Alternative ground covers and strip-tilling in CBD hemp production.

OBJECTIVE: Little research has been done on managing soil health for large-scale, outdoor hemp production, contributing to the possible overuse of black plastic for weed suppression. Our experiment aimed to understand the performance of alternative ground covers including forage crops and hay as well as a less disruptive tilling method called strip-tilling compared to black plastic. RESULTS: Yield and soil health data were taken from three experimental plantings from two different outdoor CBD hemp farms in Vermont, USA. We find that hay may be a competitive alternative to black plastic in terms of producing heavier plants. Our research also found that clover seed and hay are both more cost-effective options than black plastic which may sway some farmers to adopt these alternative ground cover options.

Reimagining the potential of Earth observations for ecosystem service assessments.

The benefits nature provides to people, called ecosystem services, are increasingly recognized and accounted for in assessments of infrastructure development, agricultural management, conservation prioritization, and sustainable sourcing. These assessments are often limited by data, however, a gap with tremendous potential to be filled through Earth observations (EO), which produce a variety of data across spatial and temporal extents and resolutions. Despite widespread recognition of this potential, in practice few ecosystem service studies use EO. Here, we identify challenges and opportunities to using EO in ecosystem service modeling and assessment. Some challenges are technical, related to data awareness, processing, and access. These challenges require systematic investment in model platforms and data management. Other challenges are more conceptual but still systemic; they are byproducts of the structure of existing ecosystem service models and addressing them requires scientific investment in solutions and tools applicable to a wide range of models and approaches. We also highlight new ways in which EO can be leveraged for ecosystem service assessments, identifying promising new areas of research. More widespread use of EO for ecosystem service assessment will only be achieved if all of these types of challenges are addressed. This will require non-traditional funding and partnering opportunities from private and public agencies to promote data exploration, sharing, and archiving. Investing in this integration will be reflected in better and more accurate ecosystem service assessments worldwide.

Towards globally customizable ecosystem service models.

Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The ARtificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five "Tier 1" ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multi-criteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed.

A methodology for adaptable and robust ecosystem services assessment.

Ecosystem Services (ES) are an established conceptual framework for attributing value to the benefits that nature provides to humans. As the promise of robust ES-driven management is put to the test, shortcomings in our ability to accurately measure, map, and value ES have surfaced. On the research side, mainstream methods for ES assessment still fall short of addressing the complex, multi-scale biophysical and socioeconomic dynamics inherent in ES provision, flow, and use. On the practitioner side, application of methods remains onerous due to data and model parameterization requirements. Further, it is increasingly clear that the dominant "one model fits all" paradigm is often ill-suited to address the diversity of real-world management situations that exist across the broad spectrum of coupled human-natural systems. This article introduces an integrated ES modeling methodology, named ARIES (ARtificial Intelligence for Ecosystem Services), which aims to introduce improvements on these fronts. To improve conceptual detail and representation of ES dynamics, it adopts a uniform conceptualization of ES that gives equal emphasis to their production, flow and use by society, while keeping model complexity low enough to enable rapid and inexpensive assessment in many contexts and for multiple services. To improve fit to diverse application contexts, the methodology is assisted by model integration technologies that allow assembly of customized models from a growing model base. By using computer learning and reasoning, model structure may be specialized for each application context without requiring costly expertise. In this article we discuss the founding principles of ARIES--both its innovative aspects for ES science and as an example of a new strategy to support more accurate decision making in diverse application contexts.

New perspectives in ecosystem services science as instruments to understand environmental securities.

As societal demand for food, water and other life-sustaining resources grows, the science of ecosystem services (ES) is seen as a promising tool to improve our understanding, and ultimately the management, of increasingly uncertain supplies of critical goods provided or supported by natural ecosystems. This promise, however, is tempered by a relatively primitive understanding of the complex systems supporting ES, which as a result are often quantified as static resources rather than as the dynamic expression of human-natural systems. This article attempts to pinpoint the minimum level of detail that ES science needs to achieve in order to usefully inform the debate on environmental securities, and discusses both the state of the art and recent methodological developments in ES in this light. We briefly review the field of ES accounting methods and list some desiderata that we deem necessary, reachable and relevant to address environmental securities through an improved science of ES. We then discuss a methodological innovation that, while only addressing these needs partially, can improve our understanding of ES dynamics in data-scarce situations. The methodology is illustrated and discussed through an application related to water security in the semi-arid landscape of the Great Ruaha river of Tanzania.