Open access peer-reviewed chapter

Necessity, Principle and Technique of Evaluation Model to Assess Sustainability of Oil Palm Plantation in Indonesia

Written By

Latief Mahir Rachman

Submitted: April 10th, 2021Reviewed: May 26th, 2021Published: March 23rd, 2022

DOI: 10.5772/intechopen.98585

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Abstract

Indonesia should have a scientific-based approach and evaluation system to counteract negative accusations and allegations that Oil Palm Plantations (OPP) in Indonesia are not environmentally friendly, unsustainable, and destroy forests and peatlands. The proposed evaluation instrument basically to assess the OPP sustainability and current productivity also the limiting factors for oil palm production which are useful to determine management recommendations for increasing the productivity of each type of OPP. Basically, the evaluation model assessed recent soil-land and environment conditions/quality and linked it to productivity, and then compared it with soil-land and environment conditions/quality before the land was converted into OPP. After comparing soil-land and environment qualities before and after converted into OPP, hence the balance derived from the increase or decrease in the quality of soil-land and the environment from all OPP types can be calculated as a result of the development of OPPs throughout Indonesia.

Keywords

  • environment degradation
  • evaluation model
  • oil palm productivity
  • soil-land quality
  • sustainability

1. Introduction

Indonesia is the biggest producer of palm oil in the world. Palm oil is an important commodity for various products, such as cooking oil, margarine, cosmetics, and biofuel. Until now, the Indonesian palm oil industry has played a very strategic role and has made a real contribution to Indonesia’s economic development and has become a mainstay in achieving the Sustainable Development Goals (SDGs).

Nowadays, however, Indonesia was accused and deemed to have developed oil palm plantation (OPP) with high risk to the environment by the European Union (EU) based on the Renewable Energy Directive (RED) II and its technical regulations (delegated art). As a consequence, Indonesian palm oil products will be gradually restricted and removed from the EU biofuel market. The EU’s decision to exclude palm oil as renewable energy in the RED II faced critics and protest from Indonesia. It finally creates EU-Indonesia palm oil dispute since this action threatens the 12% share of Indonesia’s palm oil exports, particularly exports destined for the EU.

The EU’s views and allegations need to be scientifically and proportionally responded to, especially to the main issues that were raised. The EU argued that the expansion of palm oil has direct link with deforestation and degradation of forest, peat land degradation and emissions escalation [1, 2]. Another hot issue is loss of biodiversity, saving peat lands, and disturbing the balance of the ecosystem. Indonesian government is considered not serious in implementing sustainable land-use policies that enabled palm oil corporation harming forest and its biodiversity [3].

In fact, many palm oil companies in Indonesia have considered and counted environmental and sustainability factors into their business development concept. The 3-P (Planet-People-Profit) concept has been widely used as a guideline in the management of the palm oil industry in Indonesia. In addition, efforts to achieve sustainability, particularly regarding oil palm plantation sustainability (OPPS) have been implemented through ISPO (Indonesian Sustainable Palm Oil) and RSPO (Roundtable Sustainable Palm Oil) certification [4, 5]. However, in reality, these efforts have not been deemed sufficient to be considered as producing a sustainable palm oil industry, not causing damage or degradation of soil-land-environment (S-L-E). Indonesia should have a scientific-based approach and evaluation system to counteract negative accusations and allegations that the OPP in Indonesia is not environmentally friendly, unsustainable and destroy forests and peat-lands. For this reason, more concrete efforts are needed in the form of a system supported by tools that can be used to evaluate the effects of OPP in Indonesia objectively, rationally, scientifically, measurably, proportionally, quantitatively and accurately to the quality of S-L-E in Indonesia which can also be used to increase productivity. and help design management to prevent S-L-E degradation.

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2. Principle of oil palm plantation sustainability (OPPS) evaluation

There are at least three principles that can be used as guidelines for assessing OPP in Indonesia. First, the evaluation of the oil palm industry effect, especially OPP, on S-L-E should not be generalized, but evaluated objectively and proportionally, particularly based on the origin of the OPP. In reality, OPP in Indonesia is very diverse, both from the type of land, climate, topography, age and plantation management, as well as the origin of the OPP. OPP in Indonesia is scattered on mineral and peat soils with very varied characteristics, with very varied slopes and climates, etc. Not all OPP comes from forest clearing, many also come from the conversion of rubber, cocoa or other plantation crops, moor or grassland, mixed gardens and even rice fields, shrubs, Imperata cylindricagrass fields, idle or abandoned land, former illegal logging, critical land, etc. OPP management differences also have different effects on S-L-E conditions. Thus, data on the origin of an OPP is important and crucial to be traced and identified.

