This study examines trends in growth in agricultural production in Peru for the period 1950-1995, identifying factors that affect agricultural growth and pinpointing where the constraints lie. Regional differences are examined along with production and supply. Econometric models are estimated for an aggregate agricultural production function and supply functions for three representative commodities. The overall findings indicate that public investment along with favourable expected prices and weather conditions are prerequisites for private investment, and hence growth in agricultural output.
A key feature of Peru's agricultural sector is that it accounts for a high proportion of the national workforce. The 1993 census shows the EAP (Economically Active Population) in agriculture represent 26.7 percent of the national EAP (Instituto Nacional de Estadística e Informática (INEI), 1995). On the other hand, agricultural production only accounts for some 13 percent of total GDP, with a downward trend since 1950. Hence the need to raise the yield of production factors, given that the sector is faced with the dual challenge of increasing national food supply and providing growth opportunities for agribusiness and exports.
The objective of this paper is to identify trends in agricultural growth in Peru and identify and quantify the factors enhancing or inhibiting agricultural growth. Special attention will be paid to regional differences within Peru. Section 6.2 focuses on production trends of major crops in Peru and trends in the factors affecting them. These factors are discussed in subsections and include, use of conventional inputs (land, labour and fertilizer), investments, technological change, agricultural policies, political violence and external debt. Subsections on investments address both public investments in infrastructure and private investment in machinery. Subsections on technological change look at the supply of technical information and investment in human capital that permits utilization and creates demand for technical information.
Section 6.3 examines regional differences in agriculture in response to the imposition of a structural adjustment program in Peru in 1990. The purpose of the subsections within 6.3 is to identify the characteristics of the farmers in greatest difficulty (and their coping strategies) as well as those of the farmers with relative success.
In section 6.4 models of the factors affecting agricultural production and supply are estimated using national data. The objective is to determine the relative importance of different factors on agricultural output in Peru by quantifying their contribution. This permits assessing the relative importance of various inputs, policy, investment, climate and political violence on Peru's agricultural sector. Conclusions from these sections are discussed in section 6.5
This section begins by examining the historical and regional trends in Peruvian agriculture. Subsequent parts of this section examine the major factors affecting agricultural production in Peru: land, labour and fertilizer, investments, technological change, agricultural policies, political violence and external debt. Table 6.1 gives a breakdown of Peru's agricultural production growth in terms of area and yield at the national and regional level for the period from 1950 to 1995. The regional units of analysis are relevant agricultural departments (government administrative units). Selected commodities include exportable goods such as cotton and asparagus, imported items such as rice and yellow flint maize and non-tradables such as potato and soft maize, which are both mainly produced on smallholdings in the Andean highlands.
TABLE 6.1
Production growth in terms of increased area and yield: 1950-1995
COMMODITY |
Annual Growth in Production |
Disaggregated Rates |
|||
( percent) |
Area |
Yield |
|||
Growth rate |
( percent) |
Growth rate |
( percent) |
||
POTATO |
|||||
National |
0.41 |
-0.95 |
0.00 |
1.36 |
100.00 |
Ancash |
-1.95 |
-1.65 |
84.62 |
-0.30 |
15.38 |
Cajamarca |
2.05 |
0.52 |
25.37 |
1.53 |
74.63 |
Cusco |
-0.56 |
-1.52 |
0.00 |
0.96 |
100.00 |
Huanuco |
3.13 |
1.68 |
53.67 |
1.45 |
46.33 |
Junin |
0.46 |
-1.38 |
0.00 |
1.84 |
100.00 |
La Libertad |
1.60 |
0.33 |
20.63 |
1.27 |
79.38 |
Lima |
0.62 |
-1.50 |
0.00 |
2.12 |
100.00 |
Puno |
-0.17 |
-2.12 |
0.00 |
1.95 |
100.00 |
COTTON |
|||||
National |
-2.66 |
-3.30 |
0.00 |
0.64 |
100.00 |
Ancash |
-1.33 |
-2.98 |
0.00 |
1.65 |
100.00 |
Arequipa |
-5.10 |
-6.35 |
0.00 |
0.42 |
100.00 |
Ica |
-1.81 |
-2.23 |
0.00 |
0.42 |
100.00 |
Piura |
-1.22 |
-1.55 |
0.00 |
0.33 |
100.00 |
Lima |
-2.75 |
-5.25 |
0.00 |
2.50 |
100.00 |
YELLOW FLINT MAIZE |
|||||
National |
1.34 |
0.82 |
66.67 |
0.52 |
33.33 |
Lambayeque |
4.94 |
0.62 |
12.55 |
4.32 |
87.45 |
La Libertad |
2.83 |
0.98 |
34.63 |
1.85 |
65.37 |
Ancash |
0.39 |
-0.53 |
0.00 |
0.92 |
100.00 |
Lima |
0.01 |
-0.82 |
0.00 |
0.83 |
100.00 |
San Martin |
10.86 |
5.30 |
48.80 |
5.56 |
51.20 |
Piura |
5.62 |
3.26 |
58.01 |
2.36 |
41.99 |
SOFT MAIZE |
|||||
National |
1.32 |
0.52 |
39.33 |
0.80 |
60.67 |
Ancash |
-0.45 |
0.35 |
100.00 |
-0.80 |
0.00 |
Apurimac |
2.65 |
-0.15 |
0.00 |
2.80 |
100.00 |
Cajamarca |
4.44 |
0.77 |
17.23 |
3.68 |
82.77 |
Cusco |
-0.38 |
0.32 |
100.00 |
-0.70 |
0.00 |
Junin |
0.27 |
-0.33 |
0.00 |
0.60 |
100.00 |
Piura |
4.39 |
0.99 |
22.46 |
3.40 |
77.54 |
RICE |
|||||
National |
5.24 |
3.12 |
59.54 |
2.12 |
40.46 |
Arequipa |
6.61 |
4.25 |
64.30 |
2.36 |
35.70 |
Cajamarca |
2.88 |
2.45 |
85.07 |
0.43 |
14.93 |
La Libertad |
0.70 |
0.22 |
31.43 |
0.48 |
68.57 |
Lambayeque |
2.04 |
0.62 |
30.39 |
1.42 |
69.61 |
Piura |
4.74 |
3.88 |
81.86 |
0.86 |
18.14 |
ASPARAGUS* |
|||||
National |
1.26 |
0.01 |
0.63 |
1.26 |
99.37 |
Ica |
9.05 |
3.06 |
33.85 |
5.99 |
66.15 |
La Libertad |
11.30 |
-0.64 |
0.00 |
11.93 |
100.00 |
Source: MAG, 1992 and 1994; MAG Estadistica Agraria Mensual, selected years.
* The data for asparagus date from 1966.
Note: By definition Q = A*Y, where: Q = Total production; A = Harvested area; Y = Yield. The sum of the growth rates A and Y equals the growth rate for Q. The following equations were estimated to determine the growth rates for A and Y: LnA = a + b (time) + Dummy and LnY = c + d (time) + Dummy. Thus, Q = b + d. The dummy variable covers the effects of climate in the years 1957-1958, 1991-1992 and 1993. The regressions were corrected for autocorrelation problems.
From Table 6.1 it is evident that national potato production increased exclusively due to improvements in yield. For cotton, output decreased at the annual rate of 2.7 percent driven by reductions in the area sown, despite gains in yield. For yellow flint maize, production increases are due primarily to increases in area, but yield increases are also strong. A similar pattern is found for rice. Increases in soft maize production are largely due to increases in yield, but also to hectares sown. Growth in asparagus production was overwhelmingly due to increased yield.
These results are at the national level, but it is also important to look at regional differences shown by the departmental data. The regional yield situation for 1950-1995 shows varied performance, with no quantum leaps in production to indicate widespread technological innovation.
The vast majority of farmers have medium and smallholdings but control a small portion of cultivated land in Peru. The agrarian reform of 1969 abolished large estates (latifundio) and the subsequent restructuring of the coastal cooperatives and Sociedades Agrícolas de Interés Social (SAIS) in the sierra in the 1980s produced a tenure structure which has increased the area held by small producers. Table 6.2 indicates that in 1972, two percent of farmers had holdings of more than fifty hectares but controlled 79 percent of the total area. By 1994 three percent of farmers had access to 77 percent of the land area. Agricultural censuses for 1972 and 1994 show an increase in agricultural land area of 51 percent.
In a recent study, Zegarra (1999) used data from the 1961, 1972 and 1994 agricultural censuses to examine changes in the concentration of land. Using non-standardized land data in hectares, he calculated a Gini coefficient of 0.94 in 1961. A small reduction in concentration to 0.88 in 1972 was the result of the first phase of agrarian land reform. By 1994, the coefficient reached 0.51, the result of the land redistribution process. Standardizing the data according to access to irrigation, the Gini coefficients are 0.57 in 1972 and 0.25 in 1994. The values indicate a pattern of improving equality in the distribution of land.
