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- ItemN 2 O emissions and NO 3 − leaching from two contrasting regions in Austria and influence of soil, crops and climate: a modelling approach(Dordrecht [u.a.] : Springer Science + Business Media B.V, 2019) Kasper, M.; Foldal, C.; Kitzler, B.; Haas, E.; Strauss, P.; Eder, A.; Zechmeister-Boltenstern, S.; Amon, B.National emission inventories for UN FCCC reporting estimate regional soil nitrous oxide (N 2 O) fluxes by considering the amount of N input as the only influencing factor for N 2 O emissions. Our aim was to deepen the understanding of N 2 O fluxes from agricultural soils, including region specific soil and climate properties into the estimation of emission to find targeted mitigation measures for the reduction of nitrogen losses and GHG emissions. Within this project, N 2 O emissions and nitrate (NO 3 − ) leaching were modelled under spatially distinct environmental conditions in two agricultural regions in Austria taking into account region specific soil and climatic properties, management practices and crop rotations. The LandscapeDNDC ecosystem model was used to calculate N 2 O emissions and NO 3 − leaching reflecting different types of vegetation, management operations and crop rotations. In addition, N input and N fluxes were assessed and N 2 O emissions were calculated. This approach allowed identifying hot spots of N 2 O emissions. Results show that certain combinations of soil type, weather conditions, crop and management can lead to high emissions. Mean values ranged from 0.15 to 1.29 kg N 2 O–N ha −1 year −1 (Marchfeld) and 0.26 to 0.52 kg N 2 O–N ha −1 year −1 (Grieskirchen). Nitrate leaching, which strongly dominated N-losses, often reacted opposite to N 2 O emissions. Larger quantities of NO 3 − were lost during years of higher precipitation, especially if winter barley was cultivated on sandy soils. Taking into account the detected hot spots of N 2 O emissions and NO 3 − leaching most efficient measures can be addressed to mitigate environmental impacts while maximising crop production. © 2018, The Author(s).
- ItemDATAMAN: A global database of nitrous oxide and ammonia emission factors for excreta deposited by livestock and land-applied manure(Hoboken, NJ : Wiley, 2021) Beltran, Ignacio; van der Weerden, Tony J.; Alfaro, Marta A.; Amon, Barbara; de Klein, Cecile A. M.; Grace, Peter; Hafner, Sasha; Hassouna, Mélynda; Hutchings, Nicholas; Krol, Dominika J.; Leytem, April B.; Noble, Alasdair; Salazar, Francisco; Thorman, Rachel E.; Velthof, Gerard L.Nitrous oxide (N2 O), ammonia (NH3 ), and methane (CH4 ) emissions from the manure management chain of livestock production systems are important contributors to greenhouse gases (GHGs) and NH3 emitted by human activities. Several studies have evaluated manure-related emissions and associated key variables at regional, national, or continental scales. However, there have been few studies focusing on the drivers of these emissions using a global dataset. An international project was created (DATAMAN) to develop a global database on GHG and NH3 emissions from the manure management chain (housing, storage, and field) to identify key variables influencing emissions and ultimately to refine emission factors (EFs) for future national GHG inventories and NH3 emission reporting. This paper describes the "field" database that focuses on N2 O and NH3 EFs from land-applied manure and excreta deposited by grazing livestock. We collated relevant information (EFs, manure characteristics, soil properties, and climatic conditions) from published peer-reviewed research, conference papers, and existing databases. The database, containing 5,632 observations compiled from 184 studies, was relatively evenly split between N2 O and NH3 (56 and 44% of the EF values, respectively). The N2 O data were derived from studies conducted in 21 countries on five continents, with New Zealand, the United Kingdom, Kenya, and Brazil representing 86% of the data. The NH3 data originated from studies conducted in 17 countries on four continents, with the United Kingdom, Denmark, Canada, and The Netherlands representing 79% of the data. Wet temperate climates represented 90% of the total database. The DATAMAN field database is available at http://www.dataman.co.nz.
