# Climate change impacts on Robusta coffee production in Vietnam

Lan Anh Dinh, Filipe Aires, Eric Rahn

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## To cite this version:

Lan Anh Dinh, Filipe Aires, Eric Rahn. Climate change impacts on Robusta coffee production in Vietnam. Environmental Research Letters, 2025, 20 (11), pp.114059. 10.1088/1748-9326/ae14d1 hal-05375883

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Climate change impacts on Robusta coffee production in
Vietnam

Lan Anh Dinh<fnref n="1" />,*, Filipe Aires<fnref n="1" />,<fnref n="2" /> and Eric Rahn<fnref n="3" />

E-mail: lan-anh.dinh@obspm.fr

**Keywords:** Climate change, Robusta coffee production, Suitability impact model, Regional climate simulations

**Abstract**

The Central Highlands of Vietnam is the largest Robusta coffee (*Coffea canephora* Pierre ex A.Froehner) growing region in the world. This study identifies the most important climatic variables that determine the current distribution of coffee in the Central Highlands and builds a “coffee suitability” model to assess changes in this distribution due to climate change scenarios. A neural network-based suitability model was trained on coffee occurrence data derived from national statistics on coffee-growing areas. Bias-corrected regional climate models, adjusted to reduce systematic deviations from observed patterns, were used for two climate change scenarios (RCP8.5 and RCP2.6) to assess changes in suitability for three future time periods (2038-2048, 2059-2069, 2060-2070) relative to the 2009-2019 baseline. Average expected losses in suitable areas were 62% and 27% for RCP8.5 and RCP2.6, respectively. The loss in suitability due to RCP8.5 is particularly pronounced after 2060. Increasing mean minimum temperature during the harvest (October-November) and growing season (March-September), and decreasing precipitation during the late growing season (July-September) mainly determined the loss in suitable areas. Given these risks, adaptation strategies such as shade management, soil conservation, and the development of climate-resilient varieties are essential to sustain coffee production in the region.

**1 Introduction**

Agriculture is very vulnerable to climate change, particularly in the tropics. Coffee, a perennial tropical crop providing a vital livelihood to millions of smallholder farmers, has been identified as a highly vulnerable crop, with farmers mostly lacking the resources to invest in adaptation measures (Bunn, Laderach, Ovalle Rivera & Kirschke 2015, Verburg et al. 2019). There are two main economically important coffee species: Arabica coffee (*Coffea arabica* L.) and Robusta coffee (*Coffea canephora* Pierre ex A.Froehner). The latter has a broader environmental niche and higher genetic diversity (Herrera & Lambot 2017) and therefore is expected to be less sensitive to climate change than Arabica coffee. Supra-optimal temperatures as a result of climate change negatively affect the coffee quality and yields, which is further exacerbated by unsuitable rainfall distribution (Bertrand et al. 2012, Läderach et al. 2017, Kath et al. 2021). Due to the high sensitivity of Arabica coffee to changes in temperature, most studies have focused on this species, while only a few studies have studied potential climate change impacts on Robusta coffee. Recent research, however, suggested that Robusta coffee yield is more sensitive to temperature increase than previously expected (Kath et al. 2020).

Most climate change impact assessments for coffee relied on global climate models (GCMs) as inputs for future climate projections (Baca et al. 2014, Bunn, Laderach, Pérez Jimenez, Montagnon & Schilling 2015, Ovalle-Rivera et al. 2015). However, their coarse spatial resolution often lacks detailed regional information. In addition, GCMs could introduce large uncertainties, especially over some specific regions, e.g., Southeast Asia (SEA), where the climate is complex and spatially heterogeneous. Regional climate models (RCMs) could be considered as a better alternative source for climate change impact studies (Teutschbein & Seibert 2010, Nguyen et al. 2022). Therefore,

<footnote n="1">¹LIRA, Sorbonne Université, Observatoire de Paris, Université PSL, CNRS, 75014 Paris, France</footnote>

<footnote n="2">²Estellus, 75014 Paris, France</footnote>

<footnote n="3">³International Center for Tropical Agriculture (CIAT), Cali, Colombia</footnote>

<footnote>* Author to whom any correspondence should be addressed.</footnote>
this study considers several RCMs of the Coordinated Regional Climate Downscaling Experiment for Southeast Asia (CORDEX-SEA) (Giorgi et al. 2008, Juneng et al. 2016) to provide climate projections for future decades.

