Analysis of Implementation of Best Management Practices in Oil Palm

Plantations in Indonesia

Tiemen Rhebergen

MSc thesis Plant Production Systems

May 2012

Analysis of Implementation of Best Management Practices in Oil Palm Plantations in Indonesia

Tiemen Rhebergen

MSc thesis Plant Production Systems

PPS-80436

May 2012

Supervisors:

Prof. Dr. KE Giller; Plant Production Systems, Wageningen University, Wageningen, Netherlands

Dr. T Oberthür; IPNI Southeast Asia Program, Penang, Malaysia

Examiner:

Dr. Katrien Descheemaeker

Prof. Dr. KE Giller

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Contents Abstract ................................................................................................................................................... 4

Introduction ............................................................................................................................................. 5

I. Background & Problem statement .............................................................................................. 5

II. Research objectives & hypothesis ............................................................................................... 6

III. Concepts .................................................................................................................................. 6

Yield gap ............................................................................................................................................ 6

BMP as a management tool .............................................................................................................. 7

Material & Methods ................................................................................................................................ 8

I. Area and site description ............................................................................................................. 8

II. Experimental design .................................................................................................................... 9

III. Data collection & management ............................................................................................. 11

IV. Data analysis .............................................................................................................................. 14

i. Analysis 1 ............................................................................................................................... 14

ii. Analysis 2 ............................................................................................................................... 15

Results ................................................................................................................................................... 17

I. Analysis 1 ................................................................................................................................... 17

i. Between treatments.............................................................................................................. 17

ii. Between treatments within and between sites .................................................................... 18

iii. Between treatments within and between estates ................................................................ 21

iv. Between treatments within and between climatic zones..................................................... 24

v. Between treatments within and between soil textures ....................................................... 25

vi. Between treatments within and between islands ................................................................ 26

vii. Between treatments within and between (site-) conditions ................................................ 27

II. Analysis 2 ................................................................................................................................... 28

i. Site (Plantation) level ............................................................................................................ 28

ii. Estate level ............................................................................................................................ 29

Discussion & Conclusions ...................................................................................................................... 30

Acknowledgements ............................................................................................................................ 32

References .......................................................................................................................................... 33

Abstract With an increasing world population and a growing demand for palm oil, an extra 12M ha will be

needed over the next 40 years. The growing demand can be met by improving yields in existing oil

palm plantations through Best Management Practices (BMPs) by using cost-effective and practical

agronomic methods. The main objective of this research is to analyse BMP implementation and the

main drivers of yield intensification across and within six sites encompassing a wide range of

environments that represent major production areas in Indonesia. Results of the BMP

implementation were compared to standard commercial practices (REF), and showed that average

yields in BMP were greater compared with REF blocks. This indicates that BMP significantly closes

yield gaps in mature oil palm plantations and proves the immense potential for increasing yields in

existing plantations in Indonesia. BMP implementation was highly variable across a wide range of

environments, indicating that the BMP concept is site-specific and applicable across a wide range of

conditions. Further analysis showed that environmental, management and genetic factors influence

yield variability. In particular, where management practices such as crop recovery were not carried

out properly in REF treatments, this had a greater effect on yield. Implementing BMP will benefit the

palm oil industry in the short term through operational management such as crop recovery and in

the long term through better site-specific agronomic management. It is advised that plantations

monitor and evaluate environmental conditions before implementing site-specific BMP’s. When

implemented correctly to site-specific conditions, productivity in existing plantations can be

increased.

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Introduction

I. Background & Problem statement

Oil palm (Elaeis guineensis) accounts for nearly 30% of global vegetable oil production [2] and is an

important driver in the economic development of many tropical countries [12]. It is a very efficient

crop in terms of input utilization [3] and produces the highest oil yield compared with other oil

producing crops [5]. Southeast Asia is the largest producer of palm oil [8], where planted area and

production of palm oil has increased exponentially since the 1970s [4]. During the past decade, the

most significant increases in production have occurred in Indonesia and Malaysia, which now

account for around 85% of global palm oil production [6]. However, to keep up with an increasing

world population and a growing demand for palm oil, an extra 12M ha will be needed over the next

40 years [2]. If all production were to take place in Indonesia and Malaysia, more than a 100%

increase in area under production is required [12]. Whilst area under production has increased

rapidly during the past 40 years, average achieved oil yields in Indonesia and Malaysia have remained

far below potential levels [4].

Area expansion raises concern on environmental impacts such as forest destruction and loss of

biodiversity [12]. It has therefore been proposed to expand oil palm production into so-called

‘degraded lands’ to reduce pressure on forest reserves [12]. Because of less favourable conditions –

in terms of resource quality and infrastructure – lower site yield potentials and higher production

costs per unit yield occur on these degraded lands [18]. Alternatively, increasing productivity in

already existing plantations offers scope for improvement and reduces the need to increase area for

oil production. Financial returns through yield intensification are expected to be larger because there

is no need to invest in new plantings and plantation infrastructure. In addition, financial returns are

expected to develop more rapidly, because production starts to increase as soon as agronomic

constraints are removed [4].

Increases in production can be met through intensification by improving yields per area and/or oil

extraction rates. Best Management Practices (BMPs) developed by The International Plant Nutrition

Institute’s (IPNI) Southeast Asia Program (SEAP) focuses on increasing palm oil yield in existing

mature plantations by using agronomic methods and techniques that are cost-effective and practical.

Yield improvement efforts in existing plantations thereby focuses on identifying and rectifying

management practices that contribute to the emergence of a gap between the yield potential and

actual achieved yield; the yield gap [4].

The main objective of this research is to analyse BMP implementation and the main drivers of yield

intensification across and within six sites that represent major production areas in Indonesia.

Understanding the mechanisms and variables regulating yield can help eliminate yield differences in-

and between sites and enable us to improve crop response to natural variation and variation in

management practices. Such an analysis will give insight on the site-specific nature of BMPs and

provide the appropriate tools to address them accordingly. The ultimate goal of this study is to

contribute evidence and incentives for plantation managers across Southeast Asia to adopt and

adapt BMP as a management tool in intensifying oil palm production.

II. Research objectives & hypothesis

Objectives

1) Build a database with data from six sites (plantations) encompassing 18 estates in

Indonesia where BMP has been implemented

2) Analyse the yield performance under Best Management Practices (BMP) and Reference

Estate Management Practices (REF) at six sites in Indonesia

3) Compare the performance of BMP and REF management practices across six sites in

Sumatra against Kalimantan with different agro-ecological potential for oil palm

production

4) Develop and implement an approach to identify the contribution of different factors to

yield variability

Hypothesis

1) Best Management Practices (BMP) significantly close existing yield gaps in mature oil

palm plantations across a wide range of environments

2) Yield gap reduction is variable within plantations and across plantations, and thereby

leads to site-specific responses to BMP

3) Variability is caused by site-specific environmental, genetic and management factors

whose impact on yield gap can be quantified

III. Concepts

Yield gap

Yield gaps (Yg) are calculated as the difference between the actual achieved yield (Ya) and the yield

potential (or potential yield) (Yp) for a given crop. The YP is based on the assessment of site-specific

characteristics such as climate and is therefore highly variable across and within regions [20]. YP is

defined as:

“the yield of a crop cultivar when grown with water and nutrients non-limiting and biotic stress

effectively controlled [20].”

Yield gap analysis (YGA) is used to identify poorly performing blocks where corrective management

can be implemented to increase actual yields. YGA is consequently apportioned into three parts,

where:

1) Yield Gap 1 (G1) arises from inefficiencies during the development of a plantation until the

end of the immature period

2) Yield Gap 2 (G2) arises due to nutrient constraints in the production phase

3) Yield Gap 3 (G3) arises due to poor harvesting and management in the mature stand

While G1 offers limited opportunities for improvement in existing plantations (which occur only at

the initial establishment of the plantation and at each occasion of replanting), G2 and G3 can be

corrected in existing mature stands by identifying and rectifying management practices that

contribute to the emergence of such a gap [8].

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Because oil palm is a perennial crop, oil palm yields are not only affected by planting material,

environment and management (G x E x M), but also by tree age. For most environments in Southeast

Asia, yields peak between 7 to 10 years after planting (YAP), after which they decline due to reduced

tree stand (pests & disease infestations) and difficulties with harvesting tall palms, resulting is less

complete crop recovery [12]. To analyse yield trends accurately, YGA should be performed according

to YAP. Furthermore, agronomic data for each block and year of production together with its

projected YP profile is needed. YGA is a powerful tool in the identification, selection and prioritization

of best management practices (BMPs) for intensifying yield [12].

BMP as a management tool

The International Plant Nutrition Institute’s (IPNI) Southeast Asia Program (SEAP) has developed a

concept Best Management Practice (BMP) as a management tool for correcting yield gaps (G1, G2) in

existing mature stands. The BMP concept is a programme for plantation companies to improve their

agronomic performance and accordingly increase profit. Hence, the key-point of the BMP concept is

to improve yields by using cost-effective and practical agronomic methods and estate organization

and planning [4]. BMP aims to stimulate productivity in the short term without the need to increase

in land area and focusses on identifying and rectifying management practices that contribute to yield

gaps in existing mature oil palm plantations.

