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Capital Productivity Indexes in the Brazilian Agribusiness
Carlos Feu Alvim
Cláudio David Dimande
The 1970, 1975, 1980, 1985 and 2006 agribusiness censuses have given the capital stock for the respective reference years. This information was not available in the 1996 census and therefore there is a long period without intermediary information. Furthermore, due to their complexity, they are carried out approximately every five years and so the complete and fast analysis regarding the productivity evolution necessary for corrective measure is difficult
The objective of this study is to present preliminary results regarding capital productivity in the Brazilian agribusiness using two indexes: agribusiness product/ tractors fleet (wheel plus track tractors) ratio and meat production/cattle herd ratio. Data from the Brazilian Institute of Geography and Statistics (IBGE) and the historical series from the Brazilian Association of Automotive Vehicles (ANFAVEA) were used to calculate the first index
When data from the two sources were compared they were incoherent. Data from ANFAVEA (2009) were based on tractors sales and their wearing out along the years whereas the IBGE data came from census carried out in places where tractors are used. In order to approximate these values it was constructed a wearing out curve so that the IBGE data could be reproduced using sales data from ANFAVEA. The first index revealed that capital productivity (agribusiness product/tractor fleet) remained constant during 10 consecutive years and then presented a slight recovery.
For the second index, the bovine herd was used as capital stock and the meat produced as a measure of product. Capital productivity calculated by the inverse of this ratio has grown but not in the expected levels if there was an effective productivity increase resulting from reduction of fattening period due to handling technique improvement and herd quality improvement.
Agribusiness plays a vital role in the Brazilian economy and the perspectives of growth in the next years are very encouraging because the country has abundant natural resources such as water, electricity, land and man-power (Fergie and Satz, 2007).
Prospective studies (Contini, et. al, 2006and Gasques, et. al., 2009) have shown that there is an enormous potential for growth in the next 10 years. While in other countries, mainly the populous ones, difficulties are envisaged concerning satisfying their demands due in part to the exhaustion of agricultural areas, Brazil has extended virgin areas and technology to face such challenges.
According to the Ministry of Agriculture and Fishing (2009) Brazilian agribusiness exports totaled 71.9 billion dollars in 2008, the commercial balance surplus reached a record of 60 billion dollars, its share in the total Brazilian exports was 36.3%, and in 2004 the agribusiness area employed 37% of the Brazilian workers along its productive chain.
For Alvim et al (2005), Brazil presents low capital productivity relative to its development level. Efforts to increase productivity in the country have almost always been directed to the man-power input in spite of the fact that it is abundant whereas capital is scarce. Moreover whenever productivity is mentioned most of the time it refers to physical production and not to aggregated value.
The objective of this document is to present some results of a preliminary evaluation of capital productivity in the agribusiness sector using two indexes, namely: agricultural GDP / tractors fleet and meat production/herd.
After a brief introduction, the capital stock and capital productivity concepts are introduced then the methodology and the used data sources are presented. Then the capital stock of the Brazilian agribusiness is presented followed by the topics referring to capital productivity indexes and finally conclusions, bibliographic references and annexes
2. Concepts of Capital Stock and Capital Productivity
Capital stock is the value of capital or durable goods (machines, equipment, construction material, etc.) that is included in the fixed capital gross formation of the national accounts system. Capital stock can be gross or net. Gross capital stock expresses the value of goods assuming that they have not been depreciated along time. Each good is evaluated by the acquisition price as if it were new. On the other hand, net capital stock expresses the values by which the goods would be bought if they would be put in the market in their actual condition. Obviously, this value will be lower if compared with the new equipment. This happens because the durable goods have been depreciated.
In its turn, depreciation is a way to measure the reduced capacity of the capital stock to aggregate value to the product, caused by a normal wear out, be it physical, accidental or due to time or obsolescence (Feu, 2003, Alvim, et. al., 1995, Morandi, 2005, Souza and Feu, 2003). There are different ways to depreciate capital goods, namely linear, linear with (defasagem), bell shape and sudden death.
