Understanding Water Quality Dynamics in Aquaculture Ponds in Sunyani, Ghana: Insights from Partial Least Squares (PLS) - Path Modeling

Understanding Water Quality Dynamics in Aquaculture Ponds in Sunyani, Ghana: Insights from Partial Least Squares (PLS) - Path Modeling
JFAR
Understanding Water Quality Dynamics in Aquaculture Ponds
in Sunyani, Ghana: Insights from Partial Least Squares (PLS) -
Path Modeling
Nicole E.G Otoo1*, Peter O. Sanful2, Wahab A. Iddrisu3, Solomon Amfoh4, Okyere K. Boateng5
1,2,4Department of Fisheries and Water Resources, University of Energy and Natural Resources, P. O Box 214, Sunyani,
Brong Ahafo Region, Ghana
3Department of Mathematics and Statistics, University of Energy and Natural Resources, P. O Box 214, Sunyani, Brong
Ahafo Region, Ghana
5Fisheries Commission, Sunyani
Knowledge of water quality in aquaculture ponds in Ghana is limited due to lack of qualitative and
quantitative field data. We conducted detailed field measurements to assess the effect of
hydrographic and production factors on water quality. Ponds cultured with Nile tilapia
Oreochromis niloticus, and African catfish Clarias gariepinus, were selected for the study. Eleven
fish ponds with stock ranging between 7-21 days were randomly selected and sampled at monthly
intervals for five months, with the aim of capturing water quality patterns through a full production
cycle. Seventeen parameters were measured and analyzed using Partial Least Squares (PLS) -
Path Modeling. Most ponds had unusually shallow depths, characterized by excessive stocking
densities of 200% on average above recommended rates. This necessitates high feeding rates,
thereby reducing the assimilative capacity of ponds. The effect of feeding intensity on water
quality increased with stock age. Persistent algal blooms, low DO, high temperatures and elevated
levels of ammonia, nitrite, phosphate, TDS and conductivity were indicative of generally poor
water quality. The model showed that 82 % variability in water quality was due to production
inputs. Standard stocking rates, feeding rates and construction of deeper ponds are
recommended.
Keywords: water quality, aquaculture ponds, hydrographic factors, path model, tilapia (Oreochromis niloticus), catfish
(Clarias gariepinus), Sunyani
INTRODUCTION
The maintenance of good water quality in a pond culture
system is critical for the success of raising fish to meet
various human needs (Devi, 2013). Good water quality
supports the efficient growth and survival of culture
species and promotes good fish health, reduces stress,
improves growth performance, increases productivity and
reduces environmental impacts (Boyd and Tucker 1998;
Parven et al., 2013). Poor water quality however, may be
detrimental to fish health through increased susceptibility
to stress, disease and mortality, and may also have
serious environmental consequences (Boyd, 2017).
Furthermore, susceptibility to infectious diseases by fish is
heightened by deterioration in water quality (Boyd, 2017).
Thus, poor fish growth and reduced reproductive
performance often is a consequence of poor water quality
(Keremah et al., 2014). The ability of fish to maintain
optimum growth and reproductive performance is
dependent largely on the water quality characteristics of
the aquatic medium in which they live (Devi, 2013). Water
quality therefore is a major constraint to the success of
aquaculture operations and affects the quality of
aquaculture products and its suitability for human
consumption (Boyd and Tucker, 1998). The knowledge
and application of good water quality management
principles by aquaculturists in culture systems will help
meet the increasing demand for safe and quality fisheries
products (Boyd and Tucker 1998).
*Corresponding Author: Nicole E.G Otoo; Department of
Fisheries and Water Resources, University of Energy and
Natural Resources, P. O Box 214, Sunyani, Brong Ahafo
Region, Ghana. Email: nicole.otoo.stu@uenr.edu.gh
Tel: +233541890481
Research Article
Vol. 4(1), pp. 028-042, August, 2019. © www.premierpublishers.org, ISSN: 9901-8810
Journal of Fisheries and Aquaculture Research

Otoo et al. 029
A variety of factors interact to influence the water quality
characteristics in a culture system. These are soil
properties of site selected for aquaculture, chemical
composition of source water, atmospheric deposition,
management inputs and pollutants from anthropogenic-
driven activities in the watershed which come into contact
with water used in aquaculture production (Adhikari,
2003). Once in the pond system, the constituents of water
quality may act individually or in concert to determine
conditions that potentially cause lethal or sub-lethal effects
on fish health (El- Sayed, 2002). Furthermore, the
interactions of physical, chemical, biological factors and
management inputs create dynamic and complex
environments that influence water quality and
consequently fish health (Alemu, 2003). Despite this
diversity of factors potentially influencing water quality,
evidence suggests that production activities associated
with culturing fish constitute the major determinants of
water quality (Shoko et al., 2011). Two broad groups of
water quality variables have been classified on the basis
of their functional role in pond ecosystem dynamics (Boyd
and Tucker 1998). They are those set of variables such as
alkalinity, turbidity, salinity and compounds of phosphorus
and nitrogen are known to affect pond ecosystem primary
productivity, and those classified as critical water quality
factors such as dissolved oxygen, ammonia and carbon
dioxide which affect fish physiology and growth (Boyd and
Tucker 1998). Important parameters commonly measured
in routine assessments of water quality include nitrite,
ammonia (ionized NH4
+ and unionize NH3), dissolved
oxygen, pH, carbon dioxide, phosphate, conductivity,
temperature, total dissolved solids, salinity and
transparency.
Optimum fish yield is affected principally by the impact of
managerial practices on water quality (Boyd and Tucker
1998). Management input drives various water quality
conditions through the choice of culture species,
production levels, stocking density, feeding rates, stock
age and feed type (Boyd and Tucker, 1998). These factors
are known to be central to water quality and their effective
control can lead to good water quality management and
efficiency in production (Boyd and Tucker, 1998). High
stocking density influence feeding rates, nitrate-nitrogen
concentrations in water and their interactions with pH
which affects ammonia levels resulting in significant stress
and growth inhibition in fish (Shoko, 2014). Dissolved
oxygen, temperature, pH and ammonia, also parameters
of major concern, vary diurnally with peak values of some
variables occurring during the afternoon to pre-sunset and
the lowest occurring from midnight to pre-dawn hours
(Shoko, 2014). Variability in the daily peak concentrations
of these critical factors induces various degrees of
exposure to lethal and sublethal effect (Siddiqui, 1991).
Parameters such as turbidity, electrical conductivity and
salinity are not of major concerns in inland aquaculture
settings since their effect on fish health is relatively minimal
(Devi, 2013). High stocking density of fish in ponds usually
exacerbates problems with water quality and sediment
deterioration (Bhatnagar et al., 2004). Wastes generated
by aquaculture activity (faecal matter and unconsumed
feed) first settle at the bottom, and as a consequence,
organic waste and metabolite of degraded organic matter
are accumulated in sediment and water (Fynn, 2015). Part
of the waste is flushed out of the ponds immediately or
later, after the organic matter has been degraded (Boyd,
1990). Low dissolved oxygen level is the major limiting
water quality variable in aquaculture systems as it has
implications for fish survival, feed conversion efficiencies
and resistance to infection and diseases (Boyd, 1995). A
critically low dissolved oxygen level occurs in ponds
particularly when algal blooms die-off and subsequent
decomposition of algal species can elevate ammonia
concentrations and reduce DO leading to stress or
mortality in aquaculture ponds (Parven et al., 2013). Low
dissolved oxygen levels can reduce growth, feeding and
molting frequency (Boyd, 1990). Another major effect of
aquaculture production is a high degree of variability in the
concentration of dissolved nitrates, nitrites and ammonia
(Schwartz, 1994). The environmental conditions that
create high ammonia concentrations may also cause
increase in nitrite concentration. Both ammonia and nitrite
can be directly toxic to culture organisms or can induce
sub lethal stress in culture populations that results in
lowered resistance to diseases (Boyd, 1995).
Sunyani is considered the aquaculture hub of Ghana due
to the high numbers of commercial fish farms (MoFAD,
2015). However, there is poor management of water
quality due to lack of understanding of pond ecosystem
dynamics and the complexity of water quality processes
that affect aquaculture productivity. Despite the high
density of ponds, production is relatively low (Rurangwa,
2015). Production outputs from most farms are lower that
are expected from the size of inputs into these farms which
often leads to low profitability and economic losses for
many farmers. Farmers commonly speculate that viability
of fingerlings and poor feed quality may be responsible for
the low fish productivity even though such assertions are
yet to be empirically ascertained. Yet still, the role of water
quality in fish production is overlooked. In view of the
extremely limited scientific data, a comprehensive
assessment of water quality in aquaculture ponds was
required to provide insight into factors controlling water
quality dynamics in aquaculture ponds. The aim of this
study therefore, was to assess the interaction of
hydrographic and production factors on water quality
dynamics in ponds reared with two common culture
species, Oreochromis niloticus (Nile tilapia) and Clarias
gariepinus (African catfish). The scope of the current study
will serve as an important baseline for understanding water
quality dynamics and consequently shape future research
directions.