The implementation of BMPs benefits palm growth and productivity, but also soil, water and nutrient

conservation. Accordingly, BMPs are classified to: crop recovery, canopy management and nutrient

management. Table 1 (based on Donough et al. 2010) further specifies BMPs implemented within

these categories:

Table 1. BMPs implemented at IPNI SEA project sites in Indonesia

Crop recovery BMPs Canopy management BMPs Nutrient management BMPs Harvest interval (HI) of 7 days Maintenance of sufficient fronds to

support high palm productivity Spreading pruned fronds widely in inter-row area and between palms within rows

Minimum ripeness standard (MRS) = 1 loose fruit (LF) before harvest

Removing abnormal, unproductive palms

Eradication of woody perennial weeds

Same day transport of harvested crop to palm oil mill

In-filling unplanted areas Mulching with empty fruit bunches (EFB)

Harvest audits to monitor completeness of crop recovery and quality (i.e. ripeness) of the harvested crop

Selective thinning in dense areas Management of applied fertilizers (i.e. type, dosage, timing and placement)

Good in-field accessibility (clear paths, bridges wherever needed)

Monitoring and management of pests (leaf eaters) and disease (Ganoderma)

Monitoring of plant nutrient status and growth

Clean weeded circles

Palm platforms constructed and maintained wherever needed

Minimum under-pruning in tall palms to ensure crop visibility

IPNI SEA currently aims to optimize BMP by means of operational research based on commercial

data, which provides insight on yield response under variable environments and management and

will allow the results to be scaled up to commercial levels. Commercial data provided by the estates,

including biophysical variables and management practices over a range of conditions can be used to

obtain valuable insights on how to better manage crops according to specific environmental

conditions, and also avoids the need to establish and manage a large number of costly experiments.

By quantifying the factors that impact yield, better site-specific management practices (SSBMPs) can

be identified and improved upon in standard commercial practices closing existing yield gaps in

mature oil palm plantations. This offers decision makers a better understanding of which BMPs

should be implemented as a management tool in intensifying oil palm production and enables us to

describe the uncertainties that are faced by plantation managers.

Material & Methods

I. Area and site description

Oil palm is grown under highly variable conditions such as climate, different kinds of soil and land

suitability classes. Favourable conditions for oil palm production is in areas with an annual rainfall

between 2,000-3,500 mm, evenly distributed throughout the year with a minimum of 100 mm per

month and an optimum mean annual temperature range of 22-32°C [18]. Topography and slope are

important land characteristics that determine suitability for oil palm production. Decreasing

temperatures play a role with increasing elevation, while slope determines the potential for soil

erosion and difficulties in establishing and maintaining terraces and the costs associated with

harvesting. It is therefore recommended not to plant oil palm >200 m above mean sea levels and on

slopes >38% (>20°). The ideal climatic and topographic conditions for growing oil palm are presented

in Table 2 [18].

Table 2. Ideal climatic and topographic conditions for oil palm growth

Climate Units Ideal conditions Sunshine hrs d-1 >5.5

Solar radiation MJ m-2 >16

Annual rainfall mm yr-1 2,000-2,500

Monthly rainfall mm month-1 >100 (in all months of the year)

Annual water deficit mm <200

Relative humidity % 75-85

Mean temperature °C 28

Mean wind speed m s-1 0-10

Topography

Slope % 0-4

° 0-2

As long as there is enough water, oil palm can be grown on a wide range of soils [17], such as Ultisol

(Podzolic/Latosol), Entisol (Alluvial), Inceptisol (Latosol/Podzolic), Andisol (Andosol) and Histosol

(peat soil or Organosol) [14]. Soil physical properties are thereby considered to be more important

than soil chemical properties because of the importance of soil moisture supply. The chemical

composition of soils - and nutritional deficiencies within the palm - can easily be corrected by using

cost-effective mineral fertilizers, while correcting soil physical properties is a more difficult and costly

undertaking [18]. Important soil physical properties are for example soil texture and structure. Soil

texture describes the relative amounts of (fine and coarse) sand, silt and clay found within a

particular layer, while soil structure describes how these components are aggregated. Sandy, loamy

and coarse sandy -textured soils are not desirable for oil palm cultivation because they are

susceptible to drought as well as leaching. Poorly-structured and sandy textures can partly be

improved by using organic manures and empty fruit bunch (EFB) mulching to improve soil structure,

soil moisture, and nutrient retention capacity. Due to its high porosity – and therefore its capacity to

retain more moisture and nutrients – well-structured clay (C), sandy clay (SC), clay loam (CL) and silty

clay loam (SiCL) textured soils are ideal for OP cultivation [18] as are sandy clay loam (SCL) and silty

loam [14]. Table 3 shows the suitability of soil textures for oil palm production.

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Table 3. Suitability of soil textures for oil palm production

Soil texture Suitability for oil palm production

Poor Marginal Good Clay loam

Coarse sand

Loam

Massive clay

Sand

Sandy clay

Sandy clay loam

Sandy loam

Silty clay loam

Silty clay

Well-structured clay

Silty loam

Within Southeast Asia, oil palm is mostly grown between 10°N and 10°S of the equator due to its

climatic requirements [8]. The most significant increases in production during the past decade in

Southeast Asia have occurred in Malaysia and Indonesia, which together account for an estimated

85% of global palm oil production and 23% of the world oils and fats production [6]. Indonesia, in

particular, is ideal for oil palm cultivation because of its favourable climatic and soil conditions [8].

During 2000-2009, mature palm area in Indonesia grew at 10% per annum, while palm oil production

increased by 17.4% per annum. In 2006, Indonesia took over Malaysia’s position as world’s largest

palm oil producer (producing an estimated 19.8 M tons by 2010 - 174% more than in 2000) and is

forecasted to contribute nearly half of the world’s total future production [6].

Palm oil is Indonesia’s most important agricultural export crop and many oil palm plantations are

therefore found located throughout the country in 17 provinces in Sumatra, Java, Kalimantan,

Sulawesi, Muluku and Papua [6]. In 2005, the largest plantation in the country was located in

Sumatra, with a total size of 4.3 M ha (or 76.5% of the total plantations). In Sumatra, Riau accounted

for 1.4 M ha, followed by North Sumatra with 0.96 M ha. Kalimantan has a total of 1.1 M ha (19.8%

of the total plantations), with West Kalimantan accounting for 0.47 M ha and Central Kalimantan

with 0.27 M ha. Sumatra remains Indonesia’s prime oil production region (75% of the total mature

palm area and 80% of total palm oil production) and is still expanding with an average increase of 6%

for the past ten years. Within this period, plantations also started to expand more into remote areas

on Kalimantan, Sulawesi and Papua.

II. Experimental design

In the experimental design, a parallel set of comparable blocks representative of a plantation are

selected. Within the higher yielding block, standard commercial practices are maintained (REF

blocks), while a set of SSBMPs are identified and introduced in the lower yielding block of each pair

for comparison (Figure 1 [22]). For both fields an inventory of limiting factors is prepared, but only

for the BMP block corrective action is taken.

Figure 1. BMP Experimental block design

Since July 2006, 60 paired blocks (total area 2,184 ha) have been selected, with BMPs applied on 30

blocks (total area 1,080 ha). Five plantation groups collaborated on the BMP project at six different

locations throughout Indonesia, covering a wide range of environments where oil palm is grown in

North and South Sumatra, and West, Central and East Kalimantan (Figure 2 [23]).

Figure 2. Locations of the six IPNI SEA BMP project sites in Indonesia

Table 4 provides environmental conditions for the IPNI SEA BMP project.

Table 4. Environmental conditions at the IPNI SEA BMP project sites in Indonesia

Site Soil Type Annual mean rainfall (mm)*

Annual mean temperature (°C)*

Topography & Slope (%)**

1 Alluvial & Sedentary 1923 26.7 Level (0-4%) & Undulating (4-12%)

2 Sedentary & Alluvial 3072 26.4 Level (0-4%), Undulating (4-12%) & Rolling (12-24%)

3 Sedentary 2782 27.1 Undulating (4-12%)

4 Sedentary 3080 26.6 Undulating (4-12%) & Rolling (12-24%)

5 Sedentary 3045 26.8 Level (0-4%) & Undulating (4-12%)

6 Sedentary 2509 26.6 Rolling (12-24%) & Hilly (24-38%)

* Average climatic conditions over a period of 30 to 50 years – taken from www.worldclim.org [26] ** Topography & Slope classification based on Paramananthan, 2003

Per site, five paired blocks of at least 25 ha were selected to represent the estate, while palm stands

varied between and within sites. At each site the project started at different times and is run for a

11

total of 4 years, after which BMPs are evaluated. See Table 5 for project information per site and

Figure 3 (made with CMAPS) for a hierarchal flow diagram of the project outline.