Second, OPP in Indonesia is mostly planted on suboptimal land. Suboptimal land has marginal land quality [6, 7, 8, 9]. Facts in the field show that oil palm plants are able to adapt and be cultivated on suboptimal land. In fact, the OPP was deemed able to apply regional ecological principles based on the optimization and preservation of resources, especially on suboptimal land. In other words, OPP is not always associated with a decrease in soil quality, land quality and environmental quality. In fact, in some places, OPP planted on marginal lands is believed to be able to improve soil quality, land quality and environmental quality.

Third, to resolve disagreements and polemics about OPP in Indonesia, an objective evaluation system is needed as a platform or indicator that can assess OPP in an empirical, measurable, quantitative, accurate and standardized manner. The reliable evaluation model system is also expected to be able to evaluate and provide information about the quality of S-L-E; palm oil productivity; constraining factors for the growth and production of oil palm; as well as directions for management recommendations to increase the productivity and quality of S-L-E and prevent land damage and degradation. The evaluation system needs to be supported by the Plant Potential Productivity Index (PPPI), Soil Quality Index (SQI), Land Quality Index (LQI) and Environmental Quality Index (EQI).

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3. Fundamental technique for assessing OPPS

A technique used for assessing OPPS should be based on agroecosystem principles. In Ref. [10], there are 4 principle components of sustainable agroecosystem: productivity, stability, sustainability, and equitability.

The assessment of OPPS requires evaluation of aspects of plant productivity and S-L-E quality as well as changes in these aspects compared to their original use and/or previous conditions. Plant productivity is compiled from data and production potential. Soil quality score and land quality score could be determined in the representative area where the OPP are planted. Environmental quality is compiled in a similar way to the soil quality and land quality by selecting key environmental parameters. OPPS can be analyzed from changes or differences in the productivity value of plants, soil quality score, land quality score, and environmental quality score as compared to the original land use before planted by oil palm crop. All of these would be presented as the Plant Potential Productivity Index (PPPI), Soil Quality Index (SQI), Land Quality Index (LQI) and Environmental Quality Index (EQI).

In fact, not all OPP originate from forest clearing. Majority of OPPs do not come from forests, even from abandoned land ex-illegal logging, grasslands, shrubs, gardens that are not or less productive, etc. so that the effects of OPP on S-L-E quality and S-L-E degradation vary widely. The effect is strongly influenced by the origin or history before the OPP was created [11, 12], soil type, topography (especially slopes), climate (especially rainfall and climate type), applied management [13], both during land clearing and after planting, plant age, especially related plant factors and their influence on production, surface water absorption (infiltration), surface runoff and erosion and oil palm plantation management [14, 15]. It is necessary to study the effect of OPP on the degradation and improvement of S-L-E. The study especially needs to focus on the main issues that are accused of causing effects on S-L-E, including: 1) groundwater, 2) infiltration, 3) surface and river discharge, 4) soil erosion and sedimentation, 5) gas emissions and absorption greenhouse or carbon dioxide (CO2), 6) forest area, 7) biodiversity, and 8) degradation of peat lands.

All of sustainability evaluation of OPP effects would be presented as the PPPI, SQI, LQI and EQI. SQI and LQI could be developed to provide information about: 1) soil and land quality scores, 2) quality levels (very bad to very good), 3) growth inhibiting factors and crop production that needs to be addressed by soil-land management. If this information is correlated with production data, it can produce PPPI. SQI, LQI as well as EQI score is compiled by selecting key parameters, determining the weights and assigning a score to each parameter. Supported by PPPI together with SQI, LQI and EQI of all types of OPP with their area and their originate use area, an evaluation system could be developed which is very useful for S-L-E assessments to support OPPS in Indonesia.

The need and support to be able to evaluate empirically, measurably, objectively, quantitatively, accurately and periodically is increasing. Technological advances are increasingly supporting the implementation of Agriculture 3.0 which is characterized by smart farming and precision farming and Agriculture 4.0 which is characterized by agricultural digitization. Support for quantitative land data is needed [16, 17, 18]. Software models are very helpful in simulating and predicting surface runoff, discharge, sediment loads, other environmental pollutants and helping to prepare recommendations for planning techniques for soil conservation and water improvement for SLE [19, 20, 21, 22, 23].