J. Fish. Aquacul. Res. 030
Figure 1: Map of study area showing location of sampled ponds
MATERIALS AND METHODS
The Sunyani Municipality is one of the twenty-seven
districts in the Brong Ahafo Region. It is regarded as the
hub of aquaculture production in Ghana due to the
presence of high numbers of active and inactive ponds
(MoFAD, 2015). It is located at the heart of the Brong
Ahafo Region between latitude N 070 20'47'' and longitude
W 0020 17' 06'' and covers a total land area of 506.7km2.
Fish species commonly cultured are the Nile tilapia
Oreochromis niloticus and the catfish Clarias gariepinus.
Oreochromis niloticus however accounts for the bulk of
production. Eleven randomly selected commercial fish
ponds representing 15 % of the total number of ponds
within the study area were investigated. The study ponds
are Berlin Top (BT), Abesim 1(AB1) Abesim 3 (AB3), New
Dormaa 1(ND1), ND1A, New Dormaa 2 (ND2), ND3,
Seventh Day Adventist (SDA), Magazine (MG) and
Dumasua (DS) (Table 1). Newly stocked commercial fish
ponds ranging from 7-21 days were selected in order to
capture the full range of water quality dynamics through
one full production cycle. Out of the selected fish ponds,
three contained catfish, seven tilapia-cultured ponds and
one mixed culture. Sampling usually took place from 6 am
to 2pm on each day. Hydrographic measurements of pond
area (square meters) was done only once but other related
variables such as pond depth (meters) and water quality
parameters were carried out at monthly intervals from
October 2017 to March 2018. This sampling period
marked the mid secondary rainfall period to the end of the
dry season. Production input variables measured included
stocking density, stock age, feed type and feeding
frequency.
Table 1: Coordinates of selected fish ponds in Sunyani
NORTH WEST
SITE
NAME
Deg Min Sec Deg Min Sec ELV
BT 7 20 33.2 2 21 32.9 296
ND1 7 20 48.4 2 18 13.8 297.5
NDI(A) 7 20 47 2 17 28.6 298.8
ND3 7 20 49 2 17 6.6 293.7
AB3 7 20 7.3 2 17 54.9 262.9
AB1 7 16 56.8 2 18 13.4 261.8
MG 7 19 43.2 2 18 36.8 274.6
SDA 7 20 32 2 20 44.5 314
DS 7 23 23.1 2 22 8.5 307
To determine water quality, a multiparameter probe
HANNA H19829 (HANNA Instruments) was used to make
in situ measurements of temperature, pH, dissolved
oxygen, salinity, Total Dissolved Solids (TDS) and
conductivity. Ammonia, nitrite and phosphate were
analyzed on-site with Hydrotest Kit H7100 (Trace 2 O) and
in the laboratory with UV Spectrophotometer UV-1800
240V IVDD (Shimadzu Corporation) following standard
protocols. Water colour was visually assessed whilst water
transparency was estimated using Secchi disc
measurements.
Data Analysis
Summary statistics such as standard deviation, mean and
co-efficient of variation were determined. The Partial Least
Squares - Path Model (PLS-PM) was adopted for this
study and used to examine the relationship among
hydrographic factors, production input and water quality