Table 5. Project information for each site in the IPNI SEA BMP project in Indonesia

Figure 3. Flow diagram of the project outline. *The numbers at the bottom indicate the number of paired blocks per estate

BMPs embedded within commercial operations across a variety of sites and estates will enable us to

improve crop response to natural variation and variation in management practices. Understanding

site-specific variability can help estates identify and implement particular management practices

targeted towards yield intensification. Comparisons between SSBMPs and blocks managed under

standard commercial practices will be analysed and used to provide information on how to better

manage crop production and guide decisions on a commercial scale, therefore reducing decision

uncertainty under plantation managers.

III. Data collection & management

During the project period, all field activities and data collection was overseen by the local estate

managers of IPNI SEAP’s project partners [5]. Data was collected in the field by field assistants hired

for the BMP program, while the collaborating plantations provided additional data on the project

blocks such as area (ha), stand age (yr.), stand densities (palm ha-1), planting material, seed source,

but also climate data such as rainfall (mm). Annual mean temperatures (°C) for each site was taken

from www.worldclim.org ([26] accessed on 15-12-2011).

Island Province Site No. Estates

Stand (palms ha-1) Area (ha) Start date

Stand age (yr.) BMP REF BMP REF

Sumatra North Sumatra 1 5 121-140 136-143 266.0 281.0 Aug 2006 5-12

North Sumatra 2 3 124-136 116-132 156.4 159.9 Sep 2006 8-14

South Sumatra 3 2 127-137 128-138 255.8 259.3 Feb 2007 15-18

Kalimantan West Kalimantan 4 1 143 143 142.5 147.3 Mar 2007 8-9

Central Kalimantan 5 3 112-138 128-141 124.3 121.4 Jun 2007 8-9

East Kalimantan 6 4 133-154 135-144 134.6 135.3 Jul 2007 3-12

All data was forwarded to the IPNI SEA office, where I re-arranged the data to a single format in MS

Excel. Data was separated per Site and organised in columns by Estate, Block ID and Treatment, while

each row of data was accordingly lined up to ‘Harvest date’ - making each single row of data unique.

In addition, calculations were carried out to determine Harvest Interval (number of days between

two consecutive harvests), Yield components (kg Fresh Fruit Bunches (FFB) ha-1, Number of Bunches

ha-1, Average bunch weight (kg)) and Harvester’s Productivity (area (ha) Man-day-1, Number of

bunches Man-day-1, kg FFB Man-day-1).

For sites 4 and 6, additional Loose Fruit Collection (LFC) (kg) took place and was added up to kg FFB

to determine the total harvest (kg) for a given harvest round. For these 2 sites, yield (FFB ha-1) was

calculated by taking the total harvest instead of just fruits harvested from the palm as in sites 1-3 &

5.

Ganoderma, or Basal stem rot (BSR) is a fungal disease causing severe yield losses through direct loss

of the stand and reduced yield of infected palms which are still alive [11]. Site 1 was heavily affected

by the disease, so calculations were carried out to correct stand densities due to dead and diseased

oil palms.

Percentage of coarse sand, fine sand, sand, silt and clay from the soil analysis data was used to

calculate soil texture. Measurements for each particle were averaged across samples taken at 2

depths (0-20 and 20-40 cm) and 2 locations (‘weeded circle’ and ‘frond stack’) from the beginning

and end of the project. An average soil texture for each single block was produced by using the USDA

Soil Texture Calculator developed by the United States Department of Agriculture, NRCS – Natural

Resources Conservation Service [25].

Based on a classification of climatic conditions presented in Lubis et al. (1996), five climatic zones of

oil palm cultivation is given; Extremely wet, Wet, Slightly wet, Slightly dry and Dry (Table 6). The wet

climatic zone is the optimal climate for oil palm growth, where rainfall is equally distributed

throughout the year, while the slightly wet zone is still suitable (but where rainfall is not equally

distributed). Slightly dry zones are also still possible for oil palm growth, but water deficits of about

200-400 mm a year may occur and could limit growth and production of oil palms due to low

moisture status. Oil palm cultivation in dry climatic zones is not recommended because of very low

rainfall and high annual water deficits. Marginal lands for oil palm are thus characterized by a low

average rainfall (slightly dry and dry climatic zones) and >2 dry months, while more suitable areas

have a higher average rainfall (annual average >2500 mm) [14].

Table 6. Climatic zones of oil palm cultivation based on annual average rainfall (mm), rain days (d-1) and dry months (<60 mm)

Climatic zone Annual average rainfall (mm) Annual average rainy days (d-1

) Annual average dry months (<60 mm) Extremely wet >2750 >200 0

Wet 2250-2750 150-200 0-1

Slightly wet 1750-2250 100-150 1-2

Slightly dry 1250-1750 75-100 2-3

Dry <1250 <75 >3

Monthly rainfall data (mm) from 2005-2010 was used to calculate confidence limits (with 90%

confidence interval) of minimum and maximum expected rainfall for each month for each estate. The

average rainfall (mm), number of rain days (d-1) and number of dry months (<60 mm rainfall)

13

throughout the entire year was then used to determine which climatic zone represented each estate

best. Table 7 gives an overview of climatic zones and soil textures calculated for each estate.

Table 7. Climatic zones and soil texture for each site and estate in the IPNI SEA BMP project in Indonesia

Land suitability classes for oil palm growth for each site was judged by expert knowledge (Chris

Donough) and is based on key site factors impacting yield [5]. Yield potentials for each suitability

class were taken off a poster from the Indonesian Oil Palm Research Institute (IOPRI), which was

presented at the International Oil Palm Conference, Jogjakarta, Indonesia, 2010. Site conditions

based on Donough et al. (2010) are presented in Table 8.

Table 8. Site conditions in the IPNI SEA BMP project in Indonesia

After all the data was gathered, re-arranged and sorted and all the calculations and classifications were done, all the data was compiled and stored in one large dataset in MS Access.

Site Estate Average rainfall (mm)

Average number of rain days (d

-1)

Average number of dry months (<60 mm)

Climatic zone Soil textures

1 GB 1640 100 1 Slightly dry Sandy clay loam

KP 2006 79 0 Slightly wet Sandy clay loam

SB 1762 97 1 Slightly wet Coarse sandy loam, Sandy clay loam

SE 1523 80 1 Slightly dry Sandy clay loam, Coarse sandy loam

TR 1956 127 1 Slightly wet Sandy clay loam

2 BU 3441 106 0 Extremely wet Sandy clay loam, Sandy clay, Fine sandy loam

PAP 3761 143 0 Extremely wet Coarse sandy loam, Clay

AA 3324 132 0 Extremely wet Sandy clay

3 BK 2292 125 0 Wet Clay

BT 2579 127 0 Wet Sandy clay, Sandy clay loam, Clay loam, Clay

4 SNB 3507 175 0 Extremely wet Coarse sand, Fine sand, Fine sandy loam, Loamy coarse sand, Loamy fine sand

5 SS 2320 146 0 Wet Coarse sandy loam, Loamy coarse sand

SU 2320 146 0 Wet Loamy coarse sand

WA 2320 146 0 Wet Loamy coarse sand Coarse sandy loam

6 CA 3458 188 0 Extremely wet Clay loam, Sandy clay loam

LE 5919 202 0 Extremely wet Clay loam

SEN 3751 203 0 Extremely wet Sandy clay loam, Clay loam

PE 4131 208 0 Extremely wet Clay loam

Site Suitability class*

Prior Yield (ton ha

-1)

Yield potential (ton ha

-1)

Site conditions

Site factors

1 S1 26-29 35 Good Level terrain Low rainfall Ganoderma

2 S2 24-25 32 Good Rolling terrain Planting material Terrain Variable stand

3 S2 16-24 32 Moderate Undulating terrain Severe water deficit in many years

4 S2/3 16-17 31 Poor Undulating terrain High rainfall Poor soil - sandy

5 S3 12-13 30 Very poor Poor soil - very sandy Low rainfall Water deficit in some years

6 S2 23-26 32 Good Rolling terrain Very high rainfall

*S1 = highly suitable, S2 = moderately suitable, S2/3 = moderate to marginally suitable, S3 = marginally suitable

IV. Data analysis

The data analysis was carried out in two parts; ‘Analysis 1’ was performed to describe the main

features of the BMP and REF yield data across and within sites, while ‘Analysis 2’ probed deeper into

underlying factors accounting for yield trends between the treatments by taking the site ‘North

Sumatra 1’ as an example.

i. Analysis 1

Analysis 1 focused on Hypothesis 1 & 2 where treatment means were compared across or/and within

a variety of groups. Before starting with the statistical analysis (IBM SPSS Statistics 19) data sets for

each separate analysis were compiled by selecting relevant data from the MS Access Database. Daily

yield data was summarized on a monthly basis and categorized according to project year per site.

