There is an urgent need to adapt crop breeding strategies to boost resilience in the face of a growing food demand and a changing climate. Achieving this requires an understanding of how weather and climate variability impacts crop growth and development. Using the United Kingdom (UK) as an example, we evaluate changes in the UK agroclimate and analyse how these have influenced domestic wheat production. Here we quantify spatial and temporal variability and changes in weather and climate across growing seasons over the last four decades (1981-2020). Drawing on variety trial data, we then use statistical modelling to explore the interaction between genotype and agroclimate variation.We show that changes in the UK agroclimate present both risks, and opportunities for wheat growers, depending on location. From 1981-2020, in Wales, the West Midlands, large parts of the North West, and Northern Ireland, there was an overall increase in frost risk in early spring of 0.15 additional frost days per year, whilst in the east early frost risk decreased by up to 0.29 d per year. Meanwhile, over the period 1987-2020, surface incoming shortwave radiation during grainfill increased in the east by up to 13% but decreased in Western areas by up to 15%. We show significant inter-varietal differences in yield responses to growing degree days, heavy rainfall, and the occurrence of late frost. This highlights the importance of evaluating variety-climate interactions in variety trial analyses, and in climate-optimised selection of crops and varieties by growers. This work provides guidance for future research on how climate change is affecting the UK agroclimate and resulting impacts on winter cereal production.

The demand for catchment-based flood management to adapt to climate change is growing, with natural flood management (NFM) receiving increasing attention. NFM has implications for the ‘providers’ of land for measures upstream (the farmers) and the ‘beneficiaries’ of flood reduction downstream (the public). The misalignment of interests from these stakeholder groups may pose a challenge for flood risk managers during the delivery of NFM at the catchment scale. Considering this, a rapid evidence assessment (REA) of 60 peer-reviewed articles was undertaken. This REA provides an overview of catchment perspectives, compares farmer and public preferences for NFM design, and explores key determinants of scheme acceptance. The public expressed positive perceptions and willingness to pay for NFM, with preferences for measures with large water storage capacity that deliver co-benefits alongside flood management objectives. For farmers, NFM schemes that contributed to on-farm conditions, for example, soil stability, were seen as positive, but overall, their willingness to adopt measures was limited. Nevertheless, knowledge of NFM among both groups strongly determined its acceptance. This suggests that resolving misaligned values will require policymakers and practitioners to work with these stakeholders on NFM design and farmer incentives to secure the delivery of future schemes.

To meet decarbonization targets, nations around the globe have made ambitious commitments to expand forested land. Operationalizing these commitments requires choosing a planting strategy: How many trees should be planted, of which species, and where? Given those choices must be made now but have long-term consequences, such decisions are plagued by uncertainty. For example, species that are well suited to present conditions may perform poorly under future climates, yet those future climates are themselves highly uncertain. Using the exemplar of the United Kingdom, a nation committed to achieving net zero emissions by midcentury, we quantify key uncertainties pertaining to coevolving climate and economic conditions and examine how modern methods of decision-making under uncertainty can advise on planting choices. Our analysis reveals that the best planting strategy assuming a "high-emissions" future is radically different to that for a future that remains on a "near-historic" path. Planting for the former while experiencing the latter results in substantial net costs to UK society. Assimilating uncertainty into decision-making identifies planting strategies that diversify risk and significantly reduce the probability of high-cost outcomes. Importantly, our research reveals that the scope for mitigating risk through choice of planting strategy is relatively limited. Despite this persistent risk, we find that tree planting remains a highly cost-effective carbon removal solution when compared to alternative technologies, even when those alternatives are assumed to be riskless.

Land use change is crucial to addressing the existential threats of climate change and biodiversity loss while enhancing food security [M. Zurek et al., Science 376, 1416-1421 (2022)]. The interconnected and spatially varying nature of the impacts of land use change means that these challenges must be addressed simultaneously [H.-O. Pörtner et al., Science 380, eabl4881 (2023)]. However, governments commonly focus on single issues, incentivizing land use change via "Flat-Rate" subsidies offering constant per hectare payments, uptake of which is determined by the economic circumstances of landowners rather than the integrated environmental outcomes that will be delivered [G. Q. Bull et al., Forest Policy Econ. 9, 13-31 (2006)]. Here, we compare Flat-Rate subsidies to two alternatives: "Land Use Scenario" allocation of subsidies through consultation across stakeholders and interested parties; and a "Natural Capital" approach which targets subsidies according to expected ecosystem service response. This comparison is achieved by developing a comprehensive decision support system, integrating new and existing natural, physical, and economic science models to quantify environmental, agricultural, and economic outcomes. Applying this system to the United Kingdom's net zero commitment to increase carbon storage via afforestation, we show that the three approaches result in significantly different outcomes in terms of where planting occurs, their environmental consequences, and economic costs and benefits. The Flat-Rate approach actually increases net carbon emissions while Land Use Scenario allocation yields poor economic outcomes. The Natural Capital targeted approach outperforms both alternatives, providing the highest possible social values while satisfying net zero commitments.

