Associate Professor of Economics
Montana State University
Dept of Agricultural Economics & Economics
My musings about news, publications, and general concepts in agricultural markets. The blog focuses primarily on issues relating to grain and cattle markets in the northern Great Plains, but other topics that I find interesting are also included. Your ideas for blog posts are always welcome and encouraged. Please shoot me an email or fill out a feedback/suggestion form.
As my colleague Kate Fuller astutely pointed in a previous post, several fields near my house were planted into canola this year. This is the first time in years that I've seen anything other than small grains in those fields. Then, a few days ago, I was asked to provide some thoughts on potential growth of canola production in North America. All this made me wonder: what the heck is going on with canola?!?
While Canada's canola production has been increasing steadily since the 1970s, I was pretty certain that the United States' oilseed of choice was soybeans. But, as the figure below shows, there does seem to be a consistent uptick in canola planted acres within the United States. And the table immediately following the figure indicates that the northern Great Plains have been a major contributor to this growth.
|2017 Planted Canola Acres||Trend since 2009|
Data sources: Canadian data are from StatCan and U.S. data are from the USDA National Agricultural Statistics Service.
So, what could be affecting this growth? Well, economics certainly is one likely culprit. One of the factors that goes into a farmer's land allocation decisions is the relative price of one crop to other crops that could also be grown on an acre of land. If a producers observes the price of one crop being relatively higher then the price of another, they are more likely to consider planting the more valuable crop (all else being equal). But does this relationship hold for canola?
The figure below again shows planted canola acres, but I removed the trend so that it was possible to compare planting decisions across different years. This is similar to adjusting for inflation in order to compare the price of 1 bushel of wheat, for example, in 2017 to the price of a bushel in 1950. So, the figure shows how planted acreage varied across years with 2017 as the base year for comparison. Additionally, the figure shows the average price ratio (green dotted line) between canola and four competing spring crops: spring wheat, soybeans, lentils, and peas. That is, the green line shows the average relative price (value) of canola compared to the prices of the other crops. But, I only limited these prices to the March–May period of each year.
In other words: the figure below shows the relationship between relative crop prices that producers observed at the time when they made planting decisions and the resulting planted canola acres in that year.
The figure makes fairly evident that in years when the price of canola was relatively higher than the prices of the other competing crops during the March–May planting decision period (i.e., spikes in the dashed green line), we also observed higher canola planted acres (i.e., spikes in the blue and orange solid lines).
In addition to economic reasons, northern Great Plains farmers are also likely looking to canola for disease management properties. With the boom in pulse production in the northern United States, there has been increased concerns of disease pressure associated with overly aggressive pulse–wheat rotations. Many experts recommend at least a three-year rotation when planting pulse crops in cereal systems, and in the semi-arid, drylands production practices of the northern Great Plains, canola has seen increased interest from producers.
Of course, at $7.00–8.00 per bushel spring wheat, most crops will have difficulty competing for land allocation next year. But, if pulse crops continue to be a solid player in the NGP production environment and spring wheat prices return to historical averages, fields of yellow may become a much more prominent feature of the northern U.S. landscape.
I am currently attending the 2018 annual meetings of the Agricultural and Applied Economics Association, which is the national and largest professional association for agricultural economists. One of the sessions this year provided an update about the status of the 2018 Farm Bill by a panel of three economists who are on the ground level of the legislative discussions. The panel included Callie McAdams (Deputy Economist for the House Committee on Agriculture), Matt Erickson (Chief Economist on the Senate Committee on Agriculture, Nutrition, and Forestry), and Robert Johansson (Chief Economist in the USDA's Office of the Chief Economist). Each person provided a perspective from their branch.
All three panelists agreed that the 2018 Farm Bill is being crafted at a time that's very different than when the 2014 Farm Bill was created:
These two factors are making the process difficult: while there is an increased call from producer groups to strengthen safety net programs in a time when farm returns are lower, there is also less available resources than in the past.
The House Committee has identified four key areas of focus: assessing potential problems of the ARC-County program; evaluating issues and solutions with respect to the dairy and cotton support programs; potentially developing programs that support livestock disease preparedness and management; and "doing no harm" to the federally subsidized crop insurance program.
The Senate Committee's motto is: "Producer led, producer driven." As such, they've identified several areas in which they have received feedback for increasing attention and expenditures:
Despite these identified objectives, the consensus from representatives of both chambers of Congress was that the biggest challenge with crafting the 2018 Farm Bill is a more restrictive budget. For example, Mr. Erickson noted that there are 39 existing agricultural programs that are subject to discretionary spending (i.e., the committee has leeway in allocating funds across those programs). However, there is a $3 billion shortfall in funds that can be spent on those programs. While this amount is nothing more than a blip on the radar when compared to the entire federal budget, it could mean that some of those 39 programs may be severely curtailed or eliminated.
The session concluded with a call by the Congressional staffers to those in the agricultural industry for voicing their feedback and concerns, so that these issues can be considered as part of the "producer led, producer driven" process for crafting the 2018 Farm Bill.
Microsoft, the company that has for decades dominated the personal computing world with software such as Windows, Word, and Excel, has announced plans to take a shot at bridging the technological divide still facing many rural communities in the United States. The company has proposed the development of a new way for bringing high-speed Internet access to rural communities.
Termed "super Wi-Fi," the technology would piggy-back on largely unused over-the-air television frequencies—the kinds you would pick up with rabbit-ear antennas—to beam high-speed Internet signals. Because the over-the-air television frequencies are relatively low powered, they can reach even some of the most remote places in the country, implying that over 24 million people in rural areas who still do not have high-speed Internet access could begin to experience its benefits.
Recent rural development research has almost ubiquitously shown that increased use of high-speed Internet by members of rural communities creates economic benefits for those individuals and their broader community. For example, individuals can diversify their income streams and expand opportunities for non-agricultural employment, which can result in positive economic spillover effects and reduce migration out of rural communities. Other research has shown that increased use of high-speed Internet by rural communities typically increases the median income levels of individuals in those communities and lowers unemployment rates.
But important challenges still remain for realizing this technological and economic potential. First, Microsoft would need to convince state and federal regulators that their use of over-the-air TV frequencies would not interfere with TV stations' abilities to deliver their own programming. Second, the company would need to significantly reduce the cost of consumer-level devices that receive the super Wi-Fi signals. Currently, this device costs over $1,000 per home, but Microsoft is targeting a $200 price point in the future. Despite these challenges, the size, economic resources, and political influence of the software giant substantially increase the chances that the company will be more successful in overcoming these regulatory and technological challenges than previous attempts by smaller start-ups.
A perhaps more important challenge, however, is the adoption gap that has repeatedly been shown to exist in rural communities. That is, even when high-speed Internet access becomes available, its adoption and use by members of rural communities has been lower than by those living in urban and suburban areas. Ultimately, most economic benefits of high-speed Internet are associated with the technology's use rather than simply an increase in access to the technology, so better understanding of how to increase both adoption and knowledge about effective use of high-speed Internet is critical.
I did not expect things to look as bad as they did when I drove to northeastern Montana last week to talk with wheat and pulse producers participating in the 2017 Northeast Montana Pulse Plot Tour. The primary U.S. spring wheat production region has been in an increasingly severe drought since mid-May, and with triple-digit temperatures forecasted in the next ten days, there does not appear to be respite in sight.From conversations two weeks ago with several Extension professionals from North Dakota State University, I was shocked to learn that some farmers in north-central and northwest North Dakota have begun to hay their spring wheat because they see no chance for production condition improvements. The Drought Monitor map paints a clear picture of the deteriorating conditions in the past two months.
Unsurprisingly, this has had major impacts on spring wheat markets. Since mid-May, hard red spring wheat futures prices have skyrocketed, as markets have become increasingly worried about the potential of another year with scarce spring wheat availability. Spot prices in most locations within the U.S. spring wheat growing region have followed, exceeding their 10-year average—a ten-year period during which wheat prices rose to historically-high levels.
Source: Futures prices are from Quandl and spot prices are from the USDA Agricultural Marketing Service.
A natural question to ask is whether this market reaction is due to worries purely about not having enough wheat or about not having sufficient high-quality (high-protein) wheat. A measure that I like to use is the spread between prices of spring wheat and winter wheat futures contracts. With spring wheat typically having a higher protein content than winter wheat, an increasing price spread between these two commodities would be suggestive of quality deficits (alternatively, a constant spread and rising prices in both the spring and winter wheat markets would suggest worries about overall quantities).
The data in the figure below clearly indicate that there is significant worry about the availability of higher-protein wheat. Hovering around $0.75 per bushel spread between June 2016 and May 2017, the price spread shot up to over $2.50 per bushel in early-July. News about quality concerns in France and Spain are also contributing to the global high-quality wheat supply concerns.
Source: Futures prices are from Quandl.
Without rapid improvements in production conditions, it is unlikely that these prices will return to their pre-May levels. But, while 2017 production conditions are bleak, perhaps producers have sufficient spring wheat inventories to take advantage of the high prices. While inventory data for spring wheat are not available (and using inventory data for all wheat classes would be misleading because of the record production year in 2016), an indirect measure is the quantity of U.S. hard red spring wheat exports.
The figure below shows that this year's spring wheat exports have exceeded last year's exports and their five-year average. The increased spring wheat exports reflect U.S. producers filling the voids created last year's scant global production of high-quality wheat. But what this also implies is that spring wheat inventories are likely lower than usual, potentially reducing opportunities for producers to take advantage of the seemingly unstoppable spring wheat price increases.
Source: USDA Foreign Agricultural Service
Moving forward, three factors are likely to play important roles in determining whether the spring wheat price rally will continue:
Despite the unusual May winter conditions in Kansas last month, projected U.S. winter wheat yields (49 bushels per acre) remain around the five-year average of 47 bushels per acre, but well below last year's 55 bushel per acre record high production. However, record decreases in winter wheat planted acreage places projected U.S. output at 1.25 billion bushels, down approximately 25% from last year.
While overall production remains on par with the five-year average, quality concerns remain, heightening anxiety of another marketing year with a dearth of high-quality product. Many of the issues step from high temperatures and lack of precipitation in the primary spring wheat production regions: eastern Montana, western North Dakota, and the Canadian Prairies. The latest Drought Monitor report—represented by the map below—shows that these regions are at moderate to severe drought levels.
Figure: Drought Monitor Map for June 6, 2017
USDA wheat quality ratings (early-June Crop Progress report) also show that wheat rated in the top two categories—Excellent and Good—are below last year's values for winter wheat, and well below last year's and five-year average ratings for spring wheat.
|Montana||North Dakota||United States|
|% Rated Excellent and Good, 2017||47%||—||49%|
|% Rated Excellent and Good, 2016||65%||—||62%|
|% Rated Excellent and Good, 5-year average||59%||—||44%|
|% Rated Excellent and Good, 2017||48%||52%||55%|
|% Rated Excellent and Good, 2016||74%||84%||79%|
|% Rated Excellent and Good, 5-year average||61%||80%||72%|
Table: Crop Progress Report Ratings for Winter and Spring Wheat, Week 22 (early June)
In a bit of potential good news for U.S. wheat producers, Brazil is expected to import a nearly record-high amount of wheat next year, due to increased demand and significantly reduced domestic production. Low global wheat prices have led Brazilian farmers to switch land into other crops, leading to a projected 23% average decrease in wheat production. The Brazilian crop bureau, Conab, has increased their import forecast to 7.00 million tonnes (approximately 261 billion bushels).
Proximity between the United States and Brazil could provide advantages for U.S. producers to access these markets. Additionally, Brazil's currency—the Real—has remained relatively steady in 2017 against the U.S. dollar, but has dropped by nearly 2.5% against the Russian Rubel. This could also help maintain U.S. producers' competitiveness in global markets.
Several days ago, President Trump's administration released a proposed budget, which included significant cuts to many government programs. Agricultural and food programs, too, did not escape rationed funding. And while the proposed budget has been quickly deemed "dead-on-arrival" by widespread and seemingly bipartisan declarations from lawmakers (a common outcome for presidential budget proposals during the past several administrations), it is also widely acknowledged that these proposals provide insights into the White House's legislative priorities.
As discussions and negotiations for the 2018 Farm Bill quickly approach, the proposed budget could be one of the first signals of specific farm policy components that could be affected. For example, the budget includes a provision that would set a $40,000 limit to the amount of crop insurance subsidies that participating farmers could receive. Recently, Eric Belasco provided a brief analysis of how such a policy might impact U.S. producers and taxpayers. Another proposed change in President Trump's budget is a stricter means-based eligibility limit for receiving payments from the Agricultural Risk Coverage (ARC) and Price Loss Coverage (PLC).
The stated purpose of the ARC and PLC programs described by the USDA Farm Service Agency is to provide financial assistance to commodity crop producers during periods of market downturns and/or yield decreases. The ARC-County program is structured to provide assistance when county-area revenues drop below some trigger value and the PLC program makes payments when market year average prices fall below a reference price. Currently, there are two primary limitations for these program payments: (1) total payments are limited to $250,000 per household per year and (2) households with an adjusted gross income (AGI) above $900,000 are not eligible for payments. Trump's budget proposes to reduce the AGI threshold from $900,000 to $500,000.
The new AGI threshold represents a fairly significant 45% reduction from the existing criterion. On the surface, this certainly appears as potentially having a major impact on restricting access to these safety net programs for a large proportion agricultural producers. But would this really be the case?
Data from the farm-level Agricultural Resource Management Survey (ARMS) help provide an initial understanding of the answer. Using the most recent 2015 information about farm-level ARC-County and PLC elections and payments as well as associated data describing household gross farm income and gross off-farm income (the sum of which provides a close approximation of household AGI), I consider the trade-off between potential budgetary savings of the proposed AGI threshold and the number of farms that could lose access to ARC and PLC payments.
The figure below shows how farms would have fared if in 2015 the AGI threshold was lowered from a limit of $900,000 to $500,000. The blue bars represent farmers who would not have been affected, and yellow bars pertain to those who would have lost access to payments.
Figure: Impacts of Lowering the AGI Threshold for ARC/PLC Payments
First, the data show that the lower threshold would have resulted in a total savings of approximately $177 million. That represents approximately 42% of the total ARC and PLC payments made in 2015 to the sample of farms in the dataset. The large savings occur primarily because farmers affected the change would have received nearly $30,000 per household in ARC and PLC payments. This is nearly 3.5 times the payment received by farmers who would have been unaffected by the AGI threshold change.
However, what is the trade-off in terms of farms losing access to ARC and PLC payments? The data indicate that fewer than 1 in 10 farmers would be affected. Over 91% of U.S. farmers would still remain eligible to receive financial assistance through the ARC and PLC programs in periods of market downturns and unforeseen events that adversely impact crop yields.
Additional research and analysis is needed to understand the full range of dynamics and impacts of this and other proposed policies. However, since at least some portions of Trump's proposed budget are likely to be seriously considered and potentially implemented, the outcomes presented above can certainly provide a baseline for further discussion.
Pulse crop production—mainly peas and lentils—has expanded rapidly in the past decade in the northern United States. In 2016, just around 1 million acres were planted in Montana, and that number is expected to increase in 2017 (compare this to just over 200,000 acres in 2007). The figures below provide a visual representation of just how stark and rapid the expansion has been.
(a) Planted Pulse Acreage, 2007
(b) Planted Pulse Acreage, 2016
Figure: Planted Pulse Acreage in Montana, 2007 (top) and 2016 (bottom)
Source: USDA National Agricultural Statistical Service, CropScape (Cropland Data Layer)
This growth has been (appropriately) celebrated and has provided to many Montana and North Dakota producers a much-needed opportunity to diversify their production portfolios (and for many, making it a bit easier to face recent $4.00 per bushel wheat prices). But, with so much additional crop output being brought to market, little is known about the extent to which grain handling facilities have adapted and whether they are able to provide the needed supply chain capacity to operate effectively as pulse production continues to increase.
Using data provided by the Montana Wheat and Barley Committee, I mapped and calculated several descriptive statistics about the current state of Montana's pulse handling industry. The map below shows active grain handling facilities in Montana (indicated by a circle), with green circles indicating those that accept pulse crops and gray circles showing those that primarily specialize in handling only small grains. The size of the circle indicates the relative overall storage capacity at the location, and the lines represent railways.
Figure: Locations of Active Grain Handling Facilities in Montana
The maps makes evident that despite the growth in Montana's pulse production, there are still many more elevators that do not accept pulse crops, even in the northeast part of the state (the primary production area). In fact, only 34% of elevators in Montana handle pulse crops. Moreover, an average elevator that accepts pulse crops has an average capacity of 563,909 bushels, while an average elevator that specializes in wheat handling has a capacity of 773,307. On aggregate, out of the total available capacity at active Montana elevators, 29% of that capacity represents elevators that accept pulse crops while 71% of capacity specializes in wheat.
