Empirical test of an agricultural landscape model: the importance of farmer preference for risk aversion and crop complexity

Developing models to predict the effects of social and economic change on agricultural landscapes is an important challenge. Model development often involves making decisions about which aspects of the system require detailed description and which are reasonably insensitive to the assumptions. However, important components of the system are often left out because parameter estimates are unavailable. In particular, measurements of the relative influence of different objectives, such as risk, environmental management, on farmer decision making, have proven difficult to quantify. We describe a model that can make predictions of land use on the basis of profit alone or with the inclusion of explicit additional objectives. Importantly, our model is specifically designed to use parameter estimates for additional objectives obtained via farmer interviews. By statistically comparing the outputs of this model with a large farm-level land-use data set, we show that cropping patterns in the United Kingdom contain a significant contribution from farmer’s preference for objectives other than profit. In particular, we found that risk aversion had an effect on the accuracy of model predictions, whereas preference for a particular number of crops grown was less important. While nonprofit objectives have frequently been identified as factors in farmers’ decision making, our results take this analysis further by demonstrating the relationship between these preferences and actual cropping patterns

Macroalgae Decrease Growth and Alter Microbial Community Structure of the Reef-Building Coral, Porites astreoides

This is the publisher’s final pdf. The published article is copyrighted by the Public Library of Science and can be found at: http://www.plosone.org/home.action.With the continued and unprecedented decline of coral reefs worldwide, evaluating the factors that contribute to coral demise is of critical importance. As coral cover declines, macroalgae are becoming more common on tropical reefs. Interactions between these macroalgae and corals may alter the coral microbiome, which is thought to play an important role in colony health and survival. Together, such changes in benthic macroalgae and in the coral microbiome may result in a feedback mechanism that contributes to additional coral cover loss. To determine if macroalgae alter the coral microbiome, we conducted a field-based experiment in which the coral Porites astreoides was placed in competition with five species of macroalgae. Macroalgal contact increased variance in the coral-associated microbial community, and two algal species significantly altered microbial community composition. All macroalgae caused the disappearance of a γ-proteobacterium previously hypothesized to be an important mutualist of P. astreoides. Macroalgal contact also triggered: 1) increases or 2) decreases in microbial taxa already present in corals, 3) establishment of new taxa to the coral microbiome, and 4) vectoring and growth of microbial taxa from the macroalgae to the coral. Furthermore, macroalgal competition decreased coral growth rates by an average of 36.8%. Overall, this study found that competition between corals and certain species of macroalgae leads to an altered coral microbiome, providing a potential mechanism by which macroalgae-coral interactions reduce coral health and lead to coral loss on impacted reefs

Long-term Impact of sewage sludge application on rhizobium leguminosarum biovar trifolii: an evaluation using meta-analysis

The Long-Term Sludge Experiment (LTSE) began in 1994 at nine UK field sites as part of continuing research into the effects of sludge-borne heavy metals on soil fertility. The long-term effects of Zn, Cu, and Cd on the most probable numbers of cells (MPN) of Rhizobium leguminosarum biovar trifolii were monitored for 8 yr in sludge-amended soils. To assess the statutory limits set by the UK Sludge (Use in Agriculture) Regulations, the experimental data were reviewed using statistical methods of meta-analysis. Previous LTSE studies have focused predominantly on statistical significance rather than effect size, whereas meta-analysis focuses on the magnitude and direction of an effect, i.e., the practical significance rather than its statistical significance. Results showed Zn to be the most toxic element causing an overall significant decrease in Rhizobium MPN of −26.6% during the LTSE. The effect of Cu showed no significant effect on Rhizobium MPN at concentrations below the UK limits, although a −5% decrease in Rhizobium MPN was observed in soils where total Cu ranged from 100 to <135 mg kg−1. Overall, there was nothing to indicate that Cd had a significant effect on Rhizobium MPN below the current UK statutory limit. In summary, the UK statutory limit for Zn appears to be insufficient for protecting Rhizobium from Zn toxicity effects

Combining two national‐scale datasets to map soil properties, the case of available magnesium in England and Wales

Given the costs of soil survey it is necessary to make the best use of available datasets, but data that differ with respect to some aspect of the sampling or analytical protocol cannot be combined simply. In this paper we consider a case where two datasets were available on the concentration of plant‐available magnesium in the topsoil. The datasets were the Representative Soil Sampling Scheme (RSSS) and the National Soil Inventory (NSI) of England and Wales. The variable was measured over the same depth interval and with the same laboratory method, but the sample supports were different and so the datasets differ in their variance. We used a multivariate geostatistical model, the linear model of coregionalization (LMCR), to model the joint spatial distribution of the two datasets. The model allowed us to elucidate the effects of the sample support on the two datasets, and to show that there was a strong correlation between the underlying variables. The LMCR allowed us to make spatial predictions of the variable on the RSSS support by cokriging the RSSS data with the NSI data. We used cross‐validation to test the validity of the LMCR and showed how incorporating the NSI data restricted the range of prediction error variances relative to univariate ordinary kriging predictions from the RSSS data alone. The standardized squared prediction errors were computed and the coverage of prediction intervals (i.e. the proportion of sites at which the prediction interval included the observed value of the variable). Both these statistics suggested that the prediction error variances were consistent for the cokriging predictions but not for the ordinary kriging predictions from the simple combination of the RSSS and NSI data, which might be proposed on the basis of their very similar mean values. The LMCR is therefore proposed as a general tool for the combined analysis of different datasets on soil properties

Modelling the impacts of agricultural management practices on river water quality in Eastern England

