scholarly journals Managing fire risk during drought: the influence of certification and El Niño on fire-driven forest conversion for oil palm in Southeast Asia

2017 ◽  
Author(s):  
Praveen Noojipady ◽  
Douglas C. Morton ◽  
Wilfrid Schroeder ◽  
Kimberly M. Carlson ◽  
Chengquan Huang ◽  
...  
Keyword(s):  
Palm Oil ◽  
Oil Palm ◽  
El Niño ◽  
El Nino ◽  
Fire Risk ◽  
Oil Production ◽  
Forest Loss ◽  
Fire Activity ◽  
El Niño Events ◽  
In Fire

Abstract. Indonesia and Malaysia have emerged as leading producers of palm oil in the past several decades, expanding production through the conversion of tropical forests to industrial plantations. Efforts to produce "sustainable" palm oil, including certification by the Roundtable on Sustainable Palm Oil (RSPO), include guidelines designed to reduce the environmental impact of palm oil production. Fire-driven deforestation is prohibited by law in both countries and a stipulation of RSPO certification, yet the degree of environmental compliance is unclear, especially during El Niño events when drought conditions increase fire risk. Here, we used time series of satellite data to estimate the spatial and temporal patterns of fire-driven deforestation in and around oil palm plantations. In Indonesia, fire-driven deforestation accounted for one quarter of total forest losses in both certified and non-certified plantations. After the first plantations in Indonesia received RSPO certification in 2009, forest loss and fire-driven deforestation declined in certified plantations but did stop altogether. Oil palm expansion in Malaysia rarely involved fire; only 6 % of forest loss in certified plantations had coincident active fire detections. Interannual variability in fire detections was strongly influenced by El Niño and the timing of certification. Fire activity during the 2002, 2004, and 2006 El Niño event was similar among oil palm plantations in Indonesia that would later become certified, non-certified plantations, and surrounding areas. However, rates of fire activity were 57 % and 44 % lower in certified plantations than non-certified plantations during the 2009 and 2015 El Niño events, respectively. The decline in fire activity on certified plantations, including during drought periods, highlights the potential for RSPO certification to safeguard carbon stocks in peatlands and remaining forests and support legislation banning fires. However, aligning certification standards with satellite monitoring capabilities will be critical to realize sustainable palm oil production and meet industry commitments to zero deforestation.

scholarly journals Managing fire risk during drought: the influence of certification and El Niño on fire-driven forest conversion for oil palm in Southeast Asia

2017 ◽  
Vol 8 (3) ◽  
pp. 749-771 ◽  
Author(s):  
Praveen Noojipady ◽  
Douglas C. Morton ◽  
Wilfrid Schroeder ◽  
Kimberly M. Carlson ◽  
Chengquan Huang ◽  
...  
Keyword(s):  
Palm Oil ◽  
Oil Palm ◽  
El Niño ◽  
El Nino ◽  
Fire Risk ◽  
Forest Loss ◽  
Fire Activity ◽  
El Niño Events ◽  
In Fire ◽  
El Nino Events

Abstract. Indonesia and Malaysia have emerged as leading producers of palm oil in the past several decades, expanding production through the conversion of tropical forests to industrial plantations. Efforts to produce sustainable palm oil, including certification by the Roundtable on Sustainable Palm Oil (RSPO), include guidelines designed to reduce the environmental impact of palm oil production. Fire-driven deforestation is prohibited by law in both countries and a stipulation of RSPO certification, yet the degree of environmental compliance is unclear, especially during El Niño events when drought conditions increase fire risk. Here, we used time series of satellite data to estimate the spatial and temporal patterns of fire-driven deforestation on and around oil palm plantations. In Indonesia, fire-driven deforestation accounted for one-quarter of total forest losses on both certified and noncertified plantations. After the first plantations in Indonesia received RSPO certification in 2009, forest loss and fire-driven deforestation declined on certified plantations but did not stop altogether. Oil palm expansion in Malaysia rarely involved fire; only 5 % of forest loss on certified plantations had coincident active fire detections. Interannual variability in fire detections was strongly influenced by El Niño and the timing of certification. Fire activity during the 2002, 2004, and 2006 El Niño events was similar among oil palm plantations in Indonesia that would later become certified, noncertified plantations, and surrounding areas. However, total fire activity was 75 % and 66 % lower on certified plantations than noncertified plantations during the 2009 and 2015 El Niño events, respectively. The decline in fire activity on certified plantations, including during drought periods, highlights the potential for RSPO certification to safeguard carbon stocks in peatlands and remaining forests in accordance with legislation banning fires. However, aligning certification standards with satellite monitoring capabilities will be critical to realize sustainable palm oil production and meet industry commitments to zero deforestation.

