Agency shifts in agricultural land governance and their implications for land degradation neutrality

Given current land degradation trends, Land Degradation Neutrality (LDN, SDG Target 15.3) by 2030 could be difficult to attain. Solutions to avoid, reduce, and reverse land degradation are not being implemented at sufficiently large scales, pointing to land governance as the main obstacle. In this paper, we review dynamics in agricultural land governance, and the potential this may have to enable land degradation or provide solutions towards LDN. The literature reveals agency shifts are taking place, where value chain actors are given increasing decision-making power in land governance. These agency shifts are manifested in two interrelated trends: First, through agricultural value chain coordination, such as contract farming, value chain actors increasingly influence land management decisions. Second, international large-scale land acquisitions and domestic larger-scale farms, both instances of intensified direct involvement of value chain with land management, are overtaking significant areas of land. These new arrangements are associated with agricultural expansion, and are additionally associated with unsustainable land management due to absent landowners, short-term interests, and high-intensity agriculture. However, we also find that value chain actors have both the tools and business cases to catalyze LDN solutions. We discuss how governments and other LDN brokers can motivate or push private actors to deploy private governance measures to avoid, reduce, and reverse land degradation. Successful implementation of LDN requires refocusing efforts to enable and, where necessary, constrain all actors with agency over land management, including value chain actors

State-of-the-art imaging for glioma surgery.

Diffuse gliomas are infiltrative primary brain tumors with a poor prognosis despite multimodal treatment. Maximum safe resection is recommended whenever feasible. The extent of resection (EOR) is positively correlated with survival. Identification of glioma tissue during surgery is difficult due to its diffuse nature. Therefore, glioma resection is imaging-guided, making the choice for imaging technique an important aspect of glioma surgery. The current standard for resection guidance in non-enhancing gliomas is T2 weighted or T2w-fluid attenuation inversion recovery magnetic resonance imaging (MRI), and in enhancing gliomas T1-weighted MRI with a gadolinium-based contrast agent. Other MRI sequences, like magnetic resonance spectroscopy, imaging modalities, such as positron emission tomography, as well as intraoperative imaging techniques, including the use of fluorescence, are also available for the guidance of glioma resection. The neurosurgeon's goal is to find the balance between maximizing the EOR and preserving brain functions since surgery-induced neurological deficits result in lower quality of life and shortened survival. This requires localization of important brain functions and white matter tracts to aid the pre-operative planning and surgical decision-making. Visualization of brain functions and white matter tracts is possible with functional MRI, diffusion tensor imaging, magnetoencephalography, and navigated transcranial magnetic stimulation. In this review, we discuss the current available imaging techniques for the guidance of glioma resection and the localization of brain functions and white matter tracts

The geography of megatrends affecting European agriculture

A range of intensifying pressures is making the future of European agriculture dynamic and contested. Insights into these pressures are needed to inform debates about the future of the sector. In this study, we use a foresight approach to identify, quantify and map megatrends. Megatrends are long-term driving forces which are observable today and will likely have transformational potential in the future. By mapping these megatrends at the regional scale, we establish a geography of megatrends and detect where they coincide. Four megatrends significant for the future of European agriculture at the regional scale are assessed: Climate change, demographic change, (post-) productivism shifts, and increasingly stringent environmental regulations. The direction and intensity of these megatrends differs between regions, which drives regions into different systemic lock-ins or dynamics. In most regions, megatrends converge to destabilize the current system, forewarning impending systemic changes. While the specific megatrends contributing to this instability differ regionally, this result highlights that many regions are on a dynamic rather than stable trajectory, and the governance challenge is to steer these dynamics towards a desirable future. However, some regions are found to be highly persistent, indicating that megatrends reinforce business as usual, and change needs to be triggered through purposeful governance. In a minority of regions megatrends may drive marginalization as the current system becomes increasingly unviable. We argue that research and policies concerning agricultural sustainability transitions should be cognizant of the regional diversity of European megatrends and the pressures they create

Developing context-specific frameworks for integrated sustainability assessment of agricultural intensity change: An application for Europe