Correlative species distribution models have been predominantly used to assess climate changes in coffee suitability, e.g., in Davis et al. (2012), Bunn, Laderach, Ovalle Rivera & Kirschke (2015), or Moat et al. (2017). This approach often relates the present occurrence locations with climatic variables expected to determine the environmental niche for the species distribution. A classification algorithm is used to determine a relationship between the labeled data set (i.e., the presence and absence of coffee) and the selected input variables. It, therefore, identifies areas that are similar in terms of the most important variables selected to determine the distribution of the crop occurrence data. This relationship is used to determine climate suitability, which represents the assumption that strong deviations in key climate characteristics relevant to the crop distribution will negatively affect the climate suitability of the crop. However, this approach should be treated with care as the relationship might not capture all relevant aspects determining the actual climate suitability for coffee. This can be due to biases in the occurrence data, missing climatic drivers, or biases in the climate data.

Here, we investigate the possibility of obtaining a robust coffee suitability model. We assume that the current distribution of Robusta coffee in Vietnam reflects its climatic suitability, while acknowledging that this distribution is also influenced by farmers’ decisions rather than natural dispersal. By using observed distribution data as a proxy for climatic suitability, we recognize that socioeconomic factors may also influence planting choices. For the occurrence data, we develop a systematic scheme to identify the presence of coffee based on the statistical coffee-growing areas at the district level. In addition, previous studies often used a set of bioclimatic variables (Bunn, Laderach, Ovalle Rivera & Kirschke 2015, Ovalle-Rivera et al. 2015). However, this set might not be the best option, especially in a statistical approach where the model is sensitive to overfitting (Dinh & Aires 2022). Thus, a careful selection of potential climate variables can help improve the robustness of the coffee suitability model. Moreover, a calibration (or bias correction) method is used to post-process regional climate data before deriving the potential climate variables for the suitability model under several future scenarios. This calibration is essential to minimize systematic biases and bring the climate simulations as close as possible to the observations.

In the following, section 2 introduces the data, including the study area, coffee-growing areas, potential climate predictors, and the selected climate data. Then, the methods used in this study are described in section 3. Section 4 presents the results of the model validation and climate suitability. The results are discussed and concluded in section 5.

## 2 Data

### 2.1 Study area and coffee-related data

The study area is located along the longitude 104.22 °E to 109.94 °E and latitude 9.02 °N to 17.82 °N, which covers the Vietnamese high-intensity Robusta coffee production area. A map of the study area is shown in figure 1. In detail, figure 1a shows the Southeast Asia domain covered by the regional climate models (see section 2.3). Then, figure 1b provides a close-up of the study area with the elevation information. Digital elevation data, SRTM 1 Arc-Second Global data (USGS EROS Center 2018), are downloaded from https://www.usgs.gov/centers/eros/science/ usgs-eros-archive-digital-elevation-shuttle-radar-topography-mission-srtm-1. The data on planted coffee areas at the district level are obtained from the General Statistics Office of Vietnam (GSO). Figure 2a shows the planted coffee area (in ha) averaged from 2014 to 2018, while figure S1 (supplementary material) presents the time series data for five provinces in Vietnam's Central Highlands during this period. More details about this dataset can also be found in Dinh et al. (2022) and Dinh (2022).

### 2.2 Potential predictors

We chose a set of 12 environmental variables (table 1) as the potential model predictors. These potential predictors are derived from precipitation, temperature, and evaporation, characterizing growing conditions such as the flowering (i.e., from January to March), the growing season (i.e., from March to September), or the harvest period (i.e., from October to December). They have been selected according to expert knowledge on coffee and a literature review (Bunn, Laderach, Ovalle Rivera & Kirschke 2015, Lambot et al. 2017, Kath et al. 2020, 2021, Dinh et al. 2022).
<figure><img src="image_3.png" /><figcaption>Figure 1: Study area: (a) the Southeast Asia domain covered by the regional climate models with a focus on (b) the study area (104.22 - 109.94 °E and 9.02 - 17.82 °N) with the topography elevation (m) information.</figcaption></figure>

## 2.3 Climate data

Monthly data on total precipitation, mean temperature, maximum and minimum daily temperature, and evaporation are considered for both current and future climate. For the current climate (1981-2019), we used the Era5-Land reanalysis dataset (Muñoz Sabater 2019). This dataset is available at a 0.1° × 0.1° resolution (about 10 km × 10 km at the Equator), and can be downloaded at https://cds.climate.copernicus.eu/cdsapp#!/home. We then integrated these data into a 0.22° × 0.22° grid to be consistent with the future climate data, which will be presented in the following.

