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Grazing livestock plays an important role in the context of food security, agricultural sustainability and climate change. Understanding how livestock move and interact with their environment may offer new insights on how grazing practices impact soil and ecosystem functions at spatial and temporal scales where knowledge is currently limited. We characterized daily and seasonal grazing patterns using Global Positioning System (GPS) data from two grazing strategies: conventionally- and rotationally-grazed pastures. Livestock movement was consistent with the so-called Lévy walks, and could thus be simulated with Lévy-walk based probability density functions. Our newly introduced "Moovement model" links grazing patterns with soil structure and related functions by coupling animal movement and soil structure dynamics models, allowing to predict spatially-explicit changes in key soil properties. Predicted post-grazing management-specific bulk densities were consistent with field measurements and confirmed that rotational grazing produced similar disturbance as conventional grazing despite hosting higher stock densities. Harnessing information on livestock movement and its impacts in soil structure within a modelling framework can help testing and optimizing grazing strategies for ameliorating their impact on soil health and environment., Competing Interests: 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., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Aims: The home-field advantage (HFA) hypothesis predicts faster decomposition of plant residues in home soil compared to soils with different plants ( away ), and has been demonstrated in forest and grassland ecosystems. It remains unclear if this legacy effect applies to crop residue decomposition in arable crop rotations. Such knowledge could improve our understanding of decomposition dynamics in arable soils and may allow optimisation of crop residue amendments in arable systems by cleverly combining crop-residue rotations with crop rotations to increase the amount of residue-derived C persisting in soil., Methods: We tested the HFA hypothesis in a reciprocal transplant experiment with mesh bags containing wheat and oilseed rape residues in soils at three stages of a short-rotation cropping system. Subsets of mesh bags were retrieved monthly for six months to determine residue decomposition rates, concomitantly measuring soil available N, microbial community structure (phospholipid fatty acid analysis), and microbial activity (Tea Bag Index protocol) to assess how plants may influence litter decomposition rates via alterations to soil biochemical properties and microbial communities., Results: The residues decomposed at similar rates at all rotational stages. Thorough data investigation using several statistical approaches revealed no HFA within the crop rotation. Soil microbial community structures were similar at all rotational stages., Conclusions: We attribute the absence of an HFA to the shortness of the rotation and soil disturbance involved in intensive agricultural practices. It is therefore unlikely that appreciable benefits could be obtained in short conventionally managed arable rotations by introducing a crop-residue rotation., Supplementary Information: The online version contains supplementary material available at 10.1007/s11104-022-05419-z., Competing Interests: Conflicts of interest/Competing interestsThe authors have no relevant financial or non-financial interests to disclose., (© The Author(s) 2022.)

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., Competing Interests: 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., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Under future CMIP5 climate change scenarios for 2050, an increase in wheat yield of about 10% is predicted in Great Britain (GB) as a result of the combined effect of CO 2 fertilization and a shift in phenology. Compared to the present day, crops escape increases in the climate impacts of drought and heat stresses on grain yield by developing before these stresses can occur. In the future, yield losses from water stress over a growing season will remain about the same across Great Britain with losses reaching around 20% of potential yield, while losses from drought around flowering will decrease and account for about 9% of water limited yield. Yield losses from heat stress around flowering will remain negligible in the future. These conclusions are drawn from a modelling study based on the response of the Sirius wheat simulation model to local-scale 2050-climate scenarios derived from 19 Global Climate Models from the CMIP5 ensemble at 25 locations representing current or potential wheat-growing areas in GB. However, depending on susceptibility to water stress, substantial interannual yield variation between locations is predicted, in some cases suggesting low wheat yield stability. For this reason, local-scale studies should be performed to evaluate uncertainties in yield prediction related to future weather patterns., (© 2021 The Authors.)

