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1 : Practical guidance on how to estimate soil carbon stocks and soil greenhouse gas emissions following tropical forest conversion on mineral soil December 2015

2 Authors Dr Ram Dalal, University of Queensland Dr Sanjutha Shanmugam, University of Queensland Technical and Resource Support Dr Hans Joosten Dr John Raison Goh Kah Joo The 2015 This work is licensed under the Creative Commons Attribution 4.0 International License. You are free to reuse, reprint, or republish the work, in whole or in part, without written permission, provided that the source is acknowledged. To view a copy of this license, please visit This report has been independently prepared by the authors for the High Carbon Stock (HCS) Science Study. It is part of a series of consulting studies on high carbon stock, in the areas of biomass estimation, soil carbon dynamics, remote sensing, and socio-eco nomics. Together, these consulting studies provide background information for the HCS Science Study s synthesis report, but also they constitute stand-alone research that aims to shed light on this critical area of enquiry.

3 Contents Acknowledgements 05 Executive Summary 06 Glossary of Acronyms 09 Section 1: Introduction 10 Section 2: Assessing soil organic carbon stocks and stock changes Assessing bulk density 2.2 Estimating carbon stocks 2.3 Estimating carbon stock changes 13 Section 3: SOC stocks and SOC stock changes: conversion of tropical ecosystems to other land uses SOC stocks under different ecosystems and land 18 uses 3.2 SOC stock changes following land use change SOC stock changes following land 20 conversion to oil palm plantation Magnitude of SOC losses due to soil 21 erosion Magnitude of SOC losses due to soil 22 drainage and land preparation Magnitude of SOC changes due to management practices: fertilizing, liming, organic amendment and mulching Section 4: Effects of land use change on greenhouse gases emissions Measurement of GHG fluxes 4.2 Key drivers of GHG (CO 2, CH4 and N 2 O) emissions from soil Effect of land preparation and drainage on GHG emissions Effects of application of N fertiliser and lime to soil on GHG emissions Effect of different land uses on GHG fluxes from 27 tropical mineral soils 4.4 Changes in total GHG emissions following land use changes 29 Section 5: Surrogate/proxy methods to assess SOC stock under different land use systems Measured soil properties for SOC stock estimation Tree stem basal area and circumference for SOC stock estimation Proxies to estimate SOC stock from aboveground biomass (AGB) under different land use systems 5.2 Oil palm biomass estimation 32 Section 6: Conclusions and future research needs 33 Section 7: References 34 Section 8: Appendices 45

4 Appendix 1 A1.1 Soil sampling procedures A1.2 Bulk density measurement A1.3 Analytical procedures to measure soil carbon concentration Appendix 2 47 A2.1 Analytical procedures to measure GHG fluxes: A2.1.1 Chamber methods: A2.1.2 Micrometeorological methods Appendix 3 Change of SOC stock on conversion of primary forest land 48 in subtropical and tropical (30º N- 30º S) regions Appendix 4 Change of SOC stock on conversion of secondary forest 51 land in subtropical and tropical (30º N- 30º S) regions Appendix 5 Change of SOC stock on conversion of pasture and 55 grassland in subtropical and tropical (30º N- 30º S) regions Appendix 6 Change of SOC stock on conversion of cropland in 57 subtropical and tropical (30º N- 30º S) regions Appendix 7 Default factors for calculating N added to soils from 58 crop residues (Source: IPCC 2006 Guidelines for national greenhouse gas inventories, Chapter 11) Appendix 8 Annual N 2 O fluxes in primary forests and in the associated 59 land use change in subtropical and tropical (30º N- 30º S) regions Appendix 9 Annual CH4 fluxes in primary forests and following land use 61 change in subtropical and tropical (30º N- 30º S) regions Appendix 10 Annual N2O fluxes in secondary forests and following land 62 use change in subtropical and tropical (30º N- 30º S) regions Appendix 11 Annual CH4 fluxes in secondary forests and following land 63 use change in subtropical and tropical (30º N- 30º S) regions Appendix 12 Annual N2O fluxes in natural savannah and following land 64 use change in subtropical and tropical (30º N- 30º S) regions Appendix 13 Annual CH4 fluxes in natural savannah and following land use change in subtropical and tropical (30º N- 30º S) regions 65

5 Acknowledgements Consulting Study 6 We thank the High Carbon Stock Study for financial support and the University of Queensland (Professor Neal Menzies, Dr Peter Kopittke, Mr Dean Wencke, SAFS and Science contracts staff) for administrative support. Ram Dalal also thanks the Secretariat for the High Carbon Stock Study for their organisational support to enable him to attend the HCS Technical Committee Synthesis Meeting on 27 and 28 April, 2015 in Kuala Lumpur. 05

6 Executive Summary Background/scope of the report The increasing global demand for palm oil has necessitated the increase in area for industrial cultivation of oil palm. Its expansion in the major producer and exporter countries, for example, Indonesia and Malaysia in Southeast Asia, involves significant land use change by land clearing of tropical forest lands and creates environmental and sociological concerns, which are not limited to climate change effects. Our understanding of the effects on soil carbon of land use conversion from various land use types (including tropical forests) to oil palm plantation is still incomplete, especially for mineral soils (more studies are available for peat soils). Because of the very high emissions of greenhouse gases [carbon dioxide CO 2, nitrous oxide N 2 O and methane CH 4 ] associated with the conversion of peatlands to oil palm plantation, conversion increasingly focuses on mineral soils. Relevant ecosystems include primary forests, secondary forests, rubber and other plantations, shrub lands, savannas, grasslands and croplands; all of these ecosystems carry different aboveground and belowground carbon stocks, including soil organic carbon (SOC) stocks. Change in land use from these ecosystems to oil palm plantation may result in a change in SOC stock. The amount and time course of these C stock changes and associated greenhouse gas emissions are largely unknown for most land use systems on mineral soils. In this report we estimate soil carbon stocks and greenhouse gas emissions from tropical mineral soils under forest, under oil palm plantations and under other types of land use. 2. Soil carbon stock inventory and monitoring procedures Soil carbon stocks (t C ha -1 up to a specified depth) are estimated from soil carbon content/concentration, soil depth, and bulk density, i.e. as an absolute quantity of carbon held in the soil at a specified time. Measurements of bulk density and C concentration demand careful field sampling procedures to obtain representative samples as these properties may vary temporally and spatially (both vertically and horizontally) in a soil profile. Soil carbon includes live and dead fine roots and dead organic matter (DOM) (<2 mm size). The IPCC (2006) proposes to use a default soil depth of 30 cm since changes in SOC and associated greenhouse gas fluxes upon changes in land use are in most soils primarily limited to this depth. Soil sampling for bulk density analysis should for each site be done simultaneously with soil carbon sampling. Generally, net SOC stock changes are measured at the same site over a 3-5 year or a year period since the soil C cycle is comparatively long-term and slow. Conflicting findings often result due to incomplete inventory of SOC and various SOC pools. Moreover, assessing changes in SOC stocks using repeated sampling needs to be done by revisiting the same location (i.e. the same field and soil type) and samples need to be collected at 3-5 years interval. Further, under converted land use, changes in SOC stocks can be estimated by using paired-site sampling. This type of data comparison is scarce for oil palm plantations established on lands with various previous land use and/or undisturbed natural ecosystems on mineral soils. 3. Current soil analytical procedures, default values, surrogate and cost-effective measurements: SOC, BD, GHG and GWP Several methods are available to analyse SOC concentration. Among these, widely used quantitative, cost-effective, commercial laboratory methods are: 1) Wet oxidation (Walkley and Black 1934) and 2) Dry combustion. Alternatively, SOC stocks can be estimated from pedotransfer functions (PTF) which provide an estimate of a site and regional specific soil C concentration and BD values based on other measured soil properties such as sand and clay content, TOC, total N, CEC and soil ph of the site. These PTFs are simple but robust mathematical regression models that allow estimating site specific soil properties where direct measurements are not available. Vegetation biomass may be estimated from age of the tree stand, stem/trunk height, tree basal area and tree trunk circumference (measured at a particular trunk height), using allometric equations, and these biomass estimates can be used as proxy values to estimate SOC stock under specific land use types. Soil bulk density (BD) is an important parameter that changes over time depending on cultivation and field management operations. A global meta-analysis of various major land use conversion types on tropical mineral soils revealed an increase in BD values (5-23 %) primarily associated with SOC changes in the surface 0-30 cm soil layers. As a rule of thumb, the coarser the texture the higher is the bulk density value and the larger the SOC change the larger is also the change in bulk density. Assessment and monitoring of greenhouse gases (GHG) such as CO 2, CH 4 and N 2 O and their emissions under oil palm plantation and other land use can follow the IPCC guidelines (IPCC 2006, Volume 4) for annual greenhouse gas inventories for the Agriculture, Forestry and Other Land Use (AFOLU)