TABLE 6.2
Agrarian Structure in Peru: 1972 and1994
Census |
1972 |
1994 |
||
No |
percent |
No |
percent |
|
Total Farmers |
1 390 238 |
100 |
1 745 773 |
100 |
Total area |
23 545 056 |
100 |
35 637 808 |
100 |
Less than 0.5 ha |
336 695 |
24 |
213 069 |
12.2 |
0.5 to 4.9 ha |
747 030 |
54 |
1 015 273 |
58.2 |
5 to 9.9 ha |
153 141 |
11 |
246 183 |
14.1 |
10 to 19.9 ha |
78 699 |
5.6 |
135.684 |
7.8 |
20 to 49.9 ha |
46 648 |
3.4 |
83 916 |
4.8 |
50 and more ha |
28 025 |
2 |
51 648 |
2.9 |
Source: Second National Agricultural Census of 4 to 19 September 1972.
Final national results, page 2. INEI.
Third National Agricultural Census, 1994. Page 46.
Compiled by the author.
The smallest holdings (minifundio) predominate in the coastal, highland (sierra) and tropical rainforest (selva) regions. The situation is compounded by the fact that parcels of land tend to be subdivided because of population pressure, restricting the possibility of generating sufficient income to maintain a rural household and making it difficult to innovate or adopt technology. Amat and Leon (1996) offer the following conclusion regarding land availability:
"The cultivated area equals approximately 49 percent of potential cropland of 7.6 million hectares. The arable land reserve of 4.4 million hectares is located mainly in the selva and coastal areas (18 and 82 percent, respectively). The situation in the highlands is radically different, as land is overcropped and overgrazed, i.e. unsuitable land is cultivated and even protected areas are grazed. Overuse is estimated at 500 000 hectares in the case of cropping and 2.6 million hectares in that of grazing" (Amat and Leon, 1996: 45-46).
Regarding changes in employment, the average annual rate of growth of the agricultural economically active people (EAP) was 0.81 percent for 1970-1995 (INEI, selected years). However, the agricultural EAP as a proportion of national EAP dropped from 59 percent in 1950 to 26.7 percent in 1993. The 1993 census indicates an increasing importance of trade and service activities (Instituto Cuanto,1994).
Fertilizer consumption (nitrogen and phosphorus) for 1945-1969 rose by an average 11 percent per year until the mid-1950s, then fell, rose and fell again in the late 1960s. Hopkins (1981) attributes these trends to the fall in the supply of island guano from the mid-1950s when demand had to be met through fertilizer imports. This meant the supply of fertilizer depended on the availability of hard currency, international prices and exchange rates. High production costs of the newly established local fertilizer industry coupled with import tariffs, caused price increases. Another important aspect is the increase in fertilizer use according to the size and location of land holdings. The 1972 Agricultural Census revealed regional differences in this regard:
"In 1972, only one of every six holdings in the sierra used chemical fertilizer and/or island guano; the situation in the selva was even worse (one in sixteen). The coastal area continued to be the main user of this type of fertilizer, accounting for over 84 percent of nitrogen, phosphorus and potassium consumption." (Villagarcía 1974; cited by Figueroa 1975:127). (Hopkins 1981:105).
The average annual rates of growth of fertilizer consumption have been estimated for 1970-1995 with a semi-logarithmic function and the FAO database. Although there is an aggregation problem when totalling the consumption of a variety of fertilizers, the results do portray a cyclical consumption pattern. An expansion phase from 1970 to 1980, with an annual consumption increase of 3.7 percent. This is followed by a decline of 11 percent between 1980 and 1985, an annual increase of 35 percent from 1985 to 1988, a drop of 33 percent from 1988 to 1991 and, finally, an annual expansion of 26 percent from 1991 to 1994. From 1980 to 1989, the coast accounted for 73.1 percent of sales, followed by the highlands with 24.2 percent and the rainforest with 2.7 percent (Velazco, Velazco and Sulen, 1990). Given the demand for fertilizer is highest on the coast where cash crops dominate, the fall in real prices of agricultural goods and availability of foreign exchange are probably the reasons for this particular pattern of fertilizer consumption.
Figures for 1994 indicate that 74.6 percent of coastal holdings applied chemical fertilizer (accounting for 82.3 percent of the region's agricultural land area) and that fertilizer use rose in proportion to size of holding. It should be noted, however, that fertilizer use on the coast is generally quite high on holdings of less than three hectares (74 percent) when compared to holdings of 50 hectares or more (90.8 percent). This contrasts with the highlands where only 39.6 percent of holdings use chemical fertilizer (covering 40.7 percent of the region's area). In terms of size, 41.1 percent of the area in holdings of under three hectares use chemical fertilizer against 41.3 percent of the area in holdings of 50 hectares or more. Clearly fertilizer use is low in the highlands. It is even lower in the rainforest where 11.3 percent of holdings, covering 14.7 percent of the area, use chemical fertilizer,. Farms under three hectares use fertilizer on only 11.5 percent of their area, while farms of 50 hectares or more use fertilizer on 16.2 percent of their area (Third National Agricultural Census, 1996).
This section compares the changes in macro-economic indicators, such as rate of total GDP growth and level of inflation, with components of agricultural investment. It begins by outlining economic performance for 1950 to 1995. From 1950 to 1975 the economy experienced growth (2.57 percent per year). This was followed by a recessionary phase from 1976 to the early 1990s during which GDP fell by 1.59 percent per year (Gonzales, 1996). GDP climbed from 1992 to 1996. Availability of information on the composition of agricultural investment restricts the analysis to 1970 to 1995, more precisely to the expansionary phases of 1970 to 1975 and 1993 to 1995 and to the recessionary phase of 1976 to 1992. A number of conclusions can be drawn from Table 6.3.
The economy as a whole grew faster during 1970 to 1975 than the agricultural sector. The onset of Peru's agricultural crisis can be dated from the 1970s when the rate of production growth was lower than the rate of population growth (Hopkins, 1981). The resulting deficit was covered by higher food imports. However, agriculture grew faster than the economy from 1976 to 1995.
TABLE 6.3
Total GDP, Agricultural GDP, Inflation and Investment in Agriculture for Peru
(Average Annual Rates of Growth)
Variable |
1970-75 |
1976-92 |
1993-95 |
1970-95 |
GDP |
5.02 |
0.27 |
8.76 |
2.24 |
GDP Agriculture |
0.73 |
0.83 |
9.20 |
1.82 |
Inflation |
13.78 |
785.00 |
21.70 |
534.00 |
Agricultural Investment |
29.50 |
(4.30) |
(1.60) |
2.80 |
Ag. Public Investment |
61.60 |
(2.40) |
0.80 |
10.80 |
Ag. Private Investmemt |
27.00 |
(4.60) |
(3.00) |
1.90 |
Machinery & equipment |
25.90 |
1.20 |
(2.50) |
5.70 |
Land Improvements |
73.00 |
(3.00) |
(2.00) |
12.30 |
Livestock & Plantations |
28.00 |
(4.30) |
(0.70) |
2.60 |
Note: The bracketed values are negative growth rates
All variables are expressed in soles at constant 1979 value.
Source: "Peru: Compendio Estadístico". 1990-92, 1995-96. Lima: INEI, "Los Ciclos Económicos en el Perú: 1950-1995". Lima: INEI, March 1996. Compiled by the author.
Public investment in agriculture during the general economic expansion phase increased at an annual rate of 61.6 percent during 1970 to 1975, while private investment rose at 27 percent annually, its highest level during the period under study. Figure 6.1 clearly shows the changing pattern of public and private investment and agricultural GDP. Analysis of the components of agricultural investment with the highest growth spotlights land improvements, which rose by 73 percent per year.
FIGURE 6.1
Evolution of Public and Private Investment and Agricultural GDP in Peru
The recessionary phase from 1976 to 1992 was accompanied by high levels of inflation and reductions in agricultural investment. Public and private investment fell simultaneously, the former by an annual -2.4 percent and the latter by -4.6 percent. In the post-adjustment period (1993 to 1995) of revitalized domestic and agricultural growth, public investment recovered slowly (0.8 percent) while private investment remained negative.
The trends in public and private investment (Figure 6.1) suggest that the performance of private and public investment moved very much in parallel throughout the period 1970 to 1995. The Granger causality test1 supports the hypothesis that public investment explains the behaviour of private investment suggesting that private agricultural investment grows in response to a prior increase in public agricultural investment. Thus, there would appear to be a complementary relationship between public and private investment for the agricultural sector for the period 1970 to 1995. If this is the case, the recovery of public investment during the post-adjustment period should stimulate private investment in the coming years.
This contrasts with the findings of Gonzalez (1996) for the economy as a whole and the supposed relationship between public and private investment for 1950 to 1995. The author finds that at times public and private investment are complementary, but that there was crowding-out during the recessionary phase (1976-1993) driven by the impact of the external debt on savings and public investment.
