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

Long‐term studies of agroecosystems distributed across the North American continent are providing an extraordinary understanding of regional environmental dynamics. The new Long‐Term Agro‐ecosystem Research (LTAR) network (organized in 2012) has designed an explicit cross‐site research program with multiple U.S. Department of Agriculture (USDA) experimental watersheds, ranges, and forests. Here, we report results from studies using a modified scientific method that includes learning through time that was implemented over the past five years with long‐term data from USDA experimental sites in coordination with other networks. The results offer a compelling argument for the LTAR concept of combining site‐based expertise with network‐wide coordination and collaboration to arrive at more accurate scientific conclusions than possible from individual researchers working alone. Simply put, without site‐based expertise and cross‐site communication working in parallel to provide input, feedback, and refinement to each subsequent step, similar to the way machine learning works, the interpretations and conclusions of these studies would have been incomplete, if not incorrect. Further, the up‐front time commitment to data processing and analytics above the time dedicated to place‐based studies increased the productivity of the team and the impact of the research, unlike the common perception that cross‐site research is often less efficient. In turn, this approach supported a non‐traditional system of credit for co‐authors based on citation impact of the journal selected as the publication outlet with less regard for author order. The LTAR network has embraced this modified scientific method in its shared research strategy and common experiment to address the problematic issues of mixed data quality across studies and sites, co‐author credit, research efficiency, and scientific impact on data‐intensive research. This approach can be combined with other types of collaborative and social media approaches, such as crowdsourcing, to take advantage of the wide range of expertise in the agroecological community as well as other disciplines to move science forward in the time of big data.

Soil nitrous oxide (N2O) emissions exhibit high variability in intensively managed cropping systems, which challenges our ability to understand their complex interactions with controlling factors. We leveraged 17 years (2003–2019) of measurements at the Kellogg Biological Station Long‐Term Ecological Research (LTER)/Long‐Term Agroecosystem Research (LTAR) site to better understand the controls of N2O emissions in four corn–soybean–winter wheat rotations employing conventional, no‐till, reduced input, and biologically based/organic inputs. We used a random forest machine learning model to predict daily N2O fluxes, trained separately for each system with 70% of observations, using variables such as crop species, daily air temperature, cumulative 2‐day precipitation, water‐filled pore space, and soil nitrate and ammonium concentrations. The model explained 29%–42% of daily N2O flux variability in the test data, with greater predictability for the corn phase in each system. The long‐term rotations showed different controlling factors and threshold conditions influencing N2O emissions. In the conventional system, the model identified ammonium (>15 kg N ha−1) and daily air temperature (>23°C) as the most influential variables; in the no‐till system, climate variables such as precipitation and air temperature were important variables. In low‐input and organic systems, where red clover (Trifolium repens L.; before corn) and cereal rye (Secale cereale L.; before soybean) cover crops were integrated, nitrate was the predominant predictor of N2O emissions, followed by precipitation and air temperature. In low‐input and biologically based systems, red clover residues increased soil nitrogen availability to influence N2O emissions. Long‐term data facilitated machine learning for predicting N2O emissions in response to differential controls and threshold responses to management, environmental, and biogeochemical drivers.

