Tropical deforestation continues at alarming rates with profound impacts on ecosystems, climate, and livelihoods, prompting renewed commitments to halt its continuation. Although it is well established that agriculture is a dominant driver of deforestation, rates and mechanisms remain disputed and often lack a clear evidence base. We synthesize the best available pantropical evidence to provide clarity on how agriculture drives deforestation. Although most (90 to 99%) deforestation across the tropics 2011 to 2015 was driven by agriculture, only 45 to 65% of deforested land became productive agriculture within a few years. Therefore, ending deforestation likely requires combining measures to create deforestation-free supply chains with landscape governance interventions. We highlight key remaining evidence gaps including deforestation trends, commodity-specific land-use dynamics, and data from tropical dry forests and forests across Africa. Description Forest loss for food Agricultural expansion is recognized as a major driver of forest loss in the tropics. However, accurate data on the links between agriculture and tropical deforestation are lacking. Pendrill et al. synthesized existing research and datasets to quantify the extent to which tropical deforestation from 2011 to 2015 was associated with agriculture. They estimated that at least 90% of deforested land occurred in landscapes where agriculture drove forest loss, but only about half was converted into productive agricultural land. Data availability and trends vary across regions, suggesting complex links between agriculture and forest loss. —BEL A review shows that most tropical deforestation is associated, directly or indirectly, with agriculture. BACKGROUND Agricultural expansion is a primary cause of tropical deforestation and therefore a key driver of greenhouse gas emissions, biodiversity loss, and the degradation of ecosystem services vital to the livelihoods of forest-dependent and rural people. However, agriculture-driven deforestation can take many forms, from the direct expansion of pastures and cropland into forests to more complex or indirect pathways. A clear understanding of the different ways in which agriculture drives deforestation is essential for designing effective policy responses. To address this need we provide a review of the literature on pantropical agriculture-driven deforestation and synthesize the best available evidence to quantify dominant agricultural land-use changes relating to deforestation. We consider the policy implications of this assessment, especially for burgeoning demand-side and supply-chain interventions seeking to address deforestation. ADVANCES New methods and data have advanced our understanding of deforestation and subsequent land uses. However, only a handful of studies estimate agriculture-driven deforestation across the entirety of the tropics. Although these studies agree that agriculture is the dominant land use following forest clearing, their estimates of pantropical rates of agriculture-driven deforestation during the period 2011 to 2015 vary greatly—between 4.3 and 9.6 million hectares (Mha) per year—with our synthesized estimate being 6.4 to 8.8 Mha per year. This apparent uncertainty in the amount of agriculture-driven deforestation can be disentangled by distinguishing between the different ways in which agriculture contributes to deforestation; we find that while the overwhelming majority (90 to 99%) of tropical deforestation occurs in landscapes where agriculture is the dominant driver of tree cover loss, a smaller share (45 to 65%) of deforestation is due to the expansion of active agricultural production into forests. Multiple lines of evidence show that the remainder of agriculture-driven deforestation does not result in the expansion of productive agricultural land but instead is a result of activities such as speculative clearing, land tenure issues, short-lived and abandoned agriculture, and agriculture-related fires spreading to adjacent forests. Different land uses and commodities often interact to drive deforestation. However, pasture expansion is the most important driver by far, accounting for around half of the deforestation resulting in agricultural production across the tropics. Oil palm and soy cultivation together account for at least a fifth, and six other crops—rubber, cocoa, coffee, rice, maize, and cassava—likely account for most of the remainder, with large regional variations and higher levels of uncertainty. OUTLOOK This Review points to three key areas where a stronger evidence base would advance global efforts to curb agriculture-driven deforestation: First, consistent pantropical data on deforestation trends are lacking. This limits our ability to assess overall progress on reducing deforestation and account for leakage across regions. Second, with the exception of soy and oil palm the attribution of deforestation to forest risk commodities is often based on coarse-grained agricultural statistics, outdated or modeled maps, or local case studies. Third, uncertainties are greatest in dry and seasonal tropics and across the African continent in particular. This assessment highlights that although public and private policies promoting deforestation-free international supply chains have a critical role to play, their ability to reduce deforestation on the ground is fundamentally limited. One-third to one-half of agriculture-driven deforestation does not result in actively managed agricultural land. Moreover, the majority—approximately three-quarters—of the expansion of agriculture into forests is driven by domestic demand in producer countries, especially for beef and cereals, including much of the deforestation across the African continent. These data suggest that the potential for international supply chain measures to help reduce tropical deforestation is more likely to be achieved through interventions in deforestation risk areas that focus on strengthening sustainable rural development and territorial governance. Agriculture contributes to deforestation in many ways which often interact. Most tropical deforestation occurs in landscapes where agriculture is the dominant driver of forest loss. Part of this agriculture-driven deforestation results in agricultural production (left) meeting domestic and export demand for various agricultural commodities. However, agriculture-driven deforestation also occurs without expansion of managed agricultural land through several mechanisms (right), which may lead to the deforested area being abandoned or semi-abandoned. Incomplete agricultural records also explain a share of such deforestation.

/pdf/disentangling-the-numbers-behind-agriculture-driven-tropical-rfo9s5g7.pdf

Disentangling the numbers behind agriculture-driven tropical deforestation

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.