Capital productivity is a measure of how a physical capital good is used to provide goods and services (Borsch-Supan, 1997). For OECD (2009) capital productivity is the proportion between output and input considering capital stock as input.
Feu (2005) asserts that capital productivity is a measure of the product quantity generated per unit of capital stock. Therefore capital productivity undergoes sudden changes together with contingency variations regarding the rate of productive capacity use. An alternative, according to Alvim (2005), is to use the productive value resulting from fitting the Y/K ratio which permits as well to evaluate the utilization factor or use the industry utilization factor as proxy of the economy utilization factor as a whole and therefore obtain a capital productivity value purged of oscillations introduced by capacity factor variations.
In order to determine capital productivity it is necessary to calculate first the capital stock and then relate it with the product. In this article the Perpetual Stock Method (PSM) will be used according to the following steps:
Gross Capital Stock = ∑ Vi Sit (1)
where L is the economic life time of the good, Vi refers to Sales and Si survival after i periods; that is, the fleet is given by the sum of the previous sales multiplied by depreciation of this capital.
According to the Perpetual Stock Method, we have:
Where given the life time (v) per type of capital (i) and the defasagem time (m), exogenous to the model, one calculates the depreciation rate concerning the capital good (di) as (1/ (vi-mi)). The quantity of years to be considered for depreciation (t-1-r) is the difference between the previous year (t-1) and the data when the investment was made (Ir), while the accumulated depreciation rate is calculated by multiplying d by (t-r) and the depreciation time (h = v-m). Once the capital stock value K in year t and year t-1 isdetermined, the depreciation rate in year t concerning the stock, , is give by:
Therefore, capital stock is a function of the historical investments in the Perpetual Stock Method, the depreciation rate, , is endogenous, depending on the considered life time (v), the capital composition and investment variation along time.
4. Capital Stock in the Brazilian Agribusiness
a) The land factor.
The goods values published by IBGE agribusiness census, namely land, installations and improvements can be treated as capital stock. In Brazil more than half of the assets referring to capital stock in agribusiness are immobilized in the land factor.
According to Neto and Gomes (2004), the size of a property is related to the capital availability and the existence of natural resources in the production unit. For these authors the abundance of land factor in the properties represents a larger capital stock and this facilitates credit allowance (and turns the acquisition of new and more productive factors viable) as well as allowing gains in the economy of scale regarding production and commercialization and better conditions vis-à-vis investment risk on innovation, besides better assistance from governmental policies for agribusiness.
Whenever a comparison is made between the percent shares of capital stock in the Brazilian agribusiness and that around the world as illustrated in Graphic 1 for the year 2006, it is noticed that the land and installations values in Brazil are higher but those referring to machines and animals are lower.
Capital stock in agribusiness installations
The Brazilian values are those of the 2006 IBGE Agribusiness Census and the world data are from the OECD publication (Anriquez et.al., 2009). In the Brazilian case the land values are the sum of forests (2%) and land (71%) while those of the world correspond to the land value.
b) The IBGE Agribusiness Census (2006) excluding the Land factor;
When the land factor is excluded there is an approximated distribution among the different components of the capital stock as illustrated in Graphic 2, except for natural forests. The separation of the land value is justified due to the special characteristics of this good (a natural resource) that can only be classified in the concept of capital resulting from real investments (physical capital) when the resources used to make it productive are accounted for and that represent only a fraction of the “land value”.
It should be noted that land in most cases conserves or increases its value along time and it cannot be included in the depreciation concept commonly used for capital stock evaluation. In the case of Agribusiness it is more convenient to treat the “land value” as one of the inputs that contribute to production besides other types of capital and man-power.
Graphic 2 –Accumulated capital stock by type of good (without the land value) (IBGE, 2006).
