ABSTRACT Understanding how agroecosystems respond to environmental variability is fundamental to predicting productivity and sustainability under a changing climate. We analyzed 55 site‐years of high‐frequency eddy covariance observations from five agroecosystems—two perennial grasses (miscanthus and switchgrass), two annual rotation systems (maize–soybean and sorghum–soybean), and a restored native prairie—to examine ecosystem‐scale carbon, water, and energy fluxes. Using an interpretable machine‐learning framework with regression tree ensembles, Shapley Additive Explanations, and Accumulated Local Effects, we quantified how environmental and temporal factors regulate gross primary productivity (GPP), evapotranspiration (ET), water‐use efficiency, and the Bowen ratio. Perennials exhibited stronger physiological buffering and maintained fluxes across a broader range of temperature and moisture conditions, reflecting deeper rooting and persistent canopy cover. Annuals, in contrast, showed greater short‐term variability and stronger coupling to atmospheric demand, with GPP and ET declining rapidly under low humidity or soil moisture. Differences in temperature sensitivity of Bowen ratio further revealed that perennials sustained proportionally greater sensible heat flux under cool conditions, whereas annuals exhibited constrained energy exchange when evaporative demand was low. Together, these results demonstrate that crop life cycle and canopy structure are fundamental determinants of ecosystem‐scale carbon–water–energy coupling. By integrating long‐term flux observations with interpretable machine learning, this study identifies the environmental drivers that shape agroecosystem function and highlights how conversion from annual to perennial feedstocks can enhance climatic resilience and alter land–atmosphere energy feedbacks. These findings provide a data‐driven basis for improving crop and Earth‐system models and for guiding bioenergy landscape design under future climate scenarios.

Modern soybean (Glycine max) varieties have higher than optimal leaf area index (LAI), which could divert resources from reproductive growth. Altering leaf shape could be a simple strategy to reduce LAI. To test this, we developed 204 near-isogenic lines differing in leaf morphology by introgressing the ln allele that confers the narrow-leaf trait from 2 donor parents (PI 612713A and PI 547745) into the elite, broad-leaf cultivar LD11-2170. We evaluated the lines across 2 locations and 2 row spacings (38 cm and 76 cm) to assess how reduced investment in leaf area influences canopy architecture, crop physiology, and yield. Narrow-leaf lines showed 13% lower peak LAI and 3% lower digital biomass compared to broad-leaf counterparts yet maintained yield parity (5,756 vs. 5,801 kg ha-1, P = 0.43) across environmental conditions. Photosynthetic capacity remained largely unchanged, with narrow-leaf lines showing modest increases in electron transport rate and leaf mass per area. Narrow-leaf lines achieved similar canopy closure timing despite lower LAI, suggesting architectural compensation mechanisms. The most striking difference appeared in seed packaging, with 34% of pods containing 4 seeds in narrow-leaf lines compared to only 1.8% in broad-leaf lines. There was a nonlinear relationship between peak LAI and yield, with optimal LAI values of 9 to 11 varying by environment. These findings show that the single-gene GmJAG1-controlled narrow-leaf trait offers a tractable strategy for reducing LAI and maintaining high productivity. This could reduce the metabolic costs associated with excessive canopy development and support sustainable agriculture under increasing climate variability.

Riverine nitrous oxide (N2O) emissions constitute a significant yet uncertain component of global greenhouse gas budgets. Integrating approximately 3,600 observations across the contiguous United States (CONUS), we present a monthly resolved, national-scale estimate of riverine N2O emissions (60.7 Gg N2O-N y-1; 95% CI: 41.9 to 71.2) using a machine-learning framework. Our analysis reveals that enhanced hydrologic connectivity strongly regulates nitrogen and N2O delivery to streams, driving emission hot moments during high-flow periods, especially in nutrient-rich low-order streams. The Midwest Corn Belt is identified as a major emission hot spot, where seasonal increases in connectivity (e.g., late-winter thaws and postharvest rainfall) amplify riverine emissions relative to direct soil emissions. Our watershed-specific EF5r (0.0005 to 0.029) exceeds the IPCC default (0.0026) by more than twofold on average and up to 10-fold in intensively managed watersheds. These findings highlight the importance of incorporating hydrologic connectivity and nitrogen transport into climate models and watershed nitrogen management strategies.

