A substantial effort is being made to connect LCI data across databases. To this end, both ecoSpold and ILCD aim to support “alternative modeling options and data exchange [with each other]”. The data from LCI databases are usually exported as collections of either XML or XLS files . Collections of XML data are used in most major LCA software tools, as will be discussed next. Many of the databases described in Table 3.4 have been created and modified to include LCI data for a larger variety of systems types: i.e. not just industrial production systems. The World Food LCA database focusses specifically on LCI data for agricultural production and processing and is intended asan open data project. Government-run LCI databases like the USLCI, ELCI, and the AusLCI also aim to incorporate more LCI data relevant to agricultural systems. The combination of data collection for agricultural LCI databases, and the continuous development of LCA tools means that the LCA methods as they currently stand are being incrementally improved and better supported. Still, there is a lack of domain specific LCI data, particularly for alternative agricultural systems.Four of the most popular tools that are used throughout all the LCA phases are spreadsheet tools , SimaPro, GaBi Software, and openLCA. Table 3.5 describes the basic properties and features of these software tools. The main differences between LCA software tools include modeling process, cannabis drying kit range of databases available, usability, data documentation formats, and cost. Spreadsheet tools are a natural fit for the data-intensive LCA process.
They have the capability to create inventories easily, perform impact calculations on raw data, and produce charts exportable to partner word processing software for reports. Not only can most LCI data be exported as XLS/XLST, but many plugins, templates, and guides on how to use Microsoft Excel to conduct LCAs are available. An example of a spreadsheet tool is the Athena EcoCalculator, a template that allows for getting snapshots of the environmental footprints of buildings.Pre International develops SimaPro, one of the most popular full stack proprietary LCA software tools. In direct competition is GaBi Software, a “product sustainability performance solution”, developed by PE International, that is also used to conduct LCAs . Both GaBi and SimaPro have a similar set of functionality, and are industry leaders. They are expensive, but have alternative limited access licenses for education and teaching. Many other proprietary LCA tools of varying complexity and capacities exist. These include: Sustainable Minds, Umberto NXT LCA, Quantis Suite, among others. openLCA is one of the few free and open source tools aimed at professional LCA and footprint analysts. GreenDelta, an environmental consulting group based in Germany, conducts core development for this tool. In addition to having LCIA capabilities with built-in methods, data connectivity with popular LCI data documentation formats, and reporting functionality, openLCA also allows for users to build their own plugins to extend it. GreenDelta is also responsible for the openLCA Nexus website, which aggregates LCI data from different databases and allows for them to be searchable in one interface . Modern LCA tools have provided some support for connecting to LCI databases, automated report production, basic versioning information to track changes, and simple localized user created libraries for reuse within a project . Development on each of these LCA modeling tools is ongoing with new and promising features being rolled out each year. while there is interest in bridging the gap between the need for domain-specific data, these tools are still designed for the domain-agnostic LCA modeling process.
The decomposition of an agricultural system into quantifiable unit processes, the assumed relationships between different data, and the means by which unit process data are brought together in a synergistic way in an LCA model to enable the calculation of the environmental impacts of the system of interest have been described previously. LCAs can be leveraged to do more than just retrospective evaluation, as described in Section 3.2. The current LCA modeling process can be scaffolded to enable more proactive evaluation, monitoring of systems, and to use LCA results as a decision making tool during the system design process. In this section, I present a single scenario, concerning the creation of an LCA model, as it exemplifies the modeling process and challenges that would be faced by a small- to medium scale sustainable farmer. In fact, it is part of a larger analysis in which I developed a series of scenarios describing hypothetical modeling activities enacted by potential LCA stakeholders. The goal of the full set of scenarios was to tease apart the core issues with the LCA modeling workflow and the capacity of these existing LCA data structures and tools to connect and compare agricultural system. Section 3.4.4 highlights the modeling challenges identified through the scenario presented in this chapter. The issues identified during the full scenario-based analysis, in concert with work presented in this chapter, are collectively discussed in Section 3.5.Consider the following hypothetical scenario: Alice Kidogo is the owner of a small urban farm growing an assortment of fruit and vegetables in Orange County. It is 2016, California is experiencing a drought, and she suspects that the state government will impose water rations. She currently supplies produce to certain farm-to-table restaurants in the Orange County region. She would like to apply to be a supplier at the Whole Foods in her geographic area. She is aiming to score “Good” to “Better” on the Whole Foods Responsibly Grown ratings. She wants to conduct some form of environmental assessment to help her meet these goals. Alice wants to be proactive and use this opportunity to also optimize her water usage and lower her water footprint. As the farm is composed of many different subsystems, she wants a reasonably fine-grained assessment that allows her to: identify major water sinks, detect inefficient water flows, and understand the farm’s overall relationship with water. Alice also wants to be aware of the effects of her water-saving choices with respect to other environmental