bscmsc.pps.wur.nl final report.pdf · The report the majoris output of MSc thesis work in Wageningen University, the Netherlands. The thesis study was carried out under the joint-supervision

Identification of coffee yield gap in Uganda Understand yield limiting factors for coffee production and explore the adequate plant density in coffee–banana intercropping system

MSc thesis final report

WANG Na

Department of Plant Science

Plant Production System Group

Wageningen University

Droevendaalsesteeg 1

6708 PB Wageningen

The Netherlands

March 2014

MSc thesis final report PPS-8043 March, 2014

Title: Identification of coffee yield gap in Uganda: Understand yield limiting factors for coffee

production and explore the adequate plant density in coffee–banana intercropping system

MSc thesis final report – PPS - 80436

Student: WANG Na

Department of Plant Science

Plant Production System Group


The Netherlands

March 2014

Supervisors:

Prof. Dr. K.E. Giller

Professor of Plant Production Systems


Dr. P.J.A. van Asten

Systems Agronomist

International Institute for Tropical Agriculture (IITA), Kampala, Uganda

Dr. L. Jassogne

Systems Agronomist

International Institute for Tropical Agriculture (IITA), Kampala, Uganda

Examiners:

Prof. Dr. K.E. Giller

Professor of Plant Production Systems


Dr. G.W.J. van de Ven

Assistant Professor of Plant Production Systems


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Preface

The report is the major output of MSc thesis work in Wageningen University, the Netherlands. The

thesis study was carried out under the joint-supervision of Plant Production System Group of

Wageningen University and International Institution for Tropical Agriculture in Uganda (IITA-Uganda)

during the period from August, 2013 to March, 2014. Much of the data and information used in the

study originate from the farm surveys executed by IITA team over the years 2010 and 2011. At the

point of finishing this paper, I would like to express my sincere thanks to all the people who have

supported me with guiding the thesis work and writing this paper.

First of all, I want to gratefully acknowledge Professor Ken Giller for his excellent instructions on my

thesis work. Ken is a professor in Plant Production System and is my supervisor on behalf of the

university. He recommended me to IITA group so that I got the opportunity to carry out the study.

The patience and trust he gave me is essential to undertake the work. I appreciate his conscientious

supervision in the process of developing thesis proposal and reviewing the final report.

Secondly, I would like to express my deep gratitude to my supervisors in IITA-Uganda, Laurence

Jassogne and Piet van Asten. The practical section of the thesis research was executed with the help

and support of Piet and Laurence. They instructed me the major data analysis approaches used in

this study and helped me to improve the academic competence. I appreciate the great patience they

showed during the instructions as well as the kind concern they provided in my daily life. I would not

be able to complete the study without the help of them.

Furthermore, I would like to show my gratitude for all of the IITA staffs and students for their

encouragement and company. A particular thanks to all the farmers that participated in the group

discussion and individual interviews for their contribution to my thesis work.

Last but not the least, my sincere appreciation goes to my parents and friends who have always been

helping me out of difficulties.

Na Wang

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Contents

Contents ............................................................................................................................................................... 7

Summary ............................................................................................................................................................. 9

1. Introduction ............................................................................................................................................... 11

1.1 An overview of coffee production in Uganda .......................................................................................... 11

1.2 Biotic, abiotic and management production constraints ................................................................... 14

1.3 Yield gap analysis ................................................................................................................................................ 17

1.4 Problems identification and research objectives .................................................................................. 18

2. Materials and methods .......................................................................................................................... 21

2.1 Site description .................................................................................................................................................... 21

2.2 Data collection ...................................................................................................................................................... 22

2.2.1 Coffee yield and yield related production factors ......................................................................... 22

2.2.2 Rainfall data .................................................................................................................................................. 23

2.2.3 Coffee aboveground biomass ................................................................................................................ 24

2.3 Data analysis ......................................................................................................................................................... 25

2.3.1 Identifying important yield limiting factors and evaluating coffee yield gap by boundary line analysis. ....................................................................................................................................... 25

2.3.2 Identification of yield-critical rainfall periods and effects of rainfall variation on coffee yield ............................................................................................................................................................................. 28

2.3.3 Adequate plant density in coffee-banana intercropping system ............................................ 30

2.4 Group discussion and individual interview with farmers ................................................................. 31

3. Results .......................................................................................................................................................... 33

3.1 Functional relationships between coffee yield and production constraints .............................. 33

3.1.1 Coffee yield .................................................................................................................................................... 33

3.1.2 Important yield-related factors for coffee production ............................................................... 34

3.1.3 Boundary line analysis results .............................................................................................................. 37

3.2 Important yield limiting factors and coffee yield gap in the five regions .................................... 49

3.2.1 Important yield limiting factors ........................................................................................................... 49

3.2.2 Explainable and unexplainable yield gaps ....................................................................................... 52

3.2.3 Important production constraints perceived by farmers in eastern Uganda ................... 55

3.3 Rainfall .................................................................................................................................................................... 56

3.3.1 Rainfall distribution in Uganda over five years (2006—2010) .............................................. 56

3.3.2 Yield–critical rainfall periods for coffee production .................................................................... 58

3.3.3 Effects of rainfall variation on coffee yield ...................................................................................... 59

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3.4 Relationship among yield limiting factors ................................................................................................ 61

3.5 Exploration of adequate plant density in intercropping system..................................................... 64

3.5.1 Effect of banana on coffee yield in intercropping systems of three regions ...................... 64

3.5.2 Effect of banana on coffee aboveground biomass in East Uganda ......................................... 65

4. Discussion ................................................................................................................................................... 67

4.1 Boundary line analysis ..................................................................................................................................... 67

4.2 Coffee yield and yield gap ................................................................................................................................ 69

4.3 Important limiting factors of coffee production .................................................................................... 71

4.3.1 Biotic constraints ........................................................................................................................................ 71

4.3.2 Abiotic constraints ..................................................................................................................................... 73

4.2.3 Management practices ............................................................................................................................. 78

4.3 Rainfall .................................................................................................................................................................... 85

4.3.1 Rainfall distribution in Uganda ............................................................................................................. 86

4.3.2 Effects of rainfall variation on coffee production ......................................................................... 87

4.4 Adequate plant density in intercropping system .................................................................................. 91

4.4.1 Effects of banana on coffee yield .......................................................................................................... 91

4.3.2 Effects of banana on coffee aboveground biomass ...................................................................... 92

4.3.3 Impacts of coffee on banana growth in intercropping system ................................................ 94

4.3.4 Adequate plant density in coffee-banana intercropping system ............................................ 95

Conclusions ..................................................................................................................................................... 97

References ....................................................................................................................................................... 99

Appendix I. Samples of districts and number of total, intercropped and monocropped

coffee plots .................................................................................................................................................... 103

Appendix II. Coffee harvest periods in the target districts .......................................................... 104

Appendix III. Individual farmer interview questionnaire ........................................................... 105

Appendix IV. Summary of soil properties of the surveyed regions .......................................... 107

Appendix V. Monthly rainfall distribution of study regions in Uganda through five years

(2006—2010) ............................................................................................................................................... 109

Appendix VI. Rainfall amount and rainfall days of yield-critical period ................................ 111

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Summary

Coffee is Uganda’s primary cash crop that accounts for approximately 20% to 30% of the total annual

export revenue of the country. Arabica (Coffea arabica) and Robusta (Coffea canephora) are the two

coffee species grown in Uganda and are cultivated primarily in five regions: Central and Northern

part of Uganda (Robusta) and Eastern, South-western and North-western Uganda (Arabica). Small-

scale farmers with a land holding less than 2.5 ha produce as much as 90% of Uganda’s coffee. For

smallholder farmers, coffee is of great importance as the major economic source that delivers a cash

boom once or twice a year. Nevertheless Uganda’s coffee production and export remained low in the

past decades that the actual coffee yield is far below its potential level. There is an urgent need to

increase coffee production and eventually to enhance farmers’ livelihood and improve national

exporting revenue.

Considering the population intensification and land use pressure it is difficult to expanse coffee

production area, which leaves the possibility only in increasing coffee productivity. However, coffee

production in Uganda has experienced a series of biotic, abiotic and management constraints that

restrict farmers from achieving high yield. To cope with these constraints, site-specific

recommendations should be provided which address yield improvement potential and efficient

management practices for yield enhancement. Therefore, the main purpose of this study is to

identity yield gaps and associated production constraints that can best explain yield difference.

This study is based on the data and information collected by the International Institution of Tropical

Agriculture in Uganda (IITA-Uganda) over 2010 and 2011. A total of 250 farms in the five major coffee

production regions mentioned above were sampled and surveyed, on-farm measurements were

carried out for coffee yield and various production factors (pests and diseases, soil and plant

properties and management implementations). Boundary line analysis was applied to evaluate

relationships between coffee yield and yield related factors. The relative importance of each

individual factor in limiting coffee productivity was identified. Coffee yield gaps at both farm and

regional level for coffee production in the year 2010 were quantified. The impacts of rainfall variation

(rainfall amount and rainy days) on coffee yield were evaluated based on the four yield-critical

rainfall periods: (i) one year before harvest, (ii) from flowering to harvest, (iii) fruit abortion period

and (iv) dry season before flowering. In the end, the adequate plant density for coffee production in

coffee-banana intercropping systems was evaluated addressing the effects of banana intercropping

on coffee yield (cherries) and on coffee tree aboveground biomass.

The results indicated a large yield gap for both Arabica and Robusta in year 2010. Biotic constraints

restricted coffee production in Central Robusta growing region (coffee twig borer) and the Eastern

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Arabica growing area (coffee stem borer) with the influence being relatively significant for Robusta

grown in Central Uganda. Soil properties were important limitations in almost all of the five regions

surveyed. Soil K deficiency was the principle cause of yield loss of Robusta coffee grown in the

northern region. For Arabica, unfavourable soil P was the most limiting factor in the East and the

West Nile regions and soil Mg deficiency was the primary constraint in south-western Uganda.

Elevation had a significant impact on coffee production in all Arabica growing regions. Low coffee

plant density and old coffee trees were the important constraints in the Central Robusta growing

region. Mulching and shade trees were relatively important in the North (Robusta) and the

Southwest (Arabica). Increasing mulch depth was associated with higher yield in the two regions,

while increased shade tree density indicated either positive or negative effects on coffee production

depending on the regions.

The results from rainfall analysis illustrated a considerably low precipitation combined with an

shorter dry season over 2009 and 2010 coffee growing cycle compared with that in the previous

three years (2006–2008). Seasonal rainfall shortage that identified in the Central and the Southwest

regions indicated a significant limitation for coffee production. On the other hand, excessive rainfall

across the whole growth season occurred in the eastern and the north-western part of the country

has detrimental effects on coffee production. With regard to coffee-banana intercropping, the study

indicated an adverse effect of banana on Robusta coffee yield grown in the Central area. For Arabica

coffee an adequate plant density for the achievement of maximum yield was identified. The

adequate coffee and banana density ratio should be between 2.3: 1 and 1: 1.

The study concluded that though facing diversified biotic, abiotic and management constraints, there

is still a large potential for smallholder famers to improve coffee productivity. The important

production constraints illustrated in this study provide guidance for site-specific management

practices. However, the relationship between coffee yield and production constraints and the

explainable yield gaps identified in boundary line analysis should be evaluated and interpreted with

more cautious. With regard to adequate management practices for coffee and banana intercropping,

further studies with a long-term experimental setup are needed to evaluate the inter-species

competitions of the two crops.

Key words: Uganda; Arabica; Robusta; coffee production; yield gap; boundary line analysis;

production constraints; rainfall; plant density

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1. Introduction

1.1 An overview of coffee production in Uganda

Fig. 1.1. Location of Uganda in Africa (left) and map of Uganda divided by districts (right).

Uganda is located in East Africa at 4°12´ N to 1°29´ S latitudes and 29°34´ W to 35°00´ E longitudes

(Mwebaze 2002). The country is bordered by Tanzania and Rwanda in the south, Zaire in the west,

Sudan in the north, and Kenya in the east (Fig. 1.1, left). Uganda has a total land area of 241,548

square kilometres and is divided into 39 districts (Fig. 1.1, right)(Mwebaze 2002). More than two-

thirds of its total land area is considered to be highland with an elevation of 1000–2500 m above sea

level (Mwebaze 2002). An average annual temperature varies from 25 °C in the eastern highland

regions to 31 °C in the northern lowland area (Mwebaze 2002). Tropical climate is dominating in the

southern part of the country where two distinct dry seasons can be identified between December to

February and from June to August (Mwebaze 2002). Rainfall distributions in southern Uganda are

perceived to be sufficient for crop production with annual rainfall ranging from 1200 to 1500 mm

(Mwebaze 2002). In northern Uganda, semiarid climate is dominating with only one rainy season and

relatively low precipitation between 900–1300 mm (Mwebaze 2002). Agriculture plays an important

role in Uganda as approximately 30% of the total land area is contributed to agriculture and 90% of

the Ugandan population depends on agriculture for food and household income (Shively and Hao

2012).

Coffee is the second worldwide trading commodity next to oil. It is cultivated in approximately 60

countries in the world with tropic and sub-tropic climate (UCDA, Uganda Coffee Development

Authority). The genus Coffea is derived from family Rubiaceae which consists of approximately 70

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species (Wintgens 2009). Among these, two types of coffee species, Arabica (Coffea arabica) and

Robusta (Coffea canephora) are primarily cultivated as commercial crop that account for 99% of

global coffee production (DaMatta et al., 2007; Wintgens 2009). The root system of adult coffee trees

spreads from 0 to 200 cm in the soil with 90% of the roots being concentrated in 30 cm belowground

(Wintgens 2009). Arabica coffee trees normally present a single obvious trunk while Robusta coffee

trees have multiple trunks (Vieira 2008). For both species, however, the natural structure is often

modified through pruning to avoid excessive branching and get more fruits (Vieira 2008; Wintgens

2009). In most tropical areas, coffee produce leaves consistently throughout the year and can fruit

several times annually (Vieira 2008). For Arabica, flowering occurs on branches that grew in the

previous year, while Robusta generates flowers on branches of the current year (Vieira 2008). Coffee

fruits that are often referred as cherries which are nearly round to elliptical and present red as they

ripen (Vieira 2008). Compared with Arabica, Robusta is more productive and more resistant to

unfavourable conditions though the former is perceived to produce better beverage quality and

therefore provide more economic values (DaMatta et al., 2007; Vieira 2008).

According to data obtained in 2007, Uganda is the tenth top coffee producer worldwide (Vieira 2008).

With a total planted area of 272,000 ha, coffee is Uganda’s primary cash crop that accounts for

approximately 20% to 30% of the annual export revenue (UCDA 2012). More than 3.5 million

households are engaged in coffee industry and approximately three quarters of Ugandans rely on

coffee for household earning (UCDA 2012). Small-scale farmers holding less than 2.5 ha land size are

dominant in coffee industry and contribute as much as 90% of Uganda’s coffee production (UCDA

2012). For smallholder farmers, coffee is of great importance as it is the major source of cash for the

households and it provides cash boom once or twice a year (Jassogne, 2011).

Arabica (Coffea arabica) and Robusta (Coffea canephora) are the two major coffee species grown in

Uganda and are responsible for 30% and 70% of the total coffee export respectively (UCDA 2012).

The cultivation regions of both coffee species are, to a large extent, determined by natural habitats

of plants and agro-ecological characteristics of cultivating regions. Arabica originates from Ethiopian

highland area with an elevation between 1300–2000 m, while Robusta is typically distributed in

lowland regions in tropical Africa with an elevation lower than 1000 m (Wintgens 2009). A deep

loamy acid soil (pH 5–6) with good draining and water retention capacity is perfect for coffee growth

(Kimani et al., 2002). For both Robusta and Arabica, favourable annual rainfall should be between

1200 to 1800 mm, whereas Robusta is able to resist excessive rainfall beyond 2000 mm (DaMatta et

al., 2007). The optimum average annual temperature for Arabica is between 18 to 21 °C, while for

Robusta favourable temperature is relatively higher from 22 to 30 °C (DaMatta and Ramalho, 2006).

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Across Uganda, approximately 86% of the total land area is considered to be suitable for Robusta,

while only 7% is suitable for Arabica cultivation (Van Asten et al., 2012). Five primary coffee

production regions can be recognized: Central, North, East, Southwest and Northwest (referred to as

West Nile in the report) (Van Asten et al., 2012). Their distributions are summarized in Table 1.1.

Robusta is widely cultivated in the Central region and is recently introduced to the northern part of

the country with comparably lower elevation (below 1200 m) (UCDA, 2012). Unlike Robusta, Arabica

is dominantly grown in the East, Southwest and West Nile regions with relatively low temperature

because of the high elevation (above 1500 m) (UCDA, 2012). Each region has their own agro-

ecological characteristics with respect to soil types, topography and rainfall distribution. These

characteristics combined with socioeconomic backgrounds determine a wide range of farming

systems and crop production potential.

Table 1.1. Five coffee production agro-ecological zones Region Coffee

species Percentage in total growth area(%)1

Districts2

Central Robusta 69.6 Kampala, Mukono, Mpigi, Wakiso, South Luwero, Mubende, Kalangala, Rakai, Masaka, Iganga, Kamuli and Mityan.

North Robusta 7.1 Gulu, Nwoya, Oyam, Lira and Apa.

East Arabica 12.6 Kabale, Kisoro, parts of Rukungiri, Bushenyi, Kasese, Kabarole, Bundibugyo, Mbarara, Mbale and Kapchorwa , Sironko, Bududa and Manafwa.

Southwest Arabica 9.7 Kotido, Moroto, parts of Mbarara, Ntungamo, Masala, Ntungamo Rakai, Kabalore, Kasese, Kisoro and Rubirizilbanda.

West Nile Arabica 1.0 Yumbe, Maracha, Arua, Zombo and Nebbi. 1Source : UCDA, 2012 2Source : FAO, 1999; Van Asten et al., 2012

The comprehensive information on coffee yield obtained at farm level over time and across the

whole country is barely available, while the general production trend can be evaluated based on

exporting volume. According to Uganda Coffee Development Authority (UCDA), annual export of

Robusta was 156,000, 123,000, 162,000 and 168,000 tons respectively from 2009 to 2012 (USDA,

2012 and 2013). Annual export of Arabica was reported to be 39,600, 49,200, 37,800 and 39,600 tons

during the same time interval. Arabica export implied a substantial thrive in year 2010 which

followed by a recover in the following two years. By contrast, there was a remarkable decrease in

Robusta export in the same year. In addition, the production of Robusta is demonstrated having

experienced a period of stagnation in the past decade (2000–2010) with average coffee yield of

162,000 tons (Robert, 2012).

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1.2 Biotic, abiotic and management production constraints

Uganda’s coffee industry has experienced various challenges, among which low production is one of

the most crucial problems encountered directly by smallholder farmers (UCDA, 2012). Primary

constraints for coffee production at farm level were demonstrated to be biotic constraints (pests and

diseases), abiotic limitations (unfertile soil, drought and excessive rainfall) and poor management

practices (lack of mulching, pruning and weeding etc.) (Sserunkuuma and Secretariat, 2001; Jassogne

et al., 2013 (b); Shively and Hao, 2012). These constraints have generated low yields that pose large

challenges to small-scale farmers’ livelihoods (Sserunkuuma and Secretariat, 2001).

Pests and diseases are frequently perceived by researchers as well as farmers to be the primary

reason responsible for coffee yield reduction. Coffee wilt disease (CWD) is perceived by UCDA (2012)

being the one of the most important constraints that threatens Robusta production in Uganda. The

presence of coffee wilt disease can be identified on young as well as aged Robusta trees with

symptoms of wilting of branches and stems (UCDA, 2012). In the past decades, coffee wilt disease

has affected nearly half of the total Robusta population in Uganda and caused a considerable yield

reduction (UCDA, 2012).

Abiotic limitations are dominated by poor and unproductive soils and unfavourable climate

conditions. Due to the lack of financial support, the majority of coffee producers in Uganda apply

little to no technical inputs to address fertilization, weeding, mulching and pest and disease

management (Shively and Hao, 2012). Consequently, coffee production is highly relying on existing

soil fertility and on natural climate conditions.

Minimal fertilizer application combined with continues crop harvest throughout the years facilitated

the degradation of soil fertility in Uganda’s coffee growing areas (Tenywa et al., 1999). A loss of 80–

100 kg/ha/year of soil NPK has been identified in the Central region due to soil erosion and other

types of soil nutrient losses, which caused a large yield loss of perennial crops such as coffee and

banana (Sserunkuuma and Secretariat, 2001). A current study conducted by IITA-Uganda has

demonstrated that there is a wide range of soil nutrients deficiencies (soil N, P, K, Ca and Mg) across

all coffee growing regions in Uganda (Van Asten et al., 2012).

In addition to soil constraints, the potential effects of increasing change of global climate on coffee

production have been recognized recently (Hepworth and Marisa, 2008; Jassogne, 2011; Jassogne et

al., 2013 (a); Robert, 2012). With only a few regions applying irrigation, Uganda’s agriculture is highly

susceptible to climate change (Shively and Hao, 2012). Climate change might significantly affect

coffee production and therefore the Ugandan smallholder farmers who have already encountered

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various agro-ecological and socioeconomic constraints and barely have coping strategies available

(Jassogne et al., 2013, (b)).

As an outcome of global climate change, Uganda’s temperature is likely to increase in the future two

decades by approximately 1.5 °C (Jassogne, 2011). As mentioned above Arabica favours a relatively

cool environment with the ideal average annual temperature between 18 to 21°C, while it can adapt

sub-optimum temperature as high as 26°C (DaMatta and Ramalho, 2006). On the other hand,

temperatures of either lower than 17°C or in excess of 30°C would strongly depress the growth and

development of Arabica (DaMatta and Ramalho, 2006). Due to temperature rise, suitable growth

regions for Arabica are likely to decrease in Uganda (Jassogne, 2011). This would eventually give rise

to a shift of current Arabica growing regions to a higher elevation (IITA, 2012). In the meanwhile,

Arabica growing areas will become suitable for Robusta (IITA, 2012). Higher temperature might also

result in a change to alternative crop types in the current fields (IITA, 2012) (a new Arabica variety

(“Tuzza”) is currently popular due to its well growth and high yield potential in low elevation regions

as reported by UCDA (2012)).

The effect of climate change is also characterized by the irregular onset and uncertain length of

rainfall and dry seasons as well as the inconsistent quantity of precipitation during rainy season

(DaMatta et al., 2007; Jassogne et al., 2013 (b); Robert, 2012). Either total annual rainfall or monthly

and even weekly rainfall distribution are extremely important for coffee cultivation under rainfed

condition (Wintgens, 2009). Both quantitative and qualitative aspects of coffee yield can be

significantly damaged by unfavourable rainfall patterns (DaMatta et al., 2007; Wintgens, 2009;

Jassogne, 2011; Robert, 2012).

The impact of rainfall on coffee yield has been illustrated based on its physiological requirements in

the reproductive period (DaMatta, 2004; DaMatta and Ramalho, 2006; DaMatta et al., 2007;

Wintgens, 2009). For instance, coffee trees need a spell of water deficit to initiate flowering,

therefore two to four months of dry period with little or no rain is required for the formation of

flower buds (DaMatta, 2004, Wintgens, 2009). Few months with low rainfall could also contribute to

simultaneous flowering and equal fruit ripening, and eventually to a uniform harvesting (Wintgens,

2009). On the other hand, excessive rainfall throughout the year is undesirable as it might be

associated with irregularly harvest and low productivity (DaMatta et al., 2007). Moreover, the dry

season should be followed by sufficient rainfall and appropriate atmosphere humidity in order to

achieve a good blossoming (Wintgens, 2009). Either insufficient or excessive heavy rainfall after dry

season would negatively affect coffee yield and influence coffee beans quality (Jassogne et al., 2013

(a)).

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On the other hand, intercropping coffee with shade trees has been perceived as an efficient

approach to adapt climate change and achieve sustainable coffee production socially,

environmentally and economically (DaMatta, 2004; DaMatta et al., 2007; Wingtens, 2009; UCDA,

2012; Van Asten et al., 2012; Jassogne et al., 2013 (a and b)). In fact, in certain part of the world,

coffee is traditionally grown in shaded agroforestry systems and shade trees are now frequently

found in coffee systems where temperature and rainfall are not favourable (DaMatta et al., 2007). In

Uganda, a majority of coffee fields are covered by shade trees with diverse shading intensity (Van

Asten et al., 2012).

Coffee can benefit from shade trees in diversified aspects. Shading can contribute to modifying the

microclimate so that the extreme climate conditions such as extremely high or low temperature are

reduced (DaMatta et al., 2007; Jassogne, 2011). Shade trees also serve as a buffer to mitigate the

negative influence generated by prolonged dry season, heavy rainfall and frost (DaMatta et al., 2007).

It is recommended to plant shade trees in young coffee plantation to protect coffee from sunburn

(UCDA, 2012). In addition, moderate shading can benefit slow ripening of coffee beans and promote

coffee beans’ quality (Läderach et al., 2011). On the other hand, excessive shading level can be

harmful for coffee growth and production (DaMatta, 2004). In the area where rainfall is sufficient,

too much shading would affect light interception of coffee trees, which might result in lower

accumulation of carbohydrates as well as spindly growth of coffee trees with single stem (DaMatta et

al., 2007). In addition, there might be a competition between the shade trees and coffee for water

under low rainfall condition.

Next to coffee being the primary cash crop, East African highland bananas (Musa spp. AAA-EA) are

the primary staple crop in Uganda and can grow in almost all of the country (Van Asten et al., 2011

(b)). In Uganda, banana is commonly cultivated together with coffee by small-scale farmers with the

motivation of enhancing land use sufficiency, providing shading to coffee, supplying mulch materials

and reducing soil erosion (Bongers et al., 2012; Van Asten et al., 2010 (b); Jassogne et al., 2013 (b)).

In addition, intercropping coffee with banana also contributes to enhancing food security at

household level, and to reducing farmers’ risks associated with pest and disease damage and

fluctuations of coffee beans price (Van Asten et al., 2011 (b)).