Project year 1, for example, ran from the first month of BMP implementation for 12 months. Each

site was run for 4 project years.

Yield data was analysed based on 12 month rolling yield (t ha-1), which was calculated by taking the

average yield of the first 12 months and then shifting forward, by excluding the first month and

including the next, thereby creating a series of successive averages. This process is repeated over the

entire data series and is used to decrease the impact of month-to-month or seasonal fluctuations in

yield and allow detection of trends in yield. Using this method, no rolling yield values can be

calculated for the first 11 months. In addition, actual monthly yields (t ha-1) were calculated and

analysed as well. In the results section, only rolling yields were used, because more significant

differences within and between treatments were found. Furthermore, Figures 6 and 7 illustrate why

rolling yields are preferred, as the power of detecting yield-trends in addition to actual yields is much

greater. Twelve month rolling yields (12MRY) for both treatments were compared across a variety of

groups by using a Two-Way ANOVA (General Linear model, UNIVARIATE) with 12MRY as the

Dependent variable. According to which analysis was run, Island, (site-) Conditions, Site, Estate,

Treatment, Project year, Climatic zone, Soil texture and Seed source were used as Independent

variables (or fixed factors). Custom models were built in the ‘Syntax Editor’ and run accordingly.

Where appropriate, an interaction term was added. Treatment 12MRY means were analysed as the

average of all project years together. A filter was added to the model syntax to analyse treatment

means for each separate project year as well. Table 9 shows all performed analyses with the

accompanying model syntaxes. It should be noted that results of models 5 to 8 (Table 9) could be

influenced by a ‘site’ as a confounding variable, which can adversely affect the relation between yield

and the independent variable. For models 1 & 2 (see Table 9), an interaction term

(Project_year*Treatment) was added to test if there were any significant differences in yield

between consecutive project years and treatment, and treatments within sites respectively.

Table 9. Analyses & model syntaxes performed in Analysis 1

Model Analysis of Yield performance (12MRY in ton ha-1

) Model syntax /DESIGN = 1 between Treatments Site Estate(Site) Treatment

2 between Treatments within Sites Estate Treatment

3 between Treatments between Sites Site Estate(site) Treatment Treatment*Site

4 between Treatments within and between Estates Estate Treatment Estate*Treatment

5 between Treatments within and between Climatic zones Clim_zone Site(Clim_zone) Treatment Treatment*Clim_zone

6 between Treatments within and between Soil textures Soil_tex Site(Soil_tex) Treatment Treatment*Soil_tex

7 between Treatments within and between Islands Island Site(Island) Treatment Treatment*Island

8 between Treatments within and between (site-) Conditions Condition Site(Condition) Treatment Treatment*Condition

15

ii. Analysis 2

Analysis 2 focused on answering Hypothesis 3 to identify factors that cause variability in yields. To

demonstrate this, a simple approach including partial factor contribution to yield variability was

identified. A linear regression was carried out in SPSS to analyse yield trends. Site 1 (North Sumatra

1) was selected to perform the analysis on, because it has the most complete database. The influence

of factors on yield can be variable when looking at different spatial scales. To examine this, the

analysis was carried out at two different scales: the site (plantation) and estate level. When

increasing the scale of analysis, new interactions and relationships may emerge and exhibit patterns

that occur at scales specific to those processes. Knowledge on these processes provides us with

useful information on the management of factors at different scales [15].

Twelve month rolling yields (12MRY) were used as the dependent factor while a list of independent

variables is presented in Table 10. Because there were only five estates, this limited the number of

degrees of freedom (d.f.) and in turn the number of independent variables that could be included in

the model. Variables that seemed unlikely to be highly correlated with each other were chosen.

Table 10. Independent variables at Site- and Estate level used in Analysis 2

Variable Unit/Categories* Categorical Numeric Site Estate

Treatment 1. BMP 2. REF

Topography 1. Flat 2. Flat to undulating 3. Undulating to rolling

Seed source 1. Bah Lias (Lonsum) 2. Dami (PNG) 3. Socfindo

Soil texture 1. Coarse sandy loam 2. Sandy clay loam

Climatic zone 1. Slightly dry 2. Slightly wet

Rainfall mm

Rain days d-1

Harvest days d-1

Harvest round rounds-1

Man days d-1

Mg kg block-1

N kg block-1

P kg block-1

K kg block-1

Ganoderma Adjusted stand size due to diseased and dead palms: palms ha-1

* all units per month

Analysis at the site level included a number of categorical variables, such as treatment, climatic zone

and soil texture, while analysis at the estate level excludes most of these variables, because within an

estate they are constant. Categorical predictor variables cannot be entered directly into a regression

model and interpreted meaningfully, so ‘dummy variables’ have to be created. In this process,

categorical variables are transformed into binary variables. The code ‘1’ is thereby indicated for the

level of interest and ‘0’ for all other levels. If, for example, of a 2-level categorical variable, one

categorical level is presented to be significant in the regression output (e.g. Treatment = ‘REF’), this

does not mean that ‘REF’ is significant, but the effect of treatment as a whole. This is because at least

one level will not be accounted for in the regression, as it is automatically identified by the model

intercept.

Transformations of all numeric variables (by taking the square function of itself) were included in the

analyses as well to test whether this would improve the linearity of its relation to yield. To keep the

analysis simple, no interactions were included in the model. Only main factors and their

transformations were included, which should provide enough insight into observed yield trends.

A linear regression was carried out for Site 1 and for all individual estates within Site 1 (5 estates)

separately by including ‘Treatment’ as a factor in the first run and running two separate analyses on

BMP and REF accordingly. This is done to find the most important explanatory factors for yield trends

at site and estate level, and between BMP and REF treatments.

A Backward elimination method for predictor selection was chosen and compared with Forward and

Stepwise selection to check whether all three methods would select the same predictors. Backward

and Stepwise selection gave similar results, while Forward selection always included less predictors

in the model. Based on this I chose to use Backward selection, because the models had a higher

accuracy and a greater variety of predictors. Partial correlations were included to the SPSS

‘Coefficients’ output, so each predictor’s correlation to 12MRY within the model structure was given

as well as whether it was positively or negatively related to yield. Partial correlations indicate a

variables unique contribution to the dependent variable and indicates to what extent the coefficient

of determination (R2) will decrease if that variable is removed from the regression equation [1]. It is a

measure of correlation between two variables that remains after ruling out the effects of all other

predictor variables in the model [24] and is therefore useful in explaining the variance in one

particular variable from a set of predictor variables [9]. The magnitude of the partial correlation

coefficients and significance of the predictors were chosen as criteria for explaining a factor’s

contribution to yield trends.

17

Results

I. Analysis 1

i. Between treatments

Yield with BMP was significantly (P<0.05) larger for all years (Table 11, Figure 4). BMP yield averaged

3.5 t ha-1 (+14.8%) more than the REF yield of 23.7 t ha-1. Both BMP and REF yields significantly

declined from project year 2 to 3 (-0.9 t ha-1 for BMP (-3.2%) and -0.7 t ha-1 (-3.0%) for REF). BMP

yield increased slightly again from year 3 to 4, although insignificant, while REF yield continued to

decline. The overall decline in yield from year 2 to 4 was -0.6 t ha-1 (-2.4%) for BMP and -0.8 t ha-1 (-

3.4%) for REF (Table 11).

Table 11. Average yields (ton ha-1) per treatment for each project year and yield differences between project years and treatment

1 – average annual yield in tonnes per hectare; 2 – mean values for 30 BMP blocks (across 6 sites); 3 – mean values for 30 REF blocks

(across 6 sites); 4 – difference between BMP and REF in ton ha-1; 5 – difference between BMP and REF in percentages; * - significant yield

differences between treatments (P<0.05); 6 – differences between project year; 7 – yield differences indicated in tonnes per hectare and

percentages for BMP; 8 - yield differences indicated in tonnes per hectare and percentages for REF; ** - significant yield differences

between project year per treatment.

Project year Yield (t ha-1

)1 Difference Project year

6 Difference

BMP7 REF

8

BMP2 REF

3 Yield

4 %

5 Yield % Yield %

2 27.8 24.3 3.5* +14.5 2 and 3 -0.9** -3.2 -0.7** -3.0

3 26.9 23.5 3.4* +14.3 3 and 4 0.3 +1.0 -0.1 -0.3

4 27.1 23.5 3.7* +15.7 2 and 4 -0.6** -2.4 -0.8** -3.4

Avg 27.3 23.7 3.5* +14.8

0.0

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REF

BMP

23.0

23.5

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26.0

26.5

27.0

27.5

28.0

2 3 4

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BMP

REF

Figure 4. Average yields in BMP and REF treatments across 6 sites for each project year

ii. Between treatments within and between sites

Table 12 presents BMP and REF yields achieved at six sites throughout Kalimantan and Sumatra. BMP

yield was higher for each year at every site, expect for North Sumatra 1 (NS1) in year 2 with a

difference of -0.4 t ha-1 (-1.3%). Average yields with BMP were higher at each site, although no

significant difference was found for NS1. The largest differences were found at North Sumatra 2

(NS2), South Sumatra (SS) and Central Kalimantan (CK); +26.5%, +28.7% and +24.3% respectively.

