Using farm data to validate agroecological zones in the Lake Titicaca basin, Peru S.M. Swinton1, R.A. Quiroz2, S. Paredes3, J. R. Reinoso3 and R. Valdivia3
Abstract Prior research has divided the Lake Titicaca basin into four agroecological zones based on climate: Lakeside, Suni A, Suni B, and Dry Puna. The authors hypothesize that these climatic differences result in differences in farming systems and natural resource exploitation. The validity of distinguishing among these four agroecological zones is tested statistically with farm and natural resource data from a 1999 survey of 265 farms in ten villages of Puno, Peru. Analyses include pairwise comparisons of individual variables using t-tests and chi-square tests, as well as multivariate analysis of variance for groups of related variables. On examining farming practices (including crop mix, livestock mix, soil fertility, crop pests, crop rotations and labor time), farm resources (field size, field position in landscape, cropped area and livestock inventory), and natural resource use (forest use, aquatic plant use and cooking fuel), the authors find that the agroecological zones do, in fact, differ significantly. Now that the zonal typology has been validated, it can be applied in development and diffusion of suitable technologies as well as in the design of locally suitable natural resource policies.
INTRODUCTION Ecoregions, Recommendation Domains and Agroecological Zones Over the past twenty years, the focal unit of international agricultural systems research has evolved from the farming systems recommendation domain to the ecoregion. The earlier generation of farming systems recommendation domains centered on the farm. Recommendation domains were developed to orient agricultural technology development and outreach toward relatively homogeneous farm types (Byerlee et al. 1980; Escobar and Berdegué, 1990).
1
Despite the successes of the farming systems approach at orienting agricultural research, the limitations of a farm-level focus have become increasingly evident. As salient limitations, Berdegué and Escobar (1995) list the narrow focus on increasing supply (ignoring market demand), neglect of micro-macro linkages, and focusing exclusively on the farm. To these limitations, one could further add neglect of the off-farm environmental “externalities” of on-farm production practices. The ecoregion is called upon to fulfill a mandate broader than the farm but distinct from the national economy (Rabbinge, 1995). It is conceived as a focal area not only for technology development but for natural resource policy. The price of the broader mandate is that the ecoregion becomes harder to
Dept. of Agricultural Economics, Michigan State University, E. Lansing, MI 48824-1039, USA and CIP, Lima, Peru E-mail:
[email protected] 2 Dept. of Production Systems and Natural Resource Management, CIP, Apartado 1558, Lima 12, Peru 3 Centro de Investigación de Recursos Naturales y Medio Ambiente (CIRNMA), Apartado 388, Puno, Peru
Proceedings – The Third International Symposium on Systems Approaches for Agricultural Development
define. For ecological purposes, an ecoregion may be defined one way, for administrative purposes, another. Gastó et al. (1993) offered a nine-level, ecologically based hierarchy of ecoregions. Heading the hierarchy are the five ecological kingdoms into which the world is divided, followed by subdivision into a total of sixteen domains. Being based on climatic criteria, these ecoregions bear little relation to the administrative boundaries that delineate human governments. Yet the species Homo sapiens is the one whose decisions largely shape the evolution of ecoregions. Acknowledging that policy is based on political units, Gastó et al.(1995) try to identify degrees of correspondence with a separate “ecologicaladministrative” hierarchy. In so doing, they highlight the fact that the appropriate definition of an ecoregion will hinge on the purpose for which it is defined. For the present study, our purpose is to identify causal links between agricultural management practices and natural resource outcomes. For measuring natural resource outcomes, it makes sense to work from a coherent natural area, such as a watershed. This corresponds to an ecological “province” or district in the Gastó et al. (1993) hierarchy. But an ecological province may contain a broad range of agricultural production environments. So we need to identify agroecological (AE) zones within an ecoregion. For agricultural research, such zones are the fundamental geographic units for technology development. Although AE zones correspond in large degree with farming systems recommendation domains, they differ in that their definition is based on homogeneity of natural resource conditions as well as farming systems. The Titicaca Altiplano ecoregion corresponds to the province “Secoinvernal Esteparia Transicional” in the dry winter prairie domain of the Temperate kingdom, according to the hierarchy of Gastó et al.