Agricultural diffuse water pollution remains a notable global pressure on water quality, posing risks to aquatic ecosystems, human health and water resources and as a result legislation has been introduced in many parts of the world to protect water bodies. Due to their efficiency and cost-effectiveness, water quality models have been increasingly applied to catchments as Decision Support Tools (DSTs) to identify mitigation options that can be introduced to reduce agricultural diffuse water pollution and improve water quality. In this study, the Soil and Water Assessment Tool (SWAT) was applied to the River Wensum catchment in eastern England with the aim of quantifying the long-term impacts of potential changes to agricultural management practices on river water quality. Calibration and validation were successfully performed at a daily time-step against observations of discharge, nitrate and total phosphorus obtained from high-frequency water quality monitoring within the Blackwater sub-catchment, covering an area of 19.6 km2. A variety of mitigation options were identified and modelled, both singly and in combination, and their long-term effects on nitrate and total phosphorus losses were quantified together with the 95% uncertainty range of model predictions. Results showed that introducing a red clover cover crop to the crop rotation scheme applied within the catchment reduced nitrate losses by 19.6%. Buffer strips of 2 m and 6 m width represented the most effective options to reduce total phosphorus losses, achieving reductions of 12.2% and 16.9%, respectively. This is one of the first studies to quantify the impacts of agricultural mitigation options on long-term water quality for nitrate and total phosphorus at a daily resolution, in addition to providing an estimate of the uncertainties of those impacts. The results highlighted the need to consider multiple pollutants, the degree of uncertainty associated with model predictions and the risk of unintended pollutant impacts when evaluating the effectiveness of mitigation options, and showed that high-frequency water quality datasets can be applied to robustly calibrate water quality models, creating DSTs that are more effective and reliable

Rural Development Programme measures on cultivated land in Europe to mitigate greenhouse gas emissions – regional ‘hotspots’ and priority measures

© 2016 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.Agriculture is a significant source of GHG emissions, contributing 10% of total emissions within the EU-28. Emissions from European agriculture have been reduced, albeit at the expense of crop yield and the risk of production displacement (the transfer of production, and associated emissions, to land outside of Europe). This article assesses the impact on GHG emissions of selected European Rural Development Program measures, representative of a diversity of management strategies implemented on cultivated land, within nine European Member States. Climatic zone and underlying spatial environmental variables were accounted for using a novel technique, “Regional Variation Categories,” developed with European-scale GIS data sets. Production displacement is assessed with two benchmarks: (1) compared with existing crop production and (2) relative to a “minimum requirement” land management scenario, where an emissions reduction of less than this does not constitute mitigation. Most measures reduce emissions relative to the baseline crop scenario; however, many do not reduce emissions beyond the “minimum requirement,” this being limited to measures such as catch crops and within-field grass areas to prevent soil erosion. The selection and targeting of measures to maximize agricultural GHG mitigation on cultivated land within Europe is discussed...Peer reviewedFinal Published versio

A review of the rural-digital policy agenda from a community resilience perspective

© 2016 The Authors This paper utilises a community resilience framework to critically examine the digital-rural policy agenda. Rural areas are sometimes seen as passive and static, set in contrast to the mobility of urban, technological and globalisation processes (Bell et al., 2010). In response to notions of rural decline (McManus et al., 2012) rural resilience literature posits rural communities as ‘active,’ and ‘proactive’ about their future (Skerratt, 2013), developing processes for building capacity and resources. We bring together rural development and digital policy-related literature, using resilience motifs developed from recent academic literature, including community resilience, digital divides, digital inclusion, and rural information and communication technologies (ICTs). Whilst community broadband initiatives have been linked to resilience (Plunkett-Carnegie, 2012; Heesen et al., 2013) digital inclusion, and engagement with new digital technologies more broadly, have not. We explore this through three resilience motifs: resilience as multi-scalar; as entailing normative assumptions; and as integrated and place-sensitive. We point to normative claims about the capacity of digital technology to aid rural development, to offer solutions to rural service provision and the challenges of implementing localism. Taking the UK as a focus, we explore the various scales at which this is evident, from European to UK country-level

A statistical comparison of spatio-temporal surface moisture patterns beneath a semi-natural grassland and permanent pasture:From drought to saturation

Some 60% of the agricultural land in the UK is grassland. This is mostly located in the wetter uplands of the west and north, with the majority intensively managed as permanent pasture. Despite its extent, there is a lack of knowledge regarding how agricultural practices have altered the hydrological behaviour of the underlying soils relative to the adjacent moorland covered by semi‐natural grassland. Near‐surface soil moisture content is an expression of the changes that have taken place and is critical in the generation of flood‐producing overland‐flows. This study aims to develop a pioneering paired‐plot approach, producing 1536 moisture measurements at each of the monitoring dates throughout the studied year, that were subsequently analysed by a comparison of frequency distributions, visual‐cum‐geostatistical investigation of spatial patterns and mixed‐effects regression modelling. The analysis demonstrated that the practices taking place in the pasture (ploughing, re‐seeding and drainage) reduced the natural diversity in moisture patterns. Compared to adjacent moorland, the topsoil dried much faster in spring with the effects requiring offset with moisture from slurry applications in summer. With the onset of autumn rains, these applications then made the topsoil wetter than the moorland, heightening the likelihood of flood‐producing overland‐flow. During the sampling within one such storm‐event, the adjacent moorland was almost as wet as the pasture with both visibly generating overland‐flow. These contrasts in soil moisture were statistically significant throughout. Further, they highlight the need to scale‐up the monitoring with numerous plot‐pairs to see if the observed highly dynamic, contrasting behaviour is present at the landscape‐scale. Such research is fundamental to designing appropriate agricultural interventions to deliver sustainable sward production for livestock or methods of mitigating overland‐flow incidence that would otherwise heighten flood‐risk or threaten water‐quality in rivers