Exploring the effects of land management change on productivity, carbon and nutrient balance: Application of an Ensemble Modelling Approach to the upper River Taw observatory, UK

A - Papers appearing in refereed journals

Hassall, K. L., Coleman, K., Dixit, P., Granger, S. J., Zhang, Y., Sharp, R., Wu, L., Whitmore, A. P., Richter, G. M., Collins, A. L. and Milne, A. E. 2022. Exploring the effects of land management change on productivity, carbon and nutrient balance: Application of an Ensemble Modelling Approach to the upper River Taw observatory, UK. Science of the Total Environment. 824, p. 153824. https://doi.org/10.1016/j.scitotenv.2022.153824

AuthorsHassall, K. L., Coleman, K., Dixit, P., Granger, S. J., Zhang, Y., Sharp, R., Wu, L., Whitmore, A. P., Richter, G. M., Collins, A. L. and Milne, A. E.
Abstract

Agriculture is challenged to produce healthy food and to contribute to cleaner energy whilst mitigating climate change and protecting ecosystems. To achieve this, policy-driven scenarios need to be evaluated with available data and models to explore trade-offs with robust accounting for the uncertainty in predictions. We developed a novel model ensemble using four complementary state-of-the-art agroecosystems models to explore the impacts of land management change. The ensemble was used to simulate key agricultural and environmental outputs under various scenarios for the upper River Taw observatory, UK. Scenarios assumed (i) reducing livestock production whilst simultaneously increasing the area of arable where it is feasible to cultivate (PG2A), (ii) reducing livestock production whilst simultaneously increasing bioenergy production in areas of the catchment that are amenable to growing bioenergy crops (PG2BE) and (iii) increasing both arable and bioenergy production (PG2A + BE). Our ensemble approach combined model uncertainty using the tower property of expectation and the law of total variance. Results show considerable uncertainty for predicted nutrient losses with different models partitioning the uncertainty into different pathways. Bioenergy crops were predicted to produce greatest yields from Miscanthus in lowland and from SRC-willow (cv. Endurance) in uplands. Each choice of management is associated with trade-offs; e.g. PG2A results in a significant increase of edible calories (6736 Mcal ha−1) but reduced soil C (−4.32 t C ha−1). Model ensembles in the agroecosystem context are difficult to implement due to challenges of model availability and input and output alignment. Despite these challenges, we show that ensemble modelling is a powerful approach for applications such as ours, offering benefits such as capturing structural as well as data uncertainty and allowing greater combinations of variables to be explored. Furthermore, the ensemble provides a robust means for combining uncertainty at different scales and enables us to identify weaknesses in system understanding.

KeywordsAgroecosystems modelling; Trade-offs; Ensemble modelling; Nutrients flows; Arable and livestock systems
Year of Publication2022
JournalScience of the Total Environment
Journal citation824, p. 153824
Digital Object Identifier (DOI)https://doi.org/10.1016/j.scitotenv.2022.153824
Web address (URL)https://doi.org/10.1016/j.scitotenv.2022.153824
Open accessPublished as ‘gold’ (paid) open access
FunderBiotechnology and Biological Sciences Research Council
Lawes Agricultural Trust
Funder project or codeS2N - Soil to Nutrition - Work package 3 (WP3) - Sustainable intensification - optimisation at multiple scales
The Rothamsted Long Term Experiments [2017-2022]
The North Wyke Farm Platform- National Capability [2017-22]
Publisher's version
Accepted author manuscript
Supplemental file
Supplemental file
Output statusPublished
Publication dates
Online16 Feb 2022
Print10 Jun 2022
Publication process dates
Accepted08 Feb 2022
PublisherElsevier Science Bv
ISSN0048-9697

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