(1993). Defining the Altiplano of Lake Titicaca as an ecoregion, this paper uses farm survey data from the sub-watershed formed by the Ilave and Huenque rivers in Puno Department, Peru, to validate a set of four component AE zones that were predefined based on climatic and topographical criteria.
Agroecological Zones Studied Lake Titicaca is situated in the Andean Altiplano (“high plain”) of southern Peru and western Bolivia (Figure 1). The Altiplano rises from the Lake Titicaca altitude of 3800 meters above sea level (masl). Past geographic research in the area has divided the Altiplano into three broad natural areas, which translate well into AE zones: Lakeside, Suni and Dry Puna (Pulgar Vidal, 1996; Salis, 1989; Tapia, 1996). For agricultural purposes, these zones are shaped by two major criteria: rainfall and frost risk, the latter highly correlated with altitude (Table 1). These follow a gradient by which agricultural potential declines with increasing altitude and distance from the lake. The Lakeside zone includes the area up to about 3850 masl and is strongly affected by the presence of the lake — for crop farming, livestock forage production and fishing. In addition to a fairly long frost-free season, the zone benefits from the availability of natural aquatic vegetation valued for livestock fodder such as totora (Scirpus totora) and llachu (Elodea potamogeton). The Suni zone occupies the area between 3850 and 4000 masl, where frost risk is still low enough and rainfall high enough to permit crop farming without irrigation. The dry Puna zone, which rises above 4000 masl, lacks adequate rainfall and frostfree growing days to support most crops; its residents rely overwhelmingly on livestock for their livelihood.
Table 1. Agroecological zones in the Lake Titicaca basin of the Peruvian Altiplano. AE zone
Altitude (m)
Precipitation (mm/year)
Frost-free period (days/year)
Lakeside
3800-3900
700-750
150-180
5
-1
Suni
3850-4000
600-850
90-145
3.7
-8
Humid Puna
4000-4500
800-1000
60-110
2
-16
Dry Puna
4000-4800
440-600
30-60
1
-10
Source: Tapia, 1996 (p. 69).
2
Avg Minimum Temp January (oC)
Avg Minimum Temp July (oC)
Using farm data to validate agroecological zones in the Lake Titicaca basin, Peru
Figure 1. Location of the Altiplano in South America (courtesy of Robert Hijmans, 1998). Some authors further subdivide the Suni zone into subregions A and B, where the Suni A zone “receives the direct influence of the lake or lagoons, permitting some agricultural production of cereals, quinoa and potato on slopes,” while the Suni B zone is “cooler, dedicated to livestock and forage crops” (Tapia, 1996, p. 69). This paper uses data from a 1999 survey of ten rural communities in the Ilave-Huenque river basin on the southwest side of Lake Titicaca (southern Puno department) to investigate whether differences among farms support the existence of the four AE zones: Lakeside, Suni A, Suni B, and dry Puna. The humid Puna zone is omitted in the analysis, as it is not present in the Ilave-Huenque watershed.
agroecological zones are reflected in both current farm management practices and accumulated farm resources. Specifically, our objective is to investigate whether the four AE zones cited in the literature contain significant differences in three distinct criteria: 1. Farming systems •
Do crop and livestock species vary?
•
Does soil fertility management vary?
•
Does crop pest management vary?
•
Do livestock feeding and care vary?
2. Farm resources
OBJECTIVES Recognizing that farmers are natural resource managers, the approach used here to validate these zones is based on the assumption that differences in 3
•
Do farms vary in size as measured by land and family?
•
Do fields vary in number and size?
•
Do farm livestock inventories and their species composition vary?
