Assessing the possibilities of intercropping oil palm and

Assessing the possibilities of intercropping oil palm and

pepper, under the double-row avenue system

MSc Thesis Plant Production System

Adrien-François Migeon

901219572110

June 2018

Assessing the possibilities of intercropping oil palm and

pepper, under the double-row avenue system

MSc Thesis Plant Production System

Disclaimer: this thesis report is part of an education program and hence might still contain (minor)

inaccuracies and errors.

Correct citation: Adrien-Francois Migeon, 2018, Assessing the possibilities of intercropping oil palm and

pepper, MSc Thesis Wageningen University, 33p.

Contact [email protected]

Name Student: Adrien-François Migeon

Registration number: 901219572110

Study: MSc Plant Sciences – Natural Resource Management

Chair group: Plant Production System

Code Number: PPS-80424

Supervisors: Maja Slingerlang

Meine van Noordwijk

Ni’Matul Khasanah

Examiner: Katrien Descheemaeker

Date: June, 2018

Abstract

Palm oil is the most produced oil in the world. The greatest producer is Indonesia, where 41% of the total

production is done by smallholders. More than 70% of the Indonesian smallholders rely on the palm oil

production, making them extremely vulnerable to market price changes. Of these smallholders, a great

proportion is reaching the aging phase of their plantations. Therefore, a new planting campaign will start

soon. It is the opportunity for change of paradigm. Intercropping is one of the solution mean of sustainable

intensification. The MPOB developed a new planting pattern called the Double-Row Avenue System. In this

research we look at the possibilities of intercropping oil palm and pepper (Piper nigrum) using the

WaNuLCAS model. The results show an average decrease of 3.5Mg FFB ha-1 year-1 when comparing the

conventional planting pattern and the intercropping pattern. A further decrease of 2.5Mg FFB ha-1 year-1

is observed when pepper is added to the system. On the other hand, pepper produces an additional average

of 1.9 Mg ha-1 year-1.

Table of contents

1. Introduction ................................................................................................................... 1

1.1. Sustainable production ................................................................................................. 1

1.2. Farming description ...................................................................................................... 1

1.3. Yield gap with smallholders ........................................................................................... 2

1.4. Permanent intercropping in oil palm ............................................................................... 3

1.5. Black pepper ............................................................................................................... 5

1.5.1. Plant description ...................................................................................................... 5

1.5.2. Agronomy ............................................................................................................... 5

1.5.3. Ecology ................................................................................................................... 5

1.6. Research objective ....................................................................................................... 6

2. Material and Methods ....................................................................................................... 9

2.1. Model site parameterisation ......................................................................................... 10

2.1.1. Soil sampling protocol ............................................................................................. 10

2.1.2. Climate parameters ................................................................................................ 10

2.2. Pepper parameterisation ............................................................................................. 11

2.2.1. Pepper parameters labelling protocol ......................................................................... 11

2.2.2. Pepper parameters sampling size .............................................................................. 11

2.2.3. Pepper parameters sampling protocol ........................................................................ 12

2.2.4. Biomass allocation .................................................................................................. 14

2.2.5. Tree parameterisation ............................................................................................. 14

1.7. Model validation ........................................................................................................ 14

3. Results ........................................................................................................................ 15

3.1. Pepper allometry and biomass distribution ..................................................................... 15

3.1.1. Leaf biomass ......................................................................................................... 15

3.1.2. Plagiotropic branches biomass .................................................................................. 16

3.1.3. Orthotropic branches biomass .................................................................................. 17

3.1.4. Fruit biomass ......................................................................................................... 18

3.1.5. Biomass distribution ............................................................................................... 19

3.1.6. Identification of farm effect ...................................................................................... 19

3.2. WaNuLCAS modelling results ....................................................................................... 20

3.2.1. Oil palm in monoculture .......................................................................................... 20

3.2.2. Pepper in monoculture ............................................................................................ 21

3.2.3. Oil palm and pepper under the double-row avenue system ........................................... 21

4. Discussion .................................................................................................................... 23

5. Conclusion ................................................................................................................... 25

Reference ............................................................................................................................... 27

Appendix 1 ............................................................................................................................. 29

Appendix 2 ............................................................................................................................. 31

Appendix 3 ............................................................................................................................. 31

Appendix 4 ............................................................................................................................. 32

Appendix 5 ............................................................................................................................. 32

Wageningen University and Research | Introduction 1

1. Introduction Palm oil is found in many of our daily used products, going from food products to cooking oils, to cosmetics

and bioenergy. It is the most produced oil (Woittiez et al., 2017) and the demand for palm oil is

continuously increasing. Oil palm has the highest vegetable oil yield per unit area of all oil crop (Corley et

al., 2015). Indonesia and Malaysia are the world largest producers of oil palm, with a production of 46%

and 34% of the world’s total production respectively (FaoStat, 2016). The fast increase in palm oil demand

has raised concerns. On the one hand, the increasing demand of oil leads to an increase in land-use

changes, putting at risk forests and high diversity environments. On the other hand, the palm oil production

plays an important role in the economic development of the palm oil producing countries and their rural

development.

1.1. Sustainable production The World Commission on Environment and Development of 1987 established the following definition of

sustainable development; “... development that meets the needs of the present without compromising the

ability of future generations to meet their own needs”. The delegate of the Earth Summit of Rio de Janeiro

added the concepts of “economic growth” (profit), “environmental protection” (planet) and “social equity”

(people) to the definition of sustainable development established in 1987 (Molenaar et al., 2013). This

implies that oil palm cultivations held by smallholders need to meet three characteristics to be considered

sustainable;(1) to have a long-term economic and financial viability, (2) to be responsible of the individuals

linked to the plantations and (3) to act in an environmental responsible way and aim for a conservation of

the biodiversity (Molenaar et al., 2013).

In order to meet the first characteristic (economic viability) and the third characteristic (environment

aspect), the optimisation of yield can be used thus, reducing the pressure on land-use changes. Indeed,

according to Mielke, I.S.T.A., 2010, oil palm has the highest oil yield per hectare compared to other oil

crops such as soybean, rapeseed, and sunflower, producing five to ten times more than its nearest rivals

(soybean and rapeseed) and therefore requires less land to produce the same amount of oil (Mielke,

I.S.T.A., 2010). However, with increasing demand for oil, the expansion of the oil palm production area is

inevitable. Indeed, the Indonesian ministry of agriculture reported that over 10 million hectares of land

use was dedicated to the crude palm oil production in 2014 and estimated an increase of more than 8% of

the area used for crude palm oil from the year 2014 to 2016 (Indonesian Ministry of Agriculture, 2015).

Such an expansion puts directly forests and areas with high biodiversity ecosystems at risk and creating

potential social conflicts between indigenous communities and farmers (Molenaar et al., 2013). Therefore,

optimising yields per hectare is an important element to reach sustainable farming. However, with the

increasing yields per hectare and a possible increasing income to the farmers, an additional motivation to

expand the production areas by the farmers might be created. Therefore, the reduction of pressure on land

by increasing yields can only be achieved if it is followed by adequate farming practices, agricultural

policies, and land conservation policies. In 2005, the Roundtable on Sustainable Palm Oil (RSPO) was

created to promote the sustainable palm oil production. To produce Certified Sustainable Palm Oil,

companies must comply with a series of criteria related to the protection of the environment and wildlife

but also with respect to the social, communities and workers’ interests.

1.2. Farming description The Indonesian crude palm oil production system is subdivided into three different farming categories; the

smallholders, the governmental estates, and the private estates. The smallholders represent 41% of the

land used for the palm oil production (Indonesian Ministry of Agriculture, 2015) and involve 1.7 million

smallholder households ((Indonesian Ministry of Agriculture, 2011). The Roundtable on Sustainable Palm

Oil (RSPO) defined smallholders as: “farmers growing oil palm, sometimes along with subsistence

production of other crops, where the family provides the majority of labour and the farm provides the

principal source of income and where the planted area of oil palm is usually below 25 hectares in size”

(Molenaar et al., 2013) and are characterised, in Indonesia, by an average oil palm plantation size of 3ha

(Molenaar et al., 2013). The smallholders can be classified into three subcategories; (1) the tied

smallholders, contracted to a plantation company which provides technical assistance and marketing and

supply their production to the company’s palm oil mill, (2) the independent smallholders, not bound to a

plantation company and (3) the tied + independent smallholders, who own both plantation plots tied to a

plantation company, and independent plots (Molenaar et al., 2013).

Monocropping is the main oil palm production system in all three farming categories (smallholders, private

estates, and governmental estates). The oil palm plantations are based on an equilateral triangular planting

pattern (Figure 3). This pattern is due to two main reasons; the fact that palm canopies are considered

hemispherical, an equilateral triangular pattern allows a better land use than a square pattern and

secondly, the fact that palms are monocotyledon plants, therefore, do not grow irregularly to fill in the

open spaces like dicotyledonous plants would do (Corley et al., 2003).


1.3. Yield gap with smallholders

According to Goh et al., (1994), there is a fairly consistent correlation in the yield pattern (ton FFB ha-1

year-1) related to the age of the oil palm (Figure 1). From the data analysed by Goh et al., (1994), it seems

that there is a yield decline linked to the age of the palm. This yield decline is estimated to be at 20 years

of age. The decrease in yield is due to two main reasons; the physiological decline of the plant creating a

reduction of the yield and in addition to this, the harvesting difficulties encountered due to the increasing

tree height.

Figure 1, Representation of the distribution of yield with age with the different productions phases. (After Goh et al., 1994 and

Ling, 2012)

Indonesian tied smallholders have an average palm age of 19 years and therefore will require a replanting

of the plot in the near future. On average, peak productions are reached between the age 9 to 18 years

and gradually decline after that (Figure 1). However, palms can be kept for production for up to 30 years

(United States Department of Agriculture, 2012). In addition to this, one fifth of the independent

smallholders may require replanting due to poor palm varieties (Molenaar et al., 2013).

In production ecology, based on oil palm literature, three different production levels can be distinguished;

(1) the potential yield, (2) the site yield potential and (3) the actual yield.

(1) The potential yield is determined by yield-defining factors; photosynthetically active radiations,

temperature, ambient CO2 concentrations and crop genetic characteristics (van Ittersum et al., 1997).

Evans, (1993) defines the potential yield as the yield of a cultivar, when grown in environments to which

it is adapted; with nutrients and water non-limiting; and with pests, diseases, weeds lodging, and other

stresses effectively controlled.

