ISSN: 2407-814X (p); 2527-9238 (e) AGRARIS: Journal of Agribusiness and Rural Development Research Vol. 8 No. 2 July-December 2022, Pages: 215-230 Article history: Submitted : March 25th, 2022 Revised : September 19th, 2022 July 25th, 2022 Accepted : September 23rd, 2022 Inaya Cahyaningtyas*, Arini Wahyu Utami, Lestari Rahayu Waluyati Department of Agricultural Socioeconomics, Faculty of Agriculture, Universitas Gadjah Mada, Indonesia *) Correspondence email: inaya.cahyaningtyas@mail.ugm.ac.id Indonesia’s Natural Rubber Productivity and Technically Specified Natural Rubber 20 Export: The Effect of El Nino Southern Oscillation DOI: https://doi.org/10.18196/agraris.v8i2.14320 ABSTRACT El Nino Southern Oscillation (ENSO) causes rainfall anomalies, which may disrupt Indonesia’s natural rubber production by interfering with the trees’ growth and affecting the export volume. This study analyzed the effect of ENSO dynamics on the monthly productivity of natural rubber and Technically Specified Natural Rubber (TSNR) 20 export. Monthly data from January 2006 to December 2019 were collected from the Statistics Indonesia, International Trade Centre (ITC), World Bank, Bank Indonesia, and National Ocean and Atmospheric Administration (NOAA). Descriptive statistics unveiled that strong La Nina increased the average of monthly productivity by 3.37% to 9.68%, while strong El Nino tended to decrease productivity by 1.30% to 9.27%. Moreover, the Vector Error Correction Model (VECM) demonstrated the negative effect of ENSO on Indonesia’s natural rubber export, both in the short and long term. Keywords: El Nino; La Nina; Natural rubber; Productivity; TSNR 20 export INTRODUCTION Natural rubber is one of the plantation commodities with a considerable contribution to Indonesia’s economy. The report from Statistics Indonesia (2021) revealed that natural rubber contributed 6.13% to the national income of plantation sub-sector in 2019. It also adds to the national income from international trade since 80% of its production is exported. Most types of natural rubber are exported in the form of technically specified natural rubber (TSNR 20/HS 40012220). To maintain the stability of production, water management is crucial. In this regard, climate factors, including rainfall, affect the growth and productivity of natural rubber plants (Daslin, 2013; Susetyo & Hadi, 2012). The average Indonesia rainfall is modulated by climate variability modes, such as El Nino Southern Oscillation (ENSO) (Amirudin, Salimun, Tangang, Juneng, & Zuhairi, 2020; Lestari et al., 2016; Supari et al., 2018). Rainfall anomalies induced by ENSO can interfere with growth, productivity, and tapping process of rubber plants. ENSO occurs as two distinct phenomena, El Nino, associated http://issn.pdii.lipi.go.id/issn.cgi?daftar&1420518152&1&& mailto:inaya.cahyaningtyas@mail.ugm.ac.id https://doi.org/10.18196/agraris.v8i2.14320 ISSN: 2407-814X (p); 2527-9238 (e) 216 AGRARIS: Journal of Agribusiness and Rural Development Research with lower rainfall than normal, and La Nina, linked to higher rainfall than normal. Both El Nino and La Nina causes extreme rainfall or flooding and drought throughout Indonesia (Kurniadi, Weller, Min, & Seong, 2021), with the most substantial impact seen during dry season and the weaker impact in wet season. It is strongly tied to the growth of rubber plants in Indonesia, hampered during 1994 and 1997 El Nino as well as 2015 El Nino (Saputra, Stevanus, & Cahyo, 2016). In contrast, there was no interference with rubber growth during the La Nina years (Cahyo, Murti, & Putra, 2020). In terms of agricultural commodities, ENSO also affects the productivity of oil palm (e.g., Azlan et al., 2016; Stiegler et al., 2019; Suresh, 2013), wheat (Gutierrez, 2017), and other crops (e.g., Anderson, Seager, Baethgen, & Cane, 2017; Iizumi et al., 2014; Nóia Júnior & Sentelhas, 2019). On wider scopes, studies uncovered that ENSO impacted the socioeconomic aspects of society, such as that in South America (Cai et al., 2020), civil conflict (Hsiang, Meng, & Cane, 2011), critical sectors in the society, i.e., water, agriculture, and health (Zebiak et al., 2015), as well as transportation system, such