The overall productivity in terms of land equivalent ratio (LER) and profitability expressed as annual

returns to land are proven to be significantly higher for intercropped coffee and banana compared

with mono-cropping of both crops (Van Asten et al., 2011 (b)). However, in intercropped systems,

one crop could compete with another crop due to the limited resources such as light, water and soil

nutrients which might generate yield reduction of one or both of the two crops (Van Asten et al.,

2011 (b)). Virtually, the competitive and synergistic effects of intercropping system are largely

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determined by the positions of the two crops (Vandermeer, 1992). The beneficial effect of

intercropping only work well when the plant densities are well designed and mulching and fertilizing

practice are well managed (Van Asten et al., 2011 (b)).

1.3 Yield gap analysis

Understanding the yield gap is the fundamental step in the identification of yield improvement

potentials in the given region (Van Ittersum et al., 2013). Analysing the yield gap also helps to identify

the most important production constraint in the site which should be given priority in implementing

management strategies (Fermont et al., 2009; Wairegi et al., 2010; Affholder et al., 2013; Van

Ittersum et al., 2013). The recognizing of important production constraints and socioeconomic

limitations together provides guidelines in attempt to close yield gap and improve land use efficiency

(Van Ittersum et al., 2013).

Important concepts involved in yield gap analysis are: potential yield, water limited yield, attainable

yield and actual yield (Van Ittersum et al., 2013). Potential yield can be achieved with the sufficient

supply of water and nutrient and determined only by genetic characteristics of the crop and

environmental variables such as CO2, radiation and temperature in the growth region. Water-limited

yield is used to estimate yield for crops under rainfed condition. Nutrient and water limited yield is

the yield obtained under the limitations of both water and nutrients. Actual yield is determined by

many other constraints, in addition to water and nutrients, such as pest and disease stresses and

weeds pressures, thus is considerably smaller than either potential and attainable yield. Crop yield

gap is defined as the difference between potential yield (Affholder et al., 2013) or water-limited yield

(Fermont et al., 2009; Van Ittersum et al., 2013) and the actual yield obtained by farmers. Potential

yield can be estimated employing several approaches: field experiments, yield contests, maximum

yield in the field and modelling simulations (Affholder et al., 2013; Van Ittersum et al., 2013).

The most precise way to estimate potential yield is argued to be simulation modelling which requires

the accurate information of climate features and soil properties in a given site (Van Ittersum et al.,

2013). However, it is rather difficult to address potential yield of Uganda’s coffee by modelling as the

required inputs and accurate mathematic model are not yet available. Furthermore, crop production

in Uganda is usually limited by seasonal insufficient rainfall and low soil fertility, as a result neither

potential nor water limited yield can be achieved by farmers. In comparison, attainable yield that is

explained as the maximum yield in a given region and under a given management intensity can be

easily obtained from on-farm surveys and therefore has more practical value (Fermont et al., 2009;

Wairegi et al, 2010; Van Ittersum et al., 2013). Therefore in many studies, attainable yield instead of

potential yield or water limited yield has been applied to evaluate crop yield gap (Fermont et al. 2009;

17

http://www.google.nl/search?hl=nl&tbo=p&tbm=bks&q=inauthor:%22John+H.+Vandermeer%22


Wairegi et al., 2010; Van Ittersum et al., 2013). This is based on the assumption that the potential or

water-limited yield gap can be closed by the best performing farmer.

Boundary line analysis is perceived as a reliable tool to study crop yield gap through the

understanding of yield response to the site-specific yield limiting factors (Casanova et al., 1999; Shata

and McBratney, 2004; Fermont et al., 2009; Wairegi et al., 2010; Van Ittersum et al., 2013). The

principle and application of boundary line model will be illustrated in this study.

1.4 Problems identification and research objectives

Uganda’s coffee production has experienced a series of biotic, abiotic and management stresses so

that the actual coffee yield reach only 20% to 30% of its potential level (IITA, 2012). There is an

urgent need of enhancing coffee farmers’ livelihood and increasing national export revenue through

the improvement of coffee productivity at farm level. In the progress towards yield improvement,

efforts were made primarily on agronomic and phytosanitary aspects of coffee production. This can

be viewed from government policies on coffee section of the last 20 years that has been mainly

trying to address issues such as quality control, coffee wilt disease management, replacement of old

and unproductive coffee trees and expansion of coffee cultivating area (UCDA, 2012).

Compared with cereal crops, less attention was received by coffee yield evaluation (IITA, 2012). So

far there is no complete quantitative information on coffee yield gap in Uganda and it remains

unclear that which factor limit yield to the greatest extent. In attempt to give the site-specific

recommendations on efficient management practices for yield improvement, it is important to

identity the yield gap and associated production constraints that can best explain yield difference.

This should be addressed at regional level, since production constraints and yield potential can vary

between different regions due to the variation of agro-ecological conditions.

Furthermore, the adoption of coffee-banana intercropping is not widely spread in all coffee growing

regions though various benefits have been recognized (Van Asten et al., 2012 (b)). Farmers do not

believe intercropping because the competition of the two crops for sources is so strong that one crop

tend to collapse after establish intercropping (Van Asten et al., 2012 (b)). In fact, this competition can

be largely reduced by appropriately managing the plant density of the two crops. Nevertheless,

current recommendations on production techniques such as plant density are developed mainly

based on FAO coffee guidelines that were developed outside Uganda (IITA, 2012). Therefore, they

can hardly represent the situation of Uganda’s coffee production especially of coffee-banana

intercropping systems (IITA, 2012). Adequate recommendations on plant density are essential in

order to advocate the promotion of coffee-banana intercropping systems across the whole country

and to benefit more farmers.

18


Therefore, the objectives of this study are to (i) identify and quantify the various biotic, abiotic

constraints and management practices that limit coffee production in five major coffee cultivation

regions in Uganda, (ii) understand coffee yield gap and important yield limiting factors that are

responsible for the yield loss at farm as well as regional level and (iii) explore the appropriate plant

density in coffee monocropping and coffee-banana intercropping system.

This study is carried out with dataset collected by International Institution of Tropical Agriculture in

Uganda (IITA-Uganda) over years 2010 and 2011. A total of 250 farms in five primary coffee

production regions were sampled and surveyed. Coffee yield and biotic, abiotic and management

yield-related factors were measured. The relationships between coffee yield and yield related

limiting factors were explored applying boundary line analysis. Relative importance of those limiting

factors in restricting coffee productivity was evaluated and most important constraints in a given

region were identified. Yield gap was quantified at regional level for coffee production of year 2010.

With regard to rainfall factor, four critical rainfall periods during coffee reproduction were defined: (i)

one year before harvest, (ii) from flowering to harvest, (iii) fruit abortion period and (iv) dry season

before flowering. The impacts of rainfall pattern (both rainfall amount and rainy days) on coffee yield

in yield-critical periods were estimated by linear regression analysis. The adequate plant density (for

achieving highest coffee yield) in intercropping system was explored for the eastern Arabia coffee

growth region. Adequate plant density was evaluated considering both coffee economic yield

(cherries) and agronomic yield (aboveground biomass).

19


20


2. Materials and methods

2.1 Site description

Fig. 2.1. Five major coffee growing regions in Uganda and sampled districts

(Source: Van Asten et al., 2012, Page 5)

The original data was collected by International Institution of Tropical Agriculture (IITA-Uganda) over

two years 2010 and 2011 across five major coffee production regions: the Central and Northern

Robusta growing regions and the Eastern, South-western and North-western (West Nile) Arabica

growing regions (Fig. 2.1).

Within each region, five districts were selected as samples except in the Southwest where six districts

were sampled (Fig. 2.1, LEAD districts). In each district in the Central, Southwest and East regions,

where intercropping is commonly implemented, approximately ten coffee plots consisting of five

coffee monocropping and five coffee-banana intercropping plots were visited. In the North and West

Nile regions, again, ten farm plots were sampled with number of intercropped farm plots less than

five, since coffee-banana intercropping in these regions was too rare to obtain five samples.

Eventually, a total of 40 to 60 households in each region were participated in the survey programme.

Information of coffee yields, biotic, abiotic and management production factors were obtained by

farmer interviews and by on-farm observations and measurements. Sampled districts, number of

total farms and intercropped and monocropped farms respectively are summarized in Appendix I.

21


2.2 Data collection

2.2.1 Coffee yield and yield related production factors

Information of coffee yield achieved by farmers in year 2010 was obtained by farmer interviews

(recall). Farmers described yield in various terms such as “red cherries”, “block”, “parchment” and

“Kiboko” depending on coffee types. To be able to compare, farmer-reported coffee yield was

converted into a standard form Fair Average Quality (FAQ, kg) with the conversion factors listed in

Table 2.1. Annual coffee yield was calculated by dividing yield (kg) by plot size (ha) and expressed as

kg/ha/year. Coffee yield exceeding 2500 kg/ha/year was regarded as outliers based on the empirical

results from other researches that so far the observed coffee yield in Uganda was no more than 2500

kg/ha/year. Outliers were eliminated from yield samples and the remaining yield data were used for

the further analysis.

Table 2.1. Conversion factors between different forms of coffee yield and standard form FAQ1 Types of coffee Forms Conversion factors

Robusta Red cherries2 0.17 Robusta Kiboko3 0.54 Arabica Red cherries2 0.17 Arabica Block4 0.40 Arabica Parchment5 0.80 Source: Van Asten et al., 2012, Page 9. 1 Fair Average Quality, green coffee beans ready for export or for roasting. 2Coffee fruits, coffee beans are the seeds of coffee cherry. 3Representing red cherries of Robusta coffee. 4 Representing red cherries of Arabica coffee.

5Coffee beans with endocarp derived after wet-processing of coffee cherries using pulping machine.

In each farm, Global Positioning System (GPS) location was used to locate plot position and estimate

plot size. GPS data was monitored in every corner of farm plot and used as coordinates in ArcView

software to develop a polygonal shaped file. The size of the polygon was then taken as reference in

the estimation of the real plot area.

The incidence of pest and disease (%) were evaluated by visual estimation of percentage of affected

coffee trees among total coffee population in the plot. Severity of infestation was evaluated based

on three categories defined by IITA (2012): low level, median level and high level.

Soil samples of four to five points along the diagonal of the farm plot were collected and mixed for

the analysis of physical and chemical soil properties. Soil texture (sandy, loamy and clay), soil pH, soil

organic matter (SOM, %), nitrogen (N, %), available phosphorus (P, mg/kg), exchange potassium (K,

cmol/kg), calcium (Ca, cmol/kg) and magnesium (Mg, cmol/kg) were measured in the laboratory with

the analysis methods summarized in Table 2.2.

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Table 2.2. Analysis methods for soil properties Items Methods1 Soil nutrient extraction for P, K, Ca, Mg Mehlich-3 Soil texture Hydrometer method Soil pH Using 1: 2.5 water to soil suspension Soil organic matter (%) Walkley Black method Soil N (%) Colorimetry method Soil P (mg/kg) Spectrophotometer Soil K (cmol/kg) Flame photometer Soil Ca (cmol/kg) Atomic absorption spectrophotometer Soil Mg (cmol/kg) Atomic absorption spectrophotometer 1Source: Van Asten et al., 2012

Coffee, banana and shade tree number were counted in the plot. Tree densities were calculated

through dividing tree number by plot size and expressed as trees/ha. Shade tree shading level (%)

was roughly estimated by observing the proportion of shade tree shading canopy in coffee plot.

Other management practices such as the quantity and frequency of applying chemical fertilizers,

farm yard manure, mulch materials, pesticides, herbicides and the frequency of slashing and hand

weeding were surveyed though farmer interview. Mulch depth (cm) of three randomly selected

points in the coffee plot was measured and average value of which was taken as a representative.

Pest and disease infestations were monitored by on farm observation.

2.2.2 Rainfall data

Fig.2.2. Map of GPS points for rainfall data collection. Source: CIAT-CCAFS.

The rainfall data was provided by the International Centre of Tropical Agriculture (CIAT). Daily rainfall

amount has been monitored and recorded over 15 years from 1998 to 2013. Approximately 300 GPS

23


points that evenly spread across the country were taken as monitoring points. These points are

graphically explained in the above map with numerous squared clusters (Fig. 2.2). Each cluster is

identified by a GPS point which can be located by the geographic coordinates in the X-Y axis with

latitude and longitude expressed in degrees and minutes. In the map, 34 green clusters with black

spots inside represent the surveyed districts that were identified according to their coordinates. Daily

rainfall data of those target districts were sorted for further analysis.

For each district, monthly and yearly rainfall amount over five years (2006–2010) was obtained by

adding up daily precipitation. Monthly and annual rainfall for individual region was assessed by

taking the average of annual rainfall of districts that belong to that region. In addition, annual rainfall

amount was addressed considering five regions as a whole to roughly evaluate the rainfall condition

in major coffee production regions in Uganda country over past years.

2.2.3 Coffee aboveground biomass

Together with yield and other production factors, in the eastern Arabica growing region, coffee tree

aboveground biomass (AGB) was evaluated employing an allometric model. The allometric model

used was based on easily measured variables such as stem diameters and tree height without

destroying coffee trees (Segura et al., 2006; Negash et al., 2013). The allometric model used to

estimate individual coffee aboveground biomass is:

Log10 (AGB) (kg/tree) = 1.1 - 1.6 × Log10 d15 (cm) + 0.6 × Log10 height (m)

whereby coffee stem diameter at 15 cm height (d15 (cm)) and stem height (height (m)) were taken as

independent variables. This equation was originally developed by Segura et al. (2006) and was

demonstrated being able to best predict coffee aboveground biomass in agroforestry systems in

Matagalpa, Nicaragua. The examined Arabica coffee trees in their study presented only one obvious

trunk, while Arabica coffee trees in Uganda are usually modified into multi-stem architecture (Fig.

2.3). Therefore, more than one stem was measured for stem diameter and height and the average

value of which was used in the allometric model to estimate individual stem AGB.

Fig. 2.3. Examples for girth measurement and point select on the stem of coffee trees.

Source: Technical report for workshop on climate change in coffee-banana systems (IITA, 2012).

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In each plot surveyed, five coffee trees were randomly selected and number of stems were counted,

height and girth at 15 cm height of individual stem were measured. Girth has been converted into

diameter with the transfer equation: Diameter (cm) = 2 × PI × (Girth (cm) / 2). Aboveground biomass

of individual coffee trees stem was predicted based on allometric function with averaged stem

diameters and height as explainable variables. Individual coffee tree aboveground biomass was

calculated by adding up the stem aboveground biomass of all stems of the coffee tree. Average

individual coffee aboveground biomass was identified for each plot of interest and the total

aboveground biomass was obtained by multiplying that with coffee plant density.

2.3 Data analysis

2.3.1 Identifying important yield limiting factors and evaluating coffee yield gap by

boundary line analysis.

Background of boundary line analysis

The boundary line approach was originally identified and reported by Webb (1972) in biological

experimentation where tested samples with best performance were employed as dependent

variable to estimate functional relationship with the corresponding independent variable (Y= F(X)).

The assumption was that biological material has a maximum value of growth and development in

response to a given environmental condition (Webb, 1972). Therefore for each cause and respond

relationship, there must be an upper range of response of dependent variables (Y) to the related

independent variables (X). The line describing the highest dependent variable (i.e. yield) over a range

of independent variables (i.e. soil N) is regarded as boundary line.

It is assumed that, given a large amount of data, the points on the boundary line are able to best

represent the relationship between the two variables, while the potential influence of other limiting

factors can be considered minimal (Webb, 1972; Elliott and De Jong, 1993; Schnug et al., 1996). The

inferior performance of data points that below the boundary line, on the other hand, could be

attributed to errors from measurement, material variability and other factors that are more

important than the examined one (Webb, 1972).

As a data analysis tool, boundary lines have been adopted to estimate the relationship between leaf

conductance ability and various environmental factors (Chambers et al., 1985); to predict soil

nitrogen cycle activities (Elliott and De Jong, 1993; Schmidt et al., 2000) and to identify the response

of plants to various levels of soil fertility (Schnug et al., 1996; Casanova et al., 1999; Shatar and

McBratney, 2004). Recently, boundary line analysis has been widely used to understand yield

reduction factors and to explore the largest yield increase potential (Casanova et al., 1999; Fermont

et al., 2009; Wairegi et al., 2010).

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Application of boundary line approach in this study

In this study, boundary line analysis was conducted to study the cause and response relationship

between coffee yield and production factors at regional level. Those production factors are biotic

factors such as incidence of pests and diseases; abiotic factors including soil properties (Soil pH, SOM,

soil N, P, K, Ca and Mg concentration) and cultivation elevation; management practices such as

fertilizer and manure application, coffee tree age, coffee plant, banana and shade tree densities,

shade tree shading level, mulching, slash and hand weeding frequencies.

For a range of independent variable X (indicating yield related factor), approximately 50

corresponding dependent variables Y (representing yield of 50 farms in a given region) were

identified. Scatter chart was plotted for those data sets with coffee yield as the dependent variable

and production related factors as independent variable. Boundary lines were constructed for those

scatter plots by several steps described below.

Identification of outliers Outliers in boundary line analysis were defined by Schnug et al. (1996) as

the data points that lie far away from the major data cloud and would considerably affect the

validation of boundary line model. Occurrence of outliers in this study is likely attributed to the

exaggerated statement by farmers, recording errors and mistyping by data entry. Outliers of coffee

yield and yield related constraints were identified by screening data in box-plot in SPSS. The extreme

outliers were the data points that three times interquartile (25%-75% percentile) beyond the box on

both side (lower quartile and upper quartile) and were presented as asterisk symbol in the graph.

Those extreme outliers have been double checked to ensure their validity. For coffee yield, in

addition to extreme outliers, mild outliers that are 1.5 times interquartile beyond the box on both

sides and have a symbol of circles in box-plot graph were treated as outliers as well. Because

boundary line model can be very sensitive to the highest yield data that represent the attainable

yield and is one of the constant in boundary line model. Outliers were eliminated from data sets

before further analysis.

Correlation test Spearman’s correlation analysis (SPSS Statistics 20) that used for monotonic non-

linear relationship test, was carried out to evaluate whether the given production factors had a

strong correlation with coffee yield. Important relationship is determined when the absolute value of

correlation coefficient (r) of the two variables exceed 0.25 (If the sample size is between 25 and 50,

approximately 80% sample’s r will fall in a range between -0.26 to 0.26 when population’s r is likely

to be 0 (Andy, 2009; Lyman, 2011). In this study the sample size was between 40 and 50, therefore

0.25 was selected as a boundary to make sure the sample correlation reflect the situation of the

population). Boundary line was than developed for those production factors that indicated significant

correlation with coffee yield. However, other non-linear relationships (i.e. curvilinear) might also

26


appear which cannot be detected directly by linear (Pearson’s r) or monotonic non-linear correlation

test (Spearman’s rho). Therefore, data were always plotted in scatter chart and boundary line

analysis was still performed if, by observation, an obvious boundary range along the upper boundary

points was identified.

Identification of boundary points Boundary points were identified with the principle of BOLIDES

algorithm (boundary line development system) proposed by Schnug et al. (1996) and in practice were

executed applying “IF” function in Excel (Microsoft Excel 2010). If a positive correlation between the

two variables was identified in correlation test, the data sets were sorted with X variable in an

ascending order. The first ranked data set X (X0) and Y (Y0) was regarded as the first boundary point in

the scatter-plot. For the second X (X1), if the corresponding Y (Y1) has a value larger than the previous

Y (Y0), than this X1-Y1 data pair was treated as the second boundary point. Otherwise, the previous Y

(Y0) was transformed and construct the boundary point that response to the current X (X1-Y0).

Identical algorithm was performed for the remaining X-Y data sets.

Development of boundary lines Boundary line was fitted for those boundary points identified using a

simulation model developed by Fermont et al. (2009):

Yp = Yatt / (1 + K × EXP (-(R × X)))

Where Yatt represent the attainable coffee yield which is the highest yield observed in the surveyed

region. X represents the biotic, abiotic and management variables. Yp is the maximum attainable

yield predicted under the limitation of correspondent independent variable (X). K and R are constants.

For each value of independent variable X, mean square error (MSE) between the corresponding Yp

and Y(b) (Y of boundary points) was calculated. Best fitted boundary line model was obtained by

predicting the constants K and R which were identified by minimizing the root mean squared error

(RMSE) between a range of Yp and Y(b). The minimization of RMSE was conducted with “Solver”

function in Excel (Microsoft Excel 2010).

The range of boundary line model was coincided with the range of independent variables. For the

positive correlated factors, boundary line usually has a shape of upper part of “S” curve that

indicated that the increase value of independent variable (production factors) in a certain range is

associated with the increase of dependent variable (maximum coffee yield). In some cases however,

with the increase of X value, the dependent variable Y increased firstly and then started to decrease

after peak value of Y was reached (as similar with a para-curve). In this situation, “S” curved

boundary lines were still fitted in attempt to sketch a positive curve fitting.

When there was a negative correlation, however, scattered data were ordered with X variable in a

descending order. Identical steps as addressed in positive relation were performed to determine the

27


boundary points. The trend lines of those boundary points were fitted and were regarded as

boundary line. Trend lines were either quadratic (Y = a × X2 + b × X + c) or linear (Y=a × X + b) in the

attempt to achieve the highest coefficient of determination (R2). For the negative yield-related

factors, with the increase of independent variable, dependent variable decreased immediately or

after a near stabilized stage, until the value of independent factor reached its maximum.

Important limiting factors and yield gap identification

The most limiting factor at plot level was identified according to the law of minimum: limiting factors

that give the minimum attainable yield (Ymin) (the minimum yield among the maximum attainable

yield predicted by boundary line model) can be regarded as the most limiting factor responding to

the yield reduction in the plot (Fermont et al., 2009; Wairegi et al., 2010). The important limiting

constraints in a given region were identified through ranking the number of most limiting factor at

plot level. The ranking was executed for each production factor and the factor that occurred most

often in the ranking had been identified as the most limiting factor for that region. The top three

important yield limiting factors were identified for each region surveyed.

The explainable yield gap for a given coffee plot was expressed as the gap between attainable yield

(Yatt) observed in the whole region and the minimum attainable yield (Ymin) predicted by boundary

line model. The unexplainable yield gap was defined as the difference between minimum yield

predicted (Ymin ) and the actual yield observed on coffee plot (Yobs). For an individual coffee plot, the

yield gap that caused by a particular production limiting factor was defined as the gap between

maximum attainable yield (Yp) predicted by boundary line model and the actual yield achieved under

the limitation of that constraint (Yobs). The yield gap due to a specific limiting constraint at regional

level was explained as the median of those yield gaps identified at plot level.

Evaluation of relationships between the yield limiting factors

To better understand the results of boundary line analysis, the relationships among the production

limiting factors were evaluated by Spearman’s correlation analysis (SPSS Statistics 20). Again, the

strong correlations were identified when correlation coefficient was larger than 0.25.

2.3.2 Identification of yield-critical rainfall periods and effects of rainfall variation on

coffee yield

Identification of yield-critical rainfall periods

The yield-critical phases might vary between regions and even differ across districts due to the

distinguished flowering time. In Uganda, coffee flowering in a certain growing region is largely

determined by its rainfall pattern. In northern part of Uganda where the rainfall distribution is

unimodal, coffee usually flowers and is harvested once per year. In southern part of the country

28


however, bimodal rainfall pattern is dominant. More than one flowering and harvesting cycle might

occur with the preceding harvested coffee known as “main crop” and the late harvested coffee called

“fly crop”. Since the proportion of “fly crop” in the total harvest is barely more than 20% (according

to farmer interviews, 2013), the yield-critical periods were only identified for the “main crop”

obtained in 2010. In the entire coffee growth season from 2009 to 2010, four periods were identified

as yield-critical periods during which rainfall pattern might have an impact on coffee yield of year

2010. Those periods are: one year before harvest, time from flowering to harvest, fruit abortion

period and dry season before flowering.

One year before harvest was explained as the entire year before the onset of the harvest month. The

onset of the coffee growth season (2009/2010) was evaluated by backward speculation referring to

coffee harvest time (2010). General coffee harvesting time in each district surveyed are obtained by

consulting local farmer cooperation (2013) and illustrated in Appendix II. Since most districts in a

given region have simultaneous or adjacent harvest time, for practical reason, a uniform harvest time

was used for regional generalization. For districts that have evidently different harvest time, however,

the site-specific yield-critical rainfall periods were identified particularly.

For both Robusta and Arabica, a dry span of 2-4 months is necessary to simulate flowering (DaMatta

et al., 2007). Dry month is described by Wintgens (2009) “A month where rainfall is less than twice

the monthly average temperature.” Therefore, the monthly rainfall in dry season should be less than

approximately 50–60 mm (depending on regions) for coffee grown in Uganda. To identify a dry

periods before flowering, monthly rainfall distribution across 2009/2010 was evaluated for each

region and/or individual district. A dry period was determined when the following three principals

were all complied (i) looking at monthly rainfall data for the given region and/or district that there

was one or more than one successive months where rainfall amount was apparently low (less than

60 mm), (ii) this dry period should in coincide with harvesting time based on the empirical

information from farmers that coffee normally experiences approximately ten months from

flowering to harvest (personal consultation with local farmers), (iii) if there was no obvious

successive months with low precipitation, the beginning of dry season was then derived by counting

11 months backward from the start of harvesting time and last to three months.

It was assumed that coffee flower as soon as the dry period passed and all coffee trees blossom

synchronously within a given region. Therefore, flowering period was roughly estimated to be

occurred during the next month following the dry season defined above.

During the fruit setting stage, two fruit abortion crucial periods have been identified by DaMatta et al.

(2007): (i) the first month after blossom during which flowers are under fertilised and unsuccessful

fertilisation would lead to fruit drop, (ii) the second month following blossom when early stage of

29


endosperms are formed. Since only the second period is likely to be influenced by water deficit

(DaMatta et al., 2007), fruit abortion period was considered as the second month after flowering.

Effects of rainfall variation on coffee yield

Due to the strong variation of rainfall patterns among regions, the impacts of rainfall variation on

coffee yield were evaluated for each surveyed region. Unlike other production factors for which data

were obtained for individual coffee plots, rainfall data was collected in a larger scale (at district level).

