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Rainfed farmers are among the most vulnerable farming communities to climate change in Pakistan because of the heavy reliance of crop farming on rain and of farmers’ livelihoods on crop farming. The best and most timely responses against climate change are suitable adaptation measures. Accurately perceiving the risks associated with climate change is an essential factor for planning and then implementing adaptations. Using farm household-level data of 400 rainfed farmers collected through a well-designed and field-tested questionnaire, this study examines the association between various adaptation stages (climate risk perceptions, adaptation planning, and implementation of adaptation) and their determinants using a multivariate probit (MVP) model. The findings indicate that farmers’ perceptions of climatic changes are in line with historical climatic data. Climate risk management (CRM) trainings and digital agriculture extension and communication (DAEC) services (indicators of formal institutional arrangements) show a highly significant impact on all adaptation stages. Input market distance, farmer cooperative meetings (an indicator of informal institutional arrangement), off-farm income, education, and number of male family members are among the other key determinants. A highly significant association between various adaptation stages indicates that accurate climate risk perceptions lead to planning and implementation of adaptations. When risk perceptions are underestimated or lacking, then adaptations do not occur. The results further indicate that the timely availability of reliable information on advanced agricultural inputs, weather parameters, crop farming advisory services, and market information could help rainfed farmers devise sound adaptations to minimize risks associated with climate change. The study recommends the provision of CRM trainings and DAEC services to provide a better understanding and promote sound adaptation planning through the adaptive capacity enhancement of rainfed farming communities for sustainable production and livelihood security.
Primary forests, defined here as forests where the signs of human impacts, if any, are strongly blurred due to decades without forest management, are scarce in Europe and continue to disappear. Despite these losses, we know little about where these forests occur. Here, we present a comprehensive geodatabase and map of Europe’s known primary forests. Our geodatabase harmonizes 48 different, mostly field-based datasets of primary forests, and contains 18,411 individual patches (41.1 Mha) spread across 33 countries. When available, we provide information on each patch (name, location, naturalness, extent and dominant tree species) and the surrounding landscape (biogeographical regions, protection status, potential natural vegetation, current forest extent). Using Landsat satellite-image time series (1985–2018) we checked each patch for possible disturbance events since primary forests were identified, resulting in 94% of patches free of significant disturbances in the last 30 years. Although knowledge gaps remain, ours is the most comprehensive dataset on primary forests in Europe, and will be useful for ecological studies, and conservation planning to safeguard these unique forests.
By leveraging a wide range of novel, data-driven technologies for agricultural production and agri-food value chains, digital agriculture presents potential enhancements to sustainability across food systems. Accordingly, digital agriculture has received considerable attention in policy in recent years, with emphasis mostly placed on the potential of digital agriculture to improve efficiency, productivity and food security, and less attention given to how digitalization may impact other principles of sustainable development, such as biodiversity conservation, soil protection, and human health, for example. Here, we review high-level policy and law in the German and European context to highlight a number of important institutional, societal, and legal preconditions for leveraging digital agriculture to achieve diverse sustainability targets. Additionally, we combine foresight analysis with our review to reflect on how future frame conditions influencing agricultural digitalization and sustainability could conceivably arise. The major points are the following: (1) some polices consider the benefits of digital agriculture, although only to a limited extent and mostly in terms of resource use efficiency; (2) law as it applies to digital agriculture is emerging but is highly fragmented; and (3) the adoption of digital agriculture and if it is used to enhance sustainability will be dependent on future data ownership regimes.
CONTEXT
Current research emphasises that agricultural innovation projects are influenced in multiple ways by the Agricultural Innovation Systems (AIS) in which they operate. Yet little attention has been paid to the reverse direction of this relationship, i.e. how agricultural innovation projects affect AIS in the course of their innovative activities. Accordingly, there are currently no tools to measure such AIS spillovers from agricultural innovation projects.
OBJECTIVE
This paper shows that even where agricultural innovation projects have not been designed with the explicit aim of influencing AIS they can have spillovers on the AIS in which they operate. Based on this finding, it argues that designing agricultural innovation projects in a way that maximises such positive and reduces negative AIS spillovers would be a useful tool for strengthening agricultural innovation capacities in a particular territory or sector.
METHODS
Based on the concept of agricultural innovation projects as Organisational Innovation Systems (OIS) that are embedded in AIS, the paper develops an analytical framework for assessing spillovers of such projects on AIS and applies it to a case study of an Operational Group in the German Federal State of Hessen.
RESULTS AND CONCLUSIONS
The case study shows that agricultural innovation projects may yield diverse spillovers on the AIS in which they operate. In addition to considering how agricultural innovation projects are shaped – constrained and/or enabled – by AIS, the research community on agricultural innovation should pay more attention to this side of the interrelation of AIS and agricultural innovation projects. Designing agricultural innovation projects responsibly so that spillovers on AIS are monitored can help to improve national or sectoral AIS.
