Deforestation risks posed by oil palm expansion in the Peruvian Amazon^*

Our focus was the tropical moist forest biome of Peru, an area that occupies 53.9% of the country. We defined our study area as the monitoring area for the forest change dataset [29] generated by Peruvian Government agencies, primarily the Servicio Nacional Forestal y de Fauna Silvestre (SERFOR), the Ministry of the Environment (MINAM), and the Ministry of Agriculture and Irrigation (MINAGRI). Using this dataset, we assessed yearly forest and non-forest areas from 2000–2015 at 30 m spatial resolution. A known problem in the dataset is that river meanders and oxbow lakes contribute substantial false positives to deforestation detection. To account for such errors, we further refined these data by removing all pixels identified as surface water at any time between 2000–2015 in the JRC Global Surface Water Occurrence dataset [30].

Within areas classified as non-forest in 2015, we identified oil palm and other cropland areas. To visually identify patterns of oil palm monoculture, we used high-resolution (≤0.5 m spatial resolution) Worldview-02 and Worldview-03 imagery for 2014–2016 obtained from DigitalGlobe (NextView License). Where possible we corroborated our identification of oil palm plantations with news and scholarly articles, government and company records, and geotagged photos.

We defined cropland as areas with active or fallow cultivation of annual or plantation crops other than oil palm. To identify cropland, we used an unsupervised classification implemented in Google Earth Engine, based on Landsat 8 2015 data (SI: Addl. Methods). Pastures were excluded from our analysis because they face different biophysical constraints than areas of crop production, including oil palm cultivation. Many of the region's agricultural areas are mixed-use farms that do not conform to the large-scale monoculture typical of industrial agricultural practices, increasing the likelihood of underestimating cropland extent [31].

Using the binary forest cover layers for 2000–2015, we conducted a change analysis within current oil palm and other cropland to determine the percent forested in any given year. We then determined the size of contiguous oil palm or other crop patches in 2015, and repeated this analysis again for 2000 to determine the size of non-forest patches within the 2015 sample areas.

Short distances may separate small patches of deforestation, so a cluster of such patches may be appropriately viewed as a single patch in terms of habitat fragmentation. To address this possibility, we also evaluated the spatial clustering of oil palm and other crop deforestation patches at different aggregation distances, from Euclidean distances of 0–600 m at 60 m intervals (figure 1). We examined how mean patch size increased with aggregation distance for oil palm and other crops to indicate a relative degree of clustering in both types of agricultural deforestation.

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To estimate the areas at long-term risk of deforestation for oil palm, we calculated the biophysical envelope for oil palm growth. By this, we mean the current distributions of climatic and edaphic variables for current oil palm plantations in the region. We examined physical and climatic variables likely to influence the planting of crops: mean annual precipitation, minimum monthly temperature, elevation, slope, and soil depth (SI: Addl. Methods). Using these distributions, we identified other forested areas likely suitable for oil palm.

Within forest areas at long-term risk of deforestation, we used a classification and regression tree model (CART) to determine areas of short-term, i.e. more immediate, risk of deforestation (SI: Addl. Methods). Here, explanatory variables describe population centers and accessibility for development: large urban areas, villages, distance to roads and navigable rivers.

Within areas determined at long-term risk of deforestation for oil palm, we assessed the extent of protected areas and officially recognized indigenous territories (henceforth just 'indigenous territories') by administrative region (department). Data on protected areas, including information on IUCN categories, were from MINAM and data on indigenous territories from Rede Amazônica de Informação Socioambiental Georreferenciada (RAISG) [32]. We also assessed National Forest areas degazetted in 1996 and 2000 based on data from the Protected Area Downgrading, Downsizing and Degazettement (PADDD) dataset, as well as the extent of oil palm and oil palm risk classes found within these areas [33].

We evaluated long-term and short-term risk by ecoregion as well as protected areas and indigenous territories coverage for each ecoregion. A lack of fine-scale data on species abundance and ranges in this region makes it difficult to assess the impact of deforestation on biodiversity. When such data are lacking, ecoregions [34] serve as useful proxies for the habitat and community types required to sustain biodiversity [35, 36].

We identified 845 km² of oil palm plantation area and 21 997 km² cultivated for other crops in the Peruvian Amazon (figure 2). In 2000, deforested areas within current oil palm and cropland areas showed a similar distribution of sizes with a median patch size of 0.81 km² and 0.99 km² respectively (figure 3). By 2015, we observed a highly divergent pattern for the median size of oil palm deforestation (4.50 km²), more than six times the size of cropland deforestation (0.72 km²). The statistical distributions in patch size of both the oil palm and cropland patches are long-tailed. The top 5% of patches deforested for both oil palm and cropland cultivation account for 82% and 76% of the area deforested for each land use, respectively. The distribution of patch sizes for oil palm is also bimodal, with many small patches in addition to the large ones.