By embedding a spatially explicit ecosystem services modelling tool within a policy simulator we examine the insights that natural capital analysis can bring to the design of policies for nature recovery. Our study is illustrated through a case example of policies incentivising the establishment of new natural habitat in England. We find that a policy mirroring the current practice of offering payments per hectare of habitat creation fails to break even, delivering less value in improved flows of ecosystem services than public money spent and only 26% of that which is theoretically achievable. Using optimization methods, we discover that progressively more efficient outcomes are delivered by policies that optimally price activities (34%), quantities of environmental change (55%) and ecosystem service value flows (81%). Further, we show that additionally attaining targets for unmonetized ecosystem services (in our case, biodiversity) demands trade-offs in delivery of monetized services. For some policy instruments it is not even possible to achieve the targets. Finally, we establish that extending policy instruments to offer payments for unmonetized services delivers target-achieving and value-maximizing policy designs. Our findings reveal that policy design is of first-order importance in determining the efficiency and efficacy of programmes pursuing nature recovery. This article is part of the theme issue ‘Bringing nature into decision-making’.

Abstract Spatial modelling approaches to aid land‐use decisions which benefit both wildlife and humans are often limited to the comparison of pre‐determined landscape scenarios, which may not reflect the true optimum landscape for any end‐user. Furthermore, the needs of wildlife are often under‐represented when considered alongside human financial interests in these approaches. We develop a method of addressing these gaps using a case‐study of wild bees in the UK, an important group whose declines may adversely affect both human economies and surrounding ecosystems. By combining the genetic algorithm NSGA‐II with a process‐based pollinator model which simulates bee foraging and population dynamics, Poll4pop, we ‘evolve’ a typical UK agricultural landscape to identify optimum land cover configurations for three different guilds of wild bee. These configurations are compared to those resulting from optimisations for farm income alone, as well as optimisations that seek a compromise between bee populations and farm income objectives. We find that the land cover proportions in landscapes optimised for each bee guild reflect their nesting habitat preferences rather than foraging preferences, highlighting a limiting resource within the study landscape. The spatially explicit nature of these optimised landscapes illustrates how improvement for a given target species may be limited by differences between their movement range and the scale of the units being improved. Land cover composition and configuration differ significantly in landscapes optimised for farm income and bee population growth simultaneously and illustrate how human agents are required to compromise much more when the multifaceted nature of biodiversity is recognised and represented by multiple objectives within an optimisation framework. Our methods provide a way to quantify the extent to which real‐life landscapes promote or compromise objectives for different landscape end‐users. Our investigation suggests that optimisation set‐up (decision‐unit scales, traditional choice of a single biodiversity metric) can bias outcomes towards human‐centric solutions. It also demonstrates the importance of representing the individual requirements of different actors with different landscape‐level needs when using genetic algorithms to support biodiversity‐inclusive decision‐making in multi‐functional landscapes.

Pathways to decarbonisation are commonly explored by government and industry through the use of energy system models. However, such models rarely consider where new energy infrastructure might be located. This is problematic as the spatial context of new renewable energy infrastructure will determine, in part, the environmental, social, and technical impacts of the energy transition. This paper presents the ADVENT-NEV model which brings together innovations in energy and natural capital modelling to identify the optimal locations of multiple renewable energy technologies at a national scale and high spatial resolution. Using Great Britain as a case study, the results show how the spatial distribution of renewable energy technologies changes when a natural capital approach is taken. In particular, the least-cost locations for onshore wind farms and bioenergy crops are highly influenced by the value of carbon sequestration, or emissions associated with their land use change. Siting using a natural capital approach produced appreciable ecosystem service benefits, such that the overall welfare gain to society was estimated at nearly 25 pound B. Overall, this paper demonstrates that under-standing the geospatial context of the energy transition is essential to identifying which renewable energy pathways are consistent with decarbonisation and environmental objectives.