Proceedings – The Third International Symposium on Systems Approaches for Agricultural Development
RESULTS AND DISCUSSION
3. Nonfarm natural resources • Does human exploitation of natural forest and wild species vary?
ANALYTICAL METHODS In comparing the hypothesized AE zones, our goal is to test the proposition that variability between zones exceeds variability within zones. This proposition is tested using three different methods. Continuous variables are evaluated via pairwise comparisons of means by AE zone, using the twosample t-test for unequal variances (Snedecor and Cochran 1967; Damon and Harvey 1987). Discrete variables are evaluated by comparing frequency counts of 0’s and 1’s in each AE zone with expected frequencies based on whole sample frequencies, using a χ2(1) test (Snedecor and Cochran 1967). In order to confirm that differences indicated in pairwise comparisons exist systematically across groups of related variables, multivariate analysis of variance (ANOVA) is conducted using the AE zone as the independent variable. Results are evaluated using Wilk’s lambda test (SAS Institute 1988). Based on hypothesized underlying differences among zones, the following contrasts were further tested as part of the multivariate ANOVA procedure: 1) dry Puna versus the other three zones, 2) Lakeside versus the Suni A and B zones, and 3) Suni A versus Suni B zone.
Crops Planted Which crop and livestock enterprises are practiced is the most basic characteristic of farming systems. Crops were largely absent in the dry Puna zone. In the other three hypothesized AE zones, crop plantings were pervasive, with mean planted area per farm greatest in the Lakeside and Suni A zones, both over one hectare, compared with 0.7 ha in the Suni B zone (Table 2). The three cropped AE zones were dominated by potato (Solanum spp.), quinoa (Chenopodium quinoa), barley (Hordeum vulgare), and oats (Avena sativa). Planted area in all four was greatest in the Suni A zone, with the Lakeside zone following close behind (Table 2). As total area planted was less in the Suni B zone, individual crop area tended to be less as well. Notable differences arose in secondary crops, with broad bean (Vicia faba) playing a significant role in the Lakeside area and cañihua (Chenopodium palledicuale) widely planted in the Suni B zone but nowhere else. These characteristics also proved to be key distinguishing elements in the factor analysis conducted as part of the multivariate ANOVA, which showed the four zones and all three contrasts to be significant for the set of cropped area variables.
Table 2. Mean planted area (in hectares) by major crop and agroecological zone, 265 surveyed farms, Puno, Peru, 1998-99. AE zone
Potato
Lakeside (n=57)
0.21
Suni A (n=77)
0.35
Suni B (n=63)
0.22
Dry Puna (n=68)
0.01
Quinoa ab
(0.18)
a
(0.16) a
(0.33)
0.12
b
0.11
a
0.00 (0.00)
b
0.41
a
0.25
a
0.01 (0.03)
a
0.24
ab
0.03
a
0.00 (0.00)
Cañihua
0.09
0.00
a
0.03
b
0.00
b
0.00 (0.03)
b
0.00
c
0.07
b
0.00 (0.00)
a
1.17
a
(1.10) a
(0.08) c
1.03 (1.03)
(0.00)
(0.00) b
All Crops
(0.00)
(0.08)
(0.09) c
Broad bean
(0.12)
(0.45)
(0.50) b
0.38 (0.79)
(0.52)
(0.14) c
0.20
Oats
(0.25)
(0.17)
(0.20)
(0.06)
0.09
Barley
0.68
b
(0.74) b
0.03
c
(0.15)
Note: Standard deviations in parentheses. Means in same column followed by same letter are not significantly different by pairwise t-tests (LSD) at the 95% threshold. Due to the presence of a small number of other crops (not shown), the “All crops” column may exceed the summed areas of individual crops shown.
4
Using farm data to validate agroecological zones in the Lake Titicaca basin, Peru
Crop Rotation
A and Suni B zones did not differ significantly in the crop rotation periods devoted to potato and quinoa.
