effects of moisture conditions and management on ... · effects of moisture conditions and...

Effects of moisture conditions and management

on production of cashew

A case study in the Lower Limpopo basin, Mozambique

Teshome Demissie Tolla

March 2004

International Institute for Geo-information Science and Earth Observation i

Effects of moisture conditions and management on production

of cashew

A case study in the Lower Limpopo basin, Mozambique

by

Teshome Demissie Tolla Thesis submitted to the International Institute for Geo-information Science and Earth Observation in

partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science

and Earth Observation, Sustainable Agriculture.

Degree Assessment Board Chairman: Prof. Dr. K.Harmsen (NRS Department, ITC)

External examiner: Prof. Dr.Ir.P.M. Driessen (Wageningen University)

Internal examiner: Dr. D. van der Zee (NRS Department, ITC)

Primary supervisor: Dr. Herman Huizing (NRS Department, ITC)

Second supervisor: Dr. C. A. J. M. de Bie (NRS Department, ITC)

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION

ENSCHEDE, THE NETHERLANDS

International Institute for Geo-information Science and Earth Observation ii

Disclaimer

This document describes work undertaken as part of a programme of study at the International Insti-

tute for Geo-information Science and Earth Observation. All views and opinions expressed therein

remain the sole responsibility of the author, and do not necessarily represent those of the institute.

International Institute for Geo-information Science and Earth Observation iii

Abstract

Moisture availability to plants is an important land quality that is relevant in a wide variety of cir-

cumstances. Efficient use of this available moisture particularly in most arid and semi-arid regions of

the world that suffer from insufficient and unreliable rainfall conditions is essential. Beside the prob-

lem of moisture in these areas, management aspects have a great impact on the production of annual

as well as perennial crops. In Mozambique, cashew is one of the major tree (perennial) crops that

produce by many farmers. But, the production has been declining over years thereby directly affecting

the economies of a number of families involved in the production of this crop. Climatic factors and

management practices are among the constraints that lead to low yield of this crop at present in the

area. This study was formulated based on this concept to assess the impact of climatic variation and

management on yield of cashew using a crop-climate model and comparative performance analysis

methods respectively. Magnitude of Climatic variation was estimated using Thronwaite water balance

method. The temporal and spatial variation of climate has direct influence on the variability of avail-

able soil moisture. In other words, it has an impact on the productivity of crops. Based on this fact,

the impact of the critical period of available soil moisture (i.e. June to October) on yield of cashew

was analysed. In temporal case, the linear multiple regression analysis based on 3-years available

soil moisture and yield of cashew at (�= 5%), resulted in a multiple R2 = 17%. This indicates that the

temporal variation of available soil moisture explains the yield variability of cashew nut by 17%. The

analysis result of available soil moisture variation with respect to spatial differences is statistically

significant at (�= 5%) i.e. it explains the yield variability of cashew by R2 =11%. Results of manage-

ment aspects analysis show that many factors affect the yield of cashew in the area. The yield affect-

ing factors considered in the analysis are mostly directly or indirectly related to management aspects.

Out of eleven parameters that selected for regression analysis, five parameters such as weeding,

pruning, planting pattern, incidence of pest and disease and moisture shortage significantly affected

the yield of cashew at (�= 5%). Overall they explain the yield variability of cashew nut by R2= 60%

and an adjusted R2 =57%. Generally, it can be concluded that management aspects and moisture

availability variation are factors that determine the yield variability of cashew in the area.

Keywords: Climatic variation, available soil moisture, water balance, critical period, temporal, spa-tial, management, yield variability, and cashew.

International Institute for Geo-information Science and Earth Observation iv

Acknowledgements

First of all I would like to thank Almighty God so much for having kept me in peace, good health, and successful completion of my study. I am very much grateful to the government and people of The Netherlands for fellowship grant and good hospitality. My deepest and sincere gratitude go to my primary supervisor Dr. Herman Huizing for his thorough supervision, valuable guidance, and intellectual advice, critical and conservative comments. His con-tinuous advice and suggestion were so useful and helped me a lot in my work. I wish to express my heart-felt gratitude to my second supervisor Dr. C.A.J.M de Bie, for his conser-vative and critical comments, guidance and suggestion during fieldwork and then office. I am also indebted to Agricultural Office of the Amhara Region and staff for provision and facilitation of this opportunity. The cooperation and support of the people of Mozambique from top to grassroots level was exemplary and very thrilling. I am very grateful to, and appreciate as well all their services and wise treatment. I have also a considerable respect and admiration for the people who directly or indirectly involved in teaching and helping me during my study in ITC. My warm acknowledgement goes to my beloved family my wife Aster, my daughter Bement, my brother Kassahun and our mother Zebenu, for their love, spiritual support and consistent encourage-ment throughout my study. I also offer my special thanks to Driba Demissie, Dejenu Degefa, and Assefa Guchie for their moral support and encouragement. I wish to acknowledge sincerely Berihun, Mersha, Sisay and Tesfaye for all their support and having me made feel at home. I really have a great appreciation and inspiration for that. Last but not least, I am very much indebted to my classmates Abel, Lazarus, Raj, Alberto, Kityo and Kunda for the joyful and sound relationship we have had in ITC, which is unforgettable.

International Institute for Geo-information Science and Earth Observation v

To my beloved wife Astu, my sweetest daughter Emuyie, my loving mother Ejegu and the rest of my parents

International Institute for Geo-information Science and Earth Observation vi

Table of contents

Abstract .................................................................................................................................. iii

Acknowledgement.................................................................................................................. iv

Table of contents .................................................................................................................... vi

List of figures ......................................................................................................................... ix

List of tables ........................................................................................................................... xi

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

1.1 Background ............................................................................................................................1

1.2 Tree crops...............................................................................................................................2

1.3 Problem Statement .................................................................................................................3

1.4 Objectives...............................................................................................................................4

1.5 Hypothesis..............................................................................................................................4

1.6 Research questions .................................................................................................................4

1.7 Research approach .................................................................................................................4

2 Literature review............................................................................................................. 7

2.1 Water plant relationship .........................................................................................................7 2.1.1 Evapotransipiration ........................................................................................................7 2.1.2 Soil moisture ..................................................................................................................7 2.1.3 Water holding capacity relation to soil texture ..............................................................8 2.1.4 Water balance.................................................................................................................9

2.2 Management practices: Basis for sustainability..................................................................10

2.3 Phenology of cashew............................................................................................................10 2.3.1 Root growth system of cashew Tree ............................................................................10 2.3.2 Canopy growth .............................................................................................................11 2.3.3 Flower development .....................................................................................................11 2.3.4 Fruit (Nut) ....................................................................................................................11

2.4 Management practices of cashew production in Mozambique ............................................11

2.5 Cropping calendar of cashew ...............................................................................................13

2.6 Agro-climatic models ...........................................................................................................14

3 Methods and materials.................................................................................................. 16

3.1 Study area.............................................................................................................................16 3.1.1 Location and area .........................................................................................................16 3.1.2 Climate .........................................................................................................................17

International Institute for Geo-information Science and Earth Observation vii

3.1.3 Soil ...............................................................................................................................17 3.1.4 Agriculture ...................................................................................................................17 3.1.5 Water resource .............................................................................................................18

3.2 Materials and software used.................................................................................................18

3.3 Methods................................................................................................................................19 3.3.1 Data collection .............................................................................................................19

3.3.1.1 Sample scheme .........................................................................................................19 3.3.2 Data preparation and analysis ......................................................................................21

3.3.2.1 Calculating water balance ........................................................................................21 3.3.2.2 Assessment of moisture excess and stress period ....................................................22 3.3.2.3 Mapping available soil moisture distribution pattern ..............................................23 3.3.2.4 Temporal and spatial climatic variation effect on yield...........................................23 3.3.2.5 Yield impact assessment: Management aspects.......................................................26

4 Results............................................................................................................................. 28

4.1 Water budget determination.................................................................................................28

4.2 Moisture surplus and deficit period .....................................................................................28

4.3 Available moisture distribution pattern of the area..............................................................29

4.4 Analysis of climatic variation effect on yield of cashew .....................................................30 4.4.1 Temporal effect ............................................................................................................30 4.4.2 Spatial effect.................................................................................................................32

4.5 Yield impact analysis: Management aspects and physical factors.......................................32 4.5.1 Descriptive statistics.....................................................................................................32

4.5.1.1 Normality test of yield distribution ..........................................................................32 4.5.1.2 Distribution of non categorical variables .................................................................34 4.5.1.3 Characteristics of categorical variables distribution ................................................36

4.5.2 Co-linearity test ............................................................................................................41 4.5.3 Multiple regression Analysis........................................................................................42

4.6 Age of cashew tree , weeding and pruning ..........................................................................44

5 Discussion....................................................................................................................... 45

5.1 Moisture excess and deficit period.......................................................................................45

5.2 Yield of cashew and temporal and spatial available soil moisture variation .......................45

5.3 Impact of management and physical factors on yield ..........................................................46

5.4 Age of tree and need of weeding and pruning .....................................................................48

6 Conlusions and Recommendations .............................................................................. 49

6.1 Conclusions ..........................................................................................................................49

6.2 Recommendations ................................................................................................................50

References.............................................................................................................................. 51

International Institute for Geo-information Science and Earth Observation viii

Appendices ............................................................................................................................ 54

Appendix A-1: Processed climatic data ..........................................................................................54

Appendix A-2: Tables of calculated water balance pre year for each station .................................58

Appendix B-1: Code book................................................................................................................62

Appendix B-2: Environmental requirement of cashew...................................................................67

Appendix B-3: Table of Coded data sheet .......................................................................................68

Appendix B-4: Table of yield data sheet..........................................................................................72

Appendix B-5: Table of Normalized data ........................................................................................75

AppendixB-6: Table of Available soil moisture per year and average of 3-years...........................78

Appendix B-7: Table of retained data for multiple regression analysis ..........................................80

Appendix C: Questionnaires ............................................................................................................82

International Institute for Geo-information Science and Earth Observation ix

List of figures

Figure 1.1: Conceptual framework diagram ..........................................................................................6

Figure 2.1: Classes of soil-water availability to plants and drainage characteristics ............................8

Figure 2.2: Water retained in the soil against an accumulated potential water loss. .............................9

Figure 2.3: Diagram of major crop production categories...................................................................10

Figure 2.4: Graph of total production of cashew nut in Gaza province (1981-2002)..........................12

Figure 2.5: Graph of total production of cashew nut per district of study area (1990-2001) ..............13

Figure 3.1: Study area map ..................................................................................................................16

Figure 3.2: Climatic pattern of Lower Limpopo..................................................................................17

Figure 3.3: Ipaq with GPS using for digitising the area (fieldwork in Mozambique) .........................18

Figure 3.4: Map of samples distribution over the study area...............................................................20

Figure 3.5: Picture of interviewing in the field of cashew tree............................................................21

Figure 3.6: Graphs of monthly rainfall distribution.............................................................................24

Figure 3.7: Graphs of monthly reference evapotransipiration .............................................................25

Figure 3.8: Flow diagram of dtailed research method .........................................................................27

Figure 4.1: Graph of moisture surplus and deficit preiods (average of 2000-2002)............................29

Figure 4.2: Maps of annual rainfall (mm) (a) and available soil moisture (mm) (b) distribution pattern

.....................................................................................................................................................30

Figure 4.3: Graphs of monthly moisture variation in critical period for year 2000 (a), 2001 (b),& 2002

(c).................................................................................................................................................30

Figure 4.4: Graph of yearly available soil moisture variation (average of critical months) ...............30

Figure 4.5: Graphs of the relation between yield and available soil moisture in different years ........31

Figure 4.6: Graphs of spatial moisture variation versus yield of cashew ............................................32

Figure 4.7: Graghs of cashew yield distribution ..................................................................................33

Figure 4.8: Graphs of original (a) and normalized (b) yield data normal probability test...................33

Figure 4.9: Normal probability plots for original (a) and normalized (b) yield data...........................34

Figure 4.10: Frequency distribution of altitude and its relation with yield of cashew ........................34

Figure 4.11: Frequency distribution of distance between trees and relation with yield of cashew.....35

Figure 4.12: Frequency distribution of age of cashew tree and relation with yield produced.............35

Figure 4.13: Frequency distribution of soil pH and relation with yield of cashew .............................36

Figure 4.14: Box plot of relation between pesticide application and yield of cashew ........................37

Figure 4.15: Box plot of relation between weeding practise and yield of cashew ..............................37

Figure 4.16: Box plot of relation between pruning of cashew tree and its yield .................................38

Figure 4.17: Box plot of relation between planting pattern of cashew tree and its yield ....................38

International Institute for Geo-information Science and Earth Observation x

Figure 4.18: Box plot of relation between incidence of pest and disease and yield of cashew nut.....39

Figure 4.19: Box plot of relation between moisture availability for cashew tree and its yield ...........39

Figure 4.20: Box plot of relation between soil texture and yield of cashew nut .................................40

Figure 4.21: Box plots of yields of cashew nut in relation to soil drainage and fertility.....................40

Figure 4.22: Graph of actual yield versus estimated yield of cashew..................................................43

International Institute for Geo-information Science and Earth Observation xi

List of tables

Table 2.1: Water holding capacity in (mm/m) for different soil textures ..............................................8

Table 2.2: General Information on calendar of cashew development and management practices ......14

Table 3.1: Average yield (kg/ha) of cashew nut for three years ..........................................................26

Table 4.1: Regression results of log10 (yield) of cashew versus temporal soil moisture variation ....31

Table 4.2: Soil texture versus fertility..................................................................................................41

Table 4.3: Soil texture versus drainage................................................................................................41

Table 4.4: Use of pesticide versus pest and disease problems.............................................................41

Table 4.5: Summary of co-linearity tested variables ...........................................................................42

Table 4.6: Linear multiple regression of log10 (yield) of cashew nut.................................................42

Effects of moisture conditions and management on production of cashew

International Institute for Geo-information Science and Earth Observation 1

1 Introduction

1.1 Background

Moisture availability to plants is an important land quality that is relevant in a wide variety of circum-

stances (FAO, 1976). Moisture varies spatially as well as temporally due to different factors influenc-

ing it such as the land characteristics of a given area (Gomez-Plaza, Martinez-Mena et al, 2001). Rain-

fall, potential evapotransipiration, available water capacity of soil and soil type are some of the land

characteristics, which affects moisture availability to crop growth (FAO, 1976). In general term, the

conditions of moisture availability to plants are assessed through the concept of length of growing

period.

In most arid and semi-arid regions of the world that suffer from insufficient and unreliable rainfall

conditions, moisture conservation is highly important to improve water availability for agriculture. In

these regions, a high rate of evaporation in the growing season is also very common (Ojasvi, Goyal et

al, 1999). The majority of the farmers in these areas depend on rainfed agriculture for crop produc-

tion. But the uncertainty of timely available sufficient moisture is the major constraint that the farmers

face commonly (Huirbers, 1985). Due to the variability of rainfall within a year and between years,

frequent high risk of crop failure and subsequent problems of food insecurity occur, particularly in

developing countries. In these areas, efficient use of the limited amount of moisture available for crop

production is essential.

Management is also one of the most important factors in crop production. Various crop management

interventions can be employed for improving the yield of different crops. These include land prepara-

tion, seeding rate, spacing/plant density, time of sowing, fertilizer application, weeding, chemical ap-

plication, crop variety, intercropping etc (Reddy, Sanjana Reddy et al, 2003). The conditions of these

crops management factors determine the yield of crop and the importance of them vary based on crop

type and site conditions.

Sustainable crop production is a management philosophy that will be adopted by those farmers who

are going to remain as future producers of food. This philosophy includes the implementation of crop

management strategies that provide adequate, high quality food, that are produced economically, and

with the added responsibility to safeguard the environment (Griffith, 2001). As described by (Griffith,



2001), sustainability starts with the adoption of management practices and followed by maximum

economic yield. This indicates that the importance of management practices in crop production is sub-

stantial.

Mozambique is one of the Southern African countries where about 80% of the country is classified as

semi-arid tropics, constituting the dry land agriculture belt according to the modified Thornthwaite

method as described by (Menete, 2000). Agricultural production is mostly dependent on the rainfall

pattern. Climatic uncertainty contributes to wide variations in crop production as well as high risk of

crop failure in the country.

1.2 Tree crops

Tree crops are crops that produce edible fruit, nut or legume that can serve as food for humans, live-

stock, or wildlife and as raw materials for industrial product. These trees can produce high quality

food on marginal as well as prime agricultural land (Smith, 1950). They have potentials for integra-

tion with livestock and other crop production systems. Nut and other tree crops can produce a wide

range of benefits to people directly through food production and indirectly through environmental

conservation and fuel production. Fruit and nut tree crops systems in Africa, such as cocoa, cashew

and coffee, offer significant opportunities to generate income for smallholder farmers, as well as for-

eign exchange and employment. Tree crop systems also play a critical role in increasing and sustain-

ing biodiversity and sound management of natural resources (USGS, 2003).

Cashew is a dicotyledonous evergreen (Moncur, 1986) tree crop that can be grown in areas with rain-

fall ranges from 400 to 4000 mm/year, temperature 15-35 0C, and altitude 0-1000 m above sea level.

Under typical drought situation cashew suffers in terms of yield, but it does not die owing to its inher-

ent drought tolerance nature (Venema, 1992). Traditionally cashew is grown under rainfed conditions.

But, yield increases have been demonstrated from supplemental irrigation. According to Ghosh (1995)

cited by (Grundon, 1999) yields of nuts increase about 400% when 10-year old seedlings of cashew

trees were watered during the reproductive phase. Research suggests that the average weekly water

requirement of cashew ranges from 250 to 500 L/tree depending on soil type, tree size and the irriga-

tion system used (Blaike, 1998; Grundon, 1999). Supplementary irrigation may also be required dur-

ing the wet season if the rainfall is not adequate.

According to (Moncur, 1986) cashew can be grown on low nutrient soils, well drained and light tex-

tured soil. However, to produce high yields, management inputs including irrigation, nutrients and

plant breeding are important. As described by (Mole, 2000), the yield potential of cashew trees can be

observed from the degree of flowering and fruiting. There are however, a number of genotype and



environmental factors affecting tree yields, such as soil fertility, moisture, and management. Rainfall

levels and its distribution during season are important factors thought to affect yield. In general, high

rainfall is good for cashew. But, also it is not favourable at specific times due to easy development of

fruit rot under high rainfall and humidity. At the same time lack of water can reduce the yield. Long

periods of below average rainfall make cashew trees loose their leaves and production can be up to

40% less than normal. With good rains nut production can be doubled. According to Opeke (1982) as

cited by (Mole, 2000) for good production of cashew the rainfall levels must be around 900-1100 mm

annually and also must be evenly distributed over the 9-10 months of its growing season.

The yields of cashew vary based on varieties. But, all the varieties recommended have yield potential

over 8 kg/tree or 1 to 1.5 ton per hectare. Though cashew yields from the third year, its full potential

(about 8 kg/tree) will be realized at 8-10 years of age, depending on level of management (Grundon,

1999).

1.3 Problem Statement

As mentioned in the background section, agriculture and crop production in particular, is a problem in

areas with insufficient and irregular distribution of rainfall. In these parts of the world drought is also

a big problem, and it is characterized by soil water deficiency due to natural climatic variability, such

as high evapotransipiration and precipitation deficiency (Hounam, 1975). Mozambique is one of this

world parts that is highly susceptible to weather related natural disasters with frequent drought and

flood hazards affecting the country, especially the Limpopo Basin.

More than 80% of the country’s population and about 40% of the export value of the country depend

on agriculture (Hoguane, 2000). Thus, increasing agricultural production in order to feed the popula-

tion, to produce raw materials for local industry and export in sufficient quantities to sustain a healthy

economy, is the major plan of the country (Voortman, 1985). But due to climatic conditions and poor

management practices, agriculture is full of risk, particularly when rainfed.

