Global patterns of cropland use intensity

This study presents a global scale analysis of cropping intensity, crop duration and fallow land extent computed by using the global dataset on monthly irrigated and rainfed crop areas MIRCA2000. MIRCA2000 was mainly derived from census data and crop calendars from literature. Global cropland extent was 16 million km2 around the year 2000 of which 4.4 million km2 (28%) was fallow, resulting in an average cropping intensity of 0.82 for total cropland extent and of 1.13 when excluding fallow land. The lowest cropping intensities related to total cropland extent were found for Southern Africa (0.45), Central America (0.49) and Middle Africa (0.54), while highest cropping intensities were computed for Eastern Asia (1.04) and Southern Asia (1.0). In remote or arid regions where shifting cultivation is practiced, fallow periods last 3–10 years or even longer. In contrast, crops are harvested two or more times per year in highly populated, often irrigated tropical or subtropical lowlands where multi-cropping systems are common. This indicates that intensification of agricultural land use is a strategy that may be able to significantly improve global food security. There exist large uncertainties regarding extent of cropland, harvested crop area and therefore cropping intensity at larger scales. Satellite imagery and remote sensing techniques provide opportunities for decreasing these uncertainties and to improve the MIRCA2000 inventory

Global dataset of monthly growing areas of 26 irrigated crops : version 1.0

A data set of monthly growing areas of 26 irrigated crops (MGAG-I) and related crop calendars (CC-I) was compiled for 402 spatial entities. The selection of the crops consisted of all major food crops including regionally important ones (wheat, rice, maize, barley, rye, millet, sorghum, soybeans, sunflower, potatoes, cassava, sugar cane, sugar beets, oil palm, rapeseed/canola, groundnuts/peanuts, pulses, citrus, date palm, grapes/vine, cocoa, coffee), major water-consuming crops (cotton), and unspecified other crops (other perennial crops, other annual crops, managed grassland). The data set refers to the time period 1998-2002 and has a spatial resolution of 5 arc minutes by 5 arc minutes which is 8 km by 8 km at the equator. This is the first time that a data set of cell-specific irrigated growing areas of irrigated crops with this spatial resolution was created. The data set is consistent to the irrigated area and water use statistics of the AQUASTAT programme of the Food and Agriculture Organization of the United Nations (FAO) (http://www.fao.org/ag/agl/aglw/aquastat/main/index.stm) and the Global Map of Irrigation Areas (GMIA) (http://www.fao.org/ag/agl/aglw/aquastat/irrigationmap/index.stm). At the cell-level it was tried to maximise consistency to the cropland extent and cropland harvested area from the Department of Geography and Earth System Science Program of the McGill University at Montreal, Quebec, Canada and the Center for Sustainability and the Global Environment (SAGE) of the University of Wisconsin at Madison, USA (http://www.geog.mcgill.ca/~nramankutty/ Datasets/Datasets.html and http://geomatics.geog.mcgill.ca/~navin/pub/Data/175crops2000/). The consistency between the grid product and the input data was quantified. MGAG-I and CC-I are fully consistent to each other on entity level. For input data other than CC-I, the consistency of MGAG-I on cell level was calculated. The consistency of MGAG-I with respect to the area equipped for irrigation (AEI) of GMIA and to the cropland extent of SAGE was characterised by the sum of the cell-specific maximum difference between the MGAG-I monthly total irrigated area and the reference area when the latter was exceeded in the grid cell. The consistency of the harvested area contained in MGAG-I with respect to SAGE harvested area was characterised by the crop-specific sum of the cell-specific difference between MGAG-I harvested area and the SAGE harvested area when the latter was exceeded in the grid cell. In all three cases, the sums are the excess areas that should not have been distributed under the assumption that the input data were correct. Globally, this cell-level excess of MGAG-I as compared to AEI is 331,304 ha or only about 0.12 % of the global AEI of 278.9 Mha found in the original grid. The respective cell-level excess of MGAG-I as compared to the SAGE cropland extent is 32.2 Mha, corresponding to about 2.2 % of the total cropland area. The respective cell-level excess of MGAG-I as compared to the SAGE harvested area is 27 % of the irrigated harvested area, or 11.5 % of the AEI. In a further step that will be published later also rainfed areas were compiled in order to form the Global data set of monthly irrigated and rainfed crop areas around the year 2000 (MIRCA2000). The data set can be used for global and continental-scale studies on food security and water use. In the future, it will be improved, e.g. with a better spatial resolution of crop calendars and an improved crop distribution algorithm. The MIRCA2000 data set, its full documentation together with future updates will be freely available through the following long-term internet site: http://www.geo.uni-frankfurt.de/ipg/ag/dl/forschung/MIRCA/index.html. The research presented here was funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) within the framework of the research project entitled "Consistent assessment of global green, blue and virtual water fluxes in the context of food production: regional stresses and worldwide teleconnections". The authors thank Navin Ramankutty and Chad Monfreda for making available the current SAGE datasets on cropland extent (Ramankutty et al., 2008) and harvested area (Monfreda et al., 2008) prior to their publication

