College of Agriculture

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As the foundation of the land grant mission at Montana State University, the College of Agriculture and the Montana Agricultural Experiment Station provide instruction in traditional and innovative degree programs and conduct research on old and new challenges for Montana’s agricultural community. This integration creates opportunities for students and faculty to excel through hands-on learning, to serve through campus and community engagement, to explore unique solutions to distinct and interesting questions and to connect Montanans with the global community through research discoveries and outreach.

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Now showing 1 - 10 of 93

Antecedent climatic conditions spanning several years influence multiple land-surface phenology events in semi-arid environments
(Frontiers Media SA, 2022-10) Wood, David J. A.; Stoy, Paul C.; Powell, Scott L.; Beever, Erik A.
Ecological processes are complex, often exhibiting non-linear, interactive, or hierarchical relationships. Furthermore, models identifying drivers of phenology are constrained by uncertainty regarding predictors, interactions across scales, and legacy impacts of prior climate conditions. Nonetheless, measuring and modeling ecosystem processes such as phenology remains critical for management of ecological systems and the social systems they support. We used random forest models to assess which combination of climate, location, edaphic, vegetation composition, and disturbance variables best predict several phenological responses in three dominant land cover types in the U.S. Northwestern Great Plains (NWP). We derived phenological measures from the 25-year series of AVHRR satellite data and characterized climatic predictors (i.e., multiple moisture and/or temperature based variables) over seasonal and annual timeframes within the current year and up to 4 years prior. We found that antecedent conditions, from seasons to years before the current, were strongly associated with phenological measures, apparently mediating the responses of communities to current-year conditions. For example, at least one measure of antecedent-moisture availability [precipitation or vapor pressure deficit (VPD)] over multiple years was a key predictor of all productivity measures. Variables including longer-term lags or prior year sums, such as multi-year-cumulative moisture conditions of maximum VPD, were top predictors for start of season. Productivity measures were also associated with contextual variables such as soil characteristics and vegetation composition. Phenology is a key process that profoundly affects organism-environment relationships, spatio-temporal patterns in ecosystem structure and function, and other ecosystem dynamics. Phenology, however, is complex, and is mediated by lagged effects, interactions, and a diversity of potential drivers; nonetheless, the incorporation of antecedent conditions and contextual variables can improve models of phenology.
Climate mitigation potential and soil microbial response of cyanobacteria‐fertilized bioenergy crops in a cool semi‐arid cropland
(Wiley, 2022-10) Gay, Justin D.; Goemann, Hannah M.; Currey, Bryce; Stoy, Paul C.; Christiansen, Jesper Riis; Miller, Perry R.; Poulter, Benjamin; Peyton, Brent M.; Brookshire, E. N. Jack
Bioenergy carbon capture and storage (BECCS) systems can serve as decarbonization pathways for climate mitigation. Perennial grasses are a promising second-generation lignocellulosic bioenergy feedstock for BECCS expansion, but optimizing their sustainability, productivity, and climate mitigation potential requires an evaluation of how nitrogen (N) fertilizer strategies interact with greenhouse gas (GHG) and soil organic carbon (SOC) dynamics. Furthermore, crop and fertilizer choice can affect the soil microbiome which is critical to soil organic matter turnover, nutrient cycling, and sustaining crop productivity but these feedbacks are poorly understood due to the paucity of data from certain agroecosystems. Here, we examine the climate mitigation potential and soil microbiome response to establishing two functionally different perennial grasses, switchgrass (Panicum virgatum, C4) and tall wheatgrass (Thinopyrum ponticum, C3), in a cool semi-arid agroecosystem under two fertilizer applications, a novel cyanobacterial biofertilizer (CBF) and urea. We find that in contrast to the C4 grass, the C3 grass achieved 98% greater productivity and had a higher N use efficiency when fertilized. For both crops, the CBF produced the same biomass enhancement as urea. Non-CO2 GHG fluxes across all treatments were low and we observed a 3-year net loss of SOC under the C4 crop and a net gain under the C3 crop at a 0–30 cm soil depth regardless of fertilization. Finally, we detected crop-specific changes in the soil microbiome, including an increased relative abundance of arbuscular mycorrhizal fungi under the C3, and potentially pathogenic fungi in the C4 grass. Taken together, these findings highlight the potential of CBF-fertilized C3 crops as a second-generation bioenergy feedstock in semi-arid regions as a part of a climate mitigation strategy.
