High-yield oil palm expansion spares land at the expense of forests in
               the Peruvian Amazon

Gutiérrez-Vélez, Víctor Hugo; DeFries, Ruth; Pinedo-Vásquez, Miguel; Uriarte, María; Padoch, Christine; Baethgen, Walter E.; Fernandes, Kátia; Lim, Yili

doi:10.1088/1748-9326/6/4/044029

Cited by 130 publications

(92 citation statements)

References 19 publications

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“…Phenology-based methods usually rely on the temporal signals of optical sensors to identify oil palm plantations. For example, Gutierrez-Velez et al [15] analyzed temporal changes in vegetation greenness using MODIS data to map the area deforested by industrial-scale high-yield oil palm expansion in the Peruvian Amazon from 2000 to 2010. For the same area, Gutierrez-Velez [16] used the 16 day composite EVI (Enhanced Vegetation Index) from MODIS to detect the oil palm expansion at a coarse scale and applied a temporal categorical filter to an ALOS-PALSAR scene from September 2010 and Landsat TM/ETM+ images for every year from 2000 to 2011 to map the area converted into oil palm plantations at a fine scale.…”

Section: Introductionmentioning

confidence: 99%

Mapping Oil Palm Plantations in Cameroon Using PALSAR 50-m Orthorectified Mosaic Images

Dong

Tenku

et al. 2015

Remote Sensing

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Oil palm plantations have expanded rapidly. Estimating either positive effects on the economy, or negative effects on the environment, requires accurate maps. In this paper, three classification algorithms (Support Vector Machine (SVM), Decision Tree and K-Means) were explored to map oil palm plantations in Cameroon, using PALSAR 50 m Orthorectified Mosaic images and differently sized training samples. SVM had the ideal performance with overall accuracy ranging from 86% to 92% and a Kappa coefficient from 0.76 to 0.85, depending upon the training sample size (ranging from 20 to 500 pixels per class). The advantage of SVM was more obvious when the training sample size was smaller. K-Means required the user's intervention, and thus, the accuracy depended on the level of his/her expertise and experience. For large-scale mapping of oil palm plantations, the Decision Tree algorithm outperformed both SVM and K-Means in terms of speed and performance. In addition, the decision threshold values of Decision Tree for a large training sample size agrees with the results from previous studies, which implies the possible universality of the decision threshold. If it can be verified, the Decision Tree algorithm will be an easy and robust methodology for mapping oil palm plantations. OPEN ACCESSRemote Sens. 2015, 7 1207

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Section: Introductionmentioning

confidence: 99%

Mapping Oil Palm Plantations in Cameroon Using PALSAR 50-m Orthorectified Mosaic Images

Dong

Tenku

et al. 2015

Remote Sensing

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“…Studies assessing oil palm plantation areas in the Peruvian Amazon using satellite imagery indicated areas with high and low yield, which were associated with the soil's water condition (Gutierrez-Velez et al, 2011). That shows those tools can help assess the species's performance.…”

Section: Resultsmentioning

confidence: 99%

Expansion of palm oil (Elaeis guineensis Jacq.) in the state of Maranhão and soil water deficit limitations in the Brazilian Amazon

Martorano¹,

Moraes²,

Lisboa³

et al. 2017

Aust J Crop Sci

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Oil palm is considered the crop with the highest oil production per planted area unit. This condition has driven the Brazilian government to create the Sustainable Oil Palm Production Program. Since 2009, the agroenergy production chain has used oil palm as a viable and profitable crop to recover deforested areas in the Amazon. The aim of this study was to assess hydric conditions able to indicate the potential to expand oil palm crops in the state of Maranhão, even in the Legal Amazon. The climate database used (average and extreme temperature in degrees Celsius; rainfall values; relative humidity, and vapour-pressure deficit). Water deficit values were was obtained by comparing the potential evapotranspiration of oil palm (ETc) to actual rainfall (ER). Water balance was calculated based on available water capacity of 125 mm.month -1 . Evapotranspiration was obtained using the methodology according to the climate database available to calculate the evapotranspiration rate in an area planted with oil palm in this study. The water deficit values show no restriction in the soil water replacement between January and June. However, from July to December, the water deficit varies between 200 and 300 mm. The levels showed that, in the areas evaluated, oil palm crops will require irrigation. In this period, yield was estimated at 17 tons ha -1 when the water deficit was considered at 210 mm and 14 tons ha -1 for a deficit of 380 mm. This result reinforces that oil palm production may drop by more than 50% due to water deficits and the crop will greatly impact the economy and the environment if the irrigation strategy is adopted in the areas of Maranhão.

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“…Annual cropping by Ucayali smallholders is based on traditional short fallow swiddens, with progressive clearing of forest or old-fallows and use of fire (Fujisaka 1997;Labarta et al 2008). Semi-perennial crops, usually in swidden agroforestry (Hiraoka 1986;Padoch and de Jong 1989) imply longer rotations and a less frequent need for clearing, while perennial crops tend to be associated to a level of intensification that eventually stabilizes forest clearing with positive environmental outcomes (Gutiérrez-Vélez et al 2011) 9 . Ucayali pastures, on the other hand, are often established following annual crops and managed extensively, representing low marginal cost to those who aim to extend the use of plot through grasses adapted to less fertile soils (Loker 1993).…”

Section: Methodsmentioning

confidence: 99%

Forest Use and Agriculture in Ucayali, Peruvian Amazon: Interactions Among Livelihood Strategies, Income and Environmental Outcomes

Porro

López‐Feldman

Alvarado

et al. 2014

Tropics

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Findings from a survey on sources of income and land allocation outcomes of 578 households from 26 communities with diverse ethnic composition at distinct environmental settings in Ucayali (Peruvian Amazon) are used to contrast livelihood strategies featuring high forest and high agriculture dependency, examining whether agricultural intensification can be linked to lower deforestation. A typology of households based on their land use allocation profile was used to assess current and cumulative cleared land. Recently cleared areas by households oriented to perennials, semi-perennials and pastures were similar to those focusing on annual crops. Multiple class comparisons provided evidence that land use intensification is not associated to land sparing. Near 40% of the households＇ annual income was derived from forests, followed by agriculture (25%), wages (17%) and livestock (11%). Income structure was used to determine high dependency on forests and on agriculture, featured by respectively 24% and 17% of the households, while 10% relied mostly on wages and/or businesses and half of them had a balanced income structure. Results indicate different expressions of the criticality of forest products, highlighting livelihood strategies based on the integration of income sources. Moreover, the study shows that despite the relevance of forest products, mestizo and indigenous livelihoods heavily depend on agriculture. Policy interventions aimed at environmental conservation and economic development will only be successful when strengthening the integration between agriculture and forest use featured by different social groups in the Amazon.

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High-yield oil palm expansion spares land at the expense of forests in the Peruvian Amazon

Cited by 130 publications

References 19 publications

Mapping Oil Palm Plantations in Cameroon Using PALSAR 50-m Orthorectified Mosaic Images

Mapping Oil Palm Plantations in Cameroon Using PALSAR 50-m Orthorectified Mosaic Images

Expansion of palm oil (Elaeis guineensis Jacq.) in the state of Maranhão and soil water deficit limitations in the Brazilian Amazon

Forest Use and Agriculture in Ucayali, Peruvian Amazon: Interactions Among Livelihood Strategies, Income and Environmental Outcomes

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