Therefore, it is not desirable to address a boundary line analysis in the exploration of rainfall and

yield relationship. Rainfall patterns in the view of rainfall amount and rainy days in the four yield-

critical periods were identified for the surveyed districts. Linear regression analysis was carried out

with rainfall pattern of the given district as independent variable to test its potential influence on

coffee yield of individual plot.

The relationships between coffee yield and rainfall patterns were first evaluated visually by drawing a

box-plot with rainfall properties as independent variables and coffee yield as dependent variable. For

those indicated an obvious relationship (both maximum and median yield showed the similar

increase or decrease trend with the variation of rainfall), Spearman’s correlation test was carried out

to identify statistically the strong relationship.

2.3.3 Adequate plant density in coffee-banana intercropping system

Among the five coffee growth regions surveyed, coffee-banana intercropping was frequently found

in the central, south-western and eastern regions, while in northern and north-western part of the

country intercropping was rarely observed with 7 of 48 farms in the North and 16 of 49 farms in the

West Nile operating mixed cropping. Therefore the study for intercropping systems was carried out

only for the Central, East and Southwest regions.

Five indicators were introduced to assess coffee performance under the influence of banana in

intercropping systems: (i) coffee yield (cherries, kg/ha/year), (ii) coffee stem girth (cm/tree), (iii)

coffee stem height (m), (iv) individual coffee aboveground biomass (kg/tree) and (v) total coffee

aboveground biomass (kg/ha). Significant difference of the five indicators between coffee

monocropping and coffee-banana intercropping systems were evaluated by executing independent t-

test (SPSS Statistic 20).

Furthermore, Spearman’s correlation analysis was carried out to evaluate the relationship between

relative banana density (percentage of banana trees among the total tree population in intercropping

field) and the five indicators. For those indicators that reflected significant correlation (P<0.05), linear

regression was applied to address the relationship between the two variables. Again, for those that

30


did not show a significant correlation but suggested an apparent upper range, boundary lines were

drawn across upper data points.

2.4 Group discussion and individual interview with farmers

To better understand the results derived from this study, a series of farmer group discussion and

individual interviews were organized in October, 2013. Three districts in East Uganda were selected

as samples: Kapchorwa, Manafwa and Mbale. These three districts are located in different elevation

across the eastern mountainous area (Kapchorwa located in highland (1671 m elevation), Manafwa

in the middle (1320 m elevation) and Mbale in lowland (1200 m elevation)) with an attempt to best

address the overall situation in the East region. In each district, a group discussion was organized

with approximately 15 to 25 coffee farmers participating. Farmers were asked to give their

perceptions on coffee production constraints and to rank the most important constraint from their

point of view. Three farmers who currently practice coffee and banana intercropping and have either

banana or coffee monocropping history were invited for individual interviews. Questions related with

impacts of cultivating elevation, banana and shade trees intercropping and climate change on coffee

production were addressed during individual interviews (see Appendix III “Individual interview

questionnaire” for more details).

31


32


3. Results

3.1 Functional relationships between coffee yield and production constraints

3.1.1 Coffee yield

Fig. 3.1. Coffee yield (FAQ) of five regions of Uganda in year 2010. In the graph, the three horizontal

lines of the boxes indicate 75% percentile (up), median (solid line across boxes) and 25% percentile

coffee yield (bottom); the upper and bottom bars outside the boxes explain the highest and lowest

coffee yield respectively (outliers were removed).

Coffee yield (FAQ) of 2010 of five surveyed regions is illustrated by a box-plot as presented in Figure

3.1 where highest, lowest and median coffee yield can be identified for individual region. Coffee yield

in the five regions showed nearly equal median. However, all of the five regions showed a large

variability on coffee yield and an unsymmetrical distribution. In comparison with the interquartile

range between median and minimum yield, the range between maximum yield and median were

generally larger which indicated a relatively larger variance at high yield levels.

Robusta yield ranged from 18 to 1737 kg/ha/year in the Central and from 43 to 1464 kg/ha/year in

the North. Both maximum yield (1737 kg/ha/year) and yield median (746 kg/ha/year) obtained in the

Central region were higher compared with that obtained in the North (1464 and 706 kg/ha/year

respectively for maximum and median yield), though coffee yield in the Central also indicated a

larger variation comparably. There was no significant (P≤0.05) difference in average yield between

the two regions (Central, 702 kg/ha/year and North, 647 kg/ha/year).

Arabica yielded from 167 to 1701 kg/ha/year in the East, from 164 to 2243 kg/ha/year in the

Southwest and from 178 to 1550 kg/ha/year in the West Nile. Again, average yield did not differ

Robusta Arabica

33


significantly (P≤0.05) among the three regions, while in comparison the Southwest region indicated

the highest yield value in 25%, 50% and 75% percentile.

3.1.2 Important yield-related factors for coffee production

Bivariate correlation test was implemented to estimate the correlation between coffee yield and

yield related factors. The factors that indicated significant correlation (|r|> 0.25, P≤0.05) with coffee

yield, or by observing the scatter-plot, suggested an obvious boundary line were summarized in

Tables 3.1–3.3. The production factors that implied strong relationship with coffee yield were

categorized into: (i) biotic factors such as pests and diseases incidence (%), (ii) abiotic factors such as

cultivating elevation (m) and soil properties (Soil pH, SOM, N, P, K, Ca and Mg concentration), and (iii)

management practices including coffee plant density (trees/ha), average coffee age (years/tree),

relative banana density (%), shade tree density (trees/ha), mulch depth (cm) and hand weeding

frequency (times/year). The impacts of the same production factor were inconsistent depending on

regions and the extent of correlation, if existing, was not strong with a maximum correlation

coefficient of 0.492 (Table 3.2).

By observing the scatter chart, pest and disease incidence indicated negative impacts on both

Robusta and Arabica yield (Table 3.1). The incidence of coffee twig borer (Xylosandrus compactus) in

the Central region suggested an adverse influence on maximum yield of Robusta. Coffee leaf miner

(Leucoptera coffeella) incidence indicated negative effect on maximum coffee yield in the West Nile

Arabica growing region (by observation). Coffee stem borer (Xylotrechus quadripes) in the Southwest

region indicated detrimental effect on Arabica maximum yield.

The majority of soil properties indicated a positive correlation with coffee yield of both species

through observation and statistical evaluation (Table 3.2). In Robusta growing regions, in the North,

soil pH, K, Mg indicated positive correlation with coffee yield (r=0.332, P=0.028; r=0.421, P=0.005;

r=0.329, P=0.031 respectively). In the Central Robusta growing region, soil pH, N, P, K, Ca, Mg all

suggested positive effects on maximum coffee yield, while the relationship was identified by

observing the upper boundary range of data cluster.

For Arabica, soil P in the Southwest (r=0.341, P=0.015) and soil K in the West Nile (r=0.358, P=0.02)

had significant positive influence on coffee yield. However, soil pH and soil P in the eastern region

(r=-0.492, P<0.001 and r=-0.338, P=0.027 respectively) and soil Mg (r=-0.411, P=0.003) in the south-

western Uganda reflected significant negative correlation with coffee yield.

Elevation indicated strong impacts in all of the three Arabica growing regions, while for Robusta,

elevation did not showed significant influence. Elevation implied positive association with coffee

yield in the East (r=0.385, P=0.011) and in the West Nile (by observation) (Table 3.2)). On the other

34


hand, increasing elevation suggested a significant negative influence on coffee yield in the Southwest

(r=0.312, P=0.026).

With regard to management practices, increasing coffee plant density was associated with higher

maximum attainable yield in all of the five regions (by observation) (Table 3.3). Robusta coffee tree

age had significant negative correlation with coffee yield in the Central region (r=-0.406, P=0.023).

Increased banana density in the Central region indicated a detrimental effect on maximum coffee

yield (by observation). Higher maximum yield of Robusta was associated with the increase of shade

tree density in the North (by observation). Mulch depth was positively correlated with maximum

coffee yield in the Central and the North Robusta regions (by observation). Positive influence of hand

weeding frequency on maximum coffee yield was observed in the North Robusta region.

Arabica coffee yield was negatively correlated with coffee age in the East (by observation) and in the

West Nile regions (r=0.346, P=0.025). Shade tree density had negative correlation with Arabica yield

in the West Nile (r=0.363, P=0.020), while disadvantages of shade trees were observed in the eastern

and south-western regions of Uganda. Mulch depth reflected a positive correlation with Arabica

coffee yield in the Southwest (r=0.404, P=0.004) and the West Nile (by observation).

Consequently, a total of 17 biotic, abiotic and management factors were considered as important

production constraints. Functional relationships between these constraints and maximum coffee

yield were analysed with boundary line model. Boundary lines were not developed for other factors

due to reasons that: (i) no obvious relationship (through both correlation analysis and observation)

was identified between the two variables, (ii) information of production factors was not quantitative

data (i.e. pest and disease infestation severity that was explained as light, medium and heavy) and (iii)

number of data sets were too small to generate a boundary line (i.e. fertilization was only executed

in several coffee plots (less than 10)).

Table 3.1. Correlation between coffee yield and biotic factors. Regions Coffee twig borer

incidence (%) Coffee leaf miner incidence (%)

Coffee stem borer incidence (%)

Central O (N) ns ns

North ns ns ns

East ns ns O (N)

Southwest ns ns ns

West Nile ns O (N) ns

35


Table 3.2. Correlation between coffee yield and abiotic factors. Regions Soil pH SOM

(%) Soil N (%)

Soil P (mg/kg)

Soil K (cmol/kg)

Soil Ca (cmol/kg)

Soil Mg (cmol/kg)

Elevation (m)

Central O (P) ns O (P) O (P) O (P) O (P) O (P) ns

North P (r=0.332 P=0.028)

O (P) O (P) O (P) P (r=0.421 P=0.005)

O (P) P (r=0.329 P=0.031)

ns

East N (r=-0.492 P=0.001)

O (P) O (P) N (r=-0.338 P=0.027)

O (P) O (P) O (P) P (r=0.385 P=0.011)

Southwest O (P) O (P) O (P) P (r=0.341 P=0.015)

ns O(P) N (r=-0.411 P=0.003)

N (r=-0.312 P=0.026)

West Nile O (P) O (P) O (P) O (P) P (r=0.358 P=0.020)

O (P) O (P) O (P)

Table 3.3. Correlation between coffee yield and management practices. Regions Coffee

density (trees/ha)

Banana density (trees/ha)

Shade tree density (trees/ha)

Mulch depth (cm)

Hand weeding frequency (times/year)

Average Coffee age (years)

Central O (P) N (O) ns O (P) ns N (r=-0.406, P=0.023)

North O (P) ns O (P) O (P) O (P) ns

East O (P) ns O (N) ns ns O(N)

Southwest O (P) ns O (N) P (r=0.404 P=0.004)

ns ns

West Nile O (P) ns P (r=0.363 P=0.020)

O (P) ns N (r=-0.346 P=0.025)

Note: “r”—correlation coefficient. “P”—Significant level. “P”—Significant positive correlation, P≤0.05. “N”—Significant negative correlation, P≤0.05. “ns“—No significant correlation were found, P≤0.05. “O” indicates the relationship was identified by observation. “O (P)”—positive relation by observation and “O (N)”—negative relation by observation.

36


3.1.3 Boundary line analysis results

Pest and disease

Fig. 3.2. Coffee yield and pest and disease incidence relationship in (a) Central Robusta growing reigns

and (b) East and West Nile Arabica growing areas.

Among the pests and diseases, coffee twig borer in the Central, coffee stem borer in the East and

coffee leaf miners in the West Nile indicated negative impacts on maximum coffee yield (Fig. 3.2). In

the Central Robusta region, coffee twig borers were found in 44% surveyed coffee plots. Up to 40%

of coffee trees were infected by this pest, which resulted in the maximum yield as low as 240

kg/ha/year (Fig. 3.2, a). In Arabica growing regions, coffee stem borers were observed in 20% of the

surveyed coffee plots in the eastern region. The pest caused an infection rate of up to 80% coffee

trees in individual coffee plot and gave rise to a linear decrease in maximum coffee yield to the

minimum value of 191 kg/ha/year (Fig. 3.2, b). Coffee leaf miners presented in 22% of the surveyed

coffee plots in the West Nile region. In individual coffee plot, the highest incidence of coffee leaf

miners was 60% which was associated with a minimum attainable yield of 430 kg/ha/year (Fig. 3.2, b).

It can be seen that coffee twig borers in Central caused a substantial decline on coffee yield potential,

while the negative influences of the other two pests were relatively small.

Soil properties

Soil properties (soil pH, soil organic matter (SOM), nitrogen (N), phosphorus (P), potassium (K),

calcium (Ca) and magnesium (Mg) concentration) of the coffee fields in the five regions were

illustrated with box-plots in Appendix IV. Functional relationship were identified between coffee

yield and almost all soil properties in all of the five regions, while no significant correlation or clear

boundary line was identified between yield and SOM in Central and soil K in the south-western

Arabica area. In addition, soil properties were compared between coffee-banana intercropping and

coffee mono-cropping systems in the Central, East and Southwest regions. There was no significant

difference in all soil properties examined between the two systems (P≤0.05).

0

500

1000

1500

2000

0 10 20 30 40 50

Coffe

e yi

eld

(kg/

ha/y

ear)

Pest and disease incidence (%)

Coffee yield and pest incidence relationship (Robusta)

Twig borer inCentral

0

500

1000

1500

2000

0 20 40 60 80 100

Coffe

e yi

eld

(kg/

ha/y

ear)

Pest and disease incidence (%)

Coffee yield and pest incidence relationship (Arabica)

Stem borer in East

Leaf miner inWestnile

(b)

West Nile

(a)

37


Soil pH

Fig.3.3. Relationship between coffee yield and soil pH in five coffee growing regions: (a) Central and

North Robusta areas and (b) Southwest and West Nile and (c) East Arabica regions.

Soil organic matter (SOM)

Fig.3.4. Relationship between coffee yield and soil organic matter (SOM) in four coffee growing

regions: (a) North Robusta area and (b) East, Southwest and West Nile Arabica regions.

0

500

1000

1500

2000

4 5 6 7 8

Coffe

e yi

eld

(kg/

ha/y

ear)

Soil pH

Coffee yield and soil pH relationship (Robusta)

Central

North

0

500

1000

1500

2000

2500

4 5 6 7 8

Coffe

e yi

eld

(kg/

ha/y

ear)

Soil pH

Coffee yield and soil pH relationship (Arabica)

Southwest

WestnileWest Nile

0

500

1000

1500

2000

4 5 6 7 8

Coffe

e yi

eld

(kg/

ha/y

ear)

Soil pH

Coffee yield and soil pH relationship (Arabica)

East

0

500

1000

1500

2000

0 2 4 6 8

Coffe

e yi

eld

(kg/

ha/y

ear)

SOM content (%)

Coffee yield and SOM content relationship (Robusta)

North

0

500

1000

1500

2000

2500

0 5 10 15 20

Coffe

e yi

eld

(kg/

ha/y

ear)

SOM content (%)

Coffee yield and SOM content relationship (Arabica)

East

Southwest

Westnile

(a) (b)

(c)

(a) (b)

West Nile

38


Soil N

Fig.3.5. Relationship between coffee yield and soil nitrogen (N) in five coffee growing regions: (a)

Central and North Robusta regions and (b) East, Southwest and West Nile Arabica regions.

Soil P

0

500

1000

1500

2000

0 0.1 0.2 0.3 0.4

Coffe

e yi

eld

(kg/

ha/y

ear)

Soil N concentration (%)

Coffee yield and soil N relationship (Robusta)

Central

North

0

500

1000

1500

2000

2500

0 0.2 0.4 0.6 0.8

Coffe

e yi

eld

(kg/

ha/y

ear)

Soil N concentration (%)

Coffee yield and soil N relationship (Arabica)

East

Southwest

Westnile

(b)

West Nile

0

500

1000

1500

2000

0 10 20 30 40

Coffe

e yi

eld

(kg/

ha/y

ear)

Soil P concentration (mg/kg)

Coffee yield and soil P relationship (Central)

(a)

0

500

1000

1500

2000

0 10 20 30 40

Coffe

e yi

eld

(kg/

ha/y

ear)


Coffee yield and soil P relationship (North)

0

500

1000

1500

2000

2500

0 50 100 150 200 250 300

Coffe

e yi

eld

(kg/

ha/y

ear)


Coffee yield and soil P relationship (Southwest)

0

200

400

600

800

1000

1200

0 10 20 30 40

Coffe

e yi

eld

(kg/

ha/y

ear)


Coffee yield and soil P relationship (West Nile)

(d)

(a)

(b)

(c)

39


Fig.3.6. Relationship between coffee yield and soil phosphorus (P) concentration in five coffee

growing regions: (a) Central, (b) North, (c) Southwest, (d) West Nile and (e) East.

Soil K

Fig.3.7. Relationship between coffee yield and soil potassium (K) concentration in four coffee growing

regions: (a) Central and North Robusta regions and (b) East and West Nile Arabica area.

Soil Ca

Fig.3.8. Relationship between coffee yield and soil calcium (Ca) concentration in five coffee growing

regions: (a) Central and North Robusta areas, (b) East, Southwest and West Nile Arabica regions.

0

500

1000

1500

2000

0 10 20 30 40 50

Coffe

e yi

eld

(kg/

ha/y

ear)


Coffee yield and soil P relationship (East)

0

500

1000

1500

2000

0.0 0.5 1.0

Coffe

e yi

eld

(kg/

ha/y

ear)

Soil K concentration (cmol/kg)

Coffee yield and soil K relationship (Robusta)

Central

North

(a)

0

500

1000

1500

2000

0.0 0.5 1.0 1.5

Coffe

e yi

eld

(kg/

ha/y

ear)

Soil K concentration (cmol/kg)

Coffee yield and soil K relationship (Arabica)

East

Westnile

(b)

West Nile

0

500

1000

1500

2000

0 2 4 6

Coffe

e yi

ed (k

g/ha

/yea

r)

Soil Ca concentration (cmol/kg)

Coffee yield and soil Ca relationship (Robusta)

Central

North

(a)

0

500

1000

1500

2000

2500

0 5 10 15

Coffe

e yi

eld

(kg/

ha/y

ear)

Soil Ca concentration (cmol/kg)

Coffee yield and soil Ca relatiosnhip (Arabica)

East

Southwest

Westnile

(b)

West Nile

(e)

40


Soil Mg

Fig.3.9. Relationship between coffee yield and soil magnesium (Mg) in five coffee growing regions: (a)

Central and North Robusta area, (b) East and West Nile and (c) Southwest Arabica regions.

Soil pH was positively related with maximum coffee yield in all regions of interest (Fig. 3.3, a, b)

except in the East. In the Central and Northern Robusta growing areas, soil pH varied from 4.5 to 7

with small difference between the two regions (Appendix IV, a). For both regions, soil pH showed a

symmetrical distribution among surveyed coffee plots (Appendix IV, a). Maximum attainable yield

was achieved when soil pH was 5.7 and 6.6 approximately in the Central and the North respectively

(Fig. 3.3, a).

The distribution of soil pH was not symmetrical in Arabica cultivating regions (Appendix IV, a). Coffee

fields in the Southwest revealed a large variation on soil pH ranging from 4.3 to 7.5, while highest

yields were achieved as the pH reached approximately 6.0 (Fig. 3.3, b). Soil pH varied in a relatively

narrower range from 5.9 to 7.5 in the West Nile where the maximum yield was associated with a soil

pH level of 7.0 (Fig. 3.3, b). In the East region, however, maximum yield increased firstly with the pH

increased from 5.4 to 6.0 (did not expressed in boundary line) and then decreased linearly with the

further increase of pH level from 6.0 up to 7.0 (Fig. 3.3, c).

0

500

1000

1500

2000

0 1 2 3

Coffe

e yi

eld

(kg/

ha/y

ear)

Soil Mg concentration (cmol/kg)

Coffee yield and soil Mg relatiosnhip (Robusta)

Central

North

(a)

0

500

1000

1500

2000

0 1 2 3 4

Coffe

e yi

eld

(kg/

ha/y

ear)

Soil Mg concentration (cmol/kg)

Coffee yield and soil Mg relationship (Arabica)

East

WestnileWest Nile

0

500

1000

1500

2000

2500

0 1 2 3 4 5 6

Coffe

e yi

eld

(kg/

ha/y

ear)

Soil Mg content (cmol/kg)

Coffee yield and soil Mg relationship (Arabica)

(c)

Southwest

(b)

41


Soil organic matter (SOM) indicated a nearly symmetrical distribution in Robusta areas, between

3.8%–7.6% in the Central and 2.3%–5.9% in the North; while SOM varied strongly in Arabica coffee

regions in the East (3.2%–14.8%), the Southwest (4.8%–11.4%) and the West Nile (2.4%–8.6%)

(Appendix IV, b). Higher yield was associated with larger concentrations of SOM in all regions except

the Central (Fig. 3.4, a, b). In the Northern region, maximum yield increased sharply with increase of

SOM from 2.2% to 3.6 % and remained stable with the further increase of SOM until the maximum

SOM level was reached (Fig. 3.4, a). By contrast, in the Southwest and West Nile Arabica cultivating

areas, maximum yield increased slowly with the increase of SOM in the entire range of SOM until

SOM reached its maximum level (Fig. 3.4, b).

The effect of soil N variation on coffee yield is illustrated in Figure 3.5. With a similar shaped

boundary line, soil N demonstrated a similar effect on coffee yield as SOM (Fig. 3.5, a, b). Soil N did

not affect maximum Robusta yield too much in the central and northern areas since soil N was not

limiting in most coffee plots of the two regions (beyond 0.18% and 0.23% in the Central and the

North respectively) (Fig. 3.5, a). On the other hand, there was a gradually increase in maximum

Arabica yield with the increase of soil N, until the soil N reached its maximum level of 0.56% in the

Southwest and 0.39% in the West Nile (Fig. 3.5, b). In terms of the relationship between maximum

coffee yield and SOM and soil N in the eastern Arabica area, yield tended to increase firstly and then

decrease with the increase of SOM and N (Fig. 3.4, b; Fig. 3.5, b). This gives an indication of the

optimum SOM and N of approximately 8% (SOM) and 0.3% (N) to achieve the highest Arabica yield.

The soil P concentration indicated a large variation within and between the five coffee growing

regions (Appendix IV, d). Coffee fields in the Central and North Robusta regions indicated similar level

of soil P with nearly equal median and similar variances between surveyed fields within the region

(Appendix IV, d). In the two regions, soil P varied from 2.80 mg/kg to 50 mg/kg with most data

concentrated between 2.87 mg/kg (25% percentile) and 20 mg/kg (75% percentile) (Appendix IV, d).

The largest variation in soil P among the five regions was found in the Southwest Arabica area with a

range from 5.90 mg/kg up to 277 mg/kg (Appendix IV, d). The East Arabica area also showed a

relatively strong variation between coffee plots (2.6–115 mg/kg) (Appendix IV, d).

The responses of maximum coffee yield to available soil P concentration are illustrated in Figure 3.6.

The increase of maximum Robusta yield was limited by soil P only in the lower range of soil P

concentration in the Central Robusta area (2.73–19.4 mg/kg) and in the West Nile Arabica region

(2.77–18.4 mg/kg) (Fig. 3.6, a, d). On the other hand, yield improvement was associated with

increased P concentration in the North Robusta and the Southwest Arabica regions for the whole

range of soil P concentration detected in the field (2.77–34 mg/kg in North and 5.90–277 mg/kg in

Southwest) (Fig. 3.6, b, c). In the East region, however, Arabica coffee yield was correlated negatively

42


with soil P concentration and maximum yield decreased linearly with the increase of soil P level from

2.60 to 45 mg/kg (Fig. 3.6, e).

Soil K concentration in the Central and North Robusta growing areas indicated a nearly same soil K

median (around 0.25 cmol/kg) with both region reflected relatively low variability between coffee

fields (Appendix IV, e). Arabica coffee plots in the eastern region showed the highest maximum (1.37

cmol/kg) and median (0.72 cmol/kg) soil K concentration among the five regions (Appendix IV, e).

This was followed by the Southwest Arabica region where soil K concentration was slightly lower with

a maximum value of 1.32 cmol/kg and median level of 0.56 cmol/kg (Appendix IV, e). However, both

East and Southwest showed a large variance among coffee plots (similar lowest value around 0.13

cmol/kg). In comparison, soil K concentration in the West Nile Arabica area presented a symmetric

distribution in the relatively narrow range from 0.20 cmol/kg to 1.03 cmol/kg (Appendix IV, e).

Positive relationships between soil K concentration and maximum coffee yield were identified in all

regions of interest except the Southwest (Fig. 3.7). In the Central Robusta growing region, soil K

concentration distributed between 0.08 cmol/kg to 0.79 cmol/kg and the attainable yield was

achieved when K reached a relatively low concentration of 0.19 cmol/kg (Fig. 3.7, a), which suggested

a relatively small influence of soil K on coffee production in this region. Unlike Central, in the

Northern Robusta area, maximum yield increased slowly with the enhancement of soil K in the entire

range observed on coffee fields (0.12–0.61 cmol/kg). Similarly, maximum Arabica yield increased

gently with the soil K increased from 0.13 to 1.37 cmol/kg in the East and from 0.20 to 1.03 cmol/kg

in the West Nile regions (Fig. 3.7, a, b).

With regard to soil Ca concentration, Robusta regions Central and North suggested a nearly equal soil

Ca distribution with similar median value around 2.5 cmol/kg and close maximum concentration of

approximately 5.5 cmol/kg (Appendix IV, f). Both median and maximum soil Ca concentration (5.2

and 11.03 cmol/kg respectively) in the Southwest Arabica area were the highest among the five

regions. This was followed by the East Arabica region with a slightly lower maximum (5.1 and 8.51

cmol/kg respectively), but the variation between fields was also smaller (Appendix IV, f). Soil Ca

distribution in the West Nile Arabica area indicated nearly symmetric distribution with a median of 4

cmol/kg and a maximum of 6.82 cmol/kg. As regards to soil Mg concentration, both median and

maximum soil Mg value increased in a same trend: Central< North< West Nile< East< Southwest

(Appendix IV, g). The variability within the region was also increased in the same order with the

largest variance occurred in the Southwest Arabica area (from 0.22 to 4.36 cmol/kg).