Grazing lands cover approximately one-third of the contiguous United States, support much of the nation's beef production, and are an important component of the U.S. terrestrial carbon budget. In this study, we quantified net ecosystem carbon balance (NECB), the net status of grazing lands as a carbon sink (C-sink) or source (C-source) and a key determinant of soil health and productivity. Our primary objective was to synthesize multiple years of annual NECB across heterogeneous grazing lands across the continental U.S and evaluate annual NECB against physical drivers (mean annual precipitation (MAP), mean annual temperature (MAT), vegetation, and moisture condition) and management practices (grazing pressure index (GPI) and fertilization history). We hypothesized that (1) NECB is higher in mesic and fertilized grasslands; (2) NECB increases with MAP and MAT but decreases with GPI; and (3) interactive effects exist among MAP, MAT, and GPI. Using carbon fluxes measured by eddy covariance towers and methane emissions including both enteric methane and manure derived from stocking rates across seven USDA Long-term Agroecosystem Research Network sites, we found: (1) grazing lands were a C-sink or carbon neutral at most sites; (2) vegetation type, moisture conditions, or fertilization had no significant effect on NECB; (3) NECB increased with MAP and MAT, but decreased with a higher GPI; and (4) MAT had a significant positive effect on NECB when MAP exceeded 750 mm (greater water availability). The effect of GPI on NECB was significantly negative when MAP was below 1000 mm, significantly negative when MAT < 12 °C and significantly positive when MAT > 16 °C. Thus, most grazing lands in our study acted as C-sinks unless water deficit, low temperature, or heavy grazing were interactively present. Understanding how climate and management influence NECB of grazing lands is key to maintaining resilient agroecosystems that secure beef production and sustain rural prosperity.

Rising atmospheric vapor pressure deficit (D) with warming is an increasingly important driver of crop productivity loss, yet the relative sensitivity of conventional seed crops and alternative perennial crops remains poorly resolved. In this study, we combined a 9-year eddy covariance carbon flux record (2008 – 2016) with 600+ midday leaf water potential (ψL) measurements (2024 – 2025) from adjacent maize (Zea mays), miscanthus (Miscanthus x giganteus), and switchgrass (Panicum virgatum) plots to: (1) quantify the limitations of elevated D on crop-specific productivity and (2) evaluate how these responses vary as a function of soil moisture (Θ) status. We found that maize strictly regulated ψL and its gross primary productivity (GPP) was strongly reduced by increasing D. In contrast, miscanthus and switchgrass allowed larger ψL declines and sustained higher GPP under similar moisture constraints. D exerted a larger limitation on maize GPP than Θ, but D-driven declines were less severe when accompanied by high Θ. GPP sensitivity to D was also influenced by Θ for miscanthus and switchgrass, but the buffering effect was stronger compared to maize. Specifically, high D paired with high Θ were the most productive conditions for the perennials, reflecting temperature-driven gains in photosynthetic efficiency when evaporative stress was mitigated by sufficient soil water supply. Overall, miscanthus and switchgrass displayed greater resistance to atmospheric drought and more stable GPP across hydroclimate variability than maize. These findings highlight that maize is more vulnerable to future projections of rising D than miscanthus or switchgrass.

Natural genetic variation in photosynthesis and photoprotection within crop germplasm represents an untapped resource for crop improvement. Sorghum bicolor (sorghum) is one of the world's most widely grown crops, yet the genetic basis of photoprotection in sorghum is not well understood. This study examined genetic variation in non-photochemical quenching traits by screening a field-grown panel of 861 genetically diverse natural sorghum accessions across 2 years. Broad-sense heritability ranged between 0.3 and 0.65 across different chlorophyll fluorescence parameters. A combination of genome- and transcriptome-wide (GWAS and TWAS) identification of genetic correlates with the observed trait variation uncovered a complex genetic architecture of many significant small-effect loci. An ensemble approach based on GWAS and TWAS results and the covariance between different fluorescence parameters was used to identify 110 unique candidate genes. The resulting high-confidence candidates reveal novel genetic associations with photoprotection and offer resources for further genetic studies and crop genomic improvement efforts.