Detecting motorcycle helmet use with deep learning

The continuous motorization of traffic has led to a sustained increase in the global number of road related fatalities and injuries. To counter this, governments are focusing on enforcing safe and law-abiding behavior in traffic. However, especially in developing countries where the motorcycle is the main form of transportation, there is a lack of comprehensive data on the safety-critical behavioral metric of motorcycle helmet use. This lack of data prohibits targeted enforcement and education campaigns which are crucial for injury prevention. Hence, we have developed an algorithm for the automated registration of motorcycle helmet usage from video data, using a deep learning approach. Based on 91,000 annotated frames of video data, collected at multiple observation sites in 7 cities across the country of Myanmar, we trained our algorithm to detect active motorcycles, the number and position of riders on the motorcycle, as well as their helmet use. An analysis of the algorithm's accuracy on an annotated test data set, and a comparison to available human-registered helmet use data reveals a high accuracy of our approach. Our algorithm registers motorcycle helmet use rates with an accuracy of −4.4% and +2.1% in comparison to a human observer, with minimal training for individual observation sites. Without observation site specific training, the accuracy of helmet use detection decreases slightly, depending on a number of factors. Our approach can be implemented in existing roadside traffic surveillance infrastructure and can facilitate targeted data-driven injury prevention campaigns with real-time speed. Implications of the proposed method, as well as measures that can further improve detection accuracy are discussed

How speed and visibility influence preferred headway distances in highly automated driving

While the introduction of highly automated vehicles promises lower accident numbers, a main requirement for wide use of these vehicles will be the acceptance by drivers. In this study a crucial variable for the acceptance of highly automated vehicles, the vehicle to vehicle distance expressed in time headway, was researched in a driving simulator. Research has shown that time headway distances, perceived as comfortable in self-driving and assisted driving with adaptive cruise control, remain constant over a range of different speeds. This study aims to test these findings for highly automated driving. Since time headway is perceived visually, the driving situation was varied to investigate the influence of visibility on the subjective comfort of the driver in a highly automated driving situation. In a within-subject design, drivers followed a passenger car in clear weather conditions, the same passenger car in fog which occluded parts of the traffic environment, as well as a truck that occluded the lane ahead, also in clear weather condition. Subjective comfort of drivers in each condition was rated with a haptic rating lever. Results suggest that comfortable time headway following distances in highly automated driving are not constant over different speeds, but that these distances decrease with increasing speed. Reduced visibility generally led to a shift in comfortable following distances towards larger headways. These results have implications for the introduction of highly automated vehicles and their time headway adjustments, which will need to be adaptive to speed and visibility in the road environment

Alternative = transformative? Investigating drivers of transformation in alternative food networks in Germany

In the hope for more sustainable agriculture and a stronger connection to their food, an increasing number of consumers participate in alternative food networks (AFNs) characterised by short food supply chains. However, it cannot be assumed that AFNs inherently transform the prevailing system and its respective practices around food. Thus, we apply a social innovation perspective to enable a comprehensive analysis of changed values, social practices and relations in AFNs. This article presents whether drivers of transformation occur in three different AFN models (Community Supported Agriculture (CSA), food co‐operatives (food co‐ops) and self‐harvest gardens) and how participants describe and perceive them in each model. Therefore, we conducted interviews with AFN producers and consumers, before applying qualitative content analysis. Interviewees describe a broad variety of transformed values, practices and relations: Especially CSA and food co‐ops bear transformative potential as their members report a strong reconnection of producers and consumers expressed through social interaction and community‐building. Self‐harvest gardeners predominantly seek individual capacity building and to have access to their own garden. We conclude that AFN participation fosters incremental transformation towards more sustainable practices around food and a respective value system, which can be a part of a bigger movement aimed at food system transformation.Federal Ministry of Education and Research http://dx.doi.org/10.13039/501100002347Peer Reviewe

The influence of time headway on subjective driver states in adaptive cruise control

There is no agreement on the relation between driving parameters and drivers’ subjective states. A linear as well as a threshold relationship for different subjective variables and driving parameters has been put forward. In this study we investigate the relationship between time headway and the ratings of risk, task difficulty, effort, and comfort. Knowledge about this interrelation may advance the development of adaptive cruise control and autonomous driving and can add to the discussion about driver behavior models. An earlier study (Lewis-Evans, De Waard, & Brookhuis, 2010) found a threshold effect for drivers’ ratings of subjective variables for time headways between 0.5 and 4.0 s at a speed of 50 km/h. This study aims to replicate the threshold effect and to expand the findings to time headways at different speeds. A new measure for criticality was added as a categorical variable, indicating the controllability of a driving situation to give indications for the appliance of time headway in adaptive cruise control systems. Participants drove 24 short routes in a driving simulator with predefined speed and time headway to a leading vehicle. Time headway was varied eightfold (0.5–4 s in 0.5 s increments) and speed was varied threefold (50, 100, 150 km/h). A threshold effect for the ratings of risk, task difficulty, effort, and comfort was found for all three different speeds. Criticality proved to be a useful variable in assessing the preferred time headway of drivers

