Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/289751 
Erscheinungsjahr: 
2024
Quellenangabe: 
[Journal:] Landscape Ecology [ISSN:] 1572-9761 [Volume:] 39 [Issue:] 4 [Article No.:] 90 [Publisher:] Springer Nature [Place:] Berlin [Year:] 2024
Verlag: 
Springer Nature, Berlin
Zusammenfassung: 
Both crop rotational diversity and landscape diversity are important for ensuring resilient agricultural production and supporting biodiversity and ecosystem services in agricultural landscapes. However, the relationship between crop rotational diversity and landscape diversity is largely understudied. We aim to assess how crop rotational diversity is spatially organised in relation to soil, climate, and landscape diversity at a regional scale in Brandenburg, Germany. We used crop rotational richness, Shannon’s diversity and evenness indices per field per decade (i.e., crop rotational diversity) as a proxy for agricultural diversity and land use and land cover types and habitat types as proxies for landscape diversity. Soil and climate characteristics and geographical positions were used to identify potential drivers of the diversity facets. All spatial information was aggregated at 10 × 10 km resolution, and statistical associations were explored with interpretable machine learning methods. Crop rotational diversity was associated negatively with landscape diversity metrics and positively with soil quality and the proportion of agricultural land use area, even after accounting for the other variables. Our study indicates a spatial trade-off between crop and landscape diversity (competition for space), and crop rotations are more diverse in more simplified landscapes that are used for agriculture with good quality of soil conditions. The respective strategies and targets should be tailored to the corresponding local and regional conditions for maintaining or enhancing both crop and landscape diversity jointly to gain their synergistic positive impacts on agricultural production and ecosystem management.
Schlagwörter: 
cropping systems
explainable artificial intelligence
land use
landscape heterogeneity
multiple scales
soil quality
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