Effects of Some Weather Parameters on Oil Palm Production in the Peninsular Malaysia

Ahmed, A. U.; M, Yusoff Ishak; M, Kamal Uddin; M, Yusoff Abd Samad; S, Mukhtar; S, Shehu Danhassan

doi:10.20944/preprints202106.0456.v1

Cited by 7 publications

(7 citation statements)

References 38 publications

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“…Since the temperature ranged between 25°C and 32°C, our findings revealed that LST temperature was positively correlated with yield. Ahmed et al (2021) agreed with our results, suggesting oil palm achieved the highest yields in areas with a maximum average temperature of 29-33°C and a lowest average temperature of 22-24°C. Apparently, by using backward elimination, rainfall derived from CHIRPS was discovered as a significant predictor compared to rainfall derived from meteorological stations which had low spatial resolution (Orhan et al 2014).…”

Section: Satellite-derived Climatic Variablessupporting

confidence: 93%

“…Notably, the precipitation collected from CHIRPS dropped precipitously to 170 mm and the land surface temperature was the highest (35°C) in 2015 and 2016 as a result of the very severe El Niño that caused the drought. Possibly, the oil palm yields remained low in the next few years due to the heat stress from the recent very severe El Niño that caused the drought to occur and failed to recover (Khor et al 2021;Ahmed et al 2021). In order to ascertain the results, further verification between ground-based rainfall and temperature and satellite-derived climatic variables will be undertaken.…”

Section: Satellite-derived Climatic Variablesmentioning

confidence: 99%

“…leaf area index, foliar and soil nutrients, and tree height) are typically inconsistent and can lead to statistical error and bias (Awal et al 2010;Shamshiri et al 2018). Several studies show that climate and agronomic aspects are the main elements in predicting oil palm yield (Chapman et al 2018;Hilal et al 2021;Ahmed et al 2021;Khor et al 2021). Such weather data is crucial in estimating oil palm yield because temperature increases of 1 °C, 2 °C, 3 °C and 4 °C reduce oil palm production by 10% to 41% (Sakar et al 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Early oil palm yield forecasting is critical in supporting plantation managers in making better decisions for addressing the underlying cause of crop yield decline based on pertinent factors. The loss in oil palm productivity is considerably influenced by climate change, such as severe temperature and precipitation levels and extreme events (such as floods, sea-level rise, El Nino, La Nia and drought) (Ahmed et al 2021). In addition, predicting oil palm yield and optimising yield output per unit of land usage is critical to ensure that current plantings contribute to optimal yield without expanding land use.…”

Section: Introductionmentioning

confidence: 99%

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Oil Palm Yield Prediction Across Blocks Using Multi-Source Data and Machine Learning

Ang

Shafri

Lee³

et al. 2022

Preprint

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Predicting yields on a bigger scale in a timely and accurate manner is essential for preventing climate risk and ensuring food security, particularly in the light of climate change and the escalation of extreme climatic events. Furthermore, crop yield estimates are affected by various factors including weather, nutrients and management practices. In this study, integrating multi-source data (i.e. satellite-derived vegetation indices (VIs), satellite-derived climatic variables (i.e. land surface temperature (LST) and rainfall precipitation, weather station and field-surveys), we built one multiple linear regression (MLR), three machine learnings (XGBoost, support vector regression, and random forest) and one deep learning (deep neural network) model to predict oil palm yield at block-level within the oil palm plantation. Moreover, time-series moving average and backward elimination feature selection techniques were implemented at the pre-processing stage. The yield prediction models were developed and tested using MLR, XGBoost, support vector regression (SVR), random forest (RF) and deep neural network (DNN) algorithms. Their model performances were then compared using evaluation metrics and generated the final spatial prediction map based on the best performance. DNN achieved the best model performances for both selected (R2=0.91; RMSE= 2.92 tonnes per ha; MAE= 2.56 tonnes per ha and MAPE= 0.09) and full predictors (R2=0.76; RMSE of 3.03 tonnes per ha; MAE of 2.88 tonnes per ha; MAPE of 0.10 tonnes per ha). In addition, advanced ensemble ML techniques such as XGBoost may be utilised as a supplementary for oil palm yield prediction at the block level. Among them, MLR recorded the lowest performance. By using backward elimination to identify the most significant predictors, the performance of all models was improved by 5% - 26% for R2, and that decreased by 3% - 31% for RMSE, 7% - 34% for MAE, and 1% - 15% for MAPE, respectively. DNN generates the most accurate statistical metrics, with an increase of around 15% for R2, 11% for RMSE, 32% for MAE and 1% for MAPE. Our study successfully developed efficient, effective and accurate yield prediction models for timely predicting oil palm yield over a large area by integrating data from multiple sources. These can be potentially handled by plantation management to estimate oil palm yields to speed up the decision-making process for sustainable production.