as in California, Hawaii, and the US-affiliated Pacific Islands (Kim, Chowdhury, Pant, Yamashita, & Ghimire, 2021). The link between ENSO and the macroeconomic aspects, including economic growth and inflation, has also been examined (e.g., Cashin, Mohaddes, & Raissi, 2015; Generoso, Couharde, Damette, & Mohaddes, 2020; Smith & Ubilava, 2017). Previous research analyzed the determinant factors of Indonesia’s natural rubber export. Yanita, Yazid, Alamsyah, & Mulyana (2016) discovered that determinant factors of crumb rubber export between 2005 and 2012 consisted of production quantity, exchange rate, and export in previous period. Higher production and lower exchange rate were associated with higher export of natural rubber. In another study on a particular natural rubber importer’s country, Sari, Supriana, & Rahmanta (2021) disclosed that the determinant factors of Indonesia’s natural rubber export to Japan comprised a positive sign of production and price. Nevertheless, there has been no study on the effect of ENSO climate anomaly on Indonesia’s natural rubber export. Therefore, this study assessed the effect of ENSO on the volume of TSNR 20 natural rubber export and computed the monthly productivity differences of natural rubber across the ENSO events. RESEARCH METHOD This study utilized secondary data, monthly data from January 2006 to December 2019. The variables of interest included the production quantity and land area of natural rubber, collected from the Statistics Indonesia, the export volume of TSNR 20/SIR 20 (HS 40012220) from the International Trade Centre (ITC) trade map, the international price of natural rubber from the World Bank, and the exchange rate of Indonesian Rupiah (IDR) to US Dollar (USD) from Bank Indonesia (i.e., Indonesia’s Central Bank). This study employed the Oceanic Nino Index (ONI) as indicator of El Nino and La Nina occurrence collected by the U.S. National Ocean and Atmospheric Administration (NOAA). The ONI was computed from differences in the sea surface temperature (SST) of the Pacific Ocean Nino region 3.4. El Niño occurs when the ONI is +0.5 or higher, indicating that the corresponding region is http://issn.pdii.lipi.go.id/issn.cgi?daftar&1420518152&1&& ISSN: 2407-814X (p); 2527-9238 (e) 217 Indonesia’s Natural Rubber Productivity and ….. (Cahyaningtyas, Utami, and Waluyuti) much warmer than normal. Meanwhile, La Nina occurs when ONI is -0.5 or lower, meaning that the region is cooler than normal (National Oceanic and Atmospheric Administration [NOAA], 2021). This study analyzed the differences in productivity across the ENSO phenomena descriptively. The productivity rate was measured by comparing productivity between the same month in different years to determine the difference between months across years. Then, the average monthly productivity changes were compared with the different annual ENSO intensities classified into weak or strong El Nino, weak or strong La Nina, and normal condition. Index level of +0.5 to +1.5/(-0.5 to -1.5) belongs to weak El Nino/(La Nina), while higher than +1.5/(-1.5) indicates strong El Nino/(La Nina). The influence of ENSO on the volume of natural rubber export was examined using Vector Error Correction Model (VECM) regression. The stages performed before obtaining the VECM results encompassed (1) test for stationarity with unit root test, (2) determination of optimal lag criteria, (3) stability test, (4) cointegration test, and (5) VECM regression. The estimation of the VECM equations is as follows. Long-term equation: ECTt-1 = 𝛽0 + 𝛽1Log(Exp_TSNR20t-1)+ 𝛽2𝐿og(Prodtvt-1) + 𝛽3𝐿og(Exct-1) + 𝛽4𝐿og(Price_TSNR20t-1) + 𝛽5ONIt-1 (1) Short-term equation: ∆Log(Exp_TSNR20t) = αECTt-n + 𝛽0 + 𝛽1∆Log(Exp_TSNR20t-n)+ 𝛽2∆𝐿og(Prodtvt-n) + 𝛽3∆𝐿og(Exct-n) + 𝛽4∆𝐿og(Price_TSNR20t-n) + 𝛽5∆ONIt-n (2) ECT refers to the error correction term or matrix of coefficient cointegration. Exp_TSNR20 is the export volume of Technically Specified Natural Rubber (ton per hectare). Prodtv represents the productivity of dry natural rubber (ton per hectare). Exc is IDR to the USD exchange rate. Price_TSNR20 implies the international price of TSNR 20 (USD per ton). Meanwhile, the ONI serves as the ENSO