chapter five general description of experiments— 1990/91

CHAPTER FIVE General Description of Experiments— 1990/91 Cotton Season

5.1 EXPERIMENTAL DESIGN

There were 4 experimental sites on each of 5 properties in the 1990/91 cotton season. The

Macquarie Valley properties were on Auscott Warren, Elengerah and Buttabone, while the Namoi

Valley properties were on Auscott Narrabri and Togo Station. Previous management and

management during the season differed between the properties. However, all sites had grown

cotton the previous season.

The soil structural damage for this season resulted from picking. Two sites on each property were

on areas where the picking was done when the soil was dry and the other 2 sites were on areas that

were picked after rain. The soil moisture content during picking is unknown as the sites were

chosen after picking was completed. It was hoped that by using actual soil damage caused during

normal picking operations the sites selected would give a more realistic representation of the soil

structural damage that is found on commercial cotton properties. The extent to which differences is

soil structural condition was achieved is debatable as different farm managers would have re-

commenced picking at different soil moisture contents. Therefore, the extent of soil structural

damage resulting from picking after rain is uncertain. In some cases previous management history

further complicated what sites on each properties exhibited to most soil structural damage.

To further increase the range of soil structural conditions, measurements were taken on both the

wheel rows and the guess rows. Wheel rows being the hills immediately adjacent to where

machinery wheels run while the guess rows are the hills found on the outside of the tool bars. All

soil measurements were taken at the tail drain end of the site and plant measurements towards the

head ditch. The sites on each property were referred to as wet pick wheel row, wet pick guess

row, dry pick wheel row and wet pick guess row. The experimental design consisted of:—

5 properties x 4 treatments = 20 sites (5 wet pick wheel row sites, 5 wet pick guess row sites, 5

dry pick wheel row sites and 5 dry pick guess row sites)

58

5.2 SOIL DESCRIPTION

A summary of the physical descriptions and chemical fertility for all sites is outlined below. More

detailed data is given in Appendix 1 and 2. The critical limits for cotton growth is found in Table

3 (Chapter 4). The mineralogy for all sites was similar, predominately consisting of

montmorillonite with sub–dominant fractions of koalinite and illite.

5.2.1 Auscott Warren

Physical Description

The soil classification was Ug 6.2 (Northcote, 1979). The texture was a medium to heavy clay.

The profile contains 2-10% subrounded and subangular <2 mm coarse gravel fragments dispersed

throughout the top 60-80 cm. Below this the abundance was 10-20%. Pedality varied across the

sites ranging from apedal to strong pedality and ped size from 2-50 mm. All peds were angular or

subangular blocky. Calcareous and manganiferous nodules were found on both sites, with <2%

found on the wet pick site and the dry pick site having <2% at the surface and 2-10% at depth.

Small differences between the profiles were found but they were considered to be essentially the

same soil type. The CaCO3 content was 0.5-5%. The dry pick sites at 10-20 cm consisted of

12% coarse sand, 28% fine sand, 14% silt and 44% clay, while at 60-80 cm there was 19%

coarse sand, 23% fine sand, 13% silt and 45% clay. The wet pick site at 10-20 cm had 14%

coarse sand, 27% fine sand, 13% silt and 48% clay.

Chemical Description

The soil was alkaline (pH 7.7-8.5), and the ECe was below 4.3 dSm-1 and was not limiting to

plant growth. The organic matter (<1.9%) and phosphorous contents (<0.5) are low throughout the

profile. Sodicity on the wet pick sites may become limiting to plant growth (ESP>6.3%) below 25

cm, and at 55 cm (ESP >5.1%) for the dry pick sites. The Ca/Mg ratio below 55 cm was less than

1.9 and may also cause some plant growth problems.

59

5.2.2 Elengerah


The soil classification was Ug 6.1 (Northcote, 1979). The texture was a silty to medium clay soil.

Generally, 0-2% subrounded and subangular <2 mm coarse gravel fragments were found dispersed

throughout the profile. Surface layers exhibited weak pedality with moderate to strong pedality at

depth. Peds were 2-50 mm in size and subangular to angular blocky in shape. Calcareous nodules

(<2%) were found on both sites. Manganiferous nodules were also found on the dry pick site.

The CaCO3 content was 0.5-1%. The 10-20 cm depth for the dry pick site consisted of 5%

coarse sand, 26% fine sand, 21% silt and 49% clay, while at 60-80 cm there was 4% coarse

sand, 19% fine sand, 23% silt and 55% clay. The wet pick site at 10-20 cm was comprised of

3% coarse sand, 26% fine sand, 28% silt and 45% clay and 7% coarse sand, 21% fine sand,

17% silt and 54% clay at the 60-80 cm depth.


The soil increases in alkalinity from pH 7.1 to 7.8 at depth. The organic matter (<1.9%) and

phosphorous contents (<0.5) are low throughout the profile. The salt content (ECe) is 10-21 dSm-1

for the guess row sites below 95 cm, and the dry pick wheel row site below 75 cm had and ECe of

12 dSm- 1 . Sodicity on all sites is evident at 25 cm (ESP>5.2%), with the dry pick guess row site

encountering potential plant growth problems at 15 cm (ESP>13.3). The Ca/Mg ratio for the guess

row sites was less than 1.8 from 35 cm, while wet pick wheel row site was less than 2 from 25 cm

and the dry pick wheel row site was below 1.9 throughout the profile. Differences in Ca/Mg ratio

and ESP could be causing differences in physical properties between sites. However, the primary

aim of the study was to relate measurement techniques to the soil structural index. The Ca/Mg

ratio may cause differences between sites but within a site the soil structural index can still be

compared with other measures of soil structure.

60

5.23 Buttabone


The soil classification was Ug 6.1 (Northcote, 1979). The texture was a medium clay. The profile

contains subrounded and subangular 2-6 mm coarse gravel fragments. The dry pick site had 2-

10% fragments in the top 80 cm, while the wet pick site had 0-2%. Pedality increased with depth

and consisted of subangular and angular peds. The dry pick site had <2% calcareous nodules (<2

mm) between 30 and 80 cm. Less than 2% calcareous, manganiferous and gypseous nodules, <2-

6 mm in size, were found in the wet pick site. The 10-20 cm depth for the dry pick site consisted

of 11% coarse sand, 14% fine sand, 17% silt and 59% clay, while at 60-80 cm there was 6%

coarse sand, 15% fine sand, 27% silt and 53% clay. The wet pick site at 10-20 cm was

comprised of 14% coarse sand, 24% fine sand, 13% silt and 51% clay and 2% coarse sand, 29%

fine sand, 21% silt and 50% clay at the 60-80 cm depth.


The soil pH ranges from 6.3 to 7.8. Ca/Mg ratio (<1.8), organic matter (<2.1%) and phosphorous

contents (<0.5) are very low throughout the profile. The salt content (ECe) was predominantly

below 2.7 dSm-1 and not limiting to plant growth. Sodicity occurs on all sites. In the dry pick

guess row site sodicity (ESP>7%) was encountered at 55 cm, in the wet pick wheel row site

(ESP>6.8%) at 25 cm and in the wet pick guess row and dry pick wheel row sites (ESP>5.1%) at

35 cm.

5.2.4 Auscott Narrabri



The profile contains subrounded and subangular 2-6 mm coarse fragments. Generally, the

abundance of these fragments was 2-10%. Pedality ranged from weak at the surface to moderate

then strong pedality at depth. The peds were subangular and angular blocky. Less than 2%

calcareous and manganiferous nodules 2-6 mm in size with 0.5-1% CaCo 3 content were found at

both sites. The 10-20 cm depth for the dry pick site consisted of 11% coarse sand, 20% fine

61

sand, 13% silt and 56% clay, while at 60-80 cm there was 8% coarse sand, 16% fine sand, 13%

silt and 62% clay. The wet pick site at 10-20 cm was comprised of 5% coarse sand, 14% fine

sand, 22% silt and 60% clay and 4% coarse sand, 11% fine sand, 20% silt and 65% clay at the

60-80 cm depth.


The soil pH (7.2-8.3) becomes increasingly alkaline with depth. The salt content (ECe) was below

4.5 and not limiting to plant growth but the Ca/Mg ratio (<1.9) and organic matter (<1.2%) are low

throughout the profile. Sodicity becomes limiting for plant growth on the dry pick site

(ESP>5.3%) at 55 cm and at 25 cm for the wet pick site (ESP>5.4%).

5.2.5 Togo Station



The profile contains 2-10% subrounded and subangular <2 mm coarse gravel fragments dispersed

through the profile. Generally, pedality was weak at the surface and strong at depth. The peds

were angular and subangular blocky. Less than 2% calcareous and manganiferous 2-6 mm

nodules were noted. The CaCO3 content of the profiles was 0.5-1% for the dry pick site and 1-

5% for the wet pick site. The 10-20 cm depth for the dry pick site consisted of 6% coarse sand,

15% fine sand, 19% silt and 62% clay, while at 60-80 cm there was 4% coarse sand, 11% fine

sand, 20% silt and 64% clay. The wet pick site at 10-20 cm was comprised of 9% coarse sand,

14% fine sand, 15% silt and 62% clay with 7% coarse sand, 12% fine sand, 22% silt and 62%

clay at the 60-80 cm depth.


The soil pH was slightly alkaline (7.3-8) and the ECe was below 2.5 dSm -1 . The organic matter

(<1.3) was low throughout the profile. Sodicity was greater than 5.4% for the wet pick site at 35

cm, while for the dry pick guess row site it was greater than 8.9% at 55 cm and greater than

6.2% at 15 cm for the dry pick wheel row site. The Ca/Mg ratio was low at 25 cm on the dry

pick wheel row site (<1.8), at 35 cm on the wet pick guess row site (<1.9) and at 55 cm for the dry

pick guess row (<1.85) and wet pick wheel row sites (<1.65).

62

5.3 CULTURAL PRACTICES

Management details for the experimental sites are summarised in Tables 10 to 14. They have been

included here to show the variation in management practices between the sites.

Table 10. Management details for Field 36, Auscott Warren, Warren 1990/91 cotton season.

dry pick and wet pick treatments

soil type grey clay: old alluvial backplain (McKenzie, 1992)

slope dry pick — 1:1146wet pick — 1:974

cotton variety Soikra 1-4

hill/bed system retained hills (1m): 8 row tillage system

seed treatments Terrachlor/Apron/Semevin

nitrogen type and rate applied Anhydrous ammonia —110 kgN/ha

method of nitrogen application cold flo (into side of hill)

pre—emergence herbicides Cotoran/Diuron/Treflan

sowing rate 16.4 kg/ha cotton seed

tillage operations stalk pulled, raked and burntlight listersled cultivation 9-7-90sled cultivation 15-7-90lilliston 27-8-90planting 5-10-90sled cultivation early Novsled cultivation early Dec

pre—irrigation watered up 8-10-90

number of crop irrigations 8

first square

defoliants used Dropp/DC Tron/Prep 720

picking date May 1991

yield* dry pick guess row — 2179 kg,/ha (9.68 bales/ha)dry pick wheel row — 2090 kg/ha (9.29 bales/ha)wet pick guess row — 2476 kg/ha (11.00 bales/ha)wet pick wheel row — 2134 kg/ha (9.48 bales/ha)

ids the cumulation of yields from 12 individual0.75m plots converted to a per hectare figure

63

Table 11. Management details for Field 32, Elengerah, Warren 1990/91 cotton season.


soil type silty grey clay: Macquarie alluvium and splays(McKenzie, 1992)


cotton variety Siokra 1-4


seed treatments

nitrogen type and rate applied

method of nitrogen application

pre—emergence herbicides Treflan/Cotoran/Diuron

sowing rate 16.5 kg/ka cotton seed

tillage operations stalk pulled 10-6-90raked 21-6-90planted 16-10-90sled cultivation 12-11-90sled cultivation 28-11-90sled cultivation 13-12-90


number of crop irrigations 7 excluding water up

plant emergence

first square

first flower

first green boll —

first open boll

defoliants used Harvade/Catapult/Salt

picking date April 1991

yield* dry pick guess row — 1523 kg/ha (6.77 bales/ha)dry pick wheel row — 1851 kg/ha (8.22 bales/ha)wet pick guess row — 2189 kg/ha (9.73 bales/ha)wet pick wheel row — 2423 kg/ha (10.77 bales/ha)

This is the cumulation of yields from 12 individual 0.75m plots converted to a per hectare figure

64

Table 12. Management details for Field 4, Buttabone, Warren 1990/91 cotton season.


soil type sodic grey clay


cotton variety Siokra L22



nitrogen type and rate applied Anhydrous ammonia —146 kgN/ha

method of nitrogen application side dressing rig using fallow knife

pre—emergence herbicides Stomp/Diuron/Cotoran


tillage operations stalk pulled 8-5-90raked and burnt 12-5-90slashed 13-7-90lister 18-8-90NH3 rig 23-8-90cultipacker 25-8-90lilliston 28-8-90cultipacker 26-9-90planting 5-10-90cultivation "10-11-90cultivation "14-12-90



first square 21-11-90

first flower 20-12-90

first open boll 8-2-91

defoliants used Prep/Salt

picking date 10-4-90

yield* dry pick guess row — 1463 kg/ha (6.50 bales/ha)dry pick wheel row — 1268 kg/ha (5.64 bales/ha)wet pick guess row — 2172 kg/ha (9.65 bales/ha)wet pick wheel row — 2118 kg/ha (9.41 bales/ha)

4, This is the cumulation of yields from 1 indi i u . m plots converted to a per Tiectare ure

65

Table 13. Management details for Field 10, Auscott Narrabri, Narrabri 1990/91 cotton season.


soil type grey clay

slope dry pick – 1:1270wet pick – 1:787

cotton variety Soikra 1-4

hill/bed system wide beds (2m): 8 row tillage system

seed treatments Terrachlor/Apron/Semevin •

nitrogen type and rate applied Anhydrous ammonia – 120 kgN/haUrea – 30 kgN/ha

method of nitrogen application Anhydrous ammonia – gas rigUrea – aerial

pre–emergence herbicides Diuron/Stomp/Fluometuran/Treflan

sowing rate -18 kg.ha cotton seed

tillage operations sled cultivation 20-8-90lilliston cultivation 24-9-90cultipacker 28-9-90harrows 4-10-90planting 6-10-90cultivation 8-11-90lilliston cultivation 29-11-90

pre–irrigation watered up 8-10-90


plant emergence

first square

first flower 5-12-90

first green boll

defoliants used Harvade/Catapult/Salt


yield* dry pick guess row – 1740 kg/ha (7.73 bales/ha)dry pick wheel row – 1798 kg/ha (7.99 bales/ha)wet pick guess row – 1822 kg/ha (8.10 bales/ha)wet pick wheel row – 1746 kg/ha (7.76 bales/ha)

1This is the cumulation of yields from 12 individual.75m p ots converted to a per hectare figure

66

Table 14. Management details for field 78, Togo Station, Narrabri 1990/91 cotton season.


soil type grey clay

slope dry pick — 1:1850wet pick — 1:562 •

cotton variety Siokra L22


seed treatments Terrachlor/Apron

nitrogen type and rate applied urea — 152 kgN/ha

method of nitrogen application aerial application and ground incorporation

pre—emergence herbicides Round—up/Diuron/Stomp


tillage operations planting 4-10-90

pre—irrigation watered—up 11-10-90

number of crop irrigations 5 + 2 rain events

plant emergence

first square 2-12-90

first flower

first green boll 29-1-91

first open boll —

defoliants used Dropp/Prep/Chlorat

picking date May 1991

yield dry pick guess row — 1584 kg/ha (7.04 bales/ha)dry pick wheel row — 1646 kg/ha (7.13 bales/ha)wet pick guess row — 2148 kg/ha (9.58 bales/ha)wet pick wheel row — 1992 kg/ha (8.85 bales/ha)

This is the cumulation of yields from 12 individual 0.75m plots converted to a per hectare figure

67

5.4 CLIMATIC DATA

The temperature data for the Macquarie and Nam of Valleys is shown in Fig. 7 and 8.

Figure 7. Macquarie Valley maximum and minimum temperatures for 1st October, 1990 to

March 31st, 1991.

Figure 8. Nanot Valley maximum and minimum temperatures for 1st October, 1990 to

March 31st, 1991.

68

5.5 HELD MEASUREMENTS

The sampling procedure was similar to the 1989/90 season. All techniques used in that season

were repeated for the 1990/91 cotton season. Some extra techniques however, were added to the

sampling program.