Florida ranks second in cancer burden in the United States. The Florida CAncer RESearch (FL CARES) Network (https://floridacancernetwork.org/) is a statewide collaboration dedicated to advancing cancer research and improving patient outcomes through education, collaborative research initiatives, and a focus on reducing health disparities by leveraging advanced computational approaches. Funded by the State of Florida Bankhead-Coley Cancer Research Program, FL CARES comprises six research organizations, including Florida's three NCI-designated Cancer Centers. The Network has established a collaborative research consortium with unified policies, coordinated data and metadata standards, and diverse datasets. It incorporates tools like SCAN360, developed at Sylvester Comprehensive Cancer Center, to explore the impact of social determinants of health and environmental factors on cancer outcomes across Florida communities. To further collaborative computational research, FL CARES is developing the Platform for Accelerating Collaborative Computational Cancer Research (PAC3R). This advanced informatics system enables FAIR (findable, accessible, interoperable, reusable) data management, including standardization, harmonization, and integration of multimodal cancer datasets. PAC3R facilitates secure data sharing, scalable bioinformatics tools’ deployment, and collaborative analyses across the Network. Built on the Sylvester Data Portal (https://sdp.miami.edu/), a cloud-based multi-omics platform managing clinicogenomic and research data, PAC3R integrates diverse datasets such as transcriptional perturbation signatures, cell sensitivity data, small molecule interactions, and large-scale genomics from The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC). Citation Format: Michael Sinclair, Carlos Obregon, Caty Chung, Dusica Vidovic, Lukas Rupprecht, Jeronimo Pissinis, Marcin Pilarczyk, Franklin Sotolongo, Kathleen M. Jagodnik, Till Krenz, Erin Kobetz, Mondal M. Ananda, Ling Cen, Xuefeng Wang, Appu Rathinavelu, Stuart Chalk, Jiang Bian, Ji-Hyun Lee, Qianqian Song, Vasileios Stathias, Stephan C. Schürer. Introducing the Florida Cancer Research (FL CARES) Network and the Platform for Accelerating Collaborative Computational Cancer Research (PAC3R) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1086.

The Long-Term Agroecosystem Research (LTAR) network of the United States Department of Agriculture (USDA) consists presently of 18 sites within the contiguous United States that are managed by the Agricultural Research Service (ARS) and its partners. The LTAR network focuses on developing national strategies for more efficient, resilient, and profitable agricultural production systems, improved environmental quality, and enhanced rural prosperity. The Platte River High Plains Aquifer (PRHPA) LTAR site is managed jointly by the University of Nebraska-Lincoln (UNL) and USDA-ARS and is one of the LTAR sites that conduct research on both integrated cropping and grazing systems. The PRHPA region encompasses multiple land resource areas and diverse agricultural production systems. The PRHPA sites, predominantly located in eastern Nebraska, are designated as an integrated system focused specifically on the region's dominant production practices of row crop (corn and soybean), managed pastures, and beef cattle production. Here, we focus on C3 cool-season smooth bromegrass (Bromus inermis Leyss.) pasture grazing systems under prevailing and alternative management practices for the region. The sites evaluate continuous and rotational grazing with and without pasture fertilization (prevailing practices). In an additional treatment, cattle are supplemented with dry distillers grains plus solubles, while manure supplies fertilization (alternative practice). Main measurements at the site evaluate plant and animal productivity, forage quality, greenhouse gas fluxes, and soil physical, chemical, and biological properties. This paper describes the regional characteristics of the PRHPA site, ongoing LTAR research related to pasture and livestock production, stakeholder engagement, and future research plans.

Agriculture in the United States must respond to escalating demands for productivity and efficiency, as well as pressures to improve its stewardship of natural resources. Growing global population and changing diets, combined with a greater societal awareness of agriculture's role in delivering ecosystem services beyond food, feed, fiber, and energy production, require a comprehensive perspective on where and how US agriculture can be sustainably intensified, that is, made more productive without exacerbating local and off-site environmental concerns. The USDA's Long-Term Agroecosystem Research (LTAR) network is composed of 18 locations distributed across the contiguous United States working together to integrate national and local agricultural priorities and advance the sustainable intensification of US agriculture. We explore here the concept of sustainable intensification as a framework for defining strategies to enhance production, environmental, and rural prosperity outcomes from agricultural systems. We also elucidate the diversity of factors that have shaped the past and present conditions of cropland, rangeland, and pastureland agroecosystems represented by the LTAR network and identify priorities for research in the areas of production, resource conservation and environmental quality, and rural prosperity. Ultimately, integrated long-term research on sustainable intensification at the national scale is critical to developing practices and programs that can anticipate and address challenges before they become crises.

