developed will serve to provide more price transparency in the futures markets of related commodities, and minimizing price volatility is good for both producer and consumer.

There is no single forecast group than can develop a long range weather outlook that is correct 100 percent of the time. Taking an “ensemble approach,” where results from several different forecasts are used to guide and continuously refine a seasonal outlook, is a safer way to approach the weather risk associated with an upcoming season.

Related:

• Weather data and the supply chain
• Seeing green from space
• Growing new data streams

The global agricultural community is comprised of big agribiz (think Cargill, Deere & ADM), family farms in Mato Grosso, bankers/hedge funds (many of which have no clue how things grow), and everything in between.  As I do much of my work at the first link in the global supply chain, I attend numerous events like this each year.  When working with such a diverse group, it is often difficult to achieve consensus on anything, and particular irreverence is often displayed towards new technologies.  This is notable among the grower community, whose farms and farming techniques are often passed down through generations, just like an old watch or a wedding ring.  But after going into this latest conference expecting the usual pessimism about cooperation, consensus formed around the need and application of new sources of data.

The innovations in agriculture that grab most headlines are usually related to technologies such as new seed varieties, super-combines, or physical infrastructure that increases efficiencies in drip irrigation.  So, after one panel session comprised of investors looking for opportunities in both hemispheres of the Americas, I asked about the “non-tangible” innovations that often fly under the radar: those that require access to large databases, data manipulation creativity, and computational resources. The panel agreed that these are major focal points for the next generation of agricultural investments. Nearly every discussion that followed seemed to touch upon this theme.

The nice thing about quantifiable data for this community is that it can come from subjective sources as well as those repeatedly tested in a laboratory. A grower’s logbook for instance — containing such information as how a particular crop might respond to a specific weather pattern, the amount and type of pest-fighting application used in a given season, and local market offers — can all be assembled into an index, which is another quantifiable data stream that users may have at their disposal.  And while upon first glance one might suppose that data streams are closely-guarded secrets, growers are probably among the most supportive advocates of open access and data sharing. What wiped out your neighbor’s crop a decade ago may be the very thing that hits you this year.

In several offline conversations during coffee breaks, I offered some insight based on projects that Weather Trends is pursuing. We’re working with clients to quantify relationships between weather patterns, crop disease, and agricultural yields. The potential for collaboration, and a new growth sector for this industry, was evident to everyone.

Looking ahead, I expect numerous high-quality and high-margin products to come to market that have their “roots” in both the acquisition of new types of agricultural data (ranging from genomic to planetary weather), as well as in repackaging existing data. As global food supplies are routinely subject to a number of shocks via weather, foreign exchange or geopolitics, this will be a very important platform for the global agricultural community in the years to come.

Related:

• Seeing green from space
• Weather data and the supply chain
• Sensor networks and the future of forecasting

Remote sensing is just what the name implies: a suite of tools for accessing information about a subject without actually “touching” it. Remote sensing devices range from your own eyes to satellites in orbit hundreds of miles above the surface.

Many of these Earth-orbiting satellites are in continuous data acquisition and transmission mode, capturing everything from ocean temperatures, to land reflectance at the surface of the Earth, to global chlorophyll production. Each of these satellites is equipped with a variety of instruments, which collect very specific segments of information contained in the various bands of the electromagnetic spectrum. Users then, depending on their area of interest, will take the digital data and construct profiles of their study area, analyzing individual or composite band data and building time series profiles so that these databases can start to tell a story.

One of the most common multispectral analyses uses information derived in the near infrared and visible (red) spectral regions, called the Normalized Difference Vegetation Index, or NDVI, which can be viewed as a “greenness index.” The higher the value on the scale, the more photosynthetically active the surface vegetation is, which can be used as an indicator of vegetation health. Whether the objective is to assess the health of crops in a specific growing region or across an entire continent, this index is a good indicator of how a crop region may be progressing, and where appropriate, crop failures can start to be identified. This year, the NDVI was used as an important proxy for agricultural health in India as the image below shows.

Within the graphic, the smaller image to the left shows the NDVI in July of 2009, and the smaller image on the right is the index one year later.

India is a country where agriculture and related industries make up a large part of domestic economic activity, and is therefore largely dependent on the health of the annual monsoon rains. This is not just for those directly involved in agriculture — the nation’s agrarian base consists of millions of independent farmers, who are the primary consumers of the goods purchased by the secondary industries, such as automobiles and motor scooters. So a poor monsoon not only means the potential for food shortages and less revenue for farmers, it also means less income to support other segments of India”s economy.

