Hirarchical Growth: Basic and Applied Research

We develop a model that incorporates salient features of growth in modern economies. We combine the expanding-variety growth model through horizontal innovations with a hierarchy of basic and applied research. The former extends the knowledge base, while the latter commercializes it. Two-way spillovers reinforce the productivity of research in each sector. We establish the existence of balanced growth paths. Along such paths the stock of ideas and the stock of commercialized blueprints for intermediate goods grow with the same rate. Basic research is a necessary and sufficient condition for economic growth. We show that there can be two different facets of growth in the economy. First, growth may be entirely shaped by investments in basic research if applied research operates at the knowledge frontier. Second, long-run growth may be shaped by both basic and applied research and growth can be further stimulated by research subsidies. We illustrate different types of growth processes by examples and polar cases when only upward or downward spillovers between basic and applied research are present.

Hierarchical Growth: Basic and Applied Research

We develop a model that incorporates salient features of growth in modern economies. We combine the expanding-variety growth model through horizontal innovations with a hierarchy of basic and applied research. The former extends the knowledge base, while the latter commercializes it. Two-way spillovers reinforce the productivity of research in each sector. We establish the existence of balanced growth paths. Along such paths the stock of ideas and the stock of commercialized blueprints for intermediate goods grow with the same rate. Basic research is a necessary and sufficient condition for economic growth. We show that there can be two different facets of growth in the economy. First, growth may be entirely shaped by investments in basic research if applied research operates at the knowledge frontier. Second, long-run growth may be shaped by both basic and applied research and growth can be further stimulated by research subsidies. We illustrate different types of growth processes by examples and polar cases when only upward or downward spillovers between basic and applied research are present.Basic research, applied research, knowledge base, commercialization, hierarchical economic growth

Methane Emission Characteristics of Naturally Ventilated Cattle Buildings

The mandate to limit global temperature rise calls for a reliable quantification of gaseous pollutant emissions as a basis for effective mitigation. Methane emissions from ruminant fermentation are of particular relevance in the context of greenhouse gas mitigation. The emission dynamics are so far insufficiently understood. We analyzed hourly methane emission data collected during contrasting seasons from two naturally ventilated dairy cattle buildings with concrete floor and performed a second order polynomial regression. We found a parabolic temperature dependence of the methane emissions irrespective of the measurement site and setup. The position of the parabola vertex varied when considering different hours of the day. The circadian rhythm of methane emissions was represented by the pattern of the fitted values of the constant term of the polynomial and could be well explained by feeding management and air flow conditions. We found barn specific emission minima at ambient temperatures around 10 °C to 15 °C. As this identified temperature optimum coincides with the welfare temperature of dairy cows, we concluded that temperature regulation of dairy cow buildings with concrete floor should be considered and further investigated as an emission mitigation measure. Our results further indicated that empirical modeling of methane emissions from the considered type of buildings with a second order polynomial for the independent variable air temperature can increase the accuracy of predicted long-term emission values for regions with pronounced seasonal temperature fluctuation

Heat Load-Induced Changes in Lying Behavior and Lying Cubicle Occupancy of Lactating Dairy Cows in a Naturally Ventilated Barn

Dairy cows show a high sensitivity to changes in barn climate, which can result in physiological and ethological responses because of the homeostatic mechanisms to regulate the body temperature under heat load. The objective of this study was to analyze the lying behavior and occupancy of lying cubicles of lactating high-yielding Holstein-Friesian cows throughout the day during three summer months and three winter months. The study was conducted in summer 2016 and in winter 2016/17 in a naturally ventilated barn in Brandenburg, Germany. The determined temperaturehumidity index (THI) of the barn was calculated using the measured ambient temperature and relative humidity at eight locations inside the barn. The THI was used to define the heat load the cows were exposed to. The activity of the cows was measured with accelerometers, and a video recording was made to analyze the occupancy of the three rows of lying cubicles. The results indicated that increasing heat load led to a decrease in lying time; therefore, the daily lying time differed between summer and winter months. In addition, there were different patterns of lying behavior during the course of the day, depending on the season. A sharp decline in lying time could be observed especially in the afternoon hours during the summer. The occupancy of lying cubicles was also influenced by the heat load. The data could be helpful to enable evaluation with algorithms for early detection of heat load