A modified version of Peerlkamp Soil Structural Scheme (1967) was devised by Dr Tom Batey for

the cracking clay soils in northern NSW. This was incorporated into the pre—season sampling

program as a form of visual soil structural assessment. This scheme is listed in Appendix 3.

A score was devised by making an overall assessment of the soil profile. The dimensions of the

area assessed were approximately 40 cm either side of the centre of the hill and to a depth of 50

cm. The various zones of damage were distinguished and given a score according to the modified

Peerlkamp Scheme. These scores were then weighted according to the proportion of the area they

covered and cumulated to give an overall assessment of the degree of soil structural damage. One

assessment was made on each site.

69

CHAPTER SIX Derivation of the Soil Structural Index

6.1 INTRODUCTION

The clod shrinkage technique which produces a soil shrinkage curve has been used by many

researchers investigating soil structural damage on cracking grey clays. It has proven to be a

successful and consistent technique for observing the differences between soils of varying structural

condition (Reeve and Hall, 1978; Chan, 1982; Abbott and Daniells, 1987; McGarry and Daniells,

1987; Daniells, 1989; McGarry, 1990; McKenzie et al., 1991). For this reason the clod shrinkage

technique was chosen as the baseline technique to identify the degree of soil structural damage at

each site. A difficulty with the clod shrinkage technique was determining which of the parameters

derived from the shrinkage curve best indicated the differences between soils, as the literature failed

to recommend a particular parameter. To overcome this problem all parameters were weighted and

combined to form a soil structural index (SSI) by means of principal component analysis.

6.2 SOIL SHRINKAGE CURVE AS THE REFERENCE TECHNIQUE

6.2.1 Theory

A widely accepted assessment of soil structure is the change in soil volume as the soil moisture

content changes (McGarry and Malafant, 1987). Soil volume change can be measured using either

porosity, volume change, bulk density or specific volume. The selection of the appropriate

measurement to use is dependent upon which measurement best highlights the differences in the

data set. In cracking clay soils, specific volume has been frequently used (Reeve and Hall, 1978;

Chan, 1982; Abbott and Daniells, 1987; McGarry and Daniells, 1987; Daniel's, 1989; McGarry,

1990; McKenzie et al., 1991). A description of the uses of the other measurements is outlined in

McGarry and Malafant (1987).

The whole concept is based on the fact that as soil dries it passes through 3 zones of shrinkage.

These are residual, normal and structural shrinkage. Residual shrinkage occurs where soil

volumetric change is less than the volume of water removed in the lower moisture range. Normal

shrinkage is the zone where soil volumetric change equals the volume of water lost. Structural

shrinkage is where the soil volume change is less than the volume of water removed. It is usually

associated with the draining of large pores, and occurs at the wetter end of the soil moisture range

(Fox, 1964; McGarry and Daniells, 1987).

70

Specific volume (reciprocal of bulk density) is the total volume of a sample per unit mass of oven

dried soil. From this a shrinkage curve (Fig. 9) can be derived by plotting the specific volume

against the gravimetric water content. The shrinkage curve from the experimental data is then

compared to a theoretical 1:1 curve (which represents a saturated system where normal shrinkage

without air entry is assumed throughout the water range). This enables a number of parameters to

be derived describing the relationship between the two curves. These parameters are illustrated in

Fig. 9, while a more detailed explanation of the shrinkage curves can be found in McGarry and

Malafant (1987).

6.2.2 Justification

In previous studies, soil structural condition has been assessed from parameters obtained directly, or

derived, from the soil shrinkage curve (Fig. 9). These studies have illustrated the value of the soil

shrinkage curve in determining the structural condition of cracking clay soils. Parameters used in

previous studies to identify soil structural differences between sites or treatments are outlined

below.

The structures of two clayey alluvial soils in England were compared using shrinkage curves by

Reeve and Hall (1978). The moisture content range over which structural shrinkage occurred

increased with improved soil structure, and curtailed the normal shrinkage phase.

Chan (1982) compared two clay soils from the Namoi valley: one was from an irrigated cotton

field that contained a 'massive' degraded layer at 20-40 cm, the other was from an adjacent dryland

wheat field where 'natural' soil clods were observed. It was found that OB was significantly lower

in the dryland soil. This suggests that the dryland soil had greater air—filled porosity and thus

better structure, at a particular water content, than an irrigated soil.

71

Structuralshrinkage- Ar. T5M

Normalshrinkage

/

n /6 /st

/Resickialshrinkage /

At t9A A / /

/

/ AIR

LIQUID

Figure 9. Shrinkage curve showing the structural, normal and residual zones of shrinkage and

the derived parameters.

Clod volume per unit mass of soil solids 6(m 3 mg "1)

1

0.9

0.8

0.7

0.6

0.5

0.4

0.3 e./ v0 0.1 0.2 0.3 0.4 0.5 0.6

Water Content, e (m3 mg '1)

the theoretical (2 component) line with the y–intercept at us (the reciprocal density of solids)an exemplar of observed data

Oawater content = 0Om maximum gravimetric moisture content (m3Mg-1) at saturation°B gravimetric moisture content at the swelling limit (m3Mg-1)°A gravimetric moisture content at the air–entry point (m3Mg-1)ev standard water content (0.2 m 3Mg-1) midpoint of normal shrinkage rangea clod volume at water content = 0uAclod volume at air–entry point1313

clod volume at the swelling limitum clod volume at the maximum gravimetric moisture content1), clod volume at standard water contentus reciprocal density of solidsn slope of B –• As slope of M --, Br slope A –* aK value where, if v,„=1 then (K–us) = constant specific volume of air–filled pores in normal shrinkagePB specific volume of air–filled pores at BPA specific volume of air–filled pores at APm specific volume of air–filled pores at MPaspecific volume of air–filled pores at a

D = s – nd = n – r

Nr9 .

ye a, ie,SOLID

72

Abbott and Daniells (1987) compared rotation cropping experiments in the Namoi and Macquarie

valleys, NSW. The most useful parameters were different for the two valleys. In the Namoi valley

1.)„ exhibited higher values at lower levels of compaction, while in the Macquarie Valley, P A proved

to be the most useful. The 1.).„ paramaeter refers to the clod volume at a standard water content of

0.2 kg kg-1 (0.2 m3 Mg-1). This water content was chosen as it lies near the middle of the normal

shrinkage range. Higher values of PA implied greater air–filled volume at the wetter end of the

normal shrinkage zone, which suggested better soil structure.

In the Namoi Valley, McGarry and Daniells (1987) used shrinkage curve parameters to identify

differences in structural condition between soils that had been worked in wet, moist and dry states.

Later Daniells (1989) studied the effect of rotation crops to ameliorate the soils from the previous

study. In both experiments, different parameters and variables were identified as the most sensitive

to different soil structural conditions. In the first experiment a, PA, PB and n were the most

sensitive parameters. In all cases, increases in the values of these parameters implied better soil

structural condition. It was also noted that in soils tilled wet, i.e. compacted soils, the range of

water content over which structural shrinkage occurred decreased. In the later study Daniells

(1989) found vt, to be the most sensitive parameter to assess the difference between the

ameliorating abilities of the different crops. This complies with the findings of Abbott and Daniells

(1987) where higher values of I), indicate better soil structural conditions. Data from remoulded

clods from this experiment indicated that larger slopes in the normal shrinkage phase (n) were

associated with better soil structure.

Further use of shrinkage curves was made by McGarry (1990) in comparing soil structure between

two commercial cotton fields near Dalby, Queensland. One field experienced poor crop growth and

had a very firm platy structure probably resulting from wet land preparation, while the other field

had good cotton growth and exhibited a fine granular structure. Here the parameters that best

differentiated between the two fields were P0.2, P0.3, PA and °A. The parameters Pa2 and Pa3 refer to

the specific volume of air–filled pores at 0.2 and 0.3 m3 Mg-1 water contents respectively. All four

parameters indicate improvements in structure with increasing values. P0.2, P0.3 and PA applied to

the 0.2-0.3 m depth, while °A applied to both the 0.2-0.3 m and 0.3-0.4 m depths.

McKenzie et al. (1991) compared a native pasture (grazed but uncultivated) paddock with an

irrigated cotton field in the Macquarie Valley, NSW. In this comparison n, 1),,, PB and D were used

to discriminate between the sites. All parameters below 15 cm were larger on the pasture site.

73

It is seen that the sensitivity of parameters to soil structural condition varies between studies and

that no single parameter invariably shows the greater sensitivity. This is probably related to

variations in soil physical and chemical properties between different studies which affect the

shrinkage curve parameters differently. There was also variation in the preparation of clods

between the experiments. Some clods were air—dried and then wet up on pressure plates before

measurements began, while other clods measurements commenced from the water content at which

they were collected. The clod preparation for this experiment, as described below was similar to

that used by McKenzie et al. (1991), i.e. wet up prior to coating with SARAN resin.

6.3 DETERMINATION OF CLOD SHRINKAGE

The reference technique used for characterising soil structure was that of Brasher et al. (1966) for

determining bulk density by SARAN coated clods. This produces a shrinkage curve, on which the

specific volume of soil (i.e. the volume of soil per unit mass of oven—dried soil) is plotted against

the gravimetric water content.

Intact soil clods (approximately 50 cm3 in volume) were collected from 15-20 and 35-40 cm

depths prior to sowing in August. The clods were packed in loose soil within plastic containers

and transported to the laboratory where they were air—dried. The air—dry clods were slowly wet at

a 0.1 m water suction in a sand bath (McKenzie et al., 1991). The clods were then coated in

SARAN resin, which is permeable to water vapour. The coating allows the clod to gain or lose

water atmospherically, but will not allow the penetration of liquid water when clod volume is

measured by the displacement of water (Brasher et al., 1966). The clods were hung by cotton

threads on a drying rack and their weights in water and air measured at intervals until constant

values were obtained (Abbott and Daniells, 1987; McGarry and Daniells, 1987; Daniells, 1989).

There are some weaknesses with this technique. Firstly, the clod sizes collected for analysis were

of similar size to other studies, but were smaller than those reported by Chan (1981) to be large

enough to represent whole field structure. Secondly, the collection of clods was biased towards

those clods large and firm enough to analyse. This introduced sampling bias where the soil was in

good condition, as the only clods able to be collected appear to be have been remnants of

previously damaged soil. Therefore, it appears that sampled clods may not always represent the

true structural condition of the soil. Thirdly, the ability of this technique to accurately represent the

root environment is uncertain. Roots tend to proliferate in Vertisols around the crack zones and on

74

the outside of aggregates. The shrinkage curve though, is derived from samples that include intra-

aggregate, inter—aggregate soil and crack areas. However, despite these weaknesses the clod

shrinkage appeared to be the most suitable measure of soil structural condition available.

6.4 EXPERIMENTAL DETERMINATION OF THE SHRINKAGE CURVES

Shrinkage curves consist of three shrinkage zones (Fig. 9): residual shrinkage, normal shrinkage

and structural shrinkage (McGarry and Malafant, 1987). These curves can be analysed using three

different models: the three straight lines model, the general soil volume change equation and the

logistic model. McGarry and Malafant (1987) compared all three models and concluded that the

three straight lines model was the most suitable. This model fitted the data best and the

mathematical properties of the model were consistent with the physical parameters of the three

shrinkage zones. For these reasons the three straight lines model was fitted to the data. A more

detailed description of the derivation of the three straight lines model is found in McGarry and

Malafant (1987). From this a number of parameters can be obtained. These were used as reference

values for assessing the soil structural condition at each site.

The parameters PA, PB and a from this study are similar to those derived by Abbott and

Daniells (1987); McGarry and Daniells (1987); Daniells (1989); McGarry (1990) and McKenzie et.

al. (1991). Values from the above studies and the maximum and minimum values for this study

are listed in Table 15. The clod shrinkage parameters in this study incorporated the range of

parameter values found in other studies. This suggests that a relatively comprehensive set of soil

structural conditions were represented in this study.

75

Table 15. The range of mean values for PA, uv, PB and a from other studies using the clod

shrinkage technique. The values for this study are the maximum and minimum for

each parameter.

Source Depth PA PB a

Abbott & Daniells (1987) 15cm 0.06-0.08 - - -

35cm 0.05-0.06 0.61-0.62 - -

McGarry & Daniells (1987) 15cm 0.08-0.14 - 0.08-0.15 0.54-0.59

35cm 0.08 - 0.08 0.53

Daniells (1989) 15cm 0.58-0.65

35cm - 0.58-0.64 - -

McGarry (1990) 15cm - - - -

35cm 0.05-0.07 - - -

McKenzie (1991) 15cm - 0.63-0.65 0.06-0.08 -

35cm - 0.61-0.65 0.08-0.09 -

This study 15cm 0.05-0.14 0.62-0.67 0.04-0.16 0.49-0.56

35cm 0.03-0.14 0.61-0.68 0.04-0.12 0.50-0.59

6.5 FORMATION AND DESCRIPTION OF THE SOIL STRUCTURAL INDEX (SSI)

This study has taken the findings of other authors one step further by trying to determine a way in

which all the shrinkage curve parameters can be used for soil structural assessment. It involves the

use of principal component analysis.

Principal component analysis (Sark, 1985) examines the inter-relationships between a set of

quantitative variables. In this case the variables are the parameters derived from the shrinkage

curve. The number of principal components formed is equal to the number of variables in the data

set, and each principal component is a linear combination of the original parameters. The analysis

partitions the total variation in the data into components for each of a set of new variables i.e.

principal components. This is done in such a way that the first principal component explains as

much of the total variation as possible. The amount of variation explained by each principal

component is called the eigenvalue. The eigenvalues in this analysis are expressed as percentages

of the total variation. The principal components also show the weighting of each variable i.e. the

weight given to each of the old variables in forming the principal components.

76

This approach eliminates the difficulty of knowing which single parameter to use, and at the same

time utilises all the information conveyed by the parameters obtained from the shrinkage curve. It

also determines the relative importance of each of the parameters identified by the shrinkage curve.

The first eight principal components were assessed by their eigenvalues. It was found that the first

principal component explained the largest proportion of the variation (37-53%, Table 16) and was

used to form the SSI. The other principal components were not used in the formation of the SSI as

their inclusion would have further complicated an already complex equation (Table 17). The

second principal component explained 14-27% of the variation, while the third principal

component explained 8-17% of the variation. Thus, it was also deemed that the use of extra

principal components would not have added further value to the SSI which was based only on the

first principal component (Gavin Melville, NSW Agriculture, pers. comm.). The first principal

component weighting for the various valley and depth combinations are listed in Table 16, together

with the eigenvalues.

Table 16. Weightings and eigenvalues for the first principal component for the 1989/90 and

1990/91 cotton seasons.

Parameter All sites MacquarieValley

NamoiValley

15 cm depth 35 cm depth

0A 0.1407 0.1972 -0.2488 0.1136 0.0409

uA 0.2068 0.1706 0.2719 0.3183 0.217508 0.1729 0.1176 -0.0479 0.1544 0.0220UB 0.3157 0.3432 0.1884 0.2828 0.1378uu 0.3948 0.3939 0.4071 0.3548 0.2912r -0.0135 -0.0018 -0.1814 -0.0409 0.0113n 0.1323 0.0567 -0.0112 0.2303 -0.2993s -0.2310 -0.2385 -0.2460 -0.2217 0.1011D 0.2432 0.1947 0.2387 0.2882 -0.2298d 0.1245 0.0627 0.1704 0.2239 -0.3132a 0.3543 0.3591 0.3356 0.3274 0.4041Pc, 0.3276 0.3575 0.2658 0.2838 0.4046

PA 0.3449 0.3615 0.4103 0.3338 0.3995PB 0.3931 0.3947 0.3598 0.3462 0.3307

Eigenvalue 39.05% 42.26% 37.26% 53.24% 35.18%

The parameter weighting in the first principal component show that the parameters, such as v„, a,

PA and PB, identified by earlier studies mentioned in Section 4.2 to be sensitive to soil structural

differences, are the most heavily weighted i.e. they have the largest weighting coefficients. The

77

weighting of parameters, in general, are similar for the various site and depth categories. For each

site there is a score which is the sum of the values of each parameter multiplied by their respective

weighting in the first principle component (Table 18). It is the site score from the first principal

component that is used as forms the soil structural index (SSI) for that site.

To enable a comparison between the results from different sites and experiments, the SSI's should

be calculated using the weightings from the principal component analysis for the clod shrinkage

parameters devised in this study. The equation to derive the SSI is outlined in Table 17. Details

of another equation which can be used to determine the SSI for different sites and incorporates the

parameter weightings from the principal component analysis and the mean and standard deviation

values is outlined in Greenhalgh et al. (1994).

