Is Water Oxidation-Reduction Potential (ORP) value relevant for aquaculture applications in shrimp farming?

. Intensive monitoring is essential for shrimp health and productivity, but current water management in shrimp farming is time-consuming due to the need to monitor various parameters. Oxidation-reduction potential (ORP) is a potential water quality summary parameter for shrimp farming, as it's sensitive to water chemistry changes. This study assessed ORP as a summary parameter. Real-time ORP data was collected from a pond in Cipatujah, Tasikmalaya, from September to October 2022. The physical, chemical, and biological water quality data and treatment dosage data were collected to analyze the changes in ORP value over time. The standard deviation for ORP was below 1% for hourly averaging, below 12% for daily averaging, and below 23% for weekly averaging. However, no direct correlation was found between ORP and other water quality parameters. While ORP changed before and after some treatments, it couldn't reliably determine preferred water management conditions due to scattered information. Thus, the current approach of analyzing several parameters remains preferred, as ORP didn't show significant patterns. This suggests that more research is needed to explore alternative approaches for intense yet rapid water quality monitoring in shrimp farming.


Introduction
Indonesia stands out as one of the top shrimp producers in Asia, with a particular emphasis on the farming of Litopenaeus.vannamei, the most widely farmed shrimp species in the country.This species is widespread due to its commercial value and suitability for regional aquaculture operations.The Indonesian government has set ambitious goals for shrimp production, aiming to reach a national production target of 2 million tons of vannamei shrimp by 2023 [1].Achieving such a target requires high productivity levels throughout the year.However, shrimp farming poses inherent risks and rewards due to the species' sensitivity to environmental changes, particularly water quality.Shrimp farming heavily relies on water quality, as it plays a crucial role in the growth and survival of shrimp.Therefore, maintaining optimal water quality conditions is crucial to ensure the health and productivity of shrimp farms [2].
Currently, water quality monitoring practices in Indonesia primarily involve analyzing data collected daily and weekly or through visual inspections.These methods often require the analysis of multiple parameters and the examination of correlations among them.These methods may have limitations regarding their timeliness, efficiency, and ability to provide real-time insights into water quality variations.While this approach may be feasible for monitoring a small number of ponds, it becomes challenging to implement on a larger scale when numerous ponds are involved.Moreover, there is a growing need for more rapid and comprehensive water quality monitoring techniques that can efficiently cover a larger number of ponds with more advanced and rapid water quality monitoring techniques that can support proactive decision-making and improve overall shrimp farming practices in Indonesia [3].
The ORP (oxidation-reduction potential) value represents the tendency of a substance to undergo electron transfer.Its value can be used to predict the tendency of reductionoxidation (redox) reactions.Reading the ORP value is highly beneficial in the superintensive culture of vannamei shrimp, especially in quickly assessing the farming conditions.This information can serve as a practical reference for making decisions regarding pond management, such as determining the need for water exchange, aeration, probiotic activities, or assessing the quality of the water inlet [4].In this context, The ORP value shows a promising potential parameter for summarizing water quality status in shrimp farming.Having a single parameter that can serve as a summary of water quality status would allow for more efficient decision-making and rapid action in response to changes in water quality.
ORP value is closely related to the oxygen concentration in water, which is a critical factor in maintaining water quality for shrimp.Other than that, it can indicate the presence of potentially harmful substances, such as ammonia and nitrite, which can accumulate in shrimp ponds and negatively affect shrimp health.ORP value can also be used to monitor the effectiveness of water treatment processes, such as aeration and filtration systems, commonly used in shrimp farming to maintain water quality [5].
The ORP value is widely recognized for its application in pond soil sediment, primarily for liming purposes.However, this study specifically focuses on measuring the ORP of water in shrimp farming, particularly in semi-intensive to intensive systems, as a potential indicator for rapid monitoring of shrimp farming conditions.Notably, while the application of ORP value in shrimp farming is well-established for super-intensive systems, there is still limited exploration for its implementation in other systems.Given that extensive to intensive systems are widely implemented in Indonesia, further investigation is needed to assess the feasibility and effectiveness of ORP as a monitoring tool in these systems.During the culture, the initial water source salinity of approximately 25 ppt dropped to 5 ppt due to continuous heavy rainfall.The culture systems employed a plankton-bacteria balancing approach, with specific attention given to treatments such as probiotic (Bacillus and Lactobacillus) addition, mineralization, enzyme application, feed additive support, and routine water circulation.

