Forecasting Annual Coffee Production in Lampung Province, Indonesia: Comparing Linear Regression and Smoothing Methods
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Abstract
Reliable production forecasts are useful for planning when the supply of agricultural raw materials varies from year to year. This study compared several methods for forecasting annual coffee production in Lampung Province, Indonesia, using production data for 2015–2024 from BPS-Statistics Indonesia, Lampung Province. The methods were linear regression, a three-period moving average, a three-period weighted moving average, simple exponential smoothing (α = 0.3, 0.6, and 0.9), and trend-adjusted exponential smoothing (α = 0.7; β = 0.5 or 0.7). Forecast accuracy was evaluated using mean absolute deviation (MAD), mean squared error (MSE), and mean absolute percentage error (MAPE) in POM-QM for Windows version 3. Linear regression produced the lowest errors (MAD = 7,007.457 tons; MSE = 93,557,210; MAPE = 5.8%) and the equation y = 1,290x + 110,039. The model projected production of 124,229 tons in 2025 and 130,679 tons in 2030. Among the methods examined, linear regression performed best for the available annual series. The projections should be used as planning estimates rather than fixed production targets.
IMPACT STATEMENT
The results provide a simple reference for provincial agencies, coffee processors, and other stakeholders that need an estimate of future raw-material availability. For the 2015–2024 Lampung series, linear regression produced lower errors than the moving-average and exponential-smoothing settings examined in the study. The resulting projections may support discussions of processing capacity, procurement, storage, and supply planning. Because the analysis is based on a short annual series, the forecasts should be updated when new production data become available and considered together with climate, agronomic, and market information.
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References
1
F. De Felice, M. Rehman, A. Petrillo, and I. Baffo, “Decoding the coffee supply chain: A systematic review of stakeholders, sustainability opportunities, and challenges,” Sustainable Futures, vol. 10, Art. no. 101105, 2025, https://doi.org/10.1016/j.sftr.2025.101105.
2
F. Hidalgo, X. F. Quiñones-Ruiz, A. Birkenberg, T. Daum, C. Bosch, P. Hirsch, and R. Birner, “Digitalization, sustainability, and coffee. Opportunities and challenges for agricultural development,” Agricultural Systems, vol. 208, Art. no. 103660, 2023, https://doi.org/10.1016/j.agsy.2023.103660.
3
V. M. Byrareddy, L. Kouadio, S. Mushtaq, and R. Stone, “Sustainable production of Robusta coffee under a changing climate: A 10-year monitoring of fertilizer management in coffee farms in Vietnam and Indonesia,” Agronomy, vol. 9, no. 9, Art. no. 499, 2019, https://doi.org/10.3390/agronomy9090499.
4
Y. Sarvina, T. June, S. H. Sutjahjo, R. Nurmalina, and E. Surmaini, “Projection of Robusta coffee’s climate suitability for sustainable Indonesian coffee production,” International Journal of Sustainable Development and Planning, vol. 18, no. 4, pp. 1069–1078, 2023, https://doi.org/10.18280/ijsdp.180409.
5
M. H. Basri, A. Fariyanti, and S. Suharno, “The impact of contract farming on the production risk of coffee farming in Lampung,” Jurnal Manajemen dan Agribisnis, vol. 21, no. 2, pp. 237–248, 2024, https://doi.org/10.17358/jma.21.2.237.
6
Y. Pham, K. Reardon-Smith, S. Mushtaq, and G. Cockfield, “The impact of climate change and variability on coffee production: A systematic review,” Climatic Change, vol. 156, pp. 609–630, 2019, https://doi.org/10.1007/s10584-019-02538-y.
7
C. Bunn, P. Läderach, O. Ovalle Rivera, and D. Kirschke, “A bitter cup: Climate change profile of global production of Arabica and Robusta coffee,” Climatic Change, vol. 129, no. 1, pp. 89–101, 2015, https://doi.org/10.1007/s10584-014-1306-x.
8
G. Schroth, P. Läderach, D. S. Blackburn Cuero, J. Neilson, and C. Bunn, “Winner or loser of climate change? A modeling study of current and future climatic suitability of Arabica coffee in Indonesia,” Regional Environmental Change, vol. 15, pp. 1473–1482, 2015, https://doi.org/10.1007/s10113-014-0713-x.
