Seeding Progress in Saskatchewan
Per cent seeded
May 28, 2018
May 29, 2017
May 30, 2016
May 25, 2015
June 1, 2014
May 27, 2013
5 year avg.
10 year avg.
Per cent seeded
May 28, 2018
Thanks to another week of relatively good conditions, 91 per cent of the crop is now in the ground. This is up from 70 per cent last week and remains well ahead of the five-year (2013-2017) seeding average of 81 per cent for this time of year.
The southwest region is the most advanced with 95 per cent of the crop seeded. Ninety-three per cent is seeded in the southeast, 92 per cent in the west-central region, 90 per cent in the northwest, 87 per cent in the northeast and 86 per cent in the east-central region.
Ninety-eight per cent of the lentils, 94 per cent of the durum, 92 per cent of the spring wheat, 90 per cent of the soybeans, 88 per cent of the canola, 85 per cent of the barley and 84 per cent of the flax have now been seeded.
Scattered rain showers brought varying amounts of rain this past week, helping to temporarily alleviate concerns of dry field conditions. Rainfall ranged from trace amounts to 72 mm in the Humboldt area with many areas reporting about 25 mm or less. While significant rain is still needed in the majority of the province to help crops emerge and hay land and pasture establish, some areas in the northeast have excess moisture.
Topsoil moisture conditions have slightly improved with recent rainfall. Provincially, topsoil moisture conditions on crop land are rated as one per cent surplus, 51 per cent adequate, 36 per cent short and 12 per cent very short. Hay land and pasture topsoil moisture is rated as 38 per cent adequate, 44 per cent short and 18 per cent very short.
Overall, emerged crops are in poor-to-good condition, but emergence has been patchy and delayed by dry field conditions. The majority of crop damage this past week was caused by strong winds, lack of moisture and insects such as flea beetles and cutworms in canola. Localized hail was also reported in some areas.
Farmers are busy finishing up seeding, picking rocks and starting in-crop pesticide applications.
No one has to be reminded about the upheaval in the industry over the past 14 months. As sellers of cash crops, it can be more difficult to step outside the day to day buying and selling and get a handle on how prices will interact going forward. Maybe, it really is not that difficult. Consider the AGT stock price at the end of March as it returns to below $20.00 / share. I was thinking ‘that chart is familiar.’ Here are two screenshots showing the AGT stock and the #1 Red lentil chart from Statpub.
In 2014, AGT stock price and Red Lentils tracked around $18, peaking around $45, and returning to below $20 today. It is hardly a coincidence as they chart in unison complete with a double top. When red lentils ‘work’ the industry ‘works.’ When India needs red lentils, it is enough to carry an entire industry and apparently a stock price.
Big Data and Smart Farms (excerpt – Science Direct)
In view of the technical changes brought forth by Big Data and Smart Farming, we seek to understand the stakeholder network around the farm. The literature suggests major shifts in roles and power relations among different players in existing agri-food chains. We observed the changing roles of old and new software suppliers in relation to Big Data and farming and emerging landscape of data-driven initiatives with the prominent role of big tech and data companies like Google and IBM. In Fig. 5, the current landscape of data-driven initiatives is visualized.
The stakeholder networks exhibits a high degree of dynamics with new players taking over the roles played by other players and the incumbents assuming new roles in relation to agricultural Big Data. As opportunities for Big Data have surfaced in the agribusiness sector, big agriculture companies such as Monsanto and John Deere have spent hundreds of millions of dollars on technologies that use detailed data on soil type, seed variety, and weather to help farmers cut costs and increase yields (Faulkner and Cebul, 2014). Other players include various accelerators, incubators, venture capital firms, and corporate venture funds (Monsanto, DuPont, Syngenta, Bayer, DOW etc.) (Lane, 2015).
Monsanto has been pushing big-data analytics across all its business lines, from climate prediction to genetic engineering. It is trying to persuade more farmers to adopt its cloud services. Monsanto says farmers benefit most when they allow the company to analyse their data – along with that of other farmers – to help them find the best solutions for each patch of land (Guild, 2014).