In Mozambique, including the study area, various tree crops are grown (Rui, 2000). Most of these

crops are cash crops, which are playing an important role in achieving the objective of creating and

developing income sources for the smallholders and for the country as well. According to (Rui, 2000)

cashew is among the tree crops that are harvested and marketed by a number of farmers and the lead-

ing export products in Mozambique. Smallholders produce about 95% of raw cashew nut for market-

ing purpose. In the country about 40% of the rural population have access to growing cashew tree (Ti-

ago, 1999). But, the production has been declining over recent years that directly affecting the econo-

mies of a number of families involved in the production of this crop (CFC, 2001). Climatic factors



and management practices are among the constraints that lead to low yields of this crop at present in

the area. Specially, the poor management practices contribution for poor productivity of cashew in the

area seems to be higher. Hence, to improve production levels, paying attention to these constraints is

essential. The current research is formulated based on this concept to assess the extent of these con-

straints on production of cashew tree crop in the study area.

1.4 Objectives

The overall objective of the study is to assess the impacts of climatic variation and management

practices on the yield of cashew in the Lower Limpopo basin.

Specific objectives:

1. To estimate the effect of temporal and spatial climatic variation on yield of cashew nut

2. To assess cashew based moisture deficit and surplus periods in the area.

3. To map the spatial distribution of available soil moisture for cashew in the critical period ex-

perienced by farmers

4. To assess impact of management practices on yield of cashew nut

1.5 Hypothesis

Moisture availability variation and management practices affect considerably the yield of

cashew nuts in the area.

1.6 Research questions

1. Is there a relationship between yield variability of cashew and temporal and spatial climatic

variation in the area?

2. Is there a relation between the moisture stress period determined by the Thornthwaite method

and the time of the critical moisture need for cashew that experienced by farmers in the area?

3. What is the impact of management practices on production of cashew?

1.7 Research approach

The study was carried out based on different activities and procedures. Literatures were reviewed re-

lated to moisture availability and management aspects affecting production of crops. Problem, objec-

tives, hypothesis and research questions were formulated. Based on a crop-climate model and com-

parative performance analysis methods, the research objectives and questions were addressed. Input

data such as climate, soil, crop and management data were collected for analysis. Thornthwaite water

balance method was used to compute the water budget.



Based on the selected methods, the analysis of variability of yield in relation to temporal and spatial

climatic variations was undertaken. The impact of management and related factors were also statisti-

cally analysed. Application of RS (Remote Sensing) and GIS (Geographic Information System) for

spatial presentation of sampled plots, rainfall and soil moisture distribution in the area and area under

cultivation of cashew tree crop are also part of research process. Finally, the analysed out puts were

interpreted and concluded. The framework diagram of figure1.1 shows the overall research approach.



Literatures review

R eseach top icidea

G eneral S pecific(s tudy area)

F inalize top ic

Form ulation ofproblem ,objective,

hypothesis & question

M ethods se lection

Data co llectionm ethodData analys is m ethod

Thornthwaite &M ather water ba lance

m ethod

S econdarydeta:

M eteoro log ica ldata

S tratified randomsam plingtechnique

Prim ary data

Result s

Analys is outputs

Interpretation

M oisture defic it &surp lus periods

A vailab le so ilm oisture d is tribution

m ap

Y ie ldsSoil

M anagem entfactors

Com parativeperform ance analys is

C rop -c lim atic m odel

Tem pora l & spatia l varia tionm ois ture im pac t on yie ld

G eostatis tics

Figure 1.1: Conceptual framework diagram



2 Literature review

2.1 Water plant relationship

Water is essential for plant growth. The importance of water is different from plant to plant. Some

plants need water timely and in larger quantities than others. In relation to water, plants are grouped

into hydrophytes, mesophytes and xerophytes as described by Warming (1909) cited by (Chang,

1968), which determine the crop adaptability to different moisture regimes. But such classes are not

useful to obtain the actual water needed by specific type of plant. Therefore, understanding of the

physiological plant response to water is the basic way to estimate the actual water required by a par-

ticular plant. According to Kramer (1963), cited by (Chang, 1968) the importance of water to plant is

that water is the major constituent of physiological active plant tissue, reagent in photosynthesis, used

for solvent and maintenance of plant turgidity. Because of every process in a plant is affected by wa-

ter, the relation between plant and water is not simple. Generally, the deficiency of water affects both

the yield and the growth pattern of the crop (Chang, 1968).

2.1.1 Evapotransipiration

Evapotransipiration is the combination of evaporation from all surfaces and the transpiration of plants.

It is the same as the consumptive use of water by plants. Soil moisture determines the evapotransipira-

tion i.e. when soil moisture is adequate; the evapotransipiration is at the level of potential evapotran-

sipiration (PET) where as the actual evapotransipiration (AET) occurs when soil moisture is insuffi-

cient. In general, evapotransipiration depends on (Chang, 1968) climatic factors such as temperature,

wind speed, humidity and radiation.

2.1.2 Soil moisture

Soil moisture is one of the sources of water available to plant growth. Excessive volumes of water in a

soil retard plant growth and make drainage essential. Soil moisture content can be described in terms

of weight of water per unit weight of soil or volume of water per unit volume of soil. It is influenced

by many factors for example soil texture, soil depth, soil structure and temperature (Israelsen and

Hansen, 1962).



Saturation

Field capacity

Permanent wilting

Unavailablemoisture

Hygroscopic waterEssentially no drainage

Capillary waterSlow drainage

Gravitational waterRapid drainge

Availablemoisture

Figure 2.1: Classes of soil-water availability to plants and drainage characteristics

Source: Israelsen and Hansen (1962)

The following are some concepts and definitions that are commonly used when we describe soil mois-

ture content in general and availability to plant growth in particular (Israelsen and Hansen, 1962).

Field capacity is the condition of a soil when gravitational water has been removed the remaining soil

moisture content is referred to as field capacity. The concept of field capacity is useful for determina-

tion of the amount of water available in a soil for plant growth.

Permanent wilting point refers to the moisture in a soil when the plant is permanently wilted. It is the

lower end of the soil moisture available range.

Available moisture is the water available between field capacity and permanent wilting point. Plants

use this moisture.

Readily available moisture is the available moisture that can be used by plant. Plant roots cannot ex-

tract the water available near permanent wilting point. Therefore, the water used by plant is consid-

ered as readily available moisture.

2.1.3 Water holding capacity relation to soil texture

Water holding capacity can be estimated based on soil texture. Table 2.1 shows the water holding ca-

pacity of sand and loamy sand soils.

Table 2.1: Water holding capacity in (mm/m) for different soil textures Soil Texture Sand Loamy sand

Sources

50 50 (Buckman, Brady et al, 1960) 60 60 (Foth, Turk et al, 1972)

110 110 (Israelsen, 1950) 80 110 (Israelsen and Hansen, 1962)



2.1.4 Water balance

The term water balance was used in 1944 by the meteorologist C.Warren Thornthwaite to refer to the

balance between water from precipitation and snow melt and the flow of water by evapotransipiration,

groundwater recharge, and stream flow. The budget can be computed for a soil profile or for a whole

drainage basin. The method allows the planner to compute a continuous record of soil moisture, actual

evapotransipiration, groundwater recharge, stream flow from meteorological record and a few obser-

vations on the soil and vegetation (Dunne and Leopold, 1978). Temperature and precipitation alone

do not describe the climate fully. The amount of precipitation does not indicate whether a climate is

moist or dry unless the water need of the site can be compared with it. And also temperature does not

really reveal the energy that is available for plant growth and development unless the moisture condi-

tion of the soil is known. Thus, one of the major objectives of the water balance approach in charac-

terizing climate is to arrive at a better way of determining whether a climate is moist or arid by com-

paring the climatic moisture supply with the moisture needs. Precipitation, potential evapotransipiration, actual evapotransipiration, soil moisture storage, surplus

and deficit are the parameters considered in the computation of a water balance. According to van

Hylckama (1956) as cited by (Chang, 1968), the water balance technique has been used to solve a

number of problems such as irrigation interval control, water resource planning, yield forecasting,

climate classification, stream flow, flood forecasting and worldwide fluctuation of sea level.

Figure 2.2 shows how much water will be retained in the soil after various amounts of accumulated potential water loss according to Thornthwaite and Mather (1957) cited by (Dunne and Leopold, 1978).

Figure 2.2: Water retained in the soil against an accumulated potential water loss. Source: Dunne and Leopold (1978).

Available water capac-ity of root zone of cashew (mm)



2.2 Management practices: Basis for sustainability

Best management practices are practices, which have been proven to provide optimum production po-

tential, input efficiency and environmental protection for specific site. All management practices from

seeding to harvest must be considered and packaged in a cropping system (Griffith, 2001). Figure 2.3

shows the general overview of crop production categories.

Figure 2.3: Diagram of major crop production categories

Source: Griffith (2001).

2.3 Phenology of cashew

Phenology of a plant is basic to determine the appropriate timing of many management operations,

including propagation, irrigation, and fertilizer application and plant protection. Cashew trees undergo

a period of rapid vegetative growth. The major period of vegetative growth coincides with the period

of greatest rainfall and flowering and fruiting coincide with dry seasons (Ohler, 1979). In cultivation,

the number of vegetation flushes and the length of flowering and fruiting phase depends on the tem-

perature, rainfall pattern, irrigation, and fertilizer management strategies (Grundon, 1999).

2.3.1 Root growth system of cashew Tree

Ohler (1979) stated that the cashew tree has an extensive lateral root system and a taproot that pene-

trates deeply into the soil profile. But the soil type can restrict root penetration and development. The

study in Tanzania shows that the root system of young cashew trees growing on loamy to sandy loam

soil texture is about 3m deep Tsakiris and Northwood (1967) as cited by (Ohler, 1979). The North

Queens land experiment in Australia shows that in coarse sandy loam soil root activity extends to at

least 1.2 m (Patrick, 2000). Nable (1996) also described that the root system of cashew in sand soil is

down to 2.5 m cited by (Patrick, 2000).



2.3.2 Canopy growth

The rate of growth of the cashew tree after planting, especially the rate of canopy growth will deter-

mine how rapidly the young tree comes into economic production. However, only limited studies have

been completed on the rate of growth of cashew tree or fruit-bearing canopy (Grundon, 1999).

2.3.3 Flower development

Cashew trees can flower throughout the year if sufficient water and fertilizers are available (Grundon,

1999). The age at which a cashew tree starts flowering is influenced by the growing conditions and

also by genetic factors. Under good conditions the first harvest will be an age of 3-years. But, produc-

tion of flowers takes place in the second year of growth (Ohler, 1979). In cashew development three

flowering phases have been observed i.e. a first male phase, a mixed phase and a second male phase.

2.3.4 Fruit (Nut)

The nut is attached to the lower portion of the cashew apple, which is conically shaped. The cashew

nut (seed) hangs at the bottom of the apple, and is c-shaped. The cashew seed has within the outside

shell the edible kernel or nut. In its raw form the cashew kernel is soft, white and meaty. When

roasted it changes colour and taste (Ohler, 1979).

2.4 Management practices of cashew production in Mozambique

The cashew industry is an important component of Mozambique's economy. Cashew is second in ex-

port earnings, and benefits many households who depend upon it for part of their food security. The

processing sector has been a source for employment for about a number of workers (Mole, 2000).

However, these benefits have been decreasing over the years with the decline of production and nut

quality. Rehabilitation of the national cashew tree orchards has been slow due to the incidence and

degree of the Odium Anacardium disease and the lack of a mechanism to solve it; poor economic in-

centives, particularly low cashew producer prices; unclear cashew property rights associated with land

property rights; lack of incentives to take care of existing trees or further investments in new planting

and burn agriculture that sets fire into cashew fields; less care of the cashew tree stock; and lack of

consistent research and replanting which have lead to ageing of the cashew tree orchards and the re-

duction of the cashew productive capacity.

Many smallholder cashew farmers face significant agricultural risk. Their ability to deal with it de-

pends to a large extent on their resource endowment and the level of needs they have to satisfy. Fur-

thermore, many small farmers in the country are still operating at the subsistence level and are vulner-

able to fluctuations in the environment. For many of these smallholders, food crop production may

take priority over cashew in production plans every year. This priority setting induces fewer invest-



ments in cashew production over the long run as described by Deloitte & Touche (1997) cited by

(Mole, 2000).

Cashew possesses genotype and phenotype characteristics, which make its yield variability between

trees and seasons, is one of the most difficult factors in research, particularly in breeding and im-

provement of cashew management practices Neto and Caligari (1997) cited by (Mole, 2000). Re-

search on-station with clones and individual trees in Mozambique and Tanzania has shown that rela-

tively higher cashew yield variances are associated with poor seasons of decreased management,

whereas low variability is positively correlated with 'good' years. These insights suggest that improved

management practices can be highly rewarding, despite natural and biological variations across trees

and seasons according to Neto et al (1994) as cited by (Mole, 2000).

Mozambique achieved the highest production in 1972 with commercialisation 216,000 tones of

cashew nut. Because of pest and diseases this production started to decline from 1970’s and later due

to uncontrolled forest fires (CFC, 2001). The statistical data of year 1981-2002 on production of

cashew nut shows that a high variation in total production from year to year. From the information

obtained during fieldwork, newly planted cashew trees every year is minimal i.e. increment in the

number of trees year to year is negligible. This seems to be an indicator that there are problems in

management and environmental factors that affect production of cashew in the area. Figure 2.4 and

figure 2.5 show that total production per year of the whole Gaza province and districts of particular

study area respectively.

Figure 2.4: Graph of total production of cashew nut in Gaza province (1981-2002) Data source: INCAJU- Institution of Cashew Promotion, Maputo



Figure 2.5: Graph of total production of cashew nut per district of study area (1990-2001) Data source: Agriculture Department of Gaza province, Xai-xai

2.5 Cropping calendar of cashew

The cashew biological cycle in the study area starts with the red flushing of new leaves on about

June/July, followed by panicle emergence from July onwards. Flowering is observed in October

(Mole, 2000). Cashew is normally sown or planted in the rainy season. Sowing of cashew in-situ with

or without planting holes has been the main method of establishing smallholdings and large planta-

tions. Recently use of planting pots in the nursery, especially for grafted materials has become a

common practice before planting in the field (Ohler, 1979). Most of the time the seeds are planted

directly into the field. Transplanting seedlings from nurseries to the field is only occasionally prac-

ticed (Acland, 1971). Planting It is difficult to specify the time of planting cashew in Mozambique because there are not many newly

planted trees by smallholders who only maintain the existing aged trees. In generally, sowing or plant-

ing is done when the rains have started to fall regularly and the soil does not dry out again (Ohler,

1979). This period is in wet season, i.e. November to March.

Weeding Weeding frequency depends on the age of the tree, i.e. there is strong negative correlation (Ohler,

1979) between age of the tree and the need of weeding. The growing is fast if weeding is practised at

the initial stage of the tree. In Mozambique, the time and number of weeding varies from farmer to

farmer. From fieldwork observation data many farmers were practised weeding in most of the months

of the wet season and in the beginning of the dry season. Number of weeding varies from no weeding

to not quantify. But, mostly weeding is carried out 2-3 times in a year.



Pruning Pruning with the aim of obtaining higher yields is not generally done. It is limited to removal of low-

est branches during the first year of growth (Ohler, 1979). Removing the lower branches allows the

sunlight to come under the tree. In fully-grown trees pruning of dead wood or branches, which have

been attacked by borer, is essential. According to the farmer’s experiences, pruning is also important

to get new vegetative branches when the old one is removed. In Mozambique pruning is carried out

mostly once in a year between February and October. Many farmers prune in month of August.

Harvesting Harvesting consists of collecting of the nuts that have dropped to the ground after maturing. Before

collection, nuts are allowed to drop on the ground; this insures that no unripe nuts are harvested. The

dropped nut should be not longer than a week on the ground to get a good quality nut (Acland, 1971).

However, if the apples are required for uses the fruit has to be harvested before it falls naturally (Oh-

ler, 1979). Harvesting of nut starts November to February (Mole, 2000). The information from farm-

ers also agreed with Mole’s that is harvesting extends from November to March. But the most inten-

sive harvesting time is in the month of December.

Table 2.2: General Information on calendar of cashew development and management practices

Source: Mole (2000) and fieldwork data (farmers interview)

Note: The above calendar was done based on general information. There was no sufficient informa-tion (exact day length of each development and management activity) to have exact cropping calendar of cashew in the area. Table 2.2 is only for general information.

2.6 Agro-climatic models

Agro-climatic models are the tools with which we can attempt to quantify the effects of climate on

crop productivity (Parry, 1988). Agro-climatic indices and crop-climate models are the two general

techniques of agro-climatic models for examining the response of agricultural crops to climatic varia-

tions. These terms are explained by (Parry, 1988) as follows:



Agro-climatic indices: are the combinations of several climatic variables to calculate over a network

of locations and mapped as a series of isolines.

Crop-climatic models: are mathematical devices that can be used to relate crop variability to meteoro-

logical variables. Empirical-statistical models and simulation models are the two broad classes of

these models. Empirical-statistical models relate crop yield data with weather data for the same area

and time period. Simulation models are developed on the basis of an understanding of the dynamic

relationships between the basic process of crop growth and environmental and management factors.



3 Methods and materials

3.1 Study area

3.1.1 Location and area

The geographical position of the Limpopo basin in Mozambique is between 330 0’0” to 330 57’0” East

and 240 15’0” to 250 24’0” South. Out of 412,100km2 total area of Limpopo Basin, only 19% is within

the Mozambique. Most of the Limpopo basin is located in Gaza province and some part is in Inhu-

mane province (Pereira, 2002). The location of the study area is 330 05’52” to 330 41’32” East and 240

30’14” to 240 59’44” South, which is within Gaza province. The total study area covers 3273 km2. It

includes parts of Chokwe, Chibuto, Beline, Guja and Xai-xai districts. Within this area the cashew

tree is growing in Chibuto, Beline and Xai-xai districts widely. So the study was more focused on

these areas. Out of the total study area, about 2110 km2 is widely used for cashew growing.

Figure 3.1: Study area map Data source: Topographic map, settlements map, districts map, and Aster image Jun 2003 of Gaza

province and field survey data.

Gaza



3.1.2 Climate

The area characterized by a semi-arid climate. A two-season-tropical climate type of erratic and

low rainfall pattern dominates the study area. The rainy season is from October to March when the

basin also has highest temperatures and the rest of the year is dry season. The total annual rainfall var-

ies from 400 mm to 1000 mm. The annual mean temperature generally ranges from 22.80 C to 24.40C

(Pereira, 2002).

020406080

100120140160

J F M A M J J A S O N D

Month

Temperature(0C)

Humidity(%)

Rainfall(mm)

Figure 3.2: Climatic pattern of Lower Limpopo

Data source: Mozambique National Institute of Meteorology-Maputo

3.1.3 Soil

The upland area consists of sandy soils with poor fertility that is used for cashew growing. These

soils have a very high infiltration rate and a very low water retention capacity. The lower water hold-

ing capacity together with the erratic and low rainfall aggravates the drought risk of the area (Pereira,

2002). The flood plain is characterized by alluvial soils.

3.1.4 Agriculture

Agriculture is the main activity in the country and the study area as well. Crops are produced both by

rainfed and irrigation. Irrigated areas are limited and the rainfed production pattern depends on the

regularity of the annual rainy season. A delay of one month, followed for instance by excessive rains,

may lead to serious harvest losses. Households using a mono-cropping system are therefore most se-

verely affected by droughts and/or floods due to the risk of being dependent on one single crop only.

Irrigated areas are mainly occupied by bigger farms in lowland areas and by vegetable production in

small areas.