Regional climate simulations and projections are obtained from several model simulations of the Coordinated Regional Climate Downscaling Experiment for Southeast Asia (CORDEX-SEA) (Giorgi et al. 2008, Juneng et al. 2016), with 0.22° × 0.22° resolution. The data are available at https://esg-dn1.nsc.liu.se/search/cordex/. The list of regional climate models (RCMs) carried out in this study is presented in table 2. We considered the historical period from 1981 to 2005, used for the calibration (or bias correction) in section 3.3. We will investigate the future period from 2031-2077 for the Representative Concentration Pathway (RCP) scenarios of the Coupled Model Intercomparison Project – Phase 5: the low (RCP2.6) and high (RCP8.5) greenhouse gas concentration scenarios (van Vuuren et al. 2011). The RCP2.6 scenario is a so-called “peak” scenario, which implies that the radiative forcing level reaches 3.1 W m⁻² by mid-century but returns to 2.6 W m⁻² at the end of the century. On the other hand, RCP8.5 represents a future with a radiative forcing of 8.5 W m⁻² by 2100. It is important to note that global emissions do not precisely align with any single RCP scenario due to various factors like policy choices, technological progress, and unexpected events (Pachauri & Meyer 2014). However, numerous countries are working towards substantial reductions in emissions, particularly in line with the less severe RCP2.6 scenario, to tackle the problem of global warming.

A comparison of current and future climate data for environmental variables, i.e., Tmin39 and P79, is presented and discussed in text S1 and figure S2 in the supplementary material.
<table><caption>Table 1: Description of the 12 environmental variables used in this study as potential predictors for the suitability model.</caption><thead><tr><th>No</th><th>Variables</th><th>Name</th><th>Months</th><th>Units</th></tr></thead><tbody><tr><td>1</td><td>P1012</td><td>Precipitation during harvest</td><td>Oct. - Dec.</td><td>mm</td></tr><tr><td>2</td><td>P79</td><td>Precipitation during late growing season</td><td>Jul. - Sept.</td><td>mm</td></tr><tr><td>3</td><td>P39</td><td>Precipitation during growing season</td><td>Mar. - Sept.</td><td>mm</td></tr><tr><td>4</td><td>P13</td><td>Precipitation during flowering</td><td>Jan. - Mar.</td><td>mm</td></tr><tr><td>5</td><td>Tmin1012</td><td>Mean minimum temperature during harvest</td><td>Oct. - Dec.</td><td>°C</td></tr><tr><td>6</td><td>Tmin39</td><td>Mean minimum temperature during growing season</td><td>Mar. - Sept.</td><td>°C</td></tr><tr><td>7</td><td>Tmax39</td><td>Mean maximum temperature during growing season</td><td>Mar. - Sept.</td><td>°C</td></tr><tr><td>8</td><td>Bio2</td><td>Annual mean diurnal range</td><td></td><td>°C</td></tr><tr><td>9</td><td>Bio5</td><td>Maximum temperature of warmest month</td><td></td><td>°C</td></tr><tr><td>10</td><td>Bio7</td><td>Annual temperature range</td><td></td><td>°C</td></tr><tr><td>11</td><td>Bio15</td><td>Precipitation seasonality (CV)</td><td></td><td>Percent</td></tr><tr><td>12</td><td>NDM</td><td>Maximum number of consecutive dry months</td><td></td><td>Month</td></tr></tbody></table>

<table><caption>Table 2: List of regional climate models used in this study.</caption><thead><tr><th>No.</th><th>Abbreviation of RCM experiment</th><th>Driving model (GCM)</th><th>Realisation</th><th>RCM</th><th>RCP2.6</th><th>RCP8.5</th></tr></thead><tbody><tr><td>1</td><td>SMHI_CNRM</td><td>CNRM-CM5 (CNRM, France)</td><td>rli1p1</td><td>RCA4 (SMHI, Sweden)</td><td></td><td>x</td></tr><tr><td>2</td><td>SMHI_Had</td><td>HadGEM2-ES (Hadley Centre, UK)</td><td>rli1p1</td><td>RCA4 (SMHI, Sweden)</td><td></td><td>x</td></tr><tr><td>3</td><td>ICTP_Had</td><td></td><td>rli1p1</td><td>RegCM4 (ICTP, Italy)</td><td>x</td><td>x</td></tr><tr><td>4</td><td>GERICS_Had</td><td></td><td>rli1p1</td><td>REMO2015 (GERICS, Germany)</td><td>x</td><td>x</td></tr><tr><td>5</td><td>ICTP_NCC</td><td>NCC-NorESM1-M (NCC, Norway)</td><td>rli1p1</td><td>RegCM4 (ICTP, Italy)</td><td>x</td><td>x</td></tr><tr><td>6</td><td>GERICS_NCC</td><td></td><td>rli1p1</td><td>REMO2015 (GERICS, Germany)</td><td>x</td><td>x</td></tr><tr><td>7</td><td>ICTP_MPI</td><td>MPI-ESM-MR (MPI-M, Germany)</td><td>rli1p1</td><td>RegCM4 (ICTP, Italy)</td><td>x</td><td>x</td></tr><tr><td>8</td><td>GERICS_MPI</td><td></td><td>rli1p1</td><td>REMO2015 (GERICS, Germany)</td><td>x</td><td>x</td></tr></tbody></table>