Regarding the relative proportions of agricultural investment, private investment for 1970 to 1992 was greater, accounting for 75 percent of the total against 25 percent from public investment (INEI, selected years). The private sector accounted for 70 percent of the purchase of machinery and equipment during 1970 to 1979, but public investment increased its share to over 50 percent in the following period. Investment in land improvement, on the other hand, was overwhelmingly government-initiated (94 percent). In contrast, private investment in permanent plantations and livestock was 98 percent of the total. These figures suggest a degree of specialization of agricultural investment which has remained relatively unchanged since 1970 (INEI, selected years).
Comparison of investments in terms of domestically produced or imported items indicates that imports (including transportation equipment, machinery, agricultural and industrial equipment and other capital goods) accounted for 31 percent of the total for 1970 to 1983 but only 15 percent for 1984 to 1995 (INEI, selected years).
Public Investment in Agricultural Infrastructure
Various authors have investigated the importance of public investment in infrastructure on agricultural production. Dutt (1991) uses a two-sector model (agriculture and industry) to demonstrate that "agricultural growth is constrained by agricultural infrastructure, which in turn is constrained by public expenditure. Given that the main source of tax revenue for governments in less developed economies is the industrial sector, faster industrial growth - due to a more equal distribution in industry or agriculture - will increase government tax revenues, increase public investment in agricultural infrastructures and remove the agrarian barrier" (Op cit.:344). With regard to agriculture in India, Kenya and Sudan, Von Oppen, Njehia and Tifijaimi (1997) show that improvement in market access and sites for producers has a positive impact on the efficient allocation of resources on small, medium and large holdings, thereby increasing overall productivity. Barro (1990) proposes a model that ties growth to fiscal variables.
Public investment in Peru has targeted the agricultural sector since the beginning of the century. This is because the mainstay of economic growth was export-based agriculture in the coastal region. The primary concern was to make sure that this region received sufficient water, thus removing a constraint to expanding the area under cultivation. Thus, public investment in irrigation in 1905 accounted for 8.7 percent of the total, reaching 18.62 percent in 1912, a trend that continued in the 1920s. This investment was financed by fiscal revenue from a progressive income tax and taxation on mineral export earnings. Another important source was the policy of external debt (Portocarrero, Beltrán and Zimmerman, 1988).
In general, the largest share of public investment in agriculture was directed towards irrigation in the coastal region. Between 1950 and 1980, 90 percent of irrigation investment was directed to the coastal region, the rest to the highlands. Some 76 percent of investment in the coastal region between 1978 and 1982 was concentrated in three major irrigation projects at Majes, Chira-Piura and Tinajones (Guerra, 1986). This trend was maintained in the 1990s.
TABLE 6.4
Allocation of Public Investment in Agriculture
Year |
Technology transfer and agricultural development |
Agro-industry and marketing |
Expansion of cultivated area |
Forestry/ wildlife |
Public investment |
1971 |
9.6 |
1.6 |
78.2 |
2.2 |
4.8 |
1972 |
18.0 |
4.2 |
70.4 |
0.2 |
7.9 |
1973 |
8.6 |
2.2 |
76.4 |
1.9 |
15.3 |
1974 |
13.9 |
4.4 |
77.5 |
1.6 |
20.9 |
1975 |
3.9 |
0.3 |
93.9 |
0.9 |
41.6 |
1976 |
5.7 |
3.1 |
87.6 |
1.5 |
47.5 |
1977 |
4.5 |
3.0 |
90.6 |
1.1 |
33.1 |
1978 |
7.7 |
4.3 |
85.7 |
1.4 |
31.0 |
1979 |
8.1 |
2.9 |
86.6 |
1.5 |
22.9 |
1980 |
2.9 |
7.7 |
73.6 |
2.1 |
26.5 |
1981 |
3.8 |
1.3 |
80.8 |
1.2 |
36.6 |
1982 |
6.2 |
7.6 |
62.1 |
1.3 |
39.0 |
1983 |
5.9 |
2.8 |
68.5 |
0.5 |
36.7 |
1984 |
5.0 |
3.9 |
62.8 |
0.8 |
32.5 |
1985 |
5.9 |
6.1 |
42.5 |
0.5 |
30.2 |
1986 |
10.2 |
6.7 |
58.1 |
0.8 |
25.9 |
1987 |
6.8 |
7.5 |
67.0 |
0.5 |
31.9 |
1988 |
1.4 |
3.6 |
74.4 |
0.3 |
32.1 |
1989 |
0.6 |
1.3 |
90.0 |
0.2 |
25.9 |
1990 |
2.6 |
3.6 |
85.4 |
0.3 |
25.5 |
1991 |
3.6 |
5.8 |
80.3 |
0.5 |
25.3 |
1992 |
6.9 |
8.2 |
71.2 |
0.8 |
23.9 |
1993 |
6.1 |
3.1 |
75.1 |
1.1 |
24.7 |
1994 |
5.7 |
0.5 |
76.7 |
1.2 |
23.2 |
1995 |
6.8 |
0.2 |
74.3 |
1.0 |
26.0 |
1996 |
9.2 |
0.3 |
56.9 |
2.3 |
25.0 |
Sources: 1971-1987: IICA, 1990, page 53.
1988-1989: Values for these years have been estimated on the basis of a linear trend.
1992-1996: MAG. Reports of the Office of Agricultural Investment, Ministry of Agriculture, mimeo.
Public agricultural investment has been a key policy instrument employed to promote the expansion of area under cultivation, to establish and maintain agricultural infrastructure and to pursue programs of technology transfer and agricultural development (Table 6.4). Analysis of the allocation of public investment from 1971 to 1995 indicates that on average 75.8 percent of funds were used to expand area under cultivation and that investment in technology transfer and agricultural development was limited and piecemeal (IICA, 1990; and MAG, 1992).
When examining central government expenditure in agriculture as a percentage of GDP, Gonzales (1996) observed a proportional decline in 1980-1991, before structural adjustment. Gonzales notes that government investment in the rural sector fell by two-thirds over this period, compromising its ability to carry out basic functions. A similar trend was noted for social expenditure and current expenditure on infrastructure, energy, transport and communications, housing and construction and multi-sector programmes.
Priority in road infrastructure has been given to repair, with 1 101 kilometres repaved as of May 1994. This reflects the stronger focus on transport and communications by the central government, rising from 11 percent of infrastructure investment in 1991 to 29 percent for 1992-1995 (Webb and Fernandez Baca, 1996). Such investment is important as "it enables agricultural inputs and production to be mobilized more cheaply and in less time, bringing new regions and populations into the market network and creating conditions for greater investment in the agricultural sector" (Vásquez, 1995:85).
The present government continues to invest in large public works in the coastal region, although more recent infrastructure investment focuses on smaller farmers. The budget report for 1992 to 1995 indicates a beneficiary population of over 130 000 households and shows that 54 percent of expenditure was for construction or renewal of small irrigation channels (Alfaro, Monge and Figueroa, 1997).
Capital Investment in Tractors and Agricultural Machinery
Alvarez (1974), Maletta and Foronda (1980) and Hopkins (1981) review the status of investment in tractors and agricultural machinery in Peru since the 1940s. In 1961, the distribution of tractors by region was: 80 percent in the coastal region, 17 percent in the highlands and three percent in the rainforest (Alvarez, 1974). These proportions were much the same in 1972, when 73 percent of tractors were in the coastal departments of Ica, Piura, Lima and Callao, Lambayeque and La Libertad. National tractor distribution by crop was: 35 percent for cotton production, nine percent for sugar cane, seven percent for rice and 17 percent for cereals (Alvarez, 1974). This corresponds to coastal agriculture production patterns. Hopkins' concludes from this:
"In 1972, mechanization and semi-mechanization continued to be very limited, only existing on any significant scale in the coastal region. In the highlands, some 97 percent of agricultural households function exclusively with animal and human energy and the percentage is even higher in the rainforest. There are four basic reasons for this:
(Hopkins 1981:111)."
The 1994 Agricultural Census corroborates the sparse distribution of machinery ownership. It reveals that, nationally, 0.8 percent of holdings, accounting for 2.5 percent of cultivated area, had cultivators. Further, 0.6 percent had a wheeled tractor accounting for 5.6 percent of cultivated area; one percent had an engine-driven sprayer accounting for 4.5 percent of cultivated area; and 1.6 percent had a vehicle for transportation accounting for 7.2 percent of cultivated area.
The 1994 Census also provides information on ownership of traditional farm implements. It showed that 41 percent of farmers did not have ploughs, four percent had an animal-powered iron plough, 32 percent had an animal-powered wood plough and 22 percent had a human-powered wood plough (a chaquitaclla). Respectively, these represent 68, three, 12 and ten percent of cultivated area. A mere 17 percent of farmers had a manual crop sprayer, accounting for 18 percent of cultivated area. Table 6.5 compares farmer technical profiles in the two census years of 1972 and 1994. The conclusion is that in spite of the increase in use of fuel-based energy (from 2.1 to 4.3 percent of holdings), electrical power (from 0.2 to 0.6 percent), ploughs (from 46.6 to 58.9 percent) and tractors (from 5.4 to 15.9 percent), most farmers still use traditional farm implements.