Impacts of El Nino and La Nina on Palm Oil Production: Evidence from Selected Palm Oil Producing Countries

2021 ◽  
Vol 8 ◽  
pp. 108-119
Author(s):  
Athirah Suhailah Jalil ◽  
Yan-Ling Tan
Keyword(s):  
Palm Oil ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Oil Production ◽  
Least Square ◽  
La Niña ◽  
Economic Losses ◽  
Global Changes ◽  
La Nina

Palm oil is one of the most important edible oils commercialized in world oils and fats market. However, agriculture is highly vulnerable to climate change. For instance, El Nino is the warm phase of El Nino Southern Oscillation (ENSO) while La Nina is the opposite of El Nino, the cold phase of ENSO. Strong El Nino and La Nina events cause global changes especially in temperature and rainfall, subsequently affect agricultural areas and causing considerable economic losses. Therefore, the objective of this study is to explore the impacts of El Nino and La Nina on palm oil production in selected palm oil producing countries namely Indonesia, Malaysia and Thailand from 1980 to 2019 using the Ordinary Least Square (OLS) multiple regression analysis. The results revealed that area harvested and palm oil price affect palm oil production significantly in all palm oil producing countries. Nevertheless, the ENSO effects are not apparent. Only the El Nino dummy coefficients are negative and statistically significant in Thailand, suggesting that the occurrence of El Nino events is more pronounced than the La Nina events on palm oil production in Thailand. Hence, this study suggests that a good water management system is needed to allow the palm oil to achieve optimum production.

scholarly journals Impact of El Niño on Oil Palm Yield in Malaysia

Agronomy ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2189
Author(s):  
Jen Feng Khor ◽  
Lloyd Ling ◽  
Zulkifli Yusop ◽  
Wei Lun Tan ◽  
Joan Lucille Ling ◽  
...  
Keyword(s):  
Oil Palm ◽  
El Niño ◽  
El Nino ◽  
Predictive Ability ◽  
Fresh Fruit ◽  
Economic Losses ◽  
Fresh Fruit Bunch ◽  
El Niño Events ◽  
The Impact ◽  
El Nino Events

Oil palm crop yield is sensitive to heat and drought. Therefore, El Niño events affect oil palm production, resulting in price fluctuations of crude palm oil due to global supply shortage. This study developed a new Fresh Fruit Bunch Index (FFBI) model based on the monthly oil palm fresh fruit bunch (FFB) yield data, which correlates directly with the Oceanic Niño Index (ONI) to model the impact of past El Niño events in Malaysia in terms of production and economic losses. FFBI is derived from Malaysian monthly FFB yields from January 1986 to July 2021 in the same way ONI is derived from monthly sea surface temperatures (SST). With FFBI model, the Malaysian oil palm yields are better correlated with ONI and have higher predictive ability. The descriptive and inferential statistical assessments show that the newly proposed FFBI time series model (adjusted R-squared = 0.9312 and residual median = 0.0051) has a better monthly oil palm yield predictive ability than the FFB model (adjusted R-squared = 0.8274 and residual median = 0.0077). The FFBI model also revealed an oil palm under yield concern of the Malaysian oil palm industry in the next thirty-month forecasted period from July 2021 to December 2023.

Changing fire regimes in the temperate rainforest region of southern Chile over the last 16,000 yr

2008 ◽  
Vol 69 (1) ◽  
pp. 62-71 ◽  
Author(s):  
Ana M. Abarzúa ◽  
Patricio I. Moreno
Keyword(s):  
Forest Fires ◽  
El Niño ◽  
El Nino ◽  
Summer Precipitation ◽  
Moisture Stress ◽  
Moderate Increase ◽  
Southern Chile ◽  
Precipitation Regimes ◽  
Fire Activity ◽  
In Fire

A high-resolution macroscopic charcoal record from Lago Melli (42°46′S, 73°33′W) documents the occurrence of forest fires in the lowlands of Isla Grande de Chiloé, southern Chile, over the last 16,000 yr. Our data suggest that fire activity in this region was largely modulated by the position/intensity of the southern westerlies at multi-millennial time scales. Fire activity was infrequent or absent between 16,000–11,000 and 8500–7000 cal yr BP and was maximal between ∼ 11,000–8500 and 3000–0 cal yr BP. A mosaic of Valdivian/North Patagonian rainforest species started at ∼ 6000 cal yr BP, along with a moderate increase in fire activity which intensified subsequently at 3000 cal yr BP. The modern transition between these forest communities and the occurrence of fires are largely controlled by summer moisture stress and variability, suggesting the onset of high-frequency variability in summer precipitation regimes starting at ∼ 5500 cal yr BP. Because negative anomalies in summer precipitation in this region are teleconnected with modern El Niño events, we propose that the onset of El Niño-like variability at ∼ 5700–6200 cal yr BP led to a reshuffling of rainforest communities in the lowlands of Isla Grande de Chiloé and an increase in fire activity.

