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Doing Field Research Research Techniques for Tree Crops

Research Techniques for

Tree CropsD.G. Mayer and K.R. Chapman

Table of Contents

Research Techniques for Tree Crops................................................................................................1

Research Methodology.................................................................................................................3Types and Length of Trials.......................................................................................................3Sequence of Trials....................................................................................................................3Use of Existing Trees................................................................................................................3

Experimental Layout.....................................................................................................................4Experimental Unit.....................................................................................................................4Guard vs. Datum Trees.............................................................................................................4Degree of Replication...............................................................................................................5Blocking Considerations...........................................................................................................5Importance of Randomisation...................................................................................................5Refinement of Designs..............................................................................................................6

Designs for Various Types of Experiments...................................................................................71. Clonal, Varietal and Rootstock Evaluations.........................................................................72. Breeding Experiments...........................................................................................................73. Soil Fertiliser and Irrigation Trials.......................................................................................74. Spacing or Density Trials.....................................................................................................75. Thinning Trials.....................................................................................................................86. Pruning and Tree-shaping Experiments................................................................................87. Pathology and Entomology Studies......................................................................................8

Page 1 of Research Techniques for Tree Crops


8. Spraying Trials......................................................................................................................89. Population Surveys...............................................................................................................910. Post-Harvest Trials.............................................................................................................911. Laboratory or Greenhouse Trials......................................................................................1112. On-Farm Extension Trials................................................................................................12

Methods of Data Collection with Tree Crops.............................................................................12Data Types..............................................................................................................................12Primary Variables...................................................................................................................13Secondary Variables...............................................................................................................16Derived Data...........................................................................................................................25Auxiliary Data.........................................................................................................................28Concluding Remarks...............................................................................................................30

Sampling Techniques..................................................................................................................31Introduction.............................................................................................................................31Sampling Methods..................................................................................................................31Field Recording Techniques...................................................................................................33

Data Analysis - Interpretation and Reporting.............................................................................34

Table of Figures

Figure T- 1The total growth cycle of cv Fuerte avocado at Palmwoods..........................24



Research MethodologyTypes and Length of TrialsPrior to a research program being initiated, a number of issues must be addressed. The first is to decide what type of experiment is intended. There are three main types, namely:

1. Screening trialsThese are necessarily the first steps in a research program, and are useful in unknown situations. The most obvious of these is selection of a number of suitable varieties or clones from a wide range of material. It is also a useful technique for 'pre-testing' unknown treatments and concentrations on bearing trees, or pot trials on a range of nutrient applications. These type of trials are usually minimally replicated (at one site only) and short-term, and after one or two years will reveal which treatments or varieties are suited to more rigorous testing.

2. Proving trialsThese may be viewed as the usual type of experiment. The researcher has some knowledge about the effectiveness of the treatments, and is trying to estimate differences with a view to economic considerations. These trials should be spread across as many soil, climatic and management types as possible to obtain most information, and need to run for at least two or three years of bearing. There should be (at least) minimal replication at each site, to ensure a statistically valid test of the treatment by site interaction in the cross-site analysis.

3. Demonstration trialsThese are the final stage, and concentrate on the most effective treatments. They are generally located in environments of maximum impact, preferably within easy access of the target audience. Length of the trial depends very much on the degree of differences obtained.

Sequence of TrialsThere is no necessity for these three types of trial to be sequential. For example, growth parameters after one year may be used to identify eight varieties from a screening trial, which are planted in a proving trial the next year. After a further five years with say two crops, three of these may be superior, and be planted to a demonstration trial in the location (sj of best yield. Also, there is no necessity for the three separate types. Following a conclusive screening trial, the researcher may plan a large 'proving and extension' trial, with results suited to economic extension available after six or so years.

Use of Existing TreesTrials conducted from planting, which constitute a sizeable proportion of all trials, are necessarily long-term. One alternative, which gives quick answers, is to test treatments in an orchard of bearing trees. Calibration or uniformity data (for example, previous season's yield) is quite valuable, but is rarely available. It can be used as a covariate



to correct for individual tree pre-treatment differences, and has been shown to have good correlation with subsequent yields (Pearce, 1976). If

these data are not available, it is essential to have some measure, and preferably more than one, of tree vigour, health or size to use as covariates.

Experimental Layout Experimental UnitOne of the most frequently asked questions of statisticians is 'What is an experimental unit?' Invariably, the answer is that 'It depends ....', on various things such as crop type and size, treatment types, environment, and (probably most importantly) resources available for the experiment. In general terms, an experimental unit can be taken as the smallest unit to which a treatment can be applied such that measurement of treatment performance in adjacent plots is independent. Smaller units are preferred to larger because:1. with a given amount of resources,

smaller units result in higher replication,

2. in blocked designs, smaller units give smaller blocks, which tend to be more homogeneous.

In tree crops, one can thus have three basic plot descriptions, namely:

1. Sub-tree plots These may be an individual branch, flower or fruit, but are used relatively infrequently. Treatments which affect the tree as a whole, or which the tree can mobilise (e.g., foliar sprays or systemic pesticides) cannot be used. Hence, the only types of treatments possible are those that are physically applied to the single branch or fruit and have no effect on the neighbouring unit, e.g., hand-pollination, wrapping or shading of fruit, etc. It is not advisable to test surface sprays (i.e., not systemic) by this

method, as the chance of spray drift to the next unit is high.

2. Single-tree plotsThese are normally accepted as the minimum plot size, and are quite useful under a range of experimental types. Remembering that adjacent units must be independent, single trees cannot be used in situation where:1. there is some drift or leakage of

treatments from one tree to the next,2. there are differences in

competitiveness for resources between trees, as an average tree surrounded by competitive trees should perform poorer than an average tree surrounded by inferior trees.

3. Multiple-tree plotsGiven the resources required for even minimal replication of multiple tree plots, these are less attractive. However, they must be used in situation where:1. planting material is extremely

variable, e.g. with seedlings,2. treatment or management

considerations preclude the use of single tree plots.

Guard vs. Datum TreesWith multiple tree plots, in situation (1.) all trees may be measured and averaged to obtain the plot average, i.e., all trees are datum trees. In situation (2.), it is usual to have one- or two dimensional guarding, with the 'outside'



trees (usually only a single row on each side) being treated but not measured, i.e., used as guard trees. They must be treated so that the response of the inner (datum) trees is the same as if the whole orchard were treated, but must not be measured because their performance may well be influenced by the treatment applied to the guard row in the adjacent plot. It is often a good idea with mature trees to paint a band around each tree, with one colour for guard trees and another for datum trees. This idea can be extended further with datum trees for different treatments having different colours, but leave all guard trees the same colour.

Degree of ReplicationA statistical formula is available to calculate the desired degree of replication, provided the researcher can quantify the required difference between treatments, size of the random variation (standard deviation) and probability that a difference will not be found. Rarely (if ever) will this information be available with any degree of accuracy, particularly with regard to new treatments, crops and environments.The alternate approach is to design a trial that has a reasonable to good chance of significantly detecting a treatment difference that is real. This chance of ‘success’ is increased by raising the level of replication, and is dependant on adequate error degrees of freedom. It is a waste of time and resources, especially with long-term tree crops, to conduct an experiment that shows an (economically) important treatment difference, but fails to prove that difference due to insufficient replication.A general 'rule of thumb' which can be adopted is that the error should have 12 to 15 degrees of freedom if multiple (e.g., four or more) datum trees are available per plot, but 25 or more if

single datum trees are used. Note that this rule applies to the number of datum trees per plot, not to the total plot size. Note also that each missing plot costs the analysis one degree of freedom, so it is wise to initially have higher replication than the minimum required, especially in unknown or potentially disaster-prone experiments.

Blocking ConsiderationsA researcher confronted by a new field faces a range of considerations. Having first determined size of the plot, he/she usually has to arrange these into blocks such that within block variation is minimal. To determine this, he/she is faced with slope (often in two directions), drainage lines and varying soil types and fertility. Hopefully each individual block can be restricted to one soil type only. Even so, the problem is far from simple. Soil analysis and topography may be used to determine blocks, or topography only and use soil analysis results as covariates in the analyses of variance. There is no reason that blocks have to be regularly shaped, or even physically together. For example, say the researcher is faced with the following field, and has to include four blocks of four treatments. On hillside orchards, where the rows run across the slope, it is usual to consider each row a block, and impose treatments within rows.One further complication is the possibility of initial plant size differences. These may be recorded and later used as a covariate (remember, each covariate costs the analysis one degree of freedom), or confounded with blocks (i.e., the biggest all planted into one block; second biggest lot into a second block, etc.).

Importance of RandomisationProbably every researcher realises that statistical theory and analyses are



based on the assumption of randomisation, and that it is invalid to use these techniques on trials that have not been properly randomised. That is sometimes all that the researcher thinks about randomisation.Randomisation is crucial in all experiments in that it guards against bias. It is very easy, especially in demonstration trials, to ignore randomisation and arrive at a design that 'looks random'. However, the danger here is that the (expected) best treatment is usually put at the 'front', which may have better access to water or get the run-off from the pig-pen. Of special danger is informing the farmer of expectations; naturally he/she will wish to put the best variety in his best paddock. In these situations, an equal (or superior) variety will never be found, as all other varieties get inferior resources. This is one example of bias.Another can be found by considering the harvesting of fruit. It is wrong to go up to a tree and sample 10 or so fruit 'which catch your eye'. These are usually the most prominent, and may avoid inferior fruit, i.e., they are not at all representative of the crop as a whole. All harvestable fruit must be considered, and a random selection taken.Randomisation is one of the basic elements of research, and its importance is often underestimated.