With regard to innovation of selected commodities, considerable progress has been made in the research and generation of improved varieties. The Agricultural University of La Molina (UNALM), the National Institute of Agricultural Research (INIA) and the International Potato Centre (CIP) are all actively engaged in improving existing technologies and adapting them to national agricultural conditions (Table 6.6). However, there are still important technological bottlenecks. One problem restricting innovation potential is inadequate government spending on research and technology transfer (Table 6.4).
TABLE 6.5
Technical Profile of Agricultural Holdings in Peru
TECHNICAL CHARACTERISTICS |
YEAR: 1972 |
YEAR: 1994 |
||
number |
percent |
number |
Percent |
|
Total Agricultural Holdings |
1 390 877 |
100.00 |
1 755 180 |
100.00 |
1. Sources of energy |
||||
a) Only human |
387 368 |
27.85 |
542 817 |
30.93 |
b) Only animal |
631 646 |
45.41 |
926 755 |
52.80 |
c) Only engine |
29 653 |
2.13 |
76 009 |
4.33 |
d) Engine and animal |
55 969 |
4.02 |
199 803 |
11.38 |
2. Use of electrical energy |
2 609 |
0.19 |
9 796 |
0.56 |
3. Use of tractors |
75 278 |
5.41 |
279 667 |
15.93 |
a) Ownership of tractors |
5 265 |
0.38 |
9 406 |
0.54 |
b) Hiring of tractors |
1 095 674** |
78.76 |
- |
- |
c) No tractor use |
1 018 201 |
73.21 |
1 475 513* |
84.07 |
4. Use of ploughs |
648 323 |
46.61 |
1 032 891 |
58.85 |
a) Owned |
654 995 |
47.09 |
- |
- |
b) Rented |
447 696** |
32.19 |
- |
- |
c) Not used |
456 499 |
32.82 |
725 603* |
41.34 |
5. Use of seeds |
||||
a) Bought |
264 956 |
19.05 |
- |
- |
b) Improved seeds and/or seedlings |
- |
- |
291 407 |
16.60 |
6. Use of fertilizer and/or island guano |
210 495 |
15.13 |
- |
- |
a) Chemical fertilizer |
- |
- |
662 678 |
37.76 |
b) Bio-manure |
- |
- |
959 573 |
54.67 |
7. Use of pesticides |
||||
a) Insecticides |
- |
- |
706 742 |
40.27 |
b) Herbicides |
- |
- |
182 262 |
10.38 |
c) Fungicides |
- |
- |
400 821 |
22.84 |
8. Use of credit |
74 935 |
5.39 |
269 287 |
15.34 |
9. Technical assistance |
47 465 |
3.41 |
163 739 |
9.33 |
10. Irrigation system |
||||
a) Only river |
245 114 |
17.62 |
416 164 |
23.71 |
b) Groundwater/well |
12 739 |
0.92 |
18 584 |
1.06 |
c) Other sources |
146 591 |
10.54 |
292 893 |
16.69 |
11. Irrigated holdings |
419 862 |
30.19 |
792 543 |
45.15 |
Source: INEI - Second National Agricultural Census 1972 (Tables 28,29,31,32,33,34,35 and 36)
INEI - Preliminary results of the Third National Agricultural Census 1994.
(Tables 36,41,47,51,53,54,75 and 76).
The percentages refer to each item for all of the agricultural holdings.
* "do not have".
** "do not own tractors".
Referring back to Table 6.1 on yield and area, these findings suggest that while yield increases have been important in augmenting production of most crops, there is much unexploited potential for research to increase yields further. The accumulation of expertise in the agricultural research centres has not reached the majority of farmers. Clearly, the system of disseminating information and research results is one of the culprits in the lacklustre increase in yields and production.
TABLE 6.6
Comparison of Yields (kilograms per hectare)
Product |
Experimental Plot |
National Average |
Rice |
9 010 |
3 170 |
Soft maize |
4 200 |
1 140 |
Yellow maize |
6 500 |
2 900 |
Potato |
47 000 |
8 600 |
Raw cotton |
3 737 |
1 910 |
Source: Torero (1992:375, Table 2)
There has been limited progress in mechanization between 1972 and 1994, especially in the highlands and rainforest (Table 6.5). Much of the farmer-owned equipment is traditional. The situation is compounded by inadequate infrastructure and a poor quality irrigation system.
Given that only one in two farmers in the coastal region and one in four farmers in the highlands are using modern inputs, this suggests wide scope for the transfer of new technology. Further, few farmers are using fertilizer and insecticide in sufficient quantities. Case studies by Barrera and Robles (1994) and Gallardo (1994) show that farmers stopped purchases because of financial problems. Reverting to traditional practices has negative consequences on yield and income.
The pattern of access to technology is influenced by many factors, but chief among them is the availability of credit. A general idea of the impact of technical assistance and credit on farmer income can be obtained from the estimates of the National Survey on Rural Households (ENAHR), which indicates the average annual farmer income, in constant July 1984 intis, for each region. According to ENAHR, agricultural holdings had higher incomes in all the regions when they had access to credit and technical assistance alone did not guarantee a better economic situation. The ideal scenario is therefore access to both technical assistance and credit. This explains the limited impact of technical assistance in situations where farmers have limited pre-season funds, which directly affect the purchase of inputs, improved seeds and machinery and technical assistance services.
For maize, a UNALM expert identified lack of money as the main bottleneck to production and increasing yield. Farmers simply do not have funds to invest in their crops, which is why they sometimes fail to purchase improved seeds or apply fertilizer in appropriate quantities. The problem is therefore economic and not necessarily technical, as farmers generally do not have money or access to credit to purchase desired inputs (Figueroa, 1988).
The Demand for Human Capital Investment: Education and Technical Assistance
The literature on the determinants of agricultural innovation emphasize socio-economic factors such as farm size, risk, human capital, availability of labour, credit and form of land tenure (Feder, Just and Zilberman, 1982; Feder, 1982; Feder and Slade, 1984). In the southern highlands, Figueroa (1986) and Cotlear (1989) identify education as a key factor behind the adoption of new technologies.
Table 6.7 provides a social and economic profile of Peru's farmer population. Between 1972 and 1994, the percentage of farmers without education decreased as farm size increased. However, the proportion of uneducated farmers still accounted for 20.4 percent of farms in 1994. That 59 percent of farmers have only a primary education can be seen as an obstacle to the delivery of extension programmes and technology transfer, which require specialized knowledge on the part of the farmer.
The problems are compounded by regional differences in climate and soils (Velazco and Beteta, 1993). In coastal areas water use is the most pressing concern, as mismanagement results in flooding, land degradation and pest infestation. By-products are soil erosion, lower productivity and ecological disequilibrium. Coastal farmers also have problems with soil fertility, sanitary control and planting for particular crops: rice in Arequipa, Lambayeque and Piura; potato in La Libertad and Arequipa; yellow flint maize in Lima, Lambayeque and La Libertad; soft maize in Arequipa; and cotton in Piura. This lack of technical expertise also affects export crops such as asparagus in La Libertad and fruit in Lima, where yields would be considerably higher if technology were adequate.
TABLE 6.7
Socio-Economic Profile of Peru's Agricultural Population
CHARACTERISTICS |
YEAR: 1972 |
YEAR: 1994 |
||
Farms |
Percent |
Farms |
Percent |
|
Total Agricultural Holdings |
1 390 877 |
100.00 |
1 742 267 |
100.00 |
I. LEVEL OF EDUCATION (Farmers) |
1 385 819 |
1 750 649 |
||
a. None |
380 756 |
27.48* |
357 187 |
20.40* |
b. Primary |
655 884 |
47.33* |
1 039 598 |
59.38* |
c. Secondary |
50 479 |
3.64* |
258 998 |
14.79* |
d. Higher |
59 195 |
4.27* |
63 231 |
3.61* |
e. Not stated |
231 505 |
17.28* |
31 635 |
1.81* |
II. LAND TENURE |
||||
a. Simple forms |
||||
a.1. Owner |
561 242 |
40.35 |
1 104 938 |
63.42 |
a.2. Tenant |
43 625 |
3.14 |
40 690 |
2.34 |
a.3. Collective |
52 180 |
3.75 |
397 397 |
22.81 |
a.4. Other |
231 224 |
16.62 |
68 067 |
3.91 |
b. Mixed forms |
||||
b.1. Over 50 percent owned |
99 811 |
7.18 |
68 263 |
3.92 |
b.2. Other |
94 557 |
6.80 |
62 912 |
3.61 |
III. LAND USE |
||||
a. Annual crops |
967 777 |
69.58 |
1 340 647 |
76.95 |
b. Permanent crops |
126 791 |
9.12 |
262 096 |
15.04 |
c. Sown pasture |
112 730 |
8.10 |
176 531 |
10.13 |
IV. CROPLAND |
1 053 531 |
75.75 |
1 671 594 |
95.94 |
a. Irrigated |
547 334 |
39.35 |
792 543 |
45.49 |
b. Rainfed |
898 886 |
64.63 |
1 174 018 |
67.38 |
V. AGRICULTURAL LABOUR |
||||
a. Remunerated |
447 813 |
32.20 |
591 785 |
33.97 |
a.1. Casual |
422 512 |
30.38 |
545 293 |
31.30 |
a.2. Permanent |
25 301 |
1.82 |
46 492 |
2.67 |
b. Not remunerated |
668 246 |
48.04 |
- |
- |
Source: INEI - Second National Agricultural Census 1972. (Tables 4, 9, 11 and 12)
INEI - Preliminary results of Third National Agricultural Census 1994.
