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. 2025 Jun 20;54(6):1500–1514. doi: 10.1002/jeq2.70051

Selecting performance indicators for farms and ranches engaged in collaborative agroecosystem research

Megan Donovan 1, Sheri Spiegal 1,✉, Nicole Kaplan 2, David Archer 3, Alycia Bean 4, Sarah E J Beebout 5, Brandon T Bestelmeyer 1, Patrick Clark 6, Alia DeLong 7, Ann‐Marie Fortuna 8, Claire N Friedrichsen 3, David L Hoover 2, David Huggins 9, Peter J A Kleinman 10, Matthew M McIntosh 11, Chris S Renschler 12, John Ritten 13, Douglas R Smith 14, Nicholas P Webb 1, J D Wulfhorst 15
PMCID: PMC12593285  PMID: 40538348

Abstract

The pace of global change complicates the assessment of the outcomes of agricultural management, hindering decision‐making by producers, researchers, and consumers. The Long‐Term Agroecosystem Research Network (LTAR) is in a unique position to advance monitoring to inform decision‐making. Here, we describe how the network selected performance indicators designed to measure the trade‐offs from various farming and ranching approaches. Indicator selection was motivated by the need for common indicators that apply to the diversity of LTAR sites, but they are intended for widespread use by producers and other managers via the Agricultural Performance Indicator and Context Knowledge System (AgPICKS). An initial set of domains, attributes, and indicators was developed via synthesis of structured conversations at national LTAR meetings. Early use revealed the need for a systematically inclusive process toward improvement. We designed and implemented an iterative decision‐making protocol to reach a consensus for a new version. The indicator framework differs from others in its attention to production and social outcomes and its grounding in networked agricultural science. Next steps entail developing web tools and personnel for AgPICKS that use LTAR's data and knowledge ecosystem to guide users in setting benchmarks of the desired conditions for their prioritized indicators, collect data, and visualize data to assess how well their management meets their benchmarks, toward the accurate measurement of management outcomes in a changing world.

Core Ideas

  • Long‐Term Agroecosystem Research Network's (LTAR's) performance indicator knowledge system facilitates systematic measurement of management outcomes.

  • Users can customize benchmarks and visualize management trade‐offs with their prioritized indicators.

  • Indicators were selected by a team of multidisciplinary LTAR scientists via an iterative consensus process.

  • The four domains grouping indicators were Production, Economics, Natural Resources, and Society.

  • LTAR's standardized yet flexible approach aids decision support critical to agroecosystem sustainability.

Abbreviations

AgPICKS

Agricultural Performance Indicator and Context Knowledge System

LandPKS

Land Potential Knowledge System

LTAR

Long‐Term Agroecosystem Research Network

1. INTRODUCTION

Rapid and cascading changes in weather, markets, and communities cause society's goals for agriculture to change quickly, creating moving targets of what agriculture must deliver over space and time. As the goals change, so do best practices for measuring outcomes. The Long‐Term Agroecosystem Research Network (LTAR)—a network of government, university, and nongovernmental organization scientific professionals across many disciplines throughout the United States and Canada (United States Department of Agriculture‐Agricultural Research Service (USDA‐ARS), 2024b)—is well poised to advance methods for measuring outcomes of management as goals change, to inform decision‐making for producers and researchers, and ultimately, consumers.

Currently, common goals for agriculture range from resilience to regeneration to climate smart to sustainable. The original mandate for LTAR centered around sustainability and sustainable intensification (Kleinman et al., 2018; Spiegal et al., 2018). While many in LTAR are interested in applying resilience theory to managing and measuring management outcomes (Bagchi et al., 2017; A. G. Davis et al., 2023; Hoover et al., 2021; Leimer et al., 2019; Sundstrom et al., 2023; Tracy et al., 2018; Tsegaye et al., 2024; Webb et al., 2017; Wilmer et al., 2024), sustainability has been a persistent organizing framework for many of the network's activities.

The network's working definition of sustainability entails preserving finite resources to meet the needs of future generations by minimizing trade‐offs among economic, environmental, and social outcomes (Kleinman et al., 2018). LTAR explores sustainability via the “Common Experiment,” a collection of 25 experiments conducted across 19 LTAR sites that compares strategies hypothesized to improve agricultural production outcomes (“alternative”) to “prevailing” (formerly called “aspirational” and “business as usual,” respectively) management practices across different landscapes, geographies, economies, and communities (Liebig et al., 2024; Spiegal et al., 2018; Spiegal, Webb, et al., 2022; Tsegaye et al., 2024). Engagement and knowledge coproduction with stakeholders within and beyond the Common Experiment informs LTAR's perspectives on sustainability (e.g., Augustine et al., 2020; K. P. Davis et al., 2019; DeLong et al., 2024, 2025; Derner & Augustine, 2016; Derner et al., 2021, 2022; Fernández‐Giménez et al., 2019; Guo et al., 2025, 2024; Lassa et al., 2020; Porensky et al., 2021; Reimer et al., 2023; Spiegal et al., 2024; Wilmer & Sturrock, 2020; Wilmer et al., 2018, 2019, 2022). Further understanding has been built by the LTAR Regionalization Working Group (Working Group), which incorporates point‐level data on management with raster datasets to understand regional boundaries and change (Kumar et al., 2023). LTAR has augmented its sustainability knowledge base by modeling outcomes of alternative strategies of agricultural supply chain management (Castaño‐Sánchez et al., 2023; Spiegal et al., 2020; Spiegal, Vendramini, et al., 2022). The explicit integration of social science research and inquiry in recent years has taught many in LTAR to draw its notions of sustainability from coupled social‐ecological systems (Bentley Brymer et al., 2020; Cumming, 2023; Meredith et al., 2022; Pickett et al., 2021; Selin & Selin, 2023; Wilmer et al., 2024).

