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Measuring uncertainties: the risk of rural production

You might have heard that rural farms are “roofless factories”, a concept that represents uncertainties regarding weather and biological factors of the rural activity. In other words, different from a conventional factory, there is no guarantee that the set of inputs will create a product with the expected characteristics. Another source of uncertainty for producers is price fluctuation for products and inputs. For both markets, rural producers are price takers, with a significant difference concerning market power between the producer (who only accepts prices that are offered) and players (generally few and commercially big), being input sellers or purchasers of agricultural products. Uncertainties worsen because the decision-making on farming production systems (and, therefore, on costs) in a year-crop precedes expressively the moment where the production will be available to trade. Between these moments, market conditions may change significantly.


Uncertainties, however, are a problem that may be reduced to levels that can coexist with the use of information and analytical techniques. In the financial market, the concept of uncertainty is part of the routine, and players use it as the concept of risk. How? The difference is in the possibility of quantifying characteristics of uncertainties. A good example of it in the agricultural scenario is the weather. When it is said that it might or might not rain in a specific period, favoring planting activities and good productivity (or not favoring), uncertainties are involved. However, when a historical series of daily rainfall is used, applying statistical analysis, the risk can be quantified. For example: analysis may forecast 80% chance of rain in a specific period; therefore, producers may consider in the decision-making that the productivity will be a weighted average, with 80% weight for x bags and 20% for y bags per hectare.


The question is “how much?”. In the financial market, the problem is not losing money, but how much one is willing to risk losing in order to earn a certain amount. What is the average gain or loss that one can statistically expect? What is the probability to obtain a certain gain? Or even find trades with the same average remuneration and lower risks. Maybe rural managers are still not used to this point of view for the decision-making, but it is important to have in mind that every project may have losses at some point; therefore, it would be interesting to know the size of this potential.


In practical terms, the risk analysis involves forecasting the future by knowing the past, which requires statistical knowledge and a vast database. The analysis identifies sources of uncertainty that affect the production and, from behavior patterns registered in the past, projects possible standards. As a result, the more data we have regarding risk variables, the better is the ability to anticipate future behaviors.


Several mathematical models have been developed to measure risk in the financial market, and some of them are proved to be applicable to the rural production scenario. Possible validated uses of the risk analysis are: quantify the probability of losses during the period of choosing crops, calculate environment and economic risks related to the use of chemical and biological products; measure the operational capacity of machines that reduce the economic risk; the form of contractual trading structure that reduces execution risk; the risk related to the irrigation use and others.


These analyses are more feasible thanks to computer science advances. However, the biggest challenge for the Brazilian agriculture is how to gather data efficiently in order for these analyses to become reliable and helpful in the decision-making. Experiences show that it is not common to find farms with systematized database; however, big farms are already concerned about having the information of the entire producing process quickly.


Working with risk analysis would change significantly the management of rural producers, avoiding surprises and indicating how much they need to be prepared for every possibility. An example for that is: based on the history of the business, how much should be the financial reserve to go through possible crises?


Evaluating risks could improve the capacity of trade resources, because it would indicate potential gains and losses of the business to investors. In other words, if the business is less risky, lower rates should be applied to loans.


The risk management may also favor to find which variables present more fluctuation of results and, when it is not acceptable, decide what is the best tool to reduce it.


It is suggested to change to: your business has a maximum “L” potential of losses and it can have “P” profits, remunerating “Z”, on average. As a result, uncertainties are considered and the risk size is known.

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