Quantifying the value of ecosystem services: a case study of honeybee pollination in the UK

There is concern that insect pollinators, such as honey bees, are currently declining in abundance, and are under serious threat from environmental changes such as habitat loss and climate change; the use of pesticides in intensive agriculture, and emerging diseases. This paper aims to evaluate how much public support there would be in preventing further decline to maintain the current number of bee colonies in the UK. The contingent valuation method (CVM) was used to obtain the willingness to pay (WTP) for a theoretical pollinator protection policy. Respondents were asked whether they would be WTP to support such a policy and how much would they pay? Results show that the mean WTP to support the bee protection policy was £1.37/week/household. Based on there being 24.9 million households in the UK, this is equivalent to £1.77 billion per year. This total value can show the importance of maintaining the overall pollination service to policy makers. We compare this total with estimates obtained using a simple market valuation of pollination for the UK.

Simulating the effects of grassland management and grass ensiling on methane emission from lactating cows

A dynamic, mechanistic model of enteric fermentation was used to investigate the effect of type and quality of grass forage, dry matter intake (DMI) and proportion of concentrates in dietary dry matter (DM) on variation in methane (CH(4)) emission from enteric fermentation in dairy cows. The model represents substrate degradation and microbial fermentation processes in rumen and hindgut and, in particular, the effects of type of substrate fermented and of pH oil the production of individual volatile fatty acids and CH, as end-products of fermentation. Effects of type and quality of fresh and ensiled grass were evaluated by distinguishing two N fertilization rates of grassland and two stages of grass maturity. Simulation results indicated a strong impact of the amount and type of grass consumed oil CH(4) emission, with a maximum difference (across all forage types and all levels of DM 1) of 49 and 77% in g CH(4)/kg fat and protein corrected milk (FCM) for diets with a proportion of concentrates in dietary DM of 0.1 and 0.4, respectively (values ranging from 10.2 to 19.5 g CH(4)/kg FCM). The lowest emission was established for early Cut, high fertilized grass silage (GS) and high fertilized grass herbage (GH). The highest emission was found for late cut, low-fertilized GS. The N fertilization rate had the largest impact, followed by stage of grass maturity at harvesting and by the distinction between GH and GS. Emission expressed in g CH(4)/kg FCM declined oil average 14% with an increase of DMI from 14 to 18 kg/day for grass forage diets with a proportion of concentrates of 0.1, and on average 29% with an increase of DMI from 14 to 23 kg/day for diets with a proportion of concentrates of 0.4. Simulation results indicated that a high proportion of concentrates in dietary DM may lead to a further reduction of CH, emission per kg FCM mainly as a result of a higher DM I and milk yield, in comparison to low concentrate diets. Simulation results were evaluated against independent data obtained at three different laboratories in indirect calorimetry trials with COWS consuming GH mainly. The model predicted the average of observed values reasonably, but systematic deviations remained between individual laboratories and root mean squared prediction error was a proportion of 0.12 of the observed mean. Both observed and predicted emission expressed in g CH(4)/kg DM intake decreased upon an increase in dietary N:organic matter (OM) ratio. The model reproduced reasonably well the variation in measured CH, emission in cattle sheds oil Dutch dairy farms and indicated that oil average a fraction of 0.28 of the total emissions must have originated from manure under these circumstances.

The precipitation climate of Central Asia: intercomparison of observational and numerical data sources in a remote semiarid region

In this study, we systematically compare a wide range of observational and numerical precipitation datasets for Central Asia. Data considered include two re-analyses, three datasets based on direct observations, and the output of a regional climate model simulation driven by a global re-analysis. These are validated and intercompared with respect to their ability to represent the Central Asian precipitation climate. In each of the datasets, we consider the mean spatial distribution and the seasonal cycle of precipitation, the amplitude of interannual variability, the representation of individual yearly anomalies, the precipitation sensitivity (i.e. the response to wet and dry conditions), and the temporal homogeneity of precipitation. Additionally, we carried out part of these analyses for datasets available in real time. The mutual agreement between the observations is used as an indication of how far these data can be used for validating precipitation data from other sources. In particular, we show that the observations usually agree qualitatively on anomalies in individual years while it is not always possible to use them for the quantitative validation of the amplitude of interannual variability. The regional climate model is capable of improving the spatial distribution of precipitation. At the same time, it strongly underestimates summer precipitation and its variability, while interannual variations are well represented during the other seasons, in particular in the Central Asian mountains during winter and spring

