1,720,962 research outputs found
Supporting decision-making in agricultural water management under data scarcity using global datasets – chances, limits and potential improvements
Full text linkAssessing alternative agricultural water management strategies requires long-term field trials or vast data collection for model calibration and simulation.This work aims to assess whether an uncalibrated agro-hydrological model using global input datasets for climate, soil and crop information can serve as a decision support tool for crop water management under data scarcity.This study employs the Cool Farm Tool Water (CFTW) at eight eddy covariance sites of the FLUXNET2015 dataset. CFTW is tested using global (CFTWglobal) and local (CFTWlocal) input datasets under current and alternative management scenarios.Results show that the use of global datasets for estimating daily evapotranspiration had little effect on the median Root Mean Square Error (RMSE) (CFTWglobal: 1.70 mm, CFTWlocal: 1.79 mm), while, however, the median model bias is much greater (CFTWglobal: − 18.6%, CFTWlocal: − 4.3%). Furthermore, the periods of water stress were little affected by the use of local or global data (median accuracy: 0.84), whereas the use of global data inputs led to a significant overestimation of irrigation water requirements (median difference:110 mm). The model performance improves predominantly through the use of more representative local precipitation data, followed by local reference evapotranspiration and soil for some European growing seasons.We identify model outputs that can support decision-making when relying on global data, such as periods of water stress and the daily dynamics of water use. However, our findings also emphasize the difficulty of overcoming data scarcity in decision-making in agricultural water management. Furthermore, we provide recommendations for enhancing model performance and thus may increase the accessibility of reliable decision support tools in the futur
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Modelling of nitrous oxide emissions from clover grass ley : wheat crop rotations in central eastern Germany : an application of DNDC
Full text linkNitrous oxide (N2O) is one of the three primary anthropogenic greenhouse gases. The agricultural sector accounts for 75.7 % of all anthropogenic N2O emissions in Germany (Umweltbundesamt, 2012). Thus, accurately estimating N2O emissions as well as mitigation strategies for N2O are crucial. This study optimizes the process-based model DNDC to simulate N2O emissions by conventional winter wheat and three different organic clover grass ley – wheat rotations at a site in central eastern Germany (Bad Lauchstädt: 51°24' N, 11°53' E ). The model simulates the soil environment (temperature, moisture, oxygen content etc.), plant growth and decomposition to determine nitrification, denitrification as well as fermentation. The central focus of this study is to assess the ability of DNDC to simulate N2O emissions in Bad Lauchstädt, followed by a comparison of the different crop rotations with respect to their N2O emissions based on weekly measurements and DNDC simulations. The study concludes with an investigation of emissions under future climate conditions. DNDC is able to reproduce monthly patterns of emissions in Bad Lauchstädt. Underlying processes such as plant growth and soil moisture are not represented with sufficient precision. The mean modelling efficiency (Nash Sutcliff Efficiency) of the validation runs for the monthly N2O fluxes is 0.136 and ranges from -0.526 to 0.446. Predicted daily and annual fluxes show a great offset compared to measured values. Emissions in Bad Lauchstädt are very low if compared to other observations in Germany and are primarily constrained by soil moisture and not by nitrogen availability. Neither the measurements nor the modelling results are able to resolve significant differences between the four crop rotations. According to the measurements, conventional winter wheat emits 836 g N ha-1 a-1, while the organic treatments release between 645 g N ha-1 a-1 and 1044 g N ha-1 a-1. DNDC simulates no significant change of N2O emissions under future climate conditions; this finding is not robust due to the abovementioned drawbacks of DNDC in this study. Improved estimates could be obtained by adjusting the ability of DNDC to capture the situation in Germany and in Bad Lauchstädt. Special attention should be given to the implementation of plant growth and evapotranspiration. Better comparison of treatments requires a longer measurement period and a higher temporal resolution, so that duration and height of peak emission events can be captured.In the media climate change is often linked to high emissions of carbon dioxide produced by industrial plants, plains or cars. However, the agricultural sector contributes significantly to the change of climate by emitting next to carbon dioxide also nitrous oxide. This has a 300 times greater effect on climate compared to carbon dioxide. Therefore, it is particularly important to reduce emissions of nitrous oxide and estimate emissions accurately. This study compares typical winter wheat crop rotations in organic and conventional farming in Bad