SIGNIFICANCE
This paper points to an underexplored issue in research on agricultural innovation and, related to this, a thus far unused potential policy tool for improving national and sectoral AIS. It further develops the concept of innovation projects as OIS into an approach for assessing the effects of projects on AIS; an area of project assessment that until now has not been adequately covered.
Since the Soviet breakup, rural communities in the Kazakhstan–China borderlands have faced cataclysmic social, economic, and ecological transformations. Collective and state farms have been replaced by precarious smallholder agriculture suffering from a decline in financial inputs and the deterioration of infrastructure. At the same time, dynamic transformations of border permeability (and trans-border reconnections) between Kazakhstan and China have challenged the until relatively recent widespread Soviet rhetoric on Western China (or rather Xinjiang) as an underdeveloped space across the border. This article is based on several months of fieldwork between 2019 and 2021 in Panfilov District, located at major cross-border road and railway intersections and hosting important infrastructural objects, such as a dry port, a major railway transhipment hub, and a trans-border special economic zone. The article explores how the border situation (and the border’s changing permeability for people, commodities, and ideas since the early 1990s) as well as the implementation of new trans-border infrastructures eventually shaped local discourses and practices in the socio-economic development of south-east Kazakhstan’s agricultural sector. It is scrutinized herein how various forms of trans-border socio-economic interactions (although heavily regulated) and China’s (agri-) economic policy in Xinjiang started to affect farmers’ ambitions and practices across the border.
Islands have unique vulnerabilities to biodiversity loss and climate change. Current Nationally Determined Contributions under the Paris Agreement are insufficient to avoid the irreversible loss of critical island ecosystems. Existing research, policies, and finance also do not sufficiently address small islands’ social-environmental challenges. For instance, the new Global Biodiversity Framework (GBF) mentions islands in the invasive species management target. This focus is important, as islands are at high risk to biological invasions; however, this is the only GBF target that mentions islands. There are threats of equal or greater urgency to small islands, including coastal hazards and overexploitation. Ecosystems such as coral reefs and mangroves are crucial for biodiversity, coastal protection, and human livelihoods, yet are unaddressed in the GBF. While research and global policy, including targeted financial flows, have a strong focus on Small Island Developing States (SIDS), the situation of other small islands has been largely overlooked. Here, through a review of policy developments and examples from islands in the Philippines and Chile, we urge that conservation and climate change policies place greater emphasis on acknowledging the diversity of small islands and their unique governance challenges, extending the focus beyond SIDS. Moving forward, global policy and research should include the recognition of small islands as metacommunities linked by interacting species and social-ecological systems to emphasize their connectivity rather than their isolation. Coalition-building and knowledge-sharing, particularly with local, Indigenous and traditional knowledge-holders from small islands, is needed to meet global goals on biodiversity and sustainable development by 2030.
Since the 1990s, Mongolia's capital city Ulaanbaatar has recorded steady and rapid population growth due to rural-urban migration. So far, little is known about how the social and ecological conditions of migration interact. Thus, in our study we investigated the rural-urban migration process from the countryside to the capital in the Eastern Steppe conducting semi-structured interviews. Our results show that this rural-urban migration often follows a stepwise process from smaller settlements to larger urban agglomerations. The results also demonstrate a complex interplay between the prevailing social, economic, and ecological factors. While social factors (kinship, education) show the same relevance at each step of migration, ecological factors such as the occurrence of dzud seem more relevant at the early stages, from the steppe to the sum centres. Ecological factors only seem to rank second in importance, after social reasons. Economic reasons are also revealed to be very significant but seem most relevant the closer the migrations are to the capital. These results are essential in order to cope with current and future challenges of population development and find solutions to enable traditional Mongolian nomadism even under a modern lifestyle.
Extensively used grasslands (meadows and pastures) are ecologically valuable areas in the agricultural landscape and part of the multifunctional agriculture. In Germany, the quality of these grasslands is assessed based on the occurrence of certain plant species known as indicator or character species, with indicators being defined at regional level. Therefore, the recognition of these indicators on a spatial level is a prerequisite for monitoring grassland biodiversity. The identification of indicator species for the status quo of grassland using traditional methods was found to be challenging and tedious. Deep learning-algorithms applied to high-resolution UAV imagery could be the key solution, where UAV with remote sensors can map a large area of grassland in comparison to manual or ground mapping methods and deep learning-algorithms can automate the detection process. In this research work, we use an EfficientDet based algorithm to train an object detection model capable of recognizing indicators on RGB data. The experimental results show that this approach is very promising in contrast to the difficult and time-consuming manual recognition methods. The model was trained with the momentum-SGD optimizer with a momentum value of 0.9 and a learning rate of 0.0001. The model was trained and tested on 1200 images and achieves 45.7 AP (and 85.7 AP50) on test data set. The dataset includes images of four distinct indicator plant species: Armeria maritima, Campanula patula, Cirsium oleraceum, and Daucus carota
Digitale Plattformen avancieren zu einem wichtigen Teil der kommunalen Daseinsvorsorge. Richtig eingesetzt, können plattformbasierte Leistungen ein wirksames Steuerungsinstrument für eine nachhaltige Kommunalentwicklung darstellen. Für diese kommunale Gestaltungsaufgabe wird in der Kurzstudie ein Kriterienkatalog erarbeitet, anhand dessen die Nachhaltigkeitspotenziale und -risiken unterschiedlicher Plattformmodelle beschrieben und untersucht werden können.