9
P. Läderach, J. Ramirez-Villegas, C. Navarro-Racines, C. Zelaya, A. Martinez-Valle, and A. Jarvis, “Climate change adaptation of coffee production in space and time,” Climatic Change, vol. 141, no. 1, pp. 47–62, 2017, https://doi.org/10.1007/s10584-016-1788-9.
10
O. Ovalle-Rivera, P. Läderach, C. Bunn, M. Obersteiner, and G. Schroth, “Projected shifts in Coffea arabica suitability among major global producing regions due to climate change,” PLOS ONE, vol. 10, no. 4, Art. no. e0124155, 2015, https://doi.org/10.1371/journal.pone.0124155.
11
R. Grüter, T. Trachsel, P. Laube, and I. Jaisli, “Expected global suitability of coffee, cashew and avocado due to climate change,” PLOS ONE, vol. 17, no. 1, Art. no. e0261976, 2022, https://doi.org/10.1371/journal.pone.0261976.
12
J. Kath et al., “Not so robust: Robusta coffee production is highly sensitive to temperature,” Global Change Biology, vol. 26, no. 6, pp. 3677–3688, 2020, https://doi.org/10.1111/gcb.15097.
13
F. Ceballos-Sierra and S. Dall’Erba, “The effect of climate variability on Colombian coffee productivity: A dynamic panel model approach,” Agricultural Systems, vol. 190, Art. no. 103126, 2021, https://doi.org/10.1016/j.agsy.2021.103126.
14
L. Kouadio et al., “Probabilistic yield forecasting of Robusta coffee at the farm scale using agroclimatic and remote sensing derived indices,” Agricultural and Forest Meteorology, vol. 306, Art. no. 108449, 2021, https://doi.org/10.1016/j.agrformet.2021.108449.
15
L. Kouadio et al., “Performance of a process-based model for predicting Robusta coffee yield at the regional scale in Vietnam,” Ecological Modelling, vol. 443, Art. no. 109469, 2021, https://doi.org/10.1016/j.ecolmodel.2021.109469.
16
E. Rahn et al., “Exploring adaptation strategies of coffee production to climate change using a process-based model,” Ecological Modelling, vol. 371, pp. 76–89, 2018, https://doi.org/10.1016/j.ecolmodel.2018.01.009.
17
L. C. Gomes et al., “Agroforestry systems can mitigate the impacts of climate change on coffee production: A spatially explicit assessment in Brazil,” Agriculture, Ecosystems & Environment, vol. 294, Art. no. 106858, 2020, https://doi.org/10.1016/j.agee.2020.106858.
18
R. Sarmiento-Soler et al., “Effect of cropping system, shade cover and altitudinal gradient on coffee yield components at Mt. Elgon, Uganda,” Agriculture, Ecosystems & Environment, vol. 295, Art. no. 106887, 2020, https://doi.org/10.1016/j.agee.2020.106887.
19
R. Cerda et al., “Effects of shade, altitude and management on multiple ecosystem services in coffee agroecosystems,” European Journal of Agronomy, vol. 82, pp. 308–319, 2017, https://doi.org/10.1016/j.eja.2016.09.019.
20
F. M. DaMatta, “Ecophysiological constraints on the production of shaded and unshaded coffee: A review,” Field Crops Research, vol. 86, no. 2–3, pp. 99–114, 2004, https://doi.org/10.1016/j.fcr.2003.09.001.
21
M. Jayakumar, M. Rajavel, U. Surendran, G. Gopinath, and K. Ramamoorthy, “Impact of climate variability on coffee yield in India—with a micro-level case study using long-term coffee yield data of humid tropical Kerala,” Climatic Change, vol. 145, pp. 335–349, 2017, https://doi.org/10.1007/s10584-017-2101-2.
22
B. B. Lin, “The role of agroforestry in reducing water loss through soil evaporation and crop transpiration in coffee agroecosystems,” Agricultural and Forest Meteorology, vol. 150, no. 4, pp. 510–518, 2010, https://doi.org/10.1016/j.agrformet.2009.11.010.
23
C. Durand-Bessart et al., “Analysis of interactions amongst shade trees, coffee foliar diseases and coffee yield in multistrata agroforestry systems,” Crop Protection, vol. 133, Art. no. 105137, 2020, https://doi.org/10.1016/j.cropro.2020.105137.
24
C. Muñoz-Villers et al., “Coffee and shade trees show complementary use of soil water in a traditional agroforestry ecosystem,” Hydrology and Earth System Sciences, vol. 24, pp. 1649–1668, 2020, https://doi.org/10.5194/hess-24-1649-2020.
