#model reliability

[zimbabox-2024]

Ai receives boost from restored traffic to dark web sources Dark web traffic restoration gives AI training data a new lifeline. Discover how it affects model accuracy and reliability in this analysis....

Ai receives boost from restored traffic to dark web sources

[oaresearchpaper]

Herbage availability predictive modelling using rising plate meter for improved grazing management in Ugandan pasture swards

This study investigated the botanical composition, nutritional quality, and herbage mass availability in mixed native grazing pastures across Mbarara and Nakasongola districts of Uganda during the 2023 seasons. Further, herbage mass prediction equations/models based on the rising plate meter for mixed pasture swards in Uganda were developed. The results revealed that the wet season (Season A) tended to favour a greater diversity of grasses and legumes, enhancing crude protein (CP) content, while the dry season (Season B) resulted in grass-dominated swards, particularly with the invasive Sporobolus pyramidalis, and a significant decline in legume prevalence. Nutritional profiles indicated high dry matter (DM) content across seasons; however, CP levels dropped markedly in Nakasongola, with increases in neutral detergent fiber (NDF) during the dry season. Herbage mass availability showed significant seasonal effects, with Season A yielding higher DM across both districts. The rising plate meter method effectively predicted herbage availability, with Model 1 outperforming Model 2 in reliability. However, both models exhibited underestimation bias, needing ongoing calibration and validation. These findings highlight the need for strategic supplementation during the dry season and highlight the importance of local environmental conditions in pasture quality. The developed herbage availability prediction equations could be used to compute available feed for grazing cattle using rising plate meters which could support improved herbage management strategies that enhance livestock productivity and sustainability in Uganda’s agricultural sector.

[oaresearchpaper]

This study investigated the botanical composition, nutritional quality, and herbage mass availability in mixed native grazing pastures across Mbarara and Nakasongola districts of Uganda during the 2023 seasons. Further, herbage mass prediction equations/models based on the rising plate meter for mixed pasture swards in Uganda were developed. The results revealed that the wet season (Season A) tended to favour a greater diversity of grasses and legumes, enhancing crude protein (CP) content, while the dry season (Season B) resulted in grass-dominated swards, particularly with the invasive Sporobolus pyramidalis, and a significant decline in legume prevalence. Nutritional profiles indicated high dry matter (DM) content across seasons; however, CP levels dropped markedly in Nakasongola, with increases in neutral detergent fiber (NDF) during the dry season. Herbage mass availability showed significant seasonal effects, with Season A yielding higher DM across both districts. The rising plate meter method effectively predicted herbage availability, with Model 1 outperforming Model 2 in reliability. However, both models exhibited underestimation bias, needing ongoing calibration and validation. These findings highlight the need for strategic supplementation during the dry season and highlight the importance of local environmental conditions in pasture quality. The developed herbage availability prediction equations could be used to compute available feed for grazing cattle using rising plate meters which could support improved herbage management strategies that enhance livestock productivity and sustainability in Uganda’s agricultural sector.