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Indian Journal of Agricultural Research, volume 57 issue 1 (february 2023) : 73-78

What does Modelling Tells us on the Influence of Certain Weather Parameters on Oil Palm Production in Peninsular Malaysia

A. Abubakar1, M.Y. Ishak1,*, M.K. Uddin2, A. Abu Bakar1, M.U. Mohammed3
1Faculty of Forestry and Environment, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia.
2Faculty of Agriculture, University Putra Malaysia, 43400 Serdang, Selangor, Malaysia.
3Department of Geography, Bayero University, Kano, Nigeria.
Cite article:- Abubakar A., Ishak M.Y., Uddin M.K., Bakar Abu A., Mohammed M.U. (2023). What does Modelling Tells us on the Influence ofCertain Weather Parameters on Oil Palm Production in Peninsular Malaysia . Indian Journal of Agricultural Research. 57(1): 73-78. doi: 10.18805/IJARe.AF-715.

ABSTRACT

Background: Oil palm has been recognized as one of the most important crops especially in Southeast Asia and the rest of the world. Palm oil production has recently been lowered as a result of the influence of various factors, which include weather and climate. The distribution of temperature, wind speed, relative humidity, solar radiation and rainfall all influence the growth and development of palms, which later reflects in the production quantity of the fresh fruit bunches and palm oil. The objective of this study is to investigate the impacts of various weather factors especially (temperature, wind speed, relative humidity, solar radiation and rainfall) on oil palm production in Peninsular Malaysia. 

Methods: The Statistical Package for Social Sciences (SPSS) 20.0 version was used to analyse the data, which include descriptive statistics and multilinear regression (MLR). The MLR model evaluated the strength of the relationship between oil palm yield (as a dependent variable) and temperature, solar radiation, wind speed, relative humidity and precipitation. Temperature, wind speed, relative humidity, solar radiation and rainfall, on the other hand, have been shown to have little effect on oil palm production and yield. 

Result: According to the R2 value, the independent variables only explained 20.2% of the variation in palm oil production. This study recommends operating within a comprehensive framework that includes scientific research, planting improved varieties, enhancing regional intellectual and academic leadership, engaging the participation of private and public stakeholders, highlighting participatory initiatives with researchers in consumer countries and enhancing growers’ ability to adapt best agroecological practises.

INTRODUCTION

The oil palm (Elaeis guineensis) is native to West Africa, where it can be found between Angola and Gambia (Paterson et al., 2013). Oil palm was first planted in South-east Asia in 1848 at the Bogor Botanical Garden in Indonesia (Nambiappan et al., 2018). In the 1870s, Malaysia received its first batch of oil palm from the Royal Botanical Garden, United Kingdom (Singh et al., 2021). The first commercial oil palm plantation in Malaysia was established in 1917 at Tennamaran Estate, Selangor (Begum et al., 2018). In total, there are ten important oil palm producing countries, including Indonesia, Malaysia, Thailand, Nigeria, the Democratic Republic of the Congo, the Ivory Coast, Brazil, Colombia, Costa Rica and Ecuador (Ahmed et al., 2021; Abubakar et al., 2021). Indonesia and Malaysia produce approximately 83% of the world’s palm oil, which makes a significant contribution to their economies (Paterson and Lima, 2018). Malaysian palm oil exports contribute to 45% of the global oil palm needs (Shevade and Loboda, 2019). The economy of Malaysia benefits greatly from the financial returns from the sale of palm oil (Tang and Al Qahtani, 2020). Crude palm oil is used in cooking, detergent, soap, plastics, oleo-chemicals, cosmetics, pharmaceuticals, biodiesel, etc. (Paterson et al., 2009). Large-scale oil palm cultivation has made a tremendous transformation in tropical regions, people’s lives and the profits of palm oil companies (Maluin et al., 2020). The crop’s high output, ease of establishment and low costs make it very profitable and the most economically efficient oil crop (Dislich et al., 2017).