Indicators are used worldwide to communicate the conditions that affect agricultural systems, the effects of agricultural systems on conditions, or both (Stevens et al., 2023). LTAR's science creates and improves both types of indicators while seeking to enhance the understanding of relationships between indicators. The network is currently developing an interactive, user‐friendly knowledge system to house indicators to inform decision‐making: the Agricultural Performance Indicator and Knowledge System (AgPICKS). LTAR set out to develop its own set of indicators for AgPICKS because published sets fell short of our needs. Here, we explain how the LTAR network developed performance indicators for use in AgPICKS. First, we outline the vision for the knowledge system. Next, we explain why existing sustainability indicator sets were not sufficient for use in it. Then, we briefly describe how LTAR initially developed indicators for the system and how they fell short. We then detail a novel, iterative consensus process to select improved indicators. We close by highlighting the unique features of the indicators we developed and future plans for AgPICKS.

Core Ideas

  • Long‐Term Agroecosystem Research Network's (LTAR's) performance indicator knowledge system facilitates systematic measurement of management outcomes.

  • Users can customize benchmarks and visualize management trade‐offs with their prioritized indicators.

  • Indicators were selected by a team of multidisciplinary LTAR scientists via an iterative consensus process.

  • The four domains grouping indicators were Production, Economics, Natural Resources, and Society.

  • LTAR's standardized yet flexible approach aids decision support critical to agroecosystem sustainability.

2. VISION FOR AGRICULTURAL PERFORMANCE INDICATOR AND CONTEXT KNOWLEDGE SYSTEM (AGPICKS)

We envision AgPICKS as a platform that will allow any producer to (a) understand and critique data about their regional context, (b) set benchmarks of desired conditions for their priority indicators, (c) collect data against their benchmarks, and (d) visualize how their management performs against their enterprise's benchmarks and the benchmarks of other enterprises (though de‐identified) (Figure 1). The Land Potential Knowledge System (LandPKS) is a United States Department of Agriculture Agricultural Research Service knowledge system focused on soil and land health (Herrick et al., 2013) to understand land use potential at specific locations using cataloged environmental conditions, land use classifications, and local soil characteristics. Development of a knowledge system containing indicators and benchmarks in multiple domains—to capture knowledge beyond the realms of natural resources covered by LandPKS—will allow producers to understand trade‐offs among biophysical and human systems. Web and data tools for AgPICKS are still under development, but a vision has been created and early‐tested via network research and engagement with stakeholders at LTAR sites (e.g., Spiegal et al., 2024).

FIGURE 1.

FIGURE 1

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Steps taken by producers and scientists when applying Agricultural Performance Indicator and Context Knowledge System (AgPICKS) at a farm or ranch.

With AgPICKS, scientists work with producers to characterize their operations, list their management goals, and choose the priority indicators. Drawing upon the LTAR data ecosystem and local knowledge for the farm or ranch, AgPICKS is then used to generate draft benchmarks, or desired conditions, for each indicator. The draft benchmarks are then reviewed and adjusted by the participating farm or ranch manager. Site‐specific metrics that capture the status of the indicators are then measured, and departure from the benchmark per indicator is analyzed. This approach allows for a normalized comparison of departure from benchmarks among indicators and among participating locations, providing better understanding of trade‐offs of management. The Indicators Working Group designed the benchmarking approach to meet the challenge of meaningfully interpreting and comparing the outcomes of management across the highly variable agroecosystems represented by LTAR sites and other potential users of the indicator system. The approach was derived from expertise in monitoring rangelands, which vary greatly in vegetation, soils, and human systems over space and time (Spiegal, Webb, et al., 2022; Webb et al., 2024).

3. REVIEW OF PUBLISHED SUSTAINABILITY INDICATOR SETS FOR AGPICKS

We reviewed published sets of indicators designed to measure agricultural sustainability for potential use in AgPICKS. The review was not comprehensive; it focused only on sets that were put forth as potentially viable by members of the LTAR Indicators Working Group. The review process was iterative and discussion‐based. Dialogue about the strengths and weaknesses of the indicator sets took place mainly in monthly Working Group meetings (2020–2023) and in conversations among lead researchers.

