A new approach to characterising and predicting crop rotations using national-scale annual crop maps

Upcott, Emily V. ORCID: https://orcid.org/0000-0003-1133-3102; Henrys, Peter A. ORCID: https://orcid.org/0000-0003-4758-1482; Redhead, John W. ORCID: https://orcid.org/0000-0002-2233-3848; Jarvis, Susan G. ORCID: https://orcid.org/0000-0002-6770-2002; Pywell, Richard F. ORCID: https://orcid.org/0000-0001-6431-9959. 2023 A new approach to characterising and predicting crop rotations using national-scale annual crop maps. Science of the Total Environment, 160471. 13, pp. https://doi.org/10.1016/j.scitotenv.2022.160471

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Official URL: http://dx.doi.org/10.1016/j.scitotenv.2022.160471

Abstract/Summary

Cropping decisions affect the nature, timing and intensity of agricultural management strategies. Specific crop rotations are associated with different environmental impacts, which can be beneficial or detrimental. The ability to map, characterise and accurately predict rotations enables targeting of mitigation measures where most needed and forecasting of potential environmental risks. Using six years of the national UKCEH Land Cover® plus: Crops maps (2015–2020), we extracted crop sequences for every agricultural field parcel in Great Britain (GB). Our aims were to first characterise spatial patterns in rotation properties over a national scale based on their length, type and structural diversity values, second, to test an approach to predicting the next crop in a rotation, using transition probability matrices, and third, to test these predictions at a range of spatial scales. Strict cyclical rotations only occupy 16 % of all agricultural land, whereas long-term grassland and complex-rotational agriculture each occupy over 40 %. Our rotation classifications display a variety of distinctive spatial patterns among rotation lengths, types and diversity values. Rotations are mostly 5 years in length, short mixed crops are the most abundant rotation type, and high structural diversity is concentrated in east Scotland. Predictions were most accurate when using the most local spatial approach (spatial scaling), 5-year rotations, and including long-term grassland. The prediction framework we built demonstrates that our crop predictions have an accuracy of 36–89 %, equivalent to classification accuracy of national crop and land cover mapping using earth observation, and we suggest this could be improved with additional contextual data. Our results emphasise that rotation complexity is multi-faceted, yet it can be mapped in different ways and forms the basis for further exploration in and beyond agronomy, ecology, and other disciplines.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1016/j.scitotenv.2022.160471
UKCEH and CEH Sections/Science Areas:	Biodiversity (Science Area 2017-) Soils and Land Use (Science Area 2017-)
ISSN:	0048-9697
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	rotation classification, crop sequence, crop prediction, spatial pattern, transition probability matrix, UKCEH Land Cover® plus: Crops
NORA Subject Terms:	Ecology and Environment Agriculture and Soil Science
Date made live:	19 Jan 2023 15:28 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/533819

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1016/j.scitotenv.2022.160471

UKCEH and CEH Sections/Science Areas:

Biodiversity (Science Area 2017-)
Soils and Land Use (Science Area 2017-)

ISSN:

0048-9697

Additional Information. Not used in RCUK Gateway to Research.:

Open Access paper - full text available via Official URL link.

Additional Keywords:

rotation classification, crop sequence, crop prediction, spatial pattern, transition probability matrix, UKCEH Land Cover® plus: Crops