(2) The site yield potential is defined as the yield obtained on a specific site, with natural water supply,

nutrients supplied at optimum rates, and agronomic and disease control measures implemented to a high

standard (Corley et al., 2015). The site yield potential includes management decisions such as planting

material and density (Woittiez et al., 2016).

(3) The actual yield is determined by yield reducing factors; weeds, pests and diseases (van Ittersum

et al., 1997).

Under the current agricultural practices, the actual yields of FFB (t ha-1 year-1) for Indonesia and Malaysia

are 17 and 21 respectively and the potential yields are of about 36 and 28 respectively (Table 1).

Therefore, the foreseen upcoming replanting of the plantation may create an opportunity to close down

the yield gap and, thus, improve yields.

Table 1, Fresh fruit bunch (FFB) and Crude Palm Oil (CPO) yields comparison between actual yield and potential yield (After Woittiez et al., 2016)

Country Fresh Fruit Bunch (t ha-1 year-1) Crude Palm Oil (t ha-1 year-1)

Actual Yield Potential Yield Actual Yield Potential Yield

Indonesia 17 32 - 40 3.8 8 - 10

Malaysia 21 24 - 32 4.2 6 - 8


1.4. Permanent intercropping in oil palm

Despite the needs of replanting an ageing plot, the financial aspect of replanting is a major drawback for

the farmer. For smallholders, the replanting stage is a very costly practice. Various factors such as the

investment needed for replanting, the lack of income during the immature stage, the low yield in the early

stage of production and the long-term payback of the investment make the replanting of the plots very

challenging for the farmers. It is reported that 35% of the farmers save money for replanting on a regular

basis and 28% occasionally (Molenaar et al., 2013). However, they do not believe that their savings are

sufficient to cover the full financial needs during replantation. The effect of the investment needed for

replanting will differ with the importance of the income brought in the household through the production

of palm oil. It is known that 18% of the smallholders fully rely on oil palm as the main income and for 69%

of the farmers, it represents more than half of their income, representing over 300.000 and 1.100.000

farmers respectively.

This financial burden induced by the replanting process can, however, be diminished by the use of

alternative strategies such as intercropping. Intercropping is not interesting to the big plantations due to

managerial difficulties (Corley et al., 2003). However, on average smallholders have plots of around 2ha,

it may be an interesting solution to them. Indeed, the use of the additive intercropping design may bring

an income during the immature phase of the oil palm and an additional income during the production

phases of the oil palm in the case of a cash crop (Nuertey et al., 2000), or food in the case of a subsistence

crop. In addition to this, intercropping also helps in reducing the cost of weeding as well as maximising the

land use and therefore limit the pressure exerted by farmers on forest areas and high biodiversity

ecosystems (Nchanji et al., 2015). Okyere et al., (2014) and Hartley, (1988), state that according to their

research, in the case of a previous intercropping during the immature phase of the oil palm, the

intercropping has no adverse residual effect on the growth, development and yield of the oil palm during

the following production phases. This suggests that under good management practices, the food crop

previously intercropped does not influence the nutrient uptake of the oil palm.

Despite the many advantages brought by intercropping oil palm with cash or subsistence crops, the

intercropping stage can only be maintained for the beginning of the immature phase of the oil palm.

Indeed, under a conventional triangular oil palm planting system (Figure 2a), certain crops can be

integrated for a period of 1.5 to 2 years after the establishment of the plantation. Beyond that period of

time, it becomes less suitable for intercropping as the fronds of the palm start to encroach the surrounding

palms (Suboh et al, 2009). This leads to a limitation of light penetration through the canopy (Figure 3).

Figure 2, Equilateral triangular pattern (A), double row avenue planting pattern (B) (After Suboh et al., 2009)

However, in order to reduce the effect of light limitation induced by the intercropping of oil palm under the

conventional equilateral triangular pattern (Figure 2a), the Malaysian Palm Oil Board (MPOB) has developed

a different intercropping pattern called “double row avenue system” (Figure 2b). This plantation pattern

allows the integration of other crops in the avenue between the rows of oil palm. Indeed, Mohd Haniff et

al., (2003), measured that the photosynthetically active radiation (PAR) penetrating through the canopy

was increased by as much as 50% in the double-row avenue as compared to the conventional triangular

planting pattern. He suggests that the open space on the avenues between the oil palm rows is suitable

for establishing a crop as the mean PAR transmitted through the canopy was 40%. This change in PAR was

correlated to the leaf area index of the two systems, as it was higher under the conventional planting

pattern.


Figure 3, Distribution of the photosynthetically active radiation penetrating the oil palm canopy reaching the soil measured along

a transect between two palms, for the conventional triangular pattern (dashed line) and double-row avenue (solid line) (After

Mohd Haniff et al., 2003 and Suboh et al., 2009)

With the use of the double-row system, the density is of 138 trees ha-1 on the overall system which is a

similar value compared to the conventional triangular system, with a density 139 trees ha-1. However, the

density within the row is of 192 trees ha-1. According to Goh, (1982), there is a decrease in yield of FFB

palm-1 corresponding to an increase of the planting density, therefore a lower yield would be expected in

the double-row avenue system (Figure 4). However, according to the results from the various experiments

conducted by the MPOB, it appears that the yield of FFB under the double-row avenue system is similar to

the yield in the conventional planting pattern (Table 2). This response in yield may be due to the effect of

the planting density within the palm rows and to the additional PAR made available by the border effect of

the canopy adjacent to the avenue. No studies have been found on this matter.

Figure 4, Yield of Fresh Fruit Bunch per palm according to the planting density (Goh, 1982)

Table 2, Average FFB yields (t ha-1 year-1) in the first four years of harvest at Maah Plantation, KLIA, Sepang, Malaysia (After Suboh et al., 2009)

Oil palm Planting System

FFB Yield (t ha-1 year-1)

1st year harvest

2nd year harvest

3rd year harvest

4th year harvest

Conventional triangular Control plot 3.52 9.04 24.66 26.31 Double-row avenue Integrated with hill paddy (Oryza sativa) 3.21 10.71 23.25 23.15 Integrated with sorghum (Sorghum bicolor) 4.07 11.67 25.40 22.96 Integrated with tongkat ali (Eurycoma longifolia) 3.48 9.08 21.97 22.10 Integrated with pigeon pea (Cajanus cajan) 4.21 9.73 23.20 25.19 Control plot 3.98 9.77 27.12 23.36

The integration of crops with oil palm will optimise the land-use and provide additional income to the farmer

or subsistence food. Table 2 shows that several crops are suitable to be integrated with the oil palm under

the double-row avenue system.

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8 10 12 14 16

Ph

oto

syn

thec

ally

Act

ive

Rad

iati

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(%

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Distance between palms (m)


1.5. Black pepper

Black pepper (Piper nigrum L.), also known as the King of Spices is the most important and widely used

spice in the world. It originates from the tropical forests of Western Ghats, South India and was taken

later, due to European colonisation of the 17th century, to Indonesia and Malaysia. It is now produced in

over 26 countries, around the tropical belt. In Malaysia and Indonesia, it is considered a smallholder’s crop,

with an average holding of 0.1 to 0.4 ha per family (Ravindran et al., 2003).

The product consumed is a dried drupe that grows on a spike and is harvested before ripening. White

pepper is from the same plant, it is the post-harvest process that differs. Indeed, the fruits are collected

when the drupe is ripe (red or orange colour), the pericarp is removed and the seeds are dried (Ravindran

et al., 2003).

1.5.1. Plant description

Black Pepper is a perennial, woody and climbing plant from the family of the Piperaceae. Under farming

conditions, the adult plant grows on a support. Pepper does not usually have more than 10 main roots.

The feeder roots are distributed in the first 30–40cm of the soil. The water absorbing roots, however,

penetrate the soil to a depth of up to 4m (Terada et al., 1971). Independently of the age, most roots (67-

85%) are found on the first 0-30 cm of the soil depth and at 35-70cm of the base of the plant (Ramos et

al., 1984). The pepper plant has two distinct types of branches, the orthotropic branches and the

plagiotropic branches, where each has their own functions. The orthotropic branches are the climbing

branches. They remain vegetative and have short adventitious roots present at the nodes, allowing

adherence to the support on which it grows. The plagiotropic branches, on the other hand, have the ability

to become generative. They produce higher-order branches as well as inflorescences (Guigaz, 2002).

Inflorescences are spike shaped and appear opposite to the leaves on plagiotropic branches. They hold 50

to 150 flowers on each spike. The flowers are usually bisexual. Geitonogamy is believed to be the common

mode of autogamous pollination (Corley et al., 2003). Geitonogamy is a self-pollination mechanism by

which the pollen descends due to the effect of gravity combined with the rain water or dew drops. This is

an efficient mechanism amongst plants with long pendent inflorescences. However, heavy rain has a

negative effect on pollination (Ananda, 1924). After fertilisation, the ovary takes 8 to 9 months to mature.

The leaf area per plant in bush pepper is primarily dependant on the increase of leaf area per leaf whereas

for vine pepper, the leaf area per plant is dependent on the number of leaves produced. There is a

significant difference in the biomass distribution, the number of leaves and, the total leaf area, between

vine pepper and bush pepper (Geetha et al., 1992).

The productivity of a plant depends on its efficiency in harvesting solar energy for the metabolite production

and its partitioning efficiency of the metabolites produced (Bindra et al., 1977). It has been shown for

black pepper that a higher light availability before flowering induced a greater leaf area and a more compact

canopy structure with shorter lateral shoots. This leads to a higher accumulation of metabolites, more

spikes, more flowers and therefore, a greater number of fruit and a higher dry matter of fruits per plant

(Mathai et al., 1988). Mathai et al., (1990) observed a significant relationship between biomass production

and economic yield. However, when illumination exceeds 900 µmol s-1 m-2, the photosynthetic rate

decreases (Mathai, 1983).

1.5.2. Agronomy

Even though black pepper produces drupes, the commercial propagation is only done through vegetative

propagation, with the use of cuttings made from orthotropic shoots. Cuttings are usually kept in a nursery

until transplanting. One or two months after transplanting, the plant becomes vigorous. Posts have to be

installed on the field to the required distance before transplanting.

Pepper can be harvested from April-June until August-September in South-East Asia. Harvesting is done

every two weeks. Black pepper plantations have an economic lifespan of 15 to 20 years.

1.5.3. Ecology

Black pepper is suited for the tropical climate with a well distributed annual rainfall of 2000-4000mm and

with a temperature of 25-30°C and a relative humidity of 65-95%. The crop grows best below 500m on

the equator but can be grown at altitudes of up to 1500m. It grows on soils ranging from heavy clay to

light sandy clays. The soil needs to be deep, well drained but with a sufficient water-holding capacity for

the plant to deal with water stress (Westphal et al., 1989).