Otoo et al. 031
variables based on multiple regressions multivariate
statistics. For the PLS-PM analysis, the data was classified
into input (latent) and output (manifest) variables. Latent
variables comprised hydrographic and production input
parameters, whereas water quality parameters constituted
manifest variables. The PLS-PM measures the size of
correlations between different sets of direct and indirect
variables classified as the latent and manifest variables. It
includes a qualitative theorized path and quantitatively
measured actual path yielding estimates of the magnitude
and statistical significance of hypothesized and actual
causal relationships between hydrographic, production
and water quality variables. The statistical levels of
significance for the PLS-PM were set at p < 0.05 and p <
0.01 probability levels.
Model specification of PLS-PM
Partial Least Squares (PLS) methods are analytical tools
with algorithmic origins aimed at solving models in a
practical way (Sanchez, 2013). The Path model (Figure 2)
consists of two sub-models namely, the structural or inner
model and the measurement or outer model. While the
inner model is concerned with the relationships between
the latent variables (represented by spheres), the outer
model shows relationships of a latent variable with its block
of manifest variables (represented by rectangles).
Inner Model
The first aspect of the inner model is to treat all structural
relationships as linear relationships as shown in equation
(1).
0j ji i j
i j
LV LV error 
→
= + + (1)
Where, the subscript i of LVi refers to all the latent variables
that are supposed to predict LVj. The coefficients ji are
the path coefficients and they represent the strength and
direction of the relations between the response LVj and the
predictors LVi. 0 is the intercept term, and the errorj term
accounts for the residuals. It must also be noted that the
paths formed by the arrows of the inner model cannot form
a loop.
Outer Model
Reflective mode is the most common type of
measurement, where the latent variable is considered as
the cause of the manifest variables. Similar to the inner
model, the outer model relationships are also considered
to be linear as shown in equation (2).
Figure 2. Conceptualized Path Model for latent
(production and hydrographic) variables and manifest
(water quality) variables
0jk jk jk j jkX LV error = + + (2)
Where, the coefficients jk are called loadings; 0 jk is the
intercept term, jkX is the response variable, and the
jkerror terms account for the residuals.
Data analysis was done in R (R Core Team, 2014; Rstudio
Team, 2016) using the package PLS-PM (Sanchez, 2013).
RESULTS
Variation in hydrographic, production and water
quality parameters
Pond size represented by both pond area and pond depth
varied widely across the studied ponds but highest
variation was recorded in tilapia ponds (Table 2). Mean
pond area for both catfish and tilapia ponds were 307.71 ±
216.59 m-2 ranging from 19.52 -722.70 m2 implying wide
variation in the size and depth of commercial ponds used
for culture purposes. Mean pond area was 286.6 ± 19.84
m-2 and 328.8 ± 251.3 m-2 for catfish and tilapia ponds
respectively. The size of ponds used for tilapia culture was
15 % larger than catfish ponds. Depth of fish ponds were
exceptionally shallow across most catfish and tilapia
ponds and less variable, differing by less than 10 %.
Mean pond depth was 0.49 ± 0.16 m ranging between 0.09
- 0.86 m (Table 2). Thus, the average volume of ponds was
approx. 150 m-3, holding average stocking densities of
2125 individuals for the two types of culture ponds.
Variability in stocking density across both pond types was
exceptionally high at 2125 ± 2778 individuals with a
percentage difference of 96 % between upper and lower
limits of stocking density. Mean stocking densities in
catfish and tilapia ponds were 2761 ± 1820 and 2142 ±
3077 individuals respectively. Maximum stocking density
in tilapia ponds was 10,000 compared with the relatively
low 4567 recorded in catfish ponds. Production inputs
variables measured were stocking density, feed type and
feeding frequency. Three types of culture feed were
identified in the study; these are formulated feed Raanan,
local feed and wheat bran. Raanan is a commercially

formulated feed whereas local feed and wheat bran are
feed formulations prepared by fish farmers without
proximate analysis of nutritional content of the feed.
Raanan was the dominant feed type fed to culture species.
Feeding frequency which is a measure of the daily feeding
rate was 2.09 ± 0.29 with a range of 2.00 – 3.00.
Table 2: Mean and standard deviation of water quality
variables in catfish and tilapia ponds. Ranges are given in
brackets
Variable Catfish pond Tilapia pond
DO (mgL-1) 2.70 ± 0.22
(2.40 - 3.03)
2.978 ±1.070
(2.4-7.4)
pH 7.59 ± 0.83
(6.43 - 8.79)
7.511 ± 0.254
(5.62-8.9)
Salinity (psu) 0.11 ± 0.04
(0.06 - 0.22)
0.111 ± 0.074
(0.03-0.4)
TDS (mgL-1) 117. 4 ± 48.83
(61.0 – 235.0)
105.23 ± 57.34
(33.0-239.0)
Cond. (µS/cm) 241.5 ± 96.47
(129.0 - 470.0)
241.8 ±185.432
(495-74)
Temp. (o C) 27.219 ±1.51
(25.77 - 30.8)
27.83 ± 1.56
NH3 (mgL-1) 0.306 ±0.328
(0.04 - 1.06)
0.206 ± 0.395
(0.025-1.00)
PO4
2- (mgL-1) 0.246±0.195
(0.04 - 0.50)
0.298 ± 0.254
(0.05-1)
Transparency (m) 0.106 ± 0.050 0.172 ± 0.324
(0.01-2.13)
Pond water colour Green Brown
Pond area (m2) 286.6 ± 19.84
(271.4 - 307.8)
328.776 ± 251.324
(19.52-722.7)
Pond water depth
(m)
0.524 ± 0.230
(0.086 - 0.86)
0.486 ± 0.158
(0.22-0.85)
NO2
- (mgL-1) 0.103 ± 0.033
(0.08 - 0.15)
0.236 ± 0.303
(0.08-1)
Stocking density 0.486 ± 0.158
(1035 - 4567)
2761.0 ± 1819.61
(200 – 10000)
Alkalinity (mgL-1) 5.00 ± 0.23 5.00 ± 0.23
Hardness (mgL-1) 2.00 ± 0.01 2.00 ± 0.01
The water quality environment in both ponds showed
remarkable similarities in the parameters measured (Table
2). Generally, with the exception of conductivity and TDS,
all water quality parameters fluctuated little with stable
levels throughout the period. Levels of pH, salinity,
electrical conductivity, temperature, phosphate and
turbidity were uniform across both ponds. Significant
variations were found between the ponds in respect of
dissolved oxygen and nitrites but higher concentrations
were observed in tilapia ponds. By contrast, catfish ponds
had elevated TDS (117 ± 48.8 mgL-1) and high ammonia
concentrations (0.306 + 0.328 mgL-1). High variability in
nitrite and conductivity was found in tilapia ponds.
Variation in salinity of the ponds were the lowest among all
the parameters measured. The difference between high
and low salinity levels was 74%. Variations in TDS were
also significant with a mean value of 107.07 ± 53.52mgL-1
and a range of 33.00-239.00 (Table 2).
Temporal trends in water quality conditions
Catfish ponds
Ponds containing catfish were BT, ND1b and ND3 (Fig 3a
and b). Eight water quality parameters were monitored
seasonally to determine seasonal fluctuations in
environmental conditions affecting growth and
development of culture species. Common patterns were
not apparent among the eight parameters measured in the
culture ponds. Temperature and ammonia showed similar
seasonal variations with a maximum in January at site BT.
Salinity, TDS and conductivity declined sharply at ND3
even though consistently similar patterns had existed
throughout the study period. Nitrite levels at BT and ND1
were consistently low and stable with little fluctuations
throughout the period but declined progressively at ND1
from the onset of the study to the end of sampling.
Phosphate concentrations showed mixed patterns across
sites. Peaks in phosphates occurred in different months in
the three ponds. pH increased at all sites, reached a peak
in December and declined in February. Dissolved oxygen
concentrations showed a consistent rise throughout the
study period reaching a maximum at the end of sampling
in February. The patterns of variation in the amount of
dissolved oxygen in the ponds were similar throughout the
study. Maximum and minimum dissolved oxygen
concentrations were 2.4mgL-1
and 3.03 mgL-1
recorded at
ND1b and BT respectively. Differences in seasonal
characteristics among the measured parameters were
observed. During the dry season, ammonia, phosphate
and DO increased while pH declined and vice versa. The
pH of the ponds was characterized as weakly acidic to
alkaline based on the observed range of values from 6.43
and 8.79. Variation in pH was not significant over the
seasons across ponds (p > 0.05). (Fig 3a).

