InsectSound1000 An insect sound dataset for deep learning based acoustic insect recognition.

InsectSound1000 is a dataset comprising more than 169000 labelled sound samples of 12 insects. The insect sound level spans from very loud (Bombus terrestris) to inaudible to human ears (Aphidoletes aphidimyza). The samples were extracted from more than 1000 h of recordings made in an anechoic box with a four-channel low-noise measurement microphone array. Each sample is a four-channel wave-file of 2500 kHz length, at 16 kHz sample rate and 32 bit resolution. Acoustic insect recognition holds great potential to form the basis of a digital insect sensor. Such sensors are desperately needed to automate pest monitoring and ecological monitoring. With its significant size and high-quality recordings, InsectSound1000 can be used to train data-hungry deep learning models. Used to pretrain models, it can also be leveraged to enable the development of acoustic insect recognition systems on different hardware or for different insects. Further, the methodology employed to create the dataset is presented in detail to allow for the extension of the published dataset.

Influence of the urease inhibitor suspension (Atmowell®) on the fluorescent dye pyranine and its spray and drift behavior in wind tunnel measurements.

Too many ammonia emissions are released into the environment from cattle farming. These damage the environment and have an impact on animal and human health. Ammonia Emissions could be reduce by urease inhibitors. Before using the urease inhibitor suspension Atmowell® in cattle farming a risk assessment is required. This includes exposure data on the animal and human in the barn. As there is no method for exposure measurements yet the approach of fluorometry was taken. The fluorescent dye pyranine shall replace Atmowell® in later studies as a tracer. Before Atmowell® can be replaced, the interaction between Atmowell® and pyranine-according to the fluorescence and storage stability under the influence of ultraviolet light, has to be observed and excluded. Also, the spray and drift behavior must be examined in the wind tunnel with three different nozzles. The results show that Atmowell® has no effect on neither the fluorescence nor the degradation rate of a pyranine-solution. Furthermore, it is shown that a pyranine + Atmowell® mixture does not differ in drift behavior from a pure pyranine-solution. Because of these findings, an Atmowell®-solution can be substituted by a pyranine-solution without any effects on the results of an exposure measurement being expected.

Amount and variation of strip yields collected by a defined hand-milking method after machine milking of Holstein dairy cows.

Hand-milking methods to assess the completeness of milking in dairy cows need to be reliable as well as quick to apply in order to avoid delays in group milking parlours. A previous study comparing different methods demonstrated that a defined milking handgrip with a strip frequency of 1 Hz was most suitable to assess the completeness of milk-out in dairy cows. In a first step, the present study aimed to investigate how much milk can be hand-milked by the defined handgrip, strip frequency and within a time limit of 15 s per quarter. In a second step, the question was how many udder quarters of a cow needed to be hand-milked for a reliable prediction of the amount of rest milk in the udder. The experiment comprised 28 German-Holstein cows of one herd. The cows were hand-milked after cluster detachment by an experienced milker using the defined milking handgrip. All four quarters per cow were hand-milked during nine consecutive milking sessions. The strip yield per quarter per 15 s hand-milking (SY15) was collected in four different containers and weighed with a digital scale. Afterwards, the remaining milk of all four quarters was collected and weighed in a fifth container with a maximum volume equivalent to a net weight of 540 g milk. The analysis showed that neither the position of the quarter nor the chronological order, in which hand-milking was carried out, had an influence on SY15. The amount of rest milk in the udder could be estimated best by hand-milking all four quarters.

Transcriptome analysis of North American sweet birch (Betula lenta) revealed a higher expression of genes involved in the biosynthesis of secondary metabolites than European silver birch (B. pendula).

The North American Betula lenta L. (sweet birch) has been used for medicinal reasons for centuries by native Americans. Although sophisticated technologies have rapidly been developed, a large information gap has been observed regarding genetic regulators of medicinally important compounds in sweet birch. Very little is known on the different genes involved in secondary metabolic biosynthesis in sweet birch. To gain a deeper insight into genetic factors, we performed a transcriptome analysis of each three biological samples from different independent trees of sweet and European silver birch (B. pendula Roth). This allowed us to precisely quantify the transcripts of about 24,000 expressed genes including 29 prominent candidate genes putatively involved in the biosynthesis of secondary metabolites like terpenoids, and aromatic benzoic acids. A total number of 597 genes were differentially expressed between B. lenta and B. pendula, while 264 and 210 genes showed upregulation in the bark and leaf of B. lenta, respectively. Moreover, we identified 39 transcriptional regulatory elements, involved in secondary metabolite biosynthesis, upregulated in B. lenta. Our study demonstrated the potential of RNA sequencing to identify candidate genes interacting in secondary metabolite biosynthesis in sweet birch. The candidate genes identified in this study could be subjected to genetic engineering to functionally characterize them in sweet birch. This knowledge can be beneficial to the increase of therapeutically important compounds.

