Decision Support Systems Based on Gaseous Emissions: Comparison
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Please note this is a comparison between Version 4 by Lindsay Dong and Version 3 by Evangelos Alexandropoulos.

To achieve national and global air quality and climate change objectives, the agricultural sector increasingly requires dependable decision support tools for gaseous emissions at the farm level. While most decision support systems (DSSs) provide information for facilitating their use, only four are suitable for inexperienced users, and stakeholder participation in DSS development is infrequent. The dominant methodology for farm-level GHGgreenhouse gas (GHG) estimation is IPCC 2006, with quantitative models primarily used for indicators’ assessment. Scenario and contribution analyses are the prevailing decision-support approaches. Soil, crop, and fertilizer types are the most implemented non-livestock-related inputs, while climate- and feed-related costs are the least required. All DSSs assess farm-level mitigation measures, but less than half offer sustainability consultation. These tools promote environmental sustainability by evaluating mitigation strategies, disseminating farm sustainability information, and guiding sustainable farm management. Yet, challenges such as disparate estimation methods, result variations, comparison difficulties, usability concerns, steep learning curves, the lack of automation, the necessity for multiple tools, the limited integration of the results, and changing regulations hinder their wider adoption.

  • GHG-emissions-based decision support
  • multi-pillar sustainability assessment
  • livestock systems

1. Introduction

Greenhouse gas (GHG) emissions have a negative impact on the environmental sustainability of farm systems and globally [1]. In the year 2020, the agri-food sector was responsible for 31% of the global anthropogenic GHG emissions estimated in terms of carbon dioxide equivalents (CO2 eq) [2]. Specifically, the agri-food sector emitted 21%, 53%, and 78% of the global CO2, CH4, and N2O gases, respectively [2]. In addition, in the year 2020, sources from livestock production systems (i.e., enteric fermentation, manure management, and manure left on pasture) emitted approximately 4 billion tons of CO2 eq, corresponding to 54% of the GHG emissions from agriculture, forestry, and land use (AFOLU) [3]. CO2, CH4, and N2O emissions from the agricultural sector and their cumulative effect on climate change’s impact can be assessed by employing the global warming potentials (GWPs in kg CO2 eq per kg of gas emission) of these gases [4][5][6]. The IPCC guidelines provide the most widely accepted methodological approach for estimating GHG emissions from livestock systems at the country level which, due to the definition of the relevant emission factors, could also be used at the farm level [7]. GHG emissions constitute a significant group of indicators for the sustainability assessment of agricultural systems [4][8][9]. Emission management has a strong impact on environmental sustainability, not only related to climate change, but also to pollution and air quality [10].
Today, there is a growing urgency to convey accurate information regarding GHG emissions and the impact of mitigation practices at the individual livestock farm level to a diverse range of interested parties [11][12][13]. Apart from the increased effect of livestock production on global GHG emissions, the willingness for the direct promotion of mitigation actions in a sustainable way for the livestock farmer dictates this need. Moreover, it is nearly unfeasible for farmers to obtain accurate measurements of GHG emissions—and not only of these emissions—from all potential sources at the livestock farm level. Consequently, there is an anticipated rise in the significance of software-based farm-scale decision support systems (DSSs) centered on GHG emissions [14]. These tools aim to provide targeted, comprehensible advice to the user for acting in the direction of reducing GHG emissions from the farming system of interest. The wider use of DSSs related to agriculture by the targeted end-users is an important challenge, since the use of these tools, even by qualified and well-trained users, is still limited [11][15]. The GHG DS tools are not used by the majority of livestock farmers, since it is not yet obligatory for them to have knowledge of the GHG emitted from their farms. Moreover, GHG DS tools have been developed relatively recently in relation to the other agricultural DSSs (e.g., crop inputs management and herd management) [16][17].

2. Decision Support Systems Based on Gaseous Emissions

2.1. Aims of Using a Gas-Emission-Based DS Tool

The most important objective when using a gas-emission-based DS tool is getting informed with regard to which on-farm management strategies could improve the sustainability of a livestock farm. In this respect, the user aims to receive results that are comprehensive, specific, and practical to the livestock farm of interest from user-friendly software-based tools [18][19] which are developed by trustworthy groups of professionals and have already successfully been used for similar purposes by other people in their wider working environment [18]. Furthermore, the user expects to see an improvement in decision making during the production process (e.g., mitigation methods of emissions, the emissions sources, and their amount), as a result of the interaction with a DSS. This interaction between the user and the tool should be adjustable and versatile based on the changing requirements of the user at any given time [18][20].