Barley (Hordeum vulgare ssp. vulgare) is cultivated globally across a wide range of environments, both in highly productive agricultural systems and in subsistence agriculture and provides valuable feedstock for the animal feed and malting industries. However, as the climate changes there is an urgent need to identify adapted barley varieties that will consistently yield highly under increased environmental stresses. Our ability to predict future local climates is only as good as the skill of the climate model, however we can look back over 100 years with much greater certainty. Historical weather datasets are an excellent resource for identifying causes of historical yield variability. In this research we combined recently digitised historical weather data from the early 20th century with published Irish spring barley trials data for two heritage varieties: Archer and Goldthorpe, following an analysis first published by Student in 1923. Using linear mixed models, we show that interannual variation in observed spring barley yields can be partially explained by recorded weather variability, in particular July maximum temperature and rainfall, and August maximum temperature. We find that while Archer largely yields more highly, Goldthorpe is more stable under wetter growing conditions, highlighting the importance of considering growing climate in variety selection. Furthermore, this study demonstrates the benefits of access to historical trials and climatic data and the importance of incorporating climate data in modern day breeding programmes to improve climate resilience of future varieties.

New biomass crop hybrids for bioeconomic expansion require yield projections to determine their potential for strategic land use planning in the face of global challenges. Our biomass growth simulation incorporates radiation interception and conversion efficiency. Models often use leaf area to predict interception which is demanding to determine accurately, so instead we use low-cost rapid light interception measurements using a simple laboratory-made line ceptometer and relate the dynamics of canopy closure to thermal time, and to measurements of biomass. We apply the model to project the European biomass potentials of new market-ready hybrids for 2020-2030. Field measurements are easier to collect, the calibration is seasonally dynamic and reduces influence of weather variation between field sites. The model obtained is conservative, being calibrated by crops of varying establishment and varying maturity on less productive (marginal) land. This results in conservative projections of miscanthus hybrids for 2020-2030 based on 10% land use conversion of the least (productive) grassland and arable for farm diversification, which show a European potential of 80.7-89.7 Mt year-1 biomass, with potential for 1.2-1.3 EJ year-1 energy and 36.3-40.3 Mt year-1 carbon capture, with seeded Miscanthus sacchariflorus × sinensis displaying highest yield potential. Simulated biomass projections must be viewed in light of the field measurements on less productive land with high soil water deficits. We are attempting to model the results from an ambitious and novel project combining new hybrids across Europe with agronomy which has not been perfected on less productive sites. Nevertheless, at the time of energy sourcing issues, seed-propagated miscanthus hybrids for the upscaled provision of bioenergy offer an alternative source of renewable energy. If European countries provide incentives for growers to invest, seeded hybrids can improve product availability and biomass yields over the current commercial miscanthus variety.

Context: The current pace of yield increase for major crops is not fast enough to meet future demand. Crop breeding programmes are under increasing pressure to improve existing crops further. Quantifying the contribution of these programmes to observed yield increases is important for evaluating their success and identifying if crop improvement goals are likely to be met.Objective: In this paper we explore methods to study the genetic gain of two cereal species, wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.). Specifically, the objective of this research was to identify sources of bias in genetic gain estimates of UK variety trials data.Methods: Genetic gain was estimated for fungicide-treated and untreated UK winter wheat, winter barley and spring barley for 1982-2018 using UK National List and Recommended List variety trials data. Subsets of the winter wheat variety trials dataset were used to replicate shorter breeding cycles to quantify the impact of the number and choice of long-term check varieties on estimating genetic gain.Results: While genetic and non-genetic contributions to changes in UK cereal performance are in line with previous estimates, we were able to identify previously undetected changes and biases in estimates of variety performance. Specifically, we observed an increasing yield difference between fungicide treated and untreated variety trials as varieties age, driven by both a breakdown in disease resistance and a previously unobserved long-term increase in yield as varieties age in treated trials. This shows that yields of long-term check varieties cannot be assumed to be stable over time. We found that genetic gain estimates were highly sensitive to the longterm check varieties chosen, whilst the inclusion of multiple checks decreased the standard error of the estimate.Conclusion: The estimation of genetic gain is highly susceptible to bias. We provide recommendations on how to reduce the risk of bias for estimating genetic gain. Implications: Accounting for sources of bias in genetic gain calculations is important in any programme of selection to prevent inaccurate quantification of yield progress.