Soybean (Glycine max) offers an important source of plant-based protein. Currently much of Europe's soybean is imported, but there are strong economic and agronomic arguments for boosting local production. Soybean is grown in central and eastern Europe but is less favoured in the North due to climate. We conducted field trials across three seasons and two sites in the UK to test the viability of early-maturing soybean varieties and used the data from these trials to calibrate and validate the Rothamsted Landscape Model. Once validated, the model was used to predict the probability soybean would mature and the associated yield for 26 sites across the UK based on weather data under current, near-future (2041-60) and far-future (2081-2100) climate. Two representative concentration pathways, a midrange mitigation scenario (RCP4.5) and a high emission scenario (RCP8.5) were also explored. Our analysis revealed that under current climate early maturing varieties will mature in the south of the UK, but the probability of failure increases with latitude. Of the 26 sites considered, only at one did soybean mature for every realisation. Predicted expected yields ranged between 1.39 t ha -1 and 1.95 t ha -1 across sites. Under climate change these varieties are likely to mature as far north as southern Scotland. With greater levels of CO 2, yield is predicted to increase by as much as 0.5 t ha -1 at some sites in the far future, but this is tempered by other effects of climate change meaning that for most sites no meaningful increase in yield is expected. We conclude that soybean is likely to be a viable crop in the UK and for similar climates at similar latitudes in Northern Europe in the future but that for yields to be economically attractive for local markets, varieties must be chosen to align with the growing season., Competing Interests: 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., (Copyright © 2021 Rothamsted Research. Published by Elsevier B.V. All rights reserved.)

To manage agricultural landscapes more sustainably, we must understand and quantify the synergies and trade-offs between environmental impact, production, and other ecosystem services. Models play an important role in this type of analysis as generally it is infeasible to test multiple scenarios by experiment. These models can be linked with algorithms that optimise for multiple objectives by searching a space of allowable management interventions (the control variables). Optimisation of landscapes for multiple objectives can be computationally challenging, however, particularly if the scale of management is typically smaller (e.g. field scale) than the scale at which the objective is quantified (landscape scale) resulting in a large number of control variables whose impacts do not necessarily scale linearly. In this paper, we explore some practical solutions to this problem through a case study. In our case study, we link a relatively detailed, agricultural landscape model with a multiple-objective optimisation algorithm to determine solutions that both maximise profitability and minimise greenhouse gas emissions in response to management. The optimisation algorithm combines a non-dominated sorting routine with differential evolution, whereby a 'population' of 100 solutions evolves over time to a Pareto optimal front. We show the advantages of using a hierarchical approach to the optimisation, whereby it is applied to finer-scale units first (i.e. fields), and then the solutions from each optimisation are combined in a second step to produce landscape-scale outcomes. We show that if there is no interaction between units, then the solution derived using such an approach will be the same as the one obtained if the landscape is optimised in one step. However, if there is spatial interaction, or if there are constraints on the allowable sets of solutions, then outcomes can be quite different. In these cases, other approaches to increase the efficiency of the optimisation may be more appropriate-such as initialising the control variables for half of the population of solutions with values expected to be near optimal. Our analysis shows the importance of aligning a policy or management recommendation with the appropriate scale.