We assessed the published indicators using the following set of criteria: (a) enterprise scale (i.e., appropriate for the scale of a farm or ranch enterprise), (b) holistic (i.e., equal attention to indicators in distinct environmental, economic, and social domains), (c) inclusive (i.e., applicable to croplands and grazinglands), and (d) user‐driven (i.e., the user selects indicators and benchmarks of desired condition). Table 1 contains the indicator sets considered and whether the sets aligned with each of our criteria. We indicate alignment with yes, no, or not addressed, meaning not explicitly covered by the publication where the indicator set was described.

TABLE 1.

Published sustainability indicator sets considered for LTAR performance indicators framework.

Citation and description or title Enterprise scale Holistic Inclusive User‐driven
Ahlering et al. (2021)— 20 indicators synthesized from 21 published range and pastureland indicator sets Yes (range and pastureland) No (aggregation of social and economic domains) No (range and pastureland only) N/A
Fernández‐Giménez et al. (2019)—Collaborative Adaptive Rangeland Management (CARM) indicators Yes (rangeland) Yes No (rangeland only) Yes
Field to Market (2025)—The Fieldprint Platform Yes No (environmental only) no (cropland only) Yes
Food and Agriculture Organization of the United Nations (2014)—Sustainability Assessment of Food and Agriculture Systems (SAFA) No (food system) Yes Yes No (user receives lower “score” if indicators are omitted from measurement; “acceptable” vs unacceptable” performance is prescribed use)
Kipling et al. (2024)—Global Farm Metric Yes (farm) Yes Yes no (assessment is complete only if all indicators are addressed)
Häni et al. (2003)—Response‐Inducing Sustainability Evaluation (RISE) Yes (farm) Yes Yes no (user interview must be conducted by farm advisor)
Hubeau et al. (2017)—Agri‐Food Systems Sustainability Approach (AFSSA) No (agri‐food system) Yes Yes Yes
Musumba et al. (2017)—Sustainable Intensification Assessment Framework Yes (can be applied to field, farm, household and landscape) Yes Yes Yes
Sustainable Agriculture Initiative (SAI) (2024)—Farm Sustainability Assessment (FSA) Yes (farm) Yes Yes No (prescribes benchmarks)
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Abbreviation: LTAR, Long‐Term Agroecosystem Research Network; N/A, not addressed.

Musumba et al. (2017) was the only published indicator set with a perfect score in Table 1 (all “yes” across its rows in Table 1). Despite its suitability per criteria in Table 1, we did not adopt it because of perceived shortcomings in its social indicators (deemed to be too narrow in scope). Accordingly, the Indicators Working Group was not able to adopt a previously published set of indicators.

4. SELECTING INDICATORS VIA CONSENSUS

Given our perceived shortcomings of existing sustainability indicator sets (Table 1), LTAR sought to build its own indicator framework for AgPICKS. An initial version was developed via structured, cross‐site discussions at national LTAR meetings in 2019 and 2020 (USDA‐ARS, 2024a). Synthesis of those network‐wide discussions, conducted by a core group of researchers, many from rangeland‐oriented sites, yielded “version 2022” (v.2022). V.2022 comprised an architecture with four tiers: (1) three domains of sustainability (Economic, Environmental, and Social), (2) attributes of a sustainable farm or ranch per domain, (3) indicators to assess the status of each attribute, and (4) metrics to quantify each indicator (Spiegal, Webb, et al., 2022) (Figure S1.1). The LTAR Indicators Working Group members decided that the names of indicators should be normative to reflect aspirational goals in anticipation of the departure from user‐set benchmarks compared with data collected on the farm or ranch.

Pilot use of the v.2022 framework revealed that priorities from the network were missing from the indicator framework (e.g., three indicators represented management impacts on water, while only two captured impacts on the entire theme of economics). A more methodical approach for indicator selection was warranted. In response, we designed and implemented a systematic, iterative, collective decision‐making protocol to reach consensus among LTAR network scientists, yielding v.2024 (version 2024) (Figure 2, Table 2).

FIGURE 2.

FIGURE 2

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The Long‐Term Agroecosystem Research Network (LTAR) performance indicators framework with four sustainability domains, two attributes per domain, and up to three indicators per attribute. Metrics, not pictured, are to be chosen by users to represent local conditions and priorities.

TABLE 2.

Indicator definitions for the v.2024 framework.