These statistics suggest that despite the rapid development of the northern U.S. pulse markets and increased focus on developing improved pulse crop varieties that can maximize farm productivity, there are still significant "growing pains" and questions that the industry must answer in order to realize its full potential, including:
The map of the U.S. rail infrastructure (see below) illustrates the extent of the challenge described in the last bullet point. Northern U.S. wheat has traditionally been shipped either west to export facilities in the Pacific Northwest or east to the Great Lakes region. As such, rail lines through much of Montana and western North Dakota—the primary pulse production regions—have no north–south routes, and the only major north–south throughway does not occur until Minnesota. Therefore, shipping pulse crops to domestic destinations in the Midwest and to southern export terminals requires significant additional shipping costs on the part of the grain handler, which are likely to be passed down to grain elevators and to farmers in the form of lower price bids.
Figure: U.S. Rail Infrastructure
Undoubtedly, the development and growth in northern U.S. pulse production is a boon to Montana and North Dakota producers. However, challenges within the supply chain beyond the farm level are critical to understand when considering the future evolution of this market.
As the 2018 Farm Bill negotiations creep closer and closer, the usual discussions about the efficacy and efficiency of government programs that make it into the Farm Bill. In the 2014 Farm Bill, two new programs were introduced—Agricultural Risk Coverage (ARC) and Price Loss Coverage (PLC)—and they are likely to be topics of discussion.
The stated purpose of the ARC and PLC programs is to provide financial assistance to commodity crop producers during periods of market downturns and/or yield decreases. The ARC-County program is structured to provide assistance when county-area revenues drop below some trigger value and the PLC program makes payments when market year average prices fall below a reference price.
While the efficiency and cost-effectiveness of the specific program structures has been and continues to be debated, little has been said about whether these programs equally benefit farmers across the farm income distribution. When market downturns occur, small and medium-sized farms are arguably more vulnerable to these instabilities. How have ARC and PLC program payments been allocated to these farms, especially in relation to program allocations to larger farms that may have greater resource to sustain market volatilities?
To begin answering these types of questions, I used responses from the farm-level Agricultural Resource Management Survey (ARMS) to assess how ARC-County and PLC payment were distributed in 2015, when payments from both programs were triggered by agricultural market downturns. Next, I looked at how these payments differed across the farm sales distribution to determine whether there were differences in how much operations received based on the size of their gross revenues. I focused on the three largest crops produced in the United States: corn, soybeans, and wheat.
Figure 1 shows the per farm average payment to operations across the farm sales distribution. The figure shows that, on average, farms that earned the median amount of gross revenues from farm sales received approximately $4,000 per farm in total ARC and PLC payments in 2015. However, farms that were in the top 10% of farm sales received, on average, approximately $65,000 per farm in program payments. Farms in the lowest 10% of farm sales received just under $300 per farm.
Figure 1: Per Farm ARC+PLC Payments in 2015, by Farm Sales Decile
Figure 2 shows the total payments received by farms across the gross revenue distribution. The figure shows that farms that were in the top 10% of the farm sales distribution received, in total, approximately $500 million in ARC and PLC program payments. This represents 58% of all program payments. Farms that had the median amount of farm sales collected approximately $23 million in program payments, representing about 3% of total outlays.
Figure 2: Total ARC+PLC Payments in 2015, by Farm Sales Decile
As discussions about the 2018 Farm Bill are beginning to take place and the future of farm program structures are considered, using data-driven approaches to evaluate existing safety net programs are key to understanding whether these programs are efficient, effective, and equitable, and whether changes are needed to attain those goals.
In the past several years, global wheat markets have been productive. Really productive. Partly this was triggered by higher plantings in response to higher prices in 2011 and 2012 and partly due to favorable production conditions in the past several years. At the same time, global wheat demand stagnated and world inventories have risen to historical highs. As such, wheat prices have tumbled, both in absolute terms (relative to the average in the past decade) and relative to prices of other crops.
The natural question, of course, is will wheat prices rise? As the adage goes, the remedy to low prices is low prices. Most economists will agree that as the price of a commodity decreases, producers will typically reduce their production of that commodity, which would eventually nudge the market price upward. But, as I've discussed in the past, sometimes even producers' best intentions may go awry. For example, last year, U.S. producers significantly reduced wheat plantings, but favorable weather conditions that led to higher yields ultimately resulted in unexpectedly large production.
If you're a sports fan, you've probably somewhat a believer in the "due" principle. You know, a baseball player who strikes out 10 times in a row is "due" for a hit. Or a hockey goalie who's made 40 saves in a row is "due" to let a puck in. So, is global wheat production "due" for a supply crash?
I looked into historical global wheat production data between 1960 and 2016 available in the USDA Foreign Agricultural Service's Production, Supply, and Distribution database. Then, I detrended these data to ensure that the analysis wasn't biased by the fact that wheat production has increased simply because of technological advances. That is, I wanted to account for the fact that the efficiency of inputs into wheat production in 2016 was quite different than input efficiency in 1960. In other words, I "normalized" the historical data in order to make meaningful comparisons across time.
Using the normalized wheat production data, I calculated a 10-year moving average (i.e., for each year, what was the average production in the ten preceding years) and noted for which years production was above the 10-year moving average. Here's what those normalized production data (in 1,000 metric tons) and the 10-year moving average look like. The figure makes evident that in the past four years, world production has been above the average production in the preceding 10 years.
So, what are the chances that after seeing four straight years of above-average production, we'll see a fifth above-average year? Here's what the data indicate:
|4 consecutive above-average production years||5 consecutive above-average production years||6 consecutive above-average production years|
|World wheat output||15.38%||13.16%||10.81%|
The empirical probability analysis indicate that, historically, there's only approximately an 13% chance that we would observe five consecutive years of above-average production. Conversely, there appears to be an 87% chance that world wheat markets will see production dip below the 10-year moving average.
Will this result in an immediate upward price response? Probably not, given the historically high global wheat inventories. But it potentially does mean that further wheat price drops are not likely.
I was asked to put together a webinar presentation to provide an update about domestic and trade policy related to agricultural markets. While the webinar ended up being cancelled due to unexpectedly severe winter weather conditions (yes, winter doesn't truly go away in Montana until about mid-May), I thought it might be useful to share it with the AgEconMT community and get your feedback about what I may have missed and what you might be interested to learn more about.
Earlier this week, I attended the annual meetings for a regional US Department of Agriculture group, NC-213, which focuses on research-based analysis of marketing and delivery of quality grains and bioprocess coproducts. The meeting brings together a multidisciplinary group of researchers from land-grant universities (mostly across the Plains states) and industry professionals to share and discuss the latest innovations in maintaining and improving grain quality, valuation of those innovations, and industry perspectives about how those innovations can be used in commercial environments.
One of the highlights of these annual meetings is an industry panel, which brings together leaders from the grains industry. This year, one of the panel members was David Green, who was recently elected as the executive director of the US Wheat Quality Council. Green discussed a number of issues—both opportunities and challenges—currently facing the wheat industry. But overall, these were summarized into two broad categories.
So, while there was a bit of somberness during the discussion given the state of the 2016/17 agricultural markets, there was a definite streak of optimism and a call for renewed energy in research and innovation across the entire wheat supply chain. As someone who lives in a state where wheat production is a major economic driver and works on research that is intended to better evaluate wheat markets to help farmers make more informed decisions, this optimism was definitely a bit of comfort that I could bring back home.
High quality, high protein wheat can make or break the budget for a northern U.S. grain producer. But, there are two major aspects that determine which scenario a farmer will face: how much are markets valuing quality and whether growers have the right production conditions to capitalize in these markets. So, it's never too early to start watching how markets are valuing quality and what one can expect in terms of production.
When considering how markets are value, it's useful to look at various measures such as the spread between spring wheat and winter wheat futures and the forward carry for the spring wheat futures contract, factors which we've discussed in past posts and podcast. Looking at current futures market data, there's some evidence that markets continue to place high value on existing high-protein wheat (as we continue to operate in a deficit) and that high protein wheat will continue to be valued for the 2017 new crop.
So what about farmers' ability to capitalize on this continued marketability of high-quality wheat? One of my favorite places to look when asked that question is the USDA Weekly Weather and Crop Bulletin, which not only provides a summary of the past week's weather but also provides conditions across wheat production states for five categories: excellent, good, fair, poor, and very poor. While certainly not a perfect predictor, these condition reports do provide at least a signal to markets of the potential supply of higher-quality grain in the upcoming marketing year.
The table below shows the proportion of surveyed winter wheat that falls into the excellent and good quality categories. As a quick reference, the states with red cells indicate a more than 10 percentage point decline in the proportion of excellent/good quality wheat since this time last year, yellow indicates a decrease or approximately the same quality level, and green is a quality increase.
The data show that in the major winter wheat production states such as Kansas, Oklahoma, and Texas, wheat conditions are significantly lower than when surveyed at a similar time last year. If these conditions continue, it may result in a continuing deficit of high quality wheat in the 2017/18 marketing year.
However, Montana and North Dakota winter wheat producers may have a competitive marketing advantage, as 70% of Montana winter wheat and 82% of North Dakota wheat is currently rated as good or excellent quality. If this translates into high-quality wheat that can be marketed to supplement the poorer quality wheat produced in the central Plains, northern Great Plains producers could reap the benefits.
One of the unique aspects that President Donald Trump has brought to political information dispersion is his prolific use of social media—primarily Twitter—to communicate news and his personal thoughts about domestic and global happenings. You may have also heard that President Trump's tweets may affect markets and could potentially be used as investment tools. And, entrepreneurs have already developed tools that will automatically trading trigger algorithms based on a President Trump tweet or will send automatic notifications to traders when a new tweet is posted.
Having read about some of these tools, I became curious whether the president's tweets might similarly affect agricultural commodity markets and prices. I've seen and read at least some concern that this could be the case (here's an example), but I wanted to put some data together to see whether there was any teeth to the argument.
The figure below overlays corn and hard red winter wheat March 2017 futures contract prices with instances of "pertinent" tweets associated with the @realDonaldTrump Twitter account. A few notes about my decisions for choosing the data. First, I chose the period between November 8, 2016 (election day) and January 23, 2017 (when I'm writing this post), because this is the period when Trump's tweets may have had the most impact (it seemed unlikely that his tweets would be as influential before he was elected to office as they would be after election day). Second, I focused and selected only tweets that related to trade policy or foreign relations. Because there were no tweets that directly related to agricultural markets, the next most plausibly influential posts seemed to be those that could impact agricultural trade markets. You can read the partial tweets by hovering over the green lines in the figure below.Chart notes: Futures price data represent settlement prices for hard red winter wheat and corn futures contracts traded on the CME Group's platforms. Twitter posts are associated with the username "@realDonaldTrump".
The figure shows that there really doesn't seem to be much of an identifiable relationship. For example, including election and inauguration days (which seemed relevant to include), there were 12 trading days prior to which a pertinent tweet was posted. In six of those days, wheat and corn prices declined and in the other six days prices increased. There also does not appear to be any additional volatility in prices after a tweet, which has been a major worry among some agricultural market specialists.
The table below provides a more detailed market behavior (change in cents per bushel of prices from the previous day's settlement price) associated with the business day after a pertinent tweet was posted or event occurred. To me, the table shows that even if President Trump's tweets impacted agricultural markets, the effect was likely very small and likely difficult to separate from the daily variability in market prices.
|Next business day price change|
|Date||KCBT futures||Corn futures||Event / Tweet text|
|11/8/16||-7.25||-13.5||Election Day, Trump is elected president|
|11/18/16||0.75||4.25||"Just got a call from my friend Bill Ford, Chairman of Ford, who advised me that he will be keeping the Lincoln plant in Kentucky - no Mexico"|
|11/22/16||-1||-0.25||"Many people would like to see @Nigel_Farage represent Great Britain as their Ambassador to the United States. He would do a great job!"|
|12/5/16||-3.25||1.5||"Did China ask us if it was OK to devalue their currency (making it hard for our companies to compete), heavily tax our products going into.."|
|12/13/16||1.25||-4.5||"I have chosen one of the truly great business leaders of the world, Rex Tillerson, Chairman and CEO of ExxonMobil, to be Secretary of State."|
|12/19/16||-1.25||-3||"China steals United States Navy research drone in international waters - rips it out of water and takes it to China in unprecedented act."|
|1/3/17||12.5||4||"General Motors is sending Mexican made model of Chevy Cruze to U.S. car dealers-tax free across border. Make in U.S.A.or pay big border tax!"|
|1/4/17||8||1.5||"Thank you to Ford for scrapping a new plant in Mexico and creating 700 new jobs in the U.S. This is just the beginning - much more to follow"|
|1/5/17||-1||-3.25||"Toyota Motor said will build a new plant in Baja, Mexico, to build Corolla cars for U.S. NO WAY! Build plant in U.S. or pay big border tax."|
|1/6/17||5||2||"The dishonest media does not report that any money spent on building the Great Wall (for sake of speed), will be paid back by Mexico later!"|
|1/9/17||-0.25||-1.75||"I look very much forward to meeting Prime Minister Theresa May in Washington in the Spring. Britain, a longtime U.S. ally, is very special!"|
So, it seems to me that there is much ado about not much at all when thinking about the potential connection between President Trump's tweets and agricultural commodity price impacts. Perhaps there are more substantial impacts immediately following a tweet (e.g., within the first 2-5 minutes), but I don't have access to the data to assess that hypothesis. Or perhaps market participants understand that a tweet (even from the President-elect of the United States) may not be as powerful a market signal as other more fundamental supply and demand factors.
I am curious, however, to continue watching how markets pay attention to this new form of information dissemination and the extent to which the transition from President-elect Trump to President Trump may alter the potential weight of the information. #waitandsee
Ready to start 2017? If you're a cattle producer, then you're likely answering that question with a resounding YES!
Indeed, between January 2015 and January 2016, feeder cattle futures prices dropped from approximately $225 per hundredweight to $167 per hundredweight, a 26% reduction. And as we near January 2017, prices are hovering right around $130 per hundredweight, representing an additional 22% year-on-year slide. But, despite futures prices being helpful in indicating overall market trends, they are not very informative for understanding how these price reductions may have differed across different locations.
Using historical sales price data from beefbasis.com, I put together a quick characterization of the extent of the spatial distribution of feeder cattle price changes. The first figure below shows the change in the sales volume-weighted average prices across Great Plains states between 2011-15 and 2016.Chart notes: Data are from beefbasis.com and represent monthly sales prices at auctions for 550-600 pound, 1-2 class, medium and large frame steers. Annual averages for each state are calculated by weighting each reported price by the proportion of cattle sold at the sales location relative to all cattle sold in the state during a particular year.
The figure shows that in 2016, the average price drop for feeder cattle relative to the average price between 2011 and 2015 was highest in Texas, Wyoming, Oklahoma, and Montana. In those states, respectively, prices dropped by 14.87%, 12.10%, 11.75%, and 11.68%. In all of the other Great Plains states, price reductions were less than 10% relative to the 2011-15 average price.
Here's a more stark picture: changes between average 2015 and average 2016 feeder cattle prices.
Between 2015 and 2016, Montana and Texas cattle producers experienced the largest price reductions, with prices dropping by nearly 50%. Wyoming, South Dakota, and Oklahoma also saw price drops that ranged between 38% and 44%. The states that experienced the least price reductions were Colorado, North Dakota, and Kansas.
Why such disparity across states? My hypothesis is that it's a return to a long run spatial price relationship. As I discussed in a previous post, feeder cattle prices in Montana, Wyoming, and Texas are typically higher than average. During the 2014-2015 price increases in the cattle markets, prices in those states increased disproportionately more than price increases in other states. As markets adjusted and price began to decrease to their long run averages, prices in states with faster increases also experienced much more rapid and proportionately larger reductions.
Without more historic price data across the Great Plains states, it's difficult to know whether this spatial distribution of price changes is consistent across time or a one-time behavior. But it's certainly worth keeping an eye on when another price run-up and eventual decrease occurs.
In regions where commodity crops are produced and exported, the rule of thumb is that basis (the difference between cash and futures prices) are typically stronger in locations that are closer to delivery markets and weaker for farther locations. But is the same the case in feeder cattle markets?
On the surface, it would seem that it would be. With some reconnaissance work by my colleague Gary Brester and some of my own personal communications, I confirmed my initial assumption that the cattle buyer is the party that typically pays for transportation expenses after purchasing the animals. Some of those costs are likely to be passed on to sellers in the form of lower prices (similar to crops markets). Therefore, basis (and, thus, prices) should be highest in Colorado, Kansas, and Nebraska—locations of many feedlots and processing facilities—with ranchers receiving lower prices in Wyoming, South Dakota, and Oklahoma, because there would be higher costs to ship from those locations to the central Great Plains. And, producers in Montana, North Dakota, and Texas should see the lowest prices.
