CERES :: Browsing by Author "Corstanje, Ron"

Browsing by Author "Corstanje, Ron"

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Open Access
An agent-based model of farmer decision making: application to shared water resources in Arid and semi-arid regions
(Elsevier, 2025-04-01) El Fartassi, Imane; Milne, Alice E.; Metcalfe, Helen; El Alami, Rafiq; Diarra, Alhousseine; Alonso-Chavez, Vasthi; Zawadzka, Joanna Ewa; Waine, Toby W.; Corstanje, Ron
The study presents an agent-based modelling framework that integrates behavioural and biophysical models to investigate shared irrigation water management in an arid region. The behavioural model simulates farmers' decisions about their water irrigation sources (dam or groundwater) and whether to continue cultivating in the face of drought. This model was parameterised using survey data. The biophysical model component quantifies the impact of water availability and irrigation sources on soil salinity accumulation and its effects on crop productivity. Applied to the Al Haouz Basin, in Morocco, the integrated model reveals several key findings: (1) Increased groundwater access through water abstraction authorization can initially boost productivity but leads to widespread salinisation and farm abandonment, particularly under climate change scenarios. (2) Scenarios with reduced dam water availability demonstrate that mixed irrigation strategies mitigate short-term productivity losses but fail to prevent long-term soil salinity issues. (3) Land abandonment is significantly influenced by the level of water abstraction authorizations, with higher abstraction leading to more severe environmental degradation and social impacts. (4) Policy scenarios reveal that there is a theoretical optimal level of groundwater abstraction that maximises productivity while minimising land abandonment and salinity build-up. These results highlight the complex trade-offs between short-term gains and long-term sustainability, emphasising the need for holistic water governance policies that balance individual and collective interests.
Open Access
Bundling ecosystem services at a high resolution in the UK: trade-offs and synergies in urban landscapes
(Springer, 2021-04-29) Karimi, James D.; Corstanje, Ron; Harris, Jim A.
Context Ecosystem service bundles can be defined as the spatial co-occurrence of ecosystem services in a landscape. The understanding of the delivery of multiple ecosystem services as bundles in urban areas is limited. This study modelled ecosystem services in an urban area comprising the towns of Milton Keynes, Bedford and Luton. Objectives The objectives of this study were to assess (1) how ecosystem service bundles scale at a 2 m spatial resolution and (2) identify and analyse the composition of ecosystem service bundles. Methods Six ecosystem services were modelled with the InVEST framework at a 2 m resolution. The correlations between ecosystem services were calculated using the Spearman rank correlation coefficient method. Principal Component Analysis and K-means cluster analysis were used to analyse the distributions, spatial trade-offs and synergies of multiple ecosystem services. Results The results showed that regulating services had the tendency to form trade-offs and synergies. There was a significant tendency for trade-offs between supporting service Habitat quality and Pollinator abundance. Four bundle types were identified which showed specialised areas with prevalent soil erosion with high levels in water supply, areas with high values in nutrient retention, areas with high levels in carbon storage and urban areas with pollinator abundance. Conclusions This study demonstrates the existence of synergies and trade-offs between ecosystem services and the formation of ecosystem service bundles in urban areas. This study provides a better understanding of the interactions between services and improve the management choices in ecosystem service provision in urban and landscape planning.
