Nonlinear impacts of climate anomalies on oil palm productivity

Abstract

Oil palm contributes to various global needs as one of the most productive oil crops, but there exist ongoing concerns regarding its yield reductions and associated environmental impacts resulting from land conversion. This is the first detailed report investigating the nonlinear threats to estate-level oil palm yields posed by El Niño Southern Oscillation (ENSO) in the equatorial Pacific Ocean, a major driver of climate variability. Using the Malaysian Palm Oil Board administrative records on monthly performances reported by oil palm estates through the e-submissions portal spanning from January 2015 to June 2023, we focused on elucidating the impacts of ENSO on fresh fruit bunch yield, oil extraction rate, and oil yield. We found that both El Niño and La Niña conditions, characterized by extreme levels of ENSO indices cumulated over lags of 0-23 months prior to harvest, were associated with statistically significant reductions in yields. Lag association patterns unveiled that production risks were linked to pre-harvest exposure to extreme ENSO indices in various time windows. Subgroup analyses further revealed that the effects were pronounced in labor-intensive estates and those lacking fertilizer investments. This study underscores the necessity for adaptation strategies in response to future climate anomalies.

Fig. 1

Distribution of the study areas with ENSO tele-connected impacts. (A) is the geographic locations of 13 states with data on FFB yield included in this analysis, with the top 5 FFB production in millions (mil.) of metric ton (t) during January 2015 to June 2023 depicted on the map; (B) shows the monthly correlations between the two-month lag of MEI and meteorologic parameters (total precipitation and air temperature respectively) in within each estate's nearest 9 km by 9 km grid based on quadratic fits; (C) provides the FFB yield (t/ha/month) distribution over time in tele-connected states (n = 424,185 observations from 4785 estates). Box plots indicate median (middle line), 25th, 75th percentile (box), and 5th and 95th percentile (whiskers) as well as outliers (single points). Estate composition within each month is different due to entries or exits of estates. Abbreviations: ENSO, El Niño Southern Oscillation; MEI, multivariate El Niño index; FFB, fresh fruit bunch.

Fig. 2

Cumulative exposure-response associations between ENSO and the relative changes of FFB yield (in 0–23), average OER (in 0–11), and oil yield (in 0–23 lagged months). The red lines (with 95 % confidence intervals [CIs], shaded grey) indicate effect estimates of El Niño conditions, and blue lines (with 95 % CIs, shaded grey) indicate effect estimates from La Niña conditions. The association of each ENSO measure with each outcome is computed as the effect of a given value of ENSO measure relative to the reference value (set at zero) Histograms of ENSO indices are plotted at the bottom, with proportions measured by the second (right) vertical axis. Abbreviations: t/ha, metric ton per hectare; ENSO, El Niño Southern Oscillation; MEI, multivariate El Niño index; ONI, Oceanic Niño Index; SOI, Southern Oscillation Index; BEST, Bivariate ENSO Timeseries. Refer to Fig. S2 for the results controlling for estate random effects.

Fig. 3

Lag association pattern between ENSO (up to 23 months ago) and the FFB yield, average OER, and oil yield from contour plots. The X-axis indicates intensity of each ENSO measure, and Y-axis indicates lags of 0–23 months. The color gradient represents the relative change. Abbreviations: t/ha/month, metric ton per hectare per month; ENSO, El Niño Southern Oscillation; MEI, multivariate El Niño index; ONI, Oceanic Niño Index; SOI, Southern Oscillation Index; BEST, Bivariate ENSO Timeseries. Refer to Fig. S3 for the results controlling for estate random effects.

Fig. 4

Cumulative associations between the relative changes of FFB yield (t/ha in 0–23), average OER (% in 0–11), and oil yield (t/ha in 0–23 lagged months) and MEI exposure at the value of 2 stratified by characteristics of study estates. The association of MEI with each outcome is computed as the effect of a given value of MEI relative to the reference (i.e., zero). Abbreviations: t/ha, metric ton per hectare; MEI, multivariate El Niño index; FFB, fresh fruit bunch; OER, oil extraction rate.

Fig. 5

Cumulative associations between the relative changes of FFB yield (t/ha in 0–23), average OER (% in 0–11), and oil yield (t/ha in 0–23 lagged months) and MEI exposure at the value of −1 stratified by characteristics of study estates. The association of MEI with each outcome is computed as the effect of a given value of MEI relative to the reference (i.e., zero). Abbreviations: t/ha, metric ton per hectare; MEI, multivariate El Niño index; FFB, fresh fruit bunch; OER, oil extraction rate.