Field Trials: Principles - Case Study: A comparison of weight gain in sheep attacked and unatta

Case Study: A comparison of weight gain in sheep attacked and unattacked by the sheep headfly, Hydrotaea irritans (Appleyard et al.,

3.8 Field Trials: Principles

Experimental field trials are common to most components of insect pest management since every type of control option ultimately has to be tested in the field. With small and specific variations field trial techniques that have been developed in relation

to agronomic evaluations are applicable to evaluations of yield loss. The coverage pre- sented here provides an overview of the principles of field trials methodology to complement those methods relevant to yield loss assessment already mentioned above. Specific variations of the general methodology are dealt with in more detail in the relevant chapters.

Field experimentation can only be carried out with good knowledge of sampling methodology, experimental design and statistics. Sampling methodology has been dealt with in Chapter 2 and readers requiring further information should refer to Southwood (1978) and Cochran (1977).

The advice of statisticians should always be sought at the planning stage of the trials although entomologists should them- selves have a good understanding of the principles and statistical techniques to be used in such trials. Most standard statistical textbooks cover both trial design and the statistical analysis of trial data; useful books on the subject include: Bailey (1981), Puntener (1981) and Snedecor and Cochran (1978). An excellent review of investigated. The weights of the lambs were recorded at the beginning and end of

the trial and the weight gain was calculated for each lamb. The lambs’ heads were examined weekly for the presence of wounds which were quantified as fol- lows: 0 = no lesions detected; 1 = minor lesions detected only on close inspec- tion; 2 = moderate lesions readily detected; 3 = severe lesions requiring therapy;

and 4 = extreme damage. The level of headfly damage during the trial was low but affected animals had damage assessed as lesion score 2 on one or more occa- sions during the trial. The unaffected animals were free of clinically significant lesions throughout the trial. The effect of headfly damage on weight gain is shown in Table 3.2. Lambs which suffered clinically obvious damage had a mean weight gain of 12.18 ±2.31 kg compared with 14.05 ±1.77 kg for those that were unaffected (P < 0.001).

Table 3.2. The effect of headfly damage on weight gain of lambs (after Appleyard et al., 1984).

Affected Unaffected

(n = 23) (kg) (n = 102) (kg)

Mean SD Mean SD

Starting weight 18.45 ± 2.38 17.68 ± 2.49^NS

Finishing weight 30.63 ± 3.52 31.72 ± 3.01^NS

Weight gain 12.18 ± 2.31 14.05 ± 1.77^**

NSnot significantly different; ^**significant at P < 0.001.

principles has also been written by Perry (1997).

Field experimentation should begin with small plot observations and if the results are positive then larger scale trials will be considered. Field trials are costly and may take many months or years to complete, hence it is important that avoid- able mistakes are kept to a minimum. This requires a great deal of thought and careful planning, and supervision and monitoring of the experiment. The objectives of the experiment must be carefully defined and the experiment only started after consider- ation of the crop and the environment in which the pest is to be tackled and of the biology, ecology and epidemiology of the pest (Unterstenhofen, 1976; Reed et al., 1985). Under-estimation of the importance of this understanding can seriously jeopar- dize the experiment and the usefulness of the results obtained. Reed et al. (1985) warn of anomalies that may occur as a result of conducting experimental trials at research stations. Research stations often have pro- longed cropping seasons, irrigation facili- ties, pesticide free crops, sick plots for maintaining pathogens and weed plots for maintaining weeds. There is a clear need to quantify the differences between crops on research stations and those in typical farmers’ fields with regard to the crop itself, the pests and their natural enemies in order to determine just how relevant are results obtained on research stations to the real world (Reed et al., 1985).

The properties of an experimental area are rarely uniform; the land might have a slight gradient in height, soil conditions or exposure to weather such as the number of sunshine hours. To ensure that these effects are distributed over all treatments, i.e. to prevent plots of one treatment experiencing conditions not occurring in plots of other treatments, it is necessary to incor- porate randomization and replication into the trial design.