Refinement of DesignsIt is hoped that this is never necessary. However, due to the long-term nature of tree crop research, it is sometimes better to admit that an inappropriate design was used, or that the treatments were not giving the expected results, and refine or extend the experiment.

The most obvious case is if one or more treatments are a 'failure', and alternate treatments can be found or proposed. The changeover should be made as early in the trial as possible, preferably before the initial treatment inflicts a lasting detrimental effect on the trees. This is rarely the case, however. In these situations, the best 'salvage job' which can be conducted is to record some measure of tree performance at the time of treatment changeover, and use this as a covariate. This, on the other hand, may hide some of the other treatment differences, especially if the trial has been going for a number of years. In these circumstances, it may be prudent just to delete the failed treatment from the analysis, and perhaps use those plots for observational studies of the new treatments.The other example of design refinement is with multiple datum-tree plots, say in a varietal experiment. Independent of the varietal treatments, the orchard is run under a fixed management system, governing levels of irrigation, fertiliser, sprays, pruning methods, etc. Provided that other design considerations are followed, there is every reason for the researcher to alter one or more of these management factors within each plot, i.e., imposing a further treatment combination in a split-plot design. Given that the researcher is already committing resources to the management and measurement of the trial, this extra information is available at minimal cost and effort. In particular, it will indicate whether the main and split treatments interact, i.e., whether the effect of the main treatment (say variety) is consistent across different management techniques.



Designs for Various Types of ExperimentsThere can be no hard and fast rules as to which designs should be used with various types of experiments, as many other considerations must also be taken into account. The following sections should be used as a guide only.

1. Clonal, Varietal and Rootstock EvaluationsThe objects of these three types of trials are similar. Varietal by rootstock factorials can be used. In all these trials, it is preferable to use grafted material when possible, as opposed to seedlings, as seedlings tend to be far more variable. When determining plot size, the expected size and vigour of the plants is important. If all treatments are expected to be similar, or if the planting distance is large enough to minimise inter-plot competition, single-tree plots are usually sufficient. Alternatively, if differences are expected and/or close plantings are to be used, guard trees are required. These may be circumvented if the treatments can be split into groups of similar vigour, with each group forming a separate trial. A degree of overlap between adjacent groups should exist for between-group comparisons, as outlined in Pearce (1976), p 82-83.

2. Breeding ExperimentsThese are mainly suited to simple screening trials, as each generation contributes a large number of crosses for selection. It is preferable if the screening (e.g., for disease resistance) can be conducted at an early age, to speed developments.

3. Soil Fertiliser and Irrigation TrialsThese two important types of trial have been grouped because they represent

the more 'mobile' types of treatment. In particular, the roots from the control treatment may well be expected to intrude into adjacent plots, especially these with higher levels of the treatment. One possible exception to this is micro-irrigation and fertigation, where the treatments are applied very near the tree base and each tree develops an extensive system of feeder roots in that region, especially if the soils are sandy and restrictive to lateral movement of water. Generally, however, larger plots with guard trees are required. These treatments are thus ideally suited as the main treatments of a split-plot design.

4. Spacing or Density TrialsOver the years, a number of authors (including Pearce, 1976) have recommended, and quite a number of trials have been conducted, using systematic designs. In these the planting density is varied from very high to very low in a continuum, the usual layout being circular, fan-shaped or in rows. Each tree is considered an experimental unit, and a response surface fitted.We would tend to view these with caution. Perhaps they are useful in a screening trial to roughly determine the range of densities to be considered, but their use in proving and demonstration trials is suspect. This is because the treatments are not randomised, and unknown fertility gradients or patches within the experimental field may produce unacceptable bias. For example, serious bias occurs if the centre of the circle lies on an unusually fertile or deficient area.We would suggest, instead, that a properly randomised and replicated trial would produce the desired, unbiased, result. The researcher should select five to ten discrete densities, ranging from



low to high density. Replicating these a number of times, it will be possible to fit a response surface to density. Any unknown fertility patches in the field will increase variability about the `true' response surface, but by randomising the treatments there is an equal chance of positive or negative errors, and over a number of replicates these (should) even out, producing an unbiased result.Unfortunately, density trials of this type require guards, as the competition between trees is of primary importance. If the orchard is planted in rows, where the between-row width is similar to the between-tree distances in the lower densities, one-dimensional guarding is required.In the case of square, rectangular or triangular plantings, two-dimensional guarding is necessary. Thus 9-tree plots are required for a single datum tree, or 16-tree plots for four datum trees. With these trials, the researcher faces the choice between equal numbers of trees per plot with varying areas, or equal plot areas with varying numbers of trees. Logistically, the second is usually preferred.

5. Thinning TrialsThese occur when a high-density planting is to be thinned when inter-tree competition becomes a problem, or after a set time period. After thinning, competition effects may be assumed negligible, allowing single-tree plots. Hence, the initial design should consist of multiple-tree plots, with guards, which will be thinned to single-tree plots.

6. Pruning and Tree-shaping ExperimentsDepending on the types of treatments, competition between plots may be affected, requiring guard trees. If, however, the treatments result in trees being independent of each other, single-

tree plots can be used. All treatments of this type tend to influence most of the 'auxiliary data' (for example, tree height, diameter, canopy volume, etc.). As a result, yield and trunk circumference are the most useful variables for these trials.

7. Pathology and Entomology StudiesThe general non-uniformity of infestation causes problems with these trials. Increased sampling and replication is often required because of the higher variances. Binary data (for example, presence/absence of pathogen) is quite common, as are counts per unit area. Data on counts frequently exhibit skewness and heterogeneity of variances, and require a normalising transformation such as 1n (x + 1) prior to analysis.

8. Spraying TrialsThe treatments here are usually chemical sprays aiming at control of pests or diseases, but may include growth regulators or foliar applications of fertilisers. The principles involved are similar, and depend mainly on the type of application. Screening trials involving a number of treatments are more suited to back-pack spray units, which (in the absence of significant wind) can be assumed to affect only the sprayed tree, hence single-tree plots can be used. As a safe-guard, portable physical barriers (wood or cloth) may be used either down-wind or where trees are close. Large-scale proving or demonstration trials, involving commercial-type applications, require larger plots with guards, as the method of application usually affects adjacent trees.One major problem with pest and disease trials is the choice of treatments, especially if their efficiencies are different. When this occurs, 'effective' treatment plots will be



subjected to heavier pest pressures by the surrounding 'non-effective' treatment plots than if the whole orchard were effectively treated. This results in under-estimation or a 'conservative' estimate of their effectiveness. Whilst some researchers see this as acceptable (stating the producers will achieve a similar or better response), this is not acceptable for two reasons. Firstly, there is no indication of the amount of bias, that is, how much better, and secondly, any economic assessment on the type or number of sprays required will be incorrect.Perhaps the most satisfactory compromise is to use a flexible treatment design, where noneffective treatments are recognised early in the trial and replaced by effective treatments before the pest population builds up too high. This also covers the problem of the need for two controls, namely one of the standard commercial spray and one unsprayed. Obviously, in bad years, the pest numbers in the unsprayed plots will rapidly build up, and this in fact may occur in most years. A number of authors have suggested incorporating only a sprayed control in the design and analysis of the trial, and sampling a number of unsprayed plots in areas surrounding the trial orchard to determine the 'natural' pest incidence.

9. Population SurveysThese cover experiments where there are no formal treatments and where the aim is to estimate mean incidence and dispersion of a population. In studies of rate and direction of the spread of a population, statistical methods for directional data may be used (Mardia

1972), although these are not common. The main problems of this type of trial are associated with sampling considerations, which will be covered later on in these notes. (Refer page 31)

10. Post-Harvest TrialsThis is a large and relatively complex area of experimental types, involving different considerations to those found in tree-crop trials. Post-harvest trials usually deal with treatments aimed at extending storage life of the produce. Thus, fruit quality is the key variable, and this may be measured by chemical or physical analysis, or by taste panel assessment. It is usual to consider a number of fruit as the experimental unit, unless the fruit is extremely large (for example, durian) or the supply is limited. Source of the fruit (for example, single tree or number of trees of the same variety at the same location) is the usual blocking method, although incomplete block designs using sessions are needed for taste panel trials. In these, source of fruit is used to define complete replicates.Objective tests, such as chemical analysis, are preferred on the basis of simplicity and accuracy. However, there are certain characteristics, such as flavour, odour, and individual preference, for which no satisfactory objective tests are available. Therefore, if one wishes to obtain information on these types of properties, he/she must be prepared to deal with the problems of subjective tests. Some of these problems, and the ways in which experiments are designed to overcome them, are discussed below.



Problems associated with taste panel experiments1. The taster. Since he/she is human, he/she may unaccountably change his mind, or

be inconsistent in his ratings.2. Number of samples per panellist. Except for highly trained panelists, it is generally

accepted that four to six samples per panelist per session is the maximum number of samples that can be rated consistently (Mayer and Mulder 1989).

3. Timing of session. It has been shown that the time of day, and day of week of a session has a large effect on a panelist's ratings.

4. Motivation. Keen panellists are usually more discriminating. In large experiments panelists can lose their desire to discriminate before they lose their ability. Panelists who dislike all products of the type being tasted are unlikely to be discriminating.

5. Training. In some types of experiments, panelists should be trained to detect differences. This presents problems if there are a variety of products to be tested.

6. Scoring. The score that one sample receives can depend quite markedly on the score of the sample before it.

7. Environment. Panellists who are comfortable, relaxed and unhurried are usually more discriminating. This can be difficult to attain.

8. Availability. It is rare for all panelists to be present at all sessions. Relatively large numbers of missing values can be a problem.