Otoo et al. 033
Figure 3a: Monthly pH, phosphate (mgL-1), ammonia (mgL-1), dissolved oxygen (mgL-1), nitrate (mgL-1) and conductivity
(µS/cm) levels in catfish ponds

Figure 3b: Monthly salinity (psu), total dissolved solids (mgL-1) and temperature (0C) levels in catfish ponds.
Tilapia ponds
Ammonia levels at all sites followed the same pattern with
peak concentrations of 2.157 mgL-1 occurring at site AB3
in February (Fig. 4a). Lowest ammonia concentrations of
0.025 mgL-1 were recorded at AB1 in October during the
secondary rainfall period of the year (Fig. 4a). pH
increased from October to December and remained high
at all sites till the end of the study in February. Salinity,
TDS and conductivity showed similar temporal patterns
comparable with observations made in the catfish ponds
(Fig. 4b). Phosphate levels varied from site to site
throughout the study with the least value of 0.05 mgL-1
recorded at site-SDA in October and the highest value of
1.2 mgL-1 at site DS in November (Fig. 4a). Maximum
concentrations of ammonia occurred in January similar to
catfish ponds. Except at sites ND1, ND2 and SDA, nitrite
concentrations were low throughout the study. DO levels
were higher in November but there was a gradual
decrease from December to February. This pattern of DO
sharply contrasts the situation observed in catfish ponds
where DO concentrations increased progressively over the
study period. Temperature conditions were variable
among the ponds fluctuating between 27.83 and 31.60 0 C
with a mean of 25.04 ± 1.580C. The highest water
temperature of 31.6 0 C was recorded at site-ND2 in
January. Dissolved oxygen levels of 7.4 mgL-1 recorded at
site DS in February during the dry season was the highest
recorded but lowest DO of 2.4 mgL-1 occurred in October
at ND1. pH conditions fluctuated between moderate
alkaline of 8.9 in December to weakly acidic levels of 5.6
in October. Overall, mean pH conditions showed neutral
conditions the mean of 7.5 ± 0.9 measured. TDS, salinity
and conductivity showed mixed patterns increasing and
decreasing in different months.

Otoo et al. 035
Figure 4a: Monthly pH, phosphate (mgL-1), ammonia (mgL-1), dissolved oxygen (mgL-1), nitrate (mgL-1) and conductivity
(µS/cm) levels in Tilapia ponds

Figure 4b: Monthly salinity (psu), total dissolved solids (mgL-1) and temperature (0C) levels in tilapia ponds
Analysis of variance (ANOVA) and correlations among
cluster variables
Relationship between hydrographic and water quality
variables
Hydrographic factors had little effect on general water
quality as shown by the weak correlations and the lack of
statistical significance (p > 0.05) (Table 3). Most of the
water quality parameters showed a weak negative
correlation with pond area and pond depth with few
showing weak positive correlations. Correlation of pond
area and conductivity showed the weakest relationship
among all the negatively correlated variables with a
correlation coefficient (R2 = -0.187, p < 0.05, n = 55) whiles
correlation of pond area and nitrite showed a weak
negative correlation. Correlation of pond depth with pH
was low and negatively correlated among all the variables
with a value of (R2 = -0.349, p < 0.05, n =55) while the
relationship between pond depth and temperature showed
a weak positive correlation (R2 = 0.103, p < 0.05, n =55)
(Table 3).
Table 3: Correlation coefficients and significance of statistical relationships among hydrographic factors, production input
and water quality variables
Variable Nitrate Ammonia Phosphate pH DO Conductivity Temp TDS Salinity Transparency
Pond Area -0.045 -0.053 -0.093 0.010 0.307 -0.186 0.369 -0.164 0.057 -0.055
Pond Depth 0.131 -0.263 -0.012 -0.349 -0.164 -0.035 0.103 -0.018 -0.101 -0.071
Stock Density -0.256 -0.012 -0.088 0.008 0.078 0.308 -0.010 0.348 0.230 0.036
Mortality -0.065 -0.084 -0.168 -0.045 0.026 0.064 -0.098 0.061 0.017 -0.050
Feed Type -0.091 -0.117 0.252 -0.046 0.007 -0.399 -0.208 -0.377 -0.378 -0.056
Feeding Freq. 0.075 -0.079 -0.074 -0.035 -0.151 0.531* -0.158 0.517* 0.366 -0.065
Stock Age -0.407 0.449* 0.257 0.616* 0.253 0.025 -0.219 0.039 0.083 -0.168
* Correlation is significant at the p < 0.05 probability level
Table 4: Relationship between feed type and fish production variables
Fish production
indicators
Group means Overall
mean
F-value P-value
Raanan Local Feed Wheat Bran
Stock density 1705.800a 3745.000b 200.000c 2125.055 4.719 0.013
Mortality 3.428 0.067 0.000 2.200 0.386 0.682
Feeding frequency 2.142 2.000 2.000 2.091 1.576 0.217
Stock age 103.714 105.067 90.000 102.836 0.184 0.832
Note: Means in the same row that do not share a common alphabet are significantly different at p <0.05