Irrigated dryland agroecosystems could become more sustainable if crop and soil management enhanced soil organic carbon (SOC). We hypothesized that combining high inputs from cover crops with no-tillage will increase long-term SOC stocks. Caatinga shrublands had been cleared in 1972 for arable crops and palm plantations before implementing field experiments on Mango and Melon systems (established in 2009 and 2012, respectively). Each of the two experiments were managed with no-till (NT) or conventional till (CT), and three types of cover cropping, either a plant mixture of 75% (PM1) or 25% (PM2) legumes, or spontaneous vegetation (SV). The RothC model was used with a daily timestep to simulate the soil moisture dynamics and C turnover for this dry climate. Carbon inputs were between 2.62 and 5.82 Mg C ha -1  year -1 and increased the depleted SOC stocks by 0.08 to 0.56 Mg C ha -1  year -1 . Scenarios of continuous biomass inputs of ca. 5 Mg C ha -1  year -1 for 60 years are likely to increase SOC stocks in the mango NT beyond the original Caatinga SOC by between 19.2 and 20.5 Mg C ha -1 . Under CT similar inputs would increase SOC stocks only marginally above depletion (2.75 to 2.47 Mg C ha -1 ). Under melon, annual carbon inputs are slightly greater (up to 5.5 Mg C ha -1  year -1 ) and SOC stocks would increase on average by another 8% to 22.3 to 20.6 Mg C ha -1 under NT and by 8 Mg C ha -1 under CT. These long-term simulations show that combining NT with high quality cover crops (PM1, PM2) would exceed SOC stocks of the initial Caatinga within 20 and 25 years under irrigated melon and mango cultivation, respectively. These results present a solution to reverse prior loss of SOC by replacing CT dryland agriculture with irrigated NT plus high input cover crops agroecosystems., Competing Interests: 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., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Agricultural landscapes provide many functions simultaneously including food production, regulation of water and regulation of greenhouse gases. Thus, it is challenging to make land management decisions, particularly transformative changes, that improve on one function without unintended consequences for other functions. To make informed decisions the trade-offs between different landscape functions must be considered. Here, we use a multi-objective optimization algorithm with a model of crop production that also simulates environmental effects such as nitrous oxide emissions to identify trade-off frontiers and associated possibilities for agricultural management. Trade-offs are identified in three soil types, using wheat production in the UK as an example, then the trade-off for combined management of the three soils is considered. The optimization algorithm identifies trade-offs between different objectives and allows them to be visualised. For example, we observed a highly non-linear trade-off between wheat yield and nitrous oxide emissions, illustrating where small changes might have a large impact. We used a cluster analysis to identify distinct management strategies with similar management actions and use these clusters to link the trade-off curves to possibilities for management. There were more possible strategies for achieving desirable environmental outcomes and remaining profitable when the management of different soil types was considered together. Interestingly, it was on the soil capable of the highest potential profit that lower profit strategies were identified as useful for combined management. Meanwhile, to maintain average profitability across the soils, it was necessary to maximise the profit from the soil with the lowest potential profit. These results are somewhat counterintuitive and so the range of strategies supplied by the model could be used to stimulate discussion amongst stakeholders. In particular, as some key objectives can be met in different ways, stakeholders could discuss the impact of these management strategies on other objectives not quantified by the model., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

Efficient farm management can be aided by the identification of zones in the landscape. These zones can be informed from different measured variables by ensuring a sense of spatial coherence. Forming spatially coherent zones is an established method in the literature, but has been found to perform poorly when data are sparse. In this paper, we describe the different types of data sparsity and investigate how this impacts the performance of established methods. We introduce a set of methodological advances that address these shortcomings to provide a method for forming spatially coherent zones under data sparsity.

Developing sustainable food systems is essential, especially for emerging economies, where food systems are changing rapidly and affect the environment and natural resources. We explored possible future pathways for a sustainable food system in China, using multiple environmental indicators linked to eight of the Sustainable Development Goals (SDGs). Forecasts for 2030 in a business as usual scenario (BAU) indicate increases in animal food consumption as well as increased shortages of the land available and the water needed to produce the required food in China. Associated greenhouse gas emissions and nitrogen and phosphorus losses could become 10-42% of global emissions in 2010. We developed three main pathways besides BAU [produce more and better food (PMB), consume and waste less food (CWL), and import more food (IMF)] and analyzed their impacts and contributions to achieving one or more of the eight SDGs. Under these scenarios, the demand for land and water and the emissions of GHG and nutrients may decrease by 7-55% compared to BAU, depending on the pathway followed. A combination of PMB and CWL was most effective, while IMF externalizes impacts to countries exporting to China. Modestly increasing feed or food imports in a selective manner could ease the pressure on natural resources. Our modeling framework allows us to analyze the effects of changes in food production-consumption systems in an integrated manner, and the results can be linked to the eight SDGs. Despite formidable technological, social, educational, and structural barriers that need to be overcome, our study indicates that the ambitious targets of China's new agricultural and environmental strategy appear to be achievable.