2.2. Current Use of DSSs in the Livestock Sector

Today, DSSs in agriculture are mostly used by stakeholders in order to make informed decisions about the management of agricultural producing systems and processes (e.g., livestock management, livestock welfare, and economical management), by using the results of the relevant scientific research [21]. Widely used DSSs in animal production specifically focus on livestock population management, livestock welfare, and management of farm economics, and not often on improvement of environmental performance. Easily communicable and practical advice for the sustainability improvement of livestock production systems, also taking into account the environmental pillar of sustainability, is required for livestock farmers and other relevant stakeholders’ support (e.g., livestock farmers’ advisors for providing advice with regard to improving both the livestock farm’s economic and environmental performance, policy makers for compilation of regulations for the reduction of environmental pollution, etc.) [22]. As a result, the need for reliable, modern, and accessible decision support systems, which can effectively illustrate the importance of GHG and other pollutants’ emissions for the sustainability of these systems, emerges. The current and previous works indicate that various tools that use GHG and other pollutants’ emissions estimations for decision making are available. The use of these tools needs to be further justified and promoted to the potential stakeholders (i.e., livestock farmers, livestock farmers’ advisors, inventory compilers, and policy makers) by further highlighting their strengths and addressing their weaknesses.

2.3. Assurance of Wider Use of Emission-Based DSSs

There are several DS tools available, such as KSNL, RISE, DLG, and the Carbon Navigator (Dairy/Beef) tools, that provide detailed advice reports to the user. However, these reports are the result of data management by the tool’s expert groups and lack automated, easy-to-understand advice for the user.
In the case of the GHG emission calculators (without a consulting provision), the user must possess the experience and knowledge to comprehend the results. Additionally, they must conduct tests using various mitigation methods to determine the most effective approach.
Regarding the multi-pillar sustainability assessment tools (i.e., SAFA, DLG, RISE, and KSNL), users receive a sustainability score that enables them to understand the strengths and weaknesses of their farms. However, the tools do not provide specific automated advice on how to modify their practices for a better farm sustainability performance, but some of them (i.e., KSNL and RISE) provide consultation services by specialized staff.
The focus on inputs readily available at the farm level (or that are easy to acquire) is considered of importance, as it suggests facilitation in working with the DS tool by the end-user and minimizes the chances of abandoning the use of the tool [18]. It is important to underline that entering data and receiving outputs from these tools require a lot of time in most cases, which is unsuitable for the farmers’ and advisors’ daily working schedule. As a result, DSSs associated with less time consumption should be developed to attract stakeholders who work manually or/and away from the office (e.g., crop cultivation, farm management, and agricultural advisors) and do not have much time to spend on the use of a DS tool [20][23][24][25][26]. To make DSS tools more effective, they should be developed with a user-friendly interface that allows the user to enter their farm’s data in minimal time. It is important to note that user input could be minimized through the automation of data collection through remote-sensing techniques (e.g., pasture growing measurement, animal tracking system, electronic ear tags, electronic weighing system, and camera monitoring) [27][28]. The remote-sensing techniques’ editing and the integration of the data in a DSS environment are developed by big data analytics [28]. Furthermore, apart from the minimization of the input values inserted, the quality and the quantity of the data are major characteristics of the sensor system, since the sensors collect a huge amount of accurate real-time data [29]. When input insertion is reduced and inputs are deemed less reliable (e.g., approximate data declaration), the tool will provide less accurate results which will be less closely linked to the conditions found on a specific farm. This is because some users may find the process of entering data laborious and may not complete all forms, leading to less reliable outputs and decreased accuracy. An example could be the use of FarmAC, since this tool provides the ability of parametrization to the user. Consequently, should the user input farm-specific data into the tool rather than relying on its default settings, the output will be more tailored to the specific characteristics of that farm.
The ultimate goal of a widely used DSS tool is to provide practical and instant advice to end-users. Rather than providing a high number of non-comprehensible outputs, such as external arithmetic reports, the tool should provide precise and concrete advice. This will ensure that users can easily understand the outputs and act upon them. In order for a DS tool to be widely adopted, its usability should be improved, and it should provide reliable, well-targeted, and easily comprehensible outputs [18]. Achieving this will guarantee that users can quickly access the advice they need, leading to increased use of the tool [18].
Thus, the increase in the DS tool’s usability, combined with reliable and well-targeted and comprehensible outputs, could be an assurance of its wide use [30]. Easy understanding by the average end-user is closely related to the simplicity that the sustainability outputs are provided [18]. Ideally, a proper form of results could be a pointed report of the emergence of the problems (e.g., high GHG emissions) and the solutions (e.g., mitigation method), in a well-written and comprehensive report. The simplification of the results is a doubtful action since their complicated nature is difficult to manage. The conversion of the inputs into actionable recommendations for the end-user is of high importance for the adoption of such DS tools. Furthermore, it is of increased importance for a DS tool to shift from scientific outputs into practical advice [30]. More specifically, livestock producers should receive information and training in order to understand the usability of the DS tool in improving the sustainability of their farms, and, most notably, to apply them.
The inputs and outputs of a tool relate to the aim of the tool. In addition to having some common aims and functions, many tools have considerable differences which account for discrepancies in their final results. For example, GHG calculators like Cool Farm Tool, FarmAC, Overseer, HOLOS, Carbon Navigator (Dairy/Beef), GLEAM, and EX-ACT aim to provide information about GHG emissions. FarmAC and Overseer can also estimate nitrogen and carbon circles while they contribute to the decision making related to these. Overseer presents information about the phosphorus cycle. On the hand, the sustainability assessment tools (KSNL, SAFA, RISE, and DLG) provide information about the sustainability impact of GHG emissions, and some of these DS tools (KSNL and RISE) also estimate the GHG emissions.
The constant enhancement of communication between the DS tool’s developers and data providers (e.g., HOLOS) for the future updating and upgrading of the tools is of major importance [20][25][26]. If a tool is not user-friendly and the outputs are difficult to interpret, then the tool might be abandoned in the future. Furthermore, if interested end-users participate in the development of a DS tool, this will lead to a tool design which satisfies their expectations and considers the reality of the systems they manage [30]. A direct, targeted, and simple communication of the relevant information is necessary for a DS tool which would be preferred by the end-users in the livestock sector (e.g., farmers and farmers’ advisors) [30]. Furthermore, the tools’ potential to add value to agricultural products and services should be clearly presented and established [31]. Moreover, sustainability practices should be connected to the economic value (e.g., cost, profit, and incentives), of the agricultural enterprise, in order to be adopted by the producers [32]. Finally, the tool’s user interface and programming group should promote the economic value (e.g., additional product value, enhancement of decision making, and better advice) of using a DSS contrary to the time-consuming processes of learning to use and using a DSS. This promotion will lead to an increase in the number of potential end-users [20][25][26].