/pdf/prediction-of-oil-palm-yield-using-machine-learning-in-the-1lr4a6a2.pdf

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

Deforestation and forest degradation are among the leading global concerns, as they could reduce the carbon sink and sequestration potential of the forest. The impoundment of Kenyir River, Hulu Terengganu, Malaysia, in 1985 due to the development of hydropower station has created a large area of water bodies following clearance of forested land. This study assessed the loss of forest carbon due to these activities within the period of 37 years, between 1972 and 2019. The study area consisted of Kenyir Lake catchment area, which consisted mainly of forests and the great Kenyir Lake. Remote sensing datasets have been used in this analysis. Satellite images from Landsat 1–5 MSS and Landsat 8 OLI/TRIS that were acquired between the years 1972 and 2019 were used to classify land uses in the entire landscape of Kenyir Lake catchment. Support vector machine (SVM) was adapted to generate the land-use classification map in the study area. The results show that the total study area includes 278,179 ha and forest covers dominated the area for before and after the impoundment of Kenyir Lake. The assessed loss of carbon between the years 1972 and 2019 was around 8.6 million Mg C with an annual rate of 0.36%. The main single cause attributing to the forest loss was due to clearing of forest for hydro-electric dam construction. However, the remaining forests surrounding the study area are still able to sequester carbon at a considerable rate and thus balance the carbon dynamics within the landscapes. The results highlight that carbon sequestration scenario in Kenyir Lake catchment area shows the potential of the carbon sink in the study area are acceptable with only 17% reduction of sequestration ability. The landscape of the study area is considered as highly vegetated area despite changes due to dam construction.

Assessing changes of carbon stock in dipterocarp forest due to hydro-electric dam construction in Malaysia

. Tropical forest degradation is a global concern since most deforestation study supports that the region seems to suffer a great loss in the recent decades. The main objective of this paper is to evaluate the forest dynamic in Rompin and Pekan district of Pahang state using satellite image and land use model. Pekan district has the biggest peat swamp forest (PSF) in Peninsular Malaysia and also covers with inland dipterocarp forest. The land use of study area was categorized using Maximum Likelihood Classification (MLC) and further analysed using Land Change Modeler (LCM). From the results, forest land use has a negative growth (15% net loss) in the last 25 years, mostly converted to other vegetation class with overall 45% loss and 20% gain. The PSF itself was continuously degraded during the 1990 to 2017 periods, with a total loss of more than 700 km2. This study demonstrates the forest land use exploitation and the needs to consider a details forest monitoring analysis to extract the actual contributors for this phenomenon. The role of Permanent Reserved Forest (PRF) seem likely to be taking effect since the deforestation only massively happen outside of the PRF and part of the disturbed area are being reforested.

/pdf/forest-degradation-analysis-using-geospatial-techniques-4whuxu04xp.pdf

Forest degradation analysis using geospatial techniques approach in tropical region of pahang, malaysia

The long-tailed macaque (Macaca fascicularis) is commonly used for biomedical researches. However, genetic variation within a population or among populations can significantly affect phenotypical outcomes of treatments tested on model organisms. As such, it is important for studies involving model organisms originating from different, or even the same geographical locations to have sufficient genomic and transcriptomic background of the model organisms that is used. This paper summarises the utilisation of next-generation sequencing (NGS) technology to sequence genomes and transcriptomes of long-tailed macaques from various geographical populations in general and the Malaysian long-tailed macaque in particular, and its importance in the context of population genetic studies.

A glimpse into the genomic outlook of the long-tailed macaque (Macaca fascicularis).

Increasing human population and the rapid growth of Malaysia's economy are often associated with various environmental disturbances which have been contributing to depletion of natural resources and climate change. The need for more spaces for numerous land development activities has made the existing forests suffer deforestation. The study was carried out in Peninsular Malaysia, which currently has about 5.9 million ha of forests. Phased array type L-band SAR (Palsar) and Palsar-2 images over the years 2010 and 2015, respectively were used to identify forest cover and deforestation occurrences resulted from various conversion of forests to other land uses. Forests have been identified from horizontal-vertical (HV) polarization and then classified into three major categories, which are inland, peat swamp and mangrove. Pixel subtraction technique was used to determine areas that have been changing from forests to other land uses. Forest areas have been found declined from about 6.1 million ha in year 2010 to some 5.9 million ha in 2015 due to conversion of forests to other land uses. Causes of deforestation have been identified and the amount of carbon dioxide (CO2) that has been emitted due to the deforestation activity has been determined in this study. Oil palm and rubber plantations expansion has been found the most prominent factor that caused deforestation in Peninsular Malaysia, especially in the states of Pahang, Terengganu, Johor and Kelantan. The rate of deforestation in the period was at 0.66% yr-1, which amounted a total of about 200,225 ha over the five years. Carbon loss was estimated at about 30.2 million Mg C, which has resulted in CO2 emission accounted at about 110.6 million Mg CO2. The rate of CO2 emission that has been resulted from deforestation was estimated at 22.1 million Mg CO2 yr-1. The study found that the use of a series of Palsar and Palsar-2 images, with a consistent, cloud-free images, are the most appropriate sensors to be used for monitoring of deforestation over the Peninsular Malaysia region.

https://iopscience.iop.org/article/10.1088/1755-1315/37/1/012028/pdf

Quantifying rate of deforestation and CO2 emission in Peninsular Malaysia using Palsar imageries

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Quantifying rate of deforestation and CO2 emission in Peninsular Malaysia using Palsar imageries