Land rents drive oil palm expansion dynamics in Indonesia

Using distribution maps of oil palm plantations across Indonesia for different time points spanning 2000–2015, and spatial variation in potential oil palm yields, we built a model explaining oil palm expansion using an agricultural land rent approach. This model allows us to examine the spread of oil palm plantations both spatially—from variations in crop yields and market accessibility—and temporally—according to changes in palm oil prices and production costs. We then projected the extent of further oil palm expansion beyond 2015 based on hypothetical projections of future prices, and from which we predict the effectiveness of Indonesia's Forest Moratorium.

We obtained spatially explicit distributions of oil palm plantations, other land-use types and vegetation classes across Indonesia in 2000, 2010 and 2015 [21, 22]. These were mapped as grid cells, each representing an area of 250 m by 250 m. For each cell, we obtained information of potential palm oil yield across space [13] (table S1 available online at stacks.iop.org/ERL/14/074024/mmedia). We also obtained information on the areas across Indonesia set aside for conservation from Indonesia's Forest Moratorium [23], legally protected areas [24] and locations of oil palm concessions [25]. We restricted our analyses to cells with positive potential palm oil yields, and cells available for conversion to oil palm plantation from 2000, i.e. existing oil palm plantations, concessions and all vegetation types across lowlands [22]. Our model therefore did not permit oil palm expansion into cells within protected areas and other plantations. Because the spatial distribution of oil palm plantations was not distinguished from other plantations in the map for the year 2000, we determined the distribution of oil palm plantations in 2000 as cells that were classified as plantations in 2000 and as oil palm plantations in 2010.

We based yearly production costs attributed to labour on annual reports of mean monthly national minimum wages [26]. We also obtained yearly national prices of fuel [27], fertilisers, oil palm fresh fruit bunches and timber [2]. Prices were deflated to USD 2015 values, and yearly prices were used where available: when prices were not available, we assumed constant prices from the previous year (table S1).

We based our crop expansion model on variation in agricultural rent across space and time [16]. Here, the decision to convert a cell for palm oil production is based on whether the amount earned from agricultural and timber harvests outweighs the costs involved to convert and manage a plantation, and, exceeds a minimum threshold. This threshold represents the opportunity costs of other land uses, including conversion to other crops: rent exceeding this threshold indicates a cell is more likely to be converted into oil palm plantation over other land uses. Rent for a cell i in a single year is calculated as

$$\begin{eqnarray}&&{{\rm{Rent}}}_{i}=({y}_{i}p+w)-\left(f+l+\displaystyle \frac{{y}_{i}}{c}{{vd}}_{i}\right),\end{eqnarray} \tag{ 1 }$$

where y_i is the potential yield per hectare in cell i, p is the price of oil palm fruit bunches, and w represents revenue from sale of timber from first clearing the land, given a set timber harvest of 23.1 m³ per hectare [28]. f and l represent capital costs attributed to fertiliser and labour per hectare respectively, with labour requirement set constant at 43.6 man days per hectare [29]. $\tfrac{{y}_{i}}{c}{{vd}}_{i}$ represents the cost (per hectare) of transporting fresh fruits, which we calculated from the number of trips needed given the yield y_i and the maximum capacity of oil palm fruit bunches a truck can carry (c, assumed as 18 m³), fuel cost per driving hour v, and the travel time d_i to the nearest large city (with at least a population of 50 000), therefore a measure of accessibility (S1).

For every cell i, we evaluated the rent net present value (NPV), i.e. the discounted sum of yearly agricultural rents across the lifespan of an oil palm plantation. The rent calculation from (1) is embedded within the formula for NPV given in equation (2), where t is a time index $t\in [0,T]$, with t = 0 as the base year for the plantation and T the final year in a crop cycle, and r is the discount rate.

$$\begin{eqnarray}&&{{\rm{NPV}}}_{i}=\displaystyle \sum _{0}^{T}\displaystyle \frac{{{\rm{Rent}}}_{i,t}}{{\left(1+r\right)}^{t}}.\end{eqnarray} \tag{ 2 }$$

NPV was calculated based on a typical 25 year life cycle (T = 25) of an oil palm plantation, accounting for time taken for crops to mature: oil palm crops typically start producing fruits after the third year, therefore we only considered returns from the harvest of fruits (y_ip) from the fourth to twenty-fifth years. Because our analyses relied on spatial variation of potential yields, we were limited to assuming constant yearly agricultural output upon maturity to maintain average values, instead of varying with age. Timber sales (w) were recorded as a one-off gain in the first year (t = 0).