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The pace of forest transformed to oil palm or cropland, as a percent of that cover type in 2015, varied. In the 2015 oil palm area, 47% was forested in 2000 (figure 4(A)). About 1.4% was deforested each year from 2000–2005. This accelerated to ∼5% per year from 2006–2012. While the rate of deforestation appears to decrease dramatically in 2013–2015, this is at least partially an artifact of the difficulty of verifying the presence of oil palm seedlings within the first year of planting. Some of the deforested areas may indeed be oil palm, but it is too soon to confirm using remote sensing. In the area planted in other crops in 2015, 21% was forest in 2000, with an average of 1.4% of this area converted from forest each year between 2000–2015.

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The exact contribution of oil palm to overall agricultural deforestation fluctuated over the study period, reaching 20% in 2013, assuming current oil palm areas deforested since 2000 were converted directly to oil palm from forest (figure 4(B)). From 2001–2006, oil palm accounted for an average of 5% of annual agricultural deforestation, in the second part of the study period, from 2007–2013, oil palm accounted for an average of 11% of annual agricultural deforestation. The same detection difficulties mentioned above affected the final two years evaluated for this metric. Over the entire study period, deforestation within the largest oil palm patches accounted for 88% of total oil palm deforestation.

We considered how patches of oil palm and other cropland deforestation would aggregate at distances up to 600 m. Using a linear regression of aggregation distance (d_A) to mean patch area after aggregation (figure 4(C)), we found that the rate of increase in mean patch area was five times greater for oil palm patches (0.50 d_A + 32.0, R² = 0.99) than for cropland patches (0.10 d_A − 2.22, R² = 0.97), indicating a much greater degree of clustering for oil palm.

The distributions of oil palm and other crop patches were different with respect to minimum monthly temperature, mean annual precipitation, elevation, slope, and soil depth (Kolmogorov–Smirnov test, p < 0.001; figure S1 is available online at stacks.iop.org/ERL/13/114010/mmedia). Although the ranges of oil palm and other crops for each environmental factor examined overlap, oil palm tended to occupy a narrower biophysical niche. We determined threshold values that included all the variability in oil palm patch values for each explanatory variable: ≥1678 mm mean annual precipitation, ≥17.9 °C for minimum monthly temperature, ≤705 m for elevation, ≤30° for slope, and ≥1 m for soil depth. Forest areas meeting all these conditions are considered at long-term risk of deforestation for oil palm plantations (figure 5(A), table S1). Most of these areas were in Loreto (75%) and Ucayali (14%). None of the other regions contained more than 5% of the area at long-term risk.

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Based on cross-validated error, we selected a tree with five terminal nodes for the CART model representing the short-term risk of deforestation for oil palm in unprotected forests within the long-term risk areas (figure S2). The rarity of oil palm deforestation in the area sampled for this model (0.1%) impacts the precision of the model, 92.6%. Two scenarios emerged for short-term risk of deforestation for oil palm within the long-term vulnerable forest areas: (1) forest that occur close to roads (<23 km) and urban areas (<104 km) and (2) forests that are further from urban areas, but even closer to roads (<12 km) and near previously deforested areas (>1.3% of the area within 300 m radius deforested). The greatest proportions of forest at short-term risk are in the Loreto (31%), Ucayali (15%), Huánuco (6%), and Madre de Dios (5%) regions (figure 5(A), table S1).

Protected Areas cover 19 2988 km² (24.6%) of the Peruvian Amazon (figure 5(A)). Officially recognized indigenous territories cover another 12 4626 km². No oil palm is within current protected areas and <0.1% is within indigenous territories. We determined that 107 102 km² (23.6%) of forests at long-term risk are protected. Of these, 30 636 km² are within IUCN I-IV protected areas (6.7%). Another 75 589 km² (16.7%) of forest at long-term risk was within indigenous territories. We determined that 2708 km² of forest would be at short-term risk but for its protected areas status. Another 16% of forest at short-term risk is located within indigenous territories.

Peru removed the National Forest designation from its protected areas system in 2000 (SI: appendix B). These areas formerly covered 34 594 km² of the study area. In areas formerly protected by the Humboldt and Iparía National forests, we found 96 km² of oil palm, 11.3% of total estimated Peruvian oil palm. Some 81 of 100 random samples of oil palm in the former Humboldt and Iparía National forests were established after 2005. Later plantation establishment occurred within the interior of the formerly protected areas (SI: appendix B). We found 16 810 km² (3.7%) of forests at long-term risk and 2381 km² (12.6%) of forests at short-term risk of deforestation for oil palm are within degazetted National Forest areas. Some 19.2% (6654 km²) of these former National Forests became officially recognized indigenous territory.