Table 17. Equation to calculate a standard SSI from the clod shrinkage measurements.

SSI = (-0.063536*OA) + (0.226895*uA) + (0.092168*OB) + (0.265624*vB) + (0.403189*uv) —

(0.057215*r) + (0.042540*n) — (0.229345*s) + (0.213948*D) + (0.078838*d) +

(0.380914*a) + (0.380364*Pa) + (0.380758*PA) + (0.398058*PB)

The SSI for the 3 seasons ranged from 7.6 to —4.9 (Table 18), with the higher values indicating

better soil structure. This was ascertained from tree regression analysis where the SSI was

regressed against parameters derived from the shrinkage curve, and split into subsets (Fig. 10). The

mean SSI for the subset is printed at the bottom of each tree branch. At each split there is a 2 way

decision, where the value of the shrinkage parameter is either smaller or larger than a certain value.

Using the interpretation the clod shrinkage parameters from the literature (Abbott and Daniells,

1987; McGarry and Daniells, 1987; Daniells, 1989; McGarry, 1990; McKenzie et al., 1991), it was

possible to determine the meaning of the SSI value. For example, to achieve a SSI of —2.89, the

IA, value should be less than 0.63 and the a value less than 0.52. The smaller the values of these

clod shrinkage parameters means the worse the soil structural condition. On the other hand to

obtain a SSI of 5.25 the u. should be greater than 0.66 and the P A greater than 0.11. Similarly, the

greater these clod shrinkage parameters are the better structural condition of the soil.

78

Figure 10. Tree regression splits illustrating the interpretation of the SSI.

u„<0.6565

N.B.

v,,<0. 29

soil structure (AbbottMcGarry, 1990; McKenzie,

PA<0.1

1987;

1 a<0.4

1.78 5.25

and Daniells,1991).

—2.89 —1.18 —0.

Larger values of u„, PA andMcGarry and Daniells, 1987;

0.21

a indicate betterDaniells, 1989;

These regressions, along with previously published values for shrinkage curve parameters from soils

of varying structural conditions, allowed the classification of the SSI scores. The tree regression

splits are based upon certain criteria for parameters from the shrinkage curve e.g. .I), either greater

or less than 0.6565. By comparing these criteria to the parameter values obtained in previous

studies (which illustrated the usefulness of using the shrinkage curve to assess soil structural

condition) it was possible to ascertain which of the SSI scores were indicative of poorly structured

or well structured soils.

6.6 SITE RANK ACCORDING TO THE SOIL STRUCTURAL INDEX

Sites from the three cotton seasons were able to be ranked according to their SSI values. The sites

were originally classified by assuming certain levels of soil structural damage. In both years there

were sites of varying structural conditions, where the non—compacted and dry pick sites were

assumed to be in better soil structural condition than the compacted and wet pick sites. In the

1990/91 cotton season further degrees of soil structural damage were achieved by having sites

located on wheel rows and guess rows. The wheel rows were assumed to have greater structural

damage than the guess rows as the former are more likely to be subjected to the compaction and

ia<0.215 55

79

smearing associated with machinery passes. Therefore the structural condition for the 1989/90 sites

in order from best to worst should be non—compacted>compacted, while the 1990/91 sites should

be dry pick guess row>dry pick wheel row>wet pick guess row>wet pick wheel row.

Ranking the sites using the SSI (Table 18) or the individual parameters from the shrinkage curve

showed little relationship between the order described in the paragraph above and SSI classification

of soil structural conditions. There are a number of possible explanations for this. In the 1990/91

cotton season the damaged sites were those that had been picked after rain in the previous season.

Cotton pickers have four row spacings so the picker wheels passed next to the guess rows as well

as the wheel rows while the soil was still moist. If the soil moisture content during picking was

high, there may be little difference between the structural conditions of guess and wheel rows. For

the 1989/90 sites the structural damage was induced by driving a tractor on the top of the hills. In

most cases though, the tractor used was too light and/or the moisture content at which this was

done was not sufficient to cause any substantial compaction at the measurement depths. The

moisture content of the soil during subsequent machinery passes and tillage operations will also

influence the degree of soil structural damage and further complicate the original site

classifications. There is often a large degree of inherent and induced spatial variation in the

physical properties of cropping soils. This may result from changing slope, depressions in the

field, slight changes in soil type and chemical characteristics across the field, erratic driving by

machinery operators, wheel slip, positioning of tillage implements within the hill, inadvertent

placement of large compacted clods under hills following dry soil chisel ploughing, deep ripping

and relisting. Potential sampling bias mentioned earlier associated with the collection of clods for

clod shrinkage analysis may also cause misrepresentation of the true condition of the soil.

6.7 STATISTICAL ANALYSIS

Most statistical analyses were performed using the SAS statistical package (SAS, 1985). The

exceptions to this were the derivation of the structural shrinkage curves using Genstat (Alvey et al.,

1982), and the tree regression analysis using Splus (Chambers and Hastie, 1992).

80

6.7.1 Regression analysis

In all regression analyses there is a response variable and regressor variables. The analysis assumes

that the response variable can be predicted by a combination of the regressor variables.

Linear Regression

Linear regression analysis was used to determine prediction models for the SSI from the different

soil structural assessment techniques and also for cotton yield predictions using root morphological

characteristics. The response variables were the SSI and lint yield, while the regressor variables

were soil structural and plant measurements or root morphological characteristics.

Multiple Linear Regression

Multivariate regression analysis was used as an alternative to linear regression to improve the

predictive ability of the regression model. This type of procedure is useful in determining which

variables should be included in a regression model when there is a large number of independent

variables.

Maximum R2 improvement (maxr) regression was used to determine predictor models for the SSI

from the different soil structural techniques and also yield prediction models using root

morphological characteristics.

The maxr regression is a type of stepwise regression (Sarle and Goodnight, 1985). However,

unlike other model selection methods, such as forward selection, backward selection and stepwise,

it does not settle on a single model. Rather, it finds the best one variable model, the best two

variable model, etc. for each dataset. Initially, the maxr method selects the one variable model that

has the largest R2. The R2 is the variance in the response variable that is accounted for by the

regression model. It then adds another variable that generates the greatest increase in the R 2. Once

the two variable model has been found each of the variables in the model are compared to all the

variables not included in the model. Maxr determines for each comparison whether the removal of

one variable and the addition of another variable will improve the R 2. Once all the comparisons

have been made then the exchange that rendered the largest R 2 is added to the model. This process

continues, unless otherwise specified, until all variables are included in the model.

81

Tree Regression

Tree regression analysis (Clark and Pregibon, 1992) using the statistical program Splus (Chambers

and Hastie, 1992) was performed on the variables used to construct the SSI. This procedure fits

models by binary recursive partitioning, whereby a data set is successively split into increasingly

homogeneous subsets. It is a useful technique to use when there is a large set of predictor

variables and a single response variable. In this study it was used to relate the parameters obtained

from the structural shrinkage curve (the regressor variables) to the SSI (the response variable).

These tree—based models can show diagrammatically (Fig. 10) how the response variable can be

predicted. The tree regression or dendrogram consists of a pathway from a top node on the tree,

called the root that, via a series of interior nodes called splits, reaches the terminal nodes, called

leaves. By following these nodes it is possible to use predictor variables to determine what the

average response will be. For example, by using some of the shrinkage curve parameters it is

possible to assign to each site an estimate of its SSI. An example of how this is determined was

outlined earlier in Section 4.5.

In theory, this type of analysis can have as many terminal nodes as there are observations.

However, in reality this is far too many and constraints are applied to reduce the number of

terminal nodes. Two criteria were used to decide if a node was suitable for splitting; a node was

not split if the node deviance was less than 1% of the root node deviance or if the number of

observations within the node was smaller than an absolute minimum size of 10.

6.7.2 Optimum Sample Size

Soil properties show considerable spatial variation. If these properties are to be used as predictors,

it is necessary to know how many measurements are needed to attain a given level of precision

within each set of measurements. For each variable the coefficient of variation and sample mean

were used to obtain the optimum number of replicates (sample size), using relative standard errors

of 5% and 10% (Gavin Melville, NSW Agriculture, pers. comm.). The formula is:—

Sample size = (standard deviation/(relative standard error*mean of sample))2

82

6.7.3 Predicted Soil Structural Index

The predicted SSI was calculated using the models derived from the maxr regression. These

predicted values were compared to the actual SSI. The studentized residuals were used to ascertain

whether the difference between the two values was significant.

Table 18. Soil Structural Index ranking for the separate site and depth combinations.

The sites are ranked from worst to best structured soils.

N.B. The 1992 site descriptions are listed in Chapter 8.

Experimental Sites Soil Structural Index(SSI)

Namoi Valley at 15 cmTogo 1990 wet pick guess row —1.672Auscott Narrabri 1989 compacted —1.619Togo 1990 dry pick wheel row —0.775Auscott Narrabri 1990 dry pick guess row —0.729Myall Vale Research Station 1989 compacted 0.903Auscott Narrabri 1990 wet pick guess row 1.529Togo 1990 wet pick wheel row 2.142Auscott Narrabri 1989 non—compacted 2.178Myall Vale Research Station 1989 non—compacted 2.236Togo 1990 dry pick guess row 2.582Auscott Narrabri 1990 wet pick wheel row 4.720Auscott Narrabri 1990 dry pick wheel row 4.957

Namoi Valley at 35 cmAuscott Narrabri 1990 wet pick wheel row —1.573Togo 1990 wet pick wheel row —1.292Togo 1990 dry pick wheel row —0.208Auscott Narrabri 1990 wet pick guess row —0.115Myall Vale Research Station 1989 compacted —0.014Auscott Narrabri 1990 dry pick guess row 0.190Auscott Narrabri 1989 compacted 0.533Myall Vale Research Station 1989 non—compacted 0.962Togo 1990 dry pick guess row 1.307Auscott Narrabri 1990 dry pick wheel row 1.328Togo 1990 wet pick guess row 1.540Auscott Narrabri 1989 non—compacted 1.946

83

Table 18 cont.

Experimental Sites Soil Structural Index(SSD

Macquarie Valley at 15 cmButtabone 1990 dry pick guess row -3.822

Buttabone 1989 compacted -2.197

Buttabone 1990 wet pick wheel row -1.982

Elengerah 1990 wet pick wheel row -1.929

Auscott Warren 1992 compacted early plant (site 36, rep 1) -1.918

Buttabone 1990 dry pick wheel row -1.731

Buttabone 1989 non-compacted -1.604

Elengerah 1990 wet pick guess row -1.580

Auscott Warren 1992 non-compacted early plant (site 33, rep 1) -1.5711


Auscott Warren 1992 non-compacted late plant (site 34, rep 1) -1.3611

Auscott Warren 1992 compacted late plant (site 38, rep 2) -1.338

Auscott Warren 1989 compacted -1.073

Buttabone 1990 wet pick guess row -0.294

Auscott Warren 1990 dry pick guess row -0.235

Auscott Warren 1990 wet pick guess row -0.212

Auscott Warren 1990 wet pick wheel row -0.200

Elengerah 1990 dry pick guess row 0.441

Auscott Warren 1990 dry pick wheel row 1.167

Auscott Warren 1989 non-compacted 1.579

Auscott Warren 1992 compacted late plant (site 35, rep 1) 1.853

Auscott Warren 1992 non-compacted early plant (site 40, rep2) 4.022

Carlisle 1989 non-compacted 5.341

Carlisle 1989 compacted 7.693

Macquarie Valley at 35cmAuscott Warren 1989 non-compacted -4.904

Buttabone 1990 wet pick guess row -3.891Elengerah 1990 dry pick guess row -3.143Auscott Warren 1992 non-compacted early plant (site 33, rep 1) -2.686

Buttabone 1990 wet pick wheel row -2.603

Buttabone 1989 non-compacted -2.593

Elengerah 1990 wet pick wheel row -2.155


Elengerah 1990 wet pick guess row -1.899

Auscott Warren 1990 dry pick wheel row -1.800

Auscott Warren 1989 compacted -1.547

Buttabone 1989 compacted -1.440

Auscott Warren 1990 wet pick guess row -1.386

Auscott Warren 1990 wet pick wheel row -1.236Buttabone 1990 dry pick wheel row -0.890

Auscott Warren 1990 dry pick guess row -0.585

Carlisle 1989 non-compacted -0.543

Buttabone 1990 dry pick guess row -0.537

Carlisle 1989 compacted 0.076


Auscott Warren 1992 non-compacted late plant (site 39, rep 2) 1.934

Auscott Warren 1992 non-compacted late plant (site 34, rep 1) 2.674

Auscott Warren 1992 compacted early plant (site 37, rep 2) 2.758


Auscott Warren 1992 non-compacted early plant (site 40, rep 2) 3.012

Elengerah 1989 compacted 3.513

_Iod shrinkage analysis done on clods that were remnants of previous yamage soils and was notrepresentative of the site. Statistical analysis was performed using an SSI of 8 in these casesas the soil was in better structural condition than any site in the 1989/90 and 1990/91 cotton seasons.

84

CHAPTER SEVEN Results and Discussion— 1989/90 and 1990/91 Cotton

Seasons

7.1 ANALYSIS OF TECHNIQUES TO ASSESS SOIL STRUCTURE IN THE FIELD

To determine which techniques were most appropriate for field soil structural assessment, all

techniques were compared to the soil structural index (SSI). How this index was formed and the

justification for its use are explained in Chapter 6.

For statistical analysis the experimental data was divided into the categories: combined valleys and

depths; separate valleys; and separate depths. There was insufficient degrees of freedom to divide

the data into separate valley and depth categories. This allowed the optimal grouping of sites for

prediction of structural condition to be identified. It was not possible to make measurements at all

depths throughout the profile because of time constraints. Thus, two depths were chosen where

compaction is commonly found: 15-20 cm and 35-40 cm. It was assumed that if compaction was

present in the profile that it would be shown by either or both of these two layers. Rimik cone

penetrometer data showed that a majority of the areas with higher soil strength readings were

incorporated by these 2 depths. The data used to ascertain this is found on the disks accompanying

this thesis.

As the analysis proceeded it became evident that two different packages for predicting structural

condition were needed: an 'extension' package for extension personnel, consultants, agronomists and

growers; and a 'research' package for research personnel. Both groups of users have different

requirements when diagnosing soil structural condition. The extension package is aimed at those

who must make rapid decisions for farm management purposes, and may not have the time, money,

experience or the equipment for the more sophisticated techniques. The research package is aimed

at users who require more precise measures of soil structural condition, and who have more time,

labour, money and equipment at their disposal.

7.1.1 Relationship between Soil Structural Index and Yield

The relationship between SSI and yield is very poor (Fig. 11 & 12), with both regression and

correlation analyses giving non—significant results. There are a number of possible explanations for

this. Probably the most significant effect is that of management. By adjusting management

strategies, such as increasing the irrigation frequency and nitrogen rate it is possible to mask the

effects of soil structural damage.

85

g x-x x , x■

860

4 —• o

2 —

o-6 -4 -2 0

SSI I-8

i i 1

X• x

0

I i I I2 4 6 8

••

12 —

• A

121'—

0 s'e A ■

8 —x• ■ x

0

so 6— O

a

4 —• 0 0

2

SSI F-8 -6 -4 -2

0 2 4 6 8

■ aw89 0 car189 • bb89 * an89 • nars89 A aw90 • e190 0 bb90 X an90 x togo90

Figure 11. The relationship between SSI and yield at 15 cm.

12 —

• A+

20 oA•0 A

X

••

x3:

• 3:x x

■ aw89 0 car189 • bb89 0 an89 • nars89 A aw90 • e190 0 bb90 x an90

x togo90 + e189

Figure 12. The relationship between SSI and yield at 35 cm.

86

The location of compacted areas in the profile will also influence yield. Lowry et al. (1970) found

that the depth of the compaction layer influenced plant growth, and that cotton growth was more

adversely affected by shallow rather than deeper compaction. Therefore, a soil that has compaction

(i.e. a lower SSI) at 35 cm, but not at 15 cm yields better than soils with a high SSI at 15 cm and

a low SSI at 35 cm, as the cotton plants are able to obtain adequate water and nutrients from the

soil above 35 cm. This, was evident in the 1989/90 Auscott Warren, 1989/90 and 1990/91

Buttabone, and 1990/91 Auscott Narrabri cotton seasons. This of course, is dependent upon good

irrigation scheduling, adequate nutrient supply and the absence of unfavourable climatic conditions

such as cold shock. Buttabone 1990/91 increased the irrigation frequency in response to the

presence of compaction. This resulted in some waterlogging effects on the 'dry pick' site, which

was detected by the dipyridyl test (Heaney and Davison, 1977; Childs, 1981; Batey and Childs,

1982). For this reason, variation in yield would be expected when compaction occurs at different

depths between profiles. Thus, compaction models were considered separately for 15 cm and 35

cm depths.

