The Hidden Health Burden of Environmental Degradation:Disease Comorbidities and Dryland Salinity

Peter C. Speldewinde,1,2 Angus Cook,2 Peter Davies,1 and Philip Weinstein2,3

1Centre of Excellence in Natural Resource Management, The University of Western Australia, PO Box 5771, Albany, WA 6332, Australia2School of Population Health, University of Western Australia, Perth, WA, Australia3Graduate Research Centre, University of South Australia, Adelaide, SA, Australia

Abstract: Previous studies have linked poor mental human health with environmental degradation, but none

have assessed additional diseases that may co-exist with these mental disorders. In previous work, depression

was found to be associated with a major form of environmental degradation; dryland salinity. However, little is

known about diseases co-morbid with depression in this environmental setting. In rural Australia, dryland

salinity is a major form of environmental degradation contributing widely to deterioration and non-viability of

farmland. Using georeferenced health record data, Bayesian spatial methods were used to determine the

relationship between dryland salinity and a range of human health outcomes. Initial modelling found an

increased relative risk for asthma, suicide and ischaemic heart disease in relation to dryland salinity (adjusted

for Indigenous and socio-economic status). However, in this follow-up study, a further evaluation of the role of

co-morbidities in this population revealed that: (i) the presence of depression was consistently linked to

residence in areas with high salinity and (ii) the association of asthma, suicide and heart disease with salinity

was most likely attributable to the co-morbidity of the conditions with depression. Given the predicted increase

in dryland salinity and the elevated relative risk of depression in impacted areas, the relative risk of the

co-morbid conditions can be expected to increase in rural areas in the future, further adding to the burden of

disease associated with environmental degradation.

Key words: depression, asthma, IHD, suicide, salinity, environment

INTRODUCTION

Environmental degradation has been linked with increased

health risks in a number of studies (Ostfeld and Keesing,

2000; Ostfeld and LoGiudice, 2003; Norris, 2004; Temel,

2004; Moiseenko et al., 2006). Poor mental health, in par-

ticular, has been consistently associated with environmental

degradation across a range of settings. For example, in Cote

d’Ivoire, people living in and around a degrading forest were

more likely to experience stress disorders than people living

in non-degrading areas (Van Haaften and Van de Vijver,

1996b). Soil degradation through overgrazing in the Sahel

region of Africa was associated with higher levels of reported

psychological ‘‘stress’’ (Van Haaften and Van de Vijver,

1999, 2003). In China, individuals living in more degraded

environments (including areas undergoing salinisation)

were more likely to report psychological ‘‘stress’’

(Van Haaften et al., 2004). In both the African studiesPublished online: May 21, 2011

Correspondence to: Peter C. Speldewinde, e-mail: peter.speldewinde@uwa.edu.au

EcoHealth 8, 82–92, 2011DOI: 10.1007/s10393-011-0686-x

Original Contribution

� 2011 International Association for Ecology and Health

(Van Haaften and Van de Vijver, 1996b, 1999, 2003), wo-

men in the degraded areas were found to be more likely to

report the condition than men. There is also a strong

association between many forms of stress reaction/disorder

and the onset of depressive illness (Wheatley, 1994). A re-

cent study found elevated risk of depression in areas with

high levels of dryland salinity, a form of environmental

damage largely caused by the removal of perennial vegeta-

tion that resulted in salts in the soil being mobilising to the

surface (Speldewinde et al., 2009). Although mental health

issues have been documented to be associated with envi-

ronmental degradation, no studies have analysed the po-

tential increase in comorbid disease associated with these

mental health issues in degraded landscapes.

Depression has been found to co-occur with a number

of other psychological and physical disorders. It is well

established that individuals with depression are at greater

risk of suicide than those without the condition (Kennedy

and Foy, 2005; Aina and Susman, 2006). There is also

evidence for the presence of co-morbid depression with

asthma (Goldney et al., 2003; Eisner et al., 2005; Opolski

and Wilson, 2005; Moussavi et al., 2007) and ischaemic

heart disease (IHD) (Bunker et al., 2003; Jiang et al., 2003;

Stamatakis et al., 2004; Bruce and Musselman, 2005; Jiang

and Davidson, 2005).

