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470 results for “Coffee”
Data from: Coffee berry borer (Hypothenemus hampei) (Coleoptera: Curculionidae) development across an elevational gradient on Hawai'i Island: applying laboratory degree-day predictions to natural field populations
Coffee berry borer (CBB, Hypothenemus hampei) (Coleoptera: Curculionidae: Scolytinae) is the most destructive pest of coffee worldwide. Information on CBB development times can be used to predict the initiation of new infestation cycles early in the coffee-growing season and thus inform the timing of insecticide applications. While laboratory estimates of CBB development under constant conditions exist, they have not been applied under the heterogeneous environmental conditions that characterize many coffee-growing regions. We measured CBB development times and abundance in commercial coffee farms across an elevational gradient on Hawai'i Island and applied thermal accumulation models from previous laboratory studies to test their fit to field data. Artificial lures were used to infest coffee berries at five farms ranging in elevation from 279-792 m, and weather variables were monitored at macro (farm-level) and micro (branch-level) scales. CBB development was followed in the field from the time of initial berry infestation by the founding female through the development of F1 mature adults. Mean development time from egg to adult across all sites was 38.5 ± 3.46 days, while the mean time required for the completion of a full life cycle (from time of infestation to presence of mature F1 females) was 50.9 ± 3.35 days. Development time increased with increasing elevation and decreasing temperature. Using macro-scale temperature data and two different estimates for the lower temperature threshold (14.9°C and 13.9°C), we estimated a mean requirement of 332 ± 14 degree-days and 386 ± 16 degree-days, respectively, from the time of berry infestation to the initiation of a new reproductive cycle in mature coffee berries. Similar estimates were obtained using micro-scale temperature data, indicating that macro-scale temperature monitoring is sufficient for life-cycle prediction. We also present a model relating elevation to number of CBB generations per month. Our findings suggest that CBB development times from laboratory studies are generally applicable to field conditions on Hawai'i Island and can be used as a decision support tool to improve IPM strategies for this worldwide pest of coffee.
Data from: The assembly and importance of a novel ecosystem: the ant community of coffee farms in Puerto Rico
<p>Agricultural ecosystems are, by their very nature novel, and, by definition, the more general biodiversity associated with them must likewise constitute a novel community. Here we examine the community of arboreally foraging ants in the coffee agroecosystem of Puerto Rico. We surveyed 20 coffee plants in 25 farms three times in a period of one year. We also conducted a more spatially explicit sampling in two of the farms and conducted a species interaction study between the two most abundant species, Wasmannia auropunctata and Solenopsis invicta in the laboratory. We find that the majority of the most common species are well-known invasive ants and that there is a highly variable pattern of dominance that varies considerably over the main coffee producing region of Puerto Rico, suggesting an unusual modality of community structure. The distribution pattern of the two most common species, W. auropunctata and S. invicta, suggests strong competitive exclusion. However, they also have opposite relationships with the percent of shade cover, with W. auropunctata showing a positive relationship with shade, while S. invicta has a negative relationship. The spatial distribution of these two dominant species in the two more intensively studied farms suggests that young colonies of S. invicta can displace W. auropunctata. Laboratory experiments confirm this. These results suggest the existence of a spatially explicit intransitive loop that includes these two species and is mediated by a phorid fly parasitoid that attack the larger workers of S. invicta. In addition to the elaboration of the nature and extent of this novel ant community, we speculate on the possibilities of its active inclusion as part of a biological control system dealing with several coffee pests, including one of the ants itself, W. auropunctata.</p>
Fig. 1 in First Record of the Coffee Berry Borer, Hypothenemus hampei (Ferrari) (Coleoptera: Curculionidae: Scolytinae) on Hainan Island, China
Fig. 1. Coffee berry borer, Hypothenemus hampei, infesting coffee on Hainan Island, China. A) Female, B) Male, C) Infestation on fresh coffee berry, D) Infestation on old and dry coffee berry, E) Beetle gallery in the coffee bean, F) Gallery and adult. Scale in A–B: 1 mm.