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Section: Satellite-derived Climatic Variablessupporting

confidence: 93%

Section: Satellite-derived Climatic Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Oil Palm Yield Prediction Across Blocks Using Multi-Source Data and Machine Learning

Ang

Shafri

Lee³

et al. 2022

Preprint

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“…require an average rainfall of 150 mm month -1 or 1,750 -3,000 mm year -1 with a dry period of no more than 2 -3 months (Harahap et al, 2013). Rainfall above 2,500 mm is considered unfavorable because it reduces solar radiation (Ahmed et al, 2021). Rainfall of more than 3,500 mm year -1 , less than 2,000 mm year -1 , or less than 100 mm month -1 can also reduce oil palm production and CPO levels (Kamil & Omar, 2017;Woittiez et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Effect of water deficit of Ultisols, Entisols, Spodosols, and Histosols on oil palm productivity in Central Kalimantan

Sukarman

Saidy

Rusmayadi

et al. 2022

Sains Tanah J. Soil Sci. Agroclimatology

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The same rainfall can cause different degrees of water stress depending on soil type, so the production response shown by plants can be different. This study is essential for growers, especially in predicting oil palm production based on water deficit for each soil type. The study was conducted on oil palm plantations in Central Kalimantan, Indonesia, with four soil types in 1,446.15 ha (40 blocks). The source of data collected from oil palm plantations included bunch number, average bunch weight, rainfall, and soil physical and chemical properties for the last 15 years (2007 - 2021). This experimental study used a two-stage cluster sampling method. The results showed that the best productivity, bunch number, and average bunch weight were found on Ultisols. The four soil types tested showed the same annual production distribution dynamic, but the response rate from each soil type showed differences. Entisols and Spodosols were more prone to drought stress due to water deficit than Ultisols and Histosols because of the differences in soil texture. Water deficit causes a decrease in oil palm productivity by 5 - 22% in the first year (Ultisols 12 - 22%; Entisols 12 - 22%; Spodosols 7 - 19%; Histosols 5 - 15%) and 1 - 8% in the second year (Ultisols 3 - 7%; Entisols 2 - 4%; Spodosols 5 - 8%; Histosols 1 - 5%) compared to previous years production. A decrease in oil palm productivity occurs at 3 - 5 months (bunch failure phase), 1 year (abortion sensitive phase), and 2 - 2.5 years (sex differentiation phase) after a water deficit appears.

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Functional Use-Based Positioning of Conventional Vehicles in Conjunction with Alternate Low-Emission Fuels

Kumar

Majumdar

2023

Energy, Environment, and Sustainability

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Effects of Some Weather Parameters on Oil Palm Production in the Peninsular Malaysia

Cited by 7 publications

References 38 publications

Oil Palm Yield Prediction Across Blocks Using Multi-Source Data and Machine Learning

Oil Palm Yield Prediction Across Blocks Using Multi-Source Data and Machine Learning

Effect of water deficit of Ultisols, Entisols, Spodosols, and Histosols on oil palm productivity in Central Kalimantan

Functional Use-Based Positioning of Conventional Vehicles in Conjunction with Alternate Low-Emission Fuels

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