Search Results
Assessment of continuous vaginal logger-based temperature monitoring in fever-induced gilts
2013, Schmidt, Mariana, Ammon, Christian, Schon, Peter, Manteuffel, Christian, Hoffmann, Gundula
The aim of this study was to identify a low risk method to induce fever in gilts and to evaluate vaginal sensors for temperature monitoring. Therefore, a rhinitis atrophicans vaccine was used to induce fever to evaluate sensors and behavioral patterns. During 11 trials, two of four animals were injected with 2 mL of the vaccine Porcilis AR-T DF (Intervet) and the other two animals were treated with 2 mL of 0.9% NaCl as controls. A temperature logger (TRIX-8, LogTag Recorders, Auckland, New Zealand) was used to continuously monitor the vaginal temperature. Additionally, rectal temperatures were measured four times daily. The water consumption, food intake and locomotion behaviors of the animals were analysed one day before treatment and during the day of the treatment. The vaccine induced fever in all gilts, which occurred approximately 5.87 h after vaccination. The vaginal logger temperatures and the rectal temperatures showed a linear correlation in 21 vaccinated gilts (r = 0.86; p<0.0001) and 22 non-vaccinated gilts (r = 0.65; P<0.0001). The vaginal logger can be continuously used to measure the core temperature. Water intake decreased (p<0.0001) in the gilts with fever, whereas food intake did not change (p = 0.9411). The vaccinated gilts spent 79±16 more minutes per day lying after the vaccination (p<0.001) in comparison to the day before the vaccination. Treatment with this vaccine provides the opportunity to induce fever over a short time period (5.13 h) and mild sickness behavior in gilts to investigate disease indicators.
Hydrothermal carbonization (HTC): Near infrared spectroscopy and partial least-squares regression for determination of selective components in HTC solid and liquid products derived from maize silage
2014, Reza, M. Toufiq, Becker, Wolfgang, Sachsenheimer, Kerstin, Mumme, Jan
Near-infrared (NIR) spectroscopy was evaluated as a rapid method of predicting fiber components (hemicellulose, cellulose, lignin, and ash) and selective compounds of hydrochar and corresponding process liquor produced by hydrothermal carbonization (HTC) of maize silage. Several HTC reaction times and temperatures were applied and NIR spectra of both HTC solids and liquids were obtained and correlated with concentration determined from van-Soest fiber analysis, IC, and UHPLC. Partial least-squares regression was applied to calculate models for the prediction of selective substances. The model developed with the spectra had the best performance in 3–7 factors with a correlation coefficient, which varied between 0.9275–0.9880 and 0.9364–0.9957 for compounds in solid and liquid, respectively. Calculated root mean square errors of prediction (RMSEP) were 0.42–5.06 mg/kg. The preliminary results indicate that NIR, a widely applied technique, might be applied to determine chemical compounds in HTC solid and liquid.
Livelihood and climate trade-offs in Kenyan peri-urban vegetable production
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.
Risk efficiency of irrigation to cereals in northeast Germany with respect to nitrogen fertilizer
2016, Meyer-Aurich, Andreas, Gandorfer, Markus, Trost, Benjamin, Ellmer, Frank, Baumecker, Michael
The potential role of irrigation of cereals as a response to climate change is under debate. Especially under temperate continental conditions empirical evidence of crop yield response to irrigation in interaction with nitrogen fertilizer supply is rare. Besides mean yield effects, irrigation reduces yield variance, which may be an incentive for farmers to use irrigation. This paper investigates the risk-efficiency of irrigation in cereal production in a temperate continental climate, based on data from a long term field experiment on a sandy soil. Irrigation and no irrigation of winter rye (Secale cereale) and winter barley (Hordeum vulgare) were investigated in three different nitrogen (N) fertilizer levels. Crop yield response data (1995–2010) to irrigation and N fertilizer were used to calculate net returns, certainty equivalents (CE) for different levels of risk aversion and the conditional value at risk (CVaR) as a downside risk indicator in two price scenarios. The scenarios were calculated with a total cost and a partial budget approach. Irrigation was found to be profit-maximizing in all partial budget calculations, which sometimes required higher N input to be profit-maximizing. Irrigation and N fertilizer reduction were identified as risk mitigation strategies, even though their impact was limited. Irrigation reduced the downside risk only in the partial budget calculations. The analysis based on the CE did not show improved risk efficiency with irrigated management options. In contrast, reduced fertilizer input proved to be risk efficient at specific levels of risk aversion. The price expectations of winter rye and winter barley had a much higher impact on the ranking of the management options than risk aversion based on the crop yield variances. At low crop prices for all levels of risk aversion, irrigation of winter barley and winter rye was only economically justified if fixed costs for irrigation were not taken into account. At high crop prices, irrigation of winter barley was also justified based on the total cost calculation. However, this advantage was only given at a very low level of risk aversion. With increasing levels of risk aversion irrigation was not efficient based on the CE in the total cost accounting scenario. In conclusion, irrigation of cereals can contribute to downside risk mitigation and increased profits, if fixed costs for irrigation are covered. However, this conclusion holds only when irrigation is combined with an increased N intensity. If total costs need to be accounted for, irrigation in cereals is not an appropriate risk reduction strategy and a reduction of N input is more effective.