LC-DAD separation of Kahweol and cafestol oleate and palmitate from coffee
<p>Analysis of diterpene esters (kahweol oleate -KO; cafestol oleate -CO; kahweol palmitate -KP; cafestol palmitate -CP) by LC DAD in standards and coffees.</p> <p><strong>Separation conditions: </strong></p> <p>Coffee brews were extracted using 5 mL of diethyl ether. The mixture was vortexed for 2 min, and after centrifugation, the upper phase was transferred to a clean test tube. Next, the aqueous solution was re-extracted using diethyl ether, and the combined ether phase was washed with 5mL of 2 M NaCl solution followed by centrifugation (4000 rpm, 10 min. Finally, the clean ether phase was dried under an azote stream. Samples were kept at –22ºC until analysis using LC–DAD. Separation was achieved using a Purospher STAR LichroCART RP 18 end-capped (250 × 4 mm, 5 µm) column attached. Before injection, the dried extract was dissolved in 2.5 mL of acetonitrile and filtered through 0.45 µm filter membrane (PTFE, VWR, USA). Twenty microliters of sample were injected, and the separation was achieved using isocratic conditions for 35 min with the mobile phase made of acetonitrile/isopropanol (70:30, v/v) and pumped at 0.4 mL/min. A diode array detector, in the range of 200–400 nm, was used. After each run, the acquisition software exported data as a comma-separated-values format (EZChrom Elite 3.1.6).</p> <p>For the calibration curves, mixtures of the four diterpenes esters at similar concentrations were used in the range of 2 to 200 mg/L. Eight samples were prepared and run in duplicates</p> <p> </p> <p><strong>Files: </strong></p> <p><a href="https://zenodo.org/api/files/9c2c7a63-966d-4b4b-a1bf-5b8d8228ac48/FTIRATR_Pulse.mat">Data.mat</a>: Matlab structure with, as fields:<br> <Time> : a 3751 length vector. Time axis in min<br> <Wavelength> : a 201 length vector, Wavelength of detection<br> <Names_calibration> : Name of the samples for calibrations<br> <Data_calibration> : a 3751x201x16 matrix with in first dimension the retention time, in second dimension the wavelength for detection and third dimension the calibration samples.<br> <Data_samples> : a 3751x201x4 matrix with in first dimension the retention time, in second dimension the wavelength for detection and third dimension the coffee samples.<br> <Names_Samples> : Name of the samples<br> <Concentrations_calibration>: Table with the real concentration (mg/L) used in the standard samples.</p>
Positive forest cover effects on coffee yields are consistent across regions
<p class="PargrafodaLista1CxSpFirst"><span>1. Enhancing biodiversity-based ecosystem services can generate win-win opportunities for conservation and agricultural production. Pollination and pest control are two essential agricultural services provided by mobile organisms, many depending on native vegetation networks beyond the farm scale. Many studies have evaluated the effects of landscape changes on such services at small scales. However, several landscape management policies (e.g., selection of conservation sites) and associated funding allocation occur at much larger spatial scales (e.g., state or regional level). Therefore, it is essential to understand whether the links between landscape, ecosystem services, and crop yields are robust across broad and heterogeneous regional conditions.</span></p> <p class="PargrafodaLista1CxSpMiddle"><span>2. Here, we used data from 610 Brazilian municipalities within the Atlantic forest region (~50 Mha) and show that forest is a crucial factor affecting coffee yields, regardless of regional variations in soil, climate and management practices. We found forest cover surrounding coffee fields was better at predicting coffee yields than forest cover at the municipality level. Moreover, the positive effect of forest cover on coffee yields was stronger for <i>Coffea canephora</i>, the species with higher pollinator dependence, than for <i>C. arabica</i>. Overall, coffee yields were highest when coffee fields were near to forest fragments, mostly in landscapes with intermediate to high forest cover (> 20%), above the biodiversity extinction threshold. </span></p> <p class="PargrafodaLista1CxSpMiddle"><span>3. Coffee cover was the most relevant management practice associated with coffee yield prediction. An increase in crop area was associated with a higher yield, but mostly in high forest covers municipalities. Other localized management practices like irrigation, pesticide use, organic manure, and honey-bee density had little importance in predicting coffee yields than landscape structure parameters. Neither the climatic or topographic variables were as relevant as forest cover at predicting coffee yields. </span></p> <p class="PargrafodaLista1CxSpLast"><span><a>4. <i>Synthesis</i></a><i> and application. </i>Our work provides evidence that landscape relationships with ecosystem service provision are consistent across regions with different agricultural practices and environmental conditions. These results provide a way in which landscape management can articulate small landscape management with regional conservation goals. Policies directed towards increasing landscape interspersion of coffee fields with forest remnants favor spillover process, and can thus benefit the provision of biodiversity-based ecosystem services, increasing agricultural productivity. Such interventions can generate win-win situations favoring biodiversity conservation and increased crop yields across large <a>regions.</a></span></p>