As countries decarbonise, the competition for land between energy generation, nature conservation and food production will likely increase. To counter this, modelling, and sometimes energy policies, use exclusion zones to restrict energy deployment from land deemed as important to society. This paper applies the spatially-explicit ADVENT-NEV model to Great Britain to determine the cost imposed on the energy system when either environmental or food production exclusion zones are applied. Results show that exclusion zones impose a cost of up to £0.63 billion (B), £19.17 B and £1.33 B for the solar, wind, and bioenergy pathways. These costs give an indication of the value being placed on protecting these areas of land. When multiple exclusions are imposed on bioenergy, the high pathway is infeasible indicating a more flexible approach may be needed to meet net zero ambitions. The model also shows how the value of ecosystem services changes when exclusion zones are applied, highlighting how some exclusions increase non-market costs whereas others decrease them. In several cases exclusion zones are shown to increase social costs, the opposite of their intended use. For these exclusions to be justifiable, the unobserved values missing from the model must be as large as these increases.

The pressure of nutrient pollution derived from wastewater treatment works and agricultural runoff is a reason for the decline in the ecological health of aquatic habitats. Projected residential development in catchments creates further nutrient loading that can be offset by nutrient management solutions that maintain ‘nutrient neutrality’ either onsite or elsewhere within the same catchment. This study developed an export coefficient model in conjunction with detailed farm business data to explore a nature-based solution to nutrient neutrality involving seven scenarios of crop conversion to mixed woodland or grazing grass in an area of intensive arable cultivation in the groundwater-fed Blackwater sub-catchment of the River Wensum, UK. When compared with the monitored riverine export of nutrients, the calculated nitrogen (N) and phosphorus (P) inputs under current land use showed that subsurface denitrification is removing 48–78% of the leached N and that P is accumulating in the field soils. The addition of 235 residential homes planned for 2018–2038 in the Blackwater will generate an additional nutrient load of 190 kg N a−1 and 4.9 kg P a−1. In six of the seven scenarios, the modelled fractions of crop conversion (0.02–0.21) resulted in the required reduction in P loading and more than sufficient reduction in N loading (196–1874 kg a−1 for mixed woodland and 287–2103 kg a−1 for grazing grass), with the additional reduction in N load above the requirement for nutrient neutrality potentially contributing to further improvement in water quality. The cost of land conversion is modelled in terms of crop gross margins and nutrient credits generated in the form of 0.1 kg units of N or P. For the range of scenarios considered, the annual cost per credit ranged from GBP 0.78–11.50 for N for mixed woodland (GBP 0.74–7.85 for N for grazing grass) and from GBP 160–782 for P for both scenarios. It is concluded that crop conversion is a viable option to achieve nutrient neutrality in arable catchments in eastern England when considered together with other nutrient management solutions.

We present C-LLAMA 1.0 (Country-level Land Availability Model for Agriculture), a statistical–empirical model of the global food and agriculture system. C-LLAMA uses simplistic and highly traceable methods to provide an open and transparent approach to modelling the sensitivity of future agricultural land use to drivers such as diet, crop yields, and food-system efficiency. C-LLAMA uses publicly available FAOSTAT food supply, food production, and crop yield data to make linear projections of diet, food-system, and agricultural efficiencies, as well as land use at a national level, aiming to capture aspects of food systems in both developing and developed nations. In this paper we describe the structure and processes within the model, outline an anchor scenario, and perform sensitivity analyses of key components. The model land use output behaves as anticipated during sensitivity tests and under a scenario with a prescribed reduction in animal product consumption, in which land use for agriculture is reduced by 1.8 Gha in 2050 when compared with the anchor scenario.

Over the last 1-2 decades, the UK has been one of many countries observing plateaus in key crop yields, despite farming practice innovations such as the adoption of precision agriculture and advanced plant breeding programmes. The food production sector also continues to suffer strong yield impacts arising from inter-annual weather variability. This is in the context of a rapidly growing global population and a changing climate, which combined threaten future food security worldwide. With ever increasing competition for land use, identification and further development of climate-resilient crop varieties is a key priority. In this research, we present examples of the development of ‘best in class’ historical time-series records of key agro-meteorological variables and metrics by inter-comparing and combining different types of in-situ and remotely sensed observational and re-analysis products. We demonstrate the added value of applying these climate datasets to the modelling of UK crop yield and the interpretation of inter-annual crop production data using statistical models. Specifically, we combine this high-resolution climate data with Recommended List variety trials data in linear mixed models to isolate the genetic, environmental and genotype-by-environment drivers of winter wheat yield variability in the UK. Through this we demonstrate that the rate of genetic gain in winter wheat is slowing in the UK. Through our analysis of historical time-series climate data we generate the first “State of the UK Agroclimate”. This climate service will provide breeders, farmers and the wider agricultural sector with long-term UK-wide and regional agro-climatological averages, trends and extreme weather metrics of relevance to crops widely grown in the UK, including winter wheat. Our agro-climate analysis and results from our statistical models can be combined with projections of future climate, to identify current varieties that may perform well in the face of UK climate change and provide breeders with guidance of traits to breed into future varieties for enhanced resilience.

