What a Balance Sheet Is For
The goal of a commodity balance sheet is essentially to forecast if ending stocks are expected to rise due to excess supply, or to fall as a result of a deficit, and what that implies for future prices. Identifying the largest exporters of a commodity, and then combining the balance sheets for each of those countries, can provide a clearer understanding of available supplies and more accurate price signals, as compared with looking at production and stocks of individual countries or the world as a whole.
Measuring available supply, as opposed to total supply, helps to distinguish between countries with large exportable supplies and those that have large production but are relatively self-sufficient. In wheat, for example, China and India are the second- and third-largest producers in the world, but they are only the 10th- and 18th-largest exporters, respectively. Both countries export less than 1% of production compared to the next two largest producers, Russia and the US, which export nearly half of their output. Rarely are either China or India going to be a significant driver of global wheat prices.
How to Identify Major Exporters
Gro’s free web app allows users a number of ways to find the top exporting countries for a given commodity. A tree map chart type is one example. After logging into the Gro web app, click Add New Chart and in the Add Data Series window, select Corn as the Item, Export Volume (mass) as the Metric, and World as the Region, then choose the tree map option to display all exporters by relative size (see charts below). The United States, Brazil, Argentina, Ukraine, and Russia comprise the top five. Changing the visual display to a line chart and selecting those countries reveals their export history and shows how rapidly Brazil, Argentina, and Ukraine have ramped up corn exports to compete with the US’ traditional dominance of the market. As recently as 2010, the US exported more corn than the other four countries combined, but now faces stiff competition.
This display shows two different ways of looking at the major corn exporting countries. The tree map on the left is a snapshot of a single season showing all exporters by relative size. A different season can be selected by clicking on the gray timeline at the bottom. The line chart on the right shows the export history of the five largest origins. Click on the image to go to an interactive display on the Gro Intelligence web app.
Once you have the list of top exporters, you need to select the most important, which may require a little research. A good place to start is to look at the destinations from each country. In the case of corn, for instance, South Africa is the world’s sixth-largest exporter. But by looking at the export destinations it becomes clear that South Africa ships mainly to neighboring countries, which makes South Africa less important for global trade analysis. It is impossible for most market participants to closely analyze every country’s supply and demand balance and associated trade flows, so picking the three to six most important countries is essential for efficiency.
How to Combine Major Origin Balances
After identifying the most important exporting countries, it’s time to create an individual supply and demand balance sheet for each country. In an earlier Featured Insight we described how to build a balance sheet using the Gro web app. (Gro also has a suite of models to aid in areas like yield forecasts as well as a searchable archive of Insight articles analyzing events that impact balance sheets, such as a possible end to the US/China trade dispute.) Since the aim is to forecast ending stocks, it makes sense to create a single-country balance sheet on an annual basis where there is a clear low point in stocks right before the next harvest.
Individual country balances are important, but combining them offers a more complete picture. That gets complicated because of the counterseasonal nature of the Northern and Southern Hemisphere crop cycles. In the case of soybean production in the US, Brazil, and Argentina, the three biggest exporters of that commodity, using local marketing years means the start and end dates for each season don’t line up. The USDA’s WASDE report addresses this by shifting Brazil and Argentina to an October-September crop year to more closely align the export periods with the September-August US marketing year. But this has the drawback of measuring ending stocks in the middle of the South American growing season.
A better solution is to break down all three countries’ balances to a monthly level to capture their seasonality and to forecast export availability at different points during the season. This approach offers a more robust picture of exportable supplies. It also allows for stocks-to-use calculations, a key input into price models, at multiple points during the season. Monthly export data can be found in Gro from customs agencies in the US and Brazil, from the International Grains Council covering major exporters across 11 commodities, and from the UN’s COMTRADE database, which has extensive coverage.
The corn market over the last several months offers a good example of the benefit of monthly calculations. Historic rain and flooding this spring threatened US corn planting, which led to a significant rise in price. At the same time, record crops were being harvested in Argentina and Brazil, and exports surged from those countries, as well as from Ukraine, to take advantage of the high prices. Despite a below trend crop in the US for this year, US corn inventory isn’t expected to decrease much as other major exporters meet world demand. Having a monthly breakdown of the balance sheets was helpful in multiple ways: It showed how well supplied the world market was in June when corn prices rose, and it indicated that high prices were likely temporary unless the US crop turned out worse than expected. In the next section, we describe a technique to predict US exports.