# 3 Methods

## 3.1 Learning database identification

To develop a coffee suitability model, we need a database of samples indicating the presence (coffee samples) or absence (background samples) classes. Such a database can therefore be used to calibrate a statistical suitability model. Here, the presence data are based on observations (i.e., data from GSO); the absence data are generated and not observed. To represent the current distribution, we averaged the observed coffee area over the most recent five-year period (2014–2018), assuming relative stability in planted area. This assumption is supported by provincial statistics, which show average annual changes ranging from 0.05% to less than 2% (see figure S1 in the supplementary material). The detailed methodology to identify these two datasets is presented as follows.

The current coffee samples are built from the available coffee areas with respect to the climate-gridded cell (0.22° × 0.22°). Here, we neglected coffee-growing districts with less than 500 ha, as they are relatively small compared to the size of one climate cell. To assess robustness, we tested alternative thresholds ranging from 300 to 800 ha and found consistent results, with 500 ha providing the best balance between data quality and spatial coverage. For districts with a coffee-growing area of 500 ha, one corresponding gridded cell will be selected as a coffee sample (or coffee cell): (1) if the shape of the district is smaller than the cell or it contains only one cell, we chose that gridded cell; (2) if the shape is overlapped by several cells (e.g., often two cells), the cell with higher overlap surface was selected; (3) if the shape contains several cells which was seldom the case, the coffee cell was randomly chosen among these cells. For districts with coffee-growing areas higher than 500 ha, several corresponding cells were considered and classed as current coffee samples. For each district, the number of cells was defined by a scaling factor $S_f$, which is computed by the ratio of the coffee area over 500 ha. For instance, let's consider a district with 5000 ha of coffee. The corresponding scaling factor is $S_f = \frac{5000}{500} = 10$, meaning that there will be ten coffee cells associated with this district. We identified these ten cells by replicating the corresponding gridded cell(s).

The background samples were randomly selected from a 4.4° buffer around present region. This buffer size was chosen to provide an adequate area for selecting background points, consistent with previous studies (Bunn, Laderach, Ovalle Rivera & Kirschke 2015). The ratio of background samples to current coffee data was set to 1:1, as recommended in Barbet-Massin et al. (2012). Too few (or too many) background samples can lead to false alarms as the model is biased toward the coffee samples (or the background samples).

The learning database was used to explore the corresponding elevation of the coffee and
background samples. This information was used to exclude points that are unsuitable for coffee for both current climatic conditions and future climate projections. Figure 2a shows the map of the learning database — coffee (red points) and background (blue points) samples — together with the coffee area data over the study area. We also plot the normalized histogram of the elevation (in m) corresponding to these coffee and background points in figure 2b. The learning database suggests that Robusta coffee in the Central Highlands of Vietnam requires an elevation higher than 100 m and mostly from 450 m to 1100 m. Thus, in the climate change impact assessments, we excluded all cells below 100 m.

<figure><img src="image_4.png" /><figcaption>Figure 2: (a) The coffee planted area (in ha) averaged from 2014 to 2018, and the learning database including current coffee (in red) and background (in blue) samples; (b) normalized histogram of the learning database in terms of elevation (in m), the overlapped values appear in purple.</figcaption></figure>

## 3.2 Suitability model

### 3.2.1 Model selection
Our suitability model uses a feedforward neural network for binary classification (coffee versus no coffee) trained using backpropagation and stochastic gradient descent (Schmidhuber 2015, Goodfellow et al. 2016). Neural networks were chosen because they are well suited to capture the non-linear and multidimensional relationships between climate variables and coffee distribution, and they have been successfully applied in ecological and crop modeling (Pacifico et al. 2018, Dinh & Aires 2022, Shafik et al. 2025). The network consists of an input layer corresponding to the selected predictors, one hidden layer containing 10 neurons, and an output layer producing binary predictions. A schematic of the architecture is provided in figure S3, while additional technical details are available in text S2 in the supplementary material.