9. Nuisance factors. The colour, odour, or general appearance of a sample can influence its taste rating.

10. Knowledge. If the panelist knows or can determine the identity or origin of a sample, he/she may have preconceived ideas on how it should rate.

Quality evaluation panels are used most by horticultural researchers. Panelists should be as highly trained as possible; the number of panellists depends on their expertise (generally ten to twenty). The major difference of quality evaluation testing is that interest is in an absolute taste score as well as comparative scores for several products, which are usually sufficient in the other types of panel testing. Panelists attempt to conform to a uniform scoring system over long periods of time. Taste testing for quality evaluation can be one phase of a more elaborate evaluation procedure. Composite quality scores consist of weighted averages of scores on a variety of attributes.

Designs for taste panel experiments1. Paired sample test. The taster is given two samples, and must pick the samples higher in some constituent or must state the one preferred. This test is often used in consumer preference trials. Analysis of the results is relatively straightforward. A person who cannot discriminate has a 50% chance of choosing a particular sample (success). Simple probability indicates the level of significance by comparing the number of successes with the number of trials. To save calculating this each trial, tables have been constructed.



2. Triangle test This test involves three samples, two of which are known to be alike and one different. The odd sample must be found. Analysis of results is similar to the paired sample test, the difference being that alternate tables must be used, as the chance of success is now 33.3%. This test is more widely used than the paired sample test.

3. Block designs. These designs are used widely in quality evaluation. At each session the panelist must rate a number of samples (Treatments). As there are many sources of variation in taste panel experiments, blocked designs are considered far more suitable than completely randomised designs.Designs are available to test virtually any number of treatments (within reason). It is advisable, however, to keep the number of treatments as small as possible, as simpler trials both provide more information and are easier to conduct and analyse. If the number of treatments is less than or equal to six, randomised complete blocks can be used, with each treatment being given to each panelist at each session. Factors blocked are usually sessions and/or panelists. For more than six treatments, special experimental designs, such as randomised incomplete blocks, and square and rectangular lattices need to be used. These designs are rather complex, and care needs to be taken in their planning. An experimenter intending to use them should either consult with a biometrician, or use a standard statistical text such as Cochran and Cox (1957).In rating treatments, the panelist can either rank or score them. These two methods are discussed below.A. Ranking methods

These involve ranking a number of treatments in order of preference. An advantage of these methods is that they remove the problem of varying severity amongst panelists who score samples. If this is a problem with scored data, it may be better to convert to ranked data.Disadvantages include the rigidity of the scale: two samples, which a panelist perceives to be greatly different, receive the same ranked difference as two, which are very similar. If a panelist cannot distinguish between two samples, he/she must still rank one higher than the other (although in some types of tests ties are allowed).Except for relatively simple experiments, analysis can be difficult. Results are either converted into a form suitable for an analysis of variance, or analysed using a non-parametric test.B. Scoring methodsThe panellist indicates a score for each sample, either by giving a number (which can include fractions) on an absolute scale (for example, one to nine) or by placing a mark on a continuous scale, which is converted to a score.The standard analytical tool for scored data is the analysis of variance, despite the fact that some assumptions of this technique appear suspect when considering tasting data. If necessary, one can convert scored data back to ranked data, and then do an appropriate analysis of the ranks. Here, however, there is a trade-off; whilst the test is safer, information is lost in going from scores to ranks, so it is a less powerful test. Further reading for taste panel experiments can be found in Bradley (1953), Cochran and Cox (1957), Kramer and Twigg (1966) and O'Mahony (1986).



11. Laboratory or Greenhouse TrialsAgain, this is an area of experimental types associated with tree crops, but with different considerations. The most common use of greenhouse trials is with initial screening of fertiliser requirements. In these, a number of fertilisers (usually five to eight) are tested at nil and 'adequate' levels, with growth rate (for example, weight or height change) being measured over (usually) one year to determine which nutrients the trees are responding to. Nutrients with non-significant results can effectively be excluded from further experiments at that site. If the number of nutrients to be tested is large (more than five), designs using fractional replication can be used.The experimental unit is usually a pot or tub containing one (or more) plants. Blocks can be sources of plant material or soil, or location within the greenhouse. It is generally accepted that pots should not be re-randomised daily,

due to probable physical damage; rather one to three re-randomisations should be done during the length of the trial. If resources are limited, replication by locations or across time is acceptable.

12. On-Farm Extension TrialsThese tend to be the demonstration trials, where the researcher is confident of the result and wishes to extend this information to the farming community. The number of treatments are small, frequently only two - the existing practice and the new recommendation. Relatively few replicates are used, and the plots tend to be longer than with other experiments due to practical considerations (for example, soil management techniques). Given the expected good management and even response of these plots, they are ideally suited to split-plot designs for further treatments, provided the researcher does not use the first few rows, which are visually assessed by local farmers during field days, meetings, etc.

Methods of Data Collection with Tree CropsIn this section of the course we examine the types of data commonly collected in tree crop experiments: field sampling and field recording techniques that may be employed.The most important thing to remember about data collection is that the data gathered should not only be sufficient to show treatment differences but should be able to explain these differences and the hypotheses proposed and thus meet the specific aims of the experiment.

Data TypesThere are many different types of data that may be collected during the course of a tree crop experiment. Most of the emphasis here will be for horticulturists, breeders and physiologists rather than for other research disciplines.The data types presented here do not represent an exhaustive list, since data type and records may be as diverse as the imagination of the researcher.What we have attempted is to give a broad coverage of the more commonly measured variables and to explain in



brief their importance in the interpretation of experiments (See Pearce 1976). For convenience we may classify data types as follows:(i) Primary Variables:(A) Pre-planting Data(B) Pre-bearing Data (C) Post-bearing Data(ii) Secondary Variables(A) Fruit Quality Data(B) Phenological Data(C) Insect and Disease Population Data(iii) Derived Data(A) Relative Increase in Trunk Cross Sectional Area (B) Yield-Tree Size Relationships(C) Primary, Secondary and Derived Data Time and Climatic Relationships(D) Classical Growth Analysis Data (iv) Auxiliary Data(A) Meteorological Data (B) Leaf analysis Data (C) Soils Data(D) Water Quality Data (E) Management Data

Primary VariablesThe words primary variables simply mean basic attributes or characters of the tree, which we can measure, e.g. plant weight, leaf number, fruit weight, girth (butt circumference etc). Again for convenience we have split the primary variables into pre-planting, pre-bearing and post-bearing phases of development in a tree crop.Some measurements we find we can often make only once or twice, e.g. tree height, while others such as girth may be made regularly, e.g. yearly, weekly etc.

(A) Pre-planting dataBefore planting a tree crop experiment there are a number of measurements that can be made on trees either for a pot or field experiment. Essentially all of the pre-planting data pertain to vegetative growth aspects of tree fruits. Many of these data can be used for determining derived growth analysis data. Examples of pre-planting primary data are listed below.

(i) Total plant weight and weights of plant componentsFresh weight of plants can be measured by washing all soil from the roots and by drying and weighing the plant. Fresh weight can be used to grade plants into replicates prior to planting in the same way plants can be graded visually in pots or plastic bags before planting. The large, intermediate and small plants are allocated to different blocks to remove a source of variation in the experiment.Where required, an additional sample of approximately 3 plants per treatment in each replicate allocated, may be cut into small sections for weighing (fresh wt) and drying at 65°C for about 7 days and reweighing to give dry weights, and adding to give total plant dry weights and fresh weights. Plants are oven dried until weight is constant.The usual break-up for weight purposes is:-1. Leaf weight-fresh and dry 2. Shoot weight-fresh and dry 3. Stem weight-fresh and dry 4. Root weight-fresh and dry 5. Total weight-fresh and dry

These data can then be used to estimate the proportions of the above



components in the trees to be planted out in the experiment by using only total fresh weights of the trees being planted. If required further break-ups of components can be used, e.g. weights of primary, secondary and tertiary roots or shoots etc.The data on total plant weight and weights of plant components are commonly used in shorter-term pot experiments and occasionally applied in longer term field experiments, especially where the whole tree is to be removed and sampled at the conclusion of the experiment.In some experiments, where grafted or budded plants have grown, the break-up of the components may be:1. Leaf weight2. Shoot weight above graft union 3. Shoot weight below graft union 4. Root weight5. TotalSimilarly, another break-up commonly used is the ratio of tops: roots. With grafted plants again it is usual to assume the top as that part of the tree above the graft union.For the above measurements weights to the nearest whole gram will be of sufficient accuracy.

(ii) Leaf characteristicsIn conjunction with (i) above it is common to record total leaf number/plant along with total leaf weight (fresh and dry) and leaf area.Leaf area may be determined by measuring the leaf area of all leaves by tracing, leaf area meter etc. However, it is more common to use a fresh weight: leaf area ratio, determined by weighing say 20 leaves and measuring their area in cm2, and then determining total leaf area from the total fresh weight of leaves per plant. Usual accuracy is to the nearest 0.5 cm2 and nearest gram.