7 Sector. Alternatively, the GHG exchange between soil and atmosphere can be measured using chamber methods and micrometeorological methods (eddycovariance). However, high spatial variability even at short distances requires a large number of samples (>20) for each ecosystem to detect significant changes due to management. IPCC (2006) presents default emission factors (EF) for N 2 O emission from agricultural lands with various sources of N input such as mineral N fertilizers, organic amendments and crop residues. Default CO 2 -C emission values are available for limestone (0.12 or 12% equivalent to CO 3 content) and dolomite (0.13 or 13% equivalent to CO 3 content) application (C values can be reported as CO 2 emission by multiplying with 44/12). Annual emissions of CH 4 and N 2 O can be converted into CO 2 equivalents (CO 2 e) by multiplying the values by the global warming potential (GWP) of the respective gases (28 for CH 4 and 265 for N 2 O on a 100-year time horizon (Myhre et al. 2013). 4. Land use and SOC change The SOC stock of a soil depends on the relative rates at which organic material C is added to the soil and lost from it through decomposition (and transport and soil erosion), that is, dsoc/dt = -ksoc +A, where k is the rate of SOC loss (yr -1 ), A is the rate of addition (t C ha -1 yr -1 ) and SOC is the SOC stock (t C ha -1 ) at time t (yr). It is well established that the rate of SOC loss is inversely related to the clay content of the soils, so that coarsetextured soils, for example sandy soils require higher rates of C addition than clay soils to maintain the SOC stock at steady state. Loss of SOC is inevitable during the initial land clearing and establishment phase of an oil palm plantation. Also conversion of forest land to other agriculture and pasture lands may induce immediate SOC loss from the original ecosystem including due to soil erosion. The reverse process, i.e. regaining the SOC levels by afforestation/reforestation always takes longer, for example, up to years in the tropics. SOC losses can be limited when oil palm plantations are established on degraded mineral soils compared with, for example, peatlands where SOC stocks as well as loss rates are very high. SOC changes following land use change vary widely although within a given soil type and region SOC changes have a consistent magnitude. A detailed metaanalysis of the effect of land use change on tropical mineral soils showed that largest SOC stock losses (0-30 cm depth) of 21 and 32 t C ha -1 (i.e. 25 and 30%) occurred when primary forests were converted into cropland and perennial crops, respectively. These SOC losses were primarily due to reduced C inputs to the soil although SOC removal by erosion cannot be ruled out, especially when primary forests are converted through extensive land preparation (as with oil palm plantation) and land use involves annual cropping. If land use change results in increased C input, SOC stock increases, provided that other SOC losses such as from soil erosion are small. This was the case when cropland was converted to grassland, bush fallow or secondary forest, showing SOC stock increases of 16, 14 and 35 t C ha -1, or 26, 32 and 50%, respectively. Smaller SOC increase occurred when grassland was converted into secondary forest (10 t C ha -1 or 17%) (Don et al. 2011). Findings from various sources can be simplified to a rule of thumb on the magnitude of SOC stock change following land use change to oil palm plantation on mineral soils (Table 1). Similarly, default (Tier 1) values can be derived for SOC stock change following land use change to oil palm plantation as a first approximation, which later can be modified as site-specific data become available (Tier 2) or site-specific verified and validated models are developed and made available (Tier 3). Table 1: Rule of thumb magnitude of SOC stock change following land use change to oil palm plantation after 20 years on mineral soil (Values have uncertainty of ±30% at 95% confidence intervals). Original land use Original SOC stock [t C ha -1 30cm -1 ] SOC stock in oil palm plantation [t C ha cm -1 ] Overall Sandy soil Silty-clay loam Clay soil Primary forest Secondary forest Grassland Cropped land Degraded land

8 The magnitude and rate of SOC losses are timedependent, that is, most losses occur in the initial years (following an exponential decay function). Therefore, ignoring the time since conversion gives an incomplete picture of SOC losses (and conversely SOC gains). As a result, the conclusions derived from these studies are preliminary. Time-series monitoring of SOC stocks under oil palm plantation is recommended to improve the knowledge on SOC stock changes over time. 5. Land use change, GHG flux and total GHG emissions SOC content and net GHG emissions are affected by both land-use change and land management associated with the new land use such as tillage, drainage, irrigation, fertilization, and manuring practices. In particular, N 2 O and CO 2 emissions may increase with oil palm cultivation since N fertilisers can promote N 2 O and CO 2 emissions. If anaerobic conditions occur, e.g. in ditches, CH 4 emissions from oil palm plantations on mineral soil may also increase compared to the former land use/ cover. The magnitude of greenhouse gas emissions from conversion of secondary forest to oil palm and rubber plantations depends on the method of land preparation, land management and age of plantation. In the first years, mineral fertiliser application, which may not be fully used due to early crop growth, will increase N 2 O emissions while in older plantations N 2 O emissions may again be reduced because of low mineral N available from efficient N uptake. Direct measurements of N 2 O and CH 4 fluxes for land use conversion on tropical mineral soils are, however, scarce. Among the land use changes, conversion from primary forests to crop and bare fallow had the largest total GHG emissions (7.4 t CO 2 e ), followed by rubber plantations (4.9 t CO 2 e ), and oil palm plantations (2.5 t CO 2 e ). 6. Conclusion Among the various land cover types on mineral soil, primary and secondary forests have globally the highest SOC stocks, closely followed by grasslands. When converted to oil palm plantations, the highest SOC losses ensue from traditional slash and burn agricultural land, closely followed by primary and secondary forests. Degraded and old pasture lands had the lowest loss of SOC compared to the other land uses. When converted to rubber plantations, the SOC losses were: primary forests > secondary forests > grasslands. Land use changes to cropland were always accompanied by heavy loss of SOC in the order: primary forest > secondary forests > grasslands. The total GHG emissions followed the order: primary forest converted to young pasture/crop/fallow lands > primary forest converted to rubber plantations secondary forests converted to cultivated pasture/crop land. Total GHG emission trends for non-forested land were: pasture and crop lands > oil palm > rubber > other perennial trees. However, only very few emission estimates (with large errors) exist for conversion to oil palm plantations on mineral soils and hence there is a poor basis for policy planning for reducing emissions from oil palm plantation establishment and management. Site-specific data on GHG emissions are required for soils under different management conditions to enable measures for reducing GHG emissions from soil under oil palm plantation. 08

9 Glossary of Acronyms Consulting Study 6 IR GC EF BD SOC POC AGB DOM GHG GWP HCS PTF N 2 O CH 4 CO 2 Gt C yr -1 t C ha -1 yr -1 CO 2e CO 2 IPCC AFOLU REDD+ UNFCCC Infra-red Gas chromatography Emission factor Bulk density Soil organic carbon Particulate organic carbon Above-ground biomass Dead organic matter Greenhouse gases Global warming potential High carbon stock Pedotransfer function Nitrous oxide Methane Carbon dioxide Giga tonnes carbon per year Tonnes carbon per hectare per year equivalents Intergovernmental Panel on Climate Change Agriculture, Forestry and Other Land Use Reducing Emissions from Deforestation and Forest Degradation United Nations Framework Convention on Climate Change 09