2.4. Benefits from the Use of Emission-Based DS Tools

The use of such DS tools could promote the assessment of the effect of various farm-scale mitigation strategies (direct: Carbon Navigator (Dairy/Beef), SAFA, and SMART; indirect: FarmAC, Cool Farm tool, Overseer, HOLOS, KSNL, DLG-REPRO, RISE, BEK, EX-ACT, and GLEAM) and, therefore, the environmental sustainability of livestock farming systems [11][33]. Additionally, their use could stimulate the dissemination of information about the farms’ sustainability indicators and the effect of the mitigation strategies, and has the potential to lead to better-informed stakeholders and, consequently, better farm management [34]. These tools could also contribute to the education of livestock farmers regarding GHG emissions, the environmental sustainability of their system, and its improvement. This is one of the most important steps in order to achieve environmental sustainability improvement, as well-informed farmers are more willing to adopt innovative modifications in the management of their production systems and their decision making, leading to an improvement of farm resources management [34].

2.5. Weaknesses of the Emission-Based DS Tools

A significant weakness of DS tools is the absence of a common estimation methodology among the various tools when estimating common indicators for the same set of data. Although many tools use the IPCC 2006 methodology, they differ in many aspects, such as the use of different Tier methodologies for parameter calculation (e.g., CH4 and N2O emissions from manure handling, and enteric CH4 emissions), default values for certain parameters, previous versions of estimation methodologies, and varying emission factors.

Furthermore, there are variations between the indicator results generated by a DS tool, associated with the estimation methodology for the various indicators [35]. For example, more complex estimation methods (e.g., higher IPCC Tiers) for GHG emissions are developed for higher data availability, resulting in more reliable results and lower uncertainties. In the GHG emissions’ estimation methods, variations between the indicator results, that were estimated by the tools, arise from the activity data used as inputs, climate data, and emission factors that may differ in each location [35].

Furthermore, obtaining more information to develop a sustainability assessment or estimation based on multiple parameters when using DS tools can be expensive. Nevertheless, it cannot be neglected that spending time on using a DS tool could be beneficial for the users, as they further work with the characteristics of their individual farming systems and investigate farm practices and measures specific to the farm, that can improve the DS tool’s indicators’ values.

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