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4. Basic configuration and structure of model for assessing OPPS

4.1 Identification, goals, and boundaries

A model can be set up and operated to assess or evaluate sustainability of OPP in Indonesia empirically, objectively, measurably, quantitatively, and accurately to measure the effects of OPP on quality of S-L-E. This model is targeted to be able to produce information on: 1) evaluation on S-L-E qualities, 2) potential productivity of oil palm, 3) factors that are constraining to the growth and production of oil palm, and 4) direction for management recommendations to increase productivity, maintain and improve the quality of S-L-E and preventing the damage and degradation of S-L-E.

The issue that the model hopes to address is doubts about sustainability of OPP regarding the degradation of S-L-E. Regarding the limitation of this model, this model is only to assess the sustainability of OPP in terms of physical and environmental resources, not to assess sustainability of the economic and social aspects.

4.2 Conceptualization of model

The model is built using an input - process - output model (see Figure 1).

Figure 1.

Model input – Proses – Output of OPPS model.

Input consists of controllable input and uncontrollable input. Meanwhile, the output consists of the desirable output and the undesirable output. Feedback will also be analyzed thoroughly. To facilitate organization, the model will be divided into several sub-models, each sub-model will be further divided into several sub sub-models.

The model consists of 5 subsystems: 1) Plant (Crop), 2) Soil, 3) Land, 4) Environment, and 5) OPPS. The interaction of the five subsystems is presented in Figure 2.

Figure 2.

Subsystem components of oil palm plantation system and their interactions.

Model input – proses - output will be applied. Input of this model consists of all parameters related to the all components (see Table 1). While output this model involves both expected outputs and unexpected output.

SubsystemParameters ComponentParameters Management
Plant-CropProduction, Productivity, Land cover, Biomass, Plant-Crop (CN) factors*, Emissions and Carbon sequestrationTypes and sources of seeds, foliar fertilizers, Use of PHF**, Pest-Disease Control
SoilPhysical, chemical, biological properties of soilInorganic and organic fertilization, liming, soil amendment, soil and water conservation
LandSoil, topography, climate, erosion, subsidence, flood, surface, rockMicro reservoir, irrigation, drainage, erosion prevention
EnvironmentLand fires, air quality, Greenhouse Gas Effects, biodiversity, discharge, runoff, drought, infiltration, water balance, ground water level, water quality, industrial and agrochemical waste pollutionISPO and RSPO certification, High Conservation Value Area, Wastewater Treatment Plant, Land Fire Task Force, Zero Waste Program, Environmental Impact Analysis Reporting
OPPSChange and / or trend of productivity, quality of S-L-E (increased, fixed or decreased)Planning, management recommendations for productivity improvement, preventing degradation to achieve OPPS

Table 1.

Component variables and management of each sub-system.

Information: To predict erosion (USLE).


Pesticides, Herbicides and Fungicides.


The model that has been developed, validated and mutually agreed upon will then be used to assess aspects of crop productivity, soil quality, land and environment as well as OPPS in various types of oil palm plantations in Indonesia.

4.3 Specification of model

The model consists of 5 sub models, plants, soil, land, environment and OPPS.

  1. The crop sub-model assesses aspects of production/productivity and crop management.

  2. Soil sub-models to assess soil quality, soil factors that inhibit oil palm growth and production, and soil management. This sub-model will be based on the SQI.

  3. Land sub-model to assess land quality, land factors that inhibit oil palm growth and production, and land management. This sub-model will be based on the LQI.

  4. Environmental sub-model to assess environmental quality, environmental factors causing environmental damage or degradation and environmental management.

  5. Meanwhile, the OPPS sub-model assesses the aspects of S-L-E damage, S-L-E degradation as well as the concepts and management needed to increase the productivity of oil palm plants and prevent S-L-E degradation.

At this stage, the meaning of each conceptual model relation is also carried out. Followed by quantification of the sub-model and use of numerical equations and formulas as required and determining the software selected and used.

4.4 Model evaluation and validation

Basically, at this stage, testing or validation is carried out about has the model produced logical outputs in common sense and is in line with the real world or existing facts, are the existing relationships are logical, are the model’s behavior is in accordance with expectations or goals. Model design, are the model outputs appropriate or very close to the field data, and so on. Some of the references that will be used are OPP references that have received ISPO and/or RSPO certification, as well as for OPP which are considered to be reference plantations that are very well managed.

