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Adaptivity of eating and rumination behavior are assumed to play a particular role for cows in low-input systems, because they are more frequently challenged by altering forage quality. The present study examined relations between forage quality, chewing behavior and efficiency estimates in dairy cows from Swiss zero-concentrate organic farming systems. A total of 102 Swiss Fleckvieh cows on two organic dairy farms were observed during one full production year. Each farm was visited eight times. At each visit, up to 45 cows were equipped with RumiWatch® (Itin and Hoch GmbH, Liestal, Switzerland) sensor head collars, from which eating and rumination time and the frequency of activity changes were obtained for 48 h. Milk from one complete day was analyzed individually. All offered roughages (pasture herbage, grass silages and hay) were sampled at each visit and analyzed for crude fiber, crude protein and net energy, and a feed quality score was calculated. Metabolic production efficiency was estimated based on entire lactation data, and feed efficiency was estimated based on the individual farm visits. Lactation stage and forage quality significantly affected the chewing sensor variables. Eating time increased and rumination time decreased with the improved nutritive quality of feed. Coefficients of variance of the factor animal in the sensor variables showed a contribution of the individual cow to chewing behavior. Significant correlations between chewing sensor variables and efficiency estimates were not found. In conclusion, chewing behavior under on-farm conditions in low-input dairy farms alters during lactation and during changing forage quality, with significant animal effects, indicating potential for new phenotypes, albeit with no indications for efficiency.
Adaptivity of eating and rumination behavior are assumed to play a particular role for cows in low-input systems, because they are more frequently challenged by altering forage quality. The present study examined relations between forage quality, chewing behavior and efficiency estimates in dairy cows from Swiss zero-concentrate organic farming systems. A total of 102 Swiss Fleckvieh cows on two organic dairy farms were observed during one full production year. Each farm was visited eight times. At each visit, up to 45 cows were equipped with RumiWatch® (Itin and Hoch GmbH, Liestal, Switzerland) sensor head collars, from which eating and rumination time and the frequency of activity changes were obtained for 48 h. Milk from one complete day was analyzed individually. All offered roughages (pasture herbage, grass silages and hay) were sampled at each visit and analyzed for crude fiber, crude protein and net energy, and a feed quality score was calculated. Metabolic production efficiency was estimated based on entire lactation data, and feed efficiency was estimated based on the individual farm visits. Lactation stage and forage quality significantly affected the chewing sensor variables. Eating time increased and rumination time decreased with the improved nutritive quality of feed. Coefficients of variance of the factor animal in the sensor variables showed a contribution of the individual cow to chewing behavior. Significant correlations between chewing sensor variables and efficiency estimates were not found. In conclusion, chewing behavior under on-farm conditions in low-input dairy farms alters during lactation and during changing forage quality, with significant animal effects, indicating potential for new phenotypes, albeit with no indications for efficiency.
China has implemented several ecological projects in the Loess Plateau region to address severe land degradation and soil erosion. Accurately assessing ecological restoration and its driving factors remains challenging. Previous studies in this area concentrated on driving factors have mainly focused on natural factors at the regional or watershed scale, with limited consideration of socioeconomic factors at the county scale. In this study conducted in Huanglong County on the Loess Plateau, the focus was to fill the gaps in previous research and provide insights into the socioeconomic driving forces behind vegetation greening. Remote sensing image data (NDVI) from 1999 to 2019 were used to analyze vegetation greenness dynamics in the region. Five socioeconomic variables were considered, including afforestation intensity, deforestation intensity, agricultural intensity, village intensity, and road intensity layers, to characterize the impact of afforestation, agriculture, and urbanization policies. The RESTREND (residual trends) method was employed to assess the relative importance of climate and human activities on vegetation dynamics. This study found that temperature–NDVI relationships are more suitable for building RESTREND models than precipitation–NDVI relationships. Human activity was the main driver of vegetation dynamics, contributing 62% compared to 38% from climate change. Agricultural practices and afforestation were found to have a positive impact on ecological restoration, while deforestation and urbanization had no significant impact. These findings highlight a conceptual framework for understanding the intricate relationship between ecological restoration, climatic factors, and human activity on the Loess Plateau. This study suggests that significant progress has been made in ecological restoration through human efforts in combating land degradation. However, it emphasizes the need to strengthen natural conservation efforts and gradually transition toward restoration processes driven by natural forces for sustainable socioeconomic development. The methodology used in this study can be applied to explore the driving forces of ecological restoration in other regions facing human-driven land degradation.
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