BRIC agricultural yields: Trends and theory

Although BRIC countries (Brazil, Russia, India, and China) have quickly developed modern diversified economies over the last 20-30 years, agriculture remains an important part of their economies.  If one believes food self-sufficiency is beneficial to a country, this is an especially important component of the Indian and Chinese economies because of their huge populations.  For Brazil and Russia, food self-sufficiency no longer seems a critical issue, but agriculture is nonetheless critical to their economies: Russia’s second leading export after weapons sales is agricultural products, and Brazil has gone from being a food importer in the 1970s to the 4th largest food exporter in the world as of 2014.  This all suggests that it’s important to understand agricultural production and yields in the BRIC countries.

This first article in a series on BRIC agricultural yields explores trends in yields for three major crops–maize (corn), soybeans, and sugarcane–and, to the extent data is available, related trends in major agricultural production inputs: land, equipment, fertilizers, pesticides.  In connection with these trends, a basic economic theory of agricultural production is introduced as the basis for econometric analyses in future articles in the series.  This article shows clearly that it’s basically impossible to know (or estimate) how the many factors known to influence agricultural yields actually affect the yields; a method is needed to aggregate the effects of causal factors in non-linear way.  Please read on!

1.  BRIC agricultural yield trends

To begin, note that agricultural crop yields are measured in terms of the average number of tonnes (1 tonne = 1 metric ton = 1000 kilograms) per hectare (1 hectare = 2.47 acres = 10,000 square meters).  The following graph, as also shown at the beginning of the article, shows trends in BRIC maize yields (corn, in American English) between 1960 and 2014:

Yield data for Russia begins in 1992 because the Russian Federation, as successor to a portion of the USSR, began its existence in late 1991.  As shown, Brazil, Russia, and China experienced substantially higher maize yield growth than did India; perhaps because of more capital investment in agricultural equipment, related developments in agricultural methods and technology.  It is interesting that maize crop yields in Brazil, Russia, and China seem to be converging over time, suggesting that optimal agricultural practices are perhaps being dispersed across countries.

BRIC soybean crop yield trends for the same period can be seen in following graph:

In contrast to maize yields where China experienced the highest growth, Brazil experienced the highest soybean yield growth among the BRIC countries.  In this comparison, it is difficult (for a non-expert like me) to speculate on the likely causes of such differences in yield growth.  It is interesting to note, however, that soybean crop yields across the BRIC countries do not seem to be converging as they are with maize yields.

Sugarcane crop yields for the 1990-2014 period are shown below, noting that sugarcane is not cultivated in substantial quantities in Russia:

In contrast to maize and soybean yield trends, it can be seen that the average yield growth rates across Brazil, India, and China are substantially equivalent–on average–over the 44 year period.

Now then, if such trends and differences across the BRIC countries are important–and I can think of many reasons they are, which I’ll avoid for the sake of brevity–then what we should be asking ourselves is, What is causing the trends in BRIC agricultural yields, and relatedly, the differences between the BRIC countries?  Let’s begin …

2.  A simple economic theory of agricultural production

Why theory?  Why is theory necessary to answer the question of what’s causing BRIC agricultural yield trends?  There are two, complementary ways to answer this.  The first way relates to a famous quote …

” … theory … decides what can be observed.”  Albert Einstein

… which in some sense is an abstract way to say there is reflexivity between theory–i.e., explanation and prediction of the relationships among causal factors and their effects–and the data identified, collected, and measured with respect to the cause and effect factors.  That is, without theory we basically don’t know what to look for, observe, measure, etc.; so theory leads to data collection.  A somewhat less abstract way of understanding why we need theory to answer the question is a paraphrasing of something Professor Jeffrey Wooldridge said to me many years ago:

There are basically an infinite number of regression models [“equations”] we can estimate.  What is important is understanding which regression models are important and meaningful.  Theory helps decide which models are important and meaningful.

Because we’re interested in the economic aspects of BRIC agricultural yields (at least I am), it makes sense to use economic theory in combination with some knowledge of agricultural practices to develop an econometric model, which will then be used in future articles to estimate the relationship between agricultural yields and various causal factors.

Agricultural production function.  Beginning with the definition of agricultural yield–which is a function of both agricultural production and agricultural land usage / input–and noting that production is a function of potentially many factors, consider the following:

The agricultural production function shown on the right is a commonly-used production function in economics, termed a Cobb-Douglas production function. The production inputs shown in the function, suggested by a basic knowledge of agricultural practices are …

  • Land:  l, hectares of land planted and harvested.
  • Seed:  s, tonnes of seed planted.
  • Equipment: k, number of agricultural tractors (as a proxy variable).
  • Fertilizer: f, tonnes of fertilizers used.
  • Pesticides: p, tonnes of pesticides used.

What will become most important during course of this article series are the exponents in the production function (the “betas“), which represent the elasticity of production and yield with respect to the input factors.