Crop rotation provides a measure of both crops grown and the use of fallows for fertility restoration. By most measures, the Lakeside zone stands apart. At an average of only 3.8 years, crop rotations are significantly shorter in the Lakeside zone than in the Suni A and B zones, which had rotation lengths of 5.3 and 5.7 years, respectively. The short rotations of the Lakeside zone include virtually no fallow periods. Occupying a mean of only 3.3% of the rotation cycle, these were absent from most crop rotations (Table 3, Figure 2). The absence of fallows leaves more time for other crops, and farms in the Lakeside zone devote significantly more time than those of other zones to potato, oats and broad bean in their rotations. Potato is the leading food and cash crop in these rotations, accounting for fully 28% of the short rotation cycle. Fodder crops for feeding livestock constitute the other distinct aspect for the Lakeside zone, with barley, oats and broad bean serving more as livestock fodder than for human consumption. The Suni zones are distinct from the Lakeside in that land is more abundant, especially in the Suni B. With fallow periods making up 42% of their mean rotation length, the farms in Suni B devote significantly more time to fallow than those of the Suni A zone (Table 3, Figure 2). As a result of having greater access to natural rangeland for livestock grazing, Suni B farms devote significantly less crop rotation time to forage crops than Suni A farms. In this sense, the Suni A zone is intermediate between Suni B and Lakeside in its commitment to forage crops. The Suni
The multivariate ANOVA confirmed differences among the four zones across all three contrasts for the set of five crop rotation length variables in Table 3. The associated factor analysis highlighted two factors as accounting for 97 percent of the interzonal variability, the first factor being dominated by fallow and forage crops and the second one by potato and quinoa.
Crop Inputs Used The use of crop physical inputs offers insights into fertility maintenance restoration and pest control. Organic manure (from cattle, sheep, alpaca and llama) is used pervasively for fertility maintenance in the three AE zones where cropping is practiced (Table 4). While virtually all households in the three zones use manure, the same is not true for mineral fertilizers. Mineral fertilizer use varies significantly across zones, correlating strongly with potato planted area and ranging from 94% in the Suni A zone, to 72% in the Lakeside zone, to only 18% in the Suni B zone and negligible use in the dry Puna (Table 4). Fertilizer application rates per hectare followed a similar pattern, from over 90 kg/ha in the Suni A zone to only 2 kg/ha in Suni B, with the Lakeside zone between in (Table 5). The heavier use of manure and fertilizers in the Lakeside and Suni A zones may be viewed as intensification of the fertility restoration process in response to demographic pressure that is forcing shorter fallow periods.
Table 3. Mean percent time in major stages of crop rotation by AE zone, 200 farms with crops, Puno, Peru, 1998-99. AE zone
Fallow
Potato
Lakeside
3.3
(n=56)
(10.8)
Suni A
29.9
(n=76)
(13.0)
Suni B
42.0
(N=63)
(17.7)
(7.8)
(8.5)
(9.9)
(0.0)
(0.0)
Dry Puna (n=5)
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
Overall (n=200)
25.9
21.9
17.2
25.4
3.6
4.4
(20.9)
(8.1)
(9.5)
(14.7)
(10.4)
(9.7)
c
28.1
Quinoa a
(7.5) b
19.1 20.1
19.3
a
(12.5) b
(4.4) a
Barley a
(13.4)
17.4
a
(6.3) b
26.1
Oats
27.8
a
18.8
a
(16.6) a
(9.2)
16.6
9.0
Broad bean
2.9 0.0
a
(13.2) b
(7.1) b
13.1
b
2.0 (6.5)
b
0.0
c
Note: Standard deviations in parentheses. Means in same column followed by same letter are not significantly different by pairwise t-tests (LSD) at the 95% threshold.
5
Proceedings – The Third International Symposium on Systems Approaches for Agricultural Development
100%
90%
80%
70%
Other Fava bean Oats Barley Quinoa Potato Fallow
60%
50%
40%
30%
20%
10%
0%
Lakeside
Suni A
Suni B
Figure 2. Crop rotation proportions by agroecological zone. The annual labor and power input committed to crop management is greatest in the Lakeside zone, declining sharply in the other zones with distance from Lake Titicaca (Figure 3). This pattern is especially pronounced for manual work (with significant differences between each pair of zones). However, the same pattern is discernible for tractor and oxen draft power time (Table 6).
Insecticide use in the region is chiefly targeted at the Andean potato weevil. In the three AE zones where crops prevail, its use ranges from 32% of farms in the Lakeside zone to 62% in Suni A (Table 4). Aldrin is the favored ingredient. Rates of use average 1.1 to 3.5 kg/ha (Table 5), though these means are strongly influenced by a small number of farms applying high rates. Fungicides are virtually absent from cropping systems in the watershed (Tables 4 and 5).
