Patch cropping- a new methodological approach to determine new field arrangements that increase the multifunctionality of agricultural landscapes

  • Agricultural intensification decreased land cover complexity by converting small complex arable field geometries into large and simple structures which then were managed uniformly. These changes have led to a variety of negative environmental effects and influence ecosystem services. We present a novel small-scale and site-specific cropping system which splits a large field into small homogeneous sub-fields called ‘patches’ grouped in different yield potentials. A detailed workflow is presented to generate new spatially arranged patches with special focus on preprocessing and filtering of multi-year yield data, the variation in patch sizes and the adaptation of maximum working width to use available conventional farm equipment and permanent traffic lanes. The reduction of variance by the used cluster algorithm depends on the within-field heterogeneity. The patch size, the number of growing seasons (GS) used for clustering and the parallel shift of the patch structure along the permanent traffic lane resulted in a change in relativeAgricultural intensification decreased land cover complexity by converting small complex arable field geometries into large and simple structures which then were managed uniformly. These changes have led to a variety of negative environmental effects and influence ecosystem services. We present a novel small-scale and site-specific cropping system which splits a large field into small homogeneous sub-fields called ‘patches’ grouped in different yield potentials. A detailed workflow is presented to generate new spatially arranged patches with special focus on preprocessing and filtering of multi-year yield data, the variation in patch sizes and the adaptation of maximum working width to use available conventional farm equipment and permanent traffic lanes. The reduction of variance by the used cluster algorithm depends on the within-field heterogeneity. The patch size, the number of growing seasons (GS) used for clustering and the parallel shift of the patch structure along the permanent traffic lane resulted in a change in relative variance. Independent cross validation showed an increased performance of the classification algorithm with increasing number of GS used for clustering. The applied cluster analysis resulted in robust field segregation according to different yield potential zones and provides an innovative method for a novel cropping system.show moreshow less

Download full text files

Export metadata

Additional Services

Share in Twitter Search Google Scholar Statistics
Metadaten
Author:Marco Donat, Jonas Geistert, Kathrin Grahmann, Ralf BlochORCiDGND, Sonoko D. Bellingrath-KimuraORCiD
URN:urn:nbn:de:kobv:eb1-opus-4365
DOI:https://doi.org/10.1016/j.compag.2022.106894
ISSN:0168-1699
Parent Title (English):Computers and Electronics in Agriculture
Publisher:Elsevier
Document Type:Article
Language:English
Year of Completion:2022
Date of first Publication:2022/04/12
Publishing Institution:Hochschule für nachhaltige Entwicklung Eberswalde
Release Date:2023/04/14
Tag:Soil Management Zone Delineation; Yield Productivity Zones; Python; Clustering
Issue:197
Article Number:106894
First Page:1
Last Page:15
Institutions / Departments:Fachbereich Landschaftsnutzung und Naturschutz
open_access (DINI-Set):open_access
University Bibliography:University Bibliography
Zweitveröffentlichung
Peer-Review / Referiert
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International
Einverstanden ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.