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Keywords = agro–aqua cycle

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13 pages, 701 KiB  
Article
Transfer of the Neurotoxin β-N-methylamino-l-alanine (BMAA) in the Agro–Aqua Cycle
by Sea-Yong Kim and Sara Rydberg
Mar. Drugs 2020, 18(5), 244; https://doi.org/10.3390/md18050244 - 6 May 2020
Cited by 14 | Viewed by 3366
Abstract
The neurotoxic non-protein amino acid β-N-methylamino-l-alanine (BMAA) is connected to the development of neurodegenerative diseases. BMAA has been shown to accumulate in aquatic ecosystems, and filter-feeding molluscs seem particularly susceptible to BMAA accumulation. The blue mussels farmed along the [...] Read more.
The neurotoxic non-protein amino acid β-N-methylamino-l-alanine (BMAA) is connected to the development of neurodegenerative diseases. BMAA has been shown to accumulate in aquatic ecosystems, and filter-feeding molluscs seem particularly susceptible to BMAA accumulation. The blue mussels farmed along the Swedish coastline in the Baltic Sea are, due to their small size, exclusively used to produce feed for chicken and fish in the agro–aqua cycle. We have investigated the possible biotransfer of BMAA from mussels, via mussel-based feed, into chickens. Chickens were divided into two groups, the control and the treatment. BMAA was extracted from the muscle, liver, brain, and eye tissues in both chicken groups; a UPLC-MS/MS method was subsequently used to quantify BMAA. The results indicate detectable concentrations of BMAA in both chicken groups. However, the BMAA concentration in chicken was 5.65 times higher in the treatment group than the control group, with the highest concentration found in muscle tissue extracted from the treatment group chickens. These data suggest that there is a BMAA transfer route within the agro-aqua cycle, so further investigation is recommended before using mussel-based feed in the chicken industry. Full article
(This article belongs to the Special Issue Structure–Activity Relationship of Marine Bioactive Compounds)
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24 pages, 7832 KiB  
Article
A Weekly Indicator of Surface Moisture Status from Satellite Data for Operational Monitoring of Crop Conditions
by Francesco Nutini, Daniela Stroppiana, Lorenzo Busetto, Dario Bellingeri, Chiara Corbari, Marco Mancini, Enrico Zini, Pietro Alessandro Brivio and Mirco Boschetti
Sensors 2017, 17(6), 1338; https://doi.org/10.3390/s17061338 - 9 Jun 2017
Cited by 5 | Viewed by 5190
Abstract
The triangle method has been applied to derive a weekly indicator of evaporative fraction on vegetated areas in a temperate region in Northern Italy. Daily MODIS Aqua Land Surface Temperature (MYD11A1) data has been combined with air temperature maps and 8-day composite MODIS [...] Read more.
The triangle method has been applied to derive a weekly indicator of evaporative fraction on vegetated areas in a temperate region in Northern Italy. Daily MODIS Aqua Land Surface Temperature (MYD11A1) data has been combined with air temperature maps and 8-day composite MODIS NDVI (MOD13Q1/MYD13Q1) data to estimate the Evaporative Fraction (EF) at 1 km resolution, on a daily basis. Measurements at two eddy covariance towers located within the study area have been exploited to assess the reliability of satellite based EF estimations as well as the robustness of input data. Weekly syntheses of the daily EF indicator (EFw) were then derived at regional scale for the years 2010, 2011 and 2012 as a proxy of overall surface moisture condition. EFw showed a temporal behavior consistent with growing cycles and agro-practices of the main crops cultivated in the study area (rice, forages and corn). Comparison with official regional corn yield data showed that variations in EFw cumulated over summer are related with crop production shortages induced by water scarcity. These results suggest that weekly-averaged EF estimated from MODIS data is sensible to water stress conditions and can be used as an indicator of crops’ moisture conditions at agronomical district level. Advantages and disadvantages of the proposed approach to provide information useful to issue operational near real time bulletins on crop conditions at regional scale are discussed. Full article
(This article belongs to the Special Issue Precision Agriculture and Remote Sensing Data Fusion)
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