The simplest method of ensuring that particular treatments are not positioned in such a way as to produce any bias is to randomly allocate treatment replicates to spe-

cific locations within the trial area. One way of dealing with this is to use a randomized block design, which is based on the principle that patches of ground that are close together tend to be similar, while more distant patches differ. The blocks in the trial (consisting of a small number of plots) and the plots in each block are considered to be experiencing similar soil, pest and environmental conditions. Within each block, each treatment is randomly allocated a plot position. The conditions may differ between blocks (Fig. 3.9a). The layout and shape of blocks will depend on the conditions in each experimental area; although

(b)

C (a)

D D

B A

B B

Gradient

Fig. 3.9. A complete randomized block design: (a) the blocks arranged according to the gradient; and (b) according to various levels of infestation, depicted by the intensity of shading.

the ideal shape for a block is a square (this reduces the edge length relative to the area occupied) the experimenter should use his knowledge of the differences in soil fertil- ity, yield uniformity, drainage etc. to determine the exact shape of the blocks. A pre-count of pest numbers may reveal differences in the relative size of pest infestation over the experimental area, and blocks could be allocated to take this distribution into account (Fig. 3.9b). The blocks act as replication for each treatment.

Thus, the randomization of treatment plots within a block ensures that localized variability in conditions does not cause bias and the blocking ensures that any differences over the experimental area do not bias treatment differences. However, when variation gradients within a site are not known, the use of blocks may result in their being positioned across gradients, in which case the assumptions made in any subsequent analysis would be incorrect. In such situations a complete randomized design would be more appropriate. The complete randomized block design is fairly common in experiments where the number of treatments varies between five and 20 with fewer replicates than treatments (Simmonds, 1979).

A variation of the basic complete randomized block design is the Latin Square design. This is used in experiments where there are only a small number of treatments. The basic property of the Latin Square is that each treatment must appear once in every row and once in every col- umn (Fig. 3.10). Differences in conditions between rows and differences between columns are both eliminated from the comparison of the treatment means, with a resultant increase in the precision of the experiment (Snedecor and Cochran, 1978).

The use of the Latin Square is limited to situations where the number of replicates can equal the number of treatments. To construct a Latin Square, write down a sys- tematic arrangement of the letters and rearrange rows and columns at random.

Then assign treatments at random to the letters (Snedecor and Cochran, 1978).

When a large number of treatments is used then the size of the blocks in a randomized block design is large and variation in conditions within the block might occur.

For large numbers of treatments a range of experimental designs collectively referred to as ‘Incomplete Block’ designs may be used. They all have in common the use of compact, smallish blocks, any of which contains only a proportion of the total entries (Simmonds, 1979). These designs make comparisons between pairs of treatments ensuring that all pairs of treatments are equally accurate, that differences between blocks can be eliminated. In bal- anced incomplete block designs every pair of treatments occurs together in the same number of blocks and hence all treatment comparisons are of equal accuracy (John and Quenoulille, 1977). For example, five treatments may be arranged in ten blocks of three treatments; ABC, ABD, ABE, ACD, ACE, ADE, BCD, BCE, BDE and CDE. In this design each treatment occurs six times and each pair of treatments occurs together in three blocks (John and Quenoulille, 1977). However, this design uses every possible combination of three treatments which requires a large number of replications. The number of replications may be Fig. 3.10. A Latin Square trials design, with five

treatments: A, B, C, D and E.

decreased by using designs in which every possible combination of treatments are not used. This subject is dealt with further in Chapter 5.

More complex experimental designs will be required if the interaction of a number of treatments needs to be assessed, for example, when testing the effects of different dosage levels of two insecticides on the yield of resistant and susceptible crop cul- tivars. In this situation, where the interaction of a number of combinations of different variables needs to be studied, a factorial trial design can be used (Fig.

