Multi-tier archetypes to characterise British landscapes, farmland and farming practices
Multi-tier archetypes to characterise British landscapes, farmland and farming practices
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描述英国景观、农田和农业实践的多层原型
DOI：
10.1088/1748-9326/ac810e
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发表时间：
2022
影响因子：
6.7
通讯作者：
Goodwin C
中科院分区：
文献类型：
--
作者：
Goodwin C
由于对粮食和环境服务的需求不断增加，农业越来越需要提供多种成果。通过确定广泛的原型群体，在农业景观中描述差异，是探索土地提供这些潜在竞争功能的空间模式的重要一步。在景观和农场管理的多个层面上创建特征，可以使政策制定者和土地管理者在不同的干预规模上协调生态系统服务的提供。这可以确定如何增加公共产品的互补性和农业景观的可持续性。我们使用数据驱动的机器学习在三个层次上创建景观和农业管理原型（1公里分辨率），由适应机会定义。一级原型量化了英国各地土壤、土地覆盖和人口的广泛差异，这些差异不容易受到土地管理者行为的影响;二级原型捕捉了英国以农田为主的景观中更细微的变化，土地管理者可能会对这些变化产生一定程度的影响。第3级原型是在英格兰和威尔士的国家一级建立的，重点是农田主导景观中的社会经济和农业生态特征，以农场管理的差异为特征。通过使用非嵌套的层次结构，我们确定了哪些类型的管理仅限于某些景观设置，哪些适用于多个景观环境。了解农业景观和耕作方法内部和之间的变化对规划环境可持续性和粮食安全具有影响。它还可以帮助了解干预措施最有效的规模，从激励农民行为的变化到大规模土地使用变化的政策驱动因素。
Due to rising demand for both food and environmental services, agriculture is increasingly required to deliver multiple outcomes. Characterising differences, across agricultural landscapes, via the identification of broad archetypal groupings, is an important step in exploring spatial patterns in the capacity of land to deliver these potentially competing functions. Creating characterisations at multiple levels, for landscape and farm management, can allow policy-makers and land managers to harmonise delivery of ecosystem services at different intervention scales. This can identify ways to increase the complementarity of public goods and the sustainability of farmed landscapes. We used data-driven machine learning to create landscape and agricultural management archetypes (1 km resolution) at three levels, defined by opportunities for adaptation. Tier 1 archetypes quantify broad differences in soil, land cover and population across Great Britain, which cannot be readily influenced by the actions of land managers; Tier 2 archetypes capture more nuanced variations within farmland-dominated landscapes of Great Britain, over which land managers may have some degree of influence. Tier 3 archetypes are built at national levels for England and Wales and focus on socioeconomic and agro-ecological characteristics within farmland-dominated landscapes, characterising differences in farm management. By using a non-nested hierarchy, we identified which types of management are restricted to certain landscape settings, and which are applicable across multiple landscape contexts. Understanding variation within and between agricultural landscapes and farming practices has implications for planning environmental sustainability and food security. It can also aid understanding of the scale at which interventions could be most effective, from incentivising changes in farmer behaviour to policy drivers of large-scale land use change.
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气候变化中的林地网络：土地利用变化的威胁
影响因子：
5.9
作者：
A. Gimona;L. Poggio;I. Brown;M. Castellazzi
通讯作者：
M. Castellazzi
影响因子：
4.2
作者：
Levers, Christian;Mueller, Daniel;Kuemmerle, Tobias
通讯作者：
Kuemmerle, Tobias
DOI：
10.1109/bigdata.2018.8622558
发表时间：
2018-08
期刊：
2018 IEEE International Conference on Big Data (Big Data)
影响因子：
--
作者：
Wenbin Zhang;Jianwu Wang;Daeho Jin;L. Oreopoulos;Zhibo Zhang
通讯作者：
Wenbin Zhang;Jianwu Wang;Daeho Jin;L. Oreopoulos;Zhibo Zhang
DOI：
10.1073/pnas.0812540106
发表时间：
2009-12-08
影响因子：
11.1
作者：
Rudel, Thomas K.;Schneider, Laura;Grau, Ricardo
通讯作者：
Grau, Ricardo
用于分析和设计从农场到地区层面的综合作物-牲畜系统的社会生态框架
影响因子：
2.7
作者：
M. Moraine;M. Duru;O. Thérond
通讯作者：
O. Thérond