The analyzed indexes in the present work refer to two components of the capital stock (machines and animals) and when they are added they represent more than half of the capital stock (without the land factor) calculate by IBGE in 2006.
c) Tractors Sales and Agriculture GDP
Man-power productivity and consequently the final product are highly influenced by mechanization of the different steps of the productive process (Balsadi, 2007, Souza Lima et. al., 2009). Graphic 3 shows a relationship between tractors sales (wheel and track types) and the agriculture GDP at constant prices and it is noticed that the coincidence of the two lines in the graphic, specially in 1975, 1985, 1994, 2002 and 2009 strengthen first that there is a directly proportional relation between these two variables and second the idea that when the GDP grows the tractors sale grows as well because the producers have an encouraging perspective of gains.
Graphic 3 – Wheel and track tractors sales (ANFAVEA, 2009) and agribusiness GDP and its deflator (IPEA, 2010)
The 1994 e 2002 years have some common factors that are worth mentioning such as the record in tractor sales stimulated by easy access to machines market as a function of the high exchange rate that has made possible the import of its components at lower prices and of the interest rates that were subsided by the federal government regarding machines acquisition through the Finame Agrícola and Moderfrota programs (Balsadi, 2007), which caused a higher investment capacity.
Concurrently, Graphic 4 illustrates the tractor sales and the evolution of the agriculture GDP but at variable prices showing a consistent GDP growth.
Graphic 4 - Wheel and track tractors sales (ANFAVEA, 2009) and agribusiness GDP and its deflator (IPEA, 2010)
d) Tractors Fleet.
Data concerning tractors fleet given by ANFAVEA (2009) are quite different from those estimated by IBGE, and the former are much lower than the latter. The IBGE agribusiness census (2006) represents the country but includes a limited number of establishments. It would be even plausible that the tractors fleet from IBGE would be lower than that of ANFAVEA (supposed to be of the whole country) but not higher (more than the double in 2006) as shown in Graphic 5.
Graphic 5 –Tractors fleet
The growth of IBGE tractors fleet between 1970 and 1980 is higher than that estimated by ANFAVEA but from 1980 on ANFAVEA fleet grows less than that of IBGE and decreases in the following years. There is also a difference in the first year (1970) that is not due to ANFAVEA sales statistics but the fundamental difference in the behavior of the two curves is due to the net growth of vehicles that could be explained by the vehicle wear out rate that is supposedly used in ANFAVEA estimate.
It should be noticed that the amortization time used here to reproduce the tractors fleet given by the IBGE census (42 years) seems excessive if compared with those used by several OECD countries like Holland for vehicles or machines used in agriculture that considers an average lifetime of 14 years (OCDE, 2009).
On the other hand the fleet growth of both IBGE and ANFAVEA (1970 to 1980) are in good agreement and therefore the growth of the two statistics is coherent and the difference might be explained by the use of different wear out criteria.
It was tried to determine which wear out curve might be used regarding ANFAVEA data that would permit to obtain values coherent with those of IBGE. For this purpose it was used linear depreciation with a period of 42 years (a rate of 2.38% annually and average life of 21 years) and the results are coherent with those of IBGE as well as the fleet calculated using ANFAVEA sales data and linear wearing out.
It should be noted that a wear out S-shaped (logistic) curve was first used with different coefficient values in order to change the shape of the curve and the best results were those that were very close to linear depreciation, therefore this approach was adopted due to its simplicity. The fleet results are those shown in Graphic 6.
Graphic 6 –Estimated fleet using ANFAVEA sales data and a depreciation time of 42 years.
e) Agricultural Mechanization Index
According to Ferreira Filho & Costa (1999), consumption of agriculture tractors is associated with events related to agriculture evolution such as change of cultivation composition, opening of agricultural boundaries, agricultural and/or economical policies, technology and innovative processes. Graphic 7 shows the mechanization index behavior (planted area/tractor sales) where ANFAVEA and IBGE data were used.
Graphic 7 – Agriculture mechanization index using ANFAVEA and IBGE data
The lower the mechanization index the more efficient it is because it permits to carry out operations in less time. Graphic 7 shows that using ANFAVEA data the Brazilian agriculture mechanization index was 350 hectares per tractor in 1970 and reached its lowest level in 1985 with 90 hectares per tractor. From the middle of the nineties on, while the cultivated area increased, the Brazilian tractors fleet decreased and that is why the mechanization index curve decreased in this period.
