John W. M. Bastiaansen

Farmers face challenges in improving the production and profitability of their tilapia and shrimp polyculture ponds. These challenges stem from the use of low input systems, limited access to technical knowledge, and the complexity of species interactions. While most tilapia and shrimp polyculture experiments have focused on stocking strategies and harvest outcomes, the dynamics of species interactions remain underexplored. Here, we developed a multispecies bioeconomic model, PoleeBEM, that incorporates species interactions to project the production and profitability of tilapia and shrimp polyculture systems. The model was constructed using expert knowledge, insights from a scoping visit, and a literature review. By simulating feed intake and mortality patterns, the model projects daily growth and mortality rates based on input parameters such as the biological characteristics of tilapia and shrimp and key management decisions. The model then calculates economic parameters, such as revenue, operating costs, and operating profit per square meter per year. To evaluate model performance, we conducted sensitivity analyses for the main input parameters. A 10–50% variation of daily mortality parameters has minimal effects on model outcomes, demonstrating robustness. Stocking densities and feeding rate levels were varied to explore their impact on production and profitability performance. Validation through expert discussions confirmed that most projected outcomes for tilapia and shrimp were within realistic ranges, with the exception of projections of shrimp harvest weight and tilapia cumulative mortality. We conclude that the multispecies bioeconomic model, PoleeBEM, is applicable as a tool for projecting the performance and profitability of tilapia and shrimp polyculture ponds for smallholder farmers in brackish water areas.

Production in smallholder tilapia and shrimp polyculture ponds can be projected using PoleeBEM, a multispecies bioeconomic model that incorporates interspecies interactions. To ensure its reliability, model performance must be evaluated using field data. The aim of this study was to evaluate the performance of PoleeBEM with data from the field and to identify directions for adjusting the biological coefficients of the model to support future model improvement. Recalled data from farmers in Tayu, Pati, Central Java were assessed and used for simulation. Projected outputs from the model, including harvest weight and survival rate of tilapia and shrimp, were compared with the values recalled by the farmers. Differences between recalled and projected harvest weight and survival rates were evaluated using error indices. The results showed that harvest weights of both tilapia and shrimp were severely underestimated, with percent bias values of -32.9% and -83.6%, respectively, whereas survival rates were overestimated by 24.7% for tilapia and 448.0% for shrimp. These systematic differences suggested potential mis-parameterization of biological coefficients governing growth and mortality dynamics. Based on these differences, a one-at-a-time sensitivity analysis was conducted to identify influential biological coefficients and to assess whether changes in coefficient values could move model outputs toward recalled farmer values. The analysis revealed that growth-related coefficients of tilapia influenced tilapia harvest weight, while growth-related coefficients of both tilapia and shrimp affected shrimp harvest weight. Growth coefficients, together with mortality-related coefficients, also influenced survival rates for both species. Trends of sensitivity analyses were evaluated against recalled farmer outputs to determine the direction of coefficient adjustment required to reduce differences between projected and recalled outputs. The results indicated that decreasing growth-related coefficient values and increasing mortality-related coefficient values would increase harvest weight, while the opposite adjustments would reduce survival rates. These findings should be considered in future calibration of PoleeBEM.

Production in smallholder tilapia and shrimp polyculture ponds can be projected using PoleeBEM, a multispecies bioeconomic model that incorporates interspecies interactions. To ensure its reliability, model performance must be evaluated using field data. The aim of this study was to evaluate the performance of PoleeBEM with data from the field and to identify directions for adjusting the biological coefficients of the model to support future model improvement. Recalled data from farmers in Tayu, Pati, Central Java were assessed and used for simulation. Projected outputs from the model, including harvest weight and survival rate of tilapia and shrimp, were compared with the values recalled by the farmers. Differences between recalled and projected harvest weight and survival rates were evaluated using error indices. The results showed that harvest weights of both tilapia and shrimp were severely underestimated, with percent bias values of -32.9% and -83.6%, respectively, whereas survival rates were overestimated by 24.7% for tilapia and 448.0% for shrimp. These systematic differences suggested potential mis-parameterization of biological coefficients governing growth and mortality dynamics. Based on these differences, a one-at-a-time sensitivity analysis was conducted to identify influential biological coefficients and to assess whether changes in coefficient values could move model outputs toward recalled farmer values. The analysis revealed that growth-related coefficients of tilapia influenced tilapia harvest weight, while growth-related coefficients of both tilapia and shrimp affected shrimp harvest weight. Growth coefficients, together with mortality-related coefficients, also influenced survival rates for both species. Trends of sensitivity analyses were evaluated against recalled farmer outputs to determine the direction of coefficient adjustment required to reduce differences between projected and recalled outputs. The results indicated that decreasing growth-related coefficient values and increasing mortality-related coefficient values would increase harvest weight, while the opposite adjustments would reduce survival rates. These findings should be considered in future calibration of PoleeBEM.