Sustainability innovations influence societal transformations through the development of new products, processes, organizations, behaviors or values. Although various research approaches have tackled technological innovations in the last few decades, the specificities and enabling conditions of individual sustainability innovations remain rather unknown. We therefore propose an analytical framework, built on learning from the social–ecological systems and transitions literature. The sustainability innovation framework features four dimensions: context, actors, process and outcomes, which are detailed in 31 variables. We use the sustainability innovation framework to analyze two case studies selected in the Schorfheide-Chorin Biosphere Reserve, Germany. The first refers to technological and organizational innovation in mobility, while the second relates to social and organizational innovation in agriculture. As a result, we highlight commonalities and differences in enabling conditions and variables between the two cases, which underpin the influence of trust, commitment, resource availability, experimenting, learning, advocating, and cooperating for innovation development. The cases further demonstrate that sustainability innovations develop as bundles of interdependent, entangled novelties, due to their disruptive character. Their specificity thereby resides in positive outcomes in terms of social–ecological integrity and equity. This study therefore contributes to transitions studies via a detailed characterization of sustainability innovations and of their outcomes, as well as through a generic synthesis of variables into an analytical framework that is applicable to a large and diverse range of individual sustainability innovations. Further empirical studies should test these findings in other contexts, to pinpoint generic innovation development patterns and to develop a typology of sustainability innovation archetypes.
The project presented here has pursued two main goals. First, the so-called Tourism Sustainability Satellite Account (TSSA), an accounting system for measuring the sustainability of tourism in Germany, which was initially developed and applied in the previous project phase,
was repopulated with the currently available data. The TSSA is an extended tourism satellite account (TSA) and indicator system respectively that is essentially based on the statistical
frameworks of national accounts (NA) and environmental economic accounts (EEA). In addition, the TSSA includes social indicators that measure the sustainability of labour relations in tourism.
The TSSA thus enables a systematic attribution of the economic, environmental and social impacts of tourism to tourism-related economic sectors at the national level.
The results of the TSSA update show that tourism in Germany continues to make a significant contribution to value added and job creation. Around 3.6 % of total gross value added and 6.1 % of employment were attributable to tourism in Germany in 2019. However, labour productivity in the tourism sector remains relatively low.
In terms of environmental impact, energy consumption in the tourism sector has slightly increased compared to the last measurement (based on data from 2015 or 2016). However, energy intensity has decreased over the same period. Energy intensity in tourism is about as
high as for the average of the German economy. Regarding tourism-induced greenhouse gas (GHG) emissions, and particularly emissions intensity, the situation has also improved, even though the GHG emissions intensity of tourism is still well above the average of the German economy. Within the tourism sector, transportation (in particular aviation and shipping) contribute the most to energy use and GHG emissions. By contrast, tourism-induced water consumption appears to be less of a problem in Germany. The water intensity of tourism is significantly lower than the average of the German economy. Within the tourism sector it is the highest in accommodation and gastronomy.
In terms of the social sustainability dimension, the relatively low and decreasing income gap between men and women (known as the Gender Pay Gap; unadjusted) in the tourism sector over time is worth mentioning. By contrast, all other indicators regarding the working conditions in tourism continue to perform less favourably. Consequently, employees in the tourism sector still assess the working conditions as worse compared to the average of the overall economy.
It has already been shown in the previous project phase that for some sustainability indicators, especially from the ecological dimension, a consideration within the framework of the TSSA approach is not possible without restrictions. This is due, among other things, to the insufficient data available from official statistics and the complexity of causally attributing certain ecological effects to tourism activities. In the second part of the project presented here, a detailed research of existing data bases was therefore conducted for the environmental indicators "biodiversity", "water quality", "noise pollution", "land use", "air emissions" and "waste generation" as well as for the social indicator "tourism acceptance". Based on this, it was evaluated to what extent alternative accounting approaches - as a supplement to the TSSA measurement system - can indicate the tourism effect. The data screening initially confirmed the insufficient data
availability at national level.