In 2009, the Monsoon was officially declared a “failure” (see image below left) as seasonal rainfall totals for the country came in 22 percent below normal. In the midst of a poor global macroeconomic picture, the lack of rains last year could not be repeated. This year has produced a much better monsoon (below right), and fortunately, Weather Trends clients were able to make longer range decisions with this forecast in mind. While 2010 is not a complete recovery, as India’s north eastern states are still low, the pattern has been much more beneficial to the agricultural sector in the central and southern states.

Just receiving more rain does not necessarily mean economic recovery, so we look to the NDVI to measure the change. As we can see, the year-over-year images reflect the better ground conditions with the “greenness” across central and southern India indicating better crop potential.

The NASA Ocean Color Web is a treasure trove of research-grade data that can be used to analyze these environmental variables, and combinations of these data sources can lead to the construction of new indices that may be used an stand-alone analyses, or for incorporation into longer time series models.

Related:

• Weather data and the supply chain
• Sensor networks and the future of forecasting

Identifying extreme events is just one area where it’s useful to apply long-range weather intelligence. In addition to the employment of various techniques to avoid human suffering, there are also numerous commercial interests who can apply this information as well. Just as a property reinsurer might want to know how either an acute or a seasonal weather event might affect premiums, growers may also want to assess crop potential, retailers might want to project seasonal product demand, and traders may want to employ a pricing strategy.

Food, energy, and weather

There are two things that each and every one of the roughly 7 billion people on the planet need: food and energy. Weather is central to both.

On the food side, advanced weather outlooks can help assess crop potential, both in terms of actual production/yield, and potential for disease pressure. As noted in my previous post, growers who anticipated crop losses from excessive heat or a lack of moisture (or both) may have purchased crop protection insurance, while manufacturing companies may have secured forward prices or managed their exposures through a hedge.

But even at a more basic level, there is a lot of environmental data available at the public’s disposal. If structured and viewed in the right way, that data can provide insights on the supply side of many basic commodities. Assessing food production potential for basic necessities is what I like to call the first true step in understanding the global agricultural supply chain.

Now, there’s no such thing as a perfect forecast. Even with forward-looking metrics that can be deemed 100-percent accurate, there are variables that go into determining, for instance, how many bushels per acre a particular region’s wheat crop will yield. Forecasting is part art, part science. From the science perspective, the more clean, reliable data that we can obtain and plug into a model, the better we may get at determining production potential, or more importantly, highlighting areas that may be susceptible to a weather risk.

How La Niña affects milk production

Here’s a simple example of the climate/food relationship: Casual weather observers are probably familiar with the El Niño Southern Oscillation cycle (referred to as ENSO). This particular phase of this large-scale physical weather driver often governs the global pattern. While an El Niño dictated much of the US pattern in 2009 (remember snow being trucked in for certain Olympic events in Vancouver?), the opposite La Niña has developed in 2010, which brings its’ own set of variables. The current La Niña is one reason that our January-February outlook at Weather Trends is for a little cooler than last year for the western US. We also can use the ENSO index to assess yield potential for a commercially important commodity: milk.

For some time, we have known about the positive relationship between La Niña-like conditions and milk production for US dairy producing herds.  During an El Niño, conditions for much of the US dairy production regions may tend to be warmer and wetter, and as dairy cows exhibit sensitivity to heat stress and/or muddy fields, these conditions correlate with decreased milk production, particularly during the key months (March-July) in the annual cycle.  As the opposite La Niña pattern has been developing for the last several months, cooler temperatures have been present across much of California (the largest producing state), limiting heat stress and contributing to an active grazing season, both of which are good for milk yields.  This particular La Niña is actually shaping up to be a pretty strong event, and as a result, we have been expecting better US production numbers to follow.  Note that this does not take into account decreased herd size, so the emphasis is on milk yield per cow.

To test this idea, we can look closer at the relationship between the Southern Oscillation Index (SOI), which is an ENSO guide, and US milk production over the last decade.  Specifically, we can highlight periods where there has been a stronger trend toward positive-phase SOI in recent months relative to the 6 month moving average; the assumption being a stronger relative acceleration toward positive phase supports better milk production weather. 

Using a simple decision-tree scheme, the time series was split by grouping all months where the more recent period showed stronger positive SOI characteristics (as defined by a quantitative index).  Of this reduced group of months, we then looked at monthly normalized US milk year-over-year (y/y) production to see if stronger numbers may have been related to the index.  Therefore, using a sample size (n) of 60 months, y/y milk yields increased in 52 of these months (87 percent), verifying that a positive correlation exists.

This is not to suggest that the SOI, or any other weather parameter, is the primary factor in assessing potential milk production.  Remember, a forecast is a blend of art and science, so some subjectivity is involved. But this simple analysis does demonstrate that weather can be a key driver in the amount of milk that is flowing from producers to consumers, and it bears watching as a signal for forward pricing, and assessment of global stocks.