1.6. Research objective

Raja Zulkifli et al., (2016) conducted a research on the integration of black pepper (Piper nigrum) under

the double-row avenue system, in the MPOB research stations of Belega located in the state of Sarawak

and Keratong in the state of Pahang, Malaysia. They concluded that black pepper is a suitable crop to be

integrated with oil palm under the double-row avenue system as it is technically and economically feasible

to be practised by the smallholders. In addition to this, it is stated that the integration of crop with oil palm

optimises land use and provides and an additional long-term income to the farmer. However, in comparison

to the results found by Suboh et al., (2009), the measured yield is lower in the 3rd and 4th year of harvest

(Table 3). This decrease in yield is not explained. The results obtained by Raja Zulkifli et al., (2016) have

been compared to the MPOB oil palm production statistics at the Malaysian national level and to the

corresponding regional level (Pahang and Sarawak) however, no comparable decreasing trends in yields

have been found.

Table 3, Comparison of the FFB yields (t ha-1 year-1) in the first four years of harvest obtained by Suboh et al., (2009) and Raja Zulkifli et al., (2016).

Reference Oil palm Planting System FFB Yield (t ha-1 year-1)

1st year harvest

2nd year harvest

3rd year harvest

4th year harvest

Suboh et al., (2009) Conventional triangular

Control plot 3.52 9.04 24.66 26.31

Double-row avenue

Mean value of integrated crops 3.74 10.29 23.45 23.35

Control plot 3.98 9.77 27.12 23.36

Raja Zulkifli et al., (2016) Conventional triangular

Control plot n.a n.a 15.10 13.60*

Double-row avenue

Integrated with black pepper 6.00 13.00 16.90 13.20*

Control plot 5.50 10.30 14.40 13.00*

Note: * Nine months record, we assume a yield of 18.13 t ha-1 year-1 for the conventional triangular control plot, 17.6 t ha-1 year-1 for the double-row avenue system integrated with black pepper and 17.3 t ha-1 year-1 for the double-row avenue system control plot ; n.a = not available

Intercropping is of little interest to the large plantations. Therefore, there is a scarce amount of literature

available on this matter (Corley et al., 2003). There is little research on oil palm intercropped with black

pepper. The use of model simulations to investigate the potentialities and restrictions of the intercropping

of oil palm and black pepper is an important step in order to evaluate the possibilities of the system in a

less costly and time-consuming way. The use of model simulation would allow exploring the system in the

future and, to identify its limiting factors and management strategies (Pinto et al., 2005)

The WaNulCAS model (Water, Nutrient and Light Capture in Agroforestry Systems) is a tree-soil-crop

interaction model used for agroforestry systems. It allows the assessment of the water, nutrients and light

interactions in the system. The model represents a four-layer soil profile (vertical), with four spatial zones

(horizontal) (Figure 5) and allows a description of the uptake of water and nutrients on the basis of root

length densities of both plants grown on the plot (van Noordwijk et al., 1999). The WaNulCAS model

simulates dynamic processes in a spatial and field scale environment, such as the simulation of above and

below ground plant growth within a dynamic biophysical environment (Walker et al., 2007).

The vegetative and generative duration of the crops chosen is used as an input. Each crop should be

specified on the basis of its maximum dry matter production rate per day (kg DM m-2 day-1) and on its

relative light use efficiency (dry matter produced per unit of light intercepted) and its leaf weight ratio as

a function of the crop stage (van Noordwijk et al., 1999).

The model incorporates management options such as tree spacing, pruning and choice of species (van

Noordwijk et al., 1999).


Figure 5, General layout of the zones and layers in the Wanulcas model (After van Noordwijk et al., 1999)

The interactions between the species selected for the modelling are characterised by many parameters.

These parameters are defined under a crop library in the case of a crop or a tree library in the case of a

tree. The crop library holds a database for specific crop parameters and crop related input-output for the

system simulated. Overall, there are 58 inputs parameters (Appendix 1) including 5 growth parameters as

a function of crop stage (van Noordwijk et al., 2011). However, the crop library for black pepper does not

exist.

The aims of the study are twofold:

1. Develop allometric models for pepper, to represent quantitative relations between the growth of

different organs on the plant.

2. Aim at the creation of a crop library for pepper, using the previously created allometric models.

a. First, integrate the allometric models and biomass distribution equation in the WaNuLCAS

model.

b. Second, parametrisation and calibration of the WaNuLCAS model

The aim of this study is the creation of a crop library for black pepper, enabling the use of the WaNuLCAS

model to research the growth and production of oil palm and black pepper (Piper nigrum) under the double-

row avenue system, in the MPOB research station in Belega, Malaysia. This will allow to answer the research

questions:

R.Q.1: Is the production of oil palm and black pepper possible in the long term under the double-row

avenue system?

R.Q. 2: Does the oil palm and black pepper use resources from different niches? Is there a competition

for resources such as light, nutrients and water?

Wageningen University and Research | 8

Wageningen University and Research | Material and Methods 9

2. Material and Methods

Raja Zulkifli et al., (2016) conducted an experiment measuring the yields obtained for both oil palm and

black pepper. However, it only lasted for the time of four years after the first harvest of oil palm. The

objective of this study is to use the WaNuLCAS Model to investigate, in the long term, the biophysical

interactions and the performance of oil palm and black pepper under the double-row avenue system.

The experiment consists of twofold; the creation of allometric equations allowing the prediction of the

growth and yield of black pepper, and the simulation of intercropping black pepper and oil palm under the

double-row avenue system. The experimental phase was conducted in four distinctive steps; (1) a model

site parameterisation was performed. This involves a site parameterisation describing the abiotic conditions

of the investigation site (rainfall, temperature, soil description) and the definition of the four spatial zones

with their attributed ground cover; (2) a crop parameterisation and the creation of a crop library for the

black pepper, including the creation of allometric equations; (3) a model simulation was carried out using

WaNuLCAS to predict the growth of pepper and oil palm under the double-row avenue system; (4) a model

validation was performed, based on a comparative data analysis between the model predicted data and

the observed data on the MPOB research station, in Belaga.

In order to collect the data allowing the creation of allometric equations of black pepper and the creation

of a pepper model used in the crop library, a partnership was established with the World Agroforestry

Center (ICRAF), in Bogor, Indonesia. This allowed us to collaborate with three local farmers and access

pepper fields in the region of Konawe, Southeast Sulawesi, Indonesia (Figure 6). Interviews were

performed with farmers. This allowed us to choose the farmers with whom we would collaborate, based on

the farming practices, the pepper variety used, the maintenance of the plantation, the fertilizer application

and the presence of disease (Table 4).

Table 4, Agricultural practices description for the three farmers

Farming Practice Farmer Mr. Muksin Mr. Ramli Haji Tonga

Pepper variety Local Local Local

Propagation Orthotropic Branch Cuttings Orthotropic Branch Cuttings Orthotropic Branch Cuttings

Pepper support Glyricidia Glyricidia Glyricidia

Planting distance 2.5m*2.5m 2.5m*2.5m 2.5m*2.5m

Fertiliser Application Chicken manure + NPK NPK Chicken manure

Presence of diseases No Occasional borer No

Figure 6, Study zone where the three farms are located. The inset map on the left-top corner shows in the grey frame the

location of the region of Sulawesi in Indonesia. The greyed zone on the map shows the administrative area of Konawe.


2.1. Model site parameterisation

In the WaNuLCAS Model, four zones spatial zones need to be defined, where the ground cover and the

zone width needs to be defined. Moreover, the model has a mirror effect at each of the borders of the four

zones. Thus, the defined zones are repeated oppositely to the left of zone 1 and to the right of zone 4

(Figure 7). The horizontal zones (Figure 7) were established in the model according to the double-row

avenue system pattern established by Suboh et al., (2009) (Figure 2). Indeed, a distance of 8.5m between

the palms within the palm row and a distance of 15.2m of open avenue are respected. Moreover, a distance

of 2.5m between the pepper plants is established. The soil layer depth has to be set accordingly to the

rooting depth of black pepper and oil palm. The palm density is set at 138 trees ha-1.

Figure 7, the double row-avenue system configuration as used in the WaNuLCAS model. The oil palm tree is located on the right

of zone 1. Zone 2 is left empty, as the palm tree will grow and create shade within the open avenue. Zone 3 has a pepper plant

implemented in its middle. Zone 4 has a pepper plant implemented to its right. The model is parameterised in order to reduce

the production of pepper in zone 4 by 50 percent, thus allowing the simulation of a full pepper plant when taking in consideration

the mirror effect occurring when using the WaNuLCAS model.

2.1.1. Soil sampling protocol

In order to determine the soil characteristics (Table 5), a soil sampling and analysis was carried out. A

random sampling was carried out across the plots in order to cover soil variability. Three samples per

depth, per farm, for the three farms were collected. The depth of the sample collection was 0 to 20cm, 20

to 40 cm and 40 to 60cm. The samples were collected using a soil auger. In order to keep track of the

sampling locations, the coordinates were recorded using a handheld GPS receiver (Garmin Montana 600).

Once the samples collected, they were bulked together, according to the soil depth, and thoroughly mixed.

Sub-samples were collected into smaller batches to match the recommended amount of soil needed by the

laboratory conducting the soil analysis.

Table 5, Soil Analysis for soil site parameterisation

Horizons Texture pH Nutrient Content & Cation Exchange Capacity

Soil Depth Sand Silt Clay H2O KCl C organic P2O5 CEC

cm % % % [] [] % ppm Cmolc kg-1

0-20 13 54 33 4.5 3.7 1.13 15.3 13.20

20-40 14 55 31 4.7 3.8 0.75 4.9 12.43

40-60 12 61 27 4.6 3.7 0.56 4.9 12.38

2.1.2. Climate parameters

The climate parameters (Table 6) used in the model were based on the weather data gathered by the local

weather station.