Other factors may also have produced lint yield abnormalities. Salinity, resulting from reservoir

leakage (Lubbers, 1992) adversely affected yields on Elengerah during the 1990/91 season. Hail

reduced 1989/90 cotton yields in the Namoi Valley. Poor water and crop management i.e.

randomly running excess water down furrows that was apparent at the 1989/90 Carlisle site also led

to unusually low yields. Insect pressure and disease incidence can also play a major role in the

final determination of yield. These aspects were noted, but no detailed account was keep for the

duration of the study. Therefore, it was not possible to ascertain the exact effect they had on yield.

In previous studies, the relationship between shrinkage curve parameters and yield has been varied.

McGarry (1990) showed that lower PA values corresponded to yield declines. However, this study

was carried out on 2 separate fields with different management practices. Daniells (1989), on the

other hand, found that v, was a good indicator of yield reduction after degradation, but a poor

indicator of yield improvement after soil restoration.

A closer investigation of the relationship between SSI and yield and how it is affected by

management is needed to clarify this relationship. Clarification is also needed to assess how

accurately the clod shrinkage technique represents the true root environment. However, relating

yield and soil properties across different sites is always difficult due to the variation in

management, such as rate of nitrogen application and irrigation which greatly affects yield.

87

7.1.2 Linear Regression

Although a number of measurements are significantly (P<0.05) correlated with the SSI, most have

low predictive values (R2), the highest being 0.49 (Table 19). Bulk density and air-filled porosity

generally showed the highest R2 values, ranging from 0.15 to 0.49. It would be expected, however,

that these techniques would have a good correlation to the SSI as it is based on parameters derived

from the soil shrinkage curve, in which specific volume (reciprocal of bulk density) is plotted

against the gravimetric water content. It is clear that no one technique alone explains sufficient

variation so that it can be used to predict SSI.

Table 19 . R2 values of SSI individually regressed against the measurements of soil structure.

Soil Structural Measurements

Combined 15 cm Depth 35 cm DepthDepthsn..37-58 n..24-28 n..17-29

core bulk density* (-)0.382*** (-)0.291* (-)0.490***air-filled porosityt0.323*** 0.363** 0.154*Rimik penetrometer: 15cm & 35cm* (-)0.099* (-)0.150* nsroot diameter 0.068* ns nsroot diameter ratio: 5/15 cm & 25/35cm (-)0.078* ns nsroot obliquity ratio: 30-40/40-50cm ns ns 0.254*no. of lateral roots: 10-20cm & 30-40cm 0.118* ns 0.163*no. of lateral roots ratio

: 0-10/10-20cm & 20-30/30-40cm (-)0.117* ns nsvisual assessment (Batey's modified

Peerlkamp Scheme) (-)0.198* ns (-)0.336*soil strength frequency 15cm & 35cm"

: 0-0.3 Mpa 0.077* ns nssoil strength frequency 15cm & 35cm"

: 0.7-2.5 Mpa (-)0.073* ns nswater extraction: 20cm 0.201** 0.292* 0.161*water extraction: 40cm 0.135* 0.198* nswater extraction: 60cm 0.125* 0.193* nswater extraction: 80cm 0.128* 0.259* nswater extraction: 20-40cm 0.137* 0.199* nswater extraction: 20-60cm 0.147* 0.209* nswater extraction: 20-80cm 0.178* 0.248* 0.153*water extraction: 40-80cm 0.088* ns nsdaily water extraction: 80cm 0.119* 0.244* nsoxygen flux density: 6 DAI ns (-)0.183* ns* P<0.05 ** P<0.001 *** P<0.0001 ns- not significant§ at field water content 1- at reference water content of 20%1 refers to the number of readings that lie within the 0-0.3 Mpa or 0.7-2.5 Mpa soil strength ranges(-) indicates a negative correlation, while the unsigned values are positive correlationsn" refers to the range of degrees of freedom used in the analysis

88

7.1.3 Multiple Linear Regression

As there was no outstanding single technique for assessing soil structural condition in the field,

multivariate analysis was used to identify the best combination of techniques to evaluate SSI. The

techniques included in the extension package were those that were relatively simple and could be

performed rapidly. These included the root morphological characteristics, soil strength

measurements, water extraction measurements (neutron probe measurements which are used for

irrigation scheduling), bulk density and air—filled porosity. Visual assessment using Batey's

modified Peerlkamp Scheme was only used in the 1990/91 cotton season, and could not be

incorporated into these models due to the statistical problems associated with the missing visual

assessment scores from the 1989/90 cotton season and not enough degrees of freedom when only

one years data analysed. The research package incorporated techniques that were more complex

and relatively time consuming in addition to those used in the extension models. These were root

distribution patterns, oxygen flux density, rhodamine dye infiltration, root obliquity frequency, soil

strength frequency and changes in air—filled porosity and oxygen flux density measurements over

an irrigation cycle. The frequency measurements can be time—consuming to calculate, but the use

of software packages would speed up this process.

The analysis was divided into 5 different categories— combined valleys and depths, Macquarie

Valley, Namoi Valley, 15 cm depth and 35 cm depth. Separate valley categories were included as

the Namoi Valley soils tend to have less variablity within fields than the Macquarie Valley and

therefore the models for separate valleys explained a greater degree of variation. Within each

category a point was reached where the addition of another variable or technique made no

significant improvement to the variance in SSI accounted for. However, a complete list of the

regression models and their associated R2 values have been listed in Appendix 4. The higher R2

values for the research package are attributed to the use of more sophisticated techniques for

measuring soil structural condition (see Chaper 9). As this is a multivariate regression it is not

possible to look at the relationship between SSI and each regressor variable in isolation, as some of

the variables are correlated.

89

The best models for the extension package are:—

1. Combined valleys and depths— 5 variable model (R2 = 0.534, n = 47)

SSI = -36.18 - (0.0005*yield) + (34.24*bd) + (77.59*afp) - (25.00*rflat) + (0.05*ext, 20 cm)

2 Macquarie Valley— 5 variable model (R2 = 0.687, n = 32)

SSI = 28.97 + (0.001*Chatillion) + (2.82*rdia) — (35.44*rflat) + (0.11*ext, 20 cm) +

(0.76*dwu, 40 cm)

3. Namoi Valley— 7 variable model (R2 = 0.642, n = 22)

SSI = —32.33 + (58.62*bd) + (136.70*afp) + (0.14*robl) + (1.76*rdiar, 0/20 & 20/40 cm) —

(0.84*nlat) — (3.22*nlatr, 0/10 & 20/30 cm) — (66.16*rflat)

4. 15cm Depth — 7 variable model (R 2 = 0.726, n = 24)

SSI = —63.48 + (65.69*bd) + (123.11*afp) + (0.003*Chatillion) — (4.85*roblr, 0/10 cm) —

(1.45*nlatr, 10/20 cm) — (49.35*rflat) + (0.16*ext, 20 cm)

5. 35 cm Depth — 5 variable model (R2 = 0.821, n = 23)

SSI = 18.14 — (8.55*bd) + (0.66*rdiar, 20/30 cm) + (0.10*robl) + (4.40*roblr, 20/30 cm) —

(13.41*rflat)

bd — core bulk density (Mg m-')dwu—daily water use (mm)Rimik — Rimik penetrometer (Mpa)nlat — number of lateral rootsrflat — root flatness

afp — air—filled porosity (m' m-') nlatr — number of lateral roots ratiordia — root diameter (cm) roblr — root obliquity ratioext — water extraction (mm) robl — root obliquity (°)Chatillion — Chatillion penetrometer (Mpa) rdiar — root diameter ratio

90

The best models for the research package are:-

1. Combined valleys and depths-8 variable model (R 2 = 0.618, n = 43)

SSI = -2.26 + (11.08*afp) - (0.003*Chatillion) + (0.05*robl) + (0.06*robl:freq, 10 0-60°) -

(0.12*robl:freq, 90°+) + (0.02*ss:freq, 0-0.3 Mpa) - (0.04*ss:freq, 0.3-0.7 Mpa) +

(18.78*afp 2 dai)

2 Macquarie Valley- 7 variable model (R2 = 0.760, n = 30)

SSI = 26.98 + (0.002*Chatillion) + (0.27*rdist) + (3.42*rdia) - (38.90*rflat) + (0.04*robl:freq,

0°-10°) + (0.10*ext, 20 cm) + (1.45*dwu, 40 cm)

3. Namoi Valley- 9 variable model (R2 = 0.757, n = 22)

SSI= -167.53 + (98.23*bd) + (192.79*afp) - (0.27*rdist) + (1.70*rdiar, 10/20 & 30/40 cm) -

(0.07*robl:freq, 0°) - (0.34*robl:freq, 90° +) + (0.03*ss:freq, 0.7-2.5 Mpa) + (0.02*ss:freq 15

& 35 cm, 0.3-0.7 Mpa) - (0.003* ofd 2 DAD

4. 15 cm Depth - 8 variable model (R2 = 0.806, n = 22)

SSI = -0.60 + (0.005*yield) - (0.006*Rimik) + (0.43*rdist) - (15.04*rdia) + (12.09*rdiar, 0/10

cm) - (2.50*nlatr, 0/10 cm) - (3.16*nlatr, 10/20 cm) - (0.16*ss:freq, 0.3-0.7 Mpa)

5. 35 cm Depth - 6 variable model (R2 = 0.876, n= 20)

SSI = -7.83 - (0.002*yield) + (13.16*afp) + (0.15*robl) + (3.17*roblr, 20/30 cm)

(0.08*robl:freq 40°-60°) + (0.001*ofd 4 DAD

bd - core bulk density (Mg m')dwu - daily water use (mm)robl:freq - root obliquity frequency (%)nlat - number of lateral rootsrflat - root flatnessDAI- days after irrigation

afp - porosity (m'rdia - root diameter (cm)rdist - root distribution (%)nlatr - number of lateral roots ratiordiar - root diameter ratioRimik - Rimik penetrometer (Mpa)

ss:freq - soil strength frequency (%)roblr - root obliquity ratiorobl - root obliquity (°)ext - water extraction (mm)Chatillion - Chatillion penetrometer (Mpa)ofd - oxygen flux density (g s-1)

The choice of categories to use in both the extension and research package will depend on the

operator's preferences. However, in terms of maximising accuracy the use of models for the

individual depths is the best. Ideally, an operator will quickly assess the structural condition at

both 15cm and 35cm by profile examination and noting how 'tight' the soil is. Once this is done a

more complete assessment using the SSI models can be made at the depths with suspected

compaction. To ensure that a comprehensive record of changing soil structural conditions over

time is available for each field, both the 15 and 35 cm depths should be routinely assessed.

91

Profile examination is important to give the operator an initial impression of the soil structural

condition. The importance of visual assessment in describing soil structural condition is difficult to

ascertain, as it is dependent upon operator experience in identifying structurally degraded soil. As

an operator becomes more experienced at assessing soil profiles, they tend to become more

confident in their soil diagnosis. Depending on how many soil profiles an operator has assessed

would affect the score given to a profile. Visual assessment using Batey's modified Peerlkamp

Scheme showed a significant relationship with SSI (Table 19), but it only explained 20-34% of

variation between the sites. This may be due to the visual assessment score being a weighted

average of a profile area 50 cm x 80 cm. If the area of compaction was small but in a zone that

would effect plant growth, while the rest of the profile was in good condition, then the influence of

the compacted area in the score would be reduced by the larger uncompacted area. The

development of more objective profile assessment schemes and training packages for operators

would alleviate these problems and may make profile assessment an important tool for soil

structural assessment.

7.1.4 Techniques Used in Soil Structural Index Predictor Models

The soil structural measurements that occur most frequently in the regression models are the root

morphological characteristics, water extraction, soil strength, bulk density and air—filled porosity.

All of the root morphological characteristics occur at least once. Gill and Bolt (1955) stated that a

root distorts when it encounters unsurmountable external resistance, and suggested that roots could

be used as a way of diagnosing areas of impedance i.e. compaction. Other authors have also

shown various ways that roots react to different soil structural conditions.

The cross—sectional shape of the root was shown by Gerard et al. (1972) to reflect soil bulk

density. At lower bulk densities the roots were circular, while at higher densities the roots tended

to be irregular and rectangular to platy in shape. The flatness of the roots in this study from both

cotton seasons showed a similar relationship with bulk density. Sutton (1969) also noted that roots

were flattened, and tend to exhibit numerous changes in direction of growth when confronted by

structurally damaged areas. Roots that encounter areas of compacted soil can either enter this area

through vertical cracks or are diverted horizontally (Pearson, 1971; Taylor, 1971). Wild et al.

(1992) on a grey cracking clay in the Macquarie Valley observed larger root obliquity

measurements on structurally degraded soils. Even though the relationship between root obliquity

and SSI was not significant in this study the trend indicated that larger root obliquities reflected a

92

lower SSI. Thus, the finding that root obliquity and root flatness measurements are influenced by

soil structural form are consistent with other studies. When root growth is restricted by compacted

layers then root diameter increases in the zone above the restriction (Camp and Lund, 1964;

Mathers and Welch, 1964), implying that root diameters would successfully indicate zones of

structural damage. This is evident by the significant negative relationship between root diameter

and SSI shown in Table 19. Another possible indicator of soil structural damage is the number of

lateral roots at a certain depth. When roots meet mechanical impedance, the entire root system

tends to become stunted with a proliferation of lateral roots at that point (Russell, 1977). The

relationship between SSI and ratio of the number of lateral roots between depths (Table 19)

indicated that this type of relationship existed in this study (R2 = 0.12).

The pattern of root distribution can indicate the presence of compacted or waterlogged zones, as

roots tend to concentrate above these zones. Boone et al. (1978) found that the effective rooting

depth, i.e. where 80% of the roots are located, was much shallower for compacted subsoils than for

soils without compaction. Thus, the root distribution at certain depths should enable damaged

zones to be isolated. In this study, however, there appeared to be little evidence to support these

conclusions.

Compacted soils were found by Hodgson and Chan (1982) to have slow rates of oxygen diffusion

and low air—filled porosities. Taylor and Burnett (1963), Gerik et al. (1987) and McKenzie et al.

(1991) have also noted that compacted soils had higher soil strengths, higher bulk densities and

lower porosities than non—compacted soils. The results from this study mirror the findings of these

authors (Table 19). The actual soil strength, bulk density and air—filled porosity data can be found

on the disk attached to this thesis.

Water extraction patterns have been found to reflect the depth of compacted layers within the soil

(Browne, 1984; Hodgson and Chan, 1984; Thompson and Cull, 1989). The patterns represent the

depths to which plant roots are able to extract water from. Usually, there are large extraction rates

above compacted areas and little or none below this area. In general, water extraction profiles from

this study did show higher extraction rates from above compacted areas. Table 19 illustrates that

higher rates of water extraction are indicative of higher SSI values.

93

7.2 OPTIMUM SAMPLE SIZE

Table 20. Optimum number of replicates for techniques used to assess soil structural

condition with relative standard errors of 5% and 10%.

Soil StructuralMeasurements

Depth Recommended sample sizeMacquarie Valley Namoi Valley5% 10% 5% 10%

Bulk density 15 cm 3 1 1 135 cm 2 1 1 1

Air—filled porosity 15 cm 15 5 5 235 cm 18 5 10 2

Rimik penetrometer 15 cm 14 5 18 735 cm 11 4 9 3

Chatillion penetrometer 15 cm 18 7 7 235 cm 12 5 6 2

Root diameter 0 cm 16 5 14 410 cm 43 11 41 1130 cm 30 9 39 10

Root diameter ratio 0/10 cm 31 8 42 140/20 cm 31 10 32 1310/20 cm 35 10 45 1020/40 cm 23 6 47 12

Root flatness 10 cm 2 1 1 130 cm 2 1 3 1

Root obliquity 10 cm 457 120 397 11030 cm 143 47 172 43

Root obliquity ratio 0/10 cm 996 228 927 22220/30 cm 165 57 388 84

No. of lateral roots 10 cm 70 24 69 1930 cm 116 27 133 40

No. of lateral roots ratio 0/10 cm 440 127 400 10010/20 cm 210 61 156 4620/30 cm 150 44 154 34

Water extraction 20 cm 20 6 13 3Daily water use 20 cm 19 6 10 3

40 cm 45 15 14 4

SOILpak scorer 10 cm 14 4 —20 cm 118 30 —30 cm 133 34 —40 cm 120 30 —50 cm 122 31 — —

Shear vanes 10 cm 324 81 —20 cm 351 88 —30 cm 87 22 —40 cm 23 6 — —

t refers to techniques used in the 1992/93 cotton season

94

The optimum number of replications for the techniques that were included in the prediction models

were determined. Table 20 provides the number of replications needed to ensure that the standard

errors of the measurements are within 5% or 10%. If the number of replications are less than the

optimum then the variation within the data increases leading to more inaccurate predictions of the

SSI. There is a large amount of variability in some of the techniques, which casts some doubt on

the practical use of these techniques in the SSI predictive models. This is especially so with root

obliquity, number of lateral roots, SOILpak scores and shear vane readings.