A collection of fully-annotated soundscape recordings from neotropical coffee farms in Colombia and Costa Rica
<p>This collection contains 34 hour-long soundscape recordings, which have been annotated by expert ornithologists who provided 6,952 bounding box labels for 89 different bird species from Colombia and Costa Rica. The data were recorded in 2019 at two highly diverse neotropical coffee farm landscapes from the towns of Jardín, Colombia and San Ramon, Costa Rica. This collection has partially been featured as test data in the 2021 BirdCLEF competition and can primarily be used for training and evaluation of machine learning algorithms.</p> <p><strong>Data collection</strong></p> <p>Monitoring the avifauna of coffee farms is a useful tool to measure the impact of sustainability efforts in productive landscapes. Diverse bird communities provide services to coffee farms that are enhanced with increased tree cover (e.g. shade trees, wind breaks), and the protection of forest remnants within or in close proximity to coffee farms. Our limited knowledge on the relative contributions of different types of tree cover has incentivized the scientific community to collect bird data in coffee landscapes in order to correlate different management strategies with the presence or absence of target species using bioacoustics. To accomplish this, we collected a set of bird recordings to track the presence of target species on coffee farms. The annotated data set is currently being used to measure the impact of pesticide applications and other types of management practice (e.g. pruning) to test for differences in bird activity before and after one of these interventions. In addition, the bird call annotations help us train machine learning models that will help us monitor these farms in an automatic way.</p> <p>Soundscapes for this collection were recorded using SWIFT recorders, positioned 3m above the ground. We recorded 48kHz one-hour long sound files from 4:30 to 7:30 to capture the most active time frame of the avian dawn chorus. In the same way, we recorded from 16:00 to 19:00, to capture the second avian bioacoustics activity peak that occurs before sunset, and to include nocturnal species. </p> <p>All audio was unified, converted to FLAC, and resampled to 32 kHz for this collection. Parts of this dataset have previously been used in the 2021 BirdCLEF competition.</p> <p><strong>Sampling and annotation protocol</strong></p> <p>We subsampled data for this collection by randomly selecting recordings coming from different farm locations and dates.</p> <p>Using Raven Pro, annotators were asked to create a selection box around every bird call they could recognize, ignoring those that were too faint or unidentifiable. We allowed overlapping selections. Provided labels contain full bird calls that are boxed in time and frequency. Annotators were allowed to combine multiple consecutive calls of the same species into one bounding box label if pauses between calls were shorter than 5 seconds. We converted labels to eBird species codes, following the 2021 eBird taxonomy (Clements list). Unidentifiable calls have been marked with “????” and were added as bounding box labels to the ground truth annotations.</p> <p><strong>Files in this collection</strong></p> <p>Audio recordings can be accessed by downloading and extracting the “soundscape_data.zip” file. Soundscape recording filenames contain a sequential file ID, site ID, recording date, and timestamp in local time (Costa Rica: GMT-6; Colombia: GMT-5). As an example, the file “NES_001_S01_20190914_043000.flac” has sequential ID 001 and was recorded at site S01 on Sep 14th, 2019 at 04:30:00 local time. Ground truth annotations are listed in “annotations.csv” where each line specifies the corresponding filename, start and end time in seconds, low and high frequency in Hertz, and an eBird species code. These species codes can be assigned to the scientific and common name of a species with the “species.csv” file. Geographical coordinates of San Ramon and Jardín regions can be found in the “recording_location.txt” file. Recording location coordinates are not included due to data privacy.</p> <p><strong>Acknowledgements </strong></p> <p>Compiling this extensive dataset was a major undertaking, and we are very thankful to the domain experts who helped to collect and manually annotate the data for this collection. Specifically, we want to thank (in alphabetical order): Alejandro Quesada, José Castaño, Luis Parra. We also thank Carlos Gamboa-Venegas (RedCONARE, CeNAT), for providing technical assistance for information transfer.</p> <p>We would also like to acknowledge our funding source: Nespresso AAA Sustainable Quality<sup>TM</sup> Program.</p>