PurposeThis study aims to assess the response of the Association of Southeast Asian Nations (ASEAN) to cleaner production and environmental sustainability, with a specific focus on identifying the leading countries and research networks driving these efforts.Design/methodology/approachA benchmarking academic journal was chosen, and the journal’s archive was comprehensively examined. To construct the data set, a conventional keyword search technique was applied in February 2023 to filter for ASEAN affiliations. The study used hybrid bibliometric analyses and multi-criteria decision analysis (MCDA) to analyze the collected data and address the research purpose.FindingsThe data analysis revealed a rising research trend, particularly after 2014. Malaysia had the most publications, followed by Thailand and Singapore, and their publications had the most cumulative citations among ASEAN countries. Research collaborations between Malaysia, Thailand and Singapore were frequent, but participation from other countries was low. The research topics on which ASEAN members focused were also identified, but it became apparent that there was little coordination. A scant few collaborations involving more than two countries were observed; thus, the MCDA analysis concluded that research leadership was absent in ASEAN countries.Originality/valueThis study contributes insights to the existing literature and offers a valuable overview of the research direction and collaboration status of cleaner production and environmental sustainability in the ASEAN region, thus benefiting policymakers. Additionally, this study introduces a novel approach combining bibliometrics analysis with MCDA to assess research collaboration, thus providing a novel methodology for future research policy evaluations.

The paper explores the research impact and collaboration of Central Agricultural University (CAU) and Manipur University (MU), India, between 2013 and 2022. A bibliometric analysis of 1,932 publications and 23,519 citations extracted from the Scopus database reveals a significant increase in publications. Notably, 77.64 % of these papers resulted from national collaborations, while 22.41 % had international, particularly with the United States, the United Kingdom, and South Korea. The analysis of co-authorship patterns indicates a decline in single and two-authored papers, while multi-authored papers have increased significantly. The study highlights the rapid growth of collaboration networks, with CAU, Imphal leading in national collaborations across 61 countries. These findings provide that both CAU and MU have significantly expanded their research networks, resulting in improved research output and citation impact.

The Eastern Corn Belt (ECB) Long-Term Agroecosystem Research (LTAR) network site is one of the most agriculturally productive areas in the United States; however, nutrient and sediment losses from this region directly contribute to water quality impairment in both the Gulf of Mexico and Lake Erie. One of the largest challenges facing agricultural production in the ECB is water management, especially under a changing climate. Shifting precipitation patterns in combination with evolving infrastructure (i.e., tile drainage, irrigation) and management (i.e., crops planted; 4R nutrient management: source, timing, rate, and placement) in response to changing climate are likely to alter water quantity and quality and agricultural productivity. Through plot- and field-scale research, the ECB Common Experiment (CE) is focused on crop, soil, and water management to support sustainable agricultural intensification, with the goal of maximizing profitability, minimizing agriculture's environmental footprint, and enhancing ecosystem services. At both spatial scales, the CE aims to examine differences in water quantity and quality between the primary prevailing crop production system in the ECB (i.e., corn [Zea mays L.]-soybean [Glycine max L.] rotation, tillage prior to corn planting, free drainage, and fertility management consistent with tri-state recommendations) and an alternative system (e.g., adding small grains into the crop rotation, cover crops, limited phosphorus fertilizer, and drainage water management). Aligning producer and stakeholders needs with research objectives and long-term data collection, the ECB CE will tackle both ongoing and newly emerging research priorities and explore the effectiveness of conservation strategies to decrease nutrient and sediment losses from agricultural land.