7.3 YIELD ESTIMATIONS USING ROOT MORPHOLOGICAL PROPERTIES

Presently cotton growers and consultants use boll counts to make yield estimations (Harvey

Gaynor, Auscott Pty. Ltd., pers. comm.). This can be a time consuming and tedious task. The

present study offered an opportunity to ascertain whether root morphological characteristics could

be related to cotton lint production. This might provide a quicker and perhaps more accurate

estimation of yield, despite the large number of samples needed for some of the root morphological

characteristics (Table 20).

A new method of predicting yield is being developed by Neutron Probe Services (Peter Cull,

Neutron Probe Services, pers. comm.). This involves monitoring the cumulative water content

through the season, which can be related to lint yield. This technique, even though it is in its early

developmental stage is achieving yield predictions within 0.2-0.4 bales/ha of the actual yield. The

accuracy of these predictions is greater than those of boll counts and also of yield predictions using

root morphological characteristics. Therefore the use of root morphological characteristics to

predict yield would only be of use where moisture profile readings using a neutron moisture meter

were not available.

95

Table 21. The correlation (R values) of root morphological characteristics with lint yield.

Root MorphologicalCharacteristics

root diameter, 0cm (surface)root diameter, 10cmroot diameter, 20cmroot diameter, 30cmroot diameter ratio, 0/10cmroot diameter ratio, 10/20cmroot diameter ratio, 20/30cmroot diameter ratio, 0/20cmroot diameter ratio, 10/30cmroot obliquity, 0-10cmroot obliquity, 10-20cmroot obliquity, 20-30cmroot obliquity ratio,

0-10/10-20cmroot obliquity ratio,

10-20/20-30cmno. of lat. roots, 0-10cmno. of lat. roots, 10-20cmno. of lat. roots, 20-30cmno. of lat. roots ratio,

0-10/10-20cmno. of lat. roots ratio,



0-10/20-30cmroot flatness, 10cmroot flatness, 20cmroot flatness, 30cm* P<0.05 ** P<0.001

Combined Valleys0 cm root 10 cm root yielddiameter diameter n'

Macquarie Valley Namoi Valleyyield yield

n..231-432 n..232-427 1068-2897 532-1671 536-1222

0.711*** 0.788*** 0.640***0.657*** 0.613*** 0.725*** 0.617***0.523*** 0.755*** 0.471*** 0.567*** 0.448***0.435*** 0.523*** 0.411*** 0.455*** 0.440***-0.127* -0.805*** -0.339*** -0.488*** -0.396***0.210*** 0.345*** ns 0.081* ns0.258*** 0.387*** 0.061* 0.150** nsns -0.425*** -0.158*** -0.203*** -0.208***0.320*** 0.547*** ns 0.197*** ns0.140* -0.200*** -0.134*** -0.167*** nsns ns -0.111*** -0.135*** -0.068*ns ns -0.043* -0.114*** 0.069*

ns -0.156* -0.104*** -0.145*** ns

-0.142* ns -0.051* ns ns0.265*** ns -0.098*** -0.254*** ns0.387*** 0.534*** 0.393*** 0.398*** 0.409***0.363*** 0.444*** 0.290*** 0.406*** 0.234***

-0.102* -0.353*** -0.285*** -0.429*** -0.238***

ns ns ns -0.094* ns

ns 0.163** ns

ns -0.267*** -0.224*** -0.131* 0.106* 0.051* ns nsns -0.147** ns ns -0.073*ns -0.141* -0.082* ns -0.136**** I3/40.0001 ns- not significant

It might be expected that differences in root morphology between plants would be reflected in yield

differences, as the root is the part of the plant that is directly affected by soil chemical and physical

properties. Of the root morphological characteristics measured, root diameter shows the highest

correlation with lint yield (Table 21), indicating that larger root diameters produce higher yielding

plants. Root diameters are better correlated than root obliquity measurements, as root diameter

tends to be less variable. This agrees with earlier work by Taylor et al. (1963), who noted that

larger root diameters within restricted zones corresponded to higher lint yields. Mathers and Welch

(1964) also showed that when taproots were restricted to diameters of 0.79 cm or less for two

weeks or longer then cotton yields declined. This decrease in yield was more pronounced the

96

longer the period of restriction and the greater the decrease in root diameter. Other morphological

characteristics were significantly correlated but had relatively low R 2 values in comparison to root

diameter measurements. Due to the different sampling regimes it is not possible to extrapolate and

suggest that yield and compaction (SSI) are correlated from the correlation between the root

morphological characteristics and yield (on a per plant basis) and root morphological characteristics

and SSI (based on the mean of all plants combined).

Either linear or maxr regression models can be used to estimate yield, as both types of model

explain a similar proportion of the variation.

The models from the linear regression are:-

1. Combined valleys and depths

a) Yield = -25.76 + (45.24*root diameter 0cm) R2=0.5633

b) Yield = -5.63 + (32.31*root diameter 10cm) R2=0.4240

2. Macquarie Valley



3. Namoi Valley



The models from the maxr regression are:-

1. Combined valleys and depths- 2 variable model (11`.0.573)

Yield= -24.01 + (36.69*root diameter 0cm) + (8.57*root diameter 10cm)

2. Macquarie Valley- 2 variable model (R2=0.662)


3. Namoi Valley- 2 variable model (R2=0.412)


97

7.4 CONCLUSION

Two separate packages have been developed to cater for two different user groups. One package is

aimed at cotton consultants, agronomists and growers. It uses techniques that are simple to

perform. The other package has been devised for researchers. It incorporates more laborious and

time consuming techniques as well as the simple and rapid techniques. The accuracy and speed at

which the SSI for a site can be determined is decided upon by the choice of predictors. Appendix

4 shows the increases in R2 can be obtained by adding more variables to the regression models.

Increases in R2 however, diminish with increasing number of predictors added to the equations.

This information enables an operator to determine how many variables should be measured to attain

a desired level of precision.

No single technique showed exceptional promise in estimating soil structural condition in the field.

The only techniques that showed reasonable R 2 regression values with SSI were bulk density and

air—filled porosity. This is to be expected as the soil structural index is largely based on bulk

density as measured by the clod shrinkage technique, and air—filled porosity is calculated using the

bulk density data.

With multivariate regression analysis it was possible to derive several models for determining the

SSI. The research package models described 62-88% of the variation in soil structural condition,

while the extension package described 53-82%. The choice of model used will vary according to

the required precision for predicting SSI. A model that explains more than 50% of the variation

would be suitable to use in ranking fields within a property (Gavin Melville, NSW Agriculture

biometrican, pers. comm.). The techniques that appeared most frequently in the models were bulk

density, air—filled porosity, root morphological characteristics, water extraction and penetrometer

data.

Cotton yield has frequently been shown to be correlated with soil structural condition (Taylor et.

al., 1964; Melville and Rasbury, 1981; Hadas et. al., 1985; McConnell and Wilkerson, 1987;

McConnell et al., 1989; Thompson and Cull, 1989). In this study no correlation was found

between soil structure and yield. The study did, however, suggest that compaction occurring closest

to the soil surface caused the largest yield declines. This reflects the findings of Mathers and

Welch (1964), where early season root restriction was noted to have the greatest detrimental effects

on yield.

98

This lack of correlation between yield and SSI can be explained by the use of different

management strategies adopted for structurally damaged soils. In recent years it has become

evident that it is possible to obtain reasonable yields on poorly structured commercial fields. When

a field is compacted higher rates of nitrogen fertilizer can be used to override the adverse effects of

structural damage (Hearn, 1986; Daniells and Larsen, 1991; Constable et al., 1992). Many studies

have also noted that the effects of compaction are reduced by more frequent irrigations (Taylor et.

al., 1964; Melville and Rasbury, 1981; McConnell and Wilkerson, 1987; McConnell et al., 1989;

Thompson and Cull 1989). This however, appears to only be successful where high ridges are

present. This provides a small aerated zone that is suitable for root growth.

The severity of the restrictive layer is dependent upon soil aeration and strength, both of which are

inversely related to moisture content. Letey (1985) used the concept of the non—limiting water

range (NLWR) to describe the relationship between these characteristics. This defines the optimum

range of water contents for plant growth where both aeration and soil strength are not limiting.

One limit is where the soil becomes too strong to permit root growth while the other is where the

lack of air porosity limits root growth. In well structured soils the NLWR is large, but as the bulk

density increases and soil structural condition declines this NLWR decreases. This infers that if

soil strengths can be reduced through increasing the irrigation frequency without causing

waterlogging, then any potential yield declines from the compacted zones can to some extent be

avoided (Mathers and Welch, 1964). The restriction of roots by mechanical stress in the soil is

only detrimental to plant growth when the supply of water and nutrients in the shallow rooting zone

is inadequate (Russell, 1977). Therefore, by shortening the time between irrigations and applying

higher rates of nitrogen it is possible to override the effects of compaction.

However, problems can arise when extra irrigations are scheduled for a compacted field. The

common tool for irrigation scheduling is the neutron moisture meter, and the siting of neutron

moisture meters to monitor soil water status will affect the timing of irrigations. Frequently, there

are varying degrees of compaction found within a field, and as a result there will be waterlogged

areas and areas suffering water stress after an irrigation. Therefore, as fields are irrigated at the one

time the position of the neutron moisture meters in relation to compacted areas is important.

Growers must ensure that their neutron moisture meters are located in areas that are representative

of the field.

99

Another point to note is that in cracking clay soil even though overall soil strength and porosity

may indicate poor growing conditions, there may be many failure zones and fissures through which

plant roots are able to pass. Hulme et al. (1991) observed that where aeration and strength were

suboptimal for root growth, there were still sufficient interaggregate fissures for root growth. This

was indicated by Rhodamine dye infiltration which showed preferential dye flow through these

fissures. Wild et al. (1992) used another technique, the SOLICON system (Moran et al., 1989) to

assess the macroporosity and locate interaggregate fissures within the soil.

One weakness of the SSI, as used in this study is that is does not indicate how compacted a field

has to be before plant development is inhibited and subsequent yield declines occur for a particular

level of management inputs. It was not possible to determine this from the present experiment as

each field had different management strategies. These strategies often masked and obscured the

effects of compaction on lint yields. In essence, growers are able to overcome certain degrees of

compaction by careful water and fertiliser management.

The SSI is an effective tool however, in ranking fields from best to worst. In areas or years where

there are water shortages, or a grower wants to put a proportion of his property into rotation crops

then it would be useful to be able to accurately rank fields (Table 18, Chapter 6).

One other question that was not addressed in this study was the number of soil structural

assessments needed across a field to obtain a reliable estimation of soil structural condition for the

whole field. Thus, the operator must be careful to do any soil measurements in areas that are

typical of the whole field. Areas to be avoided are those near tail drains or edges of the field,

depressions in the field, and areas where large cuts or fills have been made during laser levelling.

There are some techniques that have been recently used by other research and extension personnel

to assess soil structure that were not included in this experiment. These techniques include the

shear vane for soil strength measurements (Koppi and Douglas, 1991), SOLICON for porosity

assessment (Moran et al., 1989) and the latest SOILpak score for visual soil structural assessment

(Daniells and Larsen, 1991). For completeness the shear vane and SOILpak score were included in

the 1992/93 experiment.

100

PART 111: 1992/93 Cotton Season

CHAPTER EIGHT General Description of Experiments and Experimental

Methods

8.1 INTRODUCTION

A series of models were devised, using data from the 1989/90 and 1990/91 cotton seasons to

predict the SSI from simpler measures of soil structural form. These models and the lint yield

prediction models, however, needed to be validated. So a third cotton season (1992/93) was

studied to provide validation data. Questions regarding the effect of compaction on seedling

growth, and how the different soil and plant properties reacted to extreme levels of compaction,

were also addressed in this last season.

8.2 EXPERIMENTAL DESIGN

A split—plot design was used during the 1992/93 cotton season, with 4 treatments replicated twice.

The 4 treatments were 2 levels of compaction and 2 planting dates. This gave a total of 8 sites in

the experimental area. The study was carried out on Field 33, Auscott Warren. The compaction

levels consisted of the existing structural condition of the field (non—compacted) and induced

compaction (compacted). The compaction was caused by driving a 'Caterpillar' D7 crawler on the

top of the hills twice at a time when the gravimetric moisture content at a depth of 10-20 cm was

within the range 23-25%. The non—compacted sites were guess rows in the field. Compaction, as

noted visually, was evident at depths below 8-10 cm after the treatment had been applied. The

trial area was not managed for compaction, so additional irrigations or nitrogen applications were

not applied. The position of the compacted and non—compacted treatments could not be

randomised due to problems associated with future irrigation water subbing from the non—

compacted areas to the compacted areas, which would be drier due to a lower infiltration capacity

(McConnell et al., 1989).

Initially, the experiment was designed to have two initial irrigation dates to determine the effect on

seedling development of delaying the first irrigation. However, frequent early season rainfall meant

that this part of the experiment had to be abandoned. Instead, two planting dates were used, as the

seedlings after the first planting were subjected to long periods of rain and apparent waterlogging.

101

In contrast, the later planting date was followed by climatic conditions that were more typical of a

normal season. This allowed an assessment of differences in seedling behaviour under contrasting

climatic conditions and how this behaviour was affected by compaction.

The later planting date was replanted onto sites that had been originally planted in October. The

seedlings from the earlier planting were either removed during the replanting process or by hand.

These treatments were referred to as non—compacted early plant, non—compacted late plant,

compacted early plant and compacted late plant. The experimental design is summarised in Fig. 13

and consisted of:—

1 property x 4 treatments x 2 replicates = 8 sites (2 non—compacted early plant sites, 2 non—

compacted late plant sites, 2 compacted

early plant sites and 2 compacted late plant

sites)

102

Figure 13. Experimental design showing the treatments on each of the sites for the 1992/93

cotton season.

N N C C C C N N

12m P1 P2 P2 P1 P1 P2 P2 P1 25 m

R1 R1 R1 R1 R2 R2 R2 R2

32 m

where N was existing field structure

C was soil compacted with a 'Caterpillar' D7 crawler when the soil was moist

P1 was the early planting date — 14/10/92

P2 was the later planting date— 2/12/92

R1 was replicate 1

R2 was replicate 2

8.3 SOIL DESCRIPTION

No physical profile descriptions were done as the site covered a small area (approximately 20 m x

16 m). It was assumed to have a uniform soil physical profile across the trial area. The

mineralogy for all sites was similar, predominantly consisting of montmorillonite with sub-

dominant fractions of koalinite and illite.

Chemical analysis

The Ca/Mg ratio and ESP ranges in the top 40 cm are 2.17-2.56 and 1.93-4.18%, respectively.

The pH was between 7.47 and 7.66 while the ECe was between 1.13 and 2.33 for this depth.

None of these values were considered to be deleterious to cotton growth.

103

8.4 CULTURAL PRACTICES

Table 22. Management details for Field 33, Auscott Warren, Warren 1992/93 cotton season.

non–compacted and compacted treatments

soil type grey clay: old alluvium meander plain (McKenzie, 1992)

slope - 1:500

cotton variety Sicala VI

hill/bed system retained hills (1m)


fertilizer type and rate applied Anhydrous ammonia (95 kgN/ha), Urea (68 kgN/ha) andMAP (45 kgP/ha)

method of nitrogen application gas rig

pre–emergence herbicides Trifluralin/Fluometuron/Diuron


tillage operations Seedbed preparation: Lister 10-1-92Seedbed preparation: Sled 10-3-92Seedbed preparation: Lilliston 1-9-92Planting 14-10-92Cultivation 28-11-92Second planting 2-12-92

pre–irrigation


first square

first flower

first green boll –

first open boll

defoliants used Ethepon


yield non–compacted early plant – 1697.5 kg/ha (7.54 bales/ha)non–compacted late plant – 960.0 kg/ha (4.27 bales/ha)compacted early plant – 1555.4 kg/ha (6.91 bales/ha)compacted late plant – 730.3 kg/ha (3.25 bales/ha)

104

90 —

80 —

70 —

wk 8: late plant

wk 11: 241 dd

wk 13: 369 dd

wk 14: 451 dd

Week

60

IE 50 —

40 —

(24

30

20

10 —

0 II MIN

I I I 4 I fill, I I 1 I III I MI IIIIII I I I Mill I I I•-• e4 en er VI V.) r- 00 CT 0... C•1 01 d• VI VD r- oo On a

F't11/10/92r.._, — .- .-. .. ..... ... .... .-4 (4

8.5 CLIMATIC DATA

The rainfall inform ation for the 1992/93 cotton season is shown in Fig. 15. The 2 planting dates

and the day degrees at which seedling measurements took place are also listed.