Maize, sweet potatoes, cassava and beans are the main staple crops produced in the area. Vegetables

frequently produced are tomatoes, cabbage, garlic, piri-piri, pepper, cucumber and onions. The plant-

ing of tree crops has been a priority in the area, and in many cases fruits are an important complement



to the household diet. Cashew, mango, banana, papaya, and citrus, are among the fruits produced in

the area. Besides their nutritional value, trees are also of social and economic value to the population.

For instance, they are sources of shade, energy, construction and firewood and charcoal (Menete,

2000).

3.1.5 Water resource

Surface water is the main source of fresh water in the country. In many rivers, flows are intermittent

with high water flow for three to four months during summer season and low flows for the remaining

part of the year. Drought is common in the area, and this is related to the erratic distribution of the

rainfall during the wet season. Heavy storms and high floods are common in this area and usually fol-

low months and/or years of drought. In Mozambique most river basins are shared with other countries.

As it is located at the downstream end of those basins, it is dependent on the imported flows in terms

of their quantity and quality. Groundwater is the main source of water for the domestic water supply

as well as for villages and small towns (Menete, 2000).

3.2 Materials and software used

Materials GPS (Global Positioning System), Ipaq (mobile GIS), soil auger, measuring tape and pH

meter (chemical solution and test paper) are the main materials used during fieldwork. The

following picture shows two of these materials.

Figure 3.3: Ipaq with GPS using for digitising the area (fieldwork in Mozambique)



Software During the research work different computer software were used to carry out different activities. IL-

WIS, Ecocrop, Microsoft (Word, Excel), Visio, Minitab, SPSS and Endnote-5 are the software pack-

ages used.

3.3 Methods

Crop-climatic models: as mentioned in section 2.6, out of the two classes of crop-climate models, it

was not possible to apply a simulation model due to lack of data. Based on the available data, empiri-

cal-statistical models were applied.

Comparative Performance Analysis (CPA): this method was used to compare production situations at

actual on-farm sites i.e. quantifying production constraints and environmental impacts as a function of

land and land use (deBie, 2000). The assumption is that the land users operate at various technological

levels and apply different management packages. The analysis of management aspects and other

physical factors affect yield of cashew were carried out using the CPA method.

3.3.1 Data collection

The following are the required input data

• Climatic data such as rainfall, evaporation, temperature, humidity, radiation, sunshine hours

and wind speed. And also plant pests and disease related to climate conditions.

• Soil data such as soil texture, soil depth, pH, water holding capacity, and drainage conditions.

• Crop data like rooting depth and yields in 2000-2002 for cashew tree crops

• Management data such as water management, fertilizer application, pesticide application,

planting pattern, weeding, pruning, age of tree, and spacing between trees.

• RS and GIS data: Aster 14 Jun 2003 image, NDVI class map, Meteorological station point

map,

3.3.1.1 Sample scheme

Sampling is important for data collection. In this case a stratified random sampling technique was

used for dividing the study area into homogeneous strata (Moor, 1995). The study area is not as such

heterogeneous when we see physical features, but the NDVI (Normalized Difference Vegetation In-

dex) class map shows slight heterogeneity within the area. Based on this major NDVI classes the

study area was divided into strata to simplify data collection and to obtain as much as possible an even

distribution of sample plots. The samples within each stratum were taken randomly. The farmer who

has cashew was selected randomly from the village within a particular stratum and went to his field of



cashew to be interviewed where also other measurable and observable data were collected. All strata

were covered by the same procedure. The following map indicates the sample distribution over the

study area.

Figure 3.4: Map of samples distribution over the study area

The input data soil, crop and management practices were collected interviewing the farmers and field

observations.

Interview: after selection of the sample plot, owners of cashew tree orchards were interviewed accord-

ing to a checklist and by asking open-ended questions. Information on management practices like

weeding, pruning, and age of tree, application of fertilizer and pesticide and three year yields of

cashew nut were collected interviewing farmers. Problem of pest and disease, shortage of moisture,

critical moisture period occurred, wind problem, soil suitability (fertility level) for cashew and drain-

age condition etc were also obtained from farmers interview.

Measurement and observation: using Ipaq and GPS together sample plots were digitised. From this

the area of the plot, location and altitude were read. The number of trees within the plot was counted.

Because the distance between the trees is irregular, the average distance was estimated by measuring

largest and smallest distance. The soil depth was measured by auguring and pH measured using

chemical solution indicator and pH testing paper. The soil drainage condition and the general stand of

cashew tree were observed.

3.3.1.1.1 Primary data collection



Figure 3.5: Picture of interviewing in the field of cashew tree

Climatic data were collected from meteorological stations in and nearby of the study area for the peri-

ods of 2000-2002. Production data of cashew for 22 years were collected from INCAJU- Institution of

Cashew Promotion and Agriculture Department of Gaza province. The production data were collected

to observe the variation of overall production of cashew in the area. In other words, to have the back-

ground information of production conditions in the area. The climatic data were used directly as an

input in the analysis.

3.3.2 Data preparation and analysis

The raw data collected during fieldwork were processed before analysis. The climatic and manage-

ment data were processed using different methods.

Data coding: coding and structuring of data in tables (spreadsheets) is required before analysis. Struc-

turing is done for data standardization. A codebook is used as a reference table during data analysis.

Normalization of data: for the purpose of analysis, the categorical data were changed to numbers.

Therefore, binary numbers were replaced all categorical variables. Co-linearity test was undertaken

i.e. related explanatory variables were tested. The response variable was normalized based on statisti-

cal methods. Before normalizing the data, the original data distribution was inspected to see whether it

was normal or not. In this case because of the original data was not normal, logarithmic (log10) data

transformation method was applied to make the response variable normal (Moor, 1995). The trans-

formed data was used for statistical analysis.

3.3.2.1 Calculating water balance

A method for calculating water balance was introduced by Thornthwaite. In this study the modified

method by Thornthwaite and Mather (1997) was used for water budget determination (Dunne and

Leopold, 1978). Using data of precipitation and evapotransipiration, the other parameters were deter-

3.3.1.1.2 Secondary data collection



mined. To calculate the water balance the soil water holding capacity and rooting depth of the specific

crop are also important. These were obtained based on field observation and the literature.

As described in section 2.3, different sources stated different root depths of cashew tree. The root

depth of cashew on sandy soil is 2.5 m according to Nable (1996). The values stated by others are

somewhat fuzzy as compared to Nable (1996). From observations during fieldwork, the study area is

covered by deep sand and loamy sand soils, which have similarity with the soil mentioned by Nable

(1996). Therefore, based on this information, a root depth of cashew 2.5 m was taken into account.

As shown in table 2.1, different scientists determined different values of water holding capacity for

different soil texture classes. In this research, the available water holding capacity determined by

(Foth, Turk et al, 1972) was used for analysis. According to these scientists, the value for both sand

and loamy sand soils is 60 mm/m.

From the values of root depth and water holding capacity, the available water capacity of root zone of

cashew is 150 mm. Using precipitation, evapotransipiration and available water capacity of root zone,

the water balance was calculated. Water retained in the soil was read from figure 2.2 based on the

accumulated potential water loss and available water capacity of root zone of cashew. Using the same

steps, the water balances in different duration and places were calculated.

3.3.2.2 Assessment of moisture excess and stress period

The assessments were undertaken by comparing precipitation, potential evapotransipiration and actual

evapotransipiration. The actual evapotransipiration (AET) is obtained based on precipitation (P), po-

tential evapotransipiration (PET), and available soil moisture changes (�SM). In other words, actual

evapotransipiration is the same as potential evapotransipiration if P>PET and the sum of P and �SM

(AET = P + �SM) if P<PET (see section 4.1 and appendix A-2). The period in which the actual

evapotransipiration is less than potential evapotransipiration is the period of moisture deficit for

cashew. And the period with precipitation greater than potential evapotransipiration is the time of

moisture surplus. These periods were determined using Thronthwaite water balance method.

Critical period Critical in this sense is that the period in which cashew needs water and lack of water affects its pro-

ductivity. According to the experience of the farmers in the area, this time is mostly between the

month of June and October i.e. vegetative (flushing of new leaves) and flowering of cashew. The

magnitude of moisture at this time was determined using Thornthwaite water balance method. The

analysis of moisture effect on cashew yield was limited to this critical moisture need period experi-

enced by farmers.



3.3.2.3 Mapping available soil moisture distribution pattern

The available soil moisture obtained by water balance calculation based on the climatic data of Chi-

buto, Chokwe, Macia and Xai-xai meteorological stations is the input data for mapping. A point map

of moisture was produced and the moisture distribution over the study area was mapped using a geo-

statistical model called “moving average”. This output was used for statistical analysis of temporal

and spatial moisture variation impact on cashew yield.

3.3.2.4 Temporal and spatial climatic variation effect on yield

Rainfall and evapotransipiration are the main input data to analyse temporal and spatial climatic varia-

tions. Reference evapotransipiration was obtained by the Pan Evaporation method. This method was

selected on the basis of available data. Pan evaporation value was obtained from meteorological sta-

tions. Class-A pan coefficients (Kp) were estimated from table in FAO’s Irrigation and Drainage pa-

per No.24 (Allen, Pereira et al, 1998) based on monthly relative humidity and wind speed of the area.

Using these data, reference evapotransipiration was calculated by the following formula. Determined

values are shown in appendix A-1. ETo = Kp * Epan

Where: ETo = Reference evapotransipiration (mm/day)

Kp = Pan coefficient

Epan = Pan evaporation (mm/day) Temporal Analysis: based on the monthly moisture variation within critical moisture need periods

experienced by farmers i.e. June to October. The average available soil moisture of these months per

year was used for analysis of impact variation from year to year.

Spatial analysis: based on the same period as the temporal case. But, in this case the variation of

available moisture is not considered per year. The average of three-years variation from one place to

another was taken into account in the analysis. Climatic data Rainfall distribution and reference evapotransipiration pattern in each year (2000-2002) based on each

station called Chibuto, Chokwe, Macia and Xai-xai are shown by graphs in figure 3.6 and figure 3.7

respectively. The tabulated data for these graphs are in appendix A-1.



Figure 3.6: Graphs of monthly rainfall distribution Data source: Mozambique National Institute of Meteorology-Maputo Figure 3.6 shows the rainfall distribution per year and average of three years. As we can see each

graph the trend of rainfall follows the same pattern. But, the amount of rainfall (per meteorological

station) within year and from year to year is different. The pattern of year 2002 is slightly different

from the other two years.



Figure 3.7: Graphs of monthly reference evapotransipiration Data source: Mozambique National Institute of Meteorology-Maputo Reference Evapotransipiration is determined by considering different climatic factors like rainfall,

temperature, humidity, wind speed and radiation. Figure3.7 shows the reference evapotransipiration

per year and average of three years. In each graph the trend follows the same pattern. But, the amount

(per meteorological station) within year and from year to year is different. The pattern of year 2000 is

slightly different from the other two years and the average.

The soil moisture variation value obtained from water balance calculation was used for analysis of the

relation between yield variability and climatic variations. The reason why soil moisture is chosen is

that soil moisture is a function of all other climatic parameters considered in the water balance calcu-

lation. The variability of soil moisture depends on the variability of precipitation and evapotransipira-

tion. Therefore, taking soil moisture for analysis takes into account the other parameters. Yield of cashew: the yields of 3-years (2000-2002) were considered for analysis. The general over-

view of the yield obtained per year is shown in table below.



Table 3.1: Average yield (kg/ha) of cashew nut for three years Year Yield (kg/ha) Y-2000 226 Y-2001 160 Y-2002 256

Source: Fieldwork data from farmers interview (Oct, 2003)

3.3.2.5 Yield impact assessment: Management aspects

The assessment was carried out using the comparative performance analysis method. Based on this

method descriptive and regression analysis were undertaken. The following parameters are considered

in the yield impact analysis.

• Management aspects: use of pesticide, tree spacing, age of tree, cropping pattern, weeding

and pruning data.

• Climate related problems: rainfall (moisture) problem, wind problem, pests and disease

problem

• Topographic factor: Altitude

• Soil data: Soil pH, soil texture, soil fertility and soil drainage.

• Yield data: average yield data of year 2000-2002.

Figure3.8 shows the overall procedure used. Note: In figure 3.8: O: Objective

Q: Question



Crop data

observed yieldof year

2000 -2002

Rootdepth

Climatic data(Jan,2000-Dec,2002)

Percipitation andevapotranspiration per

meteorological station ofchibuto, chokwe, macia &

xai-xai

Thornthwaitewater balance

method

Available soil moisture, AET,moisture defecit and surplus permonth per 4 stations in each year

Crop-climatemodel(emperical-statistical model)

Soil data( texture, drainage,

pH, fertility,)

soil waterholdingcapacity

Aster image14 Jun 2003 ofthe study area

Station locationtable with

moisture column

Create pointmaps usingstudy area

georeference

Meteorologicalstations point

map

Geostatisticalanalysis

Graphical presentation ofmoisture stress and

surplus periods in a year

Map of availablemoisture distribution

pattern of the study area

Statistical out put oftemporal and spatial

climatic effect on yield

meteorologicalstations

location table

Attribute pointmap of moisture

Samplepointtable

Sample pointmap

Rastrized samplepoint map

Crossoperation

Table of availablesoil moisture value

at each sample point

Managementpractices

Planting pattern,treespacing, tree age,

weeding, pruning, Useof pesticide

(CPA)Descriptive statistics &

regression analysis

Statistical result ofyield impact

AltitudeClimate relatedproblems

-moisture problem -pest and disease -Wind problem

End offlowO-2 Q-2

End offlow O-3

End of flowO-1& Q-1

End of flowO-4 & Q-3

Figure 3.8: Flow diagram of dtailed research method



4 Results

4.1 Water budget determination

The calculated water balances in each year for each station are shown in Appendix A-2. The calcula-

tion was done based on soil water holding capacity (table 2.1), cashew root depth described in section

3.2.2.1 and climatic data (precipitation and reference evapotransipiration) of the four meteorological

stations.

Description of parameters:

• P= Monthly precipitation per climatic stations

• ETo =Reference evapotransipiration derived from pan evaporation measurements (see appen-

dix A-1)

• P-ETo=Difference of precipitation and reference evapotransipiration

• Acc Pot WL=Accumulated potential water loss obtained by adding of monthly P-ETo starting

from end of wet and beginning of dry season

• SM= Available soil moisture; if water loss occurs, the soil moisture is read from figure 2.2

against the corresponding accumulated water loss and soil water holding capacity of root

zone. If no water loss, the available soil moisture is the same as soil water holding capacity of

root zone.

• �SM = Soil moisture change is the difference of available soil moisture between consecutive

months

• AET=Actual evapotransipiration is the same as potential evapotransipiration if P>ETo and

the sum of P and �SM (AET = P + �SM) if P<ETo.

• MD=Moisture deficit is the difference between AET and ETo (MD = AET – ETo)

• S= Moisture surplus is the difference of P-ETo and �SM (S = P-ETo- �SM) if P-ETo >0 and

S = 0 if P-ETo<0

The detail calculations are shown in the tables of appendix A-2 (Dunne and Leopold, 1978)

4.2 Moisture surplus and deficit period

In section 4.1 the moisture variation per month was assessed. Based on the average values obtained in

this section as indicted in Appendix A-2 tables, the graph of a moisture surplus and deficit period was

produced (figure 4.1).



Figure 4.1: Graph of moisture surplus and deficit preiods (average of 2000-2002) Figure 4.1 shows that moisture deficit occurs from mid of June to mid of November, which coincides

with the period of critical moisture need for cashew mentioned by farmers. This strengthens the

farmer’s information for further analysis.

4.3 Available moisture distribution pattern of the area

The moisture variability pattern follows the rainfall pattern as shown in figure 4.2. Both rainfall and

soil moisture distribution pattern were determined by interpolation based on a geo-statistical method

called moving average. Moving average is a point interpolation that performs a weighted averaging on

point values and returns a raster map as output. For each output pixel, an output value is calculated as

the sum of the products of calculated weight values and point values, divided by sum of weights. The

inverse distance weight function was used for purpose of local variation within a pixel. It is expressed

by:

Inverse distance = (1 / d n) - 1

Where: d = D/D0 relative distance of points towards pixels

D = Euclidean distance of point to output pixel

D0 = Limiting distance n = weight exponent

Output pixel value = � (w i * vali)/ �w i

Where: w i = weight value of point i, Val i = point value of point i

Based on the above mathematical formula (algorithm), maps were produced using ILWIS software.

Map (a) shows annual rainfall of the whole study area and map (b) shows available soil moisture dis-

tribution pattern of cashew-growing area. Both are averages of the years 2000-2002.



(a) (b)

Figure 4.2: Maps of annual rainfall (mm) (a) and available soil moisture (mm) (b) distribution pattern

4.4 Analysis of climatic variation effect on yield of cashew

The relationship between climatic variation and the yield variability was analysed by considering both

temporal and spatial climatic variations. The details for both are described below.

4.4.1 Temporal effect

From water balance result, available soil moisture for the period of June to October in each year was

taken into account. This period is the time in which the moisture availability is critical for the cashew

crop according to farmer’s experience. Figure 4.3 shows the trend of available soil moisture variations

in critical period per year.

(a) (b) (c)

Figure 4.3: Graphs of monthly moisture variation in critical period for year 2000 (a), 2001 (b),& 2002 (c)

Figure 4.4: Graph of yearly available soil moisture variation (average of critical months)



To assess the effect of temporal moisture variation on yield, it is important to see the contributions of

available soil moisture in each year in explain the average yield variability of the three years. Using

the data in appendix B-6 (columns 2-5), linear multiple regression analysis was carried out.

Table 4.1: Regression results of log10 (yield) of cashew versus temporal soil moisture variation

R square=17% and Adjusted R square = 14%

Soil moisture Coefficients Standard Error P-value-2 tail

Constant -8.702 3.780 0.024

Available soil moisture of year 2000 0.051 0.021 0.047



The above result shows that the temporal variation of available soil moisture explains the yield vari-

ability of cashew by R square =17% at (� = 0.05). Overall the available soil moisture of year 2002

contribution in affecting the average yield of cashew is the highest. The contribution of moisture in

each year to yield variability is mentioned below.

Figure 4.5: Graphs of the relation between yield and available soil moisture in different years In all the above three cases the relationship between yield and moisture is positive. But each of them

explains the yield variability in different degrees. The year 2000 available soil moisture explains the

yield variability by R2 =11%. The year 2001 available soil moisture explains the yield variability by R2

=2%. The year 2002 available soil moisture explains the yield variability by R2 =15%. Generally, it can

be concluded that, the degrees of impact of moisture on yield of cashew vary with the temporal varia-

tion in moisture availability or climate.



4.4.2 Spatial effect

The analysis of spatial effect is focused on the spatial variation of available moisture within the study

area. The average of three years available soil moisture of critical period and yield were considered as

input data appendix B-6 (columns 2 and 6).

Figure 4.6: Graphs of spatial moisture variation versus yield of cashew

The right side graph of figure 4.6 shows that the linear regression at 95% confidence interval yield

versus moisture. The middle line is fit line and the lower and upper lines are the lower and upper con-

fidence limit lines. The spatial variation of available soil moisture explains the yield variability at (�

= 0.05) by R2 =11%, adjusted R2 =9% and p = 0.005, which is statistically significant. Therefore, from

result it can be concluded that the spatial variation of available moisture has a significant effect on

cashew yield variability.

4.5 Yield impact analysis: Management aspects and physical factors

4.5.1 Descriptive statistics

4.5.1.1 Normality test of yield distribution

Before go to the analysis it is important to check whether the data are normal or not. In this case the

original yield data is not normal. So, to make normal the lognormal method was applied.

The histogram with normal curve, the box plot confidence interval of the mean, and the median and

standard deviation graphs show the distribution of log10 yield of cashew.