Table 4. Percentage of households using different crop inputs, by AE zone, 197 farms with crops, Puno, Peru, 1998-99. Manure
Mineral fertilizer
Insecticide
Fungicide 1.8
Lakeside
98.2
*
71.9
*
31.6
Suni A
100.0
*
93.5
*
61.0
Suni B
98.4
*
17.5
*
44.4
Dry Puna
57.4
*
1.5
*
1.5
Whole sample
88.3
47.2
35.5
*Differs significantly from whole sample, based on Chi2(1) test of 0/1 frequency counts.
6
*
3.9 1.6
*
0.0 1.5
Using farm data to validate agroecological zones in the Lake Titicaca basin, Peru
Table 5. Mean input use rates by AE zone (in kg/ha), 197 farms with crops, Puno, Peru, 1998-99. Sheep/ Alpaca droppings Lakeside
982
Cow manure a
(1116) Suni A
1470 1703
a
(497) a
(1916) Suni B
161
Ashes
112 21
a
46.8
(26) ab
(256) a
5
Mineral fertilizer (dry)
6 0
a
(51.2) a
96.0
(23) b
Insecticides
2.1
a
(11.6) a
(149.6) a
3.5
Fungicides
1.3 2.5
a
(2.9) a
(3.0) b
0.4 0.0
a
(0.1) a
0.0
(1849)
(114)
(0)
(8.3)
(6.0)
(0.0)
Dry Puna
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
Whole sample
1362
96
4
51.3
2.4
0.1
(1687)
(316)
(20)
(104.5)
(7.5)
(1.5)
a
Note: Standard deviations in parentheses. Means in same column followed by same letter are not significantly different by pairwise t-tests (LSD) at the 95% threshold. Due to missing values, sample sizes vary across variables.
Farm Resources Certain farm resources both shape agricultural possibilities and reflect the results of past farming practices. Farm size, for example, is sometimes related to the carrying capacity of the land, so subsistence farms may be small on highly productive lands and large on marginal lands. Climatic risk may be reflected in management responses such as crop diversification and the spatial diversification of fields across different landforms. Likewise, livestock inventories may indicate the degree of land productivity. Species such as sheep, goats, alpacas and llamas are capable of scavenging for pasture over large areas, whereas cattle require higher quality pastures or else fodder provided. One major farm resource that we would not expect to vary by AE zone would be family size. Indeed, family size did not differ across zones, all of which had mean household sizes of 4.6 to 5.0 members.
differences in topography. The Lakeside zone is flat and prone to flooding (Table 7). The Suni A and B zones are hillier, offering a range of ecological levels along hillsides, with Suni B farmers having better pastures available for risk spreading. The multivariate ANOVA found the zones to differ in field position and inferred topography (P < 0.01), but among the three component contrasts, the contrast between Suni A and B zones was not significant.
Livestock Inventories Differences by species in livestock inventories reflect clearly the differences in climate and vegetation among the four AE zones (Figure 4). The pastoral dry Puna zone completely dominates in holdings of alpacas and llamas, species of limited profitability 120
100
Field Fragmentation and Location
Tractor hours Ox-team days Worker days
80
Crop fields were highly fragmented near Lake Titicaca, with fragmentation declining with distance away from the lake. On average in the Lakeside zone, farms had 31 fields (0.03 ha/field) versus 24 fields (0.05 ha/field) in Suni A and 9 fields (0.07 ha/field) in Suni B. In the dry Puna zone, only 21 of 68 farms had any crops at all. Figueroa (1981) has linked such field fragmentation to crop production risk from seasonal frost and flooding, as well as longterm risks from soil erosion. In the Ilave-Huenque watershed, this hypothesis is consistent with
60
40
20
0
Lakeside
Suni A
Suni B
Dry puna
Figure 3. Mean household work time devoted to crops, by agroecological zone. 7
Proceedings – The Third International Symposium on Systems Approaches for Agricultural Development
Table 6. Mean crop work effort by AE zone, 237 farms, Puno, Peru 1998-99. AE zone
Tractor hours
Oxen days
Lakeside
8.7
(n=57)
(8.2)
Suni A
4.4
(n=77)
(5.6)
Suni B
2.2
(n=63)
(3.1)
Dry Puna
0.0
(n=41)
(0.0)
(0.0)
(20.2)
(211)
Whole sample
4.1
5.9
73.2
821
(n=237)
(6.1)
(8.3)
(67.8)
(875)
a
10.9
Person days a
116.1
(10.9) b
6.9
b
b
(62.1)
3.9
c
64.9
d
a
852
b
(778) b
(38.4)
0.0
1403 (1209)
82.3
(5.2) d
a
(86.2)
(8.1) c
Total value of crop work (Peru soles)
743
b
(487)
10.5
c
91
c
Note: Standard deviations in parentheses. Means in same column followed by same letter are not significantly different by pairwise t-tests (LSD) at the 95% threshold. The Peruvian sol (S/.) exchanged with the US dollar at a rate of US$1.00=S/. 3.14 on Jan. 1, 1999.