Open Access
The Coastal Carbon Library and Atlas: open source soil data and tools supporting blue carbon research and policy
(Wiley, 2024-01-01) Holmquist, James R.; Klinges, David; Lonneman, Michael; Wolfe, Jaxine; Boyd, Brandon; Eagle, Meagan; Sanderman, Jonathan; Todd‐Brown, Kathe; Belshe, E. Fay; Brown, Lauren N.; Chapman, Samantha; Corstanje, Ron; Janousek, Christopher; Morris, James T.; Noe, Gregory; Rovai, André; Spivak, Amanda; Vahsen, Megan; Windham‐Myers, Lisamarie; Kroeger, Kevin; Megonigal, J. Patrick
Quantifying carbon fluxes into and out of coastal soils is critical to meeting greenhouse gas reduction and coastal resiliency goals. Numerous ‘blue carbon’ studies have generated, or benefitted from, synthetic datasets. However, the community those efforts inspired does not have a centralized, standardized database of disaggregated data used to estimate carbon stocks and fluxes. In this paper, we describe a data structure designed to standardize data reporting, maximize reuse, and maintain a chain of credit from synthesis to original source. We introduce version 1.0.0. of the Coastal Carbon Library, a global database of 6723 soil profiles representing blue carbon‐storing systems including marshes, mangroves, tidal freshwater forests, and seagrasses. We also present the Coastal Carbon Atlas, an R‐shiny application that can be used to visualize, query, and download portions of the Coastal Carbon Library. The majority (4815) of entries in the database can be used for carbon stock assessments without the need for interpolating missing soil variables, 533 are available for estimating carbon burial rate, and 326 are useful for fitting dynamic soil formation models. Organic matter density significantly varied by habitat with tidal freshwater forests having the highest density, and seagrasses having the lowest. Future work could involve expansion of the synthesis to include more deep stock assessments, increasing the representation of data outside of the U.S., and increasing the amount of data available for mangroves and seagrasses, especially carbon burial rate data. We present proposed best practices for blue carbon data including an emphasis on disaggregation, data publication, dataset documentation, and use of standardized vocabulary and templates whenever appropriate. To conclude, the Coastal Carbon Library and Atlas serve as a general example of a grassroots F.A.I.R. (Findable, Accessible, Interoperable, and Reusable) data effort demonstrating how data producers can coordinate to develop tools relevant to policy and decision‐making.
Open Access
Earthworm distributions are not driven by measurable soil properties. Do they really indicate soil quality?
(PLOS (Public Library of Science), 2021-08-30) Hodson, Mark E.; Corstanje, Ron; Jones, David T.; Witton, Jo; Burton, Victoria J.; Sloan, Tom; Eggleton, Paul
Abundance and distribution of earthworms in agricultural fields is frequently proposed as a measure of soil quality assuming that observed patterns of abundance are in response to improved or degraded environmental conditions. However, it is not clear that earthworm abundances can be directly related to their edaphic environment, as noted in Darwin’s final publication, perhaps limiting or restricting their value as indicators of ecological quality in any given field. We present results from a spatially explicit intensive survey of pastures within United Kingdom farms, looking for the main drivers of earthworm density at a range of scales. When describing spatial variability of both total and ecotype-specific earthworm abundance within any given field, the best predictor was earthworm abundance itself within 20–30 m of the sampling point; there were no consistent environmental correlates with earthworm numbers, suggesting that biological factors (e.g. colonisation rate, competition, predation, parasitism) drive or at least significantly modify earthworm distributions at this spatial level. However, at the national scale, earthworm abundance is well predicted by soil nitrate levels, density, temperature and moisture content, albeit not in a simple linear fashion. This suggests that although land can be managed at the farm scale to promote earthworm abundance and the resulting soil processes that deliver ecosystem services, within a field, earthworm distributions will remain patchy. The use of earthworms as soil quality indicators must therefore be carried out with care, ensuring that sufficient samples are taken within field to take account of variability in earthworm populations that is unrelated to soil chemical and physical properties.
Open Access
Emerging resilience metrics in an intensely managed ecological system
(Elsevier, 2024-03-01) Toumasis, Nikolaos; Simms, Daniel; Rust, Will; Harris, Jim A.; White, John R.; Zawadzka, Joanna Ewa; Corstanje, Ron
There is growing interest in understanding resilience of ecosystems because of the potential of abrupt and possibly irreversible shifts between alternative ecosystem states. Tipping points are observed in systems with strong positive feedback, providing early warning signals of potential instability. These points can be detected through metrics like critical slowing down (CSD), such as increased recovery time, variance, and autocorrelation. These indicators have been tested in laboratory experiments and field settings, ignoring trait changes. Here we present a long-term temporal analysis of several large, intensely monitored constructed wetlands, the Everglades Stormwater Treatment Areas (STAs), in which sudden changes in plant community composition have been observed. Using wavelet analysis, significant increases and decreases of variance properties (long-term flow data, water quality and nutrient TP loads) across these systems can indicate when and which STAs are less resilient to perturbations. In this study, continuous wavelet transform (CWT) was used to determine the periodicity of any cyclical activity in the data and to determine changes in autocorrelation and variance as measures of CSD. The change detection methods were used to find significant changes in variations and correlations across the time series. By employing these techniques, we were able to spot substantial shifts in model-observed wavelet correlation and model residual wavelet variance and thereby identify where these systems exhibit CSD. Although our analysis is limited to historical data, the proposed approach has practical value in that it identifies STAs that may be vulnerable to perturbation. The study also presents one of the few studies in which CSD is observed in practice rather than modelled in theory.