4.5.1.1.1 Statistical summery of yield of cashew



Figure 4.7: Graghs of cashew yield distribution

The distribution of the original data is not normal. It is highly skewed toward the left and about 93% of

the values are lower than 600 kg/ha. Therefore, it is important to normalize the data for further analy-

sis. Lognormal method is appropriate to normalize this data. Figure 4.7 shows the distribution of log10

(yield in kg/ha). The summarized statistical output also shows the distribution is highly close to the

standard normal distribution. The mean and median are almost the same, which indicate normality of

the distribution.

Normal probability test generates a normal probability plot and performs a hypothesis test to examine

whether or not the observations follow a normal distribution. For the normality test, the hypotheses

are, H0: data follow a normal distribution vs. H1: data do not follow a normal distribution. The fol-

lowing graphs show the normal probability versus the original and the normalized yield data.

(a) (b) Figure 4.8: Graphs of original (a) and normalized (b) yield data normal probability test

4.5.1.1.2 Normal probability test



The data depart from the fitted line most evidently in the extremes, or distribution tails. The Ander-

son-Darling test’s p-value in case of original data is equal to zero i.e. the probability of normality of

the original data is zero. And the p-value in case of normalized data indicates that, at levels greater

than 0.338, there is evidence that the data do not follow a normal distribution. The following graphs

are also shows the normal probability plot within lower and upper confident limits.

(a) (b) Figure 4.9: Normal probability plots for original (a) and normalized (b) yield data The above two plots of yield distribution are drawn at CI=95%. The fit line for normal probability

distribution is not good. Almost all of the points are out side the confidence limit in both directions

(Anderson-Darling statistic or goodness of fit = 12.7, which is a high value). In case of lognormal

probability distribution, almost all points are within the confidence limit and closer to the fit line,

which is good distribution (Anderson-Darling statistic = 0.499).

4.5.1.2 Distribution of non categorical variables

A) Altitude

Figure 4.10: Frequency distribution of altitude and its relation with yield of cashew



The histogram in figure 4.10 shows the distribution of altitude. It skewed toward the right with value 1.0 and

standard deviation of 32.4 m from the mean. According to the graph and skewness value, the distribution is

approximately symmetric and close to normal. The scattered plot shows yields of cashew in relation to

altitude variation i.e. at higher altitude the cashew production becomes lower.

B) Spacing

(a) (b)

Figure 4.11: Frequency distribution of distance between trees and relation with yield of cashew

The distance between trees ranges from 6 m to 35 according to the data collected. The histogram (a) of

figure 4.11 shows the distribution is symmetric and close to normal distribution. It skewed toward the

right with value 0.4 and standard deviation of 7.3 m. The scattered plot (b) of figure 4.11 shows the

increase the distance between the trees reduces the yield of cashew nut per hectare.

C) Age of cashew tree

(a) (b)

Figure 4.12: Frequency distribution of age of cashew tree and relation with yield produced



The age of cashew ranges from 5 years to 80 years. According to the data most of the trees are within the

range of 25 to 35 years. The histogram (a) of figure 4.12 shows the distribution is skewed toward the right

with value 0.9 and standard deviation of 13 years. The scatter plot of figure 4.12 (b) shows that the

production of cashew nut decreases as the tree becomes older.

D) Soil pH

(a) (b)

Figure 4.13: Frequency distribution of soil pH and relation with yield of cashew

The soil pH in the area ranges from 4 to 8 according to the data collected. But the dominant value is 4.5

to 5. The histogram (a) of figure 4.13 shows the distribution is skewed toward the right with value 1.4

and standard deviation of 0.7. The relation between yield and soil pH is shown by the scattered plot

(b) of figure 4.13. The linear relation yield versus soil pH is not clear. But, the higher yield of cashew

indicated around soil pH value 4.5. This indicates that non-linear function more explains the relations

than linear. But, the relation is also not significant in case of second-degree function.

4.5.1.3 Characteristics of categorical variables distribution

Box plots, also called box-and-whisker plots, are particularly useful for showing the distributional

characteristics of data. The following box plots show the distribution of normalized yield data with

respect to different yield affecting factors.



A) Use of pesticide

Figure 4.14 shows that out of 72 farmers 53

did not apply chemical (yes = applied, no = not

applied). The average log10 yield that applied

chemicals is higher than that of did not applied

(yes = 2.30, no = 1.55). The yield varies from

very low to very high for farmers who did not

apply chemicals, which indicate that there are

differences between farmers in other manage-

ment aspects that affect the yield of cashew.

Figure 4.14: Box plot of relation between pesticide application and yield of cashew

B) Weeding

The box plot of figure 4.15 shows 63 farmers

practised weeding (yes) of cashew tree and the

rest do not. The general trend of box plot also

shows that the farmers who practised weeding

obtained higher yields on average (yes = 1.89,

no = 1.04).

639N =

Weeding

yesno

Log

yiel

d

4.0

3.5

3.0

2.5

2.0

1.5

1.0

.5

0.0

Figure 4.15: Box plot of relation between weeding practise and yield of cashew



C) Pruning

Figure 4.16 shows that 65 farmers practised

pruning (yes) of cashew tree. There is one out-

lier in case of no pruning. The log10 yield of

cashew for farmers who practised pruning is

higher on average (yes = 1.85, no = 1.17).

657N =

Pruning

yesno

Log

yiel

d

4.0

3.5

3.0

2.5

2.0

1.5

1.0

.5

0.0

7

Figure 4.16: Box plot of relation between pruning of cashew tree and its yield

D) Planting pattern

The box plot of figure 4.17 shows 59 farmers

who planted cashew tree mixed with other

(perennial and annual) crops. The average

log10 (yield in kg/ha) of monocropping is

higher than that of mixed cropping (Mono =

2.17, mixed = 1.70).

1359N =

Planting pattern

MonoMixed

Log

yiel

d

4.0

3.5

3.0

2.5

2.0

1.5

1.0

.5

0.0

Figure 4.17: Box plot of relation between planting pattern of cashew tree and its yield



E) Pest and disease problem

Figure 4.18 shows that 61 farmers were re-

ported to have the problem of pest and disease,

which affects the yield of cashew. On average

the farmers with no problem of pest and dis-

ease harvested higher yields (yes = 1.61 no

=2.75). The high variability of yield in case of

presence of pest and disease problem shows

the differences in other management aspects,

which affect yields of cashew.

6111N =

Pest and disease problem

yesno

Log

yiel

d

4.0

3.5

3.0

2.5

2.0

1.5

1.0

.5

0.0

Figure 4.18: Box plot of relation between incidence of pest and disease and yield of cashew nut

F) Moisture Problem

Figure 4.19 shows that 60 farmers who reported

to have moisture shortage for cashew produc-

tion during the critical period mentioned in

chapter 3 (Jun to Oct). The farmers with no

moisture shortage obtained higher yield i.e. av-

erage log10 (yield in kg/ha) of no moisture

problem is higher (yes = 1.57, no = 2.65). There

is also higher yield variability for presence of

moisture shortage shows that the differences in

management practices affect the cashew yields.

6012N =

Moisture problem

yesno

Log

yiel

d

4.0

3.5

3.0

2.5

2.0

1.5

1.0

.5

0.0

72

Figure 4.19: Box plot of relation between moisture availability for cashew tree and its yield



G) Soil texture

The box plot of figure 4.20 shows out of 72

farmers 45 grow cashew on loamy sand soil

and the rest grow on sand soil. Average log10

(yield in kg/ha) on loamy sand is 1.68 and that

of sand is 2.03, which mean higher yields on

sand soil on average.

2745N =

Soil texture

sandloamy sa

Log

yiel

d

4.0

3.5

3.0

2.5

2.0

1.5

1.0

.5

0.0

Figure 4.20: Box plot of relation between soil texture and yield of cashew nut

H) Soil drainage and fertility

4527N =

Drainage condition

goodExcessiv

Log

yiel

d

4.0

3.5

3.0

2.5

2.0

1.5

1.0

.5

0.0

9576N =

Soil fertility

PoorBetterBest

Log

yiel

d

4.0

3.5

3.0

2.5

2.0

1.5

1.0

.5

0.0

17

Figure 4.21: Box plots of yields of cashew nut in relation to soil drainage and fertility The box plot drainage condition of figure 4.21 shows on average higher yields of cashew for soil with

excessive drainage i.e. average log10 (yield in kg/ha) (excessive = 2.03, good = 1.68). The soil fertil-

ity and yield of cashew nut has a positive relation. As shown on soil fertility box plot, the maximum

and minimum yield was obtained from soil with better fertility. But, all of the yields obtained from



soil of best fertility are higher in general. According to Turkey’s pair wise comparisons, there is no

difference in mean yield obtained from poor and better soil fertility. So, both are merged to make one

level soil fertility for analysis. The three levels were reduced to two levels, i.e. the better and the best

soil fertility levels. For analysis, if better soil fertility = 1 and if best soil fertility = 0 were used. Ac-

cording to the two classes, the average log10 (yield in kg/ha) for better soil fertility is 1.75 and best

soil fertility is 2.10.

4.5.2 Co-linearity test

To exclude use of two or more related independent variables at the same time in an analysis, co-

linearity should be tested. Using Pearson’s chi-square test and regression at 5% significance level, the

association between parameters was determined. The detail is shown as follows Table 4.2: Soil texture versus fertility

Soil texture and fertility relation was tested based on table 4.2 using chi-square test. Calcu-lated chi-square = 0.10, p> 0.25 with degree of freedom = 2. This shows that the relation is not significant at p-value = 0.05

Table 4.3: Soil texture versus drainage

Soil texture and drainage relation was tested based on the information in table 4.3 using chi-square test. Calculated chi-square = 72, p=0 with degree of freedom = 1. This shows that the relation is significant at p-value = 0.05

Table 4.4: Use of pesticide versus pest and disease problems

Use of Pest and disease problems Pesticide Yes No Total Yes 12 10 22 No 49 1 50 Total 61 11 72

Relation of pesticide application and pests and diseases problem was tested based on the table 4.4. Calculated chi-square=22.3, p< 0.0005 with degree of freedom=1. This shows that the relation is significant at p= 0.05.

From regression result, the relation between soil texture and soil pH is not significant (p=0.737 and

correlation= -0.04). Table 4.5 shows the related parameters and significance of co-linearity.

Soil fertility Soil texture Loamy sand Sand Total Poor 5 3 8 Better 36 21 57 Best 4 3 7 Total 45 27 72

Soil drainage Soil texture Loamy sand Sand Total Good 45 0 45 Excessive 0 27 27 Total 45 27 72



Table 4.5: Summary of co-linearity tested variables

Variables Model used Significance of

co-linearity Variable considered for analysis

Soil texture Vs soil drainage Chi-square Yes Soil texture Soil texture Vs soil pH Regression No Both Soil texture VS soil fertility Chi-square No Both Use of pesticide Vs pest and disease problems

Chi-square Yes Pest and disease problem

4.5.3 Multiple regression Analysis

Normalizing and exclusion of parameters with low or no variability, and also co-linear test were un-

dertaken before regression analysis. After this process, 11 parameters were retained for multiple re-

gression analysis. The regression was done using multiple regression equation indicated below:

Y= �0 + �1 X1 + �2 X2 + �3 X3 +…+ �n xn

Where: Y (yield) is the dependent parameter, �0 is constant, �1… �n are the regression coefficients, and

X1… Xn are the independent parameters. The results are shown in table below.

Table 4.6: Linear multiple regression of log10 (yield) of cashew nut

R Square = 60% and R square adjusted =57%

Parameters Coefficients Standard Error P-value (2-tail) Constant 2.21 0.27 0.000 Weeding (yes=1, no=0) 0.46 0.18 0.011

Pruning (yes=1, no =0) 0.62 0.20 0.003

Pest and disease problem (yes=1, no =0) -0.56 0.21 0.009

Moisture problem (yes=1, no=0) -0.62 0.19 0.002

Planting pattern (mixed =1, mono= 0) -0.51 0.15 0.001

• The regression results show that out of eleven variables, five are statistically significant at (� = 0.05).

• The overall R2 =60%, shows the extent by which these variables explain the yield variability

of cashew in the area.

• The production function is expressed by the equation:

Log10 (yield in kg/ha) = 2.21 + 0.46 * W + 0.62 *P - 0.56 * PDP - 0.62 * MS – 0.51 * PP



Where:

W = 1 if weeding is practiced P = 1 if pruning is practiced PDP = 1 if problem of pest and disease MS = 1 if Moisture shortage PP = 1 if Mixed planting pattern

Figure 4.22: Graph of actual yield versus estimated yield of cashew.

Figure 4.22 shows that the relation between actual and estimated yields of cashew. The estimated

yield was determined using the above production function equation. It shows the same value of R-

square obtained by multiple regressions (table 4.6).

1) Weeding

The need of weeding is principally based on the competition for water and light (Ohler, 1979). Weed-

ing and yield have a positive relation. The impact of weeding is statistically significant (R2 = 15%)

The relationship between weeding and yield is expressed by Log10 (yield in kg/ha)=1.04 + 0.84 * if

weeding is practiced.

2) Pruning

Yield and pruning have a positive relationship and the relation is expressed by Log10 (yield in

kg/ha)=1.17 + 0.68 *if pruning is practiced. It affects the yield of cashew significantly (R2 = 8%).

3) Planting pattern

The result shows that mixed cropping has a negative impact on yield. It affects the yield of cashew

significantly (R2 = 7%). The relation of planting pattern and yield is expressed by Log10 (yield in

kg/ha) = 2.17 – 0.47*if mixed planting pattern is practiced.

4) Incidence of pest and disease

Pest and disease is one of the problems in the area. The statistical analysis shows that a negative cor-

relation with yield. The effect is statistically significant (R2= 34%). The relation between yield and

pest and disease problem is expressed by Log10 (yield in kg/ha) = 2.75 – 1.15 * if there is pest and

disease problem.



5) Moisture shortage

The yield and shortage of moisture has a negative relation and the relation is expressed by Log10

(yield in kg/ha) = 2.65 – 1.08 * if there is moisture shortage problem. It affects the yield significantly

(R2 = 36%).

The above multiple regression equation suggests that yields of cashew can be higher if:

��Weeding and pruning practices are undertaken in a proper way

��Pest and disease are controlled

��Sufficient moisture is available during periods with high water needs

��Mono cropping pattern is practiced

4.6 Age of cashew tree , weeding and pruning

There is a relation between age of cashew tree and needs for weeding and pruning. For the analysis of

this relation, logistic regression is the appropriate method that deals with two possible values, such as

presence or absence. Logistic regression in this case is also based on binary response variables. The

result of this method shows that both weeding and pruning have a negative relation with age of

cashew. The relation is expressed by:

Weeding = 6.03 - 0.12 * age of tree and pruning = 6.31 – 0.12 * age of cashew. Generally, the output

shows that weeding and pruning practices decrease as the age of cashew increases.



5 Discussion

5.1 Moisture excess and deficit period

According to the water balance calculation results, moisture shortage occurs from mid of June to mid

of November (figure 4.1). In the other months of the year there is moisture surplus. The critical period

from farmer’s experience is in agreement with the moisture deficit period obtained by the Thorn-

thwaite water balance method.

Rainfall and moisture distribution patterns of the area are related to each other. In areas of high rain-

fall a relatively more soil moisture is expected. As shown on figure 4.2, the spatial distribution pattern

of available soil moisture follows the same pattern as the rainfall distribution pattern.

5.2 Yield of cashew and temporal and spatial available soil moisture variation

The temporal and spatial soil moisture values used for analysis were derived from climatic parameters

(precipitation and evapotransipiration) using the Thronthwaite water balance method.

The variability of yield in relation to the available soil moisture variation during the critical period

(i.e. June to October) was analysed in chapter 4. The regression analysis based on 3-years data on

available soil moisture as independent variables and average yield as dependant variable at (�= 5%) in

temporal case, resulted in a multiple R2 = 17% and an adjusted R2= 14 %( table 4.1). This indicates

that the temporal variation of available soil moisture explains the yield variability of cashew nut by

17%. In other words, the variation in climate through time has an impact on the production of cashew

nut. The available soil moisture contribution to the yield variability of cashew varies from year to

year. For instance, available soil moisture in year 2002 shows higher contribution in affecting yield of

cashew.

The spatial impact on yield was analysed based on the average of 3-years available soil moisture and

yield of cashew nut. The linear regression analysis result shows that available soil moisture variation

with respect to spatial differences is statistically significant at (�= 5%) (R2 =11%, adjusted R2 =9%

and p = 0.005). The spatial climatic variation explains the yield variability of cashew by 11%. Gener-

ally, the result indicates that the yield variability of cashew nut depends on the spatial available soil

moisture variation of the area.



5.3 Impact of management and physical factors on yield

The analysis of yield impact parameters was carried out using different statistical methods (section

4.5). Totally, fifteen independent variables were considered that are expected to affect the production

of cashew. Not all of them were used for regression analysis. The variables which have no and very

small variability were removed from the analysis (e.g. use of fertilizer, wind problem and soil depth).

Co-linearity of variables also tested. a) Distance between trees

The statistical results show that the yields per hectare and spacing between trees have a negative rela-

tionship. Statistically the impact on yield is not significant at (�= 5%). In terms of yield per hectare,

the highest plant density produces more yield than lowest density. But, in terms of yield per tree the

lowest density plant produces more. A study in Tanzania on spacing and yield also shows this fact

(Ohler, 1979). As the distance between trees increases, the number of trees per hectare decreases and

the yield per hectare becomes lower.

b) Age of cashew tree

Age of cashew is one of the factors that determine the yield. But statistically the effect is not signifi-

cant at the 5% significance level. The scatter plot drawn using yield data collected by interviewing

shows that the higher yields are obtained at tree ages of 25 to 35 years. The non-linear relation follows

the same pattern as scatter plot. But, still it does not explain the relation between age and yield in bet-

ter way than the linear function.

c) Weeding and pruning

Weeding and pruning are factors, which explain the yield variability of cashew significantly at (�=

5%). Proper weeding and pruning result in higher cashew yield. Weeding should preferably be done

before the end of the rainy season. Then the tree will have sufficient moisture available for its flush

and inflorescence development (Ohler, 1979).

d) Planting pattern

Cashew is planted alone or mixed with other perennial and annual crops. This has its impact on pro-

duction. Planting pattern affects significantly the yield of cashew in the study area at (�= 5%). The

negative sign (table 4.6) of the coefficient shows that intercropping (mixing) of cashew with other

crops reduces the yield. Generally, the statistical analysis indicates the higher yield of cashew for

monocropping pattern. This result is in disagreement with the result obtained by (Mole, 2000) i.e. in

his case; mixed cropping seems to show relatively higher yields than monocropping. Ohler (1979)



described the importance of intercropping. He said; intercropping is important where trees are widely

spaced to replace weed. As compared to weeds, plant density of an intercrop is much lower. He also

underlined that the type of crop to be intercropped is important. Low crops (e.g. ground nut) are suit-

able for cashew intercropping. Higher crops such as sorghum and millet should not be planted be-

tween young cashews according to (Ohler, 1979). In the study area the mixed crops are mostly fruit

crops and maize. The difference in the results of this study and that of Mole, 2000 may be a matter of

the types of crops intercropped. e) Incidence of pests and diseases

The wide spread incidence of Oidium and Helopelits in Mozambique is the most challenging factor in

cashew production (Mole, 2000). Also according to farmer’s information, pests and diseases are

among the biggest problems in the area. The statistical analysis shows a highly negative correlation

with yield and its effect is statistically significant at (�= 5%). The negative impact of pests and dis-

eases on yield and the statistical significance confirm these challenges.

f) Moisture shortage problem

Shortage of moisture according to farmers is another important factor that affects the yield of cashew.

