Jan 01, 2025
USDA LTAR Cropland Common Experiment: Standardized primary metric protocols
- Lori J. Abendroth1,
- Mark A. Liebig2,
- G Philip Robertson3,4
- 1USDA Agricultural Research Service, Cropping Systems and Water Quality Research Unit, Columbia, MO;
- 2USDA Agricultural Research Service, Northern Great Plains Research Laboratory, Mandan, ND;
- 3W.K. Kellogg Biological Station, Michigan State University, Hickory Corners, MI;
- 4Dept. of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI
External link: https://ltar.ars.usda.gov
Protocol Citation: Lori J. Abendroth, Mark A. Liebig, G Philip Robertson 2025. USDA LTAR Cropland Common Experiment: Standardized primary metric protocols. protocols.io https://dx.doi.org/10.17504/protocols.io.261ge59pyg47/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: July 30, 2024
Last Modified: January 01, 2025
Protocol Integer ID: 104278
Keywords: Long-Term Agroecosystem Research, LTAR, USDA LTAR, Common Experiment, crops, standardization, protocols, metrics, indicators
Funders Acknowledgements:
United States Department of Agriculture
Grant ID: -
Abstract
A component of the USDA ARS Long-Term Agroecosystem Research (LTAR) network is a Common Experiment standardized across all sites. In this overview protocol we describe the development of standardized protocols for the biophysical metrics collected in the Common Experiment. Throughout this collection, we refer to “metric” as the physical sample that can be quantified and used to inform the status of performance indicators within production, environment, economic, and society domains. We refer to “protocol” as the methods used to collect that metric so that all experimental sites are compatible. This set of protocols were developed for the Cropland Sites although some are also usable for Grazingland and Integrated Common Experiment Sites. This collection allows the LTAR network to ensure research is scalable and robust. All Cropland Common Experiment Sites within the network started following these protocols with the 2024 growing season.
Attachments
Context
Context
The Common Experiment was envisioned for more than a decade, was formally introduced in 2018, and is now implemented at all LTAR sites (Robertson et al., 2008; Spiegal et al., 2018). It contrasts prevailing and alternative/aspirational production systems, with the latter including novel innovations hypothesized to advance sustainable intensification in locally appropriate ways. The Common Experiment as it is currently deployed is described by Tsegaye et al. (2024) and Liebig et al. (2024). Readers are encouraged to access these papers for a greater understanding of the LTAR network, Common Experiment, and challenges and opportunities associated with coordinated, cross-site research; together they provide valuable context for the procedural steps outlined below. Additionally, the Common Experiment at each LTAR site (“Site”) is described in a series of papers included in the Journal of Environmental Quality Special Section: The USDA LTAR Common Experiment—Research to Support a Sustainable and Resilient Agriculture (https://acsess.onlinelibrary.wiley.com/doi/toc/10.1002/(ISSN)1537-2537.USDA-LTAR-Common-Experiment). The development of metrics and protocols briefly described in Liebig et al., 2024 are further detailed with a workflow provided here.
Framework
Framework
The LTAR network is focused on working lands across the U.S. which includes croplands, grazing lands, and integrated crop/grazing land management. Researchers internal and external to USDA will advance science more rapidly when standardized metrics and protocols are utilized as much as possible. Standardization of metrics and protocols for field-based research has been documented as an essential component of multi-site research activities (Kladivko et al., 2014).
The metrics and protocols in this collection address research questions associated with agroecosystem efficiencies, sustainability, and tradeoffs (Kleinman et al., 2018). The metrics inform the status of performance indicators across four domains: production, economics, environment, and society (Figure 2 in Liebig et al., 2024; Spiegel et al., 2022). This collection includes protocols that provide measurements under the production and environmental domains. The process of determining metrics for economic and society domains are currently under development.
Step 1. Identify indicators within production and environmental domains
Step 1. Identify indicators within production and environmental domains
The production and environmental domains provide the biophysical constraints for evaluating agroecosystems in the LTAR network (Spiegal et al., 2022). Within these two domains, several indicators exist and serve as the secondary conceptual framework (Table 1). There are six indicators (productivity, water quality, and so forth) which were determined as the most critical when characterizing productivity and environment domains. The metrics and protocols that are described here are categorized by these six indicators.