Wild pollinator numbers are known to be positively associated with amounts of flower-rich habitat at landscape level. Increasing floral resources can be particularly beneficial in relatively nectar-poor agricultural systems and having a baseline understanding of the temporal and spatial availability of resources can allow targeted habitat management. Very high-resolution remote sensing has potential to facilitate accurate mapping of fine-scale, within-habitat pollinator foraging resources, thereby allowing spatial and temporal gaps to be identified and addressed, improving predictions of pollinator numbers, and enabling remote monitoring of pollinator conser-vation measures.Concentrating on hedgerow and flower-rich field margins in a UK agricultural landscape, we showed that multispectral airborne imagery with 3 cm and 7 cm spatial resolutions can be used to classify five nectar-rich flowering plant species (Prunus spinosa, Crataegus monogyna, Rubus fruticosus, Silene dioica and Centaurea nigra) using a maximum likelihood classification algorithm. In 2019, we separately acquired 3 cm and 7 cm imagery for the months of March, May and July, respectively. Overall accuracies were above 90% for each month at both 3 cm and 7 cm resolutions (range 92.32%-98.72%), supporting previous research that suggests higher spatial resolutions do not necessarily lead to higher accuracies, as pixel variability is increased.Remaining challenges include determining which co-flowering species of similar colours in the visible range can be distinguished from one another within classifications and quantifying floral unit density from classifi-cations so that the nectar sugar supply can be calculated. Nonetheless, we provided a prototype approach for mapping pollinator foraging resources in an agricultural context, which can be extended to other nectar-rich species. The foundation is set for developing a remote sensing pipeline that can provide valuable data on the availability of nectar-rich flowering plant species at different time-points throughout the year.

Onshore wind electricity generation is key to mitigating greenhouse gas emissions.​ Poorly sited wind farms degrade high carbon soils and habitats, diminishing overall emission reductions. We explore the viability of the Scottish Government's renewable energy plan with respect to land use, natural capital and low carbon storage. With avoidance of sensitive peatlands a main consideration, six constraining factors were combined to determine areas of least habitat and soil sensitivity to onshore wind development in Scotland. Currently, 14 out of 21 terrestrial habitats have been impacted by installation of 389 onshore wind sites. Accounting for 73% of the total area, Coniferous Woodland, Acid Grassland, Bog, and Heather Grassland have been the largest habitats impacted. The most common soils of the least sensitive areas available for installation are brown earth and podzols, and construction of new wind farms on environmentally sensitive areas can be minimised by targeting relatively disturbed habitats such as improved grasslands. Scotland has a potential of 2.75 Mha of relatively low sensitive land, the largest areas sited in the Highlands, Dumfries and Galloway and Aberdeenshire. Additional to current installed capacity (13.9 GW), Scotland would require 6.6 GW of installed onshore wind capacity to function without nuclear energy generation and 464 GWh additional storage capacity (provided by 8.2 GW wind capacity). This equates to an installed and additional total of 346.676 ha required for wind electricity generation, potentially satisfied by shared land use with 23% of Scottish improved grasslands. Scotland has the available land area to achieve the Scottish Government's policy to move towards carbon-neutral, nuclear-free electricity generation through the use of renewables alone. Questions remain on which source of low carbon dispatchable (immediately accessible) energy to use in the case of a several day wind lull.