Agriculture plays a central role in achieving most Sustainable Development Goals (SDGs). Sustainable intensi- fication (SI) of agriculture has been proposed as a promising concept for safeguarding global food security, while simultaneously protecting the environment and promoting good quality of life. However, SI often leads to context-specific sustainability trade-offs. Operationalising SI thus needs to be supported by transparent sus- tainability assessments. In this article, we propose a general systematic approach to developing context-specific frameworks for integrated sustainability assessment of agricultural intensity change. Firstly, we specify a comprehensive system representation for analysing how changes in agricultural intensity lead to a multitude of sustainability outcomes affecting different societal groups across geographical scales. We then introduce a procedure for identifying the attributes that are relevant for assessment within particular contexts, and respective indicator metrics. Finally, we illustrate the proposed approach by developing an assessment framework for evaluating a wide range of intensification pathways in Europe. The application of the approach revealed pro- cesses and effects that are relevant for the European context but are rarely considered in SI assessments. These include farmers’ health, workers’ living conditions, cultural heritage and sense of place of rural communities, animal welfare, impacts on sectors not directly related to agriculture (e.g., tourism), shrinking and ageing of rural population and consumers’ health. The proposed approach addresses important gaps in SI assessments, and thus represents an important step forward in defining transparent procedures for sustainability assessments that can stimulate an informed debate about the operationalisation of SI and its contribution towards achieving SDGs

Farmer surveys in Europe suggest that specialized, intensive farms were more likely to perceive negative impacts from COVID-19.

It has been shown that the COVID-19 pandemic affected some agricultural systems more than others, and even within geographic regions, not all farms were affected to the same extent. To build resilience of agricultural systems to future shocks, it is key to understand which farms were affected and why. In this study, we examined farmers' perceived robustness to COVID-19, a key resilience capacity. We conducted standardized farmer interviews (n = 257) in 15 case study areas across Europe, covering a large range of socio-ecological contexts and farm types. Interviews targeted perceived livelihood impacts of the COVID-19 pandemic on productivity, sales, price, labor availability, and supply chains in 2020, as well as farm(er) characteristics and farm management. Our study corroborates earlier evidence that most farms were not or only slightly affected by the first wave(s) of the pandemic in 2020, and that impacts varied widely by study region. However, a significant minority of farmers across Europe reported that the pandemic was "the worst crisis in a lifetime" (3%) or "the worst crisis in a decade" (7%). Statistical analysis showed that more specialized and intensive farms were more likely to have perceived negative impacts. From a societal perspective, this suggests that highly specialized, intensive farms face higher vulnerability to shocks that affect regional to global supply chains. Supporting farmers in the diversification of their production systems while decreasing dependence on service suppliers and supply chain actors may increase their robustness to future disruptions. Supplementary Information The online version contains supplementary material available at 10.1007/s13593-022-00820-5

Spatial concordance of DNA methylation classification in diffuse glioma.

BACKGROUND: Intratumoral heterogeneity is a hallmark of diffuse gliomas. DNA methylation profiling is an emerging approach in the clinical classification of brain tumors. The goal of this study is to investigate the effects of intratumoral heterogeneity on classification confidence. METHODS: We used neuronavigation to acquire 133 image-guided and spatially separated stereotactic biopsy samples from 16 adult patients with a diffuse glioma (7 IDH-wildtype and 2 IDH-mutant glioblastoma, 6 diffuse astrocytoma, IDH-mutant and 1 oligodendroglioma, IDH-mutant and 1p19q codeleted), which we characterized using DNA methylation arrays. Samples were obtained from regions with and without abnormalities on contrast-enhanced T1-weighted and fluid-attenuated inversion recovery MRI. Methylation profiles were analyzed to devise a 3-dimensional reconstruction of (epi)genetic heterogeneity. Tumor purity was assessed from clonal methylation sites. RESULTS: Molecular aberrations indicated that tumor was found outside imaging abnormalities, underlining the infiltrative nature of this tumor and the limitations of current routine imaging modalities. We demonstrate that tumor purity is highly variable between samples and explains a substantial part of apparent epigenetic spatial heterogeneity. We observed that DNA methylation subtypes are often, but not always, conserved in space taking tumor purity and prediction accuracy into account. CONCLUSION: Our results underscore the infiltrative nature of diffuse gliomas and suggest that DNA methylation subtypes are relatively concordant in this tumor type, although some heterogeneity exists