NS1, NS2, SS and WK all experienced a decline in yield from year 2 to 4, while EK and especially CK

experienced an increase in yield for BMP (Figure 5). When comparing sites and treatments with each

other, a lot of yield differences occur as well (Figure 5). Only no significant differences were found for

BMP between East Kalimantan (EK) and NS2 for year 2 and NS1 and EK for year 3 and 4, while NS2

and West Kalimantan (WK) achieved similar yields as REF for years 3 and 4. Figures 6 & 7 show the

yield-data presented on a monthly basis, calculated for both actual and rolling yields for all sites. The

graphs don’t all start in the same year/month, because project implementation at each site is

different. In addition, these graphs also illustrate why I chose to work with rolling yields, as it

smooth’s out yield fluctuations and allows a clearer detection in yield trends.

Table 12. Average yields (t ha-1) per treatment and project year for each site

1 – NS1 = North Sumatra 1, EK = East Kalimantan, NS2 = North Sumatra 2, SS = South Sumatra, WK = West Kalimantan, CK = Central

Kalimantan; 2 - mean values for 5 BMP blocks; 3 – mean values for 5 REF blocks; * - significant yield differences between treatments

(P<0.05).

Site1 Project year

2 3 4 Avg

BMP2 REF

3 Difference BMP REF Difference BMP REF Difference BMP REF Difference

Yield % Yield % Yield % Yield % NS1 30.7 31.1 -0.4 -1.3 29.5 28.6 0.9 +3.2 29.1 25.8 3.2* +12.6 29.7 28.5 1.2 +4.4

EK 28.9 27.0 2.0 +7.3 29.8 27.5 2.3 +8.4 29.9 27.2 2.7 +9.9 29.5 27.2 2.3* +8.5

NS2 29.2 22.7 6.4* +28.3 26.7 21.3 5.4* +25.1 26.4 21.0 5.4* +25.9 27.4 21.7 5.7* +26.5

SS 27.7 21.8 5.9* +26.9 19.9 14.8 5.1* +34.6 20.5 16.3 4.2* +25.8 22.7 17.6 5.1* +28.7

WK 23.3 18.8 4.5* +24.2 23.3 21.3 2.0 +9.5 21.5 20.3 1.2 +6.1 22.7 20.1 2.6* +12.9

CK 16.6 14.1 2.5* +17.8 21.4 16.9 4.5* +26.8 24.7 19.5 5.2* +26.7 20.9 16.8 4.1* +24.3

2 3 1 2 3 1

a b a b

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

2 3 4 2 3 4 2 3 4 2 3 4 2 3 4 2 3 4

North Sumatra 1 East Kalimantan North Sumatra 2 South Sumatra West Kalimantan CentralKalimantan

Yie

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Site and project year

BMP

REF

Figure 5. Average yields in BMP and REF treatments for site and project year. Similar notations above the bars (1,2,3 for BMP treatments and a,b for REF treatments) indicate no significant treatment differences (P>0.05) between sites

19

Figure 6. Average actual monthly yields (t ha-1) of BMP and REF treatments for all sites during project implementation

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0Y

ield

(t

ha-1

)

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Actual yields - BMP

NS1

NS2

SS

WK

CK

EK

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45.0

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Actual yields - REF

NS1

NS2

SS

WK

CK

EK

Figure 7. Average rolling monthly yields (t ha-1) of BMP and REF treatments for all sites during project implementation

0

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CK

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Rolling yields - BMP

NS1

NS2

SS

WK

CK

EK

21

iii. Between treatments within and between estates

Because there was too much data to show in tables or figures, I chose two contrasting sites and their

estates to describe in this chapter. NS1 is one of the highest yielding sites, but shows a decline in

yield over the years (-1.6 t ha-1 (-5.5%) from year 2 to 4 for BMP and -20.5 t ha-1 (-20.5%) for REF;

Figure 8.C), while CK is one of the poorest yielding sites, but shows a major increase in yields over the

years (+8.1 t ha-1 (+32.8%) from year 2 to 4 for BMP and +27.7 t ha-1 (+27.7%) for REF; Figure 8.D).

NS1 consists of 5 estates and CK of 3. Yield differences for estates at NS1 and CK are given in Table 13

and are fairly variable. Average yield differences for NS1 vary between 0.4 t ha-1 (+1.5%) for SB and

2.4 t ha-1 (+9.6%) for GB, while Figure 8.A shows that there is not much improvement in BMP at the

estate level. BMP yields decline gradually for KP, while yields at all other estates are stable or show a

minor decrease. Differences in REF yields are clearer and decline at a higher rate. For 3 of 5 estates

(KP, SB and SE) REF yields are higher than BMP yields in year 2.

Yields achieved at estates in CK were much smaller than in NS1, but showed much stronger

improvements for both BMP and REF. Average yield differences vary between 3.9 t ha-1 (+22%) for SU

and 4.4 t ha-1 (+25.8%) for SS, while BMP and REF yields increase over the years for all 3 estates as

well (Figure 8.B). The observed yield trends for BMP and REF at the estate level most likely explain

the yield trends at the site level (Figure 8.C,D). Figure 9 shows a more detailed representation of the

data by presenting yields on a monthly basis for all estates at both sites. It must be noted that palm-

age in estates at NS1 and CK are highly variable (Table 5) and could affect yield-differences to some

extent.

Table 13. Average yields (ton ha-1) per treatment and project year for two estates in North Sumatra 1 and Central Kalimantan

Site Est.1 Project year

2 3 4 Avg

BMP2 REF

3 Difference BMP REF Difference BMP REF Difference BMP REF Difference

Yield % Yield % Yield % Yield %

NS1 GB 27.0 25.8 1.3* +5.0 27.6b

26.5c 1.1* +4.0 27.0 22.2

d 4.8* +21.7 27.2

e 24.8 2.4* +9.6

KP 30.6 31.8a

-1.2* -3.9 27.0b

25.1 1.9* +7.5 24.8 23.0d

1.8* +7.8 27.4e

26.6 0.8 +3.1

SB 28.3 31.0a

-2.7* -8.7 27.1b

27.5c -0.4 -1.6 29.2 24.7 4.4* +17.8 28.2

e 27.8 0.4 +1.5

SE 32.8 33.2 -0.4 -1.3 30.6 30.0 0.5 +1.8 31.0 29.0 2.0* +6.9 31.5 30.8 0.7 +2.3

TR 36.8 35.7 1.1* +3.1 37.3 35.8 1.6* +4.3 35.8 32.2 3.7* +11.4 36.7 34.6 2.1* +6.1

CK SS 16.5f 14.0

h 2.4* +17.4 22.3

j 17.0

k 5.3* +31.4 25.4

l,m 20.0

n 5.4* +26.8 21.4

o 17.0

p 4.4* +25.8

SU 17.7g

14.5h,i

3.2* +22.0 21.2j 18.8 2.4* +12.7 26.0

l 20.1

n 5.9* +29.6 21.6

o 17.7

p 3.9* +22.0

WA 17.3f,g

15.0i 2.3* +15.5 21.6

j 16.8

k 4.8* +28.6 24.2

m 19.6

n 4.6* +23.5 21.1

o 17.1

p 3.9* +22.8

1 – estate; 2 - mean values for 1 BMP block for NS1 and 2 (SS), 1 (SU) and 2(WA) for CK; 3 - mean values for 1 REF block for NS1 and 2 (SS),

1 (SU) and 2(WA) for CK; a-p – similar notations indicate no significant differences (P>0.05) between treatments; * - significant yield

differences between treatments (P<0.05).

0.0

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2 3 4 2 3 4 2 3 4 2 3 4 2 3 4

GB KP SB SE TR

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Estate and project year

A North Sumatra 1

BMP

REF

0.0

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35.0

40.0

2 3 4 2 3 4 2 3 4

SS SU WA

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Estate and project year

B Central Kalimantan

BMP

REF

**

**

**

14

16

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26

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30

32

2 3 4

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C

BMP

REF *

*

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**

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**

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32

2 3 4

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D

BMP

REF

Figure 8. A - average yields in BMP and REF treatments for estates in North Sumatra 1 for each project year; B - average yields in BMP and REF treatments for estates in Central Kalimantan for each project year; C – average yields in BMP and REF treatments for North Sumatra 1 and project year; D - average yields in BMP and REF treatments for Central Kalimantan and project year; * - significant yield differences between project year for BMP treatment; ** - significant yield differences between project year for REF treatment

23

15

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A North Sumatra 1

BMP GB

BMP KP

BMP SB

BMP SE

BMP TR

REF GB

REF KP

REF SB

REF SE

REF TR

101112131415161718192021222324252627282930

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B Central Kalimantan

BMP SS

BMP SU

BMP WA

REF SS

REF SU

REF WA

Figure 9. Average monthly rolling yields (t ha-1) of BMP and REF treatments for estates at North Sumatra 1 (A) and Central Kalimantan (B) during project implementation

iv. Between treatments within and between climatic zones

Table 14 shows BMP and REF yields achieved in 4 different climatic zones; Extremely wet (EW), Wet

(W), Slightly wet (SW) and Slightly dry (SD). Average yield differences were greatest in W and EW

zones; +4.7 (+27.1%) t ha-1 and +3.7 t ha-1 (+15.6%) respectively, while SW and SD zones gave higher

yields for both BMP and REF (Figure 10). All climatic zones showed a decline in yield from year 2 to 4

for BMP, except for W which increased with +2.2 t ha-1 (+2.2%). Between climatic zones and

treatments, no differences were found between SW and SD for year 3 and 4 for BMP and REF (Figure

10). All other climatic zones and years gave significantly different yields.