Both Ca and Mg concentration demonstrated a positive relationship with maximum coffee yield in

almost all of the five regions (Fig. 3.8, a, b; Fig. 3.9, a, b). However, a significant negative effect of

increasing soil Mg on coffee yield was identified in the Southwest Arabica area (P≤0.001) (Fig. 3.9, c).

43


Maximum yield was limited by Ca with concentration below 1.08 cmol/kg in the North Robusta and

1.57cmol/kg in the East Arabica regions (Fig. 3.8, a, b). Maximum yield was constrained by soil Mg

concentration that less than 1.08 cmol/kg in the North and 1.61 cmol/kg in the East (Fig. 3.9, a, b).

However in the Central Robusta and the West Nile Arabica regions, the limiting effect of Ca and Mg

existed within the entire range of detected nutrient concentration where soil Ca ranged from 4.51 to

9.08 cmol/kg in the Central and from 2 to 6.82 cmol/kg in the West Nile; soil Mg varied between 0.25

and 1.29 cmol/kg in the Central and from 0.56 to 2.17 cmol/kg in the West Nile (Fig. 3.9, a, b). In the

Southwest Arabica region, increasing soil Mg concentration from 0.22 to 4.36 cmol/kg was associated

with a significant reduction of maximum yield (from 2242 kg/ha/year to 169 kg/ha/year) (Fig. 3.9, c).

Elevation

Fig. 3.10. Elevation distribution of surveyed coffee plots in (a) Robusta and (b) Arabica growing

regions. Relationship between Arabica yield and elevation in (c) East and West Nile and (d) Southwest.

Robusta coffee farms located in the elevation between 1030 and 1300 m above sea level (Fig. 3.10, a).

There were two ascent elevation gradients in the North region (ranged 1030–1060 m and 1000–1120

m) with little variation between coffee fields. By contrast, the elevation of target coffee plots in the

Central region indicated a steady increase from 1090 up to 1300 m.

0%

20%

40%

60%

80%

100%

120%

1000

1030

1060

1090

1120

1150

1180

1210

1240

1270

1300

Cum

ulat

ive

Elevation (m)

Elevation distribution of Robusta growing regions

Central

North

0%

20%

40%

60%

80%

100%

120%

900

1050

1200

1350

1500

1650

1800

1950

2100

2250

2400

Mor

e

Cum

ulat

ive

Elevation (m)

Elevation distribution of Arabica growing regions

East

Southwest

Westnile

(b)

0

500

1000

1500

2000

500 1000 1500 2000 2500

Coffe

e yi

eld

(kg/

ha/y

ear)

Elevation (m)

Coffee yield and elevation relaitonship (Arabica)

East

Westnile

0

500

1000

1500

2000

2500

500 1000 1500 2000 2500

Coffe

e yi

eld

(kg/

ha/y

ear)

Elevation (m)

Coffee yield and elevation relationship (Arabica)

Southwest

(c)

(a)

(d)

West Nile

West Nile

44


In Arabica growing regions, elevation of surveyed coffee plots ranged from 1000 to 2000 m (Fig. 3.10,

b). In the East region, a majority of coffee plots located in a elevation between 1050–1650 m with a

few farms located at a relatively high elevation (1800–2050 m). Three elevation gradients can be

categorized for coffee plots in the Southwest: lower elevation (1050–1200 m), median (1400–1600 m)

and higher elevation (1750–2000 m). In the West Nile, coffee plots distributed on slope with two

distinguished elevation gradient: 900–1050 m and 1400–1750 m.

There was no strong correlation between elevation and coffee yield in Robusta coffee areas.

However, maximum coffee yield was associated positively with elevation in Arabica growing regions

in the East (P≤0.05) and the West Nile (by observation) (Fig. 3.10, c). On the other hand, elevation

indicated a significantly negative correlation with maximum coffee yield in the Southwest (P≤0.05)

where maximum yield declined with the increase of elevation from 1000 to 2000 m (Fig. 3.10, d).

Coffee density

Fig. 3.11. Coffee yield and coffee plant density in five regions : (a) Central and North Robusta area and

(b) East, Southwest and West Nile Arabica area.

In Robusta growing areas (both monocrop and intercrop), coffee plant density ranged from 100 to

1244 trees/ha (Fig. 3.11, a). In Arabica cultivating regions, coffee trees were planted in a relatively

higher density between 500 and 3000 trees/ha (Fig. 3.11, b). It can be seen that, in all regions of

interest, the increase of coffee tree density was associated with higher maximum yield. In general,

maximum yield showed a gradual growth with the increase of plant density and remained stable

after the attainable yield has been achieved, while the increase tendency of coffee yield was

relatively sharp in the East in the initial phase of increasing coffee density from 736 to 1172 trees/ha.

When consider Arabica coffee as a whole (including the three regions), an optimum range of coffee

density can be identified within which maximum yield achieved the highest value (Fig. 3.11, b).

Arabica yield reached a peak when the plant density was around 1500 trees/ha by observation.

Unlike Arabica, Robusta suggested a potential to continually grow as coffee density increased beyond

the current level (1244 trees/ha).

0

500

1000

1500

2000

2500

0 500 1000 1500 2000

Coffe

e yi

eld

(kg/

ha/y

ear)

Coffee density (trees/ha)

Coffee yield and coffee density (Robusta)

North

Central

(a)

0

500

1000

1500

2000

2500

0 1000 2000 3000

Coffe

e yi

eld

(kg/

ha/y

ear)

Coffee density (trees/ha)

Coffee yield and coffee density (Arabica)

East

Southwest

Westnile

(b)

West Nile

45


Coffee age

Fig. 3.12. Coffee yield and coffee age relationship in (a) Central Robusta area and (b) East and

West Nile Arabica regions.

Robusta tree age in the surveyed plots varied from 5 to 35 years in the Central region (14 on average),

from 5 to 15 years in the North area (10). Arabica trees has an age between 5 to 60 years in the East

(28), between 5 to 43 years in the Southwest (21) and between 5 to 50 years in the West Nile (31).

As illustrated in Figure 3.12, Robusta yield tended to decline with the increase of coffee tree age in

the Central region (P≤0.05). Arabica yield decreased with the increase of tree age in the West Nile

Uganda (P≤0.05) and in the East region (by observing the upper boundary points).

A linear boundary line was identified for Robusta grown in the Central region and Arabica cultivated

in the Eastern area where maximum coffee yield dropped dramatically in Central and declined

relatively slowly in the East as coffee trees became older (Fig. 3.12, a, b). In the West Nile, polynomial

line was sketched above the upper boundary points which implied that the Arabica production did

not limited by coffee tree age until the population exceeded approximately 20 years (Fig. 3.12, b).

Banana density

Fig. 3.13. Coffee yield and relative banana plant density relationship in Central Uganda.

0

500

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Coffe

e yi

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Coffee yield and coffee tree age relationship

Central

(a)

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Coffee yield and coffee tree age relationship

Westnile

East

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Relative banana density

Coffee yield and relative banana density relationship in intercropping systems (Central)

(b)

46


There was no significant (P≤0.05) correlation between relative banana density (%) and coffee yield in

all of the five regions, while in the Central region a downward boundary line was observed along the

upper boundary points of the data cloud (Fig. 3.13). Increasing relative banana density from 0 to

approximately 80% suggested a negative effect on maximum coffee yield with the minimum

attainable yield as low as 185 kg/ha/year.

Shade tree density

Fig. 3.14. Coffee yield and shade tree density relationship in (a) North Robusta growing region and (b)

West Nile, (c) East and (d) Southwest Arabica growing areas.

Shade trees can be found in all the surveyed coffee growing regions. Shade tree density expressed by

trees/ha, ranged from 0 to 92 in the Central, from 10 to 100 in the North Robusta growing areas.

Shade tree density ranged from 0 to 120 in the East, from 0 to 1100 in the Southwest and from 0 to

150 in the West Nile Arabica regions. Relative shade tree density (%) explained by the proportion of

shade trees in the total tree population, varied between 0 to 6% in the south-western region.

The influence of shade trees on coffee yield differed among different regions (Fig. 3.14). Neither

obvious boundary line nor significant correlations between shade tree density and coffee yield were

identified in the Central region. Positive relationships between coffee yield and shade trees density

were observed in the North Robusta area and in the West Nile Arabica region (Fig. 3.14, a, b). In the

Northern Uganda, maximum coffee yield increased rapidly with shade trees density increased from

0

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Coffee yield and shade tree desity relationship (North)

(a)

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Coffee yield and shade tree density relationship (West Nile)

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Coffee yield and shade tree density relationship (East)

(c)

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e yi

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Shade tree percentage

Coffee yield and shade tree percentage relationship (Southwest)

(d)

(b)

47


10 to 44 trees/ha and then remained stable as shade tree density continued to increase (Fig. 3.14, a).

In comparison, in the West Nile, maximum Arabica yield increased slowly with the increase of shade

tree density in the entire range between 0 to 120 trees/ha (Fig. 3.14, b). By contrast, increasing

shade trees affected maximum coffee yield negatively in the East and Southwest Arabica growing

areas (Fig. 3.14, c, d). Maximum coffee yield declined linearly with shade tree density went up from 0

to 120 trees/ha in the East (Fig. 3.14, c) and with relative shade tree density increased from 0 to 6%

in the Southwest (Fig. 3.14, d).

Mulch depth

Fig. 3.15. Coffee yield and mulch depth relationship in (a) Central and North Robusta coffee growing

areas and (b) Southwest and West Nile Arabica growing regions.

Mulch depth presented large variation among the five coffee growing regions. Average mulch depth

ranged from 0.3 to 1.9 cm and between 0.2 to 1.0 cm respectively in the Central and Northern

Robusta cultivating regions. In the Arabica production areas, mulch depth varied from 0 to 1.1 cm in

the East, from 0 to 2.0 cm in the Southwest and from 0.7 to 1.2 cm in the West Nile. No significant

(P≤0.05) difference in mulch depth was identified between coffee monocropping and coffee-banana

intercropping systems.

Positive correlation between coffee yield and mulch depth was indicated in all regions examined

except the East. The correlation was statistically significant in the Southwest (P<0.01), while in

Central, Northern and North-western (West Nile) regions the relationship were identified by

observation. The effects of mulch depth on maximum coffee yield are illustrated in Figure 3.15. In the

Central region, a high Robusta yield was never been achieved when mulch depth was less than 1.0

cm (Fig. 3.15, a). On the other hand, there was a potential for Robusta in the North and Arabica in

the Southwest areas to further improve the maximum yield through enhancing mulch application (Fig.

3.15, a, b). In the West Nile region the maximum Arabica yield increased sharply within a small

variation of mulch depth so that mulching did not limited maximum yield too much (Fig. 3.15, b).

0

500

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2500

0.0 0.5 1.0 1.5 2.0 2.5

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Mulch depth (cm)

Coffee yield and mulch depth relationship (Robusta)

Central

North

0

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0.0 0.5 1.0 1.5 2.0 2.5

Coffe

e yi

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Mulch depth (cm)

Coffee yield and mulch depth relationship (Arabica)

Southwest

Westnile

(a) (b)

48


Hand weeding frequency

Fig. 3.16. Coffee yield and hand weeding frequency relationship in the North Robusta region.

There was no significant (P≤0.05) correlation between frequency of hand weeding (expressed by

number of hand weeding per year) and coffee yield in all regions of interest. However, by observing

the scatter-plot, there was a potential increase of maximum Robusta yield with the increased

number of weeding times in the Northern region (Fig. 3.16). The relationship between yield and

weeding was not observed for other regions because of the low weeding frequency.

In the North region, most surveyed coffee farmers conducted hand weeding two to four times each

year (Fig. 3.16). Weeding could significantly improve maximum coffee yield in comparison with those

without weeding (0 times/year) (Fig. 3.16). However, the beneficial effect of weeding was not

obvious when weeding frequency increased beyond two times per year (Fig. 3.16).

3.2 Important yield limiting factors and coffee yield gap in the five regions

3.2.1 Important yield limiting factors

Central

0

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Hand weed frequency (times/year)

Coffee yield and hand weeding frequency relationship (North)

0%

5%

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Observed yield (kg/ha/year)

49


North

East

Southwest

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Pred

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0%

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Pred

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50


West Nile

Fig. 3.17. Percentage of coffee plots that has minimum predicted yield and corresponding yield

limiting factors in the given region (left); Actual yield against minimum predicted yield (right).

As mentioned before, the most limiting factor at plot level was the factor that resulted in the lowest

predicted yield which was the minimum attainable yield predicted by boundary line model. Based on

principle of “Law of minimum” (von Liebig, 1855), constraint that repeated most often in the given

region was regarded as the most limiting constraint at regional level and are demonstrated in the bar

chart in Figure 3.17 (left). Three top ranked limiting constraints are identified, while others are

assumed to be relatively unimportant.

The important yield limiting factors indicated a strong variation among the different regions. In the

Central Robusta region, the most limiting factor for coffee production (2010) was coffee tree density

(trees/ha). Due to inadequate plant density, the proportion of farmers obtaining the minimum

predicted yield was 24% and unfavourable plant density reduced coffee production by 122.91

kg/ha/year (median). This was followed by coffee twig borer (%) which accounted for 18% coffee

plots with minimum predicted yield and caused yield reduction of 510 kg/ha/year. The next one was

old coffee trees which associated with 14% of coffee plots with minimum predicted yield and

resulted in a yield loss of 333 kg/ha/year.

For the Northern Robusta region, the most important constraint was low soil K concentration

(cmol/kg) which gave an explanation of 23% of coffee plots with lowest predicted yield and

generated a yield reduction of 265 kg/ha/year (median). The second ranked coffee production

constraint in the Northern Uganda was mulch depth (cm) that accounted for 15% minimum yield

plots and caused a yield loss of 94 kg/ha/year. This was followed by low soil Mg concentration

(cmol/kg), poor weeding (times/year) and low shade tree density (trees/ha), each of them

contributed lowest attainable yield of 10% coffee plots.

0%

5%

10%

15%

20%

25%

0

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2000

0 500 1000 1500 2000

Pred

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51


For Arabica grown in the Eastern area, low soil P concentration (mg/kg) was identified being the

principal cause of yield loss in 30% of surveyed plots, which was followed by higher shade tree

density (trees/ha) (21% plots) and high soil pH (13% plots). Yield reduction caused by soil P

deficiencies, competition effect of shade trees, and unfavourable soil pH was 561, 613 and 501

kg/ha/year respectively (median).

In the south-western Arabica growing area, soil Mg concentration (cmol/kg) (25% plots), elevation (m)

(18% plots) and mulch depth (cm) (14% plots) were proven to be the top three limiting constraints

for coffee production that resulted in yield reduction of 633, 474 and 413 kg/ha/year (median).

In the West Nile Arabica region, soil P concentration (mg/kg) (22% plots), coffee plant density

(trees/ha) (20% plots) and soil K concentration (cmol/kg) (16% plots) were the primary yield loss

factors. Deficiency of soil P and K was associated with yield reduction of 483 kg/ha/year and 385

kg/ha/year respectively (median). Inadequate coffee plant density gave rise to annual yield loss of

372 kg/ha.

3.2.2 Explainable and unexplainable yield gaps

For each region, actual coffee yield of individual coffee plot was plotted against minimum predicted

yield as explained in the scatter-plots in Figure 3.17. The coefficient of determination (R2) that

represent the linear correlation between actual observed yield and minimum predicted yield was

0.294 (Central), 0.483 (North), 0.387 (East), 0.469 (Southwest) and 0.178 (West Nile). On the other

words, the predicted yield can explain 29.4%, 48.3%, 38.7%, 46.9% and 17.8% of the variation of the

actual yield in the five regions.

A 1: 1 diagonal line was sketched for each scatter-plot that is presented as dashed line in the graph

(Fig. 3.17, right). The fitness of plotted points on those equivalence lines indicated the predictive

ability of boundary line approach to estimate actual yield. A horizontal line was also drawn in the

graph which represents the attainable yield in the given region (Fig. 3.17, right). For individual data

point that scattered above the 1: 1 line, the explainable yield gap was the vertical distance between

that point (indicating minimum predicted yield) and the horizontal line (representing attainable yield).

The unexplainable yield gap was the vertical distance between the data point and the diagonal line

(indicating actual yield). On the other hand, data points distribute below that 1: 1 equivalence line

indicated an overestimation of actual yield. Data points that lay exactly on the equivalence line

illustrated the perfect estimation of actual yield by boundary line model and the yield gap of this

point can be explained fully by the most limiting factor responding to the predicted yield that

identified in this plot. The average explainable and unexplainable yield gap of individual plot was

quantified to explain the yield gap at regional level (Table 3.4).

52


In Robusta growing regions, the explainable yield gap at regional level was 778 kg/ha/year in the

Central and 760 kg/ha/year in the North region. The unexplainable yield gap averaged 515

kg/ha/year in the Central and 266 kg/ha/year in the North respectively.

For Arabica, the average explainable yield gap in the East region was 966 kg/ha/year, while the

unexplainable yield gap was 298 kg/ha/year. The explainable and unexplainable yield gap for Arabica

grown in the Southwest was 994 kg/ha/year and 595 kg/ha/year respectively. The explainable yield

gap in the West Nile was 774 kg/ha/year on average and the unexplainable yield gap was 342

kg/ha/year in the region.

Table 3.4 Yield gap and important limiting factors in five coffee production regions. Regions Attainab

le yield (kg/ha/ year)

Explainable yield gap (kg/ha/ year)

Unexplainable yield gap (kg/ha/ year)

Important yield limiting factors

Percentage of influenced farmers in the region

Caused yield losses (median, kg/ha/year)

Central 1737 778 515 Coffee density (trees/ha) 22% 123 Coffee twig borers incidence (%)

16% 510

Coffee age (years) 14% 333

North 1464 760 266 Soil K concentration (cmol/kg)

23% 265

Mulch depth (cm) 15% 94

East 1701 966 298 Soil P concentration (mg/kg)

30% 561


21% 613

Soil pH 13% 501

South west

2244 1090 486 Soil Mg concentration (cmol/kg)

25% 633

Elevation (m) 16% 474 Mulch depth (cm) 14% 413

West Nile

1550 774 342 Soil P concentration (mg/kg)

22% 483

Coffee density (trees/ha) 20% 373 Soil K concentration (cmol/kg)

16% 385

53


Fig. 3.18. Yield related production factors and proportions of associated farms (P%) that had the

minimum predicted yield due to the corresponding factors (expressed as 1-P% in the radar graph).

Figure 3.18 provides an overview of the important limiting factors in five regions as a whole. The

factors with endpoints lie on the corresponding radar axis indicated a limiting effect on coffee yield.

For a certain constraint in the given region, if the corresponding endpoints present the lowest value

among other constraints, it was the most limiting factor in that region.

Biotic constraints restricted Robusta production in the Central region (coffee twig borer) and Arabica

the East area (coffee stem borers), and the effect was relatively significant in the Central. Soil

properties were important limitation in almost all coffee growing regions. Unfavourable soil P level

was the most limiting constraint for Arabica production in both Eastern and West Nile regions. Soil P

deficiency also limited coffee production for Robusta in the Central and Northern regions and Arabica

in the Southwest area to some extent. Soil K deficiency was the principal cause of yield loss in

Northern Robusta growing area, and it suggested a limiting effect in the Central region as well.

Inadequate soil Mg level was the primary constraint for Arabica yield in the Southwest, while soil Mg

deficiency also limited coffee production in the Central Robusta area and in the Eastern and West

Nile Arabica regions. Unfavourable soil pH and insufficient SOM, N and Ca concentration all restricted

coffee yield to a certain degree across the five regions. Elevation affected coffee yield in all Arabica

cultivating regions and it was an important limitation in the Southwest particularly. Inappropriate

coffee plant density was the most important constraint in the Central Robusta region, and the second

ranked limiting factor in the West Nile Arabica area. Shade tree density was an important reason

explaining Arabica yield reduction in the East area. Mulch depth restricted Robusta production in the

North and Arabica yield in the Southwest. Other sub-optimum management practices such as too

high banana density and poor weeding had a relatively small impact on coffee yield.

0%

20%

40%

60%

80%

100%Coffee density (trees/ha)

Coffee age (years)

Banana density (trees/ha)


Mulch depth (cm)

Hand weeding frequency(times/year))

Soil pH

SOM (%)Soil N (%)

Soil P (mg/kg)

Soil K (cmol/kg)

Soil Ca (cmol/kg)

Soil Mg (cmol/kg)

Coffee twig borer incidence(%)

Coffee stem borer incidence(%)

Elevation (m)

CentralNorthEastSouthwestWest Nile

54


3.2.3 Important production constraints perceived by farmers in eastern Uganda

Group discussion and farmer interviews were conducted in three districts with distinct elevation

along the slope of mountain region in eastern Uganda Arabica growing region: Kapchorwa (1671 m)

in high elevation, Manafwa (1319 m) in the middle and Mbale (1209 m) in the lower area. In general,

majority of the farmers in the East perceived low soil fertility and pests and diseases pressures as the

important production constraints.

A group of 15 farmers in Kapchorwa participated in the group discussion. All farmers (15/15) believed

that over-shading by shade trees, coffee die back and poor management practice were important

limitations. Nine of 15 farmers perceived poor soil fertility as an important constraint, the same

proportion was given to coffee leaf rust and coffee berry disease that were explained by farmers to

be very common in their coffee fields. A slightly higher proportion (10/15) was given to the old coffee

trees. Moreover, coffee berry borers, coffee stem borers and mealy bugs were also present, but not

an important limitation. In the end, they ranked the top three limiting constraints as: coffee die back,

poor soil fertility and coffee leaf rust infestation.

In Manafwa, all of 26 farmers attended in the group discussion perceived that the prolonged dry

period and poor soil fertility are the important constraints for their coffee production (26/26). These

are followed by poor management practices in terms of low implementation of pruning and weeding

(19/26). Pest stresses such as coffee stem borer (18/26), coffee berry borer (1/26), root mealy bugs

and coffee leaf miners (12/26) and diseases like coffee leaf rust (8/26) and coffee berry disease

(11/26) were also mentioned by farmers to be limiting in coffee production. Others constraints like

old coffee trees (7/26) and low plant density (2/26) were proposed as well. In the end, the top three

most important constraints ranked by farmers were: poor management practices, coffee stem borer

infestation and poor soil fertility.

In the lowest region Mbale, 20 farmers joined the group discussion. Poor soil fertility (20/20), old

coffee trees (18/20) and impropriate plant density (17/20) were perceived to restrict coffee

production. Coffee berry borer (13/20), coffee stem borer (14/20) and root mealy bugs (2/20) were

explained by farmers as the most frequently occurred pests and coffee leaf rust (12/19) was the most

frequent diseases. A few farmers explained the prolonged dry season and insufficient rainfall as

important constraints in this region (6/20). Eventually, farmers ranked the poor soil fertility as the

largest production constraint which is followed by pest pressure (coffee stem borers and coffee berry

borers) and old coffee trees.

55


3.3 Rainfall

3.3.1 Rainfall distribution in Uganda over five years (2006—2010)

Fig. 3.19. Annual rainfall distribution of 25 surveyed districts across Uganda from 2006 to 2010. The

horizontal lines of the boxes indicate 75% percentile (upper), median (solid line across boxes) and 25%

percentile (bottom). The upper and bottom bars outside the boxes explain the highest and lowest

annual rainfall amount. Circles above the box-whisker represent the outliers by 1.5 to 3 times higher

than the interquartile range (25%-75% percentile).

Figure 3.19 illustrated the general rainfall distribution of 25 surveyed districts across Uganda country

over the five successive years 2006–2010. There were two districts where annual rainfall exceeded

2000 mm in 2006 and 2007 respectively, which can be indicated by a cycle in the box-plot graph (Fig.

3.19). These values were all included in the following analysis after carefully checking the database.

Annual rainfall indicated an asymmetrical distribution across the country. Therefore, median instead

of average rainfall value was used to evaluate the rainfall variation across the five years.

Among year 2006, 2007 and 2008, annual rainfall median indicated a little difference across the years,

with a median fluctuating between 1400–1500 mm (Fig. 3.19). The lowest precipitation occurred in

2009 when either maximum (1513 mm), median (1000 mm) and lowest (754 mm) rainfall was the

smallest among the five years. Both maximum and median annual rainfall amount increased in the

following year 2010, though the amount (1090 mm) was still low compared with the previous years.

Regional rainfall distributions across the five years and monthly rainfall distribution over year 2009

and 2010 were also identified. In Robusta growing area, across the five years, average annual rainfall

ranged from 950 mm to 1520 mm in Central Uganda, from 1000 to 1500 mm in the Northern region.

In Arabica production areas, annual precipitation ranged from 1422 to 1926 mm in the East, from

941 to 1185 mm in the Southwest and from 1000 to 1348 mm in the West Nile.

56


Fig. 3.20. Annual rainfall distribution in the surveyed districts of five regions from 2006 to 2010. For

each region, the boxes in the graph from left to right represent each year from 2006-2010

respectively. The horizontal lines of the boxes indicate 75% percentile (upper), median (solid line

across boxes) and 25% percentile (bottom). The upper and bottom bars outside the boxes explain the

highest and lowest annual rainfall amount. Circles below the box-whisker represent the outliers that

are 1.5 to 3 times lower than the interquartile range (25%-75% percentile).

Figure 3.20 demonstrated annual rainfall distribution of individual region over the five years (2006–

2010). In each year from 2006 to 2010, the eastern region indicated the highest average, median and

maximum annual rainfall among the five regions, while the south-western area had the lowest

average and median annual rainfall in three years (2006, 2007 and 2009) (Fig. 3.20). It can be seen

that, for each region, there was a remarkable drop in annual precipitation in year 2009, which

followed by a slightly remission in 2010. This pattern is most obvious in Central where the annual

rainfall was significantly less in 2009 and 2010 compared with the prior three years (2006–2008).

The length of the box-whisker indicated the variability of rainfall distribution of different districts in

the given region. In the East and the West Nile Arabica production areas, the variation of annual

rainfall between the districts was relatively small and did not change too much over years (Fig. 3.20).

