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Yoder, J
Pasquel, D
Odoom, E
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Authors
Pasquel, D
Roux, S
Tisseyre, B
Taylor, J.A
Islam, M
Yoder, J
Gan, H
Odoom, E
Frimpong, K.A
Phillips, S
Topics
Geospatial Data
Farm Animals Health and Welfare Monitoring
On Farm Experimentation with Site-Specific Technologies
Type
Oral
Year
2022
2024
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Filter results3 paper(s) found.

1. Comparison of Different Aspatial and Spatial Indicators to Assess Performance of Spatialized Crop Models at Different Within-field Scales

Most current crop models are point-based models, i.e. they simulate agronomic variables on a spatial footprint on which they were initially designed (e.g. plant, field, region scale). To assess their performances, many indicators based on the comparison of estimated vs observed data, can be used such as root mean square error (RMSE) or Willmott index of agreement (D-index) among others. However, shifting model use from a strategic objective to tactical in-season management is becoming a significant... D. Pasquel, S. Roux, B. Tisseyre, J.A. Taylor

2. Automatic Body Condition Score Classification System for Individual Beef Cattle Using Computer Vision

Body condition scoring (BCS) is a widely used parameter for assessing the utilization of energy reserves in the fat and muscle of cattle. It fulfills the needs of animal welfare and precision livestock farming by enabling effective monitoring of individual animals. It serves as a crucial parameter for optimizing nutrition, reproductive performance, overall health, and economic outcomes in beef cattle. The precise and consistent assessment of BCS relies on personal experience using visuals that... M. Islam, J. Yoder, H. Gan

3. Analysis of Yield Gaps in Sub-Saharan African Cereal Production Systems

Food production in sub-Saharan Africa (SSA) is one of the lowest and keeps declining across farmers’ fields season after season (Assefa et al., 2020; F Affholder, 2013). Yield gaps in cereal cropping systems have been reported by many researchers, attesting to the existence of huge variability in production levels of cereals such as corn, wheat, sorghum, rice and millet. across SSA. It is still unclear whether the yield gaps are similar in size or driven by similar factors across different... E. Odoom, K.A. Frimpong, S. Phillips