Rent for each year t was discounted annually by a discount rate r, set at 10% following [30, 31], and NPV was derived from the summed discounted rents across all 25 years (2). We calculated the equivalent annual costs (EAC) of each cell i, i.e. the equivalent constant annual revenue that leads to a similar NPV value. Having calculated NPV and EAC for each cell in a given year, we then adjusted the EAC (EACadj), based on additional factors that could potentially influence the distribution and spread of oil palm plantations across time and space.

$$\begin{eqnarray}&&{{\rm{EACadj}}}_{i}={{\rm{EAC}}}_{i}-{P}_{i}-S\times {A}_{i,t-1}-K\end{eqnarray} \tag{ 3 }$$

K represents the minimum threshold rent needed to establish plantations, set constant across space and time. This includes the opportunity cost of capital, recognising the capital could have been invested elsewhere achieving some baseline profit. P_i adjusts EAC_i based on soil type, allowing for additional costs incurred from draining peat swamps prior to conversion. Finally, S accounts for adjustments in rent associated with the location of the cell in relation to existing oil palm plantations. This parameter captures the impact of local resources, labour skills and transport systems which result from having existing plantations in the area and which result in lower costs on the basis that the necessary infrastructure already established from neighbouring plantations would reduce costs of further expansion [8, 9, 32]. S therefore relates to the proportion of cells devoted to oil palm surrounding each cell. ${A}_{i,t-1}$ refers to the percentage of plantation area within a buffer (set at 0.1°) for cell i in period $t-1$ to capture this potential accelerating factor in crop expansion, where higher percentages of existing plantations surrounding a cell relate to reduced establishment costs for that cell.

We fitted our model to land-use maps in 2000 and 2015, simulating spatial predictions of Indonesian oil palm expansion every year from 2001 to 2015 based on yearly changes in agricultural rent across space from 2001 to 2014. We assumed a one-year time lag between changes in prices and establishing a plantation. Although we incorporated yearly changes in prices, we assumed that investment decisions were based on expectations of future prices, allowing current prices to represent future expectations in real terms. Starting from 2001, we calculated EACadj for cells not classified as oil palm plantations, based on deflated prices of oil palm fruits, labour, fertiliser and fuel in that year. Cells with agricultural rent exceeding the minimum threshold K (i.e. ${{\rm{EACadj}}}_{i}\gt 0$) were considered economically viable for oil palm agriculture, and we simulated conversion to plantation. We then updated prices and distribution of existing plantations to re-evaluate agricultural rent across the remaining unconverted cells the following year (2002). We repeated this process every year until 2015 (S1).

We determined parameter values that returned an outcome of oil palm expansion by 2015 with closest resemblance to the known distribution of oil palm plantations via an optimisation approach (S1), and across multiple iterations we selected as our fitted model the combination of parameter values that returned the highest recall, i.e. the highest average proportion of cells correctly predicted across both classes of oil palm plantations and non-plantations. This selects the model that produced the highest average proportion of both correctly predicted converted and unconverted cells. To determine magnitudes of the parameters and relationship of the spatial contagion effect, we repeated the optimisation process across different sets of models (i.e. ways of evaluating EACadj) and selected the model with the highest average recall as the final, best performing model (S1). We also compared our analyses with oil palm expansion models that only account for suitability and yield (S1).

Due to computational limitations, models were fitted on a subset of cells stratified-randomly sampled across the total dataset (∼24000 of 25 111 235 cells), ensuring the same proportion of cells across all provinces. Given the limitations of this single-crop expansion model, we did not model displacement of other crops by oil palm and, therefore, cells classified as other plantations were excluded from this analysis except where oil palm concessions had been awarded. Additionally, we did not account for oil palm abandonment due to the lack of spatial information of area and extent of abandoned fields. We validated our final model against a larger subset of the overall data (10%, ∼2 400 000 cells), and model performance was similarly evaluated by comparing the predicted with the observed distribution of oil palm.