(Tables 2, 26, 27, 29, 30, 33 and 34)
* Represents percentage of total farmers.
In the highlands, given the adverse climate, inappropriate uses of land and forest resources have caused land degradation, soil erosion and landslides. Technology needs include: better information on the density and spacing of plants, the use of improved seeds, and disease control and soil fertility. These are of particular concern for soft maize in Cuzco, Cajamarca, Ancash and Junín, kidney bean in Cuzco and Cajamarca, broadbean in Cuzco, potato in Cuzco, Ancash and Junín and fruit crops in Cuzco.
Farmers in the rainforest have to deal with irregular rainfall and increasing deforestation which undermine the ideal conditions for growing tropical products. The technical problems also concern the planting, soil fertility and disease control. They affect economically important crops such as rice in San Martín and Amazonas and yellow flint maize in San Martín.
Any action taken to improve technological practices must take local characteristics and farmer profile into account. Assessment of appropriate technologies is required if local farmers are to overcome their suspicion of technical advice based on the purchase of modern inputs, which increase costs but do not necessarily reduce risk.
Human Capital Investment: The Supply of Technical Assistance
Table 6.5 highlights the low percentage of farms receiving technical assistance: 3.41 percent in 1972, 3.6 percent for the 1983 to 1984 season2 and 9.4 percent in 1994. Although the percentage of holdings receiving technical assistance is very low, this does not necessarily indicate a lack of interest. It may be due to lack of information or economic constraints on the part of the farmer, who may need technical assistance and training but is unable to afford it, particularly in times of economic crisis. There may also be a problem of inadequate quality or insufficient quantity of services supplied.
Statistics show that most technical assistance was provided by the Ministry of Agriculture and the Agrarian Bank in 1972 and 1983-84, while universities, farmer associations, associative enterprises and independent professionals played an insignificant role. The "other" category includes non-governmental organizations and international cooperation and was relatively significant (12.5 percent). In the present context of government restructuring, reduced public expenditure and dissolution of the development bank, the relative importance of public assistance has declined. As a result, in 1994 NGOs have acquired greater importance (12.9 percent), as did independent professionals (19 percent), while the role of the Ministry of Agriculture declined (45.2 percent).
This subsection examines the role of policy on agricultural production in Peru. Special attention is devoted to pricing policy. The 1950s saw a change in pattern of economic growth in Peru and the emergence of new export sectors such as mining and fisheries. The Government encouraged a process of industrial import substitution with significant foreign investment. Macro-economic policy to promote domestic industrial growth created an environment that was hostile to agricultural development, with lower agricultural prices, profitability and dynamism. Agrarian policy under the military government of 1969-1979 had two central themes: agrarian reform and low-cost urban supply (Alvarez, 1983).
The APRA3 government that took power in 1985 viewed the agrarian problem as one of low agricultural profitability and introduced a series of measures to raise farm prices, lower costs and increase productivity. Large sums were allocated to subsidize credit and basic inputs, such as fertilizer and pesticide. The results were good until 1987 when the symptoms of failed economic management affected agricultural performance. The leading beneficiaries of this policy were the modern agricultural holdings in the coastal and rainforest regions. The coastal region accounted for 74.1 percent of fertilizer sales and subsidized credit was overwhelmingly directed towards coastal and rainforest crops such as cotton and yellow flint maize. Fertilizer prices fell in real terms until the end of 1988. The highland benefited minimally from these policies (Velazco, Velazco and Sulen 1990).
The populist policies of 1985 to 1990 increased aggregate demand and pushed up imports, triggering a fiscal deficit in 1988/89 and a balance of payments crisis that in turn caused hyperinflation and recession. To quote Gonzales (1996:25), "the impact on agriculture and smallholders was negative in this period since what had been gained in the first years of the APRA government was lost in the last two years, culminating in greater impoverishment than had existed in 1985".
The programme of stabilization introduced in the early 1990s to bring inflation under control was based on restricted control of monetary variables, readjustment of prices and public tariffs, elimination of subsidies, increased taxation, reduction of public expenditure and the free movement of exchange and interest rates. These measures were underpinned by a package of structural reforms aimed at efficient resource management through market deregulation and liberalization and a reduced entrepreneurial role of the state through privatization and the closure of monopoly concerns. The latter included PESCA PERU (a fish corporation), CPV (Compañía Peruana de Vapores), ENCI and ECASA (Empresa Comercializadora de Arroz Sociedad Anónima) (León, 1994; Mendoza, 1992; Dancourt and Mendoza, 1994).
The policy measures affecting agricultural performance included the removal of subsidies and price controls for agricultural products and inputs and their unrestricted marketing for export. Variable import tariffs were introduced to provide a degree of protection against the subsidies of the main exporting countries and exchange rate lags (Dancourt and Mendoza, 1994). The tariff surcharge in 1995 on commodities such as wheat and sugar was zero.
In the financial market, interest rates were freed and loans to the agricultural sector declined seriously following the dissolution of the Agrarian Bank of Peru (BAP). The Bank had already begun to reduce its credit operations in the 1988/89 season when it provided funding for 800 000 hectares versus 1 200 000 hectares in the previous year (Escobal, 1989). The situation was particularly acute among small farmers, where only 7.6 percent had access to BAP credit in 1984 (Portocarrero, 1987). This is indicative of the importance of the informal sources of credit and of the impact on interest rates. The credit situation worsened with the demise of the Bank and the rise in interest rates. Coastal farmers were the most affected since BAP funding had been biased towards their products. For example, from 1980 to 1988 average funding for cotton amounted to 22.6 percent of total loans, while rice accounted for 32.2 percent of allocated funds (Agrarian Bank, selected annual reports).
The restriction of credit through the BAP led to a restructuring of agricultural lending. During 1986 to 1990, 88 percent of bank credit for agriculture was from the BAP and 22 percent from the Banca Comercial, whereas in 1991 the BAP accounted for 25 percent and the Banca Comercial 75 percent. In 1992, the latter was the only bank operating in the sector. However, the sums involved were relatively small: US$92.5 million in 1992, US$131.3 million in 1993 and US$202.7 million in 1994, compared to US$ 789.7 million in 1989 (Bank and Insurance Authority, reports).
Given the limited involvement of the Banca Comercial in agriculture, the Government set up the Fondeagro (Fondo para el Desarollo Agrícola), which provided US$280 million in loans from 1992 to 1994. Village banks were also formed. Data from Corporación Financiera de Desarollo (COFIDE) and the Bank and Insurance Authority indicate US$587.6 million in agricultural lending from 1993 through November 1994, with 56.3 percent provided by the Banca Comercial, 1.4 percent by village banks and 42.3 percent by Fondeagro and revolving funds. The efficiency and effectiveness of the credit institutions set up to fill the vacuum left by the BAP have not been assessed.
The liberalization of the financial market led to the elimination of the preferential rates of interest charged by the BAP, increasing financial costs to farmers. The real quarterly lending rate rose from -34 percent in the third quarter of 1990 to 4.3 percent in the first quarter of 1991. Banca Comercial real interest rates for the same quarter in 1993 and 1994 were 4.4 percent and 5.1 percent respectively (BCR, selected issues).
In December 1995, the Government introduced the Special Taxation Program (PERT) which provided farmers, livestock producers and agribusiness with easier terms for tax payments due 31 December 1994. The Program also exempted three forms of taxation (sales tax, income tax, municipal and development tax) for farmers whose annual sales did not exceed 50 taxation units for the 1995 financial year (Arias, 1995).
At the same time, the main institutional reforms in the agricultural sector were the liberalization of the property market, water and the ending of state monopolies such as ENCI and ECASA. The Land Act of 1995 removed size limits on property and permitted the privatization of land belonging to peasant and indigenous communities. Another important reform pending is the Water Act, which would define rights of water ownership. Yet another law in the pipeline concerns natural resources (Alerta Agraria, selected issues).
The Role of Agricultural Prices in Stimulating Investment and Growth in Agriculture
State intervention in agricultural markets dates from 1939 (Alvarez 1983) when the aim was to promote the cultivation of products such as pulses and rice. Pricing policy under the military government of 1969 to 1979 was essentially geared towards reducing the cost food to urban consumers, with measures to control and regulate food prices. Figure 6.2 traces price changes of rice, potato and cotton . It indicates a slight increase in the 1960s and late 1970s, fluctuations in the early 1980s and a downward trend until the 1990s.
