Species Richness and Carbon Footprints of Vegetable Oils: Can High Yields Outweigh Palm Oil’s Environmental Impact?

The balance of n-6 and n-3 fatty acids in canine, feline, and equine nutrition: exploring sources and the significance of alpha-linolenic acid

Both n-6 and n-3 fatty acids (FA) have numerous significant physiological roles for mammals. The interplay between these families of FA is of interest in companion animal nutrition due to the influence of the n-6:n-3 FA ratio on the modulation of the inflammatory response in disease management and treatment. As both human and animal diets have shifted to greater consumption of vegetable oils rich in n-6 FA, the supplementation of n-3 FA to canine, feline, and equine diets has been advocated for. Although fish oils are commonly added to supply the long-chain n-3 FA eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA), a heavy reliance on this ingredient by the human, pet food, and equine supplement industries is not environmentally sustainable. Instead, sustainable sourcing of plant-based oils rich in n-3 α-linolenic acid (ALA), such as flaxseed and camelina oils, emerges as a viable option to support an optimal n-6:n-3 FA ratio. Moreover, ALA may offer health benefits that extend beyond its role as a precursor for endogenous EPA and DHA production. The following review underlines the metabolism and recommendations of n-6 and n-3 FA for dogs, cats, and horses and the ratio between them in promoting optimal health and inflammation management. Additionally, insights into both marine and plant-based n-3 FA sources will be discussed, along with the commercial practicality of using plant oils rich in ALA for the provision of n-3 FA to companion animals.

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.

A bibliometric analysis on the tribological and physicochemical properties of vegetable oil-based bio-lubricants (2010-2021)

Vegetable oil-based bio-lubricants possess potential as an alternative to mineral oil-based lubricants due to their biodegradability and renewability. However, a detailed examination of the publication focus, trend, and future direction related to these bio-lubricants' tribological and physicochemical properties is scarce. Therefore, the study presents a bibliometric analysis of vegetable oil-based bio-lubricant. One hundred sixty-five publications were extracted from Web of Science (WoS) from 2010 to 2021. During this period, the total citation was 2,240, recording an average citation per publication of 13.58. Proceedings of The Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology was the top productive journal, publishing 10.3% of the publications selected on the studied topic. From 2010 to 2021, India was the most productive country working on bio-lubricants due to its abundance of coconut products, followed by Malaysia due to its abundance of palm products. The keyword analysis indicated that a significant amount of work emphasised the derivation of bio-lubricants with an increasing shift towards tribological performance characterisation. From the analysis, palm is the most studied bio-lubricant, followed by castor oil. The reported viscosity and viscosity index values cover an extensive range, allowing these bio-lubricants to be adopted for a wide range of applications. For different vegetable oil-based bio-lubricants, the coefficient of friction is reported from 0.001 to 0.78, with the wear scar diameter being reported from 0.075 μm to 4.59 mm. Even though these bio-lubricants' friction and wear performances can be tabulated, the dataset is still unreliable for lubricant-selection purposes because of the varying test conditions. Such a scenario also limits the ability to correlate the role of fatty acid composition in the vegetable oil-based bio-lubricants in fulfilling their various application-specific potentials. Therefore, this study recommends that a unified correlation between the fatty acid composition and its tribological performance be attained consistently to better elucidate the potential of vegetable oil-based bio-lubricants.

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.