Table 14. Average yields (ton ha-1) per treatment and project year for each climatic zone

1 – EW = Extremely wet, W = Wet, SW = Slightly wet, SD = Slightly dry; 2 - mean values for 15 BMP blocks for EW, 10 for W, 3 for SW and 2

for SD; 3 – mean values for 15 REF blocks for EW, 10 for W, 3 for SW and 2 for SD; * - significant yield differences between treatments

(P<0.05).

Climatic zone

1

Project year

2 3 4 Avg

BMP2 REF

3 Difference BMP REF Difference BMP REF Difference BMP REF Difference

Yield % Yield % Yield % Yield %

EW 27.7 23.4 4.3* +18.6 27.2 24.0 3.2* +13.3 26.6 23.1 3.5* +15.0 27.2 23.5 3.7* +15.6

W 22.5 18.2 4.3* +23.6 21.1 16.3 4.8* +29.5 23.0 17.9 5.1* +28.4 22.2 17.5 4.7* +27.1

SW 31.9 32.8 -0.9 -2.9 30.5 29.5 1.0 +3.4 29.9 26.6 3.3* +12.3 30.8 29.6 1.1* +3.8

SD 29.9 29.5 0.4 +1.4 29.1 28.3 0.8 +2.8 29.0 25.6 3.4* +13.3 29.3 27.8 1.5* +5.5

1 2 1 2 a

b a

b

0.0

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25.0

30.0

35.0

2 3 4 2 3 4 2 3 4 2 3 4

Extremely wet Wet Slightly wet Slightly dry

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Climatic zone and project year

BMP

REF

Figure 10. Average yields in BMP and REF treatments for climatic zone and project year. Similar notations above the bars (1,2 for BMP treatments and a,b for REF treatments) indicate no significant treatment differences (P>0.05) between climatic zones

25

v. Between treatments within and between soil textures

While not all soil textures occurred on both BMP and REF blocks (e.g. Coarse sand (CrS) only on REF

blocks and Fine sand (FiS) only on BMP blocks), average yields with BMP were larger on each soil

texture (Table 15), ranging from +1.6 t ha-1 (+6.8%) for Fine sandy loam (FiSL) to +8.3 t ha-1 (+48.2%)

for Sandy clay (SC). Table 16 shows in which years significant differences between different types of

soil textures occur. For example, Clay (C) soils are significantly higher yielding than Clay Loam (CL)

soils in project year 2, while in project year 4, CL soils are significantly higher yielding than C soils for

BMP treatments.

Table 15. Average yields (ton ha-1) per treatment and project year for each soil texture

1 - C = Clay, CL = Clay loam, CrS = Coarse sand, CrSL = Coarse sandy loam, FiS = Fine sand, FiSL = Fine sandy loam, LCrS = Loamy coarse sand,

LFiS = Loamy fine sand, SC = Sandy clay, SCL = Sandy clay loam; 2 - mean values for 3 BMP blocks for C, 3 for CL, 3 for CrSL, 3 for FiSL, 6 for

LCrS, 1 for LFiS, 4 for SC and 7 for SCL; 3 – mean values for 4 REF blocks for C, 6 for CL, 1 for CrS, 4 for CrSL, 1 for FiS, 4 for FiSL, 3 for LCrS, 1

for SC and 6 for SCL; * - significant yield differences between treatments (P<0.05).

Table 16. Significant yield differences between soil textures presented by project year

Treatment Soil texture

CL CrS CrSL FiS FiSL LCrS LFiS SC SCL

BMP C 2,4 2,4 2 2,3,4 2,3 2,3 2,4

CL 3 2,4 2,3,4 4 2,4 2

CrSL 2,3,4 2,3,4 3,4 2,3,4 2

FiSL 2,3 2 2,3 3,4

LCrS 2 2 2,3,4

LFiS 2 2,3,4

SC 3,4

REF C 2,3,4 2 2,3,4 2,3 2,3,4 2 2,3,4 3,4

CL 3,4 2,4 2,3 2,3,4 2,3,4 2,3

CrS 2,3,4 3 2,3,4 2 2,3,4

CrSL 2,4 3 2,3,4 2,3,4 2,3

FiS 2,3,4 3 3 2,3,4

FiSL 2,3,4 2,3,4 2

LCrS 4 2,3,4

SC 2,3,4

2,3,4 - project year. When indicated within a cell, it means that there is a significant difference in yield (P<0.05) between the two

corresponding soil textures in that particular year.

Soil texture

1

Project year

2 3 4 Avg

BMP2 REF

3 Difference BMP REF Difference BMP REF Difference BMP REF Difference

Yield % Yield % Yield % Yield % C 30.2 25.6 4.6* +17.9 25.2 19.2 5.9* +30.9 24.8 17.9 6.9* +38.7 26.7 20.9 5.8* +27.8

CL 23.9 21.9 2.0* +9.3 24.7 21.7 3.0* +13.7 26.9 22.6 4.4* +19.3 25.2 22.1 3.1* +14.1

CrS 20.4 19.2 16.8 18.8

CrSL 24.8 22.9 1.9* +8.4 26.4 22.1 4.3* +19.6 26.9 22.8 4.1* +17.9 26.0 22.6 3.4* +15.2

FiS 18.8 21.8 17.6 19.4

FiSL 26.9 23.5 3.4* +14.4 24.8 24.0 0.8 +3.4 23.9 23.3 0.7 +2.8 25.2 23.6 1.6* +6.8

LCrS 20.8 18.3 2.5* +13.8 22.7 18.8 3.8* +20.4 23.3 18.0 5.3* +29.3 22.3 18.4 3.9* +21.1

LFiS 24.9 23.2 23.8 23.9

SC 28.7 19.3 9.4* +48.6 23.5 17.3 6.3* +36.4 24.0 14.8 9.1* +61.5 25.4 17.1 8.3* +48.2

SCL 28.0 26.4 1.6* +6.1 26.1 23.9 2.2* +9.1 26.6 23.2 3.4* +14.7 26.9 24.5 2.4* +9.8

vi. Between treatments within and between islands

Average yield differences of +3.0 t ha-1 (+13.8%) and +4.0 t ha-1 (+17.4%) were obtained between

BMP and REF yields on Kalimantan and Sumatra respectively (Table 17). Plantations on Sumatra

declined in yield for both BMP and REF (-3.8 t ha-1 (-14.6%) for BMP and -4.1 (-18.9%) for REF from

year 2 to 4), while yields on Kalimantan increased (+2.5 t ha-1 (+9.7%) for BMP and +2.8 t ha-1

(+12.2%) for REF) (Figure 11). Between islands and treatments, no differences were found for year 4

for BMP and year 3 for REF (Figure 11). All other years yielded significantly different from each other.

Table 17. Average yields (ton ha-1) per treatment and project year for Kalimantan and Sumatra

1 - mean values for 15 BMP blocks; 3 – mean values for 15 REF blocks; * - significant yield differences between treatments (P<0.05).

Figure 11. Average yields in BMP and REF treatments for Kalimantan and Sumatra and project year. Similar notations (1 for BMP treatments and a for REF treatments) indicate no significant treatment differences (P>0.05) between islands

1

1

a

a

20.0

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Kalimantan - BMP

Sumatra - BMP

Kalimantan - REF

Sumatra - REF

Island Project year

2 3 4 Avg

BMP1 REF

2 Difference BMP REF Difference BMP REF BMP REF Difference

Yield % Yield % Yield % Yield % Kalimantan 23.3 20.2 3.0* +15.0 25.3 22.4 2.9* +13.1 25.8 22.8 3.1* +13.4 24.8 21.8 3.0* +13.8

Sumatra 29.8 25.8 4.0* +15.7 26.0 22.2 3.8* +17.1 26.0 21.7 4.3* +19.8 27.3 23.2 4.0* +17.4

27

vii. Between treatments within and between (site-) conditions

Table 18 shows BMP and REF yields achieved on different (site-) conditions; good, moderate,

poor and very poor. Average yield differences ranged from +2.6 t ha-1 (+12.6%) for poor, to +5.1 t

ha-1 (+28.1%) for moderate site conditions. Between year 2 and 4, BMP yields declined in good,

moderate and poor site conditions, while BMP in very poor sites showed the greatest scope for

improvement with a yield increase of 7.9 t ha-1 (+31.6%) (Figure 12). Between site conditions and

treatments, no differences were found for year 4 for BMP between moderate and poor sites and

for REF between poor and very poor sites (Figure 12). Yields were significantly different from

each other in all other years.