issues. For example, one concern that she has is the relationship between the heavy use of plastics within her irrigation system and the farm’s carbon footprint. She wants to consider alternatives to reduce her water footprint in case of rationing. She needs to find ways to improve her water footprint without compromising the farm’s overall environmental performance. Alice begins by performing a Google search with the phrase water or carbon footprint calculator farm. She finds two online tools: The Water Footprint Assessment Tool, and the AgroClimate carbon footprint tool. They provide her with interesting information about her local watershed, and some geography based statistics regarding water use. Unfortunately, even after spending some time trying to model her farm using the tools, they only allow her to get a rough estimate of the water footprint. As she wants to use the results of the water footprint assessment to make decisions about how to reduce the water consumption of different systems on her farm, these online tools do not suffice. She then browses through the United States Department of Agriculture website, to see if they have any recommendations on conducting an environmental assessment of her farm. The website lists “Quantification Tools” in the “Environmental Markets” section, including water quality, vertical farming equipment carbon and greenhouse gas emissions, and energy estimation tools. Once again, they aim to provide a snapshot footprint of the environmental performance of the system. They are specifically geared toward enabling the farmer to participate in emerging environmental markets involving, for example, the trading of offsets. Alice decides that a Life Cycle Assessment would provide her with a potential means to quantify and understand the environmental performance and of her farm. However, LCA seems to be a complex and time consuming venture, and Alice worries that she may have to resort to hiring professionals to provide her with the most reliable water footprint.
One online guide to LCA informs Alice that it could cost from $10,000 to $60,000 to outsource the LCA to a consulting company. Due to financial constraints, Alice chooses to try and conduct an LCA of her farm on her own.Alice begins by creating a flow diagram. Since no dedicated LCA flow diagramming tool is available, she uses Microsoft PowerPoint to create a simple block and arrow diagram to represent the major systems in the farm: the irrigation system, solar power system, grey water reclamation system, vermicomposting boxes, the nursery, and the farm grow beds themselves. As no formal guidelines regarding flow diagramming are available, she simply connects these blocks with arrows to represent directionality and types of flows within the system. The boundary of the system can be scope in many ways. For example, Alice chooses to include the build of the solar power system and the irrigation system, as she custom built many of the components. In contrast, her gray water reclamation and vermicomposting systems are direct from vendors. She creates an initial flow diagram, as shown in Figure 3.10.Alice goes through a variety of LCA educational materials, hoping to answer the following questions: how should she break down these subsystems, and what level of granularity is needed to calculate a useful water footprint? She converts her original flow diagram to the process based LCA flow diagram shown in Figure 3.11, created based on an introduction tutorial to LCA. This represents her systems as a series of high-level processes: these would later be decomposed into unit processes, with relevant data potentially available in existing LCI databases.No standard or generic LCA models are readily available to be explicitly built upon. Alice essentially begins from scratch when creating the LCA model, with minimal guidance on how to collect her data, what kinds of things to consider, and how to connect unit processes. Alice tries to create an LCI for just the irrigation system to try and see how far she can get. She has two options, pull data from an LCI database, or manually collect the data required. Unit process data are contained in several LCI databases. Alice chooses to use the USLCI database, as it would likely contain a geographically appropriate dataset for her southern California based farm. She uses Microsoft Excel to create a basic LCI. She tries to source much of her equipment and materials used on the farm from local vendors, and hopes that relevant data will be available in the USLCI.For example, the irrigation system on the farm is based on Harmony Farm Supply & Nursery’s sprinkler irrigation setup. The most complex part of the system is the “system head or manifold assembly”. It is responsible for distributing water among the main lines , and shown in Figure 3.12. Each of the sprinkler lines would result in a network of yet more tubes, fittings, and other parts. A complete accounting would require the knowledge of the environmental impacts of each of these sub-components of the irrigation system, and potentially even background information on their origins. Ideally, manufacturers of these parts would provide these data. Alice looks up irrigation in the USLCI to see what kind of data is available. Figure 3.13 shows the list of data available to her. None of the available data is relevant to her specific setup. The publicly available USLCI appears to mainly contain data for large-scale industrial processes, and the farming data is therefore also of that scale. She does note that the USLCI has three phases of data under development: field crop production data, Irrigation, manure management, and farm equipment operation unit process data, and mineral, fertilizer, herbicide, insecticide, and fungicide data. However, these data are not available yet. The USDA crop LCI database contains some of these data, but as with the USLCI database, it is missing certain kinds of data relevant to her system.Each modeling step required a different tool. While this alone may not be problematic, the modeling effort put into one step is lost in the next. The largest gap is between flow diagram and inventory, as no current tooling can support the connection of the two. openLCA does have the capability to import an entire external LCI database, as well as spreadsheet based inventories.