Farmers face challenges in improving the production and profitability of their tilapia and shrimp polyculture ponds. These challenges stem from the use of low input systems, limited access to technical knowledge, and the complexity of species interactions. While most tilapia and shrimp polyculture experiments have focused on stocking strategies and harvest outcomes, the dynamics of species interactions remain underexplored. Here, we developed a multispecies bioeconomic model, PoleeBEM, that incorporates species interactions to project the production and profitability of tilapia and shrimp polyculture systems. The model was constructed using expert knowledge, insights from a scoping visit, and a literature review. By simulating feed intake and mortality patterns, the model projects daily growth and mortality rates based on input parameters such as the biological characteristics of tilapia and shrimp and key management decisions. The model then calculates economic parameters, such as revenue, operating costs, and operating profit per square meter per year. To evaluate model performance, we conducted sensitivity analyses for the main input parameters. A 10–50% variation of daily mortality parameters has minimal effects on model outcomes, demonstrating robustness. Stocking densities and feeding rate levels were varied to explore their impact on production and profitability performance. Validation through expert discussions confirmed that most projected outcomes for tilapia and shrimp were within realistic ranges, with the exception of projections of shrimp harvest weight and tilapia cumulative mortality. We conclude that the multispecies bioeconomic model, PoleeBEM, is applicable as a tool for projecting the performance and profitability of tilapia and shrimp polyculture ponds for smallholder farmers in brackish water areas.

This review critically evaluated the development of vision-based individual re-identification technologies in aquaculture as a potential alternative to PIT tags. While the non-invasive nature of vision-based methods is appealing, based on a review of 24 studies, we identify multiple limitations and challenges in current implementations regarding data acquisition, data analysis, and practical implementation. To address these gaps, we recommended a new aquaculture-centered flowchart emphasizing high-tech data acquisition, adapting multi-dimensional data quality frameworks, and benchmarking. Importantly, we call for a realistic view of technology's potential: while vision-based methods hold long-term promises, they are currently insufficient to replace PIT tags. Future advancements depend on coordinated efforts in dataset standardization, methodological refinement, and the alignment of model development with practical aquacultural systems.

The ability of the animal to cope with environmental changes may be measured by log-transformed variance of deviations from expected weights (LnVar). We calculate LnVar by fitting the expected individual growth curve based on longitudinal weights (LnVar _ind ) of Nile tilapia that were grown in either an aerated or a non-aerated freshwater pond. We estimated genetic parameters for LnVar _ind in Nile tilapia, the genetic correlation between LnVar _ind and growth and the genetic correlation for LnVar _ind between aerated and non-aerated pond. The heritability estimate for LnVar _ind (0.28) in the non-aerated pond was higher than in aerated pond (0.06). In the aerated pond, genetic correlations of LnVar _ind were − 0.44 ± 0.23 with daily growth coefficient (DGC) and − 0.45 ± 0.24 with harvest weight (W ₅ ). In the non-aerated pond, genetic correlations with DGC and W ₅ were − 0.68 ± 0.12 and − 0.52 ± 0.17, respectively. These values suggest that selection for fish with high growth rate will reduce LnVar _ind . However, genetic correlation of LnVar _ind between aerated and non-aerated pond was 0.50, suggesting that genetic improvement in the aerated environment will reduce LnVar _ind in the non-aerated environment. Therefore, incorporating records from relatives in non-aerated pond is beneficial for breeding programs targeting this environment.

The present study was applied to evaluate the effects of alternate feeding and feed restriction on gene expression, growth, proximate composition and biochemical indices in European seabass, Dicentrarchus labrax. Fish were randomly divided into six indoor tanks with 90 fish per tank in a recirculating aquaculture system. Two feeding strategies were applied, in which the first group was fed daily to satiation and the second was intermittently fed (8 days feeding to satiation–2 days starvation) for 40 days. At the end of the experiment, outlier fish were sorted as fast growers (FG) and slow growers (SG) according to their final body weight. The differential gene expression tested was related to glycolysis (pk, ldha, hk, g3pdh, eno1 and alda), fatty acid metabolism (lpl and acc) and cholesterol synthesis (7dhcr and sqle). In addition, muscle ldha and gpi expressions were positively correlated with fish weight. The concentrations of glucose, triglycerides, cholesterol and non-esterified fatty acids (NEFA) were not affected by the dietary treatments. Glucose and NEFA differed significantly between SG and FG fed groups. Overall, the physiological responses of glucose and fatty acid metabolism in fish, as recorded by gene expression assays, were triggered by minor interventions in feeding rather than the different growth rates. Expression of specific genes and biochemical parameters could be used as potential biomarkers to improve aquaculture practices and benefit fish husbandry through selective breeding, feeding strategies and farm management. The study provides new insights on the impact of intermittent feeding of European seabass, with gene markers and their potential effects, for European seabass aquaculture.