Increasing global demands for food and energy necessitate innovative land-use solutions. Agrivoltaics, colocating solar photovoltaics with agriculture, shows promise, but its widespread adoption faces complex biophysical and economic trade-offs in a changing climate. Here, we develop an integrated biophysical-economic modeling framework to quantify how agrivoltaics affect biophysical and economic impacts across the Midwestern United States under both current and project climate conditions. We find strong regional divergences driven by climate gradients. In the humid eastern Midwest, solar panel shading limits photosynthesis, leading to reduced yields (maize-24%; soybean-16%) and lower farmers' profitability (maize-16%; soybean-2%) compared to conventional agriculture. Conversely, in the semiarid western region, shading alleviates heat and water stress, moderating yield reductions for maize (-12%) and even boosting soybean yields (+6%), resulting in improved economic returns (-6% for maize; +9% for soybean), for a scenario with 33% photovoltaic ground coverage ratio. Although agrivoltaics generate substantial electrical energy across all regions, high upfront installation costs challenge solar developers compared to standalone solar photovoltaics. However, our analysis identifies "win-win" opportunities where soybean-based agrivoltaics in the semiarid region produce economic benefits for both farmers and solar developers, highlighting the necessity for region-specific designs tailored to local climate conditions. Critically, future climate projections indicate eastward expansion of semiarid conditions, broadening areas where agrivoltaics can mitigate crop yield penalties (even boosting yield) and improve overall profitability, especially under high-emission scenarios. The results provide a mechanistic and economically integrated understanding essential for developing evidence-based and region-specific strategies to scale agrivoltaics in a changing climate.

Cultivar evolution through plant breeding is a cornerstone of contemporary food security, but the extent to which genetic adaptation to climatic variability and shocks contributes to yield gains is not well known. Here, we compile 48,797 cultivar-site-year observations from 2001 to 2020, covering the four prominent maize production regions in China with differing shifts in climatic conditions. The data shows that cultivar evolution underlies long-term yield gains, with productivity increasing by 0.3-2.8 Mg ha-1 per decade. Yields in Northeast China (NEC) and North China (NC) are most vulnerable to heat stress during July and August, whereas high or insufficient precipitation during the growing season is a foremost constraint to yield gains in Southwest China (SWC) and Northwest China (NWC), respectively. Cultivar evolution has significant impacts on yield sensitivity to climate, with genotypic sensitivities to heat stress amplifying in NEC and diminishing over time in NC, respectively. In contrast, yield sensitivity to precipitation increases in SWC and NWC as a result of breeding. These results underscore the importance of breeding climate-resilient cultivars that account for contextualised in situ environmental constraints and climatic adversities in obtaining high yield.

ABSTRACT The expansion of sugarcane (cane), a high‐yielding perennial crop, will likely reshape the bioenergy landscape in the Southeastern US. However, its ecohydrological implications, particularly following conversion from grazed pastures, a dominant land use in the region, remain highly uncertain. We investigated the impact of cane expansion on evapotranspiration (ET) and its partitioning, and the mechanisms influencing both ET components and water use efficiency (WUE) across multiple scales and growth cycles in subtropical Florida. We combined eddy covariance, biometric measurements, and process‐based stomatal conductance (gs) models. ET was 1.7% lower in cane than in improved pasture (IMP) but exceeded that in semi‐native pasture (SN) by 21%. Transpiration (T) followed a similar pattern, consistent with lower gs in cane relative to IMP. Cane had more conservative water use and greater sensitivity of gs to vapor pressure deficit (VPD) compared to IMP pasture, suggesting cane may be more tolerant of increasing atmospheric water demand. In contrast, SN showed lower gs and weaker stomatal sensitivity to VPD compared to cane, resulting in lower T. In cane, stomatal regulation and T varied across growth cycles, with stomata becoming less water conservative as stands matured, highlighting the importance of incorporating stand age‐dependent stomatal regulation into hydrological models. Evaporation (E) was higher in cane than pastures (19%–26%), partially offsetting WUE gains. Cane exhibited higher intrinsic WUE (GPP/gs; Gross Primary Productivity), ecosystem WUE (GPP/ET), and harvest WUE (harvest/ET) than both pasture types. Large‐scale pasture‐to‐cane conversion could produce widely contrasting hydrological outcomes. The net regional impact will depend on the proportion of each pasture type converted and on cane's high gs sensitivity to VPD, which triggers tight stomatal regulation and conservative water use, both of which will become increasingly consequential under intensifying atmospheric water demand.