If you answered "yes," you're in the same boat as me. But the figure below, which presents average 2011–2015 volume-weighted prices per hundredweight across the Great Plains, shows a bit of a different marketing landscape.Chart notes: Data are from beefbasis.com and represent monthly sales prices at auctions for 550-600 pound, 1-2 class, medium and large frame steers. Annual averages for each state are calculated by weighting each reported price by the proportion of cattle sold at the sales location relative to all cattle sold in the state during a particular year.
The figure shows that between 2011 and 2015, average prices in states with a large density of feedlots (and the lowest transportation costs to deliver feeder cattle to the feedlots) are 8-9 cents per hundredweight lower than in states that are farther away. This seems to indicate the inverse relationship between prices and delivery locations that exists in crop markets.
While I haven't been able to find any research to identify the specific reasons for this relationship, some conversations with colleagues and a bit of anecdotal evidence has led to two possibilities: quality and economies of scale. First, northern U.S. cattle may just be more hardy animals, produced in cool-temperature climates and on operations that specialize in cattle rather than having animals as a small portion of a crop operation's portfolio. These animals are more likely to outperform others when delivered to feedlots and may have higher quality carcass characteristics. A white paper that considered the economic returns to Montana's Beef Quality Assurance Program showed that cattle in the program (thus signaling a higher quality) received a $1.00-$1.56 per hundredweight premium.
A second reason—related to production specialization—is the ability to take advantage and better market economies of scale. Larger, more focused operations are more likely to sell pens of cattle (rather than individual or a small number of animals) to feedlots, which could also improve these animals' performance in feedlots as well as provide a more homogeneous, consistent product quality across animals. This can reduce the variability in quality and, ultimately, returns that feedlot operators face when marketing their finished animals.
Next week, I'll take a look at how feeder cattle prices changed in the past year and how these changes differed across the Great Plains states.
I recently had the pleasure of presenting at the 2016 Montana Seed Trade Association's annual conference. When putting together the talk, I faced a unique (to me) perspective of seeing farmers as consumers (rather than the traditional view that farmers are the first participants in the food supply chain), and seed marketers as the suppliers. There was a lot of interesting aspects of the agricultural inputs markets that I considered, but I ended up settling on exploring two in particular: how the current status of grain markets and farmers' future expectations for those markets might impact the demand for agricultural inputs and the trade-offs and potential economic effects of the two proposed mergers by four of the largest agricultural input producers in the world.
A project funded by the Montana Wheat and Barley Committee resulted in a Montana-specific variable cost of production budget for winter wheat.
A condensed version of the budget worksheet can be downloaded here.
A more detailed and adjustable Excel version of the budget can be downloaded here.
What happened in the United States in 1970? The Beatles broke up. The voting age was lowered to 18. The U.S. Environmental Protection Agency began operations. Simon & Garfunkel's "Bridge Over Troubled Water" ended as the year's top song.
What else happened in 1970? This was the last year that U.S. farmers planted fewer than 49 million acres of wheat. That is, until 2017.
The U.S. Department of Agriculture is predicting that producers will only plant 48 million acres of wheat. The two figures below show the decreasing trend in planted wheat acres in the United States and the four major wheat producing states in the northern Great Plains (with Montana being the only exception).
What's driving this trend? This is a natural response by producers to lower wheat prices, which both USDA long term projections and futures markets expect to continue over the next several years. Last year, we observed a similar reduction in planted wheat acres, but particularly favorable production conditions in the United States led to historically high yields, foiling the market's attempt to "cure" low prices by reducing supply.
As a result, data compiled by the USDA Economics Research Service show that 2016 U.S. stocks are 17.2% higher than already historically high stocks in 2015, and 64.5% higher than the average wheat stocks over the preceding 25 years. Similarly, global wheat stocks were 3.4% higher than in 2015 and 35.8% higher than the average stocks between 1996 and 2015.
What's the good news for producers? First, unless there is another historically good production year or a major reduction in the demand for wheat, the continued decline in wheat production is likely to stabilize and potentially prop up wheat prices. Second, it's an excuse to dust off those bell-bottoms and find those Donna Summer LPs.
Imagine two questions:
The difference is subtle, a simple changing of the word "willing" to "unwilling." Despite the fact that these two questions are attempting to elicit the same information—a consumer's view toward genetically modified foods—the different connotations of the questions may result in quite different research inferences.
Recently published research in the European Review of Agricultural Economics intended to investigate whether the connotation of questions in GMO food perception surveys may have led to European consumers' overly critical inferences about GMO foods. The study examined 1,713 different questions asked across 214 different surveys and found that, indeed, when questions had more negative connotations toward biotechnology, respondents tended to view GMO foods less favorably. Conversely, questions that had a more positive connotation toward biotechnology led to more favorable views toward GMO foods. Moreover, these results were unaffected across food products.
Interestingly, the research also found that when questions mentioned potential price discounts or increases in food supply resulting from the use of biotechnology, respondents tended to more negatively view GMO foods. This might seem counterintuitive to agricultural producers because, often, production of genetically modified commodities does lower production costs (thus, likely retail prices as well) and increases overall supply. So, what might be advantageous to producers and processors may not necessarily be appreciated in the same manner by consumers.
Perhaps the most interesting result of the research was this:Past findings that European consumers perceive GMO foods more negatively than people in other countries may have been primarily due to the fact that those studies asked survey questions with more negative connotations toward biotechnology.
After accounting for these "negative connotation effects," the researchers were no longer able to find that European consumers perceived GMO foods differently from consumers in other countries.
One implication of this result is the potential for continued growth for US agricultural products. As more accurate assessments of European consumers' food preferences become available, US food marketers may be more able to take advantage of opportunities that may have seemed previously non-existent.
After my most recent post that attempts to illustrate a market-based explanation for the recent declines in feeder cattle prices, there was an interesting discussion about how factors such as Brazilian beef imports and country-of-origin labeling (or lack thereof, since the law that required beef processors to label the country from where the beef product originated was repealed in 2015) might also play a role in determining prices. Especially their role in the recent price decline.
First, I wanted to look at the relationship between Brazilian imports of beef products and live animals and at least superficially assess whether there appears to be a "negative" or "inverse" relationship between the level of those imports and feeder cattle prices. Intuitively, one would expect to see a negative relationship because if US processors are able to import Brazilian beef, then their demand for US beef would decrease, which would then result in lower prices for US feeder cattle (i.e., processors substitute Brazilian beef for US beef, which would lower their willingness to pay for US cattle).
I gathered historical Brazilian beef and cattle import data from the USDA Foreign Agricultural Service's Global Agricultural Trade System database, US red meat production data from the USDA Economics Research Service, and feeder cattle prices for Montana from beefbasis.com. Lastly, I calculated the proportion of total US red meat sales is represented by imported Brazilian red meat. Here is what the data look like:
Here are my thoughts after assessing the data:
The relationship between the repeal of the country-of-origin labeling regulation and cattle prices is even more difficult to assess. At least two studies (here's the link to the first study and the second study) have shown that COOL would have adverse impacts on both US beef producers and consumers because the legislation would be associated with higher input costs for packagers (i.e., traceability and labeling), which would be passed on up and down the beef marketing channel. However, both studies estimated that feeder cattle producers would be less impacted than others in the production and marketing channel, and only slightly more affected than beef consumers.
While it might arguably be naive to assume that an opposite, positive effect on US beef producers is expected as a result of the COOL repeal, it is perhaps more difficult to conclude that the adverse effects found to be associated with COOL would be somehow compounded by its cancellation. Of course, the cattle and beef industries have likely changed since 2002 when the COOL provision was enacted and additional economic analysis would be necessary to accurately identify and estimate the effects.
Last Tuesday, one of the students in my Economics of Agricultural Markets class asked me a question that I hadn't heard in several years, but is inevitably brought up when prices drop: Why is the U.S. meatpacking industry allowed to unfairly bring down prices for cattle producers and, as such, extract higher profit margins for themselves? The question was in part prompted by the fact that after a couple of years in which cattle producers received historically high prices (reaching approximately $2.60/lb for feeder cattle), the current feeder cattle futures price hovers around $1.21/lb, representing a 46.5% reduction in market prices.
Of course, the student is not the only one asking this type of question. The United Stockgrowers of America, for example, submitted a letter earlier this year to the U.S. Senate Judiciary Committee to investigate potential anti-competitive practices in the meatpacking industry. There have also been numerous news reports (here's one as an example) of concern by the those in the feeder cattle industry who, too, believe that there is something fishy going on and that it's the cattle producers who end up taking the brunt of the impacts.
Economics research has repeatedly shown, however, that there is little evidence that specifically anti-competitive behavior in the meatpacking industry substantially impacts feeder cattle market prices, especially in the short run. Instead, the issue is much more about market fundamentals.
Consider this question: How easily can feeder cattle producers adjust their herd sizes within a one-year period in response to changes in market prices?
Now consider this question: How easily can meat processors and packers adjust their production within a one-year period in response to changes in market prices?
Economists typically think of answering these questions in terms of "supply price elasticities," which are measures that indicate the degree to which producers alter their decisions to sell more or fewer goods in response to a change in market prices. For example, if feeder cattle producers are highly elastic (versatile) to changes in prices, then they could easily and quickly adapt their herd sizes to adjust to changes in cattle markets.
However, that is not what economics research has repeatedly shown. One of the more recent studies has shown that in the short run, feeder cattle producers are highly unadaptable and unresponsive to changes in prices (for a 1% decrease in market prices, cattle producers only reduce their sales by 0.22%). That is, cattle producers cannot magically reduce their herd sizes and instantly adjust to new market conditions. They still need to manage, raise, and sell their animals.
This implies that feeder cattle producers have little leverage and are more likely to take on a larger incidence of market price reductions (i.e., they have less opportunities to not sell their cattle when market prices are low and, as a result, have to accept what the market gives them). For this reason, another study found that for every 1% decrease in the price of beef at the retail level, feeder cattle prices decrease, on average, 1.30%.
Processing facilities, on the other hand, have more flexibility in managing slaughter quantities and inventories, and are thus able to use this additional leverage to pass on the costs to feeder cattle producers. Markets observe similar conditions (albeit in reverse) when the demand for beef products increases. As was the case 2-3 years ago—when the 2014 Porcine epidemic diarrhea virus (PEDv) and the 2015 avian influenza may have led some consumers to temporarily switch to higher beef consumption—processors and packers responded by increasing their production and, in turn, increasing market demand for feeder cattle. Because this demand occurred during a period when herd sizes were already relatively small after significant cattle culling in response to the 2012-14 droughts and the biological constraints of cattle production limit the speed with which new cattle can be produced to meet the demand, it was the feeder cattle market that most benefited in the form of historically high prices, while the fed cattle and packers were left with lower profit margins.
In both cases—expansion or contraction of production—processing and packing facilities are not necessarily doing anything that is anti-competitive or illegal, but simply acting efficiently in the marketing landscape they face. Market fundamentals and the nature of cattle production are, thus, likely to blame.
The silver-lining for the current price decreases is that in the longer run, research has found that feeder cattle producers become much more responsive to market conditions. As such, producers are able to adjust their production and sales capacity to again gain leverage and become more competitive in the cattle market.
After several weeks of rumors during one of the largest wheat harvests in a decade, Russia's prime minister Dmitry Medvedev announced on September 2, 2016 that the Russian government will essentially eliminate the year-and-a-half old wheat export tariff. As of September 15, 2016, the tariff will be lowered to zero.
In a year of already historic U.S. and world wheat production that has led to the lowest wheat prices in a decade, Russia's considerable lowering of its barriers to wheat trade could lead to yet another downward jolt in the wheat markets. Consider the following:
With only these factors, Russian wheat exports are making a considerable impact on global wheat prices. It is reasonable, then, to expect that the significant tariff reduction—the tariff will be reduced to 0% from the most recent value of 50% of the wheat's export value minus 6,500 rubles—would lead to a further lowering of the global wheat price.
So what's going on? Why aren't we seeing a market response that would be anticipated with news that carries a potential influx of supply into the global wheat markets? There are several factors that are likely key to answering this question.
First, consider the chart below, which presents data from the International Grains Council about production, total availability, and export for Russia over the past decade.Chart source: International Grains Council, Supply and Demand, Russia.
The data show that although the Russian wheat tariff was in place, Russian exports continued to increase fairly proportional to Russian production. Consequently, the tariff reduction is not likely to change Russia's existing trade volumes and affect global prices.
Next, consider the exchange rate of the Russian ruble. The chart below shows the exchange rate between the ruble and the U.S. dollar over the past year.
Chart source: Indexmundi Exchange Rates.
The data indicate that since January 2016, the Russian currency has strengthened by approximately 25%. As a result, Russian wheat is now more expensive on the global market, making it less attractive for world buyers. As such, even with a tariff reduction, the demand for Russian wheat is likely not as strong due to the exchange rate as it would have been earlier this year.
Lastly, as I discussed in a previous post, despite Russia's large production, the quality (protein levels) of the wheat is quite lacking. Because the market is currently feverishly looking for high protein wheat, as discussed by my colleague Joe Janzen, the influx of lower-quality wheat may not substantially move the global markets.
So, despite the potential for yet another piece of bad news for wheat market participants in a year already filled with whammies, Russia's export tariff reduction seems, as of now, to be a benign blip on the amber-colored radar.
A few days ago, the USDA published a report, "Spotlight on Guatemala as Trade Flourishes Under CAFTA-DR." The report uses data from the Global Agricultural Trade System of the USDA Foreign Agricultural Service to examine changes in the value of exports and imports of agricultural and food products among the United States and the six central American countries—Costa Rica, Dominican Republic, El Salvador, Guatemala, Honduras, and Nicaragua—in the Central American Free Trade Agreement (CAFTA), which was passed in 2004.
With the U.S. presidential elections upcoming in 10 weeks and much of the Democratic and Republican parties' candidates platforms centered around changing the current structure Trans-Pacific Partnership (TPP) agreement, I wanted to compare the USDA's reports conclusions about the CAFTA to what might be expected if the TPP agreement is adopted. I used the exact same data as the USDA report in order to ensure consistency between the two assessments.
First, I wanted to examine the general trends in U.S. exports and imports of agricultural products. I compared four categories:
The figures below show the value of exports and imports for the United States between 2002 and 2015 (in million 2015 US dollars).
The figures show that across all regions, the value of exports and imports has increased. As the USDA report points out, there is an increase in the CAFTA-DR region. However, the charts indicate that the rate with which exports and imports have increased for these countries may have been lower than the increases in trade values across partners in other FTAs, countries that are part of the proposed TPP, and all countries regardless of their FTA status with the United States.
Additionally, we can use these figures to consider differences in the slope of the TPP category and the slope of the World category. Specifically, the slope of the World category is steeper (not shown) than the slope of the other two categories. This is perhaps suggestive that there may exist opportunities for additional value to be added by reducing the trade barriers that might currently exist.
In other words, consider this. Trade of agricultural products between the United States and all countries in the world (which includes countries with higher and lower trade barriers) has grown faster than the trade value between the U.S. and TPP countries. It is reasonable to consider, then, the fact that if trade barriers that currently exist between U.S. and TPP countries were lowered or removed, there could exist opportunities for growth in the rate of U.S. trade value growth for this region. Without further, more rigorous analysis, it is difficult to ascertain these opportunities quantitatively.
I also disaggregated the export trade data to look at several commodities that are particularly relevant for producers in the northern Great Plains region. The table below provides the value of U.S. exports and the percent growth in value between the pre-CAFTA period (2002-2004) and the current period (2013-2015).
|Beef & Beef Products||$11.1||$91.6||728%|
|All US FTA Partners||Wheat||$1,221.1||$2,241.5||84%|
|Beef & Beef Products||$1,936.5||$3,098.8||60%|
|Beef & Beef Products||$2,399.3||$3,658.3||52%|
|Beef & Beef Products||$3,529.8||$6,537.1||85%|
The table provides a more detailed comparison of growth outcomes and opportunities for potential growth. For example, the data indicate that exports of wheat, pulses, beef products, and live animals in the CAFTA-DR region grew faster than the export growth to all other countries and all countries in existing FTAs with the United States. However, growth in the values exports of sugar, corn, and soybeans was well below the overall trade growth in other regions.
Performing a similar comparison for countries in the TPP region, the largest growth opportunities appear to exist in the corn, beef product, and live animal sectors. The latter two categories are particularly relevant because the TPP includes significant reductions to barriers in export beef products to Japan, a historically major importer of U.S. beef.
So, while my version of the USDA's analysis does provide some support for their conclusions, it also offers a more detailed perspective of where the CAFTA-DR may have been more and less successful. Moreover, it shows that the areas in which opportunities for agricultural producers could exist if the TPP is passed by the U.S. legislature.