This paper describes an agricultural model (Roth-CNP) that estimates carbon (C), nitrogen (N) and phosphorus (P) pools, pool changes, their balance and the nutrient fluxes exported from arable and grassland systems in the UK during 1800-2010. The Roth-CNP model was developed as part of an Integrated Model (IM) to simulate C, N and P cycling for the whole of UK, by loosely coupling terrestrial, hydrological and hydro-chemical models. The model was calibrated and tested using long term experiment (LTE) data from Broadbalk (1843) and Park Grass (1856) at Rothamsted. We estimated C, N and P balance and their fluxes exported from arable and grassland systems on a 5km×5km grid across the whole of UK by using the area of arable of crops and livestock numbers in each grid and their management. The model estimated crop and grass yields, soil organic carbon (SOC) stocks and nutrient fluxes in the form of NH 4 -N, NO 3 -N and PO 4 -P. The simulated crop yields were compared to that reported by national agricultural statistics for the historical to the current period. Overall, arable land in the UK have lost SOC by -0.18, -0.25 and -0.08MgCha -1 y -1 whereas land under improved grassland SOC stock has increased by 0.20, 0.47 and 0.24MgCha -1 y -1 during 1800-1950, 1950-1970 and 1970-2010 simulated in this study. Simulated N loss (by leaching, runoff, soil erosion and denitrification) increased both under arable (-15, -18 and -53kgNha -1 y -1 ) and grass (-18, -22 and -36kgNha -1 y -1 ) during different time periods. Simulated P surplus increased from 2.6, 10.8 and 18.1kgPha -1 y -1 under arable and 2.8, 11.3 and 3.6kgPha -1 y -1 under grass lands 1800-1950, 1950-1970 and 1970-2010., (Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.)

Soils are fundamental to terrestrial ecosystem functioning and food security, thus their resilience to disturbances is critical. Furthermore, they provide effective models of complex natural systems to explore resilience concepts over experimentally-tractable short timescales. We studied soils derived from experimental plots with different land-use histories of long-term grass, arable and fallow to determine whether regimes of extreme drying and re-wetting would tip the systems into alternative stable states, contingent on their historical management. Prior to disturbance, grass and arable soils produced similar respiration responses when processing an introduced complex carbon substrate. A distinct respiration response from fallow soil here indicated a different prior functional state. Initial dry:wet disturbances reduced the respiration in all soils, suggesting that the microbial community was perturbed such that its function was impaired. After 12 drying and rewetting cycles, despite the extreme disturbance regime, soil from the grass plots, and those that had recently been grass, adapted and returned to their prior functional state. Arable soils were less resilient and shifted towards a functional state more similar to that of the fallow soil. Hence repeated stresses can apparently induce persistent shifts in functional states in soils, which are influenced by management history.

We describe a model framework that simulates spatial and temporal interactions in agricultural landscapes and that can be used to explore trade-offs between production and environment so helping to determine solutions to the problems of sustainable food production. Here we focus on models of agricultural production, water movement and nutrient flow in a landscape. We validate these models against data from two long-term experiments, (the first a continuous wheat experiment and the other a permanent grass-land experiment) and an experiment where water and nutrient flow are measured from isolated catchments. The model simulated wheat yield (RMSE 20.3-28.6%), grain N (RMSE 21.3-42.5%) and P (RMSE 20.2-29% excluding the nil N plots), and total soil organic carbon particularly well (RMSE3.1-13.8%), the simulations of water flow were also reasonable (RMSE 180.36 and 226.02%). We illustrate the use of our model framework to explore trade-offs between production and nutrient losses., (Copyright © 2017 Rothamsted Research. Published by Elsevier B.V. All rights reserved.)

Earthworms benefit agriculture by providing several ecosystem services. Therefore, strategies to increase earthworm abundance and activity in agricultural soils should be identified, and encouraged. Lumbricus terrestris earthworms primarily feed on organic inputs to soils but it is not known which organic amendments are the most effective for increasing earthworm populations. We conducted earthworm surveys in the field and carried out experiments in single-earthworm microcosms to determine the optimum food source for increasing earthworm biomass using a selection of crop residues and organic wastes available to agriculture. We found that although farmyard manure increased earthworm populations more than cereal straw in the field, straw increased earthworm biomass more than manures when milled and applied to microcosms. Earthworm growth rates were positively correlated with the calorific value of the amendment and straw had a much higher calorific value than farmyard manure, greenwaste compost, or anaerobic digestate. Reducing the particle size of straw by milling to <3 mm made the energy in the straw more accessible to earthworms. The benefits and barriers to applying milled straw to arable soils in the field are discussed.