which tolerate the cold, dry conditions of the dry Puna zone. Sheep are also important in the Puna, with cattle less so (Table 8). The moister pastures of the Suni B zone allow it to lead in inventories of sheep and cattle. The Lakeside zone follows in importance of livestock holdings, with notable holdings of cattle, pigs, poultry and donkeys (Table 8). As hinted by the forage crop information above, livestock production relies on intensive feeding of stationary animals in the Lakeside zone, as opposed to pastoral grazing in the Puna and Suni B zones. Cows, sheep and pigs are of modest importance to farm households in the Suni A zone, although these rely chiefly on crop production. These differences
were confirmed by the multivariate ANOVA on livestock species across the four AE zones and three component contrasts evaluated.
Nonfarm Natural Resource Use Farmers exploit certain natural resources not for farming purposes, but rather for consumption and sale. Consumption uses include cooking fuel, wild foods, and construction materials. Resources collected for sales include virtually all-home consumption types as well as others used as inputs to agricultural production in other areas.
Table 7. Mean proportion of cropped fields by landscape position and zone, 218 farms, Puno, Peru, 1998-99. Plain
Hill foot a
Hillside
Lakeside
0.93
0.04
c
(n=57)
(0.16)
Suni A
0.63
(n=77)
(0.53)
Suni B
0.56
(n=63)
(0.28)
Dry Puna
0.31
(n=21)
(0.46)
(0.48)
(0.31)
Whole sample
0.65
0.18
0.12
(n=218)
(0.42)
(0.26)
(0.19)
(0.12) b
0.20
b
0.13
b
(0.15)
0.18
b
(0.23) c
c
(0.00)
(0.19) b
0.00
0.22
a
(0.23)
0.44
a
0.13
ab
Note: Standard deviations in parentheses. Means in same column followed by same letter are not significantly different by pairwise t-tests (LSD) at the 95% threshold. 8
Using farm data to validate agroecological zones in the Lake Titicaca basin, Peru
90
80
Lakeside 70
Suni A Suni B Dry puna
60
50
40
30
20
10
0 Alpacas
Sheep
Cows
Llamas
Pigs
Poultry
Equine
Figure 4. Mean farm livestock inventory by species and agro-ecological zone. Exploitation of forest species by farm households surveyed was chiefly limited to four species, tola (34%; including Baccharis spp., Diplostephium spp., Parastrephia lepidophylla, and Lepidophyllum cuadrangulare), k’anlli (19%; Tetraglochin strictum), queñua (3%; Polylepis incana, P. racemosa) and kolli
(3%; Buddleia coreacea, B. incana) (Scientific names from ONERN 1984, pp. 110-113; Gomez G. 1991; Pulgar Vidal, 1996). Exploitation varied sharply by region, with the Lakeside region reporting less than 4% of households using any forest species. In Suni A, 58% of households used k’anlli and 9% kolli, with
Table 8. Mean livestock inventory per household on March 31, 1999, by AE zone and species, 265 farms, Puno, Peru. Alpacas
Sheep B 5.7
Cows b
(2.9)
Suni A
0.1
(n=77)
(0.6)
Suni B
6.4
(n=63)
(12.2)
Dry puna
80.0
(n=68)
(49.8)
(21.3)
(5.1)
(35.8)
(1.9)
(1.9)
(1.1)
Whole sample
22.2
20.1
4.4
8.3
1.2
1.9
0.9
(42.7)
(24.2)
(4.2)
(22.9)
(2.3)
(2.5)
(1.1)
b
(16.8) B 32.8
(0.0) b
(2.3) a
(33.8) A 28.5
2.9 7.0 3.7
0.0
a
0.3
b
32.2
A
2.2
b
0.4
A
0.7
a
1.6
B
1.8
b
1.3
a
1.1
b
(0.9) b
(2.3) B
1.6 (1.2)
(2.1)
(0.9) a
2.9 (3.4)
(3.2)
(1.2) b
1.5 (2.1)
(0.0)
(5.0) a
b
Equine
(n=57)
(2.3)
0.0
Poultry
0.6
B 13.1
b
Pigs
Lakeside
(4.9)
4.3
Llamas
0.5
c
(0.9) b
0.5
c