Otoo et al. 037
Relationship among production, feed type and water
quality variables
Most of the correlations between production factors and
water quality parameters were negatively correlated with
each other (Table 3). Among production factors, stocking
density, feeding frequency and stock age influenced
critical water quality parameters such as ammonia, pH and
dissolved oxygen. The variation in stock age accounted for
significant variations in ammonia concentrations (R2 =
0.45, p < 0.05, n = 55), pH and dissolved oxygen.
Correlation between stock age and pH showed a strong
positive relationship of R2 = 0.617, p < 0.05, n = 55 while
the relationship between stock age and ammonia had a
relatively weak but positive correlation of R2 = 0.449.
Feeding frequency determined significant variations in
conductivity, TDS and salinity with correlation coefficients
indicating that 50 % of variations in these water quality
parameters could be attributed to feeding rates of the
culture species in the ponds. The correlations between fish
production variables and water quality indicators show
varied relationships (Table 3). The results of the statistical
analysis between feed type and fish production indicators
showed that farms that had significantly high stock density
utilized local feed than farms that use Raanan or wheat
bran (Table 4). There was however no significant
difference in mortality, feeding frequency and stock age
across farms that used local feed, Raanan or wheat bran.
The ANOVA results from the analysis of the relationship
between feed type and water quality showed significantly
higher phosphate concentrations in ponds that use wheat
bran than ponds fed with Raanan and local feed (Table 5).
On the other hand, farms that use Raanan recorded
significantly higher conductivity, TDS, and salinity values
than farms that use local feed or wheat Bran. There was
however no significant difference in nitrite, ammonia, pH,
dissolved oxygen, temperature and transparency across
the farms irrespective of the feed type administered (Table
5).
Table 5: Results of the ANOVA showing relationship between feed type and water quality variables
Water quality indicators Group means Overall mean F-value P-value
Raanan Local Feed Wheat Bran
Nitrate 0.251 0.273 0.108 0.244 0.641 0.531
Ammonia 0.310 0.130 0.248 0.256 0.780 0.464
Phosphate 0.257a 0.245a 0.544b 0.280 3.901 0.026*
pH 7.553 7.506 7.406 7.527 0.060 0.942
Dissolved Oxygen 2.798 2.701 2.912 2.782 1.027 0.365
Conductivity 250.171a 188.600b 114.800c 221.073 4.967 0.011*
Temperature 28.098 27.423 27.326 27.844 1.332 0.273
TDS 120.314a 93.400b 55.400c 107.073 4.378 0.017*
Salinity 0.127a 0.088b 0.050b 0.109 4.357 0.018*
Transparency 0.178 0.125 0.160 0.162 0.179 0.837
Correlation matrix of water quality parameters
Covariation of the ten water quality variables assessed
showed predominantly negative correlations lacking any
meaningful statistical significance (Table 6). pH was
positively and significantly correlated with ammonia and
phosphate explaining 42 % and 30 % of variations among
those parameters. Temperature did not covary
significantly with ammonia concentrations and levels of
dissolved oxygen. Nitrite and phosphate were negatively
correlated but the strength of relationship was weak but
significant (R2 = -0.278, p < 0.05, n =55). TDS and
conductivity were highly correlated with each other.
Conductivity showed a strong positive correlation with TDS
(R2 = 0.9929, p < 0.05, n =55) and a moderate positive
correlation with salinity (R2 = 0.6657, p < 0.05, n =55)
explaining 99 % and 66 % respectively for the variations in
water quality (Table 6). Salinity was moderately correlated
with TDS (R2 = 0.6689, p < 0.05, n =55) accounting for
66% of the variations in water quality (Table 6). Other
water quality parameters showed weak and insignificant
correlations with each other.
Table 6: Covariation among water quality variables at p < 0.05 and p < 0.01 significant levels
Variables Nitrate Ammonia Phosphate pH DO Conductivity Temp TDS Salinity Transparency
Nitrate 1
Ammonia -0.251* 1
Phosphate -0.278** 0.063 1
pH -0.331** 0.415** 0.300** 1
DO 0.018 0.032 -0.110 -0.027 1
Conductivity -0.222 0.192 -0.159 0.092 -0.211 1
Temp 0.223 -0.089 -0.240* -0.298 0.191 -0.247** 1
TDS -0.233* 0.208 -0.158 0.108 -0.182 0.992** -0.254* 1
Salinity -0.243* 0.169 -0.159 0.143 -0.079 0.665** 0.061 0.668** 1
Transparency -0.037 0.006 -0.103 -0.155 -0.175 0.058 0.002 0.045 0.184 1
** Correlation is significant at the 0.05 level * Correlation is significant at the 0.01 level

Model Outputs from Path Analysis
Hydrographic and production factors constitute latent
variables that yield inputs into water quality manifest
variables. From the hypothesized path, hydrographic and
production factors each exert direct effects on water
quality but hydrographic factors can also influence water
quality indirectly through the structure of the production
system and its dynamics. The measured path (actual path)
gives estimates of the actual size of correlations between
latent and manifest variables. Hydrographic and
production factors were both negatively correlated with
water quality variables through direct and indirect effects.
Production factors had a dominant effect on water quality
(R2 = -0.821, p < 0.05, n = 55) explaining 82% of the
variations in water quality of the ponds investigated.
Production was inversely correlated with water quality
showing that an increase in production inputs reduced
water quality significantly. Direct hydrographic effects on
water quality were minimal as they could not explain a
large proportion of the variation in the water quality of the
ponds (R2 =- 0.1231, p < 0.05, n =55), but direct effects of
hydrography on production dynamics were large and
significant but also characterized by an inverse
relationship (R2 =-0.613, p < 0.05, n =55). Variation in
hydrographic factors accounted for 61 % of the variability
in production factors. (Fig.5).
Figure 5: Outputs from the actual path showing
correlations coefficients between groups of latent and
manifest variables
Model fit statistics
Table 7 shows the general fit of the canonical correlation
model reporting Pillais, Hotellings, Wilks, and Roys
multivariate criteria. We observe that all these values are
significant with p < 0.05. The canonical correlation
coefficients and the eigenvalues of the canonical roots
show that the first canonical correlation coefficient is 0.813
with an explained correlation variance of 61.01% and an
eigenvalue of 1.953, thus, indicating generally that fish
production factors are positively correlated with water
quality variables (Table 8). The significance of each of the
roots was tested. Among the four possible roots, only the
first root is significant with p < 0.05. Hence, we interpret
coefficients corresponding to only the first canonical
function. The unstandardized canonical coefficients are
interpreted in a manner similar to the coefficients in linear
regression and can be used to calculate the canonical
scores. However, interpreting the standardized canonical
coefficients is much easier as shown that stock age had
the strongest influence on the first canonical root (Table 8).
pH had the strongest influence on the first canonical
variate among the covariates while transparency had the
least influence.
Table 7: General fit of the canonical correlation model
Pillais 1.39897 2.36656 0.000
Hotellings 3.202 3.162 0.000
Wilks 0.132 2.772 0.000
Roys 0.661
Table 8: Eigenvalues and canonical correlations
Root
No.
Eigen
value
Pct. Cum. Pct. Canon.
Cor.
Sq.
Cor
1 1.953 61.007 61.007 0.813 0.661
2 0.956 29.865 90.873 0.699 0.488
3 0.214 6.709 97.582 0.420 0.176
4 0.077 2.417 100.000 0.268 0.071
Table 9: Canonical coefficients for multivariates
Variable Canonical Function I Canonical Function II
Standardized
Coefficients
Unstandardized
Coefficients
Standardized
Coefficients
Unstandardized
Coefficients
Nitrate -0.380 -1.327 0.194 0.678
Ammonia 0.326 0.699 0.207 0.445
Phosphate -0.020 -0.087 0.001 0.005
pH 0.500 0.546 -0.172 -0.188
Dissolved Oxygen 0.331 1.079 -0.202 -0.658
Conductivity 0.026 0.002 1.497 0.013
Temperature -0.075 -0.048 0.032 0.021
TDS -0.447 -0.008 -2.348 -0.043
Salinity -0.045 -0.682 -0.064 -0.959
Transparency -0.013 -0.049 0.096 0.342