While corporates are very much engaged with Big Data and agriculture, start-ups are at the heart of action, providing solutions across the value chain, from infrastructure and sensors all the way down to software that manages the many streams of data from across the farm. As the ag-tech space heats up, an increasing number of small tech start-ups are launching products giving their bigger counterparts a run for their money. In the USA, start-ups like FarmLogs (Guild, 2014), FarmLink (Hardy, 2014) and 640 Labs challenge agribusiness giants like Monsanto, Deere, DuPont Pioneer (Plume, 2014). One observes a swarm of data-service start-ups such as FarmBot (an integrated open-source precision agriculture system) and Climate Corporation. Their products are powered by many of the same data sources, particularly those that are freely available such as from weather services and Google Maps. They can also access data gathered by farm machines and transferred wirelessly to the cloud. Traditional agri-IT firms such as NEC and Dacom are active with a precision farming trial in Romania using environmental sensors and Big Data analytics software to maximize yields (NEC, 2014).
Venture capital firms are now keen on investing in agriculture technology companies such as Blue River Technology, a business focusing on the use of computer vision and robotics in agriculture (Royse, 2014). The new players to Smart Farming are tech companies that were traditionally not active in agriculture. For example, Japanese technology firms such as Fujitsu are helping farmers with their cloud based farming systems (Anonymous, 2014c). Fujitsu collects data (rainfall, humidity, soil temperatures) from a network of cameras and sensors across the country to help farmers in Japan better manage its crops and expenses (Carlson, 2012). Data processing specialists are likely to become partners of producers as Big Data delivers on its promise to fundamentally change the competitiveness of producers.
Beside business players such as corporates and start-ups, there are many public institutions (e.g., universities, USDA, the American Farm Bureau Federation, GODAN) that are actively influencing Big Data applications in farming through their advocacy on open data and data-driven innovation or their emphasis on governance issues concerning data ownership and privacy issues. Well-known examples are the Big Data Coalition, Open Agriculture Data Alliance (OADA) and AgGateway. Public institutions like the USDA, for example, want to harness the power of agricultural data points created by connected farming equipment, drones, and even satellites to enable precision agriculture for policy objectives like food security and sustainability. Precision farming is considered to be the “holy grail” because it is the means by which the food supply and demand imbalance will be solved. To achieve that precision, farmers need a lot of data to inform their planting strategies. That is why USDA is investing in big, open data projects. It is expected that open data and Big Data will be combined together to provide farmers and consumers just the right kind of information to make the best decisions (Semantic Community, 2015).
4.4. Network management
Data ownership is an important issue in discussions on the governance of agricultural Big Data generated by smart machinery such as tractors from John Deere (Burrus, 2014). In particular, value and ownership of precision agricultural data have received much attention in business media (Haire, 2014). It has become a common practice to sign Big Data agreements on ownership and control data between farmers and agriculture technology providers (Anonymous, 2014a). Such agreements address questions such as: How can farmers make use of Big Data? Where does the data come from? How much data can we collect? Where is it stored? How do we make use of it? Who owns this data? Which companies are involved in data processing?
There is also a growing number of initiatives to address or ease privacy and security concerns. For example, the Big Data Coalition, a coalition of major farm organizations and agricultural technology providers in the USA, has set principles on data ownership, data collection, notice, third-party access and use, transparency and consistency, choice, portability, data availability, market speculation, liability and security safeguards (Haire, 2014). And AgGateway, a non-profit organization with more than 200 member companies in the USA, have drawn a white paper that presents ways to incorporate data privacy and standards (AgGateway, 2014). It provides users of farm data and their customers with issues to consider when establishing policies, procedures, and agreements on using that data instead of setting principles and privacy norms.
The ‘Ownership Principle’ of the Big Data Coalition states that “We believe farmers own information generated on their farming operations. However, it is the responsibility of the farmer to agree upon data use and sharing with the other stakeholders (…).” While having concerns about data ownership, farmers also see how much companies are investing in Big Data. In 2013, Monsanto paid nearly 1 billion US dollars to acquire The Climate Corporation, and more industry consolidation is expected. Farmers want to make sure they reap the profits from Big Data, too. Such change of thinking may lead to new business models that allow shared harvesting of value from data.