indicator. Data were analyzed in log form, except the ONI data. The symbol of α indicates the coefficient of ECT, 𝛽0 refers to a constant, 𝛽1-𝛽5 are coefficient of all independent variables, t-1 implies the period in one previous month, and n signifies the optimal period on analysis. RESULTS AND DISCUSSION ENSO-Monthly Productivity Rate Rubber trees is an annual plan that begins to produce latex five years after planting. Tapping can be carried out every day until the plants are unproductive. Rubber trees are perennial and sensitive to weather or climatic changes, unlike seasonal crops, of which climatic necessities during cultivation can be easily modified. ENSO consists of normal, La Nina, and El Nino phases of different durations, as described in Table 1. http://issn.pdii.lipi.go.id/issn.cgi?daftar&1420518152&1&& ISSN: 2407-814X (p); 2527-9238 (e) 218 AGRARIS: Journal of Agribusiness and Rural Development Research TABLE 1. LA NINA AND EL NINO EVENTS FROM JANUARY 2006 TO DECEMBER 2019 La Nina Period ONI Average Duration (Month) El Nino Period ONI Average Duration (Month) Jan 2006–Mar 2006 Jun 2007–Jun 2008 Nov 2008–Mar 2009 Jun 2010–May 2011 Juli 2011–Apr 2012 Feb 2014 Agt 2016–Dec 2016 Oct2017–Apr 2018 -0.76 -1.09 -0.7 -1.18 -0.77 -0.5 -0.62 -0.79 3 13 5 12 10 1 5 7 Sep 2006–Jan 2007 Jul 2009–Mar 2010 Oct 2014–Apr 2016 Sep 2018–Jun 2019 Nov 2019–Dec 2019 0.76 1.03 1.39 0.68 0.5 5 9 19 10 2 Total 56 Total 45 SOURCE: NOAA (2021) From January 2006 to December 2019, El Nino lasted 45 months, and La Nina persisted for 56 months. The most prolonged El Nino occurred for 19 months, from October 2014 to April 2016, with an average ONI of 1.39, considered one of the strongest El Nino recorded since the 1980s. Another El Nino arrived in September 2018 and took place for ten months. The most decisive phase of La Nina happened from June 2010 until May 2011, with a duration of 12 months and an average ONI of -1.18. A month later, La Nina returned, occurring from July 2011 to May 2012, lasting ten months. ENSO has a seasonal cycle and impacts varied atmospheric circulations (Timmermann et al., 2018). In this case, monthly Indonesia’s natural rubber productivity was graphed against the ONI along the 2006 to 2019 study period to exhibit the fluctuation in its productivity during the El Nino and La Nina events. Some notable high productivity of 110 kg per hectare per month or more was associated with La Nina events, as depicted by negative downward bars in Figure 1. On the other hand, El Nino episodes, indicated by positive upward bars, were associated with lower productivity of 60 to 100 kg per hectare per month. FIGURE 1. MONTHLY NATURAL RUBBER PRODUCTIVITY IN INDONESIA VERSUS THE OCEANIC NINO INDEX, 2006-2019 SOURCE: NOAA (2021); STATISTICS INDONESIA (2021) Neverthless, it should be noted that La Nina could caused also lower productivity, as occurred in 2008 and 2010, with a monthly productivity of 70 to 80 kg per hectare per month. However, the effect of El Nino could be inconsistent and asymmetries (Ubilava & Abdolrahimi, 2019). For instance, the strong 2015 El Nino was linked to the monthly 50.00 60.00 70.00 80.00 90.00 100.00 110.00 120.00 130.00 -2 -1 0 1 2 3 20 06 M 01 20 06 M 06 20 06 M 11 20 07 M 04 20 07 M 09 20 08 M 02 20 08 M 07 20 08 M 12 20 09 M 05 20 09 M 10 20 10 M 03 20 10 M 08 20 11 M 01 20 11 M 06 20 11 M 11 20 12 M 04 20 12 M 09 20 13 M 02 20 13 M 07 20 13 M 12 20 14 M 05 20 14 M 10 20 15 M 03 20 15 M 08 20 16 M 01 20 16 M 06 20 16 M 11 20 17 M 04 20 17 M 09 20 18 M 02 20 18 M 07 20 18 M 12 20 19 M 05 20 19 M 10 RU BB ER P RO DU CT IV IT Y EN SO MONTH ONI Index Natural Rubber Productivity (Kg/Ha) http://issn.pdii.lipi.go.id/issn.cgi?daftar&1420518152&1&& ISSN: 2407-814X (p); 2527-9238 (e) 219 Indonesia’s Natural Rubber Productivity and ….. (Cahyaningtyas, Utami, and Waluyuti) productivity of 80 to 90 kg per hectare, higher than those from 2009 to 2010 El Nino with only 60 to 80 kg per hectare. The 2015 El Nino was noted as the extreme one, as it fits the description of environmental disasters (Chen, Li, Behera, & Doi, 2016; Meijide et al., 2018; Santoso, Mcphaden, & Cai, 2017). By itself, El Nino is characteristically stronger, if not more