Table 1. Disciplinary indicators under each domain.
| A | B | |
| Productivity Domain | Environmental Domain | |
| Productivity (encompasses yield, biomass, quality) | Water quality | |
| Water quantity | ||
| Soil | ||
| Biodiversity and pest | ||
| Greenhouse gas and air quality |
Step 2. Determine which metrics have the highest value
Step 2. Determine which metrics have the highest value
Each potential metric was discussed and assigned as either primary, secondary, or tertiary (Table 2). A classification schema was necessary as resources are limited and Sites cannot collect every metric due to infrastructure, equipment, labor, financial, expertise, or other constraints.
Table 2. Classification schema for metrics. Primary metrics are those most important for all Common Experiment Sites to implement.
| A | B | |
| Metric classification | Attributes | |
| Primary | Very high value. Inclusion of the metric is intended unless significant barriers exist or the metric is not applicable. | |
| Secondary | High value. Inclusion of the metric is strongly encouraged but not essential for all Sites. | |
| Tertiary | Moderate value. Inclusion of the metric is valuable if the Site has the necessary resources or it is particularly important in the region. |
Metrics were assessed for their value to science now, expectations for the future, and practicality in carrying them out (Table 3). This approach ensured that any Site not able to collect a particular metric immediately would be working towards collecting it. Sites vary in their readiness to collect some metrics while others are well-situated to do more. This collection outlines what is needed to best meet the goals of the Common Experiment.
Table 3. Criteria for selecting primary metrics in the LTAR Common Experiment.
| A | B | |
| Consideration | Specific questions asked | |
| Evidence-based | Has the metric shown to be robust and valuable in documenting change? | |
| Sensitive to change | How much time is needed to see change in this metric? Is it responsive to the treatments employed or is there a different metric that is more appropriate? | |
| Logistically feasible | Can this metric be collected at most Sites? If not, is there access to other facilities/laboratories to help? | |
| Accurate and precise | Have the results from this metric shown to be repeatable and representative of the true response? | |
| Broadly applicable | Is the metric applicable across plant types, regions, and climates? | |
| Useful for models | Is the metric required as an input in several biophysical or climate models? | |
| Useful for efficiencies | Is the metric necessary to calculate efficiencies such as water use efficiency, nitrogen use efficiency, etc.? | |
| Reasonable cost | What costs exist for processing the sample and could this be prohibitive in collecting it? Are there adjustments to lessen the cost? | |
| Interpretable and valued by multiple stakeholders | Is the metric valuable to stakeholders outside of LTAR such as other scientific communities, farmers, ranchers, policy and actionable science, and general citizens? | |
| Scale | Is the metric relevant at the plot, field, or both scales? | |
| Frequency | What is the preferred frequency of measurement? What would be the minimum frequency? | |
| Reporting | What units are associated with the metric? | |
| Additional considerations based on the discipline | Are there other metrics already established as standards that must be included? |
Significant discussion among scientists occurred regarding language and how metrics were termed to ensure each would be properly understood. There were variations in terminology based on the region and the type of plant grown. For example, crop biomass terminology varies based on the harvested product (e.g., grain, fruit, vegetable, forage), yet all of these represent the exportable product from the field that is economically valued. In this scenario, scientists determined that referring to biomass as either “staying” in the field or “leaving” the field would limit confusion and be most robust across different crop types. Biomass staying in the field represents the plant residue important for soil cover and nutrient cycling (input) while the biomass leaving the field represents the exportable fraction.
Metrics were defined at the finest resolution, each reflecting a unique entry in a spreadsheet. For example, a biomass sample submitted for nutrient analysis of nitrogen, phosphorus, potassium, sulfur, and carbon would have five associated metrics. Accordingly, each metric represents a standalone value in a data set.
Step 3. Develop protocols for all primary metrics
Step 3. Develop protocols for all primary metrics
There are fewer protocols in this collection than metrics as metrics were grouped when appropriate. By grouping similar metrics, it reduced the number of written protocols and simplified implementation for field and lab personnel. An example of this is provided in Table 4 with a subset of productivity metrics shown in the first column. If the description, frequency, units, and general methodology (not shown in Table 4) were similar, one protocol was written. The process to distill written protocols required two actions of consolidation. Using aboveground biomass as an example, a first grouping was established for biomass staying or leaving the field. The in-field collection, processing, and analyses were similar whether biomass was retained or exported. The second grouping occurred when analytical methods were identical and carried out at the same time for multiple metrics such as the dry combustion method for carbon and nitrogen. Using this example, 12 metrics could be represented by three protocols.