Big data applications in Smart Farming will potentially raise many power-related issues (Orts and Spigonardo, 2014). There might be companies emerging that gain much power because they get all the data. In the agri-food chain these could be input suppliers or commodity traders, leading to a further power shift in market positions (Lesser, 2014). This power shift can also lead to potential abuses of data e.g. by the GMO lobby or agricultural commodity markets or manipulation of companies (Noyes, 2014). Initially, these threats might not be obvious because for many applications small start-up companies with hardly any power are involved. However, it is a common business practice that these are acquired by bigger companies if they are successful and in this way the data still gets concentrated in the hands of one big player (Lesser, 2014). Gilpin (2015b), for example, concluded that Big Data is both a huge opportunity as a potential threat for farmers.
Markets aren’t “efficient” at finding the truth; they are just very efficient at converging on a conclusion—often the wrong conclusion. – Horowitz
The markets have been slugging it out for what feels like forever and change is not coming soon. In honor of our difficult markets, here are 5 tips from Investopedia which hold a certain relevance for this crop year.
1. Remember that cash is a position too
Everyone wants the same thing during dangerous or volatile markets. They are waiting for more conditions to become easier and more familiar and in the meantime, they throw money at the tape whenever a price impulse looks similar to one that produced a profit in better times. This is a bad approach because the market knows you’re there and flashes those familiar moments just to get your attention and pick your pocket.
This is the time to remember that cash is a position too. If you don’t believe me, go back and recalculate results for a year after eliminating those trades when sitting on your hands offered a better option. It proves the point that saving a dollar is more important than making a dollar in our modern financial markets.
2. Step back and focus on weekly and monthly charts
High volatility can dull your concentration and force you to second guess your time-tested strategies. Overcome this headwind by stepping back and trading off weekly and monthly price patterns. .This approach demands patience because days can pass between actionable buy and sell signals. In between, the market will tempt you with wild swings that look like instant moneymakers. Stay the course because long-term support and resistance levels work exceptionally well in corrective environments.
3. Become a big game hunter
All bad markets present hugely profitable opportunities but we get so distracted by adverse price action that we fail to comprehend the signals when they finally go off. Decide now to set your sights on big game when markets go haywire or when choppy conditions rule the roost. These brilliant trades may only occur at major turning points so be prepared to sit and stare at your screen for days or weeks, doing nothing, until the magic moment arrives. It will be worth the effort because months of profits can be booked in a few days when your timing is perfect.
4. Embrace uncertainty and volatility
All market movement is good because it leads to new opportunities. Understand that volatile conditions signal periods of high conflict and high commitment when two sides are battling it out for tape supremacy. It isn’t your job to pick a winner. Rather, you need to wait until one side emerges as the victor and then take advantage of the victory.
Even bad markets generate good opportunities from time to time but expect to miss them when focused on big game and the bigger picture. Keep your cool when that happens, understanding you’re playing new inefficiencies and won’t be taking advantage of trade setups that don’t match your new criteria. More importantly, you’re passing on more familiar setups because the odds are no longer in your favor
5. Survive first and the rest will follow
Preserve capital at all costs. Reduce trade frequency by 60% to 90% compared to periods when good markets are spitting out all sorts of low-risk setups. It’s a perfect time to listen to the calm inner voice that knows what to do and what not to do when a crisis hits the ticker tape. That’s the voice of the survivor that wants to come out and play after the dark clouds finally pass.
The Bottom Line
Tough markets undermine profitability and lower self-confidence. Adapt and adjust with higher cash levels, lower trade frequency, focusing on the most profitable setups and embracing uncertainty.
Why & How Decentralized Prediction Markets Will Change Just About Everything.
Excuse the superlatives. I’m usually a lot more guarded with these. However, the more I think about it, the more I feel censorship-resistant & decentralized prediction markets will change almost everything about our society, and it has to do with one core difference: the automation of it. An open, permission-less market for rewarding those (humans and programs alike) who predict the future.
It will hold implications for sharing wealth, redefining organisations in the digital age & even help stop climate change. It sounds grand, but here’s how it will happen.
Prediction markets (or the original coined term by Robin Hanson: “idea futures”) aren’t new. What are they?
Prediction markets (also known as predictive markets,information markets, decision markets, idea futures, event derivatives, or virtual markets) are exchange-traded markets created for the purpose of trading the outcome of events.