Open Access
Evidence of collaborative opportunities to ensure long-term sustainability in African farming
(Elsevier, 2023-03-15) El Fartassi, Imane; Milne, Alice E.; El Alami, Rafiq; Rafiqi, Maryam; Hassall, Kirsty L.; Waine, Toby W.; Zawadzka, Joanna Ewa; Diarra, Alhousseine; Corstanje, Ron
Farmers face the challenge of increasing production to feed a growing population and support livelihoods, whilst also improving the sustainability and resilience of cropping systems. Understanding the key factors that influence farming management practices is crucial for determining farmers' adaptive capacity and willingness to engage in cooperative strategies. To that end, we investigated management practices that farmers adopt and the factors underlying farmers' decision-making. We also aimed to identify the constraints that impede the adoption of strategies perceived to increase farming resilience and to explore how the acceleration of technology adoption through cooperation could ensure the long-term sustainability of farming. Surveys were distributed to farming stakeholders and professionals who worked across the contrasting environments of Morocco. We used descriptive statistics and analysis by log-linear modelling to predict the importance of factors influencing farmers’ decision-making. The results show that influencing factors tended to cluster around environmental pressures, crop characteristics and water availability with social drivers playing a lesser role. Subsidies were also found to be an important factor in decision-making. Farming stakeholders generally believed that collaborative networks are likely to facilitate the adoption of sustainable agricultural practices. We conclude that farmers need both economic incentives and technical support to enhance their adaptive capacity as this can lessen the socioeconomic vulnerability inherent in arid and semi-arid regions.
Open Access
Evolution of green space under rapid urban expansion in Southeast Asian cities
(MDPI, 2021-10-30) Muhamad Nor, Amal Najihah; Abdul Aziz, Hasifah; Nawawi, Siti Aisyah; Muhammad Jamil, Rohazaini; Abas, Muhamad Azahar; Hambali, Kamarul Ariffin; Yusoff, Abdul Hafidz; Ibrahim, Norfadhilah; Rafaai, Nur Hairunnisa; Corstanje, Ron; Harris, Jim A.; Grafius, Darren R.; Perotto-Baldivieso, Humberto L.
Globally, rapid urban expansion has caused green spaces in urban areas to decline considerably. In this study, the rapid expansion of three Southeast Asia cities were considered, namely, Kuala Lumpur City, Malaysia; Jakarta, Indonesia; and Metro Manila, Philippines. This study evaluates the changes in spatial and temporal patterns of urban areas and green space structure in the three cities over the last two decades. Land use land cover (LULC) maps of the cities (1988/1989, 1999 and 2014) were developed based on 30-m resolution satellite images. The changes in the landscape and spatial structure were analysed using change detection, landscape metrics and statistical analysis. The percentage of green space in the three cities reduced in size from 45% to 20% with the rapid expansion of urban areas over the 25-year period. In Metro Manila and Jakarta, the proportion of green space converted to urban areas was higher in the initial 1989 to 1999 period than over the latter 1999 to 2014 period. Significant changes in green space structure were observed in Jakarta and Metro Manila. Green space gradually fragmented and became less connected and more unevenly distributed. These changes were not seen in Kuala Lumpur City. Overall, the impact of spatial structure of urban areas and population density on green space is higher in Jakarta and Metro Manila when this is compared to Kuala Lumpur. Thus, the results have the potential to clarify the relative contribution of green space structure especially for cities in Southeast Asia where only a few studies in urban areas have taken place.