10 Section 1: Introduction Oil palm (Elaeis guineensis, originally from Africa) is cultivated on approximately 17 million ha around the world (FAO 2015, Carron et al. 2015). The crop is mainly grown in tropical countries for its oil; 60-90% of the palm oil is traded in the global vegetable oil market (UNEP 2011, UNCTAD 2012). Of the traded oil, 74-80% has multiple usages in household and food industry (UNEP 2011, UNCTAD 2012; Figs. 3 and 4). Approximately, million tonnes of oil palm was produced around the world in 2013 (FAO 2015). In general, the edible oil is extracted from the fruit pulp but the kernel is also used for oil extraction. Both sources of palm oil and its derivatives are increasingly used in the cosmetics industry and as a biofuel (UNEP 2011, UNCTAD 2012). The by-products such as kernel shells, pulp fibres, liquid and solid effluents, and oilcakes are used for bioenergy, as organic-fertiliser and feed for livestock. Oil palm is the most effective vegetable oil producer in the world market with average oil yields of 4 t ha -1 yr -1 compared to less than 0.8 t ha -1 yr -1 for other vegetable oils, such as soybean, sunflower or rapeseed (UNCTAD 2012). As a result, the global demand for palm oil for food and fuel has been increasing exponentially ever since the tree species was commercially cultivated worldwide from the 1960s onward (UNEP 2011, UNCTAD 2012). Worldwide, the major areas of oil palm cultivation are found in the tropical regions of Southeast Asia (Indonesia, Malaysia and Thailand), Africa (Nigeria), South America (Colombia, Ecuador and Brazil) and Oceania (Papua New Guinea) (Figure 1 and 2) (FAO 2010, UNEP 2011). Among these areas, the top 2 world leading palm oil producers are Indonesia (23.9 Mt) and Malaysia (18 Mt), which together produce 85% of the palm oil and export 90% of the palm oil worldwide (UNCTAD 2012). The life cycle of an oil palm plantation spans around years after which the area is cleared and replanted (UNCTAD 2012). Oil palm as a mono-crop produces fruit bunches from 2-5 years after planting. The yield increases with plantation age (fruit bunches up to 40 t ha -1 yr -1 that equals 9 t of palm oil ha -1 ) and peaks at 20 years (Wahid et al. 2005). Figure 1. Area suitability of oil palm plantations around the world (Source: UNEP 2011, Plantations.pdf). 10

11 Figure 2: Top ten palm oil producers, importers and consumers at a global scale (Source: UNEP 2011, Dec_11_Palm_Plantations.pdf). A significant area of the forest lands in Southeast Asia, especially in Indonesia (Sumatra and Kalimantan) and Malaysia (Peninsular Malaysia and Sarawak) occur on peat soils (Gunarso et al. 2013). Peat soils are a distinct category of organic soils (Histosols) and generally have a histic horizon with more than 40 % of organic carbon content (equivalent to 82 % organic matter). These soils have organic soil materials in more than half of the upper 80 cm or of any thickness if overlying rock or fragmental materials that have interstices filled with organic soil materials (Soil Science Glossary Terms Committee 2008). Less oil palm plantation conversion has been taking place on peatlands than on well-drained mineral soils (<11.6% organic carbon or <20% organic matter; IPCC 2006). In Indonesia and Malaysia together, 89% of the oil palm is cultivated on mineral soils compared to 11% on peat soils (Koh et al. 2011). More than 50-60% of oil palm plantations were established during (Gunarso et al. 2013) in most states of Malaysia, including Sabah and Sarawak, where oil palm plantations were commonly established on cleared forest land. In addition, lands under agroforestry, rubber, cocoa, coconut, and other plantations were converted to oil palm plantation in Sumatra (Indonesia), Peninsular Malaysia and Papua New Guinea (Wicke et al & 2011, Tan et al. 2009, Gunarso et al. 2013). Furthermore, considerable new oil palm plantations (up to 1.3 M ha) were established on cleared shrub and grasslands in other parts of Indonesia (Kalimantan). In general, predominantly forest-, shrub- and grasslands on mineral soils were targeted for the establishment of oil palm plantation across Malaysia, Indonesia and Papua New Guinea (Gunarso et al. 2013). Since the soil organic carbon stocks and greenhouse gas emissions differ substantially between the original forests on mineral soils and the subsequent plantations, we assessed soil carbon stocks and greenhouse gas emissions following land use change in order to estimate the total greenhouse gas emissions (expressed in CO 2- equivalents). The objective of this assessment was to provide practical guidance on how to estimate soil carbon stocks and soil greenhouse gas emissions. For this purpose we performed: 1. A comprehensive literature search on soil carbon stock data under oil palm plantations and adjacent relatively undisturbed lands, and other land uses. These data resulted from i. Standard soil sampling methodologies ii. Up-to-date analytical procedures to measure soil carbon concentration iii. Measured or default bulk density values to estimate soil carbon stocks from soil carbon concentration iv. Surrogate and cost-effective measures of soil carbon stocks. 2. A literature search on measurements and estimates of greenhouse gas emissions from oil palm plantations and other land use on mineral soil, including agricultural systems. 11

12 Section 2: Assessing soil organic carbon stocks and stock changes The soil organic carbon (SOC) stock from a given area is assessed by sampling the site of interest using statistically rigorous soil sampling methods and analytical techniques. If a treatment and/ or time comparison of SOC stocks is required then all SOC stocks are estimated on an equivalent soil mass (ESM) basis to account for differences in soil sampling depth and soil mass due to change in bulk density between treatments over time, for example, over the 25-year palm oil plantation cycle. Details of soil sampling procedures, bulk density measurements and analytical techniques are given in Appendix Assessing Bulk Density Soil carbon stocks (t C ha -1 ) are estimated from soil carbon content/concentration, soil depth and area, and bulk density following Equation 1. SOC stock (t C ha -1 ) = n Σ SOC concentration x soil depth x area x BD [1] i=1 Where n = number of soil depth intervals, SOC concentration expressed in %, soil depth in cm or m, area in hectare (10,000m 2 ) and bulk density in t m -3 or g cm -3 or Mg m -3. Bulk density changes over time, depending on cultivation and field management operations (Don et al. 2011). Thus, soil BD measurements are crucial for estimating SOC stocks in a changed land use system (Adams 1973, Eswaran et al. 1993, Don et al. 2011). A global meta-analysis (> 380 studies) of land use change in tropical mineral soils (0-30 cm soil depth) reported increased relative BD values (5-32 %) primarily associated with SOC losses in the surface 0-20 cm or 0-30 cm soil layers (Table 2). Considering the variation in bulk density values, a simple rule of thumb can be used for estimating relative bulk density changes following land use change from primary forest to cropland, to perennial cropland, and to grassland of 30, 20 and 20%, respectively (Table 2). When considering conversion of primary forest to secondary forest and secondary forest to cropland or secondary forest to grassland, the relative bulk density changes are 10, 20 and 5%, respectively. While land use change from grassland to cropland led to an increase in relative bulk density by 5%, that from cropland to secondary forest led to a decrease by 5%. No significant changes in bulk density were observed with respect to conversion of grassland to secondary forest, cropland to grassland and cropland to fallow according to Don et al. (2011) (Table 2). Land use change Relative bulk density change (%) Rule of thumb rel. bulk density change (%) Primary forest to cropland +32.3± Primary forest to perennial +21.3± cropland Primary forest to grassland +21.7± Secondary forest to Cropland +22.2± Primary forest to secondary +12.9± forest Secondary forest to grassland +4.2± Grassland to secondary forest -1.8±5.4 0 Grassland to cropland +5.1± Cropland to secondary forest -6.5±3.7-5 Cropland to grassland -2.8±5.5 0 Cropland to fallow +1.8±5.1 0 Table 2: Relative bulk density changes (%) in tropical mineral soils (0-30 cm depth) following land use change. Data extracted from Figure 3, Don et al. (2011). As a rule of thumb, soils with a coarser texture have a higher bulk density value. For example, sandy soils have bulk density values >1.75, silty clay loam soils 1.1 to 1.55 g cm -3, loam 1.25 to 1.6 g cm -3, sandy clay 1.25 to 1.65 g cm -3 and sandy clay loam 1.35 to 1.75 g cm -3. Since Vertisols swell and shrink on wetting and drying, soil moisture content must be specified along with bulk density values. Reference bulk density values for these soils are determined at their field capacity. Alternatively, site and regional specific BD values can be assessed by using pedotransfer functions (PTF). Pedotransfer functions are simple but robust regression models for estimating site specific soil properties (e.g., BD values) where direct measurements are not available (Wösten 2002, De Vos et al. 2005), using correlations with measured soil properties such as sand and clay content, TOC, total N, CEC and soil ph from the site (Manrique and Jones 1991, Asadu et al. 1997, Bernoux et al. 1998, Kaur et al. 2002, Heuscher et al. 2005, Benites et al. 2007). 12