This happens in the critical period June to October in which moisture is important for cashew. Statis-

tical analysis shows that the impact is significant at the 5% significance level.

g) Soil texture, pH and fertility

In the cashew growing area, there are two major soil textures i.e. loamy sand and sand according to

field data obtained by (Thien, 1979) feeling method. The analysis is carried out based on these two

soil textures. The result shows that the effect of soil texture variation is not statistically significant.

Generally, the production of cashew is higher on sand soil.

Different sources indicated that soil pH value between 4.5 and 6.5 are considered as suitable for

cashew growing. The scatter plot is in agreement with this fact. The linear relation shows that there is

a negative relationship between soil pH and yield of cashew. Higher yields are expected at lower pH

values than higher one. The second-degree function explains in better way the relation between

cashew yield and soil pH.

Cashew is one of the crops that can be grown on soils with a poor fertility (Ohler, 1979). The soil fer-

tility level in relation to suitability for cashew growing was classified into three levels i.e. poor, better

and best according to farmers. The statistical analysis shows that the impact on yield of cashew is not



significant at (�= 5%). The general trend indicates the higher average yield for best soil fertility (Fig-

ure 4.21).

h) Altitude

Altitude is one of the geographical factors that contribute to the yield variability of cashew and affect

time of harvest. As mentioned by (Mole, 2000), higher altitude seems to put back the main harvesting

period by about one or two weeks and the altitude above 1200m have a negative impact on yield of

cashew. The regression analysis result of this study shows insignificant impact of altitude on yield.

The maximum altitude recorded in the study area is 152m above sea level. Generally, the differences

in altitude is small that makes insignificant effect on yield of cashew in the area.

5.4 Age of tree and need of weeding and pruning

Weeding and pruning have a negative relationship with age of tree. The idea mentioned in section 2.5

by (Ohler, 1979), is in agreement with this result. The negative relationship may not imply that as the

tree becomes old, there is no need of weeding and pruning. When the tree becomes older, the yields

obtained become lower. Due to less production, the farmers do not give appropriate attention to the

tree regarding management aspects. This condition may cause the negative relationship between

cashew tree age and weeding and pruning.



6 Conlusions and Recommendations

6.1 Conclusions

The temporal and spatial variation of climate has direct influence on variability of available soil mois-

ture. In other words, it has an impact on the productivity of crops. Based on this fact, the impact of

available soil moisture in the critical period (June to October) on yield of cashew was analysed. The

temporal and spatial climatic variation or the variation in available soil moisture explains the yield

variability of cashew by R2 = 17% and R2 = 11% respectively. These results addressed the specific

objective (1) regarding the effect of temporal and spatial climatic variation on yield of cashew and

answers research question (1) on the relation existing between yield variability and climatic (mois-

ture) variation.

Specific objective (2) regarding moisture stress and surplus periods is addressed by water balance

calculation. The result is shown by figure 4.1 and shows that the moisture deficit period begins mid of

June and extends to November. The other months of the year are moisture surplus period. Research

question (2) on the coincidence of critical period according to farmers experience and that obtained by

calculation is answered by the water balance calculation as shown by figure 4.1. The result shows that

there is critical time from farmers experience and the calculated critical period from month of June to

October coincide. Specific objective (3) regarding mapping of the spatial distribution of available soil

moisture is addressed by figure 4.2.

Specific objective (4) and research question (3) regarding management impact on yield of cashew are

addressed by the analysis part of section 4.5. Totally fifteen independent variables were considered in

the analysis that are expected to affect the production of cashew in the area. Most of these variables

are directly or indirectly related to management aspect. From the analysis, each variable has impact in

different degree on yield of cashew. Out of eleven variables selected for regression analysis, five vari-

ables (weeding, pruning, planting pattern, incidence of pest and disease and moisture shortage prob-

lem) significantly affect the yield of cashew at (�= 5%). They explain the yield variability of cashew

by R2= 60% and an adjusted R2 =57%.

Generally, from the overall analysis it can be concluded that management aspects and moisture avail-

ability variation are factors that determine the yield variability of cashew in the area.



6.2 Recommendations

This study shows that a number of factors affect the productivity of cashew tree. The real challenge is

facing these factors in a way that develops cashew to provide benefits to the rural growers. As is

known cashew is a tree crop, which can produce with less management, poor soil fertility and low

moisture. This study has presented empirical evidence on the determinant of cashew productivity at

the farm level. The following points need attention to improve productivity of cashew.

��In the area particularly, management practices are poor and lead to high variability of cashew

yields. The study results show that important factors such as weeding, pruning, planting pat-

tern, incidence of pest and disease and moisture deficit affect significantly the productivity of

cashew tree. In order to improve the productivity of cashew giving infancies to these factors is

essential.

��Mozambique is among the countries, which produces cashew in large scale. About 40% of ru-

ral population are cashew producers. But, as compare to number of producers, the attention

gave to this crop was less according to the observation during this study. Additionally there are

no much research outputs, which are referring to cashew tree crop. Therefore, development of

research activities is the important issue that help to improve the productivity of cashew.

��Generally, improved technologies and management practices have potentials to raise on-farm

cashew productivity. However, this needs support by institutions, research and extension ser-

vices in order to bring about the possible increases in cashew productivity to raise small-

holder’s income, to improve food security conditions and to reduce poverty.



References

Acland, J. D. (1971). East African crops: an introduction to the production of field and plantation

crops in Kenya, Tanzania and Uganda. London, Food and Agricultural Organization

Allen, R. G., L. S. Pereira, et al. (1998). Crop-evapotransipiration: guidelines for computing crop wa-

ter requirements. Rome, Food and Agricultural Organization.

Blaike, S. J., Chacko, E. K., Lu, P., W. J. Muller (1998). Productivity and water relations of field-

grown cashew: a comparison of sprinkler and drip irrigation. Australian Journal of Experi-

mental Agriculture 41(5): 663-673. http://www.publish.csiro.au/nid/72/paper/EA00158.htm

18/07/2003

Buckman, H.O., N. C. Brady, et al. (1960). Nature and properties of soil: a college text of edapoogy.

New York, Macmillan.

CFC. (2001). The cashew sub-sector in Eastern and Southern Africa: Proceedings of a sub-regional

workshop, Common Fund for Commodities.

Chang, J. (1968). Climate and Agriculture: an ecological survey. Chicago, Aldine Pub. Co.

deBie, C.A. J. M. (2000). Comparative performance analysis of agro-ecosystems: International Insti-

tute of Aerospace and Earth observation. Enschede, Wageningen university.

Dunne, T. and L. B. Leopold (1978). Water in environmental planning. San Francisco, W. H. Freeman.

FAO. (1976). A framework for land evaluation: FAO soil bulletin no. 32. Rome, Food and Agriculture

Organization of the United Nations: Vii, 72.

Foth, H. D., L. M. Turk, et al (1972). Fundamentals of soil science. New York, Wiley.

Gomez-Plaza, A., M. Martinez-Mena, et al. (2001). Factors regulate spatial distribution of soil water

content in small semiarid catchments. Journal of hydrology 253 91-4): 211-226.

Griffith, B. (2001) Efficient fertilizer use: Maximum Economic Yield Strategies.

http://www.imcglobal.com/general/efumanual/pdf/mey.pdf. 23/11/2003

Grundon, N. J. (1999). Overview of Australian Cashew Literature:

http://www.clw.csiro.au/publications/technical99/tr25-99.pdf. 18/07/2003

Hoguane, A. (2000). Mozambique: General background information.

http://www.aaas.org/international/africa/moz/overview.html.17/06/2003

Hounam, C. E., et al (1975). Drought and agriculture: Report of the CagM Working group on the As-

sessement of Drought. Geneva, Secretariat of the World Meteorological Organization: xv, 127

Huibers, F. P. (1985). Rainfed agriculture in a semi-arid in tropical climate: aspects of land and water

management for red soil in India, Agricultural University Wageningen: 193

Iseraelsen, O.W. (1950). Irrigation principles and practices: New York, Wiley.



Iseraelsen, O.W. and V.E. Hansen (1962). Irrigation principles and practices: New York, Wiley.

Parry, M.L., Carter, T.R., N.T., Konijn (1988). The impact of climatic variation on agriculture: as-

sessment in semi-arid regions. Dordrecht, Kluwer Academic, International Institute for Ap-

plied Systems Analysis (IIASA), United Nations Environmental programme (UNEP)

Menete, M. Z. L. (2000). The Need for Collaborative Research on Environment, Soil, and Water

Management for Sustainable Agriculture.

http://www.aaas.org/international/africa/moz/menete.html. 17/06/2003

Mole, P. N. (2000). An economic analysis of smallholder cashew development opportunities and link-

ages to food security, in Mozambique. Northen province of Nampula, Michigan state univer-

sity, Department of Agricultural Economics.

http://www.aec.msu.edu/agecon/fs2/mozambique/moledissertation.pdf. 20/08/2003

Moncur, M. W., A. J., Wait (1986). Tabular descriptions of crop grown in tropics: 15. Cashew (Ana-

cardium Occidentale L.)

Moore, D. S. (1995). The basic practices of statistics. New York, W.H. Freeman

Ohler, J. G. (1979). Cashew. Amsterdam, Koninklijk Institute voor de Tropen.

Ojasvi, P. R., R. K. Goyal, et al. (1999). The micro-catchments: water-harvesting technique for the

plantation of jujube (Zizyphus mauritiana) in an agroforestary system under arid conditions.

Agricultural Water Management 41(3): 139-147.

Patrick, O., Johan, A., R., David (2000). The effects of nitrogen on cashew in North Queen land, 1995

to 99. Rural Industries Research and Development.

http://www.ridic.gov.au/reports/NPPwo2-001. 08/12/2003

Pereira, I. J. F. (2000). The Geographic impacts of the year 2000 great floods in the Limpopo river

basin, Mozambique.

Reddy, B. V. S., P. Aanjana Reddy, et al. (2003) Crop management factors influencing yield and qual-

ity of crop residues. Field Crops Research 84(1-2): 57-77

Rui, B., et al. (2000). Representative characteristics of rural house holds in areas of Central and

Southern Mozambique affected by the 2000 floods, research paper series no. 4, Ministry of

Agriculture and Rural Development, Mozambique.

http://www.aec.msu.edu/agecon/fs2/mozambique/wps40e.pdf. 10/07/2003

Smith, J. R. (1950). North Tree crops: Permanent Agriculture. Devin-Adair. Old Greenwich and CT

Organization.

http://www.daviesand.com/Papers/Tree_Crops/Northern_Tree_Crops/.11/08/2003

Thien, A. (1979). A flow diagram for teaching texture-by-feeling analysis. Journal of Agronomic

Eduction, 8, 54-55



Tiago, S. W., G.M., Julio (1999). Cash cropping in Mozambique: Evaluation and prospect, Food secu-

rity unit Mozambique European Commission.

http://www.daviesand.com/Papers/Tree_Crops/Northern_Tree_Crops/ 10/07/2003

USGS. (2003). Sustainable Tree Crops: Africa and Latin America, USGS.

http://edcintl.cr.usgs.gov/index.html. 11/08/2003

Venema J. H., D. F. (1992). Land evaluation and land use planning for agricultural expansion and di-

versification.

Voortman, R. L. (1985). Guidelines on land evaluation for rainfed agriculture in Mozambique: base-

ment complex. Maputo, National Institute of Agronomic investigation.



Appendices

Appendix A-1: Processed climatic data

Xai-xai station climatic data Year

2000 2001 2002

Month Precipitation

(mm) Evaporation (mm /day)

ClassA Kpan

ETo (mm/d)

Precipitation (mm)

Evaporation (mm/day)

ClassA Kpan

ETo (mm/d)

Precipitation (mm)


ClassA Kpan

ETo (mm/d)

January 417.1 1.6 0.85 1.4 118.7 3.5 0.85 3.0 26.5 3.7 0.85 3.2 February 567.7 1.6 0.85 1.3 329.7 2.6 0.85 2.2 59 3.6 0.85 3.1 March 751.7 1.9 0.85 1.6 218.7 2.0 0.85 1.7 54.3 3.7 0.85 3.2 April 81.0 2.0 0.85 1.7 77.6 2.5 0.85 2.1 131.3 2.3 0.85 1.9 May 136.7 2.5 0.8 2.0 15.2 2.2 0.8 1.8 25.1 2.5 0.8 2.0 June 62.0 1.3 0.85 1.1 0 2.3 0.85 1.9 143.7 2.2 0.85 1.9 July 82.6 1.4 0.85 1.2 23 3.3 0.85 2.8 32 2.6 0.85 2.2 August 19.4 2.4 0.85 2.1 2.9 3.0 0.85 2.6 5.4 2.7 0.85 2.3 September 156.7 2.5 0.85 2.2 25 3.8 0.85 3.2 41.9 3.4 0.85 2.9 October 24.7 2.6 0.85 2.2 28.9 3.3 0.85 2.8 80 3.9 0.85 3.4 November 567.8 2.2 0.85 1.9 106.3 3.1 0.85 2.6 85.2 4.3 0.85 3.6 December 58.9 4.4 0.85 3.7 224 2.9 0.85 2.4 88.2 3.0 0.85 2.6



Chokwe station climatic data

Year

2000 2001 2002 Month

Precipitation (mm)


ClassA Kpan

ETo (mm/d)

Precipitation (mm)


ClassA Kpan

ETo (mm/d)

Precipitation (mm)


ClassA Kpan

ETo (mm/d)

January 202.3 2.1 0.8 1.7 71.4 3.9 0.8 3.1 60.3 5.5 0.8 4.4 February 286.7 2.0 0.8 1.6 179.1 2.9 0.8 2.3 22.2 3.9 0.8 3.1 March 382.2 2.3 0.8 1.9 74.3 2.3 0.8 1.8 53.4 3.6 0.8 2.9 April 34 2.5 0.8 2.0 46.5 2.5 0.8 2.0 2 3.2 0.8 2.6 May 62.9 3.0 0.8 2.4 18.6 2.9 0.8 2.3 8.6 2.6 0.8 2.1 June 24.1 1.8 0.8 1.4 1.6 3.0 0.8 2.4 27.2 2.3 0.8 1.8 July 34.8 1.9 0.8 1.5 8.4 3.6 0.8 2.8 1 2.9 0.8 2.3 August 2 2.5 0.8 2.0 0.2 4.0 0.8 3.2 2.6 3.0 0.8 2.4 September 52.4 3.3 0.8 2.6 11.8 5.2 0.8 4.2 58.2 4.1 0.8 3.3 October 9.1 3.8 0.8 3.0 41 4.1 0.8 3.3 70.4 4.0 0.8 3.2 November 288.5 2.5 0.8 2.0 128.3 2.9 0.8 2.3 89.5 4.0 0.8 3.2 December 37.3 3.9 0.8 3.1 254.8 2.7 0.8 2.2 19.3 4.0 0.8 3.2



Macia station climatic data Month Year

2000 2001 2002

Precipitation (mm)


ClassA Kpan

ETo (mm/d)

Precipitation (mm)


ClassA Kpan

ETo (mm/d)

Precipitation (mm)


ClassA Kpan

ETo (mm/d)

January 193.3 4.5 0.8 3.6 81.7 3.0 0.8 2.4 37.3 4.3 0.8 3.4 February 277.7 4.9 0.8 3.9 286.2 2.4 0.8 1.9 26.7 3.9 0.8 3.1 March 373.1 4.1 0.8 3.3 80.7 1.9 0.8 1.5 37.1 3.5 0.8 2.8 April 100.2 3.5 0.8 2.8 136.2 2.3 0.8 1.9 66.3 3.2 0.8 2.6 May 110.9 2.1 0.8 1.7 28.4 2.5 0.8 2.0 13.5 3.3 0.8 2.6 June 29.4 4.9 0.8 3.9 13 2.5 0.8 2.0 58.2 2.7 0.8 2.1 July 48.7 1.6 0.8 1.3 32.9 3.0 0.8 2.4 2.3 3.6 0.8 2.9 August 10.5 2.3 0.8 1.9 5.4 3.2 0.8 2.6 3.7 3.2 0.8 2.6 September 40 4.4 0.8 3.5 13.1 4.4 0.8 3.5 57 4.4 0.8 3.5 October 19.5 5.7 0.8 4.6 49.6 3.9 0.8 3.1 84.7 5.0 0.8 4.0 November 78.2 5.1 0.8 4.1 257.9 3.2 0.8 2.6 72.6 4.5 0.8 3.6 December 28.5 5.8 0.8 4.6 322.4 3.1 0.8 2.5 39.5 4.7 0.8 3.8

Average monthly precipitation and ETo for four meteorological stations (2000) Xai-xai Chokwe Macia Chibuto Month

P (mm) ETo (mm/d) P (mm) ETo (mm/d) P (mm) ETo (mm/d) P (mm) ETo (mm/d) Av.P (mm)

AV.ETo (mm/d)

January 417.1 1.4 202.3 1.7 193.3 3.6 196.8 1.3 252.4 2.0

February 567.7 1.3 286.7 1.6 277.7 3.9 283.0 1.2 353.8 2.0

March 751.7 1.6 382.2 1.9 373.1 3.3 381.0 1.6 472.0 2.1

April 81.0 1.7 34 2.0 100.2 2.8 70.1 1.8 71.3 2.1

May 136.7 2.0 62.9 2.4 110.9 1.7 88.5 2.2 99.7 2.1

June 62.0 1.1 24.1 1.4 29.4 3.9 22.1 1.0 34.4 1.9

July 82.6 1.2 34.8 1.5 48.7 1.3 38.5 1.3 51.1 1.3

August 19.4 2.1 2 2.0 10.5 1.9 1.4 2.1 8.3 2.0

September 156.7 2.2 52.4 2.6 40 3.5 41.9 2.3 72.7 2.6

October 24.7 2.2 9.1 3.0 19.5 4.6 9.6 2.4 15.7 3.1

November 567.8 1.9 288.5 2.0 78.2 4.1 159.9 1.8 273.6 2.4

December 58.9 3.7 37.3 3.1 28.5 4.6 25.8 3.6 37.6 3.8



Average monthly precipitation and ETo for four meteorological stations (2001) Xai-xai Chokwe Macia Chibuto Month

P (mm) ETo (mm/d) P (mm) ETo (mm/d) P (mm) ETo (mm/d) P (mm) ETo (mm/d) Av.P (mm) AV.ETo (mm/d) January 118.7 3.0 71.4 3.1 81.7 2.4 72.5 3.2 86.1 2.9 February 329.7 2.2 179.1 2.3 286.2 1.9 243.2 2.3 259.6 2.2 March 218.7 1.7 74.3 1.8 80.7 1.5 76.8 1.7 112.6 1.7 April 77.6 2.1 46.5 2.0 136.2 1.9 96.4 2.1 89.2 2.0 May 15.2 1.8 18.6 2.3 28.4 2.0 17.9 2.0 20.0 2.0 June 0.0 1.9 1.6 2.4 13.0 2.0 2.1 2.2 4.2 2.1 July 23.0 2.8 8.4 2.8 32.9 2.4 19.6 2.9 33.7 2.8 August 2.9 2.6 0.2 3.2 5.4 2.6 0.0 2.9 2.1 2.8 September 25 3.2 11.8 4.2 13.1 3.5 6.5 3.8 14.1 3.7 October 28.9 2.8 41 3.3 49.6 3.1 38.9 3.1 39.6 3.1 November 106.3 2.6 128.3 2.3 257.9 2.6 200.0 2.6 173.1 2.5 December 224 2.4 254.8 2.2 322.4 2.5 286.4 2.4 271.9 2.4

Average monthly precipitation and ETo for four meteorological stations (2002) Month Xai-xai Chokwe Macia Chibuto

P (mm) ETo (mm/d) P (mm) ETo (mm/d) P (mm) ETo (mm/d) P (mm) ETo (mm/d) Av.P (mm) AV.ETo (mm/d)