Combined increases in atmospheric CO2 and warming temperatures impact photosynthesis in complex yet mechanistically predictable ways. Seminal work in this area showed that temperatures above a thermal optimum reduce photosynthesis primarily by increasing photorespiration, but that elevated CO2 and higher temperature can act synergistically to raise the thermal optimum of photosynthesis and increase absolute photosynthetic rates. The modeling work that led to this prediction outlined both the synergistic effects of CO2 and temperature and hypothesized scenarios that could deviate from theory. Here, the assumptions underlying models of CO2 × temperature interactions in photosynthesis are reviewed, with emphasis on advances in in vivo enzyme kinetics, diffusive limitations, photosynthetic acclimation, and atmospheric feedbacks. Advances in Rubisco biochemistry, mesophyll conductance, field experiments, and canopy-scale modeling show why photosynthetic responses under future climates often deviate from the theoretical CO2 × temperature response. Scaling from leaf-level physiology to canopy carbon uptake is addressed to identify priorities for next-generation experiments and models. Together, this review highlights the work of the late Prof. Stephen P. Long, beginning with his pioneering manuscript that first addressed the synergistic effects of CO2 and temperature on photosynthesis.

Tropospheric ozone (O3) is a phytotoxic air pollutant. The stomatal uptake of O3 is a substantial sink of O3, but it can create oxidative stress within plants, reducing photosynthesis and crop yields. In seasonally dry climates, stomatal regulation causes temporal decoupling between stomatal conductance and atmospheric O3 concentrations protecting vegetation from high stomatal uptake of O3 during peak ambient concentrations in the afternoon, but less is known about this temporal decoupling over agricultural fields in continental humid climates. Here, we investigate how maize (Zea mays L.) ecophysiology impacts O3 dry deposition and diurnal O3 exposure-dose dynamics at an agricultural field in the central Corn Belt of the United States. We measured the field-scale eddy covariance flux of O3 using a UV-absorption based instrument, the NASA Rapid Ozone Experiment (ROZE). The stomatal component of total O3 flux was estimated with an inversion of the Penman-Monteith equation using observed latent heat flux and a model of stomatal conductance using gross primary productivity. We found that maize stomatal conductance remained high as vapor pressure deficit increased during the afternoon. The diurnal synchrony between O3 concentrations and stomatal conductance resulted in high stomatal uptake of O3 during peak O3 concentrations. The monthly mean stomatal flux reached > 70% of the total O3 flux to the land surface during high leaf area index. Furthermore, the total deposition velocity of O3 was tightly coupled with stomatal conductance. Our findings suggest that maize ecophysiology in our field in the Corn Belt of the United States couples high O3 stomatal uptake with high O3 exposure. Furthermore, our study demonstrates the first use of NASA ROZE to measure growing season O3 flux over an agricultural field, and NASA ROZE will be crucial for expanding O3 flux measurements necessary for studying O3 dry deposition and field-scale phytotoxic dose across other fields.

