Inferring management and predicting sub-field scale C dynamics in UK grasslands using biogeochemical modelling and satellite-derived leaf area data
Introduction
Grasslands cover more than 20% of northwest Europe (Ireland, United Kingdom, France, Netherlands, Belgium, Luxembourg) and represent around 50% of the region’s agricultural land (Smit et al., 2008). In the United Kingdom (UK), grasslands are the dominant type of land use (% of agricultural area), are part of the rural landscape and life, support a large livestock industry and are a critical, direct and indirect, agricultural greenhouse gas (GHG) contributor (DEFRA, 2020, Qi, Holland, Taylor, Richter, 2018). Grasslands in the UK are dominated by C3 plants whose physiology, combined with generally cool and wet climatic conditions, leads to relatively slow litter decomposition rates, which are beneficial for C accumulation in soils and highlight their potential for climate change mitigation through C sequestration (Gibson, 2010, Kipling, Virkajärvi, Breitsameter, Curnel, De Swaef, Gustavsson, Hennart, Höglind, Järvenranta, Minet, Nendel, Persson, Picon-Cochard, Rolinski, Sandars, Scollan, Sebek, Seddaiu, Topp, Twardy, Van Middelkoop, Wu, Bellocchi, 2016, Vertès, Delaby, Klumpp, Bloor, 2018).
Grassland management intensity, in the form of livestock grazing and grass cutting, defines a vegetation C stock that is much more dynamic than the soil C stock (Conant, Cerri, Osborne, Paustian, 2017, Erb, Kastner, Plutzar, Bais, Carvalhais, Fetzel, Gingrich, Haberl, Lauk, Niedertscheider, Pongratz, Thurner, Luyssaert, 2018, Soussana, Lemaire, 2014). Vegetation plays a key role in grassland C cycling as it supplies inputs to the soil C pool directly through the production of litter and indirectly through livestock excrement. Grassland biomass productivity and management are tightly coupled. The application of inputs, including fertilisers and lime, and the combination of sward composition, soil properties and climatic conditions control the productivity of a grassland (Qi et al., 2017). The timing and yield of grass harvests along with the timing and intensity of livestock grazing determine the final biomass production and the net C budget of a grassland.
The quantification of the C balance of managed grasslands across time and space is an important research area, particularly when considering the C-neutrality ambitions and Nationally Determined Contributions (NDC) of developed countries following the Paris agreement (Bell, Cloy, Topp, Ball, Bagnall, Rees, Chadwick, 2016, Committee on Climate Change, 2019). In this context, in 2019 the government of the UK has set a legally binding target of net zero GHG emissions by 2050 (UK Government, 2019). Existing approaches to quantifying the productivity and C budget of managed grasslands include purely statistical modelling tools, built on agricultural census and experimental farm data (Herrero, Havlík, Valin, Notenbaert, Rufino, Thornton, Bluemmel, Weiss, Grace, Obersteiner, 2013, Qi, Holland, Taylor, Richter, 2018, Qi, Murray, Richter, 2017, Smit, Metzger, Ewert, 2008). Nevertheless, statistical approaches do not provide an in-depth description of grassland functioning, limiting their utility for climate change and C cycling studies and for management scenario explorations. For these purposes, biogeochemical models that describe the processes controlling C accumulation and partitioning must be used. Such process models incorporate information on grassland management (i.e. grazing and cutting) and, if relevant data are available, can be implemented at any location (Chang, Ciais, Viovy, Vuichard, Sultan, Soussana, 2015, Chang, Viovy, Vuichard, Ciais, Wang, Cozic, Lardy, Graux, Klumpp, Martin, Soussana, 2013, Kipling, Virkajärvi, Breitsameter, Curnel, De Swaef, Gustavsson, Hennart, Höglind, Järvenranta, Minet, Nendel, Persson, Picon-Cochard, Rolinski, Sandars, Scollan, Sebek, Seddaiu, Topp, Twardy, Van Middelkoop, Wu, Bellocchi, 2016, Puche, Senapati, Flechard, Klumpp, Kirschbaum, Chabbi, 2019, Rolinski, Müller, Heinke, Weindl, Biewald, Bodirsky, Bondeau, Boons-Prins, Bouwman, Leffelaar, Roller, Schaphoff, Thonicke, 2018, Vuichard, Soussana, Ciais, Viovy, Ammann, Calanca, Clifton-Brown, Fuhrer, Jones, Martin, 2007). However, process models typically require local information on model parameters and exogenous drivers that can be challenging to obtain.