In a recent post, I discussed several ways to think about whether conditions in wheat markets are likely to get worse or improve next year. Today, I want to share a fairly recent (and recently discovered by me) joint project between the agricultural economics department at Purdue University and the CME Group: the Ag Economy Barometer.
The Ag Economy Barometer project is based on a monthly survey of over 400 large producers from across the United States about their sentiment of numerous measures of the agricultural economy. While the project began in October 2015 and there is a somewhat limited scope of comparison, it does provide several year-on-year indicators as well as sentiment about the future.
You can look at all of the available statistics on the Ag Economy Barometer project website, but I will discuss a few here that I think provide some key insights.
First, here is a general index of the sentiment about current and future expectations of the ag economy. To interpret the indices, consider that the base period is October 2015 through March 2016, at which the index is 100. Therefore, any value above 100 means that the sentiment is better than it was in the October through March period, and values below 100 imply a less optimistic sentiment than the base period.
The index of current conditions (brown) shows that the most recent sentiment about current ag economy conditions is that conditions are somewhat worse (93) than they were between October 2015 and March 2016. However, the expectation sentiment (green) is that of optimism—that future ag economic conditions will be substantially better than they were last year.
The second indicator is based on the question of whether agriculture will enjoy widespread good times or widespread bad times over the next five years.
The indicator shows that in the most recent survey of July 2016, only 40% of respondents were pessimistic about the agricultural economy in the next five years. This is in contrast to the sentiment in the first part of 2016 when more than half of respondents were pessimistic.
Lastly, I want to show the results of the question regarding the sentiment about commodity prices.
Similar to the optimism signaled by the charts above, there appears to be a growing optimism that prices of all four major commodities produced in the United States will rise. For wheat, the greatest pessimism about prices occurred in April 2016, when just over 15% of respondents expected prices to be higher than in the past. Now, as I've discussed in a previous post, there seems to be a signal of acknowledgement that prices have likely bottomed out and will begin to rise.
In general, there appears to be optimism for the performance of the U.S. agricultural economy and the Ag Economy Barometer is a good tool to keep track of future sentiment changes.
Despite the one of the lowest wheat planted acreage in three decades in response to already record high inventories in the United States and the world, the 2016 production conditions have been arguable "too" good. Milder and wetter spring and summers across the Great Plains and globally has resulted in bumper crops. In the United States, winter wheat yields reached a record 51.3 bushels per acre. In Russia, early reports are indicating yields that exceed 71 bushels per acre.
So, despite market attempts to respond to lower 2015/16 marketing year prices by reducing quantities supplied, storage bin space in the United States and across the world has not increased. This has resulted in further declines of wheat prices and an increased global trade competition from France, Russia, Ukraine, Kazakhstan, and Australia. The situation has been exacerbated by record yields of other crops, such as corn—which is projected to yield of a record 175.1 bushels per acre—and soybean—with a project yield of a record 48.9 bushels per acre.
What's the impact? Well, just as an example, consider that as August 11, 2016, the September 2016 futures price for winter wheat was $4.10 per bushel, nearly $1.00 per bushel lower than the price on the same date in 2015. Similarly, the August 11, 2016 spring wheat futures price was $0.25 per bushel lower at $5.00 per bushel. In Montana, winter wheat was, on average, $0.95 per bushel higher last year than the $3.25 per bushel average price in August 2016, while spring wheat was relatively on par across years (likely due to higher demand for protein in the 2016/17 marketing year).
The natural question to ask: Are the 2016/17 marketing year prices here to stay?
When I'm asked this question, I typically go to four sources:
So, overall, it seems that after weathering this year's marketing environment, wheat producers could expect improvements next year.
We've heard this line before: Russia sees high yields but there are concerns over quality.
Well, that's again the case after harvest statistics have begun to emerge as Russian producers continue to harvest their winter wheat crop, as reported by Agrimoney.com. Similar to what's happening in the United States, Russia is headed for a higher-than-average winter wheat harvest after a mild winter and moisture-rich spring and summer seasons. Specifically, the average reported yield is approximately 73 bushels per acre.
Higher yields in the second largest world wheat exporter mean that wheat prices are certain to remain at relatively low levels. But the news of higher yields in Russia may not be all bad for northern Great Plains producers.
As is usually the case due to the biological inverse relationship between wheat yield and protein content, the protein content in Russian wheat has been tested at below average levels and well below typical quality levels required for milling. This compounds the already protein-deficient global wheat market, where other major exporters such as France, Canada, and the United States have all seen high yields but generally lower protein levels. As such, it is likely that the premium for higher protein wheat will grow even larger.
One way to glean information about changes in protein premiums is to look at the spread between the futures contract price of a lower protein wheat (such as the winter wheat KCBT contract) and the price of a higher protein wheat (such as the spring wheat MGEX contract). The higher the spread, the more markets demand higher protein wheat. Here is what the daily prices and spread has looked like over the past two months.
Chart notes: Data are daily closing prices for the KCBT September 2016 winter wheat contract and MGEX September 2016 spring wheat contract. The spread is calculated as the difference between the MGEX and KCBT closing day prices.
The data indicate that spreads between an 11.0% protein content winter wheat and 13.5% protein content spring wheat have been as high as $1.02 per bushel. As of August 11, the spread was $0.91 per bushel and in an upward trend, perhaps suggesting a market response to continuing devaluation of wheat quality around the world.
Northern Great Plains farmers who were able to grow higher protein wheat or have higher quality wheat stored in on-farm storage may be able to benefit from these global effects. If Russia's and Europe's wheat quality does not improve as those two regions finish their harvests, taking advantage of higher than average premiums may be a means by which NGP farmers can, to some degree, offset the adverse impacts of low wheat prices.
This marketing advantage is something that central Great Plains farmers might only dream about.
I recently posted about the premiums that can be expected for protein level differences in 2016. In the post, I mentioned one of my recent academic journal publication, "Forecasting a Moving Target: The Roles of Quality and Timing in Determining Northern U.S. Wheat Basis." In this research project, I, along with two other colleagues, wanted to use historical wheat basis (the difference between a cash and futures price) to develop economic models that would help (a) better understand market forces that affect prices of wheat in the northern Great Plains around harvest and (b) better forecast those prices.
Without going into too much detail about the modeling aspects, we considered 52 different models of historical hard red winter and hard red spring wheat prices. Here is what we found:
In addition to better understanding the wheat marketing landscape in the northern United States, we used these findings to put together an interactive, web-based tool that can be used to forecast wheat basis based on the wheat class, wheat protein level, and delivery location. You can access the tool here: http://wheatbasis.montana.edu and here's a snapshot of what it looks like.
The neat part about that tool is that it updates on a daily basis to incorporate current market conditions and provide the most up-to-date price forecast!
(Cross listed with AgEconMT.com)
Montana wheat is special! The northern Great Plains wheat marketing landscape is unique to other U.S. wheat markets in that grain processors and elevators not only pay producers for how much wheat they market but also the quality of that wheat. The quality assessment is largely based on the protein levels—the proportion of protein content in a wheat kernel. Because wheat protein contents are important for milling flour that can produce different types of baked foods—higher protein wheat is needed to produce flour for foods such as breads, pizza crusts and bagels while lower protein wheat is needed for soft noodles, pastries, and cakes—there are numerous markets for wheat with different protein levels.
Montana producers primarily grow hard red winter and hard red spring wheat. Moreover, the dryland production methods in Montana and relatively low rainfall levels (11-16 inches annually) lead to the potential for producing wheat with high protein levels. This is unlike other major hard red wheat production regions, such as Kansas and Nebraska, where higher moisture content during the growing season results in lower protein levels. Consequently, while in these central Great Plains markets all wheat is treated the same and farmers get paid only for the quantity of wheat they grow, in Montana's wheat markets, processors and elevators differentiate wheat by its quality.
So, how much are protein premiums? Well, just like almost every other answer that you will get from an economist, the answer to this question is: it depends. Just like commodity prices, the amount that processors and elevators are willing to pay for higher protein (higher quality) wheat depends on the production and market conditions at the time that a sale occurs. As I, along with my co-authors Dr. Gary Brester from Montana State University and Dr. Mykel Taylor from Kansas State University, discuss in a recent paper, "Forecasting a Moving Target: The Roles of Quality and Timing in Determining Northern U.S. Wheat Basis," there are two primary drivers in determining this premium: (1) how many farmers produced high or low protein wheat in a particular region, such as Montana, and (2) whether the wheat crop in the central Great Plains had an average (or above or below average) protein content.
Unfortunately, there are no direct tools that are currently available (at least those that I am familiar with) that predict protein content. However, because there is a natural biological relationship between wheat yield and its protein content (higher yield typically implies lower protein content vice versa), it is possible to at least get some sense of what protein premiums may be in the northern Great Plains by observing realized yield levels in the central Great Plains (where winter wheat harvest occurs approximately 1 month earlier) and yield forecasts for the northern Great Plains. This information can be readily access from the USDA Wheat Outlook reports. Using the July 2016 report, here is what the market appears to look like.
In 2016, favorable growing and harvesting conditions in the central Great Plains resulted in nearly record high wheat yields. The current estimate is 53.9 bushel per acre yields, which would exceed the previous historical high by 6.1 bushels per acre. Such high yields suggest that the protein levels in central Great Plains' winter wheat will be lower than average, creating a potential deficit of protein in the overall market. Adding to that is increased yields and production almost in every other major wheat production region around the world. In the northern Great Plains, spring wheat (typically the higher protein wheat) production is expected to be down by 8% due to lower plantings, but yields are expected to higher. Similarly, winter wheat yields are expected to be above average. Both of these factors suggest that, on aggregate, protein levels in Montana and the northern Great Plains are also likely to be low.
The combination of lower protein levels across the Great Plains region suggests that protein premiums are going to be relatively higher this marketing year, in order to increase incentives for farmers to deliver their higher-protein wheat. Conversely, discounts for lower protein wheat will be higher as well. To obtain a more detailed forecast of protein premiums and discounts for the upcoming harvest, visit the Wheat Basis and Price Forecasting Tool, developed to for Montana and Washington marketing locations.
(Cross listed with AgEconMT.com)
The data in the graph indicate that retail expenditure on wheat-based food products is expected to grow by 58% between 2007 and 2017, organic wheat production in the United States actually declined by 2%. This decline is despite a growth of 55% in organic wheat production in the northern Great Plains (NGP). While the expenditure and production are not comprehensive indicators of supply and demand trends, these data suggest that consumption growth is outpacing production.
Here's why the New York Times article made me so happy. The disparity between consumption and production trends seems to indicate that even $15 per bushel premiums for organic wheat relative to conventional wheat (as of July 2016) do not create sufficient incentives for enough farmers to enter organic production to meet market demand. However, because the excess demand at the consumer level is so apparently high, food processors and marketers see a sufficiently high profit opportunity that they are willing to subsidize agricultural producers' transition process.
That is, the current share of the organic dollar paid by the consumer that is going to the farmer did not sufficiently incentivize a sufficient number of producers to enter the organic industry (because the costs of entry potentially exceeded the returns from entry). As a result, food processors are passing down a greater share of that dollar down to the farmers in order to raise the incentive bar.
Could we see a similar scenario play out in Montana and the northern Great Plains? Given the current trends in organic wheat-based food production, it's certainly a distinct possibility.
(Cross listed with AgEconMT.com)
I was recently asked to give a talk about what the future of agricultural research looks like. The previous 60-100 years have focused primarily on getting more food for less effort: efficiency. Furthermore, the primary model of doing and delivering research information has been top-down: researchers perform a project and then simply relay the information to producers.
I don't think that this is the most successful model going forward. As consumer income continues to grow (both in the United States and globally), there is a greater demand for food with particular characteristics. Whether it's how a food is produced, what inputs and how much of those inputs were used, or what features a product has to benefit specific lifestyle choices, consumers are more and more choosing with their wallets. In order to capture these potential premiums, producers must respond by growing foods that match those demands. However, how does one do so in a cost-effective manner?
That was the main question that I posed to myself and attempt to partially comment on in the infographic below
Each year on February 2, the groundhog Punxsutawney Phil comes out to predict whether the United States should expect an early spring. Such knowledge could be invaluable to agricultural producers, allowing them to better gauge the length of their growing season and perhaps get an early start on planting. To commodity market investors, this knowledge would also be useful because they would be able to immediately incorporate into futures prices the potential impacts of the changes in producers' behaviors.
Naturally, I became curious whether this lovable marmot's powers lie not only in the ability to predict an early end to winter (by the way, his accuracy has only been 37% since 1887), but also to move agricultural commodity markets. To test this hypothesis, I used daily futures price data between 1959 and 2014 for corn, soybeans, and soft red winter wheat (all commodities traded on the Chicago Mercantile Exchange). My idea was to see whether there was a statistically significant difference between prices of those commodities on February 2 (when Punxsutawney Phil makes his prediction) and, just in case all of the potential market effects did not get incorporated into the price of each commodity within the same day, I also included February 3. In cases when February 2 was a weekend, I used the first business day to measure any potential effects.
Using these data, I needed to estimate a reasonable econometric model that could somewhat accurately represent the daily price behaviors of these three commodities. I did not want to get too fancy, so I simply decided to estimate an ARIMA model for each commodity. Just in case you're interested in the gory details, I first-differenced each price series and used the SCAN (Tsay and Tiao, 1985) and minimum BIC search methods to identify the best ARIMA specifications; these turned out to be ARIMA(1,1,2) for corn, ARIMA(1,1,1) for soybeans, and ARIMA(3,1,0) for wheat. Additionally, in each ARIMA estimation, I used an indicator variable for days after an "early spring" prediction to determine whether markets had any reaction to this meteorological news event.
Alas, in all cases, the venerable ground squirrel's ability to sway the markets was even less successful than his ability to predict the start of a spring season. On the other hand, to borrow a line from the film Groundhog Day (1993), perhaps "this is one time where [academic research] really fails to capture the true excitement of a large squirrel predicting the weather."
I recently presented at a producers' meeting organized by the Montana State University's Northwestern Agricultural Research Center. The goal was to provide a quick overview of the tools and resources available to Montana farmers to develop their own costs of production budgets. As I've done in the past, I'm posting the infographic version of the presentation here.
A few days ago, I paid $1.78 for a gallon of gas. That made me really pleased, and that feeling is probably shared by the majority of the population (except those who are in the oil industry). The low gasoline prices reflect the continued slumps in the oil market. Today, U.S. oil futures price was approximately $33 per barrel. This is the lowest price since March 2004.
In Russia, where the economy has become highly linked to oil sales, continuing drops in oil prices mean continuing economic declines. This has led to the tumbling of the ruble (the Russian currency) relative to other countries' currencies and, coupled with the Russian government's restrictions on many food imports, has resulted in the precipitous rise of food in Russia.
As a result, the Russian government is about to decide whether to eliminate or reduce its current export tariff. The export tariff significantly reduced the flow of wheat out of Russia, a major global wheat producer that can sway the global wheat price, has contributed to the most recent drops in wheat prices. However, as reported by Reuters, Russian officials are holding a meeting on January 29 to decide whether to maintain or even strengthen the tariff in an effort to maintain a sufficient domestic wheat supply.
The continuation or increase in Russian export restrictions would continue to restrict the availability of wheat for importing countries, which would potentially lead to a rebound in wheat prices.
The nearby March 2016 winter wheat futures contract is currently trading at $4.68 per bushel. This is the lowest prices fell rapidly in 2010 after the run up in prices in 2008 and 2009. This most recent price decline is observed across all agricultural commodities (and many non-agricultural commodities), and according to price forecast put out by the Food and Agricultural Policy Research Institute, crop prices are expected to remain low in the 2015-16 and the 2016-17 marketing years, before beginning to rise in the 2017-18 marketing year.
Economic theory indicates that producers who face decreasing expected prices would respond by reducing their quantity supplies. In practice, we would observe this rational behavior by seeing a reduction in the seeded acreage.
The latest winter wheat seeding report published by the USDA National Agricultural Statistical Service shows that this is exactly what has occurred over the past three years.
2016 Winter wheat seedings, in 1,000
Data source: USDA National Agricultural Statistical Service, Winter Wheat Seeding Report, January 12, 2016
Across the United States, the 2016 winter wheat acreage is down by 7% since from 2015 and 14% since 2014. The 2016 planted acreage is 36.61 million, which is the lowest acreage seeded since 2010, when 36.57 million acres were planted. The next lowest occurred all the way back in 1970, when there were 37.62 million acres were planted in the United States.
U.S. wheat farmers are clearly smart economists.
I recently put together a presentation of some recent research about the impacts of shuttle-loading grain-handling facilities on the basis bids that we observe at these locations. Because these facilities are higher capacity, can load grain at a faster speed, and can obtain rail cars at lower costs, they have the opportunity to pass these cost savings to farmers in the form of higher prices bids for grain. However, does this actually happen? And if it does, what are the marketing landscapes that make this pass-through more likely?