As in a classification task, NN trains the generic feedforward neural network to map each input vector into its corresponding target vector. The target is based on the learning database introduced previously. The inputs are chosen from 12 potential predictors (section 2.2). A forward selection method is used: the first selected input is the one that gives the best-performing network (i.e., the smallest mean squared error). The second one is selected among the remaining potential predictors. The two selected inputs define a two-input model that gives the best-performing network. We continue this process until all potential predictors are selected to obtain the hierarchy of the explanatory variables. However, for the final model, we only used four inputs as the use of more inputs leads to overfitting related to a poor generalization ability (Dinh & Aires 2022).
### 3.2.2 Model training and evaluation
To assess the model’s generalization, we divided our database into three sets: training (60%), validation (20%), and testing (20%). The model is then evaluated using performance metrics derived from the confusion matrix (see figure S4 in the supplementary material), which are commonly used for evaluating classification models (Provost & Fawcett 2001, Sokolova & Lapalme 2009). We considered two common metrics:

* precision *p*: the measure of correctly identified coffee samples over the number of all coffee predicted samples;

* recall *r*: the measure of correctly identified coffee samples over the count of actual coffee samples.

### 3.2.3 Impacts
We first trained and tested the model on the learning database for the current climate (2009-2019) to obtain optimal model parameter values. Then, the model is applied to all cells in the study area for different periods: current (i.e., 2009-2019) and future (i.e., 2038-2048, 2049-2059, and 2060-2070 periods). The baseline climate (2009–2019) was defined as an 11-year window to ensure consistency with the future projection periods, and does not necessarily coincide with the coffee planted area data, which are assumed to be generally unchanged. The results are visualized as maps with continuous scores, which are normalized from 0 (not suitable) to 1 (suitable). A threshold is used to determine if a cell is suitable or not for coffee. The threshold is based on the probability density functions of these two classes in the actual learning database (figure 3) and the coffee area. As shown in figure 3, a threshold of 0.5 helps to distinguish very well between the coffee and background samples. In addition, with this threshold, our model was able to identify about $70 \times 10^4$ ha of suitable areas in the Central Highlands, which is comparable to the actual coffee-growing area in this region (i.e., $57 \times 10^4$ ha). The area is computed by summing the entire land area within suitable cells (i.e., a pixel cell of $0.22^\circ \times 0.22^\circ$).

<figure><img src="image_5.png" /><figcaption>Figure 3: Normalized histogram of the coffee suitability prediction. A threshold of 0.5 is chosen for the coffee or background (no coffee) classification.</figcaption></figure>

For suitability assessments of future climate projections, we first applied the suitability model for each of the eight calibrated RCMs (table 2) for the high-end emission scenario RCP8.5. Then, we compared the suitability changes under RCP2.6 and RCP8.5 scenarios using only six RCMs, i.e., all models presented in table 2 except SMHI_CNRM and SMHI_Had as they do not have simulations for the RCP2.6 scenario. We investigated the area changes respective to elevation.
## 3.3 Calibration of climate simulations

### 3.3.1 Calibration method
A calibration (also known as a bias correction) is a necessary prerequisite for climate change impact studies as climate models often suffer from substantial biases and errors (i.e., structural biases or parametric uncertainties) compared to the observations (Hawkins & Sutton 2011, Teutschbein & Seibert 2012, Chen et al. 2013, Maraun & Widmann 2018, Dinh & Aires 2023). This study used the equidistant quantile mapping (EqQM) (Li et al. 2010, Pierce et al. 2015) or equiratio CDF matching (Wang & Chen 2014) to calibrate the CORDEX-SEA data in order to enhance the accuracy of future climate projections. The calibration was done using three datasets, including:

* the historical observations $X_{O,h}$ (integrated Era5-Land, 1981-2005),

* the corresponding simulations $X_{M,h}$ on the historical record (CORDEX-SEA22, 1981-2005),

* and the simulations $X_{M,f}$ for the future (CORDEX-SEA22 for 3 periods 2031-2055, 2042-2066, and 2053-2077).

The calibrated data $X_{C,f}$, for the 2031-2055 period, for instance, are computed as:

$$X_{C,f}(i) = X_{M,f}(i) + F_{O,h}^{-1}[F_{M,f}(X_{M,f}(i))] - F_{M,h}^{-1}[F_{M,f}(X_{M,f}(i))] \quad (1)$$

for temperature variables, and

$$X_{C,f}(i) = X_{M,f}(i) \times \frac{F_{O,h}^{-1}[F_{M,f}(X_{M,f}(i))]}{F_{M,h}^{-1}[F_{M,f}(X_{M,f}(i))]} \quad (2)$$

for precipitation and evaporation variables. In equations (1) and (2), i is the time step, F is the cumulative distribution function, and $F^{-1}$ is the inverse of F. After these calibration processes, the adjusted climate variables more accurately represent future conditions. These corrected variables were then used to derive potential predictors (as detailed in section 2.2) that act as inputs for the coffee suitability model for future predictions.