Considering the IBGE data the mechanization index in 1970 was approximately 200 hectares/tractor and it decreased (higher mechanization) year after year reaching the lowest level in 2006 with 76 hectares and still a little far from that of developed countries.
Comparatively, the Agricultural Mechanization Index in Brazil in 2003 was 171 hectares per wheel tractor while in the same period that index was 124 in Australasia, 59 in Asia, 43 in North America and 26 in Europe.
Graphic 8 – Agriculture productivity and harvested area relative to the values of the year 2000 (Source: IBGE apud IPEA, 2010).
Similarly Graphic 8 shows the productivity and harvested area evolution. The data used for this calculation as well as those relative to agriculture GDP for a given year, the deflating factors used here, among other data, are available at the virtual page of the Economy & Energy periodical (www.ecen.com).
It should be noted the proportionality regarding the increase of both factors since the 1940s until the 1960s. At the end of the sixties and during the seventies the use of modern input in the Brazilian agriculture has had a considerable growth, for example the use of fertilizers has increased from 630 thousand tons in 1969 to almost two million tons in 1974 (Nogueira, 2001).
In the years that followed the cultivated area has increase a little more relative to the product. It should be noted that in 1969, when the cultivated area curve grows more firmly there is an expressive growth of tractor sales that was approximately 10,000 in 1969 and escalated to 62,700 units in 1976, already shown in Graphic 4.
Graphic 9 – Agriculture Productivity (Source: IPEA, 2010).
All these factors had impacted both the cultivated area and the product and consequently the agricultural productivity, which is shown in Graphic 9. It gained vigor in 1980 and in the nineties it had an accelerated growth possibly due to technological innovations introduced by EMBRAPA, the acquisition of more efficient and resistant machines, besides governmental policies aiming at accelerating land productivity.
The economical result per hectare (named economical productivity in the present article) did not grow at the same pace of the physical productivity. In order to make this comparison, shown in Graphic 10, the agriculture products values calculated from the GDP share (Graphic 3) and those from the real production (Graphic 40) were used.
Graphic 10: Agriculture Productivity per hectare relative to 2000 using the agriculture products based on the GDP share and real production.
It should be noted in Graphic 10 that the physical production (tons per ha) has doubled from 1980 on but the same thing did not happen to the aggregated value per harvested ha that has remained practically constant oscillating with the annual prices that have considerably decreased relative to the other economical products, as can be seen in Graphic 11. The farmer transfers part of the productivity gain to the consumer or to the intermediary trader. This suggests that there was a large gain in the national agriculture that made it competitive which gives Brazil excellent conditions in a time when commodities prices have experienced some increase.
Graphic: 11 – Deflator of the agriculture GDP /general GDP
The ratio between the agriculture GDP and that of the deflator is shown in Graphic 11. The decrease of this ratio means that the agriculture prices have grown less relative to those of the economy as a whole. The agriculture product values from 2005 on are 40% of the values in the 1975/1985 period.
5. Capital Productivity Indexes
a) Agriculture GDP X Fleet GDP
The productivity of the agriculture sector mainly grains (cotton, rice, beans, soybean, sugarcane, etc.) is given by the land yield factor and generally this is associated with the reduction of agricultural prices (Buainain and Viera, 2009) as seen in the previous item.
Graphic 12 – Agriculture GDP and capital stock.
Graphic 12 shows the consistent increase of the tractors fleet from 1990 on and the decrease is due to the fact that the integral replacement is not made through sales. Surprisingly production grows. This can be explained by the higher efficiency of the modern tractors and other technological gains.
– Capital productivity index in Agribusiness
Considering the tractors fleet as a capital indicator, Graphic 13 shows that the product/capital ratio increases from 1990 on after a period when it remained practically constant. From this period on productivity has only grown and reached a peak in 2006. The combination of several factors (EMBRAPAS research results, governmental subsidies for machines acquisition, more efficient and resistant machines, the stability of the Brazilian currency, etc.) previously mentioned seem to justify such growth.
b) Meat Production X Bovine Herd.
