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Respiration experiments with high-yielding dairy cows in Northern Ireland have shown higher energy maintenance requirements than those used in the requirements standards of, e.g. France, UK, USA and Germany. Therefore, the current net energy for lactation (NEL) system of Germany was tested by comparing measured NEL intake with calculated NEL requirements based on a comprehensive dataset from feeding trials conducted at nine research institutions in Germany, Austria and Switzerland. The relationship between NEL requirements and NEL intake is described by the equation: NELrequirementsMJ/d=26.6±0.4+0.82±0.004⋅NELintakeMJ/dwithCoefficient of Determination R2=0.677,Root Mean Square Error RMSE =15.9 MJ NEL. The equation indicates a systematic over-estimation of NEL requirements in the lower performance range and an under-estimation at higher energy intake levels. A multiple regression analysis was conducted by calculating metabolisable energy (ME) requirements [MJ/d] using metabolic body size (MBS) [kg0.75], milk energy performance (LE) [MJ/d] and body weight change (BWC) [kg/d]: ​​​​​​​MEintake(MEI)[MJ]=0.651(±0.004)⋅MBS+1.37(±0.006)⋅LE+16.6(±0.31)⋅BWC withR2=0.717,RMSE=24.0 MJ. These results indicate that the energy maintenance requirements are markedly higher than presumed in the feed evaluation systems commonly in use but confirm the results from Northern Ireland (0.600-0.660 MJ ME/kg0.75 MBS). ME efficiency for lactation is also higher (kL = 1/1.37 = 0.73) than that used in the systems and is also similar to the results of Northern Ireland with 0.64-0.69. The energy contribution of BWC derived by this equation is 12.1 MJ/kg (16.6 · 0.73) and distinctly lower than that of 21-25 MJ/kg presumed by the feeding standards, e.g. in Germany. Further, maintenance requirements were linked to milk yield (energy corrected milk (ECM) [kg/d]), as is practiced in the standard Australian energy system: ​​​​​​​(MEI)[MJ]=0.640 +0.0070⋅ ECM)]⋅MBS+1.12)⋅LE +16.7⋅ BWC withR2=0.719,RMSE=24.0 MJ. These results demonstrate that maintenance energy requirements are partly dependent on milk yield. A differentiated analysis by stage of lactation showed that the regressions coefficients for MBS, LE and BWC change with lactation month; however, these findings apply especially to the first lactation months (i.e. in phases of intensive mobilisation).

Studies on soil organic carbon (SOC) stocks mostly focus on topsoils (< 30 cm). However, 30 to 63% of the SOC are stored in the subsoils (30 to 100 cm), and the factors controlling SOC storage in subsoils may be substantially different than in topsoils. The low mean SOC content in subsoils makes its quantification and characterization challenging. Thus, new approaches are required to depict the SOC stocks distribution in full soil profile. Hyperspectral imaging of soil core samples can provide high spatial resolution of the vertical distribution of SOC in a soil profile. The main objective of the ongoing study, within the Horizon 2020 European Project Circular Agronomics, is to apply laboratory hyperspectral imaging with a variety of machine learning approaches for the mapping of OC distribution in undisturbed soil cores. Soil cores were collected down to a depth of one meter in grasslands of 15 organic farms located in the Lungau Valley, in Austria. Some samples were divided into five depths in the field for classical bulk soil measurements (total carbon and nitrogen, texture, pH, EC and bulk density) on disturbed samples. Undisturbed soil cores were sliced vertically for laboratory hyperspectral imaging in the range of Vis-NIR (400-1000 nm). We were able to reveal the hotspots of OC and map the OC distribution in soil profile by applying a variety of machine learning approaches (i.e. partial least square and random forest regression) as a function of spectral responses. A digital elevation model was further exploited to investigate the effects of topographical factors such as elevation, aspect and slope on SOC profile distribution. Landsat 8 data were also used to depict the spatial variability of land insensitive cover/vegetation in study area.