extreme, than La Nina (Dommenget, Bayr, & Frauen, 2013; Hsiang & Meng, 2015). Moreover, Indonesia’s natural rubber productivity amounted to 1,095.17 kg per hectare in 2019, or about 91.26 kg per hectare per month (Statistics Indonesia, 2021). The productivity of natural rubber demonstrated seasonal patterns, as exhibited in Figure 2. Average productivity experienced two peak harvests in June and December, while low average productivity occurred from March to April and August to September. Nonetheless, La Nina and El Nino events classified in weak and strong intensities resulted in differences in annual productivity patterns. Seasonal patterns tended to be more regular from 2013 to 2019. However, in 2015, a strong El Nino caused a decrease in average productivity from January to August compared to two years earlier, despite the different production risks in other regions (Qian, Zhao, Zheng, Cao, & Xue, 2020). FIGURE 2. MONTHLY PRODUCTIVITY OF NATURAL RUBBER IN INDONESIA (KG PER HECTARE) DIFFERENTIATED BY EL NINO AND LA NINA INTENSITY: (A) IN 2006-2012 AND (B) IN 2013-2019 SOURCE: STATISTICS INDONESIA (2021) 55 65 75 85 95 105 115 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC PR O DU CT IV IT Y (K G/ HA ) MONTH 2006-Weak El Nino 2007-Strong La Nina 2008-Weak La Nina 2009-Strong El Nino 2010-Strong La Nina 2011-Weak La Nina 2012-Normal a. 55 65 75 85 95 105 115 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC PR O DU CT IV IT Y KG /H A MONTH 2013-Normal 2014-Weak El Nino 2015-Strong El Nino 2016-Weak La Nina 2017-Weak La NIna 2018-Weak El Nino 2019-Normal b. http://issn.pdii.lipi.go.id/issn.cgi?daftar&1420518152&1&& ISSN: 2407-814X (p); 2527-9238 (e) 220 AGRARIS: Journal of Agribusiness and Rural Development Research Productivity change analysis described the rate of productivity growth across months. Table 2 displays the analysis results. The average productivity growth rate of natural rubber tended to increase during strong or weak La Nina and decrease following strong or weak El Nino. Strong La Nina raised the average monthly productivity rate by 3.37 to 9.68%, while the strong El Nino event lowered the average monthly productivity by 1.30 to 9.27%. La Nina, causing higher rainfall, could affect the natural rubber supply. Hence, increasing rainfall will enhance the natural rubber supply (Arunwarakorn, Suthiwartnarueput, & Pornchaiwiseskul, 2019). TABLE 2. NATURAL RUBBER PRODUCTIVITY GROWTH (%) Year Event Month Average Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec 2007 Strong LN 12.90 2.54 -14.95 -6.05 -1.01 -6.42 -3.50 4.42 11.79 24.58 22.01 -5.83 3.37 2008 Weak LN -13.54 -12.85 8.43 15.58 13.27 30.95 9.64 -19.80 -20.55 0.34 -3.86 0.70 0.69 2009 Strong EN -9.20 -11.04 -11.92 -12.54 -11.36 -12.47 -10.17 -6.52 -6.21 -5.53 -9.80 -4.46 -9.27 2010 Strong LN 9.49 9.40 9.68 9.77 9.70 10.06 9.80 9.45 9.46 9.94 9.81 9.56 9.68 2011 Weak LN 8.38 8.38 8.32 8.28 8.29 8.23 8.29 8.39 8.39 8.28 8.30 8.38 8.33 2012 Normal 0.00 0.07 0.12 0.24 0.33 0.55 0.41 0.09 0.18 0.48 0.36 0.03 0.24 2013 Normal 5.67 10.08 12.18 6.88 -4.51 -14.97 -6.60 11.39 8.43 -0.35 2.87 -7.18 1.99 2014 Weak EN -1.87 -1.60 -1.31 -1.42 -2.09 -2.84 -2.39 -1.33 -4.97 -5.13 -4.46 -4.10 -2.79 2015 Strong EN -7.85 -7.75 -7.14 -7.32 -5.20 -5.19 -7.70 -7.81 13.84 9.58 6.19 10.78 -1.30 2016 Weak LN 6.55 6.54 6.46 6.53 6.61 7.01 6.93 6.54 6.46 6.45 6.36 6.29 6.56 2017 Weak LN 5.69 5.99 7.02 6.18 7.38 7.39 5.96 5.81 15.80 13.51 11.98 15.16 8.99 2018 Weak EN -0.31 -0.47 -0.96 -0.22 -1.57 -1.36 -0.09 -0.29 -10.52 -8.23 -6.73 -9.57 -3.36 2019 Normal -4.07 -4.48 -5.13 -5.75 -2.51 -4.34 -3.80 -4.34 -8.67 -8.46 -7.87 -7.83 -5.61 Note: Strong LN = Strong La Nina; Weak LN = Weak La Nina; Strong EN = Strong El Nino; Weak EN = Weak El Nino In contrast, El Nino, lowering rainfall, could decrease natural rubber productivity, as demonstrated by the 2015 El Nino (Saputra, Stevanus, & Cahyo, 2016). To compare these results to another critical and valuable commodity, lower yield due to El Nino also occurring in palm oil plantations (e.g., Azlan et al., 2016; Khor et al., 2021; Oettli, Behera, & Yamagata, 2018; Stiegler et al., 2019). Khor et al. (2021) computed that the opportunity losses