Note: Standard deviations in parentheses. Means in same column followed by same letter are not significantly different by pairwise t-tests (LSD) at the 95% threshold. 9
Proceedings – The Third International Symposium on Systems Approaches for Agricultural Development
few using other species. In Suni B, 44% used tola and 8% used k’anlla. Finally, in the dry Puna zone, nearly all households collected wood, either tola (88%) or queñua (10%). In every zone, the balance of forest resource use differed significantly from the overall frequency, based on χ2(1) tests. Among the households surveyed, cooking fuel is often gathered as firewood or livestock dung. In fuel sources, the hypothesized AE zones again differ significantly, based on availability of natural forest resources and livestock habitat. Firewood was not used for cooking by any households in the Lakeside zone, but it was almost universally used in the dry Puna (Table 9). The Suni A households also used more firewood than the overall sample average. Although cattle manure was used by a great majority of households in all zones, it was less common in the dry Puna, where cattle are relatively fewer. Likewise, sheep, alpaca and llama dung were little used in the Lakeside zone, but common in the dry Puna and Suni A zones, where these animals are more numerous (Table 9). Table 9. Households (percent) using different natural resourcebased cooking fuels, 265 farms, Puno, Peru, 1998-99. AE zone
Firewood
Cattle manure
Lakeside
0.0
* 97.7
4.7
Suni A
75.3
* 98.6
32.9
Suni B
59.7
98.4
19.4
*
Dry Puna
97.1
* 80.9
77.9
*
Whole sample
64.2
93.5
*
Sheep/camellid droppings *
37.0
*Differs significantly from whole sample, based on Chi2 (1) test of 0/1 frequency counts.
DISCUSSION OF RESULTS These statistical results validate differences among the four hypothesized AE zones in the Ilave-Huenque watershed of the Altiplano ecoregion. They reinforce the broad ecological distinctions of geographers like Pulgar Vidal (1996), who have defined the Suni and Puna ecological zones on a national basis. But they further highlight the more subtle distinctions made by scholars studying the Altiplano, who have underlined the uniqueness of the Lakeside zone (Salis, 1989; Tapia, 1996). The AE zone distinctions extend to the subtle difference between the Suni A and B zones,
based largely on land aspect in relation to Lake Titicaca. Yet agricultural production and natural resource conditions are consistent with distinct zones, as argued by Reinoso and Valdivia (1994) and speculated by Tapia (1996). These differences are manifested statistically across farming systems, farm resources and nonfarm natural resources. The dry Puna zone relies heavily on livestock and forest resources. By contrast, the other three zones rely in major part on crop production. In the land-scarce Lakeside zone, intensive cropping is practiced on small fields and relies on manure and mineral fertilizers for fertility restoration. Intensive livestock feeding is necessary due to the lack of space for natural pasture. In the Suni zones, crop production relies more on fallow for fertility replenishment, especially in the more landabundant Suni B zone. Drier, cooler weather in the Suni B zone makes crop production less important and frost-tolerant crops such as cañihua more important than in the Suni A zone.