Indicator Attribute Domain Definitions
Financial Efficiency Financial Health Economics Proportion of farm or ranch assets being used to generate revenue
Liquidity Financial Health Economics Ability of the farm or ranch to generate sufficient cash to meet total cash demands without disturbing the ongoing operation of the business
Solvency Financial Health Economics Dollar value that would remain if all assets were converted into cash and all debts paid
Net Farm Income Profitability Economics Returns (both monetary and nonmonetary) to farm operators for their labor, management and capital, after all production expenses have been paid
Profit Margin Profitability Economics The percentage of profit earned by the farm or ranch in relation to its revenue
Returns to Land, Labor, and Management Profitability Economics Earnings to labor and management from the farm or ranch business
Particulate Control Healthy Air Natural Resources Degree to which the ambient air on or affected by the farming or ranching system is free of smoke, dust, and other particulates
Agricultural Emissions Management Healthy Air Natural Resources Degree to which the farming or ranching system actively manages emissions of methane, ammonia, and other gasses of concern
Microclimate Suitability Healthy Air Natural Resources Degree to which microclimate for plant growth and human comfort is regulated by the farm or ranch's agroecosystem structure and function
Biodiversity Water and Land Health Natural Resources Variety of life within a farm or ranch that provides an array of supporting or regulating ecosystem services to the agroecosystem on or our outside of the farm/ranch, such as pollination, pest suppression, nutrient cycling, productivity
Soil Health Water and Land Health Natural Resources Capacity of the farm's or ranch's soil to sustain plant and animal productivity, maintain or enhance water and air quality, and support human health and habitation
Water Quantity and Quality Water and Land Health Natural Resources Suitability of water on or flowing from the farm or ranch for a particular use based on selected physical, chemical, and biological characteristics and amount of water on or flowing from the farm that provides an array of supporting or regulating ecosystem services such as groundwater recharge, wildlife habitat, erosion protection
Conditions Community Well‐Being Society Contributions of farm or ranch to the physical, economic, and social infrastructure of the community
Capabilities Community Well‐Being Society Contributions by farm or ranch to the skills and agency of a community to respond and take action to make the community a better place
Connections Community Well‐Being Society Contributes by the farm or ranch to the relationships within a community and between the community and its environment
Self‐Determination Producer Well‐Being Society Ability to choose and control one's own actions and goals
Self‐Actualization Producer Well‐Being Society Realization of one's potential and the development of one's abilities and appreciation for life
Mental Health Producer Well‐Being Society Perceived stress, anxiety, and ability to cope with challenges to be able to meet one's full potential
Product Quality Agroecosystem products Production Characteristics of farm or ranch products valued by buyers along agricultural supply chains
Product Safety Agroecosystem products Production Degree to which farm or ranch products are safe for consumption
Yield Agroecosystem Products Production Amount of agricultural production harvested per unit of land area
Energy Balance Resource Reserves Production Degree to which energy inputs and outputs are balanced
Nutrient Balance Resource Reserves Production Degree to which nutrient inputs and outputs are balanced
Water Use Efficiency Resource Reserves Production Degree to which water inputs (unit of potentially available water (precipitation [including snowfall when relevant] + any irrigation) is balanced with outputs (crop harvest, biomass produced, or livestock yield)
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Note: Economics domain definitions were adapted from Farm Financial Standards Council (2014) and McCorkle et al. (1999). Soil Health definition was adapted from Soil Science Society of America (SSSA) (Natural Resources Conservation Service [NRCS], n.d.). Society definitions were adapted from Breslow et al. (2016). Water Use Efficiency definition was adapted from Hoover et al. (2023).

Our consensus process drew from several methods, including a modified Delphi technique (Beiderbeck et al., 2021; Taghipoorreyneh, 2023), articulation work (Baker & Millerand, 2007), multiple criteria decision analysis (Picone et al., 2021), and multistage qualitative exploratory approach involving stakeholders (Velasco‐Muñoz et al., 2022) (details in Section S2). Because the process involved human subjects, the lead researchers sought and received approval from New Mexico State University's Institutional Review Board, with protocol 2404123904.

The process comprised several stages. First, lead researchers developed a structured suggestion form to elicit feedback on v.2022 from network scientists (Section S3). For clarification, the leads conducted individual follow‐up conversations with all who made suggestions. All information gathered during the “Clarifying Conversations” was entered into the online database structured by each domain of the framework. Soon after the Clarifying Conversations, an Indicators Selection Committee was recruited via a request for volunteers from lead researchers to the LTAR Indicators Working Group. Volunteers were 17 scientists representing multiple disciplines including soil science, rangeland science, ecology, agronomy, social science, data science, and economics. To prepare the Selection Committee for voting, lead researchers synthesized ideas from the Suggestion Box and Clarifying Conversations into reference materials to organize the choices to be made in or between the Indicator Selection Committee meetings (Table S4.1). LTAR scientists inside and outside of the Indicator Selection Committee were invited to present their perspectives on the choices at the Indicator Selection Committee meetings.

Indicator Selection Committee members were instructed to follow three rules when making their selections. Rule 1 stipulated adhering to a hierarchical structure that included domains at the broadest level, followed by attributes, indicators, and metrics. Rule 2 specified two attributes per domain and three indicators per attribute. Rule 3 forced a directionality of framework elements where “more is better,” allowing analysts and users to quantify departure from benchmarks in a consistent direction (e.g., the indicator name “Water Quantity and Quality” was more effective than the indicator name “Water”). Indicator Selection Committee members were asked to read a set of criteria for useful indicators (Karl et al., 2017) and to keep in mind the eventual integration of enterprise‐level indicator data with LTAR modeling efforts, such as the Soil and Water Assessment tool (Yost et al., 2024). Voting forms were administered via the online survey software Qualtrics (Sections S5–S7). Detailed voting results are included in Section S8. Production emerged as a fourth domain by a narrow voting margin (Figure S8.1).

