This study aimed to assess the influence of bees’ floral preference on cashew agronomics performances in Côte d’Ivoire. Therefore, a sampling design with a total of 40 cashew trees preferred by bees and 40 trees that were not preferred by bees was established in 4 main producing regions. In addition, bees’ foragers and agronomics performances of trees were sampled. As results, a total of 46 bee’ species with a foraging activity of 4±0.32 visits per minute were observed. Apis mellifera (60% of visits, with 2.27±0.17 of visitors per minute) followed by Meliponula bocandei (23% of visits with 0.91±0.18 of visits per minute) contributes significantly to the reproduction of cashew trees, compare to the 44 other bees’ species (17% of visits; with an activity of 0.69±0.03 of visitors per minute). The preferred trees recorded 40.54±0.57 kg of nuts per tree, with 18.39±0.48 fruits per inflorescence, including 37.12±0.4% of useful kernel per raw nut (yield ratio of 65.45±0.66 pound of useful kernel). Conversely, the non-preferred trees obtained 5.24±0.44kg of nuts per tree, with 1.7±0.21 fruits per inflorescence, including 28.69±0.65% of useful kernel per raw nut (50.6±1.15 pound of useful kernel). Hence, the foraging preference of these two Apidae significantly increased the fruiting rate (83.7±0.01%), the yields (87.08±0.0%), and the kernel rate (22.68±1.76%) in raw cashew nuts. Based in these results, we suggest the foraging preference of Apis mellifera as good indicator of high-yielding cashew plants. Moreover, we suggests combination of apicultural and meliponicultrual in cashew farming to boost the yields and farmers livelihoods.
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RBINS Staff Publications 2022
Hyperspectral remote sensing reflectance (Rrs) derived from PRISMA in the visible and infrared range was evaluated for two inland and coastal water sites using above-water in situ reflectance measurements from autonomous hyper- and multispectral radiometer systems. We compared the Level 2D (L2D) surface reflectance, a standard product distributed by the Italian Space Agency (ASI), as well as outputs from ACOLITE/DSF, now adapted for processing of PRISMA imagery. Near-coincident Sentinel-3 OLCI (S3/OLCI) observations were also compared as it is a frequent data source for inland and coastal water remote sensing applications, with a strong calibration and validation record. In situ measurements from two optically diverse sites in Italy, equipped with fixed autonomous hyperspectral radiometer systems, were used: the REmote Sensing for Trasimeno lake Observatory (RESTO), positioned in a shallow and turbid lake in Central Italy, and the Acqua Alta Oceanographic Tower (AAOT), located 15 km offshore from the lagoon of Venice in the Adriatic Sea, which is characterised by clear to moderately turbid waters. 20 PRISMA images were available for the match-up analysis across both sites. Good performance of L2D was found for RESTO, with the lowest relative (Mean Absolute Percentage Difference, MAPD 25\%) and absolute errors (Bias 0.002) in the bands between 500 and 680 nm, with similar performance for ACOLITE. The lowest median and interquartile ranges of spectral angle (SA 8°) denoted a more similar shape to the RESTO in situ data, indicating pigment absorption retrievals should be possible. ACOLITE showed better statistical performance at AAOT compared to L2D, providing R2 0.5, Bias 0.0015 and MAPD 35\%, in the range between 470 and 580 nm, i.e. in the spectral range with highest reflectances. The addition of a SWIR based sun-glint correction to the default atmospheric correction implemented in ACOLITE further improved performance at AAOT, with lower uncertainties and closer spectral similarity to the in situ measurements, suggesting that ACOLITE with glint correction was able to best reproduce the spectral shape of in situ data at AAOT. We found good results for PRISMA Rrs retrieval in our study sites, and hence demonstrated the use of PRISMA for aquatic ecosystem mapping. Further studies are needed to analyse performance in other water bodies, over a wider range of optical properties.
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