The stacked bar chart on the left shows the seasonal cyclicality of corn exports from the US (red bars), Brazil (green bars) and Argentina (blue bars). The crop calendar on the right shows the different times of year when planting, growing, and harvesting take place in several major corn producing regions. Click on the image to go to an interactive display on the Gro Intelligence web app.
Forecasting Residual Corn Demand for 2019/20
One way to approach the world import/export balance is through residual demand, which is the demand not met by other countries. In this section, we walk through the steps of forecasting residual demand for corn in the 2019/20 season, using Gro data on major corn exporting countries.
The US has a superior capacity to store grain on-farm and off-farm and excellent infrastructure to move that grain when the world export market needs it. As a result, the US has effectively become the residual supplier of corn, wheat, and soybeans as the primary holder of inventory among major exporters. At the end of last season the US was holding 81% of corn stocks among the five largest exporters. In this exercise we map out the export potential of the other four origin countries and calculate the corn balance that is needed from the US.
First we look at each of the major exporter balance sheets individually based on the current USDA WASDE 2019/20 crop year projections. We double check assumptions made in the forecast, and then break down the export projections on a monthly basis to align them on an October-September crop year, the most common trade year for countries that import or export corn.
Argentina and Brazil are in the middle of exporting record crops harvested in the first half of 2019, a good portion of which will be competing with US exports in the October-September season that has just begun, followed by exports from the South American countries’ next crop that is being planted now.
Argentina is forecast to export 36 million tonnes of corn in its March-February marketing year. There are a few ways to assess this forecast. Exports as a percentage of annual production is a fairly consistent number for countries that have established themselves as significant exporters, and can be a quick check to see if a forecast is out of line and requires more research. In this case 36 million tonnes is just above the five- and 10-year averages for percentage of production headed for export, and therefore seems reasonable. Looking at the rest of the balance sheet forecast, the USDA is estimating that Argentina’s ending stocks will expand by 1.2 million tonnes, after taking into account a robust increase in domestic consumption, so there is more corn on paper available for export.
Now let’s examine various export pace metrics, as it is seven months into Argentina’s marketing year. Marketing-year-to-date exports are up 10.4 million tonnes or 68% over last year. If you assume the final exports will be 68% higher than the previous year, that implies a figure of 37.7 million tonnes. Extending that same measure to the last three- and five-year periods, the implied final exports are 37.3 million tonnes for both periods. A comparison of free-on-board (FOB) export prices among the major exporters shows that Argentina is the lowest, and therefore should continue to push exports in the short term.
Combining the year-to-date export pace, the projected available ending stocks, and the current competitive price, it is fair to assume Argentina will export 37.3 million tonnes. That adjustment implies ending stocks will be slightly lower than last season, and below the USDA’s current forecast, but still above the five- and 10-year average ending stocks.
Looking at the season that will start March 2020, the USDA is forecasting Argentina trend production of 50 million tonnes (down 1 million tonnes from the current season) with exports of 33.5 million tonnes and an increase in ending stocks of 1.5 million tonnes. That increase in ending stocks, however, suggests Argentina should be able to match its 10-year average of exports as a percentage of production, which implies exports of 34.5 million tonnes.
Our combined adjustments for the 2018/19 and 2019/20 seasons added 1.9 million tonnes of corn exports from Argentina to the current October-September period as compared with the latest USDA forecast. Now, we will look at the other major corn exporters.
The chart on the left highlights the seasonality of Argentina’s monthly corn exports. The current season’s exports (blue line with markers) were records for six of the first seven months. The chart on the right shows free-on-board (FOB) export prices for four major regions, the Black Sea (blue), the US Gulf of Mexico (green), Brazil’s Paranagua port (red), and Up-River in Argentina (purple). Argentina has been the cheapest origin for the majority of the year. Click on the image to go to an interactive display on the Gro Intelligence web app.