Toward the end of the meetings and iterative voting, the lead researchers conducted conversations with each Indicator Selection Committee member to capture the nuance of individual perspectives and gather feedback on aspects of the framework missing from the voting forms. The conversations centered around a version of the framework diagram that combined information from the voting results. Any new naming suggestions from these conversations were discussed with each committee member until consensus was reached. Lead researchers and Indicator Selection Committee members shared final comments and ratified the new set of indicators in the final Indicator Selection Committee meeting (May 2024).

In May 2025, after initial use of the framework ratified in May 2024 (Figure S9.1), the names of a handful of framework elements were changed for clarity and salience (one domain, two attributes, and three indicators). Underlying indicator definitions (Table 2) were slightly modified to make descriptions more specific or incorporate a broader range of phenomena.

5. INDICATORS SELECTED

5.1. Suggestion Box and Clarifying Conversations

The network‐wide Suggestion Box received comments from 17 scientists from across LTAR, for a total of 44 unique suggestions for changes at the level of domain, attribute, indicator, or metric. Of the 44 suggestions to v.2022, 21 pertained to its Production/Economics domain, 12 to its Environment domain, and 11 to its Social domain. The LTAR Human Dimensions Working Group and LTAR Resilience Working Group conducted their own consensus processes to develop coordinated suggestions to v.2022.

An important discussion during the Clarifying Conversations (with people who contributed to the Suggestion Box) was the use of resilience terminology in a sustainability‐oriented framework. Lead researchers determined that some concepts pertaining to social systems and resilience that were named in the Suggestion Box exist beyond the relatively small container of the performance of management at enterprise scale designated for AgPICKS. Lead researchers pledged that while those broader ideas may not fit into the enterprise‐scale indicator framework per se, they would be captured in the multiscalar knowledge system of AgPICKS.

5.2. Production and Economics domains

After the results from the Suggestion Box were in and the convenings began, the treatment of production‐oriented performance indicators in a sustainability framework was the biggest challenge for the Indicator Selection Committee. As such, concepts for Economics and Production were included in all three rounds of voting (details in Table S10.1). A few members of the Indicator Selection Committee presented their case for Production to be its own domain. Disagreement ensued about whether Production indeed belongs in a separate domain and, if so, whether to place it as a fourth domain that is an outcome of management on par with the three other domains of Natural Resources, Economics, and Society, or to place it as the central activity with the Economics, Society, and Natural Resources domains surrounding a core of Production. Other groups encountered such debates and noted disproportionate representation of productivity and profit indicators among the published collections for measuring agricultural performance (Geck et al., 2023). Indicator Selection Committee members presenting arguments in favor of a separate Production domain noted the apparent lack of emphasis on production in v.2022, while opposing views expressed concern with overemphasizing production in the performance indicators framework and implications for farm or ranch sustainability. Strategies to intensify production and boost efficiency to increase production can have natural resource ramifications that are not necessarily sustainable as improved efficiency leads to increased resource use and can lead to increased resource consumption (York & McGee, 2016). Intensification may not be sustainable from an economic standpoint either, with the potential for diminishing returns as investment increases. Ultimately, the Indicator Selection Committee decided to use a fourth domain of Production treated with equal emphasis alongside Economics, Society, and Natural Resources (Environment in v.2022), demonstrating LTAR's holistic perspective of sustainability (Figure 2, Table 2).

Agricultural economists in the LTAR Indicators Working Group joined the Indicator Selection Committee to provide domain expertise, and their proposal for indicators within an Economics domain was ratified (Economics domain, Figure 2). Their suggestions entailed attributes and indicators that align with concepts already used by agribusiness and insurance brokers.

Concerns over how to treat labor were raised repeatedly throughout the selection process. Discussions explored the difficulty of setting benchmarks for labor, as the impacts differ by scale. For instance, decreased labor costs (hiring fewer people or working fewer hours) are generally better for agricultural operational profitability (Bora et al., 2012), but can be detrimental to overall employment in the surrounding community (and thus, have potential implications for both well‐being and regional economies) (Christiaensen et al., 2020). Further, as more technology is adopted, an operation may shift from labor involving traditional skills (lower cost) to labor involving specialized skills (higher cost) (Erickson et al., 2018; Ogunyiola et al., 2024). The Indicator Selection Committee ultimately decided that the indicator Returns to Land, Labor, and Management, and nested metrics will adequately address these implicit trade‐offs to maintain a holistic perspective of sustainability performance.

The idea to include indicators of Byproducts and Waste Minimization as part of the resource‐focused attribute of Production (i.e., Resource Reserves) was discussed in the Individual Conversations at the end of the voting process. These concepts were rejected by the Indicator Selection Committee as they were determined to be management approaches to be assessed rather than indicators of the outcomes resulting from management.

5.3. Natural Resources domain

After early initial use of the indicators ratified by committee in May 2024, the domain previously called Environment was renamed Natural Resources, toward alignment with terminology used in planning by the United States Department of Agriculture Natural Resources Conservation Service.

Overall, suggestions for this domain in the Suggestion Box and committee meeting and voting process were relatively minor, possibly reflecting the prevalence of biophysical scientists in the network with expertise and comfort in investigating environmental outcomes. Many ideas did not change much from the start of the network‐wide discussions on indicators, and so this consensus process entailed changing names more than concepts (details in Section S11).

