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- ItemNitrous oxide emissions from winter oilseed rape cultivation(Amsterdam [u.a.] : Elsevier, 2017) Ruser, Reiner; Fuß, Roland; Andres, Monique; Hegewald, Hannes; Kesenheimer, Katharina; Köbke, Sarah; Räbiger, Thomas; Quinones, Teresa Suarez; Augustin, Jürgen; Christen, Olaf; Dittert, Klaus; Kage, Henning; Lewandowski, Iris; Prochnow, Annette; Stichnothe, Heinz; Flessa, HeinzWinter oilseed rape (Brassica napus L., WOSR) is the major oil crop cultivated in Europe. Rapeseed oil is predominantly used for production of biodiesel. The framework of the European Renewable Energy Directive requires that use of biofuels achieves GHG savings of at least 50% compared to use of fossil fuel starting in 2018. However, N2O field emissions are estimated using emission factors that are not specific for the crop and associated with strong uncertainty. N2O field emissions are controlled by N fertilization and dominate the GHG balance of WOSR cropping due to the high global warming potential of N2O. Thus, field experiments were conducted to increase the data basis and subsequently derive a new WOSR-specific emission factor. N2O emissions and crop yields were monitored for three years over a range of N fertilization intensities at five study sites representative of German WOSR production. N2O fluxes exhibited the typical high spatial and temporal variability in dependence on soil texture, weather and nitrogen availability. The annual N2O emissions ranged between 0.24 kg and 5.48 kg N2O-N ha−1 a−1. N fertilization increased N2O emissions, particularly with the highest N treatment (240 kg N ha−1). Oil yield increased up to a fertilizer amount of 120 kg N ha−1, higher N-doses increased grain yield but decreased oil concentrations in the seeds. Consequently oil yield remained constant at higher N fertilization. Since, yield-related emission also increased exponentially with N surpluses, there is potential for reduction of the N fertilizer rate, which offers perspectives for the mitigation of GHG emissions. Our measurements double the published data basis of annual N2O flux measurements in WOSR. Based on this extended dataset we modeled the relationship between N2O emissions and fertilizer N input using an exponential model. The corresponding new N2O emission factor was 0.6% of applied fertilizer N for a common N fertilizer amount under best management practice in WOSR production (200 kg N ha−1 a−1). This factor is substantially lower than the linear IPCC Tier 1 factor (EF1) of 1.0% and other models that have been proposed. © 2017
- ItemAmmonia and greenhouse gas emissions from slurry storage : A review(Amsterdam [u.a.] : Elsevier, 2020) Kupper, Thomas; Häni, Christoph; Neftel, Albrecht; Kincaid, Chris; Bühler, Marcel; Amon, Barbara; VanderZaag, AndrewStorage of slurry is an important emission source for ammonia (NH3), nitrous oxide (N2O), methane (CH4), carbon dioxide (CO2) and hydrogen sulfide (H2S) from livestock production. Therefore, this study collected published emission data from stored cattle and pig slurry to determine baseline emission values and emission changes due to slurry treatment and coverage of stores. Emission data were collected from 120 papers yielding 711 records of measurements conducted at farm-, pilot- and laboratory-scale. The emission data reported in a multitude of units were standardized and compiled in a database. Descriptive statistics of the data from untreated slurry stored uncovered revealed a large variability in emissions for all gases. To determine baseline emissions, average values based on a weighting of the emission data according to the season and the duration of the emission measurements were constructed using the data from farm-scale and pilot-scale studies. Baseline emissions for cattle and pig slurry stored uncovered were calculated. When possible, it was further distinguished between storage in tanks without slurry treatment and storage in lagoons which implies solid-liquid separation and biological treatment. The baseline emissions on an area or volume basis are: for NH3: 0.12 g m−2 h-1 and 0.15 g m−2 h-1 for cattle and pig slurry stored in lagoons, and 0.08 g m−2 h-1 and 0.24 g m−2 h-1 for cattle and pig slurry stored in tanks; for N2O: 0.0003 g m−2 h-1 for cattle slurry stored in lagoons, and 0.002 g m−2 h-1 for both slurry types stored in tanks; for CH4: 0.95 g m-3 h-1 and 3.5 g m-3 h-1 for cattle and pig slurry stored in lagoons, and 0.58 g m-3 h-1 and 0.68 g m-3 h-1 for cattle and pig slurry stored in tanks; for CO2: 6.6 g m−2 h-1 and 0.3 g m−2 h-1 for cattle and pig slurry stored in lagoons, and 