Given the emerging evidence of the effects of envi-

ronmental degradation on mental health, it was hypothe-

sised that typically comorbid diseases may also be

influenced by environmental degradation. In Western

Australia, a recent study (Speldewinde et al., 2009) found

an association between dryland salinity and depression.

This rural population was re-examined for a range of other

physical and psychological conditions—particularly asth-

ma, ischaemic heat disease and suicide—to identify whe-

ther any corresponding increased risk was associated with

the presence of a depressive illness.

METHODS

This study examined disease rates in relation to dryland

salinity in rural south-west Western Australia. The study

area excluded the major population centre of Perth (the

capital city of Western Australia), as well as the sparsely

populated areas in the north and east of the state (Fig. 1).

The City of Perth was excluded from the analysis as the

urban area was not relevant to the environmental data

analysed. The study area was selected because of (i) the

availability of extensive data on salinity and the range of

saline conditions encompassed in the region and (ii) the

high level of completeness and coverage of health data for

the resident population. The more remote parts of the State

(the far north and east) were also not included due to

sparse populations and the lack of environmental data. The

methods used in this study are fully described in Spel-

dewinde et al. (2009). Briefly, the case populations with the

three diseases (asthma, suicide and ischaemic heart disease)

studied between 1996 and 2001 were extracted from Wes-

tern Australia’s Data Linkage Unit database. This database

geocodes the place of residence for all hospital patient

admissions to the census collection district level (a spatial

unit of approximately 225 households). These health re-

cords have unique individual identifiers that allow linkage

of health records. Population data from the 1996 and 2001

Australian Census were used to generate expected numbers

of cases for each disease (standardised using 5-year age

intervals), and these estimates were used to model the

relative risk for each disease (see below). To avoid the

double counting of individuals, only the first admission for

each individual for each disease was used in the analysis.

Three conditions were included in the analysis: asthma

(ICD-9 code 493), suicide (ICD-9 codes E950-E959) and

ischemic heart disease (ICD-9 codes 410-414).

As depression (ICD-9 codes 300.4 & 296) can be

comorbid with all three of these conditions, models were

also analysed with comorbid depression removed from the

analysis. However, it must be noted that there was no

longitudinal component to the analysis. It was therefore

not possible to determine the temporal relationships: that

is, whether hospitalisation with heart disease and asthma

preceded the depression, or whether depression was already

present and acted as a risk factor for subsequent develop-

ment of disease.

Indicators of socio-economic status and Indigenous

status were included in the analysis. The socio-economic

index for areas (SEIFA) from the 2001 Australian Bureau of

Statistics census was used as a measure of socio-economic

status. The SEIFA uses attributes such as occupation,

education, employment and income to derive an index of

socio-economic status for an area (Pink, 2008). The pro-

portion of the population identified as Indigenous (IND)

was also derived from the 1996 census and 2001 census.

Levels of dryland salinity (SAL) for each census collection

district were calculated using the Western Australian

Department of Agriculture’s soil and landscape mapping

database (Fig. 1).

Dryland Salinity and Comorbid Disease 83

A well validated and extensively used Besag, York,

Mollie (BYM) model (Besag et al., 1991) was used for the

analysis. The BYM model is expressed as:

Log hi ¼ aþ bXi þ hi þ bi;

where hi is the relative risk for area i, a is a constant, Xi are

the risk factors for area I, b is the vector of the regression

parameter, hi is the non-spatial heterogeneity for area i and

bi is the spatial heterogeneity for area i. The model is

considered one of the optimal Bayesian models for the

spatial modelling of disease (Lawson et al., 2000) and is less

susceptible to extreme values in relative risk due to low case

numbers or low population numbers. Models were run

using WinBUGS (version 1.4.1)� using a ‘burn in’ of

10,000 iterations before a sample of 5,000 iterations.

Models were compared using the deviance information

criterion (DIC) of Spiegelhalter et al. (2002). The DIC is a

widely used statistic which tests the statistical goodness of

fit (Lawson et al., 2003) and can be used to assess model

complexity and compare different models. When compar-

ing models, the model with the lower DIC value is con-

sidered to best represent the data. For this study, a decrease

in the DIC value of more than three was considered sig-

nificant (Spiegelhalter et al., 2002). For each disease,

models were compared using the DIC from the basic model

(a + b + h) which includes terms for correlated and

uncorrelated spatial heterogeneity, with the models con-

taining the environmental variables (a + variable + b + h).

