Search Results
Uncertainty in the measurement of indoor temperature and humidity in naturally ventilated dairy buildings as influenced by measurement technique and data variability
The microclimatic conditions in dairy buildings affect animal welfare and gaseous emissions. Measurements are highly variable due to the inhomogeneous distribution of heat and humidity sources (related to farm management) and the turbulent inflow (associated with meteorologic boundary conditions). The selection of the measurement strategy (number and position of the sensors) and the analysis methodology adds to the uncertainty of the applied measurement technique. To assess the suitability of different sensor positions, in situations where monitoring in the direct vicinity of the animals is not possible, we collected long-term data in two naturally ventilated dairy barns in Germany between March 2015 and April 2016 (horizontal and vertical profiles with 10 to 5 min temporal resolution). Uncertainties related to the measurement setup were assessed by comparing the device outputs under lab conditions after the on-farm experiments. We found out that the uncertainty in measurements of relative humidity is of particular importance when assessing heat stress risk and resulting economic losses in terms of temperature-humidity index. Measurements at a height of approximately 3 m–3.5 m turned out to be a good approximation for the microclimatic conditions in the animal occupied zone (including the air volume close to the emission active zone). However, further investigation along this cross-section is required to reduce uncertainties related to the inhomogeneous distribution of humidity. In addition, a regular sound cleaning (and if possible recalibration after few months) of the measurement devices is crucial to reduce the instrumentation uncertainty in long-term monitoring of relative humidity in dairy barns. © 2017 The Authors
Risk efficiency of irrigation to cereals in northeast Germany with respect to nitrogen fertilizer
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.
Investigation on the potential of applying bio-based edible coatings for horticultural products exemplified with cucumbers
Plastic packaging for fresh horticultural produce has many advantages but generates plastic waste and ecological alternatives are required. Edible coatings can retard many processes related to loss of quality. Hydrophobic lipid-based coatings are preferably applied for fresh fruits and vegetables. The approval of such coatings for products with edible peels in EU is increasingly under discussion. However, investigations on the efficiency of various edible coatings on soft-skinned fruit and vegetables are rare and it is currently unclear whether the consumer will accept them. Therefore, this study investigates (1) important characteristics of a lipid-based coating and (2) its ability to maintain the post-harvest quality of fresh cucumbers. This was evaluated by a comparative storage test under common suboptimal retail conditions (20 °C; 65% RH). The study also evaluates (3) the general perception of consumers about and their acceptance of the application of edible coatings on fresh fruit and vegetables with edible peels. The investigated coating was able to drastically reduce water loss (54–68%) and fruit respiration (approx. 33%) of fresh cucumber. The reduction of tissue stiffness was delayed by 2 days, thus, prolonged shelf life. Majority of consumer (77%) endorse the application of edible coatings as an alternative to plastic packaging, but emphasized important requirements for them.
This looks More Like that: Enhancing Self-Explaining Models by prototypical relevance propagation: This Looks More Like That
Current machine learning models have shown high efficiency in solving a wide variety of real-world problems. However, their black box character poses a major challenge for the comprehensibility and traceability of the underlying decision-making strategies. As a remedy, numerous post-hoc and self-explanation methods have been developed to interpret the models’ behavior. Those methods, in addition, enable the identification of artifacts that, inherent in the training data, can be erroneously learned by the model as class-relevant features. In this work, we provide a detailed case study of a representative for the state-of-the-art self-explaining network, ProtoPNet, in the presence of a spectrum of artifacts. Accordingly, we identify the main drawbacks of ProtoPNet, especially its coarse and spatially imprecise explanations. We address these limitations by introducing Prototypical Relevance Propagation (PRP), a novel method for generating more precise model-aware explanations. Furthermore, in order to obtain a clean, artifact-free dataset, we propose to use multi-view clustering strategies for segregating the artifact images using the PRP explanations, thereby suppressing the potential artifact learning in the models.
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
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.
A brief dataset on the model-based evaluation of the growth performance of Bacillus coagulans and l-lactic acid production in a lignin-supplemented medium
The data presented in this article are related to the research article entitled “Model-based characterization of growth performance and l-lactic acid production with high optical purity by thermophilic Bacillus coagulans in a lignin-supplemented mixed substrate medium (R. Glaser and J. Venus, 2016) [1]”. This data survey provides the information on characterization of three Bacillus coagulans strains. Information on cofermentation of lignocellulose-related sugars in lignin-containing media is given. Basic characterization data are supported by optical-density high-throughput screening and parameter adjustment to logistic growth models. Lab scale fermentation procedures are examined by model adjustment of a Monod kinetics-based growth model. Lignin consumption is analyzed using the data on decolorization of a lignin-supplemented minimal medium.
Enzyme-based lignocellulose hydrolyzation – Brief data survey for cellulase performance characterization on behalf of the Sauter mean diameter of raw material particles
The data presented here supports the informational background of enzyme-based lignocellulose hydrolyzation, cellulase characterization, and sugar yield prediction for the work “Enzyme-based lignocellulose hydrolyzation – Sauter mean diameter of raw materials as a basis for cellulase performance characterization and yield prediction” by Glaser [1]. Glucose yields from the enzymatic hydrolysis of the raw materials were shown as a function of cellulase enzyme loading as well as of particle size with different solid loading. The data for the proposed methods of the determination of enzyme activity in inhomogeneous samples of lignocellulosic raw materials are presented. The data of the empirical model that was developed for the prediction of hydrolysis yields for different enzyme concentrations, substrate specific particle size, and solid loadings, are given. Data are also given in relation of terms of scale-up opportunities.
The MiLA tool: Modeling greenhouse gas emissions and cumulative energy demand of energy crop cultivation in rotation
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.
Perspectives from CO+RE: How COVID-19 changed our food systems and food security paradigms
[no abstract available]
Awassi sheep keeping in the Arabic steppe in relation to nitrous oxide emission from soil
Sheep husbandry is the main source of income for farmers in arid zones. Increasing sheep production on steppes may increase the greenhouse gas production. The objective of this study was to investigate the nitrous oxide (N2O) emissions from the steppes for Awassi sheep keeping and feed cropping in arid zones such as Syria. The methodology developed by the Intergovernmental Panel on Climate Change (IPCC) was used to estimate N2O emissions. A survey was conducted on 64 farms in Syria to gather data for analysis. Precipitation and crop yield data from 2001 to 2009 were also used for calculation and modelling. Sheep-keeping systems, precipitation, year and the region have significant effects on N2O emissions (p<0.05). Emissions of N2O from lands with extensive, semi-intensive and intensive systems were 0.30 ± 0.093, 0.598± 0.113 and 2.243± 0.187 kg sheep1year1, respectively. Crop production was higher in regions with high precipitation levels, which helped to reduce N2O emissions. Using more residuals of wheat, cotton and soya as feed for sheep in the keeping systems evaluated may decrease the overuse of steppe regions and N2O emissions. Nitrous oxide emissions of N2O from sheep-keeping areas can be reduced by changing sheep-keeping systems and increasing the crop production in arid zones through artificial irrigation.