Table 18. Average yields (ton ha-1) per treatment and project year for each (site-) condition

1 – mean values for 15 BMP blocks for Good, and 5 for Moderate, Poor and Very poor; 2 – mean values for 15 REF blocks for Good, and 5

for Moderate, Poor and Very poor; * - significant yield differences between treatments (P<0.05).

(Site-) Condition

Project year

2 3 4 Avg

BMP1 REF

2 Difference BMP REF Difference BMP REF Difference BMP REF Difference

Yield % Yield % Yield % Yield % Good 30.2 27.5 2.7* +9.8 29.3 26.4 2.9* +10.9 29.1 25.3 3.8* +15.1 29.5 26.4 3.1* +11.8

Moderate 27.9 21.9 6.1* +27.7 20.5 15.4 5.1* +33.3 21.0 16.9 4.1* +24.3 23.1 18.0 5.1* +28.1

Poor 23.8 19.2 4.6* +23.9 23.7 21.8 1.9* +8.8 22.0 20.7 1.2* +6.0 23.2 20.6 2.6* +12.6

Very poor 17.1 14.5 2.5* +17.5 21.8 17.3 4.5* +26.2 25.0 19.9 5.2* +26.1 21.3 17.2 4.1* +23.8

1 1

a a

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

2 3 4 2 3 4 2 3 4 2 3 4

Good Moderate Poor Very poor

Yie

ld (

ton

ha-1

)

(Site-) Condition and project year

BMP

REF

Figure 12. Average yields in BMP and REF treatments for (site-) condition and project year. Similar notations (1 for BMP treatments and a for REF treatments) indicate no significant treatment differences (P>0.05) between (site-) conditions

II. Analysis 2

i. Site (Plantation) level

Table 19 shows model statistics of the linear regression performed at the site level. All models (Site,

BMP, REF) were significant (P<0.05) and have a very high R-square. Figure 13.A,B,C shows the partial

correlation coefficients to 12MRY. The most top variables had a higher correlation with 12MRY and

were more significant in explaining yield trends. In the site model (which included treatment as a

factor), climatic zone, treatment and seed source were the strongest explanatory factors (Figure

13.A) , while soil texture, topography, climatic zone and incidence of Ganoderma were most strongly

correlated with BMP (Figure 13.B), and seed source, Ganoderma, climatic zone and harvest days with

REF (Figure 13.C). Ganoderma showed a positive correlation with yield, because it was expressed in

units of the number of palms ha-1, by correcting stand size due to diseased and dead palms. Thus, the

more palms per hectare, the higher the yield.

Table 19. Linear regression model statistics for North Sumatra 1 with 12MRY as dependent variable

Model R-Squared Adjusted R-Squared Std. Error of the Estimate Total Degrees of Freedom F Sig. Site .819 .809 1.8385 179 85.494 .000

BMP .879 .870 1.4151 89 100.677 .000

REF .840 .822 1.7681 89 46.727 .000

Figure 13. Explaining variables at the site level with 12MRY as dependent variable. Solid black bars indicate a positive correlation with 12MRY and white-dotted bars indicate a negative correlation with 12MRY; A – partial correlation coefficients for North Sumatra 1; B – partial correlation coefficients for North Sumatra 1 – BMP; partial correlation coefficients for North Sumatra 1 – REF; Scf - Socfindo; D - Dami (PNG); ^ - indicates transformed variable; * - significant partial correlation coefficient (P<0.05); n.s. – no significant partial correlation coefficient (P>0.05)

n.s.

*

*

*

*

*

*

*

*

.000 .100 .200 .300 .400 .500

Rainfall

Topography (Flat)

Harvest days

Ganoderma^

Ganoderma

Seed source (D)

Seed source (Scf)

Treatment (REF)

Climatic zone (SW)

Partial correlation coefficient

Var

iab

le

A North Sumatra 1

*

*

*

*

*

*

.000 .100 .200 .300 .400 .500

Rainfall

Ganoderma

Ganoderma^

Climatic zone (SW)

Topography (Flat)

Soil texture (SCL)

Partial correlation coefficient

Var

iab

le

B BMP

n.s. * * * * *

* *

*

.000 .200 .400 .600

MgTopography (Flat)

Harvest round^Harvest days^Harvest round

Harvest daysClimatic zone (SW)

GanodermaSeed source (Scf)

Partial correlation coefficient

Var

iab

le

C REF

29

ii. Estate level

Table 20 shows model statistics of the linear regression performed at the estate level. All models are

significant (P<0.05), while coefficients of determination varied from weak (R2 = 0.382 for SB) to very

strong (R2 = 1.000 for SE - REF). For each estate, models were run three times. First by including

‘Treatment’ as a factor (in models GB, KP, SB, SE and TR), and then to BMP and REF yields separately.

Table 21 shows the five most explanatory variables to 12MRY and their partial correlation

coefficients for all the models. Treatment was significant for all estates, while Ganoderma was also

important. Ganoderma was important also in BMP models, while Rain days and fertilizer application

(N, P, K, Mg) had strong correlations. For REF models, Ganoderma was important in explaining yield

trends, while ‘harvesters productivity’ such as Harvest round, Man days and Harvest days, as well as

rain (rainfall and rain days) also had strong correlations with yield.

Table 20. Linear regression model statistics for estates at North Sumatra 1 with 12MRY as dependent variable

Model R-Squared Adjusted R-Squared Std. Error of the Estimate Total Degrees of Freedom F Sig. GB .889 .832 1.0511 35 15.418 .000

GB - BMP .886 .784 .3488 17 8.715 .002

GB – REF .945 .915 .8020 17 31.597 .000

KP .799 .774 1.0446 35 30.898 .000

KP – BMP .966 .902 .4904 17 15.267 .002

KP – REF .999 .995 .1646 17 273.457 .000

SB .382 .302 1.9886 35 4.789 .004

SB – BMP .832 .714 1.2559 17 7.066 .003

SB – REF .937 .903 .5985 17 27.366 .000

SE .660 .639 .7985 35 31.976 .000

SE – BMP .386 .348 .8119 17 10.070 .006

SE – REF 1.000 .996 .0851 17 294.480 .003

TR .842 .787 1.2471 35 15.362 .000

TR – BMP .973 .934 .3454 17 25.005 .000

TR - REF .998 .987 .3245 17 95.100 .002

Table 21. Explanatory variables & partial correlation coefficients at the estate level

Dependent variable: 12MRY; ^ - indicates transformed variable. All partial correlation coefficients are significant (P<0.05)

Model Explanatory variables + partial correlation coefficients

1 2 3 4 5 GB Treatment (REF) (-0.538) Ganoderma^ (.299) N^ (.292) N (.255) Man days (2.54)

KP Treatment (REF) (-.824) Ganoderma (.679) Rain days^ (.162) Ganoderma^ (.154)

SB Treatment (REF) (-.414) Ganoderma (.340) Ganoderma^ (-.334) Harvest days (.270)

SE Ganoderma^ (.675) Treatment (REF) (-.611)

TR Treatment (REF) (-.559) Mg^ (-.341) K^ (-.286) Rainfall (.223) Harvest days^ (.219)

GB – BMP Mg^ (.528) Rain days^ (-.511) P^ (.485) Rain days (.477) N^ (-.466)

KP – BMP Ganoderma (.550) Rain days^ (-.366) Rain days (.313) Ganoderma^ (.275) Harvest days (-.271)

SB – BMP N^ (.381) N (-.367) Ganoderma^ (-.354) Ganoderma (.349) K (.343)

SE – BMP Ganoderma^ (.622)

TR – BMP Rain days^ (.613) Rain days (-.595) Harvest round^ (-.440) Rainfall (.380) Man days (-.330)

GB – REF Ganoderma^ (.745) Mg (-.325) N^ (.228) N (-.248) Harvest round (-.209)

KP – REF Ganoderma (.362) Man days^ (.294) Man days (-.232) Rain days^(-.220) Mg (-.201)

SB – REF Harvest round (-.261) Ganoderma^ (.245) Ganoderma (-.207) Man days (.194) Harvest days (.192)

SE – REF Rainfall (-.275) Rainfall^ (.272) Rain days (.231) Rain days^ (-.209) Ganoderma (.199)

TR - REF Rainfall (.359) Rainfall^ (-.344) Man days (-.317) Man days^ (.301) Mg (.283)

Discussion & Conclusions Results of Analysis 1 showed that average yields in BMP were greater compared with REF blocks in all

cases (P<0.05), indicating that BMP significantly closes yield gaps in mature oil palm plantations and

proves the immense potential for increasing yields in existing plantations in Indonesia [5].