Multiple UAV Flights across the Growing Season Can Characterize Fine Scale Phenological Heterogeneity within and among Vegetation Functional Groups
(MDPI AG, 2022-03) Wood, David J. A.; Preston, Todd M.; Powell, Scott; Stoy, Paul C.
Grasslands and shrublands exhibit pronounced spatial and temporal variability in structure and function with differences in phenology that can be difficult to observe. Unpiloted aerial vehicles (UAVs) can measure vegetation spectral patterns relatively cheaply and repeatably at fine spatial resolution. We tested the ability of UAVs to measure phenological variability within vegetation functional groups and to improve classification accuracy at two sites in Montana, U.S.A. We tested four flight frequencies during the growing season. Classification accuracy based on reference data increased by 5–10% between a single flight and scenarios including all conducted flights. Accuracy increased from 50.6% to 61.4% at the drier site, while at the more mesic/densely vegetated site, we found an increase of 59.0% to 64.4% between a single and multiple flights over the growing season. Peak green-up varied by 2–4 weeks within the scenes, and sparse vegetation classes had only a short detectable window of active phtosynthesis; therefore, a single flight could not capture all vegetation that was active across the growing season. The multi-temporal analyses identified differences in the seasonal timing of green-up and senescence within herbaceous and sagebrush classes. Multiple UAV measurements can identify the fine-scale phenological variability in complex mixed grass/shrub vegetation.
The spatial variability of NDVI within a wheat field: Information content and implications for yield and grain protein monitoring
(Public Library of Science, 2022-03) Stoy, Paul C.; Khan, Anam M.; Wipf, Aaron; Silverman, Nick; Powell, Scott L.
Wheat is a staple crop that is critical for feeding a hungry and growing planet, but its nutritive value has declined as global temperatures have warmed. The price offered to producers depends not only on yield but also grain protein content (GPC), which are often negatively related at the field scale but can positively covary depending in part on management strategies, emphasizing the need to understand their variability within individual fields. We measured yield and GPC in a winter wheat field in Sun River, Montana, USA, and tested the ability of normalized difference vegetation index (NDVI) measurements from an unoccupied aerial vehicle (UAV) on spatial scales of ~10 cm and from Landsat on spatial scales of 30 m to predict them. Landsat observations were poorly related to yield and GPC measurements. A multiple linear model using information from four (three) UAV flyovers was selected as the most parsimonious and predicted 26% (40%) of the variability in wheat yield (GPC). We sought to understand the optimal spatial scale for interpreting UAV observations given that the ~ 10 cm pixels yielded more than 12 million measurements at far finer resolution than the 12 m scale of the harvester. The variance in NDVI observations was “averaged out” at larger pixel sizes but only ~ 20% of the total variance was averaged out at the spatial scale of the harvester on some measurement dates. Spatial averaging to the scale of the harvester also made little difference in the total information content of NDVI fit using Beta distributions as quantified using the Kullback-Leibler divergence. Radially-averaged power spectra of UAV-measured NDVI revealed relatively steep power-law relationships with exponentially less variance at finer spatial scales. Results suggest that larger pixels can reasonably capture the information content of within-field NDVI, but the 30 m Landsat scale is too coarse to describe some of the key features of the field, which are consistent with topography, historic management practices, and edaphic variability. Future research should seek to determine an ‘optimum’ spatial scale for NDVI observations that minimizes effort (and therefore cost) while maintaining the ability of producers to make management decisions that positively impact wheat yield and GPC.