25
K. Akpoti, A. T. Kabo-bah, and S. J. Zwart, “Agricultural land suitability analysis: State-of-the-art and outlooks for integration of climate change analysis,” Agricultural Systems, vol. 173, pp. 172–208, 2019, https://doi.org/10.1016/j.agsy.2019.02.013.
26
R. P. D. Gunathilaka, J. C. Smart, and C. M. Fleming, “Adaptation to climate change in perennial cropping systems: Options, barriers and policy implications,” Environmental Science & Policy, vol. 82, pp. 108–116, 2018, https://doi.org/10.1016/j.envsci.2018.01.011.
27
J. S. Choi et al., “Potential effects of perfect seasonal climate forecasting on agricultural markets, welfare and land use: A case study of Spain,” Agricultural Systems, vol. 133, pp. 177–189, 2015, https://doi.org/10.1016/j.agsy.2014.10.007.
28
F. Petropoulos et al., “Forecasting: Theory and practice,” International Journal of Forecasting, vol. 38, no. 3, pp. 705–871, 2022, https://doi.org/10.1016/j.ijforecast.2021.11.001.
29
R. J. Hyndman and A. B. Koehler, “Another look at measures of forecast accuracy,” International Journal of Forecasting, vol. 22, no. 4, pp. 679–688, 2006, https://doi.org/10.1016/j.ijforecast.2006.03.001.
30
D. Koutsandreas, E. Spiliotis, F. Petropoulos, and V. Assimakopoulos, “On the selection of forecasting accuracy measures,” Journal of the Operational Research Society, vol. 73, no. 5, pp. 937–954, 2022, https://doi.org/10.1080/01605682.2021.1892464.
31
H. Hewamalage, K. Ackermann, and C. Bergmeir, “Forecast evaluation for data scientists: Common pitfalls and best practices,” Data Mining and Knowledge Discovery, vol. 37, pp. 788–832, 2023, https://doi.org/10.1007/s10618-022-00894-5.
32
S. Makridakis, E. Spiliotis, and V. Assimakopoulos, “The M4 Competition: 100,000 time series and 61 forecasting methods,” International Journal of Forecasting, vol. 36, no. 1, pp. 54–74, 2020, https://doi.org/10.1016/j.ijforecast.2019.04.014.
33
S. Makridakis, E. Spiliotis, and V. Assimakopoulos, “Statistical and machine learning forecasting methods: Concerns and ways forward,” PLOS ONE, vol. 13, no. 3, Art. no. e0194889, 2018, https://doi.org/10.1371/journal.pone.0194889.
34
S. Kolassa, “Why the ‘best’ point forecast depends on the error or accuracy measure,” International Journal of Forecasting, vol. 36, no. 1, pp. 208–211, 2020, https://doi.org/10.1016/j.ijforecast.2019.02.017.
35
S. Kim and H. Kim, “A new metric of absolute percentage error for intermittent demand forecasts,” International Journal of Forecasting, vol. 32, no. 3, pp. 669–679, 2016, https://doi.org/10.1016/j.ijforecast.2015.12.003.
36
H. Hewamalage, C. Bergmeir, and K. Bandara, “Recurrent neural networks for time series forecasting: Current status and future directions,” International Journal of Forecasting, vol. 37, no. 1, pp. 388–427, 2021, https://doi.org/10.1016/j.ijforecast.2020.06.008.
37
S. Smyl, “A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting,” International Journal of Forecasting, vol. 36, no. 1, pp. 75–85, 2020, https://doi.org/10.1016/j.ijforecast.2019.03.017.
38
D. R. Khan, A. B. Patankar, and A. Khan, “An experimental comparison of classic statistical techniques on univariate time series forecasting,” Procedia Computer Science, vol. 235, pp. 2730–2740, 2024, https://doi.org/10.1016/j.procs.2024.04.257.
39
E. Spiliotis, F. Petropoulos, and V. Assimakopoulos, “On the disagreement of forecasting model selection criteria,” Forecasting, vol. 5, no. 2, pp. 487–498, 2023, https://doi.org/10.3390/forecast5020027.
40
E. R. Abraham et al., “Time series prediction with artificial neural networks: An analysis using Brazilian soybean production,” Agriculture, vol. 10, no. 10, Art. no. 475, 2020, https://doi.org/10.3390/agriculture10100475.
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