Table 6, Data required for site parameterisation

Climate

Parameters Unit Source

Rainfall (daily or monthly) mm Weather station data

Daily Soil temperature °C Weather station Data


2.2. Pepper parameterisation

In order to simulate the phenological development of black pepper in the model, a black pepper Crop

Library was established. Given the fact that the crop library comprises 58 different input parameters

(Appendix 1). The crop library is partly based on field data collected in three black pepper plantations

(Table 7), in the region of Konawe, Southeast Sulawesi, Indonesia, as well as through information found

in literature and defaults values set in the WaNuLCAS model (M. van Noordwijk, personal communication,

8 June 2017).

Table 7, Black pepper parameters collected in monocultures

Parameters unit Acronym

Ratio of height increment to biomass increment [] Cq_HBiomConv Production of dry matter kg m-2 day-1 Cq_GroMax Length of the vegetative stage days Cq_TimeVeg Length of the generative stage days Cq_TimeGen Maximum Leaf Area [] Cq_LAIMax

Relative light intensity at which shading starts to affect the crop growth [] Cq_RelLightMaxGr

The WaNuLCAS model includes a Crop Management section where all the different agricultural practices

conducted through the experiment are referenced in the model. Therefore, in addition to the physiological

characteristics needed to run the model, managerial information was provided by the farmers, allowing

the model to consider those various agricultural practices during the results computation. These practices

include; timing of planting, timing of fertiliser and organic matter application and harvesting of crop

biomass.

2.2.1. Pepper parameters labelling protocol

The measurements and samples were classified according to each plant thus, assigned with the plant

number and the farm it was located on. In addition to this, each plant was subdivided into sections of 50cm

high. The number of sections present per plant was based on the plant height (Figure 8). The sampling

classification method was used for all material sampled.

Figure 8, Representation of a black pepper plant growing on a pole.

The plant is divided into sections set according to the height.

2.2.2. Pepper parameters sampling size

During the pepper data collection, we decided to sample 30 plants non-destructively. We measured 10

pepper plants in three different farms with similar agricultural practices (Table 4). We opted for the sample

size of 10 plants per farm on the basis of expecting to have homogeneous data within each farm. It was

decided to collect data from three different farms as this would enable us to detect a farm effect in the

data in case of its presence. Moreover, due to time limitations, the above-mentioned sample size seemed

the most feasible. A total of more than 15.000 measurements were performed for the plagiotropic branches

alone.

In addition to this, 2 plants were used for destructive measurement in order to create volume conversion

coefficients and density coefficients.


2.2.3. Pepper parameters sampling protocol

For the non-destructive measurement of the 30 plants, a total of 6 general parameters were collected per

plant; (1) Plant height, (2) Plant DBH, (3) Number of intersections between the orthotropic branches and

a vertical axis following the plant support, (4) the diameter of each orthotropic branch at each section (0,

50, 100, 150, 200cm), (5) the number of leaves per section, and (6) the number of generative organs per

section, according to their development stage. Moreover, 5 plagiotropic branches were analysed per section

per plant. The measurements consist of measuring the length and diameter of each internode and recording

its position in the branch structure. In addition to this, each leaf and generative organ supported on the

measured branch is recorded according to its position on the branch. Finally, 5 leaves per section per plant

were collected in order to establish leaf area and leaf biomass.

2.2.3.1. General parameters

1) Plant height

The plant height is determined by the distance between the ground and the highest leaf observed. The

pepper plant heights were measured using a plumb line and a measuring tape. The plumb line was attached

to a rod with a spirit level to ensure the horizontality of the rod when placing it at the top of the plant to

do the height measurement. The plumb line was allowed to slide through the rod allowing its adjustment

to the ground level. Sections of 50 cm were created allowing for classification of the samples. The highest

section was determined based on the highest multiple of 50cm below the highest measured leaf.

2) Plant diameter

The diameter of the canopy of the pepper plant was measured at breast height. In order to determine the

diameter, the circumference of the plant was measured using a flexible measuring tape.

3) Leaf area

The leaf area is based on the average individual leaf area observed per section. The leaf area is

characterised by the one-sided area of a fresh leaf. Five leaves per section were sampled, thus the leaf

area of 745 leaves was measured.

Each leaf included the leaf blade and its petiole. An image of each leaf was taken and analysed with an

image analysis software. The leaves were placed under a transparent plexiglass cover allowing a flat

positioning of the leaves before the capture of the image and were placed on a white background. The

measurements were obtained using an image analysis software, ImageJ, with the image processing

package FIJI. The software measures digital images by using the RGB (Red, Green and Blue) value of each

pixel to identify the leaf and scale regions in each image (Appendix 2). The conversion factor between the

pixel size and the true size (cm) represented by each pixel was set by establishing a scale. The imaging of

each leaf was done using a digital camera (Olympus OMD-EM5) placed on a tripod with the centre-column

installed upside down.

4) Leaf Biomass

The total number of leaves per section was recorded. In addition to this, for each plant, 5 leaves were

sampled per section. Each sampled leaf was analysed. The fresh biomass and dry biomass was measured.

To determine the individual leaf biomass (kg DM), the leaf samples were placed in a drying oven at 70°C

for a minimum time of 72 hours. Once the leaves dry, they were removed from the oven and their biomass

was determined by measuring the dry matter of each leaf using an electronic scale with a resolution of

0.01g. The dry matter biomass of 745 leaves was established.

Based on these measurements, we can establish the leaf biomass per section of 50cm, thus, if the height

of the plant is known, the total leaf biomass per plant (equation 1).

(1) 𝑇𝑜𝑡𝑎𝑙 𝐿𝑒𝑎𝑓 𝐵𝑖𝑜𝑚𝑎𝑠𝑠 = ∑ (𝑛 𝑙𝑒𝑎𝑣𝑒𝑠 𝑝𝑒𝑟 𝑠𝑒𝑐𝑡𝑖𝑜𝑛 ∗ 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐿𝑒𝑎𝑓 𝐵𝑖𝑜𝑚𝑎𝑠𝑠 𝑝𝑒𝑟 𝑠𝑒𝑐𝑡𝑖𝑜𝑛 (𝑔))

𝑛 𝑠𝑒𝑐𝑡𝑖𝑜𝑛𝑠

𝑖=0

2.2.3.2. Plagiotropic branch biomass

The plagiotropic branches for the 30 plants measured non-destructively were measured. For each plant, 5

branches were measured per each section of 50cm high. Each of the internodes composing these branches

were measured, based on their length and diameter, in order to establish a Theoretical Volume for each

internode (equation 2). The lengths and diameters were measured with an electronic digital calliper with a

resolution of 0.01 mm.


(2) 𝑇ℎ𝑒𝑟𝑜𝑟𝑒𝑡𝑖𝑐𝑎𝑙 𝐵𝑟𝑎𝑛𝑐ℎ 𝑉𝑜𝑙𝑢𝑚𝑒 (𝑐𝑚3) = (𝐿𝑒𝑛𝑔𝑡ℎ ∗ 𝐷𝑖𝑎𝑚𝑒𝑡𝑒𝑟2 ∗ 𝜋

4) ∗ 10−3

In addition to this, the structure of the branch was recorded. Moreover, the number of leaves at their given

position was recorded for the plagiotropic branches.

In order to convert the Theoretical Volume of the plagiotropic branches into their Biomass, we needed to

determine two transformation coefficients allowing to estimate the Real Branch Volume and the Branch

Biomass. The plagiotropic branches collected during the destructive sampling of the 2 pepper plants were

used to create the two transformation coefficients. In total, 737 plagiotropic internodes, originating from

the destructive measurement, were analysed. Their

length, diameter, fresh biomass, dry biomass and volume

were recorded. The length and diameter were measured

with an electronic digital calliper with a resolution of 0.01

mm. All weight measurements were carried out using an

electronic scale with a resolution of 0.01g. The fresh

biomass was measured using the electronic scale. In order

to measure the dry matter, the samples were placed in a

drying oven at a temperature of 70 degree Celsius for a

minimum of 72 hours and weighted using an electronic

scale. The volume of each internode, at its fresh stage, was

measured using a suspended hydrostatic measurement

technique, where the measured branch is immersed under

the surface of a liquid of known density (demineralised

water at 24 degree Celsius) in a container placed on the

electronic scale. The branch suspended in the water is

stationary, therefore we may conclude that the forces

exerted on the branch are balanced. Indeed, the gravitational (g) force is canceled out by the buyancy

force (b) and the tension force (t) (Figure 9). The branch immersed in water is equivalent to a volume of

water exactly the same size and shape. Therefore, the volume of the immersed branch is the increase in

weight divided by the density of the fluid (equation 2).

Where ρ is the density of the fluid at a given temperature, 𝛥𝑊 is the change in weight expressed on the

scale after the immersion of the branch and V is the unknown volume.

These recorded measurements allow to establish a transformation coefficient enabling the conversion of

the Theoretical Volume to the Real Volume (equation 3) and a density coefficient to transform the Real

Volume to Branch Biomass (equation 4). These two coefficients are obtained using linear regression

models, where Theoretical Volume is plotted against Real Volume and Real Volume is plotted against

Biomass. Indeed, the relationship between the dependent variables and independent variables will be

described by the regression analysis, thus allowing to estimate the values of the dependent variables

according to the independent variable.

(3) 𝑅𝑒𝑎𝑙 𝐵𝑟𝑎𝑛𝑐ℎ 𝑉𝑜𝑙𝑢𝑚𝑒 (𝑐𝑚3) = ((𝐿𝑒𝑛𝑔𝑡ℎ ∗ 𝐷𝑖𝑎𝑚𝑒𝑡𝑒𝑟2 ∗ 𝜋

4) ∗ 10−3) ∗ 𝑡𝑟𝑎𝑛𝑠𝑓𝑜𝑟𝑚𝑎𝑡𝑖𝑜𝑛 𝑐𝑜𝑒𝑓𝑓.

(4) 𝐵𝑟𝑎𝑛𝑐ℎ 𝐵𝑖𝑜𝑚𝑎𝑠𝑠 (𝑔) = [((𝐿𝑒𝑛𝑔𝑡ℎ ∗ 𝐷𝑖𝑎𝑚𝑒𝑡𝑒𝑟2 ∗ 𝜋

4) ∗ 10−3) ∗ 𝑡𝑟𝑎𝑛𝑠𝑓𝑜𝑟𝑚𝑎𝑡𝑖𝑜𝑛 𝑐𝑜𝑒𝑓𝑓. ] ∗ 𝐷𝑒𝑛𝑠𝑖𝑡𝑦

The biomass of each of the plagiotropic branch measured non-destructively may now be assessed based

on the two transformation factors previously established. Thus, to assess the total biomass of the

plagiotropic branches per plant, the relationship between a given branch biomass and the number of leaves

it holds was analysed (equation 5). In order to analyse the relationship between each branch biomass and

the number of leaves it holds, a linear regression analysis is conducted. The number of observations used

to carry out the regression analysis is n=646.