Understanding changes in plant-soil C, N and P using data alone is difficult due to the linkages between carbon, nitrogen and phosphorus cycles (C, N and P), and multiple changing long-term drivers (e.g. climate, land-use, and atmospheric N deposition). Hence, dynamic models are a vital tool for disentangling these drivers, helping us understand the dominant processes and drivers and predict future change. However, it is essential that models are tested against data if their outputs are to be concluded upon with confidence. Here, a simulation of C, N and P cycles using the N14CP model was compared with time-series observations of C, N and P in soils and biomass from the Rothamsted Research long-term experiments spanning 150years, providing an unprecedented temporal integrated test of such a model. N14CP reproduced broad trends in soil organic matter (SOM) C, N and P, vegetation biomass and N and P leaching. Subsequently, the model was used to decouple the effects of land management and elevated nitrogen deposition in these experiments. Elevated N deposition over the last 150years is shown to have increased net primary productivity (NPP) 4.5-fold and total carbon sequestration 5-fold at the Geescroft Wilderness experiment, which was re-wilded to woodland in 1886. In contrast, the model predicts that for cropped grassland conditions at the Park Grass site, elevated N deposition has very little effect on SOM, as increases in NPP are diverted from the soil. More broadly, these results suggest that N deposition is likely to have had a large effect on SOM and NPP in northern temperate and boreal semi-natural grasslands and forests. However, in cropped and grazed systems in the same region, whilst NPP may have been supported in part by elevated N deposition, declines in SOM may not have been appreciably counteracted by increased N availability., (Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.)

Factors governing the turnover of organic matter (OM) added to soils, including substrate quality, climate, environment and biology, are well known, but their relative importance has been difficult to ascertain due to the interconnected nature of the soil system. This has made their inclusion in mechanistic models of OM turnover or nutrient cycling difficult despite the potential power of these models to unravel complex interactions. Using high temporal-resolution respirometery (6 min measurement intervals), we monitored the respiratory response of 67 soils sampled from across England and Wales over a 5 day period following the addition of a complex organic substrate (green barley powder). Four respiratory response archetypes were observed, characterised by different rates of respiration as well as different time-dependent patterns. We also found that it was possible to predict, with 95% accuracy, which type of respiratory behaviour a soil would exhibit based on certain physical and chemical soil properties combined with the size and phenotypic structure of the microbial community. Bulk density, microbial biomass carbon, water holding capacity and microbial community phenotype were identified as the four most important factors in predicting the soils' respiratory responses using a Bayesian belief network. These results show that the size and constitution of the microbial community are as important as physico-chemical properties of a soil in governing the respiratory response to OM addition. Such a combination suggests that the 'architecture' of the soil, i.e. the integration of the spatial organisation of the environment and the interactions between the communities living and functioning within the pore networks, is fundamentally important in regulating such processes.

When dry soils are rewetted a pulse of CO 2 is invariably released, and whilst this phenomenon has been studied for decades, the precise origins of this CO 2 remain obscure. We postulate that it could be of chemical (i.e. via abiotic pathways), biochemical (via free enzymes) or biological (via intact cells) origin. To elucidate the relative contributions of the pathways, dry soils were either sterilised (double autoclaving) or treated with solutions of inhibitors (15% trichloroacetic acid or 1% silver nitrate) targeting the different modes. The rapidity of CO 2 release from the soils after the drying:rewetting (DRW) cycle was remarkable, with maximal rates of evolution within 6 min, and 41% of the total efflux over 96 h released within the first 24 h. The complete cessation of CO 2 eflux following sterilisation showed there was no abiotic (dissolution of carbonates) contribution to the CO 2 release on rewetting, and clear evidence for an organismal or biochemical basis to the flush. Rehydration in the presence of inhibitors indicated that there were approximately equal contributions from biochemical (outside membranes) and organismal (inside membranes) sources within the first 24 h after rewetting. This suggests that some of the flux was derived from microbial respiration, whilst the remainder was a consequence of enzyme activity, possibly through remnant respiratory pathways in the debris of dead cells.

