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Delineating the cropping area of cocoa agroforests is a major challenge in quantifying the contribution of land use expansion to tropical deforestation. Discriminating cocoa agroforests from tropical transition forests using multispectral optical images is difficult due to the similarity of the spectral characteristics of their canopies. Moreover, the frequent cloud cover in the tropics greatly impedes optical sensors. This study evaluated the potential of multiseason Sentinel-1 C-band synthetic aperture radar (SAR) imagery to discriminate cocoa agroforests from transition forests in a heterogeneous landscape in central Cameroon. We used an ensemble classifier, Random Forest (RF), to average the SAR image texture features of a grey level co-occurrence matrix (GLCM) across seasons. We then compared the classification performance with results from RapidEye optical data. Moreover, we assessed the performance of GLCM texture feature extraction at four different grey levels of quantization: 32 bits, 8 bits, 6 bits, and 4 bits. The classification&rsquo s overall accuracy (OA) from texture-based maps outperformed that from an optical image. The highest OA (88.8%) was recorded at the 6 bits grey level. This quantization level, in comparison to the initial 32 bits in the SAR images, reduced the class prediction error by 2.9%. The texture-based classification achieved an acceptable accuracy and revealed that cocoa agroforests have considerably fragmented the remnant transition forest patches. The Shannon entropy (H) or uncertainty provided a reliable validation of the class predictions and enabled inferences about discriminating inherently heterogeneous vegetation categories.

Scholars and experts in air transport generally assume the distance flown between airports is the shortest route (also known as the great-circle distance or the orthodromic route). Howev-er, in the real world, planes follow longer itineraries. This paper reviews the factors of de-tours, based on interviews with pilots and experts in air navigation. Factors relate to (1) technical constraints, (2) natural processes (including meteorological conditions) and obsta-cles, (3) geopolitical factors and (4) social factors, which are all explored in this paper. Their temporary vs. permanent and spatial impact (small vs. long detours) varies signifi-cantly among factors and among cases, as well as their avoidable nature. Appropriate poli-cies could lower detours. In addition, these results echo academic debates on the meaning of distance. info:eu-repo/semantics/published

This paper is the first to present panel data evidence on the longer-term impact of expansion of global value chains and large-scale export-oriented farms in developing countries. Using panel data from two survey rounds covering a seven-year period and fixed effects regression, we estimate the longer-term income effects of wage employment on large-scale farms in the rapidly expanding horticultural export sector in Senegal. In addition to estimating average income effects, we estimate heterogeneous income effects using fixed effects quantile regression. We find that poverty and inequality reduced much faster in the research area than elsewhere in Senegal. Employment in the horticultural export sector significantly increases household income and the income effect is strongest for the poorest households. Expansion of the horticultural export sector in Senegal has been particularly pro-poor through creating employment that is accessible and creates substantial income gains for the poorest half of the rural population. These pro-poor employment effects contrast with insights in the literature on increased inequality from rural wage employment.

Abstract Despite long-standing efforts in terracing, limited field-based evidence of its effectiveness as implemented within rural farming systems of humid tropical regions, such as Rwanda, is available. This study aimed to reveal regional differences in effectiveness of two widely used terracing techniques. Traditional slope farming (NP) was compared to bench (BT) and farmers’ based progressive terraces (PT) in terms of runoff, soil losses, and topsoil fertility in two contrasting agro-ecological zones, the Eastern Plateau (Murehe) and Buberuka Highlands (Tangata). During four consecutive rainy seasons, event-based data were collected using erosion plots (5 m width x 22.2 m length). Effectiveness indices of both terracing systems, as well as (R)USLE P-factor values, were calculated. The annual average soil losses under NP ranged from 4.71 ± 5.02 ton ha−1 to 46.01 ± 7.28 ton ha−1 in Murehe (14% slope gradient) and Tangata (43% slope gradient), respectively. Bench terracing clearly outperformed the farmer-based progressive terrace at both locations, leading to negligible soil losses. In terms of runoff reduction, an effectiveness of 70 and 85% respectively, was observed at Murehe and Tangata. The effectiveness of PT reached 52% for runoff control and 93% for soil loss control at Tangata, thereby confirming its huge potential as erosion control measure, even in mountainous areas. In the hilly landscape of Murehe, the runoff generated by PT – in some years - can exceed that under traditional farming, while the measure reduced soil losses by half on average. Associated USLE P-factors varied between seasons with an annual average values of 0.001–0.02 for BT, and 0.07 to 0.55 for PT at Tangata and Murehe, respectively. These variations in performance by site and terracing system also resulted in differences in topsoil chemical fertility, with BT generally outperforming both PT and NP at Tangata. At Murehe, PT showed a significantly lower chemical fertility compared to BT and NP. Poor quality risers explained the overall lower performance of PT at Murehe. The study thus confirmed the huge potential of (bench) terraces to sustainably reduce soil erosion rates when established within an integrated approach, paying attention to correct installation and fertility-supporting agronomic practices. More attention should be given to riser installation (e.g. distance) and maintenance of PT. Adoption of these erosion control measures can be recommended to similar agro-ecological zones for sustainably protecting the lands while mitigating or adapting the effects of climate change.