Figure 15. Rainfall data for the 1992/93 cotton season.

wk 1: early plant

wk 6: 241 dd

wk 8: 369 dd

wk 9: 451 dd

105

8.6 EXPERIMENTAL METHODS

8.6.1 Field Measurements

8.6.1.1 Soil profile descriptions

On each site a visual soil assessment using Batey's modified Peerlkamp scheme (Appendix 3) was

made. This was using the same procedure as the 1990/91 cotton season, with 1 assessment made

for each profile to a depth of 40 cm (see Section 5.2). A visual assessment was also made using

the SOILpak assessment scheme that was devised mid—way through this project (Daniells and

Larsen, 1991). Three assessments were made at 0, 10, 20, 30 and 40 cm for each site. Here soil

aggregates at each depth were assessed for soil structural condition using the assessment criteria in

Appendix 5. The actual scores can be found on the disk attached to this thesis.

8.6.1.2 Physical properties

Bulk density and air—filled porosity

Bulk density and air—filled porosity measurements were taken at 15 and 35 cm, with 3 replicates

per site. The technique used was the same as the 1989/90 and 1990/91 cotton seasons (see Section

3.2.2).

Soil strength

Soil strength measurements were taken using the Rimik cone penetrometer and shear vane, with 3

replicates per site. The Rimik penetrometer readings were taken in a similar fashion to the 2 earlier

seasons (see Section 3.2.2). The shear vane used was a 'Geonor' hand—held shear vane with 2 right

angled vanes that were 15 mm wide and 31.5 mm in length. The readings were taken in triplicate

at depths of 0, 10, 20, 30 and 40 cm.

Water usage

Water usage was assessed using a neutron moisture meter, as in the previous two seasons (see

Section 3.2.2). However, readings in 1992/93 were confined to one irrigation cycle (4-2-93 to

12-2-93) with readings taken every 2 days after irrigation.

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8.6.1.3 Plant measurements

Cotton lint yield

Each site had five 1 m randomly allocated row lengths that were hand—picked to determine cotton

lint yields. Of these 5 strips, 3 were picked and ginned on a per plant basis while at the other 2

strips the lint samples were bulked and ginned on a per plot basis. Only 3 strips were done

individually due to time and labour constraints with the root morphological measurements.

Root morphological characteristics

Root morphological characteristics were assessed on the 3 plots where the plants had been

individually picked and ginned. As in the earlier season measurements included taproot diameter,

taproot diameter ratio, root obliquity, root obliquity ratio, the number of lateral roots, the number of

lateral roots ratio and the root flatness (see Section 3.2.3).

Seedling root development and soil strength

The root obliquity of seedlings was measured weekly until 8 weeks after planting. At approximate

weekly intervals, six seedlings from each site were carefully excavated from the soil and the shape

of the root system was drawn. In the laboratory, the obliquity of each taproot was measured at 2.5

cm intervals along the length of the root.

Prior to the seedlings being excavated, soil strength readings (Rimik cone penetrometer) were taken

within 1 cm of the seedlings, with the aim of determining how soil strength affected the root

growth of seedlings.

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8.6.2 Laboratory Measurements

8.6.2.1 Chemical measurements

Samples for chemical analysis were collected from the 12-18 cm and 33-37 cm depths.

Exchangeable cation content, pH and electrical conductivity analysis were assessed using the same

methods as in the previous analyses (see Section 3.3.1).

Clay mineralogy analysis was also evaluated on the samples from the 15 cm and 35 cm depths.

8.6.2.2 Physical measurements

Shrinkage curve indices were determined for each site using a modification of the clod shrinkage

technique of Brasher et al. (1966). As with the first two seasons, these measurements were made

at 15 and 35 cm. Problems were encountered at the 15 cm depth for the non—compacted sites as

the seedbed was in a friable condition and the collection of clods proved to be difficult. The only

clods that could be collected were considered to be remnants of soil damaged in previous seasons.

It was recognised that these samples may not have been representative of the whole soil profile.

8.6.3 Statistical Analysis

Correlation and regression analyses (SAS, 1985) were used to test the lint yield and SSI predictor

models.

Comparisons between the 1992/93 treatments were made using analysis of variance statistical

methods within the general linear model (GLM) procedure in SAS (SAS, 1985). The GLM

procedure was used as in a number of the analyses there was an unequal number of observations

for the different sites due to sample damage.

As the data was analysed using analysis of variance, it meant that the interaction between the

planting dates and compaction treatments could not be determined.

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CHAPTER NINE Results of 1992/93 Cotton Season

9.1 VALIDATION OF SSI PREDICTOR MODELS

9.1.1 Robustness of Models to Derive the Soil Structural Index

The models derived from the 1989/90 and 1990/91 season were tested for robustness using 1992/93

cotton season data. The SSI was predicted using the models and then compared to the actual SSI

using correlation and linear regression analysis.

It was found that there was no significant correlation between the predicted SSI (based on the

1989/90 and 1990/91 models) and actual SSI (measured in 1992/93). Upon further investigation, it

was seen that the range of soil structural conditions for the 1992/93 season was wider than for the

previous years. Thus, air—filled porosity, bulk density and root obliquity ratio and soil strength

frequency data from the 1992/93 cotton season had a greater range of values than the data from

which the models from the first 2 cotton seasons were derived.

The data from the 1992/93 season was combined with that from the previous two seasons in order

to generate new models which could be applied to a wider range of soil conditions than

encountered previously.

9.1.2 Development of Models Incorporating the Wider Range of Soil Structural Conditions

The techniques used in the third season were a subset of those evaluated during earlier seasons,

with only the techniques that showed potential for predicting SSI selected. Thus, for the research

package the more laborious techniques that were not well correlated with SSI were not measured.

Similarly, for the extension package those techniques that were not correlated with the SSI in the

first two seasons were not included. The techniques incorporated into the maxr regression analysis

were air—filled porosity, bulk density, yield, soil strength (Rimik cone penetrometer), root

morphological characteristics, water use and extraction data. Therefore, the difference between the

two sets of models probably resulted from the different techniques that were included in the

analysis and the greater range of soil structural conditions that was incorporated into the analysis of

the three seasons. The two sets of models are very similar with respect to the techniques

incorporated in them. Any discrepancy between the models is largely attributed to a larger number

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of techniques needed in the three season models to attain the same degree of precision. The

models developed can be used by both research and extension personnel, but may not have

sufficient precision for researchers.

The best models from the combined data of the three cotton seasons are:-

1. Combined valleys and depths-10 variable model (R z = 0.471, n = 61)

SSI = -32.63 - (0.002*yield) + (35.02*bd) + (68.02*afp) + (0.002*Rimik) - (2.10*rdia) -

(0.08*robl) - (2.09*roblr, 0-10/10-20 & 20-30/30-40 cm) - (24.49*rflat) + (0.11*ext,

20cm) - (0.40*dwu, 20cm)

2. Macquarie Valley- 5 variable model (R 2 = 0.645, n = 32)

SSI = 25.39 + (7.43*afp) + (1.95*rdia) - (30.14*rflat) + (0.08*ext, 20 cm) + (0.68*dwu, 40cm)

3. Namoi Valley- 7 variable model (R 2 = 0.626, n = 22)

SSI= -37.71 + (60.92*bd) + (139.64*afp) + (1.83*rdiar, 0/20 & 20/40 cm) + (0.14*robl) -

(0.81*nlat) - (3.15*nlatr 0-10/10-20 & 20-30/30-40 cm) - (65.30*rflat)


SSI = -180 + (104.52*bd) + (183.92*afp) + (2.94*rdiar, 10/20 cm) - (0.24*robl) - (0.70*nlat) -

(0.70*nlatr, 10-20/20-30 cm) + (0.18*ext, 20cm)


SSI = 12.28 - (0.002*yield) + (0.002*Rimik) + (4.41*rdia) - (1.91*roblr, 20-30/30-40 cm) +

(0.35*nlat) - (11.04*rflat) + (0.08*ext, 20 cm) - (0.65*dwu, 20 cm)

bd - core bulk density (Mg m-')rdiar - root diameter ratiodia - root diameter (cm)dwu - daily water use (mm)

afp - air-filled porosity (m"roblr - root obliquity ratiorflat - root flatnessRimik-Rimik penetrometer (Mpa)

nlatr - number of lateral roots rationlat - number of lateral rootsrobl - root obliquity (°)ext - water extraction (mm)

There are two disadvantages with the practical application of these models by advisers. Firstly, for

more accurate predictions to be made all of the variables in the model must be measured. If time,

expertise or equipment limits the ability of the operator to conduct the full range of measurements,

then it is possible to use models that encompass less variables. However, this is at the expense of

precision. Greater precision requires the use of more variables. The complete list of models is

shown in Appendix 4. Secondly, some of the measurements such as yield, root morphological

characteristics and water extraction, included in the models relate to the previous crop when used to

110

predict soil structural condition, and the future performance of a cotton crop. Thus, if any soil

damage occurs during picking or subsequent land preparation it will not be identified by these

models. To assess post-harvest soil structural damage a series of models based only on pre-season

soil measurements were developed. These models listed below have, however, low precision and

cannot be used to reliably predict the soil structural index. With multivariate analysis it is not

possible to compare individual regressor variables with the SSI, as there is correlation between the

regressor variables.

The models that will identify post picking soil structural damage are:-

1. Combined valleys and depths-3 variable model (R' = a187, n = 73)

SSI = -16.13 + (7.88*bd) + (26.96*afp) + (0.0003*Rimik)

2 Macquarie Valley- 3 variable model (R2 = 0.134, n = 49)

SSI = -16.35 + (8.19*bd) + (25.36*afp) + (0.0002*Rimik)

3. Namoi Valley- 3 variable model (R 2 = 0.176, n = 23)

SSI= -75.90 + (42.99*bd) + (86.22*afp) + (0.0009*Rimik)


SSI = -32.11 + (18.29*bd ) + (39.28*afp) - (0.0007*Rimik)

5. 35 cm Depth - 3 variable model (R2 = 0.218, 37)

SSI = -21.21 + (10.10*bd ) + (34.81*afp) + (0.0009*Rimik)

bd — core bulk density (Mg nfi)

afp — air—filled porosity (m Rimik — Rimik penetrometer (Mpa)

9.1.3 Predicted Soil Structural Indices

Predicted SSI values were calculated for each site using the models derived from the regression

analysis (Section 9.1.2: models from the 3 cotton seasons). A full list of predicted SSI values are

given in Appendix 6. Studentized residuals were used to determine if the predicted SSI values

were significantly different from the measured SSI values. Any predicted SSI that has a

studentized residual greater than +2 or less than -2 is significantly different from the actual SSI. A

positive residual means that the predicted SSI is less than the actual SSI, while a negative residual

indicates that the predicted SSI is greater than the actual SSI. The greater the absolute value of the

residual the greater is the difference between the predicted and measured SSI values.

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In the models derived from the three cotton seasons data there were 6 predicted values that were

significantly different from the measured values. There were 2 out of 80 in the combined valley

and depth category, 2 out of 56 in the Macquarie Valley category, 1 of 24 in the Namoi Valley

category and 1 of 40 in the 15 cm category. This confirms that the most appropriate models to use

are those for individual depths as they give consistent estimations of soil structural indices for each

site.

9.2 PRE—SEASON MEASUREMENTS

Analysis of variance was used to compare the sensitivity of a number of soil structural

measurements to applied compaction. In the 1992/93 season, the levels of compaction achieved

represented extreme differences in soil structural condition. At 15 cm, the seedbed on the non—

compacted sites was assessed to be in favourable condition (recently tilled), and the soil structural

measurements were outside of the range sampled in the earlier seasons. The compacted sites

showed fairly similar values between depths for bulk density, air—filled porosity soil strength, shear

vane and SOILpak scores listed in Table 23.

Bulk density, air—filled porosity, soil strength frequencies and shear vane data highlighted the

differences between compaction treatments at 15 cm, while only the Rimik penetrometer

demonstrated the differences at 35 cm (Table 23). The lack of significant difference between the

treatments at 15 cm for the Rimik penetrometer is due to the large variation in the readings on the

recently cultivated non—compacted sites. Recently tilled soil tends to be a mixture of friable soil

and remnant clods. Thus, the greatest difference between the sites is found at 15 cm, with

differences below this being less marked. The above techniques, with the exception of shear vane

readings and SOILpak scores, were incorporated in the SSI prediction models.

The SSI did not identify any significant differences between the compaction treatments in the

original analysis of variance due to the large number of missing SSI values in the 1992/93 cotton

season and therefore is not listed. This is most likely due to the difficulties associated with

collecting clods for the clod shrinkage analysis in friable soils. The non—compacted sites had very

fine and loose surface tilth, making it extremely difficult to locate clods large enough to sample.

As mentioned earlier, the clods collected from the non—compacted sites tended to be larger

remnants of previously damaged soil, which were not representative of the site. A number of the

collected clods also disintegrated during analysis and the shrinkage curve was not able to be

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computed for the sites. Consequently, the SSI was biased towards lower values than actually

occurred. To enable a more realistic analysis of the 15 cm depth to be carried out an SSI value of

8 was substituted for those non-compacted sites where no clods were able to be collected to derive

the soil shrinkage curve. This value was arbitrarily chosen to represent a SSI slightly higher than

7.7, which was the highest SSI previously obtained from either the 1989/90, 1990/91 or 1992/93

cotton seasons. An SSI of 8 was thought to more accurately reflect the true field conditions at that

depth, as higher SSI values are indicative of better soil structural condition (Section 6.5).

Table 23. Comparison between soil structural measurements at different levels of compaction

taken at 15 and 35 cm on field 33, Auscott Warren for the 1992/93 cotton season.

Soil Structural Characteristics Non-compacted Compacted

15 cm depth

SSIt 8.0b -0.43aBulk density (Mg m') 1.06a 1.56bAir-filled porosity (m 3 m') 0.39a 0.09bSoil strength-Rimik (Mpa) 169a 1771aSoil strength freq: 0.01-0.3 Mpa (%) 96.3a 0.0bSoil strength freq: 0.301-0.7 Mpa (%) 3.7a 0.0bSoil strength freq: 0.701-2.5 Mpa (%). 0.0a 93.5bSoil strength freq: 2.501-3.5 Mpa (%) 0.0a 6.5aShear vane- 10cm (kPa) 0.56a 7.16bShear vane- 20cm (kPa) 0.41a 5.62aVisual assessment (Batey) 3.86a 4.92aSOILpak score- 10cm 1.71a 0.58aSOILpak score- 20cm 1.65a 0.51a

35 cm depth

SSIt 1.83b 1.09bBulk density (Mg m") 1.44a 1.55aAir-filled porosity (m3 m") 0.17a 0.10aSoil strength-Rimik (Mpa) 987a 1413bSoil strength freq: 0.01-0.3 Mpa (%) 11.0a 0.0aSoil strength freq: 0.301-0.7 Mpa (%) 18.5a 0.0aSoil strength freq: 0.701-2.5 Mpa (%) 70.4a 98.2aSoil strength freq: 2.501-3.5 Mpa (%) 0.0a 1.9aShear vane- 30cm (kPa) 3.34a 5.89aShear vane- 40cm (kPa) 5.29a 5.95aVisual assessment (Batey) 3.86a 4.92aSOILpak score- 30cm 1.18a 0.53aSOILpak score- 40cm 0.93a 0.51aMeans with the same letter within the same row are not significantly different at the 5% level.t using estimated SSI of 8 for the 15 cm non-compacted sites as the clod samples could not be collected

113

The analysis also highlights the difficulties associated with visual soil assessment techniques in

identifying any significant difference between compaction treatments. This suggests there is a large

amount of variation between the readings, which depends upon the experience of the operator for

accuracy. As experience is gained with soil profile assessment, these score discrepancies decrease.