(5) 𝑃𝑙𝑎𝑔𝑖𝑜𝑡𝑟𝑜𝑝𝑖𝑐 𝐵𝑟𝑎𝑛𝑐ℎ 𝐵𝑖𝑜𝑚𝑎𝑠𝑠 𝑝𝑒𝑟 𝑃𝑙𝑎𝑛𝑡 = [ ∑ (𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑙𝑒𝑎𝑣𝑒𝑠 𝑝𝑒𝑟 𝑠𝑒𝑐𝑡𝑖𝑜𝑛)

𝑛 𝑠𝑒𝑐𝑡𝑖𝑜𝑛𝑠

𝑖=0

] ∗ 𝐵𝑟𝑎𝑛𝑐ℎ 𝑐𝑜𝑒𝑓𝑓.

(2) 𝑉 =𝛥𝑊

𝜌

Figure 9, Hydrostatic measurement set-up.

(Hugues, 2005)


2.2.3.3. Orthotropic branch biomass

The data used was recorded during the non-destructive measurement of the 30 plants. In order to

determine the volume of the orthotropic branches measured, we used the formula used to find the volume

of a conical frustum (equation 6). This equation is commonly used in forestry to establish branch volume.

The diameter of the internode of the orthotropic branches were measured at each of the intersections of

50cm, including ground level (0cm). In order to find the volume of the internodes of the orthotropic

branches at a given section, the sum of the diameters at the lower section (𝐷𝑎) and the sum of the

diameters at the upper section (𝐷𝑏) of the orthotropic branches were used.

(6) 𝑇𝑜𝑡𝑎𝑙 𝑉𝑜𝑙𝑢𝑚𝑒 𝑂𝑟𝑡ℎ𝑜𝑡𝑟𝑜𝑝𝑖𝑐 𝐵𝑟𝑎𝑛𝑐ℎ𝑒𝑠 𝑝𝑒𝑟 𝑆𝑒𝑐𝑡𝑖𝑜𝑛 =𝜋 ∗ ℎ + (𝐷𝑎

2 + 𝐷𝑏2 + 𝐷𝑎 ∗ 𝐷𝑏)

12

To determine the biomass of the sum of the orthotropic branches per section (equation 7), we used a

density coefficient that is determined using the orthotropic branches collected during the destructive

sampling. The procedure to determine the density was the same as for the plagiotropic branches.

(7) 𝑇𝑜𝑡𝑎𝑙 𝐵𝑖𝑜𝑚𝑎𝑠𝑠 𝑂𝑟𝑡ℎ𝑜𝑡𝑟𝑜𝑝𝑖𝑐 𝐵𝑟𝑎𝑛𝑐ℎ 𝑝𝑒𝑟 𝑆𝑒𝑐𝑡𝑖𝑜𝑛 = 𝜋 ∗ ℎ + (𝐷𝑎

2 + 𝐷𝑏2 + 𝐷𝑎 ∗ 𝐷𝑏)

12∗ 𝐷𝑒𝑛𝑠𝑖𝑡𝑦

2.2.3.4. Fruit biomass

During the destructive measurement of the two pepper plants, a total of 619 generative organs were

collected. During this sampling phase, no distinction was made between the different development stages

of the generative organs; flower, semi-developed spike and fully developed spike. All the 619 samples

were analysed. The fresh biomass and dry biomass was established using an electronic scale with a

precision of 0.01g. In order to dry the samples, the generative organs were placed in a drying oven at 70

degrees Celsius for a period of 72 hours. Based on the data obtained, we were able to establish an average

generative organ biomass.

During the non-destructive measurement of the 30 plants, the number of generative organs and the

number of leaves were counted for each section. The relationship between the total number of leaves per

plant and the total number of fruits per plant allowed to estimate the fruit biomass per section, based on

the number of leaves observed. In order to analyse the relationship between the number of fruits per

section and the number of leaves per section, a linear regression analysis was conducted between the

independent variable; Number of leaves per section and the dependent variable; Number of fruits per

section. The number of observations used to carry out the regression analysis is n=124.

In order to find out the fruit biomass, we need to multiply the amount of fruits by the average biomass

(equation 8);

(8) 𝐹𝑟𝑢𝑖𝑡 𝐵𝑖𝑜𝑚𝑎𝑠𝑠 = 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑓𝑟𝑢𝑖𝑡𝑠 ∗ 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑓𝑟𝑢𝑖𝑡 𝑏𝑖𝑜𝑚𝑎𝑠𝑠

2.2.4. Biomass allocation

Pepper yield estimation may be now conducted through the use of allometric equations. Indeed, an

allometric equation represents the quantitative relation between the growth of two or more organs of the

same plant (Richards, 1969). It, therefore, becomes possible to assess a physiological status of a growing

plant by analysing the allometric relationship to a different organ.

The allometric equations used were derived from the different empirical parameters collected. This allowed

to establish the distribution of growth over the different plant organs (equation 9).

(9) 𝑃𝑙𝑎𝑛𝑡 𝐵𝑖𝑜𝑚𝑎𝑠𝑠 = (𝛼 𝐿𝑒𝑎𝑓 𝐵𝑖𝑜𝑚𝑎𝑠𝑠) + (𝛽 𝐹𝑟𝑢𝑖𝑡 𝐵𝑖𝑜𝑚𝑎𝑠𝑠) + (𝛿 𝑂𝑟𝑡ℎ𝑜 𝐵𝑖𝑜𝑚𝑎𝑠𝑠) + (𝜖 𝑃𝑙𝑎𝑔𝑖𝑜 𝐵𝑖𝑜𝑚𝑎𝑠𝑠)

2.2.5. Tree parameterisation

In the case of the oil palm, the library already exists, therefore, no parameters needed to be collected in

order to use it in the WaNuLCAS model.

1.7. Model validation

A comparison was conducted between the modelled yield results and field data observed on the trial

plots at the Belaga research station. If the comparison suggests that the results produced by the model

are close to the data obtained from the field, the model will be considered valid.

Wageningen University and Research | Results 15

3. Results

3.1. Pepper allometry and biomass distribution

3.1.1. Leaf biomass

The leaf biomass per individual section was determined using the average number of leaves per section and the average leaf biomass per section (Table 8). Plotting the biomass increment with the increase in

height represented by the sections (Figure 10a) gives us a model allowing to establish the leaf biomass increment (g DM) per increase of 50cm. This relationship is described by the equation: 𝑦 = 2.0868𝑥 + 1.9842.

In figure 10b, we observe that the leaf biomass at 0-50cm is lower than at 50-100. The lower leaf biomass at 0-50 is explained by the fact that they are less leaves present in the section (Table 8). A smaller amount of leaves may be explained by the fact that pepper is propagated generatively using cuttings made from orthotropic branches and the first plagiotropic branch does not appear at ground level but higher. As it is the plagiotropic branches that hold the leaves, this results in a smaller number of leaves, thus biomass. The decrease of leaf biomass starting at 100cm onwards (Figure 10b) is explained by two factors; there are less leaves present per section as we look into higher sections and the leaf size is reduced as we look higher into the sections (Table 8). Indeed, the leaf area per individual leaf decreases as it is located higher on the plant and therefore leads to a lower individual leaf biomass. This trait may be phenotypically related.

Between 200 to 250cm, there is an increase in leaf biomass due to an increase in the number of leaves

present. A greater number of leaves may be due to fact that there is more light available at the top of the

pepper plant, inducing the production of leaves. However, the mean leaf size is smaller. Moreover, only 4

plants measured reached the category of 200-250. Thus, the biomass is not representative to the rest of

the plants measured as they were taller. The influence of the four higher plant on the average leaf biomass

of the lower sections was checked. No significant effect was observed thus, we chose to keep these four

plants in the sample batch.

Table 8, Statistical summary of leaf description

Section Parameter Number of leaves (n)

Biomass leaf (g DM)

Leaf Area (cm²)

Specific Leaf Area (g DM cm-2)

0-50 Mean 274.6 0.3603 7.810 0.0473

Standard Deviation 128.65 0.1032 1.8305 0.0079

Min 100 0.0600 4.187 0.0143

Max 531 0.66 12.109 0.0648

n 30* 150 150 150

50-100 Mean 327.4 0.3521 7.610 0.0463


Min 163 0.1100 4.185 0.0187

Max 529 0.6100 12.207 0.0647

n 30* 150 150 150

100-150 Mean 343.0 0.3342 7.279 0.0459


Min 183 0.1600 4.243 0.0252

Max 536 0.5900 12.606 0.0626

n 30* 150 150 150

150-200 Mean 274.3 0.3355 7.103 0.0474


Min 81 0.14 3.883 0.0255

Max 508 0.60 12.811 0.0842

n 29* 145 145 145

200-250 Mean 340.8 0.2981 6.622 0.0447


Min 240 0.09 3.519 0.0177

Max 466 0.63 11.318 0.0644

n 4* 145 145 145

*= number of trees on which the number of leaves per section were recorded

Figure 10, (a)Relationship between cumulated leaf biomass and section,

(b) relationship between non-cumulated leaf biomass and section.


3.1.2. Plagiotropic branches biomass

The linear model used to convert the theoretical plagiotropic branch volume to the real branch volume

(equation 3) uses the following function: 𝑦 = 1.442𝑥 + 0.079 , with an R² value of 0.97 (Figure 11a). The

model used to convert the real plagiotropic branch volume to its biomass in dry matter (equation 4) uses

the following formula: 𝑦 = 0.385𝑥 − 0.028 , with an R² value of 0.97 (Figure 11b). Both regression models

are based on n=735 measurements of plagiotropic branches.

The transformation coefficients were used to determine the biomass (g DM) of the branches measured

non-destructively. The model used to estimate the total plagiotropic biomass based on the number of

leaves present per branch biomass uses the following equation: 𝑦 = 3.784𝑥 + 6.126 and allows to explain

54.2% of the variation.

Based on the cumulative leaf biomass (Figure 10a), we can model the plagiotropic branch biomass to

height increment (Figure 13a) defined by the following equation: 𝑦 = 24.252 + 137.321. There is a low

plagiotropic branch biomass in the first 50cm, it may be explained by the fact that pepper is propagated

generatively using cuttings made from orthotropic branches and the first plagiotropic branch does not

appear at ground level but higher. Between 50cm to 150cm, the plagiotropic branch biomass increases.

This is due to a greater amount of branches per sections. The specific branch biomass per sections was

analysed and showed a decrease in biomass as the branch was in higher sections (Appendix 3). We may

therefore conclude that the increase in biomass is related to the increase in the number of branches present

per section. This trait, like the leaf biomass, may be phenotypically related.