because of El Nino, beginning from 1986 (excluding 2018, 2019), were around USD 9.55 billion, while Oettli et al. (2018) discovered that La Nina was favorable for improving profit. These results are consistent with Selvaraju (2003), examining the impact of ENSO on food grain production, uncovering that total food grain production increased from normal during La Nina. Vector Error Correction Model (VECM) Estimation Before running the VECM regression, Augmented-Dickey Fuller (ADF) test was conducted to ensure data stationarity. The VECM required stationary variables in the first difference (I). Table 3 exhibits three insignificant variables at this level. However, all variables of interest were significant in the first difference level (p-value < 0.05). After all, these variables were stationary, and the subsequent step was determining the optimum lag to be utilized in the regression analysis. Optimal lag testing could take advantage of some information by using Akaike Information Criterion, Schwarz Criterion, and Hannan- http://issn.pdii.lipi.go.id/issn.cgi?daftar&1420518152&1&& ISSN: 2407-814X (p); 2527-9238 (e) 221 Indonesia’s Natural Rubber Productivity and ….. (Cahyaningtyas, Utami, and Waluyuti) Quin Criterion. The optimum lag length was based on the smallest value among the other lags. The second lag was optimum from AIC, SC, and HQ values marked with asterisks (*) (Table 4). TABLE 3. RESULTS OF STATIONARY TEST Variable Level First Difference ADF Probs ADF Probs Log (Exp_TSNR20) -3.891322*** 0.0026 -21.51016*** 0.0000 Log (Prodtv) -1.321500 ns 0.6189 -3.602440*** 0.0067 Log (Exc) -0.832823 ns 0.8069 -10.07921*** 0.0000 Log (Price_TSNR20) -1.857535 ns 0.3518 -9.095177*** 0.0000 ONI -5.223822*** 0.0000 -6.043501*** 0.0000 Notes: Log (Exp_TSNR20) = Export volume of TSNR 20; Log (Prodtv) = Rubber Productivity; Log (Exc) = Exchange rate; Log (Price_TSNR20) = Price of TSNR 20; ONI = ENSO indicator ***) Significant at the 0.01 alpha, ns) insignificant TABLE 4. RESULTS FOR LAG LENGTH FOR ENDOGENOUS WITH LAG LENGTH CRITERIA Lag LogL LR FPE AIC SC HQ 0 95.59894 NA 2.28e-07 -1.104865 -1.010357 -1.066498 1 865.8632 1484.168 2.57e-11 -10.19345 -9.626404 -9.963253 2 1036.428 318.2494 4.37e-12* -11.96864* -10.92905* -11.54660* 3 1058.429 39.70848 4.54e-12 -11.93206 -10.41993 -11.31819 4 1080.086 37.76870* 4.75e-12 -11.89130 -9.906627 -11.08560 Note: (*) indicates lag order selected by the criterion. LR = Sequential Modified LR test statistics (each test at 5% level); FPE = Final Prediction Error; AIC = Akaike Information Criterion; SC = Schwarz Information Criterion; HQ = Hannan- Quinn Information Criterion Subsequently, the stability test was run, ensuring the modulus was less than one for the regression model to be stable. Table 5 displays less than one modulus on the model. Thus, the second lag fit and the established VARM was stable. TABLE 5. RESULTS OF THE STABILITY TEST Root Modulus 0.986685 0.986685 0.866536 - 0.251219i 0.902217 0.866536 + 0.251219i 0.902217 0.884816 0.884816 0.349903 - 0.685643i 0.769765 0.349903 + 0.685643i 0.769765 0.684584 0.684584 0.423816 0.423816 -0.417256 0.417256 0.093150 0.093150 A cointegration test followed to confirm the short- and long-term equilibrium. Table 6 describes that trace and maximum eigenvalue test indicated three co-integrating equations and might be a long-term equilibrium relationship, confirming the appropriateness of using VECM regression. Table 7 exhibits an ECT coefficient of -0.236381, significant at 0.10 alpha, implying the model’s validity. The ECT coefficient determined how quickly the equilibrium http://issn.pdii.lipi.go.id/issn.cgi?daftar&1420518152&1&& ISSN: 2407-814X (p); 2527-9238 (e) 222 AGRARIS: Journal of Agribusiness and Rural Development Research was recovered. An ECT of -0.236381 signifies that its equilibrium and the development of the previous TSNR 20 export volume were corrected for the current period of 23.64%. TABLE 6. COINTEGRATION TEST UNRESTRICTED COINTEGRATION RANK TEST (TRACE) Ho Eigenvalue Trace Statistic Critical Value 0.05 Prob.