The highest yields and smallest yield differences were found in North Sumatra 1 and East Kalimantan

(Table 22, Figure 5). Both sites have good site conditions and are already close to their yield

potential. The lowest yields and largest yield differences are found at sites with moderate to very

poor growing conditions (Table 18, Figure 12). The difference to the yield potential is also much

greater, which shows more scope for improvement under these prevailing conditions. All sites but

East Kalimantan and Central Kalimantan showed a decrease in yield over the experimental period. EK

increased with +1.0 t ha-1 (+3.3%) from project year 2 to 4 and CK with 8.1 t ha-1 (+32.8%). The major

increase in yield in CK also validates the impact of BMP under very poor conditions and its immense

potential in increasing yields.

Table 22. Yields (t ha-1), suitability classes and site conditions for each site in the IPNI SEA BMP project in Indonesia

Analysis 2 was more a simple exploratory study, in which the main factors that cause yield variability

was analysed. Partial correlation coefficients for yield were given for NS1 at the site and estate level.

The influence of factors on yield was variable when looking at different spatial scales and also when

taking ‘Treatment’ up as a factor in the analysis. At the site level, environmental (climatic zone),

management (treatment) and genetic (seed source) factors seem to have the largest influence on

yield variability (Figure 13.A). These effects are extended when looking at BMP and REF yield trends

separately, with management practices (number of harvest days and harvest round) having a larger

impact on yield for REF treatments. Because categorical variables were constant on the estate level

and could not be taken up in the analysis, results yielded a slightly different set of explaining factors.

Treatment and Ganoderma are still important factors, while climatic factors (rainfall and rain days)

explain both BMP and REF yield trends. Management factors such as Man days, Harvest round and

Harvest days on the other hand have a larger influence on REF yields than in BMP.

North Sumatra 1 was heavily affected by Ganoderma, causing stand densities to significantly drop in

size due to dead and diseased palms. This had a major effect on yields at all estates, except for TR,

which was not affected by the disease. GB – REF was the most affected (partial correlation coefficient

of .745), with a decline from 141 palms ha-1 at the beginning of the project to 96 palms ha-1 at the

end of the project. Preventing Ganoderma outbreaks and management of diseased stands is thus

essential in maintaining high yields.

Site Avg Suitability

class

Site

conditions

Yield

potential

(t ha-1)

Difference with yield potential

BMP REF Difference BMP REF

Yield % Yield % Yield %

NS1 29.7 28.5 1.2 +4.4 S1 Good 35 -5.3 15.1 -6.5 18.6

EK 29.5 27.2 2.3 +8.5 S2 Good 32 -2.5 7.8 -4.8 15.0

NS2 27.4 21.7 5.7 +26.5 S2 Good 32 -4.6 14.4 -10.3 32.2

SS 22.7 17.6 5.1 +28.7 S2 Moderate 32 -9.3 29.1 -14.4 45.0

WK 22.7 20.1 2.6 +12.9 S2/3 Poor 31 -8.3 26.8 -10.9 35.2

CK 20.9 16.8 4.1 +24.3 S3 Very poor 30 -9.1 30.3 -13.2 44.0

31

BMP is a management tool for yield gap correction and yield intensification in existing mature oil

palm plantations and can be split in two components; operational management (“yield taking”) and

agronomic management (“yield making”). Operational management stimulates yield increases at the

short-term through crop recovery, such as shortened harvest intervals (more frequent harvesting),

while the effects of agronomic management (canopy and nutrient management) are expected to be

seen from the 3rd year of project implementation [5].

Where management practices were not carried out properly, this has a greater effect on yield.

Results of Analysis 2 showed that for REF treatments on the site and estate level, management

practices (e.g. harvest round) are highly correlated with yield trends, because there is much less

consistency in harvest interval and crop recovery (Table 23). When harvesting intervals (HI) are

irregular or too long (fruit becomes rotten), this has a greater impact on yield. BMP adopts a

constant and shorter harvest interval of 7 days, which corresponds to an average of 4 harvest rounds

per month. With BMP harvesting, harvesters also cover an average of 33% more ground daily

compared to REF and has demonstrated higher recovered yields in past oil palm studies [5].

Table 23. Average Harvest intervals (HI) for BMP and REF treatments for estates at NS1 during BMP implementation

Estate Avg HI1 Max HI

2

BMP REF BMP REF

GB 7 10 9 14

KP 7 11 12 22

SB 7 10 9 11

SE 7 11 9 21

TR 7 10 9 15

1 – average interval days between successive harvest occasions;

2 – maximum recorded interval (days) between successive harvest occasions

In existing mature oil palm plantations, genotype and environmental conditions (G x E) are fixed (or

at least extremely difficult to manipulate), but management practices can be altered and

implemented immediately. Immediate yield increases can therefore be achieved by eliminating crop

loss by maintaining a more regular and controlled harvest interval. Previous studies have showed

that for yield improvements in the short term, a short harvest interval combined with other crop-

recovery BMPs is essential [5].

Results of Analysis 1 in which the different sites were compared showed that BMP implementation is

highly variable across a wide range of environments, as well as between and within sites. This shows

that the BMP concept is site-specific and applicable across a wide range of conditions. The highest

yields were found on soil textures considered good for oil palm growth, while the lowest yields occur

on the poor soils (Table 3, Table 15). Only poor soils are found at WK and CK, which are also the

poorest performing sites. According to Table 2 and 6, the most ideal climatic zone for oil palm growth

lies somewhere between the Slightly wet and Wet zone (2,000 – 2,500 mm yr-1), while Slightly dry is

considered poor because of severe water deficits which may occur in some months [14]. Slightly wet

had the highest average yield, followed by the slightly dry zone, while the wet zone had the lowest

yields. This seems to contradict the former statement, but it should be noted that on areas

considered having marginal climatic conditions, favourable soil conditions may help to overcome the

effects of unfavourable climatic factors. This suggests an interaction between soil and climate, which

affects oil palm growth and its production [14].

Oil palm plantations in Indonesia are found in areas having highly variable land conditions. With

different climatic zones and soil textures found within and across sites, BMPs should be adapted

accordingly. Plantations should focus on and develop ‘site-specific’ agronomic management, when

dealing with highly variable climates, soils and their interactions. In particular, agronomic practices

before and after low rainfall periods (especially in slightly dry climates) should be selected cautiously

[14]. Prolonged moisture stress due to drought decreases the sex ratio, which in turn decreases palm

oil yield 19-22 months later, while very severe drought may even abort female flowers. Adequate

moisture supply is also needed during fruit ripening and for the formation of bunches with a wide

oil:bunch ratio [18].

Before adapting a management program, each site should be evaluated in terms of soil fertility

(chemical fertility, physical properties, drainage, and topography), the condition of the palms and the

general environment such as climatic conditions [19]. Agronomic management should then be

adapted to each climatic zone, by taking soil physical and chemical conditions into consideration. This

is important when defining e.g. the kind and dosage of fertilizer, the application system, its frequency

and timing [14]. Other agronomic management practices which should adapted to site and should be

taken into account include; canopy management and pruning, pest and disease control (e.g.

Ganoderma) and drainage and transportation network maintenance. Plantation managers are

therefore advised to monitor and evaluate these characteristics before taking corrective measures.

By adapting the BMP concept, the oil palm industry will benefit in the short term through operational

management such as crop recovery and in the long term through better site-specific agronomic

management. When implemented correctly to site-specific conditions, productivity in existing

plantations can be increased. Increasing productivity in already existing plantations reduces costly

undertakings such as establishing new plantations, but also more forest land could be spared for

(biodiversity) conservation [7].

While differences between BMP and REF treatments across a wide range of environments are clearly

established in Analysis 1, solid conclusions for Analysis 2 are difficult. However, this analysis offers

insights and scope for future analysis. Results for North Sumatra 1 are less detailed and indicate that

yield variability is driven by environmental, genetic as well as management factors at different scales

and treatment level. To fully understand the dynamics behind yield trends, a more thorough analysis

of the project data is needed across all sites. More factors and interactions should be included in the

analysis, while comparisons between years should also offer more insight into the contribution of

different factors to yield variability.

Acknowledgements

I would like to thank:

- IPNI SEAP’s project partners and collaborating plantations for all the data; Permata Hijau

Group, PT Bakrie Sumatera Plantations Tbk, PT REA Kaltim Plantations, PT Sampoerna Agro

Tbk, and Wilmar International Ltd.

- Rob Verdooren for his help with the statistical analysis.

- Chris Donough, Thomas Oberthür, Ken Giller and Thomas Fairhurst for their supervision and

valuable suggestions.

33

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Cover picture taken from http://sawitresources.com/tamora.html and modified with Adobe Photoshop

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