Image 8_Swine influenza surveillance in Italy uncovers regional and farm-based genetic clustering.tiff по L. Cavicchio (21751946)

“...</p>Material and methods<p>Passive surveillance, conducted from 2013 to 2022, involved 253 farms located in three regions, collecting over 3,000 samples that were tested for swIAV. ...”

Image 7_Swine influenza surveillance in Italy uncovers regional and farm-based genetic clustering.tiff по L. Cavicchio (21751946)

“...</p>Material and methods<p>Passive surveillance, conducted from 2013 to 2022, involved 253 farms located in three regions, collecting over 3,000 samples that were tested for swIAV. ...”

Image 4_Swine influenza surveillance in Italy uncovers regional and farm-based genetic clustering.tiff по L. Cavicchio (21751946)

“...</p>Material and methods<p>Passive surveillance, conducted from 2013 to 2022, involved 253 farms located in three regions, collecting over 3,000 samples that were tested for swIAV. ...”

Image 2_Swine influenza surveillance in Italy uncovers regional and farm-based genetic clustering.tiff по L. Cavicchio (21751946)

“...</p>Material and methods<p>Passive surveillance, conducted from 2013 to 2022, involved 253 farms located in three regions, collecting over 3,000 samples that were tested for swIAV. ...”

Image 9_Swine influenza surveillance in Italy uncovers regional and farm-based genetic clustering.tiff по L. Cavicchio (21751946)

“...</p>Material and methods<p>Passive surveillance, conducted from 2013 to 2022, involved 253 farms located in three regions, collecting over 3,000 samples that were tested for swIAV. ...”

Image 5_Swine influenza surveillance in Italy uncovers regional and farm-based genetic clustering.tiff по L. Cavicchio (21751946)

“...</p>Material and methods<p>Passive surveillance, conducted from 2013 to 2022, involved 253 farms located in three regions, collecting over 3,000 samples that were tested for swIAV. ...”

Image 3_Swine influenza surveillance in Italy uncovers regional and farm-based genetic clustering.tiff по L. Cavicchio (21751946)

“...</p>Material and methods<p>Passive surveillance, conducted from 2013 to 2022, involved 253 farms located in three regions, collecting over 3,000 samples that were tested for swIAV. ...”

Image 6_Swine influenza surveillance in Italy uncovers regional and farm-based genetic clustering.tiff по L. Cavicchio (21751946)

“...</p>Material and methods<p>Passive surveillance, conducted from 2013 to 2022, involved 253 farms located in three regions, collecting over 3,000 samples that were tested for swIAV. ...”

Sea lice infestation dataset for wild and farmed salmon populations on the Pacific coast of Canada (2001-2023) по Crawford Revie (20463748)

“...(This is very similar to data in the [industry- _farm_abundance] table, but provides average values over a more highly resolved time-frame. ...”

Table 1_Under pressure: effect of first-parity sows’ early social experience and genetic line on behavior and lesions in a paired interaction test with an older sow.docx по Linda Marie Backeman Hannius (22818311)

Table 1_Public attitudes, perceptions, and suggested strategies for managing free-roaming dogs in selected urban and rural settings in Uganda.docx по Dickson Stuart Tayebwa (6733610)

Table 2_Public attitudes, perceptions, and suggested strategies for managing free-roaming dogs in selected urban and rural settings in Uganda.docx по Dickson Stuart Tayebwa (6733610)

Data Sheet 1_Swine influenza surveillance in Italy uncovers regional and farm-based genetic clustering.pdf по L. Cavicchio (21751946)

“...</p>Material and methods<p>Passive surveillance, conducted from 2013 to 2022, involved 253 farms located in three regions, collecting over 3,000 samples that were tested for swIAV. ...”

Data Sheet 1_Biosecurity implementation in poultry farms across Europe and neighboring countries: a systematic review.zip по Ronald Vougat Ngom (18239415)

“...Despite relatively broad geographical coverage, including eight multi-country studies involving 36 national assessments, the distribution of studies was uneven. ...”

Table 6_AI-based predictive modeling for enteric methane mitigation: cross-farm validation using an allicin based essential oil.docx по Yaniv Altshuler (645084)

“...Since the wide variety of feed additives available in the market, validating the model across a diverse range of additives is critical for its adoption in commercial farming practices. In this study, we extensively validate the model across ten commercial farms over a three-month period, involving 339 Holstein cows, and using an allicin-based essential oil (Allimax), an organosulfur compound obtained from garlic with potential to reduce enteric methane emissions. ...”

Table 4_AI-based predictive modeling for enteric methane mitigation: cross-farm validation using an allicin based essential oil.docx по Yaniv Altshuler (645084)

“...Since the wide variety of feed additives available in the market, validating the model across a diverse range of additives is critical for its adoption in commercial farming practices. In this study, we extensively validate the model across ten commercial farms over a three-month period, involving 339 Holstein cows, and using an allicin-based essential oil (Allimax), an organosulfur compound obtained from garlic with potential to reduce enteric methane emissions. ...”

Table 5_AI-based predictive modeling for enteric methane mitigation: cross-farm validation using an allicin based essential oil.docx по Yaniv Altshuler (645084)

“...Since the wide variety of feed additives available in the market, validating the model across a diverse range of additives is critical for its adoption in commercial farming practices. In this study, we extensively validate the model across ten commercial farms over a three-month period, involving 339 Holstein cows, and using an allicin-based essential oil (Allimax), an organosulfur compound obtained from garlic with potential to reduce enteric methane emissions. ...”

Table 1_AI-based predictive modeling for enteric methane mitigation: cross-farm validation using an allicin based essential oil.docx по Yaniv Altshuler (645084)

“...Since the wide variety of feed additives available in the market, validating the model across a diverse range of additives is critical for its adoption in commercial farming practices. In this study, we extensively validate the model across ten commercial farms over a three-month period, involving 339 Holstein cows, and using an allicin-based essential oil (Allimax), an organosulfur compound obtained from garlic with potential to reduce enteric methane emissions. ...”

Table 2_AI-based predictive modeling for enteric methane mitigation: cross-farm validation using an allicin based essential oil.docx по Yaniv Altshuler (645084)

“...Since the wide variety of feed additives available in the market, validating the model across a diverse range of additives is critical for its adoption in commercial farming practices. In this study, we extensively validate the model across ten commercial farms over a three-month period, involving 339 Holstein cows, and using an allicin-based essential oil (Allimax), an organosulfur compound obtained from garlic with potential to reduce enteric methane emissions. ...”

Table 3_AI-based predictive modeling for enteric methane mitigation: cross-farm validation using an allicin based essential oil.docx по Yaniv Altshuler (645084)

“...Since the wide variety of feed additives available in the market, validating the model across a diverse range of additives is critical for its adoption in commercial farming practices. In this study, we extensively validate the model across ten commercial farms over a three-month period, involving 339 Holstein cows, and using an allicin-based essential oil (Allimax), an organosulfur compound obtained from garlic with potential to reduce enteric methane emissions. ...”