Influence of weather and endogenous cycles on spatiotemporal yield variation in oil palm
2022
Monzon, Juan P. | Jabloun, Mohamed | Cock, James | Caliman, Jean-Pierre | Couëdel, Antoine | Donough, Christopher R. | Vui, Philip Ho Vun | Lim, Ya Li | Mathews, Joshua | Oberthür, Thomas | Prabowo, Noto E. | Edreira, Juan I Rattalino | Sidhu, Manjit | Slingerland, Maja A. | Sugianto, Hendra | Grassini, Patricio
Oil palm is the major source of vegetable oil in the world and Indonesia is the main palm oil producing country. There is limited knowledge on the factors accounting for spatial and temporal variation in fresh fruit bunches (FFB) yield. Here we investigated relationships between weather and endogenous factors with FFB yield and its components (bunch number and individual bunch weight) using data collected from well-managed plantations in Indonesia. The database included many sites and years (total of 136 block-years observations), portraying a wide range of FFB yield and environmental conditions. We used average annual values to detect spatial variations in yield associated with weather, and monthly values to detect temporal yield variations in yield associated with weather and endogenous cycles. We found that water stress was the key factor accounting for the spatial and/or temporal variation in FFB yield. Our analysis also highlights the importance of vapor pressure deficit (VPD) as a stress factor in oil palm, with this study being the first to demonstrate the negative relationship between yield and VPD and yield and water-use efficiency at the block level. Meteorological anomalies during the bunch failure, anthesis, and sex differentiation periods had the largest impact on yield. Besides climate factors, we confirmed the existence of endogenous yield cycles, with high-yield cycles typically followed by low-yield cycles and vice versa. Our findings extend current knowledge about sources of variation in oil palm yield, providing useful information to describe oil palm production environments and improve oil palm modeling and yield forecasting.
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