Otoo et al. 039
Hydrographic, Production and Water Quality Loadings
Pond depth showed a very strong negative correlation with
hydrographic loadings (R2 = -0.929, p < 0.05, n =55) but
pond area had a weak effect, and accounted for 92% of
the collective effect of hydrographic loadings (Fig. 6).
Stock age had the largest influence on production, exerting
a negative but strong correlation with production loadings
(R2 =-0.921, p < 0.05, n =55), and explaining 92% of the
variations in production loadings (Fig. 6). Ammonia, pH
and nitrite produced strong effects on water quality. Nitrite
had a moderately negative correlation with water quality
(R2 =-0.592, p < 0.05, n = 55) while ammonia was positive
and highly correlated with water quality (R2 =0.682, p <
0.05, n =55). Nitrite and ammonia thus explained 59 %
and 68 % of the variation in water quality loadings
respectively (Fig. 6). Effect of other variables on water
quality was significantly low.
Figure 6: Loadings for various constructs
DISCUSSION
The results obtained from the study indicate there are
predominantly large defects in the water quality of
commercial fish ponds used to rear culture species. A
prevalence of suboptimal conditions under which fish are
commercially cultured for consumption implies that rate of
fish production may be hampered by the stressful growth
environment in which the fish live. Both catfish and tilapia
ponds were characterized by low average DO (< 4 mgL-1),
high temperatures and elevated concentrations of
ammonia and phosphates. Algal blooms occurred in most
of the ponds and persisted through the entire production
cycle. The high levels of ammonia and phosphates
combined with high temperatures may accelerate
biological uptake of these dissolved limiting nutrients
required for photosynthetic activity. Nitrogen and
phosphorus compounds are critical for algal growth and
may explain the prevalence of algal blooms in most ponds
investigated. pH ranged between moderate alkalinity to
low acidity. The low fluctuations in pH suggests good
buffering capacity of the pond water despite sharp diurnal
changes in carbon dioxide concentrations arising from
photosynthesis and respiration processes which regulate
the pH and its effect on ammonia dynamics. Detrimental
effects of the poor water quality on fish health may be
reflected in increased stress and susceptibility to diseases,
infections and mortality which ultimately affects the
efficient and profitable production of fish (Isyiagi et al.,
2009). This is confirmed by the high mortality of catfish
observed in ponds with algal blooms, and may be evident
of low amounts of DO during the late night to early morning
periods where respiratory activities of organisms consume
the available DO and renders DO concentrations below
tolerable limits.
Pond area and pond depth both constitute important
hydrographic factors that influence the physical properties
of pond ecosystem.. The studied ponds were generally of
small sizes, even though there is no general restriction on
the size of ponds used in aquaculture production. On the
basis of the fact that the ponds are used for commercial
purposes rather than subsistence, they can be
characterized as small compared with ponds in other
geographical regions used primarily for commercial
production. The choice of pond size is entirely based on
the discretion of the farmer, but is mainly influenced by
expected production outputs of the farm within the
economic and resource constraints of the farmer’s planned
operations.
Extremely high stocking rates were found which exceeded
the carrying capacities of the small ponds overstocked by
200 % on average above recommended stocking rates
(Gindaba and Mulugeta, 2017). For example, a 217 m2
earthen pond was stocked with 4567 catfish far in excess
of the recommended stocking rates of 8-9 individuals/m2
for catfish and 3-5 individuals/m2 for tilapia respectively
(Isyiagi et al., 2009). Pond water depths were remarkably
shallow in the aquaculture ponds, a pervasive
phenomenon which can be considered as a key
management decision that characterizes the industry. The
reason given by the farmers for maintaining such high
production intensities in extremely shallow ponds is to
ease harvesting, without cognizance of the serious and
deleterious consequences of combining shallow water
depths and high tropical temperatures with high stocking
densities on water quality in the ponds. The average pond
water depth of 0.52 ± 0.23 m for catfish and 0.49 ± 0.16 m
for tilapia contrasts the recommended depths for grow-out
aquaculture production of 1.0 – 1.5 m or 1.0 – 2.0 m (Fynn,
2015). This phenomenon indicates that aquaculture
farmers and perhaps water quality managers have a poor
understanding of the importance of greater pond water
volumes on dilution of potentially toxic concentrations of
dissolved constituents such as ammonia, nitrite, carbon
dioxide and hydrogen sulphide. Furthermore, high
temperature conditions prevailing in the tropics does not
favour water retention in the shallow ponds due to the rapid
water loss from high evaporation rates. Deeper ponds of
1-2 m, compliant with standard pond metrics will among
other benefits hold larger volumes, increase dilution of