Data collection
The physical, chemical, and biological data were collected daily and weekly, serving as the primary dataset for further analysis of the observed ORP values.Daily physical data included water transparency measurements, temperature, dissolved oxygen, pH, and salinity.Weekly chemical data encompassed ammonium, nitrite, phosphate, calcium, magnesium, total alkalinity, and organic matter.In addition, weekly biological data were gathered, including total bacteria count (TBC), total vibrio count (TVC), and plankton abundance and diversity assessment.The treatment dosage data were also collected to examine the changes in ORP values before and after each treatment application.
The transparency data was collected using a Secchi Disk.The temperature and dissolved oxygen (DO) were measured using a DO Meter AZ 8403.The pH was determined using a pH meter HANNA HI-9871, and salinity was measured using an Atago Salinity Refractometer Master.Water ammonium, nitrite, and phosphate levels were analyzed using test kits in a commercial laboratory.Calcium, magnesium, total alkalinity, and total organic matter (TOM) data were obtained through laboratory analysis performed by a commercial laboratory using titration methods.Bacteria and vibrio counts were determined using the Total Plate Count (TPC) method in a commercial laboratory.Plankton data were collected using a counting chamber.

Data analysis
The collected ORP data were analyzed by averaging the values at hourly, daily, and weekly intervals to assess the most suitable interval for measuring ORP.The standard deviation between these intervals was examined to determine the optimal frequency of ORP measurements.Moreover, the maximum and minimum values of the coefficient of variation were determined using the following equation to provide a comprehensive understanding of the variability in the data. ( Where: : coefficient of variation  : standard deviation of population  : average of population The ORP values were compared with other water quality parameters through graphical representations and descriptive analysis to investigate direct correlations.Furthermore, the ORP values before and after treatment were compared to evaluate the sensitivity of ORP in responding to water treatment.