The North Wyke Farm Platform was established as a United Kingdom national capability for collaborative research, training and knowledge exchange in agro-environmental sciences. Its remit is to research agricultural productivity and ecosystem responses to different management practices for beef and sheep production in lowland grasslands. A system based on permanent pasture was implemented on three 21-ha farmlets to obtain baseline data on hydrology, nutrient cycling and productivity for 2 years. Since then two farmlets have been modified by either (i) planned reseeding with grasses that have been bred for enhanced sugar content or deep-rooting traits or (ii) sowing grass and legume mixtures to reduce nitrogen fertilizer inputs. The quantities of nutrients that enter, cycle within and leave the farmlets were evaluated with data recorded from sensor technologies coupled with more traditional field study methods. We demonstrate the potential of the farm platform approach with a case study in which we investigate the effects of the weather, field topography and farm management activity on surface runoff and associated pollutant or nutrient loss from soil. We have the opportunity to do a full nutrient cycling analysis, taking account of nutrient transformations in soil, and flows to water and losses to air. The NWFP monitoring system is unique in both scale and scope for a managed land-based capability that brings together several technologies that allow the effect of temperate grassland farming systems on soil moisture levels, runoff and associated water quality dynamics to be studied in detail., Highlights: Can meat production systems be developed that are productive yet minimize losses to the environment?The data are from an intensively instrumented capability, which is globally unique and topical.We use sensing technologies and surveys to show the effect of pasture renewal on nutrient losses.Platforms provide evidence of the effect of meteorology, topography and farm activity on nutrient loss.

Soil organic carbon (SOC) and nitrogen (N) contents are controlled partly by plant inputs that can be manipulated in agricultural systems. Although SOC and N pools occur mainly in the topsoil (upper 0.30 m), there are often substantial pools in the subsoil that are commonly assumed to be stable. We tested the hypothesis that contrasting long-term management systems change the dynamics of SOC and N in the topsoil and subsoil (to 0.75 m) under temperate conditions. We used an established field experiment in the UK where control grassland was changed to arable (59 years before) and bare fallow (49 years before) systems. Losses of SOC and N were 65 and 61% under arable and 78 and 74% under fallow, respectively, in the upper 0.15 m when compared with the grass land soil, whereas at 0.3-0.6-m depth losses under arable and fallow were 41 and 22% and 52 and 35%, respectively. The stable isotopes 13 C and 15 N showed the effects of different treatments. Concentrations of long-chain n-alkanes C 27 , C 29 and C 31 were greater in soil under grass than under arable and fallow. The dynamics of SOC and N changed in both topsoil and subsoil on a decadal time-scale because of changes in the balance between inputs and turnover in perennial and annual systems. Isotopic and geochemical analyses suggested that fresh inputs and decomposition processes occur in the subsoil. There is a need to monitor and predict long-term changes in soil properties in the whole soil profile if soil is to be managed sustainably., Highlights: Land-use change affects soil organic carbon and nitrogen, but usually the topsoil only is considered.Grassland cultivated to arable and fallow lost 13-78% SOC and N to 0.6 m depth within decades.Isotopic and biomarker analyses suggested changes in delivery and turnover of plant-derived inputs.The full soil profile must be considered to assess soil quality and sustainability.

There are several conceptual definitions of resilience pertaining to environmental systems and, even if resilience is clearly defined in a particular context, it is challenging to quantify. We identify four characteristics of the response of a system function to disturbance that relate to "resilience": (1) degree of return of the function to a reference level; (2) time taken to reach a new quasi-stable state; (3) rate (i.e. gradient) at which the function reaches the new state; (4) cumulative magnitude of the function (i.e. area under the curve) before a new state is reached. We develop metrics to quantify these characteristics based on an analogy with a mechanical spring and damper system. Using the example of the response of a soil function (respiration) to disturbance, we demonstrate that these metrics effectively discriminate key features of the dynamic response. Although any one of these characteristics could define resilience, each may lead to different insights and conclusions. The salient properties of a resilient response must thus be identified for different contexts. Because the temporal resolution of data affects the accurate determination of these metrics, we recommend that at least twelve measurements are made over the temporal range for which the response is expected.