The subsequent feasibility analysis yielded that a spatial differentiation according to the degree of tourism relevance (based on tourism density and intensity) and an ensuing analysis of the
change in environmental conditions in regions strongly influenced by tourism in comparison to the overall German average represents an opportunity to quantify the contribution of tourism e.g. to land use. On the one hand, existing official regional data could be used here. On the other hand, additional data collections at the local level would also be essential for certain environmental indicators in the future. In other cases, in particular for biodiversity impacts and
solid waste generation, the preconditions for suitable data availability must be created first. By contrast, the acceptance of tourism by residents can easily be measured through standardised
surveys.
The aim of the presented project was to develop a practicable system for measuring sustainability of national tourism in Germany. Initially, 18 sustainability criteria for tourism were identified. In a second step, these criteria were analyzed with regard to their measurability using indicators in a coherent accounting system in compliance with international recommendations. The outcome is a Tourism Sustainability Satellite Account (TSSA), a system of
indicators which is mainly based on statistical frameworks of national accounts and environmental-economic accounts. In addition, social indicators have been added that mainly measure decent job creation in tourism. Thus, the TSSA allows a systematic allocation of the
economic, ecological and social impacts of tourism to the tourism-relevant economic sectors at a national level. However, there is still a need for development of some sustainability indicators,
especially from the management and, to some extent, the ecological sector.
As a test, the TSSA indicators have been filled with currently available data. The results show that tourism in Germany contributes significantly to creating added value and jobs, although labor productivity is low. In terms of ecological impacts, climate impacts with a slightly above-average greenhouse gas intensity compared to the economy as a whole are at the top of the list, although this intensity varies significantly within the tourism sub-sectors. Working conditions are generally considered to be less sustainable than in other industries. Only the pay gap between men and women is significantly smaller than in other sectors of the economy.
Tourism needs to reduce emissions in line with other economic sectors, if the international community's objective of staying global warming at 1.5°-2.0 °C is to be achieved. This will require the industry to half emissions to 2030, and to reach net-zero by mid-century. Mitigation requires consideration of four dimensions, the Scales, Scopes, Stakeholders and Strategies of carbon management. The paper provides a systematic review of these dimensions and their interrelationships, with a focus on emission inventory comprehensiveness; allocation principles at different scales; clearly defined responsibilities for decarbonization; and the identification of significant mitigation strategies. The paper concludes that without mitigation efforts, tourism will deplete 40% of the world's remaining carbon budget to 1.5 °C. Yet, the most powerful decarbonization measures face major corporate, political and technical barriers. Without worldwide policy efforts at the national scale to manage the sector's emissions, tourism will turn into one of the major drivers of climate change.
Random year intercepts in mixed models help to assess uncertainties in insect population trends
(2023)
1. An increasing number of studies is investigating insect population trends based on time series data. However, the available data is often subject to temporal pseudoreplication. Inter-annual variability of environmental conditions and strong fluctuations in insect abundances can impede reliable trend estimation. Temporal random effect structures in regression models have been proposed as solution for this issue, but remain controversial.
2. We investigated trends in ground beetle abundance across 24 years using generalised linear mixed models. We fitted four models: A base model, a model featuring a random year intercept, a model featuring basic weather parameters, and a model featuring both random year intercept and weather parameters. We then performed a simple sensitivity analysis to assess the robustness of the four models with respect to influential years, also testing for possible spurious baseline and snapshot effects.
3. The model structure had a significant impact on the overall magnitude of the estimated trends. However, we found almost no difference among the models in how the removal of single years (sensitivity analysis) relatively affected trend coefficients. The two models with a random year intercept yielded significantly larger confidence intervals and their p-values were more sensitive during sensitivity analysis. Significant differences of the model with random year intercept and weather parameters to all other models suggest that the random year effects and specific weather effects are rather additive than interchangeable.
4. We conclude that random year intercepts help to produce more reliable and cautious uncertainty measures for insect population trends. Moreover, they might help to identify influential years in sensitivity analyses more easily. We recommend random year intercepts in addition to any variables representing temporally variable environmental conditions, such as weather variables.
Learning and transdisciplinary research are widely acknowledged as key components for achieving sustainability; however, the links between these concepts remain vague in the sustainability literature. Recently, emphasis has been given to transdisciplinary learning, highlighting its potential as an approach that contributes to solving real-world problems. To better understand and foster transdisciplinary learning for sustainability transformations, it is relevant to pay attention to two dimensions that define transdisciplinary learning: social interaction (individual learning in a social setting, as a group, or beyond the group), and learning forms (single-, double-, or triple-loop learning). This article introduces a conceptual framework built upon these two dimensions to understand three specific forms of transdisciplinary learning as a) individual competence development, b) experience-based collaboration, and c) societal interaction. This framework helps to clarify the design of learning processes as well as their interactions in transdisciplinary processes to support transformative change.