Hyperspectral field measurements to determine dry matter yield and nutritive value of legume-grass swards

Summary: Productivity, botanical composition and forage quality of legume-grass swards are important factors for successful arable farming in both organic and conventional farming systems. As these attributes can vary considerably within a field, a non-destructive method of detection while doing other tasks would facilitate a more targeted management of crops, forage and nutrients in the soil-plant-animal system. This study was undertaken to explore the potential of field spectral measurements for a non destructive prediction of dry matter (DM) yield, legume proportion in the sward, metabolizable energy (ME), ash content, crude protein (CP) and acid detergent fiber (ADF) of legume-grass mixtures. Two experiments were conducted in a greenhouse under controlled conditions which allowed collecting spectral measurements which were free from interferences such as wind, passing clouds and changing angles of solar irradiation. In a second step this initial investigation was evaluated in the field by a two year experiment with the same legume-grass swards. Several techniques for analysis of the hyperspectral data set were examined in this study: four vegetation indices (VIs): simple ratio (SR), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and red edge position (REP), two-waveband reflectance ratios, modified partial least squares (MPLS) regression and stepwise multiple linear regression (SMLR). The results showed the potential of field spectroscopy and proved its usefulness for the prediction of DM yield, ash content and CP across a wide range of legume proportion and growth stage. In all investigations prediction accuracy of DM yield, ash content and CP could be improved by legume-specific calibrations which included mixtures and pure swards of perennial ryegrass and of the respective legume species. The comparison between the greenhouse and the field experiments showed that the interaction between spectral reflectance and weather conditions as well as incidence angle of light interfered with an accurate determination of DM yield. Further research is hence needed to improve the validity of spectral measurements in the field. Furthermore, the developed models should be tested on varying sites and vegetation periods to enhance the robustness and portability of the models to other environmental conditions.

Heat stress response for physiological traits in dairy and dual purpose cattle populations on phenotypic and genetic scales

The main objective of this thesis was to determine the potential impact of heat stress (HS) on physiological traits of lactating cows and semen quality of bulls kept in a temperate climate. The thesis is comprised of three studies. An innovative statistical modeling aspect common to all three studies was the application of random regression methodology (RRM) to study the phenotypic and genetic trajectory of traits in dependency of a continuous temperature humidity index (THI). In the first study, semen quality and quantity traits of 562 Holstein sires kept on an AI station in northwestern Germany were analyzed in the course of THI calculated from data obtained from the nearest weather station. Heat stress was identified based on a decline in semen quality and quantity parameters. The identified general HS threshold (THI = 60) and the thermoneutal zone (THI in the range from 50 to 60) for semen production were lower than detected in studies conducted in tropical and subtropical climates. Even though adult bulls were characterized by higher semen productivity compared to younger bulls, they responded with a stronger semen production loss during harsh environments. Heritabilities (low to moderate range) and additive genetic variances of semen characteristics varied with different levels of THI. Also, based on genetic correlations genotype, by environment interactions were detected. Taken together, these findings suggest the application of specific selection strategies for specific climate conditions. In the second study, the effect of the continuous environmental descriptor THI as measured inside the barns on rectal temperatures (RT), skin temperatures (ST), vaginal temperatures (VT), respiration rates (RR), and pulse rate (PR) of lactating Holstein Friesian (HF) and dual-purpose German black pied cattle (DSN) was analyzed. Increasing HS from THI 65 (threshold) to THI 86 (maximal THI) resulted in an increase of RT by 0.6 °C (DSN) and 1 °C (HF), ST by 3.5 °C (HF) and 8 °C (DSN), VT by 0.3 °C (DSN), and RR by 47 breaths / minute (DSN), and decreased PR by 7 beats / minute (DSN). The undesired effects of rising THI on physiological traits were most pronounced for cows with high levels of milk yield and milk constituents, cows in early days in milk and later parities, and during summer seasons in the year 2014. In the third study of this dissertation, the genetic components of the cow’s physiological responses to HS were investigated. Heat stress was deduced from indoor THI measurements, and physiological traits were recorded on native DSN cows and their genetically upgraded crosses with Holstein Friesian sires in two experimental herds from pasture-based production systems reflecting a harsh environment of the northern part of Germany. Although heritabilities were in a low range (from 0.018 to 0.072), alterations of heritabilities, repeatabilities, and genetic components in the course of THI justify the implementation of genetic evaluations including heat stress components. However, low repeatabilities indicate the necessity of using repeated records for measuring physiological traits in German cattle. Moderate EBV correlations between different trait combinations indicate the potential of selection for one trait to simultaneously improve the other physiological attributes. In conclusion, bulls of AI centers and lactating cows suffer from HS during more extreme weather conditions also in the temperate climate of Northern Germany. Monitoring physiological traits during warm and humid conditions could provide precious information for detection of appropriate times for implementation of cooling systems and changes in feeding and management strategies. Subsequently, the inclusion of these physiological traits with THI specific breeding values into overall breeding goals could contribute to improving cattle adaptability by selecting the optimal animal for extreme hot and humid conditions. Furthermore, the recording of meteorological data in close distance to the cow and visualizing the surface body temperature by infrared thermography techniques might be helpful for recognizing heat tolerance and adaptability in cattle.

A probabilistic approach to the estimation of regional photovoltaic power generation using meteorological data

In Germany the upscaling algorithm is currently the standard approach for evaluating the PV power produced in a region. This method involves spatially interpolating the normalized power of a set of reference PV plants to estimate the power production by another set of unknown plants. As little information on the performances of this method could be found in the literature, the first goal of this thesis is to conduct an analysis of the uncertainty associated to this method. It was found that this method can lead to large errors when the set of reference plants has different characteristics or weather conditions than the set of unknown plants and when the set of reference plants is small. Based on these preliminary findings, an alternative method is proposed for calculating the aggregate power production of a set of PV plants. A probabilistic approach has been chosen by which a power production is calculated at each PV plant from corresponding weather data. The probabilistic approach consists of evaluating the power for each frequently occurring value of the parameters and estimating the most probable value by averaging these power values weighted by their frequency of occurrence. Most frequent parameter sets (e.g. module azimuth and tilt angle) and their frequency of occurrence have been assessed on the basis of a statistical analysis of parameters of approx. 35 000 PV plants. It has been found that the plant parameters are statistically dependent on the size and location of the PV plants. Accordingly, separate statistical values have been assessed for 14 classes of nominal capacity and 95 regions in Germany (two-digit zip-code areas). The performances of the upscaling and probabilistic approaches have been compared on the basis of 15 min power measurements from 715 PV plants provided by the German distribution system operator LEW Verteilnetz. It was found that the error of the probabilistic method is smaller than that of the upscaling method when the number of reference plants is sufficiently large (>100 reference plants in the case study considered in this chapter). When the number of reference plants is limited (<50 reference plants for the considered case study), it was found that the proposed approach provides a noticeable gain in accuracy with respect to the upscaling method.