Tropical countries, in particular, are negatively affected by climate change, which also has a detrimental impact on oil palm agronomy, whereas the cultivation of oil palm increases the impact of climate change (Uning et al., 2020). Climate has changed over centuries and will undoubtedly continue to change in the future and affect crop production worldwide (Chen et al., 2004; Corley and Tinker, 2015). Although awareness of the effects of climate change on crop production and disease has grown (Lobell et al., 2006), the effects on tropical crops are less understood (Ghini et al., 2011). As noted by Paterson (2020) climate changethreatens the sustainability of oil palm production. Various factors influence oil palm production, including planting material, cultivation management, soil and the environment, or climate, usually rainfall, temperature, relative humidity, wind and solar radiation (Herdiansyah et al., 2020).

Weather and climate are prominent drivers that influence oil palm production systems. In spite of advances in technology and crop science, a fluctuation in oil palm yield has been noticed recently (Kukal and Irmak, 2018; Sarkar et al., 2020). Oil palm requires at least 2000 mm of rainfall distributed evenly throughout the year, which equates to about 167 mm per month (Rhebergen et al., 2016). Furthermore, minimum temperatures should be between 22 and 24°C and maximum temperatures should be between 29 and 33°C, with relative humidity greater than 85% (Zainal et al., 2012). The solar radiation level should be at least 16 or 17 MJ m-1 d-1 (Oettli et al.,  2018). Variation in the climatic variables might have been responsible for the substantial changes. The distribution of rainfall affects the growth and development of palm trees, which in turn affects FFB production (Kamil and Omar, 2016). Excessive rainfall also harms the fresh fruit bunch (FFB), stifles harvest activity and causes flooding. Oil palm yield is limited by the length of the annual dry season, so areas with consistent high rainfall throughout the year, such as parts of Southeast Asia, have particularly high yields (Munévar and Munévar, 2004; Pirker et al., 2016; Fleiss et al., 2017). Preceding studies have shown that a 100-mm increment in water shortfall in a year can reduce output by 8-10% in the following year and also by 3-4% in the following year (Caliman and Southworth, 1998). Ambar Suharyanti et al., (2020) stated that a 100 mm water shortfall might affect FFB output during the flowering stage. Specifically, floral initiation, yield might be lost by about 1-3%, whereas sex determination and floral abortion might experience 3-4% and 8-10% yield loss, respectively. 

When the temperature rises by 1-4°C, oil palm cultivation is expected to decline by 10-40% in Malaysia (Sarkar et al., 2020). The number of dry periods is expected to increase as the temperature rises, resulting in a loss of oil palm yield. Because soil water vaporises more rapidly as temperatures rise, the effects of dry spells become more severe (Merten et al., 2016). The average monthly temperature of 27.83°C, eight months prior to harvest, led to a low FFB yield (Shanmuganathan et al., 2014). In addition, wind speed was also found to have an impact on oil palm cultivation (Sasirat et al., 2019). The solar radiation hours are not only the site-specific factors influencing oil palm production (Keong and Keng, 2012). The simultaneous availability of soil moisture also plays an important role in determining the effective solar radiation hour for maximizing FFB yield (Lim et al., 2011). Direct sunlight boosts palm productivity. The lower incidence of cloud over greater parts of Southeast Asia is thought to be one of the reasons why oil palm yields are mostly higher than in West Africa (Sheil et al., 2009). Photoperiod response regulates oil palm flowering (Legros et al., 2009). In 2014, 2015 and 2016, the palm oil yield dropped by 0.3%, 1.9% and 17%, respectively, to 3.84, 3.78 and 3.21 t ha-1, compared to the previous year’s record of 3.84, 3.78 and 3.21 t ha-1  (Hilal et al.,  2018). The decrease in palm oil yield has been attributed to a decrease in FFB yield in recent years (Darmawan et al., 2016).

With oil palm production accounting for the highest agricultural production in Malaysia, research into the effects of climatic elements on oil palm cultivation does not receive the same level of attention as cereal crops. As a result, this study seeks to investigate the effects of these climatic elements on oil palm cultivation in Peninsular Malaysia. The study also makes some recommendations to improve oil palm cultivation in Malaysia.