FIGURE 6.2
Changes in Peruvian Farmgate Price Indices Adjusted by the Consumer Price Index
The most important consequences for agriculture of the new macro-economic environment starting in 1990 have been a significant fall in production and prices of both tradable and non-tradable products. Escobal (1994) found that agricultural earnings for 1992/93 were down 61 percent from 1989-90. When identifying the effects of the Program of Stabilization and Structural Adjustment on agriculture, it is essential to take into consideration the initial conditions of the sector and the effect of events that cannot be controlled, such as natural disasters and climate change, but affect agricultural production. Historically, the agrarian crisis in Peru can be dated back to the 1970s when production increases failed to keep pace with population growth (Hopkins, 1981).
The noteworthy characteristic of this situation in the 1990s is that earnings in both tradable and non-tradable agriculture have been affected. The terms of trade deteriorated because of more expensive credit and climate change, which directly impacted production conditions. In addition, the determining factors of demand, reduced employment and income appear to have reduced the capacity to purchase agricultural products (Dancourt and Mendoza, 1994).
The impact of the macro-economic context on agricultural production can be illustrated by examining the effect on production. Production declined 5.5 percent between 1989/90 and 1990/91, 26 percent between 1990/91 and 1991/92 and 8.4 percent between 1991/92 and 1992/93 (Statistics Bulletin of the Ministry of Agriculture: selected years), along with a similar pattern in cropped area.
Peruvian society was deeply affected in the 1980s by two important factors: the economic crisis that led to recession and hyperinflation and the spread of political violence that embraced most of the country, especially the rural sector. The peasant farmer population noted the economic crisis in the changing terms of trade and in the depressed labour market with fewer work opportunities and lower wages.
These were the two largest determining factors of income generation and consumption in rural households. Another important element of the macro-economic context was the introduction of a policy of stabilization and extensive structural adjustment. The economic crisis and political violence can be seen as external events that changed the economic, social and political structure of the rural sector.
Over 30 000 people are estimated to have died during the "dirty war" waged by the Shining Path, the Túpac Amaru Revolutionary Movement, para-military groups and the armed forces. The urban areas most affected by subversive activity were, in order of importance, Ayacucho, Junín, Huancavelica, Apurímac, Puno, Cusco and Lima. One of the direct consequences of the violence was population displacement to other provinces in the department and to more distant urban areas, particularly Lima, Huancayo, Ica, Huamanga and Abancay.
Most of the migrants were rural inhabitants. Coral (1994) found that middle income and wealthier rural people tended to move to distant destinations such as Lima, Ica or Huancayo. Where the risk was not so great, families moved to intermediary towns and department capitals, provinces or rainforest areas. In contrast, poor people either remained in their communities or moved to small nearby communities.
Although the fighting occurred in the central-southern highland and in the rainforest, the political violence affected all of Peruvian society. Coronel (1997) lists the following consequences: 25 927 dead, 6 000 disappeared, 430 075 displaced, 1 600 000 directly affected and 9 000 lost complete freedom and rights.
Most acts of political violence in the departments of the central region occurred in Ayacucho, which accounted for 43 percent, followed by Junín with 34 percent. Among civilian casualties, 69 percent were peasant farmers (SEPAR, 1992:15,36). In addition, the political violence had the following negative effects: disinvestment; low productivity; destruction; loss of technology and stock; disruption of market infrastructure; material destruction of production infrastructure, services and roads; decline in public and social institutional presence; and an increase in drug trafficking. In response, new forms of organizations have emerged to deal with these problems, such as defense committees and women's associations (Coronel, 1997).
TABLE 6.8
Coefficients of Simple Correlation, 1970-1994
Variables |
Coefficient |
Private investment |
-0.56 |
Public investment |
-0.03 |
Investment in agriculture |
-0.48 |
Investment in machinery |
-0.28 |
Note: The values in brackets are the t-statistics
*= five percent significance level
Source: Webb and Fernandez Baca. 1995. Compiled by the author.
Estimates of simple correlation coefficients between private and public investment in agriculture and the number of subversive actions recorded by the National Police (Instituto Cuanto, 1995) show a negative relationship between acts of political violence and investment (Table 6.8). Figure 6.3 compares agricultural GDP in the departments most affected by the political violence (Junín, Apurímac and Ayacucho) and agricultural GDP in the country as a whole. The graph shows that political violence in rural areas adversely affected agricultural development from its outbreak in the early 1980s, leading to lower agricultural GDP in Ayacucho and Apurímac. This was followed by recovery in the early 1990s during the relatively successful process of national pacification. The GDP in Junín differed from that in the other departments because most of the food supplies for the seven million inhabitants of the city of Lima come from the valley of Mantaro in Junin and also because more severe military actions occurred in the southern highlands and the rainforest.
FIGURE 6.3
Evolution of Agricultural GDP in Peru and in Departments Affected by Political Violence
The problem now facing the areas hit by political violence is rehabilitation and reconstruction. Young returnees to a conflict-hit community of Apurímac, one of the poorest parts of the country, indicated that they needed credit and technical assistance (Rodríguez, 1997).
TABLE 6.9
Coefficients of Simple Correlation, 1970-1994
Variables |
Coefficient |
Private investment |
-0.51 |
Public investment |
0.104 |
Investment in agriculture |
-0.41 |
Investment in agriculture |
-0.24 |
N.B.: The values between brackets are the t-statistics
* = five percent significance level
Source: Webb and Fernandez Baca (1995). Lima: Instituto Cuanto S.A.
Compiled by the author
A glance at economic fluctuations during the period from 1959 to 1996 shows six recessions, five coinciding with external factors such as deterioration in the terms of trade, increase in international interest rates and/or reduced availability of external credit (Dancourt, Mendoza and Vilcapoma, 1997). The coefficients of partial correlation between investment, external debt and inflation (Table 6.9) suggest that private investment is more sensitive to changes in the macro-economic context than public investment or investment in agriculture.
However, the impact on agri-cultural investment may be indirect. Twomey's study (1989) of the external debt crisis in Latin American found that the payment of debt affected import capacity which, in turn, impacted directly on agriculture and decreased production because of the lower availability of hard currency for fertilizer imports. The author also found that restrictive monetary policy reduced the supply of credit to the agricultural sector.
The 1990s have been a period of fundamental change in agricultural performance. Implementation of the stabilization programme and structural reforms has modified the institutional context and the conditions in which agricultural producers participate in the market. The following section puts forward a number of hypotheses and explanations as to how the new economic rules at national level have affected micro-economic decisions in agriculture.
For ease of analysis, farmers have been divided into those producing for export and those supplying the domestic market. Each category is further divided between those who have performed badly and those who have adapted successfully to the new market conditions.
When examining the impact of adjustment policies on small-scale commercial agriculture which had no or limited diversification, Escobal (1994) found that the farmers of Serrán and Malacasi in the Alto Piura were seriously hurt. The medium and small commercial fruit growers of Motupe, Olmos and San Lorenzo in Piura were also hard-hit, especially since they had no access to credit. The rice farmers of Ferreñafe, Lambayeque also suffered a collapse of income despite selling part of their livestock and equipment.
A different case study by Barrera and Robles (1994) includes the results of the Organización Nacional Agraria (ONA) coastal survey of 20 farmers growing asparagus and yellow flint maize in the valley of Chicama in the coastal department of La Libertad. One important finding was that, although there was a greater supply of agricultural inputs, farmer demand for inputs fell because of lower real farm-gate prices and higher credit costs.
These trends are corroborated by the first agrarian survey conducted by the Ministry of Agriculture from 31 August to 8 September 1993 among 2 931 farmers in the valleys of Chira, San Lorenzo, Alto Piura, Medio Piura and Bajo Piura. The survey figures show that 52 percent of farmers did not use inputs in sufficient quantity and of adequate quality because of high prices. The main problems facing agriculture were lack of credit (82 percent of respondents), shortage of water (55 percent) and high input prices (42 percent) (CIPCA, 1994).
The same survey in 72 irrigated coastal areas in June through December 1993 in the departments of Tumbes, Piura, Lambayeque, La Libertad, Ancash, Lima, Ica, Arequipa, Moquegua and Tacna produced similar results. One of the principal reasons for leaving arable land uncultivated was the precarious economic situation in which small and medium farmers found themselves, a situation compounded by shortage of water and lack of access to credit.
The rice growers of Arequipa are a good example of how farmers react to difficult conditions. The large decrease in some farm-gate prices made them turn to other more profitable crops . This "crop conversion" (El Comercio, 1995) involved growing marigolds for export and planting annual crops, such as sweet potato, onion, garlic and soft maize in valleys that were clearly suited to rice-growing such as Tambo, Majes, Ocoña and Camaná.
The 1993 agrarian survey of farmers in 72 coastal valleys revealed the existence of partnerships between farmers and processors to promote a particular product. For example, rice and wheat millers provided credit for inputs on the condition that they could then purchase the farmer's harvest at pre-set prices. (SINIA, 1993). Among export crops agreements evolved between farmers and exporters who provide credit for the cultivation of marigold, asparagus, sorghum and sunflower. Further study is needed on the results of these contracts to determine whether they have been beneficial to farmers. In the case of marigolds in Aplao, Valley of Majes, yields per hectare and hence income have been lower than anticipated, because of inadequate technical assistance.