Some 99.9% of oil palm deforestation is within two ecoregions, Ucayali Moist Forest (80.9%) and Iquitos Várzea (19.0%) (table S2, figure 5(B)). This reflects the two types of ecoregions most at risk of long-term development for oil palm: moist forests (Napo, Solimões-Japurá, Ucayali and Southwest Amazon) and floodplain forests known as várzea (Iquitos and Purus). Higher elevation forests (E. Cordillera Real Montane Forests, Marañón Dry Forests, Peruvian and Bolivian Yungas) have 7% or less of their area at risk. The greatest amount of forests at short-term risk occur in the Southwest Amazon Moist Forests (35.9%), Ucayali Moist Forests (34.8%), and Iquitos Várzea (18.0%) ecoregions.

In each of the six moist forest and várzea ecoregions, 38%–43% of the long-term risk area is under some form of protection or within indigenous territories. However, these differ among those that have primarily protected areas (Iquitos Várzea, Solimões-Japurá and Southwest Amazon Moist Forests), indigenous territories (Ucayali Moist Forest and Purus Várzea) or a nearly equal amount of both (Napo Moist Forest). Less than 7% of the area at long-term risk is under strict (IUCN I-IV) protection.

Between 1990 and 2010, demand for palm oil resulted in an expansion of plantation area from 60 000–160 000 km² globally [3]. As suitable land becomes limited in Southeast Asia, producers are targeting expansion in tropical areas, including in South America [37]. The impacts of such expansion are of concern when viewed in conjunction with those of mining, logging, petroleum extraction, infrastructure development and urban expansion in the Peruvian Amazon [38–46].

We identified 845 km² of oil palm plantation area in the Peruvian Amazon (figure 2). The area is greater than reported in previous studies and nearly twice the FAO estimate of harvested oil palm area in 2015 (431 km²) [3]. Almost half of the oil palm plantations we mapped were forest in 2000, compared to about 21% of other crops (figure 4(A)). Oil palm has become a major source of agricultural deforestation in the Peruvian Amazon, accounting for 11% of agricultural deforestation from 2007–2013, while occupying less than 4% of the agricultural area. These results support general trends from previous studies suggesting recent deforestation for oil palm [9–11], including increases in the rate of deforestation since 2006 (figure 4).

The spatial pattern of oil palm agriculture differed from other crops, with larger and more clustered deforestation patches (figures 3 and 4). Most oil palm cultivation comes from a small number of large plantations. However, the bimodal nature of oil palm patch distribution suggests both smallholder and large industrial actors are responsible for plantation establishment. A recent study of deforestation in the Peruvian Amazon noted that small patch deforestation is not always attributable to smallholder or subsistence agriculture but to a diversity of actors [47]. This motivates a need for further field surveys in the region. A greater percentage (50%) of large plantations came from deforestation since 2000 than small ones (30%). This is in broad agreement with earlier studies of this region [9, 11]. The dramatic shift we observed in median patch sizes from 2000–2015 suggests a fundamental change in the scale of deforestation within areas currently occupied by oil palm.

While small plantations may each have a limited impact, oil palm plantations were also more spatially clustered than cropland areas (figure 4(C)). Clustering of oil palm plantations may result from the need to transport oil palm fruits to mills soon after harvest. Rapid processing of the fruit is needed to prevent a rapid rise in free fatty acids that reduces the quality of the crude palm oil produced [48]. Such aggregation possibly leads to larger effective patches of oil palm deforestation, when we consider the effects of fragmentation on the remaining forest between patches. Studies indicate that increasingly fragmented forest areas support fewer species in the long-term and affect the function of forest areas [49–53].

Oil palm occupies sites at lower elevations, higher precipitation, lower slopes, and greater soil depths than many crops in this region (figure S1). Such habitat envelope differentiation suggests that oil palm development leads to deforestation in different areas than for other crops. If oil palm agriculture continues to expand, it could greatly change the distribution of agriculture on this landscape.

Large areas of forest are biophysically suitable for oil palm. We do not suggest that the entirety of this area will convert to oil palm. Rather we suggest that deforestation for oil palm could become a substantial part of overall deforestation within this region, given the extent of suitable forest area. Within the area determined suitable for oil palm, approximately 13% are seasonally or permanently flooded wetlands, including lowland peatlands [54]. We have not observed conversion of wetlands for oil palm in Peru, though logging, agriculture, and grazing activities increasingly degrade and convert these systems [55]. Peatland conversion for oil palm is an issue in Southeast Asia [56] and in the flooded savannah ecosystems of the Colombian Llanos [57]. Because of the uncertainty around future oil palm development scenarios in the Peruvian Amazon, we include wetlands in our areas projected to be at risk of development.