Identifying grassland management across time and space remains an outstanding challenge (Reinermann and Asam, 2020). Data on daily livestock density and the timings of harvests are available for a limited number of experimental farms but this information is uncertain across large areas (Chang et al., 2017). Earth observation (EO) satellite sensors, however, can provide spatially and temporally consistent data on grasslands at local to regional extents (Ali et al., 2016). Empirical models (e.g. Huang, He, Niu, 2013, Ullah, Si, Schlerf, Skidmore, Shafique, Iqbal, 2012), machine learning algorithms (e.g., Ali, Cawkwell, Dwyer, Barrett, Green, 2016, Ghosh, Behera, 2018, Wang, Wu, Deng, Tang, Wang, Sun, Shangguan, 2017) and physical-based retrieval approaches (e.g., He, Li, Yin, Nan, Bian, 2019, Punalekar, Verhoef, Quaife, Humphries, Bermingham, Reynolds, 2018) are typically used to produce estimates of biomass in forests and grasslands from EO data.
The recent availability of open-access EO data from the European Space Agency’s (ESA) Sentinel-2 dual-satellite constellation, with a spatial resolution as fine as 10 m and an average global revisit time of 5 days, has the potential to provide frequent observations of grassland vegetation at the sub-field scale. The fact that fields of managed grassland in the UK are enclosed by hedgerows and most of them cover an area between 5–8 ha highlights the importance of high resolution EO data for monitoring vegetation dynamics in UK grasslands (CEH, 2017, DEFRA, 2020, Deverell, McDonnell, Devlin, 2009, Guiomar, Godinho, Pinto-Correia, Almeida, Bartolini, Bezák, Biró, Bjørkhaug, Bojnec, Brunori, Corazzin, Czekaj, Davidova, Kania, Kristensen, Marraccini, Molnár, Niedermayr, O’Rourke, Ortiz-Miranda, Redman, Sipiläinen, Sooväli-Sepping, Šmane, Surová, Sutherland, Tcherkezova, Tisenkopfs, Tsiligiridis, Tudor, Wagner, Wästfelt, 2018, Robinson, Sutherland, 2002). In addition, for use on an operational basis the overall extent to which EO data can provide timely information on UK grassland dynamics is dependent on the frequency of available cloud-free imagery. Although there is typically an inverse relationship between temporal and spatial resolution, the use of EO-derived data at higher temporal resolutions is required for resolving fields of intensively managed grasslands in the UK (Gao, 2006).
The combined use of EO data and process modelling will produce more robust estimates of managed grassland C balance when compared to using each approach independently. This synergy of model and observational data, commonly known as model-data fusion (MDF), requires a framework in which ecosystem model parameters, fluxes and pools are adjusted accordingly to match observations (data assimilation) (Raupach et al., 2005). Probabilistic approaches for parameter optimisation and for quantifying predictive uncertainty in model-based studies are central to MDF (Ben Touhami, Bellocchi, 2015, Gottschalk, Wattenbach, Neftel, Fuhrer, Jones, Lanigan, Davis, Campbell, Soussana, Smith, 2007, Oenema, Burgers, van Keulen, van Ittersum, 2015, van Oijen, 2017, Patenaude, Milne, Van Oijen, Rowland, Hill, 2008). The dependence of MDF on process-modelling means that EO data for grasslands can be translated into estimates of C present in above and below-ground vegetation, removed via grazing or cutting, transferred to the soil and lost as CO. In this respect, and in contrast to EO data processing methods, MDF provides a more holistic and informative approach to C balance quantification. Considering the increasing volume and quality of EO data, MDF has the potential to be used in order to monitor the C balance of managed grasslands across space. Model-data fusion has been used in studies focusing on different aspects of terrestrial ecosystem functioning in the past (Bloom, Williams, 2015, Fox, Williams, Richardson, Cameron, Gove, Quaife, Ricciuto, Reichstein, Tomelleri, Trudinger, Van Wijk, 2009, Keenan, Davidson, Moffat, Munger, Richardson, 2012, Kuppel, Peylin, Maignan, Chevallier, Kiely, Montagnani, Cescatti, 2014, Peaucelle, Bacour, Ciais, Vuichard, Kuppel, Peñuelas, Belelli Marchesini, Blanken, Buchmann, Chen, Delpierre, Desai, Dufrene, Gianelle, Gimeno-Colera, Gruening, Helfter, Hörtnagl, Ibrom, Joffre, Kato, Kolb, Law, Lindroth, Mammarella, Merbold, Minerbi, Montagnani, Šigut, Sutton, Varlagin, Vesala, Wohlfahrt, Wolf, Yakir, Viovy, 2019, Peylin, Bacour, MacBean, Leonard, Rayner, Kuppel, Koffi, Kane, Maignan, Chevallier, Ciais, Prunet, 2016, Scholze, Buchwitz, Dorigo, Guanter, Quegan, 2017, Smallman, Exbrayat, Mencuccini, Bloom, Williams, 2017, Wang, Trudinger, Enting, 2009, Xiao, Davis, Urban, Keller, 2014). However, the focus of studies that considered grasslands relied exclusively on plant functional type identification and they have not examined C cycling and the role of grassland management (Kuppel, Peylin, Maignan, Chevallier, Kiely, Montagnani, Cescatti, 2014, Peaucelle, Bacour, Ciais, Vuichard, Kuppel, Peñuelas, Belelli Marchesini, Blanken, Buchmann, Chen, Delpierre, Desai, Dufrene, Gianelle, Gimeno-Colera, Gruening, Helfter, Hörtnagl, Ibrom, Joffre, Kato, Kolb, Law, Lindroth, Mammarella, Merbold, Minerbi, Montagnani, Šigut, Sutton, Varlagin, Vesala, Wohlfahrt, Wolf, Yakir, Viovy, 2019, Peylin, Bacour, MacBean, Leonard, Rayner, Kuppel, Koffi, Kane, Maignan, Chevallier, Ciais, Prunet, 2016).