You essentially bet against each other (the market) how an outcome will turn out. For example, Bernie Sanders will be the Democratic candidate vs Hillary Clinton. You buy shares of each outcome that basically correlate to a percentage chance that the event will occur. Once the event has occurred, the prediction market will allow your shares to be redeemed for $1, while the other shares become worthless. For example, if you buy a Bernie Sanders outcome at $0.6 and Hillary Clinton outcome at $0.4, and Bernie becomes the candidate, you believed with a 60% chance that it will happen. Your Bernie token becomes worth $1. And your Hillary Clinton token becomes worth $0. Getting tokens is as simple as paying $1, upon which you get both. You can then precede to buy or sell them with others at various prices. If you don’t trade either of the outcomes that you bought, you will just get your money back (since one will go to zero and the other to 1).
Prediction markets have existed and still do exist. Intrade was popular but had to exclude US traders. It’s not an easy space to be in, since in some jurisdictions it is seen gambling, while in others it is seen options trading. The other worry is that it could create controversial incentives such as predicting the death of a global leader.
A global prediction market has thus not flourished as well as it could have. Even if it worked properly, building on top of it as a platform (APIs and such) is also not an easy job, and there’s little guarantee that there won’t be another clamp down.
Decentralized systems where innovation can happen without permission have allowed new (& old ideas) to flourish in wondrous new ways. We wouldn’t have Facebook, Wikipedia & Twitter if the web wasn’t open. Free permission to innovate with information has led to where we are today. An open prediction market platform will come. It will most likely come to live on a blockchain. It not only helps with maintaining an open infrastructure for it, but it also allows separation of concerns (who is doing what in this market).
Currently there are 3 known decentralized prediction market efforts underway: Truthcoin/Hivemind (Bitcoin-based sidechain), Augur (recently raised $5.3m from a crowdsale, built on Ethereum) & Gnosis (working prediction market platform on Ethereum). I will focus on what this will look like on Ethereum.
Ultimately, a prediction market will exist that will allow anyone to create markets, bet on outcomes and resolve/report the outcomes. The difference comes in when you add the following to this:
An Arbitration Market for Reporting:
At the end of the limit the outcome of an event must be reported. In the past, this was usually reported by the people who ran the prediction market itself (and you had to trust them to report correctly). With a decentralized system you can swap this out for various systems. A market for an event can have one person decide.
If this person is trusted, then liquidity will come. If they are not, then multiple persons can report an outcome (where 2 of 3 need to agree, for example). Market participants can vote for who they want to report as well. Systems such as Augur has a token system where those who hold the token, vote on outcomes as a crowd. All these styles are swappable. In some circumstances you wouldn’t even need a report to happen. If the information is already on Ethereum, the resolution will happen without requiring a trusted source. ie, if someone is selling their song on Ethereum, and that is publicly accessible by other contracts, you can use that as is.
Finally, and perhaps the most interesting, is that all you need is a threat of an outcome for the market converge to the right outcome. The closer to the time an event comes, the more it starts to converge to the actual outcome as clarity increases. Thus, in a way, the tokens become worth zero on the one side and 1 on the other, automatically resolving itself. In a scenario where this actually ended up wrong, users can put up a deposit to dispute it: which results in arbitrator that has to come in and decide.
This is arguably the most important. If it is an open layer any program can start predicting. Prediction markets only played by slow humans who have do the thinking won’t ever have enough liquidity to be useful. Programs can absorb much more and make much better predictions about the future.
Automatically gathering knowledge about the world & combining it correctly will result in financial gain. Thus, companies like Google & Facebook will be at a considerable advantage, betting on these prediction markets. Dumb sensors in every avenue could either predict themselves or sell the information (more on this later). And finally, you can build bots that predict based on some model: it could be based off a person, a group, or any combination (also more on this later).
The difference here vs a traditional market maker is subtle. The purpose is not facilitate trading, but to automate predictions.
Once you add these parts, you get wonderful potential emergent behaviour. Here are some examples.