In other cases, the problems of small farmers in funding their cropping activities and engaging wage labour have given rise to new practices such as that observed in the department of Piura where labour is exchanged between relatives and/or friends (Zevallos, 1994). The immediate consequence of this practice has been to slash demand for labour and make it even harder for landless labourers to find work. The author also identified another strategy used by small landowners which was to lease their land and sharecrop. Such agreements have mainly been made with agribusiness corporations and have in effect turned farmers into wage earners on their own land. In terms of land area, this practice is most prevalent among medium-size farms. In terms of numbers of leaseholders, it is most prevalent among smallholders.
Coping Mechanisms of Peasant Farmers
The availability of off-farm activities has helped diversify sources and averted sharp declines in peasant farmer income. For farmers in the Napo River basin, in the rainforest department of Loreto, this has meant prioritizing extractive activities. In the highland department of Cusco, in the upper Urubamba valley, Cavassa, Carpio and Gómez (1992) showed that the strategy adopted by farmers was to produce seed potato instead of potato for consumption.
In addition, the impact of adjustment on the peasant farmer communities of Pomacanchis revealed that the changes in relative prices had significantly affected the pattern of demand for inputs and factors of production, as well as yields. Modern inputs and factors of production have been replaced by more traditional methods (Gallardo, 1994).
A study of four small settlements in Bambamarca, in the rural province of Cajamarca, found that the most affected were the very poor who had limited access to land and who were net market purchasers, sellers of handicrafts and livestock producers (Velazco and Caballero, 1996). The study concluded that the most effective peasant strategy to mitigate the impact of the structural adjustment programme was to diversify, increase autoconsumption and seek wage labour. Families who did not diversify their agricultural production were more affected by structural adjustment than those producing a range of items. Increasing autoconsumption and decreasing purchase of market goods gave families the material means to absorb the adjustment. Thus, the decline in farm prices and income was accompanied by reduced demand for purchased goods such as rice, oil, detergents and salt. Finally families reduced their reliance on cash crops and increased off-farm income through petty trade and urban wage labour.
The more successful farmers were involved in the non-traditional agricultural export sector. Production organization and linkage with agribusiness facilitated this success. Those more directly involved in agricultural exports have been coastal farmers, while the sierra and rainforest farmers have been largely bypassed. The change in composition of agricultural exports indicates that the share of traditional exports fell from 97 percent in 1970 to 34 percent in 1993. The primary non-traditional exports are asparagus, fruit, pulses, canned goods, olives and flowers. Escobal (1994) discovered that medium and large commercial producers of green asparagus in Chincha posted good production performance thanks to access to funding and guaranteed demand that enabled them to maintain and raise their incomes. The case of asparagus is particularly important, as its share of non-traditional agricultural exports rose from 7.8 percent in 1980, 15 percent in 1985, 35.3 percent in 1990, to 47 percent in 1992. The average annual rate of growth of asparagus exports for the period 1980-1993 was 24 percent and the annual rate of growth of cropped area for 1970-1993 was 19.7 percent (Compendium of Agricultural Statistics, 1994). A 1993 survey by the Ministry of Agriculture indicates that asparagus is cultivated on 7.1 percent of cropland in La Libertad and 3.2 percent of Ica.
The expansion of coastal agricultural exports has not been without difficulties in adapting to the new economic context. Fieldwork by Marañon (1994) in the valleys of Chao and Virú in the coastal department of La Libertad indicated that large holdings employed modern technology and tended to specialize. In contrast, small farmers were faced with constraints to innovation. For example, a small export concern in Piura shipping frozen green pigeon peas to the United States drew up production agreements in 1992 with small farmers to meet the demands of the international contractor. However, getting these farmers to adopt new cropping practices was not easy. While production costs and water usage are lower than for traditional crops, farmers lacked commercial experience and technical knowledge. This was compounded by lack of technical assistance, funding and faith in the market for the product (Olaechea and San Miguel, 1993).
Asparagus operators had similar problems when they started to promote activities in the valleys of Chira and Cieneguillo. The processors found that they needed to provide much more than seed. Technical assistance with harvest and post-harvest handling proved to be the lynchpin to maintain quality and prices (Olaechea and San Miguel, 1993).
Flower exporters belonging to the Exporters Association (ADEX) Committee on Flora and Fauna showed that the surface area planted in flowers could be increased from 200 to 400 hectares with a positive impact on foreign exchange earnings and absorption of human resources, currently employing 2 500 families. They sought a reduction in airfreight charges and called for the producers, exporters and the State to collaborate in research and access to the latest technology. Rather than subsidies, they asked the State for macro-economic policies that promote exports (El Comercio, 1995).
The Agricultural Census of 1994 identified the main restrictions to farming. Over 16 percent of farmers reporting uncultivated land. The reasons were: 74 percent lacked water, 61 percent lacked seed, 49 percent lacked credit, 31 percent lacked labour, ten percent had problems with salinity, erosion or poor drainage and four percent were obstructed by terrorism. The primary reason for lack of water is the absence of infrastructure. To counteract some of these problems, investment by the Compensation and Social Development Fund (FONCODES) in resource-poor rural areas includes small irrigation schemes, as well as forestation and reforestation programmes, soil rehabilitation and marketing infrastructure. 4.
A second group of reasons for failure to cultivate land includes farmers' economic status and macroeconomic policies with adverse impact on agriculture. Limited access to credit or poor returns make it difficult to acquire seed and fertilizer, make farm improvements, purchase machinery, accede to modern technology packages and technical services, or hire wage labour.
A potential problem among the more successful farmers was the relative concentration of processors. From 1986 to 1994 the number of mango export companies fell from 47 to six (Gómez 1995). Marañon (1991) found high levels of processor concentration for exported canned asparagus in Trujillo, Huaral and Lima, maracuya juice in Chiclayo, Chanchamayo and Piura and flowers in Callejón de Huaylas. For example, four companies accounted for 80.3 percent of the canned asparagus. One company accounted for almost half of the export market of marcuya juice, while four companies processed 88.4 percent. Market concentration was even higher among flower exporters; one company accounted for over three-quarters of total exports. The author attributed their position to membership in regional or national entrepreneurial groups investing in agribusiness, which provided funding and technology transfer.
To quantify the relative importance of such factors as land, capital and labour discussed in section 6.2, models of agricultural production and supply are estimated. Using cointegration techniques, a model of aggregate agricultural production is estimated. Since potato, rice and cotton are major crops, aggregate supply functions are also estimated.
The following Cobb-Douglas production function was estimated for Peruvian crops.
Ln(Yt ) = ao + a1 Ln (Tt ) + a2 Ln(Lt )+ a3 Ln(Ft ) + a4 Ln(Mt )
where Y is value of agricultural GDP at constant 1979 soles, T is cropped area in hectares, L is labour measured as the economically active population involved in agriculture, F is aggregate consumption of a range of fertilizers and M is the stock of machinery measured by the number of tractors, with observations by time, t. The coefficients ai (i=1,..4) are the elasticities of the respective variables with respect to agricultural production, with the assumption that a i > 0. Translog production functions were estimated but multicollinearity hampered estimation of the coefficients of regression.
FAO data wereused for all variables. Although there are informational shortcomings in, for example, the summation of fertilizer variables, the calculation of agricultural EAP and the failure to allow for machinery depreciation, the estimated functions do give an indication of the relative importance of these factors in agricultural production.
Given this is time series data, stationarity tests were conducted and the production function was estimated using the cointegration method suggested by Johansen (1988) for the period 1970-1995. Under this methodology, two or more variables can be considered to be in long-term equilibrium if they move closely and in parallel over time, even though they may momentarily move in opposite directions. For this reason, the technique is particularly useful to identify the determining factors of output. The long-term relationship is known as the cointegration vector.
An augmented Dickey-Fuller (ADF) test that the variable levels are integrated in single order was not rejected. Moreover, the null hypothesis that the first differences have unit roots was rejected. The Trace test indicated at most two cointegration vectors using Johansen and Juselius' (1990) criterion and one cointegration vector using Reimers' (1992) criterion. The maximum characteristic value test showed there were at most two cointegration vectors.
The resulting normalized cointegration vector provides an estimate of the relative contribution of land stock, agricultural labour, fertilizer and machinery to Peru's agricultural output.
Ln(Y) = 0.76Ln(L) + 0.24Ln(T) + 0.10Ln(F) + 0.06Ln(M)
What stands out is the magnitude of the elasticity of labour; a one percent increase in agricultural labour would have the greatest impact on boosting production, 0.76 percent, followed by land, fertilizer and tractors.
By way of comparison, Twomey (1989) estimated aggregate Cobb-Douglas production functions for Latin America for 1969-1982, using FAO data on fertilizer, land and tractors as independent variables. Estimates of the elasticities of agricultural supply for fertilizer use were between 0.1 and 0.2; for land (arable plus permanent cropland) 0.44 and 0.58; and for tractors the estimated values fluctuated between 0.18 and 0.39. Agricultural labour data were not included.