13 For example, Manrique and Jones (1991) and Benites et al. (2007) developed the following simple regression equations for predicting soil BD values (D b ) at 0-30 cm depth from clay content, SOC, and the sum of basic cations (SB: Ca ++, Mg ++, K + ) for Brazil (R 2 values ranging from 0.63 to 0.66): D b = SOC (%) 1/2 D b = Clay (g kg -1 ) SOC (g kg -1 ) D b = Clay (g kg -1 ) 0.01 SOC (g kg -1 ) SB (cmol kg -1 ) When measured SOC concentrations are not available for a particular site, even more simple regression models, which use only soil textural classes and basic pedon descriptions of a particular horizon, can be used to predict the BD values (Sequeira et al. 2014). [2] [3] [4] responses of SOC and associated fluxes to changing land use are in most soils largely limited to the upper 30 cm of the soil (Sombroek et al. 1993, Batjes and Dijkshoorn 1999, Ng et al. 2011, Griffin et al. 2013). 2.3 Estimating Carbon Stock Changes Generally, net SOC stock changes are determined by repeated sampling (Hartemink 2003, 2006) of soil organic carbon stock at the same site at different intervals. Timeframes typically vary between 5 to 20 years (IPCC 2006) to account for the slow character of the soil C cycle (Dalal and Allen 2008). Alternatively, changes in SOC stock can be quickly estimated by using paired-site sampling (Hartemink 2003, 2006, Harms et al. 2005). However, if more measured data on basic soil properties are available, mixed models can estimate the BD values more accurately through multiple regression. Suuster et al. (2011), for example, developed Equation 5 using about 20 measured soil properties to estimate soil BD (Db) values for the surface humus horizon (A-horizon, 3-40 cm depth) of cultivated minerals soils in Estonia: Db = β1 depth + β2 soil type + β4 A(cm) + β6 texture + β7 texture*wc + β8 depth*wc + β9 soil type*wc + β10 depth* SOC + β11 soil type*soc + [5] β12 soil type*cf + β13 depth*cf + A site + B site*year + C year + D transect + E point 2.2 Estimating Carbon Stocks Carbon stock is defined as the absolute quantity of carbon held within a pool at a specified time (IPCC 2006). The soil organic carbon (SOC) stock is the product of soil organic carbon concentration and bulk density. It is assessed up to a certain depth (usually 30 cm) and is expressed as unit weight carbon per unit area. IPCC (2006) distinguishes six carbon pools (above ground biomass, below ground biomass, dead wood, harvested wood products, litter, and soil; Figure 3). There are inputs to and outputs from the total system and carbon is transferred between the six pools. Universal methods are available to measure the total carbon stock for various types of land use. These methods consider both above and below ground biomass, SOC generally to a depth of 30 cm, and surface organic litter (such as dead and decaying leaf and woody fractions of 2-10 mm size) (IPCC 2006). For estimating the C stock in mineral soil under native vegetation, the IPCC (2006) suggests to include by default the living and dead fine roots and dead organic matter (DOM) present within the upper surface layers of the soil fractions (<2 mm size) since Figure 3: Carbon cycle and associated changes between all the six carbon pools and atmosphere in terrestrial ecosystem (Adapted from IPCC 2006). IPCC (2006) proposes a three Tier (1-3) approach to estimating C stock changes in a particular system, region or country. Tier 1 is applied when no local/ first hand measured data are available and thus default values have to be used. IPCC provides these default values for different climate zones and types of land use (see below). Tier 2 involves the use of country/region/ crop specific data on C stock changes. Tier 3 uses simulation models and can be applied when sufficient knowledge is available of processes that underlie stock changes as well as sufficient data for parameterisation and validation. 13

14 Annual carbon stock changes in any pool can be estimated using the difference-based Stock-Difference Method (IPCC 2006, Equation 6), in which the carbon in a particular pool (e.g. soil C pool) is determined at different points in time (t 1 and t 2, see repeated sampling above). ΔC = Where (C t2 C t1 ) (t 2 t 1 ) ΔC = annual carbon stock change in a pool, tonnes C yr -1 C t1 = carbon stock in the pool at year t 1, tonnes C C t2 = carbon stock in the pool at year t 2, tonnes C Alternatively, the process-based Gain-Loss Method (IPCC, Equation 7) can be used, which estimates annual carbon stock changes in a particular pool as the result of all carbon fluxes to and from this pool. ΔC = ΔC G ΔC L [7] Where ΔC = annual carbon stock change in a pool, tonnes C yr -1 ΔC G = annual gain of carbon, tonnes C yr -1 ΔC L = annual loss of carbon, tonnes C yr -1 IPCC (1996, 2006) has compiled a set of default factors and equations (Table 3, Equations 8a,b) to scale the soil carbon stock in croplands according to major management factors. Other general methodologies to calculate total carbon stock changes over multiple strata, multiple C pools, and multiple land use systems are explained in detail in Chapter 2 of Volume 4 of the IPCC (2006) Guidelines. [6] SOC (0-T) = soil organic carbon stock at the beginning of the inventory time period, t C SOC 0 and SOC (0-T) are calculated using the SOC equation 8b where the reference carbon stocks and stock change factors are assigned according to the land-use and management activities and corresponding areas at each of the points in time (time = 0 and time = 0-T) T = number of years over a single inventory time period, yr D = Time dependence of stock change factors which is the default time period for transition between equilibrium SOC values, yr. Commonly it is 20 years, but depends on assumptions made in computing the factors FLU, FMG and FI, it may be varied. If T exceeds D, use the value for T to obtain an annual rate of change over the inventory time period (0-T years). c = represents the climate zones, s the soil types, and i the set of management systems that are present in a region or country. SOC REF = the reference carbon stock, t C ha -1 FLU = stock change factor for land-use systems or subsystem for a particular land-use, dimensionless [Note: FND is substituted for FLU in forest soil C calculation to estimate the influence of natural disturbance regimes]. F MG = stock change factor for management regime, dimensionless F I = stock change factor for input of organic matter, dimensionless A = land area of the stratum being estimated, ha. All land in the stratum should have common biophysical conditions (i.e. climate and soil type) and management history over the inventory time period to be treated together for analytical purposes. (SOC 0 SOC 0-T ) ΔCMineral = D [8a] SOC = Σc,s,i (SOC REF c, s,i x F LU c,s,i x F MG c,s,i x F I c,s,i x A c,s,i ) [8b] Note: T is used in place of D in this equation if T is 20 years, details are given below. Where: ΔC Mineral = annual change in carbon stocks in mineral soils, t C yr -1 SOC 0 = soil organic carbon stock in the last year of an inventory time period, t C 14