Maximum carbon uptake rate dominates the interannual variability of global net ecosystem exchange
(2019-10-19) Fu, Zheng; Stoy, Paul C.; Poulter, Benjamin; Gerken, Tobias; Zhang, Zhen; Wakbulcho, Guta; Niu, Shuli
Terrestrial ecosystems contribute most of the interannual variability (IAV) in atmospheric carbon dioxide (CO2) concentrations, but processes driving the IAV of net ecosystem CO2 exchange (NEE) remain elusive. For a predictive understanding of the global C cycle, it is imperative to identify indicators associated with ecological processes that determine the IAV of NEE. Here, we decompose the annual NEE of global terrestrial ecosystems into their phenological and physiological components, namely maximum carbon uptake (MCU) and release (MCR), the carbon uptake period (CUP), and two parameters, α and β, that describe the ratio between actual versus hypothetical maximum C sink and source, respectively. Using long‐term observed NEE from 66 eddy covariance sites and global products derived from FLUXNET observations, we found that the IAV of NEE is determined predominately by MCU at the global scale, which explains 48% of the IAV of NEE on average while α, CUP, β, and MCR explain 14%, 25%, 2%, and 8%, respectively. These patterns differ in water‐limited ecosystems versus temperature‐ and radiation‐limited ecosystems; 31% of the IAV of NEE is determined by the IAV of CUP in water‐limited ecosystems, and 60% of the IAV of NEE is determined by the IAV of MCU in temperature‐ and radiation‐limited ecosystems. The Lund‐Potsdam‐Jena (LPJ) model and the Multi‐scale Synthesis and Terrestrial Model Inter‐comparison Project (MsTMIP) models underestimate the contribution of MCU to the IAV of NEE by about 18% on average, and overestimate the contribution of CUP by about 25%. This study provides a new perspective on the proximate causes of the IAV of NEE, which suggest that capturing the variability of MCU is critical for modeling the IAV of NEE across most of the global land surface.
The exchange of water and energy between a tropical peat forest and the atmosphere: Seasonal trends and comparison against other tropical rainforests.
(2019-09) Tang, Angela C. I.; Stoy, Paul C.; Hirata, Ryuichi; Musin, Kevin K.; Aeries, Edward B.; Wencelslaus, Joseph; Shimizu, Mariko; Melling, Lulie
Tropical rainforests control the exchange of water and energy between the land surface and the atmosphere near the equator and thus play an important role in the global climate system. Measurements of latent (LE) and sensible heat exchange (H) have not been synthesized across global tropical rainforests to date, which can help place observations from individual tropical forests in a global context. We measured LE and H for four years in a tropical peat forest ecosystem in Sarawak, Malaysian Borneo using eddy covariance, and hypothesize that the study ecosystem will exhibit less seasonal variability in turbulent fluxes than other tropical ecosystems as soil water is not expected to be limiting in a tropical forested wetland. LE and H show little variability across seasons in the study ecosystem, with LE values on the order of 11 MJ m−2 day and H on the order of 3 MJ m−2 day−1. Annual evapotranspiration (ET) did not differ among years and averaged 1579 ± 47 mm year−1. LE exceeded characteristic values from other tropical rainforest ecosystems in the FLUXNET2015 database with the exception of GF-Guy near coastal French Guyana, which averaged 8–11 MJ m−2 day−1. The Bowen ratio (Bo) in tropical rainforests in the FLUXNET2015 database either exhibited little seasonal trend, one seasonal peak, or two peaks. Volumetric water content (VWC) and VPD explained a trivial amount of the variability of LE and Bo in some of the tropical rainforests including the study ecosystem, but were strong controls in others, suggesting differences in stomatal regulation and/or the partitioning between evaporation and transpiration. Results demonstrate important differences in the seasonal patterns in water and energy exchange across different tropical rainforest ecosystems that need to be understood to quantify how ongoing changes in tropical rainforest extent will impact the global climate system.
The surface-atmosphere exchange of carbon dioxide in tropical rainforests: Sensitivity to environmental drivers and flux measurement methodology
(2018-12) Fu, Zheng; Gerken, Tobias; Bromley, Gabriel T.; Araújo, Alessandro; Bonal, Damien; Burban, Benoit; Ficklin, Darren L.; Fuentes, Jose D.; Goulden, Michael L.; Hirano, Takashi; Kosugi, Yoshiko; Liddell, Michael; Nicolini, Giacomo; Niu, Shuli; Roupsard, Olivier; Stefani, Paolo; Mi, Chunrong; Tofte, Zaddy; Xiao, Jingfeng; Valentini, Riccardo; Wolf, Sebastian; Stoy, Paul C.