An-Ci curves are used to infer Vcmax,25 and J25 for terrestrial biosphere models, but fitting-tool assumptions can alter both parameters; although Vcmax,25 varies more, J25 differences can also shift inferred limitation regimes and thereby affect propagation from leaf to canopy simulations. We compared 11 widely used An-Ci fitting configurations and one optimal model configuration across four C3 crops (soybean, perennial ryegrass, red clover, and winter wheat), ran two complementary sensitivities (one-at-a-time toggles relative to a baseline and variance-based decomposition), and used the resulting parameters in the Soil-Canopy Observation, Photochemistry and Energy (SCOPE) model to simulate An and gross primary productivity (GPP). Vcmax,25 differed by up to fourfold across tools, with consistent fingerprints across crops. Pooled sensitivities identified mesophyll conductance (gm) and limiting-rate selection as the dominant drivers of between-tool spread. In SCOPE, using the same assumptions in simulation as in parameter estimation kept leaf An errors below 9%, while mismatched assumptions produced An deviations -68% to +81% across crops and soybean GPP shifts -12% to +20%. Vcmax,25 is transportable only with its assumptions. Users should align gm, limiting-rate, and kinetic settings with intended simulations or treat catalogued values as priors unless recomputed from raw An-Ci data.

ABSTRACT Water-use efficiency (WUE), the ratio of accumulated plant biomass to water lost through transpiration has conventionally been determined using a destructive single-point measurement. Recent advances in high-throughput phenotyping now enable repeated, non-destructive estimation of biomass and WUE. However, these digital measurements must be statistically validated against conventional destructive methods to validate their use as reliable proxies. Therefore, we compared digital biomass determined point clouds produced from multispectral camera scanners with destructive harvests across eight harvests using Samsun tobacco grown under both drought and high-water conditions. WUE efficiency, calculated using the digital biomass estimated from a point cloud and gravimetric water use determinations, were compared to destructive harvest determinations. The coefficient of variation (CV) showed there were no significant differences in digital and destructive measurements for either biomass or WUE. Indicating that digital measurements can be used in place of destructive measurements. Drought plants used significantly less water and were significantly smaller than high-water plants from Harvests 4 through 8. However, there were no significant differences in the ratio of evapotranspiration to leaf area or WUE, indicating that drought plants were simply smaller and used less water than the high-water plants. This work validates that estimating plant biomass from a digital point coupled with continuous gravimetric determination of water use provides a reliable nondestructive measure of WUE in high-throughput measurements across the full plant life cycle. PLAIN LANGUAGE SUMMARY We grew tobacco plants under either a drought or high-water treatment and harvested a portion of the plants every few days for a total of eight harvests. Throughout the experiment, we collected 3D images of the plants and continuously measured pot weight to track plant growth and water use across different developmental stages. Destructive biomass served as the gold-standard measurement. We then compared biomass and water-use estimates generated from the digital measurements with the destructive measurements. The digital approach provided accurate estimates of plant biomass and water use while requiring little hands-on labor and no plant destruction. These nondestructive methods could help plant breeders identify water-efficient plants earlier in the breeding process, accelerating the development of crops that use water more efficiently.

This study aims to (a) investigate growing-season weather conditions with the yield of soybean and corn commercially grown in alternate rotation on a farm in Champaign, Illinois, (b) identify anomalies in this relationship, and (c) evaluate changes in microclimate variables during crop growing stages and associated changes in annual yield. To achieve these aims, we evaluated 21 years (1997-2017) of annual yield data alongside hourly microclimate measurements from a 10-m flux tower at the crop site. While the annual yield for the study period varied about the mean of 350 +/- 63 g m-2 for soybean and about 1200 +/- 185 g m-2 for corn, our evaluations using linear regression best-fit equations showed generally low correlations between microclimate variables and the annual yield. The growing season daily soil water within the top 1-m soil depth showed consistent values above 300 mm that were close to the field capacity (330 mm) of the dominant field soil of silty clay loam. Fitted regression lines between yield and microclimate variables averaged over June, July and August showed modest correlations for corn yield versus growing degree days (R2 = 0.52), precipitation (R2 = 0.43) and evapotranspiration (R2 = 0.41), as well as a modest correlation between soybean yield and precipitation (R2 = 0.31). Regressions between yield and vapor pressure deficit, solar radiation, soil water content and carbon dioxide flux showed poor correlations that were less than R2 of 0.21. This study offers a useful evaluation of the yield in rainfed corn and soybean conditions and demonstrates that simple regression analysis may be insufficient in representing complex crop-microclimate interactions. However, the microclimate evaluation presented in this study could serve as a valuable contribution to the application of dynamic crop models.