(iii) Other pre-plant dataIn many experiments it may be useful to record other pre-plant data including:1. Number of terminal growing points2. Individual length and diameter of

shoots (mm) 3. Shoot numbers4. Tree girth, i.e. circumference of the

stem or butt 5. Tree canopy surface area or

silhouette area

With terminal bearing crops such as lychee, longan, mango, avocado etc, the number of terminals/plant, and thus/hectare, have a big influence on the ability of the plant to carry fruits - thus terminal number is a very important attribute of yield.With plants such as custard apple, sugar apple and cherimoyer, the number of small shoots/plant dictates the number of fruits that can be carried. Therefore, shoot number and shoot size are important attributes of yield.Tree girth (circumference of the stem or butt) is often left to be measured in the field or pot immediately after planting out the experiment. A point about 300 to 600 mm above ground and at least 150 mm above the graft union is chosen for the measurement of girth. This point is marked on the tree with a plastic paint ring painted on the tree, so that each year the same part of the stem can be measured. Tree girth is measured with a flexible tape of the non-stretching type (e.g. Fiberglass or thin steel) to the nearest whole mm. The tape is wrapped around the stem and the circumference read. With long term experiments girth is usually measured yearly on the same date in the dormant part of the season. For shorter experiments or monitoring particular growth phases and responses, sensitive dendrometers can be used to



measure very accurately tree girth changes throughout the day.Tree canopy surface area or silhouette area can be measured with a simple sketching device (see papers by Chapman, Bell and Bell, 1986; and Bell and Bell, 1983). Because tree canopy surface area or silhouette area and tree girth are well correlated with yield, long-term yearly records are of a great deal of benefit in tree crop research.Other measures of tree size which have been utilized over the years include:-1. East-west spread 2. North-south spread 3. Tree height4. Stem height5. Number of primary branches and

tree volume and 6. Index of crown height

The paper by Chapman, Paxton and Maggs (1986), summarises the use of this information for guava (Psidium guajava L.) and gives formulae for calculation.However, in general, I believe that girth and tree canopy surface area or silhouette area will be more useful in interpreting treatment response with most tree fruit crops, and the data are easy to collect.N.B. Girth data are usually converted to cross-sectional area of the butt or stem, when related to yield attributes etc with trees. Pruning weights. Where pruning is done to shape up a developing tree it is useful to keep weight records of the vegetative growth removed from a tree. This is particularly important where the tree is to be sampled at a later date for total weight of vegetative growth at the conclusion of an experiment. The data are also useful in determining the onset

of level of cropping and how pruning levels influence these factors.Pruning at any stage in the life of a tree pushes the trees balance back towards vegetative growth and reduces cropping. However, pruning does have a number of desirable effects such as reducing the tendency towards biennial bearing in heavy cropping cultivars, improving fruit quality, reducing fruit blemish, improving fruit colour and strengthening and opening up canopies and improving fruit size etc.Where pruning is done, notes should be kept on the type of pruning practiced, e.g. heading back, thinning out, blossom pruning etc. and the approximate amount of material removed at each cut, e.g. 100 mm, 300 mm etc along with pruning weights if required.

(iv) General commentsA number of the pre-planting primary variables listed above may be repeated at the conclusion of an experiment or on many occasions during the course of a long-term experiment. In the latter instance, the non-destructive measurements will be the ones often repeated.

(B) Pre-bearing dataMost tree crops have a juvenile growth period of two years or more (sometimes up to 6 years) before the tree is ripe to flower and produce fruits.During this period measurements can be made on a number of different attributes of the tree. Commonly tree girth is recorded as a basic primary variable, along with other measures of tree size, e.g. canopy surface area or silhouette area, or canopy volume.Dependant on the nature of the crop and the treatments employed, the intent of the experiment and labour available, any or all of the primary data listed under A(i), A(ii) and A(iii) above may be measured and data recorded to explain



the influences of treatments imposed over time. In most instances with field experiments data listed under A(iii) would be more commonly recorded.Again we should keep firmly in mind that with our data records we are attempting to build a concise picture of the growth and development of a tree crop in relation to the treatments imposed and the climate and management prevailing. Interpretation of the data helps us achieve this aim and provides an understanding of why a plant behaves and reacts to treatments in a given way. With this understanding we are then in a good position to prescribe management practices that give good yields of high quality produce, with economically viable and practical inputs.

(C) Post-bearing dataAs tree crops grow and develop there comes the time when the tree begins to flower and produce fruits. At this time data relating to the fruiting of the plant must be recorded along with the other vegetative growth variables such as those listed under (A) and (B) above.The standard types of primary variable data records are: 1. Total fruit weight (kg), marketable

fruit weight (kg) and non-marketable fruit weight (kg)

2. Mean fruit size - (g) 3. Mean harvest date

(i) Total fruit weight, marketable fruit weight and non marketable fruit weightTotal Fruit Weight is measured in the field with a set of scales (e.g. Spring Balance) to the nearest 0.5 kg. This measurement includes all fruit, both those that are on the tree and on the ground, even if beginning to decay. This measurement gives an important idea of the tree's potential to produce.

By selecting the fruit that would be suitable for marketing from the total crop picked, the Marketable Fruit Weight is found along with the amount of Non-Marketable Fruits.If desired a further break-up of Marketable Fruit can be made by grading the fruit into three or more size categories and the amounts recorded in each category.Similarly Non-Marketable Fruit can be size graded in the same way and classified on the basis of rejection, e.g. fruit rots, insect damage, overripe fruit, hail damaged fruit, bat damaged fruit etc.

(ii) Mean fruit sizeIt is usual with longer-term field experiments to have an index of mean fruit size of marketable fruits. This index is usually determined by weighing 100 fruits from each tree during the second harvest each year where weekly sequential harvests are required. Mean fruit size for one fruit is then determined and expressed to the nearest 0.1g. Alternatively all fruit may be graded by machine or by hand for size or for weight and the distribution of various size ranges recorded for all fruit or for 100 randomly picked fruits. For young trees cropping for the first time, 10 or 20 fruits may have to be used, as the fruit load per tree may be very small.

(iii) Mean harvest timeMean harvest date is determined for each treatment by calculating a weighted average harvest date; the weightings being yield at each harvest. Alternately, mode harvest time can be estimated by examining the harvest data records to determine at which date the largest pick of fruit was taken from the tree.With different crops more and different attributes may be measured, e.g. with



grapes the Merbein Bunch Count Method may be employed to indicate which part of the cane (2nd, 3rd, 4th, 5th or 6th spur) carries the most fruit, has the biggest bunches and the biggest berry size. Such attributes are under both genetic and environmental influences and have important consequences for management. (See Antcliff et al 1972).

Secondary VariablesData classified under Secondary Variables refers to data derived from the plant and which is not of a primary nature, i.e. developmental data, fruit quality data etc.For convenience we may classify secondary variables as follows:-A. Fruit quality data B. Phenological dataC. Insect & disease population data

(A) Fruit quality dataAssessment of fruit quality is perhaps one of the most important aspects of tree crop research. If the quality of a tree fruit cultivar is poor, there is little point proceeding further with yield evaluation, environmental adaptability or productivity management studies. Fruit quality is often assessed on trees in Plant Introduction Blocks, before formal experiments begin, and cultivars are often rejected at this time because of poor fruit quality.However, fruit quality should be assessed over at least two and preferably three cropping seasons before a cultivar is rejected on the basis of poor quality, since fruit from young trees often differs from fruits borne in later years, especially with respect to keeping quality.Many quality aspects are important in horticultural distribution (See Schoorl, 1984; Schoorl and Holt, 1983c; Schoorl

and Holt, 1985; Mayer, Holt and Schoorl, 1984.)The list of fruit quality data is almost as long as the mind can imagine. However, there are a number of commonly measured aspects that will be described here to illustrate important principles, viz.1. Fruit size 2. Fruit colour 3. Brix (total soluble solids) 4. Titratable acidity 5. Percentage flesh (edible portion),

Percent seed, Percent skin, and Percent flesh recovery

6. Taste, Appearance, Texture7. Instron measurements, Bruise

resistance and crack resistance8. General acceptability9. Transpiration coefficient and

respiration coefficient 10. Post-harvest shelf life11. Other attributes(i) Fruit Size was discussed previously under Primary Variables (C) Post Bearing Data. (Refer page 16).(ii) Fruit Colour can be recorded in a number of ways, e.g. by description, by photography, by 0-4 scales, or by using the full standardized Munsil Colour Chart, where every colour has a given standard for hue, value and chroma. This chart is an internationally accepted standard.(iii) Brix - total soluble solids is measured with a refractometer either hand-held or bench model compensated for temperature changes. Brix, titratable acidity, percent flesh, percent skin and percent flesh recovery measurements are often done together. Juice is extracted from fruits, mixed into a composite sample and smeared on the refractometer and the brix reading noted. With small fruits such as grapes, up to 50 fruits are used to make a composite sample. For larger fruits



such as peach, orange, kiwifruit etc, 10 fruits provide an adequate sample. For intermediate sized fruits such as lychee and longan, 20 fruits provide a representative sample.(iv) Titrateable acidity10ml of juice diluted with 10ml of distilled water are titrated with N/10 Sodium Hydroxide and Phenolphthalein indicator to the end point. Alternatively titrate to an absolute endpoint of pH 9.0.The titre (mls of N/10 NaOH) multiplied by 0.064 gives the Citrus Acid Equivalent – an accepted standard used for many fruits. The higher the value the more acid the fruit. Also Titratable Acidity may be expressed simply as mls of N/0 NaOH. The larger the value the more acid the fruit.If desired a sugar/acid ratio can be used for fruits and is equal to:

Fruits with a good sugar/acid balance are more acceptable to eat.As for Brix measurements, the sample taken for determining Titratable Acidity should be derived from a composite sample of at least 10 to 50 fruits, depending on fruit size.(v) Percent flesh, percent seed, percent skin, and percent flesh recovery are all done essentially in one operation. After weighing the sample of 10 to 50 fruits, the fruit is peeled (skin removed), the seeds extracted and the flesh (edible portion) separated.The weights of skin, seed and flesh are recorded and percent flesh recovery calculated.Percent Flesh Recovery

Juice is extracted from the flesh for brix and titratable acidity measurements.