Lauchstädt, central eastern Germany, based on measurements and the computer model DNDC. Organic and conventional farming differ with respect to fertilizer application, manure amendment and tilling practice and thus by the availability of nitrogen in the soil to form nitrous oxide. The main aim was to test the capability of the computer model to simulate emissions. Furthermore, this report compares different crop rotations and investigates emissions under future climate conditions. Overall the model showed a good performance when simulating monthly emissions, but had a great offset when compared to daily or annual observations. The central weakness of DNDC was its disability to simulate some of the main factors that control nitrous oxide emissions, such as soil moisture, plant growth and soil organic carbon. This study did not reveal differences between various organic and conventional farming practices. Nitrous oxide emissions in Bad Lauchstädt are mostly constrained by low precipitation and thus soil moisture, but not by the availability of nitrogen. The computer model does not show higher nitrous oxide emissions under future climate conditions. Due to drawbacks in DNDC this result is not reliable. Better simulations and comparison could be achieved in several ways. The computer model was built to be applicable all-around the globe. However, this adds great uncertainty. DNDC could be improved by adjusting some parts of it as the plant growth to better fit the conditions in Germany. More profound comparison of crop rotations could be attained by long-term series of measurements with a higher spatial and temporal resolution
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Bilanzierung und Entscheidungsfindung im landwirtschaftlichen Wassermanagement unter begrenzter Datenverfügbarkeit : Chancen, Grenzen und Verbesserungsmöglichkeiten
No full textAlleviating water scarcity and ending hunger are two Sustainable Development Goals that are inextricably linked, as 70% of water is used for agricultural production. However, assessing and identifying improved water management requires costly field trials or knowledge and data to run agro-hydrological models. This thesis addresses this challenge by integrating an agro-hydrological model with global soil, crop and climate datasets into a user-friendly, web-based agricultural water management assessment tool, the Cool Farm Tool Water (CFTW). This dissertation substantially contributes to the development and assessment of this modelling framework as well as identifies levers to enhance the model performance. Model results were compared to published scientific field trials, eddy covariance observations as well as Water Footprint Network data. Finally, the model framework was applied to five cereals across India between 2005 and 2014, representing a high spatial, inter- and intra-annual heterogeneity. The modelling framework explained 96% of the variability in seasonal water footprints and reduced the error of water footprints by 70% compared to state-level averages by the Water Footprint Network. However, CFTW captured only 50% of the variance in seasonal crop water use. CFTW enables the assessment of the daily dynamics of evapotranspiration, can reproduce daily crop water stress but overestimates irrigation requirements by an average of 40%. It helps identify relative improvements in agricultural water management but lacks the accuracy for absolute assessments. Using local input data reduced the bias from 18.6% to 4.3% for evapotranspiration. While results showed site- and season-specific differences, the greatest model improvement is replacing global precipitation data with local observations, reducing e.g. the bias by 70.6% for daily water use. Water footprints and water use of India revealed the importance of considering crop-, season-, year- and location-specific differences. Total Rabi (dry season) water footprints were between 33.4% and 45.0% lower than Kharif (monsoon) water footprints. However, the Rabi blue water footprints accounted for up to 78.3%. Overall, India increased cereal production by 26.4% without utilising additional water resources, mainly through increases in yield, partly by shifting production to the higher-yielding Rabi season. CFTW supports addressing water scarcity and food security at seasonal timescales and, to some extent, at daily timescales. Global datasets combined with an uncalibrated agro-hydrological model can inform improved agricultural water management by providing water footprints, water use and water stress. However, model outcomes also revealed considerable offsets. These results highlight the difficulties of overcoming data scarcity in decision support tools for agricultural water management. The rapid development of global datasets may help to overcome identified limitations in the future.Die Bekämpfung von Wasserknappheit und Hunger sind eng miteinander verknüpft, da 70% der globalen Wassernutzung auf die landwirtschaftliche Produktion entfallen. Die Bewertung und Identifizierung von Verbesserungsmöglichkeiten im landwirtschaftlichen Wassermanagement erfordern jedoch kostenintensive Feldversuche oder fundierte Kenntnisse sowie geeignete Daten für den Einsatz agrarhydrologischer Modelle.