Open Access
Exploring the role of hydrological pathways in modulating multi-annual climate teleconnection periodicities from UK rainfall to streamflow
(European Geosciences Union, 2021-04-23) Rust, William; Cuthbert, Mark; Bloomfield, John; Corstanje, Ron; Howden, Nicholas J. K.; Holman, Ian P.
An understanding of multi-annual behaviour in streamflow allows for better estimation of the risks associated with hydrological extremes. This can enable improved preparedness for streamflow-dependant services, such as freshwater ecology, drinking water supply and agriculture. Recently, efforts have focused on detecting relationships between long-term hydrological behaviour and oscillatory climate systems (such as the North Atlantic Oscillation – NAO). For instance, the approximate 7 year periodicity of the NAO has been detected in groundwater-level records in the North Atlantic region, providing potential improvements to the preparedness for future water resource extremes due to their repetitive, periodic nature. However, the extent to which these 7-year, NAO-like signals are propagated to streamflow, and the catchment processes that modulate this propagation, are currently unknown. Here, we show statistically significant evidence that these 7-year periodicities are present in streamflow (and associated catchment rainfall), by applying multi-resolution analysis to a large data set of streamflow and associated catchment rainfall across the UK. Our results provide new evidence for spatial patterns of NAO periodicities in UK rainfall, with areas of greatest NAO signal found in southwest England, south Wales, Northern Ireland and central Scotland, and show that NAO-like periodicities account for a greater proportion of streamflow variability in these areas. Furthermore, we find that catchments with greater subsurface pathway contribution, as characterised by the baseflow index (BFI), generally show increased NAO-like signal strength and that subsurface response times (as characterised by groundwater response time – GRT), of between 4 and 8 years, show a greater signal presence. Our results provide a foundation of understanding for the screening and use of streamflow teleconnections for improving the practice and policy of long-term streamflow resource management
Open Access
Future restoration should enhance ecological complexity and emergent properties at multiple scales
(Wiley, 2021-12-07) Bullock, James M.; Fuentes-Montemayor, Elisa; McCarthy, Ben; Park, Kirsty; Hails, Rosie S.; Woodcock, Ben A.; Watts, Kevin; Corstanje, Ron; Harris, Jim A.
Ecological restoration has a paradigm of re-establishing ‘indigenous reference' communities. One resulting concern is that focussing on target communities may not necessarily create systems which function at a high level or are resilient in the face of ongoing global change. Ecological complexity – defined here, based on theory, as the number of components in a system and the number of connections among them – provides a complementary aim, which can be measured directly and has several advantages. Ecological complexity encompasses key ecosystem variables including structural heterogeneity, trophic interactions and functional diversity. Ecological complexity can also be assessed at the landscape scale, with metrics including β diversity, heterogeneity among habitat patches and connectivity. Thus, complexity applies, and can be measured, at multiple scales. Importantly, complexity is linked to system emergent properties, e.g. ecosystem functions and resilience, and there is evidence that both are enhanced by complexity. We suggest that restoration ecology should consider a new paradigm to restore complexity at multiple scales, in particular of individual ecosystems and across landscapes. A complexity approach can make use of certain current restoration methods but also encompass newer concepts such as rewilding. Indeed, a complexity goal might in many cases best be achieved by interventionist restoration methods. Incorporating complexity into restoration policies could be quite straightforward. Related aims such as enhancing ecosystem services and ecological resilience are to the fore in initiatives such as the Sustainable Development Goals and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. Implementation in policy and practice will need the development of complexity metrics that can be applied at both local and regional scales. Ultimately, the adoption of an ecological complexity paradigm will be based on an acceptance that the ongoing and unprecedented global environmental change requires new ways of doing restoration that is fit for the future.
Open Access
The importance of non-stationary multiannual periodicities in the North Atlantic Oscillation index for forecasting water resource drought
(European Geosciences Union (EGU), 2022-05-11) Rust, William; Bloomfield, John P.; Cuthbert, Mark; Corstanje, Ron; Holman, Ian P.