Figure 1. Map of study area showing extent of

dryland salinity

84 Peter C. Speldewinde et al.

Socio-economic and Indigenous variables were initially

evaluated in all models. However, if the variable did not

make a significant contribution to the model, it was sub-

sequently excluded. The expected number of cases for each

area was standardised using 5-year age groups.

RESULTS

The total population of the study area averaged 408,111

over the period 1996–2001, this was distributed across 882

census collection districts. Approximately 2% of the pop-

ulation of the study area identified as Indigenous

(Aboriginal or Torres Strait Islander). The number of cases

of depression, asthma, ischaemic heart disease and suicide/

self-inflicted injury are detailed in Table 1.

Asthma

The optimal model for describing spatial variation in the

relative risk for asthma was achieved using socio-economic

status, percentage of the population identified as Indige-

nous, the presence of salinity and, the standard terms for

correlated and uncorrelated heterogeneity (Table 2). The

initial map of SMR showed extreme values between 0 and

6.41 with areas of elevated SMR associated with the loca-

tion of towns. The modelled relative risk using the BYM

model incorporating SEIFA, proportion of the population

identified as Indigenous and salinity, showed no distinct

spatial pattern of excess risk. The relative risk attributable

only to the salinity variable ranged from 1 up to 1.36 in the

more saline areas. The model shows spatial clustering (i.e.

correlated heterogeneity) of asthma but no uncorrelated

heterogeneity. The spatial heterogeneity was positive for

most census collection districts in the study area, with the

exception of the south-east quadrant of the study area

which had negative spatial correlation.

The relative risk models (incorporating SEIFA, indig-

enous population and dryland salinity) were re-analysed

after stratifying the population into adults and children.

For the purpose of this analysis, children were classified as

aged 19 years and younger because of the 5-year age strata

used for the standardisation of the data. For children, the

DIC did not alter when salinity was added to the model,

but adults (subjects aged 20 years or over) did show a

decrease in DIC with the inclusion of the salinity variable.

The effects of socio-economic status and Indigenous pop-

ulation were still evident (Table 2). The relative risk asso-

ciated with salinity increased from a maximum of 1.3 in the

total population (children and adults) to 1.5 when the adult

population alone was assessed (Fig. 2). This model for

adults in relation to salinity showed a strong spatial com-

ponent, with the south-west corner of Western Australia

showing high spatial autocorrelation.

Table 1. Number of individual of hospitalisations by condition and gender; 1996–2001 (number of individuals with co-morbid

depression in brackets)

Depression Asthma Ischaemic heart disease Suicide/self-inflicted injury

Male 1,016 2,493 (16) 5,122 (49) 1,719 (187)

Female 1653 2,689 (71) 2,721 (47) 1,741 (391)

Total 2,669 5,182 (87) 7,833 (86) 3,460 (578)

Table 2. Deviance information criterion (DIC) for SEIFA, Indigenous and salinity model for asthma for total population, all adults and

all adults without co-morbid depression

Model DIC total population DIC all cases (�20 years) DIC without depression (�20 years)

a þ SEIFA þ ind þ sal þ b þ h 4009.770 3153.530 3111.280

a þ SEIFA þ ind þ b þ h 4018.840 3157.610 3113.000

a þ SEIFA þ b þ h 4036.532 3187.690 3143.410

a þ b þ h 4082.350 3237.780 3199.270

SEIFA socioeconomic index, ind percentage of the population Indigenous, sal percentage of area saline

Dryland Salinity and Comorbid Disease 85

Re-analyses of the risk models after stratification by sex

did not identify any substantial differences between males

and females when the salinity variable was added to the

model. The influence of SEIFA and Indigenous population

parameters remained evident.

Given that asthma and depression may co-occur

in the same individuals, the adult asthma model

(a + SEIFA + IND + SAL + b + h) was re-run with

the subset of asthma records in which those with co-

morbid depression was removed. With this revised

model, the effect of salinity was reduced to a level not

considered significant (Table 2), with a DIC change of

only 1.7. This suggested that co-morbid depression may

be the factor associated with salinity, as opposed to

asthma per se.