We highlight a subset of the biophysically suitable envelope as under short-term threat due to population and accessibility factors. In the short-term, the proximity of urban areas, roads and previous deforestation increase the risk of deforestation from oil palm (figure 5(A)). Another important factor is the proximity to existing oil palm plantations, as evidenced by the spatial clustering of oil palm plantations (figure 4(C)). Other studies find similar factors to be important in other regions of the world [58–60].

Frequent satellite images are few in areas of active oil palm expansion. Thus, the exact sequence of events in deforestation for new plantations and the development of infrastructure to bring palm oil to market requires further investigation. Our fieldwork near Pucallpa supports the idea that, while major road infrastructure is predictive, the establishment of large-scale plantations involves construction of new roads and improvement of existing ones. Our short-term risk analyses suggest that monitoring should continue throughout this region, including areas where there is no oil palm currently. A notable example is the province of Madre de Dios, which shares many of the biophysical, population and accessibility factors with areas that already have oil palm development.

Less than 7% of the area at long-term risk is under strict (IUCN I-IV) protection. Although Peru's forestry laws mandate preservation of vegetation cover and forestry resources, agricultural deforestation has occurred under the 'best land use capacity' exemption to them [61]. This highlights the importance of protected areas and indigenous territories. A new governmental resolution, signed by the Vice-Minister of Culture in 2017, mandates consultation with communities in indigenous territories (Resolution 014-2017-VMI-MC). Recent news items indicate that some indigenous communities are vocal in their concern about development within their territories [62].

It is also important to maintain long-term protections. From 1996–2000, Peru degazetted National Forests from their protected area system. This reduced the total protected areas in the study region by 12.3%. After degazetting, former National Forests opened to commercial logging and agriculture. From 2000–2010, deforestation rates exceeded those in both protected areas and areas that were never protected [63]. Most of the oil palm we identify in the former Humboldt and Iparía National Forests was planted after 2005 (SI: appendix B). Maintaining and enforcing protections preventing agricultural deforestation in National Forests could have reduced the areas at long-term and short-term risk of deforestation for oil palm by 3.7% and 12.6% respectively. While our findings support the conclusion that protected status prevents development of protected lands, it could displace development to other areas and, therefore, may not reduce overall risk of deforestation.

The two ecoregions currently containing oil palm (Ucayali Moist Forest and Iquitos Várzea) also contain much of the area at short-term risk of oil palm deforestation. In the long term, all six ecoregions of low elevation moist forests and várzea have large areas at risk. Little of these are under strict protection (<7%), though indigenous territories and other categories of protection cover ∼40% of each. This suggests that increasing protections of already designated areas could be a major component of conservation in this region. Indigenous territories are critical to the protection of the Purus Várzea, Napo Moist Forests and Solimões-Japurá Moist Forests, which have more than 90% of their area at long-term risk. Eighty percent of at-risk area currently under IUCN I-IV protection lies within the Southwest Amazon Moist Forest. It has considerable area under short-term risk despite containing no oil palm currently.

Peru's recent National Oil Palm Plan [64] indicates that an area of more than 14 000 km² may be suitable for oil palm development, a claim that conflicts with the goal of reducing deforestation within the Amazonian region. In recent years, companies have experienced increasing pressure to purchase responsibly produced palm oil, certified through the Roundtable on Sustainable Palm Oil (RSPO) [65]. RSPO certified palm oil now accounts for about 20% of global oil palm production. A recent study of RSPO certification effectiveness in Indonesia found it to be insufficient in reducing deforestation for oil palm [66].

In a recent instance of RSPO activity in Peru, the Native Community of Santa Clara de Uchunya made a formal complaint regarding the activities of one of its member companies: plantations of Pucallpa. An RSPO investigation ruled the company guilty of land clearing within traditional forest lands, violating the code of conduct for member companies [67]. Although Plantations of Pucallpa withdrew from the RSPO prior to the verdict, the Peruvian government fined it for compliance issues related to the case. Outside of the RSPO framework, a combination of societal pressure and legal action prevented deforestation of 230 km² of primary forest by Grupo Romero, a Peruvian producer in 2015 [61].

Voluntary commitments form an important part of deforestation regulation in Peru, but their effectiveness is limited when unaccompanied by policy interventions. Plantations will likely continue to expand in the coming years with a combination of international and national producers active in the region. As evidence of this, after the end of our study period in 2016 and 2017, deforestation occurred to establish a large plantation in Northern San Martin and for smaller plantations in the Ucayali and Huánuco regions.