Drought forecasting and early warning systems for water resource extremes are increasingly important tools in water resource management in Europe where increased population density and climate change are expected to place greater pressures on water supply. In this context, the North Atlantic Oscillation (NAO) is often used to indicate future water resource behaviours (including droughts) over Europe, given its dominant control on winter rainfall totals in the North Atlantic region. Recent hydroclimate research has focused on the role of multiannual periodicities in the NAO in driving low frequency behaviours in some water resources, suggesting that notable improvements to lead-times in forecasting may be possible by incorporating these multiannual relationships. However, the importance of multiannual NAO periodicities for driving water resource behaviour, and the feasibility of this relationship for indicating future droughts, has yet to be assessed in the context of known non-stationarities that are internal to the NAO and its influence on European meteorological processes. Here we quantify the time–frequency relationship between the NAO and a large dataset of water resources records to identify key non-stationarities that have dominated multiannual behaviour of water resource extremes over recent decades. The most dominant of these is a 7.5-year periodicity in water resource extremes since approximately 1970 but which has been diminishing since 2005. Furthermore, we show that the non-stationary relationship between the NAO and European rainfall is clearly expressed at multiannual periodicities in the water resource records assessed. These multiannual behaviours are found to have modulated historical water resource anomalies to an extent that is comparable to the projected effects of a worst-case climate change scenario. Furthermore, there is limited systematic understanding in existing atmospheric research for non-stationarities in these periodic behaviours which poses considerable implications to existing water resource forecasting and projection systems, as well as the use of these periodic behaviours as an indicator of future water resource drought.
Open Access
Improved soil moisture estimation with Sentinel-1 for arable land at the field scale
(EGU: European Geophysical Union, 2021-04-30) Beale, John; Waine, Toby; Corstanje, Ron; Evans, Jonathan
Open Access
A Moroccan soil spectral library use framework for improving soil property prediction: evaluating a geostatistical approach
(Elsevier, 2024-12-01) Asrat, Tadesse Gashaw; Breure, Timo; Sakrabani, Ruben; Corstanje, Ron; Hassall, Kirsty L.; Hamma, Abdellah; Kebede, Fassil; Haefele, Stephan M.
A soil spectrum generated by any spectrometer requires a calibration model to estimate soil properties from it. To achieve best results, the assumption is that locally calibrated models offer more accurate predictions. However, achieving this higher accuracy comes with associated costs, complexity, and resource requirements, thus limiting widespread adoption. Furthermore, there is a lack of comprehensive frameworks for developing and utilizing soil spectral libraries (SSLs) to make predictions for specific samples. While calibration samples are necessary, there is the need to optimize SSL development through strategically determining the quantity, location, and timing of these samples based on the quality of the information in the library. This research aimed to develop a spatially optimized SSL and propose a use-framework tailored for predicting soil properties for a specific farmland context. Consequently, the Moroccan SSL (MSSL) was established utilizing a stratified spatially balanced sampling design, using six environmental covariates and FAO soil units. Subsequently, various criteria for calibration sample selection were explored, including a spatial autocorrelation of spectra principal component (PC) scores (spatial calibration sample selection), spectra similarity memory-based learner (MBL), and selection based on environmental covariate clustering. Twelve soil properties were used to evaluate these calibration sample selections to predict soil properties using the near infrared (NIR) and mid infrared (MIR) ranges. Among the methods assessed, we observed distinct precision improvements resulting from spatial sample selection and MBL compared to the use of the entire MSSL. Notably, the Lin's Concordance Correlation Coefficient (CCC) values using the spatial calibration sample selection was improved for Olsen extractable phosphorus (OlsenP) by 41.3% and Mehlich III extractable phosphorus (P_M3) by 8.5% for the MIR spectra and for CEC by 25.6%, pH by 13.0% and total nitrogen (Tot_N) by 10.6% for the NIR spectra in reference to use of the entire MSSL. Utilizing the spatial autocorrelation of the spectra PC scores proved beneficial in identifying appropriate calibration samples for a new sample location, thereby enhancing prediction performance comparable to, or surpassing that of the use of the entire MSSL. This study signifies notable advancement in crafting targeted models tailored for specific samples within a vast and diverse SSL.