### 3.3.2 Temporal configuration
To better preserve the temporal evolution of climate change, the calibration is done for several time blocks (Switanek et al. 2017). In detail, we used 25-year periods with an 11-year sliding window to calibrate the 11 middle years. For instance, the 2038-2048 period was calibrated using a model set up in the 2031-2055 (future) versus the 1981-2005 (historical) periods. Next, we calibrated 2049-2059 data using the future 2042-2066 period and the same historical period. Finally, we used the model set up in the 2053-2077 and 1981-2005 periods to obtain the 2060-2070 calibration.

# 4 Results

## 4.1 Model validation and variable contribution

The suitability model, which is trained and tested over the learning database, shows a good predictive performance for the current period (2009-2019). The model precision is $p = 95.4\%$, meaning that less than 5% of background samples are misclassified as coffee regions. We also obtain a high model recall of $r = 96.9\%$, which implies that the model can identify the actual coffee cells very well on the historical record, based on actual observations.

Among 12 potential predictors, the most important variables are (1) the mean minimum temperature during the harvest and growing season (i.e., Tmin1012 and Tmin39 shown in table 1); (2) the precipitation variables are also crucial for robusta coffee, including precipitation during the late growing season (P79) and flowering (P13). The least important variables are the mean maximum temperature during the growing season (Tmax39) and the maximum temperature in the warmest months (Bio5) (in the supplementary table S1).

## 4.2 Climate suitability

### 4.2.1 Current and future suitabilities
The (trained and tested) suitability model is applied over all study cells for the current period, i.e., 2009-2019 (figure 4). The highly suitable areas are located in higher elevation areas of the Central Highlands of Vietnam. We then applied this suitability model for eight RCMs (presented in table 2) under the RCP8.5 emission scenario. Figure 4 shows the mean and standard deviation of the future suitabilities resulting from these eight simulations for three different future periods, i.e., 2038-2048, 2049-2059, and 2060-2070. Compared to the current suitability, highly suitable areas will decrease significantly in the future.
<figure><img src="image_6.png" /><figcaption>Figure 4: Current and future suitability for coffee. The mean and standard deviation (Std) of future suitabilities are computed from eight RCMs presented in table 2 under the RCP8.5 scenario. Dark blue (or 100%) indicates high suitability, and white (or 0%) means low suitability. The corresponding suitable areas (in $10^4$ ha) are indicated in each panel. The area is computed by summing the entire land area within suitable cells.</figcaption></figure>

These areas become smaller and smaller in time. For example, the suitable area is about $81 \times 10^4$ ha for the current period (2009-2019); however, it will be reduced by half in the 2065s ($\approx 42 \times 10^4$ ha). In addition, the eight simulations give similar predictions by showing small standard deviation values, i.e., 15% on average, for all three projected periods.

### 4.2.2 Sensitivity to climate scenarios
We now study the suitability model's sensitivity to different climate scenarios. As presented in table 2, only six of the eight RCMs provide the simulations for both RCP2.6 and RCP8.5 scenarios. Therefore, in the following comparisons, we will use only six RCM simulations (all models in table 2 except SMHI_CNRM and SMHI_Had).

First, let us look at the changes in suitability (the difference between future and current suitability) for different future periods under the RCP2.6 and RCP8.5 scenarios shown in figure 5. As expected, climate change impacts are less pronounced in the low CO₂ emission scenario (a1 to a3) than in the high CO₂ emission scenario (b1 to b3). For the 2038-2048 period, for instance, the number of red cells, which signify the negative change, is much less and lighter in (a1) than (b1). Similar behaviours are obtained for two other considered periods, as shown in (a2) versus (b2) and (a3) versus (b3). In addition, both scenarios show that most of the study regions suffer negative impacts or could become unsuitable for coffee, i.e., shown in many red cells in figure 5. Nevertheless, a small area in the northern Central Highlands could actually become more suitable for coffee. This area covers high mountains ranging from 875 to 1200 m. The change in climate is compatible with the increase in elevation. For the RCP2.6 scenario, the impact of climate change is relatively constant over time. On the other hand, for the RCP8.5 scenario, the negative impacts
<figure><img src="image_7.png" /><figcaption>Figure 5: Suitability change (future suitability - current suitability) for three future periods (2038-2048, 2049-2059, and 2060-2070) in the RCP2.6 and RCP8.5 scenarios. Dark red (or -100%) presents areas with drastic changes from suitable to unsuitable, and dark blue (or 100%) indicates positive changes.</figcaption></figure>

become stronger toward the end of the century: more regions suffer from negative impacts in the 2060-2070 period (b3) than in the 2038-2048 period (b1).

