We propose stochastic models for predicting and analysing the production of tilapia (Oreochromis niloticus), lettuce (Lactuca sativa), and cucumber (Cucumis sativus) cultivated in an aquaponic system. Fish and plants were cultivated in a shade house using 30, 60, and 90 fish/m3 employing an NFT system. Results from Monte Carlo simulation showed that higher yields of tilapia and cucumber, as well as larger plant sizes, were obtained by stocking at the highest density (90 fish/m3). At this density, with 95% confidence, yields of tilapia varied from 39.60 to 59.26 kg/m3, the final length of lettuce leaves varied from 13.53 to 28.5 cm, the final length of cucumber plant varied from 119 to 235.3 cm, and biomass of cucumber varied from 0.98 to 0.99 kg/m2. Regression and sensitivity analyses showed that dissolved oxygen, density, temperature, and electrical conductivity significantly affected the production of tilapia; density, nitrites, pH, and temperature influenced lettuce production; ammonium, pH, and density affected the production of cucumber plants; and ammonium and density influenced yields of cucumber. The greatest certainty to achieve higher yields of large tilapia was found at low densities. For plants, there was more certainty of harvesting larger products when cultivated with tilapia stocked at the highest density. A preliminary economic analysis of tilapia production showed that net revenues ranged from USD$ 18.50 to 81.76 per system, and that the best results were obtained when using the highest stocking density. We conclude that the models are useful for predicting and analysing the production of an aquaponic system.
The production of Litopenaeus vannamei was analysed when affected by the acute hepatopancreatic necrosis disease using a dynamic stock model and primary data of seven production cycles from a shrimp farm in Mexico from 2013 to 2016. Significant results (p < .05) of the correlation analysis indicated that during those years mortalities by the disease were more severe when water salinity was high and productivity was low. Significant results from ANOVA showed that throughout the period, disease severity and salinity diminished while pond productivity initially declined but subsequently improved. Significant results from regression analyses conducted for each production cycle also indicated the importance of salinity and productivity on disease severity and showed that early mortality by the disease occurred in ponds with warmer water. Within the observed range of water quality parameters, increases of 1 cm in water transparency and 1 g/L in salinity resulted in increments within 0.17%–0.25% and 1.7%–3.1% in shrimp mortality by the disease. When increases of 1°C in water temperature were recorded, outbreaks occurred 0.2–1.57 weeks earlier. In conclusion, the disease strongly determines the dynamics of shrimp production, and the role of salinity, productivity and temperature is worthy of further delving.
This study uses a stochastic bioeconomic approach to estimate the COVID-19 pandemic economic impact on shrimp farming in Mexico. Seeding-harvesting schedules — March–June, May–August, and August–November — were analyzed using shrimp prices and production costs corresponding to 2017–2019 (pre-pandemic) and 2020 (pandemic). The analyses estimated net revenue varied within 597.97–2758.88 USD$ ha
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and 1262.40–1701.32 USD$ ha
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under the pre-pandemic and pandemic scenarios, respectively. Significant decreases (38%) were estimated in net revenue values in March–June and May–August under the pandemic scenario. However, probability distributions estimated that uncertainty on the expected net revenues was not affected by the pandemic conditions, and the probability of losing was null or negligible in all the cases. Unfavorable conditions under the pandemic also required significantly higher break-even production for March–June (25.7%) and May–August (28.5%) schedules. The cost of post-larvae was the most important economic factor influencing net revenue. To conclude, although the operating conditions during the pandemic were conducive to worsening the economic outcome, no evidence still exists that uncertainty and economic risk increased compared with pre-pandemic conditions.
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