CONCLUSIONS Understanding differences among agroecological zones is the first step toward developing technologies and policies to enhance agricultural productivity and sustainability. The characteristics of farming systems and farm production resources, which shape them, remain central, as in past farming systems research endeavors. But the characteristics of the natural environment and its exploitation by humans are equally important, if the productivity of natural resources is to be sustained. The research presented here examined agricultural and related natural resource attributes that validate the existence of four agroecological zones in the Ilave-Huenque watershed of the South American Altiplano ecoregion. These zones can become the basis for development and diffusion of suitable agricultural and pastoral practices in the region. They can also be used for the design of locally adapted natural resource policies, such as ones designed to maintain productive soil, water and forest resources.
REFERENCES Berdegué J A, Escobar G (1995) Nuevas Direcciones del Enfoque de Sistemas para la Modernización de la Agricultura Campesina de América Latina. In Julio A. Berdegué and Eduardo Ramírez (eds.), 10
Using farm data to validate agroecological zones in the Lake Titicaca basin, Peru
Investigación con Enfoque de Sistemas en la Agricultura y el Desarrollo Rural. Santiago de Chile: RIMISP (Red Internacional de Metodología de Investigación de Sistemas de Producción). Pages 13-43. Byerlee D, Collinson M et al. (1980) Planning Technologies Appropriate to Farmers: Concepts and Procedures. CIMMYT (Centro Internacional de Mejoramiento de Maiz y Trigo), Mexico. Damon R A Jr, Harvey W R (1987) Experimental Design, ANOVA, and Regression. New York: Harper & Row. Escobar G, Berdegué J (1990) Conceptos y Metodología para la Tipificación de Sistemas de Finca: La Experiencia de RIMISP. In Germán Escobar and Julio Berdegué (eds.) Tipificación de Sistemas de Produccion Agrícola. Santiago de Chile: RIMISP (Red Internacional de Metodología de Investigación de Sistemas de Producción). Pages 13-44. Figueroa A (1981) La Economía Campesina de la Sierra del Perú. Lima: Pontífica Universidad Católica del Perú Fondo Editorial. Gastó J, Cossio F, Panario D (1993) Clasificación de Ecorregiones y Determinación de Sitio y Condición: Manual de Aplicación a Municipios y Predios Rurales. Santiago, Chile: Red de Pastizales Andinos. Gomez García, O A (1991) Vocabulario Agricola: Nombres Vulgares y Cientificos. Puno, Peru: Universidad Nacional del Altiplano.
ONERN (Oficina Nacional de Evaluación de Recursos Naturales) (1984) Inventario, Evaluación e Integración de los Recursos Naturales de la Micro Región Puno. Pulgar Vidal J (1996) Geografía del Perú: Las Ocho Regiones Naturales. Décimo edición. Lima: PEISA. Rabbinge R (1995) Eco-Regional Approaches, Why, What and How. In J. Bouma, A. Kuyvenhoven, B. A. M. Bouman, J. C. Luyten, and H. G. Zandstra (eds.) Eco-Regional Approaches for Sustainable Land Use. Dordrecht, Netherlands: Kluwer Academic Publishers. Reinoso J, Valdivia R (1994) Impacto ambiental y socioeconómico del uso de los recursos renovables en el altiplano de Puno-Perú. Informe no publicado. Centro de Investigación de Recursos Naturales y Medio Ambiente (CIRNMA), Puno. Salis A (1989) Caracterización de 8 comunidades seleccionadas representativas de 4 zonas agroecológicas de Puno. Informe no publicado. Proyecto PISA, Puno, Perú. SAS Institute, Inc. (1988) SAS/STAT User’s Guide, Release 6.03 Edition, Cary, NC: SAS Institute, Inc. Snedecor G W, Cochran W G (1967) Statistical Methods. Sixth edition. Ames, IA: Iowa State University Press. Tapia M E (1996) Ecodesarrollo en los Andes Altos. Fundación Friedrich Ebert. Lima.
11