Using projected palm oil prices from 2016 to 2025 [2, 33], while keeping all other costs at 2015 values, we ran our model forwards to determine areas susceptible to future expansion as palm oil prices vary and identified areas that become economically viable for oil palm expansion each subsequent year. In keeping other prices constant in real terms, our projections show the direct impact of oil palm prices on future oil palm expansion. Given our model only focuses on the spread of oil palm plantations, we do not examine future displacement of other crops by oil palm, and excluded other plantations from projections of oil palm expansion beyond 2015. From these projections, we identified the proportion of areas vulnerable to crop expansion that fall under protection by Indonesia's 2011 Forest Moratorium.

A land rent framework was more effective in explaining Indonesia's oil palm expansion than just relying on suitability (S2). Of the models run, Model 4 performed best (average recall = 75.8%; S2) and was used for validation and projection. This model included a minimum threshold K of USD10,053 per hectare before a new plantation is established, adopting a discount rate of 10%. We also captured a spatial contagion effect in relation to agricultural rent: lower costs are incurred (S = USD987 per hectare) as the percentage of existing surrounding plantations increases, following a square-root relationship. We excluded additional costs of establishing plantations on peat soils in this model (i.e. P = USD0 per hectare). Considering an overall relationship across fifteen years, our model showed gradual increase in the area cleared for oil palm each year. As prices of oil palm fruits (relative to other costs) increased from 2000 to 2010, so did the extent of oil palm expansion into forests and peatlands. Additionally, with the spatial contagion process, even with the slight drop in fruit prices beyond 2011, the extent of oil palm plantations continued increasing.

Against our validation data-points (10% of the total area), our model showed an overall accuracy of 85.84%. We correctly identified 70.07% of cells converted to plantations in 2015 (58 483 out of 83 460 cells). Our model performed particularly well in Kalimantan, Jambi, Riau, North and West Sumatra (figure 1). The model also correctly identified 79.23% of peat swamps converted into oil palm plantations by 2015, particularly in Riau, North and West Sumatra (S4). The model could not identify 29.93% of the converted cells (24 977 out of 83 460 cells) as having agricultural rents high enough to establish plantations. Of these cells, 17 286 (69.2%) had been classified as other plantations in 2000 but converted to oil palm by 2015, thus had not been detected by our model. Other cells were located within areas and provinces (e.g. West Papua, East Kalimantan) with no detected oil palm plantations in 2000 (figure 1).

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Our model also had a false positive rate of 13.53%, i.e. cells predicted to be economically profitable for conversion into plantations but were not classified as oil palm plantations in 2015 (figure 1). These cells were mainly located within proximity to existing plantations, especially across provinces in Sumatra and Kalimantan. Of these cells, 50.49% were classified as plantations: while the returns from oil palm expansion was high, these areas had been converted to other crops instead (figure S1). Provinces such as West Papua, Bengkulu, Jambi, and Southeast Sulawesi, for instance, showed high false positive rates (>65%, S4).

Keeping other costs constant at 2015 values and assuming no other land-use changes, the extent of oil palm plantations based on projected annual prices of oil palm fruits could grow by as much as 4.5 times by 2020 (figure 2), and six times by 2025 (S5). Areas economically viable for further crop expansion were mainly located near existing oil palm plantations. Projected oil palm expansion was therefore highest across Sumatra and Kalimantan. Only 9.79% of the areas susceptible to oil palm expansion by 2020 (10.27% by 2025) fall within Indonesia's Forest Moratorium. 80.67% of natural areas (i.e. forests, peatlands and mangroves) vulnerable to oil palm expansion by 2020 (83.9% by 2025) were not protected by the Forest Moratorium (table S5). Provinces like Riau, Papua and West Papua were better protected against oil palm expansion, with a higher proportion of areas with high agricultural rents by 2025 falling within the Forest Moratorium areas (0.22–0.27, table S6). Conversely, within Kalimantan, large proportions of natural areas susceptible to expansion by 2025 were not protected by the Forest Moratorium (≥0.89, table S6).

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