8.0 g m−2 h-1 for both slurry types stored in tanks; for H2S: 0.04 g m−2 h-1 and 0.01 g m−2 h-1 for cattle and pig slurry stored in lagoons. Related to total ammoniacal nitrogen (TAN), baseline emissions for tanks are 16% and 15% of TAN for cattle and pig slurry, respectively. Emissions of N2O and CH4 relative to nitrogen (N) and volatile solids (VS) are 0.13% of N and 0.10% of N and 2.9% of VS and 4.7% of VS for cattle and pig slurry, respectively. Total greenhouse gas emissions from slurry stores are dominated by CH4. The records on slurry treatment using acidification show a reduction of NH3 and CH4 emissions during storage while an increase occurs for N2O and a minor change for CO2 as compared to untreated slurry. Solid-liquid separation causes higher losses for NH3 and a reduction in CH4, N2O and CO2 emissions. Anaerobically digested slurry shows higher emissions during storage for NH3 while losses tend to be lower for CH4 and little changes occur for N2O and CO2 compared to untreated slurry. All cover types are found to be efficient for emission mitigation of NH3 from stores. The N2O emissions increase in many cases due to coverage. Lower CH4 emissions occur for impermeable covers as compared to uncovered slurry storage while for permeable covers the effect is unclear or emissions tend to increase. Limited and inconsistent data regarding emission changes with covering stores are available for CO2 and H2S. The compiled data provide a basis for improving emission inventories and highlight the need for further research to reduce uncertainty and fill data gaps regarding emissions from slurry storage.
- ItemClassifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change(Amsterdam [u.a.] : Elsevier, 2017) Fronzek, Stefan; Pirttioja, Nina; Carter, Timothy R.; Bindi, Marco; Hoffmann, Holger; Palosuo, Taru; Ruiz-Ramos, Margarita; Tao, Fulu; Trnka, Miroslav; Acutis, Marco; Asseng, Senthold; Baranowski, Piotr; Basso, Bruno; Bodin, Per; Buis, Samuel; Cammarano, Davide; Deligios, Paola; Destain, Marie-France; Dumont, Benjamin; Ewert, Frank; Ferrise, Roberto; François, Louis; Gaiser, Thomas; Hlavinka, Petr; Jacquemin, Ingrid; Kersebaum, Kurt Christian; Kollas, Chris; Krzyszczak, Jaromir; Lorite, Ignacio J.; Minet, Julien; Minguez, M. Ines; Montesino, Manuel; Moriondo, Marco; Müller, Christoph; Nendel, Claas; Öztürk, Isik; Perego, Alessia; Rodríguez, Alfredo; Ruane, Alex C.; Ruget, Françoise; Sanna, Mattia; Semenov, Mikhail A.; Slawinski, Cezary; Stratonovitch, Pierre; Supit, Iwan; Waha, Katharina; Wang, Enli; Wu, Lianhai; Zhao, Zhigan; Rötter, Reimund P.Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (−2 to +9°C) and precipitation (−50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern. The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index. Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.
- ItemLivelihood and climate trade-offs in Kenyan peri-urban vegetable production(Amsterdam [u.a.] : Elsevier, 2017) Kurgat, Barnabas K.; Stöber, Silke; Mwonga, Samuel; Lotze-Campen, Hermann; Rosenstock, Todd S.Trade-offs between livelihood and environmental outcomes due to agricultural intensification in sub-Saharan Africa are uncertain. The present study measured yield, economic performance and nitrous oxide (N2O) emissions in African indigenous vegetable (AIV) production to investigate the optimal nutrient management strategies. In order to achieve this, an on-farm experiment with four treatments – (1) 40 kg N/ha diammonium phosphate (DAP), (2) 10 t/ha cattle manure, (3) 20 kg N/ha DAP and 5 t/ha cattle manure and (4) a no-N input control – was performed for two seasons. Yields and N2O emissions were directly measured with subsampling and static chambers/gas chromatography, respectively. Economic outcomes were estimated from semi-structured interviews (N = 12). Trade-offs were quantified by calculating N2O emissions intensity (N2OI) and N2O emissions economic intensity (N2OEI). The results indicate that, DAP alone resulted at least 14% greater yields, gross margin and returns to labour in absolute terms but had the highest emissions (p = 0.003). Productivity-climate trade-offs, expressed as N2OI, were statistically similar for DAP and mixed treatments. However, N2OEI was minimized under mixed management (p = 0.0004) while maintaining productivity and gross margins. We therefore conclude that soil fertility management strategies that mix inorganic and organic source present a pathway to sustainable intensification in AIV production. Future studies of GHG emissions in crop production need to consider not only productivity but economic performance when considering trade-offs.
