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, el...

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An early and precise yield estimation in intensive managed grassland is mandatory for economic management decisions. RGB (red, green, blue) cameras attached on an unmanned aerial vehicle (UAV) represent a promising non-destructive technology for the assessment of crop traits especially in large and remote areas. Photogrammetric structure from motion (SfM) processing of the UAV-based images into point clouds can be used to generate 3D spatial information about the canopy height (CH). The aim of this study was the development of prediction models for dry matter yield (DMY) in temperate grassland based on CH data generated by UAV RGB imaging over a whole growing season including four cuts. The multi-temporal study compared the remote sensing technique with two conventional methods, i.e., destructive biomass sampling and ruler height measurements in two legume-grass mixtures with red clover (Trifolium pratense L.) and lucerne (Medicago sativa L.) in combination with Italian ryegrass (Lolium multiflorum Lam.). To cover the full range of legume contribution occurring in a practical grassland, pure stands of legumes and grasses contained in each mixture were also investigated. The results showed, that yield prediction by SfM-based UAV RGB imaging provided similar accuracies across all treatments (R 2 = 0.59-0.81) as the ruler height measurements (R 2 = 0.58-0.78). Furthermore, results of yield prediction by UAV RGB imaging demonstrated an improved robustness when an increased CH variability occurred due to extreme weather conditions. It became apparent that morphological characteristics of clover-based canopies (R 2 = 0.75) allow a better remotely sensed prediction of total annual yield than for lucerne-grass mixtures (R 2 = 0.64), and that these crop-specific models cannot be easily transferred to other grassland types.Destructive biomass sampling is considered to be the most accurate yield estimation method but can also be considered as the most labor-intensive method [4]. Another approach for estimating biomass in grasslands is the assessment of canopy height (CH), which was frequently found to be positively correlated with crop biomass [5,6]. Traditional height measurements in grassland are often conducted with a rising plate meter, determining the compressed sward height, or with a ruler stick [7,8]. Furthermore, several portable technical devices for non-destructive biomass estimation were developed in the recent years, which so far were not widely distributed in agricultural practice, e.g., leaf area meter to asses leaf area index (LAI) [9], electronic capacitance meter, which measures the difference of capacitance between air and biomass [1] and a reflectometer, which measures intensity of spectral reflectance by light emitting diodes (LED) [10]. Biomass sampling, manual height measurement and the above mentioned technical devices need a substantial number of repetitions in combination with a spatially uniform distributions of the measurements to generate a reliable yield estimation [11]. Therefore, much time an...

who plan row crops and livestock around their grassland hectares are grassland farmers" (Barnes, 1995). Before Grassland agriculture is an important industry for livestock produc-World War II, agriculture in the USA was very diverse tion and land management throughout the world. We review the and integrated, agricultural markets were primarily loprinciples of nutrient cycling in grassland agriculture, discuss examples of grassland farming systems research, and demonstrate the usefulness cal, and nutrients were cycled mainly within farms and of whole-farm simulation for integrating economic and environmental among local farms. With the advent of mechanization, components. Comprehensive studies conducted at the Karkendamm chemical fertilizers, improved seeds, and agrichemicals, experimental farm in northern Germany and the De Marke experifarm size increased, agricultural markets became namental farm in the Netherlands have quantified nutrient flows and tional and international in scope, and nutrient cycles developed innovative strategies to reduce nutrient losses in grassland became more fragmented. Animal agriculture became farming systems. This research has focused on improving the utilizaspecialized and concentrated, relying on off-farm sources tion of manure nutrients on the farm by including grain crops in of feeds and fertilizers, which resulted in nutrient accucropping systems with grassland and by incorporating manure hanmulation on farms. dling techniques that reduce nitrogen losses. Although the information generated in experimental farms is not always directly applicable to Similar changes in farm structure occurred in northother climates and soils, it is being transferred to other regions through western Europe during the 1960s and 1970s (de Wit et computer simulation. A whole-farm model calibrated and verified al., 1987). Farms located in regions with good soils were with the experimental farm data is being used to evaluate and refine converted to crops like winter wheat (Triticum aestivum these strategies for commercial farms in other areas. Simulation of L.), oilseed rape (Brassica napus L.), potato (Solanum farms in northern Europe illustrate that on the sandy soils of this tuberosum L.), and sugarbeet (Beta vulgaris L.), whereas region, maize (Zea mays L.) silage can be used along with grasslands farms on poorer sandy soils specialized in milk producto increase farm profitability while maintaining or reducing nutrient tion with permanent grassland as the main crop. These loss to the environment. Use of cover crops, low emission barns,farms also intensified their use of purchased fertilizers covered manure storages, and direct injection of manure into soil and feed concentrates, and farm-scale nutrient budgets greatly reduces N losses from these farms, but their use creates a net cost to the producer. By integrating experimental farm data with became less balanced because of low conversion rates whole-farm simulation, more sustainable grassland production sys-of nutrients in milk and meat production...