Influence of barn climate, body postures and milk yield on the respiration rate of dairy cows

The main objective of this study was to identify the influences of different climatic conditions and cow-related factors on the respiration rate (RR) of lactating dairy cows. Measurements were performed on 84 lactating Holstein Friesian dairy cows (first to eighth lactation) in Brandenburg, Germany. The RR was measured hourly or twice a day with up to three randomly chosen measurement days per week between 0700 h and 1500 h (GMT + 0100 h) by counting right thoracoabdominal movements of the cows. Simultaneously with RR measurements, cow body postures (standing vs. lying) were documented. Cows’ milk yield and days in milk were recorded daily. The ambient temperature and relative humidity of the barn were recorded every 5 min to calculate the current temperature-humidity index (THI). The data were analyzed for interactions between THI and cow-related factors (body postures and daily milk yield) on RR using a repeated measurement linear mixed model. There was a significant effect of the interaction between current THI category and body postures on RR. The RRs of cows in lying posture in the THI < 68, 68 ≤ THI < 72 and 72 ≤ THI < 80 categories (37, 46 and 53 breaths per minute (bpm), respectively) were greater than those of standing cows in the same THI categories (30, 38 and 45 bpm, respectively). For each additional kilogram of milk produced daily, an increase of 0.23±0.19 bpm in RR was observed. Including cow-related factors may help to prevent uncertainties of RR in heat stress predictions. In practical application, these factors should be included when predicting RR to evaluate heat stress on dairy farm

Influence of Preprocessing Methods of Automated Milking Systems Data on Prediction of Mastitis with Machine Learning Models

Missing data and class imbalance hinder the accurate prediction of rare events such as dairy mastitis. Resampling and imputation are employed to handle these problems. These methods are often used arbitrarily, despite their profound impact on prediction due to changes caused to the data structure. We hypothesize that their use affects the performance of ML models fitted to automated milking systems (AMSs) data for mastitis prediction. We compare three imputations—simple imputer (SI), multiple imputer (MICE) and linear interpolation (LI)—and three resampling techniques: Synthetic Minority Oversampling Technique (SMOTE), Support Vector Machine SMOTE (SVMSMOTE) and SMOTE with Edited Nearest Neighbors (SMOTEEN). The classifiers were logistic regression (LR), multilayer perceptron (MLP), decision tree (DT) and random forest (RF). We evaluated them with various metrics and compared models with the kappa score. A complete case analysis fitted the RF (0.78) better than other models, for which SI performed best. The DT, RF, and MLP performed better with SVMSMOTE. The RF, DT and MLP had the overall best performance, contributed by imputation or resampling (SMOTE and SVMSMOTE). We recommend carefully selecting resampling and imputation techniques and comparing them with complete cases before deciding on the preprocessing approach used to test AMS data with ML models

Assessment of the biochemical methane potential of in-house and outdoor stored pig and dairy cow manure by evaluating chemical composition and storage conditions

Biogas production is a suitable option for producing energy from dairy and pig manure types. During manure storage, organic matter degradation results in methane emissions decreasing the potential biogas yield. The present research advances the understanding of the biochemical methane potential (BMP) and the chemical characteristics of manure collected year-round from sequential stages of the liquid manure management chain of commercial dairy cow and pig farms. To this end, manure samples from six livestock farms in Germany were analyzed. The results showed that changes in chemical composition during storage led to a 20.5% decrease in the BMP of dairy manure from the barn to outdoor storage. For fattening pig manure samples, there was a 39.5% decrease in the BMP from intermediate to outdoor storage. An analysis of BMP according to manure age showed that pig manure degrades faster than dairy manure; the importance of promptly feeding manure to the biogas plant in order to avoid significant CH4 emission losses and reduction in energy producing capacity was highlighted. The best BMP predictors for dairy manure were the contents of dry matter, volatile solids and lignin, whereas best BMP predictors for pig manure were dry matter and volatile fatty acid (VFA) content. Prediction models performed well for samples from outdoor storages; refinements for predicting BMP of less aged samples presenting lower chemical variability would be necessary

Methane emissions from the storage of liquid dairy manure: Influences of season, temperature and storage duration