It is estimated that only 20% of fertilizers applied annually in the European agricultural systems are converted to finished products for human consumption. These low efficiencies result in large loss of nutrients into the environment with severe negative influences on soils, water and air, and constitute unacceptable health and environmental costs. In addition, around 45% of soils in the European countries have less than 2% organic carbon (OC). Low soil OC storage is linked with negative environmental impacts including soil and water quality, climate change and biodiversity. A relevant strategy to enhance soil OC is the transformation of waste products into organic amendments for application on soils. The aim of the H2020 European project Circular Agronomics is to provide a comprehensive synthesis of practical solutions to improve the current C, N and P cycling in European agro-ecosystems. This project explores the medium and long-term effects of new and classical organic fertilizers in six countries including Germany, Spain, Italy, Netherlands, Czech Republic and Austria. The study sites will be sampled before and after applying the new organic amendments using a hydraulic corer. A full profile assessment of the C, N and P distribution, stability and bioavailability will be released up to one meter depth using a combination of classical bulk chemical analyses and state-of-the-art imaging techniques. Undisturbed soil cores will be scanned using a hyperspectral camera to reveal hotspots of C, N and P storage in the soil profile, at the micro-scale. Soil C, N and P will be modelled as a function of spectral response using a variety of machine learning approaches. These results will provide essential information to develop management strategies that reduce nutrient surplus and increase C stocks.

Besides producing food for humanity’s nutrition, agriculture also fulfills other functions such as providing a livelihood for farmers and preserving an attractive and biodiverse landscape. These functions of agriculture were considered in a novel eco-efficiency assessment concept applied to Austrian farms within this study. The joint application of life cycle assessment (LCA) and data envelopment analysis (DEA) was used to evaluate Austrian farms’ eco-efficiency. Data from 47 farms from different farm types (crop production, milk production, beef production, and wine production) were used to implement the concept. Cumulative exergy demand (CExD), global warming potential (GWP), normalized eutrophication potential (EP), and aquatic ecotoxicity potential (AE) were included as environmental impacts in an LCA and were consequently used as input values for the DEA. Considering multiple functions of agriculture, the farm net income (FNI), the net food production of crude protein and human-edible energy, and High Nature Value farmland (HNVf) were selected as output variables for the DEA. Results show that the purchase of resources causes a substantial share of environmental impacts, highlighting the importance of efficient utilization of on-farm resources. The results further revealed the use of high amounts of human-edible energy and protein as animal feed to cause lower eco-efficiency scores of livestock keeping farms (i.e., milk production and beef production). Overall, the eco-efficiency of farms depends on the fulfillment of different functions of agriculture, and individual strategies for improvement could be identified.

FarmClim aims at contributing to a more considerate use of nitrogen in Austrian agriculture. The transdisciplinary research project attempts to tackle the “science-policy gap” by using a participatory approach, that is, stakeholders influence the research process as much as the scientists strive for the implementation of their ideas. This paper describes the project design and communication processes. Full integration of practice partners adds to the complexity of the project's structure, but brings consider able benefits right from the outset. Taking advantage of the existing institutional setting of FarmClim partners, we expect to maintain expert consultancy beyond the lifetime of the project, helping agriculture to meet the challenges of environmental and economic performance of a producing agriculture.

The occurrence of transferable diseases in wild animals generally reflects the causative agent’s distribution. The influence of climate change on the prevalence of pathogenic organisms is either exerted directly by causative agents with an increased survival rate as related to higher average annual temperatures, or indirectly because vectors (e.g. ticks, mosquitoes) experience better environmental conditions similarly promoting their viability and allowing them to invade new areas of circulation. The latter option explains why pathogen-carrying vectors such as ticks and mosquitoes prevail at higher sea levels than observed two decades ago. In recent years, vectors with seasonal cycles show longer periods of incidence. In addition, parasitic eggs, larvae and parasitic organisms at intermediate stages can be found at higher sea levels as they too benefit from changing climate conditions. For this reason cumulative purulent and abscess forming pneumonia is observed in chamois which originate from lungworm infections. This contribution demonstrates the relationship between climate factors and transferable diseases. The geographical dissemination and incidence of many diseases interferes with environmental changes. Additionally, risk factors increasing the diseases’ prevalence and incidence are discussed. Finally, selected examples of our own findings on the impact of climate changes on diseases (tularaemia) and on habitats of alpine wild animal species (black grouse, snow grouse, chamois and ibex) are highlighted.