Ischaemic Heart Disease

The optimal model for ischaemic heart disease (IHD) was

achieved using variables for socio-economic status, percent-

age of the population classified as Indigenous, the presence of

environmental salinity, and the standard terms for correlated

and uncorrelated heterogeneity (Table 3). The initial map of

SMR showed no distinct spatial pattern, with extreme values

between 0 and 35.63. The modelled relative risk using the

BYM model incorporating SEIFA, proportion of the popu-

lation identified as Indigenous and salinity, showed no dis-

tinct spatial pattern of excess risk (Table 3). When the relative

risk was separated into components, the relative risk for IHD

due only to the salinity variable ranged from 1 up to 1.46 in

the more salt-effected areas.

Figure 2. Modelled relative risk of asthma for

adults (RR[i] = exp(a + b1 9 SEIFA[i] + b2 9

ind[i] + b3 9 sal[i] + b[i] + h[i]), where a�x ¼�1:000; b1�x ¼ 0:204; b2�x ¼ 0:052 and b3�x ¼0:005)

86 Peter C. Speldewinde et al.

Sub-analyses of the relative risk model by sex revealed

no changes in the DIC when the salinity factor was added

to the model for females (Table 3), but for males a decrease

in DIC was observed when the salinity variable was added

(Fig. 3). The areas in the eastern section of the study area

showed negative spatial autocorrelation, whereas the wes-

tern half displayed positive spatial autocorrelation.

Given that, as with asthma, IHD may display co-

morbidity with depression the model for males (a + SEI-

FA + IND + SAL + b + h) was re-run with all the set of

Table 3. DIC for ischaemic heart disease, by sex, and with co-morbid depression removed for males

Model DIC total population DIC only females DIC only males DIC males without depression

a þ SEIFA þ ind þ sal þ b þ h 4505.910 3183.480 4026.910 1350.650

a þ SEIFA þ ind þ b þ h 4510.760 3182.860 4031.300 1352.980

a þ SEIFA þ b þ h 4523.860 3201.910 4048.100 1345.530

a þ SEIFA þ b þ h 4551.700 3225.230 4077.790 1373.700

SEIFA socioeconomic index, ind percentage of the population Indigenous, sal percentage of area saline

Figure 3. Modelled relative risk of ischaemic

heart disease in males (RR[i] = exp(a + b1 9

SEIFA[i] + b2 9 ind[i] + b3 9 sal[i] + b[i] +

h[i]), where a�x ¼ �0:555; b1�x ¼ 0:089; b2�x ¼0:034 and b3�x ¼ 0:005)

Dryland Salinity and Comorbid Disease 87

IHD records with co-existing depression now removed. In

this model, the effect of salinity was reduced to a level

where it was not considered significant (Table 3), again

suggesting that it was the presence of co-morbid depression

that was most likely associated with the salinity variable.

Suicide and Other Self-Inflicted Injury

The optimal model for estimating the relative risk for

suicide (and other self-inflicted injury) was achieved using

the proportion of the population identified as Indigenous,

the presence of salinity and the standard terms for corre-

lated and uncorrelated heterogeneity (Table 4). The initial

map of SMR showed extreme values between 0 and 6.68,

with areas of high SMR values associated with the location

of towns. The relative risk due only to the salinity variable

ranged from 1 up to 1.61 in the more saline areas. The

model shows spatial clustering (i.e. correlated heterogene-

ity) of suicide but no uncorrelated heterogeneity, suggest-

ing that a number of suicide cases within a census

collection district were related to the number of suicide

cases in surrounding census collection districts.

With re-analyses after stratification by sex, the DIC did

not alter when salinity was added to the model for males

(Table 4), but did show a decrease in DIC for suicide in

females (Table 4). The modelled relative risk of suicide for

females was similar to the modelled relative risk for the

entire population (Fig. 4) although the maximum relative

risk increased up to 12. The relative risk due to salinity

alone increased from a maximum of 1.6 in the total pop-

ulation up to 1.7 in the female population. The model for

females with salinity shows a strong spatial component, but

the addition of non-spatial heterogeneity did not improve

the model for suicide.

As suicide and other self-inflicted injury commonly co-

occurs with a diagnosis of depression, the model for female

suicides and other self-inflicted injuries was rerun using

only those females who had no record of depression in the

linked data set. The predictive effect of salinity was lost

when the subset of females with no record of depression

were analysed (Table 4).