However, in the Northern Robusta region and the Southwest Arabica area, the variation of annual

precipitation was obviously larger in 2009 and 2010 compared with the previous years 2006 – 2008.

The monthly rainfall distribution of the individual region during period from January, 2006 to

December, 2010 were illustrated in Appendix V. Compared with the preceding three years, monthly

rainfall showed an irregular distribution in year 2009 and 2010 in each region. For instance, Eastern

Uganda used to have one long rainy season combining with two dry seasons in each year from 2006

to 2008. However over 2009 and 2010, the rainfall distribution changed into a pattern of two rainy

seasons associated with three dry periods.

(mm

)

57


3.3.2 Yield–critical rainfall periods for coffee production

Table 3.5 demonstrates the coffee crop calendar across 2009/2010 (including dry season, flowering,

fruit setting and harvest). In general, coffee harvest season of most districts surveyed started from

September or October, 2010 and can last three to four months to the next year 2011. Only in two

districts Rubiriz and Ibanda in the Southwest region, the harvest was started on May, 2010.

According to monthly rainfall distribution (Appendix V), through 2009/2010, there was no obvious

successive months with low rainfall level less than 60 mm in all regions. However, in most districts,

the monthly rainfall distribution indicated a similar pattern across 2009/2010 that the lowest

monthly precipitation occurred in January of 2010 (less than 60 mm) which was followed by

November and December of 2009 (only in the Southwest region, identified based on monthly rainfall

distribution of the individual district that did not illustrated in the report).

Consequently, the assumption has been made that dry season associated with coffee yield in 2010,

might have occurred in the end of 2009 (December, only for two districts in the Southwest) and the

beginning of year 2010 (January, for majority of districts in the other four regions), which also

matches with the harvest time after approximately nine to ten months. Flowering that followed the

dry season occurred in February of 2010 in most districts. The fruit abortion period was assumed

being occurred in the month following the flowering month (March, 2010). In Rubiriz and Ibanda of

the Southwest Arabica region, a dry period occurred in June and July of 2009, therefore blossom was

probably occurred in August, 2009 and fruit abortion period was likely to be in September, 2010.

Table 3.5. Yield-critical rainfall periods in five coffee growing regions in Uganda. Regions Districts Dry period Flowering

time Fruit abortion period

Harvest started time

Central Luwero, Mubende, Mityana, Mokono, Mipigi

January 2010 February, 2010

March, 2010 October, 2010

North Gulu, Oyam, Lira, Apa



East Kapchorwa, Sironko, Mbale, Bududa, Manafa



Southwest Kasese, Kisoro

November and December 2009 and January 2010

February, 2010


Rubiriz, Ibanda May, June and July, 2009

August, 2009

September, 2009

May, 2010

West Nile Maracha, Yumbe, Arua, Zumbu, Nebbi



58


3.3.3 Effects of rainfall variation on coffee yield

Fig. 3.21. Relationship between coffee yield and rainfall amount (left) and rainfall days (right) in (A) one year before harvest, (B) from flowering to harvest, (C) fruit abortion period and (D) dry season.

0

500

1000

1500

2000

500 1000 1500 2000

Coffe

e yi

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ear0

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Rainfall amount in one year before harvest and coffee yield

East

Westnile

Central

0

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180 200 220 240 260 280 300

Coffe

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Rainfall days in one year before harvest and coffee yield

Southwest

0

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500 700 900 1100 1300 1500

Coffe

e ye

ild (k

g/ha

/yea

r)


Rainfall amount from flowering to harvest and coffee yield

East

WestnileWest Nile

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Southwest

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Rainfall amount in fruit abortion period and coffee yield

Southwest

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10 15 20 25 30

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Rainfall days in in fruit abortion period and coffee yield

East

Central

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Coffe

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Southwest

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30 50 70 90

Coffe

e yi

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Rainfall days in dry season and coffee yield

Southwest

(A)

(B)

(C)

(D)

59


Detailed data of rainfall amount and rainy days of individual district surveyed in the four yield-critical

periods are summarized in Appendix VI. The correlations between coffee yield of 2010 and those

rainfall properties were evaluated with Pearson correlation test (Table 3.6). Both rainfall amount and

rainfall days indicated significant correlation (|r|>0.25; P≤0.05) with coffee yield, while the effect of

which varied in different regions (Table. 3.6). Linear regression was implemented for those that

showed strong relationship and illustrated in Figure 3.21.

Rainfall amount in one year before harvest suggested a significant positive effect on coffee yield

(r=0.431, P=0.006) in the Central Robusta growing region where rainfall amount ranged from 930 to

1090 mm (Fig. 3.21, A, left). On the other hand, increased rainfall amount from 1033 to 1300 mm in

this period suggested a negative impact on Arabica coffee yield in the West Nile (r=-0.332, P=0.034).

Over 2009/2010, eastern Uganda experienced a relatively higher precipitation between 1561 to 1970

mm compared with other coffee production regions. However, the increased rainfall amount during

the whole growth season suggested a significant negative impact on Arabica yield (r=-0.495, P=0.001)

(Fig. 3.21, A, left). Rainfall days in this period was positively related to Arabica yield in the Southwest

area where increased rainy days from 207 to 254 days was associated with a significant yield

improvement (r=0.433, P=0.007) (Fig. 3.21, A, right).

Rainfall amount from flower to harvest that varied from 1023 to 1176 mm indicated a significant

negative correlation with coffee yield in the eastern Arabica production region (r=-0.495, P=0.001)

(Fig. 3.21, B, left). This result was in line with the disadvantage of excessive rainfall amount identified

in the period of one year before harvest. In the West Nile, rainfall amount from flower to harvest

ranged from 665 to 864 mm and increased rainfall amount related with Arabica yield decline (r=-

0.332, P=0.034) (Fig. 3.21, B, left). By contrast, increasing rainfall days in this period indicated a

positive relationship with Arabica yield (r=0.387, P=0.011) in the East where higher yields were

associated with increased rainfall days from 148 to 171 (Fig. 3.21, B, right). The impact of rainfall days

in this period was opposite to that of the rainfall amount, rainfall amount and rainfall days in this

period were negatively correlated with each other (r=-0.554, P<0.001). In south-western Uganda,

increasing rainy days appeared to have a favourable effect on Arabica yield as rainy days increased

from 104 to 131 (Fig. 3.21, B, right).

During the fruit abortion period, rainfall amount was positively correlated with Arabica yield in the

Southwest (r=0.433, P=0.007). Increasing rainfall amount from 91 to 141 mm was related to an

improvement of coffee yield (Fig. 3.21, C, left). Increased rainfall days in this period were associated

with higher yield for Robusta production in the Central (r=0.564, P≤0.001) and Arabica production in

the East (r=0.387, P=0.011) (Fig. 3.21, C, right) with rainfall days varied from 20 to 26 in the Central

and between 14 to 18 in the East region.

60


In dry season, both rainfall amount and rainy days indicated a positive effect on Arabica coffee yield

in the south-western Uganda (Fig. 3.21, D). Higher yield was identified as rainfall amount increased

from 82 to 262 mm in the south-western Arabica coffee growing region (r=0.433, P=0.007) (Fig. 3.21,

D, left). Coffee yield also increased with the increase of rainfall days from 42 to 76 in the Southwest

(r=0.663, P<0.001) (Fig. 3.21, D, right).

Table 3.6. Pearson correlation test for yield and rainfall variation in the four yield-critical periods. Regions Rainfall properties One year before

harvest From flower to harvest

Fruit abortion period

Dry season

Central Rainfall amount (mm)

P (r=0.431, P=0.006)

ns ns ns

Rainfall days (days) ns ns P (r=0.564, P<0.001)

ns

North Rainfall amount (mm)

ns ns ns ns

Rainfall days (days) ns ns ns N (r=-0.357, P=0.026)

East Rainfall amount (mm)

N (r=-0.495, P=0.001)

N (r=-0.495, P=0.001)

ns ns

Rainfall days (days) ns P (r=0.365, P=0.019)

P (r=0.365, P=0.019)

ns

Southwest Rainfall amount (mm)

ns P(r=0.433, P=0.007)

P(r=0.642, P<0.001)

P (r=0.556, P<0.001)

Rainfall days (days) P (r=0.552, P<0.001)

ns ns P (r=0.663, P<0.001)

West Nile Rainfall amount (mm)

N (r=-0.332, P=0.034)

N (r=-0.332, P=0.034)

ns ns

Rainfall days (days) N (r=-0.410, P=0.007)

N (r=-0.446, P=0.003)

ns ns

Note: “r”—correlation coefficient; “P” significant level. “P”—Significant positive correlation, P≤0.05. “N”—Significant negative correlation, P≤0.05. “ns“—No significant correlation were found.

3.4 Relationship among yield limiting factors

Presence of pest and disease and management practice

Compared with young coffee trees population, older trees tended to be more vulnerable to pests

and diseases infestation. The presence of coffee weaver ants (Oecophylla smaragdina) in the

northern Robusta growing region (P<0.01), coffee stem borer (Xylotrechus quadripes) in the south-

western Arabica production area (P<0.05) and coffee leaf skeletonizers (Leucoplema dohertyi) in the

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West Nile Arabica area (P<0.05 for both) was significantly higher in older coffee tree population

compared with that in young plant community.

The results also implied the impacts of shade trees on the dynamics of pests and diseases. Shade tree

density was associated positively (P≤0.05) with presence of pests and diseases in all regions. In the

Central Robusta region, the presence of coffee twig borer was positively related with shade tree

density (P<0.05). In the Northern Robusta area, the presence of coffee berry borers (Colletotrichum

coffeae) was significantly higher with increased shade tree shading level than that under low shading

level (P<0.05). In the eastern Arabica area, the occurrence of coffee mites (Tetranychus spp.) was

significantly greater with increased shading (P<0.05). Coffee leaf miners (Leucoptera coffeella) in the

Southwest Arabica growing region occurred more in coffee plots with more shade trees (P<0.05).

Coffee leaf skeletonizers in the West Nile Arabica growing area presented more with higher shade

tree shading level (P<0.05).

For Robusta, there was a negative correlation between pests and diseases and soil properties. In the

Central region, coffee twig borer was negatively correlated with soil pH (r=-0.309, P=0.029). Coffee

berry borer was negative correlated with soil pH in northern Uganda (r=-0.461, P=0.001).

Correlation between soil properties

The majority of soil properties were correlated with each other. Soil pH and SOM indicated a

significant correlation (P<0.05) with almost all of the other soil properties (soil N, P, K, Ca and Mg

concentration). Soil pH was correlated positively with soil N (P<0.05), with soil P (P<0.01), with soil K

(P<0.05), Ca (P<0.01) and Mg (P<0.01) in Central and with soil P, K, Ca and Mg (P<0.01 for all) in

Northern Robusta production regions. Soil pH has a positive relationship with soil P and Ca (P<0.01

for both) in the East; with soil Ca, Mg and K (P<0.01 for all) in the Southwest and with soil K (P<0.05)

in the West Nile Arabica growing areas. Soil pH had a negative correlation with SOM and N in the

West Nile (P<0.05 for both).

SOM was positively related with soil N, Ca and Mg in the Central Robusta area (P<0.01 for all). SOM

correlated positively with N, P and Ca (P<0.05 for all) in the northern Robusta region. SOM has a

positive relation with soil N, Mg and K (P<0.01 for all) and Ca (P<0.05) in the East Arabica growing

area. SOM has positive correlation with soil N and Ca (P<0.01 for both) in the Southwest and with N,

Ca, Mg (P<0.01 for all) and P (P<0.05) in the West Nile Arabica regions.

Soil N was positively related to Ca and Mg (P<0.01 for both) and K (P<0.05) concentration in the

Central Robusta production area. In Arabica growing areas, soil N was positively related with Ca, Mg

and K (P<0.01 for all) in the eastern region. Soil N has positive correlation with Ca (P<0.01) and K

62


(P<0.05) in the Southwest. Positive correlation was also identified between N and Ca and Mg in the

West Nile (P<0.01).

Soil P concentration was positively related to soil K, Ca and Mg (P<0.01) in the Central Robusta region.

Soil P and Ca were correlative positively (P<0.05) in the Northern Robusta area. Soil P indicated

positively correlation with Ca (P<0.01), Mg and K (P<0.05) in the East; with Ca and K in the Southwest

(P<0.01) and with Mg and Ca (P<0.05) in the West Nile Arabica regions. In addition, soil K, Ca and Mg

are positively correlated with each other in all of the five regions (P<0.01 in Central, Northern,

Eastern and South-western regions and P<0.05 in the West Nile).

Furthermore, soil P, K and Ca were positively correlated with farmyard manure (kg/ha/year) in the

Central Robusta region (P<0.05). Soil Ca and Mg was positively correlated with fertilizer application

(kg/ha/year) in the Central area (P<0.05). Farmyard manure had a positive relationship with soil pH

(P<0.05) and a negative relationship with soil Mg (P<0.05) in the Eastern Arabica area.

The influences of elevation in Arabica growing regions

The elevation appeared to be correlated with a range of biotic, abiotic factors and management

practices in Arabica growing areas. In the Southwest area, the presence of coffee leaf miners was

significantly less at higher elevation compared with that at lowland (P<0.05), while coffee berry

disease occurred more at higher elevation (P<0.05). In the West Nile region, coffee leaf skeletonizers

were found more frequently at higher elevation (P<0.05).

Elevation had a significant negative correlation with soil P in the East and Southwest (P<0.01 for

both). Elevation had a significant positive correlation with soil K in eastern area (P<0.05). Elevation

was negatively correlated with soil K and soil Ca in the Southwest (P<0.05, P<0.01 respectively).

Elevation correlated negatively with soil pH in the West Nile, while positive correlation between

elevation and SOM was identified in the region (P<0.01).

The relationship between elevation and rainfall pattern (amount and days) that related to coffee

yield (2010) was evaluated. Table 3.7 illustrated the correlation between elevation and rainfall that

occurred in one year before harvest and dry season. Elevation was negatively correlated with rainfall

amount in the period of one year before harvest in the West Nile (P<0.01). Negative correlation was

identified between elevation and rainfall amount of the dry season in all Arabica growing areas

(P<0.05 for East; P<0.001 for Southwest and West Nile). In addition, in the Southwest area, higher

elevation was associated with less rainfall days in the dry season (P<0.001).

Moreover, elevation associated negatively with mulch depth in the Southwest (P<0.01). Higher

elevation was significantly related with higher shade tree density in the West Nile (P<0.01). On the

contrary, shade tree density was negatively correlated with elevation in the East region (P<0.05).

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Table 3.7. Correlations between elevation and rainfall patterns in Arabica growing regions Regions Rainfall amount Rainfall days

One year before harvest

Dry season One year before harvest

Dry season

East ns N (r=0.292, P=0.04)

ns ns

Southwest ns N (r=-0.878, P<0.001)

ns N (r=-0.570, P<0.001)

West Nile N (r=-0.375, P=0.011)

N (r=-0.713, P<0.001)

ns ns

All Arabica growing areas

N (r=-0.428, P<0.001)

N (r=-0.554, P<0.001)

ns N (r=-0.209, P=0.015)

Note: “r”—correlation coefficient; “P” significant level. “P”—Significant positive correlation, P≤0.05. “N”—Significant negative correlation, P≤0.05. “ns“—No significant correlation were found.

Other correlations

Mulch depth was positively correlated with banana density in the East and Southwest Arabica

regions (P<0.05 for both) and with coffee density in the Northern Robusta area (P<0.01). Mulch

depth was positively related to soil K (P<0.01) in Central Robusta region. In Arabica areas, mulch

depth was negatively correlated with soil pH and soil Mg in the Southwest (P<0.05 for both). Coffee

density was positively correlated with SOM (P<0.05), soil N (P<0.05) and soil Mg (P<0.01) in the East.

Coffee density had a significant negative correlation with soil P (P<0.01) in the West Nile. In the East,

coffee individual aboveground biomass had a positive relationship with coffee yield (P<0.01). Coffee

individual aboveground biomass was also positively related with SOM and soil N (P<0.05), with soil K

(P<0.01) and with mulch depth (P<0.01) in Eastern region.

3.5 Exploration of adequate plant density in intercropping system

3.5.1 Effect of banana on coffee yield in intercropping systems of three regions

Fig. 3.22. Coffee yield and banana density in intercropping system of Arabica.

0

500

1000

1500

2000

2500

0% 20% 40% 60% 80%

Coffe

e yi

eld

(kg/

ha/y

ear)

Relative banana denisty

Coffee yield and banana density (2010)

East

Southwest

64


There was no significant (P≤0.05) difference in coffee yield (kg/ha/year) between coffee

monocropping and coffee-banana intercropping systems in the Central Robusta area and East and

Southwest Arabica regions in 2010 at current production levels. In addition, coffee density and

average coffee tree age did not differ significantly between the two systems (P≤0.05).

As illustrated above, increased relative banana density had a negative effect on maximum Robusta

yield in the Central region (by observation). No significant (P≤0.05) correlation between coffee yield

and relative banana density were identified in the East and the Southwest Arabica areas. However,

when look at the data cluster of the two Arabica regions as a whole, an optimum range of relative

banana density appeared where coffee yield reached the peak level (Fig. 3.23).

A boundary curve was sketched by hand enclosing the upper boundary points of the data cloud for

Arabica grown in East and Southwest regions. The boundary lines indicated an optimum relative

banana density for a good coffee production that range from 30% to 50% (Fig. 3.23). This suggested a

coffee and banana density ratio between 1: 1 to 2.3: 1 (a structure of one or two coffee trees

associated with one banana tree). However, the largest yield observed in the intercropping system

was lower than the maximum yield that indicated in Y axis which was the yield achieved in coffee

monocropping system (Fig. 3.23).

3.5.2 Effect of banana on coffee aboveground biomass in East Uganda

Fig. 3.23. Aboveground biomass of coffee trees in intercropping and monocropping systems (a)

average individual AGB and (b) total coffee aboveground biomass (left) and total tree aboveground

biomass (right). Error bars are standard deviation with 95% CI.

Coffee stem girth (cm) at 15 cm above bottom; coffee stem height (cm); individual (kg/tree) and total

coffee aboveground biomass (kg/ha), were used as indicators to explored the effect of banana on

coffee performance in intercropping system.

(b) (a)

65


Neither coffee tree girth nor stem height differed significantly (P≤0.05) between coffee

monocropping and coffee-banana intercropping systems. However, intercropping coffee with banana

indicated a negative effect on both individual and total coffee aboveground biomass. Individual

coffee tree aboveground biomass in coffee monocropped field (Mean=3.05 kg/tree, SE=0.187) was

significantly (P≤0.05) higher compared with that in coffee-banana intercropped field (Mean=2.20,

SE=0.180) (Fig. 3.24, a). In addition, total aboveground biomass of coffee trees in coffee-banana

intercropping system (Mean=3650.77 kg/ha, SE=416.977) was significantly (P≤0.05) lower than that

in coffee monocropping system (Mean=5490.61 kg/ha, SE=661.807) (Fig. 3.24, b, left).

On the other hand, total land productivity (kg AGB/ha) that expressed as total aboveground biomass

in the coffee plot was significantly different between the two systems (P≤0.05). Total land

productivity of coffee-banana intercropping (Mean= 10397.73 kg/ha, SE=845.454) was significantly

(P≤0.05) greater than that of coffee sole-cropping (Mean=5494.61 kg/ha, SE=661.803) (Fig. 3.23, b,

right).

Fig. 3.24. The effect of relative banana density on (a) individual coffee tree aboveground biomass

(AGB) and (b) coffee tree girth in coffee-banana intercropping system.

There was a significant correlation between individual coffee tree aboveground biomass (AGB)

(kg/tree) and relative banana density (%) in coffee-banana intercropping systems in the eastern

region (r=0.54, P≤0.05). Individual coffee AGB increased linearly with the increase of relative banana

density from 20% to 50% (in other words, coffee and banana density ratio varied from 1: 1 to 4: 1)

(Fig. 3.25, a). In addition to individual coffee AGB, stem girth of the surveyed coffee trees was

positively correlated with relative banana density with a relatively higher correlation coefficient

(r=0.71, P<0.05) (Fig. 3.25, b). Further exploratory analysis of the regression line was not

implemented here as it was assumed that coffee individual AGB as well as stem girth might decrease

if banana density exceeds a certain level.

0

1

2

3

4

5

0% 20% 40% 60%Indi

vidu

al c

offe

e tr

ee A

GB (k

g/tr

ee)


Individual coffee tree AGB and relative banana density

0

2

4

6

8

0% 20% 40% 60%

Coffe

e tr

ee g

irth

(cm

)


Coffee tree girth and relative banana density

(b) (a)

66


4. Discussion

4.1 Boundary line analysis

The usefulness of boundary line approach as a data analysis tool has been evaluated in a number of

studies. Boundary line analysis has been considered as a reliable method to analyse yield response to

various biophysical factors and consequently to identify crop yield gaps (Casanova et al., 1999; Shatar

and Mchbratney, 2004; Fermont et al., 2009; Wairegi et al., 2010; Van Ittersum et al., 2013). Unlike

the linear regression approach that enables the evaluation of average yield responses across all the

fields, boundary line analysis has the advantage to reflect the site-specific characteristics of individual

farms in any growing season (Shatar and Mchbratney, 2004; Van Ittersum et al., 2013). By

recognizing the relevant importance of production constraints and yield improvement potential, an

individual farmer is able to improve management practices in the most efficient way and achieve a

better yield over time.

Nevertheless, this approach has many limitations in terms of the reliability and representativeness of

the results and therefore requires critical evaluation and appropriate interpretation based on

agronomic knowledge and principles. One of the primary limitations of boundary line model in

predicting crop yield gaps was that the boundary line approach focuses on the relative importance of

a certain factor, while cannot address the interactive effect of multiple variables, therefore might

underestimate the real situation (Shatar and Mchbratney, 2004). This can be revealed from the 1: 1

scatter-plot in Figure 3.8 that some of the predicted yields were higher than the actual yields.

Moreover, attainable yield instead of potential yield has been used as a reference to evaluate coffee

yield gaps by the assumption that best performing farmers were able to close the potential yield.

However, attainable yield might be far below potential yield for farmers lacking financial support

(Van Ittersum et al., 2013). Furthermore, boundary line approach is not able to address the yield

response to the variability of temperature, radiation and specific yield-related rainfall distribution

(Van Ittersum et al., 2013). High attainable yield obtained in an individual coffee field might due to

the good soil and climate conditions which are not the same in other fields (Shatar and Mchbratney,

2004). As such, attainable yield does not completely take into account the variability of soil,

temperature and rainfall distribution between different sites and can hardly represent the entire

agro-ecological condition across the region. Consequently, the yield gap expressed as the difference

between the attainable yield and the actual yield might underestimate the real yield gap that is

explained by the potential yield and the actual yield.

The most limiting factor in the boundary line analysis was defined considering the proportion of

farmers that reflected minimum attainable yield attained by boundary line model. However, in some

67


cases yield loss (in median) caused by a certain constraint was not the highest although it indicated

the largest proportion of farms with minimum attainable yield. For instance, in the Central region,

coffee density was the most limiting factor on 22% of total surveyed farms with the minimum

attainable yield, while unfavourable plant density only caused a yield loss of 123 kg/ha/year in

median which was less than the yield reduction of 510 kg/ha/year that caused by coffee twig borer.

In this sense, the effect of plant density was relatively small for each individual farm, but the

magnitude of this constraint was large in the whole region. For an individual coffee plot, the

production constraint that can explain the maximum yield gap was also evaluated. However, due to

the time limitation, this issue was not addressed in the ranking process in this study, while it should

be taken into consideration when rank the most important production constraint in the further

studies.

The construction of boundary line is based on boundary points which are only a small proportion of

the marginal data. Therefore, boundary line is rather sensitive to the selected samples. Any change in

the upper data points will have a significant influence on the accuracy of the final prediction.

Boundary points in this study are the yield achieved by the best performing farmer under the

limitation of an individual biophysical factor and can be evaluated accurately only given enough

samples corresponding to a certain limiting factor. On the other hand, if there were insufficient farms

related to a given constraint, the real maximum attainable yield might be underestimated, the

boundary line constructed from these boundary points might not be reliable to estimate yield gap

and the associated most limiting factor.

In addition, the identification of outliers is extremely important in order to give an accurate

prediction of boundary points. Outliers were identified by Schnug et al. (1996) graphically with two

criteria, “Rectangle criteria” and “Cycle criteria”. This method considered the both dependent and

independent variables, defined a rectangle or cycle with data points as centre, the size of which was

determined by standard deviation of both variables. Schmidt et al. (2000) chose 99% percentiles of

data points to address boundary line while the outliers can be identified as the data above the 99%

percentile. Outliers were identified by Shatar and Mchbratney (2004) using Mahalanobis distance

that takes into account the relative distance of a residual from the main data points.

Instead of consider the two variables as a whole, outliers in this study were identified for each

variable separately through simple approaches: (i) by experiences that the observed coffee yield on

the farm was no more than 2500 kg/ha/year (ii) identified in boxplot chart based on the principle

that outliers are the points with value of more than 1.5 times interquartile higher than the upper

quartile and (iii) by observation that the points were apparently separated from the remaining data

clusters in scatter-chart. However, the approach did not address the two variables as a whole, and

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might be over-simplified especially consider the possible inaccuracy of entries in the database that

was based primarily on farmer surveys, observations and on farm measurements. Further efforts

should be made on the identification of outliers and determination of boundary points to develop a

more precise boundary line model that address specific problems.

It has been emphasised by Wairegi et al. (2010) that yield gap can be best closed only by

implementing site-specific management that consider the temporal and spatial variation of

production constraints. The results presented also identified that the most limiting factors varied

significantly across coffee fields within the same region. This might suggests a site-specific variability

across fields in terms of accessibility of inputs and markets. In fact, boundary line model is

considered to be more precise to address problems of a small magnitude than that in a large

magnitude (Wairegi et al., 2010). Therefore, it is worthy to carry out boundary line analysis based on

sub-unit (districts) rather than at regional level. However this was not achievable given the available

data in this study (ten farms per district), further surveys are needed to address the specific problems

of individual districts.