3.11). Factorial experiments compare all treatments that can be formed by combin- ing the different levels of each of the different factors (variables). A variation of the straightforward factorial design is the split- plot or nested design where precise information is required on one factor and on the

interaction of this factor with a second, but less precision is required on the second factor. This type of design is particularly useful where small scale experiments need to be tested on different large scale schemes such as irrigated and non-irrigated land or land cultivated by different means.

The irrigated/non-irrigated plots provide the main plots which are then divided into X1

Z1 X1

Z1 X2

Z1 X1

Z2 X1

Fig. 3.11. An example of a factorial trial design with three factors. Factor X had two treatments while factors Y and Z have three treatments each.

The number of plots is: 2 33 33 = 18.

Fig. 3.12. The first two blocks of a split-plot experiment on alfalfa, illustrating the random arrangement of main and subplots (after Snedecor and Cochran, 1978).

BLOCK I

COSSACK RANGER LADAK

BLOCK II

LADAK COSSACK RANGER

smaller subplots, for the smaller scale experiments (Fig. 3.12). The essential fea- ture of a split-plot design is that the subplots are not randomized within each block (Snedecor and Cochran, 1978).

All of the above experimental designs have been used successfully for many years for experiments carried out on research stations. Ultimately trials should be carried out to assess the effectiveness of treatments under conditions experienced by farmers and growers. In the real world it may not be possible or even desirable to set up and carry out experiments using classical experimental design procedures. In the tropics for instance, it can often be difficult to use permanent markers for plots because local people find the materials useful for other purposes and an experiment can easily be ruined if the plot markers are removed.

An alternative to conventional experimental designs that is used in, for instance, mating disruption experiments is the use of whole field experiments or area-wide experiments where plots tend to be large fields or a number of fields and the treatments are not replicated. There are many disadvantages with this approach: system- atic errors may arise because treatments are not replicated and uniform trial areas are unlikely on such a large scale; there is a resultant increase in variability and only relative rather than absolute measurements can be obtained (Puntener, 1981). However, if treatments are effective then differences have been demonstrated under realistic practical conditions and can provide tangi- ble evidence of the value of a treatment.

Certainly all prospective pest management control options should be tested in on-farm trials as the final proof of their value before being recommended to users.

The choice of plot size for experimental trials will depend on:

1. The type of crop used.

2. The type of equipment needed or used to apply treatments.

3. The amount of plant/pest material required for sampling and evaluation of treatments.

4. The size required to maintain plot variability at a suitable level.

The use of too large a plot will be wasteful of land and resources, while too small a plot will increase the variability of the data. Such variability will be partly dependent on inter- ference or inter-plot effects, when the treatment of one plot interacts with an insect population on an adjacent plot. This effect is thought to be caused by movement of the pest and/or its natural enemies between treatment and control (untreated) plots. In insecticide trials spray drift can also have an effect. The inter-plot effect can potentially influence the results of experimental trials, with yield and insect numbers affected by the proximity of a trial plot to other treated or untreated plots. For instance, the yield of untreated plots of cotton and the size of pest infestation of plots (each 4.2 ha) were affected by the presence of insecticide treated plots 150 m away (Joyce and Roberts, 1959). The insect pests can move from untreated plots where infestation may be high to treated plots where they are killed;

thus without equivalent immigration into the untreated plots the infestation is reduced and yield increased to a level greater than would normally be expected in true untreated field conditions. Movement and death of predators and parasites in treated plots would confound the difference in the untreated plots. Joyce and Roberts (1959) proposed an ideal layout of treatments to determine the size of the untreated area that would be required for a plot to approximate to being in an entirely unsprayed environment (Fig. 3.13). Measures to reduce the inter-plot effect have however become com- monplace in field trial methodology. These include the use of discarded or disregarded guard rows or border areas and the sampling and harvesting of only the most central area of each plot (Fig. 3.13).

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