Subacute rumen acidosis (SARA) of ruminants is an important factor in terms of animal health, especially in high yielding cattle. In order to find an accurate method to determine the ruminal pH-value, three methods using eight rumen cannulated cattle were compared. An indwelling measuring unit (sensor) was used for continuous measurement of the ruminal pH-value. These results were compared to the pH-values of samplings via rumen fistula and to the results of samplings via oral stomach tube. Due to the different rations, mean pH-value of trial 1 (average of all methods) was 6.64 +/- 0.37 (hay ad lib., 2 kg concentrate/ animal) and 6.24 +/- 0.36 for trial 2 (75% maize silage, 1 kg hay, 2 kg soybean meal). In trial 1 no statistically significant differences between all methods could be observed. Under more acidic ruminal conditions of trial 2 all methods differed significantly (p0.05). In the lower pH-range of trial 2, a difference of 0.32 that data can be collected continuously. The sensor system was evaluated by a comparison with standardized pH-dilutions (pH 4, pH 7). The sensor system has proven to be an accurate and reliable instrument (r = 0.9984) and it represents an innovative system for answering scientific questions in terms of rumen physiology and rumen pathology.

Studies on soil organic carbon (SOC) stocks mostly focus on topsoils (< 30 cm). However, 30 to 63% of the SOC are stored in the subsoils (30 to 100 cm), and the factors controlling SOC storage in subsoils may be substantially different than in topsoils. The low mean SOC content in subsoils makes its quantification and characterization challenging. Thus, new approaches are required to depict the SOC stocks distribution in full soil profile. Hyperspectral imaging of soil core samples can provide high spatial resolution of the vertical distribution of SOC in a soil profile. The main objective of the ongoing study, within the Horizon 2020 European Project Circular Agronomics, is to apply laboratory hyperspectral imaging with a variety of machine learning approaches for the mapping of OC distribution in undisturbed soil cores. Soil cores were collected down to a depth of one meter in grasslands of 15 organic farms located in the Lungau Valley, in Austria. Some samples were divided into five depths in the field for classical bulk soil measurements (total carbon and nitrogen, texture, pH, EC and bulk density) on disturbed samples. Undisturbed soil cores were sliced vertically for laboratory hyperspectral imaging in the range of Vis-NIR (400-1000 nm). We were able to reveal the hotspots of OC and map the OC distribution in soil profile by applying a variety of machine learning approaches (i.e. partial least square and random forest regression) as a function of spectral responses. A digital elevation model was further exploited to investigate the effects of topographical factors such as elevation, aspect and slope on SOC profile distribution. Landsat 8 data were also used to depict the spatial variability of land insensitive cover/vegetation in study area.

It is estimated that only 20% of fertilizers applied annually in the European agricultural systems are converted to finished products for human consumption. These low efficiencies result in large loss of nutrients into the environment with severe negative influences on soils, water and air, and constitute unacceptable health and environmental costs. In addition, around 45% of soils in the European countries have less than 2% organic carbon (OC). Low soil OC storage is linked with negative environmental impacts including soil and water quality, climate change and biodiversity. A relevant strategy to enhance soil OC is the transformation of waste products into organic amendments for application on soils. The aim of the H2020 European project Circular Agronomics is to provide a comprehensive synthesis of practical solutions to improve the current C, N and P cycling in European agro-ecosystems. This project explores the medium and long-term effects of new and classical organic fertilizers in six countries including Germany, Spain, Italy, Netherlands, Czech Republic and Austria. The study sites will be sampled before and after applying the new organic amendments using a hydraulic corer. A full profile assessment of the C, N and P distribution, stability and bioavailability will be released up to one meter depth using a combination of classical bulk chemical analyses and state-of-the-art imaging techniques. Undisturbed soil cores will be scanned using a hyperspectral camera to reveal hotspots of C, N and P storage in the soil profile, at the micro-scale. Soil C, N and P will be modelled as a function of spectral response using a variety of machine learning approaches. These results will provide essential information to develop management strategies that reduce nutrient surplus and increase C stocks.

Poster presentation at at the 7th International Symposium on Soil Organic Matter (SOM2019), in Adelaide, South Australia, from the 6th to the 11th of October.