A farmer's decision on whether to control a pest is usually based on the perceived threat of the pest locally and the guidance of commercial advisors. Therefore, farmers in a region are often influenced by similar circumstances, and this can create a coordinated response for pest control that is effective at a landscape scale. This coordinated response is not intentional, but is an emergent property of the system. We propose a framework for understanding the intrinsic feedback mechanisms between the actions of humans and the dynamics of pest populations and demonstrate this framework using the European corn borer, a serious pest in maize crops. We link a model of the European corn borer and a parasite in a landscape with a model that simulates the decisions of individual farmers on what type of maize to grow. Farmers chose whether to grow Bt-maize, which is toxic to the corn borer, or conventional maize for which the seed is cheaper. The problem is akin to the snow-drift problem in game theory; that is to say, if enough farmers choose to grow Bt maize then because the pest is suppressed an individual may benefit from growing conventional maize. We show that the communication network between farmers' and their perceptions of profit and loss affects landscape scale patterns in pest dynamics. We found that although adoption of Bt maize often brings increased financial returns, these rewards oscillate in response to the prevalence of pests.

Nitrous oxide emitted to the atmosphere via the soil processes of nitrification and denitrification plays an important role in the greenhouse gas balance of the atmosphere and is involved in the destruction of stratospheric ozone. These processes are controlled by biological, physical and chemical factors such as growth and activity of microbes, nitrogen availability, soil temperature and water availability. A comprehensive understanding of these processes embodied in an appropriate model can help develop agricultural mitigation strategies to reduce greenhouse gas emissions, and help with estimating emissions at landscape and regional scales. A detailed module to describe the denitrification and nitrification processes and nitrogenous gas emissions was incorporated into the SPACSYS model to replace an earlier module that used a simplified first-order equation to estimate denitrification and was unable to distinguish the emissions of individual nitrogenous gases. A dataset derived from a Scottish grassland experiment in silage production was used to validate soil moisture in the top 10 cm soil, cut biomass, nitrogen offtake and N2O emissions. The comparison between the simulated and observed data suggested that the new module can provide a good representation of these processes and improve prediction of N2O emissions. The model provides an opportunity to estimate gaseous N emissions under a wide range of management scenarios in agriculture, and synthesises our understanding of the interaction and regulation of the processes., (Copyright © 2015. Published by Elsevier B.V.)

National governments are becoming increasingly aware of the importance of their soil resources and are shaping strategies accordingly. Implicit in any such strategy is that degradation threats and their potential effect on important soil properties and functions are defined and understood. In this paper, we aimed to review the principal degradation threats on important soil properties in the UK, seeking quantitative data where possible. Soil erosion results in the removal of important topsoil and, with it, nutrients, C and porosity. A decline in soil organic matter principally affects soil biological and microbiological properties, but also impacts on soil physical properties because of the link with soil structure. Soil contamination affects soil chemical properties, affecting nutrient availability and degrading microbial properties, whilst soil compaction degrades the soil pore network. Soil sealing removes the link between the soil and most of the 'spheres', significantly affecting hydrological and microbial functions, and soils on re-developed brownfield sites are typically degraded in most soil properties. Having synthesized the literature on the impact on soil properties, we discuss potential subsequent impacts on the important soil functions, including food and fibre production, storage of water and C, support for biodiversity, and protection of cultural and archaeological heritage. Looking forward, we suggest a twin approach of field-based monitoring supported by controlled laboratory experimentation to improve our mechanistic understanding of soils. This would enable us to better predict future impacts of degradation processes, including climate change, on soil properties and functions so that we may manage soil resources sustainably.