Long-term research is essential for guiding the development of agroecosystems to meet escalating production demands in a manner that is environmentally sound and socially acceptable. Research must integrate biophysical and socioeconomic factors to provide geographically scalable knowledge that involves stakeholders across the research-education-extension-policy spectrum. In response to this need, the Long-Term Agroecosystem Research (LTAR) network developed a "Common Experiment," which seeks to develop and disseminate multi-region, science-based information to enable implementation of visionary agricultural innovations while simultaneously promoting food security, well-being, environmental quality, and climate adaptation and mitigation. The core design of the Common Experiment contrasts prevailing and alternative/aspirational production systems, with the latter including novel innovations hypothesized to advance sustainable intensification in locally appropriate ways. Treatments in the Common Experiment represent a diversity of production systems under cropland, grazing land, and integrated crop/grazing land management. Where possible, treatments are evaluated at multiple spatial scales (e.g., from plot to enterprise) and are designed to evolve over the course of the experiment with stakeholder input. A common assessment framework guides data collection for the experiment and is complemented by metric-specific protocols and an emerging data management infrastructure. Currently, there are large differences among sites in the application of the experimental framework and degree of stakeholder engagement; differences largely grounded in pragmatic issues related to land access, site expertise, and resource availability. The full potential of the LTAR Common Experiment may be realized with strategic investments in network capacity.

Across agroecosystems, water is a key driver of primary production, and the relationship between precipitation and production (i.e., water-use efficiency; WUE) provides an important indicator for evaluating agroecosystem resilience to changes in water availability. While this relationship has been well-characterized in relatively unmanaged, native ecosystems, cross-site syntheses spanning diverse agroecosystems and climate gradients are lacking. We leveraged the USDA's Long-Term Agroecosystem Research (LTAR) network to assess the relationship between annual precipitation and aboveground net primary production (ANPP) across an extensive set of climate conditions and agroecosystems, representing native rangelands, croplands, and pasturelands and various management intensities. We utilized long-term ANPP data (mean=17years) from fifteen sites spanning a large precipitation gradient (265 to 1347mmyr-1). We observed a positive relationship between annual precipitation and productivity across precipitation gradients; however, this nonlinear pattern differed from native ecosystems and varied by agroecosystem type. Rangeland ANPP was strongly coupled to annual precipitation, increasing nearly 20% for every 100mm of precipitation. Croplands and pasturelands showed significantly decreased sensitivity, although grouping crops by photosynthetic pathway and crop type revealed some significant patterns. Underlying these patterns in sensitivity were large differences in overall ANPP among agroecosystems; cropland ANPP was up to 6.7-fold greater than rangelands and 2.6-fold greater than pasturelands, despite overlapping precipitation gradients. While agroecosystem type captured much of the variability in the precipitation-production relationship at the continental scale, understanding the more subtle differences in precipitation sensitivities will be fundamental for identifying production vulnerabilities and adapting to changing water resources.

Infusing ‘long-term’ into social science rangelands research

Knowledge of weather, particularly precipitation, is fundamental to interpreting watershed and hydrologic processes. The long-term weather record in the Goodwater Creek Experimental Watershed (GCEW) complements hydrologic and water quality data in the region. The GCEW also is the core of the Central Mississippi River Basin (CMRB) node of the Long-Term Agroecosystem Research network. Our objectives are to (i) describe the climatological context of the GCEW and CMRB settings, (ii) document instrumentation and the data collection, quality assurance, and reduction processes; (iii) provide examples of the data obtained and descriptive statistics; and (iv) document the availability of and access methods to obtain the data from the web-based data access portal at . These objectives support an overall goal to make these long-term data available to the public for use in further analyses and modeling in support of research and public policy on watershed management.