Results and discussion
The ORP data were collected every minute for 60 days in Pond B1, resulting in 86,400 data points.The data were grouped and analyzed based on hourly, daily, and weekly intervals, and the average and standard deviation were calculated for each group.The coefficient of variance was then determined for each group.
The results in Table 1 demonstrate that the minimum and maximum coefficients of variance values increase as the group size becomes larger.The minimum coefficient of variance values for the hourly, daily, and weekly data groups was 0.02%, 0.98%, and 2.93%, respectively.These values indicate the significance of the differences within each group, with the minimum variance coefficient representing the smallest variability level.It is worth noting that these values are considered low, given that the ORP values of the pond water typically consist of three digits.The maximum coefficients of variance for the hourly, daily, and weekly data groups were found to be 0.83%, 11.03%, and 22.32%, respectively.It is important to acknowledge that the maximum coefficients of variance for the daily data group exceed 10%, indicating a significant spike in variability within that specific interval.This observation highlights the potential daily fluctuations or deviations in the ORP values, warranting further investigation and consideration in water quality management.
Each line represents the ORP data points for a single day, showcasing the range of ORP values from 97 to 250 mV.The graph demonstrates the fluctuating nature of ORP throughout each day, highlighting the variability in water quality conditions over the 60 days.
Each data point on the graph represents the average ORP value calculated for a specific day.The graphs allow for comparison with other water quality parameters.The transparency data exhibits an inverse relationship with the ORP data, indicating that higher transparency values are associated with lower ORP values and vice versa.Additionally, when the ORP values remain constant, the water transparency values remain constant.However, the ORP value exhibits no clear trend or inverse relationship with the other parameters.
The daily data interval is more suitable for analysis than the weekly interval as it allows for a more detailed examination of fluctuations and patterns.The broader time frame of the weekly data may not capture the full extent of variability, leading to a less distinct pattern.
Furthermore, it is important to note that this research's analysis primarily focuses on visual observation and trend identification through graph plotting.The available data may not be sufficient for conducting advanced statistical analyses, such as multiparameter correlational analysis, due to limitations in the dataset.Various treatments were implemented at Pond B1, including feed, liming, minerals, enzymes, probiotics, and various combinations of these treatments.The ORP values were collected before and after each treatment.The post-treatment data were obtained at the maximum or minimum point reached after the treatment application, with the time of checking the ORP value varying depending on the treatment application time and the point of maximum or minimum value.
The analysis revealed an average difference of 2.43 mV in the ORP values after treatment compared to before treatment.This indicates an average change of 2.43 mV in the ORP value.The maximum change observed was 193 mV, while the minimum was 0.03 mV.These results demonstrate a wide range of ORP value fluctuations following the treatments.To utilize ORP value as a rapid monitoring strategy for water quality management, it is crucial to consider the interval of ORP measurements carefully.If the ORP value does not exhibit significant changes during certain intervals, it may not provide valuable insights into the overall pond condition.For instance, the salinity parameter changes slowly as it is influenced by rainfall, evaporation, or recirculation factors.Therefore, it may not be necessary to monitor salinity as frequently as parameters like dissolved oxygen (DO) or pH, which exhibit diurnal variations due to the influence of sunlight [2].
Based on the results presented in Table 1, the hourly fluctuations in ORP values were not found to be statistically significant.The maximum and minimum values of the coefficient of variation remained below 1%, indicating that the changes in ORP values were within the range of a single-digit difference.Since ORP values are typically represented as three-digit numbers, these fluctuations can be considered insignificant regarding practical implications for water quality management.
The lack of significant changes in ORP values within an hourly interval is surprising, considering that water quality parameters, including those influenced by diurnal variations and the application of treatments, are expected to undergo notable fluctuations within such a timeframe.However, the daily and weekly averages analysis reveals that the ORP values can exhibit significant changes, with maximum variations exceeding 10%.This suggests that the ORP value can demonstrate noticeable fluctuations over a day or week.
ORP, which stands for oxidation-reduction potential, refers to the tendency of a chemical substance to oxidize or reduce another substance [6].In water quality, various parameters in the water system interact to establish a specific redox potential [7].Therefore, it is crucial to examine the correlation between ORP and other water quality parameters to understand how the ORP value can reflect changes in water quality.
The measurement of ORP utilizes an inert metal electrode that either releases electrons to oxidizing agents or accepts electrons from reducing agents.This electrode continues the electron transfer process until a potential is established.The ORP measurement utilizes a silver-silver chloride reference electrode, similar to a pH meter [6].t should be noted that a certain offset level in the ORP measurement is acceptable, given that the values measured often consist of three to four digits.
The analysis of ORP data plotted against other water quality parameters reveals that there is no direct correlation between them.While single data analysis may display a scattered pattern, a multiparameter analysis could provide better insights into the ORP data.This is because ORP is influenced by various chemical reactions within the pond [8,9].
The analysis of treatments also revealed a scattered pattern, with an average difference of approximately 2.43 in ORP values.These changes are relatively low, below 1% of the actual ORP value.However, it is important to note that there were notable variations, with the minimum difference at 0.03 mV and the maximum at 193 mV.This indicates a complex pattern in understanding the changes in ORP values following the application of various treatments.
According to the article "Fundamentals of ORP Measurement" by Emerson Process Management in 2008, it is noted that the ORP value may not be a suitable method for directly measuring concentration due to its logarithmic relationship with concentration.Therefore, when using ORP as a measurement parameter, it is important to have a specific target value in mind rather than relying on an uncertain value of measurements [6].

Conclusion
In conclusion, applying ORP value as a rapid monitoring strategy for water quality management in shrimp farming presents challenges and opportunities.While the hourly fluctuations in ORP values were not statistically significant, the daily and weekly averages analysis revealed noticeable changes, indicating the potential for capturing variations in water quality over more extended time frames.The lack of direct correlations between ORP values and other water quality parameters suggests that ORP should be considered a complementary parameter rather than a sole indicator of water quality.In practical terms, ORP value is better suited for controlling and monitoring specific systems rather than being the sole parameter for interpreting overall pond dynamics.It is important to acknowledge that ORP data has limitations in providing comprehensive insights into the broader phenomena occurring in the pond.

Table 1 .
Coefficient of variation analysis for ORP data in Pond B1.

Table 2 .
Sample of Changes in ORP Values Before and After Various Treatments at Pond B1.