N.B. With nut crops such as Macadamia and Cashew, standard methods have been developed for assessing Kernel Recovery % and Percent No.1 Grade Kernels etc.(vi) Taste, appearance, texture, general eating quality and acceptabilityThe attributes of taste, appearance, texture, general eating quality and acceptability may be rated on the Hedonic Scale 1-9, or using a 0-4 abbreviated scale, which may be more objective. The Hedonic Scale, recognised internationally is as follows:

1 - Dislike extremely2 - Dislike very much3 - Dislike moderately4 - Dislike slightly5 - Neither like nor dislike6 - Like slightly7 - Like moderately 8 - Like very much 9 - Like extremelyAn example of a 0-4, five point scale may be as follows for an appearance rating: 0 - Very poor appearance1 - Poor appearance2 - Good appearance3 - Very good appearance 4 - Excellent appearanceThese ratings may be applied to the whole fruit or samples of the edible flesh.General eating quality is usually applied to one fruit type subjected to different treatments or different cultivars of the same species.General acceptability is the attribute usually applied to testing out a new fruit or fruit product with a taste panel or the



general public, i.e. where you are looking for a yes/no answer.As one might imagine there are many ways that fruit attributes may be rated, apart from those mentioned, e.g. pulp appearance, pulp texture, fruit discolouration, skin rots etc.(vii) Instron measurementsThe Instron Universal Testing Machine gives internationally accepted measurements of fruit firmness, when fitted with standard heads and driven with standard cross-head speed and set for standard depth of probe penetration for a given chart speed.More importantly the Instron machine can make measurements of tensile strength of fruit samples, which is an important character in transportability, and handling ability of fruits (See Schoorl and Holt, 1983a).The machine is very expensive to purchase, but is a must for a well set-up post-harvest handling and management laboratory.Instron measurements are commonly made in post harvest research but are less common in production research.(viii) Bruise resistance and crack resistanceBruise resistance (Schoorl, Mayer and Holt 1983; Schoorl and Holt 1980; Holt and Schoorl 1983) and crack resistance (Schoorl and Holt 1983b) are simple measurements which can be made on fruits and which are essential in determining how fruits will stand-up to transporting and handling.(ix) Transpiration coefficient and respiration coefficientTranspiration Coefficient of fruits or vegetables refers to moisture weight loss/unit time for a given weight of product under a given set of conditions. It is an important character in determining post-harvest life of fruit, vegetable and flower products.

Transpiration Coefficient is simply determined by weighing to determine water loss (Holt, Schoorl and Mayer 1984). Respiration Coefficient refers to the energy output from fruits and vegetables during the post harvest phase. Again Respiration Coefficient is important in determining post-harvest life of products under given conditions, e.g. shelf or cool storage etc. Respiration Coefficient is usually measured with an infrared gas analyser or gas chromatograph, which can monitor carbon dioxide evolution from the product. (Schoorl and Holt, 1984).

(x) Post-harvest Shelf LifePost-harvest shelf life is simply assessed by selecting say 50 fruits from any one treatment or cultivar and setting them out on a bench or shelf in the laboratory and assessing the time taken for fruit to still have general acceptable eating quality.Fruits are sampled each two (2) days for generally acceptable eating quality. On each occasion 2 or 3 of the most mature fruits are sampled and rated on a 0-4 scale. (See Schoorl, Mayer and Holt, 1984).(xi) Other attributesThere are many other attributes that may be rated on fruits, e.g. symmetry of shape, susceptibility to bruising, smoothness of skin, seed numbers, number of aborted seeds, seed percentage, astringency, grittiness of flesh, calyx dehisence or respiration from the fruit, uniformity of shape, ease with which fruits may be packed for market (depends on shape, firmness, skin toughness etc.).The attributes selected for measurement depend to a large extent on the fruit and the use to which a fruit may be put, e.g. with guava and acerola, vitamin C level



is a very important attribute for fresh and processed fruit etc. etc.Intelligent selection of the important attributes is required and the researcher should ensure that he/she understands these requirements.(xii) General commentsAs with all other data, fruit quality data ratings etc. are subjected to biometrical analyses such as "t" tests, analyses of variance, regressions, Chi-square tests etc.

(B) Phenological dataDefinitions: Phenology is a word that has been variously defined, e.g. 1 "As the branch of science concerned with relations between climate and periodic biological phenomena".e.g. 2 "The descriptive study of behavioural characteristics of organisms in relation to their environment".Phenometry has been defined as the quantitative measure of life cycles of specific phenophases (e.g. growth phenophase, flowering phenophase etc).The value of Phenology and Phenometry in ecosystem analysis lies in the understanding they provide of plant responses to climate.For modem land users, a knowledge of Phenological relationships of a crop gives us an opportunity to select plant genotypes and develop management practices in a way which will optimise plant productivity, and/or obtain particular products from the land at the time of season at which they are required.Once we have chosen a particular genotype or in our case a particular fruit

cultivar, the next most important concern is plant manipulation and how this modifies Phenology and thus optimal productivity.In some instances we may find that the fruit cultivar readily adapts to a range of climates, while other cultivars are not at all adaptable.Phenology and Phenometry help us to identify adaptable and non-adaptable genotypes as cultivars and provide an understanding of interactions with the environment and thus clues on how to manipulate the plant.When we begin to examine plants in this way we quickly begin to appreciate that certain plant activities or phenophases may be under either exogenous or endogenous control. While initially it will be easier to document exogenous controls, we must be ever aware that the final response may be under endogenous control or modification.With trees and especially with tree crops where the fruit is the end product harvested, we are acutely aware of development phases or phenophases with the passing of a season, e.g. shoot growth flushes, flower initiation, flower emergence, anthesis, fruit set, fruit drop, fruit maturation, leaf shed, etc.What we are suggesting in the collection of phenological data is that we record these phenophases in relation to the calendar year and especially in relation to the meteorological data, plus soil moisture and soil temperature data.Phenometrv essentially quantifies the phenophases, e.g. the length of a shoot growth flush, the intensity of flowering (a rating of inflorescence appearance or an actual count of inflorescences per tree), the number of fruit set per terminal or per lateral etc.Listed below are the usual exogenous phenophases that we record with tree fruit crops and these comprise the Phenological Data Base.



Exogenous phenophases1. Shoot growth flushes - dates2. Inflorescence emergence and

anthesis - dates3. Fruit set, fruit development and fruit

drop - dates 4. Harvest dates5. Post harvest shoot growth flushes -

dates 6. Root growth activity - dates7. Leaf shed periods - dates8. Growth cessation - dormancy - dates9. Honey bee and pollinating insect or

animal activity - dates

All of the above data are accumulated by continual and periodic visits to the trees on a regular basis, e.g. every week, every two weeks or every month, depending on the stage of tree development, e.g. when flowering commences we may inspect the trees each two to three days to monitor the length of the male, female and hemaphrodite flower stages in a crop such as lychee. During fruit development a record each two weeks may be adequate and so on, depending on the data details needed. With each of the above phenophases we may attempt to quantify the event as well as recording the dates. This is Phenometry.Examples of Phenological and Phenometric measurements are given below:(i) Shoot growth flushesRecord the date of flush emergence in an experiment or orchard by visiting the site each 2 to 4 weeks. Standing about 8-10 metres from the tree estimate the percentage of non-fruiting terminals showing a young growth flush, e.g. 10%, 30%, 60%, 90%, etc. Using three or four visits over the flushing period, the peak

flush date or period can be determined for different cultivars or treatments.If desired the average length of a growth flush on each tree can be measured by tagging at least six (6) and preferably ten (10) growth flushes with a bright yellow ribbon or plastic strip as they emerge. At the end of the growth flush when fully expanded mature green leaves are present and the growth has ceased, the length of the flush (in mm can be measured. If required, the number of leaves present can be recorded. Also leaves can be removed from some flushes and the areas measured and recorded. In this way the intensity and size of the growth flush can be quantified.(ii) Inflorescence emergence and anthesisAs well as recording the date of inflorescence emergence, by tagging at least ten (10) terminals or laterals with fruiting buds and frequently visiting the site, again the event can be quantified, e.g. by rating or measuring the length of the inflorescence at anthesis, counting the number of flowers or flower buds and counting the number of different types of flowers (male, female, hermaphrodite) etc.With trees such as lychee where one is not sure if an inflorescence or vegetative growth will emerge from a bud, it is advisable to tag at least 15 terminals per tree, or 20 terminals on young trees and those which are known to be poor croppers.(iii) Fruit set, fruit drop and fruit developmentFruit set dates can be recorded by frequent visits to the site (e.g. weekly), and rating the percent of fruits set on tagged terminals or laterals. This is done by rating the percentage of fruits of a predetermined arbitrary size, e.g. pea size, soybean size etc. Again more detailed data can be collected by



counting the number of fruits set. Continued rating over the following weeks or continued counting will yield data on fruit drop and development week by week.Fruit development can be judged by counting or rating the fruits of a larger given size, e.g. 2 cm in diameter etc. and bigger as time passes. Fruit drop can also be assessed on a per tree basis by collecting in containers (about 0.5 - 0.75 m2 in area) fruits that have fallen each week from a tree. About four catching containers spread under the four sectors of the tree canopy should be adequate. Fallen fruits can be counted, weighed, dried, weighed and dissected to determine the reasons for fruit drop, e.g. insect damage, aborted embryo, no embryo, etc.(iv) Harvest datesSee previously mentioned in the Primary Variables (C) Post Bearing Data section of this paper. (Refer page 16).(v) Post-harvest shoot growth flushesAs for Shoot Growth Flushes mentioned previously. (Refer page 21).(vi) Root growth activityRoot growth activity is comparatively easy to assess with trees such as avocado, which are surface rooting and where under the tree is covered with a good depth of leaf mulch. A root window is used. A root window is an area where the surface leaf litter mulch and surface soil are removed to a depth of about 25 mm to expose the root system. A hessian bag of double thickness is placed over the roots and then covered with newspaper and the original mulch and leaves replaced. Root activity and root health of the tree can be monitored by regular inspections and rating the percentage of active roots and healthy roots that have grown through the hessian bag.