** None*** 0.570496 221.2900 76.97277 0.0000 At most 1*** 0.218516 80.99939 54.07904 0.0000 At most 2 ** 0.154764 40.07027 35.19275 0.0138 At most 3 ns 0.056362 12.15921 20.26184 0.4347 UNRESTRICTED COINTEGRATION RANK TEST (MAXIMUM EIGENVALUE) Ho Eigenvalue Trace Statistic Critical Value 0.05 Prob.** None*** 0.570496 140.2906 34.80587 0.0000 At most 1*** 0.218516 40.92913 28.58808 0.0008 At most 2*** 0.154764 27.91106 22.29962 0.0074 At most 3 ns 0.056362 9.630022 15.89210 0.3692 Notes: The null hypothesis, Ho was no-cointegrating vector. ‘None’ = No co-integrating equation; ‘At most (1 to 3)’ = The number of co-integrating equations. ***), **) Denotes rejection at the 0.01 and 0.05 alpha, ns) insignificant Prob.** MacKinnon-Haug-Michells (1999) p-values TABLE 7. ESTIMATION OF THE VECTOR ERROR CORRECTION MODEL (VECM) Variable Coefficient Statistics-t Prob. Long-Term LogExp_TSNR20(-1) 1.000000 LogProdtv(-1) -1.556677ns -12.7314 0.12227 LogExc(-1) 0.516614ns 3.53543 0.14612 LogPrice_TSNR20(-1) 0.027125* 0.50275 0.05395 ONI(-1) -0.020219** -1.32340 0.01528 C 2.388028 Short-Term D(LogExp_TSNR20(-1)) -0.487927* -5.20763 0.09369 D(LogExp_TSNR20(-2)) -0.174404* -2,08602 0.08361 D(LogProdtv(-1)) -0.286970* -3.34544 0.08578 D(LogProdtv(-2)) -0.245177ns -2.35612 0.10406 D(LogExc(-1)) -0.221982ns 0.54001 0.41107 D(LogExc(-2)) -0.251514ns -0.61428 0.40945 D(LogPrice_TSNR20(-1)) 0.128369ns 1.10268 0.11642 D(LogPrice_TSNR20(-2)) 0.400922ns 3.48682 0.11498 D(ONI(-1)) 0.042009* 0.62032 0.06772 D(ONI(-2)) -0.065942* -0.06645 0.06645 ECT -0.236381* -3.00134 0.07876 Note: LogExp_TSNR20(-1) = TSNR 20 export volume; LogProdtv(-1) = Rubber Productivity; LogExc(-1) = Exchange rate; LogPrice_TSNR20(-1) = Price of TSNR 20; ONI(-1) = ENSO indicator. All variable were differentiated in the short-term equation and lag 2 was the optimal period. D(LogExp_TSNR20(-1)) = TSNR 20 export volume in lag 1; D(LogExp_TSNR20(-2)) = TSNR 20 export volume in lag 2; D(LogProdtv(-1)) = Rubber productivity in lag 1; D(LogProdtv(-2)) = Rubber productivity in lag 2; D(LogExc(-1)) = Exchange rate at lag 1; D(LogExc(- 2)) = Exchange rate at lag 2; D(LogPrice_TSNR20(-1)) = Price of TSNR 20 at lag 1; D(LogPrice_TSNR20(-2)) = Price of TSNR 20 at lag 2; D(ONI(-1)) = ENSO indicator at lag 1; D(ONI(-2)) = ENSO indicator at lag 2; ECT = Error Correction Term. **) Significant at the 0.05 alpha, *) 0.10 alpha, ns) insignificant http://issn.pdii.lipi.go.id/issn.cgi?daftar&1420518152&1&& ISSN: 2407-814X (p); 2527-9238 (e) 223 Indonesia’s Natural Rubber Productivity and ….. (Cahyaningtyas, Utami, and Waluyuti) Table 7 portrays the VECM regression results. Regarding the effect of ENSO on TNSR export volume, an increase of one unit of the ONI, or the tendency of El Nino event, both in the long- and short-term equilibrium, was related to a reduction in the TSNR 20 export volume. A higher ONI indicated the occurrence of El Nino, signifying that El Nino decreased natural rubber export, while La Nina was beneficial for it. It was evidenced by the statistically significant ONI variable in the model, at 5% alpha in the long term and 10% alpha in the short term. One unit increase in the ONI variable (ONI (-1)) in the long term harmed decreasing the TSNR 20 export volume by 100.0201 = 1.05 ton. In the short term, the ONI variable from two previous months (D (ONI (-2)) was associated with declining the TNSR 20 export volume by 1.16 tons. These results emphasized the effect of non-economic factors, such as El Nino, on natural rubber export. These findings align with Gutierrez (2017), disclosing a negative association between global wheat export and ENSO anomalies. The study, however, discovered that La Nina put a higher burden on wheat export than El Nino. La Nina’s highest impact on wheat export was -2.23% after six months following the event, compared to El Nino’s highest impact of -0.62% after three months of its occurrence. ENSO can affect a country’s export, especially concerning the national income, including Indonesia. Cashin et al. (2015) uncovered a relative short-term decrease in GDP during El Nino episodes. If traced back to the effect of El Nino on the decline of natural rubber productivity, downstream industries, such as TSNR 20 processing, could be impacted by the availability of natural rubber. It could inhibit export, which consequently lowered the national income. Moreover, excessive heat due to the strong El Nino phase could reduce economic growth (Dell, Jones, & Olken, 2012). Moreover, the TSNR 20 price variable (LogPrice_TSNR20(-1)) demonstrated a significant and positive value in the long term, meaning that one unit increase in TSNR 20 price, on average, increased the TSNR 20 export volume by 1,064 ton, holding constant other variables. This result is supported by Khin, Bin, Kai, Teng, & Chiun (2019), revealing that when the price dropped by USD 1, the export of natural rubber decreased by up to 30 tons in the ASEAN market. Higher natural rubber price in the world market is an indication of more profit to obtain. It also highlights the importance of natural rubber as one export commodity for the national income (Claudia, Yulianto, & Mawardi, 2016). Natural rubber export remained steady despite declining the natural rubber price globally (Perdana, 2019). The TSNR 20 price highly affected export since it signaled natural rubber producers of the prospective high profit to earn. Accordingly, it encouraged producers to improve the maintenance of rubber plantations, increasing output and more export (Yanita et al., 2016). On the micro-scale, research by Syarifa, Agustina, Nancy, & Supriadi (2016) revealed that some farmers remained to tap rubber even during falling prices. Regarding natural rubber productivity, the variable was insignificant in the long-term analysis (LogProdtv (-1)) but negatively significant in the short term on one previous month (D(LogProdtv(-1))). These results contradict Amoro & Shen (2013). The sign on the variable of natural rubber productivity was positive, signifying that an increase in production http://issn.pdii.lipi.go.id/issn.cgi?daftar&1420518152&1&& ISSN: 2407-814X (p); 2527-9238 (e) 224 AGRARIS: Journal of Agribusiness and Rural Development Research stimulated an escalation in export. However, this research had a lag time between productivity and export volume. Farmers sometimes reduced the tapping days and delayed the dry rubber trade to the factory or middlemen because of falling prices (Suwardin, 2015). Indeed, it could cause the buildup of dry rubber products to be sold in the following month. Concerning the effect of TSNR 20 export, the variable was negatively significant in the short term. In other words, the lower export volume in one (D(LogExp_TSNR20(-1))) and two previous months (D(LogExp_TSNR20(-2))) was responded by the higher TSNR export volume. Crumb rubber processing capacity in South Sumatra was only 76.5% fulfilled (Suwardin, 2015). Hence, to optimize the processing and export capacity of natural rubber, the low quality of rubber material, rubber processing technique, and supply chain should be enhanced (Antoni & Tokuda, 2019). Exchange rate variable depicted insignificant effect in both long (LogExc(-1)) and short term (D(LogExc(-1)) and D(LogExc(-2))). Empirically, it is in line with Klaassen (2004), revealing that the exchange rate had no significant effect on export. In another study, however, the exchange rate affected export due to the depreciation of the Indonesian Rupiah (IDR) currency in the importing country, causing the product price to be lower to improve trade flows (Arumta, Mulyo, & Irham, 2019). On the other hand, another factor excluded in this model was variation in domestic policies across countries. Some policies in some countries, such as using tires, vehicles, and crude oil, could influence the demand for natural rubber. The crude oil price could be an essential factor in the natural rubber price. The input cost of natural rubber products depends on the crude oil price, which is the raw material for synthetic rubber (Fong, Khin, & Lim, 2018). However, although natural rubber is currently less produced and less consumed than synthetic rubber, natural rubber remains irreplaceable by synthetic rubber. In many ways, the advantages of the quality of natural rubber are difficult to match with synthetic rubber (Wahyudy, Khairizal, & Heriyanto, 2018). FIGURE 3. IMPULSE RESPONSE FUNCTION (IRF) OF THE TECHNICALLY SPECIFIED NATURAL RUBBER (TSNR) 20 EXPORT VOLUME TO SHOCK AFFECTED BY ENSO, PRODUCTIVITY, EXCHANGE RATE, AND