The UK government has made formal commitments to reduce GHG emissions (e.g. under the Climate Change Act 2008 and subsequent amendments) and to protect/improve natural capital and the environment (e.g. as part of the 25 Year Environment Plan published in 2018). Meeting these objectives requires an integrated approach to two parallel challenges i) decarbonising the energy system and ii) better understanding and valuation of natural capital and ecosystem services. From an academic perspective this involves bringing together two substantial, but rather weakly connected bodies of research, while also acknowledging that this integration in a UK setting needs to recognise the international context (i.e. a whole systems perspective). The ADVENT project (ADdressing Valuation of Energy and Nature Together) has been funded by the UK National Environment Research Council to develop conceptual frameworks and modelling tools which ‘integrate the analysis of prospective UK energy pathways with considerations relating to the value of natural capital’. A methodology has been implemented to downscale the outputs of pathways from national energy system models and incorporate environmental impacts into the assessment of different options. This has required defining spatially-optimised distributions of investments in new energy infrastructure using a range of financial and welfare criteria. These distributions are then compared in terms of their construction, transport and land opportunity costs, as well as the implications for biodiversity, greenhouse gas emissions, recreation, visual amenity and water resources. This paper will present results from comparing different UK energy pathways through to 2050 in terms of the implications of electricity generation from three types of renewables (bioenergy, solar and onshore wind). The results illustrate that i) individual pathways can vary appreciably in their environmental impacts, ii) overall societal welfare can be enhanced by using spatial modelling to incorporate valuations of such impacts into implementation of pathways and iii) assessment outcomes can be sensitive to modelling assumptions (e.g. regarding the proportion of biomass feedstock from domestic or international sources). More broadly, the results demonstrate how important improvements can be achieved in the integration of environmental considerations into the assessment of future energy pathways at regional and national scales. The approach is now being further refined through the UK Energy Research Centre Phase 4 programme and ADVANCES Landscape Decisions project in the UK, as well as the five-country IRENES project funded by Interreg Europe.

In 2010, the UK government launched the Demonstration Test Catchments (DTC) platform to evaluate the extent to which on-farm mitigation measures can cost-effectively reduce the impacts of agricultural water pollution on river ecology whilst maintaining food production capacity. In this paper, we compare the impacts on soil health of two types of conservation tillage (direct drill and shallow non-inversion) against conventional mouldboard ploughing after five years (2013-2018) of adoption within the River Wensum DTC. Across the 143 ha conservation tillage trial area, temporal changes in the physical, chemical and biological condition of the soils were examined through the analysis of 324 soil samples, whilst the impacts on soil water chemistry were assessed through the analysis of 1176 samples of subsurface field drainage. Riverine water pollution was also explored through high-resolution (30 min) hydrochemistry measurements generated by an automated, in-situ bankside monitoring station located 650 m downstream of the trial area. Results revealed that conservation tillage did not significantly alter the soil physical, chemical or biological condition relative to conventional ploughing during the first five years. In addition, conservation tillage did not reduce nutrient leaching losses into field drainage and did not significantly impact upon river water quality, despite the trial area covering 20% of the catchment. Economically, however, conservation tillage yielded net profit margins 13% higher than conventional ploughing after five years of practice due to a combination of operational efficiency savings and improved yields. Overall, the results of this study demonstrate that conservation tillage alone is ineffective at improving the short-term environmental sustainability of farming practices in this lowland intensive arable setting and indicates that a broader, integrated approach to conservation agriculture is required incorporating aspects of cover cropping, crop rotations and precision farming techniques. The improvements in farm business performance do, however, demonstrate land managers can make important financial gains by converting to a conservation tillage system.

In 2010, the UK government established the Demonstration Test Catchment (DTC) initiative to evaluate the extent to which on-farm mitigation measures can cost-effectively reduce the impacts of agricultural water pollution on river ecology whilst maintaining food production capacity. A central component of the DTC platform was the establishment of a comprehensive network of automated, web-based sensor technologies to generate high temporal resolution (30 min) empirical datasets of surface water, groundwater and meteorological parameters over a long period (2011-2018). Utilising 8.9 million water quality measurements generated for the River Wensum, this paper demonstrates how long-term, high-resolution monitoring of hydrochemistry can improve our understanding of the complex temporal dynamics of riverine processes from 30 min to annual timescales. This paper explores the impact of groundwater-surface water interactions on instream pollutant concentrations (principally nitrogen, phosphorus and turbidity) and reveals how varying hydrochemical associations under contrasting flow regimes can elicit important information on the dominant pollution pathways. Furthermore, this paper examines the relationships between agricultural pollutants and precipitation events of varying magnitude, whilst demonstrating how high-resolution data can be utilised to develop conceptual models of hydrochemical processes for contrasting winter and summer seasons. Finally, this paper considers how high-resolution hydrochemical data can be used to increase land manager awareness of environmentally damaging farming operations and encourage the adoption of more water sensitive land management practices. (C) 2020 Elsevier B.V. All rights reserved.