January 26.5 3.2 60.3 4.4 37.3 3.4 37.6 3.8 40.4 3.7 February 59.0 3.1 22.2 3.1 26.7 3.1 19.7 3.2 31.9 3.2 March 54.3 3.2 53.4 2.9 37.1 2.8 36.3 3.2 45.3 3.0 April 131.3 1.9 2 2.6 66.3 2.6 40.5 2.2 60.0 2.3 May 25.1 2.0 8.6 2.1 13.5 2.6 5.7 2.0 13.2 2.2 June 143.7 1.9 27.2 1.8 58.2 2.1 44.3 1.8 68.4 1.9 July 32.0 2.2 1 2.3 2.3 2.9 0.0 2.3 8.8 2.4 August 5.4 2.3 2.6 2.4 3.7 2.6 0.0 2.4 2.9 2.4 September 41.9 2.9 58.2 3.3 57 3.5 49.7 3.1 51.7 3.2 October 80 3.4 70.4 3.2 84.7 4.0 72.4 3.4 76.9 3.5 November 85.2 3.6 89.5 3.2 72.6 3.6 71.5 3.6 79.7 3.5 December 88.2 2.6 19.3 3.2 39.5 3.8 27.9 2.8 49.0 3.1



Appendix A-2: Tables of calculated water balance pre year for each station

Average monthly water balance of year 2000(Chibuto)

Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

P (mm) 197 283 381 70 88 22 38 1 42 10 160 26 ETo (mm) 55 50 61 63 70 45 44 70 81 92 68 127 P-ETo (mm) 141 233 321 7 18 -23 -5 -69 -39 -83 92 -101 Acc Pot WL (mm) -23 -28 -97 -136 -219 -127 -229 SM (mm) 150 150 150 150 150 132 128 80 70 40 72 38

�SM (mm) 112 0 0 0 0 -18 -4 -48 -10 -30 32 -34 AET (mm) 55 50 61 63 70 62 40 33 52 40 68 60 MD (mm) 0 0 0 0 0 -4 -37 -29 -52 0 0 S (mm) 29 233 321 7 18 0 0 0 0 0 60 0

Average monthly water balance of year 2001(Chibuto) Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

P (mm) 72 243 77 96 18 2 20 0 7 39 200 286 ETo (mm) 98 67 54 64 60 65 91 90 113 97 78 74 P-ETo (mm) -26 176 23 32 -42 -62 -72 -90 -106 -58 122 213 Acc Pot WL (mm) -26 -42 -104 -176 -266 -372 -430 SM (mm) 130 150 150 150 110 76 56 30 15 11 150 150

�SM (mm) -20 20 0 0 -40 -34 -20 -26 -15 -4 139 0 AET (mm) 92 67 54 64 58 36 40 26 22 43 78 74 MD (mm) -6 0 0 0 -2 -28 -52 -64 -91 -54 0 0 S (mm) 0 156 23 32 0 0 0 0 0 0 122 213

Average monthly water balance of year 2002(Chibuto)


P (mm) 38 20 36 41 6 44 0 0 50 72 71 28 ETo (mm) 118 94 99 65 60 40 70 74 93 105 108 88 P-ETo (mm) -80 -74 -63 -25 -54 4 -70 -74 -43 -32 -37 -60 Acc Pot WL (mm) -396 -470 -533 -558 -612 -70 -144 -187 -219 -256 -316 SM (mm) 12 8 6 4 2 150 100 65 54 45 35 20

�SM (mm) -58 -4 -2 -2 -2 148 -50 -35 -11 -9 -10 -15 AET (mm) 96 24 38 43 8 40 50 35 61 81 81 43 MD (mm) -22 -70 -61 -23 -52 0 -20 -39 -32 -23 -27 -45 S (mm) 0 0 0 0 0 4 0 0 0 0 0 0



Average monthly water balance of year 2000(Chokwe)


P (mm) 202 287 382 34 63 24 35 2 52 9 289 37 ETo (mm) 52 47 57 60 71 43 47 62 79 94 59 97 P-ETo (mm) 150 240 325 -26 -8 -19 -12 -60 -26 -85 229 -60 Acc Pot WL (mm) -26 -34 -53 -66 -126 -153 -237 -60 SM (mm) 150 150 150 127 125 108 105 73 60 35 150 106

�SM (mm) 44 0 0 -23 -2 -17 -3 -32 -13 -25 115 -44 AET (mm) 52 47 57 57 65 41 38 34 65 34 59 81 MD (mm) 0 0 0 -3 -6 -2 -9 -28 -13 -60 0 -16 S (mm) 106 240 325 0 0 0 0 0 0 0 114 0

Average monthly water balance of year 2001(Chokwe)


P (mm) 71 179 74 47 19 2 8 0 12 41 128 255 ETo (mm) 96 68 56 60 70 72 88 98 126 102 70 68 P-ETo (mm) -24 111 18 -13 -51 -71 -80 -98 -114 -61 58 187 Acc Pot WL (mm) -24 -13 -64 -135 -215 -313 -427 -488 SM (mm) 132 150 150 142 106 70 42 20 12 8 150 150

�SM (mm) -18.0 18 0 -8 -36 -36 -28 -22 -8 -4 142 0 AET (mm) 89 68 56 55 55 38 36 22 20 45 70 68 MD (mm) -6 0 0 -5 -15 -35 -52 -76 -106 -57 0 0 S (mm) 0 93 18 0 0 0 0 0 0 0 58 187

Average monthly water balance of year 2002 (Chokwe)






Average monthly water balance of year 2000 (Macia)


P (mm) 193 278 373 100 111 29 49 11 40 19 78 29 ETo (mm) 111 113 103 85 50 117 40 57 104 142 124 143 P-ETo (mm) 82 165 270 15 61 -87 9 -47 -64 -122 -45 -114 Acc Pot WL (mm) -87 -47 -111 -233 -279 -393 SM (mm) 150 150 150 150 150 95 150 109 75 36 26 15

�SM (mm) 145 0 0 0 0 -55 55 -41 -34 -39 -10 -11 AET (mm) 111 113 103 85 50 84 40 52 74 58 88 40 MD (mm) 0 0 0 0 0 -32 0 -6 -30 -83 -35 -103 S (mm) 82 113 103 85 50 0 0 0 0 0 0 0



P (mm) 82 286 81 136 28 13 33 5 13 50 258 322 ETo (mm) 73 55 47 56 59 59 75 79 105 96 77 78 P-ETo (mm) 8 231 34 80 -31 -46 -42 -74 -92 -46 181 245 Acc Pot WL (mm) -31 -77 -119 -193 -286 -332 SM (mm) 150 150 150 150 128 98 74 54 25 17 150 150

�SM (mm) 0 0 0 0 -22 -30 -24 -20 -29 -8 133 0 AET (mm) 73 55 47 56 50 43 57 25 42 58 77 78 MD (mm) 0 0 0 0 -9 -16 -18 -54 -63 -38 0 0 S (mm) 8 231 34 80 0 0 0 0 0 0 181 245







Average monthly water balance of year 2000 (xai-xai)


P (mm) 417 568 752 81 137 62 83 19 157 25 568 59 ETo (mm) 43 38 49 52 60 34 37 64 65 68 57 116 P-ETo (mm) 374 529 703 29 77 28 45 -45 92 -44 511 -57 Acc Pot WL (mm) -45 -44 -57 SM (mm) 150 150 150 150 150 150 150 110 150 110 150 106

�SM (mm) -44 0 0 0 0 0 0 -40 40 -40 40 -44 AET (mm) 43.0 38 49 52 60 34 37 59.0 65.0 65.0 57.0 103 MD (mm) 0 0 0 0 0 0 0 -5 0 -3 0 -13 S (mm) 43 38 49 52 60 34 37 0 92 0.0 511 0.0



P (mm) 119 330 219 78 15 0 74 3 25 29 106 224 ETo (mm) 92 64 52 64 54 58 87 80 97 88 79 75 P-ETo (mm) 27 266 167 13 -38 -58 -13 -77 -72 -59 27 149 Acc Pot WL (mm) -38 -96 -109 -186 -259 -318 SM (mm) 150 150 150 150 124 71 78 55 30 20 150 150

�SM (mm) 0 0 0 0 -26 -53 7 -23 -25 -10 130 0 AET (mm) 92 64 52 64 41 53 81 26 50 39 79 75 MD (mm) 0 0 0 0 -12 -5 -6 -54 -47 -49 0 0 S (mm) 27 266 167 13 0.0 0.0 0.0 0 0 0.0 27 149



P (mm) 27 59 54 131 25 144 32 5 42 80 85 88 ETo (mm) 98 90 98 58 59 56 69 71 86 104 109 80 P-ETo (mm) -71 -31 -44 73 -34 88 -37 -66 -44 -24 -23 8 Acc Pot WL (mm) -71 -102 -145 -34 -37 -103 -147 -171 -194 SM (mm) 102 80 65 150 126 150 124 80 64 58 53 150

�SM (mm) 48 -22 -15 85 -24 24 -26 -44 -16 -6 -5 97 AET (mm) 75 81 69 58 32 56 58 49 58 86 90 80 MD (mm) -23 -9 -29 0 -27 0 -11 -22 -28 -18 -18 0 S (mm) 0 0 0 73 0 64 0 0 0 0 0 80



Appendix B-1: Code book

General D: Date of the data collected SNN: Strata number, which is based on NDVI classes NR: Name of respondent i.e. the farmer who is the owner of the plot gave the information by inter-

viewing NP: Local name of the area sample taken SN: It indicate the code of the sample plot and the interviewed number X: The longitude locations of the point within sample plot. The value is recorded using ipaq and

GPS together on the spot Y: The latitude location of the point within sample plots. The value is recorded using ipaq and GPS

together on the spot *Alt: Elevation above sea level in (m) of the point within sample plot. Read on the spot from ipaq

connected to GPS AP: Area of the plot in (m2, ha) sampled digitised using ipaq and GPS together by walking around the

plot NTH: Number of trees per hectare. Obtained from the digitised sample plot and number of tree on it NT: Number of cashew trees counted in the digitised plot *AST: Average space between cashew trees in (m) within the digitised plot. Because of the space between trees is not regular it was done by measuring the shortest and longest distance and take the average distance Age of cashew *AT: Age of trees within the sample plot starting from planting to the time of interviewed carried out

(year) Application of fertilizer and pesticides UF: Use of fertilizer i.e. whether the farmer applied fertilizer to cashew or not Use =1 and Not use=0 *UP: Use of pesticide i.e. whether the farmer applied chemicals for pests and diseases control to

cashew or not Use= 1 and Not use = 0 Weeding

*W: Weeding or cleaning under the trees. Time and number of weeding is taken into consideration during interviewing

Weeding = 1 and Not weeding =0



NWY: Number of weeding per year tc= timely cleaning (not quantified) TW: Months of weeding 1. 1st - Apr, 2nd -Oct 2. 1st -Apr, 2nd -Jun, 3rd -Nov 3. 1st -Apr, 2nd -Jun 3rd -Oct

4. 1st -Apr, 2nd -Oct 5. 1st -Apr, 2nd -Sep 6. 1st -Apr, 2nd- sep, 3rd -Dec 7. 1st -April, 2nd -Aug 8. 1st -April, 2nd -Nov 9. 1st -Aug, 2nd -Oct 10.1st -Feb, 2nd -Jun, 3rd -Aug, 4th -Nov 11.1st -Feb, 2nd -Jun, 3rd -Oct 12.1st -Jan, 2nd -Jul 13.1st -Jan, 2nd -Mar, 3rd -Oct 14.1st -Jun, 2nd -Aug 15.1st -Jun, 2nd -Aug-sep 16.1st -Mar, 2nd -Aug Pruning *P: Pruning of the old branches of the trees. Time and number of Pruning is taken into consideration

during interviewing *Pruning = 1 and Not pruning = 0 NPY: Number of pruning per year TP: Months of pruning carried out 1. 1st -Apr, 2nd -Aug 2. 1st -Apr, 2nd -Sep 3. 1st -Feb, 2nd -May 4. 1st -mar, 2nd -Aug 5. 1st -Mar, 2nd -Oct 6. After harvest 7. Apr 8. Apr-May 9. Aug 10.Aug-sep 11. Before flowering 12. Feb 13. Feb-Mar



Planting pattern PP: Planting pattern of cashew tree, whether mixed with other tree or annual crops or mono cropping * Mixed cropping= 1 and Monocropping = 0 0. Monocropping 1.Mixed with, mango, banana 2. Mixed with cassava 3. Mixed with cassava, banana 4. Mixed with lemon, mango 5. Mixed with lemon, papaya 6. Mixed with maize 7. Mixed with maize mesala 8. Mixed with maize, cassava 9. Mixed with maize, cassava, papaya 10. Mixed with maize, cassava, s. potato, mango 11. Mixed with maize, banana 12. Mixed with maize, mango 13. Mixed with maize, mango, papaya, lemon 14. Mixed with maize, mango, papaya, lemon, mesala 15. Mixed with maize, mango, banana 16. Mixed with mango 17. Mixed with mango, lemon, maize 18. Mixed with mango mesala 19. Mixed with mango, banana 20. Mixed with mango, banana, lemon, papaya 21. Mixed with mango, banana, lemon, papaya, mesala 22. Mixed with mango, banana, papaya 23. Mixed with mango, lemon 24. Mixed with mango, lemon, mesala 25. Mixed with maize, mango, papaya, lemon, banana 26. Mixed with mesala 27. Mixed with papaya Harvesting time TH: is the harvesting time in which the farmers collecting the nut from cashew tree. 1. Dec 2. Dec to Apr 3. Dec to Feb 4. Dec to Jan 5. Dec to Mar 6. Jan to Feb 7. Nov to Dec 8. Nov to Jan



9. Nov to Mar Yields of cashew tree YTT: Yield collected from the trees per the digitised sample plots in (kg) Y0: Yield of cashew nut obtained in year 2000 in (kg) Y1: Yield of cashew nut obtained in year 2001 in (kg) Y2: Yield of cashew nut obtained in year 2002 in (kg) YPT: Yield per tree YT0: Yield of cashew nut obtained in year 2000 in (kg/tree) YT1: Yield of cashew nut obtained in year 2001 in (kg/tree) YT2: Yield of cashew nut obtained in year 2002 in (kg/tree) YPH: Yield per hectare YH0: Yield of cashew nut obtained in year 2000 in (kg/ha) YH1: Yield of cashew nut obtained in year 2001 in (kg/ha) YH2: Yield of cashew nut obtained in year 2002 in (kg/ha) *AHY: Average yield of cashew nut (kg/ha) N = young trees not start to give yield (new planted) Soil SD: The effective soil depth in cm. measured using soil auger and tape ST: Soil Texture: Determined using feeling method (Thien, 1979) SS= sand soil LS= loamy sand soil *0=Sand soil *1=Loamy sand PH: Soil pH measured at field level by chemical solution indicator and paper *CT: Chemical solution for testing of soil pH PT: Paper for soil pH test Soil drainage condition SDC: The drainage condition of the soil in the area, based of site observation and secondary data in-formation GD = Good ED = Excessive *0=Good *1=Excessive Farmer’s observation on suitability (fertility level) of sand and loamy sand soils for cashew SSA (SF): Suitability (fertility) of soil for cashew tree growing according to farmer’s experience



1= Poor 2 =Better 3 =Best *1= Poor + better (after testing significant difference between the three classes) *0 = Best Farmer’s observation on water requirement for cashew IRF: Importance of rainfall for cashew according to farmer’s experience 1 =Need much water during vegetative and flowering 2 =Need normal rainfall all over the year 3 =Need low rainfall all over the year 4 =Need water the whole season before fruiting Impact on yield YI: Yield impacts i.e. environmental factors and management practices affect the yield of cashew

nut in the area 0=None 1=Pest and disease problem 2= Rainfall shortage problem 3 =Wind problem Impact priority according to farmer’s observation 0= No problem 1 = First problem 2 = Second problem 3 = Third problem *PDP: Problem of pest and disease in affecting cashew production Present=1 and absent=0 *WP: Problem of wind in affecting cashew production Present (yes)=1 and absent (no)=0 *MS: Moisture (rainfall) shortage during Production period Yes=1 and no=0 N.B * indicates the variables taken into account for multiple regression analysis



Appendix B-2: Environmental requirement of cashew

Crops Descriptions and re-quirements Cashew (Anacardium occidentale L.)

Family name Anacardiceae Life form Tree Habitat Erect Life span Perennial Physiology Evergreen, single stem, multi stem, photosynthesis pathway

C3 Category Fruits and nuts, medicinal and aromatic

Plant attributes Grown on large scale Min. 15 Temperature

(0c) Max. 35 Min. 750 Annual rainfall

(mm) Max. 1600 Min. 0 Latitude

(Degree) Max. 25 Min. 0 Altitude (m)

(Absolute) Max. 1200 Min 4.5 Soil pH Max. 6.5 Min. Very bright Light intensity Max. Very bright

Soil depth (cm) >150 Soil texture Medium, light Soil fertility Moderate, low Soil salinity Low (<4 ds/m) Soil drainage Well (dry spells) Climatic zone Tropical wet and dry (Aw), steppe or semiarid (Bs), sub-

tropical dry summer (Cs), subtropical dry winter (Cw) Photoperiod Short day (<12 hours), neutral day (12-14 hours), long day

(>14 hours) Killing temperature 0 Crop cycle Min 190 Max. 260 Notes

Well developed and deep root system, can tolerate drought conditions, spread root system with the spread of canopy, require little water for survival during non-flowering

Source: Ecocrop.FAO.org



Appendix B-3: Table of Coded data sheet

D SN SNN NR NP X Y Alt AP NT AST AT UF UP TW NWY TP NPY PP TH Y0 Y1 Y2 CT PT SD ST DC YI SSA IRF PDP RFS WP

23-9-2003 P1 44 Anna Chimonzo 528510 7242574 61 5130 22 6 15 0 0 21 2 6 1 24 1 0 200 240 5.5 7 >120 LS G 1,2 2 4 1 2 0

23-9-2003 P2 44 Domingush Mutomba Chimonzo 529981 7247921 58 28920 53 15 32 0 0 28 0 25 1 24 7 0 0 100 6.5 7.5 LS G 1,2 2 2 1 2 0

24-9-2003 P3 35 Armindo Timbe Sinshbane 520154 7251583 56 25330 87 17 14 0 1 18 2 19 1 26 7 150 900 600 4.5 6.5 LS G 1,2 2 1 1 0 0

24-9-2003 P4 35 Alfredo

Massango Tlacula 521742 7253905 57 13470 49 18 36 0 1 8 2 9 1 0 5 1600 1300 1200 5 6.5 S E 0 2 1 0 0 0

25-9-2003 P5 14 Celestina Hokwe 518255 7266834 42 1452 4 10 27 0 0 21 tc 11 1 6 6 50 15 30 4.5 6.5 S E 1,2 1 1 1 2 0

25-9-2003 P6 13 Davide Hokwe 517675 7268130 42 2072 17 17 10 0 0 21 tc 6 1 6 3 0 0 7.5 7 7 S E 1,2,3 2 4 3 2 1

26-9-2003 P7 17 Marta Hokwe 519471 7265524 43 2078 6 13 29 0 0 22 5 7 1 0 3 15 30 100 6.5 7 S E 1,2 2 1 1 2 0

26-9-2003 P8 17 Marta Makhu Nene 525527 7250620 26 424 4 8 24 0 0 21 1 21 1 0 9 0 0 20 5.5 6.5 LS G 1,2 2 3 1 2 0

30-9-2003 P9 42 Marta Zitha Mazivila 512998 7250620 65 882 26 15 21 0 1 21 tc 17 1 0 4 300 300 100 4.5 6.5 S E 0 2 3 0 0 0

30-9-2003 P10 35

Jose Cham-bal Mazivila 512292 7249280 65 4257 6 10 21 0 1 28 2 9 1 16 3 15 50 50 5.5 6.5 S E 1,2 2 1 1 2 0