One or two such root windows per tree about 600 x 600 mm is sufficient to monitor root activity and general root health.With deeper rooting trees a root observation chamber with glass or perspex sides may be needed to monitor root activity with better precision. These are more costly to install, as they must allow a person to slide down into the chamber to make the rating, although a wide angled lens camera could be used.(vii) Leaf shedLeaf shed can be monitored in one of two main ways. Again ten (10) main shoots or branches can be tagged throughout a tree and leaf counts made on a regular basis, e.g. fortnightly or monthly. Alternatively, leaf shed can be monitored as for fruit drop, by placing collecting containers under a tree - four containers each 0.5 - 0.75 m2 in area per tree will be sufficient. Leaf counts and weights etc. can be made weekly through the fruit shed period.(viii) Growth cessation - dormancyMany of the tropical and sub-tropical fruits don't enter a stage of deep dormancy but they do cease to grow for long periods, which more often than not coincide with very dry conditions. Growth cessation is a very important characteristic of many tree fruit crops since it is a prerequisite for floral induction in the plant, e.g. mango, longan, lychee, citrus etc. Thus an understanding of the process and the regularity or irregularity of occurrence has important consequences for productivity.Growth cessation can be simply monitored as for growth flushes by regular inspection of the tree for flushing and measurements of shoot growth on tagged terminals or laterals.(ix) Honey bee and pollinating insect or animal activity



During the flowering period it is useful to note if honeybee or pollinating insect activity is good or poor, since this may have an important bearing on productivity. Notes on activity can be simply made during regular inspections of trees from inflorescence emergence through anthesis to early fruit set. Additional notes on the insect or animal, and whether they are pollen or nectar collecting may be useful in assessing their importance as pollinators and explaining good or poor fruit set.Notes may also be kept about why pollinator activity was low e.g. very dry conditions and thus little nectar flow; wet overcast conditions with low bee activity and high fungal attack of flowers; hot dry conditions where flower burning, drying off and shed was excessive etc. Such notes at a later date can be easily checked back to local Meteorological Records for verification.

Endogenous phenophasesIt is far less obvious what processes and changes are taking place within the tree and which have no obvious external (exogenous) morphological expression.In more recent years we have come to appreciate that to understand how to manage a tree to give optimum productivity, we must understand both the exogenous and endogenous phenophases.Phenophase is probably not the right word to refer to metabolic, hormonal, nutritional and water process changes within a tree, since they are changes in the level of activity or components rather than changes in morphology or phases of development. However, these endogenous process changes can and are often the driving force behind exogenous phenophase change, which may be triggered by exogenous and/or endogenous stimuli. Again, there are indeed hundreds of endogenous tree attributes that we can measure,

however, there are but a few which most horticulturists would be able to measure on a regular basis.These are:- (i) Hormonal Status Photosynthesis and Respiration Plant Water Status(ii) Nutritional Status of Plant Carbohydrate Status of Plant(i) Hormonal Status, Photosynthesis and Respiration and Plant Water Status Measurements These require some fairly sophisticated and expensive equipment and trained personnel to measure accurately these endogenous processes. It is usual for these measurements to be of shorter duration rather than monitoring changes throughout the year. Plant water status may be an exception as it can now be monitored regularly, and with the portable photosynthesis equipment more regular monitoring is feasible.(ii) Nutritional Status of Plant Carbohydrate Status of PlantNutritional status of the tree and carbohydrate levels, lend themselves to more regular monitoring, e.g. monthly.Leaf nutritional status is monitored by sampling 5 leaves per tree from about 10 trees monthly and subjecting the washed, dried and ground tissue to complete analysis for major and minor nutrients (results may be related back to soil nutrient status). (See Stephenson et al (1986) and Stephenson et al (1986 a).For carbohydrate levels a piece of bark and wood tissue is removed from the trunk of a tree at a given height above the graft union (say 25 to 50 cm above) with a clean cutting wood boring bit about 20 mm in diameter. The cut into the tree is made about 5 to 10 mm deep depending on the depth of bark. Trees may be sampled monthly by taking samples in a spiral pattern around the tree. Both bark and wood are analysed separately for total ethanol soluble



sugars and starch on a residual dry weight basis - this essentially measures the amount of these substances present in the cell walls of bark and wood. Samples are taken according to treatment or layout in an experiment, or in an orchard at least 10 trees of each cultivar under similar management would be sampled.

How to use phenological data:With tree fruits it is important to know what the end point is in the use of Phenological Data. Essentially we use the data to build a picture or model of a tree's development and what it is doing month by month throughout the year, so we can use this information to schedule our management practices, to give optimum productivity.Figure T- gives a diagrammatic representation of the Phenological cycle of the avocado throughout a year in south-east Queensland, Australia. The diagram shows all the important events or phenophases of the avocado's development and the important cultural practices in relation to phenophase and time of the year (See Cull, 1986).

The next step from this point is to relate the above phenophases and cultural practices to average climatic conditions that prevail in the region to show how these affect phenophase and cultural practices. This can be done by graphing such attributes as minimum and maximum temperature, rainfall, soil temperatures, evaporation, radiation, soil water balance etc. throughout the year and comparing these with the Figure 1 diagram.

With this comparison we can identify beneficial and adverse climatic effects on phenophase and thus management practices.Finally, again it is worth noting that with some tree fruits, unless the genotype or cultivar is suited to an environment which is conducive to flowering and fruit set, it can be very difficult to manipulate such a plant to advantage with management practices.

Figure T- The total growth cycle of cv Fuerte avocado at Palmwoods. Growth forms are dependent on each other, but all compete for tree resources. Management of this cycle leads to improved fruit yields.



Phenophase monitoring by collection of Phenological Data and relating it to climate will help to quickly identify adapted and non-adapted cultivars or genotypes. Once the adaptable cultivars are identified, Phenological Data then become a powerful tool in helping to fine tune management for optimum productivity.

(C) Insect and disease population dataHorticulturists, physiologists and breeders should get to know the insects and diseases, which attack the crops they are researching. Furthermore, with their regular visits to field and with pot experiments, researchers should note disease and insect incidence in their crop experiment diary and indicate the action taken on a given date to overcome the problem.If these simple records are not kept along with the honeybee and pollinating insect or animal activity, then results obtained at harvest or the end of an experiment may be meaningless eg. one cultivar may simply be better than another because of resistance to disease and not because it is a superior

genotype. Such issues are very important to understand.Thus, during your experimentation carefully monitor disease and insect activity and note the effects on fruit yield and quality.Remember we are not only in the business of obtaining research results but we are also required to explain the results.

Derived DataDerived data are simply data derived, calculated, deduced or extracted from measurements of Primary or Secondary variables listed earlier in this paper.The range of derived data is as large as one's imagination and to attempt to cover the range in this paper is an impossible task. What I have done is to concentrate on derived data commonly used in tree crop experiments and in particular those concerning yield and vegetative growth and their relationships. Balancing these relationships in tree crops is essentially the key to optimising productivity once desirable fruit quality is found.



(A) Relative increase in trunk cross sectional area (R – cm2/year)Trunk girth (G) or butt circumference measured in mm each year can be used directly to statistically compare treatments and cultivars in experiments. R, the Relative Increase in Trunk Cross Sectional Area (%/year) is simply defined as:

where A is the cross sectional area of trunk in cm2 at times t2 and t1, usually one year apart.A is determined from G as follows:

Where R is calculated each year during an experiment, beginning with young trees, it is easy to see how the relative increase in trunk cross sectional area declines as the trees being to crop. This shows the diversion of assimilates from vegetative growth into crop. The R value for different treatments of cultivars may differ considerable as cropping commences, showing the precocity of bearing and the relative diversions of assimilates.

(B) Yield tree size relationshipsEast-West Spread, North-South Spread, Tree Height, Stem/trunk Height, Number of Primary Branches, Tree Volume and Index of Crown Height together with various products of these, e.g. Tree Height X N-S Spread X E-W Spread may be related to Total Annual Yields, Total Accumulated Yield, Marketable Annual Yields, Marketable Accumulated Yields, etc. This can be done using regression on either raw data or transformed data, etc., or by using a multiple regression approach.

(See Chapman, Paxton and Maggs, 1986 and George and Nissen (1982)).Tree canopy volume can be estimated as:

where h is tree height (m), d is canopy diameter (m, average of North-South and East-West diameters), S is canopy skirt height (m), and V is canopy volume (cubic metres). Depending on the tree species and the spatial arrangement of trees, either good or poor R2 values for regressions may emerge, to explain relationships and difference between treatments or cultivars.Alternatively, Yield per Cross Sectional Area of Butt or Trunk (Yc) may provide a good measure of yield efficiency with apples (Westwood and Roberts, 1970) and guavas (Chapman, Paxton and Maggs, 1986). Other versions of this simple relationship for marketable annual and marketable accumulated yields may be treated as a regression fit using yield as the dependent (y) variable and growth as the independent (x) variable. This latter approach is more useful in selection programs and may benefit from applying a log-log transformation of data, e.g. with Macadamia (Chapman, Bell and Bell, 1986).The simple girth measurement with guava and use of yield per cross sectional area of butt give a more simple useful index of yield efficiency than more complex parameters including a Fruitfulness Index of Moore (1968) and multiple regressions after Shikhamany, et. al. 1978. Also with guava, we found that trunk girth provided at least as good an estimate of tree size as the Vigour Index of Moore (1968).