15 Table 3: Default factor used in default procedures for estimating carbon stocks in tropical mineral soils (Source: Chapter 5 of Volume 4, IPCC 2006) Relative stock change factors (F LU, F MG, and F I ) over 20 years for different management activities on cropland Factor value type Land use (F LU ) Land use (F LU ) Land use (F LU ) Land use (F LU ) Level Long-term cultivated Temperature regime Moisture regime IPCC defaults Error 1,2 Description Temperate/Boreal Dry 0.80 ± 9% Represents area that has been continuously Moist 0.69 ± 12% managed for >20 yrs, to predominantly annual crops. Input and tillage factors are also Tropical Dry 0.58 ± 61% applied to estimate carbon stock changes. Moist/Wet 0.48 ± 46% Land use factor was estimated relative to use Tropical montane n/a 0.64 ± 50 % of full tillage and medium carbon input levels. Paddy rice All Dry and Moist/Wet Perennial/ Tree crop Set aside (<20 yrs) All Dry and Moist/Wet 1.10 ± 50% Long-term (>20 yrs) annual cropping of wetlands (paddy rice). Can include doublecropping with non-flooded crops. For paddy rice, tillage and input factors are not used ± 50% Long-term perennial tree crops such as fruit and nut trees, coffee and cacao. Temperate/ Boreal Dry 0.93 ± 11% Represents temporary set aside of annual and Tropical Moist/Wet 0.82 ± 17 % cropland (e.g. conservation reserves) or other idle cropland that has been revegetated with Tropical montane NA 0.88 ± 50 % perennial grasses. Tillage (F MG ) Full All Dry and Moist/Wet 1.00 NA Substantial soil disturbance with full inversion and/or frequent (within year) tillage operations. At planting time, little (<30%) of the surface is covered by residues. Tillage (F MG ) Reduced Temperate/ Boreal Dry 1.02 ± 6 % Primary and/or secondary tillage but with Moist 1.08 ±5 % reduced soil disturbance (usually shallow and without full soil inversion). Normally leaves Tropical Dry 1.09 ± 9 % surface with >30% coverage by residues at Moist/ Wet 1.15 ± 8 % planting. Tropical montane NA 1.09 ± 50% Tillage (F MG ) No-till Temperate/ Boreal Dry 1.10 ± 5 % Direct seeding without primary tillage, with Moist 1.15 ± 4 % only minimal soil disturbance in the seeding zone. Herbicides are typically used for wed Tropical Dry 1.17 ± 8 % control. Moist/ wet 1.22 ± 7 % Tropical montane NA 1.16 ± 50% Input (F I ) Low Temperate/ Boreal Dry 0.95 ± 13 % Low residue return occurs when there is Moist 0.92 ± 14 % due to removal of residues (via collection or burning), frequent bare-fallowing, production Tropical Dry 0.95 ± 13 % of crops yielding low residues (e.g. vegetables, Moist/ wet 0.92 ± 14 % tobacco, cotton), no mineral fertilization or Tropical montane NA 0.94 ± 50% N-fixing crops. 1 ± two standard deviations, expressed as a percent of the mean; where sufficient studies were not available for a statistical analysis to derive a default, uncertainty was assumed to be ±50 %. 2 This error range does not include potential systematic error due to small sample sizes that may not be representative of the true impact for all regions of the world. NA -Not applicable. 15

16 Factor value type Level Temperature regime Moisture regime Input (F I ) Medium All Dry and Moist/Wet Input (F I ) Input (F I ) High without manure High with manure IPCC defaults Error 1,2 Description 1.00 NA Representative for annual cropping with cereals where all crop residues are returned to the fields. If residues are removed then supplemental organic matter (e.g. manure) is added. Also requires mineral fertilization or N-fixing crop in rotation. Temperate/ Boreal Dry 1.04 ± 13 % Represents significantly greater crop residue and Tropical Moist/Wet 1.11 ± 10 % inputs over medium C input cropping systems due to additional practices, such as Tropical montane NA 1.08 ± 50 % production of high residue yielding crops, use of green manures, cover crops, improved vegetated fallows, irrigation, frequent use of perennial grasses in annual crop rotations, but without manure applied. Temperate/ Boreal Dry 1.37 ± 12 % Represents significantly higher C input over and Tropical Moist/Wet 1.44 ± 13 % medium C input cropping systems due to an additional practice of regular addition of Tropical montane NA 1.41 ± 50 % animal manure. 16

17 Section 3: SOC Stocks and SOC Stock Changes: Conversion of Tropical Ecosystems to Other Land Uses Organic carbon is an integral component of soil organic matter (SOC = 58% of SOM, IPCC 2006) that is essential for soil stability. It provides long term storage of C in the Earth system. Storing almost three times the amount of C stored in the atmosphere soils play an important role in global C cycling. Soil C sequestration can thus help mitigate global warming and associated climate change issues (Schlesinger 1995, Metz et al. 2007). The SOC stock of a soil depends on the relative rates at which organic matter is added to and lost from the soil. Loss takes place by decomposition, transport and soil erosion. Thus, the change in SOC over time can be expressed as: dsoc/dt = -ksoc +A [9] where k is the rate of SOC loss (yr -1 ), A is the rate of addition (t C ha -1 yr -1 ) and SOC is the SOC stock (t C ha -1 ) at time t (year, yr). Assuming one SOC pool Equation (9), on integration, yields: Figure 4: Rate of loss of SOC as a function of clay content (Dalal and Mayer 1986). SOC t = SOC o + (SOC o SOC e ) exp (-kt) [10] Where SOCo is the initial SOC stock (t C ha -1 ) and SOCe is the SOC stock at equilibrium or at steady state (t C ha -1 ). SOC reaches an equilibrium or steady state after a long period of similar land management, when the rate of C addition equals the rate of SOC loss: SOC e = A/k [11] If the rate of C addition exceeds SOC loss then SOC stock increases and vice versa. Dalal and Mayer (1986) found that the rate of SOC loss was inversely related to the clay content of the soils, so that coarse-textured soils, for example sandy soils required higher rates of C addition than the clay soils to maintain the SOC stock at steady state. This is illustrated in Figures 4 and 5. Figure 5: The amount of organic C inputs required to maintain SOC at steady state as a function of clay content (Dalal and Mayer 1986). 17

18 3.1 SOC Stocks Under Different Ecosystems and Land Uses SOC stocks are influenced by vegetation characteristics, such as root distribution with depth and root biomass as well as litter quality (chemical composition) and quantity. In addition, conversion of forest lands to other agriculture and pasture lands may result in immediate loss of SOC from the original ecosystem including due to soil erosion. The reverse process, i.e. regaining the SOC levels by afforestation/reforestation always takes longer, however (up to years in the tropics; Cunningham et al. 2012, Cook et al. 2013). Both below-ground and above-ground C stocks vary widely between ecosystems and types of land use. Aboveground C is usually the highest (>200 t C ha -1 ) in tropical forests and lowest (<5 t C) in grasslands. Yet, SOC stocks in the latter are still considerable and exceed 100 t C ha -1 (Figure 6). Forests play an important role in supplying large amounts of organic matter (e.g. forest surface litters and root biomass) to the terrestrial soil C pool. Tropical forests typically store similar amounts of carbon in their soil as in their vegetation. It is estimated that approximately 12 % of all native vegetation on the earth surface (15.12 x 10 9 ha) are tropical forest (1.76 x 10 9 ha; IPCC 2000). Globally, tropical forests store nearly 212 Gt C in their biomass and a similar amount belowground as SOC (216 Gt C), making a total C of 428 Gt (Figure 6; IPCC 2000, Dalal and Allen 2008). Tropical rainforests have an average SOC stock of 243 t C ha -1. Mature forests still accumulate 0.37 t C ha -1 yr -1, possibly due to CO2 fertilisation and atmospheric N deposition, and tropical forests act as the largest terrestrial C sink (Dalal and Allen 2008), Figure 6: Global C stock (soil and vegetation biomass, Gt C) in various natural ecosystems. (Source: Adapted from IPCC 2000, Dalal and Allen 2008). 18