The aim of this study is to quantify C dynamics in fields of managed grassland in the UK using MDF. We use a process model of grassland C dynamics (DALEC-Grass) within a probabilistic MDF algorithm (CARDAMOM) to quantify the C impact of harvest and grazing events at three fields located on a experimental farm in Southwest England (UK) between 2015 and 2018. DALEC-Grass has been tested against an extensive set of field-measured data (chamber and tower-based) of C fluxes (including net ecosystem change, ecosystem respiration), C pools (above and below-ground biomass) and LAI collected at two variably-managed fields in the UK (Myrgiotis et al., 2020). In that study, daily livestock numbers and cutting dates were known. In the present study, the model is provided with a weekly time-series of vegetation reduction, which is derived from a high-temporal and low-spatial resolution EO-based dataset (Proba-V). These data, along with meteorological drivers, are used by DALEC-Grass to simulate weekly grass biomass removals via harvest and grazing. Temporally infrequent, but high spatial resolution, leaf area index (LAI) time series derived from EO-based data (Sentinel-2) are assimilated within CARDAMOM to (1) infer the simulated management from the vegetation reduction inputs according to biophysical and biogeochemical rules and (2) refine the distribution of DALEC-Grass parameters. The probabilistic MDF approach allows us to consider (i) the temporal uncertainties around the EO-derived vegetation reduction drivers, (ii) the model’s parametric uncertainty and (iii) the uncertainty around the assimilated LAI EO data. The combined impact of these uncertainties on MDF-estimates is quantified.
We compare MDF predictions to independent ground-based data on livestock density and harvest yields in order to assess the effectiveness of DALEC-Grass and CARDAMOM. The MDF-estimated net C balance and its different components are presented and discussed. Since livestock density is a key element of a grassland’s economic, ecological and environmental performance, we conducted a sensitivity analysis to assess how it affects the simulated C dynamics. As far as we are aware this study is the first example of a validated analysis of biomass removals and of C cycling in managed grasslands at field scale using MDF and relying exclusively on EO data (i.e. no in-situ data used). Based on the obtained results we discuss how monitoring the grassland C balance across time and space could be achieved by up-scaling the MDF framework.
Section snippets
Field data
The fields modelled in this study are part of the North Wyke Farm Platform, a systems scale research facility in Southwest England for investigating the sustainability of lowland ruminant production systems (Orr et al., 2016). Rothamsted Research’s North Wyke site is located near Okehampton, Devon (50N, 30W) and was established in 2010. The platform is 63 ha in size divided into 15 hydrologically isolated catchments across three 21 ha farmlets with five catchments in each. Six of
MDF performance
For Great Field, the CARDAMOM-produced LAI estimates show a good fit with the assimilated Sentinel-2 LAI data (overlap = 75%) (Table 3). The initial MDF implementation identified that this field was harvested only once, in 2017, and the harvest that occurred in 2018 was not captured. When implementing CARDAMOM using this information (i.e. annual harvest probability EDC) provided, the single harvest event was simulated in June 2017 even though it actually occurred a month earlier (May 2017). For
The performance of the MDF
Our results show that by using available EO datasets within a MDF framework we can determine with acceptable accuracy (1) the timing and yield of annual grass harvests and (2) the amount of grazing-induced biomass removals. DALEC-Grass depends on weekly vegetation reduction inputs in order to simulate biomass reduction due to grazing and cutting. Because the vegetation reduction data contain temporal uncertainties, time lags between the modelled and the actual timing of harvest events were
Conclusions
Climate change, increasing human population and changing demand for livestock products together with national C neutrality targets create the need for better estimates of the C balance of managed grasslands and of its spatial variation. This study demonstrated how estimates of grassland utilisation and C budget at the farm scale can be produced through the synergy of EO data and process modelling. The level of accuracy of the predicted annual utilisation rates (grazing/harvest) was shown to be
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
The authors would like to thank the four anonymous reviewers for improving this article through their constructive comments. VM and MW devised the study concept. VM developed DALEC-Grass, implemented the model-data fusion, and undertook the analysis. PH and HS provided ground based data and other information from North Wyke. VM and MW led the analysis of the results. VM led the writing, with support from MW, AR, PH and HS. This study was supported by the Natural Environment Research Council
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