### 4.2.3 Distribution of climate change impacts
Figure 6 shows the distribution of the suitable regions by elevation for three future periods (i.e., 2038-2048, 2049-2059, and 2060-2070). Here, we compute the suitable areas by summing the entire land area within suitable cells across 100 m elevation classes.

In general, suitable areas under the RCP2.6 scenario (blue lines and shaded areas in figure 6) are somewhat comparable to the currently suitable areas (black lines in figure 6). The average losses are about 27±9% over three projected periods. The most considerable loss of suitable area could be up to 36±5% for the 2060-2070 period. For the high-impact scenario RCP8.5, the suitable areas decrease significantly for three projected periods, with the losses ranging from 39±4% up to 83±9%. These estimates, along with associated uncertainties, are derived from ensemble simulations using six climate models for RCP2.6 and eight climate models for RCP8.5 (see table 2). Following established community practice, we use the inter-model spread as a proxy for uncertainty; however, it is important to note that this spread only represents a portion of the total uncertainty. Overall, the total suitable area remains relatively stable between 2038–2048 and 2049–2059. However, we observe a substantial decrease (i.e., about 15%) after the 2060s compared to the two previous projected periods. The suitability is very sensitive to the elevation. The major losses are found in low-elevation areas (i.e., below 800 m). In contrast, the total suitable areas do not change much in higher elevations, especially above 850 m.
<figure><img src="image_8.png" /></figure>

Figure 6: Distribution of suitable areas by elevation for three future periods (i.e., 2038-2048, 2049-2059, and 2060-2070). Black lines indicate the current (2009-2019) suitable area. Color lines and corresponding shaded areas present the mean and standard deviation of future suitable areas induced from six RCMs (all models in table 2 except SMHI_CNRM and SMHI_Had) under RCP2.6 and RCP8.5 scenarios. The areas are calculated by summing the entire land area within suitable cells across 100 m elevation classes.

**5 Conclusion**

This study assessed the climate suitability of Robusta coffee in the Central Highlands of Vietnam, the world's largest Robusta-producing region, under future climate change scenarios. Our modeling framework was based on a neural network approach, incorporating a carefully selected set of predictors and a systematic partitioning of the dataset into training, validation, and testing subsets. This strategy ensured robust evaluation on unseen data and provided a reliable estimate of the model's generalization capacity. While effective, an alternative approach would be spatial cross-validation, which accounts for spatial autocorrelation and may better capture real-world prediction uncertainties. Investigating this strategy represents a valuable direction for future research.

A key contribution of this work is the application of bias-corrected RCMs rather than GCMs to assess crop suitability. The RCMs are considered here as they provide finer spatial resolution outputs and more adequately represent the climate and weather processes over a complex and spatially heterogeneous Southeast Asia region that includes mountains and coastal areas. The coffee suitability model indicates that projected climate change scenarios will negatively impact the suitability for growing coffee in the Central Highlands of Vietnam. The degree of suitability change depends on the emission scenarios (RCPs) and time periods. As expected, suitability decreases over time, particularly in the period 2060-2070. Towards the mid-century (2038-2048), suitability is negatively affected under the RCP8.5 scenario with a loss in suitable areas of about 56.5%, while changes are more subtle under the RCP2.6 scenario with 21.7% losses. The RCP8.5 scenario is the worst-case emissions pathway assuming no policy success towards climate change mitigation, high population growth, and continued reliance on fossil fuels. The RCP2.6 is more consistent with current trajectories pointing to a 2°-3°C warming range (Hausfather & Peters 2020, Pielke Jr et al. 2022). However, there is still a possibility of the worst-case scenario, which could result in an 83% reduction in suitable areas, with significant losses occurring below 800 m. Losses in suitable areas are particularly striking in Dak Lak province, where the current climate is hotter and drier than other coffee-growing areas in the Central Highlands.