Evidence of time-lag in the provision of ecosystem services by tropical regenerating forests to coffee yields
<p>Abstract Restoration of native tropical forests is crucial for protecting biodiversity and ecosystem functions, such as carbon stock capacity. However, little is known about the contribution of early stages of forest regeneration to crop productivity through the enhancement of ecosystem services, such as crop pollination and pest control. Using data from 610 municipalities along the Brazilian Atlantic Forest (30 m spatial resolution), we evaluated if young regenerating forests (less than 20 years old) are positively associated with coffee yield and whether such a relationship depends on the amount of preserved forest in the surroundings of the coffee fields. We found that regenerating forest alone was not associated with variations in coffee yields. However, the presence of young regenerating forest (within a 500 m buffer) was positively related to higher coffee yields when the amount of preserved forest in a 2 km buffer is above a 20% threshold cover. These results further reinforce that regional coffee yields are influenced by changes in biodiversity-mediated ecosystem services, which are explained by the amount of mature forest in the surrounding of coffee fields. We argue that while regenerating fragments may contribute to increased connectivity between remnants of forest fragments and crop fields in landscapes with a minimum amount of forest (20%), older preserved forests (more than 20 years) are essential for sustaining pollinator and pest enemy's populations. These results highlight the potential time lag of at least 20 years of regenerating forests' in contributing to the provision of ecosystem services that affect coffee yields (e.g., pollination and pest control). We emphasize the need to implement public policies that promote ecosystem restoration and ensure the permanence of these new forests over time.</p>
FIGURE 2 in Description of Aporcella daklakensis sp. n. (Nematoda: Dorylaimida: Aporcelaimidae), associated with coffee plantations in Central Highland of Vietnam
FIGURE 2. Aporcella daklakensis sp. n. (Krong Ana, type population, female, LM). A: Entire. B–D: Anterior region, in lateral median view. E: Posterior body region. F: Anterior region, in lateral surface view. G: Pharyngo-intestinal junction. H, J: Vagina region. I: Caudal region. [Scale bars: A = 200 µm; B, G, I = 10 µm; C, D, F, H, J = 5 µm; E = 20 µm].
FIGURE 1 in Description of Aporcella daklakensis sp. n. (Nematoda: Dorylaimida: Aporcelaimidae), associated with coffee plantations in Central Highland of Vietnam
FIGURE 1. Aporcella daklakensis sp. n. (Krong Ana, type population, female). A: Entire. B, C: Anterior region, in lateral median view. D: Anterior region, in lateral surface view. E: Neck region. F: Pharyngo-intestinal junction. G: Vagina region. H: Anterior genital branch. I: Posterior body region. J: Rectum and caudal region. K: Caudal region. [Scale bars: A = 200 µm; B, F, K = 10 µm; C, D, G = 5 µm; E = 100 µm; H–J = 20 µm].
FIGURE 3 in Description of Aporcella daklakensis sp. n. (Nematoda: Dorylaimida: Aporcelaimidae), associated with coffee plantations in Central Highland of Vietnam
FIGURE 3. Aporcella daklakensis sp. n. (Di Linh population, female, LM). A, B: Anterior region, in lateral median view. C: Pharyngo-intestinal junction. D: Anterior region, in lateral surface view. E: Rectum and caudal region. F: Posterior genital branch. G, I: Vagina region. H: Caudal region. [Scale bars: A, C, E, H = 10 µm; B, D, G, I = 5 µm; F = 20 µm].
Fig. 3 in Dispersion Pattern of Giant Cicada (Hemiptera: Cicadidae) in a Brazilian Coffee Plantation
Fig. 3. Schematic representation of the experimental area with capture, release, and recapture points of Quesada gigas adults marked with colored synthetic nail enamels.The numbers inside the recapture points represent the sequential order of recapture.
Fig. 1 in Dispersion Pattern of Giant Cicada (Hemiptera: Cicadidae) in a Brazilian Coffee Plantation
Fig. 1. Location of the coffee plantation (Coffea arabica) used for the experiments of Quesada gigas dispersion and mating and oviposition behaviors. State of Minas Gerais (MG), Brazil.
Fig. 5 in Dispersion Pattern of Giant Cicada (Hemiptera: Cicadidae) in a Brazilian Coffee Plantation
Fig. 5. Time course recapture of Quesada gigas marked in green and released on 17 October 2017 until the end of recaptures on 13 November 2017. Recapture rates represent the percentage of recaptured of males and females in defined points throughout the area of study.