19 3.2 SOC Stock Changes Following Land Use Change In undisturbed ecosystems, addition of aboveground and belowground biomass increases the SOC stock depending on the prevailing climate, temperature, soil and vegetation. C inputs change considerably when ecosystems are converted for land use. The SOC changes following land use change vary widely, but seem to be consistent within a given soil type and region. A detailed meta-analysis of the effect of major land use change on the SOC stock in tropical mineral soils showed that highest losses occurred when primary forests were converted into cropland or perennial crops (-21 and -32 t C ha -1 or -25 and -30%, respectively; Figure 7 a-b). Low SOC stock losses were observed when primary forests were converted into secondary forests (-8 t C ha -1 or -9%), when secondary forests were converted into perennial crops and grassland (-2 to -5 t C ha -1 or -2 to 6 %) and when grassland were converted into cropland (-7 t C ha -1 or -10%) (Figure 7a & 7b). The observed SOC losses were primarily due to reduced C inputs to the soil, although SOC removal by erosion cannot be ruled out (Guillaume et al. 2015), especially when primary forests are converted into lands used for annual cropping. If land use change results in increased C inputs compared with the previous land use and losses do not change, then the SOC stock increases (cf. Equation [11]). Such increase was observed when cropland was converted into grassland, bush fallow or secondary forest, showing SOC stock increase of 16, 14 and 35 t C ha -1, or 26, 32 and 50%, respectively. Smaller SOC increase occurred when grassland was converted into secondary forest (10 t C ha -1 or 17%) (Don et al. 2011) (Figures 8a & 8b). Figure 7b: Loss in SOC stock (per cent decrease from previous vegetation) following land use change (adapted from Don et al. 2011). Figure 8a: Gain in SOC stock (absolute SOC stock) following land use change (adapted from Don et al. 2011). Figure 8b: Gain in SOC stock (per cent increase) following land use change (adapted from Don et al. 2011). Figure 7a: Loss in SOC stock (absolute SOC stock) following land use change (adapted from Don et al. 2011). 19

20 20 The magnitude and rate of SOC losses depend on the time since conversion with most of the losses occurring in the initial years (following an exponential decay function, Equation 11). As a result, the actual loss strongly depends on the initial stock, which may vary within land cover classes (Fig. 9 & 10). The assumptions made in chronosequence studies should therefore be considered carefully. For example, Bahr et al. (2014), using a chronosequence approach in the Southern Equadorian Andes, found higher SOC stocks in 5-10 year old new perennial croplands and pasture lands than in a reference forest land. However, the actual forest conversion by slash-and-burn had resulted in a 14-19% decrease of SOC stocks (0-30 cm depth) in the first 5 years, irrespective of the established land use (annual: maize, tapioca; perennial: banana, coffee, cacao; pasture). Also when using the repeated sampling method the initial period of the land use change must be included and ideally be sampled at higher frequency. A simplified rule of thumb for the magnitude of SOC stock change following land use change to oil palm plantation (after 20 years) on mineral soil is given in Table 1 (Executive Summary) and reproduced below. The changes in SOC stock following land use change are based on soil texture effects as shown in Figures 4 and 5, since clay content affects both rate of SOC loss (or gain) and rate of organic C inputs. However, SOC losses from erosion are not considered. Table 1: (from the Executive Summary). Rule of thumb magnitude of SOC stock change following land use change to oil palm plantation after 20 years on mineral soil (Values have uncertainty of ±30% at 95% confidence intervals). Original land use Original SOC stock [t C ha cm -1 ] Overall SOC stock in oil palm plantation [t C ha -1 30cm -1 ] Sandy soil Siltyclay loam Clay soil Primary forest Secondary forest Grassland Cropped land Degraded land The IPCC default values (Penman et al. 2003) for SOC loss upon land use change are expressed as remaining fraction of the original stock. For a change from forest to cropland the default value is 0.69 (31% SOC loss) for dry tropical regions and 0.58 (42% SOC loss) for wet (Figure 9). For the change from forest to grassland the default value is 1 (meaning no SOC loss). Similar fractions were found by Don et al. (2011) although values were higher (0.76 [24%] and 0.68 [32%] for conversion to cropland) and lower for grassland (0.91 [9%], Figure 9). The discrepancy was primarily because Don et al. (2011) adjusted for changes in bulk density and soil depth. Figure 9: Default values for remaining SOC fraction after land use change from Don et al. (2011) and IPCC (Penman et al. 2003) SOC Stock Changes Following Land Conversion To Oil Palm Plantation Conversion of a primary forest to an oil palm plantation results in a decrease of the SOC stock of 30±10 %; conversion from secondary forest of 18±13 % (Table 4). These values are similar to those reported for rubber plantations (Table 4). SOC losses were smaller (6 t C ha -1 or 12%) when oil palm is established on degraded pasture/grasslands. However, larger SOC losses (up to 44% or 25 t C ha -1 ) may occur when oil palm replaces traditional slash and burn cropping/ swidden agriculture systems. Moreover, land use change to oil palm plantation may result in SOC losses in association with heavily mechanized land preparation (Bruun et al. 2013). Soil fertility of the converted land can be recovered over time if sufficient fertilizer and organic inputs are supplied in addition to appropriate intercropping (Tanaka et al. 2009, Ng et al. 2011) until a larger oil palm canopy cover is established. Usually, palm trees are removed and replanted after years. At that point trunks and fronds can be recycled into the soil in order to replenish the SOC stock (Khalid et al. 1999, 2000; Yamada et al. 2010, Comte et al. 2012). Generally, 96% of the total annual net primary production (dry biomass) is stored in aboveground parts like the trunk, fronds and bunches (Corley and Tinker 2003, Aholoukpè et al. 2013). Soil organic matter input to the soil might be brought about by recycling pruned fronds and other biomass into the soil during the plantation life cycle (~25-30 years) (Ng et al. 2011, Stichnothe and Schuchardt 2011, Chiew and Shimada 2013, Frazão et al. 2014). However, since fronds are usually stacked in every second row only, this will result in spatial variability in SOC stocks. For example, Frazão et al. (2014) found that SOC stocks were times higher under frond piles than in the inter-rows, possibly in relation to the year age of plantations. However, insight in the efficiency of pruned fronds conversion to SOC for different aged oil palm plantations is thus far lacking and cultivation practices are too variable to draw conclusions (Ng et al. 2011, Frazão et al. 2014).