State-of-the-art imaging for glioma surgery

Diffuse gliomas are infiltrative primary brain tumors with a poor prognosis despite multimodal treatment. Maximum safe resection is recommended whenever feasible. The extent of resection (EOR) is positively correlated with survival. Identification of glioma tissue during surgery is difficult due to its diffuse nature. Therefore, glioma resection is imaging-guided, making the choice for imaging technique an important aspect of glioma surgery. The current standard for resection guidance in non-enhancing gliomas is T2 weighted or T2w-fluid attenuation inversion recovery magnetic resonance imaging (MRI), and in enhancing gliomas T1-weighted MRI with a gadolinium-based contrast agent. Other MRI sequences, like magnetic resonance spectroscopy, imaging modalities, such as positron emission tomography, as well as intraoperative imaging techniques, including the use of fluorescence, are also available for the guidance of glioma resection. The neurosurgeon’s goal is to find the balance between maximizing the EOR and preserving brain functions since surgery-induced neurological deficits result in lower quality of life and shortened survival. This requires localization of important brain functions and white matter tracts to aid the pre-operative planning and surgical decision-making. Visualization of brain functions and white matter tracts is possible with functional MRI, diffusion tensor imaging, magnetoencephalography, and navigated transcranial magnetic stimulation. In this review, we discuss the current available imaging techniques for the guidance of glioma resection and the localization of brain functions and white matter tracts

Smart sensors and communication using Internet of Things in supermarkets: Sensor to Server communication

During 9 weeks of investigation multiple solutions for implementing a communication module in a supermarket have been explored. These results led to three options which have been further researched. Bluetooth Low Energy (BLE), 433 MHz and ZigBee have been selected as the best communication protocolsfor sensor to gateway communication. Chips for these protocols have been bought and for BLE and 433 MHz libraries have been developed. For the gateway and the communication to the server a Raspberry PI and Mobile Internet have been selected and these have been integrated. During the measurements we came to the conclusion that for the RFM69HCW (433 MHz) and the BLE Nano (BLE) the sleep mode did not work as specified. Resulting in high power consumption and thus worse battery life (sleep mode consumption for RFM69HCW 3.37 mA, BLE Nano 4.38 mA and for XBee 43.2 휇A). The final conclusion of this thesis is that, with the current research, XBee (ZigBee) isthe best communication protocol for communicating within a supermarket. But in further research BLE Nano and RFM69HCW can perform better if the sleep mode is improved to work according to what is specified

Representing large-scale land acquisitions in land use change scenarios for the Lao PDR

Agricultural large-scale land acquisition (LSLA) is a process that is currently not captured by land change models. We present a novel land change modeling approach that includes processes governing LSLAs and simulates their interactions with other land systems. LSLAs differ from other land change processes in two ways: (1) their changes affect hundreds to thousands of contiguous hectares at a time, far surpassing other land change processes, e.g., smallholder agriculture, and (2) as policymakers value LSLA as desirable or undesirable, their agency significantly affects LSLA occurrence. To represent these characteristics in a land change model, we allocate LSLAs as multi-cell patches to represent them at scale while preserving detail in the representation of other dynamics. Moreover, LSLA land systems are characterized to respond to an explicit political demand for LSLA effects, in addition to a demand for various agricultural commodities. The model is applied to simulate land change in Laos until 2030, using three contrasting scenarios: (1) a target to quadruple the area of LSLA, (2) a moratorium for new LSLA, and (3) no target for LSLA. Scenarios yield drastically different land change trajectories despite having similar demands for agricultural commodities. A high level of LSLA impedes smallholders’ engagement with rubber or cash crops, while a moratorium on LSLA results in increased smallholder involvement in cash cropping and rubber production. This model goes beyond existing land change models by capturing the heterogeneity of scales of land change processes and the competition between different land users instigated by LSLA