Given these risks, coffee growers need to adopt effective adaptation strategies. Farm-level practices such as shade management and soil conservation can enhance resilience across different regions (Rahn et al. 2014, Pham et al. 2021). These measures are relatively low-cost, can be implemented within a few growing seasons, and are therefore particularly feasible for smallholder farmers. Additionally, breeding and dissemination of drought-resistant and heat-tolerant varieties (Läderach et al. 2017) represent a medium- to long-term strategy, requiring sustained investment in research, extension services, and seed distribution. Another potential adaptation pathway is the relocation of coffee production to higher elevations, which are projected to become more climatically suitable (Ovalle-Rivera et al. 2015). However, this option comes with high costs related to land acquisition, infrastructure development, and market access. Such relocation would therefore represent a long-term strategy that could only be implemented with strong government and policy support, as well as careful planning to avoid negative social and environmental impacts. A phased
or gradual relocation approach may be more realistic, for example, by progressively expanding into higher-elevation zones over time. While our findings highlight that the most severe losses are expected in low-elevation areas under RCP8.5 by the 2060s, relocation should be considered a complementary strategy, rather than a replacement, for adaptation practices at the farm level. The effectiveness of these measures depends on proactive adaptation and the specific climatic conditions of each region.

Not surprisingly, and consistent with previous studies focusing on the climate sensitivity of Robusta coffee (Bunn, Laderach, Ovalle Rivera & Kirschke 2015, Kath et al. 2020, Dinh et al. 2022), temperature is a major determinant of the distribution of climate suitability of coffee. Increased temperature accelerates bean development and ripening and increases plant respiration (DaMatta et al. 2019). Under the RCP2.6 scenario, changes in agronomic practices (e.g., the use of shade trees) and the use of more adapted clones could potentially compensate for the loss of suitability. However, further research is required to translate changes in climate suitability into changes in coffee quality and yield. Kath et al. (2020) identified that Robusta coffee yields in Vietnam were highest at low temperatures and that every 1°C increase in mean minimum/maximum temperatures above 16.2/24.1°C during the growing season results in yield decreases of about 14%. The most important climate variables identified by our suitability model (i.e., mean minimum temperature during the growing season and harvest, precipitation during flowering and the late growing season) are in agreement with Kath et al. (2020) who used correlative models on actual yield time series rather than coffee occurrence alone.

In this study, we only sampled the environments close to where Robusta coffee is grown in Vietnam to assess the suitability of this coffee type (Tram et al. 2021) under specific growing conditions. Robusta coffee genotypes from other regions might thrive better under the projected future climate conditions and could therefore be considered for climate-resilient breeding efforts. Further research is needed to analyze how climate variability and climate extremes affect the economic viability of Robusta coffee growing, an aspect that cannot be fully accounted for when using correlative species distribution modeling. Short-term climate hazards, for example, heat stress during peak days of flowering, could be more harmful to Robusta coffee than what can be identified from climate predictors based on monthly averages. In addition, our study focused on climate suitability rather than yield prediction. While climate is one of the major determinants of coffee viability, actual yield is influenced by a complex interplay of agronomic practices, soil conditions, and management decisions (Rahn et al. 2018). Future research could incorporate these factors, as well as make use of longer observational datasets or process-based crop models, to provide a more comprehensive assessment of coffee yield and the impacts of extreme events under changing climate conditions.

**Acknowledgments**

The authors appreciate the interesting exchange with Christian Bunn (CIAT).

**Funding**

This work benefits from the French state aid managed by the ANR under the "Investissements d'avenir" program with the reference ANR-16-CONV-0003, and the Australian Centre for International Agricultural Research and conducted as part of activities for the project "Enhancing smallholder livelihoods in the Central Highlands of Viet Nam through improving the sustainability of coffee and black pepper farming systems and value chains" (AGB/2018/175).

**Author contributions**

LAD and FA conceptualized this research. FA and ER obtained the funding. LAD and FA developed the methodology and discussed the results. LAD collected the data, implemented the model, conducted the analysis, and wrote the manuscript. LAD and FA revised the manuscript.

**Data availability**

The coffee area data supporting this research can be obtained from Vietnam's General Statistics Office (GSO) for the period 2000-2018. These data are not publicly available but can be accessed upon reasonable request. For inquiries, please contact GSO at banbientap@gso.gov.vn. Digital elevation data (USGS EROS Center 2018) are from https://www.usgs.gov/centers/eros/science/ usgs-eros-archive-digital-elevation-shuttle-radar-topography-mission-srtm-1 (last access: 02 Mar 2022). The current climate data (Era5-Land reanalysis dataset) can be downloaded from https://cds.climate.copernicus.eu (Muñoz Sabater 2019) (last access: 22 Apr 2021).
The future climate simulations are available at https://esg-dn1.nsc.liu.se/search/cordex/
(last access: 27 Jun 2022).

**Supplementary data**

The supplement information related to this manuscript is provided.

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