30-9-2003 P11 35 Alis Mazivila 511710 7249117 57 25620 75 16 40 0 1 18 0 19 1 18 3 0 0 500 6 6.5 S E 1,2 3 2 1 2 0

30-9-2003 P12 44 Jost Zimba Zimrene 512683 7241875 99 7706 14 21 51 0 0 18 0 6 1 18 4 0 0 15 4.5 5.5 LS E 1,2 2 1 1 3 0

30-9-2003 P13 44

Antonio Chichava Nwapaco 515727 7234334 84 951 7 35 34 0 1 20 2 9 1 26 5 0 0 15 4.5 6.5 LS E 1,2 2 2 1 2 0

30-9-2003 P14 44 Litete Chimonzo 529735 7240457 60 4060 9 12 35 0 0 7 2 23 0 0 2 0 0 7 4.5 6.5 LS G 1,2 2 1 1 2 0

30-9-2003 P15 42

Amelio Khumaio Lisilo 539894 7247403 70 1524 21 12 41 0 1 27 1 2 2 20 4 0 0 100 4.5 6.5 S E 0 2 1 0 0 0

1-10-2003 P16 44

Orlando Shavan Incaia 518368 7235932 76 4213 23 22 34 0 0 23 1 16 1 22 4 50 50 15 5 6 LS G 1,2 3 1 1 3 0

1-10-2003 P17 44

Ratah Macamo Mangalatini 523506 7240626 70 14810 27 25 29 0 0 26 tc 9 1 4 9 0 0 100 5.5 6.5 S E 1,2 1 1 1 3 0

1-10-2003 P18 44 Maria Chimonzo 531515 7240525 92 4643 15 13 20 0 0 21 1 6 1 21 4 0 0 9 4.5 6.5 S E 1,2 2 1 1 3 0

1-10-2003 P19 44

Margarida Mathavele Mathanjini 537665 7244423 72 4987 23 12 18 0 0 21 2 6 1 22 3 0 0 30 4.5 6.5 LS G 1,2 1 1 1 3 0

1-10-2003 P20 37

Alda Mata-vele Lisilo 541510 7250610 41 3042 11 20 20 0 0 24 1 7 1 5 5 150 100 100 8 7.5 LS G 1,2 2 1 2 1 0



Contd… D SN SNN NR NP X Y Alt AP NT AST AT UF UP TW NWY TP NPY PP TH Y0 Y1 Y2 CT PT SD ST DC YI SSA IRF PDP RFS WP

1-10-2003 P21 37 Isaura Ubisse Canlo Pneste 541195 7251060 42 468 4 25 15 0 1 21 tc 13 1 27 9 0 0 100 4.5 6 LS G 0 2 1 0 0 0

1-10-2003 P22 37 Armindo

Cossa Bairr03 540497 7252711 43 3087 11 13 27 0 1 21 tc 19 1 16 3 250 100 200 4.5 6.5 LS G 0 2 1 0 0 0

1-10-2003 P23 13 Angelina Chiulele Zuza 539412 7255504 32 1421 11 20 34 0 1 21 tc 6 1 0 3 300 100 75 4.5 6 S E 0 2 2 0 0 0

1-10-2003 P24 13 Samuel Sim-

bin Zuza 537594 7255780 42 2682 10 20 26 0 0 1 tc 8 1 16 5 0 0 60 4.5 6.5 S E 1,2 2 1 2 1 0

1-10-2003 P25 17 Arman Mas-

vial Zuza 537954 7256158 46 2974 23 11 25 0 0 21 tc 6 1 13 8 0 0 5 6 7.5 LS G 1 1 2 2 0 0

2-10-2003 P26 37 Amelia

Nacuacua Chicombane 553625 7236711 55 6646 55 18 55 0 0 29 1 23 0 0 1 300 500 10 4.5 6.5 S E 1,2 1 2 2 2 0

2-10-2003 P27 37 Marta Wa-

musee Chicombane 552916 7237666 54 3231 12 15 44 0 0 28 0 19 1 7 1 0 0 10 5 6 LS G 1,2 2 4 1 3 0

2-10-2003 P28 37 Delfinsh Lawule Chicombane 553477 7237766 56 544 3 35 40 0 0 21 tc 4 2 2 3 0 0 5 4 6.5 LS G 1,2 2 1 3 1 0

2-10-2003 P29 37 January Mekamu Chicombane 554616 7237127 55 123 3 15 24 0 1 21 tc 12 1 4 1 19 25 15 4.5 6.5 LS G 0 1 4 0 0 0

3-10-2003 P30 44 Gloria Fra-

nusco Incaia 520042 7237400 84 9575 17 20 20 0 0 31 1 22 1 14 3 0 0 20 4 6 LS G 1,2 2 1 1 2 0

3-10-2003 P31 44 Slmerinda Muchanga Incaia 520044 7237391 86 9693 23 17 20 0 0 15 2 18 1 24 4 0 0 15 4 6 LS G 1,2 2 1 1 2 0

3-10-2003 P32 44 Uti Metabel Chissano 535181 7241965 63 12780 40 25 80 0 0 28 0 23 0 18 1 0 0 20 4.5 6.5 S G 1,2 3 4 2 1 0

3-10-2003 P33 42 Herinke Bela Gulelen 548837 7240073 63 50120 104 35 44 0 0 28 0 23 0 17 3 5 10 20 5.5 7.5 LS G 1,2 2 4 1 0 3

6-10-2003 P34 35 Fernando Nhangale Olombe 528319 7252736 78 3268 11 11 21 0 1 10 4 17 1 0 4 0 0 300 4.5 6.5 S E 0 2 4 0 0 0

6-10-2003 P35 35 Terezinha

Pavava Olombe 527605 7252728 78 2670 19 12 30 0 1 21 0 9 1 0 3 0 0 150 4.5 6 LS G 0 2 4 0 0 0

6-10-2003 P36 35 Tomash Olombe 528201 7252749 76 12150 47 11 30 0 1 21 tc 3 2 6 1 0 0 750 4.5 6 LS G 0 2 4 0 0 0

6-10-2003 P37 35 Celeste

Macamo Masavan 5261612 7257514 78 1259 6 11 32 0 1 18 2 10 1 6 1 0 0 160 5.5 6 LS G 0 2 4 0 0 0

6-10-2003 P38 14 Yosafe Azan-

dosto Macunane 523117 7261979 47 21920 109 23 28 0 0 5 2 18 1 8 4 50 50 800 5 5.5 LS G 1,2 2 4 1 3 0

6-10-2003 P39 17 Helena Asa Chambal Macunane 523399 7263056 26 3104 4 25 10 0 0 9 2 16 1 10 4 0 25 80 5 6 LS G 1,2 3 1 1 3 0

8-10-2003 P40 28 Salmina

Chambele Mapapa 513682 7260582 48 11460 23 13 20 0 1 4 2 24 1 6 1 0 0 160 5.5 6.5 LS G 1,2 3 1 1 3 0



Contd…

D SN SNN NR NP X Y Alt AP NT AST AT UF UP TW NWY TP NPY PP TH Y0 Y1 Y2 CT PT SD ST DC YI SSA IRF PDP RFS WP

8-10-2003 P41 28 Rafael Sitoe Mapapa 513696 7260712 44 3402 10 25 19 0 0 25 1 11 1 6 1 0 0 40 5 6.5 LS G 1,2 2 4 1 3 0

8-10-2003 P42 28 Antonio Chemula Mapapa 513766 7260803 44 15290 75 12 10 0 0 4 2 11 1 6 1 50 0 100 5 6.5 S E 1,2 2 4 1 2 0

8-10-2003 P43 35 Laura Rafael Mazivila 512404 7247387 68 2135 4 30 3 0 0 14 2 24 1 9 1 N N 5 5 6.5 LS G 1,2 2 4 1 3 0

8-10-2003 P44 17 Zefanias

Massingue Chiaquecane

Bairr04 513224 7257190 40 8051 21 12 5 0 0 13 3 6 1 0 1 160 0 20 8 7 S E 1,2 3 4 1 3 0

8-10-2003 P45 17 Paulo Benza-

ne Chiaquecane 513244 7257219 61 3264 20 17 5 0 0 12 2 14 1 0 1 20 5 40 8 7 S E 1,2 3 4 1 3 0

9-10-2003 P46 14 Mara Manuel Canhavane

Payaya 557017 7276947 92 78790 180 21 34 0 0 2 3 14 1 8 1 100 50 150 4.5 6 LS G 1,2,3 2 1 1 2 3

9-10-2003 P47 14 Fenias Gu-

muzane Canyavane 557093 7276551 92 17630 60 30 34 0 1 23 1 9 1 6 1 20 50 100 4.5 6 LS G 1,2 1 1 1 3 0

9-10-2003 P48 37 Alexandre

Manuel Canyavane Nupayaya 557223 7275748 93 37170 55 22 42 0 1 23 1 9 1 6 1 200 50 150 5 6 LS G 1,2 2 1 1 3 0

9-10-2003 P49 14 Albertina Maswan

Canyavane Payaya 557460 7276059 93 20310 72 23 34 0 0 32 1 23 0 6 1 30 50 30 5 6.5 LS G 1,2 2 1 1 3 0

9-10-2003 P50 14 Salome Lucas

Nyoni Canyavane

Payaya 557571 7276234 101 56300 95 25 34 0 0 11 3 12 1 6 1 15 10 30 4.5 6 LS G 1,2,3 3 4 1 2 3

10-10-2003 P51 37 Zacarias Vasco

Mudada Chi-buto 556074 7270956 149 14240 66 13 29 0 0 17 3 7 1 25 3 5 20 240 4.5 6 LS G 1,2 2 4 2 1 0

10-10-2003 P52 37 Teresina

Cuna Mudada Chi-

buto 556550 7270981 152 27580 80 28 44 0 0 3 3 7 1 6 1 5 20 50 5 6.5 LS G 1,2 2 1 1 2 0

10-10-2003 P53 37 Teresa

Ernesto Sive Mudada Chi-

buto 556122 7270878 149 1878 16 8 20 0 0 3 3 7 1 1 1 3 5 25 5 6.5 LS G 1,2 2 1 1 2 0

10-10-2003 P54 37 Lucia Manuel Mudada 555850 7270926 140 1346 10 11 10 0 0 6 3 7 1 3 1 N N 30 4.5 6 LS G 1,2 2 1 1 2 0

13-10-2003 P55 14 Erenesto Meatin Mwakulale 559812 7276939 67 39910 34 33 54 0 0 25 1 16 1 7 7 100 100 100 4 6 LS G 1,2 2 1 1 2 0

13-10-2003 P56 14 Carlos Mwakulale 559866 7276855 71 35100 74 24 44 0 1 21 tc 22 1 7 8 30 200 100 4 6 S E 1,2 2 4 1 2 0

13-10-2003 P57 14 Salamon Bala Mwakulale 559775 7276652 71 22000 45 30 30 0 0 21 tc 25 1 7 3 300 400 250 4 6 S E 1,2 2 4 1 2 0

14-10-2003 P58 14 Julieta Godido 563458 72778678 96 19940 57 32 33 0 1 30 1 19 1 0 1 300 15 400 4.5 5.5 S E 1,2,3 2 1 1 3 2

14-10-2003 P59 14 Lina Mutie-

mukulu Majawlan 564092 7278805 92 7647 43 28 40 0 1 18 2 5 2 0 4 100 15 200 6.5 7.5 LS G 1,2 2 1 1 2 0

14-10-2003 P60 14 Amelia

Macuacua Madfaulane 564554 7278104 88 5960 24 25 30 0 0 26 1 9 1 0 4 0 0 50 5.5 6.5 LS G 1,2 2 2 1 2 0



Contd… D SN SNN NR NP X Y Alt AP NT AST AT UF UP TW NWY TP NPY PP TH Y0 Y1 Y2 CT PT SD ST DC YI SSA IRF PDP RFS WP

14-10-2003 P61 14 Marta Nyate Maguiguane 561211 7278127 92 17590 41 31 24 0 0 21 tc 7 1 6 8 25 50 150 4.5 5.5 LS G 1,2 2 1 1 2 0

15-10-2003 P62 17 Carmilia Chibabel Bairr04 525662 7279849 38 3701 8 25 20 0 0 21 tc 1 2 12 7 15 15 100 5 6.5 LS G 1,2 2 1 1 3 0

15-10-2003 P63 17 Americo Chibabel Bairr04 525608 7279849 36 8763 4 15 34 0 0 28 0 23 0 0 7 0 0 30 5 6.5 LS G 1,2 2 1 1 2 0

15-10-2003 P64 17 Enelina Chibabel Bairr04 525565 7280022 33 53 5 15 10 0 1 21 tc 1 2 0 7 8 15 30 5 6.5 LS G 0 2 1 0 0 0

15-10-2003 P65 17 Jermias Chibabel Bairr04 525680 7280050 34 279 3 25 7 0 0 21 tc 19 1 6 3 250 250 250 4 5.5 LS G 0 2 1 0 0 0

16-10-2003 P66 13 Addass

Mbendane chaimite 534292 7272674 40 1279 13 20 12 0 0 19 2 19 1 6 4 0 0 3 5 5.5 LS G 1,2 2 1 1 3 0

16-10-2003 P67 13 Rosauna

cossa chaimite 533914 7272674 37 322 14 15 24 0 0 21 tc 6 1 11 4 0 0 8 5 5.5 LS G 1,2 2 1 1 2 0

16-10-2003 P68 13 Regina Er-

nesto chaimite 533789 7273107 43 812 6 12 30 0 0 28 0 4 2 0 4 15 23 19 4 5.5 S E 1,2 2 1 2 1 0

16-10-2003 P69 13 Sergio Ma-

bunda chaimite 533123 7273236 31 856 8 23 30 0 1 21 tc 4 2 12 4 15 22 45 5 6.5 S E 2 2 4 0 2 0

16-10-2003 P70 13 Alfredo Sitoe chaimite 532182 7273384 30 3013 17 25 12 0 0 16 2 9 1 15 4 5 0 15 8 7.5 S E 1,2 2 4 1 2 0

17-10-2003 P71 37 LauraBila Chibuto 556345 7268849 141 3268 6 25 25 0 0 21 tc 9 1 11 3 0 0 10 5 6.5 S E 1,2 1 1 1 3 0

17-10-2003 P72 37 Alxandre

Bila Chibuto 556214 7270163 139 1421 14 15 27 0 0 21 tc 9 1 12 1 0 0 6 5 6 LS G 1,2 2 1 1 2 0

17-10-2003 P73 37 Zita Mazivi Chibuto 556272 7270328 144 766 5 20 34 0 0 28 0 19 1 0 4 0 0 23 5 6 LS G 1,2 2 1 1 2 0

17-10-2003 P74 37 Temias

Chilengue Chibuto 556182 7270589 144 2478 12 11 15 0 0 26 1 19 1 6 4 50 125 75 5 6 LS G 1,2 2 1 1 2 0

17-10-2003 P75 37 Beni Mataret Chibuto 556439 7266851 110 1300 7 25 35 0 0 21 tc 23 0 0 3 0 0 7 5.5 6.5 S E 1,2 2 1 1 2 0



Appendix B-4: Table of yield data sheet

YTT (kg) YPT (kg/tree) YPH (kg/ha)