Indicator Selection Committee discussions (Table S11.1) elicited questions about whether the meaning of the term “biodiversity” alone represents a desired condition because biodiversity can describe the abundance and diversity of undesirable as well as desirable species. Indicator Selection Committee members proposed several alternative names to qualify biodiversity as a goal (e.g., “Biodiversity Maintenance”) (Table S11.1), but ultimately landed on the widely used Biodiversity. The group also debated the use of the term “health” for indicators and metrics, with some positing the term is too value‐laden, vague, and only appropriate for medical settings. The group ultimately ratified its use in two cases to maintain viability and visibility with partner agencies and stakeholders recognizing and using the term (Figure 2, Table 2).

Two aspects of water were prioritized in suggestions and discussions about the water‐related indicator: (1) water at field scale necessary to help plant life grow and (2) natural resource impact of the water leaving the field (runoff or groundwater). However, participants noted that water is the only major concept with two indicators in v.2022, which elicited movement to unite the two concepts into one (Figure 2, Table 2). The water topic also brought forth debate about the scale of this indicator system, especially because water impacts adjacent fields, and the role of regulations in water quality. The group was ultimately satisfied that widespread use and interpretation of AgPICKS will provide knowledge on multiple farms and how they affect each other, which will help address the relationship between environmental quality and regulations.

In 2025, the names of the attribute Atmospheric Health and its three nested performance indicators were changed. We changed Atmospheric Health to Healthy Air to retain focus on the air immediately surrounding the farm. Greenhouse Gas Mitigation was changed to Agricultural Emissions Management to include ammonia (NH3) and to emphasize the role of active management. Air Quality was changed to Particulate Control to emphasize the smoke and dust that agricultural systems can produce. Temperature Regulation was updated to Microclimate Suitability for improved clarity on the scale and scope of this indicator (cf. Figure S9.1; Figure 2).

5.4. Society domain

Social scientists on the Indicator Selection Committee advocated for indicators gleaned from ongoing research by the LTAR Human Dimensions Working Group on incorporating stakeholders’ lived experiences to develop indicators of well‐being (Bentley‐Brymer et al., 2020; Friedrichsen et al., 2021; Friedrichsen, 2025). The research entailed inductive data analysis of interviews with producers. Theories related to well‐being structured the interviews and their interpretation, including the 4 C's conceptual framework of well‐being (Breslow et al., 2016), self‐determination theory (e.g., Deci & Ryan, 2012), nature's contribution to people framework (Intergovernmental Science‐Policy Platform on Biodiversity & Ecosystem Services [IPBES], 2019), the Millenium Ecosystem Assessment (2005), Maslow's Hierarchy of Needs (Maslow, 1971), and Wilkinson's Community Health (Wilkinson, 1996).

The social scientists articulated that stakeholder insights from their research were essential to reliably identify the aspects of well‐being that characterize a sustainable farm or ranch, and that adopting indicators from the research would advance the theoretical development of well‐being in the context of agricultural management. This research‐based approach reflects the requisite qualitative methods for developing social indicators for agricultural performance assessment, as called for by global experts (de Olde et al., 2016).

Results of their research yielded attributes of a sustainable farm or ranch that distinguished well‐being at the individual level (one's well‐being as a result of one's agricultural management) from well‐being at the community level (how an individual perceives their management contributes to well‐being in their community) (Bentley‐Brymer et al., 2020; Friedrichsen et al., 2022). At the individual level, subjective well‐being (an individual's ability to pursue a fulfilling life) emerged from the data instead of objective well‐being (the absence of pain). The focus on subjective instead of objective will improve capacity for self‐assessment that is sensitive to yearly or seasonal changes in management.

Subjective well‐being indicators that were ratified are often absent from sustainability assessments due to their complex, context‐dependent nature. These indicators span scales (e.g., individual, family, and community) and are politically sensitive, and accordingly are frequently neglected in favor of more observable measures (Ait Sidhoum, 2018). However, a producer's ability to monitor their own well‐being and the well‐being outcomes of their community associated with their implementation of sustainable practices can create a valuable feedback loop that supports behavior change motivation (Prost et al., 2023). Thus, it was deemed that the new set of indicators would fill a need for a framework that producers and experts can use to evaluate and assess the perceptions of their individual management decisions on the greater community's well‐being (Breslow et al., 2016).

When the Indicator Selection Committee was given the chance to accept or reject the set during Individual Conversations, most expressed they were comfortable with the proposed attributes and indicators (Table S12.1). The lack of questioning by the biophysical scientists may suggest a strong trust in and reliance on the social science expertise built in recent years.

The name of the attribute pertaining to Individual Well‐Being was changed to Producer Well‐Being to reflect the focus on producers, the intended users of AgPICKS (c.f., Figure S9.1, Figure 2).

6. KEY TAKEAWAYS FROM THE CONSENSUS PROCESS

The iterative, adaptive nature of our consensus process allowed for elaborate discussions among scientific domain experts and Indicator Selection Committee members. Articulation work, as described by Baker and Millerand (2007), allowed for mediation of concepts and co‐design of well‐defined terms. These approaches resulted in inclusion and development of important concepts that were previously only implicit, such as rendering production distinct from economics. Iteration was also critical in improving voting forms over the course of the effort.