While the smart city gains global attention as a popular umbrella term for digitally enabled sustainable city development, city administrations are faced with the managerial challenge that comes with a strategic digital transformation. Smart city projects form the frontline of smart city strategies. In these smart projects, cities find a way to implement the principles of the smart city. Many of these are high-visibility projects with substantial budget implications, and therefore require scrutiny by means of a formal selection and evaluation process. In this research-in-progress paper we propose the outline for a project-level smart value assessment instrument. The instrument should serve at the same time as a tool for smart city managers to assess and plan upfront how a project will contribute to reach the city's smart city ambition, as well as a post factum evaluation. The conceptual instrument has been developed in action-design research mode in collaboration with practitioners in the city of Brisbane, Australia and is demonstrated by mapping four smart city initiatives in different international contexts.

AbstractUrbanization is one of the most extreme forms of environmental alteration, posing a major threat to biodiversity. We studied the effects of urbanization on avian communities via a systematic review using hierarchical and categorical meta‐analyses. Altogether, we found 42 observations from 37 case studies for species richness and 23 observations from 20 case studies for abundance. Urbanization had an overall strong negative effect on bird species richness, whereas abundance increased marginally with urbanization. There was no evidence that city size played a role in influencing the relationship between urbanization and either species richness or abundance. Studies that examined long gradients (i.e. from urban to rural) were more likely to detect negative urbanization effects on species richness than studies that considered short gradients (i.e. urban vs. suburban or urban vs. rural areas). In contrast, we found little evidence that the effect of urbanization on abundance was influenced by gradient length. Effects of urbanization on species richness were more negative for studies including public green spaces (parks and other amenity areas) in the sampled landscapes. In contrast, studies performed solely in the urban matrix (i.e. no green spaces) revealed a strong positive effect on bird abundance. When performing subset analyses on urban–suburban, suburban–rural and suburban–natural comparisons, species richness decreased from natural to urban areas, but with a stronger decrease at the urban–suburban interface, whereas bird abundance showed a clear intermediate peak along the urban–rural gradient although abundance in natural areas was comparable to that in suburban areas. This suggests that species loss happens especially at the urban–suburban interface, and that the highest abundances occur in suburban areas compared to urban or rural areas. Thus, our study shows the importance of suburban areas, where the majority of birds occur with fairly high species richness.

Abstract The aim of this study is to develop a method for continuous automated detection of aggressive behaviour among pigs by means of image processing. Five repetitions of the same experiment were performed. In each of the experiment, 24 piglets were mixed after weaning from four litters in two pens with 12 piglets each and captured on video for a total of 60 h. From these video recordings, a dataset containing 150 episodes with and 150 episodes without aggressive interactions was built through manual labelling. The Motion History Image was used to gain information about the pigs’ motion and to relate this information to aggressive interactions. Two features were extracted from the segmented region of the Motion History Image: the mean intensity of motion and the occupation index. Based on these two features, the Linear Discriminant Analysis was used to classify aggressive interactions in every episode. Applying leave-one-out cross-validation, the accuracy of the system was 89.0% with a sensitivity of 88.7% and a specificity of 89.3%. These results show that it is possible to use image analysis in order to automatically detect aggressive behaviours among pigs.

Abstract N is known to be the most limiting element for vegetation growth in temperate and boreal forests. The expected increases in global temperature are predicted to accelerate N mineralization, therefore incrementing N availability in the soil and affecting the soil C cycle as well. While there is an abundance of C data collected to fulfill the requirements for national GHG accounting, more limited information is available for soil N accumulation and storage in relation to forest categories and altitudinal gradients. The data collected by the second Italian National Forest Inventory, spanning a wide range of temperature and precipitation values (10° latitudinal range), represented a unique opportunity to calculate N content and C/N ratio of the different soil layers to a depth of 30 cm. Boosted Regression Tree (BRT) models were applied to investigate the main determinants of soil N distribution and C/N ratio. Forest category was shown to be the main explanatory factor of soil N variability in seven out of eight models, both for forest floor and mineral soil layers. Moreover latitude explained a larger share of variability than single climate variables. BRT models explained, on average, the 49% of the data variability, with the remaining fraction likely due to soil-related variables that were unaccounted for. Accurate estimations of N pools and their determinants in a climate change perspective are consequently required to predict the potential impact of their degradation on forest soil N pools.