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

Earthworm communities in an agroforest system: Impact of tree rows on the distribution in grassland and cropped land

Agroforest systems are highly discussed as new and innovative land use systems for arable land in temperate regions. They are recommended due to their beneficial impact on several ecosystem functions and for the general diversification of the cultured landscapes. Tree rows, known as alley cropping systems, are one of the most frequent applications. In May 2016 earthworm communities were sampled in an agroforest system in Reiffenhausen south of Göttingen using an electrical extraction system. Asking for the impact of tree rows on the spatial distribution of earthworms, sampling was done in distances of 0, 1, and 4.5m from the alleys (willow on grassland and poplar on cropland). Also grassland and cropland with no trees were sampled as a control. At Reiffenhausen we obtained 6 different species covering all ecological groups of earthworms. Abundances indicated a step gradient for earthworm numbers in the combination of poplar with cropland with decreasing numbers with increasing distance. However, the gradient was not indicated for willow rows on grassland. An effect of hypnotized earthworm supporting factors like litter entry and shading couldn’t be fully confirmed. More data is needed to value agroforest systems for their impact on key organisms and key functions of soil biota

Räumliche Verteilung mikrobieller Bodeneigenschaften in verschiedenen Agroforstsystemen in Deutschland

In den letzten Dekaden konnte frequentiert gezeigt werden, dass Managementpraktiken sowohl Qualität und Quantität pflanzlicher Residuen als auch Aktivität und Diversität von Bodenmikroorganismen beeinflussen. Vor dem Hintergrund nachhaltiger Landnutzung stellen Agroforstsysteme als Kombination aus Bäumen/Sträuchern und Ackerkulturen/Grünland innerhalb eines Landbausystems einen multifunktionalen Ansatz dar. Denn Bäume beeinflussen durch Veränderungen des Mikroklimas und des C-Eintrags direkt das Bodenmilieu und besitzen damit Auswirkungen auf von Bodenorganismen herbeigeführten Ökosystemleistungen (z.B. Abbau organischer Substanz, Nährstoffversorgung, C-Sequestrierung). Dennoch wurden derartige Effekte auf die mikrobielle Aktivität/Diversität und räumliche Variation nur spärlich untersucht. Wir postulieren, dass Unterschiede abiotischer und biotischer Faktoren mit zunehmender Distanz von Baumstreifen die Aktivität und Diversität von Bodenmirkoorganismen beeinflussen. Dafür wurden Oberböden in zwei Tiefen (0-5, 5-20 cm) in unterschiedlichen silvo-arablen und silvo-pastoralen Agroforstsystemen in Deutschland in den Baumstreifen sowie in verschiedenen Distanzen von den Baumstreifen beprobt. An den Bodenproben wurden Corg- und Nt-Gehalte, mikrobielle Biomasse (CMik, NMik), Ergosterol, mikrobielle Residuen (Aminozucker) und pH-Werte bestimmt. Die Ergebnisse zeigen, dass die Baumstreifen mikrobielle Indizes beeinflussen. So hat die Etablierung der Agroforstsysteme an einigen Standorten unter den Bäumen in 0-5 cm Bodentiefe bereits zu einer signifikanten Erhöhung der Corg- und Nt-Gehalte geführt. Gleichfalls konnten erhöhte Cmik-, Nmik- und Ergosterolgehalte beobachtet werden. Darüber hinaus weisen wichtige mikrobiologische Quotienten Unterschiede auf. So deuten signifikant erhöhte Cmik-zu-Corg- und Ergosterol-zu-Cmik-Quotienten auf eine verbesserte C-Nutzungseffizienz beziehungsweise einen erhöhten Anteil saprotropher Pilze an der mikrobiellen Biomasse unter den Bäumen hin. Ferner wird der Einfluss abiotischer Faktoren (z.B. pH, Tongehalt) auf die räumliche Verteilung der mikrobiellen Parameter und die Problematik der Bestimmung der Effekte bei hoher Bodenvariabilität in Agroforstsystemen und Acker-/Grünlandkontrollflächen aufgezeigt

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

Assessing the accuracy of current near infra-red reflectance spectroscopy analysis for fresh grass-clover mixture silages and development of new equations for this purpose

The purpose of this study was to ascertain whether Near Infra-Red Reflectance Spectroscopy (NIRS) prediction equations calibrated on grass silage samples, could accurately predict the chemical composition of mixed grass-clover silage samples, and furthermore, to develop and calibrate new grass-clover equations should the grass-based equations be insufficiently accurate for these silages. A set of 94 silage samples from mixed grass-clover swards (clover concentration (CC) ranging from 4 to 1000 g/kg as fed; determined manually) were analysed for chemical composition using reference laboratory techniques, in vivo digestible organic matter in the dry matter (DOMD, in sheep), and in situ degradability of dry matter and crude protein (in cows). The same samples were scanned fresh (undried and unmilled, as is standard practice for silage analysis within UK laboratories) using NIRS (at AFBI, Northern Ireland) and grass-based prediction equations applied. Predicted and observed results were compared. Of 15 chemical components that were tested for prediction accuracy, only volatile-corrected dry matter and nitrogen were well predicted (RPD values of 4.9 and 2.4 respectively, with low root mean square errors of prediction (RMSEP)). Neutral detergent fibre and DOMD showed low RPD values, however the predicted and observed datasets had no significant bias between them and were therefore also considered as fit for purpose. Variables with significant bias between predicted and observed datasets that were not considered suitably accurate included crude protein, acid detergent fibre, microbial dry matter yield and the effective degradability of protein. For many components, bias could be attributed at least in part to CC and changes in the fractionation of nutrients present. For some variables such as crude protein, grass-based equations were sufficiently accurate at low CCs but became inaccurate as CC increased, as expected. In response to inadequate prediction accuracy of certain nutrients, new grass-clover equations were calibrated using the obtained spectra. These were validated and results indicated that the grass-clover-based equations outperformed their grass-based counterparts. The adoption of new grass-clover equations, or alternatively, with further development, the use of a CC correction factor to the existing grass-based equations, is recommended for commercial laboratories offering undried and unmilled silage analysis on samples containing clover

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

P. 1-15Non-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.S