21 Table 4: Summary of relative SOC stock change from different land use changes (data source cited in Appendices 3-6). Previous land use Converted land use Age of new land use (y) (mean ±SD) SOC stock of previous land use (t /ha) (A) SOC stock of converted land use (t/ha) (B) Relative change in SOC stock (%) *(Mean ± SD) [(B-A)/A] x 100 Number of data considered Primary forest Oil palm plantation 17± ± ± ±10.1 n=6 Secondary forest Oil palm plantation 15±7 56.5± ± ±13.1 n=7 Pasture/grassland Oil palm plantation ± ± ±27.0 n=3 Cropland/ Slash and Oil palm plantation 13±3 56.6± ± ±5.3 n=3 burn Agriculture Primary forest Rubber plantation 23± ± ± ±6.7 n=5 Secondary forest Rubber plantation 33±2 70.7± ± ±7.7 n=7 Pasture/grassland Young Rubber/tree 9±5 54.8± ± ±10.2 n=3 plantation Pasture/grassland Old Rubber/ 19± ± ± ±8.3 n=3 Broadleaf-tree plantation Primary forest Perennial tree 30±1 55.9± ± ±10.7 n=11 plantation Secondary forest Tree/perennial 28±2 75.9± ± ±6.7 n=6 plantation Primary forest Pasture 27± ± ± ±13.8 n=8 Secondary forest Grass/Crop land 27± ± ± ±14.7 n=7 Primary forest Crop and fallow 29± ± ± ±14.2 n=9 land Pasture/grassland Cropland 30± ± ± ±9.8 n=4 * Relative change in SOC stock (%) values with (-) signs indicate source of soil C/emission/loss of soil C from the previous land use/release of CO 2 into the atmosphere Magnitude of SOC Losses Due to Soil Erosion Loss of the litter layer and vegetation canopy exposes the soil surface to soil erosion (Ross and Dykes 1996). SOC loss due to soil erosion is rarely taken into account, however, as its overall impact on CO 2 emission is considered uncertain. Eroded soil may indeed be deposited in wet environments where it is not or only slowly degraded. However, for a given land use, soil erosion should be accounted for as it depletes the SOC stock and affects soil fertility, especially with respect to nitrogen. The N enrichment ratio, which is the concentration of N in the eroded sediment divided by that of the parent soil, varies from 1.1 to as high as 10, depending on the soil texture, aggregation, rainfall intensity and land use. At a sediment delivery ratio of 10% (eroded sediment / bulk soil), the eroded sediment may have an N enrichment ratio of ~2 in a clay soil, ~3 in a loamy soil and > 6 in a sandy soil (Figure 10) (Foster et al. 1985, Rose and Dalal 1988). Organic C enrichment ratios of detached sediments are likely to be similar to N enrichment ratios, because soil organic C and N are closely related. Figure 10: Relationship between sediment delivery ratio and enrichment ratio (Source: Adapted from Foster et al. 1985, Rose and Dalal 1988). Organic C enrichment ratio of detached sediments is likely to be similar since soil organic C and N are closely related. 21

22 22 Soil erosion negatively affects SOC by disrupting or slaking aggregates and an associated increase in mineralisation, by preferential removal of C in runoff water, and by increased mineralisation of SOC in the displaced sediments. However, eroded sediments may be re-aggregated into organo-mineral complexes and buried as C-enriched sediments in flood plains, lakes, or the ocean floor (Lal 2003), mitigating losses to some extent. Soil erosion is increased when land use changes from relatively undisturbed land use such as forest to relatively disturbed land use such as cropland (Sidle et al. 2006). The loss of SOC may be up to 50 t C ha -1 within 5-10 years following conversion from natural vegetation to cropland, depending on climate, soil, slope, drainage, method of land preparation, and extent of residual vegetation cover (Davidson and Ackerman 1993). Almost 50% of this SOC loss could be due to soil erosion on steep sloping lands. Moreover, in the tropics, intensive rainfall may cause high rates of erosion (Yu et al. 2001, Oliveira et al. 2013). For example, Gharibreza et al (2013) estimated soil erosion losses of 47 to 99 mm per year on newly cleared land and of 3.5 to 15.6 mm per year from mature oil palm/rubber plantations in Malaysia. Assuming the soil (Ferrosol) contained 4% organic C and had bulk density of 1 Mg m -3, SOC losses would amount to18.8 and 39.6 t C ha -1 per year from newly cleared land and 1.4 to 6.2 t C ha -1 per year from the mature plantation. Losses may be higher considering that eroded sediments are generally enriched in SOC compared with the bulk soil (Figure 10). For example, Boye and Albrecht (2006) measured a SOC enrichment ratio of 3.3 in Kenya and Bilgo et al. (2006) reported 4.3 in Burkina Faso. These findings were confirmed by a recent study conducted in secondary rainforests in the lowland mineral soils of Indonesia (Jambi province, Sumatra) by Guillaume et al. (2015). They reported erosion related SOC loss of about 10 t C ha -1 after 15 years of land use change from secondary forest to rubber and oil palm plantations on a coarse-textured soil; this soil contained almost 50% lower concentrations of SOC than the Ferrosol studied by Gharibreza et al. (2013). Simulation studies using the soil redistribution model Landsoil and a SOC model based on RothC-26.3 also identified land use change (from native vegetation to cropland) as the most important factor determining SOC loss from soil erosion (Lacoste et al. 2015). Particulate organic C (POC) was the most important component of SOC lost due to soil erosion, especially by sheet (interrill) erosion, with coarse-textured soils being more prone to the POC loss than fine-textured soils (Muller-Nedebock and Chaplot 2015). Muller-Nedebock and Chaplot (2015) estimated that tropical soils, on average, lost 66 kg C year compared with only 17 kg C from temperate soils. The highest SOC loss (as POC) was 2400 kg C, reported from a sandy soil in a wet environment in Kenya (>1500 mm annual rainfall, Gachene et al. 1997). In conclusion, SOC losses, especially as POC, from soil erosion must be taken into account and minimised following land use change. Erosion can be most effectively minimised by retaining the surface vegetation cover and by reducing soil disturbance to a minimum when preparing the land for oil palm plantation. For oil palm establishment soil erosion prevention and fertility conservation techniques such as immediate establishment of legumes or other plant cover may prevent major SOC losses from erosion (Ling et al. 1979) Magnitude Of SOC Losses Due to Soil Drainage and Land Preparation Drainage is a mandatory practice when preparing land for oil palm plantation, especially in wetlands. Intensive draining of the wetland mineral soil will increase its aeration, which will lead to accelerated rate of SOC decomposition and hence accelerated SOC loss. As seen above, preparing land for oil palm plantation also leads to SOC losses due to soil erosion. In addition, mechanical preparation and excessive land disturbance such as bulldozing and bench terracing may bring unfertile and acidic sub-soils to the surface, resulting in significant fertility degradation as well as soil compaction (Hamdan et al. 2000) Magnitude of SOC Changes Due To Management Practices: Fertilizing, Liming, Organic Amendment and Mulching Addition of the required amounts of fertilisers reverses fertility degradation, increases plant biomass and hence contributes to C inputs to the SOC pool. Similarly, various organic amendments may provide C inputs to SOC pool, decreasing the rate of SOC loss or even increasing the SOC stock (Ng et al. 2011, Cheah et al. 2013, Moradi et al. 2012, 2013, Pauli et al. 2014). Fertilizing Fertiliser application of N, P and K is essential for optimal palm oil production. However, the effect of fertiliser application to tropical mineral soils on SOC stock changes has hardly been studies. The limited information available indicates that fertiliser application may reduce SOC loss. For example, Tanaka et al. (2009) found that fertilizer application of 400 to 800 kg ha -1 y -1 of NPK compound fertilisers around the palm tree bases (1 2 m radius) decreased the loss of SOC and improved soil physical properties following the land preparation and bench terracing. However, the application of fertilisers, especially ammoniac-based N fertilisers, may cause a detrimentally low soil ph (4.2 to 3.8, Kee et al. 1995) in the acidic Ultisols of Malaysia under mature oil palm plantations.

