Image 8_Swine influenza surveillance in Italy uncovers regional and farm-based genetic clustering.tiff Yazar: 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 Yazar: 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 Yazar: 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 Yazar: 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 Yazar: 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 Yazar: 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 Yazar: 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 Yazar: 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. …”

Multiple seasons spatialy distributed maize yield and soil properties data for crop modeling applications in precision agriculture Yazar: Simphiwe Maseko (21577652)

“…<p dir="ltr">This data was collected from a data-intensive farm management (DIFM) maize trial in Hennenman, Free State South Africa, from a private farm that allowed the research to be done and data used for academic purposes. …”

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

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 Yazar: Linda Marie Backeman Hannius (22818311)

Data Sheet 1_Swine influenza surveillance in Italy uncovers regional and farm-based genetic clustering.pdf Yazar: 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_Digital technology adoption and farm household income in ethnic minority areas: evidence from Xinjiang, China.docx Yazar: Yan Tang (195081)

“…Specifically, digital adoption reduces reliance on traditional labor inputs in agricultural production, boosting agricultural incomes while increasing the likelihood of non-farm employment, thereby promoting income diversification. …”

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

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

Continuous Discontinuous Irrigated Areas in China Dataset (CDIA) (2000-2022) Yazar: Xin Tian (17835848)

DRF-main.zip Yazar: Pranuthi Gogumalla (21392648)

“…There is potential in using open-source satellite data for monitoring farm fields in the future.</p>…”

Table 6_AI-based predictive modeling for enteric methane mitigation: cross-farm validation using an allicin based essential oil.docx Yazar: 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 Yazar: 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 Yazar: 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. …”