Furthermore, constraints for coffee production and associated yield gap might vary throughout the

years. In this study, yield gap analysis was conducted for only one growing season which might not be

able to represent the situation over years. Information of yield and production limiting factors over a

longer period were not considered in this study which are necessary to give a more accurate

evaluation on coffee yield gap in Uganda. Moreover, it is not sufficient to only consider the most

important constraint in a given region that demonstrated in this study. Yield response to a certain

constraint might change with the variation of other factors. A comprehensive evaluation of various

production constraints and their interactions is needed in attempt to address best recommendations.

4.2 Coffee yield and yield gap

In Uganda, the average Robusta coffee yield was reported to be 500 kg/ha/year under moderate

management (Van Asten et al., 2012). Average Robusta coffee yield observed in 2010 (based on

recall data) was higher than that reported (702 kg/ha/year in the Central and 647 kg/ha/year in the

Northern regions). However, Robusta growing areas reflected a large variance in yield between farm

plots (from less than 100 kg/ha/year to up to 1737 kg/ha/year). In addition, the attainable yield

obtained in both regions (1737 kg/ha/year in the Central and 1464 kg/ha/year in the North) were far

below the recorded maximum yield 3500 kg/ha/year for Robusta coffee observed in Thailand (Van

Asten et al., 2012).

According to UCDA, the average annual Arabica coffee yield in Uganda was 750 kg/ha/year (Van

Asten et al., 2012). Among the three Arabica growing regions surveyed, the Southwest achieved the

69


highest yield in 2010 with an average yield of 923 kg/ha/year. The average yield in the East and the

West Nile were 698 and 747 kg/ha/year respectably which were slightly less than that reported

earlier. Again, large yield variation could be identified within and across the regions with the lowest

yield of 167 kg/ha/year and the highest yield reached 2243 kg/ha/year.

For Robusta coffee, the explainable yield gap in year 2010 was demonstrated to be 778 kg/ha in the

Central and 760 kg/ha in the Northern region. An explainable yield gap of 965, 1090 and 774

kg/ha/year in the East, Southwest and West Nile regions respectively were identified for Arabica

coffee production. Yield gaps of both Arabica and Robusta coffee were almost half of the attainable

yield achieved by the two species. The large yield gap reflected enormous challenges faced by

Uganda’s coffee farmers, while it also implies a high potential for yield improvement in coffee

production across the country. The relatively higher yield gap identified in the East and the

Southwest Arabica coffee growing regions indicated a relatively larger attainable yield of the two

regions in the surveyed year compared with that of the West Nile region. This might due to the

favourable weather in the two regions, while it probably also reflects the extremely low input level in

a majority of coffee farm fields which lead to a generally poor yield.

A yield gap of 70% to 80% has been reported by African Fine Coffees Association (AFCA). In

comparison, the explainable yield gap identified in this study was generally smaller with an average

yield gap of approximately 50% referring to the attainable yield (45% in the Central, 52% in the North,

56% in the East, 49% in the Southwest and 50% in the West Nile). As mentioned above, yield

predicted by the boundary line approach was in general overestimated as can be identified from the

scatter figures (Fig. 3.17) that most data points lay above the 1: 1 line.

The correlation coefficients (R2) representing linear relationship between actual yield and minimum

predicted yield were relatively smaller in the Central Robusta area (0.294) and the West Nile Arabica

region (0.178) compared with that observed in the other three regions (0.483 in the North; 0.387 in

the East and 0.469 in the Southwest). The underestimation of actual coffee yield and low R2 observed

in the Central and the West Nile regions might attributed to (i) inherent inaccuracy of boundary line

approach (as explained in the earlier section), (ii) exclusion of other important constraints, (iii) the

elimination in boundary line analysis of potential interactive effects between the limiting factors. The

over-prediction of actual yield combined with the underestimation of attainable yield all gave rise to

the underestimation of the explainable yield gap.

Many other biotic and abiotic production constraints might also limit coffee production. Those

factors were not involved in this study due to the lack of information or because of the limitation on

boundary line construction. For instance, information of pest and disease incidence in this study was

obtained during the coffee flowering season, while pests and diseases occurring in other seasons

70


might affect coffee yield as well. Rainfall availability is an essential yield related factor for Uganda’s

coffee production and was not included in boundary line analysis (It was claimed that drought

occurred in 2009 has caused a yield loss up to 50% (Robert, 2012)). Weed pressure was not

quantified in the study which if included, might be one of the important constraints (Wingtens, 2009).

Moreover, soil texture might plays an important role in determining coffee yield due to its influence

on soil water retention, nutrient provision and plant nutrient absorption (Wairegi et al., 2010). Other

soil properties such as micronutrients might also affect coffee production but were not addressed in

the study due to the lack of information. The application of fertilizer and farmyard manure in the

surveyed farms was too rare to construct a boundary line, while these external inputs would

contribute substantially to yield improvement.

In addition to the important biophysical constraints, socio-economical limiting factors also play

important roles in Uganda’s coffee industry as a whole (Robert, 2012; UCDA, 2012). Farmers tend to

achieve only low yield when lack of social and financial supplies to implement appropriate

management (Fermont et al., 2009). Coffee market liberalization that occurred in the early 1990s

caused a large reduction of farm gate prices which discouraged the investment by farmers in coffee

production (Sserunkuuma and Secretariat, 2001). Low farm price and lack of appropriate approach to

deliver the products have been considered as important constraints for coffee production. Moreover,

coffee production relies mainly on family labour most from women and children who do not have

adequate experiences in good husbandry practice (UCDA, 2012). Increased urbanisation and

industrialization, population growth and land shortage were also proposed in the explanations of low

productivity (Robert, 2012). Last but not the least, the lack of sorting and package community and

limited buyers during harvesting period are important limitations as explained by farmers (farmer

interview in East region).

4.3 Important limiting factors of coffee production

This section focuses on the relatively important production limitations derived from boundary line

analysis. The results illustrate that various biophysical characteristics and agricultural practices have

affected coffee yield. It is difficult to give a general conclusion since the effects of these factors were

inconsistent across different regions. Therefore, the emphasis was given on the site-specific

influences of each biotic, abiotic limitations and management practices on coffee production.

4.3.1 Biotic constraints

Coffee twig borer, coffee stem borer and coffee leaf miner were negatively related with Robusta

coffee yield in the Central and Arabica production in the East and the West Nile respectively, while

71


for Arabica grown in eastern and West Nile regions, pests and diseases did not seem to be important

comparatively.

Pest pressure was particularly important in the Central region where coffee twig borer was the

second ranked important constraints for Robusta coffee production. Based on boundary line analysis,

coffee twig borers had caused a significant yield reduction (median, 510 kg/ha/year) in the Central

region in 2010, though the present of which was only observed in 48% coffee plots during the survey

period. Coffee twig borer is commonly found in tropical Africa and it can be a serious constraint for

Robusta coffee (Wintgens, 2009). The pest was described by UCDA being the “economic threshold”

for Uganda’s Robusta coffee production and occurred most frequently in the Central region (UCDA,

2012). Defoliation is a typical symptom of coffee twig borer, which result in detrimental effect on

plant photosynthesis (Wintgens, 2009).

Coffee twig borer predominant in wet seasons and less present in dry periods (Wintgens, 2009). In

the Central region, the surveys were conducted during the second flowering period (June, July and

August) which was the end of the dry season and the beginning of the rainfall season. Therefore,

shorter dry season combined with prolonged rainfall period across the whole year identified in the

study might partially explain the dominant of the pest in the Central region (based on monthly

rainfall distribution in Appendix V). Increased shading level would contribute to the establishment of

the population, therefore, pruning and suckering should be implemented and frequent thinning is

needed to avoid over-shading (Wintgens, 2009). However, this study did not identify the significant

effect of shading on presence of coffee twig borer in all regions of interest.

In addition to coffee twig borer, coffee wilt disease had also caused a considerable damage of

Robusta coffee in the Central region as reported by UCDA (2012), while the effect of this disease was

not significant during the survey period even though it was presented in more than 50% of the coffee

plots. Old populations especially those with poor management are vulnerable to coffee wilt disease

(Wintgens, 2009). The selection of resistant varieties can be the best way to cope with this problem

(Wintgens, 2009).

Arabica coffee grown in Uganda is usually affected by coffee leaf rust and coffee stem borer (Van

Asten et al., 2012). These are also heard from farmers frequently to be an important limitation of

coffee production during group discussion in the eastern Arabica region. Though the presences of

coffee leaf rust and coffee stem borer were also observed in Arabica growing areas during farm

surveys, the importance of these pests and diseases was not strong as implied by boundary line

analysis in comparison with other constraints.

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The prediction of pest and disease influence is complex since the presence and severity of pests and

diseases has a high degree of spatial and temporal variability. The surveys were conducted during

coffee flowering stage which might therefore not be able to represent pest and disease dynamics

over the entire growth season. For instance, coffee berry disease and coffee berry borers are only

dominant in fruiting season. The on-farm monitoring of pest and disease over more than one growth

season is needed to evaluate its importance in influencing coffee production.

In addition, some pests and diseases were proven to be significantly related with other biophysical

factors such as, elevation, coffee age, shade tree density and hand weeding frequency (IITA, 2012).

The presence and severity of leaf miners, coffee berry borers, coffee leaf rusts and many fungal

diseases were also associated with variation of temperature and precipitation (Robert, 2012;

Jassogne et al., 2013 (a)). The results also illustrated the influence of coffee tree age, shade tree

shading level and soil properties on the presence of pests and diseases: the occurrences of coffee

weaver ants, coffee stem borers, coffee die back and coffee leaf skeletonizers were proven to be

positively related to old coffee population; coffee berry borers, coffee mites, coffee leaf rust and

coffee leaf skeletonizers were positively associated with higher shade tree shading level; coffee twig

borer and coffee berry borer were negatively correlated with soil pH. Therefore, the presence of pest

and disease and their effects on coffee production should also consider the interactive effects of

other biophysical variables.

4.3.2 Abiotic constraints

Soil properties

Soil fertility of the five regions surveyed has been classified based on its agricultural potential

(Mwebaze, 2002). Central and Northern Uganda are dominated by alluvial soils with extremely low

agricultural productivity; volcanic soils can be found in the East and the Southwest regions that are of

medium to high productivity; in the West Nile Uganda sandy clay loam with medium to low

productivity are prevalent (Mwebaze, 2002). This study revealed that soil properties (SOM, soil N, P,

K, Mg and Ca concentration) were strongly correlated with coffee yield. Low soil fertility posed a big

challenge for coffee production in almost all coffee growth regions in Uganda. The limiting effects of

soil properties on coffee yield indicated a large variability across the five regions.

According to the boundary line analysis, coffee yield increased linearly with an increase in some soil

parameters and this was followed by a range where coffee yield remain stable and not limited by soil

properties. Negative impacts of some soil properties such as soil pH, soil P and Mg concentration

were also identified. Consequently, the results illustrated that for Arabica coffee, soil P deficiency is

particularly important in the East and West Nile regions, soil Mg deficiency was the most limiting

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constraint in the Southwest area, while for Robusta coffee, and soil K plays an important role in the

North coffee growing area. A Compositional Nutrient Diagnosis (CND) analysis has been conducted

by IITA (2012) using the same data base as this study. Based on the soil critical values and foliar

critical values, the CND analysis helps to establish the nutrient deficiency map that provides the

suggestion of deficient, sufficient and moderate soil nutrient levels (Wairegi and van Asten, 2012). A

specific nutrient is deficient if it is deficient in the soil, foliar and CND index. In this section, the

results presented in the study are illustrated in comparison with conclusion obtained in CND analysis.

The various importance of soil properties on coffee are addressed briefly as well.

Nitrogen (N) is essential in both vegetative and reproductive stages of coffee trees (Wintgen, 2009).

Soil mineral depletion is most significant for soil N and soil K and soil N deficiency that commonly

occurs after a heavy harvest and is predominant in dry environment (Wintgen, 2009). The results

from CND analysis demonstrated a general deficiency in soil N in all of the five regions. However, soil

N did not seem to be an important limitation based on boundary line analysis in comparison with

other limiting factors. Moreover, both SOM and N concentration indicated a similar effect on coffee

yield according to boundary line models. The specific effect of both factor were hard to interpret

since SOM and soil N were closely correlated with each other.

Phosphorus (P) plays an important role in development of coffee roots in particular (Wintgen, 2009).

This study indicated that low soil P concentration was the most limiting factor in the West Nile

Arabica area where soil P ranged from 3 to 30 mg/kg. This is supported by the result illustrated by

CND analysis that soil P was particularly low in the West Nile and Southwest Arabica growing regions.

On the other hand, the study found a relatively high concentration of soil P in the East Arabica region

where increased soil P (0–50 mg/kg) suggested a strong limiting effect on coffee production in this

region. The relatively high P concentration in East was also revealed by CND analysis where P was “in

sufficient supply” in part of the East region (Van Asten et al., 2012). The strong deficiency of soil P in

the Southwest was revealed by CND analysis, while soil P did not indicate a significant effect on

coffee yield based on boundary line analysis.

However, it should be emphasised that in the East Arabica region soil P concentration decreased with

the increase of elevation (P<0.01). Increasing elevation demonstrated a positive effect on yield

improvement in this region. Moreover, soil pH has a positive correlation with soil P and Ca content

(P<0.01) in the East and increasing soil pH (from 5.2 to 7) also suggested a negative effect on coffee

yield in this region. Therefore, the negative correlation between soil P and coffee yield might due to

the influences of other soil parameters.

The importance of potassium (K) for coffee production in its contribution to fruit development and

maturation has been stated by Wintgen (2009). During the later stage of coffee fruit development,

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fruits would contain more than 95% of total K, P, and N that were recently accumulated by coffee

trees (DaMatta et al., 2007). Soil K was suggested in CND study to be strongly deficient in the

Northern Robusta area and in the West Nile Arabica region, while K was in moderate supply in the

other three regions. The deficiency of soil K in the North and West Nile demonstrated by CND

supported our results that soil K was the most limiting factor for Robusta coffee production in the

North and the third ranked important constraint for Arabica production in the West Nile.

Calcium (Ca) and magnesium (Mg) are important for coffee growth and production considering its

functions in plant leaf photosynthesis and flower bud generation (Wintgen, 2009). Lack of Ca and Mg

would be detrimental to coffee production qualitatively and quantitatively (Wintgen, 2009). Soil Ca

was strongly deficient in Central Robusta area and in the Southwest and West Nile Arabica regions

and soil Mg was generally in sufficient supply in the Southwest as pointed out by CND analysis. In this

study, soil Ca deficiency did not appear to affect coffee production strongly. On the other hand, in

the Southwest region increasing Mg (0.22 cmol/kg–4.36 cmol/kg) indicated a negative influence on

maximum yield, whereas in the other four regions increasing soil Mg had a positive influence on

coffee yield. The results illustrated that soil exchangeable K, Mg and Ca are significantly correlated

with each other. The adverse effect of increased soil Mg in the Southwest region might due to the

imbalance of K, Ca and Mg, as a high level of soil Mg and Ca is often associated with crop K deficiency

due to problems of crop nutrients uptake (Wintgen, 2009).

It should be pointed out that, boundary line was performed in attempt to obtain an “S” curve for

positive yield related soil properties. The assumption was that excessive supply of soil nutrients

would not generate adverse effects on coffee yield. However, boundary curves could be performed

in other shapes as well. For instance, soil pH in the Southwest and SOM and N in the East suggested a

parabola curve along the upper boundary points. In this sense, excessive soil pH level in the

Southwest region (pH>6) suggested a negative influence on maximum Arabica coffee yield. SOM and

N seemed to be oversupplied in the East Arabica region, SOM larger than 10% and soil N greater than

40% all indicated an adverse impact on Arabica production. In this study, however, limiting effect of

excessive nutrient only appeared in a few coffee farms. Therefore, these excessive nutrient factors

were not taken into consideration in boundary line analysis by assumption that the exclusion of these

effects would not affect the final results.

Furthermore, soil nutrient level is only one of the indicators for nutrient availability of coffee trees

and should not be addressed without the consideration of other soil parameters and other

production factors. Even though soil pH did not reflected to be an important constraint, soil pH did

indicate the significant correlation with other soil properties examined (positively correlated with soil

P, Ca, Mg and K). Soil pH is one of the most important soil properties that affect the nutrient

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availabilities of crops (especially P and micronutrients) (Jensen, 2010). Soil properties were also

related to mulch depth, coffee plant density and crop systems. In this view, regression analysis that

can address interactive effects of multi-variables might be more desirable than boundary line

approach. Moreover, the influence of individual soil parameter might not be obvious in year 2010

but a long-term deficiency of which would eventually reduce productivity by the interactive effect

with other soil properties. In order to define the absolute influence of a single nutrient, on-farm

controlled experiments are needed to evaluate the balance and interactions with various other soil

properties and other biophysical factors over time during the vegetative and reproductive stage of

coffee trees.

The improvement of soil fertility can be fulfilled by adopting the Integrated Soil Fertility Management

that proposed in African Green Revolution (ISFM Africa, 2012). The ISFM is a series of soil fertility

improvement strategies including fertilization, application of organic inputs and the dissemination of

the knowledge needed for the implementation of these practices (ISFM Africa, 2012). Applying

fertilizer could be one of the most efficient ways to deal with soil nutrient deficiencies in short term.

Important soil constraints illustrated in this study provide suggestions for fertilizer recommendations.

Fertilizer recommendations should also address site-specific nutrient deficiency proposed by CND

analysis that carried out at district level. With these guidelines, demonstrations with on long-term

and large scale trial experimentations are warranted to explore the most efficient way for use of

fertilizer. Moreover, the availability of the specific fertilizer required and accessibility of farmers to

those inputs should also be considered. In addition to fertilizers application, other strategies can also

be advisable especially for small-holder farmers who lack of financial support. Technologies such as

mulching, intercropping legumes or shade trees and the recycled use of residual materials (coffee

husks) would improve soil properties and efficiency of fertilization and provide many other long-term

benefits to farmers.

Elevation

Surveyed coffee fields located in elevation from 1000 to 1300 m in the Central and North Robusta

coffee growing regions, while Arabica was found growing in the higher elevation between 1000 to

2000 m in East, Southwest and West Nile areas. Difference of elevation did not seem to be important

for Robusta. This might attributed to the fact that the surveyed sites in the Central and the North

regions are adjacent with a difference of elevation of a few hundred meters. Nevertheless, the

variation of elevation suggested a pronounced effect on coffee yield in all Arabica production regions.

Maximum Arabica yield increased with the increase of cultivation elevation in both East and West

Nile regions. Conversely, maximum yield dropped down with the increase of elevation in the

Southwest. The effect of elevation on Arabica coffee can be explained by the individual and/or

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interactive effects of various biophysical factors that related with elevation. These factors can include

the presence and severity of pests and diseases, soil fertility, soil moisture, temperature, rainfall

distribution, wind, air humidity and various socio-economic characteristics.

The study showed that better yield was associated with higher elevation in eastern Arabica area and

this is in agreement with the perspectives of local farmers who believed that coffee performs better

at higher elevation (farmer interview in the East). The reasons were explained to be higher

precipitation, better soil fertility (more volcanic soils) and lower temperature in highland area

compared with those in lower sites. Farmers in highland Kapchowa (1671 m) explained that rainfall is

in sufficiently supply over the whole growth season and therefore is not a constraint for them.

Farmers in low elevation region Mbale (1209 m) also agreed that coffee yield better at higher

elevation where rainfall is higher, soil texture and soil fertilities are better and pest and disease

incidence is lower comparably. Moreover, farmers in lowland region sometimes buy seedlings that

perform well in the higher lands, but grow them in lower sites where they do not perform very well.

Farmers’ perspectives on water potential are supported by the results and advices from the National

Agriculture Research Laboratories (NARL) (2011) that land at higher elevation receives more rainfall

which is associated with higher atmosphere humidity. On the other hand, sites from lower elevations

benefit from their topographic advantages and may receive additional water supply (Shatar and

Mcbratney, 2004). However, the results presented in this study indicated an excessive rainfall supply

in the East region which was negatively associated with coffee yield. The detrimental effect could be

even more severe for coffee farms in lower area that are vulnerable to waterlogging.

With regard to soil fertility, an opposite statement is that in eastern Uganda, lowland soil indicates

relatively higher productivity potential with volcanic ash as parent material, while soil in upper slopes

is less fertile and less productive (NARL, 2011). This study also indicated that higher elevation was

associated with lower soil P concentration in the East region. However, soil P deficiency was shown

to be limiting in the East which was in contrast with the conclusion that yield increases with the

increase of elevation. Therefore, individual or interactive effects of other biophysical factors rather

than soil P might be able to explain the higher yield observed in highland area. Furthermore, lower

elevation was significantly associated with higher shade tree density as indicated in the results, which

in turn negatively affect coffee yield. This might also explain the lower yield observed in lowland area.

In spite of biophysical relations, social-economic aspects such as engagement in agricultural activities,

availability of input and accessibility to market might also related with the variation of elevation.

Farmers at higher elevation tend to give more priority to coffee production than those at lower

elevation who are closer to town and would pay more attention to other business (farmer interview

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in the East). In this sense, coffee gardens of the high land regions could receive better management

compared with those in lower land area.

Similarly, in the West Nile Arabica growing area, coffee yield increased with the increase of

cultivation elevation. Pests and diseases such as coffee die back and coffee leaf skeletonizers were

proven to be more predominant at higher elevation than that in lower land, while pests and diseases

did not seem to have a strong limiting effect compared with other constraints. On the other hand,

rainfall amount of the entire growth season was higher at lower elevation in the West Nile and

excessive rainfall during this period appeared to have an adverse influence on coffee yield. Therefore,

relatively favourable precipitation might be able to explain the higher yield found in highland area.

Unlike the East and West Nile regions, Arabica coffee yield of the Southwest region was negatively

correlated with elevation. This study illustrated a positive relationship between elevation and

presence of coffee berry disease. The present of coffee leaf miners was also demonstrated to be

more dominant at higher elevation in the Southwest. In addition, soil P; K and Ca concentrations

were all negatively correlated with elevation in this region. Highland area in the Southwest was

proven being associated with a lower level of both rainfall amount and rainfall days during dry

season, which were all positively related to coffee yield. Furthermore, higher elevation was proven to

be associated with less mulching. The individual or combinations of those factors mentioned above

are probably the reasons for the negative relationship between elevation and coffee yield observed

in Southwest.

4.2.3 Management practices

Among other management factors, coffee density, coffee tree age, shade tree density and mulch

depth were strongly correlated with coffee yield of both Robusta and Arabica and indicated a

significant limitation for coffee production in Uganda.

Coffee density

Robusta was planted in a density ranged from 100 to 1244 trees/ha with an average density of 870

trees/ha in the Central and Northern regions. Arabica plant density was between 500 and 3000

trees/ha in the East, Southwest and West Nile regions respectively. Unfavourable plant density was

revealed to be an important yield limiting factor for both Robusta and Arabica. In the Central region,

low plant density was appeared to be the most limiting constraint for Robusta production which was

associated with 123 kg/ha (median) annual yield reduction referring to attainable yield. In the West

Nile, low coffee plant density was demonstrated being an important constraint that responsible for

median annual Arabica yield loss of 373 kg/ha. For both coffee species, increasing plant density had a

positive effect on maximum attainable yield. The results are in agreement with the statement made

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by DaMatta (2004) that increasing coffee plant density would give rise to higher productivity. For

Arabica, however, maximum attainable yield declined when coffee density exceeded a certain level.

It was also demonstrated that maximum yield of individual coffee trees decreased with an increase

of coffee plant density (results did not illustrated in the report).

The reduction of the maximum yield might be attributed to the increased intra-specific competition

for light, nutrient and water in high density plantations. For instance, increased plant density is

associated with higher mutual-shading between coffee trees which would reduce leaf photosynthesis

as demonstrated by DaMatta (2004). Too much shading would also generate adverse effect on

flowering since the exposure of solar radiation is important for flower bud induction (Wintgens, 2009;

DaMatta, 2004). The decline in individual coffee tree yield was also demonstrated in previous studies

which explained that increase plant density is associated with the decrease of number of fruit-

bearing nodes as well as the number of fruit per node (DaMatta et al., 2007). In addition, higher plant

density is associated with higher risks of pests and diseases such as coffee leaf rust and coffee berry

borer (Vieira 2008; Wintgens, 2009). Moreover, for farmers who interplant coffee with annual crops

such as beans, large plant density would be harmful for the growth of annual crops due to the

blocking of light interception and nutrient competition (Jassogne et al., 2013 (a)).

On the other hand, the reduction of individual tree yield can be counteracted by the increase of total

population, eventually coffee productivity would increase as a whole in higher density plantation.

From this view, there should be an optimum density where a balance of inter-specific competition

and population advantages can be achieved. For Robusta, the boundary line suggested a potential for

further improving coffee yield through the increase of plant density. Therefore, the optimum plant

density for Robusta coffee could be more than 1244 trees/ha (the highest density observed during

farm surveys).

Unlike Robusta, Arabica coffee had a peak yield when the plant density was approximately 1500

trees/ha, below or beyond which yield started to decrease. The results is quite close to that indicated

in recent studies that optimum plant density of Arabica was 1600 to 1700 trees/ha (with low manual

management level and under medium to poor soil fertility) (Wingtens, 2009). A lower optimum

Arabica density (1000–1200 trees/ha) is recommended by IITA-Uganda. Coffee plant density of up to

5000 trees/ha has been proven to be favourable for Arabia (for short height cultivars), and the

desirable plant density for Robusta coffee should be less than 4000 trees/ha (DaMatta, 2004).

Optimum coffee plant density suggested by authorities is less in Uganda as compared to other

countries, which might attributed to the unfavourable climate and soil conditions and poor

management practices of Uganda coffee production.

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Coffee age

According to UCDA (2012), most coffee trees cultivated in Uganda are older than 50 years which are

unproductive and susceptible to pest and disease pressures. Based on five major coffee production

regions across the country, this study identified a relatively younger coffee population with coffee

tree age ranged between 5 and 60 years. This might largely due to the fact that since 1990s,

replacement of old and diseased coffee trees has been the primary government policy strategy on

Uganda coffee production (UCDA, 2012).