The expansion of sugarcane onto land currently occupied by improved (IMP) and semi-native (SN) pastures will reshape the U.S. bioenergy landscape. We combined biometric, ground-based and eddy covariance methods to investigate the impact of sugarcane expansion across subtropical Florida on the carbon (C) budget over a 3-year rotation. With 2.3- and 5.1-fold increase in productivity over IMP and SN pastures, sugarcane displayed a C use efficiency (CUE; i.e., fraction of gross C uptake allocated to plant growth) of 0.59, well above that of pastures (0.31-0.23). Sugarcane also had greater C allocation to aboveground productivity and hence, harvestable biomass relative to IMP and SN. Cane heterotrophic respiration over the 3-year rotation (903 +/- 335 gC m-2 year-1) was 1% and 14% higher than IMP and SN pastures, respectively. These soil C losses responded largely to disturbance over the first year after conversion (1510 +/- 227 gC m-2 year-1) but declined in subsequent years to an average 599 +/- 90 gC m-2 year-1-well below those of IMP (933 +/- 140 gC m-2 year-1) and SN (759 +/- 114 gC m-2 year-1) pastures-despite a significant 40%-61% increase in soil C inputs. Soil C inputs, however, shifted from root-dominated in pastures to litter-dominated in sugarcane, with only 5% C allocation to roots. Reduced decomposition rates in sugarcane were likely driven by changes in the recalcitrance and distribution rather than the size of the newly incorporated soil C pool. As a result, we observed a rapid shift in the net ecosystem C balance (NECB) of sugarcane from a large source immediately following conversion to approaching the net C losses of IMP pastures only 2 years after conversion. The environmental cost of converting pasture to sugarcane underscores the importance of implementing management practices to harness the soil C storage potential of sugarcane in advancing a sustainable bioeconomy in Southeastern United States.

Continued greenhouse gas emissions will accelerate global warming and intensity of heat waves, which already harm crop productivity. From the stability of key enzymes to canopy processes, photosynthesis is affected by temperature. All crops suffer declines in photosynthetic rate when temperatures cross critical thresholds, with irreversible losses typically occurring above 40° to 45°C. Protective measures within plants can be induced by growth at elevated temperatures but not from the sudden temperature elevation of heat waves. Strategies to improve the heat resilience of photosynthesis include modifying surface energy balance, optimizing canopy architecture, improving enzymatic heat tolerance, and (re)engineering key metabolic pathways for greater efficiency or to remove bottlenecks. This Review summarizes present knowledge on the major mechanisms that underlie high-temperature inhibition of photosynthesis and explores opportunities for breeding and biotechnological interventions to overcome them.

As global atmospheric CO2 rapidly approaches a key tipping point, there is an urgent need to implement strategies to reverse this pattern. A generally accepted understanding of carbon (C) in agricultural fields includes: (H1) substantial C loss occurs when natural vegetation is converted to crops, (H2) soils typically reach a steady-state C concentration under contemporary practices, and (H3) improved management or crop selection can enhance soil C stocks over time. Significant variability exists, but studies consistently show large C losses from agricultural ecosystems, supporting H1. Although steady-state C levels (H2) are commonly assumed, measuring C gains or losses in mature agroecosystems is challenging. Efforts to increase soil C storage (H3) have limited data due to the diversity of potential practices, compounded by substantial variability in soil C measurements. Here, long-term (7-17 year) ecosystem C flux data from diverse cropping systems revealed that conventionally tilled annual row crops (maize and soybean) act as significant long-term atmospheric C sources, challenging H2. Furthermore, conservation tillage practices reduced C losses compared with conventional tillage but showed minimal evidence for long-term ecosystem C storage, even after 20+ years. This indicates that no-till practices reduce C losses but imply that no soil C is added, challenging H3. By contrast, perennial Miscanthus × giganteus, Panicum virgatum, and restored tallgrass prairie systems store C at the ecosystem scale more effectively than minimally tilled annual row crops. Analysis over multiple years demonstrates significant ecosystem C storage with perennial crops, varying by species, starting in the first year of transition. These findings, although focused on one region, suggest that the assumptions of steady-state C levels and increased storage from conservation practices do not universally apply and that significant changes to agroecosystems are required to increase C storage.