- ItemPerformance of seasonal forecasts for the flowering and veraison of two major Portuguese grapevine varieties(Amsterdam [u.a.] : Elsevier, 2023) Yang, Chenyao; Ceglar, Andrej; Menz, Christoph; Martins, Joana; Fraga, Helder; Santos, João A.Seasonal phenology forecasts are becoming increasingly demanded by winegrowers and viticulturists. Forecast performance needs to be investigated over space and time before practical applications. We assess seasonal forecast performance (skill, probability and accuracy) in predicting flowering and veraison stages of two representative varieties in Portugal over 1993–2017. The state-of-the-art forecast system ECMWF-SEAS5 provides 7-month seasonal forecasts and is coupled with a locally adapted phenology model. Overall, findings illustrate the dependence of forecast performance on initialization timings, regions and predicting subjects (stages and varieties). Forecast performance improves by delaying the initialization timing and only forecasts initialized on April 1st show better skills than climatology on predicting phenology terciles (early/normal/late). The considerable bias of daily values of seasonal climate predictions can represent the main barrier to accurate forecasts. Better prediction performance is consistently found in Central-Southern regions compared to Northern regions, attributing to an earlier phenology occurrence with a shorter forecast length. Comparable predictive skills between flowering and veraison for both varieties imply better predictability in summer. Consequently, promising seasonal phenology predictions are foreseen in Central-Southern wine regions using forecasts initialized on April 1st with approximately 1–2/3–4 months lead time for flowering/veraison: potential prediction errors are ∼2 weeks, along with an overall moderate forecast skill on categorical events. However, considerable inter-annual variability of forecast performance over the same classified phenology years reflects the substantial influence of climate variability. This may represent the main challenge for reliable forecasts in Mediterranean regions. Recommendations are suggested for methodological innovations and practical applications towards reliable regional phenology forecasts.
- ItemClimate change impacts on European arable crop yields: Sensitivity to assumptions about rotations and residue management(Amsterdam [u.a.] : Elsevier, 2022) Faye, Babacar; Webber, Heidi; Gaiser, Thomas; Müller, Christoph; Zhang, Yinan; Stella, Tommaso; Latka, Catharina; Reckling, Moritz; Heckelei, Thomas; Helming, Katharina; Ewert, FrankMost large scale studies assessing climate change impacts on crops are performed with simulations of single crops and with annual re-initialization of the initial soil conditions. This is in contrast to the reality that crops are grown in rotations, often with sizable proportion of the preceding crop residue to be left in the fields and varying soil initial conditions from year to year. In this study, the sensitivity of climate change impacts on crop yield and soil organic carbon to assumptions about annual model re-initialization, specification of crop rotations and the amount of residue retained in fields was assessed for seven main crops across Europe. Simulations were conducted for a scenario period 2040–2065 relative to a baseline from 1980 to 2005 using the SIMPLACE1 framework. Results indicated across Europe positive climate change impacts on yield for C3 crops and negative impacts for maize. The consideration of simulating rotations did not have a benefit on yield variability but on relative yield change in response to climate change which slightly increased for C3 crops and decreased for C4 crops when rotation was considered. Soil organic carbon decreased under climate change in both simulations assuming a continuous monocrop and plausible rotations by between 1% and 2% depending on the residue management strategy.