Figure 11, Plagiotropic internodes conversion factors. (a) Relationship between theoretical

volume of the internodes and their real volume, (b) Relationship between the internode

real volume and their biomass (DM).

Figure 12, Relationship between the number of leaves

observed per branch and the branch biomass.

Figure 13, (a)Relationship between cumulated plagiotropic branch biomass and

section, (b) relationship between plagiotropic branch biomass and section.


3.1.3. Orthotropic branches biomass

The linear model used to convert the volume of the orthotropic branches to its biomass (g DM) (Figure 14)

uses the following function: 𝑦 = 0.409𝑥 − 0.099. It is based on the analysis of 186 samples of orthotropic

brannches. The volume used in the regression is based on the conversion of the measured diameters at

each intersection using the conical frustum equation.

The relationship between the orthotropic branch biomass and the height at which it is located is following

a decreasing pattern. Indeed, Figure 15b shows that the orthotropic branch biomass is lower at the upper

part of the plant than at the lower sections. This is due to the growth of the plant, where the orthotropic

branches grow in height but in width over time.

The orthotropic branch biomass per individual section was determined using the average values of biomass

per section (Appendix 4). Plotting the orthotropic branch biomass increment with the increase in height

represented by the sections (Figure 15a) gives us a polynomial model of order 2 allowing to establish the

orthotropic branch biomass increment (g DM) per increase of 50cm. This relationship is described by the

equation: 𝑦 = −0.039𝑥2 + 18.256𝑥 − 225.853.

Figure 14, Orthotropic branch conversion factor.

Relationship between the real volume and the

biomass (DM).

Figure 15, Relationship between cumulated orthotropic branch biomass and section, (b)

relationship between orthotropic branch biomass and section.


3.1.4. Fruit biomass

The best relationship found to predict the fruit biomass per section is through the number of leaves present

per section, with an R2 of 0.62. The linear model (Figure 16) described as 𝑦 = 0.305𝑥 + 11.272 allows us to

predict the number of genererative organs present per section. The number of fruits present per given

section is multiplied by the average fruit biomass (Table 9).

The relationship between the increase in fruit biomass and plant height is constant (Figure 17a). The linear

model is expressed by the following equation: 𝑦 = 1.845𝑥 − 9.408. The trend of the fruit biomass according

to the height follows a bell-shaped pattern, where the fruit biomass per section increases until 150cm high.

The fruit biomass decreases after 150cm (Figure 17b). This is due to the fact that only the plagiotropic

branches hold the generative organs. Indeed, the decrease in fruit biomass after 150cm is related to the

decreasing amount of plagiotropic branches present in the section above 150cm as well as to the

decreasing specific biomass of the plagiotropic branches.

Table 9, Statistical summary of fruit description

Parameter Biomass Fruit (g)

Mean 0.8527

Standard Deviation 0.3927

Min 0.05

Max 2.25

n 618

Figure 16, Relationship between the number of leaves and number of generative

organs observed on the 30 plants measured non-destructively.

Figure 17, (a) Relationship between cumulated fruit biomass and section, (b) relationship

between fruit biomass and section.


3.1.5. Biomass distribution

The biomass distribution is dependent on the height of the pepper plant. Indeed, as the distribution of each

organ is not evenly spread throughout the plant, the size of the plant will determine the biomass

distribution (Figure 18). The detailed description of the biomass distribution is found in appendix 5. The

total biomass is of 1853.88 g, 3883.36g, 5717.84g, 7357.32g and 8801g, for the 50, 100, 150, 200 and

250 sections, respectively. We may observe on Figure 18 that the plagiotropic branch biomass proportion

increases with the height increment. On contrary, the orthotropic branch biomass proportion decreases.

This is due to the fact that the plant first allocates most of the photosynthates to the vertical growth of the

plant, thus through the orthotropic branches.

3.1.6. Identification of farm effect

The sampling carried out to create the allometric equations and the biomass distribution took place on

three different farms, where similar farming practices were conducted.

A first step was conducted to see if there is a difference between farmers in the data that was used to

make the pepper model. The data related to the farms and used for the allometric equations calculus are;

number of leaves per section, number of fruits per sections, orthotropic branch biomass per section. The

P-Value were 3.02e-04, 9.01e-06 and 2.59e-04 for the farm effect on leaves, fruits and orthotropic

branches, respectively. We may therefore reject the null hypothesis, stating that farm has no effect on the

three parameters.

A comparison was carried out to compare the different farms in order to assess farm effect. Figure 19

shows the cumulative biomass of all the organs for a given plant of 250cm high. We can observe that the

farm Muksin has the greatest overall biomass. However, when looking at the relative values, we can

observe that the biomass distribution is similar throughout the three farms and the general model

combining the data of all three farms. We may therefore conclude that there is indeed a farm effect

however, there is no effect on the biomass distribution.

0.580 0.589 0.612 0.640 0.673

0.337 0.273 0.248 0.234 0.228

0.663

0

0.674 0.685 0.673

0.235 0.220 0.187 0.021

0.057 0.064 0.059

Figure 18 Stacked bar plot describing the distribution of biomass of the pepper plant. The bar plot on the left describes the relative

biomass distribution according to the height increment. The bar plot on the right describes the absolute biomass distribution of

the pepper plant. The darkest grey represents the plagiotropic branch biomass, followed by the orthotropic branch biomass, leaf

biomass and the lightest grey represents the biomass allocated to the fruits.

0.060

Figure 19. The bar plot on the left describes the relative biomass distribution according to the height increment per farm and for

the general model. The bar plot on the right describes the absolute biomass distribution of the pepper plant. The darkest grey

represents the plagiotropic branch biomass, followed by the orthotropic branch biomass, leaf biomass and the lightest grey

represents the biomass allocated to the fruits.


3.2. WaNuLCAS modelling results

The WaNuLCAS model was used to assess the effect of intercropping on oil palm and pepper. The modelling

was performed using WaNuLCAS 4.3.1 on the dynamic modelling platform Stella 7.0.3 Research. The

simulations were done under two conditions; 1) monoculture, using the recommended planting pattern;

2) intercrop, using the double-row avenue system. All simulations using the WaNuLCAS model were done

using the same fertilisation regime, climatic conditions, edaphic conditions and managerial activities.

3.2.1. Oil palm in monoculture

The conventional planting pattern of oil palm was elaborated to optimise the canopy area. Indeed, the

conventional planting configuration allowed for a closed canopy, leading to an optimum area allowing the

capture of the photosynthetic active radiations. Figure 20 shows the modelled Fresh Fruit Bunch production

per annum, under the conventional planting pattern. The production of fruit starts 4 to 5 years after

planting. Figure 20 shows that the production curve follows a normal FFB production pattern, where the

immature phase takes place for 4 years after planting, followed by the young phase, where the yield

reaches its peak and finally, followed by a plateauing stage. The average yield at the mature stage is of

20.5 Mg FFB ha-1 year-1. Such values and production patterns are described in literature.

The double-row avenue pattern is not configured to optimize the canopy area. Indeed, the reduction of

space between the palm lines, leads to a smaller ground cover allocated per tree, thus a reduced canopy

area able to use the PAR. Figure 21 shows the fraction of light used by the palm trees. We can observe

that the fraction in the conventional planting pattern is greater in comparison to the double-row avenue

system. The opening in the avenues, in the double-row avenue system, could have had the effect to

compensate this decrease in canopy area. Indeed, the absence of competition for space allows the palm

fronds to capture lateral light, thus leading to a greater area for PAR capture. However, this was not

observed in the modelling results. Figure 20 shows that planting the palms under the double row avenue

system pattern, without any crop integrated into the opening, induces an average yield at the mature

stage of 18Mg ha-1 year-1. We may therefore conclude that the effect of additional light captured in the

openings of the avenue is not sufficient to compensate for the decrease in canopy area.

Figure 20, Oil palm production curves (Mg ha-1 year-1). The circles and plain line represent yield under the conventional

planting pattern. The triangles and the dotted line represent yield under the double-row avenue pattern.

Figure 21, Fraction of light received by the tree. The circles represent the light fraction used under the conventional

planting pattern. The triangles represent the light fraction used under the double-row avenue pattern.


3.2.2. Pepper in monoculture

The simulation of pepper was conducted by integrating the research results in the WaNuLCAS model. The

allometric equations and biomass distribution were used to establish the data set used in the Crop Library

in order to be able to simulate the growth and fruit yield of pepper, under various conditions. The simulation

of pepper in monoculture has only one planting pattern. The planting pattern of pepper is the same for

both in the conventional pattern and when using the double-row avenue system. Moreover, the maximum

height of pepper was set in the model at 250cm, as it is in a pepper field maintained under the best farming

practices. The time span of the simulation for pepper was 25 years, in order to follow the plantation cycle

of oil palm.

Figure 22 describes the production curve of pepper. We can observe that the beginning of production

occurs after three years. After 5 years, the production reaches its plateau. This may be explained by the

fact that the pepper plant grows in height at the beginning, by allocating more of its biomass to the

orthotropic branches. Once the plant has reached its final height, there is an increase in total biomass,

until it has reached its maximum. No production curve has been found in literature. May we therefore not

confirm the production curve established by the model. However, the yield at mature production is on

average at 3.1Mg DM ha-1 year-1. The simulated result using the WaNuLCAS model corresponds with the

yields reported in literature (Ravindran, 2003).

3.2.3. Oil palm and pepper under the double-row avenue system

Intercropping may lead to competition for resources. Figure 23 shows a decrease in yield of FFB when

pepper is added to the plantation. The average yield at mature stage is of 15.5 Mg ha-1 year -1. This may

be due to the competition for nutrients and water. Indeed, both the oil palm and the pepper are sourcing

their nutrients and water from the same niche, leading to competition for nutrients and water. However,

the stress induced by the change in nutrient availability was not expressed by the WaNuLCAS model.

Figure 22, Pepper production over time (Mg ha-1 year-1). The curve represents the

production curve.

Figure 23, Oil palm production curves (Mg ha-1 year-1). The circles and plain line represent oil palm yield under the conventional

planting pattern. The triangles and the dotted line represent oil palm yield under the double-row avenue pattern. The squares

and finely dotted line represent oil palm yield when combined with pepper under the double-row avenue system.


It is assumed that oil palm and pepper compete for nutrients. However, the competition for light is more

pronounced when looking at the shading effect of the palms on pepper. Figure 24 shows, in the initial stage

of the intercrop, that pepper has a yield corresponding to the yield observed when grown in monoculture.