Open Access
A multistep approach to improving connectivity and co-use of spatial ecological networks in cities
(Springer, 2021-01-12) Beaujean, Simon; Nor, Amal Najihah Muhamad; Brewer, Timothy R.; Zamorano, Juan Gallego; Dumitriu, Alex Cristina; Harris, Jim A.; Corstanje, Ron
Context Ecological networks are systems of interconnected components that support biodiversity, ecological processes and ecosystem services. Such structures play a crucial role for nature conservation and people well-being in anthropogenic landscapes. Assessing connectivity by using efficient models and metrics is a sine qua non condition to preserve and improve appropriately these ecological networks. Objectives This study aims to present a novel methodological approach to assess and model connectivity for species conservation (Bufo calamita; the natterjack toad) and human recreation in the city. Methods The study used a combination least cost and circuit models to identify priority corridors in the City of Liège, Belgium. Green areas, habitats and relevant movement parameters were derived based on existing studies around (i) the occurrence, ecology and biology of the natterjack toad and (ii) human behavioural studies on urban pedestrians. Combining the two models allowed the assessment of connectivity for both species via two different metrics visualised using priority corridors on maps. Results The connectivity assessments identified lack of connectivity as the potential route to extinction of natterjack toads at one of the source sites. Conclusions This study provides examples of how combining least cost and circuit models can contribute to the improvement of urban ecological networks and demonstrates the usefulness of such models for nature conservation and urban planning
Open Access
Non‐stationary control of the NAO on European rainfall and its implications for water resource management
(Wiley, 2021-02-19) Rust, William; Bloomfield, John P.; Cuthbert, Mark O.; Corstanje, Ron; Holman, Ian P.
Water resource forecasting generally centres on understanding hydrological variability over coming months or years, so that water managers can prepare for extremes such as droughts or floods (Chang & Guo, 2020; Hao et al., 2018). Some forecasting systems seek to project further into the future to allow long‐term planning of infrastructure and resilience to extremes and climate change (Svensson et al., 2015). These systems can rely directly or indirectly on outputs from Global Climate Models (GCMs; such as gridded reanalysis datasets) to forecast hydrological conditions (Bhatt & Mall, 2015; Ionita & Nagavciuc, 2020). In the North Atlantic region, in particular Western Europe, the North Atlantic Oscillation (NAO) is used as an indicator for hydrometeorological conditions given its leading control on winter rainfall totals (Hurrell & Deser, 2010; Scaife et al., 2008, 2014). A dipole of pressure anomalies over the North Atlantic, the NAO's positive phase (greater than average pressure gradient; NAO+) results in wetter conditions in northwest Europe with dryer conditions in southwest Europe (Rust et al., 2018; Trigo et al., 2004). Its negative phase (weaker than average pressure gradient; NAO−) results in the inverse effect on rainfall (Folland et al., 2015; and as shown by the correlation coefficients in Figure 1). Given this relationship, and, considering the role of winter rainfall variability in groundwater drought development (e.g., reduced winter recharge) and generation of late winter/early spring floods, the NAO offers a potential explanatory variable when understanding the behaviour of some hydrological extremes.
Open Access
The relationship between spatial configuration of urban parks and neighbourhood cooling in a humid subtropical city
(Springer, 2024-02-14) Verma, Ravi; Zawadzka, Joanna Ewa; Garg, Pradeep Kumar; Corstanje, Ron
Context Urban parks are essential for maintaining aesthetics within cities and keeping their its energy balance by helping mitigate the Urban Heat Island (UHI) effect through controlling ambient and land surface temperature (LST). Objectives To investigate the impact of cooling in terms of distance by variously configured urban parks of a humid subtropical city, using landscape metrics and open-source data. Methods Land use (LU) was obtained through maximum likelihood classification of 3 m resolution aerial RGB-NIR imagery supported by ground control points and park boundaries collected during field survey. LST at matching resolution was obtained through downscaling of Landsat-8 LST at 30/100m resolution, calculated with the Radiative Transfer Equation (RTE). Landscape metrics for patches of parks were calculated using landscapemetrics R library and related to neighbourhood distances over built-up land use (LU). Results Urban parks with homogenous cores and less complex shape provide distinctly higher cooling of neighbouring built-up LU of circa 2.55 °C over the distance of 18 m from park boundaries. Four metrics: contiguity index (CONTIG), core area index (CAI), fractal dimension index (FRAC) and perimeter-area ratio (PARA) represent significant relationship between spatial configuration of parks and their cooling distance. No cooling capacity of parks regardless of their shape and core was observed beyond the distance of 18 m, which remained constant with small fluctuations in the range of 0.5 °C up to the distance of 600 m. Conclusions The study concludes that cooling distance of urban parks in their neighbourhood extends up to 18 m, which is shorter than suggested by other studies.