Table 4. Example of a subset of productivity metrics and the consolidation process based on similarities.
| A | B | C | D | E | F | |
| Primary Metric | Short Description | Preferred Frequency | Minimum Frequency | Units | Protocol | |
| Aboveground biomass staying in the field | All vegetative above-ground biomass, dry weight. Includes leaves, stem, and non-grain components. | Maximum vegetative biomass and maximum reproductive biomass | Once (1x) at Plant Maturity, Senescence, or Grazing Cycle | kg/ha dry biomass or g/m^2 dry biomass | USDA LTAR Common Experiment measurement: Aboveground biomass | |
| Aboveground biomass leaving the field (Yield/Biomass) | ||||||
| Aboveground biomass (staying): C concentration | Average g Carbon per kg aboveground dry matter | Maximum vegetative biomass and maximum reproductive biomass | Once (1x) at Plant Maturity, Senescence, or Grazing Cycle | g/kg dry biomass | USDA LTAR Common Experiment measurement: Concentration of carbon and nitrogen in aboveground biomass | |
| Aboveground biomass (leaving): C concentration | ||||||
| Aboveground biomass (staying): N concentration | Average g Nitrogen per kg aboveground dry matter | |||||
| Aboveground biomass (leaving): N concentration | ||||||
| Aboveground biomass (staying): P concentration | Average g Phosphorus per kg aboveground dry matter | Maximum vegetative biomass and maximum reproductive biomass | Once (1x) at Plant Maturity, Senescence, or Grazing Cycle | g/kg dry biomass | USDA LTAR Common Experiment measurement: Concentration of phosphorus, potassium, and sulfur in aboveground biomass | |
| Aboveground biomass (leaving): P concentration | ||||||
| Aboveground biomass (staying): K concentration | Average g Potassium per kg aboveground dry matter | |||||
| Aboveground biomass (leaving): K concentration | ||||||
| Aboveground biomass (staying): S concentration | Average g Sulfur per kg aboveground dry matter. | |||||
| Aboveground biomass (leaving): S concentration |
A protocol template was developed and utilized across all workgroups to help standardize content and format. The template prompted workgroups to document field methodology, sampling size, laboratory procedures, calculations, quality control practices, required metadata, covariate metrics, and sample archiving. This template is attached to this protocol and also available as Template S1 in Liebig et al., 2024.
Although secondary and tertiary metrics were identified by the disciplinary workgroups, protocols have yet to be developed for them. As the existing protocols are implemented for primary metrics, scientists will assess their suitability and refine as needed as well as publish new protocols. Protocols will be included in this collection as they are published. All protocols have standardized keywords; they are readily found at https://www.protocols.io using keywords “LTAR Common Experiment” in the search function.
Step 4. Review protocols
Step 4. Review protocols
Each protocol followed a similar review process as shown in Figure 1. Once the protocol was written initially it moved through seven steps of review and editing before publication.
Figure 1. Workflow utilized by the Cropland Common Experiment in writing, reviewing, editing, and publishing the protocols in this collection.
Step 5. Implement protocols across the LTAR network
Step 5. Implement protocols across the LTAR network
Sites are working to integrate these protocols into their research workflows. Metrics may have been collected in the past following a very similar methodology as outlined in these protocols; this allows historical data to be confidently aligned with current data. However, other metrics may have been collected following a different methodology not necessarily compatible with these protocols. Some Sites are also determining whether to continue using both old and new methodologies to ensure historical data are not orphaned. Given the long history of many LTAR sites, changes in methodology over time due to technological and scientific advancements are expected. Thus, the task of reconciling different methodologies and determining how to connect historical data with current data is ongoing.
Step 6. Enter metric data into LTAR data infrastructure
Step 6. Enter metric data into LTAR data infrastructure
The standardized metrics and protocols serve as the foundational structure for data entry processes used in the LTAR Common Experiment. These standards align with the network's goal to enhance data consistency, data sharing, and research outcomes across Sites.
The entry templates will be shared in a future version of this collection as they are currently under development. The data entry templates will be in a row-column format with metric codes and units documented for those users wanting to implement the protocols in an exact fashion as the LTAR Common Experiment.
Protocol references
Journal of Environmental Quality Special Section: The USDA LTAR Common Experiment—Research to Support a Sustainable and Resilient Agriculture (https://acsess.onlinelibrary.wiley.com/doi/toc/10.1002/(ISSN)1537-2537.USDA-LTAR-Common-Experiment).
