Räumliche Vorhersage des Befallsrisikos an Winterweizen durch die Erreger Blumeria graminis f. sp. tritici (Echter Mehltau) und Puccinia triticina (Braunrost) in Schleswig-Holstein mittels maschineller Lernverfahren

Wheat is one of the most important cereals in the world. Phytopathogens such as powdery mildew or brown rust can considerably reduce wheat yields. By treatment with fungicides, infections with these pathogens can be contained. It is of decisive importance to be informed about upcoming dangerous infestation events in real time to be able to respond to them. To define which infestation events are to be classified as yield-relevant, this work uses the damage threshold concept. This concept assumes that the exceedance of a 70 % threshold value for powdery mildew and of a 30 % threshold value for brown rust of infected plants in a field would threaten the yield of the complete stock of winter wheat and, thus, suggests the application of fungicides. The main objective of this thesis is the spatial prediction of the probability of exceedance above this damage threshold. In order to achieve this goal, a concept was developed that regionalises the hourly weather data on a daily basis and subsequently uses these data as input parameters for predicting the pathogen-specific behaviour. Besides, the modelling concept uses supervised machine learning techniques to generate models based on these aggregated weather data, regionalised climate data and manually collected infestation data. The following learning methods are used to predict the occurrence of infestation spatially: k-Nearest Neighbor, Decision Trees, Boosted Decision Trees and Random Forests. The concept was examined iteratively using various evaluation methods, and thus the pathogen-specific performance of the models was tested concerning the prediction of the probability of infestation. The model results generated with the machine learning methods were then integrated into a web-based prediction system, which provides interested users with the probability of dangerous infestations.Weizen ist eines der bedeutendsten Getreide der Welt. Phytopathogene wie der Echte Mehltau oder der Braunrost können den Weizenertrag deutlich reduzieren. Durch Behandlung mit Fungiziden können Befälle mit diesen Erregern eingedämmt werden. Von entsprechender Bedeutung ist es, bevorstehende gefährliche Befallsereignisse vorherzusagen, um auf diese reagieren zu können. Um festzulegen, welche Befallsereignisse als ertragsrelevant zu klassifizieren sind, verwendet diese Arbeit das Schadschwellenkonzept nach \citet{Klink1997 . Entsprechend dieses Konzeptes, gehen von mehr als 70 % mit dem Echten Mehltau oder von mehr als 30 % mit dem Braunrost befallenen Pflanzen eines Bestandes eine Gefährdung des Weizenertrages aus, welche mit Fungiziden abgewandt werden sollte. Das Hauptziel dieser Arbeit ist die flächenhafte Vorhersage der Wahrscheinlichkeit einer überschreitung dieser Schadschwelle. Um dieses Ziel zu erreichen, wurde ein Konzept entwickelt, welches die stündlichen Wetterdaten tagesaktuell interpoliert und diese mit dem erregerspezifischen Verhalten in Beziehung setzt. Darüber hinaus verwendet das Konzept überwachte maschinelle Lernverfahren, um Modelle zu generieren, die auf diesen oben genannten Wetterdaten, den regionalisierten langfristigen Klimadaten und manuell erfassten Befallsdaten basieren. Zur räumlichen Vorhersage der Befallsereignisse werden dabei folgende Lernverfahren eingesetzt: k-Nearest Neighbor, Decision Trees, Boosted Decision Trees und Random Forests. Das Konzept wurde unter Verwendung verschiedener Evaluierungsverfahren iterativ überprüft und somit die erregerspezifische Performanz der Modelle hinsichtlich der Vorhersage der Befallswahrscheinlichkeit getestet. Die mit den maschinellen Lernverfahren erstellten Modellergebnisse wurden im Anschluss in ein webbasiertes Vorhersagesystem eingebunden, welches interessierten Nutzern die Wahrscheinlichkeit gefährlicher Befälle bereitstellt

Soil Penetration Resistance after One-Time Inversion Tillage: A Spatio-Temporal Analysis at the Field Scale