Methane emissions from livestock manure are primary contributors to GHG emissions from agriculture and options for their mitigation must be found. This paper presents the results of a study on methane emissions from stored liquid dairy cow manure during summer and winter storage periods. Manure from the summer and winter season was stored under controlled conditions in barrels at ambient temperature to simulate manure storage conditions. Methane emissions from the manure samples from the winter season were measured in two time periods: 0 to 69 and 0 to 139 days. For the summer storage period, the experiments covered four time periods: from 0 to 70, 0 to 138, 0 to 209, and 0 to 279 continuous days, with probing every 10 weeks. Additionally, at the end of all storage experiments, samples were placed into eudiometer batch digesters, and their methane emissions were measured at 20 degrees C for another 60 days to investigate the potential effect of the aging of the liquid manure on its methane emissions. The experiment showed that the methane emissions from manure stored in summer were considerably higher than those from manure stored in winter. CH4 production started after approximately one month, reaching values of 0.061 kg CH4 kg(-1) Volatile Solid (VS) and achieving high total emissions of 0.148 kg CH4 kg(-1) VS (40 weeks). In winter, the highest emissions level was 0.0011 kg CH4 kg(-1) VS (20 weeks). The out comes of these experimental measurements can be used to suggest strategies for mitigating methane emissions from manure storage

Methane Emissions from Livestock Slurry: Effects of Storage Temperature and Changes in Chemical Composition

Livestock production contributes to releasing methane into the atmosphere. Liquid manure management offers significant opportunities to reduce these emissions. A better understanding of the factors controlling methane emissions from manure is necessary to select effective mitigation strategies. Our study aimed to identify the influence of storage temperature and the associated change in chemical composition on methane emissions from dairy and fattening pig manure. Storage temperature affects microbial activity and induces changes in chemical composition that are key influences in methane emissions. Dairy and fattening pig manure samples were stored at five different temperatures (5–25 °C) for 90 days in a laboratory-scale experiment to measure the methane production. The chemical composition of the slurry samples was analyzed, and the biochemical methane potential (BMP) tests were performed before and after storage. For pig manure stored at 25 °C and 20 °C, methane emissions accounted for 69.3% and 50.3% of the BMP, respectively. Maximum methane emissions for dairy slurry were observed at 25 °C but remained at a low level. Analyses of the accumulation of volatile fatty acids (VFAs) during storage are presented in few studies, this work revealed a potential inhibition of methane production, where the accumulation of VFAs was most elevated in samples stored at 20 °C and 25 °C. This partly counteracted the increase in methane emissions expected from the higher temperatures. The degree of VFA and dissociated fatty acids accumulation in dairy cattle slurry should be assessed for more accurate estimations of methane emissions from slurry stores

Evaluating the potential of dietary crude protein manipulation in reducing ammonia emissions from cattle and pig manure: A meta-analysis

Dietary manipulation of animal diets by reducing crude protein (CP) intake is a strategic NH3 abatement option as it reduces the overall nitrogen input at the very beginning of the manure management chain. This study presents a comprehensive meta-analysis of scientific literature on NH3 reductions following a reduction of CP in cattle and pig diets. Results indicate higher mean NH3 reductions of 17 ± 6% per %-point CP reduction for cattle as compared to 11 ± 6% for pigs. Variability in NH3 emission reduction estimates reported for different manure management stages and pig categories did not indicate a significant influence. Statistically significant relationships exist between CP reduction, NH3 emissions and total ammoniacal nitrogen content in manure for both pigs and cattle, with cattle revealing higher NH3 reductions and a clearer trend in relationships. This is attributed to the greater attention given to feed optimization in pigs relative to cattle and also due to the specific physiology of ruminants to efficiently recycle nitrogen in situations of low protein intake. The higher NH3 reductions in cattle highlights the opportunity to extend concepts of feed optimization from pigs and poultry to cattle production systems to further reduce NH3 emissions from livestock manure. The results presented help to accurately quantify the effects of NH3 abatement following reduced CP levels in animal diets distinguishing between animal types and other physiological factors. This is useful in the development of emission factors associated with reduced CP as an NH3 abatement option. © 2017, The Author(s)