You sell your information to be used in markets. If the information is in the same ecosystem as the prediction markets (say, Ethereum), then you can sell this information to be used in a trust-less manner inside the market itself. This is a holy grail for several reasons. The oft talked about “Internet of Things” will be extremely useful here. A sensor can produce information, & put in the blockchain (public). If it used for reporting, it can charge for that information. It could also result in markets where the information is not made public, but encrypted, upon which it can then be sold to others to gain knowledge to predict more effectively. So, information markets can develop around selling information to help predict & report. This won’t just exist for sensor data. It would eventually become the norm to report any kind of data into the blockchain, especially for reporting purposes. It not only means that you can have audited & transparent information in there, but it also means you immediately provide a trust source for information to be bet on, at very little cost to the producer. For example, you decide to log your monthly revenue of your company to the blockchain.
New kinds of organisations.
With the rise of social media, we saw its capability to move people together in new ways: driven by a common goal, bereft of normal bureaucratic process. The Arab Spring is a good example, where these movements remain relatively head-less. Recently, in South Africa a movement to reduce tertiary tuition fees rallied around a hashtag (#feesmustfall), that formed the locus of the liquid organisation.
These organisations are unlike what we’ve seen before, because social media allows near instant communication and allows important news & events to immediately filter up (based on retweets) and then subsequently affect and inform the rest of the organisation. Affiliation is as easy as using the hashtag. It’s the network’s version of an organisation. There is no permission to be a part of it.
How does one build ways to incentivise these new organisations (financial gain) & how do you help it make decisions? You use prediction markets. Just as these hashtag organisations move like crowds do, so should its decision making. As the organisation goes about its goals, various outcomes are constantly generated, upon which the people in the organisation and those outside of it, bet on the outcomes, leading it automatically towards outcomes which serve the goals of the organisation.
Since these network organisations move at the speed of social media, you might need some help from our bot friends to bet on your behalf. And this leads to the next part:
If prediction markets will be so useful in creating wealth for those are in the know, then you might want to developed automated personalised prediction bots. These bots automatically bet on events based on who you are & what you do. If you join a hashtag organisation (the tweet appears in your feed), then the bot automatically detects this and assumes this as indicator that marginally this movement might succeed and thus bets on those outcomes (resulting in automatic financial gain). Another example, is where if you frequent a coffee shop, your bot will automatically start betting on the revenue that will be posted by this shop. You are directly influencing its success by being a patron and thus you can partake in the financial gain of it.
This presents a potentially whole new way of looking at organisations. Perhaps into the future we won’t even need things like shares/equity. Money simply flows towards some locus, upon which it is used to improve a metric that can be predicted upon. It paints a new model of “investment”. If you donate $10 000 to an organisation or group, you know that its metric of success will improve and thus you can benefit from that financial gain. There are countless metric which could be influenced in this manner, just by “being alive”, you affect them. It doesn’t have to be financial even (I’m developing an interesting application that is non-financial that will soon launch).
This opportunity will become available to all, at any scale.
Futarchies is a governance system where a metric is chosen (say GDP in 10 years). Outcomes are bet based on certain decisions (GDP in 10 years if investment in education). If yes -> what will GDP be? If no -> what will GDP be? Then you implement the market with the higher forecast. After 10 years, you reward those who predicted correctly.
Millions of programs & countless humans predicting the future will in anyway help current governments choose what to do (without even resorting to a futarchy).
Protecting Natural Systems.
Could we use prediction markets to protect our climate & environment?
If I am interested in seeing the ecosystem flourish, I can get financial gain from it, by simply protecting & fostering it (you first predict, then enact the change). You make sure that a certain amount of trees are planted. Sensors can provide much more nuanced feedback and reporting vs more concrete outcomes (such as trees planted). For example, measuring pollution. This information will be sold to interested parties. To bring these sensors into existence they can be crowdfunded by a group of environmentalists, who will earn these fees, which subsequently can result in further creating a sustainable ecosystem.
Decentralized AI Systems?
What happens if you have automated sensors flying around, collecting information for predictions? I’ll you let you do the rest of that thinking. 😉
A prediction market is a powerful idea. A decentralized prediction market is an even more powerful idea. Once we combine the capability to automate predictions combined with AI, Machine Learning and the Internet of Things, it becomes something that indeed will change just about everything. It will result in basically being able to model externalities in a much better fashion.