According to Borges and Mezzadri (2008), 50% of the world bovine herd is concentrated in 5 countries (India, Brazil, China, United States and European Union) and Brazil is one of the largest world exporters of bovine meat.
Table 2 – Herd and slaughter rate(ABIEC, 2004).
Some aspects that directly influence the Brazilian competitive position are: technology (including technological aspects of cattle-raising, slaughter, meat processing and distribution), management, traceability, certification, environmental and sanitary questions (Buaianin and Batalha, 2007).
Table 2 shows the Brazilian bovine herd and the slaughter rate among other data. It should be noted that the three indicators under analysis (bovine herd, meat production and slaughtering rate) present a constant increase except in 1996 when there was a decrease that impacted the slaughtering rate in the following year (1997).
Graphic 14 – Herd / meat production.
Graphic 14 shows the evolution of the bovine herd (including the dairy herd) and of meat production. It is noticed that during eight years the evolution in both cases was similar maybe due to the high internal demand, higher per capita consumption and opening of new exporting markets and it is noticed a small shift in 2004 when the herd had a constant behavior and a small decrease. Concerning meat production in 2004, it continued to grow and presented stability only in 2007, therefore three years after that of the bovine population.
Graphic 15 – Capital Productivity of the bovine herd.
Graphic 15 illustrates capital productivity of meat production/bovine herd. Except for the growth from 1994 to 1996, this ratio remained approximately constant in the following eight years with small insignificant oscillations. This ratio grows from 2004 on and reaches a new plateau 17% higher than the values of the beginning of the decade. This suggests that the different techniques for increasing productivity has been largely used such as genetic improvement of the herd, other forms of calf manipulation that have impact. It should be remembered that agribusiness still uses quite primary practices such as intense raising.
Non-coordination and diversity are intrinsic characteristics of the bovine meat production chain. For Moizés et. al. (2010) non-coordination is due to the fragility of the relationship among the different chain agents (cattleman, freezing storage, wholesale and retail dealers and consumers) and the diversity has as result the offer of products without a defined minimum standard and without visible quality to the consumer.
For Bankuti (1999) the main problems that immobilize the programs (such as the precocious calf) aiming at improving productivity are: advanced age for slaughtering, lack of typical animal and carcass classification, clandestine slaughtering, low integration between freezing storage and producer and idle capacity of the industrial park (over-dimensioned with high operational cost). This is a complementary indication that it is possible to have capital productivity gains in this industry.
Graphic 16 – Capital productivity of fleet and bovine meat.
Graphic 16 shows that the capital productivity indexes relative to tractors fleet (agriculture product/ tractor fleet value) and the specific one for meat production (meat production/bovine herd) show a steady growth while agriculture productivity is practically stable. All these circumstances explain certain inertia in the sector and this turns difficult the fast dissemination of innovations.
The objective of the present article is to present two indexes, agriculture GDP/tractors fleet and meat production/bovine herd in Brazil in order to measure capital productivity in the Brazilian agribusiness. The first index reveals a relationship between the agriculture GDP and the tractors fleet indicating a probable qualitative change of the used equipment (larger and with higher productivity). The fleet data based on the IBGE agriculture census and those based on ANFAVEA tractors sales were harmonized.
It elucidates as well that during approximately 10 years (1980-1990) capital productivity has remained constant with some downward oscillations but it reacted in the beginning of the 1990s and reached a peak in 2007.
Capital productivity per hectare has reached a new plateau in the last 30 years starting with an index (product per hectare with 100-base) of 40 in 1980 and has reached an approximate index of 110 in 2009.
The meat production/herd index has shown some slight progress in the last 6 years but it has a considerable margin for progress. It seems that the fact that the productive system has different breeds and technological levels, various production systems, variable sanitary conditions and different commercialization forms have hindered the coordination of this chain (Moizés, et. al., 2010).
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