toxic substances and thus minimize toxicity, stabilize pond
against daily temperature extremes, reduce wind-driven
diffusion of ammonia and hydrogen sulphide from pond
sediments into the overlying water column and improve
water quality.
The study also revealed that management decisions
regarding production inputs were the primary causes of
water quality in the aquaculture ponds. Stocking densities
were high, relative to pond size as explained in the
hydrographic context of the water quality dynamics. High
stocking densities necessitates high feeding rates which
leads to excretion of large amounts of metabolic waste
such as ammonia by fish. The primary source of ammonia
in waste excreted by fish is dietary protein, which is
metabolized to build muscle tissue and produce energy
(Hargreaves and Tucker, 2004). Ammonia may also be
produced from decomposition of organic matter such as
dead algae and feed waste from the pond sediment. Rapid
diffusion of ammonia from the sediment into the overlying
water column may be facilitated by the shallow pond water
depth, high temperatures and wind-generated
disturbances to the pond bottom (Delince, 1992). High
stocking densities also lead to elevated respiration rates
and greater consumption of DO and release of toxic
metabolites. The low average DO concentrations (< 4 mgL-
1) suggest that fish are consistently living in an oxygen-
stressed environment. Low DO concentration is inimical to
fish as it suppresses fish feeding ability, increases
susceptibility to infection and diseases and limits the
efficiency of feed conversion (El- Sayed, 2002). Reduced
feeding rate due to oxygen stress results in deposition of
large quantities of feed waste at the pond bottom through
rapid sedimentation over the shallow water column. Thus
water quality may be impaired through decomposition of
organic matter and release of toxic by-products. .
In terms of the contribution of feed type and its nutritional
composition on water quality, three main feed types were
found to be fed to fish; raanan, wheat bran and local feed.
Raanan is a commercially produced, formulated, pelleted
fish feed whose nutrient composition is known. Pelleted
feeds have high hydrostability, flotation decreased settling
rateof uneaten feed to the pond bottom (Sørensen, 2012).
This type of feed thus contributes little to sediment
decomposition of organic matter and production of
ammonia from feed waste. However, Raanan was
positively and significantly correlated with conductivity,
TDS and salinity in the ponds but values were all within the
acceptable limits of pond water quality standards. The
study therefore found no evidence of water quality
deficiencies resulting from feeding fish with Raanan. The
second type of feed, wheat bran is a composite feed
prepared by the farmers themselves and contains wheat,
oyster shells, palm kernel, soybean (rich source of
protein). It has lower hydrostability properties and rapidly
settles to the pond bottom. Wheat bran was positively and
significantly correlated with high phosphates in the ponds
(p < 0.05) in both tilapia and catfish ponds and could
possibly explain the persistent algal blooms in the fish
ponds. Algae utilize phosphate as a nutrient source in the
production of organic matter, and as a limiting nutrient in
algal growth has a threshold concentration of 0.01 PmgL-1
(Reynolds 2007). The excessive phosphate
concentrations could account for the dense algal growths
in the ponds. Phosphates may originate from agricultural
fields in the watershed of the ponds investigated and may
be carried by run-off into surface water bodies used as
source water for the ponds. Phosphates are known to
contribute prominently to eutrophication in aquatic
systems due to the important role they play in algal growth
(Hussein, 2012). The high algal biomass may result in
high quantities of dead sedimenting algae to the pond
bottom, decomposing and contributing to water quality
deterioration through the production of toxic metabolites
which are released back into the water column. The wheat
bran may however contribute indirectly to ammonia
production through feeding and excretion of fish. The third
type of feed is the local feed which is also prepared locally
by farmers with varying composition of different feed
sources. It is usually a mixture between proportions of
Raanan and other nutrient sources such as soybean and
oyster shells. These feeds do not undergo heat treatment
and pelletization and therefore like wheat bran, has lower
hydrostability. This type of feed was found to have no
negative effect on water quality.
Stock age influenced the concentrations of ammonia, pH
and DO. Rate of excretion of metabolites such as
ammonia, is a direct function of feeding rate and both
processes are known to increase linearly with increasing
body size (Ip et al., 2007). Fish growth increases as
feeding rates increase resulting in the high amounts of
ammonia excreted. Older fishes are also likely to consume
greater amounts of dissolved oxygen to metabolize the
feed ingested which explains the effect of stock age on
variations in ammonia. pH also increased with stock age
as ammonia production increased. The utilization of
ammonia by algae as a nutrient during photosynthesis
directly elevates pH of pond water when consumption of
large amounts of carbon dioxide leads to an increase in pH
typically in the afternoon. Growing fishes may respire more
at night thereby increasing the carbon dioxide
concentration, lowering pH and increasing acidity.
The Path Model predicted that production inputs would
dictate water quality in aquaculture ponds, which is in
agreement with the assertions of Boyd and Tucker (1998).
According to the model, 82 % of variation in water quality
is attributed to the production inputs. The actual path gives
estimates of the actual size of correlations between latent
and manifest variables. Even though PLS-Path Model has
its roots in the social sciences, it has gained an
increasingly popular role in empirical research from other
disciplines such as medicine (Berglund et al., 2012),
engineering and sustainability (Hussain et al., 2018), and

Otoo et al. 041
environmental science (Brewer et al., 2012; Kumar et al.,
2015; Javari, 2015). This is mainly because the technique
imposes little demand on sample sizes, measurement
scales, and residual distributions, unlike other causal
models that involve latent variables. Several metrics have
been provided (Table 8) as an overall summary of the
structural model. The coefficients of determination (R2) of
the endogenous latent variables (Production and Water
Quality) suggest that hydrographic indicators explain
about 38% and 57% of the variance in production and
water quality respectively. The mean redundancy for
production and water quality implies that hydrographic
indicators predict about 7% and 10% of the variability in
production and water quality indicators respectively. The
final measure of quality that was examined is the
goodness-of-fit index. This measure assesses the overall
prediction performance of the model by taking into account
the communality and coefficients of determination. The
goodness-of-fit index (Table 7) suggests that the
prediction power of the fitted Path Model is about 32%
which is quite significant.
Water quality is the most important factor affecting fish
health and performance in aquaculture production
systems (Blancheton et al., 2013). The path model
developed in this study has significant practical
implications for water quality management in aquaculture.
For instance, the model results (Table 7) indicate a direct
negative relationship between hydrographic factors (pond
area and pond depth) and water quality. This negative
relationship implies that water quality in aquaculture ponds
can be enhanced by paying attention to pond area and
pond depth. A bigger pond area and high stocking rates
may result in a decrease in water quality. The model
results also indicate a direct negative relationship between
fish production factors (stock density, mortality, feed type,
feeding frequency and stock age) and water quality, which
means that an increase in stock density, mortality, feeding
frequency and stock age can lead to a decrease in water
quality in aquaculture ponds.
CONCLUSION
The study revealed that aquaculture production of Nile
tilapia and African catfish in Sunyani is characterized by
small, shallow and overstocked culture ponds. The major
water quality problems found were low average DO (< 4
mgL-1), high temperatures, high ammonia, phosphates
and algal blooms, which could retard fish growth and
productivity due to the inhospitable water quality
environment. Reduced pond depth may promote the
release of ammonia from the sediment into the overlying
shallow water column, from excessive feeding rates
arising from the high stocking densities. Levels of
dissolved oxygen was within range but ammonia
concentrations were lethal to fish growth, and most of the
ponds had abnormally dense populations of algae
potentially releasing toxins into the pond with
consequences for fish growth and consumers due to the
risk of bioaccumulation. Production inputs such as feed,
stocking density and feeding frequency had very high
impact on water quality. Standard stocking rates, feeding
rates and construction of deeper ponds, together with
regular monitoring, are recommended to improve water
quality conditions in ponds.
REFERENCES
Adhikari S (2003).Fertilization, soil and water quality
management in small-scale ponds. The Gher
Revolution, 6.
Alemu K (2003). The growth performance of Oreochromis
niloticus L. reared in fresh water ponds loaded with
varying levels of poultry manure. SINET: Ethiop. J.
Sci, 26(1), 17-23.
Berglund E, Lytsy P, Westerling R (2012). Adherence to
and beliefs in lipid-lowering medical treatments: A
structural equation modeling approach including the
necessity-concern framework. Patient Education and
Counseling, 91(1), 105-112.
Bhatnagar A, Jana SN, Garg SK, Patra BC, Singh G,
Barman, (2004).Water quality management in
aquaculture. Course Manual of summerschool on
development of sustainable aquaculture technology in
fresh and saline waters, CCS Haryana Agricultural,
Hisar (India), 203, 210.
Blancheton JP, Attramadal KJK, Michaud L d’Orbcastel,
ER, Vadstein, O (2013). Insight into bacterial population
in aquaculture systems and its implication. Aqua. Eng,
53, 30-39.
Boyd CE (1990). Water quality in ponds for aquaculture.
Auburn University, Alabama.
Boyd CE (1995). Bottom Soils, Sediment, and Pond
Aquaculture. Chapman and Hall, New York, 348 pp. 38
Boyd CE (2017). General relationship between water
quality and aquaculture performance in Ponds. In Fish
Diseases (pp. 147-166). Academic Press.
Boyd CE and Tucker CS (1998). Pond Aquaculture. Water
Quality Management. Kluwer Academic Publishers,
Boston, Massachusettes, pp.700.
Brewer TD, Cinner JE Fisher, R Green, A, Wilson SK
(2012). Market access, population density, and
socioeconomic development explain diversity and
functional group biomass of coral reef fish
assemblages. Global Environmental Change, 22(2),
399-406.
Delince G (1992). The ecology of the fish pond ecosystem
with special reference to Africa .Text book, Kluwer
Academic Publishers, 230 pp.
Devi D and Bhatnagar A (2013). Water quality guidelines
for the management of pond fish culture. International
Journal of Environmental Science, 3(6):1980-1997.
El‐Sayed AFM (2002). Effects of stocking density and
feeding levels on growth and feed efficiency of Nile
tilapia (Oreochromis niloticus L.) fry. Aquaculture
research, 33(8), 621-626.