In addition to inadequate plant density, old and unproductive coffee trees were proven to be an

important constraint for Robusta production in the Central region. Robusta coffee planted in the

Central and the North regions were relatively young with an average tree age of 14 and 10 years in

respectively. The increase of tree age from 4 to 35 years indicated a substantial decline of maximum

yield in the Central, while increasing coffee tree age in the range of 5 to 15 years in the North did not

imply strong effect on maximum yield. Arabica coffee tree age was 28, 21 and 30 years in the East,

Southwest and West Nile coffee growing regions respectively. Increasing coffee tree age from 5 to 50

years in the East and from 5 to 60 years in the West Nile indicated a negative influence on maximum

yield, while the decreased trend was not significant in both regions according to boundary line

analysis.

The lifespan of coffee trees is reported to be 100 years, while the high productive stage is less than

40 years (UCDA, 2012). Coffee trees can keep productive for up to 80 years under moderate

management while the economic productive life period is only less than 30 years (Wintgens, 2009).

The maximum coffee yield can be remained for three to five years as claimed by DaMatta (2004). The

results in this study were reasonable in the sense that the optimum biological potential age (30 years)

has not been reached for Robusta grown in the North and Arabica cultivated in the Southwest, while

this age has been surpassed in the East and West Nile Arabica growing areas.

In addition to physiological disability, older coffee trees are also associated with higher risks of pests

and diseases. Coffee was lately introduced in northern part of the country so that less pest and

disease problems were presented in the North region (Jassogne, 2011). This study indicated that the

presence of pests and diseases such as coffee weaver ant, coffee stem borer, coffee die back and

coffee leaf skeletonisers were significantly higher in older coffee plantations than that in young

communities (P<0.05). Therefore, the effect of aging coffee trees on coffee yield might also be

related to higher incidence of pest and disease particularly in poor managed coffee plantations.

In the Central region, coffee trees were generally young (4–35 years with an average of 15 years),

while increasing tree age was associated with a steep decrease of yield potential. With coffee trees

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getting old, the situation of leaf loss and branches wilting would become severe and heavy pruning

and thinning are required in order to maintain a good productivity (DaMatta, 2004). In fact, the

central part of Uganda is referred as intensive coffee-banana farming system by Sserunkuuma and

Secretariat (2001) that has highest agriculture and economic potential due to its easily access to

infrastructure, good access to markets and high population intensity. However, to cope with

decreasing crop yield and increasing food security, farmers in the Central region (with high access to

urban market in Kampala) tend to engage more into rural industry and urban employment

(Sserunkuuma and Secretariat, 2001). The relatively remarkable effect of aging trees and

inappropriate plant density in the Central region might largely due to the less engagement of farmers

in agricultural practices.

Shade tree density

Both Robusta and Arabica are naturally cultivated in shaded environment under tropical forests in

Africa (DaMatta et al., 2007). The strengths of shade trees in coffee plantations have been reviewed

comprehensively in many studies. By planting shade trees in coffee garden, farmers are less

vulnerable to risks associated with some type of pest and disease (Jassogne et al., 2013 (a)) wind

stress (DaMatta and Ramalho, 2006); farmers are able to maintain high level of agro-biodiversity and

better ecosystems services in coffee garden compared that under full sun (Mendez et al., 2010; Jha

et al., 2011). Through decreasing evapotranspiration of coffee canopy, shading can reduce air

temperature to 4°C (Läderach et al., 2010) and up to 10°C (Wingtens, 2009) compared with that

under full-sun. Shade trees also contribute to improving water use efficiency of coffee trees and

maintain high atmosphere humidity in coffee plantations (Jassogne et al., 2013 (a)). In addition,

shade trees can enhance nutrient cycling (legume shade trees can also supply substantial N to coffee),

reduce soil erosion, reduce nutrients requirement by coffee trees (reduce foliar K, Zn and Ca leaching)

(Wingtens, 2009). Compared with coffee trees under full-sun, shaded coffee tend to be taller, less

vulnerable and longevous (Wingtens, 2009). Therefore, shade trees are highly advisable especially

under sub-optimum environmental conditions such as extreme temperature, prolonged dry season

and heavy wind pressure (DaMatta, 2004). Moreover, shade trees helps to maintain a well balance of

nutrient supplements between vegetative and reproductive stage of coffee trees so that reduce the

risks of “biennial production” that due to plant exhaustion from heavy harvest (DaMatta, 2004).

During farmer interview in the East Arabica coffee growing region, farmers explained their purposes

of planting shade trees and shared their perspectives on the advantages and disadvantages of shade

trees. According to farmers, shade trees were either already in coffee fields or established by farmers

later for timber and firewood that can provide them extra income. Farmers explained the advantages

of shade trees as following: (i) provide leaf residues so that supply nutrients to coffee trees (ii)

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maintain favourable air humidity (iii) protect soil from soil erosion (iv) maintain low temperature (v)

associated with less pest and disease pressures (vi) uniform ripening of coffee beans and (vii) bigger

and even-coloured cherries. The importance of shade trees was perceived by farmers especially in

lower elevation area due to the relatively low rainfall potential explained by farmers. Farmers in

lowland area of the East region were encouraged by local authorities to plant shade trees and they

found that drought damage was reduced by doing this. They also believed that shade trees can help

to maintain the same environment as that in higher land elevation relatively low temperature, high

air moisture and high soil fertility.

On the other hand, much evidence indicated that coffee plantations are more productive under

direct sunshine compared with that under shading system (Bosselmann et al, 2009; DaMatta, 2004;

Vieira, 2008; Jassogne et al., 2013 (a)). The benefit effects of shade trees might only appear when

under the favourable environmental conditions in terms of intensity and quality of radiation,

temperature, soil nutrients and relative humidity (DaMatta, 2004). For instance, if soil fertility is

insufficient, the benefiting from shading would reduce and shade tree might even negatively affect

coffee yield at all elevations gradients (DaMatta, 2004). The negative influence of shade trees on

coffee yield might be attributed to the reduced light interception of coffee trees which affect

photosynthetic of coffee canopies (DaMatta, 2004). Too much shading would therefore reduce

carbohydrate accumulation of coffee trees and consequently reduce fruit production (DaMatta,

2004). In addition, the development of flower buds could be hampered by over-shading (Wingtens,

2009). Moreover, shading might promote vegetative growth rather than reproductive growth of

coffee trees which would in the end reduce production (DaMatta et al., 2007).

The disadvantages of shade trees on Arabica production in the East region were also pointed out by

local farmers. Farmers in Kapchorwa explained that too much shading might cause low temperature

which is harmful to coffee trees. Farmers in Manafwa had observed extremely tall coffee trees under

heavy shading. Farmers in Mbale argued that some types of shade trees are not friendly to coffee

because they induce too much competition with coffee trees in term of water, nutrients and solar

radiation. In addition, farmers pointed out that leaves residues generated by some shade trees

species would take a rather long time to decompose. Moreover, falling down of shade tree branches

would destroy coffee trees as explained by farmers. Farmers also mentioned that planting shade

trees would increase the incidence of some types of pests and diseases.

Some types of shade trees were argued by farmers being unsuitable in coffee garden due to their

strong association with some types of coffee pests and diseases. For instance, jackfruit (Artocarpus

heteropHyllus) is a host for coffee berry borers; Musisis would harbour carpenter worm which would

generate larva on coffee leaves and feed on that. Coffee trees that under mango are often found to

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be water stressed. Moreover, farmers in Manafwa suggested the allelopathy effect of shade tree

Eucalyptus (Eucalyptus grandis) by explaining that this shade tree can produce some harmful

chemicals to coffee trees.

In this study, the influences of shade trees on coffee production indicated a large variation among

different regions. Shade trees suggested positive effect on Robusta yield in the North area and on

Arabica production in the West Nile, where increasing shade tree density (ranged between 10-40 and

0-120 trees/ha in the North and West Nile respectively) was associated with higher maximum yield.

The impacts was relatively less obvious in the North with a few coffee plots (10) actually affected by

lacking of shade trees. The significant disadvantage of shade trees was identified for Arabica coffee

grown in the East and the Southwest regions, increasing shade tree density from 0-120 and from 0-6%

respectively was associated with linear reduction of maximum yield. In addition, high shade tree

density was proven to be an important constraint in the East region that responsible for yield loss of

613 kg/ha. It has been proven that coffee intercropped with Grevillea robusta (grevillea) in Brazil

with a shade tree density up to 119 trees/ha did not damage coffee yield (Baggio et al., 1999). The

negative effect of shade trees in some regions of Uganda might partially due to the sub-optimum

environmental conditions and management implementations.

However, it should be pointed out that, in the West Nile, higher shading tree density was associated

with higher elevation which was also related with better yield. Therefore, the positive effect of shade

tree on Arabica yield in the West Nile region might due to the correlated influence of elevation which

makes it impossible to identify the individual influence of shade trees on coffee production.

Precipitation in the West Nile during the whole growth season was proven to be sufficient, while the

average annual temperature was regarded to be highest among the five regions. Therefore, if the

positive effects of shade trees do exist, it might suggest a mitigated effect of shade trees of

extremely high temperature. It might also due to the ability of shade trees to reduce soil erosion that

was caused by excessive rainfall in the high elevation.

Similarly, in the East area, higher shade tree density was associated with lower elevation which in

turn indicated a yield reduction. Therefore, the negative effect of shade tree density on coffee yield

might indirectly due to its negative correlation with elevation. In addition, annual precipitation was

proven to be excessive so that the advantage of shade trees in terms of mitigating drought damage

would be less obvious. Other climate condition (temperature, humidity and wind) in eastern Uganda

seems favourable for Arabica coffee growth. Therefore, shade trees tend to limit coffee production in

the East region due to the fact that the benefits of shade trees are significant only under

suboptimum environmental conditions (Wingtens, 2009).

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On the other hand, soil nutrients were proven to be strongly deficient in the Southwest Arabica

growing area. South-western Uganda also suffered from significant water shortage as indicated in

this study. The identified negative influence of shade trees might partially due to the strong inter-

specific competition of water source and soil nutrients.

Considering the sub-optimum soil fertilities and water stress in the Southwest, shade trees seem to

be unadvisable and even harmful for Arabica growth and should be reduced to improve coffee yield.

However, the role of shade trees in reducing risks of climate change and wind stress, the value of

shade trees in protecting soil and maintaining biodiversity and the contribution of shade trees to

maintaining long term coffee productivity should not be disregarded. Moreover, sudden removal of

shade trees would generate significant damage to coffee trees (i.e. foliar sunburn) (Wingtens, 2009).

Nevertheless, it is advisable to change shade tree types when apparent disadvantages of shade trees

can been identified. In addition, adequate management practice should be implemented to ensure

high canopy and adequate canopy cover especially during dry season (to provide enough sun light for

flower induction and reduce water competition) (Wingtens, 2009).

For the North Robusta area and the West Nile Arabica region, it is advisable to intercrop shade trees

in coffee gardens. The application of shade tree intercropping should consider coffee and shade tree

species, topographic features, soil properties, water availability and adequate shading level

(Wingtens, 2009). Suitable shade trees should also be site-specific and depends on the priorities of

farmers on cultivating purposes (for timber, fire wood and food etc.), also depend on availability of

original resources (shade tree seedlings). Moreover, shade trees should be well managed to maintain

a good canopy cover and minimize the competition with coffee trees. Researchers in IITA have

recently conducted a study on the effect of different type of shade trees on coffee production in the

eastern Uganda. Further studies with long term field experiments are desirable to provide the site-

specific information on favourable shade tree types and adequate shading level.

Mulch depth

As indicated in many studies, mulching can significantly contribute to mitigating soil erosion,

maintaining soil moisture and depressing weeds. Mulching can improve soil nutrients availabilities,

especially soil K (Wintgens, 2009). Mulching can also provide substantial SOM which can improve

physical, chemical and biological aspects of soil properties (Wintgens, 2009). In addition, ground

cover by mulching can reduce ground soil evaporation and enhance rainfall infiltration, which helps

to increase soil water holding capacity and modify soil temperature (Wintgens, 2009). Moreover,

mulch was demonstrated being able to strongly improve fruit-bearing nodes of coffee trees to

enhance coffee yield (DaMatta et al., 2007). This study demonstrated that, mulch depth was

positively related with coffee yield in all of the five surveyed regions except in the East. The limitation

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of lack of mulching on coffee production is relatively more significant for Robusta in the North and

Arabica in the Southwest.

The results presented illustrated that soil K deficiency is the most important limitation for Robusta

production in the North region. The importance of mulching in the North might therefore be

associated with its ability to complement soil nutrient availabilities. It should be noticed that,

however, mulch depth was also correlated positively with coffee density in the North which would in

the end, suggested a positive effect on coffee yield. Therefore, the effect of mulch depth should be

critically evaluated with long term field experiments.

Over 2009 and 2010, the south-western Uganda has experienced a severe rainfall shortage as

illustrated in this study. The significant role of mulching on Arabica coffee in the Southwest might

partially attributed to its capacity to enhance soil water retention and eventually increases water use

efficiency of coffee trees. On the other hand, the less importance of mulching in the eastern area

would likely due to the relatively high precipitation in the region.

A mulch depth of 1 to 1.5 cm is recommended for adult coffee plantations (Wintgens, 2009). Though

the benefits of mulching are commonly agreed by farmers and researchers, the application of

mulching is extremely low in Uganda, especially in the North region (mulch depth ranged from 0.2—1

cm with an average of 0.6 cm). The main reason is to avoid fire risks as pointed out by Van Asten et al.

(2012). The low application of mulch could also attribute to the non-availability and high cost of

mulch materials and labour. The advantage of mulching in the North (provide soil nutrient) can be

complemented by increasing fertilization. The application of mulching in the Southwest indicated a

large variation (mulch depth from 0 to 2 cm), while it is highly advisable to widespread mulching in

the Southwest region to help farmers cope with prolonged drought stress. This can be fulfilled by

planting cover plants and shade trees on coffee plantation in the end of rainfall season.

4.3 Rainfall

The effect of rainfall pattern on coffee yield gap was not quantified with boundary line analysis due

to the limited information. However, the role of rainfall in determining coffee yield was still

evaluated by linear regression analysis. The influence of rainfall patterns (rainfall amount and rainfall

days) were identified in four particular periods during the growing season: one year before harvest,

from flower to harvest, fruit abortion period and dry season. The results indicated that rainfall

patterns of these critical periods have significant influence on coffee yield across all the coffee

production regions surveyed, the impacts varied depending on the regions.

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4.3.1 Rainfall distribution in Uganda

Rainfall distribution across year 2009 and 2010 in Uganda did not seem to be ideal for coffee

production. When looked at the general rainfall distribution over the past five years (2006–2010), a

considerable lower precipitation in 2009 had been identified with annual rainfall amount of

approximately 1000 mm in comparison with previous three years of approximately 1400 mm.

Moreover, in year 2009, the variations of annual rainfall amount across districts among the North

and the Southwest regions were observed to be larger compared with prior years. The results are in

line with the evidence from Meteorological Department of Uganda as well as the statement made by

farmers, that there was a prolonged drought occurred over year 2009 and 2010 which significantly

reduced coffee production across the whole country (Robert, 2012). This severe yield reduction in

2009/2010 growth season can be evidenced by yield figures over the past 10 years derived from

UCDA data base (2010). The extremely low precipitation in 2009 might largely explain the lower yield

level observed in 2010 compared with previous years.

Two rainy seasons has been identified in the Central Robusta growing area and in the Southwest

Arabica coffee regions, while the unimodal rainfall pattern was observed in the North Robusta area

and in the East and West Nile Arabica regions (according to monthly rainfall distribution, Appendix V).

In addition to large variations in rainfall amount across districts, the onset of rainfall season also

showed an abnormal distribution in year 2009 and 2010 compared with the previous three years

(2006–2008). For bimodal rainfall pattern regions (Central and Southwest), in 2010, the first rainfall

season started on February which was one or two months earlier than that of previous years (2006–

2008, began from March or April). The second rainfall season in year 2010 was from August extended

into December which was also one month earlier since it used to begin in September in previous

years. The onset and duration of rainy season identified in previous years were in agreement with

farmers’ statements that first rainy season usually began on March and last to May and the second

rainfall season was in general between September and December (Jassogne et al., 2013 (b)). Similar

phenomena was observed in unimodal rainfall pattern regions (North, East and Westnile) that, in

2010, rainfall season started on February which is one month earlier compared with the prior years

and went on to October or November. Consequently, rainfall season in 2010 was approximately one

month earlier than that of previous years but the duration of which was not changed.

The earlier onset of rainfall season in 2010 was associated with the less obvious dry period between

2009 and 2010. The onset and duration of dry period can be roughly identified being started on

December, 2009 or January, 2010 (Appendix V). Except January, 2010 which indicated a significant

low precipitation (less than 50 mm), rainfall in other months was relatively high. By contrast, in

previous years, a noticeable dry season in the end of the year can be identified from December in

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current year to February of the next year with remarkably low precipitation (Appendix V). In addition,

a short span of dry season across 2009/2010 gave rise to an earlier florescence in 2010. In fact,

change of rainfall pattern over the past decades has been implied by Meteorological Department of

Uganda that the previous two distinct rainfall seasons in unimodal rainfall regions has gradually

integrated into one prolonged rainy season (Robert, 2012). This is in support of the distinctness of

dry season observed across 2009/2010 coffee growth season.

It has been pointed out by farmers in the south-western Uganda that the onset and duration of

rainfall seasons become more and more unpredictable in recent years (Jassogne et al., 2013 (b)).

During individual interview in eastern Uganda (2013), farmers also descript a strong variation of

rainfall pattern. In the past years, dry period only extended for two to three months (usually in

December, January and February), while dry season become longer nowadays that lasts almost half

year from November to April according to farmers. The results based on daily monitored rainfall data

suggested a reduction of rainfall amount over the (first) rainfall seasons during year 2009 and 2010

and reflected a shorter dry season instead of a prolonged dry period. The prolonged dry season

explained by farmers might due to the overall low precipitation through the year (every month

rainfall is less than that in prior years) and large fluctuation between the months, which gave a

perception of experiencing a long drought. Shift and variation of onset and duration of rainfall

seasons every year and between years made farmers confused about the appropriate agronomic

managements (i.e. planting date) (farmer interviews).

4.3.2 Effects of rainfall variation on coffee production

One year before harvest and from flowering to harvest

Increasing rainfall amount during period of one year before harvest and from flowering to harvest,

illustrated a negative influence on Arabica coffee yield in the East (1561–1970 mm for one year

before harvest) and in the West Nile (1033–1300 mm for one year before harvest). It is believed that

Arabica coffee could grow well without irrigation when annual rainfall is around 1100 mm (Wintgens,

2009). Optimum annual rainfall for Arabica coffee range from 1400 to 2000 mm as suggested by

Wintgens (2009) or from 1200 to 1800 mm as indicated by DaMatta and Ramalho (2006). On the

other hand, precipitation of one year before harvest that higher than 1561 mm and 1033 mm in the

East and West Nile respectively, seems to be excessive for Arabica coffee growth and optimum

rainfall amount in the two regions should be lower than the existed values. In the West Nile region,

however, rainfall amount in these two periods was negatively associated with elevation which was

positively correlated with yield. Therefore, the unfavourable effect of rainfall on yield observed in the

West Nile should be evaluated critically.

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The detrimental impact of rainfall amount in Eastern and West Nile regions might suggested sub-

optimum conditions of other environmental factors such as poor soil texture (low water retention

capacity) and high air humidity (Arabica favours a relatively low air humidity (DaMatta, 2004). Coffee

fields that located in high mountainous regions are highly susceptible to soil erosion (Wingtens,

2009). It is reported by Jassogne et al. (2013 (b)) that in Uganda, precipitation frequency during

rainfall season has reduced in recent years compared with past decades, however rainfall, if

happened, was so heavy that even generate landslides. The disadvantage of increasing precipitation

in the East and the West Nile might largely attribute to the extensive soil erosion triggered by

excessive precipitation. As mentioned before, a large proportion of coffee roots develop in 30 cm of

top soil. Loss of top soil due to rainfall run-off would significantly limit coffee growth by reduce water

and nutrient availability (Wingtens, 2009). It was also explained by farmers in the eastern area that

some roots of coffee trees are commonly exposed to the air. Moreover, heavy rainfall, if occurred

during coffee blossom period would also damage flower buds. Furthermore, rainfall pattern is highly

associated with dynamics of some types of pest and disease which tend to dominant only in wet or

dry environment (Robert, 2012; Wingtens, 2009; Jassogne et al., 2013 (b)).

On the other hand, increased rainfall amount in one year before harvest (930–1090 mm) was

associated with a yield improvement in Central Robusta growing area. The optimum annual

precipitation for Robusta is recorded to range from 2000 to 2500 mm (Wintgens, 2009) or between

1200 and 1800 mm (DaMatta and Ramalho, 2006). In addition, Robusta coffee requires a relatively

high atmospheric humidity (close saturation) (DaMatta, 2004). Therefore, for Robusta coffee rainfall

amount in the Central seems far away from sufficient level which might be one of the most

important limitations for coffee production in this region. The precipitation in periods of one year

before harvest and from flowering to harvest did not influence Robusta in the North region (1100–

1600 mm for one year before harvest). This might suggested that precipitation of 2010 in the North

region was rather favourable and did not pose any challenge for coffee production.

Though the variation of rainfall amount (880–1092 mm) did not have a significant effect on Arabica

yield in the Southwest, increasing rainfall days in the two periods all suggested a positive influence

on coffee production (207–254 days for one year before harvest). In contrast to rainfall amount,

increasing rainfall days in the period from flower to harvest reflected a positive effect on Arabica

coffee yield in the East area. Rainfall amount and rainfall days in the two periods were not

significantly correlated with each other. Therefore, it appeared to be that coffee is likely benefit from

the improvement of rainy frequency rather than total rainfall amount.

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Fruit abortion period

During fruit abortion period (one month after the flowering month), increasing rainfall amount was

associated with increased Arabica coffee yield in the Southwest (91–141 mm). Increasing rainfall days

in this period also indicated a positive influence on coffee yield in the Central Robusta area and the

East Arabica region (rainy days varied 20–26 and 14–18 in the Central and East respectively).

It is well-demonstrated that water shortage, when it occurs during fruit expansion period, will limit

the development of cherries which would in the end cause significant damage to coffee production

(DaMatta et al., 2007). Prolonged dry period combined with high temperature would generate coffee

fruit abortion (DaMatta, 2004). It was also implied by farmers that drought is a problem when it

affects coffee during flowering time (“If coffee does not receive enough rainfall in March and April,

coffee yield would drop a lot” claimed by a farmer in Manafwa). However, there is no quantitative

information on water requirement in fruit generation period and it remains unknown to what extent

does coffee fruiting affected by water limitation. Furthermore, the physiological mechanisms of

coffee flowering and fruit setting are complex and might associated with various other factors in

addition to water availability (DaMatta et al., 2007). For instance, lack of mineral nutrients or a

sudden decline of temperature would cause unsuccessful fruit set (DaMatta et al., 2007).

Dry season

During dry season, coffee flower buds experience a period of dormancy which can be broken by a

sudden relief from water deficit or a sudden reduction of temperature (Wintgens, 2009). Excessive

precipitation in dry season is harmful for flowering which eventually, suggests an adverse effect on

fruit setting (DaMatta et al., 2007; Wintgens, 2009). Low level rainfall with scattered distribution in

the end of flower bud generation period during dry season is unfavourable. This rainfall pattern

would give rise to unequal blossom so that various types of coffee cherries (green, ripen and dried

cherries) can be found during harvest (DaMatta et al., 2007). The less selective harvest might reduce

product quality since only ripen coffee beans can provide good beverage (Vieira, 2008). Moreover,

uneven flowering is associated with more harvesting times that require more labour (DaMatta et al.,

2007). On the other hand, too little rainfall in dry season is also undesirable since plant need water to

maintain basic physiological needs (photosynthesis, transfer nutrients and foliar evaporation). During

dry season, evapotranspiration of coffee canopy might easily excess water supply which would give

rise to water stress of coffee trees (Wingtens, 2009).

As demonstrated above, relevant dry season for coffee production in 2010 generally started from the

end of 2009 (December) or the beginning of year 2010 (January) and extended one to two months.

During dry season, the increase of both rainfall amount (82–262 mm, 142 mm in median) and rainy

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days (42–76 days) were associated with higher coffee yield in the Southwest Arabica coffee growing

area. Other regions did not seem to be affected by rainfall pattern in dry season. It can be identified

that during dry season, rainfall amount was relatively low in Southwest (less than 200 mm in median)

compared with other regions (in median: Central 215 mm, North 212 mm, East 353 mm). It should be

noticed that in the Southwest, there was a significant correlation between rainfall days in one year

before harvest and rainfall days in dry season (P<0.05). As illustrated before, increasing rainfall days

across the whole growth season also had a positive effect on coffee yield. Therefore, the positive

effect of increasing rainfall days in dry season might be attributed to the advantage from the

enhancement of rainfall distribution over the whole growth year.

To summarize, among the five regions examined, Eastern Arabica area reflected the highest annual

rainfall distribution in coffee growth season of 2009/2010 (1561–1970 mm) which was proven to be

rather excessive considering its negative effect on coffee yield. West Nile also had an excessive

rainfall of 1033–1300 mm per annum. Southwest indicated the lowest annual rainfall amount among

five regions (880–1092 mm), both rainfall amount and rainy days seemed insufficient for Arabica

coffee production. For Robusta coffee, in the Central region, rainfall amount in the whole growth

season (1100–1600 mm) was proven to be insufficient, while rainfall did not seem to be a limitation

for Robusta grown in the North region.

An increase or decrease in precipitation might not necessarily cause a significant change in coffee

yield. There might be other factors interactively involved in the relationship such as temperature,

environmental humidity, soil water retention capacity, cloud cover, wind and crop management

(DaMatta et al., 2007). Moreover, alternative rainfall pattern would also affect coffee production

indirectly through its potential influence on weed establishment and pest and disease dynamics

(Fermont et al., 2009; Jassogne et al., 2013 (a)). However, the dramatic decline of precipitation in

year 2009 did suggest a limiting effect of rainfall on Robusta coffee production in Central and Arabica

coffee yield in the Southwest. There is urgent need for local farmers to adapt to climate change

through improving water supply in their coffee gardens which can be fulfilled efficiently by applying

irrigation.