Conservation agriculture may lead to increased penetration resistance due to soil compaction. To loosen the topsoil and lower the compaction, one-time inversion tillage (OTIT) is a measure frequently used in conservation agriculture. However, the duration of the positive effects of this measure on penetration resistance is sparsely known. Therefore, the aim of this study was to analyze the spatio-temporal behavior of penetration resistance after OTIT as an indicator for soil compaction. A field subdivided into three differently tilled plots (conventional tillage with moldboard plough to 30 cm depth (CT), reduced tillage with chisel plough to 25 cm depth (RT1) and reduced tillage with disk harrow to 10 cm depth (RT2)) served as study area. In 2014, the entire field was tilled by moldboard plough and penetration resistance was recorded in the following 5 years. The results showed that OTIT reduced the penetration resistance in both RT-plots and led to an approximation in all three plots. However, after 18 (RT2) and 30 months (RT1), the differences in penetration resistance were higher (p < 0.01) in both RT-plots compared to CT. Consequently, OTIT can effectively remove the compacted layer developed in conservation agriculture. However, the lasting effect seems to be relatively shor

Spatio-Temporal Prediction of the Epidemic Spread of Dangerous Pathogens Using Machine Learning Methods

Real-time identification of the occurrence of dangerous pathogens is of crucial importance for the rapid execution of countermeasures. For this purpose, spatial and temporal predictions of the spread of such pathogens are indispensable. The R package papros developed by the authors offers an environment in which both spatial and temporal predictions can be made, based on local data using various deterministic, geostatistical regionalisation, and machine learning methods. The approach is presented using the example of a crops infection by fungal pathogens, which can substantially reduce the yield if not treated in good time. The situation is made more difficult by the fact that it is particularly difficult to predict the behaviour of wind-dispersed pathogens, such as powdery mildew (Blumeria graminis f. sp. tritici). To forecast pathogen development and spatial dispersal, a modelling process scheme was developed using the aforementioned R package, which combines regionalisation and machine learning techniques. It enables the prediction of the probability of yield- relevant infestation events for an entire federal state in northern Germany at a daily time scale. To run the models, weather and climate information are required, as is knowledge of the pathogen biology. Once fitted to the pathogen, only weather and climate information are necessary to predict such events, with an overall accuracy of 68% in the case of powdery mildew at a regional scale. Thereby, 91% of the observed powdery mildew events are predicted

Location Modeling of Final Palaeolithic Sites in Northern Germany

Location modeling, both inductive and deductive, is widely used in archaeology to predict or investigate the spatial distribution of sites. The commonality among these approaches is their consideration of only spatial effects of the first order (i.e., the interaction of the locations with the site characteristics). Second-order effects (i.e., the interaction of locations with each other) are rarely considered. We introduce a deductive approach to investigating such second-order effects using linguistic hypotheses about settling behavior in the Final Palaeolithic. A Poisson process was used to simulate a point distribution using expert knowledge of two distinct hunter–gatherer groups, namely, reindeer hunters and elk hunters. The modeled points and point densities were compared with the actual finds. The G-, F-, and K-function, which allow for the identification of second-order effects of varying intensity for different periods, were applied. The results reveal differences between the two investigated groups, with the reindeer hunters showing location-related interaction patterns, indicating a spatial memory of the preferred locations over an extended period of time. Overall, this paper shows that second-order effects occur in the geographical modeling of archaeological finds and should be taken into account by using approaches such as the one presented in this paper