Ethiopia is a developing nation that could highly benefit from securing food via improved smallholder poultry farming. To support farmer and breeding decisions regarding which chicken strain to use in which Ethiopian environment, G*E analyses for body weight (BW) of growing male and female chickens were conducted. Research questions were (1) if a G*E is present for BW and (2) which strain performs best in which environment in terms of predicted BW. Analyses were performed using predicted BW at four different ages (90, 120, 150, and 180 days) of five strains (Horro, Koekoek, Kuroiler, Sasso-Rhode Island Red (S-RIR), and Sasso) tested in five Ethiopian regions (Addis Ababa, Amhara, Oromia, South Region, and Tigray) that are part of three Agro-Ecological Zones (AEZ) (cool humid, cool sub-humid, and warm semi-arid). The indigenous Horro strain was used as a control group to compare four other introduced tropically adapted strains. The dataset consisted of 999 female and 989 male farm-average BW measurements. G*E was strongly present (p < 0.001) for all combinations of strain and region analyzed. In line with previous research, Sasso was shown to have the highest predicted BW, especially at an early age, followed by Kuroiler. Horro had the lowest predicted BW at most ages and in most regions, potentially due to its young breeding program. The highest predicted BW were observed in Tigray, Oromia, and Amhara regions, which are in the main part of the cool sub-humid AEZ.

In precision dairy farming there is a need for continuous and real-time availability of data on cows and systems. Data collection using sensors is becoming more common and it can be difficult to connect sensor measurements to the identification of the individual cow that was measured. Cows can be identified by RFID tags, but ear tags with identification numbers are more widely used. Here we describe a system that makes the ear tag identification of the cow continuously available from a live-stream video so that this information can be added to other data streams that are collected in real-time. An ear tag reading model was implemented by retraining and existing model, and tested for accuracy of reading the digits on cows ear tag images obtained from two dairy farms. The ear tag reading model was then combined with a video set up in a milking robot on a dairy farm, where the identification by the milking robot was considered ground-truth. The system is reporting ear tag numbers obtained from live-stream video in real-time. Retraining a model using a small set of 750 images of ear tags increased the digit level accuracy to 87% in the test set. This compares to 80% accuracy obtained with the starting model trained on images of house numbers only. The ear tag numbers reported by real-time analysis of live-stream video identified the right cow 93% of the time. Precision and sensitivity were lower, with 65% and 41%, respectively, meaning that 41% of all cow visits to the milking robot were detected with the correct cow’s ear tag number. Further improvement in sensitivity needs to be investigated but when ear tag numbers are reported they are correct 93% of the time which is a promising starting point for future system improvements.

This study evaluated the effects of dietary macauba pulp on the growth performance, carcass, and pork traits of growing-finishing pigs; and whether differences in residual feed intake breeding values could influence the pigs’ growth responses to macauba pulp inclusion in the diet. A total of 282 (34.8 ± 4.40 kg) pigs (barrows and females), progeny of sires with high (HRFI) or low (LRFI) breeding value for residual feed intake, were pair-housed on the basis of sex, breeding value, and initial BW. Diets with (macauba) or without (control) of 50 g/kg of macauba pulp inclusion were randomly assigned to the experimental pens. There were eight treatment groups: HRFI barrows were fed the control diet; HRFI barrows were fed the macauba diet; LRFI barrows were fed the control diet; LRFI barrows were fed the macauba diet; HRFI female pigs were fed the control diet; HRFI female pigs were fed the macauba diet; LRFI female pigs were fed the control diet; and LRFI female pigs were fed the macauba diet. The trial lasted 90 days and was divided into four phases: growing phase 1 (G1) and 2 (G2); and finishing phases 1 (F1) and 2 (F2). There was no interaction between sex, breeding value, and diet for performance, carcass, and pork traits. Barrows had higher feed intake (ADFI), daily gain (ADG), feed conversion rate (FCR), and final body weight than female pigs. The breeding value had no effect on performance measurements. The inclusion of macauba pulp in the pigs’ diets did not affect any growth parameter during G1, G2 and F1 phases. However, reduced ADFI and improved FCR were observed in F2. Female pigs had lower backfat thickness (BF) and higher loin eye area (LEA) than barrows. HRFI pigs had higher hot carcass weight and LEA, and lower BF than LRFI pigs. There was no effect of macauba pulp inclusion on carcass traits. Pork from barrows presented lower Warner–Bratzler shear force and higher fat content than pork from the females. There was no effect of breeding value on pork traits. Pork from pigs fed the macauba diet showed lower moisture content and water-holding capacity. In conclusion, macauba pulp can partially replace corn without reducing the performance of pigs. Regardless of sex and breeding value for RFI, pigs responded similarly to macauba pulp inclusion in diets.