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5. Type of OPP as target of evaluation of OPPS

Evaluation or assessment of the sustainability of oil palm plantations needs to be carried out in various regions throughout Indonesia, in order to represent the various types of oil palm plantations that exist today (see Table 2).

Type of LandType of OPPVariation Garden
MineralFlat, low rainfallWith variations in the age of the oil palm; plantation management (managed by companies and smallholder oil palm plantations); as well as the origin of land use / cover before it was converted into oil palm plantations
Flat, moderate rainfall
Flat, high rainfall
Medium slope, low rainfall
Medium slope, moderate rainfall
Medium slope, high rainfall
High slope, low rainfall
High slope, moderate rainfall
High slope, high rainfall
PeatThin peat, less rainfallWith variations of peat in tidal, non-tidal, lowland areas; peat maturity level; age of oil palm; variations in plantation management (managed by companies and smallholder oil palm plantations); as well as the origin of land use / cover before it was converted into oil palm plantations
Thin peat, moderate rainfall
Thin peat, high rainfall
Medium peat, less rainfall
Medium peat, moderate rainfall
Medium peat, high rainfall
Deep peat, less rainfall
Deep peat, moderate rainfall
Deep peat, high rainfall
Marine peat
Brackish peat

Table 2.

Several types of oil palm plantations (OPPs) that are targeted by the palm oil palm plantation sustainability (OPPS) evaluation system.

The validated OPPS model is used to evaluate several types of OPP in Indonesia. Basically, the types of OPP can be divided into those cultivated on mineral soils and peat lands with the following variations:

  1. Mineral soils: on land with various variations in topography, climate, age of oil palm; plantation management (companies and smallholder oil palm plantations); as well as the origin of land use/cover before it was turned into oil palm plantations, etc.

  2. Peat land: on peat with varying levels of depth and maturity of peat, topography/physiography (coastal peat /marine peat, transitional peat/brackish peat), climate, age of oil palm; plantation management (companies and smallholder oil palm plantations); as well as the origin of land use/cover before it was converted into oil palm plantations, tidal and non-tidal areas, swamps, etc.

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6. Methodology

6.1 Method

The methods used to measure, determine or obtain data for each parameter are presented in Table 3.

Sub System ParametersMethod used
  1. Plant/Crop

Production and productivityInterview and secondary data
Land coverField observations, secondary data
BiomassField observations, secondary data
Plant Factors (CN)*Field observations, secondary data
Carbon emissions and sequestrationField observations, secondary data
Types and sources of seedsInterview and secondary data
Foliar fertilizerInterview and secondary data
Use of PHF**Interview and secondary data
Pest-Disease ControlInterview and secondary data
  1. Soil

Physical, Chemical, Biological PropertiesField observation and lab analysis
Inorganic fertilizationInterview and secondary data
Soil ConservationField observation
Liming, soil amandmentInterview and secondary data
Provision of organic material (biological)Interview and secondary data
  1. Land

SoilField observation and lab analysis
Topography (slopes)Field observations, secondary data
Climate (rainfall and climate type)Secondary data processing
Runoff, discharge, floodCIA: Model Mock; Weibull, dll.
Erosion Hazard LevelUniversal Soil Loss Equation
SubsidenceSecondary data, field observations
Surface rockField observations, secondary data
Micro reservoir, irrigationField observations, secondary data
DrainageField observations, secondary data
Erosion preventionInterview and secondary data
  1. Environment

Land firesField observations, secondary data
Air qualityField observations, secondary data
Surface flow, dischargeField observations, secondary data
Greenhouse Gas EffectField observations, secondary data
Drought and water balanceInterviews, secondary data, and the Thornthwaite and Mather Model, etc.
BiodiversityField observations, secondary data
InfiltrationField observations, secondary data
Factory waste pollutionLab analysis and secondary data
Agrochemical contaminationLab analysis and secondary data
Ground water levelField observations, secondary data
High Conservation Value AreaInterview and secondary data
Water qualityLab analysis and secondary data
ISPO*** and / or RSPO**** certificationInterview and secondary data
Wastewater Treatment PlantInterview and secondary data
Land Fire Task ForceInterview and secondary data
Zero Waste ProgramField observations and interviews
Environment Analysis Impact ReportingInterview and secondary data

Table 3.