Groundwater use for irrigation - a global inventory

Irrigation is the most important water use sector accounting for about 70% of the global freshwater withdrawals and 90% of consumptive water uses. While the extent of irrigation and related water uses are reported in statistical databases or estimated by model simulations, information on the source of irrigation water is scarce and very scattered. Here we present a new global inventory on the extent of areas irrigated with groundwater, surface water or non-conventional sources, and we determine the related consumptive water uses. The inventory provides data for 15 038 national and sub-national administrative units. Irrigated area was provided by census-based statistics from international and national organizations. A global model was then applied to simulate consumptive water uses for irrigation by water source. Globally, area equipped for irrigation is currently about 301 million ha of which 38% are equipped for irrigation with groundwater. Total consumptive groundwater use for irrigation is estimated as 545 km3 yr−1, or 43% of the total consumptive irrigation water use of 1 277 km3 yr−1. The countries with the largest extent of areas equipped for irrigation with groundwater, in absolute terms, are India (39 million ha), China (19 million ha) and the United States of America (17 million ha). Groundwater use in irrigation is increasing both in absolute terms and in percentage of total irrigation, leading in places to concentrations of users exploiting groundwater storage at rates above groundwater recharge. Despite the uncertainties associated with statistical data available to track patterns and growth of groundwater use for irrigation, the inventory presented here is a major step towards a more informed assessment of agricultural water use and its consequences for the global water cycle

Introducing a multivariate model for predicting driving performance: The role of driving anger and personal characteristics

Introduction: Maladaptive driving is an important source of self-inflicted accidents and this driving style could include high speeds, speeding violations, and poor lateral control of the vehicle. The literature suggests that certain groups of drivers, such as novice drivers, males, highly motivated drivers, and those who frequently experience anger in traffic, tend to exhibit more maladaptive driving patterns compared to other drivers. Remarkably, no coherent framework is currently available to describe the relationships and distinct influences of these factors. Method: We conducted two studies with the aim of creating a multivariate model that combines the aforementioned factors, describes their relationships, and predicts driving performance more precisely. The studies employed different techniques to elicit emotion and different tracks designed to explore the driving behaviors of participants in potentially anger-provoking situations. Study 1 induced emotions with short film clips. Study 2 confronted the participants with potentially anger-inducing traffic situations during the simulated drive. Results: In both studies, participants who experienced high levels of anger drove faster and exhibited greater longitudinal and lateral acceleration. Furthermore, multiple linear regressions and path-models revealed that highly motivated male drivers displayed the same behavior independent of their emotional state. The results indicate that anger and specific risk characteristics lead to maladaptive changes in important driving parameters and that drivers with these specific risk factors are prone to experience more anger while driving, which further worsens their driving performance. Driver trainings and anger management courses will profit from these findings because they help to improve the validity of assessments of anger related driving behavior

Adjustable automation and manoeuvre control in automated driving

Current implementations of automated driving rely on the driver to monitor the vehicle and be ready to assume control in situations that the automation cannot successfully manage. However, research has shown that drivers are not able to monitor an automated vehicle for longer periods of time, as the monotonous monitoring task leads to attention reallocation or fatigue. Driver involvement in the automated driving task promises to counter this effect. The authors researched how the implementation of a haptic human–vehicle interface, which allows the driver to adjust driving parameters and initiate manoeuvres, influences the subjective experience of drivers in automated vehicles. In a simulator study, they varied the level of control that drivers have over the vehicle, between manual driving, automated driving without the possibility to adjust the automation, as well as automated driving with the possibility to initiate manoeuvres and adjust driving parameters of the vehicle. Results show that drivers have a higher level of perceived control and perceived level of responsibility when they have the ability to interact with the automated vehicle through the haptic interface. The authors conclude that the possibility to interact with automated vehicles can be beneficial for driver experience and safety

The Influence of Distance and Lateral Offset of Follow Me Robots on User Perception

Robots that are designed to work in close proximity to humans are required to move and act in a way that ensures social acceptance by their users. Hence, a robot's proximal behavior toward a human is a main concern, especially in human-robot interaction that relies on relatively close proximity. This study investigated how the distance and lateral offset of “Follow Me” robots influences how they are perceived by humans. To this end, a Follow Me robot was built and tested in a user study for a number of subjective variables. A total of 18 participants interacted with the robot, with the robot's lateral offset and distance varied in a within-subject design. After each interaction, participants were asked to rate the movement of the robot on the dimensions of comfort, expectancy conformity, human likeness, safety, trust, and unobtrusiveness. Results show that users generally prefer robot following distances in the social space, without a lateral offset. However, we found a main influence of affinity for technology, as those participants with a high affinity for technology preferred closer following distances than participants with low affinity for technology. The results of this study show the importance of user-adaptiveness in human-robot-interaction.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli