Counting starch one rai at a time…
STARCH 20.70 · ROOT 3.28 · μ 6.32 · 02 JUN 2026
KUCCI · Tapioca Outlook
Tapioca Starch Outlook · by KUCCI
05 May 2026
STARCH EX-MILL21.30 ▲ +0.60 ROOT NAKHON3.28 ▲ range 3.00–3.55 μ = S/R6.50 — above long-run mean USD/THB32.74 ▲ Baht steady CBOT CORN$4.75 ▲ +0.06 NIÑO 3.4+0.5°C ▲ EL NIÑO WATCH SHANGHAI4,112 ▲ +24% YoY DEC '26 ENSEMBLE20.02 THB/kg ▲ MACRO REGIME
Tapioca starch:
the price path to year-end
Bangkok · June 2, 2026
Section 1

Where the forecast lands

Seven contributors agree on direction; the ensemble splits the difference and stays inside the historical envelope.
May–December 2026 macro-regime forecast · 1–2–1 filtered with historical anchors
Observed history unchanged · Cambodia/China/Vietnam macro overlay · weak seasonality · 1–2–1 smoothing uses nearest observed anchors
Climatology ± 1σ
Observed
Ensemble (top 7)
Ridge α=1 (best single)
GBM
Random Forest
OLS · AR(1)
Random Walk · 3MA · Climatology
Trained on monthly data 2017–May 2026 · Historical observations unchanged · Macro overlay: Cambodia-border supply disruption, Thai root tightness, China substitution demand, Vietnam/Laos route shift · Forward values 1–2–1 smoothed
▸ Pinch / scroll to zoom · drag to pan
Monthly forecast values All available contributors · ensemble in gold
Ensemble weighting Inverse RMSE · selected contributors from walk-forward test
Why include benchmarks. Random walk and climatology each capture something the fitted models miss. Random walk anchors the near-term level. Climatology is retained only as a low-weight background check. The macro-regime overlay prevents the model from forcing a seasonal decline when the shock is trade-driven.

What the next eight months look like

May–June
Commit near-term coverage — direction is settled
Ensemble puts June near 19.95 THB/kg after smoothing against the May observed anchor. Ridge and OLS still carry upside, but the macro-stress overlay prevents the curve from obeying normal seasonality while Cambodia flows remain disrupted and China demand remains supportive.
July–September
Uptrend intact; watch ENSO confirmation
Ensemble continues rising through Q3 — Jul 18.78, Aug 18.82, Sep 18.85. Ridge sees the corn_lag15 signal sustaining; AR(1) stays near 18.5. Niño-3.4 above +1.0°C by Q3 reinforces demand but flags a 2027 yield drag at the 18-month lag — plant decisions taken now will matter.
October–December
Prices firm — begin locking 2027 Q1 volumes
Oct 20.14, Nov 20.08, Dec 20.02: the ensemble holds well above the 2025 trough. The curve is smoothed and nearly flat because current root supply has not normalized, root medians have not weakened, and trade disruption is not a seasonal variable. New-crop pressure is allowed only as mild softening, not as a forced downturn.
Through 2026
USD/THB and baht risk
USD/THB eased from 35 to 32.7 over twelve months; the model assigns a modest but consistent negative coefficient. The greater risk is asymmetric: a baht strengthening toward 31.5 trims export receipts in Q4 when starch prices are already falling. The compounding of FX and price decline warrants attention in H2 planning.
Section 3 · Decomposition

What you pay for, when you buy a kilo of starch

Two-thirds of the price is cassava root. The rest is what mills earn for converting it.
Today's starch price decomposed · 19.50 THB/kg
As at 19 May 2026 · root @ 3.20 · μ = 6.09×
68% · ROOT MASS
13% · CONVERSION
19% · MARGIN
12.50 THB/kgCassava root × (1/η) where η ≈ 0.24
Driven by yield, planting area, weather
2.50 THB/kgEnergy, drying, packaging, labour
Driven by oil price, grid tariff, wages
3.50 THB/kgDemand-side margin
Driven by China, corn substitution
Crush margin · what a mill earns per tonne processed
Industry-standard frame, comparable to soybean crush · current values
ComponentValueComment
Starch sell19.50 THB/kg starchTTSA domestic, 19 May 2026
Root cost (at gate)3.20 THB/kg root25% starch central reference; TTTA range 3.00–3.60
Root mass cost in starch13.33 THB/kg starch3.20 ÷ η   (η = 0.24)
Conversion (energy, dry, labour)2.50 THB/kg starchIndustry standard, sticky
Crush margin3.67 THB/kg starchGross spread before SG&A
Per tonne root processed≈ 880 THB / tonne root3.50 × η × 1000
A 1,000 t/day mill running 200 operating days at this spread earns ~168 M THB/year in gross margin. Floor is ~1.0 THB/kg starch (run on contracts only); break-even is ~1.5; current 3.50 is the firmest spread since early 2024.
Multiplier μ = Starch / Root · 2017–2026
When μ widens, mills earn more per kg processed
Multiplier μ (monthly)
Long-term mean (6.38×)
Authors' calculation · Root price hand-constructed from CEIC/OAE annual + Krungsri Research + measured Dec 2025–May 2026
What the decomposition shows. In 2025, μ collapsed to 5.45× — its lowest annual reading on record. Root prices stayed near 2.45 while starch fell to 13.05. That margin compression cost mills roughly 1.5 THB/kg of operating spread for an entire year. The May 2026 print pushes μ back to 6.17× — at the long-run mean — and the crush margin to 3.50 THB/kg starch (≈ 797 THB/tonne root). For SMS, SQS and the rest of the Buriram–Chaiyaphum mill cluster, that's the firmest spread since early 2024 and turns the next eight months into a meaningful margin story even if absolute starch prices stay below the 2024 peak.
Annual mean multiplier
Section 4

What is moving the price now

Niño-3.4
+0.5°C
▲ El Niño watch by Q3
USD/THB
32.74
▲ baht weakening
CBOT Corn
$4.75
▲ one-year high
Cassava yield
17.0
▼ third year low
Root price
3.20
▲ +15% from Dec '25
SSE Composite
4,112
▲ +24% YoY
Starch price against three reference series · annual averages
Standardised to common scale
Starch (target)
CBOT corn
Niño-3.4
Shanghai Composite

Three forces dominate, each on a different timescale. The first is corn. When American corn rallied past $7 a bushel during the Russia–Ukraine grain disruption, Asian starch buyers booked tapioca volume in advance. The displacement showed up in Thai mills not the next quarter but the next harvest — about fifteen months later. The 2023 starch high of 19 baht traced to corn prices set in late 2021.

The second force is climate. The 2023–24 El Niño dried out the cassava growing season; root water stress during July–September bulking reduced root size and starch content. The harvested crop reached mills through late 2024 and 2025. The lag from peak Niño 3.4 to peak Thai mill price runs around eighteen months. The current Niño watch — temperatures crossing +0.5°C in April 2026 with continued warm-phase forecasts — is therefore a 2027 problem, not a 2026 problem.

The third force is China. The Shanghai Composite, often dismissed as retail noise, works as a proxy for Chinese industrial activity. Its rises and falls foreshadow demand for paper, adhesives and bioplastics — the dominant end-uses of Thai starch — by roughly nine months. The 24% rise in Shanghai over the past year argues for firmer Thai mill prices into late 2026. The model takes that signal seriously. It does not take the S&P 500 or Dow particularly seriously: their lead times are weaker and the economic transmission less direct.

What the model does not do is treat all variables as equally informative. The cassava yield estimate, prominent in industry conversation, contributes less than corn from a year before. The dollar-baht rate, often cited as a major driver, registers modestly. The important correction is that border closure and trade rerouting are not sinusoidal. They enter as a macro-regime term that holds price level higher until confirmed supply relief appears.

News update, 21 May 2026: Reuters/Green Pool linked improved Thai cassava prices to stronger China demand and minimal Cambodian imports after the border closure; CZapp flags border closure as a raw-material cost risk; Cambodianess shows Cambodia normally exports more than half its crop to Vietnam and 35–40% to Thailand; Krungsri expects China chip demand to support 2025–2026 exports. The forecast therefore treats Cambodia/China/Vietnam flows as macro structure, not seasonality.

Section 5 · Method

How the forecast is built

Three parallel approaches. A direct multi-model ensemble for prediction, a two-equation decomposition for interpretation, and a macro-regime overlay for trade-disruption shocks.

Model horse race · walk-forward validation

▸ EXPAND

Every contributor evaluated on 89 monthly out-of-sample steps (88 walk-forward CV + the May 2026 print as a fresh holdout). RMSE in THB/kg.

The fitted linear models beat random walk by 0.01–0.06 THB/kg of RMSE — modest at one-step-ahead, but compounding over the eight-month forecast. The May 2026 print sharpened the picture: Ridge and OLS predicted within 0.5 THB/kg of the realised 18.5, while tree models and climatology missed by 2.2–2.8. That fresh evidence lifted Ridge and OLS in the inverse-RMSE weighting and trimmed the tree models. The headline win is still over the published 3-month moving average baseline (Komkul, 2017[1]), where RMSE drops nearly in half. The genuine economic information added by 15-month corn and 18-month ENSO shows up at directional turning points and over multi-month horizons, not in monthly error metrics alone.

The equations, written out

The two-equation decomposition makes the economic structure explicit. Root and multiplier are modelled separately, then combined. A macro-regime term is added when trade policy or border closure changes feedstock availability independently of normal seasonality.

Identity
Decomposition of starch price
\[ S_t = R_t \times \mu_t = R_t \times \left( \frac{1}{\eta} + \frac{c_t + m_t}{R_t} \right) \]
Where S_t is domestic starch price (THB/kg), R_t is cassava root farm-gate price, η ≈ 0.24 is the starch extraction yield (mass fraction), c_t is the conversion cost component (energy, drying, labour, packaging), and m_t is the residual demand-side margin. The multiplier μ_t = S_t/R_t averages 6.38× across 2017–2026, ranging 4.96–7.70×.
Equation 1 · Root
Physical supply equation
\[ R_t = \alpha_R + \sum_{i \in \{1,2,12\}} \phi_i R_{t-i} + \beta_1 Y_t + \beta_2 \Delta Y_t + \gamma_1 N_{t-12} + \gamma_2 N_{t-18} + \gamma_3 \bar{N}_{[t-21,t-15]} + s(t) + \epsilon^R_t \]
Root price as autoregressive in own-lags R_{t-1,2,12}, plus current and YoY-change cassava yield Y_t, ΔY_t, plus Niño-3.4 anomaly at lags 12, 18, and a 6-month average centred on lag-18. s(t) is harmonic seasonality. Coefficients estimated by Ridge regression with α=1.
Equation 2 · Multiplier
Margin equation
\[ \mu_t = \alpha_\mu + \sum_{i \in \{1,2,12\}} \psi_i \mu_{t-i} + \delta_1 C_{t-15} + \delta_2 \bar{C}_{[t-18,t-12]} + \delta_3 C_t + \theta_1 SSE_{t-9} + \omega_1 X_t + \omega_2 \Delta X_t + s(t) + \epsilon^\mu_t \]
Multiplier as autoregressive plus CBOT corn at the empirically-tuned lag-15 (substitution effect), plus contemporaneous corn, plus Shanghai Composite at lag-9 (China demand cycle), plus current and 6-month-changed USD/THB rate X_t, ΔX_t.
AR(1)
Time-series ensemble component
\[ S_t = c + \phi_1 S_{t-1} + \epsilon_t \]
First-order autoregression on starch price. One non-seasonal AR term φ_1 captures monthly persistence. The AR(1) form is the simplest pure time-series benchmark above random walk; it adds a single mean-reversion parameter relative to RW. The 2020 TTSA benchmark study[2] used ARIMA(2,0,2); AR(1) is a parsimonious counterpart that competes with the linear regression family on the same data.
Final forecast
Macro-regime ensemble + 1–2–1 smoothing
\[ \hat{S}_t^{\text{ENS}} = \sum_{k=1}^{7} w_k \hat{S}_t^{(k)} , \quad w_k = \frac{1/\text{RMSE}_k}{\sum_j 1/\text{RMSE}_j} \]
Inverse-RMSE weighting is now constrained by a macro-regime overlay. Ridge, OLS, AR(1), Gradient Boost, Random Walk and the moving average remain in the ensemble, but climatology is capped at low weight. The Cambodia-border shock, Thai feedstock tightness, China demand, and Vietnam/Laos rerouting are treated as structural inputs rather than seasonal residuals. The forecast line is smoothed with a 1–2–1 filter anchored to the nearest observed prices. After the May 2026 retrain, the top-7 ensemble RMSE is 0.366 — slightly worse than Ridge alone at 0.348, because the linear models nailed May while tree models missed badly. The ensemble retains its diversification benefit at horizons beyond one month, where Ridge is more exposed to its corn-lag-15 input failing.
Lead-lag tuning
Why the lags are what they are
\[ \tau_X^* = \arg\max_\tau \left| \rho\left( \Delta X_{t-\tau}, \Delta S_t \right) \right| \quad \tau \in [0, 24] \]
Each exogenous variable's lag is selected by the cross-correlation between its first-difference and the first-difference of starch price, over horizons 0–24 months. Result: τ*_corn = 15 (r=+0.31), τ*_Niño = 18 (r=−0.36), τ*_SSE = 9 (r=+0.21), τ*_USDTHB = 6 (r=−0.11). Cross-correlation analysis turns this from a black-box autoregression into an economically-grounded model.
Lead-lag map ▸ EXPAND
▌ Cross-correlation on first-differenced monthly data · positive lag = variable leads
Niño-3.4 at 18 months captures the cassava growing-season impact on next year's harvest. CBOT corn at 15 months reflects substitution decisions made by buyers a full crop year ahead. Shanghai Composite at 9 months leads Chinese industrial demand for paper, adhesives, bioplastics.
In-sample fit · ensemble best model
One-step-ahead walk-forward predictions vs observations
Observed
Predicted
Signal strength · climate-first ranking
▌ Cross-correlation magnitude at the optimal lag for each driver
Ranked by |r| at each variable's empirically-tuned lag — not by tree-based feature importance, which inflates AR terms. Niño-3.4 leads because cassava grows over a 12–18 month cycle and the planting decision responds to the prior wet season's rainfall, which ENSO sets a year ahead.
All contributors · walk-forward results
Section 6

Where this year sits in nine years of price

The seasonal arc is visible historically, but 2026 is a trade-disruption regime. May 2026 prints above the usual band.
Climatological envelope · 2026 highlighted
Mean ± 1σ from 2017–2025 · 2026 traced in sky blue
Climatological mean ± 1σ
Climatological mean
2026
Where May 2026 lands. Climatology mean for May is 15.09 with σ ≈ 2.46. The 19.50 print sits at +1.79σ — well above the upper envelope, inside the historical "strong Q2" regime. May 2024 peaked at 18.70 (+1.47σ); the current May print now exceeds that level. The 2025 collapse is clearly over. The prior sharp unwinding was a model artifact. The corrected forecast treats 2026 as a high-plateau regime until root supply or import flows show real normalization.
Departure from climatology · year by year
Each line: that year's biweekly price minus long-term mean
Reading the year-by-year departures. Eight of the last nine years split into three regimes. Below climatology: 2019, 2020, 2021, 2025 — buyer-friendly years with weak corn pull-through and ample root supply. At climatology: 2017, 2018. Above climatology: 2022, 2023, 2024 — the post-Ukraine corn squeeze that lifted Thai starch by 4–5 THB/kg through 2023 and held it elevated for two years. 2026 has now joined the third group. The May print is +2.81 THB/kg above the long-run May mean. Whether the year settles into the 2022 trajectory (peak July, plateau through Q4) or the 2024 trajectory (peak Apr–May, decline through Q3) depends on how Chinese chip demand behaves in the late summer, which is the central question for the China-channel tab.

Literature on Thai cassava price and export forecasting

  1. Komkul, P. (2017). Forecasting Cassava Starch Price in Thailand by Using Time Series Models. The Journal of King Mongkut's University of Technology North Bangkok. Compared Box-Jenkins, Holt's exponential smoothing, damped trend, and 3/6/12-month moving averages on TTSA monthly data 2009–2014; the 3-month moving average had the lowest MAPE for 2014.
  2. Anonymous (2020). The impact of declining export price of tapioca starch on Thai economic output and employment. ResearchGate working paper. ARIMA(2,0,2) selected by minimum AIC on TTSA monthly 2011–2020; 8-month-ahead forecast.
  3. Pannakkong, W., Huynh, V.-N., & Sriboonchitta, S. (2016). ARIMA Versus Artificial Neural Network for Thailand's Cassava Starch Export Forecasting. In Causal Inference in Econometrics, Springer. ANN models outperformed ARIMA on MSE/MAE/MAPE for native starch, modified starch, and sago export volumes 2001–2013.
  4. Pannakkong, W., Sriboonchitta, S., & Huynh, V.-N. (2019). A Novel Hybrid Autoregressive Integrated Moving Average and Artificial Neural Network Model for Cassava Export Forecasting. International Journal of Computational Intelligence Systems, Springer. Hybrid ARIMA-ANN model with seasonal index gave the lowest error on native and modified starch export forecasts.
  5. FAO (2002). Forecast of area, yield and production of Thai cassava roots. Paper 5, FAO/OAE/Kasetsart econometric study. Planted-area function on lagged own price, competing crop prices, and lagged area; yield by time-series moving average; OLS-estimated Cobb-Douglas at national and regional levels.
  6. Sriroth, K. (1999). Cassava industry in Thailand: The status of technology and utilization. International Symposium on Cassava, Starch, and Starch Derivatives, Nanning, China. Background reference on starch extraction yield (η ≈ 23–25%) used in the decomposition above.

Distinction from the published literature. All five forecasting papers cited above are univariate (price or export volume forecast from its own past). The model presented here adds exogenous regressors at empirically-tuned lags — a methodologically more ambitious approach not previously documented for Thai tapioca starch.

KUCCI/Cassava Price Terminal
FEED · waiting for cassava_feed.js
Roots 25% TTSA Kasetprice farm-gate NETTA R25 NETTA R30 R25 fcst Starch TTSA Starch fcst
OPERATIONAL SIGNALS / NETTA · DAILY
Apr 07 — May 18 · 16 obs
Chips / Ayutthayaมันเส้น
THB/kg
Upstream signal for starch — when chip price rises, roots get diverted from starch processing, tightening starch supply with 2–4 wk lag.
Dry Residue / Mueangกากมัน
THB/kg
Post-extraction byproduct sold as feed. Rising price corroborates firm feed-market demand → competitive bid for chips.
Industry CapacityUTILISATION
<50%
unchanged · ~5 wk flat
Factories running below half capacity for 5 weeks. When this flips above 50%, supply has loosened. Currently supply-constrained.
Method · TTSA 19 May + TTTA 21 May + NETTA 18 May + Kasetprice 15 May · level-shift + constrained-supply forecast
5-week trend-carry · 18 May NETTA + 15 May Kasetprice bias · factory-pressure index

Observed price arrays are kept as source records and are not back-adjusted. The 21 May TTTA starch range is retained as a market cross-check; the TTSA 19 May domestic quote remains the model anchor. Forecast = confirmed level shift + persistent supply-pressure carry + weak seasonality + median-confirmed root momentum. Seasonal reversion is capped and cannot force a downturn until root medians weaken. R25 forecast is plotted with its uncertainty spread. R30 forward calculation is retained internally and folded into the starch forecast cone instead of being plotted as a separate line.

Stage 1 — weekly baseline

$$\hat{P}^{base}_{j,h} = \hat{P}_{j,h-1} + \rho(T_{j,h}-\hat{P}_{j,h-1}) + d_h m_j$$ $$T_{j,h}=A_j\frac{1+H_j(w_t+h)}{1+H_j(w_t)}, \qquad A_j=0.70P_{j,t}+0.30\overline{P_j}^{(4w)}$$ $$\rho=0.22,\qquad d_h=[0.65,0.35,0.12,0.05,0.02]$$

Muted seasonal harmonic

$$H_R(w)=-0.020\sin\left(\frac{2\pi w}{52}\right)+0.016\cos\left(\frac{2\pi w}{52}\right)$$ $$H_S(w)=-0.012\sin\left(\frac{2\pi w}{52}\right)+0.006\cos\left(\frac{2\pi w}{52}\right)$$

Stage 2 — NETTA bias correction

$$Bias^{NETTA}=\overline{R^{NETTA}_{25}}_{(t_a,t_a+7d]}-\hat{R}^{base}_{25,1}$$ $$\hat{R}^{bias}_{25,h}=\hat{R}^{base}_{25,h}+\gamma_h Bias^{NETTA},\qquad \gamma_h=[0.85,0.55,0.30,0.15,0.05]$$

Stage 3 — factory-pressure index

$$PI_t= 0.30Z(R^{NETTA}_{30}-R^{NETTA}_{25}) +0.30Z(C^{NETTA}/2.5-R^{NETTA}_{25}) +0.15Z(G^{NETTA}) +0.15Z(R^{NETTA}_{25}-R^{weekly}_{25}) +0.20B_t +0.10U_t$$ $$B_t=+1\ \text{for constrained cross-border/import flow},\qquad U_t=0.75\ \text{when utilization}<50\%$$

Final forecasts

$$\hat{R}_{25,h}=\hat{R}^{base}_{25,h}+\gamma_h Bias^{NETTA}+\beta_h PI_t+\kappa_h(K_{25,t}-R^{NETTA}_{25,t})$$ $$\beta_h=[0.055,0.040,0.025,0.015,0.005],\qquad \kappa_h=[0.35,0.22,0.12,0.06,0.02]$$ $$\hat{R}_{30,h}=\hat{R}_{25,h}+\bar{Q}+\eta_h(Q_t-\bar{Q}),\qquad Q_t=R^{NETTA}_{30,t}-R^{NETTA}_{25,t}$$ $$\eta_h=[0.65,0.45,0.28,0.15,0.06]$$ $$\hat{S}_{h}=\hat{S}^{base}_{h}+\lambda_h PI_t+\omega_h J_t$$ $$\lambda_h=[0.105,0.075,0.050,0.030,0.015],\qquad \omega_h=[0.28,0.15,0.07,0.03,0.00]$$ $$J_t=\max\left(0,\Delta S_t-\overline{\Delta S}_{prior}\right)$$

Conversion checks

$$R^{chips}_t=\frac{C^{NETTA}_t}{2.5}$$ $$R^{starch,gross}_t=0.20S^{TTSA}_t$$