MATERIALS AND METHODS

Study area
 
Peninsular Malaysia is geographically located between latitudes 1° and 7° north and between 99° and 105° east. The region occupied a total land area of 132000 km2 and was mainly composed of the highlands, floodplains and coastal zones.  Overall, the Peninsular has a warm and humid tropical climate throughout the year, with temperature ranges from 25°C to 32°C. The region is characterized by two monsoon seasons: the southwest monsoon from May to September and the northeast monsoon from November to March, which is associated with high rainfall (Wong et al., 2009; Wong et al., 2016). The region records annual rainfall of 2000-4000 mm (Muhammad et al., 2020).
 
Methods
 
Secondary data was used for the purpose of this study. Data on oil palm yield in Malaysia between 1990-2020 was obtained from the Malaysian Palm Oil Board (MPOB). Climate historical data, particularly average annual temperature and rainfall, solar radiation, relative humidity and wind speed, were also downloaded from the climate-knowledge portal of the World Bank and the National Aeronautics and Space Administration (NASA) in July, 2021.
 
Data analysis
 
Multiple linear regression, an extension of simple linear regression which has more than one independent variable, was employed in this study (Uyanik and Güler, 2013). The model was adopted because the dependent variable was interval scale. The independent variables should be mostly interval or scale level variables, but multiple regression can also have dichotomous independent variables called dummy variables (Matthews, 2017). In this study, the independent variables refer to relative humidity (%), wind speed (m/s), mean rainfall (mm), mean temperature (°C) and solar radiation (MJ/m2/day). The dependent variable is the oil palm yield. In regression analysis, assumptions need to be considered as the samples are normally distributed and uncorrelated with the other variables (Wagschal, 2016). There is a linear relationship between the independent variables and the dependent variable and no multicollinearity issues (Daoud, 2018; Shrestha, 2020). As a result, this study analysed bivariate correlation to examine the linear relationship and continued to examine the variance inflation factor (VIF) and tolerance to confirm the presence of multicollinearity. The values of tolerance must be less than 5 and tolerance values greater than 0.2 (Jirakiattikul et al., 2021). The multiple linear regression equation is as follow:
 
Y= β0 + B1X1+B2X2+….. BnXn + e
 
The yield of oil palm is influenced by relative humidity, wind speed, mean rainfall, temperature and solar radiation. For multiple linear regression, the coefficient is estimated similar to simple linear regression. The error term is a random variable with a mean of zero and a constant variance. In the basic linear regression model, the error term reflects the fact that the regression is not perfect and will not fit the data absolutely. There are random factors other than the independent variables affecting the relationship. They are unknown and this study can only make basic assumptions about the factors in order for the model to work, such as soil types (land selection and topography), palm age, planting material (tissue culture, new varieties), machinery, manpower and available technology, technical management (financial, organizational, labour, transport, pest, disease, harvesting efficiency, unsuitable ground vegetation, etc.) (Woittiez et al., 2017).
For this case of study, the proposed model is:

 
Yield tonne (Y) = β0 + B1RelativeH + B2WindS + B3Rainfall + B4Temperature + B3SolarR + e

RESULTS AND DISCUSSION

The mean yield of oil palm was 18.719 tonne per hectare, 87.120% of relative humidity and the mean wind speed was 0.502 m/s. In addition, the mean rainfall was 243.967 mm, 26.010°C mean temperature and 35.659 MJ/m2/day was the mean of solar radiation (Table 1). The analysis indicates that the histogram was symmetrical, explained the normal distribution and met the following regression analysis assumption (Fig 1).

Table 1: Descriptive analysis.



Fig 1: Regression standardized residual.



The linear relationship was conducted using bivariate correlation. The analysis showed that a linear relationship between solar radiation and temperature had a significant and inverse relationship. In addition, temperature and solar radiation did not present a significant correlation with yield (Table 2).

Table 2: Correlation analysis.



Table 3 revealed that when the combined effect of relative humidity, wind speed, mean rainfall, mean temperature and solar radiation was regressed on the oil palm yield of the sample under study, an F-value of 1.618 was obtained, with p>0.05 at 5 and 32 degrees of freedom (df). Based on the results obtained, the climatic parameters (relatived humidity, rainfall, temperature, wind speed and solar radiation) did not contribute significantly to the change in yield variation. Furthermore, the result explained by the combined effect of relative humidity, wind speed, mean rainfall, mean temperature and solar radiation did not significantly influence the yield of oil palm.

