Depreciation of agricultural machinery

Many factors influence the depreciation that agricultural machinery suffers during its useful life. Research on tractors sought to determine the depreciation model that best represents the market value depending on the commercial brands and power ranges available.

Agriculture is an important economic sector, as in addition to generating jobs, it is largely responsible for exports. Agribusiness is strategic for the Brazilian economy, especially in times of economic crisis, representing 23% of GDP and being the only sector with notable growth in 2014 (Cepea, 2014). The sale of agricultural machinery plays a fundamental role in agribusiness GDP. According to data from Anfavea (Brazilian Association of Motor Vehicles, 2014), in 2014, 55.623 wheel tractors were sold from manufacturers registered with the association.

Used agricultural machinery suffers depreciation, which is the value that the tractor loses during its useful life, influenced by the way it is used, operated and maintained throughout its life (Machado, 2010). The simplest way to calculate the depreciation of the tractor is using the “straight line” method, which consists of amortizing the capital used to acquire the asset in equal installments over its estimated life (Custodio et al, 2013). However, the most appropriate method available is the market price, which is based on research into the value of the machinery with dealers (Consentino, 2004).

The objective of this work is to determine the depreciation model that best represents the market value depending on commercial brands and power ranges.

Data collection took place between March 1st and August 30th, 2014. The data was collected from specific online sales sites and grouped into an Excel spreadsheet.

The data was collected taking into account the commercial brand, the model, the gross power of the engine, the year of manufacture, the number of hours worked (if informed), whether the tractor is specific to any crop, the condition of the tires and the paintwork (if informed by the seller), the type of traction (4x2, 4x2 TDA or 4x4), whether it has a cab, some observations such as a 12-speed gearbox, whether the tractor has a front blade, type of tire, the city and the state (UF) where the tractor is located, the name of the sales website and the link to the vehicle, the sales price of the used machine and the sales price of the new machine, which was obtained through IPMA - Price Index of Machines on 11/9/2014.

With data on the values ​​of new tractors along with their sales value, the percentage of the price in relation to the new value was obtained, which is basically the value of the used model divided by the value of the new model. When the tractor model or brand no longer exists, prices of a similar brand with a model with the same engine power were used.

There was a division into power bands to try to improve the correlation of data and thus find a function that best suited it. The division into power bands was based on the number of tractors and did not follow an equal distribution. The ranges adopted were in hp, up to 59, 60 to 75, 76 to 90, 91 to 110, 111 to 130, 131 to 150, 151 to 180 and greater than 180 hp.

346 tractors of seven different brands were found. 100 units of the Valtra/Valmet brand, 86 units of the Ford/New Holland brand, 66 units of the Massey Ferguson brand, 31 units of the Case IH brand, 28 units of the John Deere brand, 28 units of the Agrale brand and seven units of the CBT brand. The tractors were distributed in 16 Brazilian states, where the majority are in the state of São Paulo. The tractors identified as used in sugarcane cultivation were also evaluated and compared to the others.

According to the data obtained on age and percentage of price in relation to the value of the new tractor, curves divided into commercial brands were constructed, as shown in Figure 1, which is an example of one of the commercial brands.

According to the regression curve, a correlation coefficient equal to 0,65 was found, with the best function, which adjusted to the proposed model, being y = 76,62e^-0,031x, where “y” is the percentage of the tractor in relation to the value of a new one and “x” the age in years for all brands analyzed. The other equations for different brands are found in Table 1.

It can be seen from Figure 1 that some data goes beyond the 100% value, this is due to the data source to obtain new values ​​carrying out a national average of the prices of new machines, where the price of new machines varies depending on the State.

According to the regression curve, a correlation coefficient equal to 0,845 was found, with the best function, which adjusted to the proposed model, being y = 85,423e^-0,035x, where “y” is the percentage of the tractor in relation to the value of a new one and “x” the age in years in the power range from 44,1 to 55,2kW. The other equations for different power ranges are found in Table 1.

It can be seen from Table 1 that only the power range from 151hp to 180hp had a relatively low coefficient of determination (R²), as in this power range presented there was greater divergence between the tractors with regard to the variables analyzed, such as, for example , the existence of tractors with and without cabins and tractors that operate in different crops, such as the so-called “sugarcane farmers”, which due to their intensive work can depreciate more than those that operate in grains, for example.

A similar analysis was carried out with the different commercial brands (Table 2), presented here without being identified, except for the extinct CBT. 

It is observed that only the CBT brand presents a very low correlation, this is due to the tractors analyzed presenting divergences, such as the front blade set, double wheels, cabin, and some being sold as collector's items, demanding prices well above the market value in comparison with other brands.

For the sugarcane activity, the equation determined was y = 84.776e^-0.081x (R² = 0,816), for the other activities it was y = 87,69e^-0,04x (R² = 0,396), indicating greater depreciation of sugarcane tractors.

Conclusions

The different situations to which the tractors are placed influence the behavior of the model. Stratification by brands and division by power ranges allowed for higher coefficients of determination (R²) of the data, in most cases, with a better fit of the model. The power range that showed the greatest depreciation in the first years was up to 59 hp, but over time the power range with the greatest depreciation was from 151 hp to 180 hp. The brand that suffers the greatest depreciation is E, the one that suffers the least is D. Tractors operating in sugar cane depreciate more than those operating in other activities.

Thiago L. Romanelli, USP/Esalq; Giovani A. Ghirardello, USP; Leandro M. Gimenez, USP/Esalq

Article published in issue 159 of Cultivar Máquinas.