Two papers from the Special Section that are highlighted specifically in this protocol:
- Liebig, M. A., Abendroth, L. J., Robertson, G. P., Augustine, D., Boughton, E. H., Bagley, G., Busch, D. L., Clark, P., Coffin, A. W., Dalzell, B. J., Dell, C. J., Fortuna, A.-M., Freidenreich, A., Heilman, P., Helseth, C., Huggins, D. R., Johnson, J. M. F., Khorchani, M., King, K., … Yost, J. (2024). The LTAR Common Experiment: Facilitating improved agricultural sustainability through coordinated cross-site research. Journal of Environmental Quality, 53, 787–801. https://doi.org/10.1002/jeq2.20636
- Tsegaye, T., Eve, M., Hapeman, C. J., Kleinman, P. J. A., Baffaut, C., Browning, D. M., Coffin, A. W., & Spiegal, S. A. (2024). The Long-Term Agroecosystem Research (LTAR) network: Cross-site transdisciplinary science to support a sustainable and resilient agriculture. Journal of Environmental Quality. https://doi.org/10.1002/jeq2.20649
Kladivko, E. J., Helmers, M. J., Abendroth, L. J., Herzmann, D., Lal, R., Castellano, M. J., Mueller, D. S., Sawyer, J. E., Anex, R. P., Arritt, R. W., Basso, B., Bonta, J. V., Bowling, L. C., Cruse, R. M., Fausey, N. R., Frankenberger, J. R., Gassman, P. W., Gassmann, A. J., Kling, C.L., …. Villamil, M. B. (2014). Standardized research protocols enable transdisciplinary research of climate variation impacts in corn production systems. Journal of Soil and Water Conservation, 69, 532–542. https://doi.org/10.2489/jswc.69.6.532
Kleinman, P., Spiegal, S., Rigby, J. R., Goslee, S. C., Baker, J. M., Bestelmeyer, B. T., Boughton, R. K., Bryant, R. B., Cavigelli, M. A., Derner, J. D., Duncan, E., Goodrich, D. C., Huggins, D. R., King, K. W., Liebig, M. A., Locke, M. A., Mirsky, S. B., Moglen, G. E., Moorman, T. B., … Walthall, C. L. (2018). Advancing the sustainability of US agriculture through Long-Term Research. Journal of Environmental Quality, 47, 1412–1425. https://doi.org/10.2134/jeq2018.05.0171
Robertson, G. P., Allen, V. G., Boody, G., Boose, E. R., Creamer, N. G., Drinkwater, L. E., Gosz, J. R., Lynch, L., Havlin, J. L., Jackson, L. E., Pickett, S. T., Pitelka, L. F., Randall, A., Reed, A. S., Seastedt, T., Waide, R. B., & Wall, D. H. (2008). Long-term Agricultural Research: A Research, Education, and Extension Imperative. Bioscience, 58(7), 640–645. https://doi.org/10.1641/B580711
Spiegal, S., Bestelmeyer, B., Archer, D., Augustine, D. J., Boughton, E. H., Boughton, R. K., Clark, P. E., Derner, J. D., Duncan, E., Hamilton, S., Hapeman, C., Harmel, D., Heilman, P., Holly, M., Huggins, D. R., King, K., Kleinman, P., Liebig, M. A., Locke, M., … Walthall, C. (2018). Evaluating strategies for sustainable intensification of U.S. agriculture through the Long-Term Agroecosystem Research network. Environmental Research Letters, 13, 034031. https://doi.org/10.1088/1748-9326/aaa779
Spiegal, S., Webb, N. P., Boughton, E. H., Boughton, R. K., Brymer, A. L. B., Clark, P. E., Collins, C. H., Hoover, D. L., Kaplan, N., & McCord, S. E. (2022). Measuring the social and ecological performance of agricultural innovations on rangelands: Progress and plans for an indicator framework in the LTAR network. Rangelands, 44, 334–344. https://doi.org/10.1016/j.rala.2021.12.005
Acknowledgements
This research is a contribution from the Long-Term Agroecosystem Research (LTAR) network. LTAR is supported by the United States Department of Agriculture. The use of trade, firm, or corporation names in this publication is for the information and convenience of the reader. Such use does not constitute an official endorsement or approval by the United States Department of Agriculture or the Agricultural Research Service of any product or service to the exclusion of others that may be suitable. USDA is an equal opportunity provider and employer.