Transforming landscapes: Modeling land-use patterns of environmental borderlands

How did socio-cultural transformation processes change land-use patterns? Throughout the last 50 years, outstanding comprehensive geographic, archaeobiological, and archaeological data have been produced for the area of Oldenburger Graben, Schleswig-Holstein, Germany. Based on this exceptional data set, we are able to study the land-use patterns for a period ranging from the Final Mesolithic until the Late Neolithic (4600–1700 BCE). By application of fuzzy modeling techniques, these patterns are investigated diachronically in order to assess the scale of transformations between the different archaeological phases. Based on nutrient requirements and proposed dietary composition estimates derived from empirical archaeobotanical, archaeozoological, and stable isotope data, the required extent of the areas for different land-use practices are modeled. This information is made spatially explicit using a fuzzy model that reconstructs areas of potential vegetation and land-use for each transformation phase. Pollen data are used to validate the type and extent of land-use categories. The model results are used to test hypotheses on the dynamics of socio-cultural transformations: can we observe a diversification of land-use patterns over time or does continuity of land-use practices prevail? By integrating the different lines of evidence within a spatially explicit modeling approach, we reach a new quality of data analysis with a high degree of contextualization. This allows testing of hypotheses about Neolithic transformation processes by an explicit adjustment of our model assumptions, variables, and parameters

Modelling landscape transformation at the Chalcolithic Tripolye mega-site of Maidanetske (Ukraine): Wood demand and availability

Wood was a crucial resource for prehistoric societies, for instance, as timber for house construction and as fuel. In the case of the exceptionally large Chalcolithic Tripolye ‘mega-sites’ in central Ukraine, thousands of burnt buildings, indicating huge population agglomerations, hint at such a massive use of wood that it raises questions about the carrying capacity of the sensitive forest-steppe environment. In this contribution, we investigate the wood demand for the mega-site of Maidanetske (3990–3640 BCE), as reconstructed based on wood charcoal data, wood imprints on daub and the archaeomagnetometry-based settlement plan. We developed a regional-scale model with a fuzzy approach and applied it in order to simulate the potential distribution and extent of woodlands before and after Chalcolithic occupation. The model is based upon the reconstructed ancient land surface, soil information derived from cores and the potential natural woodland cover reconstructed based on the requirements of the prevailing ancient tree species. Landscape scenarios derived from the model are contrasted and cross-checked with the archaeological empirical data. We aim to understand whether the demand for wood triggered the site development. Did deforestation and consequent soil degradation and lack of resources initiate the site’s abandonment? Or, alternatively, did the inhabitants develop sustainable woodland management strategies? Starting from the case study of Maidanetske, this study provides estimates of the extent of human impact on both carrying capacity and landscape transformations in the sensitive transitional foreststeppe environment. Overall, the results indicate that the inhabitants of the Chalcolithic site did not suffer from a significant shortage in the wood resource at any time of inhabitation in the contexts of the different scenarios provided by the model. An exception is given by the phase of maximum house construction and population within a scenario of dry climatic conditions

Compositional diversity of rehabilitated tropical lands supports multiple ecosystem services and buffers uncertainties

High landscape diversity is assumed to increase the number and level of ecosystem services. However, the interactions between ecosystem service provision, disturbance and landscape composition are poorly understood. Here we present a novel approach to include uncertainty in the optimization of land allocation for improving the provision of multiple ecosystem services. We refer to the rehabilitation of abandoned agricultural lands in Ecuador including two types of both afforestation and pasture rehabilitation, together with a succession option. Our results show that high compositional landscape diversity supports multiple ecosystem services (multifunction effect). This implicitly provides a buffer against uncertainty. Our work shows that active integration of uncertainty is only important when optimizing single or highly correlated ecosystem services and that the multifunction effect on landscape diversity is stronger than the uncertainty effect. This is an important insight to support a land-use planning based on ecosystem services

Compositional diversity of rehabilitated tropical lands supports multiple ecosystem services and buffers uncertainties

High landscape diversity is assumed to increase the number and level of ecosystem services. However, the interactions between ecosystem service provision, disturbance and landscape composition are poorly understood. Here we present a novel approach to include uncertainty in the optimization of land allocation for improving the provision of multiple ecosystem services. We refer to the rehabilitation of abandoned agricultural lands in Ecuador including two types of both afforestation and pasture rehabilitation, together with a succession option. Our results show that high compositional landscape diversity supports multiple ecosystem services (multifunction effect). This implicitly provides a buffer against uncertainty. Our work shows that active integration of uncertainty is only important when optimizing single or highly correlated ecosystem services and that the multifunction effect on landscape diversity is stronger than the uncertainty effect. This is an important insight to support a land-use planning based on ecosystem services