The multiple linear regression model obtained as follows:

 
Yield of oil palm (y) = 4.553+0.235 Relative H+2.451 WindS-0.001 Rainfall -0.380 Temperature + 0.071 Solar R

 

Table 3: ANOVA analysis.


From the model, it is evident that the relative humidity (Relative H), wind speed (WindS), mean rainfall (Rainfall), mean temperature (Temp) and solar radiation (SolarR) have a low impact on the yield of oil palm. The R2 value of 0.202 indicates that only 20.2% of the variation in the oil palm yield is explained by the independent variables (Table 4). This indicates that there are other variables besides relativehumidity, wind speed, mean rainfall, mean temperature and solar radiation that influence the yield of oil palm, such as farm management, soil characteristics and type of seed, among others. This is consistent with studies conducted at various locations in Malaysia and found that climatic elements have less impact on oil palm production and the determination of fresh fruit bunch yield (Nda et al., 2018; Kota Shafiq, 2017).

Table 4: Model summary.



Based on the results in Table 5, relative humidity (0.225), wind speed (0.841), mean rainfall (0.888), mean temperature (0.307) and solar radiation (0.904) all the values are greater than 0.05, therefore this study failed to reject the null hypothesis (H0) and concluded they are all statistically insignificant. The Variance Inflation Factor (VIF) of relative humidity (1.522), wind speed (1.241), mean rainfall (1.164), mean temperature (3.801) and solar radiation (4.297) were less than 5. For the tolerance value, relative humidity (0.657), wind speed (0.806), mean rainfall (0.859), mean temperature (0.263) and solar radiation (0.233) were greater than 0.2. From the analysis, the results confirmed that there were no issues with multicollinearity. However, Hair et al., (2010) states that “multicollinearity occurs when two or more predictors in the model are correlated and provide redundant information about the response. Multicollinearity was measured by variance inflation factors (VIF) and tolerance. If VIF value exceeding 4.0, or by tolerance less than 0.2 then there is a problem with multicollinearity”.

CONCLUSION

This study concludes that climatic elements had no significant effect on oil palm production, with the model indicating that only 20.2% of the variability in monthly oil palm yield was represented by the regression equation. Future research will most likely include the delineation of climatic regions using statistical analysis of Malaysia’s monthly grided data set using various climatic indices. It will be useful to further explore the relationship between oil palm yield, climatic and cultural factors aside. This will give the oil palm growers insight into the agricultural practices suitable for oil palm production.

ACKNOWLEDGEMENT

I wish to acknowledge Tertiary Education Trust Fund (TETFUND) for sponsoring this original research. Grant number TETF/UNIV/ JIGAWA STATE/TSAS/2019.

Authors contribution

Ahmed Abubakar, Mohd Yusoff Ishak, Murtala Uba Mohammed: Conceptualization, methodology, writing- review and editing.; Ahmed Abubakar, Mohd Yusoff Ishak, Md Kamal Uddin, Aisyah Abu Bakar: software, validation, formal, analysis; Ahmed Abubakar, Mohd Yusoff Ishak, Mohd Kamal Uddin: investigation Ahmed Abubakar: resources Mohd Yusoff Ishak, Murtala Uba Mohammed, Aisyah Abu Bakar, Ahmed Abubakar: data curation, writing-original draft, preparation, visualization, supervision, project administration.;  Ahmed Abubakar: funding acquisition.

Declaration

Ethics approval and consent to participate

This study neither involved human/animal participation, experiment, nor human data/tissue.

Consent for publication

This study did not involve children or individual details, but 100% data usage.

Data availability statement

All data generated or analysed during the study are included in the published article(s) cited within the text and acknowledged in the reference section.

Funding

The study was funded by the Tertiary Education Trust Fund, Nigeria (TETFUND).

Competing interest

All authors declare no competing interest.

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