Identification and analysis of key genes involved in methyl salicylate biosynthesis in different birch species.

Species of the perennial woody plant genus Betula dominate subalpine forests and play a significant role in preserving biological diversity. In addition to their conventional benefits, birches synthesize a wide range of secondary metabolites having pharmacological significance. Methyl salicylate (MeSA) is one of these naturally occurring compounds constitutively produced by different birch species. MeSA is therapeutically important in human medicine for muscle injuries and joint pain. However, MeSA is now mainly produced synthetically due to a lack of information relating to MeSA biosynthesis and regulation. In this study, we performed a comprehensive bioinformatics analysis of two candidate genes mediating MeSA biosynthesis, SALICYLIC ACID METHYLTRANSFERASE (SAMT) and SALICYLIC ACID-BINDING PROTEIN 2 (SABP2), of high (B. lenta, B. alleghaniensis, B. medwediewii, and B. grossa) and low (B. pendula, B. utilis, B. alnoides, and B. nana) MeSA-producing birch species. Phylogenetic analyses of SAMT and SABP2 genes and homologous genes from other plant species confirmed their evolutionary relationships. Multiple sequence alignments of the amino acid revealed the occurrence of important residues for substrate specificity in SAMT and SABP2. The analysis of cis elements in different birches indicated a functional multiplicity of SAMT and SABP2 and provided insights into the regulation of both genes. We successfully developed six prominent single nucleotide substitution markers that were validated with 38 additional birch individuals to differentiate high and low MeSA-producing birch species. Relative tissue-specific expression analysis of SAMT in leaf and bark tissue of two high and two low MeSA-synthesizing birches revealed a high expression in the bark of both high MeSA-synthesizing birches. In contrast, SABP2 expression in tissues revealed indifferent levels of expression between species belonging to the two groups. The comparative expression and bioinformatics analyses provided vital information that could be used to apply plant genetic engineering technology in the mass production of organic MeSA.

Low-Input Estimation of Site-Specific Lime Demand Based on Apparent Soil Electrical Conductivity and In Situ Determined Topsoil pH.

Site-specific liming helps increase efficiency in agricultural production. For adequate determination of the lime demand, a combination of apparent soil electrical conductivity (ECa) and topsoil pH can be used. Here, it was hypothesized that this can also be done at low-input level. Field measurements using the EM38 MK I (Geonics, Canada) were conducted on three experimental sites in north Germany in 2011. The topsoil pH was measured based on two approaches: on the field using a handheld pH meter (Spectrum-Technologies Ltd., Bridgend, UK) with a flat electrode (in situ), and in the lab using standard equipment (ex situ). Both soil ECa (0.4-35.9 mS m-1) and pH (5.13-7.41) were heterogeneously distributed across the sites. The same was true of the lime demand (-1.35-4.18 Mg ha-1). There was a significant correlation between in situ and ex situ determined topsoil pH (r = 0.89; p < 0.0001). This correlation was further improved through non-linear regression (r = 0.92; p < 0.0001). Thus, in situ topsoil pH was found suitable for map-overlay with ECa to determine the site-specific lime demand. Consequently, the hypothesis could be confirmed: The combined use of data from EM38 and handheld pH meters is a promising low-input approach that may help implement site-specific liming in developing countries.

Accuracy and application of milk fatty acid estimation with diffuse reflectance near-infrared spectroscopy.