Water footprint analysis for the assessment of milk production in Brandenburg (Germany)
2010, Drastig, K., Prochnow, A., Kraatz, S., Klauss, H., Plöchl, M.
The working group "Adaptation to Climate Change" at the Leibniz-Institute for Agricultural Engineering Potsdam-Bornim (ATB) is introduced. This group calculates the water footprint for agricultural processes and farms, distinguished into green water footprint, blue water footprint, and dilution water footprint. The green and blue water demand of a dairy farm plays a pivotal role in the regional water balance. Considering already existing and forthcoming climate change effects there is a need to determine the water cycle in the field and in housing for process chain optimisation for the adaptation to an expected increasing water scarcity. Resulting investments to boost water productivity and to improve water use efficiency in milk production are two pathways to adapt to climate change effects. In this paper the calculation of blue water demand for dairy farming in Brandenburg (Germany) is presented. The water used for feeding, milk processing, and servicing of cows over the time period of ten years was assessed in our study. The preliminary results of the calculation of the direct blue water footprint shows a decreasing water demand in the dairy production from the year 1999 with 5.98×109 L/yr to a water demand of 5.00×109 L/yr in the year 2008 in Brandenburg because of decreasing animal numbers and an improved average milk yield per cow. Improved feeding practices and shifted breeding to greater-volume producing Holstein-Friesian cow allow the production of milk in a more water sustainable way. The mean blue water consumption for the production of 1 kg milk in the time period between 1999 to 2008 was 3.94±0.29 L. The main part of the consumed water seems to stem from indirect used green water for the production of feed for the cows.
Field inter-comparison of eleven atmospheric ammonia measurement techniques
2010, von Bobrutzki, K., Braban, C.F., Famulari, D., Jones, S.K., Blackall, T., Smith, T.E.L., Blom, M., Coe, H., Gallagher, M., Ghalaieny, M., McGillen, M.R., Percival, C.J., Whitehead, J.D., Ellis, R., Murphy, J., Mohacsi, A., Pogany, A., Junninen, H., Rantanen, S., Sutton, M.A., Nemitz, E.
Eleven instruments for the measurement of ambient concentrations of atmospheric ammonia gas (NH3), based on eight different measurement methods were inter-compared above an intensively managed agricultural field in late summer 2008 in Southern Scotland. To test the instruments over a wide range of concentrations, the field was fertilised with urea midway through the experiment, leading to an increase in the average concentration from 10 to 100 ppbv. The instruments deployed included three wet-chemistry systems, one with offline analysis (annular rotating batch denuder, RBD) and two with online-analysis (Annular Denuder sampling with online Analysis, AMANDA; AiRRmonia), two Quantum Cascade Laser Absorption Spectrometers (a large-cell dual system; DUAL-QCLAS, and a compact system; c-QCLAS), two photo-acoustic spectrometers (WaSul-Flux; Nitrolux-100), a Cavity Ring Down Spectrosmeter (CRDS), a Chemical Ionisation Mass Spectrometer (CIMS), an ion mobility spectrometer (IMS) and an Open-Path Fourier Transform Infra-Red (OP-FTIR) Spectrometer. The instruments were compared with each other and with the average concentration of all instruments. An overall good agreement of hourly average concentrations between the instruments (R2>0.84), was observed for NH3 concentrations at the field of up to 120 ppbv with the slopes against the average ranging from 0.67 (DUAL-QCLAS) to 1.13 (AiRRmonia) with intercepts of −0.74 ppbv (RBD) to +2.69 ppbv (CIMS). More variability was found for performance for lower concentrations (<10 ppbv). Here the main factors affecting measurement precision are (a) the inlet design, (b) the state of inlet filters (where applicable), and (c) the quality of gas-phase standards (where applicable). By reference to the fast (1 Hz) instruments deployed during the study, it was possible to characterize the response times of the slower instruments.
