Problems and Prospects of Improving Abiotic Stress Tolerance and Pathogen Resistance of Oil Palm

Multi-Omics Approaches in Oil Palm Research: A Comprehensive Review of Metabolomics, Proteomics, and Transcriptomics Based on Low-Temperature Stress

Oil palm (Elaeis guineensis Jacq.) is a typical tropical oil crop with a temperature of 26-28 °C, providing approximately 35% of the total world's vegetable oil. Growth and productivity are significantly affected by low-temperature stress, resulting in inhibited growth and substantial yield losses. To comprehend the intricate molecular mechanisms underlying the response and acclimation of oil palm under low-temperature stress, multi-omics approaches, including metabolomics, proteomics, and transcriptomics, have emerged as powerful tools. This comprehensive review aims to provide an in-depth analysis of recent advancements in multi-omics studies on oil palm under low-temperature stress, including the key findings from omics-based research, highlighting changes in metabolite profiles, protein expression, and gene transcription, as well as including the potential of integrating multi-omics data to reveal novel insights into the molecular networks and regulatory pathways involved in the response to low-temperature stress. This review also emphasizes the challenges and prospects of multi-omics approaches in oil palm research, providing a roadmap for future investigations. Overall, a better understanding of the molecular basis of the response of oil palm to low-temperature stress will facilitate the development of effective breeding and biotechnological strategies to improve the crop's resilience and productivity in changing climate scenarios.

Future Climate Effects on Yield and Mortality of Conventional versus Modified Oil Palm in SE Asia

Palm oil is a very important commodity which will be required well into the future. However, the consequences of growing oil palm (OP) are often detrimental to the environment and contribute to climate change. On the other hand, climate change stress will decrease the production of palm oil by causing mortality and ill health of OP, as well as reducing yields. Genetically modified OP (mOP) may be produced in the future to resist climate change stress, although it will take a long time to develop and introduce, if they are successfully produced at all. It is crucial to understand the benefits mOP may bring for resisting climate change and increasing the sustainability of the palm oil industry. This paper employs modeling of suitable climate for OP using the CLIMEX program in (a) Indonesia and Malaysia, which are the first and second largest growers of OP respectively, and (b) Thailand and Papua New Guinea, which are much smaller growers. It is useful to compare these countries in terms of future palm oil production and what benefits planting mOP may bring. Uniquely, narrative models are used in the current paper to determine how climate change will affect yields of conventional OP and mOP. The effect of climate change on the mortality of mOP is also determined for the first time. The gains from using mOP were moderate, but substantial, if compared to the current production of other continents or countries. This was especially the case for Indonesia and Malaysia. The development of mOP requires a realistic appreciation of what benefits may accrue.

Implications of zero-deforestation palm oil for tropical grassy and dry forest biodiversity

Many companies have made zero-deforestation commitments (ZDCs) to reduce carbon emissions and biodiversity losses linked to tropical commodities. However, ZDCs conserve areas primarily based on tree cover and aboveground carbon, potentially leading to the unintended consequence that agricultural expansion could be encouraged in biomes outside tropical rainforest, which also support important biodiversity. We examine locations suitable for zero-deforestation expansion of commercial oil palm, which is increasingly expanding outside the tropical rainforest biome, by generating empirical models of global suitability for rainfed and irrigated oil palm. We find that tropical grassy and dry forest biomes contain >50% of the total area of land climatically suitable for rainfed oil palm expansion in compliance with ZDCs (following the High Carbon Stock Approach; in locations outside urban areas and cropland), and that irrigation could double the area suitable for expansion in these biomes. Within these biomes, ZDCs fail to protect areas of high vertebrate richness from oil palm expansion. To prevent unintended consequences of ZDCs and minimize the environmental impacts of oil palm expansion, policies and governance for sustainable development and conservation must expand focus from rainforests to all tropical biomes.

Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow

Current development in precision agriculture has underscored the role of machine learning in crop yield prediction. Machine learning algorithms are capable of learning linear and nonlinear patterns in complex agro-meteorological data. However, the application of machine learning methods for predictive analysis is lacking in the oil palm industry. This work evaluated a supervised machine learning approach to develop an explainable and reusable oil palm yield prediction workflow. The input data included 12 weather and three soil moisture parameters along with 420 months of actual yield records of the study site. Multisource data and conventional machine learning techniques were coupled with an automated model selection process. The performance of two top regression models, namely Extra Tree and AdaBoost was evaluated using six statistical evaluation metrics. The prediction was followed by data preprocessing and feature selection. Selected regression models were compared with Random Forest, Gradient Boosting, Decision Tree, and other non-tree algorithms to prove the R² driven performance superiority of tree-based ensemble models. In addition, the learning process of the models was examined using model-based feature importance, learning curve, validation curve, residual analysis, and prediction error. Results indicated that rainfall frequency, root-zone soil moisture, and temperature could make a significant impact on oil palm yield. Most influential features that contributed to the prediction process are rainfall, cloud amount, number of rain days, wind speed, and root zone soil wetness. It is concluded that the means of machine learning have great potential for the application to predict oil palm yield using weather and soil moisture data.