Near infrared spectroscopy (NIRS) has the potential to estimate contents of fatty acids (FA) in milk frequently at-farm or during daily milking routine. In this study, a total of 738 raw milk spectra collected from 33 Holstein cows over a period of 30 weeks were recorded. Reference data on FA composition in milk and in milk fat were analysed in laboratory. Calibration models were calculated for single FA and groups of FA in milk and in milk fat. Validation resulted in sufficient Ratio of Prediction to Deviation (RPD) values for some single FA and in higher RPD values for groups of FA when concentrations of FA in milk were predicted. Since the concentrations of most FA in milk are highly correlated with milk fat content, the prediction of FA contents in milk fat is more meaningful when independent predictions are intended. The accuracy of predicting single FA concentrations in milk fat is rather poor for most FA but still comparable to alternative analysing methods such as MIR analysis. The estimation of different groups of FA in milk fat resulted in an improved accuracy based on higher RPD values, which was sufficient to mirror the development in the different lactation phases. The course of cow individual long chain fatty acid (LCFA) concentration in the early lactation stage can be an indicator for body fat mobilisation. The accurate estimation of the extent and duration of body fat mobilisation in cow individuals was rather difficult with NIR predicted LCFA concentrations and would require a higher measuring frequency than applied in this study.

Effect of a multi-sided concave liner barrel design on thickness and roughness of teat-end hyperkeratosis.

In a round liner barrel, the force of the closing liner is transferred by the two opposite sides of the liner wall to the teat apex. Liners with a multi-sided barrel shape close at three or more planes and distribute their force to a larger area of the teat apex. The objective of the study was to investigate effects of a liner with a multi-sided concave barrel design on the degree of teat-end hyperkeratosis, thickness and roughness, and on the time delay until thickness or roughness of teat-end hyperkeratosis responded to the experimental liner. The investigations were done on two dairy farms, one in USA and one in Germany. A split-udder arrangement of liners was used, and control treatment was a liner with round barrel shape. The test period comprised 14 weeks in the first study and 16 weeks in the second study. Thickness of teat-end hyperkeratosis was influenced by farm and test week. Roughness was influenced by farm, test week and treatment. In the first study, the incidence of rough teat-end hyperkeratosis was about 28 and 42% lower in teats milked with the experimental liner than in teats milked with the control liner by test weeks 11 and 14, respectively. In the second study, incidence of rough teat-end hyperkeratosis was rare in general, and in addition hardly occurred in teats milked with the experimental liner. The results indicate that the barrel design of the experimental liner causes similar effects on different farms but magnitude of the effect depends on initial incidence of teat end hyperkeratosis in the herd.

Rumination activity of dairy cows in the 24 hours before and after calving.

Monitoring rumination behavior serves multiple purposes in feeding and herd management of dairy cows. The process of calving is a major event for cows, and a detailed understanding of alterations in behavioral patterns of animals in the time around calving is important in calving detection. The objective of this study was to describe the short-term changes in rumination patterns in dairy cows immediately before and after parturition. In total, 17 cows were fitted with rumination sensors that were able to monitor rumination time, number of rumination boli, and number of rumination jaw movements. Rumination time was decreased in the last 4h antepartum and in the first 8h postpartum. Cows stopped ruminating 123 ± 58 min (mean ± standard deviation) before calving and resumed ruminating 355 ± 194 min after calving. The number of rumination jaw movements and boli per day were decreased in the 24-h period postpartum. Rumination rate, the number of rumination jaw movements per rumination minute, and the number of boli per rumination minute changed little around calving. The calving event primarily influenced the duration and frequency of various rumination characteristics but not rumination intensity. Among detected characteristics, rumination time showed the greatest potential for monitoring of calving events.

Robustness of near-infrared calibration models for the prediction of milk constituents during the milking process.

The robustness of in-line raw milk analysis with near-infrared spectroscopy (NIRS) was tested with respect to the prediction of the raw milk contents fat, protein and lactose. Near-infrared (NIR) spectra of raw milk (n = 3119) were acquired on three different farms during the milking process of 354 milkings over a period of six months. Calibration models were calculated for: a random data set of each farm (fully random internal calibration); first two thirds of the visits per farm (internal calibration); whole datasets of two of the three farms (external calibration), and combinations of external and internal datasets. Validation was done either on the remaining data set per farm (internal validation) or on data of the remaining farms (external validation). Excellent calibration results were obtained when fully randomised internal calibration sets were used for milk analysis. In this case, RPD values of around ten, five and three for the prediction of fat, protein and lactose content, respectively, were achieved. Farm internal calibrations achieved much poorer prediction results especially for the prediction of protein and lactose with RPD values of around two and one respectively. The prediction accuracy improved when validation was done on spectra of an external farm, mainly due to the higher sample variation in external calibration sets in terms of feeding diets and individual cow effects. The results showed that further improvements were achieved when additional farm information was added to the calibration set. One of the main requirements towards a robust calibration model is the ability to predict milk constituents in unknown future milk samples. The robustness and quality of prediction increases with increasing variation of, e.g., feeding and cow individual milk composition in the calibration model.