Fig. 2 in Dispersion Pattern of Giant Cicada (Hemiptera: Cicadidae) in a Brazilian Coffee Plantation
Fig. 2. (A) Frontal view of the sound trap employed to capture Quesada gigas in the experimental area. Sound transmitter; Blanched fabric (2.0 × 1.5 m) positioned for insect landing. (B) Back view of the sound trap. Sound transmitter; Blanched fabric (2.0 × 1.5 m) positioned for insect landing; Battery, 12 volts; CD player and sound amplifier.
Fig. 3 in Susceptibility of Chrysoperla externa (Hagen, 1861) (Neuroptera: Crysopidae) to insecticides used in coffee crops
Fig. 3 Survival of Chrysoperla externa exposed to insecticides used in coffee crop. a Pupal stage (df = 2, Χ2 = 100.23, p> 0.05), group 1 = azadirachtin, ethiprole and teflubenzuron; group 2 = chlorpyrifos, and group 3 = control. b Adults (df = 2, Χ2 = 224.83, p> 0.05); group 1 = azadirachtin and control; group 2 = ethiprole and teflubenzuron, and group 3 = chlorpyrifos
Fig. 4 in Susceptibility of Chrysoperla externa (Hagen, 1861) (Neuroptera: Crysopidae) to insecticides used in coffee crops
Fig. 4 Reproductive parameters of Chrysoperla externa exposed to insecticides used in coffee crop. a Mean number of eggs oviposited by females (SE) exposed to insecticides when pupae (df = 3, Χ² = 247.3, p <0.05) and adults (df = 2, F = 8.1353, p <0.05. b Total eggs layed by females exposed to insecticides when pupae (df = 3, F = 29.756, p <0.05) and adults (df = 2, F = 53.006, p <0.05). c Egg viability (SE) of females exposed to insecticides when pupae (df = 3, F = 3.4328, p <0.05) and adults (ns = the means do not differ from each other, df = 2, F = 1.5769, p> 0.05)
Fig. 2 in Susceptibility of Chrysoperla externa (Hagen, 1861) (Neuroptera: Crysopidae) to insecticides used in coffee crops
Fig. 2 Number of males and females (SE) of Chrysoperla externa adults from pupae exposed to insecticides (ns = the means do not differ from each other, df = 3, Χ2 = 0.093936, p> 0.05)
Fig. 1 a in Susceptibility of Chrysoperla externa (Hagen, 1861) (Neuroptera: Crysopidae) to insecticides used in coffee crops
Fig. 1 a Percentage of adults emergence of Chrysoperla externa from pupae exposed to insecticides (df = 4, F = 7.3237, p> 0.05). b Duration of the pupal stage in days, when insects exposed to insecticides while pupae of C. externa (ns = the means do not differ from each other, df = 4, F = 1.3462, p> 0.05)
Fig. 1 in Virulence of two entomopathogenic nematode species, Steinernema sp. (strain PQ16) and Heterorhabditis indica (strain KT3987), to nymphs of the coffee cicada Dundubia nagarasingna
Fig. 1. Mortality of coffee cicada nymphs, Dundubia nagarasingna, treated with entomopathogenic nematodes, Steinernema sp. (strain PQ16) and Heterorhabditis indica (strain KT3987), at inoculation doses of 100, 200, 300, 400, 500 and 600 infective juveniles (IJ) nymph−1 at A: 24 h, B: 48 h, C: 72 h after inoculation. Values are means ± SE; different letters in each figure represent means that are statistically different between nematode concentrations (Tukey's HSDtest, P <0.05). Thecorrectedcumulative mortality axis indicates nymphal mortality increasing with IJ inoculation doses and exposure time. Mortality data were corrected by Abbott's formula (Abbott, 1925).
Fig. 4 in Virulence of two entomopathogenic nematode species, Steinernema sp. (strain PQ16) and Heterorhabditis indica (strain KT3987), to nymphs of the coffee cicada Dundubia nagarasingna
Fig. 4. Number of infective juveniles (IJ) (250 ml soil)−1 at the treatment dose of 40 × 103 and 60 × 103 IJ pot−1 10, 20 and 30 days after treatment. A: S-PQ16 strain, B: H-KT3987 strain. Letters indicate significant differences among interval times in each (lower case andupper case) IJdose (one-way ANOVA, P <0.05). Pair-treatmentcomparisonbetweeninitialinoculationsisrepresentedwithlines above thecolumns (Student's t -test, *P <0.05, **P <0.01, ***P <0.001, ns: not significant). Valuesaremeans ± SE.
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