Government has recently taken activities to develop irrigation across the country as a strategy to

enhance food security, while irrigation facilities are barely present in Uganda for the moment and if

available, are rather expensive for small-holder farmers (Shiver and Hao, 2012). On the other hand,

mulch and shade trees might be alternative choices for farmers considering their capacity to enhance

soil water holding capacity, maintain soil moisture, reduce soil erosion and increase water use

efficiency of coffee trees. In addition, efforts should be paid on plant breeding of drought tolerance

genotypes. Negative effect of excessive rainfall observed in the East and the West Nile regions might

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be attributed to high soil erosion associated with high rainfall in these mountain regions. Adequate

soil and water conservation strategies are required to cope with rainfall run-off. Moreover, the

effects of rainfall patterns on the dynamic of pest and disease and on the development of weeds

were not explored in this study which should be evaluated in further studies.

4.4 Adequate plant density in intercropping system

4.4.1 Effects of banana on coffee yield

The evaluations of intercropping systems were carried out for three regions (Central, East and

Southwest) that include both coffee monocropping and coffee-banana intercropping. The results

indicated that coffee yield did not differed significantly between the two systems. This was in line

with the demonstration proposed by Van Asten et al. (2011 (b)) in his study of agronomic and

economic benefits of coffee-banana intercropping system. In addition, Van Asten et al. (2011(b))

illustrated that increasing banana density from 300 trees/ha to approximately 1600 trees/ha in

Robusta growing regions was associated with a linear increase in coffee yield (P<0.05). Arabica coffee

yields also tend to increase with the increase of banana density (300–2000 trees/ha), though the

relationship was not statistically significant. Based on these results, he proposed a potential of

further increase banana density to more than 1600 trees/ha (Robusta) and 2000 trees/ha (Arabica) in

intercropping system without negatively affecting coffee yield. Nevertheless, it is hard to compare

the effect of banana on coffee yield without considering coffee density. For instance, the relative

effect of banana with a certain plant density can be lower when combined with a lower coffee

density compared with that intercropped with a higher coffee density. Instead of simple banana

density (tree/ha), relative banana density that explained as percentage of banana trees in total trees

population in intercropping field, was taken as reference with the capacity to indicate relative

distance between the two species.

Boundary line analysis indicated a negative effect of increasing relative banana density on Robusta

coffee yield in the Central region, while no significant relationship (either boundary line or regression

line analysis) was identified between the two variables for other regions. According to the boundary

line analysis, maximum coffee yield started to decrease when the relative banana density exceeded

30% in intercropping the Central region (coffee and banana ratio less than 2.3: 1). Previous study

illustrated an increase of Arabica coffee yield with the increase of relative banana density (from 18%

to 50%) in the East region (Van Asten et al., 2010 (b)). Compared with East, rainfall shortage and

nutrient deficiency (soil N, P, K, Ca and Mg) were the dominant problem in the Central region as

illustrated in this study. Therefore, the negative impacts of banana in Central might attributed to the

relatively strong inter-specific competition under sub-optimum environmental conditions. In addition,

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the relatively low management intensity as illustrated in the previous section can be an inherent

reason for low competitive strength of coffee trees in intercropping system in the Central region.

The benefit of banana to coffee have been illustrated as providing shading, reducing soil erosion and

supplying mulch materials (Van Asten et al., 2010 (b)). However, intercropped banana might

compete with coffee for various sources. The ability of banana to provide shading might be rather

small if banana trees do not reach a certain height even though given a high banana density. To

provide a good canopy cover, the appropriate banana tree height should exceed 2.5 m (by personal

observation). Additionally, the advantage of shading might hardly be identified when the space

between the two species is too large. From this view, coffee growth might not benefit from intercrop

anymore if the two species were not in a good spatial arrangement and there should be an optimum

plant density with which coffee achieved the highest yields.

In this study, a good banana density range for Arabica coffee production was roughly evaluated. It

was identified according to boundary line that with the increase of banana proportion in

intercropping systems in the East and Southwest, coffee yield initially increased and then started to

decrease with peak yield achieved when relative banana density ranged approximately 30% to 50%

(coffee-banana ratio was between 2.3: 1– 1: 1). This conclusion was partially in line with the

recommended plant densities of intercropping system established in fertile soil which are 7 × 7 feet

(551 trees/ha) for coffee and 14 × 14 feet (1102 trees/ha) for banana (relative banana density of 30%

and coffee-banana ratio is 2: 1) (information get from Uganda Cooperators Alliance (APEP) and

Uganda National Famer Association).

4.3.2 Effects of banana on coffee aboveground biomass

Coffee agronomic yield expressed by total aboveground biomass (kg/ha) and individual aboveground

biomass (kg/tree) were evaluated for coffee-banana intercropping system in the East region. Coffee

aboveground biomass was estimated with an allometric function proposed by Segura (2006). The

model was developed based on easily measured parameters that consist of coffee tree stem

diameter and stem height. However, the parameterization of this allometric model was conducted in

coffee agroforestry system in Nicaragua, therefore was not site-specific to Uganda coffee trees. In

addition, Arabica coffee trees in these two countries present different architectures that coffee trees

were maintained with one major trunk in Nicaragua, while in Uganda Arabcia tree usually exhibit

more than one stems derived from the bottom of the main trunk. Furthermore, coffee cultivars, tree

age, plant density and agro-ecological characteristics are all different between the two sites.

Therefore, the allometric function used in this study might not be able to fit Uganda’s situation very

well and can only give a rough prediction of coffee aboveground biomass. However, the purpose of

exploring coffee aboveground biomass in this study was to evaluate the effect of banana

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intercropping on coffee production rather than to precisely predict coffee biomass. Therefore, errors

from the prediction of coffee aboveground biomass have been neglected in this study.

In the results, both individual and total aboveground biomass of coffee trees was proven to be

significantly higher in monocropping compared with that in intercropping system, while coffee age

and coffee density did not differ significantly between the two systems. As illustrated before, rainfall

was in sufficient supply in the eastern area, while soil nutrient (especially soil P) was rather limited in

this region. The significantly lower coffee aboveground biomass in intercropping system might be

attributed to the inter-specific competition of the two species on soil nutrients. In addition, the

different input level was explained by farmers who own both coffee and coffee-banana fields. Given

a limit external input, intercropping plots tend to receive more soil inputs such as farm yard manure,

coffee husks and maize straws, therefore are more productive. Farmer also indicated a higher

weeding frequency for intercropping system (5 times per year) compared with monocropping system

(3 times per year). However, this trend varied depending on farmers’ gender. Males tend to give

priority to coffee mono-cropping field by the reason that coffee could provide more money and

support children to go to school (farmer interview). On the contrary, females tend to give priority to

coffee and banana intercropping field in attempt to maintain household food security.

On the other hand, intercropping indicated higher land use efficiency. Total biomass productivity

(kg/ha) of plant community including both coffee and banana was significantly (P<0.05) higher in

intercropping (consisted of coffee and banana trees) than that in monocropping (only include coffee

trees). This is in agreement with the conclusion made be Van Asten, et al. (2010 (b)) that relative

yield advantage expressed by land equivalent ratio (LER) was higher intercropping field compared

with that in monocropping sites. Moreover, though coffee individual aboveground biomass was

significantly lower in intercropping than that in monocropping, total coffee yield per land did not

indicated significant decline (P<0.05). Harvest index of coffee trees that explained by the weight of

cherries as a proportion of coffee tree aboveground biomass did not differ significantly between the

two systems (P<0.05). Therefore, it seems that individual coffee tree productivity was not affected

much by banana intercropping.

The results also found that, increasing relative banana density in intercropping was associated with

the increase of coffee individual aboveground biomass (P>0.05). In addition, coffee tree girth (15 cm

above bottom) increased linearly with the increase of banana density, while coffee stem height did

not differ significantly between the two systems (P<0.05). A previous study conducted by Rodrigo et

al. (1997) on banana and rubber intercropping system illustrated that both rubber and banana had a

higher plant height in intercropping system compared with that in monocropping system. By

identifying this, he proposed a conclusion that increasing banana density gives rise to an increase of

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dry matter partitioning to aboveground biomass for both species which might due to the increased

shading level in intercropping systems (Rodrigo et al., 1997).

Unlike rubber, coffee trees tend to be stronger instead of taller under intercropping (larger stem

girth rather than higher stem height). This is likely explained by the primary advantages of banana in

enhancing nutrient cycling, reducing soil erosion and depressing incidence of some type of pests and

diseases. The stronger stem is the major reason for the higher individual aboveground biomass of

coffee trees. Increasing individual aboveground biomass was significantly related with the

improvement of maximum coffee yield (P<0.05) (by boundary line analysis, result not presented).

Though did not been identified by boundary line analysis, it can be assumed that increase banana

density in the East Arabica region can improve coffee yield by its contribution to the enhancement of

coffee trees.

4.3.3 Impacts of coffee on banana growth in intercropping system

The impacts of coffee on banana yield in intercropping system were also explored in Arabica growing

region in the East, while no significant correlation was identified between coffee density/yield and

banana yield, neither there was an apparent boundary line. A few farmers in the East region

explained that the size of banana trees looks smaller after grown in intercropping system compared

with that in banana sole-crop. The poor performance of banana was explained by farmers due to the

high competition induced by coffee. Therefore, monocropping seems more suitable for banana in

order to achieve a high yield as perceived by farmers. It was also argued be farmers that, banana

started to suffer from intercropping as coffee ages. Three years after the establishment of

intercropping system, banana productivity tend to went down and external inputs are usually

required in intercropping field. In addition, in his study Van Asten, et al. (2010 (b)) demonstrated that

compared with banana monocropping, banana yield increased in intercropping system in the East

Arabica coffee growing area.

It has been illustrated that, one of the major constraints for banana production in Uganda is rainfall

shortage (Van Asten et al., 2010 (a)). Water stress can give rise to as much as 60% banana yield

reduction (Van Asten et al., 2010 (a)). Optimum rainfall for East African highland banana is reported

to be between 1200 and 1300 mm, while yield reduction could occur in areas with annual rainfall less

than 1100 mm (Van Asten et al., 2010 (a)). In general, average annual rainfall was demonstrated to

be sufficient for Arabica coffee production in eastern Uganda with annual rainfall fluctuated around

1500 mm. However, annual rainfall distribution was relatively low in the Southwest Arabica area and

in the Central Robusta regions, especially in year 2009 with annual precipitation less than 1000 mm.

To maintain a good yield for both species of intercropping system, external water supply by irrigation

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or other soil and water conservation practices that can improve water use efficiency are needed in

rainfall limiting regions.

Furthermore, various soil nutrient deficiencies have been identified for banana growth in Uganda

(Wairegi and Van Asten, 2012). Soil P and K has been demonstrated as an important constraints for

banana production in the central area; a severe K deficiency was identified in the south-western

region and soil Mg deficiency is relatively important in the eastern part of the country (Wairegi and

Van Asten, 2012). Low soil fertility across coffee growing regions has been identified in this study and

well demonstrated in the previous research. Therefore, the competition of soil nutrients could be

severe in coffee-banana intercropping systems and complementary inputs are required to provide

sufficient nutrients to intercropping system.

4.3.4 Adequate plant density in coffee-banana intercropping system

Based on the available data, the study found an optimum banana plant density for Arabica coffee

grown in the East and the Southwest regions. The highest coffee economic yield was reached when

relative banana density ranged from 30% to 50% approximately (coffee-banana ratio was between

2.3: 1 and 1: 1). Due to time limitation, the optimum density range was only evaluated roughly with

an upper boundary line drawn by hand. To achieve a more precise prediction of optimum plant

density more dataset of banana density and corresponding coffee yield at regional level and even

district level are required. It is also desirable to develop a mathematically explainable boundary line.

The fourth order polynomial model introduced by Schnug et al. (1996) might be useful to employ

here and the optimum value can be obtained by addressing the derivation of the model function.

In spite of the negative effect of banana intercropping on maximum Robusta coffee yield that

identified in the Central region, the profitability in terms of total products revenue was suggested to

be 50% higher in coffee-banana intercropping system compared with that in coffee monocropping

(Van Asten et al., 2010 (b)). This influence has not been evaluated in this study but if exist, would

considerably benefit farmers in the Central region. The optimum banana and coffee density in

attempt to achieve maximum total income is worth to explore in further studies based on long term

on site demonstrations. Site-specific recommendations should also consider the production purpose

(priority to final revenue or food security), production constraints for both species and availability of

external inputs.

In fact, the adequate plant density of intercropping could be site-specific which should be defined

under the consideration of water availability, soil conditions as well as the husbandry intensity and

topography of individual coffee field (Wingtens, 2009). For instance, the optimum plant density (in

terms of maximize agronomic yield) of banana monocropping is suggested to be lower with lower

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rainfall availability and less fertile soil compared with that with sufficient rainfall and favourable soil

conditions (Robert, 2012). In addition, a positive relationship between coffee yield and banana

density that proven for Arabica growing regions was attributed to the relatively low competition

between the two crops for water and nutrients (Van Asten et al., 2010 (b)). Furthermore, the

optimum plant density defined for coffee grown under full sun (2000 trees/ha) is higher than that

cultivated in shading (1250–1660 trees/ha), the higher the shading level is the lower the coffee plant

density should be (Wingtens, 2009). A density of more than 1700 trees/ha is suitable for coffee trees

with three stems modified by pruning, while for those maintained with four stems, less than 1700

tree/ha would be more favourable (Wingtens, 2009).

So far, the phenomenal and mechanistic knowledge on competitive effects of one crop on another

crop in coffee and banana intercropping is rather limited which discourages the development of

appropriate management strategies. To understand this, it is important to identify how the

environmental sources are allocated to the two species and to see how the two crops affect each

other under such environmental conditions (Huxley, 1985). In addition, a general view of production

constraints for both species in a given region would be helpful by assuming that the competitive

effect can be most obvious if both species suffer from the same constraint. External input is then

required to maintain a well-developed intercropping system and at the same time avoid soil

depletion. In addition to spatial arrangement, temporal variations of the two species should also be

evaluated to achieve the highest yield. According to the farmer interviews in the East region, some

farmers plant coffee prior to banana, while others plant the two species in an opposite order. It is

probably more feasible to plant banana in a high density when coffee population are in a relatively

young stage that are less competitive and generally vulnerable to unfavourable environment. The

proportion of banana can be reduced latter as coffee matures depending on the available sources

(water, soil fertility, solar radiation etc.).

96


Conclusions

Based on boundary line analysis, the study found a large yield gap for both Arabica and Robusta

which implies a substantial room to improve coffee production in Uganda. The yield gaps are caused

by diversified biotic and abiotic production constraints and poor management practices. However, it

should be pointed out that, the correlation between production constraints and yields identified in

the boundary line analysis does not necessarily imply causality. The strong correlation between

coffee yield (Y) and a certain production factor (X) might due to the third factor that give rise to the

changes in both X and Y. The results presented indicated a diversified correlation between the

production factors, while with boundary line approach it is difficult to identify the impact of

correlated variables, which is a major limitation of this type of approach. Therefore, the individual

impact of production constraints identified in the study should be evaluated with more cautious.

Abiotic constraints (especially poor soil fertility) and management practices (i.e. old trees,

inadequate coffee density and less mulching) are important limitations for coffee production in

Uganda. The important production constraints illustrated in this study provides guidance for site-

specific management practices that should be given priority in attempt to improve coffee

productivity. The management implementations for yield enhancement should also take into the

consideration of the availability and accessibility of sources (in terms of financial support; labour;

facilities; supplier of fertilizer, pesticide and herbicides; adequate knowledge and training etc.) and

the implementation capacity of local farmers. To encourage the efficient implementation of yield

improvement strategies, there is also require for governments and national institutes to provide

appropriate training and final support.

Based on boundary line analysis, the study found a negative effect of banana intercropping on

Robusta coffee production in the Central region, while in Arabia production regions an adequate

plant density was roughly identified that coffee and banana density ratio should be between 2.3: 1

and 1: 1. Further researches are needed to explore the inter-species competition from the physiology

and ecosystem point of view in the long term experimental setup. This would eventually contribute

to the estimation of adequate plant density of coffee-banana intercropping systems in terms of

maximizing agronomic and economic crop yield and land use efficiency.

97


98


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102


Appendix I. Samples of districts and number of total, intercropped and

monocropped coffee plots

Region Number of coffee

plots in total

District Number of

coffee plots

Intercropping Monocropping

Central 50 Luwero 10 5 5

Mityana 10 5 5

Mpigi 10 5 5

Mubende 10 5 5

Mukono 10 5 5

North 48 Lira 8 3 5

Apac 10 0 10

Gulu 10 1 9

Dokolo 2 2 0

Oyam 15 1 14

Nwoya 3 0 3

East 50 Bududda 10 5 5

Kapchorwa 10 5 5

Manafwa 10 4 6

Mbale 10 3 7

Sironko 10 6 4

Southwest 57 Ibanda 10 5 5

Rubirizi 10 5 5

Kasese 10 5 5

Bundibugyo 7 1 6

Kabalore 10 4 6

Kisoro 10 5 5

West Nile 49 Arua 10 0 10

Maracha 5 0 5

Nebbi 11 1 10

Yumbe 3 0 3

Zombo 20 15 5

Source: Van Asten et al., 2012

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Appendix II. Coffee harvest periods in the target districts

Regions Ditricts Coffee type Harvest periods

Central Mukono Robusta November-January

Central Luwero Robusta November-February

Central Mpigi Robusta October-January

Central Mityana Robusta October-January

Central Mubende Robusta October-January

Southwest Ibanda Arabica May-August

Southwest Lubirizi Arabica Mid April-August

Southwest Kabarole Arabica September-December

Southwest Kisoro Arabica September-December

Southwest Bundibugyo Arabica September-December

Southwest Kasese Arabica September -December

West Nile Arua Arabica September-December

West Nile Nebbi Arabica September-December

West Nile Zombo Arabica September-December

West Nile Maracha Arabica September-December

West Nile Yumbe Arabica September-December

North Lira Robusta September-December

North Apac Robusta September-December

North Oyam Robusta September-December

North Gulu Robusta September-December

North Nwoya Robusta September-December

East Kapchorwa Arabica September-November

East Sironko Arabica October-December

East Mbale Arabica September-November

East Manafa Arabica September-November

East Bududa Arabica September-November

Source: personal survey by David Mukassa, staff in IITA, 2013

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Appendix III. Individual farmer interview questionnaire

General household description:

District: County: Sub-county: Village:

Location (GPS): Date: Observer:

Farmer name: Farmer age: Farming system: Intercrop/mono-crop:

General questions:

1. What are the limiting factors for coffee production in your farm?

2. What is the most limiting factor among these constraints?

3. Results indicated that, coffee yield is generally larger in higher elevation than that in lower

elevation. What are the possible reasons do you think?

4. Do you think shade trees are important to coffee? Why?

5. Are there any problems with shade trees for coffee – if so, what are these problems (mites)?

6. Why did you plant shade trees and when did you plant them (or where they always there)?

7. Why do you intercrop coffee with banana?

8. Is intercropping doing well in terms of production?

9. What is the optimum banana plant density (coffee and banana ratio) do you think is for coffee

production?

Questions related to aboveground biomass (farmers who have both coffee mono-cropping and

coffee and banana intercropping system):

1. What was the history of the intercropping system? How many years do you have banana/coffee?

When did you start to intercrop?

2. Have you improved input after establish intercropping system?

3. Have you noticed any interaction between two crops? Are they a good marriage or is one

suffering more than the other.

4. If you have limited soil inputs, you have two fields (coffee mono-cropping system and

intercropping system), and which field would you give priority?

5. Have you found any difference after you established the second crop?

6. What was the effect of second crop on the growth/development of first crop?

7. Our results found that, individual aboveground biomass is higher in intercropping system than

105


sole cropping system. Increase banana density can improve coffee individual aboveground

biomass. In addition, coffee tree diameters increase with the increase of banana density. Have

you notice this phenomena? What reasons do you think for these results? (For instance, bananas

are established late, after coffee getting mature).

Questions related to climate change:

1. What is climate change? What causes it?

2. Current results showed that, due to climate change, optimum area for coffee production could

move from lower area to higher area. Do you think coffee is moving up the slope as climate

getting warmer?

3. What will you do if climate change does not favour coffee production in low land areas?

Others:

1. Is this feedback survey useful for you?

2. What is your biggest problem for coffee production at this moment?

3. What’s your expectation from scientific research?

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Appendix IV. Summary of soil properties of the surveyed regions

(a) (b)

(c)

(d)

(e)

(f)

(mg/

kg)

(cm

ol/k

g)

(cm

ol/k

g)

8

107


Coffee

types

Regions Criteria Soil properties

Soil pH SOM (%) N (%) P

(mg/kg)

K

(cmol/kg)

Ca

(cmol/kg)

Mg

(cmol/kg)

Robusta Central Max. 6.70 8.99 0.40 136 0.92 8.92 1.69

Min. 4.50 3.66 0.18 3 0.08 0.75 0.20

Average 5.63 5.34 0.26 21 0.32 2.81 0.74

North Max. 7.00 6.97 0.28 197 0.85 9.84 3.71

Min. 5.10 2.21 0.13 3 0.12 0.63 0.35

Average 5.93 3.84 0.20 27 0.31 2.70 1.09

Arabica East Max. 7.00 14.87 0.60 198 1.54 8.55 3.23

Min. 5.20 3.24 0.17 3 0.13 2.02 0.90

Average 6.19 7.85 0.34 48 0.70 4.96 1.74

Southwest Max. 7.50 13.26 0.56 357 1.50 11.20 4.94

Min. 4.80 3.92 0.18 6 0.14 1.06 0.62

Average 6.27 7.32 0.32 125 0.62 5.60 2.43

West Nile Max. 8.10 8.88 0.39 201 1.03 6.82 2.21

Min. 5.90 2.44 0.16 3 0.20 1.77 0.56

Average 6.89 5.79 0.26 32 0.50 4.04 1.50

(g)

(cm

ol/k

g)

3,0,.

108


Appendix V. Monthly rainfall distribution of study regions in Uganda

through five years (2006—2010)

0

50

100

150

200

250

300

350

Jan. Mar. May Jul Sep Nov Jan. Mar. May Jul Sep Nov Jan. Mar. May Jul Sep Nov Jan. Mar. May Jul Sep Nov Jan. Mar. May Jul Sep Nov

2006 2007 2008 2009 2010

Mon

thly

rain

fall

(mm

/mon

th)

Monthly rainfall distribution in the Central Robusta region (Jan.2006—Dec.2010)

0.00

50.00

100.00

150.00

200.00

250.00

300.00

350.00

Jan. Mar.May Jul Sep Nov Jan. Mar.May Jul Sep Nov Jan. Mar.May Jul Sep Nov Jan. Mar.May Jul Sep Nov Jan. Mar.May Jul Sep Nov

2006 2007 2008 2009 2010

Mon

thly

rain

fall

(mm

/mon

th)

Monthly rainfall distribution in the Northern Robusta region (Jan.2006—Dec.2010)

0.00

50.00

100.00

150.00

200.00

250.00

300.00

350.00

Jan. Mar. May Jul Sep Nov Jan. Mar. May Jul Sep Nov Jan. Mar. May Jul Sep Nov Jan. Mar. May Jul Sep Nov Jan. Mar. May Jul Sep Nov

2006 2007 2008 2009 2010

Mon

thly

rain

fall

(mm

/mon

th)

Monthly rainfall distribution in the East Arabica region (Jan.2006—Dec.2010)

109


0.00

50.00

100.00

150.00

200.00

250.00

300.00

350.00


2006 2007 2008 2009 2010

Mon

thly

rain

fall

(mm

/mon

th)

Monthly rainfall distribution in the Southwest Arabica region (Jan.2006—Dec.2010)

0.00

50.00

100.00

150.00

200.00

250.00

300.00

350.00


2006 2007 2008 2009 2010

Mon

thly

rain

fall

(mm

/mon

th)

Monthly rainfall distribution in the Westnile Arabica region (Jan.2006—Dec.2010)

110


Appendix VI. Rainfall amount and rainfall days of yield-critical period


Region District Yield-critical rainfall periods

One year

before harvest

From flower to

harvest

Fruit abortion

period

Dry

season

Central Luwero 1033 830 171 27

Mubende 1042 838 100 28

Mityana 1090 783 130 23

Mokono 1026 810 128 23

Mpigi 932 617 116 14

North Gulu 1591 1021 120 12

Oyam 1134 717 123 13

Lira 1320 758 130 15

Apa 1107 627 93 10

East Kapchorwa 1562 1023 138 40

Sironko 1755 1080 146 46

Mbale 1724 1076 154 70

Manafwa 1971 1177 184 83

Southwest Kasese 881 508 106 155

Kisoro 1194 700 141 263

Rubiriz 1093 964 107 129

Ibanda 1068 985 92 83

West Nile Maracha 1210 720 35 11

Yumbe 1310 864 51 14

Arua 1282 805 48 3

Zumbu 1103 666 94 9

Nebbi 1073 653 71 16

111



Region District Yield-critical rainfall periods

One year

before

harvest

From flower

to harvest

Fruit

abortion

period

Dry season

Central Luwero 188 142 18 11

Mubende 180 137 17 11

Mityana 190 136 18 14

Mokono 189 138 14 16

Mpigi 167 115 15 19

North Gulu 207 131 16 4

Oyam 187 117 16 6

Lira 187 117 15 4

Apa 159 94 11 5

East Kapchorwa 247 163 23 11

Sironko 239 148 20 11

Mbale 273 171 26 21

Manafwa 250 153 21 15

Southwest Kasese 221 131 19 33

Kisoro 219 112 19 61

Rubiriz 254 117 23 76

Ibanda 207 104 21 42

West Nile Maracha 183 113 12 6

Yumbe 180 118 13 5

Arua 185 114 13 2

Zumbu 183 114 11 3

Nebbi 168 100 11 5

112

Documents

bscmsc.pps.wur.nl final report.pdf · The report the majoris output of MSc thesis work in Wageningen University, the Netherlands. The thesis study was carried out under the joint-supervision