Open Access
Robust spatial estimates of biomass carbon on farms
(Elsevier, 2022-11-30) Beka, Styliani; Burgess, Paul J.; Corstanje, Ron
The drive for farm businesses to move towards net zero greenhouse gas emissions means that there is a need to develop robust methods to quantify the amount of biomass carbon (C) on farms. Direct measurements can be destructive and time-consuming and some prediction methods provide no assessment of uncertainty. This study describes the development, validation, and use of an integrated spatial approach, including the use of lidar data, and Bayesian Belief Networks (BBNs) to quantify total biomass carbon stocks (Ctotal) of i) land cover and ii) landscape features such as hedges and lone trees for five case study sites in lowland England. The results demonstrated that it was possible to develop and use a remote integrated approach to estimate biomass carbon at a farm scale. The highest achievable prediction accuracy was attained from models using the variables AGBC, BGBC, DOMC, age, height, species and land cover, derived from measured information and from literature review. The two BBN models successfully predicted the test values of the total biomass carbon with propagated error rates of 6.7 % and 4.3 % for the land cover and landscape features respectively. These error rates were lower than in other studies indicating that the seven predictors are strong determinants of biomass carbon. The lidar data also enabled the spatial presentation and calculation of the variable C stocks along the length of hedges and within woodlands.
Open Access
Spatial modelling approach and accounting method affects soil carbon estimates and derived farm-scale carbon payments
(Elsevier, 2022-02-28) Beke, Styliani; Burgess, Paul J.; Corstanje, Ron; Stoate, Chris
Improved farm management of soil organic carbon (SOC) is critical if national governments and agricultural businesses are to achieve net-zero targets. There are opportunities for farmers to secure financial benefits from carbon trading, but field measurements to establish SOC baselines for each part of a farm can be prohibitively expensive. Hence there is a potential role for spatial modelling approaches that have the resolution, accuracy, and estimates to uncertainty to estimate the carbon levels currently stored in the soil. This study uses three spatial modelling approaches to estimate SOC stocks, which are compared with measured data to a 10 cm depth and then used to determine carbon payments. The three approaches used either fine- (100 m × 100 m) or field-scale input soil data to produce either fine- or field-scale outputs across nine geographically dispersed farms. Each spatial model accurately predicted SOC stocks (range: 26.7–44.8 t ha−1) for the five case study farms where the measured SOC was lowest (range: 31.6–48.3 t ha−1). However, across the four case study farms with the highest measured SOC (range: 56.5–67.5 t ha−1), both models underestimated the SOC with the coarse input model predicting lower values (range: 39.8–48.2 t ha−1) than those using fine inputs (range: 43.5–59.2 t ha−1). Hence the use of the spatial models to establish a baseline, from which to derive payments for additional carbon sequestration, favoured farms with already high SOC levels, with that benefit greatest with the use of the coarse input data. Developing a national approach for SOC sequestration payments to farmers is possible but the economic impacts on individual businesses will depend on the approach and the accounting method.
