Abiotic Resource Efficiency of Grassland Production Systems in North-West Europe

Nitrate leaching and energy efficiency are key criteria of a resource efficient grassland production system. A four-year field experiment was conducted to evaluate the effects of N input and defoliation system on both criteria. The objective of this experiment was to develop strategies which help to facilitate future grassland production with maximum environmental friendliness

A Sward Based Method to Estimate Herbage Selection of Grazing Dairy Cows

Diet selection of grazing animals is influenced by sward composition and vertical sward structure. Grazing studies were established in northern Germany (Kiel, Schleswig - Holstein state) to determine if selective grazing behaviour in a mixed sward can be measured by a sward based method. The hypothesis that active selection of different functional groups of forages can be documented by using the selection index (Figure 1, Hodgson, 1990) and regressive approaches vs time was tested

Evaluating the Economic and Environmental Sustainability of Integrated Farming Systems

Economic and environmental sustainability has become a major concern for forage-based animal production in Europe, North America and other parts of the world. Development of more sustainable farming systems requires an assimilation of experimental and modelling research. Field research is critical for supporting the development and evaluation of models, and modelling is needed to integrate farm components for predicting the long-term effects and interactions resulting from farm management changes. Experimentally supported simulation provides a tool for evaluating and comparing farming strategies and predicting their effect on the watershed, region and beyond

High Performing Hospital Enterprise Architecture: Insights from a Multi-method Exploratory Case

The US healthcare system is a critical infrastructure grappling with as much as 16% of the GDP in its expenditures and unsatisfactory outcomes, and undergoing considerable public scrutiny. High ranking officials have both singled out the US healthcare system as the most expensive and among the least effective in the developed world. Hospitals hoping to find “The Toyota Way” so as to rid themselves of waste through lean and six sigma improvement initiatives, have mostly focused in applying tools at a process level, rather than adopting an enterprise perspective and understanding the full breadth of their socio-technical complexity. This paper adopts a systems thinking approach in describing a leading Boston hospital’s enterprise architecture through a multi-method exploratory case. The initial exploratory question proposed by hospital senior leadership was “How to speed patient flow in the Emergency Department?”, however as results became available, the scope was expanded to include the whole hospital enterprise. Both qualitative and quantitative data evidence were collected through a variety of methods, namely observation, archival records, documentation, and interviews. Analysis includes techniques consistent with the grounded theory approach, as well as more traditional quantitative data analysis. Hospital enterprise performance is hypothesized to be related to hospital enterprise architecture, and an alternative hospital enterprise architecture is proposed as well as future work

Diverse Forage Mixtures Effect on Herbage Yield, Sward Composition, and Dairy Cattle Performance

Managing complex mixtures of plants to take advantage of spatial and temporal variability in land and climate may be one ecological approach to increase productivity of pastures. We tested the hypothesis that complex mixtures of forage species would yield more herbage and reduce weed competition compared with a simple grass-legume mixture in grazed pastures

Target‐oriented habitat and wildlife management: estimating forage quantity and quality of semi‐natural grasslands with Sentinel‐1 and Sentinel‐2 data

Semi‐natural grasslands represent ecosystems with high biodiversity. Their conservation depends on the removal of biomass, for example, through grazing by livestock or wildlife. For this, spatially explicit information about grassland forage quantity and quality is a prerequisite for efficient management. The recent advancements of the Sentinel satellite mission offer new possibilities to support the conservation of semi‐natural grasslands. In this study, the combined use of radar (Sentinel‐1) and multispectral (Sentinel‐2) data to predict forage quantity and quality indicators of semi‐natural grassland in Germany was investigated. Field data for organic acid detergent fibre concentration (oADF), crude protein concentration (CP), compressed sward height (CSH) and standing biomass dry weight (DM) collected between 2015 and 2017 were related to remote sensing data using the random forest regression algorithm. In total, 102 optical‐ and radar‐based predictor variables were used to derive an optimized dataset, maximizing the predictive power of the respective model. High R2 values were obtained for the grassland quality indicators oADF (R2 = 0.79, RMSE = 2.29%) and CP (R2 = 0.72, RMSE = 1.70%) using 15 and 8 predictor variables respectively. Lower R2 values were achieved for the quantity indicators CSH (R2 = 0.60, RMSE = 2.77 cm) and DM (R2 = 0.45, RMSE = 90.84 g/m²). A permutation‐based variable importance measure indicated a strong contribution of simple ratio‐based optical indices to the model performance. In particular, the ratios between the narrow near‐infrared and red‐edge region were among the most important variables. The model performance for oADF, CP and CSH was only marginally increased by adding Sentinel‐1 data. For DM, no positive effect on the model performance was observed by combining Sentinel‐1 and Sentinel‐2 data. Thus, optical Sentinel‐2 data might be sufficient to accurately predict forage quality, and to some extent also quantity indicators of semi‐natural grassland

Biodiversity effects on ecosystem functioning in a 15-year grassland experiment: Patterns, mechanisms, and open questions