We may qualify the effect of shading of the established palms to be minim on the production of pepper for

5 years after planting. However, the shading effect of the palms increases (Figure 25b) with the increase

in palm trunk height, frond length and crown radius (Figure 25a) has a significant effect on the pepper

production, located in zone 3 (Figure 7). The pepper located in zone 4 (Figure 7) is less affected by the

growth of palm. This may be due to the fact that the pepper plants located in zone 4 are less affected by

the shade. Aravindakshan et al., 1969, report that yield is dependent on the amount of photosynthates

available.

Figure 25a shows that the length of the fronds t greater than the radius of the crown. This is may be due

to the fact that the WaNuLCAS model took into account the fact that the petiole of the fronds does not

attach to the trunk horizontally, thus creates an arc from the bending frond.

Figure 25b describes the amount of light that is used by the pepper plants in zone 3 and 4. We observe

that the amount of light captured by the pepper plant is starting to decrease after 5 years of plantation.

This effect is in concordance to the fact that the palm leaf and thus, the palm canopy radius are increasing

their size. After 5 years after plantation, the palm fronds and radiuses are of 4.98m and 4.57m respectively.

The canopy of the palm starts, after 5 years only, to extend into zone 3 (Figure 7), which is allocated to

pepper. After 19 years after planting, the amount of light captured in zone 3 is 0.2g m-2. Therefore, pepper

plant growth is impossible at this stage.

The amount of light captured by pepper in zone 4 starts to decrease 19 years after planting. This

corresponds to the time when the palm crown radius is 6.43m, thus starts to extend onto zone 4. Syakir,

1994 states that pepper yield pepper is affected when the available light goes below 25% of shading. In

case of the zone 4, the 25% decrease of available light corresponds to 23 years after planting however,

the modelled results show a yield decrease at around 15 years.

Figure 24, Pepper production (Mg ha-1 year-1). The circles and plain line represent yield in monoculture. The squares and the

dotted line represent yield under the double-row avenue pattern, in zone 4. The triangles and finely dotted line represent yield

under the double-row avenue system pattern, in zone3.

Figure 25, (a) Trunk height increase over time (circles), frond length increase (squares) and crown radius (triangles) increase

over time. (b) Amount of light intercepted in zone 3 (circle) and zone 4(triangles)

Sling001

Cross-Out

Wageningen University and Research | Discussion 23

4. Discussion

Using the WaNuLCAS model allowed us to characterize and assess the growth of oil palm and pepper under

the double-row avenue system, for a period of 28 years.

We observed in the modelled results that the production of oil palm decreases by an average of 3.5 Mg ha-

1 year-1 when the planting pattern is changed from conventional to the double-row avenue system. Goh,

1982 reported that there is a decrease in yield of FFB per palm corresponding to the increase in plantation

density. Indeed, adopting the double-row avenue system induces an increased density of 192 palms ha-1

within the row. Moreover, the expected increase in light interception, due to the additional radial light

coming through the openings of the avenues, creating a border effect, did not compensate the decrease

of yield related to the increase in density mentioned by Goh, 1982.

A decrease of 2.5 Mg ha-1 year-1 of oil palm production yield was observed when intercropping oil palm and

pepper. However, we did not witness any stress related to the competition for water or nutrients when

pepper was intercropped with oil palm under the double-row avenue system.

The effect of intercropping oil palm and pepper had a significant effect on the production of pepper. The

pepper row closest to the palm rows was the most affected by the palms. The shade induced by the palm

trees lead to significant decrease in production. Pepper plants located in the middle of the avenue were

less affected by the palm shade. This supports the idea that light had the greatest impact on pepper

production. Moreover, the fact that the pepper plants closer to the palms are more affected in comparison

to the pepper plants located in the middle of the avenue corroborates with the study of Mohd Haniff et al.,

2003 and Suboh et al., 2009, stating that there is more photosynthetically active radiation available for

the plants as we get closer to the centre of the avenue.

As proposed by Nuertey et al., 2000, one advantage of intercropping is the reduction of the financial burden

of replanting oil palms. Indeed, for the farmer, replanting induces a loss of income due to no production

by the palms and a waiting period for the first production of fruit bunches. Despite the high value of pepper,

it may not be the right crop to compensate the financial burden induced by replanting in the short term.

Indeed, pepper takes a few years to attain its mature yield. This means that the farmers may not be able

to rely on pepper only as a source of income. However, once the production of pepper has started, the

financial income is may play an important role in the farmer’s income. Prices are reported to be at around

3000US$ ton-1 for black pepper an around 5000US$ ton-1 for white pepper (International Pepper

Community, 2018).

In the long term, the application of intercropping oil palm and black pepper under the double-row avenue

system involves challenges. Indeed, pepper can be produced in the avenue for around 10 years. However,

the decrease in pepper yield induced by the shade of the palms affects greatly the production of pepper

located closest to the palm rows.

The data obtained through modelling was compared to the data published by Zulkifli et al., 2016, obtained

by a field trial where oil palm and black pepper are intercropped under the double-row avenue system.

The values obtained for oil palm production through modelling are in line with the values obtained by

Zulkifli et al., 2016 however, we may only compare the fresh bunch production for a period of 4 years as

their publication only mentions 4 years of production. Our study provides additional information regarding

the effectiveness of oil palm production in the long-term effect of intercropping. The modelled results

concerning the pepper production corroborate with the results from Zulkifli et al., 2016. Indeed, as their

experiment only lasted for 4 years after the first production of fresh bunches on the palms, they did not

yet reach the time frame needed to see the effect of shading on the pepper plants located closest to the

palms, thus concluded a satisfactory result for the long-term production of pepper. However, our study

leads to refute the conclusion stated by Zulkifli et al., 2016, where black pepper is a suitable crop for

integration in the double-row avenue system as it is not technically feasible through the entire lifespan of

the palm tree.

Various management practices are possible to encounter with the decrease in pepper yield. Farmers may

choose to grow pepper in the entire avenue and replace all pepper plants or the closest one to the palm

rows by a sciaphilic crop after ten years. Another option may be to use oil palm varieties with shorter fronts

however, this may impact the oil palm yield.


Wageningen University and Research | Conclusion 25

5. Conclusion

This study shows the challenges met when implementing intercropping as it induces a more complex

system. However, it offers to the farmer a large range of choices. In addition to the oil palm production,

the farmer may choose to plant a cash crop, a subsistence crop or both. These managerial strategies offer

the farmer the ability to be less dependent on the market prices and may offer more food security.

Intercropping oil palm also induces a greater diversification within the plantation, which may have

beneficial effects on the surrounding environment. Intercropping in oil palm may be a promising system

allowing to improve the current practices on many aspects.

This study stresses out the importance and advantages of modelling as it allows us to do long term

predictions. The modelling tools are an opportunity to skip years of trials and come to faster conclusions

regarding the best management practices to adopt when intercropping. Therefore, it could allow for a

faster change within the oil palm industry, if the institutes, governments and NGOs follow on closing down

the future knowledge gap between researchers and farmers.


Wageningen University and Research | Reference 27

Reference

Ananda, M. (1924). Observations on the habits of pepper vines with special reference to its reproductive phase.

Madras AGRI. Dept. Year book, pp. 49-69

Aravindakshan, M., & Krishnamurthy, S. (1969). Influence of leaves on the developmental physiology of

grapes (Vitis vinifera L.). Sci & Culture, 35, 111-114

Bindra, A.S., Brar, R.S. (1977). Shading may cause flower bud and flower drop in grape vines. Sci. and Culture,

43, 275-276

Causton, D. R., & Venus, J. C. (1981). The biometry of plant growth. Edward Arnold

Corley, R. H. V., & Tinker, B. (2003). The Oil Palm 4th edition. Blackwell Science LTD., Oxford.

Corley, R. H. V., & Tinker, B. (2015). The Oil Palm 5th edition. Blackwell Science LTD., Oxford.

Evans, L. T. (1993). Crop evolution, adaptation and yield.,(Cambridge University Press: New York).

FaoStat. Oil palm production by country (2011-2015)

Viewed July, 2017, retrieved from: http://www.fao.org/faostat/en/#compare

Geetha, C.K., Aravindakshan, M. (1992). A comparison of growth and dry matter production in bush pepper and

vine pepper as influenced by applied NPK. Indian Cocoa, Arecannut and Spices J., 15, 95-98.

Goh, K.J., (1982). Analyses of oil palm spacing experiments. In: The Oil Palm in agriculture in the eighties, Vol.

2. (Ed. by E. Pushparajah & Chew P.S.), pp. 393-414, Incorp. Soc. Planters, Kuala Lumpur.

Goh, K.J., Chew, P.S., Teo, C.B., (1994). Maximising and maintaining oil palm yields on commercial scale in

Malaysia. In: Chee, K.H. (Ed.), Proceedings of the 1994 Internation Planters Conference on Management for

Enhanced Profitability in Plantations. Incorporated Society of Planters, Kuala Lumpur, pp. 121–141.

Guigaz, M. (2002). Mémento de l’Agronome. Montpellier, CIRAD-GRET and Ministère des Affaires Étrangères.

Hartley, C.W.S. (1988). The Oil Palm, 3rd edition. Longman, London

Hughes, S. W. (2005). Archimedes revisited: a faster, better, cheaper method of accurately measuring the volume

of small objects. Physics Education, 40(5), 468.

Indonesian Ministry of Agriculture. (2011) Tree Crop Estate Statistics of Indonesia 2011-2012 Oil Palm.

Directorate General of Estates, Jakarta.

Indonesian Ministry of Agriculture. (2015) Tree Crop Estate Statistics of Indonesia 2014-2016 Oil Palm.

Directorate General of Estates, Jakarta.

International Pepper Community. (2018) FOB Daily Prices.

Retrieved May 25, 2018 from: http://www.ipcnet.org/index_n.php

Ling A.H. (2012). Weather Effects on Palm Oil Production: Supply Outlook 2012/ 2013. In: Proceedings of the

International Palm Oil Trade Fair and Seminar, 15–16 October 2012, Kuala Lumpur.

Loague, K., & Green, R. E. (1991). Statistical and graphical methods for evaluating solute transport models:

overview and application. Journal of contaminant hydrology, 7(1-2), 51-73.

Lott, J. E., Howard, S. B., Ong, C. K., & Black, C. R. (2000). Long-term productivity of a Grevillea robusta-based

overstorey agroforestry system in semi-arid Kenya: II. Crop growth and system performance. Forest Ecology and

Management, 139(1), 187-201.