Multi-decadal variability of forest fire risk - eastern Australia

2004 ◽  
Vol 13 (2) ◽  
pp. 165 ◽  
Author(s):  
Danielle C. Verdon ◽  
Anthony S. Kiem ◽  
Stewart W. Franks
Keyword(s):  
Climate Variability ◽  
Forest Fire ◽  
El Niño ◽  
El Nino ◽  
Fire Risk ◽  
Fire Danger ◽  
El Niño Events ◽  
Forest Fire Danger ◽  
Forest Fire Risk ◽  
El Nino Events

This study investigates the influence that the El Niño/Southern Oscillation (ENSO) and the Inter-decadal Pacific Oscillation (IPO) have on long term daily weather conditions pertinent to high forest fire danger in New South Wales, Australia. Using historical meteorological data for 22 weather stations to compute the daily value of McArthur’s Forest Fire Danger Index (FFDI), it is shown that a strong relationship exists between climate variability, on a range of time scales, and forest fire risk. An investigation into the influence of ENSO on fire risk demonstrates that the proportion of days with a high, or greater than high, fire danger rating is markedly increased during El Niño episodes. More importantly, this study also shows that the already significantly enhanced fire danger associated with El Niño events was even further increased during El Niño events that occurred when the IPO was negative. The potential to use simple indices of climate variability to predict forest fire risk is therefore demonstrated to be significant.

Sistem Pakar Mendeteksi Penyakit Tanaman Kelapa Sawit Menggunakan Metode Forward Chaining

2019 ◽  
Vol 1 ◽  
pp. 444
Author(s):  
Dimas Satria ◽  
Poningsih Poningsih ◽  
Widodo Saputra
Keyword(s):  
Expert System ◽  
Programming Languages ◽  
Palm Oil ◽  
Oil Palm ◽  
Oil Production ◽  
Plant Diseases ◽  
Accurate Information ◽  
Forward Chaining ◽  
Web Programming ◽  
Level Of Confidence

The purpose of this paper is to create an expert system to detect oil palm plant diseases in order to help farmers / companies in providing accurate information about the diseases of oil palm plants and how to overcome them and to help reduce the risk of decreasing palm oil production. This system is designed to mimic the expertise of an expert who is able to detect diseases that attack oil palm plants. The method used is forward chaining that is starting from a set of data and proving a fact by describing the level of confidence and uncertainty found in a hypothesis. The results of this study are to diagnose diseases of oil palm plants and their computerization using web programming languages.

CORRELATION BETWEEN RAINFALL PATTERN AND OIL PALM PERFORMANCE IN SUMATRA AND BORNEO ISLAND DURING EL NIÑO 2015

2018 ◽  
Vol 24 (2) ◽  
pp. 87-96
Author(s):  
Iput Pradiko ◽  
Eko Novandy Ginting ◽  
Nuzul Hijri Darlan ◽  
Winarna Winarna ◽  
Hasril Hasan Siregar
Keyword(s):  
Oil Palm ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Pearson Correlation ◽  
Rainfall Data ◽  
Rainfall Pattern ◽  
Dry Spell ◽  
Male Flowers ◽  
Central South

El Niño 2015 is one of the strongest El Niño. Drought stress due to El Niño could affect oil palm performances. This study was conducted to determine rainfall pattern and oil palm performance in Sumatra and Borneo Island during El Niño 2015. Data employed in this study is monthly rainfall data, Southern Oscillation Index (SOI) January-December 2015, andoil palm performances. Pearson correlation between SOI and rainfall data was used to analyze rainfall pattern, while oil palm performances were observed based on morphological conditions. Result shows that southern part of Sumatra and mostly part of Borneo suffer from more dry spell, dry month, and water deficit such as 37-133 days, 3-5 months, and 349-524 mm respectively. Analysis of rainfall pattern shows that Jambi, South Sumatra, Lampung, Central, South, and East Borneo are significantly (r ≥ +0,60) affected by El Niño 2015. Oil palms in southern part of Sumatra and mostly part of Borneo are suffer from drought stressmarked by the emergence of more than two spear fronds, appearing of many male flowers, malformations on bunches, fronds tend to hanging down, and lower fronds tend to dry.

Sign in / Sign up

Export Citation Format

Share Document