Limiting factors in the restoration of UK grassland beetle assemblages

Grasslands restoration is a key management tool contributing to the long-term maintenance of insect populations, providing functional connectivity and mitigating against extinction debt across landscapes. As knowledge of grassland insect communities is limited, the lag between the initiation of restoration and the ability of these new habitats to contribute to such processes is unclear. Using ten data sets, ranging from 3 to 14 years, we investigate the lag between restoration and the establishment of phytophagous beetle assemblages typical of species rich grasslands. We used traits and ecological characteristics to determine factors limiting beetle colonisation, and also considered how food-web structure changed during restoration. For sites where seed addition of host-plants occurred the success in replicating beetle assemblages increased over time following a negative exponential function. Extrapolation beyond the existing data set tentatively suggested that success would plateau after 20 years, representing a c. 60% increase in assemblage similarity to target grasslands. In the absence of seed addition, similarity to the target grasslands showed no increase over time. Where seed addition was used the connectance of plant-herbivore food webs decreased over time, approaching values typical of species rich grasslands after c. 7 years. This trend was, however, dependent on the inclusion of a single site containing data in excess of 6 years of restoration management. Beetles not capable of flight, those showing high degrees of host-plant specialisation and species feeding on nationally rare host plants take between 1 and 3 years longer to colonise. Successful grassland restoration is underpinned by the establishment of host-plants, although individual species traits compound the effects of poor host-plant establishment to slow colonisation. The use of pro-active grassland restoration to mitigate against future environmental change should account for lag periods in excess of 10 years if the value of these habitats is to be fully realised.

The REFLEX project: Comparing different algorithms and implementations for the inversion of a terrestrial ecosystem model against eddy covariance data

We describe a model-data fusion (MDF) inter-comparison project (REFLEX), which compared various algorithms for estimating carbon (C) model parameters consistent with both measured carbon fluxes and states and a simple C model. Participants were provided with the model and with both synthetic net ecosystem exchange (NEE) of CO2 and leaf area index (LAI) data, generated from the model with added noise, and observed NEE and LAI data from two eddy covariance sites. Participants endeavoured to estimate model parameters and states consistent with the model for all cases over the two years for which data were provided, and generate predictions for one additional year without observations. Nine participants contributed results using Metropolis algorithms, Kalman filters and a genetic algorithm. For the synthetic data case, parameter estimates compared well with the true values. The results of the analyses indicated that parameters linked directly to gross primary production (GPP) and ecosystem respiration, such as those related to foliage allocation and turnover, or temperature sensitivity of heterotrophic respiration, were best constrained and characterised. Poorly estimated parameters were those related to the allocation to and turnover of fine root/wood pools. Estimates of confidence intervals varied among algorithms, but several algorithms successfully located the true values of annual fluxes from synthetic experiments within relatively narrow 90% confidence intervals, achieving >80% success rate and mean NEE confidence intervals <110 gC m−2 year−1 for the synthetic case. Annual C flux estimates generated by participants generally agreed with gap-filling approaches using half-hourly data. The estimation of ecosystem respiration and GPP through MDF agreed well with outputs from partitioning studies using half-hourly data. Confidence limits on annual NEE increased by an average of 88% in the prediction year compared to the previous year, when data were available. Confidence intervals on annual NEE increased by 30% when observed data were used instead of synthetic data, reflecting and quantifying the addition of model error. Finally, our analyses indicated that incorporating additional constraints, using data on C pools (wood, soil and fine roots) would help to reduce uncertainties for model parameters poorly served by eddy covariance data.