Fruitfulness and Vigour Indices are calculated from all girth and accumulated yield values and scaling them by dividing by the Standard Error (S.E.) of all values and taking the sums and differences to give Vigour and Fruitfulness indices respectively for different cultivars or treatments.

Tree Fruit Yields whether marketable or total, annual or accumulated are frequently related to other units, e.g. Yield/tree, Yield/ha, Yield/Unit of input, Yield/Unit tree size/unit land area, Yield/unit surface area of canopy, Yield/unit silhouette area of canopy, etc. From the agronomic and the farmer's viewpoint, yield of marketable product is the measurement of most interest, e.g. with Macadamia, it is yield of First Grade Kernel which is the most important consideration. For most tree fruits yield/unit tree size is of critical importance since it is tree size and plant density that determines yield per hectare.With most tree fruit tested, spectacular increases in yield/hectare have resulted from smaller tree size and higher tree number/hectare and good illumination of canopies. This is not surprising since more of the assimilates can be directed to fruit production in small trees instead of going into structural support. These small trees often have a higher Harvest Index, once again justifying the need to relate yield to unit tree size. (See Chapman, Bell and Bell, 1985 for extra reading).As yield/unit tree size is important, we use a number of ways to measure this relationship. Some have already been discussed, e.g. Yield/Cross Sectional

Area of Butt and other Yield/tree size relationships.From studies with macadamia we have found the relationship between yield and canopy surface area and yield and silhouette area of a canopy to be of particular interest (Chapman, Bell and Bell, 1986). Canopy Surface Area or Silhouette Area can account for up to 77% of the variation in yields in Macadamia. Such good relationships are very important in identifying treatment and cultivar effects and can be used for selection purposes (Chapman, Bell and Bell, 1986, and Winks, Bell and Bell, 1986). It is also noteworthy that Canopy Silhouette Areas are simply measured with an easily constructed measuring device (Bell and Bell, 1983) and thus there is little excuse for not testing this methodology with other tree crops.The above relationships between yield and tree size can be examined over a number of cropping seasons. We would suggest that in cultivar evaluation experiments that relationships should be recorded over at least three cropping seasons for highly productive species, e.g. Guava and five seasons for less productive species, e.g. Lychee.As a further suggestion Yield/Cross Sectional Area of Butt and Yield/Canopy Silhouette Area would appear to offer the better simple parameters for measurement.Many other correlations or regressions may be examined which relate yield or yield attributes to other tree characteristics, e.g. Yield (fruit number) and number of weak laterals in custard apple (George and Nissen, 1986).

(C) Primary, secondary and derived data - time or climatic relationshipsOften it is useful to relate primary, secondary or derived data to month of



the year, time of the day or some climatic measurement such as minimum air temperature etc.Such data may be shown graphically, e.g. such as flowering period (phenophase) in relation to month of the year or some climatic variable such as minimum temperature. Alternatively, we may wish to correlate minimum air temperature at flowering with the number of nubbins (seedless small mangos) at harvest for different cultivars or sites, to establish pertinent points about flowering time, climate and desirable fruit set.Another example may be a correlation between the percent of terminal branches dormant at one time of the year with the percent of terminal branches flowering later in the year, e.g. with lychee.These are but a few examples of the many thousands of relationships that may be examined where primary, secondary and derived data may be related to time or other parameters such as climate or indeed other plant data.

(D) Classical growth analysis dataGrowth Analysis includes the following parameters:(i) Biomass Relative Growth Rate (RGR) (ii) Net Assimilation Rate (NAR) (iii) Crop Growth Rate (CGR) (iv) Leaf Area Ratio (LAR)(v) Leaf Weight Ratio (LWR) (vi) Leaf Area Index (LAI) (vii) Leaf Area Duration (LAD) (viii) Specific Leaf Area (SLA) (ix) Harvest Index (HI)(x) Biomass Duration (BMD) etc.

The following texts are recommended for study, viz. Kvet, Ondok, Necas and

Jarvis (1971), Radford (1967), Charles-Edwards (1982), Watson (1952) and Watson (1963).The above relationships are more difficult to measure in tree crops in field experiments because sequential, regular, destructive harvesting is difficult and expensive. However, they are often calculated for short-term pot experiments or for flower crop experiments, which approach field crop configurations.The Classical Growth Analysis formulae do have a place in tree crop studies, but their use is usually confined to experiments that seek a basic understanding of tree crop physiology and response to treatment, often under controlled environmental conditions.

Auxiliary DataAuxiliary Data include all other data that routinely should be collected and be available throughout the course of a tree crop experiment, viz.(A) Meteorological Data (B) Leaf Analysis Data (C) Soils Data(D) Water Quality Data (E) Management Data

(A) Meteorological dataIt is usual to record meteorological data as a matter of course with tree crop experiments. Most Research Centres and Stations will have a fully equipped Meteorological Station, but for "on-farm" research you may have to install your own. The usual measurements taken are:-(i) Maximum and minimum, grass, air and soil temperatures taken at 0900 and 1500 hrs for soil depths of 10 cm to 20 cm(ii) Rainfall (0900 hrs)(iii) Class A Pan Evaporation - 0900 hrs



(iv) Wind Run and Direction (0900 and 1500 hrs)(v) Relative Humidity - from wet and dry bulb thermometers (0900 and 1500 hrs) (vi) Solar Radiation (0900 hrs)(vii) Occurrence of frost, cyclones, hail storms, rain and wind storms, cloud type (0900 and 1500 hrs)(viii) Rainfall intensity - Pluviograph (0900 hrs)A minimum data set for "on-farm" research would be (i), (ii), (iii) and (v) above and a calculation of saturation vapour pressure deficit from wet and dry bulb temperatures.Meteorological data are very important in explaining the phenological responses of tree fruits and treatment responses - see earlier.

(B) Leaf analysis dataDuring the course of a long-term experiment or at the start of an experiment superimposed upon established trees it is useful to know something of their plant nutrient status. This is particularly important, in the latter instance if the experiment is to continue for some years.Where practical, and where leaf analysis facilities are available, I would recommend that a complete leaf nutrient analysis be undertaken at least once per year with long-term experiments. Analyse for N, P, K, Ca, Mg, Fe, Mn, Zn, Cu, B, Mo, Na, CI, S.

(C) Soils dataBefore commencing s long-term tree fruit experiment it is usual to determine both the soil physical and chemical conditions.At a Research Station or Centre during the early planning stages when blocks are being set out and a farm plan is prepared one of the first tasks should be the preparation of a complete survey

based on a 15 m grid and 0.5 m contour interval.For Soil Physical Properties the following data should be recorded on a 15 m grid basis for each block, viz.(i) Depth of surface and sub-soil horizons (ii) Texture of horizons(iii) Colour of horizons (iv) Consistence Structure of horizons (v) Drainage status rating(vi) Slope Aspect Level of water table - if present

For a Chemical Description of the soils, bulked samples from similar soils within a block should be analysed for:-(i) pH Salinity (conductivity) (ii) Alkalinity(iii) Cation Exchange Capacity(iv) Complete Soil Nutrient Status - N, P, K, Ca, Mg, Fe, Mn, Zn, Cu, B, Mo, Na, CI and S.

At a Research Station or Centre once the above Soil Physical and Chemical Conditions have been determined, it will only be necessary to determine soil chemical conditions if it hasn't been determined for some time, unless major manipulation of the soil has taken place to modify physical condition.During the course of a long-term tree fruit experiment, annual determination of the Chemical Condition of the soil is desirable. If analysis facilities are limited then pH and soil salinity should at least be recorded yearly.

(D) Water quality dataBefore commencing any tree crop experiment that is to be irrigated, tests of the water to be used should be undertaken. The tests required for water quality are:-



(i) pH Salinity (Conductivity) (ii) Alkalinity(iii) Sodium Absorption Ratio (iv) Chlorides(v) Bicarbonates and Carbonate and Sulphates(vi) Iron - if under tree, Drip/Trickle/Microjet irrigation is to be used.Where Boron levels in water are known to be high in a Region, B concentration should also be determined.At a Research Centre or Station, where one or two water sources are used for irrigation each source should be tested, at least yearly in the dry season.Routine tests of pH, conductivity and iron, are a minimum requirement for water to be used for irrigating free draining soils.Where high water tables are present in the soil, routine testing of this ground water should also be undertaken. Regular monitoring of the level of the water table in the soil, using Piezometer tubes (test wells) installed for this purpose, should also be carried out.

(E) Management dataFor each tree fruit experiment being conducted a separate Diary of activities should be kept in a book or folder together with all other data records.In the Diary a complete and detailed record of all activities carried out on the experiment must be recorded. This should include:-(i) Data Collection - Date, Typee.g. 21st May 1987 - 1st harvest, fruit weight and fruit quality test details.- Flushing records 20th June 1987 20th July 1987- Girth measurements etc.All data collected should have a date attached to each table.

(ii) Irrigation- Amount and Dates of Application along with weekly rainfall and evaporation (Keep a separate sheet of these details).- Date and Results of Water Quality tests(iii) Fertilizer Application- Date, Fertilizer type, Amounts applied per tree or per ha, and How Incorporated into soil(iv) Weedicide Applications- Date, Soil Chemical Concentration and Amount per tree or per ha. Wetting Agent etc.(v) Pruning Date- Details Recorded, Weight, Length of shoots removed etc.(vi) Planting Date and Preplant Fertilizer and Liming Details- Amounts and Chemicals used, how applied. (vii) Pesticide and Fungicide Applications- Date, Chemicals Applied, Concentration, Wetting Agent, Amount Applied per tree or Sprayed to Run-off etc.(viii) Defoliation Treatment Applied- e.g. Ethrel, Rate, Date, Amount, Applied Wetting Agent, Weather Conditions prevailing.(ix) Growth Regulators Applied- e.g. Date, Rate, Amount, Wetting Agent and Weather Conditions prevailing.

(x) Sampling Dates- Leaf Analysis, Soil Analysis, Water Quality Testing, etc. and Pertinent Details (xi) Dates at which Treatments Applied and All Details of the Treatments



(xii) Dates of Weeding, Mowing, Mulching, Cultivation, etc. along with Details- e.g. Grass Mulch applied to depth of 50 mm, under the canopy of all trees. (xiii) Dates on Which Special Operations are Undertaken- e.g. Cincturing, Blossom Thinning, Fruit Thinning etc. (xiv) Any Other Details Not Mentioned Above

It is important that very good records are kept of all management practices undertaken on tree crop experiments, and in particular with long-term experiments. In fact record keeping should be so good that another officer could take over the experiment, while the officer responsible is on study leave or if he/she is transferred.Very simply with tree crop experiments, because they are long term and expensive to run, the Golden Rule should be "If in Doubt then Record It - In Detail".

Concluding RemarksAt this point it should be quite obvious that a Researcher not only has to be a

smart thinker, but he/she must be a very good manager, who keeps good data records.It is also now much easier to appreciate the need for economical experimental design in tree crop experiments, as many data are often needed to explain treatment or cultivar result differences. These data may be readily collected from say single tree plots, adequately replicated, but impossible to collect for multiple tree plots with very large experiments.The researcher has many choices to make about the type of data he/she needs to collect. Therefore, he/she must have very clear in his mind the specific aims of the experiment and his hypotheses for testing. However, in all instances he/she must keep and have access to very good auxiliary data records.Please bear in mind that what has been discussed above is just a bare outline of Methods of Data Collection for tree fruits. Researchers should make every effort to develop, see and read about data collection techniques at every opportunity, to develop their skills in this very important aspect of research.

Sampling TechniquesIntroduction Sampling issues arise in most horticultural research. In an experiment we estimate the parameters of a population by studying only a small selection or sample of the values available in the population. The method of obtaining a sample assumes greater importance when the population is not uniform.

Variation between the individuals in a population leads to difficulties if attempts are made to generalise results based on a sample. If it were not for this ever-present variation, a sample consisting of a single value would provide all the information about the population. Sampling techniques have been developed to assist in establishing meaningful inferences about a total population from the values contained in a sample.



Reasons for sampling include:1. Experiments in which practical

limitations preclude collection of data from the entire population.

2. Experiments that involve destructive measurements.

Sampling MethodsFour basic methods for selecting a sample from a population will be discussed, namely,1. Simple random sampling ,2. Systematic sampling , 3. Stratified sampling , and 4. Cluster sampling .The method used in a particular situation will depend on the type of population being sampled and on the resources available for the investigation. As a general rule, sampling techniques involve random selection, as this guards against bias. For example, taking the first 10 trees observed in an orchard is not a random selection. These trees may be the closest to an access road and therefore the oldest or the largest, giving a biased sample. Of equal danger is selection of a few "average" or "typical" trees in each group. Again the sample is not representative of the group as a whole.

Simple random samplingThis technique is preferable when the population to be sampled is reasonably uniform and/or does not split into clearly different classes. A simple random sampling procedure gives each population unit an equal chance of selection in the sample.Two situations may arise due to the type of population being sampled: (i) Populations with discrete, easily identifiable units. An example of this type of population is trees in an orchard that can be identified by individual

numbers. A simple random sample can be drawn from such a population in the following manner. Suppose the trees are numbered 1 to 60. If a sample of eight trees is to be chosen for yield estimates, a set of eight numbers between 1 and 60 is computer generated or chosen from random number tables; for example, 46, 39, 9, 51 , 25, 37, 14, 22. Trees with these code numbers from a simple random sample.(ii) Populations with small units difficult to distinguish. Individual fruits, flowers or branches on a tree are examples of this type of population. It is impractical to take measurements on individual units so small groups of units, chosen at random, constitute the sample. For example, all flowers on a random number of branches could be measured rather than a random sample of all flowers on the tree.

Systematic samplingConsider a production line where a treatment is applied to prevent post-harvest disease in fruit. Suppose a five percent sample of the fruit is required. We could choose a random number between 1 and 20; say 17. Then take the l7th, 37th and 57th etc fruit as it comes along the line. This would be an easier system to operate than simple random sampling.This is an illustration of a systematic sampling scheme. As well as being easier to select, the sample is evenly distributed over the population. However, careful consideration should be given to the potential disadvantages of systematic sampling. They provide no protection against periodic variation within the units and subsequent statistical analysis may be a problem.

Stratified samplingThis type of sampling scheme is applicable when prior knowledge of the population indicates that there are



differences between groups of units. For example, different rootstocks may be present. Or, in an orchard, the same cultivar might be planted out in different areas, such as on flat land or on a slope, or on different farms.Simple random sampling over the whole population in situations such as this is not suitable. Rather the population should be divided into strata of blocks on the basis of prior knowledge of differences between groups of units and a random sample can be drawn from each stratum. A problem now arises of how the sample should be divided among the strata.Several methods of allocation can be used. The most popular of these is proportional allocation; that is, the size of the sample drawn from any stratum depends on the size of that stratum relative to the total population.The advantages of the stratified sampling scheme are similar to those achieved by blocking in a randomised block design. Effective stratification produces greater precision in estimates of overall population parameters than if simple random sampling were used.

Cluster samplingCluster sampling involves intense sampling on a number of sub-sections of the population. If we were interested in the incidence of disease in fruit in north-east Thailand we would simplify the field work and reduce the cost by using cluster sampling in place of simple random sampling. To use simple random sampling we would need to select from all the fruit on all farms in north-east Thailand. We would then have to examine the selected fruit. This would involve collecting fruit on almost all properties in the area, a task beyond the resources of any reasonable programme.

The required information may be obtained much more readily and with dramatically reduced cost by the use of cluster sampling. Under this system a sample of farms is selected at random to be investigated more thoroughly. Fruit are then collected at random from the farms included in this initial selection. Cluster sampling would involve collection and examination of fruit samples on only a small number of farms with corresponding reduction in costs.

Field Recording TechniquesThere are a number of methods of recording data in the field, ranging from the pencil and notebook to automatic data recorders utilising cassette tape or disk storage. There is no 'best' way, as different circumstances will require different methods. Bear in mind that recording may need to be done in rain, wind, etc.Automatic data recorders are obviously preferred where applicable. However these are usually limited to 'indoor' trials, for example greenhouse or laboratory experiments. When using these, it is essential to produce a hard-copy (i.e., paper print-out) of the data during recording, to guard against power failures, cassette malfunction, or data transfer disasters.The usual method employed is to record data into a rough scrap-book in the field, and later transcribe this onto neat data sheets for statistical analysis. This as an unnecessary step, and one that contributes probable transcription errors.Instead we recommend taking yield books into the field, and directly entering data onto sheets, which can be forwarded for statistical analysis. These should be loose-leaf data forms, held in a plastic-covered clipboard. They need to be carefully drawn-up beforehand (perhaps even computer-generated),



and arranged into field-plot order for ease of data recording. This sequence can easily be converted back to standard replicate and treatment order by computer manipulation prior to analysis.

Missing valuesMost statistical packages can cope with missing values, provided there are not too many of them. Usually, less than approximately 5 to 10% of the data should be missing.If single-tree plots are used, the decision on missing plots is relatively simple. If the tree is dead or severely diseased, it is regarded as missing, provided the reason for ill-health is not due to the applied treatment. If the treatment causes (or is suspected to cause) the problem, then data should be recorded and a decision on analysis and presentation can be made at the end of the trial. For example, the researcher may wish to quote overall growth rate of all trees, or just average growth rate of healthy trees only combined with

information of percent healthy trees for each treatment.With multiple-tree plots further problems arise. These plot types are used because inter-tree competition is expected, so resources (e.g. water, nutrients, light) left unused by a dead or diseased tree are accessible by surrounding trees. Hence, Datum trees adjacent to missing trees should not be recorded. This problem is not so severe if guard trees are missing, however missing datum trees can exclude all surrounding trees from being recorded. It is usual to take the plot data as the average of all unaffected trees (i.e., those fully surrounded by similar trees). For example, with 16 tree plots (4 by 4, with one guard row around the outside), the four inner trees are potential datum trees. Hopefully, most plots will use 4-tree averages, but due to missing trees some plots may use 3-tree, 2-tree or even single-tree averages for the plot value. Some may even be totally missing, and included in the analysis as such.

Data Analysis - Interpretation and ReportingThere will be no formal notes for this topic; the format will be a series of relatively challenging examples, each consisting of:1. Handing out a computer generated

analysis 2. Explaining the various features and

figures3. Get participants to write a paragraph

and/or table suitable for the 'Results' section of a publication

4. Feedback on a few answers, comparing similarities and differences

The examples planned are:4.1 Randomised complete block experiment4.2 Factorial experiment4.3 Multiple covariate analysis4.4 Regression analyses (yield vs. pre-bearing trunk circumference, tree height and diameter, and canopy volume and surface area).4.5 Log-linear model (multi-dimensional contingency table, using GENSTAT)


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