23 Liming Liming improves soil conditions for plant productivity by reducing Al 3+ and Mn 2+ toxicity, alleviating Ca deficiency and improving the availability of plant nutrients. Increased plant productivity increases the organic C input to the soil, thus contributing to the SOC stock (Haynes and Naidu 1998; Briedis et al. 2012a, b). Liming also improves soil structure by increasing and strengthening the clay-organic matter bonds (clay- Ca 2+ -SOM/SOC) (Oades 1984), thereby making SOC relatively inaccessible to microbes and hydrolysing enzymes from decomposition. However, liming also increases the soil faunal and microbial activity, which increases the transformation (decomposition/ mineralization, turnover) of organic matter CO 2 (Fuentes et al. 2006) and thus reduces the SOC stock. As a result, the net effect on the SOC stock is difficult to predict in the field and is largely unknown. Because of the counteracting albeit interactive processes in limed acidic soils, SOC stocks may increase, decrease or show no change depending on the relative importance of the different processes (Page et al. 2009; Ahmad et al. 2013, 2015; Paradelo et al. 2015). Other factors such as initial soil ph, rate and duration of liming, nature of the soil matrix, climate and land management may also affect the SOC stock, although Paradelo et al. (2015) in their meta-analysis on liming effects on SOC, found no significant effect of soil texture, soil use, nor of amount, duration and frequency of application. However, Paradelo et al. (2015) for lack of field data did not consider harvest rates, management practices such as residue removal or burning, and erosion, which may all affect the SOC stock (Page et al. 2009). Soil organic carbon and its fractions: Lime-induced increase in plant productivity and SOC Increase in productivity and SOC stocks were observed on a cropped Oxisol after 15 years when it was limed at 3, 6 and 9 t ha -1 of dolomite, with increases in SOC (0-20 cm depth) from 49.9 t C ha -1 to 52.8, 53.0 and 57.6 t C ha-1, respectively (Briedis et al. 2012a). Most of the SOC increase was in the POC fraction (Briedis et al. 2012b). However, although liming increased crop yields in the semi-arid region studied by Chan and Heenan 1999, the SOC content actually decreased in the first 3 years, primarily due to a decrease in the light fraction (<1.8 Mg m -3 ) of organic C, possibly due to increased mineralization (CO 2 respiration). It is likely that in oil palm plantations the harvest prevents a longterm increase in the SOC stock. Moreover, increased soil respiration in limed soils may also be result in a decrease in SOC stocks (Rangel-Castro et al. 2004, Moore et al. 2012). Lime-induced increase in aggregate stability and SOC Briedis et al. (2012) reported increased aggregate stability and SOC after liming of an Oxisol used for continuous cropping. They suggested that organic C was protected against decomposition in soil aggregates. On the other hand, although liming improved soil structure, the SOC content in fact decreased in commensurate with increased microbial respiration (Chan and Heenan 1999). In a long-term experiment (30 years) on an Alfisol in India, Hati et al. (2008) found that although liming improved the aggregate stability, it had no effect on SOC and that liming did not improve crop yields. Thus, a lime-induced increase in aggregate stability through clay-ca 2+ -SOM/SOC bonds may not be as protective of SOM against microbial decomposition as clay-al 3+ / Fe 3+ -SOM/SOC bonds in an acidic soil. Lime-induced interactive effect of plant productivity and aggregate stability on SOC The interactive effect of liming on plant productivity, return of crop residue to the soil and aggregate stability, especially the increase in macroaggregates (8-19 mm), was observed on an Oxisol in Southern Brazil (Briedis et al. 2012a,b). Increase in SOC as well as POC was closely associated with macroaggregates, indicating Ca2+ formed a cationic bridge between kaolinitic clay and SOC in the Oxisol. In contrast, Chan and Heenan (1999) observed a loss in SOC although soil aggregation improved on liming of an Alfisol in a semi-arid region. Further field work is required to demonstrate whether these processes operate in other soils and climatic conditions, especially those under oil palm plantations in tropical humid environments. For SOC stocks to remain or increase, plant productivity and microbial products for formation of aggregates must substantially increase in the limed soil compared with the unlimed soil to compensate for increased soil respiration. Lime-induced increase in Soil microbial biomass Soil microbial biomass constitutes <5 percent of SOC in most soils although it is the primary agent of organic carbon transformation as well as a source and sink of N, P, and S (Dalal 1998). Lime-induced plant production and return of plant residue provides a C substrate for the microbial population, resulting in an increased microbial biomass. Fornara et al. (2011) reported increase in microbial biomass upon liming of acidic soils, but microbial activity and soil respiration increased as well. Organic amendments and mulching It has been observed that plant residue retention, and no-till practices increase SOC stocks in mineral soils (Smith 2008), or at least reduce the rate of loss of SOC stocks (Dalal 1989). For example, Bayer et al. (2006) found an increase in SOC stock of 0.35 Mg C in an Oxisol used for cropping in Brazil. After eliminating the burning of crop residue, Ng et al. (2011) recorded an increase in SOC stock (up to 2-16 %, 0-45 cm depth) under the inter-row spaces of oil palm plantations. 23

24 Application of organic amendments to soil under oil palm plantation generally increases SOC stocks as well as soil fertility (Comte et al. 2013). For example, oil palm by-products, including empty fruit bunches (EFB) significantly improved soil aggregation and aggregate stability (up to 55 %) of sloping lands. In addition, the application of EFB mulches distributed soil water more uniformly throughout the soil profile (>30 cm deep) in non-terraced hill slopes (Ping et al. 2012; Moradi et al. 2012; Moradi et al. 2014), and increased SOC stocks (Bakar et al. 2011; Moradi et al. 2013). Besides recycling nutrients, co-composting of palm oil mill effluent (POME) waste and EFB also has the potential to reduce methane emission (Stichnothe and Schuchardt 2010). In addition, N 2- fixing legume crops, in particular Mucuana bracteata have been shown to increase SOC stocks by 1-8 t C ha -1 during the first 4 years of an oil palm plantation (Cheah et al. 2013). Moreover, legumes also contribute to maintaining soil fertility, especially with respect to N and may in the long-term reduce N fertiliser requirements. 24

25 Section 4: Effects of Land Use Change on Greenhouse Gases Emissions In the land use sector, CO 2, CH 4 and N 2 O are considered the primary greenhouse gases (GHG). These gases are responsible for approximately 60%, 20% and 10% of the total human induced global warming (Dalal and Allen 2008). Land use change alters processes in terrestrial ecosystems, which in turn modifies GHG fluxes (Figure 11). Land management such as drainage, land preparation, and fertilisation of a newly established oil palm plantation could either cause the system to become a source (-) or a sink (+) of GHG emissions to the atmosphere (Figure 11). Emissions of CH 4 and N 2 O are converted into CO 2 equivalents (CO 2e ) by their global warming potential (GWP). The GWP values are 28 for CH 4 and 265 for N 2 O on a 100-year time horizon (Myhre et al. 2013). 4.2 Key Drivers of GHG (CO 2, CH 4 and N 2 O) Emissions from Soil The key driving factors affecting the production and release of GHG gases are organic matter substrate availability, temperature, soil moisture content, aeration, porosity and ph, as well as N fertiliser or large mineral N sources for N 2 O emissions. Management practices such as the application of fertilisers, manures, herbicides, pesticides and other amendments affect GHG emissions though these key drivers (see Dalal et al. 2003, 2008 for a detailed review). Net GHG emissions are affected by both landuse change (e.g., native forests to grazing lands or croplands) and land use management such as tillage, irrigation, fertilization, and manuring and drainage practices. For example, the application of N fertilisers may result in high N 2 O emissions if mineral N, especially NO 3- N, and readily available C substrate are available under partially anaerobic conditions (up to 70-80% water-filled pore space) (Dalal et al. 2003). Significant CH4 emissions occur only when readily available organic C substrates are combined with anaerobic conditions (Dalal et al. 2008). As a general rule, an oil palm plantation on mineral soil will have significantly lower CH4 emissions than found in wet peatlands and inundated rice-paddies (Zheng et al., 2007 Bhattacharya et al. 2013). Figure 11: Land use change and its impacts on different ecosystem processes and GHG fluxes under general terrestrial AFOLU systems (Source: IPCC 2006). 4.1 Measurement of GHG Fluxes Assessment and monitoring of GHG emissions from oil palm plantations and other land use can be carried out according to the IPCC guidelines (IPCC 2006, Volume 4). The primary methods used to assess GHG fluxes are closed chamber measurements and micrometeorological (eddy covariance) measurements. These methods are described in Appendix Effect of Land Preparation and Drainage on GHG Emissions Drainage greatly affects the CO 2 emission in inland mineral soils. Also N 2 O emissions may increase when drainage leads to an increase in organic matter mineralisation and in increased amounts of NO 3- N and available C substrate. Data on N 2 O emissions in tropical regions are scarce for drained or temporarily wet conditions (in case the water-table rises during the wet season) and the range is very high (0-24 kg N 2 O-N ha -1 yr -1 ). For CH 4 flux, the default emission is insignificant (close to zero) for drained mineral soils (IPCC 2006). Emissions from rewetted mineral soils are considerable (IPCC 2014, Table 5). 25