Changing climate and the Irish landscape

The impacts of current and future changes in climate have been investigated for Irish vegetation. Warming has been observed over the last two decades, with impacts that are also strongly influenced by natural oscillations of the surrounding ocean, seen as fluctuations in the North Atlantic Oscillation and the Atlantic Multidecadal Oscillation. Satellite observations show that vegetation greenness increases in warmer years, a feature mirrored by increases in net ecosystem production observed for a grassland and a plantation forest. An ensemble of general circulation model simulations of future climates indicate temperature rises over the twenty-first century ranging from 1°C to 7°C, depending on future scenarios of greenhouse gas emissions. Net primary production is simulated to increase under all scenarios, due to the positive impacts of rising temperature, a modest rise of precipitation and rising carbon dioxide concentrations. In an optimistic scenario of reducing future emissions, CO2 concentration is simulated to flatten from about 2070, although temperatures continue to increase. Under this scenario Ireland could become a source of carbon, whereas under all other emission scenarios Ireland is a sink for carbon that may increase by up to three-fold over the twenty-first century. A likely and unavoidable impact of changing climate is the arrival of alien plant species, which may disrupt ecosystems and exert negative impacts on native biodiversity. Alien species arrive continually, with about 250 dated arrivals in the twentieth century. A simulation model indicates that this rate of alien arrival may increase by anything between two and ten times, dependent on the future climatic scenario, by 2050. Which alien species may become severely disruptive is, however, not known.

Plant-soil interactions and grassland diversity restoration

Restoration schemes aimed at enhancing plant species diversity of improved agricultural grassland have been a key feature of agri-environmental policy since the mid 1980s. Allied to this has been much research aimed at providing policy makers with guidelines on how best to manage grassland to restore botanical diversity. This research includes long-term studies of the consequences for grassland diversity of management techniques such as different hay cut dates, fertiliser additions, seed introductions and grazing regimes. Studies have also explored the role of introductions of Rhinanthus minor into species-poor swards to debilitate competitive grasses. While these studies have been successful in identifying some management features that control plant species diversity in agricultural grassland, they have taken a largely aboveground perspective on plant community dynamics.

Limiting factors in the restoration of grassland insects

Grasslands restoration is a key management tool contributing to the long-term maintenance of insect populations, providing functional connectivity and mitigating against extinction debt across landscapes. As knowledge of grassland insect communities is limited, the lag between the initiation of restoration and the ability of these new habitats to contribute to the successful enhancement of native biodiversity is unclear. Using two long term data sets, we investigate differences in successional trajectories during the establishment of butterfly (11 years) and phytophagous beetle (13 years) communities during the recreation of calcareous grassland. Overall restoration success was higher for the butterflies than the beetles. However, both shared a general pattern of rapidly increasing restoration success over the first five years, awhich approached an asymptote after c. 10 years. The use of pro-active grassland restoration to mitigate against future environmental change therefore needs to account for such time lag if the value of these habitats is to be fully realised.

Abiotic drivers and plant traits explain landscape-scale patterns in soil microbial communities

The controls on aboveground community composition and diversity have been extensively studied, but our understanding of the drivers of belowground microbial communities is relatively lacking, despite their importance for ecosystem functioning. In this study, we fitted statistical models to explain landscape-scale variation in soil microbial community composition using data from 180 sites covering a broad range of grassland types, soil and climatic conditions in England. We found that variation in soil microbial communities was explained by abiotic factors like climate, pH and soil properties. Biotic factors, namely community- weighted means (CWM) of plant functional traits, also explained variation in soil microbial communities. In particular, more bacterial-dominated microbial communities were associated with exploitative plant traits versus fungal-dominated communities with resource-conservative traits, showing that plant functional traits and soil microbial communities are closely related at the landscape scale.

Factors influencing adoption of improved grassland management by small-scale dairy farmers in central Mexico and the implications for future research on smallholder adoption in developing countries