A larger number of assessments may also be necessary to achieve a significant difference between

the treatments (Table 20, Chapter 7). Progress is also being made toward reducing the subjectivity

of these scores through detailed SOILpak training manuals (Larsen, 1994).

Correlations between the pre—season measurements were highly significant (Table 24), with

correlations between the SSI and other soil structural measurements being greater than the first two

seasons (see Table 19, Chapter 7). There is a good correlation between the SOILpak visual

assessment score and SSI but a much poorer relationship between Batey's modified Peerlkamp

Scheme. This probably due to Batey's scheme being a weighted score for a 80 cm x 50 cm soil

profile which means that if compaction occupied a small proportion of the profile it would tend to

be overshadowed by larger non—compacted zones. The SOILpak scores, on the other hand were

estimated on a per depth basis. Using Batey's scheme on a per depth basis may have produced

similar results to the SOILpak score. Bulk density and air—filled porosity also exhibited good

correlations with the SSI. This is not surprising as the soil structural curve which forms the SSI is

based on specific clod volume (reciprocal of bulk density).

114

Table 24. Correlation (R values) table of the techniques for pre-season measurements in the

1992/93 cotton season.

SSIt

BD

AFP

Rimik

SV1

SV2

Batey

SP1

SP2

SSI

1.000

BD

-0.7730.0001

1.000

AFP

0.7750.0001

-0.9970.0001

1.000

Rimik

-0.8170.0001

0.8610.0001

-0.8560.0001

1.000

SV1

-0.6740.0001

0.6680.0001

-0.6640.0001

0.8160.0001

1.000

SV2

-0.5870.0003

0.5250.0001

-0.5320.0001

0.4930.0004

0.6400.0001

1.000

Batey

-0.3880.02

0.4260.003

-0.4230.003

0.3950.005

0.2710.062

ns

1.000

SP1

0.7920.0001

-0.8400.0001

0.8330.0001

-0.7560.0001

-0.6980.0001

-0.5100.0002

-0.5310.0001

1.000

SP2

0.7780.0001

-0.8170.0001

0.8100.0001

-0.7560.0001

-0.6720.0001

-0.4890.0004

-0.5980.0001

0.9150.0001

1.000irst num•er is tie v. ue, w let e num•er •e ow is t e pro.a.i ay ns- not signs leant

t using estimated SSI of 8 for the 15 non-compacted sites as the clod samples for analysis could not be collectedSV1 = shear vane measurements at 10 and 30cm SP1 = SOILpak score at 10 and 30 cmSV2 = shear vane measurements at 20 and 40 cm SP2 = SOILpak score at 20 and 40 cm

9.3 CROP AND ROOT MEASUREMENTS

9.3.1 Root Morphological Characteristics

A comparison of the root morphological characteristics between compaction treatments was made

using analysis of variance (Table 25). The lint yields per unit area (g/m) show that there is no

significant difference between the compaction treatments at the same planting date, even though the

compacted sites did yield marginally less than the non-compacted sites. The decreasing yield trend

agrees with the finding of other studies (Taylor and Burnett, 1963; Mathers and Welch, 1964;

Carter and Tavernetti, 1968; Khalilian et al., 1983; Daniells, 1989; Constable et al., 1992; Wild et

115

al., 1992). The differences between planting dates at the same compaction level is attributed to the

later planting having fewer mature bolls at picking time. These bolls when picked may have

yielded less lint cotton than the fully mature bolls. All lint had to be picked at the same time as

the entire crop was due to be mechanically harvested the following day. The compacted sites had

substantially fewer plants per metre than the uncompacted site and as a result the yield per plant

was higher on the compacted sites for the early planting.

The root morphological characteristics on the early planted sites do not reflect the lint yield trends

on a per plant basis. The larger root diameters, root diameter ratio, root obliquities and root

flatness indicate the presence of soil structural problems as seen by the SSI at 15cm (Table 18,

Chapter 6) but do not seem to have affected yield production per plant. However, the smaller

number of plants on the compacted sites has meant that in general the lint yield per metre is lower

than the non—compacted sites. Conversely, on the late planted sites the root morphological

characteristics do reflect the lint yield trends. The exceptions to this are the root diameter at 10 cm

and the root diameter ratio (20/30cm) where the non—compacted sites had larger values than the

non—compacted sites. These two anomalies do not appear to have greatly influenced the yield

trend.

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Table 25. Comparison between root morphological characteristics at different levels of

compaction and different planting dates.

Root Morphological

Early Plant Late Plant

Characteristics Non-compacted Compacted

Non-compacted Compacted

No.of Plants/Sm (df) 69 39 62 48

Lint yield (g/plant) 24.60ab 35.58a 15.48b 15.22bLint yield (g/m)t 169.75a 155.54a 96.00b 73.03bRoot diameter- 0cm (surface) 0.933a (142) 1.050b (93) 0.963a (145) 1.029b (114)Root diameter- 10cm 0.627a (136) 0.641a (60) 0.631a (142) 0.549b (89)Root diameter- 20cm 0.266ab (130) 0.239bc (45) 0.284a (135) 0.219c (62)Root diameter- 30cm 0.180a (87) 0.174a (32) 0.177a (90) 0.169a (23)Root diameter- 40cm 0.141a (21) 0.395a (15) 0.160a (13) 0.167a (3)Root diameter ratio-0/10cm 1.570a (136) 1.771c (58) 1.599a (142) 1.977b (89)Root diameter ratio-10/20cm 2.383a (130) 3.066b (43) 2.266a (135) 2.526a (61)Root diameter ratio-20/30cm 1.586ab (87) 1.781a (34) 1.738a (90) 1.445b (23)Root diameter ratio-0/20cm 3.666a (130) 4.859b (43) 3.558a (135) 5.073b (61)Root diameter ratio-10/30cm 3.776a (84) 7.360b (35) 3.982a (90) 3.726a (23)Root obliquity- 0-10cm 17.289a (142) 45.589b (92) 15.366a (145) 46.947b(114)Root obliquity- 10-20cm 20.470a (132) 36.750b (48) 18.089a (135) 21.969a (64)Root obliquity- 20-30cm 18.874a (87) 17.839a (31) 17.764a (89) 13.913a (23)Root obliquity ratio-

0-10/10-20cm 1.281a (131) 1.325a (48) 1.188a (135) 2.193b (63)Root obliquity ratio-

10-20/20-30cm 1.387a (87) 1.823a (33) 1.594a (88) 1.500a (23)Root flatness- 0cm (surface) 1.080a (142) 1.120c (93) 1.059ab (145) 1.047b (114)Root flatness- 10cm 1.045a (136) 1.344b (61) 1.040a (142) 1.050a (89)Root flatness- 20cm 1.038a (130) 4.298b (47) 1.041a (135) 1.059a (62)Root flatness- 30cm 1.069a (87) 7.366b (35) 1.070a (90) 1.080a (23)Means within the same row with the same letter are not significantly different at the 5% levelt g/m converted to kg/ha by dividing by 10Parenthesis indicate number of measurements used in the analysis

9.3.2 Robustness of Models to Predict Yields

The yield prediction equations devised from the first 2 seasons (see Section 7.6) were validated

using the 1992/93 cotton season. The measured yields for each plant were highly correlated with

the predicted yields based on root diameter measurements (Table 26). The best models to use are

those derived from the maxr regression.

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Table 26. R values for correlation analysis between actual yield and predicted yield in the

1992/93 season using root morphological data.

Category Model' R value

Linear regression

Combined valleys and depths 1 a) 0.625***

1 b) 0.598***

Macquarie Valley 2 a) 0.625***

2 b) 0.598***

Maxr regression

Combined valleys and depths 1 0.636***

Macquarie Valley 2 0.646***

*** P<0.0001 t see Section 7.6

9.3.3 Seedling Root Morphology

Correlation analysis of seedling root obliquity and the associated soil strengths are shown in Table

27. The 7.5 cm and 10 cm depths, and combined depths indicate that there is a significant

correlation between root obliquity and soil strength, but the amount of variance explained is

extremely low. The 7.5 cm depth corresponds to the depth where significant differences in soil

strength between treatments began (Table 28). This appears to have affected the depth of rooting

for plants on the compacted sites. This is evident on the compacted sites by the decrease in

number of roots available for root obliquity measurements below 7.5 cm (Table 29).

Table 27. R values for correlation analysis of root obliquity and soil strength (Rimik cone

penetrometer).

Depths R value

All depths combined 0.288***7.5 cm depth 0.122*10 cm depth 0.156**P<0.05 *** P<0.0001

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There is a significant difference in seedling root obliquities with both the early and late planting

dates between compaction treatments at 7.5 and 10 cm (Table 28). This agrees with the significant

differences in soil strength readings below 7.5 cm with respect to the levels of compaction. The

average soil strengths however, did not reach the critical level of 2500 Kpa identified by Taylor

and Burnett (1963) where no cotton roots penetrated. Rather they lay in the range (700-2500 Kpa)

where 50% reductions in root elongation rates occurred (Taylor and Ratcliff, 1969b). At these

strengths modifications of root behaviour eg. root obliquity are likely to occur.

Table 28. Seedling root obliquity and soil strength comparisons between different levels of

compaction for the same planting dates.

Depth Non-compacted Compacted

EARLY PLANTED

Seedling Root Obliquity 02.5 cm 5.61a 5.14a5.0 cm 8.34a 10.40a7.5 cm 11.97a 18.48b10.0 cm 15.73a 24.12b

Soil Strength (Mpa)2.5 cm 307a 295a5.0 cm 463a 503a7.5 cm 653a 1225b10.0 cm 724a 1649b12.5 cm 727a 1626b

LATE PLANTED

Seedling Root Obliquity (°)2.5 cm 5.72a 5.26a5.0 cm 11.81a 15.71a7.5 cm 14.23a 31.18b10.0 cm 18.02a 32.89b

Soil Strength (Mpa)2.5 cm 207a 254a5.0 cm 355a 445a7.5 cm 570a 1095b10.0 cm 674a 1262b12.5 cm 682a 1231bMeans with the same letter are not significantly different at the 5% level.

119

This experiment consisted of comparing 2 different planting dates as well as 2 levels of

compaction. This was designed to see if the cotton crop behaved differently when subjected to

different growth conditions in the seedling stage. The early planted sites were exposed to frequent

rain events (Fig. 14, Chapter 8) which probably caused some degree of waterlogging as there was

little time for the soil profile to dry out between falls. The late planted sites, on the other hand,

experienced predominantly dry conditions with a majority of water applications resulting from

irrigations.

The comparison was made between the different compaction treatments and planting dates

according to the number of day degrees (Waddle, 1984; Constable and Shaw, 1988) that the crop

had experienced. The sites from the different planting dates were compared at 241, 369 and 451

day degrees. The actual dates for the early and late planted treatments at the various day degrees

were 17-11-92 (early plant) and 24-12-92 (late plant) for 241 day degrees; 1-12-92 (early plant)

and 5-1-93 (late plant) for 369 day degrees; and 9-12-92 (early plant) and 12-1-93 (late plant)

for 451 day degrees. The number of cotton plants with deeply penetrating roots was less for the

compacted sites than for the non—compacted sites (Table 29). This relates to the markedly higher

soil strengths found below 7.5 cm (Table 30). The non—compacted sites infrequently experienced

soil strengths that would inhibit root growth, while below 7.5 cm on the compacted sites all soil

strengths were greater than 700 Kpa. The variation in the number of seedling roots measured

between the different day degrees results from different seedlings being measured each time.

Therefore, the depth to which the roots were found is dependent upon each individual seedling's

ability to penetrate the compacted layer and whether the entire seedling root system could be

successfully extracted.

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Table 29. Comparison between the root obliquity of seedlings from the two planting dates at

different day degrees.

Sampling Depth Non-compacted CompactedTime Early plant Late plant Early plant Late plant(degree days)

241 2.5cm 6.78a (9) 6.08a (12) 5.00a (6) 4.54a (13)5.0cm 11.44a (9) 10.03a (12) 8.00a (6) 10.54a (13)7.5cm 15.89a (9) 15.58a (12) 18.60a (5) 38.57b (7)10.0cm 27.50a (6) 21.00a (10) 26.25a (4) nr

369 2.5cm 5.71a (14) 4.92a (12) 6.50a (12) 6.42a (12)5.0cm 9.00a (14) 13.17a (12) 12.75a (12) 16.50a (12)7.5cm 16.43a (14) 20.67a (12) nr 27.89a (9)10.0cm 17.58a (12) 22.70a (10) nr nr12.5cm 21.88a (8) 25.60a (5) nr nr

451 2.5cm 7.08a (12) 7.83a (12) 3.75a (12) 4.17a (12)5.0cm 9.18a (11) 16.25a (12) 8.92a (12) 16.33a (12)7.5cm 11.82a (11) 18.18ab (12) 15.46ab (11) 29.70b (10)10.0cm 13.18a (11) 17.25a (12) 30.63a (8) 25.43a (7)12.5cm 12.86a (7) 22.33a (12) nr 23.14a (7)

Means with the same letter in the same row are not significantly different at the 5% levelParenthesis indicate number of measurements used in the analysisnr- no roots found at this depth

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Table 30. Comparison between soil strength readings from the two planting dates at different day

degrees.

Sampling DepthTime(degree days)

Non—compactedEarly plant Late plant

CompactedEarly plant Late plant

241 2.5cm 188a (9) 136a (12) 199a (6) 191a (13)5.0cm 306a (9) 231a (12) 388a (6) 372a (13)7.5cm 527a (9) 405a (12) 833b (6) 1004b (6)10.0cm 686a (9) 484b (12) 1564c (6) 1122d (13)12.5cm 669a (9) 450b (12) 1478c (6) 1077d (13)

average eg (%) 25.26 28.00 23.72 24.86

369 2.5cm 339a (14) 365a (12) 165a (12) 386a (11)5.0cm 509a (14) 663a (12) 325a (12) 729a (11)7.5cm 639a (14) 1031ab (12) 1142b (12) 1805c (11)10.0cm 729a (14) 1145b (12) 2008c (12) 2101c (11)12.5cm 741a (14) 1141b (12) 1838c (12) 1930c (11)15.0cm 709a (14) 971a (12) 1481b (12) 1638b (11)

average Og (%) 15.32 18.63 15.06 18.91

451 2.5cm 434ab (12) 155a (12) 612b (12) 225ab (12)5.0cm 650a (12) 226b (12) 1015c (12) 378ab (12)7.5cm 827a (12) 358b (12) 2174c (12) 827ab (12)10.0cm 846a (12) 455b (12) 1915c (12) 908a (12)12.5cm 791a (12) 490b (12) 1844c (12) 966a (12)15.0cm 711ab (12) 560b (12) 1670c (12) 999a (12)

average Og (%) 20.53 29.43 20.15 27.53Means with the same letter in the same row are not significantly different at the 5% levelParenthesis indicate number of readings used in the analysiseg— gravimetric moisture content

The variation in the soil strength readings at 451 day degrees is primarily due to differences in

moisture content (Table 30). The readings for the late planted sites were taken 2 days after an

irrigation while the early planted sites were much drier. Thus, increased soil moisture contents

could have caused soil strengths to decrease on the late planted treatments. The soil strengths were

not corrected to standard moisture contents as there were no satisfactory calibration equations

available for these soil types and there was not sufficient data collected to derive suitable equations.

At 241 day degrees there were significant difference in seedling root obliquity between planting

dates on the compacted sites (Table 29), which corresponded to the higher soil strengths on the late

planted sites (Table 30). At 369 day degrees, an insufficient number of seedlings with root systems

deeper than 7.5 cm were able to be extracted and no conclusions can be drawn from that sampling

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period. At 451 day degrees there was no significant difference in seedling root obliquity between

compaction levels or planting dates.

The lack of roots below 5 cm on the early planted sites at 369 day degrees may be attributed to the

large amount of root necrosis observed on seedling roots from the compacted sites. There were

few disease problems observed on the uncompacted sites. The necrotic areas were assessed but

could not be identified (Steve Allen, NSW Agriculture, pers. comm.). However, they were

probably the result of the frequent rain events during this period. This made it difficult to make

any conclusions about the differences between planting dates at these depths.

9.4 INTENSIVE MEASUREMENTS OVER ONE IRRIGATION CYCLE

Both the compacted and non—compacted sites were irrigated on 3-2-93. Bulk density, air—filled

porosity, soil strength and water extraction measurements were taken at 2 day intervals after the

irrigation. Rain fell 7 days after the irrigation, and all measurements were terminated.