DISCUSSION

Speldewinde et al. (2009) found a correlation between in-

creases in relative risk for depression and dryland salinity, a

form of land degradation in Western Australia. There have

been very few studies examining the psychological impacts

of environmental degradation. The limited research has

consistently found a positive correlation between environ-

mental degradation and psychological stress (Van Haaften

and Van de Vijver, 1996a, b, 1999, 2003; Van Haaften et al.,

2004). In the case of dryland salinity, Speldewinde et al.

(2009) suggested a number of possible links for this asso-

ciation of increase risk of depression in saline areas,

including falling farm incomes, social network disintegra-

tion and solastalgia, although was not able to definitively

define the causal link between dryland salinity and risk of

depression. This current study was innovative in its

exploration of how the mental health issues due to envi-

ronmental degradation can be associated with an increase

in comorbid conditions. Our initial analysis suggested that

an increase in the relative risk for asthma, ischaemic heart

disease (IHD) and suicide were found to be associated with

dryland salinity (adjusting for socio-economic status and

the proportion of the population identified as Indigenous).

However, all three of these conditions are also known to be

co-morbid with the diagnosis of depression (Bunker et al.,

2003; Katon et al., 2004; Kennedy and Foy, 2005). When

the subset of individuals with co-existent depression was

removed from the analysis, the association between dryland

salinity and these other diseases were no longer evident.

This suggests that, for this population, depression may have

been the primary condition associated with salinity, and

that the model was in fact capturing elevated relative risks

for asthma, IHD and suicide mainly because of their co-

occurrence with depression. For example, the lack of a

Table 4. DIC for suicide and self-inflicted injury using total population, by sex, and with co-morbid depression removed for females

Model DIC total population DIC only males DIC only females DIC females without depression

a þ ind þ sal þ b þ h 3505.910 2719.690 2798.530 2499.700

a þ ind þ b þ h 3510.090 2719.700 2803.390 2500.920

a þ b þ h 3566.220 2776.400 2848.410 2557.360

ind Percentage of the population Indigenous, sal percentage of area saline

88 Peter C. Speldewinde et al.

persisting effect of salinity on asthma relative risk when

individuals with depression were removed suggests that

asthma rates primarily relate to the elevated risk of

depression in saline areas, as opposed to arising from some

direct interaction between salinity and asthma (such as an

increased exposure to wind-blown particulates in such

environments). As noted in the methods, however, there

was no longitudinal component to our analysis and

therefore, in the case of heart disease and asthma, it was not

possible to determine the temporal relationship of the

comorbidities described. For example, for hospitalisation

with heart disease and asthma, it was not possible to

examine whether these events preceded the onset of

depression, or whether depression was already present and

acted as a potential risk factor for these events.

The exact mechanisms for co-morbidity of asthma and

depression remains unclear (Katon et al., 2004; Wright,

2005). Depressive symptoms in adults with asthma are

associated with poorer health outcomes, including greater

asthma severity and risk of hospitalisation for asthma

(Eisner et al., 2005). Asthmatics with depression are more

likely to be non-compliant with medication than those

without, and hence are more likely to present at hospital

(Cochrane, 1996; Opolski and Wilson, 2005; Smith et al.,

2006). Asthmatics admitted to hospital with acute asthma

have been found to have a higher prevalence of anxiety and

depression (Kolbe et al., 2002).

This current study found that ischaemic heart disease

in males was associated with dryland salinity. The presence

of psychosocial stressors and depression has been linked

Figure 4. Modelled relative risk for suicide in

females (RR[i] = exp(a + b1 9 ind[i] + b2 9

sal[i] + b[i] + h[i]), where a�x ¼ �0:560; b1�x ¼0:086 and b2�x ¼ 0:007)

Dryland Salinity and Comorbid Disease 89

with IHD in several studies (Bunker et al., 2003; Jiang et al.,

2003; Stamatakis et al., 2004; Bruce and Musselman, 2005;

Jiang and Davidson, 2005). Individuals with depression

also have an increased relative risk for ischaemic heart

disease and a higher risk of mortality from IHD (Anda

et al., 1993; Jiang et al., 2005). The increased risk of IHD in

depression patients is thought to be due to a higher fre-

quency of detrimental health behaviours (e.g. smoking,

poor diet) and physiological mechanisms (e.g. neuroen-

docrine and platelet activation) (Rozanski et al., 1999).