TABLE 6.10
Estimation of Supply Functions for Peruvian Crops, 1970-1995
Explanatory variables |
Cotton |
Cotton |
Rice |
Potato |
Intercept |
8.254 |
8.253 |
8.005 |
7.754 |
Price cotton (t-1) |
(5.77)* |
0.465 |
||
Price rice (t-2) |
0.284 |
|||
Price potato (t-2) |
0.229 |
|||
Invmac (t-2) |
0.06 |
|||
Invlimp (t-2) |
0.005 |
0.087 |
||
Credit (t-1) |
0.034 |
0.034 |
0.012 |
|
Climate |
-0.005 |
|||
Climate (t-1) |
-0.268 |
-0.268 |
-0.083 |
|
AR(1) |
0.664 |
0.665 |
0.893 |
0.273 |
R2 |
0.863 |
0.863 |
0.628 |
0.55 |
Adjusted R2 |
0.826 |
0.835 |
0.545 |
0.418 |
F-Statistic |
22.84 |
30.13 |
7.603 |
4.16 |
Durbin Watson |
1.67 |
1.66 |
2.33 |
1.82 |
* Significant at five percent
** Significant at ten percent
Note: The variables are expressed in logarithms, t-statistics are given in parentheses.
Source: MAG, Compendio Estadistico Agrario, various years.
Price cotton (t-1): Price of cotton in the previous year.
Price rice (t-2): Price of rice two years prior.
Price potato (t-2): Price of potato two years prior.
Invmac (t-2): Investment in machinery two years prior.
Invlimp (t-2): Investment in land improvement two years prior.
Credit (t-1): Credit in the previous year.
Climate (t-1): Climate in the previous year expressed as a dummy variable which refers to the serious presence of the El Niño current.
Building on the previous analysis, the economic factors in determining the supply of cotton, rice and potato are now discussed, using the following supply function:
Ln(Q)= ao + a1Ln(Price) + a2Ln(Credit) + a3Ln(Investment) + a4Climate,
where Q is agricultural supply, Investment is investment in land improvements and agricultural machinery, Price is the average real commodity prices and Credit is the amount of credit for the product from the financial system. The variables in the model are expressed in logarithms so the coefficients ai can be interpreted as elasticities. Climate is added as a dummy variable to account for severe climate anomalies such as El Niño in 1981 and 1991. Incorporating a dummy variable for the number of terrorist attacks proved intractable because of low occurrence. The data source is the Statistical Compendium of the Ministry of Agriculture (MAG, selected years).
The models (Table 6.10) explain a substantial portion of the variation in each crop supply (42 to 84 percent). The elasticity values are within expected values. The supply levels are affected by the price of commodities in previous years and the climatic conditions in the previous season. The coefficients of agricultural investment, whether in land improvement for cotton and rice or in machinery for potato, have the expected positive signs but limited statistical significance. This may reflect the limitation of the variable used, since it does not account for accumulated capital stock.
This chapter has looked at the evolution of sources of growth in Peru's agricultural sector, in particular, we have looked at how changes in land, labour and fertilizer, the role of public and private investment, technological change, policy and political violence have influenced Peru's agricultural sector. The difficulties facing farmers in the present post-adjustment context and examination of strategies that have been most successful were examined. Finally, in an attempt to quantify the contribution of different factors to Peruvian agriculture, aggregate agricultural production function and supply functions were estimated. From this analysis, the following conclusions can be made.
While there was a 51 percent expansion of cultivated area between the censuses of 1972 and 1994, land is still concentrated in larger holdings. Although the Agrarian Reform collectivized land into farmer groups, the present agrarian structure is predominantly made up of small farmers, who account for 77 percent of the total farmer population, have holdings of less than five hectares and work 6.56 percent of the agricultural area. In contrast, 2.9 percent of the farmer population have holdings of more than 50 hectares and account for 70.4 percent of the total agricultural area.
While the economically active population engaged in agriculture appears to have grown by an annual 0.81 percent between 1970 and 1995, a large proportion has little or no education. The 1994 census indicates that 20.4 percent of farmers have no education, 59.4 percent primary education, 14.8 percent secondary education and only 3.61 percent higher education. Thus human capital investment would appear to be an obstacle to the effectiveness of extension programs and to technological change.
The use of improved inputs is concentrated in the coastal region. It is the greatest consumer of fertilizer, with the rainforest and highlands following far behind. Demand for tractors and agricultural machinery is also concentrated in the coastal region, with very limited demand in the highlands and rainforest. Technology use, particularly fertilizer use, has fluctuated according to availability of foreign currency, international prices and exchange rate policy.
Regional analysis of production growth rates in terms of cropland and yield for the major crops indicate no significant leaps in production or sweeping technological change between 1970 and 1995. The significant differences between yields from demonstration plots and farm plots suggest considerable scope for effective technology transfer. Given the erratic nature of public investment in agricultural research in Peru, it proved to be relatively insignificant. Clearly, such investment needs to be more consistent and target a wider range of producers.
As regards macro-economic indicators for 1970-1995 and agricultural investment, the period of overall economic growth was not matched by growth in the agricultural sector. On the other hand, public and private investment expanded most during this period (1970-1975) and thereafter generally declined. The bulk of public investment has been directed towards land improvement (73 percent), largely in the form of large-scale irrigation works in coastal areas. The 1994 Census identified the absence of adequate irrigation infrastructure as a major constraint to capital investment. Meanwhile, private investment has focused on establishing perennial crops, livestock production and the purchase of agricultural machinery. Limited access to credit appears to be a major obstacle to buying equipment, as well as purchasing seed, fertilizer and agricultural services.
There appears to be a relationship between public and private investment, with the latter responding to increases in the former. Given the risks inherent in agriculture, private operators may be more prone to invest in the sector once the Government has provided irrigation infrastructure or expanded cultivable land.
Macro-economic and agricultural policies have reduced profitability, with a relative fall in agricultural prices. The situation for farmers was compounded by the political violence of the 1980s in mainly rural areas. This led to sharp decreases in agricultural production in the central and southern highlands and the increasing poverty among the peasant farmer population.
Agricultural investment has been adversely affected by periods of high inflation, the external debt crisis and hence lower availability of funds and political violence, with its attendant risk and uncertainty and the loss of human, material and production capital.
Looking at regional differences and coping-mechanisms of farmers gave widely differing indications of the impact of structural adjustment on agriculture and farmers. Those producing for the domestic market were the most affected because of lower prices due to decreased demand and higher production costs. Farm holdings that diversified their sources of income mitigated the problems of lower profits by raising the contribution of off-farm income. Farmers growing for the export market fared relatively better as they had guaranteed access to credit, received high external prices and were well organized.
With respect to the key components of agricultural growth in Peru, estimation of the aggregate production function for 1970-1995 indicated that increasing agricultural employment would have the greatest impact on output, followed by land, fertilizer and tractors. A ten percent increase in labour ceteris paribus would have increased production by 7.6 percent, while similar increases in cultivated land area, fertilizer use and tractor use would increase production by 2.4 percent, one percent and 0.6 percent, respectively.
Estimation of the supply functions for cotton, rice and potato revealed the relative importance of expected price and climate conditions compared to credit and investment in land improvements and machinery. The implication is that profitable and favourable conditions for agriculture must exist to encourage investment.
From all these findings the following causal relationships can be inferred between agricultural investment and growth. Agricultural output is a function of cultivated area, labour, fertilizer and machinery. Cultivated area is a function of public investment. Private investment in machinery et cetera is a function of both credit availability and prior public investment in infrastructure and land improvements. Technological transfer is dependent upon agricultural research and extension, which is a function of both public and private investment. Public investment is a function of taxes collected, external shocks, external debt and social stability.
These relationships indicate that an increase in agricultural production requires an increase in production factors, but that this will only occur if public and private investment take place. Since public investment drives the latter, it is a crucial to expanding output. In addition, they are both necessary to develop technology through agricultural research. Clearly, macro-economic and external conditions and internal stability factors such as the political violence of the past decade have a strong bearing on agricultural investment performance.
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1 The results of the Granger causality test between the log of public investment (LnPub) and the log of private investment (LnPri) are:
Null Hypothesis F-Statistic Probability
LnPri does not Granger cause LnPub 2.55 0.0943
LnPub does not Granger cause LnPri 15.38 7.6E-05
These results refute the suggestion that public investment does not Granger cause private investment, in other words they support the existence of causality from public investment to private investment. In addition, the correlation coefficient between investments for 1970 to 1994 is 0.67, a 95 percent significance level.
2 This season was however adversely affected by floods in the North (El Niño) and drought in the South, events that probably underestimated effective demand.
3 APRA (Alianza Popular Revolucionaria Americana), Peruvian political party founded in the 1920s by Víctor Raúl Haya de la Torre and forming government for the first time in 1985-1990 under Alan García Pérez.
4 For the period January-September 1997, 14.3 percent of FONCODES project investment was for agricultural purposes, with an emphasis on small irrigation schemes (Office of the President of the Republic, 1997).
