基于GPS的隐藏马尔科夫模型记录了萨赫勒地区牧民的流动性
Arthur Scriban1,2,3, Serge Nabeneza2,3, Daniel Cornelis4,5
1CIRAD, UMR SELMET, PPZS, Dakar 11500, Senegal.
Sensors (Basel, Switzerland)
|November 9, 2024
概括
这项研究引入了一种基于GPS的方法来跟踪萨赫勒地区的牲畜流动,揭示了关键的牧养行为,如夜间放牧. 这些数据对于理解和支持萨赫里亚牧业至关重要.
科学领域:
- 农业学是一种农业学.
- 动物行为 动物行为
- 地理空间分析的研究.
背景情况:
- 在农业系统中,牲畜的流动性对于料管理,营养循环和家庭经济至关重要.
- 在萨赫勒地区,关于牧民流动和放牧实践的准确数据显著缺乏.
- 了解这些动态对于可持续的农牧主义至关重要.
研究的目的:
- 采用基于GPS的行为模型,引入和验证一种分析和记录牧民流动性的方法.
- 为了分类牛活动状态并了解它们的环境驱动因素.
- 为未来关于类似系统中牲畜移动的研究提供框架.
主要方法:
- 收集了塞内加尔半干旱牧场的过渡和居民牛群的2.5年的连续GPS数据.
- 开发了一个隐藏的马尔科夫模型 (HMM) 来将GPS数据分类为静止状态,食状态和移动状态.
- 结合HMM输出与基于卫星的土地使用数据来分析活动分布.
主要成果:
- 隐藏马尔科夫模型准确地分类了牲畜的活动:休息 (47%),食 (37%) 和旅行 (16%).
- 该模型确定了受温度影响的状态变化,并揭示了像过渡,夜间放牧和田间牧场这样的模式.
- 分析显示,在景观,季节和日常周期中,活动分布明显.
结论:
- 开发的基于GPS的方法提供了对牛的流动性和很少记录的萨赫里亚农牧业实践的精确见解.
- 这种方法可以为面临未来挑战的牧业系统提供可持续管理策略.
- 该方法和由此产生的数据集可用于支持进一步的研究和保护工作.
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