- ItemSustainable food protein supply reconciling human and ecosystem health: A Leibniz Position(Amsterdam [u.a.] : Elsevier, 2020) Weindl, Isabelle; Ost, Mario; Wiedmer, Petra; Schreiner, Monika; Neugart, Susanne; Klopsch, Rebecca; Kühnhold, Holger; Kloas, Werner; Henkel, Ina M.; Schlüter, Oliver; Bußler, Sara; Bellingrath-Kimura, Sonoko D.; Ma, Hua; Grune, Tilman; Rolinski, Susanne; Klaus, SusanneMany global health risks are related to what and how much we eat. At the same time, the production of food, especially from animal origin, contributes to environmental change at a scale that threatens boundaries of a safe operating space for humanity. Here we outline viable solutions how to reconcile healthy protein consumption and sustainable protein production which requires a solid, interdisciplinary evidence base. We review the role of proteins for human and ecosystem health, including physiological effects of dietary proteins, production potentials from agricultural and aquaculture systems, environmental impacts of protein production, and mitigation potentials of transforming current production systems. Various protein sources from plant and animal origin, including insects and fish, are discussed in the light of their health and environmental implications. Integration of available knowledge is essential to move from a dual problem description (“healthy diets versus environment”) towards approaches that frame the food challenge of reconciling human and ecosystem health in the context of planetary health. This endeavor requires a shifting focus from metrics at the level of macronutrients to whole diets and a better understanding of the full cascade of health effects caused by dietary proteins, including health risks from food-related environmental degradation. © 2020
- ItemMeasures to increase the nitrogen use efficiency of European agricultural production(Amsterdam [u.a.] : Elsevier, 2020) Hutchings, Nicholas J.; Sørensen, Peter; Cordovil, Cláudia M.d.S.; Leip, Adrian; Amon, BarbaraInputs of nitrogen to agricultural production systems are necessary to produce food, feed and fibre, but nitrogen (N) losses from those systems represent a waste of a resource and a threat to both the environment and human health. The nitrogen use efficiency (NUE) of an agricultural production system can be seen as an indicator of the balance between benefits and costs of primary food, feed and fibre production. Here, we used modelling to follow the fate of the virgin N input to different production systems (ruminant and granivore meat, dairy, arable), and to estimate their NUE at the system scale. We defined two ruminant meat production systems, depending on whether the land places constraints on farming practices. The other production systems were dairy, granivore and arable production on land without constraints. Two geographic regions were considered: Northern and Southern Europe. Measures to improve NUE were identified and allocated to Low, Medium and High ambition groups, with Low equating to the current situation in Europe for production systems that are broadly following good agricultural practice. The NUE of the production systems was similar to or higher in Southern than Northern Europe, with the maximum technical NUEs if all available measures are implemented were for North and South Europe, respectively, 82% and 92% for arable systems, 71% and 80% for granivores, 50% and 36% for ruminant meat production on constrained land, 53% and 55% for dairy production on unconstrained land and 46% and 62% for ruminant meat production on unconstrained land. The values for NUE found here tend to be higher than reported elsewhere, possibly due to the accounting for long-term residual effects of fertiliser and manure in our method. The greatest increase in NUE with the progressive implementation of higher ambition measures was in unconstrained granivore systems and the least was in constrained ruminant meat systems, reflecting the lower initial NUE of granivore systems and the larger number of measures applicable to confined livestock systems. Our work supports use of NUE as an indicator of the temporal trend in the costs and benefits of existing agricultural production systems, but highlights problems associated with its use as a sustainability criteria for livestock production systems. For arable systems, we consider well-founded the NUE value of 90% above which there is a high risk of soil N depletion, provided many measures to increase NUE are employed. For systems employing fewer measures, we suggest a value of 70% would be more appropriate. We conclude that while it is feasible to calculate the NUE of livestock production systems, the additional complexity required reduces its value as an indicator for benchmarking sustainability in practical agriculture. © 2020 The Authors
- ItemPrediction of the biogas production using GA and ACO input features selection method for ANN model(Amsterdam [u.a.] : Elsevier, 2019) Beltramo, Tanja; Klocke, Michael; Hitzmann, BerndThis paper presents a fast and reliable approach to analyze the biogas production process with respect to the biogas production rate. The experimental data used for the developed models included 15 process variables measured at an agricultural biogas plant in Germany. In this context, the concentration of volatile fatty acids, total solids, volatile solids acid detergent fibre, acid detergent lignin, neutral detergent fibre, ammonium nitrogen, hydraulic retention time, and organic loading rate were used. Artificial neural networks (ANN) were established to predict the biogas production rate. An ant colony optimization and genetic algorithms were implemented to perform the variable selection. They identified the significant process variables, reduced the model dimension and improved the prediction capacity of the ANN models. The best prediction of the biogas production rate was obtained with an error of prediction of 6.24% and a coefficient of determination of R2 = 0.9.
- ItemThe role of nitrogen in achieving sustainable food systems for healthy diets(Amsterdam [u.a.] : Elsevier, 2020) Leip, Adrian; Bodirsky, Benjamin Leon; Kugelberg, SusannaThe ‘food system’ urgently needs a sustainable transformation. Two major challenges have to be solved: the food system has to provide food security with healthy, accessible, affordable, safe and diverse food for all, and it has to do so within the safe operating space of the planetary boundaries, where the pollution from reactive nitrogen turned out to be the largest bottleneck. Here we argue that thinking strategically about how to balance nitrogen flows throughout the food system will make current food systems more resilient and robust. Looking from a material and a governance perspective on the food system, we highlight major nitrogen losses and policy blind spots originating from a compartmentalization of food system spheres. We conclude that a participatory and integrated approach to manage nitrogen flows throughout the food system is necessary to stay within regional and global nitrogen boundaries, and will additionally provide synergies with a sustainable and healthy diet for all. © 2020 The Authors