There have been limited recent advances in understanding of what influences uptake of innovations despite the current international focus on smallholder agriculture as a means of achieving food security and rural development. This paper provides a rigorous study of factors influencing adoption by smallholders in central Mexico and builds on findings to identify a broad approach to significantly improve research on and understanding of factors influencing adoption by smallholders in developing countries. Small-scale dairy systems play an important role in providing income, employment and nutrition in the highlands of central Mexico. A wide variety of practices and technologies have been promoted by the government public services to increase milk production and economic efficiency, but there have been very low levels of uptake of most innovations, with the exception of improving grassland through introduction of grass varieties together with management practices. A detailed study was conducted with 80 farmers who are already engaged with the use of this innovation to better understand the process of adoption and identify socioeconomic and farm variables, cognitive (beliefs), and social–psychological (social norms) factors associated with farmers' use of improved grassland. The Theory of Reasoned Action (TRA) was used as a theoretical framework and Spearman Rank Order correlation was conducted to analyse the data. Most farmers (92.5%) revealed strong intention to continue to use improved grassland (which requires active management and investment of resources) for the next 12 months; whereas 7.5% of farmers were undecided and showed weak intention, which was associated with farmers whose main income was from non-farm activities as well as with farmers who had only recently started using improved grassland. Despite farmers' experience of using improved grassland (mean of 18 years) farmers' intentions to continue to adopt it was influenced almost as much by salient referents (mainly male relatives) as by their own attitudes. The hitherto unnoticed longevity of the role social referents play in adoption decisions is an important finding and has implications for further research and for the design of extension approaches. The study demonstrates the value and importance of using TRA or TPB approaches to understand social cognitive (beliefs) and social–psychological (social norms) factors in the study of adoption. However, other factors influencing adoption processes need to be included to provide fuller understanding. An approach that would enable this, and the development of more generalisable findings than from location specific case studies, and contribute to broader conceptualisation, is proposed.

Identifying time lags in the restoration of grassland butterfly communities: a multi-site assessment

Although grasslands are crucial habitats for European butterflies, large-scale declines in quality and area have devastated many species. Grassland restoration can contribute to the recovery of butterfly populations, although there is a paucity of information on the long-term effects of management. Using eight UK data sets (9-21 years), we investigate changes in restoration success for (1) arable reversion sites, were grassland was established on bare ground using seed mixtures, and (2) grassland enhancement sites, where degraded grasslands are restored by scrub removal followed by the re-instigation of cutting/grazing. We also assessed the importance of individual butterfly traits and ecological characteristics in determining colonisation times. Consistent increases in restoration success over time were seen for arable reversion sites, with the most rapid rates of increase in restoration success seen over the first 10 years. For grasslands enhancement there were no consistent increases in restoration success over time. Butterfly colonisation times were fastest for species with widespread host plants or where host plants established well during restoration. Low mobility butterfly species took longer to colonise. We show that arable reversion is an effective tool for the management of butterfly communities. We suggest that as restoration takes time to achieve, its use as a mitigation tool against future environmental change (i.e. by decreasing isolation in fragmented landscapes) needs to take into account such time lags.

Simulated variations in atmospheric CO2 over a Wisconsin forest using a coupled ecosystem-atmosphere model

Ecosystem fluxes of energy, water, and CO2 result in spatial and temporal variations in atmospheric properties. In principle, these variations can be used to quantify the fluxes through inverse modelling of atmospheric transport, and can improve the understanding of processes and falsifiability of models. We investigated the influence of ecosystem fluxes on atmospheric CO2 in the vicinity of the WLEF-TV tower in Wisconsin using an ecophysiological model (Simple Biosphere, SiB2) coupled to an atmospheric model (Regional Atmospheric Modelling System). Model parameters were specified from satellite imagery and soil texture data. In a companion paper, simulated fluxes in the immediate tower vicinity have been compared to eddy covariance fluxes measured at the tower, with meteorology specified from tower sensors. Results were encouraging with respect to the ability of the model to capture observed diurnal cycles of fluxes. Here, the effects of fluxes in the tower footprint were also investigated by coupling SiB2 to a high-resolution atmospheric simulation, so that the model physiology could affect the meteorological environment. These experiments were successful in reproducing observed fluxes and concentration gradients during the day and at night, but revealed problems during transitions at sunrise and sunset that appear to be related to the canopy radiation parameterization in SiB2.

Ecological intensification: harnessing ecosystem services for food security

Rising demands for agricultural products will increase pressure to further intensify crop production, while negative environmental impacts have to be minimized. Ecological intensification entails the environmentally friendly replacement of anthropogenic inputs and/or enhancement of crop productivity, by including regulating and supporting ecosystem services management in agricultural practices. Effective ecological intensification requires an understanding of the relations between land use at different scales and the community composition of ecosystem service-providing organisms above and below ground, and the flow, stability, contribution to yield, and management costs of the multiple services delivered by these organisms. Research efforts and investments are particularly needed to reduce existing yield gaps by integrating context-appropriate bundles of ecosystem services into crop production systems.