NIRS-aided monitoring and prediction of biogas yields from maize silage at a full-scale biogas plant applying lumped kinetics.

The aim of this study was to apply near-infrared spectroscopy (NIRS), available biogas plant data and lumped degradation kinetics to predict biogas production (BPr) of maize silage. A full-scale agricultural biogas plant was equipped with NIRS-metrology at the feeding station. Continuously NIR-spectra were collected for 520 d. Substrate samples were analyzed by means of feedstuff analysis. Biogas potential of the samples was calculated from the laboratory analysis results and for a sample-subset practically assessed by "Hohenheim biogas tests". NIRS-regression-models for all mentioned parameters were calibrated. Continuously gathered spectra, NIRS-models, actual plant-feeding data and degradation kinetics were used to calculate time-series of theoretically expectable BPr. Results were validated against measured gas quantity. Determination coefficients between calculated and measured BPr were up to 58.2%. This outcome was mainly due to the positive correlation between BPr and input amount since the substrate was very homogeneous. The use of NIRS seems more promising for plants with stronger substrate heterogeneity.

Use of near infrared spectroscopy in online-monitoring of feeding substrate quality in anaerobic digestion.

In order to keep the anaerobic process stably and uniformly producing biogas it needs to be supplied with either an even amount of substrate of stable quality or varying amounts according to variations in quality. Feeding amounts are usually adjusted manually as a reaction to changing rates of biogas production. Continuous information about the actual substrate quality is not available and feedstuff analyses are costly. Aim of this study was to assess the feasibility of near infrared spectroscopic (NIRS) online monitoring of substrate quality in order to find ways towards more exact control of biogas plant feeding. A NIRS sensor system was designed, constructed and calibrated for continuous monitoring of (RMSECV in brackets) dry matter (DM) (0.75%fresh matter (FM)), volatile solids (0.74%FM), crude fat (0.09%FM), crude protein (0.22%FM), crude fiber (1.50%DM) and nitrogen-free extracts (0.93%FM) of maize silage.

Use of near infrared spectroscopy in monitoring of volatile fatty acids in anaerobic digestion.

Recently biogas production from agricultural sources has rapidly developed. Therefore the demands on biogas plants to optimise the efficiency of the anaerobic digestion (AD) process have grown immensely. At present there is no online-supervision tool available to monitor the AD process, but costly and time-consuming chemical analyses are necessary. The possibility to use near-infrared spectroscopy (NIRS) in order to track relevant process parameters like total volatile fatty acids (VFA), acetic acid and propionic acid was investigated in the present research project. A NIR-sensor was integrated into a full-scale 1 MW biogas plant and NIR-spectra of the fermenter contents were recorded semi-continuously for 500 days. Weekly samples were taken and analysed for the above mentioned parameters. Calibration models were calculated, capable of following these parameters: VFA (r(2)=0.94), acetic acid (r(2)=0.69), propionic acid (r(2)=0.89).

Development of a dynamic model to predict PM10 emissions from swine houses.

Influences on dust emissions from livestock operations are number, weight, and kind of animals and characteristics of the housing system. Differences between facilities cannot be explained solely by mechanistic input variables. The objective of this study was to characterize the main input variables for modeling emissions of particulate matter with a mass median diameter < or = 10 microm (PM10) from swine facilities using a data-based model. Investigations were performed in mechanically ventilated facilities for weaning, growing-finishing, and sows in Italy and Germany. The measurements included inside and outside concentration of airborne PM10 particles (scatter light photometry), ventilation rate (calibrated measuring fans), indoor air climate at a measuring frequency of 60 s, feeding times, and animal-related data such as weight and animal activity. Dust concentration and emission were simulated using a dynamic transfer function. The results indicated that the average PM10 emission rate was influenced considerably by housing system. The simulation of the PM10 emission rate resulted in a mean percentage error per data set of 21 to 39%, whereas the average simulated and measured emission rate per data set differed by about 4 to 19%. High prediction errors occurred especially during situations in which the absolute level and spatial location of the measured activity peaks did not correspond with the measured dust peaks. Further recommendations of the study were to improve continuous and accurate measurements of input variables, such as the activity level in animal houses, and to optimize the amount of measuring days in relation to the model accuracy.