通过采用最低限度数据收集方法填补农学数据的空白
Fatima A M Tenorio1, Juan I Rattalino Edreira1, Juan Pablo Monzon1
1Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583-0915, USA.
Field crops research
|March 18, 2024
概括
本研究提出了一种具有成本效益的方法来收集基本的农学数据,改进作物生产评估. 该方法有效地确定了关键采样区域,减少了数据收集工作,同时确保了主要作物生产地区的准确代表性.
科学领域:
- 农业科学 农业科学
- 农业学是一种农业学.
- 数据收集方法 数据收集方法
背景情况:
- 农学数据 (输入,管理,产量) 对于评估作物系统性能至关重要,但往往无法获得或汇总.
- 现有的数据限制阻碍了对生产率,营养平衡和资源利用效率的全面分析.
- 需要有效的方法来收集主要作物生产区的特定地点的农学数据.
研究的目的:
- 开发和验证分层采样方法,以确定农业数据收集的最佳地点.
- 为了平衡数据收集效率与主要作物生产地区的准确表示.
- 解决关键作物系统的农学信息中的数据缺口.
主要方法:
- 采用了分层采样方法,使用气候区的空间框架和作物分布图.
- 采样区 (SA) 被选定覆盖给定作物的全国收获面积的至少50%.
- 该方法使用在阿根廷,美国和肯尼亚对玉米,小麦和大豆的肥料应用数据进行了验证.
主要成果:
- 该方法确定了每个国家的10-28个采样区域 (阿根廷,美国,肯尼亚).
- 来自少数专家或选定的SA领域的数据提供了对平均肥料率的可靠估计.
- 使用这种方法获得的国家级肥料率与完整数据集的肥料率非常相匹配,需要不到三分之一的观察.
结论:
- 开发的最低作物数据收集方法有效地以成本有效的方式填补农学数据的空白.
- 这种方法适用于主要的作物系统,包括大规模和小规模的农业活动.
- 该方法是通用的,适用于任何作物-国家组合,需要适度的数据收集投资.
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