- ItemAmmonia and nitrous oxide emission factors for excreta deposited by livestock and land-applied manure(Hoboken, NJ : Wiley, 2021) van der Weerden, Tony J.; Noble, Alasdair; de Klein, Cecile A. M.; Hutchings, Nicholas; Thorman, Rachel E.; Alfaro, Marta A.; Amon, Barbara; Beltran, Ignacio; Grace, Peter; Hassouna, Mélynda; Krol, Dominika J.; Leytem, April B.; Salazar, Francisco; Velthof, Gerard L.Manure application to land and deposition of urine and dung by grazing animals are major sources of ammonia (NH3 ) and nitrous oxide (N2 O) emissions. Using data on NH3 and N2 O emissions following land-applied manures and excreta deposited during grazing, emission factors (EFs) disaggregated by climate zone were developed, and the effects of mitigation strategies were evaluated. The NH3 data represent emissions from cattle and swine manures in temperate wet climates, and the N2 O data include cattle, sheep, and swine manure emissions in temperate wet/dry and tropical wet/dry climates. The NH3 EFs for broadcast cattle solid manure and slurry were 0.03 and 0.24 kg NH3 -N kg-1 total N (TN), respectively, whereas the NH3 EF of broadcast swine slurry was 0.29. Emissions from both cattle and swine slurry were reduced between 46 and 62% with low-emissions application methods. Land application of cattle and swine manure in wet climates had EFs of 0.005 and 0.011 kg N2 O-N kg-1 TN, respectively, whereas in dry climates the EF for cattle manure was 0.0031. The N2 O EFs for cattle urine and dung in wet climates were 0.0095 and 0.002 kg N2 O-N kg-1 TN, respectively, which were three times greater than for dry climates. The N2 O EFs for sheep urine and dung in wet climates were 0.0043 and 0.0005, respectively. The use of nitrification inhibitors reduced emissions in swine manure, cattle urine/dung, and sheep urine by 45-63%. These enhanced EFs can improve national inventories; however, more data from poorly represented regions (e.g., Asia, Africa, South America) are needed.
- ItemProximal Soil Sensing - A Contribution for Species Habitat Distribution Modelling of Earthworms in Agricultural Soils?(San Francisco, California, US : PLOS, 2016) Schirrmann, Michael; Joschko, Monika; Gebbers, Robin; Kramer, Eckart; Zörner, Mirjam; Barkusky, Dietmar; Timmer, JensBackground: Earthworms are important for maintaining soil ecosystem functioning and serve as indicators of soil fertility. However, detection of earthworms is time-consuming, which hinders the assessment of earthworm abundances with high sampling density over entire fields. Recent developments of mobile terrestrial sensor platforms for proximal soil sensing (PSS) provided new tools for collecting dense spatial information of soils using various sensing principles. Yet, the potential of PSS for assessing earthworm habitats is largely unexplored. This study investigates whether PSS data contribute to the spatial prediction of earthworm abundances in species distribution models of agricultural soils. Methodology/Principal Findings: Proximal soil sensing data, e.g., soil electrical conductivity (EC), pH, and near infrared absorbance (NIR), were collected in real-time in a field with two management strategies (reduced tillage / conventional tillage) and sandy to loam soils. PSS was related to observations from a long-term (11 years) earthworm observation study conducted at 42 plots. Earthworms were sampled from 0.5 x 0.5 x 0.2 m³ soil blocks and identified to species level. Sensor data were highly correlated with earthworm abundances observed in reduced tillage but less correlated with earthworm abundances observed in conventional tillage. This may indicate that management influences the sensor-earthworm relationship. Generalized additive models and state-space models showed that modelling based on data fusion from EC, pH, and NIR sensors produced better results than modelling without sensor data or data from just a single sensor. Regarding the individual earthworm species, particular sensor combinations were more appropriate than others due to the different habitat requirements of the earthworms. Earthworm species with soil-specific habitat preferences were spatially predicted with higher accuracy by PSS than more ubiquitous species. Conclusions/Significance: Our findings suggest that PSS contributes to the spatial modelling of earthworm abundances at field scale and that it will support species distribution modelling in the attempt to understand the soil-earthworm relationships in agroecosystems.