In the past two decades, a large number of studies have investigated the relationship between biodiversity and ecosystem functioning, most of which focussed on a limited set of ecosystem variables. The Jena Experiment was set up in 2002 to investigate the effects of plant diversity on element cycling and trophic interactions, using a multi-disciplinary approach. Here, we review the results of 15 years of research in the Jena Experiment, focussing on the effects of manipulating plant species richness and plant functional richness. With more than 85,000 measures taken from the plant diversity plots, the Jena Experiment has allowed answering fundamental questions important for functional biodiversity research. First, the question was how general the effect of plant species richness is, regarding the many different processes that take place in an ecosystem. About 45% of different types of ecosystem processes measured in the ‘main experiment’, where plant species richness ranged from 1 to 60 species, were significantly affected by plant species richness, providing strong support for the view that biodiversity is a significant driver of ecosystem functioning. Many measures were not saturating at the 60-species level, but increased linearly with the logarithm of species richness. There was, however, great variability in the strength of response among different processes. One striking pattern was that many processes, in particular belowground processes, took several years to respond to the manipulation of plant species richness, showing that biodiversity experiments have to be long-term, to distinguish trends from transitory patterns. In addition, the results from the Jena Experiment provide further evidence that diversity begets stability, for example stability against invasion of plant species, but unexpectedly some results also suggested the opposite, e.g. when plant communities experience severe perturbations or elevated resource availability. This highlights the need to revisit diversity–stability theory. Second, we explored whether individual plant species or individual plant functional groups, or biodiversity itself is more important for ecosystem functioning, in particular biomass production. We found strong effects of individual species and plant functional groups on biomass production, yet these effects mostly occurred in addition to, but not instead of, effects of plant species richness. Third, the Jena Experiment assessed the effect of diversity on multitrophic interactions. The diversity of most organisms responded positively to increases in plant species richness, and the effect was stronger for above- than for belowground organisms, and stronger for herbivores than for carnivores or detritivores. Thus, diversity begets diversity. In addition, the effect on organismic diversity was stronger than the effect on species abundances. Fourth, the Jena Experiment aimed to assess the effect of diversity on N, P and C cycling and the water balance of the plots, separating between element input into the ecosystem, element turnover, element stocks, and output from the ecosystem. While inputs were generally less affected by plant species richness, measures of element stocks, turnover and output were often positively affected by plant diversity, e.g. carbon storage strongly increased with increasing plant species richness. Variables of the N cycle responded less strongly to plant species richness than variables of the C cycle. Fifth, plant traits are often used to unravel mechanisms underlying the biodiversity–ecosystem functioning relationship. In the Jena Experiment, most investigated plant traits, both above- and belowground, were plastic and trait expression depended on plant diversity in a complex way, suggesting limitation to using database traits for linking plant traits to particular functions. Sixth, plant diversity effects on ecosystem processes are often caused by plant diversity effects on species interactions. Analyses in the Jena Experiment including structural equation modelling suggest complex interactions that changed with diversity, e.g. soil carbon storage and greenhouse gas emission were affected by changes in the composition and activity of the belowground microbial community. Manipulation experiments, in which particular organisms, e.g. belowground invertebrates, were excluded from plots in split-plot experiments, supported the important role of the biotic component for element and water fluxes. Seventh, the Jena Experiment aimed to put the results into the context of agricultural practices in managed grasslands. The effect of increasing plant species richness from 1 to 16 species on plant biomass was, in absolute terms, as strong as the effect of a more intensive grassland management, using fertiliser and increasing mowing frequency. Potential bioenergy production from high-diversity plots was similar to that of conventionally used energy crops. These results suggest that diverse ‘High Nature Value Grasslands’ are multifunctional and can deliver a range of ecosystem services including production-related services. A final task was to assess the importance of potential artefacts in biodiversity–ecosystem functioning relationships, caused by the weeding of the plant community to maintain plant species composition. While the effort (in hours) needed to weed a plot was often negatively related to plant species richness, species richness still affected the majority of ecosystem variables. Weeding also did not negatively affect monoculture performance; rather, monocultures deteriorated over time for a number of biological reasons, as shown in plant-soil feedback experiments. To summarize, the Jena Experiment has allowed for a comprehensive analysis of the functional role of biodiversity in an ecosystem. A main challenge for future biodiversity research is to increase our mechanistic understanding of why the magnitude of biodiversity effects differs among processes and contexts. It is likely that there will be no simple answer. For example, among the multitude of mechanisms suggested to underlie the positive plant species richness effect on biomass, some have received limited support in the Jena Experiment, such as vertical root niche partitioning. However, others could not be rejected in targeted analyses. Thus, from the current results in the Jena Experiment, it seems likely that the positive biodiversity effect results from several mechanisms acting simultaneously in more diverse communities, such as reduced pathogen attack, the presence of more plant growth promoting organisms, less seed limitation, and increased trait differences leading to complementarity in resource uptake. Distinguishing between different mechanisms requires careful testing of competing hypotheses. Biodiversity research has matured such that predictive approaches testing particular mechanisms are now possible

Global application of an unoccupied aerial vehicle photogrammetry protocol for predicting aboveground biomass in non‐forest ecosystems

This is the final version. Available on open access from Wiley via the DOI in this recordData Availability Statement: The data collected for this publication, including aerial images, marker and plot coordinates and dry sample weights, as well as site and survey metadata, are available from the NERC Environmental Information Data Centre . Code for photogrammetric processing and statistical analysis is available at Zenodo Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low-stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R2 of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave-one-out cross-validation of 3.9%. Biomass per-unit-of-height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1–10 ha−1. Photogrammetric approaches could provide much-needed information required to calibrate and validate the vegetation models and satellite-derived biomass products that are essential to understand vulnerable and understudied non-forested ecosystems around the globe