Societal Impact Statement Plant viruses spread by insects decimate crop yields globally, causing food security challenges in vulnerable areas including regions of Africa. Interdisciplinary research is needed to protect future crop supplies. CONNECTED, the Community Network for African Vector‐Borne Plant Viruses, increased research capacity in Central, East, West and Southern Africa through networking, funding research and training. Here, we describe how CONNECTED developed a community of 1675 members across 97 countries, delivered new research and trained early career researchers in interdisciplinary techniques. The approach used by CONNECTED can be replicated by other initiatives to increase international plant research capacity and collaboration, particularly between the Global South and North. Summary Plant viruses, often spread by insects, decimate staple and nutritious crop yields globally. This contributes to economic and food security challenges in vulnerable areas, including regions of Africa. Interdisciplinary research is required to better understand plant virus epidemiology to protect future crop supplies, but this is often hindered by limited collaboration and knowledge exchange between plant pathology and entomology research communities. Collaborative networks are a useful model for promoting collaborative and interdisciplinary research. CONNECTED, the Community Network for African Vector‐Borne Plant Viruses, was a collaborative research network supported by the UK Global Challenges Research Fund. CONNECTED sought to convene international plant pathology and entomology communities through networking, pump‐priming new collaborative research, and training, to increase vector‐borne plant virus (VBPV) research capacity in Africa. Combining analysis of CONNECTED's activities programme with qualitative methods, we identify the outcomes achieved by CONNECTED for the global VBPV community, and the value it provided to its members. CONNECTED developed a multidisciplinary community of 1675 members across 97 countries. CONNECTED delivered 20 new strategically‐relevant research projects involving 55 researchers from 33 institutions across 14 countries working on 11 different crops. CONNECTED trained over 140 early career researchers from 23 countries in interdisciplinary VBPV techniques, providing new training materials co‐created in partnership with Africa‐based research organisations. These outcomes demonstrate the impact of CONNECTED's phased programme of activities in building the capacity of international VBPV research communities, towards plant science and food security outcomes, which can be expanded and replicated in different contexts.

In situ methods for estimating water quality parameters would facilitate efforts in spatial and temporal monitoring, and optical reflectance sensing has shown potential in this regard, particularly for chlorophyll, suspended sediment, and turbidity. The objective of this research was to develop and evaluate relationships between hyperspectral remote sensing and lake water quality parameters-chlorophyll, turbidity, and N and P species. Proximal hyperspectral water reflectance data were obtained on seven sampling dates for multiple arms of Mark Twain Lake, a large man-made reservoir in northeastern Missouri. Aerial hyperspectral data were also obtained on two dates. Water samples were collected and analyzed in the laboratory for chlorophyll, nutrients, and turbidity. Previously reported reflectance indices and full-spectrum (i.e., partial least squares regression) methods were used to develop relationships between spectral and water quality data. With the exception of dissolved NH, all measured water quality parameters were strongly related ( ≥ 0.7) to proximal reflectance across all measurement dates. Aerial hyperspectral sensing was somewhat less accurate than proximal sensing for the two measurement dates where both were obtained. Although full-spectrum calibrations were more accurate for chlorophyll and turbidity than results from previously reported models, those previous models performed better for an independent test set. Because extrapolation of estimation models to dates other than those used to calibrate the model greatly increased estimation error for some parameters, collection of calibration samples at each sensing date would be required for the most accurate remote sensing estimates of water quality.

The collaborative European funded research and development landscape drives competitiveness among innovative organisations. Recently it has seen the rise of public private partnerships significantly impacting the dynamics of these networks. Thus, the complexity of managing research networks has intensified with the increased diversity of research network members. Additionally, the emergence of the academic entrepreneur has augmented the focus of educational institutions to include innovation and building start-up organisations. The impact of research is scalable if an optimum research network is created and managed effectively. This paper investigates network embeddedness; the nature of relationships, links and nodes within a research network, specifically their structure, configuration and quality. The contribution of this paper extends our understanding for establishing and maintaining effective collaborative research networks. The effects of network embeddedness are recognized in the literature as pertinent to innovation and the economy. Network theory literature claims that networks are essential to innovative clusters such as Silicon valley and innovation in high tech industries. The concept of embeddedness is what differentiates network theory from economic theory. This study adopts a qualitative approach and uncovers some of the challenges of multi-disciplinary research through case study insights. One challenge is competition between network members over ownership and sharing of data. The contribution of this paper recommends the establishment of scaffolding to accommodate cooperation in research networks, role appointment, and addressing contextual complexities early to avoid problem cultivation. Furthermore, it suggests recommendations in relation to network formation, incubation and operations. The network capability is enhanced by the recognition of network theory, open innovation and social exchange with the understanding that the network structure has an impact on innovation and social exchange in research networks and subsequently on research output. The research concludes that the success of collaborative research is reliant upon establishing a common language and understanding between network members to realise their research objectives.