I will be presenting to the REAL Montana group this Thursday about some general trends and issues in the U.S. and Montana's agricultural landscapes as well as a brief discussion about international trade in the agricultural industry. Here's the infographic of the presentation.
This upcoming Tuesday, I will be presenting at the 2015 Beeronomics conference, which brings together economics researchers and industry participants who are interested in researcher the economics of beer markets. I will be presenting a paper about the effects of state-level beer excise taxes on differences in the size of states' craft beer markets.
As I was putting together the presentation, I thought that I rarely see researchers' presentations be available online, and rarer yet do I see available presentations be in a format that can be accessible to audiences who did not actually attend the talk. In large part, this may be because most presentations are made in the "slide" format, with few graphics, and often fairly ambiguous keywords or short phrases that are made clear during the actual presentation, but which are difficult to understand when simply looking at the slides.
In an attempt to overcome these challenges, I decided to try a new presentation creation framework: infographics. In theory, this format provides an aesthetically engaging, graphics-based informational paradigm that can be used not only during presentations to a live audience, but will also be accessible to others who did not have a chance to attend the talk. Here is my first attempt at this.
The rise in the use of video games for education purposes is unmistakable. And why not? They are fun, increasingly realistic, and require players to be engaged, stimulating their creativity, reaction, and a multitude of emotions. A 2013 EDUCAUSE report noted that there are over 1 billion people worldwide who play at least one hour of video games every day. With the increasing proliferation of electronic devices, it is unlikely that this number will not continue to rise.
As access to technology that supports games continues to become cheaper and more available to much of the U.S. population, educators at all levels have increasingly become open to and enthused about using games for educational purposes. For example, this EdTech Magazine piece describes some of the reasons that this movement is happening, a Chronicle of Higher Ed article shares some ways that students learn better from games, and this reading list for teaching with games in the classroom is a great overview of places to start when thinking about games in the classroom. The primary explanation is that games engage the audience, an increasingly relevant problem in many classrooms.
You might be wondering what all of this has to do with agricultural extension. You might also be thinking that farmers have much more important things to do than play games. And that county extension agents, university extension specialists, and university professors who make educational presentations to agricultural producers are there to deliver information, not invite audience members for an all-day Madden NFL 16 tournament. Or should they?
Well, perhaps I can concede that the Madden NFL tournament would probably not be very educational, but there is an increasing number of games that could be relevant and provide important insights that a 30-minute PowerPoint presentation may be much less effective in making. For example, this recent story about a tractor simulation game got me thinking about this whole idea in the first place. The game allows users to operate tractors that use different quality tractor hydraulic fluids. The participants are able to quickly assess differences in tractor performance and gauge whether increasing their expenses for a higher-quality fluid would be worth it. Could this information have been presented using PowerPoint slides with lots of convoluted graphs and tables with tiny font that only audience members in the first row could see? Sure. Would the impact and insights have been the same? Perhaps not.
Here's another example: Farming Simulator 16. The game provides the ability to work in a modern day farming environment with high-end graphics, equipment that you'll find on a typical farm, and the uncertainty that farmers face each year. Check out one of the game's trailers:
Here's an example of questions that extension and outreach educators can answer by engaging audiences in playing along: What's the best re-allocation strategy? What's the best hedging strategy? What are the possible returns if you acquire crop insurance? What are the risk and expected returns of using a new cropping system? What are the cost-benefit trade-offs of acquiring new equipment? Want a slightly lower-tech approach? The The Farming Game is a board game alternative (in app form) to simulating farming conditions.
Engaged learners get more out of an educational experience. Games have been shown to be an effective tool in traditional classrooms to engage students and increase their educational attainment. The agricultural extension learning environment is a natural next step. Even farmers like to play games.
Beginning on August 11, the Chinese central bank has devalued the country's currency, the renminbi, by 4.4%. For a country that has refused to change its currency's valuation for decades, this move represents a major shift in international monetary policy. Previously, the country pegged the currency to the U.S. dollar with very little room for additional fluctuations.
In general, this currency depreciation has been viewed by many as a sign of a slowdown in the Chinese economy. As the U.S. dollar has strengthened relative to other major currencies in late 2014 and 2015, the Chinese renminbi also gained strength. This made it difficult for others to purchase the currency and prevented Chinese exporters to sell in the global marketplace. Devaluing the currency may be one approach that the Chinese government is attempting to spur the country's slowing economy, by providing its manufacturers to be more competitive in trade (perhaps especially in the face of the Trans-Pacific Partnership free trade agreement that is currently being negotiated).
While the devaluation could benefit the Chinese economy, there could be potentially adverse effects for U.S. agricultural exporters who have benefited from the strength of the Chinese currency. Consider a classic economic model (below) for understanding trade impacts when there are different currencies and relative currency values are characterized by exchange rates.
Effects of Chinese Currency Devaluation
Notes: P = price; Q = quantity; RMB = renminbi; USD = US dollars; ES = excess supply; ED = excess demand
In this model, the original conditions (before Chinese devaluation) are shown by the black lines. The United States exports goods and China imports those goods. U.S. exporters want to receive US dollars for their goods (not Chinese renminbi, which would be useless in the United States). Therefore, Chinese importers must first convert their currency into the U.S. currency. This is illustrated in the "Trade market, in RMB" graph. In that panel, we see the excess demand for a good in RMB and the excess supply of goods (from the United States) after converting the USD currency into RMB.
The RMB devaluation essentially means that Chinese exporters need more renminbi to purchase US dollars. That is, the price of importing has gone up. As a result, there is a movement back along the Chinese excess demand (ED) curve to a new equilibrium marked by the intersection of the red excess supply (ES) curve and the old black ED curve. At this new intersection, the price of trade with U.S. exporters is higher and, consequently, the quantity of good traded goes down. In the United States, exporting producers now face a lower price (because Chinese importers are buying less of the good).
If you've skipped the economics lesson, let me get to the potential implication to U.S. agricultural producers. Because Chinese importers can now exchange their currency for fewer dollars, they are likely to import less of U.S. agricultural products, including agricutlural commodities and processed food goods. This is expected to lower the prices that U.S. producers receive and also reduce the amount of goods that they can export. Multiplying a lower price by smaller quantity implies lower revenues.
What impact could this have? Considering that China is the largest international market for U.S. food and agricultural products, the impact could be significant. For Montana, China also represents the major export market. For example, in 2013, Montana's top exports to China included $169 million worth of crop production, $33 million in chemicals, $30 million in metals, and $11 in minerals and ores.
What can dampen this effect? A fall in the international value of the US dollar. A cheaper dollar would reduce the price for importers of U.S. agricultural commodities and other goods, increasing the demand for those goods and, consequently, the price received and sales by U.S. producers.
I have been hearing a lot lately about the increase in the production of cattle. Yes, it's true. As I've written before, lower corn and soybean prices have resulted in large positive profit margins for feedlot operators, resulting in increased demand for cattle and a move toward rebuilding herds. And, yes, the markets have observed an increase in cattle inventory and decrease in slaughter numbers. The July 2015 USDA report on cattle showed that all cattle cattle and calf inventories were up 2% since last July, the first time the markets have seen an increase in inventories in nine years. Moreover, the 2015 calf crop is expected to be 1% higher than it was in 2014.
A skeptic (that's me) would say that this is simply a market rebound effect from the numerous years of historically high feed costs, tough weather conditions, and continued reductions in pastureland. However, today, I read a much more optimistic view of the situation. A Rabobank Food and Agribusiness Research (FAR) and Advisory group's report describes a continued expansion of the U.S. cowherd. While this in itself is unsurprising, the report also states that the expansion will occur in locations where corn and soybeans have taken over the agricultural landscape---the Dakotas and the Corn Belt.
This last part was particularly surprising to me. First, consider what has occurred in the central Plains and Corn Belt regions during the past 20 years. As shown below, every state in this experienced substantial increases in the acres allocated to corn and soybean production, which was a rational market response to increases in corn and soybean prices during much of the 2000s. Where did these acres come from? To some extent, they were the more marginal land that prior to conversion had cattle.
Change in Corn and Soybean Acres Planted, 1995-2015
Data source: USDA National Agricultural Statistical Service
Change in All Cattle Inventory, 1995-2015
Data source: USDA National Agricultural Statistical Service
Change in All Cattle Inventory and Corn and Soybean Acres, 1995-2015
Data source: USDA National Agricultural Statistical Service
Clearly, there has been a ubiquitous shift away from cattle and toward crop production. Can this land go back into cattle production? Sure. But this will depend on the relative cattle-to-crop prices that a producer faces when making the decision. In my opinion, the relative price of cattle would have to be quite a bit higher than that observed in 2011, the year that the Rabobank report predicts to be the benchmark for what to expect in cattle inventory increases. That is, because there are significantly higher costs to introducing (or even re-introducing) a cattle operation than the costs of transitioning land into cropping, producers would need to see substantially higher cattle prices and expect substantially higher returns from cattle (relative to those from crops) to return land (even marginal land) into cattle production. In other words, taking down fences is a lot cheaper then putting fences up.
So, even with $3.00-$4.00/bu corn and cattle prices around $1.50-$1.75/lb, I am skeptical that we will see a major return of cattle production into corn and soybean country.
Dr. Gary Brester, a professor in the Department of Agricultural Economics and Economics at Montana State University, puts together a monthly newsletter to participants of the Beginning Farmers and Ranchers Program in Montana. The newsletter provides an excellent overview of the previous month's grain market activity and educational insights about hedging price risk using futures and options markets.
Dr. Gary Brester, a professor in the Department of Agricultural Economics and Economics at Montana State University, puts together a monthly newsletter to participants of the Beginning Farmers and Ranchers Program in Montana. The newsletter provides an excellent overview of the previous month's grain market activity and educational insights about hedging price risk using futures and options markets.
I recently read an Agriculture.com article describing political contributions made by the agricultural sector in the 2014 election. Immediately, two things popped into my head. First, there were almost no graphics that would allow for an easy way to visualize the information. Second, given the upcoming 2016 election and that industries, companies, and individuals are not very likely to change their contribution behaviors, I wanted to look into the crystal ball of what kind of contributions from the agriculture sector are likely to occur in 2016.
All federal-level contributions data are available through Open Secrets, part of the Center for Responsive Politics. The website is dedicated to making available information about campaign contributions, monetary expenses by members of the U.S. government, and lobbying expenditures. My data radar went into overdrive when I saw that they make available raw data describing contribution amounts and contribution sources: so many possibilities!
This is when I decided to make a multi-part blog series that digs down into the data and provides visualizations that help paint a picture about the agricultural industry's role in political contributions. In today's entry, I want to provide a zoomed-out view of the political contribution situation, and with each new entry, dig deeper and examine more detailed information.
First, I wanted to look at the overall contributions and number of contributors by broadly defined agricultural industries. These data show that by far the greatest contributing sector is the crops production and processing group, which had nearly 26,000 contributors giving nearly $22 million in political campaign contributions. This group includes the big grain crops organizations and crops handling facilities, such as elevators and processors. The next largest contributing sector is the food processing and sales sector, which includes food stores, food manufacturers, and processing facilities that supply end-use products. This group contributed $11.6 million, although this came from only approximately 6,100 contributors. Overall, agricultural sectors contributed $69.7 million to campaigns in the 2014 election cycle.
Digging a bit deeper into the overall contributions, I broke up each sector into different groups. There were too many groups to provide a reasonable visualization of the data, but here is a table of contributions by specific groups within the agricultural industry. Within the largest contributing sector, crop production and processing, nearly half of all contributions came from groups associated with processing the crops. Groups and individuals representing major cash grains, including corn, soybeans, and wheat, contributed $1.1 million, the vegatable, fruit, and tree nut ssector contributed $2.4 million, and the sugar sector contributed nearly $6 million. The large contributions of the latter may have been associated with the U.S. government's 2014 negotiations with Mexico over the World Trade Organizations (WTO) sugar trade dispute.
Last for today, here is a visualization of contributions from agricultural sectors to specific groups. I wanted to see whether contributions had particular political leanings and how much of the contributions went to political action committees (PACs), which could then make their monetary allocations to candidates that each PAC supports. Of the total contributions from the agricultural industry, 44.5% was made to Republican candidates, 30% was made to PACs, and only 11% went to Democrats. Out of the four largest contributors (crop production and processing, food processing and sales, agricultural services, and livestock), all but the food processing sector had nearly equal contributions directly to Republican candidates and to PACs. The food processing and sales sector contributed almost three times as much money directly to Republican candidates than it did to PACs.
What does this all mean? On the surface, there aren't many surprises here. There is a lot of money spent by the agricultural sector on the 2014 election cycle, much of the money came from crop production and processing, and the majority of the funds were contributed to Republican candidates. In part 2, I will look deeper into who contributed during the 2014 election cycle and in part 3 I will further explore the recipients of those contributions.
In the May 21, 2015 blog post, I wrote about the creative destruction of agricultural jobs. That is, the idea that although technological advances and using more capital equipment to replace labor initially led to significant reductions in the number of jobs within the agricultural sector, there is a rapidly growing research/science based industry that will support food production.
In the June 1, 2015 post on the Farm Press Blog, David Bennett notes that the recent news about increases in jobs within the agricultural sector "...may mean that [farm] kids will be coming home." That is, that there will be job growth in the production agriculture sector. This is not likely to be the case.
Here is a graph of some of the occupational outlooks for several fields in the agricultural sector. These are data that I collected from the U.S. Bureau of Labor Statistics' Occupational Outlook Handbook. The data show that for production agriculture professions, either in running an operation or as farm labor, is going to continue to decrease (by between 3% and 19%). However, the research-based support industries will have a significant increase by 4-11%. So, the kids are probably not going to be the ones growing the wheat, but they're going to be the ones developing innovative methods to grow the wheat better.
One of the things I enjoy is looking at data in different ways. In other words, I am a data visualization geek. Today, a Tuesday, is when the USDA posts their weekly crop progress report, which describes various production conditions such as crop planting, emergence progress, and quality conditions, among others. One of the more annoying aspects of these reports is that they provide limited insights about how crop production progress has changed over the course of the growing year. Another limitation (at least for me) is that the information is provided in tabular form, which is fairly difficult to visualize and make quick inferences.
I wanted to develop a better method. I downloaded hard red winter wheat conditions data from the USDA National Agricultural Statistical Service and was able to develop a map-based characterization of changes in wheat quality over the 2014/15 growing period. Here's what this looks like. The time-lapsed condition visualization shows the decreases in quality conditions in early Spring (during the time when there was little precipitation and soil moisture) and then improvements in late Spring (after significant rainfall). This is arguably a much easier way to get a general sense of the information available to market participants and provide some insights into the price patterns observed in the winter wheat market.
If you ask a New Yorker or Baltimorian what they it's like being an agricultural producer, you might get a response that reflects the state of agricultural production as it was in the 1940s. A family operation with 100 acres of land, 10 head of cattle, 8 children, and a single tractor. Or, you might get a response that almost all of the agricultural land in the United States is owned by three or four huge (probably also evil) farming corporations that manipulate food prices and production to simply maximize their own profits without any consideration for the greater social good. Of course, neither of these descriptions characterizes reality, although the reality might be somewhere in between.
The U.S. agricultural sector has experienced tremendous changes in the 20th century. Tremendous technological improvements and adoption of those technologies led to the classic labor-capital trade-off behavior and a concurrent improvement in output. To characterize the changes to a Baltimorian, for instance, would be to say that instead of having a family of 10 work 100 acres of land, a family of 3 can now work 5,000 acres.
Here's another way to consider how the agricultural industry has changed. I obtained data from the USDA Economic Research Service about implicit quantities of farm outputs and inputs between 1948 and 2011. Then, I plotted the livestock and crop output data and the labor (number of farm workers), capial (machinery), and land inputs. The plots make quite evident the upward trends in output and the downward trends in inputs. For example, between 1948 and 2011, output in livestock and related products increased by 129% and crop output increased by 163%. These tremendous increases occurred at the same time that land use for agricultural production decreased by 27% and labor decreased by 78%. The initial reduction in labor was replaced by a higher use of equipment inputs (which increased by 174% between 1948 and 1981). After the early-1980s, equipment inputs began to decline\d, likely as a result of equipment becoming larger and more efficient. For example, between 1950 and 1980, farm workers were replaced by machinery, which was not very efficient but was more so than a person. After the 1980s, the not very efficient machinery was replaced by equipment that was much more cost-effective (e.g., upgrading from a 10 foot combine header to a 40 foot combine header).