We found that using framework diagrams as visual aids to envision scenarios was effective. A working spreadsheet of indicator definitions was another helpful resource for committee members that allowed for operationalizing the terms in real time during the consensus process. LTAR's capacity includes data managers dedicated to this effort (Kaplan et al., 2021). Data managers facilitated data to be managed in a tidy fashion, with a database well‐described and organized in rows and columns (Wickham, 2014) for use more broadly in the network and in development of web tools such as AgPICKS. During the consensus process, a committee member developed a tetrahedron as a visual aid to use when engaging with stakeholders about the domains and their relationships (Section S13). Overall, we learned that tidy data and effective visualizations are key to understanding and communicating the indicator framework. [Correction added on 30 June 2025 after first online publication: At the end of the second paragraph in the “Key Takeaways from the Consensus Process” section, in the second to last sentence “(Section S11)” was incorrect. This should have read: “(Section S13)”.]

Indicator Selection Committee members found consensus elusive when deciding how to treat production. In the end, most were satisfied that the new indicators framework would offer a standardized approach for evaluating important trade‐offs involving production.

7. UNIVERSAL APPLICATIONS

We offer transparent insight into an iterative consensus process to support comprehensive buy‐in of a performance indicators framework designed to resonate with producers. The performance indicators developed by LTAR seated within AgPICKS offer promise for widespread applicability via customizable exploration of the trade‐offs of farm or ranch scale practices. The ability of the user to assess multiple trade‐offs will allow for more informed decision‐making, including the adjustment of monitoring and management. Further, the performance indicators will be increasingly useful over time by changing to reflect the needs of stakeholders through continued coproduction. As such, AgPICKS provides enhanced capacity to producers across a wide breadth of agroecosystems globally.

8. LIMITATIONS AND FUTURE DIRECTIONS

While inclusiveness was an ideal, the Indicator Selection Committee was limited to self‐selected volunteers already in the Indicators Working Group. The use of the framework will be contingent upon building buy‐in from potential end users of the framework. We continue to engage with stakeholders to this end and to develop benchmarking approaches for on‐farm use.

Evaluation of benchmarks within and between enterprises is a key area for future coproduction with stakeholders. Comparing the potential trade‐offs of prevailing and alternative practices within and between enterprises in AgPICKS involves two primary approaches: direct indicator comparisons using the same metrics and using relativized comparisons when metrics differ in ways that direct comparisons are not possible. An array of promising analytical methods for relative comparisons include log response ratio (Su et al., 2022), effect size (Ruppert et al., 2012; Smith et al., 2015), and direct comparison of anomalies (Petrie et al., 2018). Webb et al. (2024) offer critical conceptual guidance for developing quantitative benchmarks, including the consideration of relationships among indicators, reference conditions, and existing datasets. Likewise, a collection of methods for engaging with stakeholders around developing benchmarks demonstrates potential, including interviews, facilitated discussions (e.g., Reimer et al., 2023), and serious games (e.g., Fleming et al., 2020).

We will also work with stakeholders via intentional coproduction to develop AgPICKS and a corresponding web tool. Tool development will entail iterative feedback and testing with an array of stakeholders, including environmental organizations (e.g., Environmental Defense Fund and The Nature Conservancy), producers, and agricultural service providers. Committee members plan to use the tetrahedron (Figures S13.1 and S13.2) as a visual aid.

Though applied at farms and ranches, the AgPICKS process aims to capture dimensions of agroecosystems outside of the immediate scope of the operation. The integration of farm‐ or ranch‐scale knowledge with regional context will provide cross‐scale insights for researchers and policymakers interested in agricultural resilience. Regional snapshots provided to the AgPICKS user will rely in large part on the expertise and data products of the LTAR Regionalization Working Group (e.g., Bean et al., 2021; Kumar et al., 2023).

The achievement of sustainable agroecosystems is a complex goal with multiple possible solutions. The LTAR indicators framework provides a partial solution, via standardized “language” to speak within the LTAR network, particularly regarding coordination of the Common Experiments conducted across disparate LTAR sites (Liebig et al., 2024). Outside of LTAR, the framework can provide a standardized way to track performance on disparate operations—toward more sustainable farms and ranches despite differences in local contexts.