- ItemGoverning Transactions and Interdependences between Linked Value Chains in a Circular Economy: The Case of Wastewater Reuse in Braunschweig (Germany)(Basel : MDPI, 2018-4-9) Maaß, Oliver; Grundmann, PhilippReusing wastewater in agriculture has attracted increasing attention as a strategy to support the transition towards the circular economy in the water and agriculture sector. As a consequence, there is great interest in solutions for governing the transactions and interdependences between the associated value chains. This paper explores the institutions and governance structures for coordinating transactions and interdependences between actors in linked value chains of wastewater treatment and crop production. It aims to analyze how transactions and interdependences shape the governance structures for reusing wastewater at the local level. A transaction costs analysis based on data from semi-structured interviews and a questionnaire is applied to the agricultural wastewater reuse scheme of the Wastewater Association Braunschweig (Germany). The results show that different governance structures are needed to match with the different properties and requirements of the transactions and activities between linked value chains of wastewater treatment and crop production. Interdependences resulting from transactions between wastewater providers and farmers increase the need for hybrid and hierarchical elements in the governance structures for wastewater reuse. The authors conclude that aligning governance structures with transactions and interdependences is key to efficiently governing transactions and interdependences between linked value chains in a circular economy. © 2018 by the authors.
- ItemRespiratory patterns of European pear (Pyrus communis L. ‘Conference’) throughout pre- and post-harvest fruit development(London [u.a.] : Elsevier, 2019) Brandes, Nicole; Zude-Sasse, ManuelaInformation on the developmental stage of pear pre-harvest and in shelf-life is crucial to determine the optimum timing of harvest, post-harvest treatment, and time of consumption ensuring high eating quality. In the present study, CO2 emission and fruit quality of European pear (Pyrus communis L.) ‘Conference’ were analysed pre- and post-harvest with emphasis on shelf life for three years. Additionally, cytochrome and cyanide-resistant O2 consumption were analysed in the last year of experiments. The respiration rate of pear showed typical climacteric rise of CO2 emission in two years only, despite daily measurements. However, in each year the fruit quality in shelf life was closely linked to harvest date suggesting climacteric fruit response. Thus, the developmental stage of ‘Conference’ pear should be analysed by additional methods. Particularly, the cytochrome and cyanide-resistant O2 consumption showed an encouraging potential to obtain data on characteristic respiratory patterns. © 2019
- ItemAgricultural Monitoring Using Polarimetric Decomposition Parameters of Sentinel-1 Data(Basel : MDPI, 2021) Harfenmeister, Katharina; Itzerott, Sibylle; Weltzien, Cornelia; Spengler, DanielThe time series of synthetic aperture radar (SAR) data are commonly and successfully used to monitor the biophysical parameters of agricultural fields. Because, until now, mainly backscatter coefficients have been analysed, this study examines the potentials of entropy, anisotropy, and alpha angle derived from a dual-polarimetric decomposition of Sentinel-1 data to monitor crop development. The temporal profiles of these parameters are analysed for wheat and barley in the vegetation periods 2017 and 2018 for 13 fields in two test sites in Northeast Germany. The relation between polarimetric parameters and biophysical parameters observed in the field is investigated using linear and exponential regression models that are evaluated using the coefficient of determination (R2) and the root mean square error (RMSE). The performance of single regression models is furthermore compared to those of multiple regression models, including backscatter coefficients in VV and VH polarisation as well as polarimetric decomposition parameters entropy and alpha. Characteristic temporal profiles of entropy, anisotropy, and alpha reflecting the main phenological changes in plants as well as the meteorological differences between the two years are observed for both crop types. The regression models perform best for data from the phenological growth stages tillering to booting. The highest R2 values of the single regression models are reached for the plant height of wheat related to entropy and anisotropy with R2 values of 0.64 and 0.61, respectively. The multiple regression models of VH, VV, entropy, and alpha outperform single regression models in most cases. R2 values of multiple regression models of plant height (0.76), wet biomass (0.7), dry biomass (0.7), and vegetation water content (0.69) improve those of single regression models slightly by up to 0.05. Additionally, the RMSE values of the multiple regression models are around 10% lower compared to those of single regression models. The results indicate the capability of dual-polarimetric decomposition parameters in serving as meaningful input parameters for multiple regression models to improve the prediction of biophysical parameters. Additionally, their temporal profiles indicate phenological development dependent on meteorological conditions. Knowledge about biophysical parameter development and phenology is important for farmers to monitor crop growth variability during the vegetation period to adapt and to optimize field management.