What was one of the results of these technological advances? Farms became bigger (taking advantage of economies of scale), the demand for labor decreased, older farmers could continue running their operations, and children who grew up on farms were less likely to return. It seemed as though this was just another case of technology crowding out individuals from an industry. And then, this happened:
What happened? How can an industry that's consolidating farmland and reducing labor inputs have such a deficit of qualified personnel? It appears that agriculture has fallen prey to the idea of "creative destruction," which generally describes the process of developing a "new" system only after destroying the existing system. The automation of the automobile assembly is frequently cited as an example of creative destruction. Robotic systems have replaced large portions of car assembly lines around the world, resulting in an entire industry of car assembly workers becoming unemployed. However, this automation spurred the development of a new industry for building, maintaining, and repairing the robotic systems.
The same creative destruction story is playing out in agriculture. The increasing scale and efficiency of machinery and the consolidation of farms has significantly reduced the role of traditional farm and ranch labor. However, who is going to continue advancing these technologies? Who is going to collect and analyze the huge amounts of data that all of the new machinery is generating? Who will manage the incredibly complex global infrastructure of moving food products from Glasgow, Montana to Tokyo, Japan? This is the emerging modern agricultural industry whose role is to support the revolutionized world of ag.
Recently, I've been seeing a number of discussions arguing that hedge funds have aggressively shorted (sold futures contracts) wheat markets. This has essentially created a low price bubble (my terminology). Similarly to the high price bubble that occurred in the housing market in the late-2000s, wheat markets are disproportionately skewed toward the short side, implying that the low prices that have recently been observed in wheat markets (for example, here are recent futures price charts for hard red winter wheat and hard red spring wheat) may be too low to accurately reflect market fundamentals. If there really is a low price bubble, we would expect wheat prices to increase when the bubble finally bursts.
These discussions made me curious about (a) whether there really does appear to be a low price bubble and (b) the size of this potential bubble. To try to answer these questions, I gathered Commitment of Traders (COT) data, which are published by the U.S. Commodity Futures Trading Commission (CFTC). These data show the number of contracts and position (long or short) that are held by producers, merchants, processors, and other users who "predominantly engages in the production, processing, packing or handling of a physical commodity and uses the futures markets to manage or hedge risks associated with those activities." The data also show contract positions held by swap dealers and money managers, who are market participants that are typically considered to be speculators in commodity markets.
I decided to look at a relatively simple but arguably quite instructive measure that indicates the proportion (relative to the total open positions) of long and short positions held by the second set of market participants: the speculators. The idea here is that participants that use futures markets for hedging price risk (e.g., producers, merchants, processors, and other commodity users) are unlikely to substantially "gamble" in the market and, therefore, unlikely to create a significant bubble (although I acknowledge that this can be argued and there is anecdotal evidence of price hedgers taking speculative positions).
After calculating the proportion and plotting the data for daily positions between January 3, 2006 and May 5, 2015, there does appear to be some evidence to indicate a potential low price bubble. Let's start with a less convincing indicator, the proportion of long positions held by speculators. The figure shows that in the past several months, the proportion of long positions has certainly decreased for all three wheat classes for which futures contracts are traded, but the current number of long positions does not appear to be substantially different from the long run average (and certainly not as low as it has been in the past).
However, there seems to be significantly more evidence of a low price bubble when considering the proportion of short positions held by speculators for hard red and soft red winter wheat contracts. In the hard red winter wheat market, the 64% of short positions held by speculators is 20 percentage points higher than previous peaks, which were observed only five times since 2006. It is also 40 percentage points (or 204%) higher than the historical average number of short positions held by speculators. In the soft red winter wheat market, speculators hold 71% of short positions, which is only second to the 73% observed in January 2014.
So is there a low price bubble? It certainly appears that there is at least suggestive evidence of one in the winter wheat markets, although little evidence (based on speculators' short positions) in the spring wheat market. If (or likely, when) the bubble bursts, wheat prices are likely to bounce back (and they have begun to do so; see the May 15, 2015 blog entry).
On May 05, 2015, both hard red winter and hard red spring wheat harvest period futures prices were at a five-year low. News of moisture in the central Great Plains, Russia's bumper crop, and reduction in U.S. export demand drove markets downward as hedgers overwhelmed the market by going big on short positions. Only ten days later, the wheat markets' upside "great potential" is apparently being realized as hard red spring wheat prices are trading at $5.75 per bushel (8.04% higher than the May 5th low) and winter wheat prices have rebounded to $5.42 per bushel (a 10.02% jump from its May 5th low).
What happened? It was likely the case of "too much of a good thing." The global production expectations were somewhat inhibit by the announcement that the world is an El Nino year. This raised concern about increased droughts in Australia, poor production conditions in Asia, and increased precipitation and cooler temperatures in the U.S. central Great Plains. While it is relatively easy to infer why droughts in Australia and Asia would increase prices (i.e., lower Australian supplies and higher Asian import demand), it may not be immediately evident why and how this affects price-increasing fears in the United States.
As I have mentioned in previous posts, rain is good until it isn't. High precipitation amounts have contributed to increased incidents of various wheat rust diseases in the central United States. This has increased concerns about the quality and production levels of winter wheat. This concern would result in an upward market readjustment, possibly one that we are currently observing.
Strategie Grains, a private French agricultural consulting firm, published a report that significantly reduced European Union export wheat expectations due to the increased output of Russian wheat. The European Union, and especially France, compete with Russia for buyers in northern Africa and the Middle East, and an increase in the Russian wheat harvest would make the export market more competitive. The situation could be exacerbated depending on Russia's timing to eliminate its trade tariffs on wheat exports, which were installed by Russian President Vladimir Putin as a countermeasure to the U.S. and European economic sanctions resulting from Russia's activity in Crimea and eastern Ukraine.
Russia's impact could have significant implications that last for longer than just a single year. Decreased competitiveness in the export market would imply increased inventories of wheat in the European Union. This would have additional ripple effects to the United States, where farmers similarly compete on the global market and prices reflect this competition. In the USDA's most recent World Agricultural Supply and Demand Estimates, the agency projects the European Union to export 34.5 million metric tons of wheat, with inventories (stocks) rising from 10.07 million metric tons to 14.72 million metric tons. The United States is also expected to increase its inventories by over 20%.
Higher inventories are likely to dampen wheat price levels. Moreover, the increasing global wheat inventories would likely maintain lower price levels over a longer time horizon. One potential saving grace could be the U.S. cattle market's move to rebuild herds that have been significantly reduced over the past 4-5 years. If domestic demand for feed remains steady or increases, U.S. wheat farmers could have a potential price advantage relative to other wheat producing regions.
The U.S. Wheat Quality Council organizes an annual tour of Colorado, Kansas, Nebraska, and Oklahoma winter wheat fields by crop scouts who analyze and provide forecasts about the yield potential at wheat farms across the state. In 2015, over 90 scouts visited 659 fields.
After a very low production year in 2014, the recent precipitation in the central Great Plains is expected to boost yields in 2015. The Wheat Tour results indicate an average 35.9 bushels per acre yield across Kansas, Nebraska, and Oklahoma, which represents 4.6 bushel acre increase over the average yield in 2014 across those states. While this production increase, coupled with high U.S. and global wheat inventories (among other reasons), has reduced wheat prices to a five-year low, the expected total output of 288.5 million bushels is below the 298 million bushel expected output that was predicted by industry experts prior to the tour.
Markets responded bullishly to the unofficial news of the wheat tour results (the official report has not yet been published by the Wheat Quality Council). Hard red winter wheat July futures are trading at $5.08 per bushel at the time of writing, nearly 30 cents per bushel higher than the $4.90 per bushel low observed on May 5, 2015. Similarly, hard red spring wheat September futures are trading at $5.47 per bushel, 11 cents per bushel higher than the May 5 closing price.
I followed the wheat tour using reports posted on Twitter. One of my key observations was the substantial disparity in the reports. Some fields were reported to have yields of 15-20 bushels per acre, while others were in the 60-70 range. This regional variability may play a key role in markets' ability to correctly establish prices due to the significant uncertainty in production information. Adding to this uncertainty was the number of stripe rust incidents that I noticed from the posted photos. This could lead to potential yield and quality issues.
The U.S. Department of Agriculture will release its first production and yield estimates of the season on May 12, 2015.
One of the main reasons that I became so interested in economics, and especially economics research, is the mind-blowing concept that one can make a reasonable prediction about people's behaviors just by analyzing data. The information-rich world of the Internet age has only made my addiction to data even stronger. Today, I want to share a really neat way to think about and visualize individuals' behavior in agricultural markets.
Consider, for example, a corn producer. Typically, you plant in mid-spring and market the corn in mid-fall. Prior to planting, you need to acquire fertilizer and after harvest, you might want to find a grain handling facility that offers you the highest price for your product. Twenty years ago, you might have made several phone calls to inquire about fertilizer and corn prices, and then made a purchasing or sales decision. Today, you might be as likely (if not more likely) to "Google" the information. This is where the really neat stuff begins!
In 2012, Google Inc. launched a service called Google Trends (various predecessors existed since 2008, but Google Trends represents the most current version). Google Trends tracks information about Google users' keyword search activity and makes highly aggregated statistics about this information available to the public. As an economist, these statistics are particularly interesting because they can provide insights about the demand for particular information and how this demand may be related to what is observed in markets.
Using the example above, consider search trends for the keyword "corn" and the keyword "fertilizer". Google Trends does not provide a measure of the actual search volumes, but rather a relative index of search volume, which ranges from 0 to 100. For example, if there were more searches in the month of April 2015 than there were in March 2015, then the index value for April would be higher than for March. In each figure, I plot the weekly search volume index between 2004 and 2015.
Each figure has a distinctive cyclical pattern. For corn, there is always a spike in the number of searches around late-September and early-October; that is, around harvest time. This corresponds to the period when producers are typically seeking information about where they can market their grain and which location can offer them the best price. Similarly, for fertilizer, search increases are observed in late-March and early-April, the period when many spring seeded crops are being planted and farmers purchase fertilizer for their operations. Incredibly, these patterns are very consistent across the 11-year period for which Google has maintained records of search terms. The spike in the fertilizer search volume in 2013 corresponds to the explosion of a Texas fertilizer factory, which prompted a much broader interest.
What can we learn from all of this? First, this helps very clearly verify the classic economic relationship between demand, supply, and prices. In the spring, when the demand for fertilizer sourcing and price product is in highest demand (as indicated by the demand for information about these aspects), fertilizer prices are highest. Similarly, in September and October, when the supply of corn is highest (as indicated by the demand for information about where to sell it and finding the best price), the price of corn is the lowest. Second, the data help provide insights about the potentially optimal times to buy and sell these products. Just look for the months when people are least interested!
Dr. Gary Brester, a professor in the Department of Agricultural Economics and Economics at Montana State University, puts together a monthly newsletter to participants of the Beginning Farmers and Ranchers Program in Montana. The newsletter provides an excellent overview of the previous month's grain market activity and educational insights about hedging price risk using futures and options markets.
Yesterday, Vietnam signed a free trade agreement with South Korea and is soon expected to sign similar agreements with Belarus, Kazakhstan, and Russia. These agreements are likely to be a boon to Vietnam's agricultural sector, which accounts for over 30% of the country's exports. Moreover, less expensive labor costs in the country are likely to make agricultural exports competitive on the world market.
The United States has been the major exporter of raw and processed food products to South Korea for over half a century, but Vietnam's free trade agreement with South Korea could have significant implications that may reduce United State's role as an important South Korean supplier. In 2014, the United States exported nearly $7 billion worth of agricultural products to the Asian country. In 2012-2014, grain exports accounted for 29% of that value ($1.76 billion) and meat products were approximately 18% ($1.07 billion). For Montana, South Korea is a particularly important trade partner. In 2013, the latest statistics prepared by the Montana Department of Commerce, exports to South Korea from Montana were valued at approximately $168 million.
While the United States and South Korea have enjoyed relatively amiable trade relationships in the past, the Vietnamese free trade agreement represents increasing global competition for the growing Korean middle-class consumer base. The Trans-Pacific Partnership (TPP), for which a "fast track" Trade Promotion Authority is currently being hotly debated in the U.S. Congress, may be necessary to potentially counteract Vietnam's entry as a competitor in the global trade market.
Yesterday, I had a phone conversation about, in part, recent developments in wheat markets. One of the discussion points was how representative was the U.S. Drought Monitor in providing information that can be used to understand production conditions. This tool is widely used by researchers, industry participants, and producers. However, during my conversation, it was noted that the production conditions suggested by the drought monitor may not be representative of the conditions experienced by farmers. For example, in the most recent drought monitor figure, most of Montana appears to be in normal conditions, with only a small part of the southern region designated as being Abnormally Dry. However, anecdotal evidence from Montana producers indicated that in some regions marked as normal were actually experiencing low moisture conditions.
This got me to wondering: while the drought monitor is useful in indicating longer-run weather conditions, but how representative is it of the shorter run? Perhaps a better indicator of agricultural production conditions could be provided by soil moisture measures. With this in mind, I started looking for tools that can help better understand these measures. There are three indicators that I found to be both useful and easily accessible: surface soil moisture, root zone soil moisture, and the evaporative stress index. All three measures are calculated with data that are collected by the National Oceanographic and Atmospheric Administration (NOAA) and National Aeronautics and Space Administration (NASA) using remote sensing, which typically includes satellite, radar, and/or aerial photography.
Here are the most recent surface soil moisture, root zone soil moisture, and evaporative stress index (ESI) figures. The surface soil and root zone soil moisture measures are presented in relative terms that compare current conditions to a long-term, 60-year average. For example, in Montana, the two moisture levels show that in the central, north central, and southwest regions of the state, surface soil moisture levels are nearly the same as the long-run average. However, in the northeastern, southwestern, and western parts of Montana, conditions are significantly below the average. The evaporative stress index shows anomalies (both high moisture, green, and low moisture, green) in rates of water use across locations. For Montana, the most recent ESI similarly shows that central and north central Montana are showing above-average moisture rates and the northeastern, eastern, and western regions are below-average.
These measures provide a complementary, perhaps more representative characterization of production conditions in the United States. Moreover, these tools are updated rapidly and offer more geographically precise moisture conditions. While the U.S. Drought Monitor is certainly an important instrument, it alone is unlikely to provide the type of information that can help better assess production conditions and, ultimately, economic markets.
Recently, I found a really neat data set compiled by the United States Geological Survey's (USGS) National Water-Quality Assessment (NAWQA) Program that provides county-level estimates of pesticide use for over 400 products. These data can provide interesting insights into the way that the agricultural sector has changed its behavior in pesticide use and drill down to fairly specific areas.
What I like most about spatial data that are available over time is the ability to develop dynamic maps that show trends across geographic spaces. I've put together two examples using the pesticide use data. Both examples look at trends in the use of glyphosate, which is a broad system herbicide designed to target broadleaf and grassy weeds. The herbicide was originally patented and marketed by Monsanto in 1973 under the brand name Roundup. In 2000, the Roundup patent expired and generic glyphosate products entered the market. Furthermore, Monsanto has introduced genetically modified crops that are resistant to glyphosate herbicide, which significantly reduces farmers' cost of managing weeds. For example, in 1996, Monsanto introduced Roundup Ready soybeans and in 1998, Roundup Ready Corn.
While the pesticide use data base provides information on many products, glyphosate is a herbicide used by the majority of commodity producers in the United States. First, I analyze changes in estimated glyphosate in the continental United States. Second, I consider only the northern U.S. states (Idaho, Montana, North Dakota, and Washington).
The figures provide several interesting insights. First, there appears to be a rapid adoption in use of glyphosate after the Monsanto patent in 2000. Furthermore, much of the heaviest adoption occurred in the corn belt states, which can at least partially be explained by farmers' increasing use of Roundup Ready crops. In the northern Great Plains and northwest states, the increase and cyclical nature of glyphosate use is likely also tied to increased no tillage management practices. According to the USDA Economic Research Service, the proportion of acres in no-till operations has increased from 20% to 40% for wheat between 2000 and 2010, from approximately 17% to 23% for corn, and from 35% to 45% for soybeans. In 2010, Montana had over 40% of its acres in no till practice and North Dakota had approximately 26%.
In mid-April, Bunge Ltd., a large U.S. grain trading company, teamed up with the Saudi Agricultural and Livestock Investment Company to a announce a planned purchase of the majority stake in the Canadian Wheat Board. The Canadian Wheat Board was the sole buyer and seller of Canadian grains in the world market until 2011, when the Conservative Party of Canada eliminated this power through the 2011 Marketing Freedom for Grain Farmers Act. The legislation ended Canadian farmers' obligation to market their grains only to the Canadian Wheat Board, and also ended the ability of only Canadian Wheat Board's to market Canadian grain internationally.