AUTHOR CONTRIBUTIONS

Megan Donovan: Conceptualization; data curation; formal analysis; investigation; methodology; project administration; visualization; writing—original draft; writing—review and editing. Sheri Spiegal: Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; project administration; resources; supervision; writing—original draft; writing—review and editing. Nicole Kaplan: Conceptualization; data curation; investigation; methodology; project administration; writing—original draft; writing—review and editing. David Archer: Conceptualization; writing—original draft; writing—review and editing. Alycia Bean: Conceptualization; writing—original draft; writing—review and editing. Sarah E. J. Beebout: Conceptualization; writing—review and editing. Brandon T. Bestelmeyer: Conceptualization; writing—original draft; writing—review and editing. Patrick Eugene Clark: Conceptualization; writing—original draft; writing—review and editing. Alia DeLong: Conceptualization; writing—original draft; writing—review and editing. Ann‐Marie Fortuna: Conceptualization; writing—original draft; writing—review and editing. Claire N. Friedrichsen: Conceptualization; methodology; writing—original draft; writing—review and editing. David L. Hoover: Conceptualization; writing—original draft; writing—review and editing. David Huggins: Conceptualization; writing—original draft; writing—review and editing. Peter J. A. Kleinman: Conceptualization; writing—original draft; writing—review and editing. Matthew M. McIntosh: Conceptualization; visualization; writing—original draft; writing—review and editing. Chris S. Renschler: Conceptualization; visualization; writing—original draft; writing—review and editing. John Ritten: Conceptualization; writing—original draft; writing—review and editing. Douglas R. Smith: Conceptualization; writing—original draft; writing—review and editing. Nicholas P. Webb: Conceptualization; writing—original draft; writing—review and editing. J. D. Wulfhorst: Conceptualization; writing—original draft; writing—review and editing.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

Supporting information

Figure S1.1. The v.2022 LTAR performance indicators framework with three sustainability domains, two attributes per domain, up to four indicators per attribute and dots to represent metrics, to be chosen by users to represent local conditions and priorities.

Table S4.1. Dates and themes for Selection Committee meetings and voting.

Figure S8.1. Voting outcomes for voting Round 2.

Figure S9.1. The v.2024 LTAR performance indicators framework ratified by the Indicator Selection Committee in May 2024.

Table S10.1. Selection Committee choices for Production and Economics and support stated by advocates in the Suggestion Box, Committee Meetings, and Follow‐Up Conversations.

Table S11.1. Selection Committee choices for Environment and support stated by advocates in the Suggestion Box, Committee Meetings, and Follow‐Up Conversations. In Individual Conversations, lead researchers promised Committee members that the names would be workshopped with NGO and public partners over time.

Table S12.1. Selection Committee choices for Society and support stated by advocates in the Suggestion Box, Committee Meetings and Follow‐Up Conversations.

Figure S13.1. Tetrahedron representation of the v.2024 indicators framework.

Figure S13.2. The explanation of each v.2024 indicators framework tetrahedron feature.

JEQ2-54-1500-s001.docx (5.4MB, docx)

ACKNOWLEDGMENTS

This research was a contribution from the Long‐Term Agroecosystem Research (LTAR) network. LTAR is supported by the United States Department of Agriculture. Figure 1 was created by Sheri Spiegal. Figures 2, S1.9, and S13.1 were created by Matthew M. McIntosh. Thanks to Mark Kautz for improvements to Figure 2. Figure S1.1 was created by Zach Hurst. Figure S8.1 was created by Darren James. Figure S13.2 was created by Chris S. Renschler. Support provided by the Foundation for Food & Agriculture Research (FFAR) project, “Recycling Nutrients for Robust Agricultural Supply Chains” project number 22‐000103. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the United States Department of Agriculture. [Correction added on 30 June 2025 after first online publication: In the Acknowledgments, the text “Figure S1.13 was created by Chris S. Renschler.” was incorrect. It should have read: “Figure S13.2 was created by Chris S. Renschler”.]

Donovan, M. , Spiegal, S. , Kaplan, N. , Archer, D. , Bean, A. , Beebout, S. E. J. , Bestelmeyer, B. T. , Clark, P. , DeLong, A. , Fortuna, A.‐M. , Friedrichsen, C. N. , Hoover, D. L. , Huggins, D. , Kleinman, P. J. A. , McIntosh, M. M. , Renschler, C. S. , Ritten, J. , Smith, D. R. , Webb, N. P. , & Wulfhorst, J. D. (2025). Selecting performance indicators for farms and ranches engaged in collaborative agroecosystem research. Journal of Environmental Quality, 54, 1500–1514. 10.1002/jeq2.70051

Assigned to Associate Editor Amy Shober.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1.1. The v.2022 LTAR performance indicators framework with three sustainability domains, two attributes per domain, up to four indicators per attribute and dots to represent metrics, to be chosen by users to represent local conditions and priorities.

Table S4.1. Dates and themes for Selection Committee meetings and voting.

Figure S8.1. Voting outcomes for voting Round 2.

Figure S9.1. The v.2024 LTAR performance indicators framework ratified by the Indicator Selection Committee in May 2024.

Table S10.1. Selection Committee choices for Production and Economics and support stated by advocates in the Suggestion Box, Committee Meetings, and Follow‐Up Conversations.

Table S11.1. Selection Committee choices for Environment and support stated by advocates in the Suggestion Box, Committee Meetings, and Follow‐Up Conversations. In Individual Conversations, lead researchers promised Committee members that the names would be workshopped with NGO and public partners over time.

Table S12.1. Selection Committee choices for Society and support stated by advocates in the Suggestion Box, Committee Meetings and Follow‐Up Conversations.

Figure S13.1. Tetrahedron representation of the v.2024 indicators framework.

Figure S13.2. The explanation of each v.2024 indicators framework tetrahedron feature.

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