The MiLA tool: Modeling greenhouse gas emissions and cumulative energy demand of energy crop cultivation in rotation
2016, Peter, Christiane, Specka, Xenia, Aurbacher, Joachim, Peter Kornatz, Peter Kornatz, Herrmann, Christiane, Heiermann, Monika, Müller, Janine, Nendel, Claas
Crop rotations are part of current agricultural practice, since they and their effects can contribute to a sustainable agricultural cropping system. However, in current Life Cycle Assessment (LCA) studies, crop rotation effects are insufficiently considered, since these effects are difficult to measure. LCA studies from crop production typically take only one vegetation period into account. As a result, the consideration of how the assessed crop is influenced by the previous crop (crop rotation effects) including: (1) nutrient carryover, (2) reduction in operational requirements and (3) different intensity and timing of farming activities, is outside of the system boundary. However, ignoring these effects may lead to incorrect interpretation of LCA results and consequently to poor agricultural management as well as poor policy decisions. A new LCA tool called the “Model for integrative Life Cycle Assessment in Agriculture (MiLA)” is presented in this work. MiLA has been developed to assess GHG emissions and cumulative energy demands (CED) of cropping systems by taking the characteristics of crop cultivation in rotation into account. This tool enables the user to analyze cropping systems at farm level in order to identify GHG mitigation options and energy-efficient cropping systems. The tool was applied to a case study, including two crop rotations in two different regions in Germany with the goal of demonstrating the effectiveness of this tool on LCA results. Results show that including crop rotation effects can influence the GHG emission result of the individual crop by − 34% up to + 99% and the CED by − 16 up to + 89%. Expanding the system boundary by taking the whole crop rotation into account as well as providing the results based on different functional units improves LCA of energy crop production and helps those making the assessment to draw a more realistic picture of the interactions between crops while increasing the reliability of the LCA results.
Nitrous oxide emissions from winter oilseed rape cultivation
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, Heinz
Winter 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
Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change
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.
Milk fatty acids estimated by mid-infrared spectroscopy and milk yield can predict methane emissions in dairy cows
2018-5-2, Engelke, Stefanie W., Daş, Gürbüz, Derno, Michael, Tuchscherer, Armin, Berg, Werner, Kuhla, Björn, Metges, Cornelia C.
Ruminant enteric methane emission contributes to global warming. Although breeding low methane-emitting cows appears to be possible through genetic selection, doing so requires methane emission quantification by using elaborate instrumentation (respiration chambers, SF6 technique, GreenFeed) not feasible on a large scale. It has been suggested that milk fatty acids are promising markers of methane production. We hypothesized that methane emission can be predicted from the milk fatty acid concentrations determined by mid-infrared spectroscopy, and the integration of energy-corrected milk yield would improve the prediction. Therefore, we examined relationships between methane emission of cows measured in respiration chambers and milk fatty acids, predicted by mid-infrared spectroscopy, to derive diet-specific and general prediction equations based on milk fatty acid concentrations alone and with the additional consideration of energy-corrected milk yield. Cows were fed diets differing in forage type and linseed supplementation to generate a large variation in both CH4 emission and milk fatty acids. Depending on the diet, equations derived from regression analysis explained 61 to 96% of variation of methane emission, implying the potential of milk fatty acid data predicted by mid-infrared spectroscopy as novel proxy for direct methane emission measurements. When data from all diets were analyzed collectively, the equation with energy-corrected milk yield (CH4 (L/day) = − 1364 + 9.58 × energy-corrected milk yield + 18.5 × saturated fatty acids + 32.4 × C18:0) showed an improved coefficient of determination of cross-validation R2 CV = 0.72 compared to an equation without energy-corrected milk yield (R2 CV = 0.61). Equations developed for diets supplemented by linseed showed a lower R2 CV as compared to diets without linseed (0.39 to 0.58 vs. 0.50 to 0.91). We demonstrate for the first time that milk fatty acid concentrations predicted by mid-infrared spectroscopy together with energy-corrected milk yield can be used to estimate enteric methane emission in dairy cows. © 2018, The Author(s).