SN AP (m2) AP (ha) NTP NTH Y0 Y1 Y2 YT0 YT1 YT2 YH0 YH1 YH2

P1 5130 0.5 22 43 0 200 240 0 9.1 11 0 390 468

P2 28920 2.9 53 18 0 0 100 0 0 2 0 0 35

P3 25330 2.5 87 34 150 900 600 1.7 10.3 7 59 355 237

P4 13470 1.3 49 36 1600 1300 1200 32.7 26.5 24 1188 965 891

P5 1452 0.1 4 28 50 15 30 12.5 3.8 8 344 103 207

P6 2072 0.2 17 82 0 0 7.5 0 0 0 0 0 36

P7 2078 0.2 6 29 15 30 100 2.5 5 17 72 144 481

P8 424 0 4 94 0 0 20 0 0 5 0 0 472

P9 882 0.1 26 382 300 300 100 11.5 8.9 3 4403 3403 1134

P10 4257 0.4 6 14 15 50 50 2.5 8.3 8 35 117 117

P11 25620 2.6 75 29 0 0 500 0 0 7 0 0 195

P12 7706 0.8 14 18 0 0 15 0 0 1 0 0 19

P13 951 0.1 7 74 0 0 15 0 0 2 0 0 158

P14 4060 0.4 9 22 0 0 7 0 0 1 0 0 17

P15 1524 0.2 21 138 0 0 100 0 0 5 0 0 656

P16 4213 0.4 23 55 50 50 15 2.2 2.2 1 119 119 36

P17 14810 1.5 27 18 0 0 100 0 0 4 0 0 68

P18 4643 0.5 15 32 0 0 9 0 0 1 0 0 19

P19 4987 0.5 23 46 0 0 30 0 0 1 0 0 60

P20 3042 0.3 11 36 150 100 100 13.6 9.1 9 493 329 329

P21 468 0 4 22 0 0 100 0 0 25 0 0 539

P22 3087 0.3 11 36 250 100 200 22.7 9.1 18 810 324 648

P23 1421 0.1 11 77 300 100 75 27.3 9.1 7 2111 704 528

P24 2682 0.3 10 37 0 0 60 0 0 6 0 0 224

P25 2974 0.3 23 77 0 0 5 0 0 0 0 0 17



Contd…



P26 6646 0.7 55 83 300 500 10 5.5 9.1 0 451 752 15

P27 3231 0.3 12 37 0 0 10 0 0 1 0 0 31

P28 544 0.1 3 55 0 0 5 0 0 2 0 0 92

P29 123 0 3 76 19 25 15 20.4 8.3 16 1548 636 1222

P30 9575 1 17 18 0 0 20 0 0 1 0 0 21

P31 9693 1 23 24 0 0 15 0 0 1 0 0 15

P32 12780 1.3 40 31 0 0 20 0 0 1 0 0 16

P33 50120 5 104 21 5 10 20 0 0.1 0 1 2 4

P34 3268 0.3 11 34 0 0 300 0 0 27 0 0 918

P35 2670 0.3 19 71 0 0 150 0 0 8 0 0 562

P36 12150 1.2 47 39 0 0 750 0 0 16 0 0 617

P37 1259 0.1 6 48 0 0 160 0 0 27 0 0 1271

P38 21920 2.2 109 50 50 50 800 0.5 0.5 7 23 23 365

P39 3104 0.3 4 13 0 25 80 0 6.3 20 0 81 258

P40 11460 1.1 23 20 0 0 160 0 0 7 0 0 140

P41 3402 0.3 10 29 0 0 40 0 0 4 0 0 118

P42 15290 1.5 75 49 50 0 100 0.7 0 1 33 0 65

P43 2135 0.2 4 19 N N 5 N N 1 N N 23

P44 8051 0.8 21 66 160 0 20 7.6 0 0 499 0 25

P45 3264 0.3 20 61 20 5 40 1 0.3 2 61 15 123

P46 78790 7.9 180 23 100 50 150 0.6 0.3 1 13 6 19

P47 17630 1.8 60 34 20 50 100 0.3 0.8 2 11 28 57

P48 37170 3.7 55 15 200 50 150 3.6 0.9 3 54 13 40

P49 20310 2 72 35 30 50 30 0.4 0.7 0 15 25 15

P50 56300 5.6 95 17 15 10 30 0.2 0.1 0 3 2 5



Contd…



P51 14240 1.4 66 46 5 20 240 0.1 0.3 4 4 14 169

P52 27580 2.8 80 29 5 20 50 0.1 0.3 1 2 7 18

P53 1878 0.2 16 85 3 5 25 0.2 0.3 2 16 27 133

P54 1346 0.1 10 74 N N 30 N N 3 N N 223

P55 39910 4 34 9 100 100 100 2.9 2.9 3 25 25 25

P56 35100 3.5 74 21 30 200 100 0.4 2.7 1 9 57 28

P57 22000 2.2 45 20 300 400 250 6.7 8.9 6 136 182 114

P58 19940 2 57 29 300 15 400 5.3 0.3 7 150 8 201

P59 7647 0.8 43 56 100 15 200 2.3 0.3 5 131 20 262

P60 5960 0.6 24 40 0 0 50 0 0 2 0 0 84

P61 17590 1.8 41 23 25 50 150 0.6 1.2 4 14 28 85

P62 3701 0.4 8 22 15 15 100 1.9 1.9 13 41 41 270

P63 8763 0.9 4 5 0 0 30 0 0 8 0 0 34

P64 53 0 5 274 8 15 30 10.2 5.6 6 2805 1522 1643

P65 279 0 3 108 250 250 250 83.3 83.3 83 8971 8971 8971

P66 1279 0.1 13 102 0 0 3 0 0 0 0 0 23

P67 322 0 14 435 0 0 8 0 0 1 0 0 248

P68 812 0.1 6 74 15 23 19 2.5 3.8 3 185 283 234

P69 856 0.1 8 93 15 22 45 1.9 2.8 6 175 257 526

P70 3013 0.3 17 56 5 0 15 0.3 0 1 17 0 50

P71 3268 0.3 6 18 0 0 10 0 0 2 0 0 31

P72 1421 0.1 14 99 0 0 6 0 0 0 0 0 42

P73 766 0.1 5 65 0 0 23 0 0 5 0 0 300

P74 2478 0.2 12 48 50 125 75 4.2 10.4 6 202 504 303

P75 1300 0.1 7 54 0 0 7 0 0 1 0 0 54



Appendix B-5: Table of Normalized data

SN YH0 YH1 YH2 AHY Alt AST AT NTH UF UP W P PDP MS WP PH PP ST DC

P1 0 390 468 286 61 6 15 43 0 0 1 1 1 0 0 5.5 1 1 0

P2 0 0 35 12 58 15 32 18 0 0 0 1 1 1 0 6.5 1 1 0

P3 59 355 237 217 56 17 14 34 0 1 1 1 1 0 0 4.5 1 1 0

P4 1188 965 891 1015 57 18 36 36 0 1 1 1 0 0 0 5 0 0 1

P5 344 103 207 218 42 10 27 28 0 0 1 1 1 1 0 4.5 1 0 1

P6 0 0 36 12 42 17 10 82 0 0 1 1 1 1 1 7 1 0 1

P7 72 144 481 233 43 13 29 29 0 0 1 1 1 1 0 6.5 0 0 1

P8 0 0 472 157 26 8 24 94 0 0 1 1 1 1 0 5.5 0 1 0

P9 4403 3403 1134 2980 65 15 21 295 0 1 1 1 0 0 0 4.5 0 0 1

P10 35 117 117 90 65 10 21 14 0 1 1 1 1 1 0 5.5 1 0 1

P11 0 0 195 65 57 16 40 29 0 1 0 1 1 1 0 6 1 0 1

P12 0 0 19 6 99 21 51 18 0 0 0 1 1 1 0 4.5 1 1 0

P13 0 0 158 53 84 35 34 74 0 1 1 1 1 1 0 4.5 1 1 0

P14 0 0 17 6 60 12 35 22 0 0 1 0 1 1 0 4.5 0 1 0

P15 0 0 656 219 70 12 41 138 0 1 1 1 0 0 0 4.5 1 0 1

P16 119 119 36 91 76 22 34 55 0 0 1 1 1 1 0 5 1 1 0

P17 0 0 68 23 70 25 29 18 0 0 1 1 1 1 0 5.5 1 0 1

P18 0 0 19 6 92 13 20 32 0 0 1 1 1 1 0 4.5 1 0 1

P19 0 0 60 20 72 12 18 46 0 0 1 1 1 1 0 4.5 1 1 0

P20 493 329 329 383 41 20 20 36 0 1 1 1 0 1 0 8 1 1 0

P21 0 0 539 180 42 25 15 86 0 1 1 1 1 0 0 4.5 1 1 0

P22 810 324 648 594 43 13 27 36 0 1 1 1 0 0 0 4.5 1 1 0

P23 2111 704 528 1114 32 20 34 77 0 1 1 1 0 0 0 4.5 0 0 1

P24 0 0 224 75 42 20 26 37 0 0 1 1 1 1 0 4.5 1 0 1

P25 0 0 17 6 46 11 25 77 0 0 1 1 1 1 0 6 1 1 0



Contd…


P26 451 752 15 406 55 18 55 83 0 1 1 0 0 0 0 4.5 1 0 1

P27 0 0 31 10 54 15 44 37 0 0 0 1 1 1 0 5 1 1 0

P28 0 0 92 31 56 35 40 55 0 0 1 1 1 1 0 4 1 1 0

P29 1548 636 1222 1135 55 15 24 244 0 1 1 1 0 0 0 4.5 1 1 0

P30 0 0 21 7 84 20 20 18 0 0 1 1 1 1 0 4 1 1 0

P31 0 0 15 5 86 17 20 24 0 0 1 1 1 1 0 4 1 1 0

P32 0 0 16 5 63 25 80 31 0 0 0 0 1 1 0 4.5 1 0 1

P33 1 2 4 2 63 35 44 21 0 0 0 0 1 1 1 5.5 1 1 0

P34 0 0 918 306 78 11 21 34 0 0 1 1 1 0 0 4.5 1 0 1

P35 0 0 562 187 78 12 30 71 0 1 1 1 1 0 0 4.5 1 1 0

P36 0 0 617 206 76 11 30 39 0 1 1 1 0 0 0 4.5 1 1 0

P37 0 0 1271 424 78 11 32 48 0 1 1 1 0 0 0 5.5 1 1 0

P38 23 23 365 137 47 23 28 50 0 0 1 1 1 1 0 5 1 1 0

P39 0 81 258 113 26 25 10 13 0 0 1 1 1 1 0 5 1 1 0

P40 0 0 140 47 48 13 20 20 0 1 1 1 1 1 0 5.5 1 1 0

P41 0 0 118 39 44 25 19 29 0 0 1 1 1 1 0 5 1 1 0

P42 33 0 65 33 44 12 10 49 0 0 1 1 1 1 0 5 1 0 1

P44 499 0 25 175 40 12 5 26 0 0 1 1 1 1 0 6 1 0 1

P45 61 15 123 66 61 17 5 61 0 0 1 1 1 1 0 6.5 1 0 1

P46 13 6 19 13 92 21 34 23 0 0 1 1 1 1 0 4.5 1 1 0

P47 11 28 57 32 92 30 34 34 0 1 1 1 1 1 0 4.5 1 1 0

P48 54 13 40 36 93 22 42 15 0 1 1 1 1 1 0 5 1 1 0

P49 15 25 15 18 93 23 34 35 0 0 1 0 1 1 0 5 1 1 0

P50 3 2 5 3 101 25 34 17 0 0 0 1 1 1 1 4.5 1 1 0



Contd….


P51 4 14 169 62 149 13 29 46 0 0 1 1 1 1 0 4.5 1 1 0

P52 2 7 18 9 152 28 44 29 0 0 1 1 1 1 0 5 1 1 0

P53 16 27 133 59 149 8 20 85 0 0 1 1 1 1 0 5 1 1 0

P55 25 25 25 25 67 33 54 9 0 0 1 1 1 1 0 4 1 1 0

P56 9 57 28 31 71 24 44 21 0 1 1 1 1 1 0 4 1 0 1

P57 136 182 114 144 71 30 30 20 0 0 1 1 1 1 0 4 1 0 1

P58 150 8 201 120 96 32 33 29 0 1 1 1 1 1 0 4.5 0 0 1

P59 131 20 262 137 92 28 40 56 0 1 1 1 1 1 0 6.5 0 1 0

P60 0 0 84 28 88 25 30 40 0 0 1 1 1 1 0 5.5 0 1 0

P61 14 28 85 43 92 31 24 23 0 0 1 1 1 1 0 4.5 1 1 0

P62 41 41 270 117 38 25 20 22 0 0 1 1 1 1 0 5 1 1 0

P63 0 0 34 11 36 15 34 5 0 0 0 0 1 1 0 5 0 1 0

P64 2805 1522 1643 1990 33 15 10 941 0 0 1 1 1 1 0 5 0 1 0

P66 0 0 23 8 40 20 12 102 0 0 1 1 1 1 0 5 1 1 0

P67 0 0 248 83 37 15 24 435 0 0 1 1 1 1 0 5 1 1 0

P68 185 283 234 234 43 12 30 74 0 0 1 1 1 1 0 4 1 1 0

P69 175 257 526 319 31 23 30 93 0 1 1 1 0 1 0 5 1 1 0

P70 17 0 50 22 30 25 12 56 0 0 1 1 1 1 0 6 1 1 0

P71 0 0 31 10 141 25 25 18 0 0 1 1 1 1 0 5 1 1 0

P72 0 0 42 14 139 15 27 99 0 0 1 1 1 1 0 5 1 1 0

P73 0 0 300 100 144 20 34 65 0 0 0 1 1 1 0 5 0 1 0

P74 202 504 303 336 144 11 15 48 0 0 1 1 1 1 0 5 1 1 0

P75 0 0 54 18 110 25 35 54 0 0 1 0 1 1 0 5.5 0 1 0



AppendixB-6: Table of Available soil moisture per year and average of 3-years

SN

Log10 average yield year 2000-2002

Average avail-able soil mois-ture (mm) (year 2001)



Average avail-able soil moisture (mm) (year 2000-2002)

P1 2.5 99 47 83 76 P2 1.1 98 45 84 76 P3 2.3 95 45 83 74 P4 3.0 95 44 83 74 P5 2.3 90 40 83 71 P6 1.1 91 40 83 71 P7 2.4 91 41 83 72 P8 2.2 94 42 83 73 P9 3.5 119 50 92 87

P10 2.0 94 46 82 74 P11 1.8 94 46 81 74 P12 0.8 98 43 84 75 P13 1.7 96 48 80 75 P14 0.8 100 47 84 77 P15 2.3 102 45 92 80 P16 2.0 96 50 81 76 P17 1.4 97 48 82 76 P18 0.8 101 47 84 77 P19 1.3 102 45 85 77 P20 2.6 101 44 85 77 P21 2.3 101 44 85 77 P22 2.8 100 43 88 77 P23 3.0 98 43 85 75 P24 1.9 98 43 85 75 P25 0.7 94 49 80 74 P26 2.6 118 48 83 83 P27 1.0 92 38 83 71 P28 1.5 112 47 87 82 P29 3.1 118 48 88 85 P30 0.8 95 48 83 75 P31 0.7 96 49 82 76 P32 0.7 102 46 84 77 P33 0.4 92 46 78 72 P34 2.5 97 44 84 75 P35 2.3 97 44 83 75 P36 2.3 97 44 85 75



Contd…

SN

Log10 average yield year 2000-2002




Average avail-able soil moisture (mm) (year 2000-2002)

P37 2.6 96 44 83 74 P38 2.1 93 42 83 73 P39 2.1 93 42 83 73 P40 1.7 91 42 83 72 P41 1.6 91 42 83 72 P42 1.5 91 42 82 72 P44 2.2 92 43 83 73 P45 1.8 92 43 83 73 P46 1.1 93 39 84 72 P47 1.5 93 39 84 72 P48 1.6 93 39 84 72 P49 1.3 92 39 84 72 P50 0.5 93 39 81 71 P51 1.8 91 38 83 71 P52 1.0 91 38 83 71 P53 1.8 91 38 83 71 P55 1.4 93 39 84 72 P56 1.5 93 39 84 72 P57 2.2 93 39 84 72 P58 2.1 95 40 85 73 P59 2.1 95 40 85 73 P60 1.4 95 40 85 73 P61 1.6 94 39 84 72 P62 2.1 89 38 83 70 P63 1.1 89 38 83 70 P64 3.3 114 49 83 82 P66 0.9 93 40 84 72 P67 1.9 93 40 84 72 P68 2.4 93 40 84 72 P69 2.5 93 40 84 72 P70 1.3 92 40 84 72 P71 1.0 116 47 86 83 P72 1.1 91 38 83 71 P73 2.0 91 38 83 71 P74 2.5 91 38 83 71 P75 1.3 93 39 84 72



Appendix B-7: Table of retained data for multiple regression analysis

SN AY (kg/ha)

Log10 yield Alt AST AT W P PDP MS PP

PH ST SF

P1 286 2.5 61 6 15 1 1 1 0 1 5.5 1 1 P2 12 1.1 58 15 32 0 1 1 1 1 6.5 1 1 P3 217 2.3 56 17 14 1 1 1 0 1 4.5 1 1 P4 1015 3 57 18 36 1 1 0 0 0 5 0 1 P5 218 2.3 42 10 27 1 1 1 1 1 4.5 0 1 P6 12 1.1 42 17 10 1 1 1 1 1 7 0 1 P7 233 2.4 43 13 29 1 1 1 1 0 6.5 0 1 P8 157 2.2 26 8 24 1 1 1 1 0 5.5 1 1 P9 2980 3.5 65 15 21 1 1 0 0 0 4.5 0 1

P10 90 2 65 10 21 1 1 1 1 1 5.5 0 1 P11 65 1.8 57 16 40 0 1 1 1 1 6 0 1 P12 6 0.8 99 21 51 0 1 1 1 1 4.5 1 1 P13 53 1.7 84 35 34 1 1 1 1 1 4.5 1 1 P14 6 0.8 60 12 35 1 0 1 1 0 4.5 1 1 P15 219 2.3 70 12 41 1 1 0 0 1 4.5 0 1 P16 91 2 76 22 34 1 1 1 1 1 5 1 1 P17 23 1.4 70 25 29 1 1 1 1 1 5.5 0 1 P18 6 0.8 92 13 20 1 1 1 1 1 4.5 0 1 P19 20 1.3 72 12 18 1 1 1 1 1 4.5 1 1 P20 383 2.6 41 20 20 1 1 0 1 1 8 1 1 P21 180 2.3 42 25 15 1 1 1 0 1 4.5 1 1 P22 594 2.8 43 13 27 1 1 0 0 1 4.5 1 1 P23 1114 3 32 20 34 1 1 0 0 0 4.5 0 1 P24 75 1.9 42 20 26 1 1 1 1 1 4.5 0 1 P25 6 0.7 46 11 25 1 1 1 1 1 6 1 1 P26 406 2.6 55 18 55 1 0 0 0 1 4.5 0 0 P27 10 1 54 15 44 0 1 1 1 1 5 1 1 P28 31 1.5 56 35 40 1 1 1 1 1 4 1 1 P29 1135 3.1 55 15 24 1 1 0 0 1 4.5 1 1 P30 7 0.8 84 20 20 1 1 1 1 1 4 1 1 P31 5 0.7 86 17 20 1 1 1 1 1 4 1 1 P32 5 0.7 63 25 80 0 0 1 1 1 4.5 0 1 P33 2 0.4 63 35 44 0 0 1 1 1 5.5 1 1 P34 306 2.5 78 11 21 1 1 1 0 1 4.5 0 0 P35 187 2.3 78 12 30 1 1 1 0 1 4.5 1 0 P36 206 2.3 76 11 30 1 1 0 0 1 4.5 1 1



Contd…

SN AY

(kg/ha) Log10 yield Alt AST AT W P PDP MS PP PH ST SF

P37 424 2.6 78 11 32 1 1 0 0 1 5.5 1 1 P38 137 2.1 47 23 28 1 1 1 1 1 5 1 0 P39 113 2.1 26 25 10 1 1 1 1 1 5 1 0 P40 47 1.7 48 13 20 1 1 1 1 1 5.5 1 1 P41 39 1.6 44 25 19 1 1 1 1 1 5 1 1 P42 33 1.5 44 12 10 1 1 1 1 1 5 0 1 P44 175 2.2 40 12 5 1 1 1 1 1 6 0 1 P45 66 1.8 61 17 5 1 1 1 1 1 6.5 0 0 P46 13 1.1 92 21 34 1 1 1 1 1 4.5 1 1 P47 32 1.5 92 30 34 1 1 1 1 1 4.5 1 1 P48 36 1.6 93 22 42 1 1 1 1 1 5 1 1 P49 18 1.3 93 23 34 1 0 1 1 1 5 1 1 P50 3 0.5 101 25 34 0 1 1 1 1 4.5 1 1 P51 62 1.8 149 13 29 1 1 1 1 1 4.5 1 1 P52 9 1 152 28 44 1 1 1 1 1 5 1 1 P53 59 1.8 149 8 20 1 1 1 1 1 5 1 1 P55 25 1.4 67 33 54 1 1 1 1 1 4 1 1 P56 31 1.5 71 24 44 1 1 1 1 1 4 0 1 P57 144 2.2 71 30 30 1 1 1 1 1 4 0 1 P58 120 2.1 96 32 33 1 1 1 1 0 4.5 0 1 P59 137 2.1 92 28 40 1 1 1 1 0 6.5 1 1 P60 28 1.4 88 25 30 1 1 1 1 0 5.5 1 1 P61 43 1.6 92 31 24 1 1 1 1 1 4.5 1 1 P62 117 2.1 38 25 20 1 1 1 1 1 5 1 1 P63 11 1.1 36 15 34 0 0 1 1 0 5 1 1 P64 1990 3.3 33 15 10 1 1 1 1 0 5 1 1 P66 8 0.9 40 20 12 1 1 1 1 1 5 1 1 P67 83 1.9 37 15 24 1 1 1 1 1 5 1 1 P68 234 2.4 43 12 30 1 1 1 1 1 4 1 1 P69 319 2.5 31 23 30 1 1 0 1 1 5 1 1 P70 22 1.3 30 25 12 1 1 1 1 1 6 1 0 P71 10 1 141 25 25 1 1 1 1 1 5 1 1 P72 14 1.1 139 15 27 1 1 1 1 1 5 1 1 P73 100 2 144 20 34 0 1 1 1 0 5 1 1 P74 336 2.5 144 11 15 1 1 1 1 1 5 1 1 P75 18 1.3 110 25 35 1 0 1 1 0 5.5 1 1



Appendix C: Questionnaires

Primary data Dataset number: …………… Data collection date: ………

Administrative area:……………. Location Project name: …………………... UTM zone Latitude (y) Longitude (x) Data collector’s name: …………

Interviewed name: ……………...

……….. …………. ……………

Mapping Unit ID: ……………… Dataset type: ………… Sample plot ID: ………………… Sample plot size: ………………

Info source: …………

Table1: Yield and management information by interview farmers Parameters Descriptions (respond)

Planting pattern (a) Mono (b) Mixing (c) other

Time of harvest

Cropping pattern

Spacing between trees

System:

Yes

Wee

ding

No

Number/year:

System:

Yes

Prun

ing

No

Number/year and time:

System:

Amount:

Yes

Fert

ilize

r app

lica-

tion

No


System:

Amount:

Yes

Pest

icid

e ap

-pl

icat

ion

No


Irrigation:

M

anag

emen

t pra

ctic

es

Water source

Rained:



Number of tree per plot:

Age of tree: Total nut yield (kg):

Average yield (kg/ tree):

Number of tree /ha:

Yie

ld o

btai

ned

per y

ear

Yield (kg/ha): Farmer’s experience for rainfall importance time for cashew production:

Table 2: Soil and topographic data Parameters Descriptions

Soil texture:

Soil effective depth (cm):

Soil pH:

Soil drainage condition:

Soil

Suitability for cashew growing (fertility level):

To-

pog-

Altitude:

Open-ended questions:

��Any problem encountered during production of cashew tree in the area Secondary data

��Climatic data of the study area ��Production conditions of cashew from concerned institutes

effects of moisture conditions and management on ... · effects of moisture conditions and...

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