Patrick Collison of Stripe asked this question himself this year.
And, yes, it would be a huge breakthrough, and decentralized prediction markets can do this. So excuse the superlatives, but from this vantage point, I’d bet on it.
Happy Holidays from everyone at Commodious Trading and Souris River Seeds.
Here are some trading quotes to finish up the year:
“That cotton trade was almost the deal breaker for me. It was at that point that I said, ‘Mr. Stupid, why risk everything on one trade? Why not make your life a pursuit of happiness rather than pain?’” – Paul Tudor Jones
“The elements of good trading are: (1) cutting losses, (2) cutting losses, and (3) cutting losses. If you can follow these three rules, you may have a chance.” – Ed Seykota
“When I get hurt in the market, I get the hell out. It doesn’t matter at all where the market is trading. I just get out, because I believe that once you’re hurt in the market, your decisions are going to be far less objective than they are when you’re doing well… If you stick around when the market is severely against you, sooner or later they are going to carry you out.” – Randy McKay
“Frankly, I don’t see markets; I see risks, rewards, and money.” – Larry Hite
“When I became a winner, I said, ‘I figured it out, but if I’m wrong, I’m getting the hell out, because I want to save my money and go on to the next trade.’” – Marty Schwartz
“I always define my risk, and I don’t have to worry about it.” – Tony Saliba
“The key to trading success is emotional discipline. If intelligence were the key, there would be a lot more people making money trading… I know this will sound like a cliché, but the single most important reason that people lose money in the financial markets is that they don’t cut their losses short.” – Victor Sperandeo
“I think investment psychology is by far the more important element, followed by risk control, with the least important consideration being the question of where you buy and sell.” – Tom Basso
“If I have positions going against me, I get right out; if they are going for me, I keep them… Risk control is the most important thing in trading. If you have a losing position that is making you uncomfortable, the solution is very simple: Get out, because you can always get back in.” – Paul Tudor Jones
“I learned early that there is nothing new in Wall Street. There can’t be because speculation is as old as the hills. Whatever happens in the stock market today has happened before and will happen again. I’ve never forgotten that.” – Jesse Livermore
“Learn to take losses. The most important thing in making money is not letting your losses get out of hand.” – Marty Schwartz
“The desire for constant action irrespective of underlying conditions is responsible for many losses in Wall Street even among the professionals, who feel that they must take home some money every day, as though they were working for regular wages.” – Jesse Livermore
“The goal of a successful trader is to make the best trades. Money is secondary.” – Alexander Elder
“I have learned through the years that after a good run of profits in the markets, it`s very important to take a few days off as a reward. The natural tendency is to keep pushing until the streak ends. But experience has taught me that a rest in the middle of the streak can often extend it.”– Marty Schwartz
“I’ll keep reducing my trading size as long as I’m losing… My money management techniques are extremely conservative. I never risk anything approaching the total amount of money in my account, let alone my total funds.” – Randy McKay
“In this business if you’re good, you’re right six times out of ten. You’re never going to be right nine times out of ten.” -Peter Lynch
“What seems too high and risky to the majority generally goes higher and what seems low and cheap generally goes lower.” -William O’Neil
“It takes 20 years to build a reputation and 5 minutes to ruin it. If you think about that, you’ll do things differently.” – Warren Buffett
“In investing, what is comfortable is rarely profitable.” – Robert Arnott
“I’m always thinking about losing money as opposed to making money. Don’t focus on making money, focus on protecting what you have” – Paul Tudor Jones.
“If you personalize losses, you can’t trade.” – Bruce Kovner
“Don’t focus on making money; focus on protecting what you have.” – Paul Tudor Jones
“Markets are constantly in a state of uncertainty and flux and money is made by discounting the obvious and betting on the unexpected. “– George Soros
“It’s critical for the crocodile to understand its prey and to know where to look for it and remain calm and patient until it arrives. As traders, we have to know what our trading edge looks like and where to look for it and then control ourselves enough to not over-trade before it arrives. “- Nial Fuller
GRAVITY – Pretty much everyone, (and I hope everyone reading this) would agree that a force called gravity exists all around us. It pulls us to its center and we have to exert energy to break its bounds. We can experience it even though it is considered the weakest of the four fundamental interactions in nature. Physicists, however, can not accurately calculate the probability of a particle existing at a certain time and space without by considering all four of the fundamental forces. In quantum calculations, gravity is also fudged number. The thing that we can most feel is unproven and its value is made up.
