@article{Lutz2016,
  author    = {Lutz, Johannes},
  title     = {The Validity of Crowdsourcing Data in Studying Anger and Aggressive Behavior A Comparison of Online and Laboratory Data},
  series = {Social psychology},
  volume    = {47},
  journal   = {Social psychology},
  publisher = {Taylor \& Francis Group},
  address   = {G{\"o}ttingen},
  issn      = {1864-9335},
  doi       = {10.1027/1864-9335/a000256},
  pages     = {38 -- 51},
  year      = {2016},
  abstract  = {Crowdsourcing platforms provide an affordable approach for recruiting large and diverse samples in a short time. Past research has shown that researchers can obtain reliable data from these sources, at least in domains of research that are not affectively involving. The goal of the present study was to test if crowdsourcing platforms can also be used to conduct experiments that incorporate the induction of aversive affective states. First, a laboratory experiment with German university students was conducted in which a frustrating task induced anger and aggressive behavior. This experiment was then replicated online using five crowdsourcing samples. The results suggest that participants in the online samples reacted very similarly to the anger manipulation as participants in the laboratory experiments. However, effect sizes were smaller in crowdsourcing samples with non-German participants while a crowdsourcing sample with exclusively German participants yielded virtually the same effect size as in the laboratory.},
  language  = {en}
}
@article{HampfNendelStreyetal.2021,
  author    = {Hampf, Anna and Nendel, Claas and Strey, Simone and Strey, Robert},
  title     = {Biotic yield losses in the Southern Amazon, Brazil},
  series = {Frontiers in plant science : FPLS},
  volume    = {12},
  journal   = {Frontiers in plant science : FPLS},
  publisher = {Frontiers Media},
  address   = {Lausanne},
  issn      = {1664-462X},
  doi       = {10.3389/fpls.2021.621168},
  pages     = {16},
  year      = {2021},
  abstract  = {Pathogens and animal pests (P\&A) are a major threat to global food security as they directly affect the quantity and quality of food. The Southern Amazon, Brazil's largest domestic region for soybean, maize and cotton production, is particularly vulnerable to the outbreak of P\&A due to its (sub)tropical climate and intensive farming systems. However, little is known about the spatial distribution of P\&A and the related yield losses. Machine learning approaches for the automated recognition of plant diseases can help to overcome this research gap. The main objectives of this study are to (1) evaluate the performance of Convolutional Neural Networks (ConvNets) in classifying P\&A, (2) map the spatial distribution of P\&A in the Southern Amazon, and (3) quantify perceived yield and economic losses for the main soybean and maize P\&A. The objectives were addressed by making use of data collected with the smartphone application Plantix. The core of the app's functioning is the automated recognition of plant diseases via ConvNets. Data on expected yield losses were gathered through a short survey included in an "expert" version of the application, which was distributed among agronomists. Between 2016 and 2020, Plantix users collected approximately 78,000 georeferenced P\&A images in the Southern Amazon. The study results indicate a high performance of the trained ConvNets in classifying 420 different crop-disease combinations. Spatial distribution maps and expert-based yield loss estimates indicate that maize rust, bacterial stalk rot and the fall armyworm are among the most severe maize P\&A, whereas soybean is mainly affected by P\&A like anthracnose, downy mildew, frogeye leaf spot, stink bugs and brown spot. Perceived soybean and maize yield losses amount to 12 and 16\%, respectively, resulting in annual yield losses of approximately 3.75 million tonnes for each crop and economic losses of US\$2 billion for both crops together. The high level of accuracy of the trained ConvNets, when paired with widespread use from following a citizen-science approach, results in a data source that will shed new light on yield loss estimates, e.g., for the analysis of yield gaps and the development of measures to minimise them.},
  language  = {en}
}