Pyric herbivory, a process by which grazing is driven by fire, has been shown to create heterogeneity in fire-prone grasslands. Patch-burn grazing (PBG) is a management tool used to harness pyric herbivory and contrasts with full burn (FB) which fosters homogeneity. Here we provide a comprehensive assessment of plant communities (vegetation composition, diversity, and heterogeneity), soil characteristics, and fire fuel consumption responses to PBG as compared to FB management in two different pasture-types (intensively managed pastures [IMP] vs. less intensely managed seminatural pastures [SNP]) in subtropical, humid grasslands in Florida, USA. In 2017, we established 16 experimental pastures at Archbold Biological Station's Buck Island Ranch that were 16-ha each, eight in IMP and eight in SNP. Of the eight pastures in each pasture-type, four were fully burned in 2017 (FB) while in the other four, one-third of the pasture was burned each year for three years (2017, 2018, and 2019) (PBG). PBG-treated pastures were expected to have greater plant richness, diversity, and structural heterogeneity due to the creation of patch contrast while in FB pastures, we expected homogeneous vegetation structure because patches would all have the same fire history. Fuel consumption by fire was greater in SNP vs. IMP and in burned patches within PBG vs. similar-size areas in FB. Recently burned patches had greater total native richness and Shannon diversity, driven by greater numbers and cover of forbs and sedges, but the magnitude of this response varied among years. PBG pastures had greater structural heterogeneity shortly after fire but this disappeared by the end of the growing season. PBG benefits both conservation and production goals in SNP, but incentive programs may be required to implement PBG in IMP to offset losses in forage production while gaining increased height heterogeneity and potential increases in vegetation diversity. (c) 2024 The Author(s). Published by Elsevier Inc. on behalf of The Society for Range Management. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

Environmental observation networks, such as AmeriFlux, are foundational for monitoring ecosystem response to climate change, management practices, and natural disturbances; however, their effectiveness depends on their representativeness for the regions or continents. We proposed an empirical, time series approach to quantify the similarity of ecosystem fluxes across AmeriFlux sites. We extracted the diel and seasonal characteristics (i.e., amplitudes, phases) from carbon dioxide, water vapor, energy, and momentum fluxes, which reflect the effects of climate, plant phenology, and ecophysiology on the observations, and explored the potential aggregations of AmeriFlux sites through hierarchical clustering. While net radiation and temperature showed latitudinal clustering as expected, flux variables revealed a more uneven clustering with many small (number of sites < 5), unique groups and a few large (> 100) to intermediate (15-70) groups, highlighting the significant ecological regulations of ecosystem fluxes. Many identified unique groups were from under-sampled ecoregions and biome types of the International Geosphere-Biosphere Programme (IGBP), with distinct flux dynamics compared to the rest of the network. At the finer spatial scale, local topography, disturbance, management, edaphic, and hydrological regimes further enlarge the difference in flux dynamics within the groups. Nonetheless, our clustering approach is a data-driven method to interpret the AmeriFlux network, informing future cross-site syntheses, upscaling, and model-data benchmarking research. Finally, we highlighted the unique and underrepresented sites in the AmeriFlux network, which were found mainly in Hawaii and Latin America, mountains, and at under-sampled IGBP types (e.g., urban, open water), motivating the incorporation of new/unregistered sites from these groups.