In an effort to mitigate anthropogenic effects on the global climate system, industrialised countries are required to quantify and report, for various economic sectors, the annual emissions of greenhouse gases from their several sources and the absorption of the same in different sinks. These estimates are uncertain, and this uncertainty must be communicated effectively, if government bodies, research scientists or members of the public are to draw sound conclusions. Our interest is in communicating the uncertainty in estimates of greenhouse gas emissions from agriculture to those who might directly use the results from the inventory. We tested six methods of communication. These were: a verbal scale using the IPCC calibrated phrases such as 'likely' and 'very unlikely'; probabilities that emissions are within a defined range of values; confidence intervals for the expected value; histograms; box plots; and shaded arrays that depict the probability density of the uncertain quantity. In a formal trial we used these methods to communicate uncertainty about four specific inferences about greenhouse gas emissions in the UK. Sixty four individuals who use results from the greenhouse gas inventory professionally participated in the trial, and we tested how effectively the uncertainty about these inferences was communicated by means of a questionnaire. Our results showed differences in the efficacy of the methods of communication, and interactions with the nature of the target audience. We found that, although the verbal scale was thought to be a good method of communication it did not convey enough information and was open to misinterpretation. Shaded arrays were similarly criticised for being open to misinterpretation, but proved to give the best impression of uncertainty when participants were asked to interpret results from the greenhouse gas inventory. Box plots were most favoured by our participants largely because they were particularly favoured by those who worked in research or had a stronger mathematical background. We propose a combination of methods should be used to convey uncertainty in emissions and that this combination should be tailored to the professional group., (Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Current research on macronutrient cycling in UK agricultural systems aims to optimise soil and nutrient management for improved agricultural production and minimise effects on the environment and provision of ecosystem services. Nutrient use inefficiencies can cause environmental pollution through the release of greenhouse gases into the atmosphere and of soluble and particulate forms of N, P and carbon (C) in leachate and run-off into watercourses. Improving nutrient use efficiencies in agriculture calls for the development of sustainable nutrient management strategies: more efficient use of mineral fertilisers, increased recovery and recycling of waste nutrients, and, better exploitation of the substantial inorganic and organic reserves of nutrients in the soil. Long-term field experimentation in the UK has provided key knowledge of the main nutrient transformations in agricultural soils. Emerging analytical technologies, especially stable isotope labelling, that better characterise macronutrient forms and bioavailability and improve the quantification of the complex relationships between the macronutrients in soils at the molecular scale, are augmenting this knowledge by revealing the underlying processes. The challenge for the future is to determine the relationships between the dynamics of N, P and C across scales, which will require both new modelling approaches and integrated approaches to macronutrient cycling., (Copyright © 2012 Elsevier B.V. All rights reserved.)

The nature and effect of the stresses on root growth in crops subject to drying is reviewed. Drought is a complex stress, impacting on plant growth in a number of interacting ways. In response, there are a number of ways in which the growing plant is able to adapt to or alleviate these stresses. It is suggested that the most significant opportunity for progress in overcoming drought stress and increasing crop yields is to understand and exploit the conditions in soil by which plant roots are able to maximize their use of resources. This may not be straightforward, with multiple stresses, sometimes competing functions of roots, and conditions which impact upon roots very differently depending upon what soil, what depth or what stage of growth the root is at. Several processes and the interaction between these processes in soil have been neglected. It is our view that drought is not a single, simple stress and that agronomic practice which seeks to adapt to climate change must take account of the multiple facets of both the stress induced by insufficient water as well as other interacting stresses such as heat, disease, soil strength, low nutrient status, and even hypoxia. The potential for adaptation is probably large, however. The possible changes in stress as a result of the climate change expected under UK conditions are assessed and it appears possible that wet warm winters will impact on root growth as much if not more than dry warm summers.

We compared alternate uses of cereal straw (4.25t dry matter ha(-1) containing 1.7t carbon (C)) for their effectiveness in relation to climate change mitigation. The scenarios were (1) incorporation into soil to increase soil organic carbon (SOC) content ("carbon sequestration") and (2) combustion to generate electricity. The Rothamsted Carbon Model was used to estimate SOC accumulation in a silty clay loam soil under the climatic conditions of north-west Europe. Using straw for electricity generation saved seven times more CO2 than from SOC accumulation. This comparison assumed that electricity from straw combustion displaced that generated from coal and used the mean annual accumulation of SOC over 100yr. SOC increased most rapidly in the early years, but then more slowly as a new equilibrium value was approached. We suggest that increased SOC from straw incorporation does not represent genuine climate change mitigation through carbon sequestration. In Europe, most straw not already incorporated in the field where it is grown is subsequently returned elsewhere, e.g., after use for animal bedding and production of manure. Only additional retention of C in soil compared to the alternative use represents sequestration. Maintenance of SOC for soil functioning is a more appropriate rationale for returning straw to soil than climate change mitigation. This analysis shows that considerably greater climate change mitigation is achieved through saved CO2 emissions by burning straw for electricity generation, replacing some use of fossil fuel.