Mathai, C.K. (1983). Growth and yield analysis in black pepper varieties (Piper nigrum L.) under different light

conditions. Ph.D Thesis, University of Agricultural Sciences, Bangalore, India

Mathai, C.K., Sastry, K.S.K. (1988). Productivity of black pepper as influenced by light availability during

preflowering stage. Comparative Physiology and Ecology, 13, 97-102

Mathai C.K., Nair, B.P. (1990). Biomass production levels in relation to economic yield in black pepper varieties.

J. Plantation Crops, 18, 125-128

Mielke, I.S.T.A. (2010). Oil World Annual 2010. Hamburg: ISTA Mielke GmbH.

Wageningen University and Research | Reference 28

Mohd Haniff, H., Suboh, I., Dayang Fildia, A. A., Mohd Shahabudin, I., Siti Aminah, K. (2003) Light Interception

and Leaf Area Index measurements from three different oil palm planting systems. In: 14th Malaysian Society of

Plant Physiology Conference. 3-4 September 2003, Genting Highlands.

Molenaar, J. W., Persch-Orth, M., Lord, S., Taylor, C., & Harms, J. (2013). Diagnostic study on Indonesian Oil

Palm Smallholders: developing a better understanding of their performance and potential. International Finance

Corporation, World Bank Group.

Nchanji, Y. K., Nkongho, R. N., Mala, W. A., & Levang, P. (2016). Efficacy of oil palm intercropping by

smallholders. Case study in South-West Cameroon. Agroforestry Systems, 90(3), 509-519.

Nuertey, B. N., Ofori, K., & Asamoah, T. E. O. (2000). Economics of intercropping food crops in oil palm for small-

scale farmers. Journal of the Ghana Science Association, 2(3), 170-176.

Okyere, S. A., Danso, F., Larbi, E., & Danso, I. (2014). Residual effect of intercropping on the yield and

productivity of oil palm. Int J Plant Soil Sci, 3(7), 854-862.

Pinto, L. F. G., Bernardes, M. S., Van Noordwijk, M., Pereira, A. R., Lusiana, B., & Mulia, R. (2005). Simulation of

agroforestry systems with sugarcane in Piracicaba, Brazil. Agricultural Systems, 86(3), 275-292.

Raja Zulkifli, R.O., James, C.H., Wahid, O., Norkaspi, K. (2016). Integration of Black Pepper with Oil Palm. MPOB

Information Series, ISSN 1511-7871.

Ramos, J.V., Moreira, C.S., Dossantos, R.N. (1984). The influence of age on root system distribution in pepper in

Bahia, Brazil. Theobroma, 14, 27-32

Ravindran, P. N. (Ed.). (2003). Black pepper: Piper nigrum. CRC Press.

Richards, F. J. (1969). The quantitative analysis of plant growth. Plant physiology, 3-76

Suboh, I., Norkaspi, K.,Raja Zulkifli, R. O., (2009). Double-Row Avenue System for Crop Integration with Oil

Palm. MPOB Information Series, ISSN 1511-7871.

Stomph, D. (2016). Smallholder oil palm: space for diversification, MSc Thesis Wageningen University, 71p.

Syakir, M. (1994). Pengaruh naungan, unsur hara P dan Mg terhadap iklim mikro, indeks pertumbuhan dan laju

tumbuh tanaman lada. Buletin Penelitian Tanaman Rempah dan Obat 9.2: 106-114.

Terada, S., Chiba, M. (1971). Problems of growing Piper nigrum L. in Amazon region with regards to root

development. Japanese J. Tropical Agri., 33, 11-15

United States Department of Agriculture. (2012) Malaysia: Stagnating Palm Oil Yield Impede Growth. Retrieved

July 02, 2017 from https://www.pecad.fas.usda.gov/highlights/2012/12/Malaysia/

Van Ittersum, M. K., & Rabbinge, R. (1997). Concepts in production ecology for analysis and quantification of

agricultural input-output combinations. Field crops research, 52(3), 197-208.

Van Noordwijk, M., & Lusiana, B. (1999). WaNuLCAS, a model of water, nutrient and light capture in agroforestry

systems. In Agroforestry for Sustainable Land-Use Fundamental Research and Modelling with Emphasis on

Temperate and Mediterranean Applications (pp. 217-242). Springer Netherlands.

Van Noordwijk, M., Lusiana, B., Khasanah, N., Mulia, R. (2011). WaNulCAS version 4.0, Background on a model

of water nutrient and light capture in agroforestry systems. Bogor, Indonesia. World Agroforestry Centre – ICRAF,

SEA Regional Office. 224 p.

Walker, A. P., Mutuo, P. K., van Noordwijk, M., Albrecht, A., & Cadisch, G. (2007). Modelling of planted legume

fallows in Western Kenya using WaNuLCAS. (I) Model calibration and validation. Agroforestry systems, 70(3),

197-209.

Westphal, E., Jansen, P. C. M. (1989). Plant Resources of South-East Asia. A selection. Pudoc/Prosea,

Wageningen, the Netherlands.

Woittiez, L. S., van Wijk, M. T., Slingerland, M., van Noordwijk, M., & Giller, K. E. (2017). Yield gaps in oil palm:

A quantitative review of contributing factors. European Journal of Agronomy, 83, 57-77.

Zulkifli, R. O., James, C. H., Wahid, O., Norkaspi, K., (2016). Integration of Black Pepper with Oil Palm. MPOB

Information Series, ISSN, 1511-7871.

https://www.pecad.fas.usda.gov/highlights/2012/12/Malaysia/

Wageningen University and Research | Appendix 1 29

Appendix 1

Parameters Units Source

Crop Stage

Length of generative stage days Literature

Length of vegetative stage days Literature

Annual or Perennial

Is it annual crop? 1=yes, 0 = no [] Literature

Earliest day to flower in a year Julian day Literature

Latest day to flower in a year Julian day Literature

Crop Growth

Production of dry matter per day kg m-2.day-1 Literature

Seed weight kg m-2 Literature

Water requirement for dry matter production l kg-1 Literature

Ratio of height increment to biomass incr. m kg-2 Literature

Maximum proportion of crop biomass remobilized as storage component

1/day Literature

Light Capture

Extinction light coefficient [] ??

Relative light intensity at which shading starts to affect crop growth [] ??

Canopy

Maximum Leaf Area Index [] Field measurement

Rain Interception

Rainfall water stored at leaf surface mm Field measurement

Water Uptake

Hydraulic conductivity of roots cm day-1 ??

Maximum plant potential cm ??

Minimum plant potential cm ??

Nutrient Uptake

Canopy biomass for closed canopy kg m-2 Field measurement

N concentration in crop tissue [] Field measurement

P concentration in crop tissue [] Field measurement

N concentration in young crop biomass [] Field measurement

P concentration in young crop biomass [] Field measurement

N concentration in roots [] Field measurement

N Fixation N fixation

Type of N2 fixation [] Literature

Constant

Proportion of N from atmosphere [] Literature

Responsive to N stress

Responsiveness of N2 fix. to N stress [] Literature

Fraction of reserve pool for N2 fix. 1/day Literature

Dry weight cost for N2 fixation kg(DW) g(N)-1 Literature


Parameters Units Source

Root

All root type

Root tip diameter cm Field measurement

Root type 0

Max. root length density in layer1 cm cm-3 Field measurement




Root type 1

Total root length per unit area cm cm-2 Field measurement

Root type 1 and 2

Decrease of root with depth 1/m Field measurement

Root type 2

Length per unit root dry weight m g-1 Literature

Root half life days Literature

Root affected by water or nutrient stress [] Literature

Root distribution by depth in good uptake [] Literature

Mycorrhizal fraction

Fraction of roots infected by mycorrhiza [] Field measurement

Crop effects on nutrient mobility

Reduction of constant P by root activity mg cm-1

Relative transfer rate for N pool cm2 day-1

Relative transfer rate for P pool cm2 day-1

Litter Quality

Lignin fraction of crop residue [] Literature

Lignin fraction of crop root residue [] Literature

Polyphenol fraction of crop residue [] Literature

Polyphenol fraction of crop root residue [] Literature

Soil erosion

Crop cover efficiency factor [] Field measurement

Sensitivity to pest damage

Fraction of crop eaten by pigs [] MPOB

Fraction of crop eaten by monkeys [] MPOB

Fraction of crop eaten by locust [] MPOB

Fraction of crop eaten by nematode [] MPOB

Fraction of crop eaten by goat [] MPOB

Fraction of crop eaten by buffalo [] MPOB

Fraction of crop eaten by birds [] MPOB

Agronomic yield

Standard moisture content [] []


Appendix 2 Macro for Leaf Area:

run(“Smooth”);

setOption (“BlackBackground”, false);

run (“Make Binary”);

run (Set Scale…”, “distance=294.6 known=1 unit=cm”);

run (“Fill Holes”);

run (“Create Selection”);

run (“Measure”);

rin (“Summarize”);

saveAs (“Results”, xxx);

The macro may be typed directly in the batch process window in ImageJ.

Appendix 3 Statistical summary of the number of leaves recorded and the average biomass and leaf area per section

Section Parameter Plagiotropic Branch Biomass (g DM)

0-50 Mean 41.679


Min 7.539

Max 165.659

n 145

50-100 Mean 47.016


Min 8.506

Max 126.348

n 150

100-150 Mean 44.02


Min 5.00

Max 107.78

n 150

150-200 Mean 36.300


Min 3.916

Max 84.508

n 145

200-250 Mean 33.166


Min 8.699

Max 66.875

n 20

*= number of trees on which the number of leaves per section were recorded

The above boxplot shows the decrease in biomass per specific branch per increase in height on the plant.


Appendix 4

Statistical summary of the orthotropic branch biomass per section

Section Parameter Orthotropic Branch Biomass (g DM)

0-50 Mean 589.7


Min 129.9

Max 1328.3

n 30

50-100 Mean 612.9


Min 173.7

Max 1230.1

n 30

100-150 Mean 450.23


Min 32.65

Max 932.72

n 30

150-200 Mean 193.48


Min 1.55

Max 723.89

n 30

200-250 Mean 58.24


Min 2.63

Max 215.04

n 30

Appendix 5

Pepper biomass distribution per height (g)

Height Total Biomass Plagio Biomass Ortho Biomass Leaf Biomass Fruit Biomass

50 1853.88 1075.28 589.45 106.32 82.83

100 3883.36 2287.88 1209.75 210.66 175.07

150 5717.84 3500.48 1635.05 315.00 267.31

200 7357.32 4713.08 1865.35 419.34 359.55

250 8801.80 5925.68 1900.65 523.68 451.79

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