Omitted variables.  Agricultural experts will note that there are important causal factors omitted from the production function including land quality, soil remediation, seed variety (including use of GM seed), rainfall, temperature, and solar radiation.  As I will discuss in later articles in the series, such omitted variables need to be considered carefully when estimating the effects of the above causal factors on agricultural production and yields.  For now, it will be enough to understand the basic nature of the production function.

Non-linear production.  An important aspect of the agricultural production function is its non-linearity, which can be seen by noting that when any casual factor has a value of zero, the production will also equal zero.  So, the function is multiplicative (non-linear) rather than additive (linear).  Were the function linear, a factor with a value of zero would not necessarily result in zero production.  This generally makes sense because if any fundamental agricultural input like land or seeds is missing (i.e., has a zero value), no crops or production results.  The same is true with important omitted variables like rainfall and solar radiation: no sun or rain => no crop => no production.

I will discuss more of these technical issues in the next article where I develop an econometric model to estimate effects of agricultural inputs on production.  But for now I will explore trends in selected inputs factors to get an overall idea of how they are changing over time in the BRIC countries.

3.  Trends in selected agricultural production inputs

Land area harvested.  It is possible that agricultural production and yields depend on returns to scale, which basically refers to the relationship between production efficiency and input / production volume.  Because land area is the primary agricultural production input, consider the following graphs of land area harvested (and, so, the land area planted) for maize, soybeans, and sugarcane in BRIC countries:

BRIC land usage / harvest trends over the last 50+ years, shown above, can be summarized in broad, rough terms as follows:

  • Maize land usage is fairly constant except for large increases in China.
  • Soybean land usage is fairly constant except for large increases in Brazil and India.
  • Sugarcane land usage is fairly constant except for large increases in Brazil.

Such trends can be attributed to a variety of factors.  For example, the growth in soybean and sugarcane land usage in Brazil can be attributed to a combination of expansion of government financing in the agri-business sector, increased oil and gas prices (alcohol derived from sugar is a energy substitute), etc.  For our purposes of this article series, however, simply understanding the effects of trends on BRIC agricultural production and yields will be sufficient.

Agricultural equipment usage.  Another major input for agricultural production and yields is agricultural equipment.  Unfortunately, data on agricultural equipment usage available from the Food and Agriculture Organization of the United Nations (the sole data source for this article) was fairly incomplete.  As can be seen in the following graph, equipment usage data was not available post-2003 for India, post-2006 for Brazil, and post-2009 for Russia:

Agricultural experts will note that I’ve shown tractor usage as a proxy variable for agricultural equipment.  Use of many other types of agricultural equipment influence agricultural yields, but there is substantially less data available on such other equipment.  And, indeed, the data represented in the above graph is not adequate to development meaningful estimates of the effects on agricultural yields.  So, it will be necessary to consider this lack of data later in the article series or perhaps find other available data sources.

Agricultural fertilizer and pesticide usage.  Consider now two other major agricultural inputs–fertilizers and pesticides–as shown in the following graphs:

As with the agricultural equipment data, fertilizer and pesticide data available from the Food and Agriculture Organization of the United Nations is fairly incomplete: fertilizer usage data is available only beginning in 2002, and there was only one year of pesticide data available for Russia (not shown).  Nonetheless, we can see some broad differences in trends between the BRIC countries:

  • China has the highest and growth in fertilizer and pesticide usage.
  • Brazil’s pesticide usage has increased markedly over the last 25+ years.
  • Other BRIC fertilizer and pesticide trends are fairly unremarkable.

The graphs of fertilizer and pesticide usage represent total inputs, rather than the more meaningful usage per cultivated hectare or something similar.  But the graphs do provide us with a rough hypothesis that China and Brazil agricultural yield growth is related to increased fertilizer and pesticide usage.  Given the general lack of data, however, care will be needed in estimating the effects on yields; and/or alternative data sources will need to be identified.

4.  Open questions about BRIC agricultural yields

When we look at the above trends in BRIC agricultural yields and related input factors–and then consider a simple economic theory of agricultural production–several questions follow directly:

  • What is actually causing the trends in BRIC agricultural yields?
  • What is causing differences in the trends between BRIC countries?
  • Are input factor effects on yields time-varying or constant over time?
  • Are input factor effects on yields linear or non-linear (does input level matter)?

And, of course, once these questions are answered, we would reasonably want to know if there are any implications for BRIC agricultural practices or economies.


São Paulo

Caveats.  Please note: (i) views presented above are my own and do not reflect those of others; (ii) like anyone, I’m not infallible and am responsible for any errors; (iii) I greatly appreciate being informed of any significant errors in facts, logic, or inferences and am happy to give credit to anyone doing so; (iv) the above article is subject to revision and correction; and, (v) the article cannot be construed as investment or financial advice and is intended merely for educational purposes.  MMc