Turning to Brazil the forecast for the current 2018/19 marketing year, which runs from March to February, is for 39 million tonnes of corn exports and a decline of 2 million tonnes in ending stocks to 7.3 million tonnes, which is below the 10-year average. Thirty-nine million tonnes represents a relatively high percentage of production compared to the 10-year average, so at first look the export forecast may seem high. But this season’s pace of exports through September is already running strong—8.5 million tonnes above the previous record set two seasons ago. Midmonth estimates from Brazil’s MDIC suggest at least 5 million tonnes were exported in October, matching 2016/17. If we assume this season continues to match the pace from 2016/17 then the final export figure will be 40.1 million tonnes. That is a rough technique to make a forecast, but it can also be easily updated when additional data is released. MDIC is the most timely source, issuing monthly totals the first business day after month-end.
Looking at early forecasts for Brazil’s 2019/20 season, 34 million tonnes of exports is just above the five-year average percentage of production. Ending stocks are forecast to increase by 1 million tonnes. Not much to argue with on the total. But trying to project the seasonal distribution of exports and how much will fall into the October-September time frame we are trying to analyze is unclear. This season’s exports, for example, are shifted much earlier in the season partly due to the rapid rise in corn prices in late May and early June.
The chart on the left shows a historical comparison of exports (blue bars) and production (green bars) for corn in Brazil, including the current USDA forecast. The chart on the right highlights the seasonality of Brazil’s monthly corn exports. The current season’s exports (blue line with markers) are much higher than previous years’ (other blue lines) owing to a bumper second crop harvest. Click on the image to go to an interactive display on the Gro Intelligence web app.
The current USDA forecast for 35 million tonnes of corn exports from Brazil in the 12 months started Oct. 1 look about right for now. Close attention needs to be paid to how exports develop over the next few months. If the pace remains very strong and projections are increased it will alter several assumptions in the balance sheet about available stocks. As a comparison, many analysts had to review their entire Brazil soybean balance sheet when exports last season far exceeded anyone’s expectations.
Russia and Ukraine, also among the top five corn exporters, have just begun their marketing year and therefore there is no data to conduct an export-pace analysis. The USDA’s projections for those countries’ corn exports look reasonable from the perspective of keeping ending stocks basically unchanged on unchanged domestic consumption. There is still debate on final production numbers as both countries are still harvesting the crop.
Adding the adjustments we have made to the export forecast for the major origin countries outside of the US results in a total of 1.9 million tonnes more corn being shipped to the world market.
This balance sheet display shows historical supply and demand data for corn in the US from the USDA. In addition, the current forecast from Gro’s US Corn Yield Model is shown in the red 2019 E column. The Interactive column on the far right allows users to input their own values for various balance sheet line items, which will then cause ending carryover stocks to automatically adjust. Click on the image to go to an interactive display on the Gro Intelligence web app.
The import demand side of the balance for most countries can be projected using basic trend analysis or a regression against GDP. A notable exception is the European Union. Wheat production was poor last season, which led to a large increase in corn imports as an alternative feed. This season’s EU crops were strong and thus fewer corn imports should be expected. Deeper analysis of the balance sheets of major importing countries can fine-tune the residual-demand approach or project future years’ demand. But for simplicity in this example we will take the USDA’s current forecasts of import demand.
Using the USDA’s forecasts for worldwide corn import demand, the above adjustments we made on the export side suggest the US will need to ship 1.9 million tonnes fewer than the current USDA forecast in the October-September period just begun. The USDA balance sheet uses bushels for units in the US, so 1.9 million tonnes translates into 75 million bushels.
Since the US marketing year runs from September to August, the adjustment to the 2019/20 balance sheet implied by our analysis is that the US will export 1.875 billion bushels of corn, fewer than the 1.900 billion bushels currently forecast in the latest WASDE report. The complications caused by varying marketing years for each country highlight the benefit of having monthly balance sheets, particularly for major exporting countries.
To stay current, an analysis like this one, using a residual demand framework, needs to be updated as new data becomes available on production and trade. In addition, each of these steps can be applied to other exporting countries, and pace analysis can be performed on importing countries, to improve projections on both sides of the balance sheet.