Open Access
Stepwise model parametrisation using satellite imagery and hemispherical photography: tuning AquaCrop sensitive parameters for improved winter wheat yield predictions in semi-arid regions
(Elsevier, 2024-04-01) Oulaid, Bader; Milne, Alice E.; Waine, Toby; El Alami, Rafiq; Rafiqi, Maryam; Corstanje, Ron
Crop models are complex with many parameters, which has limited their application. Here we present an approach which both removes the model complexity through reducing the parameter dimensionality through sensitivity analysis, and presents a subsequent efficient approach to model parameterisation using swarm optimisation. We do this for two key model outputs, crop canopy and yield, and for two types of observational data, hemispheric photographs and Landsat7 imagery. Importantly we compare the usefulness of these two sources of data in terms of accurate yield prediction. The results showed that the dominant model parameters that predict canopy cover were generally consistent across the fields, with the exception of those related water stress. Although mid-season canopy cover extracted from Landsat7 was underestimated, good agreement was found between the simulated and observed canopy cover for both sources of data. Subsequently, less accurate yield predictions were achieved with the Landsat7 compared to the hemispherical photography-based parametrizations. Despite the small differences in the canopy predictions, the implications for yield prediction were substantial with the parametrization based on hemispherical photography providing far more accurate estimates of yield. There are, however, additional resource implications associated with hemispherical photography. We evaluate these trade-offs, providing model parametrization sets and demonstrating the potential of satellite imagery to assist AquaCrop, particularly on large scales where ground measurements are challenging.
Open Access
Understanding the importance of landscape configuration on ecosystem service bundles at a high resolution in urban landscapes in the UK
(Springer, 2021-02-10) Karimi, James D.; Corstanje, Ron; Harris, Jim A.
Context Landscape structure is thought to affect the provision of ecosystem service bundles. However, studies of the influence of landscape configuration on ecosystem service trade-offs and synergies in urban areas are limited. This study used Bayesian Belief Networks to predict ecosystem service trade-offs and synergies in the urban area comprising the towns of Milton Keynes, Bedford and Luton, UK. Objectives The objectives of this study were to test (1) a Bayesian Belief Network approach for predicting ecosystem service trade-offs and synergies in urban areas and (2) assess whether landscape configuration characteristics affect ecosystem service trade-offs and synergies. Methods Bayesian Belief Network models were used to test the influence of landscape configuration on ecosystem service interactions. The outputs of a Principal Component Analysis (PCA) on six ecosystem services and landscape configuration metrics were used as response and explanatory variables, respectively. We employed Spearman’s rank correlation and principal component analysis to identify redundancies between landscape metrics. Results We found that landscape configuration affects ecosystem service trade-offs and synergies. A sensitivity analysis conducted on the principal components showed that landscape configuration metrics core area (CORE) and effective mesh size (MESH) are strong influential determinants of ecosystem service trade-offs and synergies. Conclusions This study demonstrates that landscape configuration characteristics affect ecosystem service trade-offs and synergies and that a core set of metrics could be used to assess ecosystem service (ES) trade-offs and synergies. The findings may be relevant to planning and urban design and improved ecosystem management.
Open Access
Using Bayesian Belief Networks to assess the influence of landscape connectivity on ecosystem service trade-offs and synergies in urban landscapes in the UK
(Springer, 2021-08-05) Karimi, James D.; Harris, Jim A.; Corstanje, Ron
Context Landscape connectivity is assumed to influence ecosystem service (ES) trade-offs and synergies. However, empirical studies of the effect of landscape connectivity on ES trade-offs and synergies are limited, especially in urban areas where the interactions between patterns and processes are complex. Objectives The objectives of this study were to use a Bayesian Belief Network approach to (1) assess whether functional connectivity drives ES trade-offs and synergies in urban areas and (2) assess the influence of connectivity on the supply of ESs. Methods We used circuit theory to model urban bird flow of P. major and C. caeruleus at a 2 m spatial resolution in Bedford, Luton and Milton Keynes, UK, and Bayesian Belief Networks (BBNs) to assess the sensitivity of ES trade-offs and synergies model outputs to landscape and patch structural characteristics (patch area, connectivity and bird species abundance). Results We found that functional connectivity was the most influential variable in determining two of three ES trade-offs and synergies. Patch area and connectivity exerted a strong influence on ES trade-offs and synergies. Low patch area and low to moderately low connectivity were associated with high levels of ES trade-offs and synergies. Conclusions This study demonstrates that landscape connectivity is an influential determinant of ES trade-offs and synergies and supports the conviction that larger and better-connected habitat patches increase ES provision. A BBN approach is proposed as a feasible method of ES trade-off and synergy prediction in complex landscapes. Our findings can prove to be informative for urban ES management.