9.4.1 Bulk density and Moisture Content

The bulk density at 15-20cm was greater on the compacted treatments than the non—compacted

treatments at 4 and 6 days after irrigation (DAI), while the 25-30 cm depth showed little difference

between the 2 treatments (Table 31). The early planted non—compacted treatments tended to have

higher gravimetric water contents than the other treatments (Table 31). Overall though the water

contents were similar for all treatments.

9.4.2 Air—filled Porosity

The air—filled porosity of the non—compacted sites was generally lower than the compacted

treatments for the duration of the irrigation cycle. Below 15 cm the air—filled porosity on the

compacted treatments rarely exceeded the critical level of 0.145 em' (Hodgson and MacLeod,

1989a). Values at the 25-30 cm depths were similar for each treatment. This is in accordance

with the SSI readings, where there was a difference in SSI at 15 cm but little difference at 35 cm.

For the first 2 DAI all air—filled porosities were below the critical level (Table 31). The air—filled

porosity 4 days after the irrigation showed a significant difference between compaction treatments.

The compacted treatment at 15-20 cm remained below the critical level whereas the non-

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compacted site had risen above this level. For the 0-5 cm depth air-filled porosity was not

limiting. Even though the air-filled porosity was still limiting at some depths on the compacted

sites 6 DAI, there was no significant difference between the sites.

Table 31. Comparison of bulk density, air-filled porosity and gravimetric moisture contents

between treatments at 2 day intervals after an irrigation.

DAL Depth Non-compacted CompactedEarly plant Late plant Early plant Late plant

Bulk Density (Mg m-3)2 0-5cm 1.35a 1.32a 1.33a 1.38a

15-20cm 1.40a 1.42a 1.48a 1.48a25-30cm 1.41a 1.41a 1.52b 1.44a

4 0-5cm 1.31a 1.15b 1.19b 1.31a15-20cm 1.34a 1.34a 1.58b 1.52b25-30cm 1.40a 1.40a 1.50a 1.44a

6 0-5cm 1.18ab 1.08b 1.18ab 1.27b15-20cm 1.33a 1.30a 1.54b 1.50b25-30cm 1.42a 1.36a 1.46a 1.52a

Gravimetric Moisture Content (g g-1)2 0-5cm 0.31a 0.28a 0.31a 0.29a

15-20cm 0.32a 0.26b 0.25b 0.25b25-30cm 0.29a 0.26ab 0.25b 0.25b

4 0-5cm 0.22a 0.21ab 0.20b 0.20b15-20cm 0.23a 0.23a 0.20a 0.21a25-30cm 0.23a 0.22a 0.20a 0.21a

6 0-5cm 0.21a 0.19ab 0.19ab 0.17b15-20cm 0.21a 0.22a 0.20a 0.17a25-30cm 0.23a 0.21b 0.19b 0.20b

Air-filled Porosity (m3 m-3)2 0-5cm 0.08a 0.14b 0.09ab 0.08a

15-20cm 0.03a 0.08a 0.07a 0.07a25-30cm 0.06a 0.10ab 0.06a 0.11b

4 0-5cm 0.22a 0.33b 0.31bc 0.24ac15-20cm 0.19a 0.19a 0.09b 0.11b25-30cm 0.15a 0.17a 0.13a 0.16a

6 0-5cm 0.31a 0.39a 0.33a 0.30a15-20cm 0.21a 0.23a 0.12a 0.18a25-30cm 0.14a 0.20a 0.17a 0.12a

ears with the same letter in the same row are not significantly different at the 5% levelDAI- days after irrigation

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9.43 Soil Strength

Table 32. Soil strength comparisons between treatments at two day intervals after an irrigation.

DAI Depth Non—compactedEarly plant Late plant


2 1.5-4.5cm 145ab 93a 168b 174b6-9cm 283a 278a 432b 556c10.5-15cm 394a 485a 951b 892b16.5-19.5cm 503a 590a 1113b 1036b21-24cm 627a 812a 1253b 1212b25.5-30cm 953a 1124a 1585b 1467b31.5-34.5cm 1293a 1378ab 2045c 1742bc36-39cm 1539a 1613a 2405b 1922a40.5-45cm 1655a 1870a 2802b 2115a

4 1.5-4.5cm 77a 278b 210bc 143ac6-9cm 355a 743b 734b 757b10.5-15cm 708a 946b 1590c 1543c16.5-19.5cm 732a 999b 1767c 1554d21-24cm 742a 1213b 1724c 1557d25.5-30cm 972a 1298b 2027c 1741d31.5-34.5cm 1287a 1346a 2680b 1903c36-39cm 1407a 1393a 3012b 2093c40.5-45cm 1462a 1375a 3274b 2309c

6 1.5-4.5cm 243a 155a 196a 357b6-9cm 621a 629a 960b 1334c10.5-15cm 783a 812a 2068b 1729c16.5-19.5cm 730a 752a 2009b 1568c21-24cm 715a 984b 1982c 1597d25.5-30cm 1035a 1269b 2157c 1706d31.5-34.5cm 1281a 1389a 2538b 1813c36-39cm 1342a 1432a 2751b 1916c40.5-45cm 1437a 1511a 3057b 2190c

Means with the same letter in the

DAI— days after irrigation

same row are not significantly different at the 5% level

The non—compacted treatments had significantly lower soil strengths than the compacted treatments

for the duration of the irrigation cycle (Table 32). Two DAI the compacted sites reached the range

of 700-2500 Kpa, at which 50% reduction in root elongation is supposed to occur (Taylor and

Ratcliff, 1969b). The non-compacted sites did not reach this level until below 20cm. At 4 and 6

DAI all treatments at 6-10 cm had soil strengths above 700 Kpa. The compacted early planted site

was the only treatment in the first 6 DAI to encounter soil strengths above the 2500 Kpa level, the

point at which no roots penetrate (Taylor and Burnett, 1963). This strength was reached at 40 cm

2 DAI, and at 30 cm 4 and 6 DAI. This information along with the air-filled porosities indicate

that the compacted early planted treatments were the least favourable for plant growth.

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9.4.4 Total Water Extraction

There were no significant differences between the compaction treatments in terms of water

extraction below 20 cm (Table 33). The compacted late planted sites extracted significantly less

water from the whole profile than the rest of the sites in the first 6 days of the irrigation cycle.

This implies that the root system had not been able to adequately establish itself. Surprisingly the

compacted early planted sites extracted the most water from the profile, and is contradictory to the

air—filled porosity, bulk density and soil strength data (Table 31 and 32). This suggests that the

access tube siting was not be representative of the site or perhaps cracks in the soil may have given

greater root access than is indicated by the bulk density, air—filled porosity and soil strength data.

The tubes may be located consistently close to scattered, large, healthy cotton plants which have

extracted large amounts of water from the immediate vicinity of the access tubes. This reinforces

the importance of access tube placement to obtain an accurate representation of crop water use.

Table 33. Comparison of water extraction rates (mm) between treatments for the duration of an

irrigation.

Depth Non—compactedEarly plant Late plant


20cm 27.89a 33.44b 35.16b 25.10a40cm 11.92a 12.12a 14.63a 9.96a60cm 6.94a 5.63a 6.46a 2.82a80cm —0.15a 1.05a 2.73a —0.97a100cm 1.30a —0.50a 0.84a —0.09a120cm 0.28a —0.63a —0.19a 0.41atotal extraction (mm) 46.99a 51.11ab 55.10b 37.24cMeans with the same letter in the same row are not significantly different at the 5% level

126

9.5 GENERAL DISCUSSION AND CONCLUSIONS

The models developed using the 1989/90 and 1990/91 data (section 7.1.3) could not be validated,

as the measurements from the 1992/93 cotton season used in the validation process lay outside the

initial range of measurements. In the 1992/93 season, the experimental site consisted of extreme

differences in soil structural condition in the top 15 cm. The non—compacted sites had a very loose

friable seedbed, whereas the compacted sites had a shallow seedbed overlying a massive and tight

zone, achieved by driving a D7 crawler on the top of the hills when moist. It was felt that by

combining the data from all three seasons, a comprehensive range of structural conditions would be

available. Unfortunately, all the techniques from the first 2 seasons were not used in the final

season. The techniques used were those found to be the most useful in the models derived from

the first 2 seasons i.e. air—filled porosity, bulk density, Rimik cone penetrometer, yield, root

morphological characteristics, water extraction and daily water use.

The major drawback with the practical application of these models for compaction assessment is

that they depend on crop measurements taken during the cotton season i.e. yield, root

morphological characteristics, water extraction and daily water use. Thus, if any compaction occurs

after the root system is established then these models cannot be used. Therefore, models were

devised using only the techniques that were not reliant on the presence of a crop. However, they

had low R2 values ranging from 0.13 to 0.22 and would not be reliable for the routine assessment

of soil structural condition.

The 1992/93 experiment was designed so that the sensitivity of a number of techniques to soil

structural damage could be evaluated. Bulk density, air—filled porosity, soil strength measurements

using the shear vane, and soil strength frequencies all showed differences between the compaction

levels in the top 15 cm (Table 23). Below this the differences were less marked and only the

Rimik penetrometer indicated any differences between the treatments.

The root morphological characteristics were influenced by the time of planting. Two different

planting dates were chosen to compare the behavioural responses of cotton subjected to prolonged

wet periods and the more usual irrigation cycles during the early growth and squaring phases of

development. All root morphological characteristics (Table 25) for the 0-10 cm depths exhibited

differences between the compaction levels. The root obliquities and ratios, root diameter ratio and

root flatness values were all greater on the compacted sites. The root diameter measurements do

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not appear at first sight to be in agreement with the earlier finding of this study, where larger root

diameters indicated higher yields. On closer inspection, however, it is noted that the root diameters

from the early planted site did increase with lint production on a per plant basis. The overall lower

lint yields for the compacted treatments was the result of fewer plants per metre compared to the

non—compacted sites. There were fewer plants on the compacted sites as a number of the seedlings

died from root disease or were unable to survive in the restricted root zone. Some seedlings

though, were able to penetrate the compacted areas and tended to be larger as a result of reduced

competition for sunlight, water and nutrients.

Below a depth of 10-15 cm some anomalous relationships existed between roots morphological

characteristics and yield. For instance, on the compacted early planted sites some root diameters

and their ratios, root obliquity and root flatness measurements (Table 25) were significantly larger

than those on the non—compacted early planted site. The corresponding lint yield trends on a per

plant basis, however, do not reflect the morphological properties of roots. The compacted late

planted sites, on the other hand, generally had lower yields per plant where the root diameter, root

obliquities and their ratios were greater than the non—compacted late planted site. The early

planted compacted site were often subjected to higher moisture content levels early in the season

due to frequent rain events (Fig. 14, Chapter 8). Therefore, some cotton seedling roots were able

to penetrate the compacted layer as the high soil strengths that would usually inhibit plant growth

were not limiting because of higher soil water contents. This is reflected in the higher lint yield

per plant trend on these sites. Slightly larger root diameters, root obliquities and root flatness in

the top 20cm of soil do, however, indicate that some seedlings did experience difficulties with

penetrating the compacted zone. Where the roots were not able to penetrate this layer they could

have been adversely affected by waterlogging above the compacted zone, as noted by Hodgson and

Chan (1982). The late planted compacted sites had experienced a number of wetting and drying

cycles, and some of the seedlings from the original planting may have established some routes into

the compacted layer prior to replanting. This meant that some seedlings may have found areas

within the compacted zone that were more amenable to plant growth.

Some of the unusual findings of the root morphological analysis may also be directly related to the

compacted layer impeding root growth. Below 7.5 cm, fewer plants on the compacted sites were

able to penetrate the compacted layer, and consequently the statistical means are based on fewer

plants than for the non—compacted sites, giving biased results.

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The robustness of the yield prediction models using root diameter measurements was tested using

the 1992/93 cotton season data. The R2 for the models that incorporate both the surface and 10 cm

root diameters ranged from 0.60 to 0.65 (Table 26). The model estimates yield on a per plant

basis. To determine the yield per hectare, the number of plants per metre must be ascertained. The

yield per plant (g) can then be converted to bales per hectare by firstly multiplying by the number

of plants per metre to give g/m. This is multiplied by 10 to give kg/ha and then divided by 225 to

give bales/ha. There are approximately 225 kg of lint in a cotton bale.

Seedling development was compared between all the treatments. Root obliquity was found to be

positively related to soil strength below 7.5 cm (Table 27). Significant differences in root

obliquities between different compaction treatments occurred at 7.5 cm, and corresponded to the

depths where significant differences in soil strengths arise (Table 28).

Comparison between data collected at the same stage of plant development from different planting

dates (based on the number of day degrees experienced by the seedlings), showed that seedlings on

the late planted sites generally had larger root obliquities, both on the compacted and non—

compacted sites. In most cases though, this difference was not significant. The corresponding soil

strengths are difficult to compare as the moisture contents at which readings were taken differed

between the planting dates. Soil strength may also have changed with moisture content in between

sampling times. Due to the late planted sites being picked before all the bolls were mature it was

not possible to determine whether any of the early season root restriction affected the final lint

yields. The immature bolls were collected and oven—dried to extract any lint they contained, but

the quantity obtained would have been less than if they had been left to mature naturally. The only

valid yield comparisons are between the compaction treatments from the early planting date where

lint yields tended to decrease with compaction.

Seedling health appeared to be adversely affected by compaction. A larger number of deformed

seedlings and necrotic areas were observed on the compacted sites. The seedling development on

the compacted sites was slower; the non—compacted sites were at the 3-4 leaf stage at

approximately 200 day degrees, while the compacted sites were still at the 2 cotyledon stage. It

was also noted that when the seedling roots did bend at the compacted layer, there was a tendency

for a proliferation of lateral roots to occur at this point. Not all roots suffered from bending when

encountering the compaction zone. This may be due to the presence of vertical macropores which

allowed the roots to penetrate the compacted area. There did not appear to be any visual signs of

nitrogen deficiency on any of the treatments.

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Air–filled porosity 2 DAI was limiting at all depths for both treatments (Table 31). After 4 days

the non–compacted site had adequate aeration to promote plant growth as indicated by air–filled

porosity. The compacted site though only had sufficient aeration at 0-5 cm. The bulk densities for

the non–compacted sites were below the critical bulk densities for root penetration of 1.46-1.63

Mg m-3 identified by Veihmeyer and Hendrickson (1948) for the clay soils in the USA. Bulk

densities below 15 cm (Table 31) on the compacted sites were greater than the critical bulk density.

Soil strength (Table 32) was significantly higher on the compacted site for the duration of the

irrigation cycle. At 2 DAI the soil strengths below 10cm on the compacted sites and below 21 cm

on the non–compacted sites were above 700 Kpa, the strength identified by Taylor and Ratcliff

(1969b) which causes a 50% reduction in root elongation. However, only the compacted early

plant treatment reached soil strength levels (2500 Kpa) where no roots supposedly penetrate (Taylor

and Burnett, 1963).

Total water extraction (Table 33) shows that there was not a significant difference between the

planting dates for the non–compacted sites, but was a significant difference between the compacted

sites. Significant differences also exist between the levels of compaction at the same planting date.

The late planted compacted site had the lowest water extraction which is attributed to the decreased

ability of roots to extract moisture from depth in the profile. The large amount of water extracted

by the early planted compacted site, in comparison to the non–compacted site, may be the result of

inappropriately placed access tubes.

In general, the results of the 1992/93 cotton season confirm the findings of other studies relating

the value of soil structural parameters and the response of cotton plants to different levels of

compaction (Taylor and Burnett, 1963; Mathers and Welch, 1964; Carter and Tavernetti, 1968;

Khalilian et al., 1983; Daniells, 1989; Constable et al., 1992; Wild et al., 1992). The lack of

significant differences between lint yields on the treatments may have resulted from the large

amounts of nitrogen added to the trial sites compared to previous cotton compaction studies on

Australian cracking clay soils. Previous studies had nitrogen application of around 80 kgN/ha

(Dave McKenzie, NSW Agriculture, pers. comm.), while in this study 163 kgN/ha was added. This

may have masked some of the effects of compaction and increased lint yields on the compacted

sites (Constable et al., 1992). It may be that where compaction exists, moistening the soil without

waterlogging enables roots to penetrate into the compacted soil layers. This implies that when a

soil is compacted an earlier first irrigation and extra nitrogen would help to boost lint production.

This is an aspect that needs to be addressed more closely to clarify the relationship between lint

production and irrigation frequency on compacted soils, and the economic feasibility of such a

practice.

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chapter five general description of experiments— 1990/91

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