PRICE Furthermore, Figure 3 presents the impulse response function (IRF), comparing the effect of variables included in the regression. The IRF demonstrated that the shock of the http://issn.pdii.lipi.go.id/issn.cgi?daftar&1420518152&1&& ISSN: 2407-814X (p); 2527-9238 (e) 225 Indonesia’s Natural Rubber Productivity and ….. (Cahyaningtyas, Utami, and Waluyuti) natural rubber export volume by one standard deviation in the first month increased natural rubber export by 11.05% and had not responded to the shock from other variables. In the second month, a shock by ENSO was responded positively by natural rubber export by 0.59%. Then, the response fluctuated until the fifth month, when the export volume negatively responded to the ENSO shock in the next period. The response of natural rubber export to the ENSO shock began to reach balance in the 23rd month, where natural rubber export responded negatively to the shock by 0.96%. Moreover, variance decomposition analysis helped explain the shock contribution from the natural rubber productivity variable, price, exchange rate, and ENSO to fluctuation in the TSNR 20 export volume. The time frame to forecast this variance decomposition was 36 months (three years). Figure 4 displays that the export shock caused export fluctuation in the first month. In the next 12 months, the export volume was affected by TNSR 20 export by 68.23%, TSNR 20 price by 17.55%, productivity by 11.64%, the exchange rate by 1.60%, and the ONI by 0.97%. However, in the next 36 months, the export volume was influenced by TNSR 20 export by 62.24%, TSNR 20 price by 20.54%, productivity by 13.38%, the exchange rate by 1.99%, and the ONI by 1.86%. The smallest proportion of the ENSO shock disclosed that ENSO in the following few periods did not significantly affect the export shock (Bastianin, Lanza, & Manera, 2018). Fluctuation in the volume of natural rubber export in some periods was predominantly influenced by the export volume rather than other variables. FIGURE 4. VARIANCE DECOMPOSITION (VD) OF THE TECHNICALLY SPECIFIED NATURAL RUBBER (TSNR) 20 EXPORT VOLUME (SHOCK CONTRIBUTION FROM ENSO, PRODUCTIVITY, EXCHANGE RATE, AND PRICE) CONCLUSION This study assessed the effect of El Nino Southern Oscillation (ENSO) on natural rubber productivity and the TSNR 20 export volume. A descriptive analysis of natural rubber productivity under the La Nina and El Nino conditions unveiled that La Nina was associated with increased productivity, while El Nino decreased natural rubber productivity. As for the effect of ENSO on the TSNR 20 export volume, in-line results were discovered. The Vector http://issn.pdii.lipi.go.id/issn.cgi?daftar&1420518152&1&& ISSN: 2407-814X (p); 2527-9238 (e) 226 AGRARIS: Journal of Agribusiness and Rural Development Research Error Correction Model (VECM) regression revealed that higher ONI, indicating El Nino, led to the lower TSNR 20 export volume, both in the long and short term. Following the effect of ENSO on natural rubber productivity, several mitigation efforts could be directed toward using drought-resistant clones and improving water management in rubber plantations, especially during El Nino. Meanwhile, to buffer the shock of ENSO on the export volume, the rubber industry could develop inventory management, specifically during high production or when El Nino is predicted to occur. Expectedly, the inventory could provide the TSNR 20 stock to be exported during low production to lessen the shock on export. Acknowledgment: The authors are grateful for the support of the Department of Agricultural Socioeconomics, Faculty of Agriculture, Universitas Gadjah Mada (UGM) in providing research facilities. Authors’ contributions: I.C. contributes on conceptualization, data collection, data analysis, writing original manuscript, review and editing manuscript, addressing review comments. A.W.U. is responsible in conceptualization, methodology and writing supervision, review and editing manuscript. L.R.W. is responsible in supervision of data collection and reviewing the manuscript. Conflict of interest: The authors declare no conflict of interest. REFERENCES Amirudin, A. 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