Main parameters of each subsystem and the methods used.

For volume and peak discharge calculations.


PHF = Pesticides, Herbicides, Fungicides.


Indonesian Sustaianable Palm Oil.


Roundtable Sustainable Palm Oil.


6.2 Processing and data analysis

Some of the main analysis methods that are needed in order to build the evaluation model are: 1) regression-correlation analysis and multi-variate analysis, 2) continuity analysis using Multi-Dimensional Scaling (MDS), 3) Interpretative Structural Modeling to select key parameters in order to determine the SQI, LQI and EQI, 4) Analytical Hierarchy Process to weight each key parameter in determining the SQI, LQI and EQI, 5) SWOT analysis (Strength, Weaknesses, Opportunity and Threat) to strengthen the formulation of management improvement strategies to achieve OPPS.

6.3 Determination of the OPPS index and the index and Categorization of degradation or improvement

6.3.1 PPPI calculation

  • Select and define key plant parameters to get SQI

  • Set the weights of each of the key plant parameters

  • Defining criteria and scoring for each of the key crop parameters

  • Calculating the PPPI score by adding the multiplication of the score and weight for each key plant parameter.

  • PPPI scores are then categorized into very bad, very bad, bad, moderate, moderate, good and very good levels.

6.3.2 SQI calculation and categorization

  • Select and define land key parameters to get SQI

  • Set the weights of each of the key soil parameters

  • Establish criteria and score for each key soil parameter

  • Calculating the SQI score by adding the multiplication of the score and weight for each key parameter.

  • The SQI scores are then categorized into very bad, very bad, bad, moderate, moderately good, good and very good levels.

6.3.3 LQI calculation and categorization

  • Select and define key land parameters to get LQI

  • Set the weight for each of the key land parameters

  • Establish criteria and score for each key field parameter

  • Calculating the LQI score by adding the multiplication of the score and weight for each key parameter.

  • The LQI scores are then categorized into very bad, very bad, bad, moderate, moderately good, good and very good levels.

6.3.4 EQI calculation and categorization

  • Select and define key parameters to get EQI

  • Set the weight for each of the EQI key parameters

  • Defining criteria and scoring for each key environmental parameter

  • Calculating the EQI score by adding the multiplication result of the score and the weight of each key parameter.

  • The EQI scores are then categorized into levels of no degradation, very bad, bad, moderate, moderate, moderately good, good and very good.

6.3.5 OPPS score calculation and categorization

  • Determine the weights of PPPI, SQI, LQI and EQI

  • Calculating the OPPS score by adding the multiplication results of the scores and weights of each PPPI, SQI, LQI and EQI

  • The KIS scores are then categorized into very bad, very bad, bad, moderate, moderately good, good and very good levels.

6.3.6 Calculation of the Index of degradation (ID) or index of improvement (IP)

  • Calculating the ID or IP by calculating the difference between subtracting the OPPS scores from the OPPS score for land use before the OPP was made or the original OPP score with the OPPS score after a certain period, then converted into a percent. If the OPPS score of OPP score is lower, then a negative score is generated and an ID is obtained. Conversely, if the OPPS score of OPP score is greater than a positive value will be generated, then an IP is generated.

  • ID and IP are then categorized into levels of no degradation, very low, low, medium, high, very high and very high.

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7. Summary and outlook

In order to respond negative accusations and allegations that the OPP in Indonesia is not environmentally friendly, unsustainable and destroy forests and peat-lands, Indonesia should have an evaluation system that is based on a scientific approach. In order to achieve this aims, concrete efforts are needed in the form of a system supported by tools that can be used to evaluate the effects of OPP in Indonesia objectively, rationally, scientifically, measurably, proportionally, quantitatively and accurately to the quality of S-L-E in Indonesia which can also be used to increase productivity and to help design management to prevent S-L-E degradation. The evaluation system needs to be supported by the Plant Potential Productivity Index (PPPI), Soil Quality Index (SQI), Land Quality Index (LQI) and Environmental Quality Index (EQI). By having the evaluation system, the effect of OPP on the natural resources and environment can be determined, whether OPP produces degradation or improvement on certain area as well as for a national scale.

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Written By

Latief Mahir Rachman

Submitted: April 10th, 2021Reviewed: May 26th, 2021Published: March 23rd, 2022