After having to relinquish its single-desk buying and selling power, the Canadian Wheat Board simply became one of the many market participants who had to compete to buy and sell Canadian wheat, although its lengthy presence as the marketer of Canadian wheat helped the Board maintain substantial market share. Now, the Wall Street Journal reports, the Canadian Wheat Board's majority share and, as Bunge and its partner hope the marketing share associated with having that majority share, is expected to be sold for $206.5 million around mid-2015. Furthermore, Bunge's CEO Soren Schroder noted that the company's plans are to focus Canadian grain movement toward export facilities on the west coast.
Unlike recent recent entry by Asian multinational corporations into large wheat production regions of the northern United States, Bunge's partnership with the Saudi Agricultural and Livestock Investment Company likely signals the growing demand for securing grain supplies from the Middle East. The Saudi Arabian population is estimated at over 31 million and continues to grow at approximately 3% annually. The increasing population, highly limited agricultural production capabilities due to water deficiency, and relatively high incomes resulting from oil sales are all factors that will continue to expand the demand for wheat in that country. Bunge's acquisition of the Canadian Wheat Board and its increasing presence in the Australian wheat export market (the company is completing its second Australian export facility in 2016) are signals that the company is taking a strong position to be competitive in the growing Middle East food market.
Favorable weather conditions in the midwest and higher production in major importing countries have contributed to a downward slide in wheat futures prices (see the hard red winter wheat price chart and hard red spring wheat price chart). A recent report by Commerzbank noted that the rapid price decrease was due to a record number of short positions in the soft red and hard red winter wheat futures. The widespread move to the short side of the market reflects investors' concerns for a greater than expected supplyin the United States (even with lower plantings) and decreased demand from major international buyers.
But is there reason to panic? Probably not. First, the most recent (April 27, 2015) USDA crop progress report shows that the recent precipitation events in the central Plains did not have an immediate impact on improving winter wheat quality. Winter wheat quality in Kansas, the largest producer of this wheat class, remained relatively stable with only 26% of wheat in good or excellent condition in both of the last two weeks. Moreover, this represents a slight decline from the 28% of wheat in good or excellent condition in Kansas three weeks ago. Similar condition persistence was observed in Nebraska and Oklahoma during the past three weeks. Montana, however, has had more favorable production conditions with 63% of its winter wheat rated as being in either good or excellent condition.
Second, continued worries about lack of moisture in top spring wheat producing states (Montana and North Dakota) has somewhat buoyed spring wheat prices. As of the April 28, 2015 USDA Weekly Weather and Crop Bulletin, the percentage of topsoil rated as having very short or short moisture levels was 37% in Montana and 33% in North Dakota. Lastly, factors that are favorable to production also come with the potential for adverse effects. The recent rains in the central Great Plains are certainly a boon to this year's winter wheat crop, but they have also sparked worry about wheat rust outbreak. While continued heavy precipitation and high wind conditions in some areas and low moisture situations may propogate production worries, they may also mitigate or reverse the recent downward wheat price trends.
In the midst of the 2013-14 grain marketing year, the northern Great Plains experienced a significant shock to the grain transportation portion of the marketing channel. An above-average production year, a cold and snowy winter, and rail line repairs were among the primary factors that led to northern Great Plains grain not being delivered in a timely manner to west coast export facilities. The major economic impacts occurred during the first two quarters of 2014. First, the lower supply of available rail cars resulted in large increases to rail car prices in secondary rail markets, where grain handling facilities can bid on cars to be delivered to their locations. For example, between 1997 and 2012, elevators paid on average approximately $50 above the original rail car price to reserve cars for their facilities. In the 2013-14 marketing year, the additional cost in secondary markets was above $750 per rail car. Some elevators paid over $3,000 per rail car for the right to obtain a car in the thin market.
As with nearly all costs, the individual or organization that pays the cost does not necessarily experience the full burden of that cost. In most cases, elevator facilities passed on a portion of their additional expenses to farmers through lower prices, thus reducing their own total costs in an attempt to reduce losses in their profit margins. To analyze these potential impacts, I performed an informal analysis of historical basis for Montana wheat. Specifically, I combined daily nearby basis data for 1998–2014 from six regions in Montana with state production and available stocks information to analyze potential deviations in basis values during the first five months of calendar year 2014. The results indicate that for hard red winter wheat (11% protein content), basis were $0.13–$0.24 per bushel weaker in April and May 2014 than historical basis averages in those months. For hard red spring wheat (13% protein content), basis were $0.12–$0.66 per bushel weaker in February through May 2014. These estimates provide suggestive empirical evidence that grain transportation constraints in early-2014 may have adversely affected local cash prices.
Naturally, I began wondering whether similar issues have been observed during the 2014-15 marketing year. To begin answering this question, I looked at the weekly carload reports published by the BNSF Railway Company, which serves the vast majority of Montana and other northern states' grain markets. The data contained in the reports provide relatively good news, which I have summarized in two graphics. First, I looked at the weekly numbers of BNSF cars that were used to transport grain between January 3, 2014 and April 18, 2015, and how these weekly number of cars compared to a relatively normal 2012-13 marketing year. The figure shows that between January and April 2014, there were 10%-25% fewer BNSF rail cars delivering grain relative to the same period in 2013. While there was some recovery between May and July 2014, the available rail car deficit was observed again during the beginning of the 2014-15 marketing period. This undoubtedly played a role in affecting northern Great Plains wheat prices.
The good news is what happened after approximately November 2014. The carload reports indicate that, on average, there were 25% more grain carloads being delivered in January-April 2015 relative to the same period in 2014. This good news is even more evident when considering the total number of carloads delivered between January 1 and the week of a published report. These data, summarized in this figure, show that the number of rail cars available to ship grain has returned to a relatively stable, historical level after experiencing significant declines in early 2014.
So, for northern Great Plains farmers, there is evidence that the transportation markets have returned to a relative normal state. However, there are several issues that are still necessary to keep in focus. First, part of the "solution" to the rail issues observed in 2013-14 was the decline in the demand for rail cars to transport oil out of eastern Montana and western North Dakota. The precipitous drop in oil prices (figure) and continued downward pressure has eased the transportation demand from the emerging U.S. oil markets. Second, higher U.S. and global wheat inventories have led farmers to reduce their 2014 winter wheat and 2015 spring wheat plantings, which is likely to reduce overall production and, thus, demand for rail cars during the 2015-16 marketing year. However, continued high production of small grains, corn, and soybeans in the central Great Plains and the Corn Belt may continue straining the U.S. rail infrastructure, which can have indirect economic impacts on northern Great Plains markets.
The Trans-Pacific Partnership (TPP) is a proposed agreement that began negotiations in 2005 and after an initial goal of launching in 2012, has picked up political steam in 2014 and 2015. The partnership is the cornernstone of President Obama's Administrations Asia-Pacific economic policy and is intended to significantly reduce trade barriers among North American nations and western South American countries and major Asia-Pacific importers, including Japan, Malaysia, Singapore, South Korea, Taiwan, and Vietnam. The 12 nations that comprise the TPP represent approximately 40% of the world's gross domestic product (GDP); that is, 40% of the world's economic output. The TPP is likely to have major implication for Montana's crop production industry, because over 80% of Montana's small grain are exported to the Asia-Pacific. Nearly the same proportion of pulse crops is exported from Montana, also primarily heading west.
Quite typical of many trade accords, the TPP has received substantial opposition from domestic groups who claim that the treaty will harm U.S. labor markets, adversely affect food safety, and create additional public health concerns, among other issues. This opposition made its way into the political realm, resulting in a significant slow down of the legislation's movement through the U.S. Congress. Specifically, while the TPP largely has the support of Republican lawmakers, Democrats are divided about the initiative due to the party's close ties to labor union organizations.
On April 22, 2015, the Senate Finance Committee approved by a 20 to 6 vote a "fast track" measure for the trade agreement. Then, on April 23, 2015, the House Ways and Means Committee voted 25 to 13 to approve a similar Trade Promotion Authority bill. The fast track provides guidelines for the the Obama administration to negotiate the partnership, which will then be presented to Congress for an up or down vote, without the possibility of changing the language of the agreement. Perhaps one of the underlying ideas for enacting the fast track measure is that few, if any, large, complex, multi-national deals would ever be enacted if each country's legislative bodies participated in negotiations. However, even though the fast track measure is a significant boon to the TPP's negotiation process, the partnership is far from being signed into law. Aside from the discontent about the measure by many of President Obama's Democratic allies (including House minority leader Nancy Pelosi) and other groups who support the Democratic party (such as trade and labor unions, environmentalists, and Latino organization), the Senate Finance Committee fast track measure set 150 negotiating objectives that will have to be satisfied by the TPP. However, there is strong support by businesses and business lobby groups as well as Republican leadership. Ironically, the loss of majority in both chambers of the U.S. Congress to the Republican party during the 2014 election may actually turn out to be rather helpful to President Obama's cornerstone trade program.
Today I presented my thoughts about the 2015 agriculture outlook at the Montana Farm Bureau's Council of Presidents Conference in Helena, MT. The 2015/16 marketing year for northern Great Plains small grains is slightly less optimistic than last year primarily due to higher U.S. inventories and global inventories of small grains. Both domestic and world production was higher than consumption last year, resulting in higher than expected supplies and, consequently, lower prices. However, continued uncertainty about below-average precipitation in the central Great Plains continues to buoy wheat prices. Unless water availability increases in the large hard red winter wheat producing regions, basis-adjusted wheat prices are expected to be between $5.00 and $5.50 per bushel for HRWW and between $5.25 and $5.75 per bushel for HRSW.
Feeder cattle prices are likely to remain relatively high for Montana producers, with basis-adjusted values ranging between $2.00 and $2.50 per pound for 550-600 pound steers. Lower corn prices have resulted in increased profits for Midwest feedlot operators, contributing to continued strong demand for feeder cattle. Moreover, drought conditions are expected to be minimal during 2015. These strong markets for feeder cattle and favorable production conditions are both very encouraging for Montana ranchers.
More details are provided in the PDF of the full presentation, which you can download below.
Anton Bekkerman joined the Department of Agricultural Economics and Economics at Montana State University as an Assistant Professor of Economics in 2009. He received a B.B.A. in Business Economics in 2005 from the Sellinger School of Business at Loyola University in Maryland, and advanced degrees in Economics from North Carolina State University.
Bekkerman's research interests include price analysis, agricultural and food marketing, production and agribusiness management, crop insurance, and applied econometrics with a concentration in spatial and temporal modeling. He is also involved in numerous inter and intradisciplinary research projects including cropping system analysis, economics of education, and the economics of public libraries.
Montana State University
Dept of Agricultural Economics & Economics
Montana State University
Dept of Agricultural Economics & Economics
SAS Institute, Inc.
Econometrics/Time Series Group
SAS Institute, Inc.
Technical Documentation Group
North Carolina State University
North Carolina State University
Business Economics, Major
Computer Science, Minor
Mathematical Science, Minor
Loyola University in Maryland
Below are my current research projects as well as a catalog of my published research, including work that has been published in peer-reviewed journals and outreach reports. If you see anything that interests you, please don't hesitate to get in touch with me to ask questions and provide feedback on my existing research and/or discuss potential future work.
Inconsistent discipline across schools can inequitably impact students' access to education by separating certain students from familiar learning environments, especially in misconduct cases that result in longer removal periods. We empirically estimate whether such inconsistencies are attributable to heterogeneity in student body demographic characteristics. The results indicate that a greater number of disciplines that remove students from school for an extended period of time are observed in schools with a higher proportion of black students, but no significant differential punishment effects are observed in schools with a higher Hispanic student population. Furthermore, results of decomposing the marginal effects into conditional and unconditional elasticities indicate that it is not the case that schools with predominantly white student bodies have the least severe punishments and schools with more minority students have the most severe punishments. Rather, schools with inconsistent disciplinary behavior have a proportion of the inconsistency attributable to the race of the student body.
This article assesses the role of simulation methods in econometrics pedagogy. Technological advances have increased researchers' abilities to use simulation methods and have contributed to a greater presence of simulation-based analysis in econometrics research. Simulations can also have an important role as pedagogical tools in econometrics education by providing a data-driven medium for difficult-to-grasp theoretical ideas to be empirically mimicked and the results to be visualized and interpreted accessibly. Three sample blueprints for implementing simulations to demonstrate foundational econometric principles provide a framework for gauging the effectiveness of simulation analysis as a pedagogical instrument.
Camelina (Camelina sativa) is a promising bioenergy crop, but a sustainable production system for this crop has not yet been well developed. There is also concern about competing land use between crop productions for bioenergy or food use. One approach to overcoming this concern and developing sustainable production systems for bioenergy crops is potentially replacing the fallow period in wheat-based cropping systems with bioenergy crops. The agronomic and economic benefits of growing camelina in rotation with winter wheat were evaluated in a replicated rotation study from 2008 to 2011 in the Northern Great Plains (NGP), focusing on the effects on wheat yield and overall profitability of the cropping system. Average winter wheat yields were 2401 and 1858 kg/ha following camelina and barley, respectively, representing a 13.2 and 32.8% winter wheat yield reduction compared to the fallow–winter wheat rotation (2766 kg/ha). Lower winter wheat yield in the alternative systems were offset by 907 kg/ha camelina and 1779 kg/ha barley yields. Economic analyses revealed that at existing market prices and production costs, the traditional fallow–winter wheat rotation provides greater net returns to growers due to substantially lower variable costs of the system. Scenario analyses that use more optimized, lower cost camelina production practices show that the net profits of camelina-wheat system could be closer to those in the fallow-wheat system. However, higher grain price and/or greater grain yield of camelina are essential to attract producers to include camelina in their cropping systems. Although the fallow–wheat system resulted in higher short-run net returns, the total biomass production and crop residue return to soil is much greater in camelina-wheat than fallow-wheat rotation, which is likely to improve soil quality and productivity in the long run.
No-till (NT) and Organic (ORG) farming systems each claim increased sustainability compared with conventional tilled systems. Our objective was to compare agro-economic productivity and soil nutrient status among diversified NT and ORG cropping systems in Montana. Five cropping systems were compared, including four NT systems and one organic system. Three NT systems were designed as 4-yr rotations, including a pulse [lentil (Lens culinaris Medik.) or pea (Pisum sativum L.)], an oilseed [canola (Brassica napus L.) or sunflower (Helianthus annuus L.)], and two cereal crops [corn (Zea mays L.), proso millet (Panicum miliaceum L.), or wheat (Triticum aestivum L.)] in alternate year broadleaf-cereal arrangements. No-till continuous wheat was also included. The organic system that included a green manure (pea), wheat, lentil, and barley (Hordeum vulgare L.) received no inputs. Winter wheat grain yields in the ORG system were equal or greater than those in the NT systems, even though 117 kg N ha-1 was applied to the NT winter wheat. After 4 yr, soil nitrate-N and Olsen phosphorus were 41 and 14% lower in the ORG system, whereas potentially mineralizable N was 23% higher in the ORG system. After 4 yr, per hectare total economic net returns were equal between NT and ORG systems, despite the inclusion of a 3-yr market transition period for ORG when grain prices were equal for both NT and ORG systems.
Growers in north central Montana ('Golden Triangle') have questions about risk management of cropping intensity and nitrogen (N) fertilizer strategies. A 10-yr study at Bozeman demonstrated economic resilience provided by variably-managed pea in rotation with wheat under contrasting available N fertility and uncertain wheat protein discount/premium schedules (Miller et al. 2015). A follow-on study was initiated in north central Montana to investigate whether this long-term response is also observed in a drier climates. Our objectives were to compare winter wheat yield and quality, agronomic efficiency of fertilizer use, and economic trade-offs in four alternative crop rotations managed with four different N fertility regimes. This study is planned to run for a minimum of six years at both sites. Here we report on the results.
The 2011 Marketing Freedom for Grain Farmers Act deregulated Canadian grain markets and removed the Canadian Wheat Board (CWB) as the sole buyer and seller of Canadian grain. We develop a rational expectations contract decision model that serves as the basis for an empirically informed simulation analysis of malt barley contracting opportunities between Canadian farmers and U.S. maltsters in the deregulated environment. Comparative statics and simulation results indicate that some new opportunities for contracting are possible, but the likelihood of favorable conditions for U.S. maltsters to contract with Canadian rather than U.S. farmers is low—between 9% and 35% over a range of possible selection rates. The effects on contracting of the termination of the Canadian grain transportation revenue cap policy and of the relaxation of criteria for the release of new spring wheat varieties are also investigated. While changes to grain transportation policies are not likely to significantly affect favorable conditions for contracting, reducing constraints on Canadian farmers' access to higher yielding wheat varieties could increase the returns from growing spring wheat but decrease the likelihood of contracting for malt barley with U.S. maltsters by an average of 5.3 percentage points.