Although it is well established that men have a higher

relative mortality rates for ischaemic heart disease than

women (e.g. Wong et al., 2006), our analysis in the cur-

rent study standardised for both age and sex and thus the

higher relative mortality of males cannot account for the

findings. Two studies (Hippisley-Cox et al., 1998; Pennix

et al., 1998) have found depression as a risk factor for

ischaemic heart disease in men but not for women.

Hippisley-Cox et al. (1998) also found that men with

depression were three times more likely to develop

ischaemic heart disease than men without the condition.

Our finding of an association between men with Ischae-

mic heart disease and depression was therefore consistent

with previous studies.

Suicide (and other self-inflicted injury) was found to

be associated with dryland salinity for women. Individuals

with depression are at greater risk of suicide than those

without the condition (Kennedy and Foy, 2005; Aina and

Susman, 2006). Many studies have found that, in rural

areas, male suicide rates are higher than female suicide rates

(Judd et al., 2006), but admission rates for females for self-

harm have been found to be higher than males in remote

areas (Serafino et al., 2000). However, a study comparing

rates of suicidal acts between genders after depression

found that the importance of risk factors differed in de-

pressed men and women (Oquendo et al., 2007). Oquendo

et al. (2007) found that women tended to have an earlier

onset of depression and more often displayed suicidal/self-

injurious behaviour (including attempted suicides),

whereas males tended to use more violent means of suicide

and were more likely to be successful. In this project,

analysis was conducted for all forms of suicide and other

self-inflicted injury, utilising data on both fatal and non-

fatal suicide attempts (as well as unintentional self-inflicted

injury). The finding of an association between suicide (and

other self-inflicted injury) in women with depression and

dryland salinity was thus in accordance with the gender

patterns observed in previous studies.

This study has a number of limitations requiring re-

sults to be treated with some caution. These data were

based on hospital admissions, and therefore visits to gen-

eral practitioners and emergency departments were not

included in the analysis. Such incompleteness in the re-

corded health events would have tended to underestimate

true community rates of illness. Furthermore, the data set

did not utilise records with post office box addresses be-

cause the exact place of residence was not known, thereby

removing a subset of cases because of geocoding issues.

This prevalence analysis used ‘‘ecological’’ estimates of

exposure (although the record-based health outcomes ap-

plied to individuals), and, as such, the findings are open to

the fallacy that aggregated assumptions about exposure do

not necessarily apply to individual results. In addition, the

environmental data used in the study were based upon

proportional mapping based on soil surveys. This approach

assigns a probability for salinity risk within each soil unit

but does not provide precise information about individual

sites. It is also assumed that the exposure of interest—the

salinity level—may have remained invariant over the 5-year

term. Short-term (such as seasonal) effects are less likely to

be of relevance given that the degree of dryland salinity is

typically gradual and does not undergo rapid change.

Finally, the relationships observe in the study popula-

tion may not be fully generalizable to other locations, given

that aspects of the interconnection of environmental effects

(extensive impacts from salinity) and health may be specific

to the Western Australian context. However, many of the

drivers for rural ill-health—such as poverty, land degra-

dation, isolation and remoteness from health services—are

common to many populations in agricultural regions

around the world, and therefore some degree of compa-

rability with other communities would be expected.

Mental health risks associated with environmental

degradation have been found in a number of previous

studies (Van Haaften and Van de Vijver, 1996a, b, 1999,

2003; Crighton et al., 2003; Van Haaften et al., 2004;

Speldewinde et al., 2009), and this present study highlights

the additional burden of disease arising from these envi-

ronmental processes. In Australia, areas at high risk of

developing dryland salinity are predicted to increase from

565 million to 17,000 million hectares over the next

50 years (NLWRA 2001). Given the human health impacts

shown by this study and results from Speldewinde et al.

(2009), the rates of all these interrelated diseases can also be

expected to increase as the areas affected by dryland salinity

continue to expand. This study highlights the need to

90 Peter C. Speldewinde et al.

include mental health in any study examining the impacts

of environmental degradation, such as dryland salinity.

Increases in physical conditions, such as asthma and

ischaemic heart disease may arise indirectly by their asso-

ciation with depression rather than directly from environ-

mental degradation.

ACKNOWLEDGMENTS

This research was funded by the Cooperative Research

Centre for the Plant Based Management of Dryland

Salinity. The research was approved by the University of

Western Australia’s ethics committee (RA/4/3/0492).

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