Die vorliegende Arbeit greift dies auf, indem sie ein agrohydrologisches Model mit globalen Datensätzen zu Boden-, Pflanzen- und Klimainformationen in einem nutzerfreundlichen, webbasierten Tool, dem des Cool Farm Tool Water (CFTW), vereint. Diese Dissertation trägt maßgeblich zur Entwicklung und Bewertung des Modellansatzes bei und erarbeitet Möglichkeiten zur Optimierung. Die Resultate wurden mit Daten aus wissenschaftlichen Feldversuchen sowie Informationen des Water Footprint Network verglichen. Darüber hinaus analysiert diese Arbeit den Getreideanbau in Indien zwischen 2005 und 2014 mit Hilfe des CFTW.
Dieser Modellaufbau kann 96% der Variabilität der saisonalen Wasserfußabdrücke abbilden und reduziert den Fehler um 70% verglichen zu den Durchschnittswerten des Water Footprint Network. Jedoch konnte CFTW nur 50% der Varianz der saisonalen Wassernutzung erfassen. Das Modell ermöglicht die Simulation der täglichen Schwankungen der Evapotranspiration, unterschätzt jedoch die absoluten Werte. Relative Verbesserungen des landwirtschaftlichen Wassermanagements können identifiziert werden. Durch die Verwendung lokaler Inputdaten kann der Bias von 18,6% auf 4,3% reduziert werden. Darüber hinaus kann CFTW den täglichen Wasserstress der Nutzpflanzen erfassen, überschätzt jedoch den Bewässerungsbedarf um durchschnittlich 40%. Trotz orts- und saisonspezifischer Unterschiede stellt die Nutzung lokaler Niederschlagsdaten den größten Hebel zur Verbesserung der Modellergebnisse dar. Diese reduzieren den Bias der täglichen Wassernutzung um 70,6%.
Die Auswertung der Wasserfußabdrücke und der Wassernutzung in Indien zeigt die große Bedeutung kultur-, saison-, jahres- und standortspezifischer Auswertungen. So ist beispielhaft der durchschnittliche Gesamtwasserfußabdruck während der Trockenzeit zwischen 33,4% und 45,0% niedriger verglichen zur Monsunzeit, zeigt jedoch mit bis zu 78,3% eine größere Abhängigkeit vom blauen Wasser. Die Ergebnisse zeigen, dass die indische Getreideproduktion um 26,4% gestiegen ist, ohne zusätzliche Wasserressourcen zu nutzen. Dies ist im Wesentlichen auf Ertragssteigerungen der Getreidekulturen zurückzuführen.
Globale Datensätze in Kombination mit einem unkalibrierten agrarhydrologischen Modell liefern zuverlässige Bewertungen des saisonalen Wasserfußabdrucks und gute Resultate für den täglichen Wasserverbrauch und Wasserstress. Sie leisten einen wichtigen Beitrag zur Bewertung und Verbesserung des Wassermanagements. Dennoch zeigten einige Modellergebnisse erhebliche Abweichungen und verdeutlichen die große Herausforderung, die sich aus fehlenden Daten für Entscheidungsprozesse ergeben
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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