Tropical rainforests play a central role in the Earth system by regulating climate, maintaining biodiversity, and sequestering carbon. They are under threat by direct anthropogenic impacts like deforestation and the indirect anthropogenic impacts of climate change. A synthesis of the factors that determine the net ecosystem exchange of carbon dioxide (NEE) at the site scale across different forests in the tropical rainforest biome has not been undertaken to date. Here, we study NEE and its components, gross ecosystem productivity (GEP) and ecosystem respiration (RE), across thirteen natural and managed forests within the tropical rainforest biome with 63 total site-years of eddy covariance data. Our results reveal that the five ecosystems with the largest annual gross carbon uptake by photosynthesis (i.e. GEP > 3000 g C m(-2) y(-1)) have the lowest net carbon uptake - or even carbon losses versus other study ecosystems because RE is of a similar magnitude. Sites that provided sub canopy CO2 storage observations had higher average magnitudes of GEP and RE and lower average magnitudes of NEE, highlighting the importance of measurement methodology for understanding carbon dynamics in ecosystems with characteristically tall and dense vegetation. A path analysis revealed that vapor pressure deficit (VPD) played a greater role than soil moisture or air temperature in constraining GEP under light saturated conditions across most study sites, but to differing degrees from -0.31 to -0.87 mu mol CO2 m(-2) s(-1) hPa(-1). Climate projections from 13 general circulation models (CMIP5) under the representative concentration pathway that generates 8.5 W m(-2) of radiative forcing suggest that many current tropical rainforest sites on the lower end of the current temperature range are likely to reach a climate space similar to present-day warmer sites by the year 2050, warmer sites will reach a climate not currently experienced, and all forests are likely to experience higher VPD. Results demonstrate the need to quantify if and how mature tropical trees acclimate to heat and water stress, and to further develop flux-partitioning and gap-filling algorithms for defensible estimates of carbon exchange in tropical rainforests.
Linking flux network measurements to continental scale simulations: Ecosystem gas exchange capacity along a European transect under non-water-stressed conditions
(2007-01) Owen, Katherine E.; Tenhunen, John; Reichstein, Markus; Wang, Quan; Falge, Eva; Geyer, Ralf; Xiao, Xiangming; Stoy, Paul C.; Ammann, Christof; Arain, M. Altaf; Aubinet, Marc; Aurela, Mika; Bernhofer, Christian; Chojnicki, Bogdan H.; Granier, Andre; Gruenwald, Thomas; Hadley, Julian; Heinesch, Bernard; Hollinger, David Y.; Knohl, Alexander; Kutsch, Werner L.; Lohila, Annalea; Meyers, Tilden P.; Moors, Eddy J.; Moureaux, Christine; Pilegaard, Kim; Saigusa, Nobuko; Verma, Shashi B.; Vesala, Timo; Vogel, Chris
This paper examines long‐term eddy covariance data from 18 European and 17 North American and Asian forest, wetland, tundra, grassland, and cropland sites under non‐water‐stressed conditions with an empirical rectangular hyperbolic light response model and a single layer two light‐class carboxylase‐based model. Relationships according to ecosystem functional type are demonstrated between empirical and physiological parameters, suggesting linkages between easily estimated parameters and those with greater potential for process interpretation. Relatively sparse documentation of leaf area index dynamics at flux tower sites is found to be a major difficulty in model inversion and flux interpretation. Therefore, a simplification of the physiological model is carried out for a subset of European network sites with extensive ancillary data. The results from these selected sites are used to derive a new parameter and means for comparing empirical and physiologically based methods across all sites, regardless of ancillary data. The results from the European analysis are then compared with results from the other Northern Hemisphere sites and similar relationships for the simplified process‐based parameter were found to hold for European, North American, and Asian temperate and boreal climate zones. This parameter is useful for bridging between flux network observations and continental scale spatial simulations of vegetation/atmosphere carbon dioxide exchange.
Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes
(2007-04) Yuan, Wenping; Liu, Shuguang; Zhou, Guangsheng; Zhou, Guoyi; Tieszen, Larry L.; Baldocchi, Dennis D.; Bernhofer, Christian; Gholz, Henry; Goldstein, Allen H.; Goulden, Michael L.; Hollinger, David Y.; Hu, Yueming; Law, Beverly E.; Stoy, Paul C.; Vesala, Timo; Wofsy, Steven C.
The quantitative simulation of gross primary production (GPP) at various spatial and temporal scales has been a major challenge in quantifying the global carbon cycle. We developed a light use efficiency (LUE) daily GPP model from eddy covariance (EC) measurements. The model, called EC-LUE, is driven by only four variables: normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and the Bowen ratio of sensible to latent heat flux (used to calculate moisture stress). The EC-LUE model relies on two assumptions: First, that the fraction of absorbed PAR (fPAR) is a linear function of NDVI; Second, that the realized light use efficiency, calculated from a biome-independent invariant potential LUE, is controlled by air temperature or soil moisture, whichever is most limiting. The EC-LUE model was calibrated and validated using 24,349 daily GPP estimates derived from 28 eddy covariance flux towers from the AmeriFlux and EuroFlux networks, covering a variety of forests, grasslands and savannas. The model explained 85% and 77% of the observed variations of daily GPP for all the calibration and validation sites, respectively. A comparison with GPP calculated from the Moderate Resolution Imaging Spectroradiometer (MODIS) indicated that the EC-LUE model predicted GPP that better matched tower data across these sites. The realized LUE was predominantly controlled by moisture conditions throughout the growing season, and controlled by temperature only at the beginning and end of the growing season. The EC-LUE model is an alternative approach that makes it possible to map daily GPP over large areas because (1) the potential LUE is invariant across various land cover types and (2) all driving forces of the model can be derived from remote sensing data or existing climate observation networks.
Interannual variability of ecosystem carbon exchange: From observation to prediction
(2017-09) Niu, Shuli; Zheng, Fu; Yiqi, Luo; Stoy, Paul C.; Keenan, Trevor F.; Poulter, Benjamin; Zhang, Leiming; Piao, Shilong; Zhou, Xuhui; Zheng, Han; Han, Jiayin; Wang, Qiufeng; Yu, Guirui
Aim Terrestrial ecosystems have sequestered, on average, the equivalent of 30% of anthropogenic carbon (C) emissions during the past decades, but annual sequestration varies from year to year. For effective C management, it is imperative to develop a predictive understanding of the interannual variability (IAV) of terrestrial net ecosystem C exchange (NEE). Location Global terrestrial ecosystems. Methods We conducted a comprehensive review to examine the IAV of NEE at global, regional and ecosystem scales. Then we outlined a conceptual framework for understanding how anomalies in climate factors impact ecological processes of C cycling and thus influence the IAV of NEE through biogeochemical regulation. Results The phenomenon of IAV in land NEE has been ubiquitously observed at global, regional and ecosystem scales. Global IAV is often attributable to either tropical or semi‐arid regions, or to some combination thereof, which is still under debate. Previous studies focus on identifying climate factors as driving forces of IAV, whereas biological mechanisms underlying the IAV of ecosystem NEE are less clear. We found that climate anomalies affect the IAV of NEE primarily through their differential impacts on ecosystem C uptake and respiration. Moreover, recent studies suggest that the carbon uptake period makes less contribution than the carbon uptake amplitude to IAV in NEE. Although land models incorporate most processes underlying IAV, their efficacy to predict the IAV in NEE remains low. Main conclusions To improve our ability to predict future IAV of the terrestrial C cycle, we have to understand biological mechanisms through which anomalies in climate factors cause the IAV of NEE. Future research needs to pay more attention not only to the differential effects of climate anomalies on photosynthesis and respiration but also to the relative importance of the C uptake period and amplitude in causing the IAV of NEE. Ultimately, we need multiple independent approaches, such as benchmark analysis, data assimilation and time‐series statistics, to integrate data, modelling frameworks and theory to improve our ability to predict future IAV in the terrestrial C cycle.

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