This study empirically investigates the potentially unintended effects of state laws that seek to improve safety in U.S. public school by mandating standardized student punishment. We estimate the effects of exogenous state-level variation in the quantity and type of such mandates on disciplinary disparities across students who commit serious offenses. Estimation results indicate that more severe punishments are imposed in schools with higher proportions of black or Hispanic students, but such disparities are significantly dampened in states that mandate a higher number of guidelines for serious offenses. However, more guidelines for less severe misconduct tend to increase race-based disciplinary disparities and increase the severity of punishments administered for serious offenses. These outcomes extend the existing sentencing guidelines literature and provide empirical implications for considering marginal deterrence effects when crafting future policies.
We investigate the effects of exposure to coal power plant emissions on school absenteeism for children with asthma, a leading cause of health-related barriers to education. We combine responses from the 2007–2009 Behavioral Risk Factor Surveillance System survey with coal power plant emission data to estimate a zero negative binomial regression model of school absences and investigate misspecification bias associated with naive assumptions about emission dispersion and self-selection into treatment groups. The results show a robust, positive relationship (P<0.001) between increases in emission exposure and the likelihood of a school absence due to an asthma episode. Exposure to higher emission volumes is associated with a 1.92–4.81% higher likelihood of missing an additional school day. Furthermore, assuming uniform emission dispersion and not controlling for self-selection underestimates the effects by 2.72–4.27 times. Access to education and the ability to develop human capital through schooling is affected for children with respiratory illnesses who are exposed to emissions. Public policies for emission regulation are likely to remain relevant for lowering pediatric respiratory health risks and lower barriers to educational opportunities.
Annual legume green manure (LGM) cover crops may have potential in dryland wheat (Triticum aestivum L.) production areas where rotation with whole-year summer fallow is practiced. No-till cropland management enhances soil water conservation, possibly enabling cover cropping, but tillage may be necessary to stimulate mineralization of LGM N in time to affect crop yield. A 2-yr LGM-wheat crop sequence study was repeated three times in Montana, with mean annual precipitation of 356 mm. Spring-planted pea (Pisum sativum L.) and lentil (Lens culinaris Medik.) The LGM were terminated at first bloom with tillage or herbicide. Post-termination weed control also was accomplished with either tillage or herbicide in a factorial combination with the termination treatments, resulting in four management regimes. Fallow and non-N-fixing cover crop controls were included and subjected to the same management regimes. Spring wheat was grown the following year in subplots with four levels of N fertilizer. Wheat tiller density increased only when LGM was tilled at least once. Tillage also resulted in reduced soil water storage and wheat kernel weight in 1 yr. Effects on grain yield were usually neutral or positive, with pea more frequently having a positive effect than lentil, and interactions with tillage varying each year. Wheat grain protein was increased by pea LGM regardless of tillage, even when LGM did not affect wheat yield, indicating that LGM N supply is accelerated by tillage. Managing LGM in dryland environments involves a tradeoff of soil water for N supply, and tillage affects this balance.
The data-driven analysis used over 4,000 observations describing wheat stem sawfly (WSS) infestation, cutting, and parasitism outcomes from 1998 to 2011 across thirty-one locations in Montana and southern Canadian prairie provinces. WSS-related damages in Montana were estimated to be approximately $80.1 million in 2012, the most recent year for which production data were available. Nearly 9.7 million bushels did not reach consumers. Per-farm economic losses ranged between $15,000 and $20,000 for spring wheat farmers and $25,000 and $47,000 for winter wheat farmers in 2012 at an assumed 2,000 acre operation. In high impact areas, winter wheat producers were estimated to have forgone between $110,000 and $120,000 per farm. Management strategies that prevent high WSS infestation levels provide the greatest long-run economic benefits. Unlike existing recommendations to swath at relatively low infestation levels, the swathing management strategy was found to be economically cost-effective only when WSS infestation levels are high.
Proximity to information resources has repeatedly been shown to affect urban development. However, individuals' increased abilities to access information content electronically may have dampened urban areas' comparative advantage of proximity-driven knowledge flows. We investigate the effects of increased high-speed Internet access on the role of information proximity by modeling changes in the demands for locally-based information resources, exploiting variation in the use of US public libraries—the most common low-cost providers of locally accessible information content. Data describing a nearly comprehensive set of US public libraries during 2000–2008 provide empirical evidence of complementary growth in Internet access and the use of public library resources, suggesting that Internet access increases the value of locally accessible information content and overall information demand. Moreover, the complementarity is found to be largest in metropolitan areas, indicating that improved Internet access in locations with greatest proximity and information spillover effects are likely to experience more substantial economic impacts.
Growing global wheat supply uncertainties and unintended impacts of U.S. domestic policies may have contributed to Asian multinational agribusinesses' increased interests in securing long-run access to reliable sources of high-quality U.S. wheat. These interests have been manifest in their increased efforts to vertically integrate wheat procurement, handling, transportation, and exports, largely by constructing and acquiring efficient, high-capacity shuttle-loading facilities. Long-run implications of these changes could include the exit or change in the role and function of less efficient, smaller elevators and subsequent increases in market-power concentration by a few multinational agribusinesses. Significant economic ramifications to northern Great Plains grain producers, traditional wheat marketing structures, and land conservation efforts could follow. Furthermore, price impacts could spill over to other U.S. wheat markets because northern Great Plains production constitutes a large share of overall U.S. grain output.
When consumers have heterogeneous perceptions about product quality, traditional parametric methods may not provide accurate marginal valuation estimates of a product’s characteristics. A quantile regression framework can be used to estimate valuations of product characteristics when quality perceptions are not homogeneous. Semi-parametric quantile regressions provide identification and quantification of heterogeneous marginal valuation effects across a conditional price distribution. Using purchase price data from a bull auction, we show that there are nonconstant marginal valuations of bull carcass and growth traits. Improved understanding of product characteristic valuations across differentiated market segments can help producers develop more cost-effective management strategies.
Uncertain and changing economic conditions can have substantial effects on price relationships in spatially separated, linked markets. Although numerous studies have analysed price relationships to characterize market linkage structures, most assume that the relationships and associated linkages are time invariant. This study extends the literature by modelling and estimating time-dependent market linkages that are conditional on changes in exogenous factors. The methodology is used to investigate price relationships in North Carolina (NC) corn and soya bean markets. Empirical results indicate that generalized market-linkage models provide a better representation of price relationships over time, improving the understanding of price discovery dynamics and marketing strategies.
Wind-borne diseases can spread rapidly and cause large losses. Producers may have little incentive to prevent disease spread because prevention may not be welfare-maximizing. This study proposes a market-based mitigation program that indemnifies producers against disease-related losses and provides an incentive to neighboring producers to take preventive action, which can substantially mitigate infestations, reduce the likelihood of catastrophic losses, and increase social welfare. An equilibrium displacement model simulates introduction of the program for U.S. soybeans. Simulations reveal that the market-based solution contributes to minor market distortions but also reduces social welfare losses and could succeed for other at-risk commodities.
The aim of this paper is to show how provisions of the Supplemental Revenue Assistance Payments (SURE) program impacts production practices, and empirically examine changes in crop insurance participation rates as a means of measuring producer responses to the program. The structure of the SURE program is described and a stylized theoretical model is used to show the SURE program’s effects on farm-level crop insurance and production decisions. A county-level cross-sectional empirical specification with regional fixed effects is used to test the hypothesis that producers who are most likely to benefit from production practice re-optimization are more likely to participate in crop insurance. Results from empirical analyses of corn, soybean, and wheat production areas show that the SURE program has had substantial impacts on crop insurance participation by producers who are more likely to receive SURE indemnities and exploit moral hazard opportunities.
Extensive literature has shown that student attainment outcomes are affected by student-to-teacher ratios and overall teacher aptitude levels, but offers little information about which method offers the greatest student attainment return relative to associated costs. This study provides empirical evidence that staffing policies should consider the cost-effectiveness of teacher-hiring decisions when multiple education policies are effective.
The rotational effects and economic potential of incorporating fall-seeded pea (Pisum sativum L.) and lentil (Lens culinaris Medik) into conventional wheat (Triticum aestivum L.)-based cropping systems in the northern Great Plains are not well understood. Two 2-yr crop rotation experiments were conducted in central Montana to investigate how winter pea hay, lentil green manure, and lentil grain affects subsequent winter wheat yield and protein content, as well as the economic returns of the systems under no-till conditions. In Exp. 1, a winter pea hay–winter wheat (WP–WW) rotation was compared to fallow–winter wheat (FW–WW) and spring wheat–winter wheat (SW–WW) rotations. In Exp. 2, a winter lentil for green manure–winter wheat [WL(m)–WW] rotation was compared to a winter lentil grain–winter wheat [WL(g)–WW] rotation. Four different rates of N were applied to the winter and spring wheat. Winter wheat yield in the WP–WW rotation was 2193 kg ha–1, which was equivalent to the yield in the FW–WW rotation (2136 kg ha–1), and much greater than the SW–WW rotation (1155 kg ha–1). Averaged over all N rates, the WP–WW, FW–WW, and SW–WW systems had $196, $116, and $41 ha–1 net return, respectively. In Exp. 2, the WL(m)–WW rotation produced greater grain yield and protein content at lower N input levels, indicating a greater N benefit. Nevertheless, the WL(g)–WW system generated $213 ha–1 net profit while the WL(m)–WW system produced $92 ha–1. Therefore, the winter pea cover crop, used for livestock feed, improves the system profitability.
The purpose of this paper is to examine the potential gains in hedge ratio calculation for agricultural commodities by incorporating market linkages and prices of related commodities into the hedge ratio estimation process. A vector autoregressive multivariate generalized autoregressive conditional heteroskedasticity (VAR-MGARCH) model is used to construct a time-varying correlation matrix for commodity prices across linked markets and across linked commodities. The MGARCH model is estimated using a two-step approach, which allows for a large system of related prices to be estimated. In-sample and out-of-sample portfolio variance comparison among no hedge, bivariate GARCH, and MGARCH models indicates that hedge ratios estimated using the MGARCH approach reduce agricultural producers and commercial consumers' risks in futures market participation.
Cow-calf producers seek to reduce costs and increase profits by selecting bulls that produce more efficient offspring. Organizers of formal bull auctions usually produce catalogs for potential buyers that advertise bull performance measures and genetic characteristics, including EPD and simple performance measures (SPM). Buyers use this information to make decisions regarding bull purchases based on heritable bull traits. Residual feed intake (RFI) is a relatively new SPM of feed efficiency. The Midland Bull Test company (Columbus, MT) measures RFI in addition to other SPM during bull performance testing. The Midland Bull Test company records individual animal feed intake by using GrowSafe (Airdrie, Alberta, Canada) technology. Residual feed intake for each bull is calculated as the difference between actual and expected feed intake. The Midland Bull Test company included RFI along with EPD and other SPM in its 2008 and 2009 sale catalogs. A linear hedonic price model was used to quantify RFI values with various bull performance measures from the Midland Bull Test sale catalogs and associated bull sale prices. Analyses indicate that buyers were willing to pay more for bulls that were RFI efficient (P < 0.01). Although other performance measures (e.g., BW gain, birth weight, and age) were valued more highly (P < 0.01) by bull purchasers, an RFI SPM could eventually be valued to the extent that an RFI EPD might be developed.
Soybean rust is a highly mobile infectious disease and can be transmitted across short and long distances. Soybean rust is estimated to cause yield losses that can range between 1-25%. An analysis of spatio-temporal infection risks within the United States is performed through the use of a unique data set. Observations from over 35,000 field-level inspections between 2005 and 2007 are used to conduct a county-level analysis. Statistical inferences are derived by employing zero-inflated Poisson and negative binomial models. In addition, the model is adjusted to account for potential endogeneity between inspections and soybean rust finds. Past soybean rust finds and inspections in the county and in the surrounding counties, weather and overwintering conditions, and plant maturity groups and planting dates are all found to be significant factors determining soybean rust. These results are then used to accordingly price annual insurance contracts or indemnification programs that cover soybean rust damages.
I currently teach the Economics of Agricultural Marketing and Managerial Economics. I have also taught Master's level Econometrics. For each course, I put together a course notes packet. If you are interested, please feel free to download them. I do ask that if you use any of the materials in the note packets, please attribute them to me appropriately. Also, if you note any issues or errors, I would very much appreciate you letting me know those errors so that I can correct them. Thanks!
This course is intended to introduce important concepts in agricultural marketing. We examine links between agricultural producers and consumers and factors that may cause changes in those links. Because agricultural markets are often fluid and changes can occur rapidly, we will seek to study current events and examine their effects on agricultural markets. Additionally, the course emphasizes the effects of local, national, and international events on agricultural markets in Montana. Students who complete this course should be comfortable with using a theoretical economic framework to assess and interpret the functions of agricultural markets.
The material is highly integrated and focused on developing an understanding of concepts and tools that are relevant to firm-level decision making. Each of the specific learning outcomes relate to the underlying microeconomic principles to which you were exposed in introductory economics courses. However, this course will provide a greater level of detail for these topics and develop insights about how these principles can be used in effective management. Students willdemonstrate proficiency in understanding supply and demand concepts; apply statistical methods to improve knowledge of economic models; use basic regression analysis to analyze and forecast demand curves; analyze production functions and understand firm choice under constraints; and be introduced to the concepts of choice under risk and uncertainty.
This course is intended to introduce important concepts in econometrics with an emphasis on probability theory, mathematical statistics, matrix algebra, and application of these concepts to developing econometric analyses. The linear regression model will be discussed in detail and students will learn how to use linear regression analysis to examine data and interpret results, especially as it relates to time-series applications in financial engineering.
Part of my outreach efforts is to make my research as accessible and useful as possible to the stakeholders who will most benefit from this research. Below you will find software (web applications) and code that attempt to bridge the gap between academic research and those for whom the research is most relevant.
The wheatbasis.montana.edu web application is a tool for forecasting wheat basis at over 70 grain elevators in Washington and Montana.
The Bozeman Public Library Geostatistics Project is a product of a collaborative effort among economists at Montana State University and the Bozeman public library. The project was supported by funding from the National Leadership Grants for Libraries program, administered by the Institute of Museum and Library Services.
The project's goal is to provide a data-driven approach for more precisely identifying trends, changes, and opportunities for growth in patrons' use of public library resources. This includes dividing a library system's service area into neighborhoods to obtain a more detailed understanding of geographical locations that may be underserved or where there is greatest potential for growth, having greater insights about periods when library services are in greatest demand and how these trends changed over time, and pinpointing information resources that are most and least important to a library's patrons.
Often, as was the case with the Bozeman public library (BPL), libraries already have much of the information necessary to gain a deeper understanding of their patrons' changing demands for information resources. The challenge was organizing the large quantity of data in a way that made these data easily accessible, allowed administrative decisions to be made quickly and accurately, and preserved patron privacy. The project team designed specialized software that confidentially identified and sorted patrons into their respective geographical neighborhoods and presented only neighborhood-level use statistics. While these aggregated statistics do not reveal information about any particular patron, they offer an immensely greater level of library use information than a single, annually-calculated value for the entire library system.
The success of the Bozeman Public Library Geostatistics Project indicates that there are significant benefits to public libraries from gaining a more precise geospatial understanding of their patrons' demands for information resources. The long-term objective is to investigate how these methods can be applied to both larger and smaller library systems in Montana and the United States. By providing library administrators technology to quickly and easily assess opportunities for their libraries, these community anchor institutions can become more effective and efficient distributors of knowledge in the 21st century.
This is code and associated paper for determining road distances (rather than as-the-crow-flies distances) among geographic locations. The code is implemented fully within SAS but calls Google Maps to acquire the necessary road distance information.
Many times, I simply don't have enough time to present everything that I think would be useful or interesting. Therefore, I will post "appendices" to presentations that will include all of the items that were not included in presentations. And, of course, I'll post the presentations as well.
Links to the session's presentations, in order of appearance.
Integrative Learning: An Overview (Anton Bekkerman, Montana State University)
Integrating Learning in the Classroom: Client Based Projects and Self-directed Learners (Lindsey M. Higgins, Cal Poly State University, San Luis Obispo)
Integrated Learning in Extension Programs (Mykel Taylor, Kansas State University)
Integrative Learning: A Discussion (Kerry K. Litzenberg, Texas A&M University)
These are resources that I have found to be useful in keeping up to date with resources, information, and techniques that help me think about transitioning my courses to an increasingly integrative framework.
The Graphic Syllabus and the Outcomes Map: Communicating Your Course (Linda B. Nilson)
Integative Learning: Mapping the Terrain (Mary T. Huber and Pat Hutchings)
Fostering Integative Learning through Pedagogy (Richard A. Gale)
I am always excited to chat economics. I am typically in my office between 8 a.m. and 5:30 p.m. Mountain time. If my door is open, then I am in the office and welcome all drop-in visitors. However, if you would like to ensure that I will be able to meet, please contact me using the information to the right.