Grassland systems frequently exhibit small‐scale botanical and structural heterogeneity with pronounced spatio‐temporal dynamics. These features present particular challenges for sensor applications, in addition to limitations posed by the high cost and low spatial resolution of many available remote‐sensing (RS) systems. There has been little commercial application of RS for practical grassland farming. This article considers the developments in sensor performance, data analysis and modelling over recent decades, identifies significant advances in RS for grassland research and practice and reviews the most important sensor types and corresponding findings in research. Beside improvements of single sensor types, the development of systems with complementary sensors is seen as a very promising research area, and one that will help to overcome the limitations of single sensors and provide better information about grassland composition, yield and quality. From an agronomic point of view, thematic maps of farm fields are suggested as the central outcome of RS and data analysis. These maps could represent the relevant grassland features and constitute the basis for various farm management decisions at strategic, tactical and operational levels. The overarching goal will be to generate low cost, appropriate and timely information that can be provided to farmers to support their decision‐making.

The use of semi-natural grasslands for the production of renewable energy through conventional conversion techniques faces major limitations because of chemical and physical properties of the biomass. A new conversion procedure was developed which separates the biomass, as silage, into a liquid phase for biogas production and into a solid fraction to be used as fuel. Separation (mechanical dehydration) is carried out with a screw press after mashing with water (hydrothermal conditioning). The effect of hydrothermal conditioning at different temperatures (5, 60 and 80°C) and mechanical dehydration on mass flows of plant compounds into the press fluid was investigated for five grassland pastures typical of mountain areas of Germany. Results show that 0AE18 of the crude fibre was transferred into the fluid, whereas more digestible organic compounds, such as crude protein and nitrogen-free extract, showed mass flows of 0AE40 and 0AE31 respectively. While 0AE52-0AE89 of potassium (K), magnesium (Mg) and chloride (Cl), which are detrimental for the combustion of the press cake, were transferred into the press fluid, more than 0AE50 of calcium, which has positive combustion properties, remained in the press cake. Significantly (P < 0AE05) higher mass flows were detected at conditioning temperatures of 60°C (K and Mg) and 80°C (crude fibre and nitrogen-free extract) compared with the 5°C treatment. Because of the separation of solids and liquids, high proportions of P (0AE61-0AE74) and K (0AE64-0AE85) but only 0AE32-0AE45 of nitrogen exported from the grassland would be recycled with an application of the digestates from the anaerobic digestion of the press liquid.

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3D point cloud analysis of imagery collected by unmanned aerial vehicles (UAV) has been shown to be a valuable tool for estimation of crop phenotypic traits, such as plant height, in several species. Spatial information about these phenotypic traits can be used to derive information about other important crop characteristics, like fresh biomass yield, which could not be derived directly from the point clouds. Previous approaches have often only considered single date measurements using a single point cloud derived metric for the respective trait. Furthermore, most of the studies focused on plant species with a homogenous canopy surface. The aim of this study was to assess the applicability of UAV imagery for capturing crop height information of three vegetables (crops eggplant, tomato, and cabbage) with a complex vegetation canopy surface during a complete crop growth cycle to infer biomass. Additionally, the effect of crop development stage on the relationship between estimated crop height and field measured crop height was examined. Our study was conducted in an experimental layout at the University of Agricultural Science in Bengaluru, India. For all the crops, the crop height and the biomass was measured at five dates during one crop growth cycle between February and May 2017 (average crop height was 42.5, 35.5, and 16.0 cm for eggplant, tomato, and cabbage). Using a structure from motion approach, a 3D point cloud was created for each crop and sampling date. In total, 14 crop height metrics were extracted from the point clouds. Machine learning methods were used to create prediction models for vegetable crop height. The study demonstrates that the monitoring of crop height using an UAV during an entire growing period results in detailed and precise estimates of crop height and biomass for all three crops (R 2 ranging from 0.87 to 0.97, bias ranging from −0.66 to 0.45 cm). The effect of crop development stage on the predicted crop height was found to be substantial (e.g., median deviation increased from 1% to 20% for eggplant) influencing the strength and consistency of the relationship between point cloud metrics and crop height estimates and, thus, should be further investigated. Altogether the results of the study demonstrate that point cloud generated from UAV-based RGB imagery can be used to effectively measure vegetable crop biomass in larger areas (relative error = 17.6%, 19.7%, and 15.2% for eggplant, tomato, and cabbage, respectively) with a similar accuracy as biomass prediction models based on measured crop height (relative error = 21.6, 18.8, and 15.2 for eggplant, tomato, and cabbage).