Fynn IEM (2015). Pond aquaculture spatial distribution,
production and productivity determinants in Ghana
(Doctoral dissertation, Virginia Tech).
Gindaba A, Getahun A, Wakjira M (2017). Growth
Performance and Yield of Nile Tilapia (Oreochromis
niloticus (Linnaeus, 1758) under Different Stocking
Densities and Fertilization Scenarios in Earthen Pond
Culture.
Hargreaves JA and Tucker CS (2004). Managing
ammonia in fish ponds (Vol. 4603). Stoneville, MS:
Southern Regional Aquaculture Center.
Hussain S, Fangwei Z, Siddiqi A, Ali Z, Shabbir M (2018).
Structural Equation Model for Evaluating Factors
Affecting Quality of Social Infrastructure Projects.
Sustainability, 10(5), 1415.
Hussein MS (2012). Effect of organic and chemical
fertilization on growth performance, phytoplankton
biomass and fish production in carp polyculture system
Egypt. J Aquat Biol Fish, 16(2), 133-143.
Ip YK, Chew SF, Randall DJ (2001). Ammonia toxicity,
tolerance, and excretion. Fish physiology, 20, 109-148.
Isyagi NA, Veverica KL, Asiimwe R, Daniels WH (2009).
Manual for the commercial pond production of the
African catfish in Uganda. Kampala, 222.
Javari M (2015). A study of impacts of temperature
components on precipitation in Iran using SEM-PLS-
GIS. Journal of Earth Science & Climatic Change, (S3),
1.
Keremah RI, Davies OA, Abezi ID (2014). Physico-
chemical analysis of fish pond water in freshwater Area
of Bayelsa State.Nigeria. Greener Journal of Biological
Sciences, 4(2):033-038.
Kumar Gorai A, Tuluri, F, Tchounwou, PB (2015).
Development of PLS-path model for understanding the
role of precursors on ground level ozone concentration
in Gulfport, Mississippi, USA. Atmospheric Pollution
Research.
Ministry of Fisheries and Aquaculture Development
(2015). Aquaculture yearly report. Retrieved from
http://www.mofad.gov.gh
Parven M, Hossain MB, Rahman MF, Jalal KCA, Jahan N
and Amin SMN (2013). Limnological Parameters
Affecting Monthly Abundance of Chironomid Larvae in
a Fish Pond and Their Role in the Diet of Catfish,
Clarias batrachus. J Biol Sci 13:1-9.
Reynolds CS (2007). Variability in the provision and
function of mucilage in phytoplankton: facultative
responses to the environment. Hydrobiol 578(1), 37-
45.
Rurangwa E, Agyakwah SK, Boon H, Bolman B (2015).
Business cases for aquaculture investment in
Ghana. Development of aquaculture in Ghana.
Analysis of the fish value chain and potential business
cases. IMARES Report C, 21, 1-9.
Sanchez G (2013). PLS path modeling with R. Berkeley:
Trowchez Editions, 383, 2013.
Schwartz M F, and Boyd CE (1994). Effluent quality during
harvest of channel catfish from watershed ponds. The
Progressive Fish‐Culturist, 56(1), 25-32.
Shoko AP, Limbu SM, Mrosso HD, Mgaya YD (2014). A
comparison of diurnal dynamics of water quality
parameters in Nile tilapia (Oreochromis niloticus,
Linnaeus, 1758) monoculture and polyculture with
African sharp tooth catfish (Clarias gariepinus, Burchell,
1822) in earthen ponds. International Aquatic
Research, 6(1), 56.
Shoko AP, Matola HD, Mzighani S, Mahika, GC (2011).
Fishpond performance of Nile tilapia Oreochromis
niloticus (Linnaeus, 1758) in the Lake Victoria basin,
Tanzania. Journal of Aquaculture in the
Tropics, 26(1/2), 1.
Siddiqui AQ, Howlader MS, Adam AE (1991). Effects of
water exchange on Oreochromis niloticus (L.) growth
and water quality in outdoor concrete tanks.
Aquaculture, 95(1-2), 67-74.
Sørensen M (2012). A review of the effects of ingredient
composition and processing conditions on the physical
qualities of extruded high‐energy fish feed as measured
by prevailing methods. Aquaculture nutrition, 18(3),
233-248.
Accepted 10 August 2019
Citation: Otoo NEG, Sanful PO, Iddrisu WA, Amfoh S,
Boateng OK (2019). Understanding Water Quality
Dynamics in Aquaculture Ponds in Sunyani, Ghana:
Insights from Partial Least Squares (PLS) - Path Modeling.
Journal of Fisheries and Aquaculture Research, 4(1): 028-
042.
Copyright: © 2019 Otoo et al. This is an open-access
article distributed under the terms of the Creative
Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium,
provided the original author and source are cited.

Understanding Water Quality Dynamics in Aquaculture Ponds in Sunyani, Ghana: Insights from Partial Least Squares (PLS) - Path Modeling

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Understanding Water Quality Dynamics in Aquaculture Ponds in Sunyani, Ghana: Insights from Partial Least Squares (PLS) - Path Modeling

Similar to Understanding Water Quality Dynamics in Aquaculture Ponds in Sunyani, Ghana: Insights from Partial Least Squares (PLS) - Path Modeling (20)

More from Premier Publishers

More from Premier Publishers (20)

Recently uploaded

Recently uploaded (20)

Understanding Water Quality Dynamics in Aquaculture Ponds in Sunyani, Ghana: Insights from Partial Least Squares (PLS) - Path Modeling