Thinking of supply and demand in calculating markets is kind of like calculating gravity. It’s an idea that can be experienced, but it is incomplete and mostly made up. Making market calculations on this idea alone will often lead to incorrect answers about the actual trajectory of a certain market.
Green lentils in 2017 are a perfect example of inaccurate results given considering a S+D chart. The logic seemed obvious. If a commodity was trading between 35 – 55 cents, with a median price of 45 cents and carryout ratio’s below 5%. If next year’s ratio is forecasted to be below 1%, how can it be possible to have a market trade below the median price? Looking at S+D ratios makes the analysis appear easy. Taking a long position below 45 cents should result in a profitable trade. Yet, within 30 days of a harvest that was significantly reduced by drought and void of any backup stocks the market is now trading at levels seen when the stocks to use ratio was over 50%! To get the answer we need to consider the other fundamental forces in our markets.
In keeping with the quantum physics theme, let’s get out of our vacuum and consider two other strong forces and a weak one to help explain what is going on.
Strong Force (India) – India is a natural force of nature that can provide but also take away. Complex and unruly. As the largest producer and consumer, she sets her own rules about how to manage her relationship. India must be fed, but how when and with what is always to constant fluctuation. India can follow its own logic which is not necessarily linear in nature. In 2017, her plans did not include Canada. While domestic pigeon pea prices crashed left no opportunity for Canadian green lentils a more substantial negative force has come from Indian government policy to control prices by bolstering domestic prices while reducing prices for retail consumers. The result is an interesting menagerie of incentives and restrictions which has often done the opposite of what was intended.
Strong Force (Wheat/Canola Prices) – This year we have realized the importance of how other markets set the relative tone pricing lentils. “If Durum was $10 bucks, Lentils would be $40” is the most accurate quote told to me this year. While canola has stayed relatively flat, Within 3 months of 2017 harvest, the Durum market has fallen around 20%. Relative to durum, lentil bids were highly overvalued (and unsustainable) in a declining world commodity market. This occurs even though the commodities have almost nothing to do with each other on the S+D stage. In year’s past, the opposite is also true. When Obama mandated a minimum ethanol percentage in gasoline corn and feed wheat prices skyrocketed. Future lentils prices had to keep pace in relative producer profitability to be considered for planting in the spring. A 5-year bull market was unleashed.
Weak Force (Competition) – It’s one of those often overlooked economic rules: If you start a company and become successful, others will notice. For all of Canada’s commendable cultural politeness, we had a hard time downplaying the ‘good thing’ we had going on with the rest of the world. Maybe it’s a human nature thing. Fear and Greed, and the inability to recognize the transient nature of both these things and tone it down a little. Canadians were making a killing but also making sure the rest of the world knew about it. Enter Russia, Enter USA, Enter Argentina, …….. with high prices and lots of opportunities to practice the trade. On the demand side, Exit India, Exit Europe, Exit North Africa, Exit Middle East…..as these countries look for other opportunities to reduce costs. While this relates to adjusting to supply and demand, the weak element is that trading countries outside Canada are willing to operate outside of the traditional pricing rules surrounding the lentil trade. Their inability to carry stocks creates a market where anyting goes. Unfortunately, Canada often ends up at the losing end of that battle.
In summary, strong forces determine the market trend, weak forces determine the market range.
For Green Lentils:
S+D (Weak Force): Current stock/use ratio under 5% = market will tend to high end of range
India (Strong Force): Low need for Canadian products = establishes negative trend
Wheat/Canola (Strong Force): Durum = down 25%. For LGL 75% of $45 = $33.75 which is where we are today.
A more thorough analysis would clearly have pointed to the necessity of re-adjusting lentil prices to remain in balance with the rest of the market forces.
Going forward, if wheat markets continue to stagnate prices will continue to decline through 2018. It will take production troubles in India could reverse this trend. Russia will sell out will give more weight to our current grower’s price demands. However, because it is a weak force it will start adding resistance but can not change the trend.