Effects of Management Practices on Grassland Plant and Bird Community Composition in Kane County Forest Preserves

Loss of grassland ecosystems over the past century has increased importance of efforts to improve and restore habitat for native plant species and the biotic communities they support. As a result of these efforts, biotic and abiotic conditions and interactions with the environment are altered. Species evolution based on these particular environmental conditions has caused many species to be mapped onto various environmental gradients which can be defined as niche separation. This study attempted to determine what environmental gradients had the strongest impact upon grassland bird and plant species niche separation, particularly those gradients defined by management activities such as burning, chemical maintenance, and mechanical maintenance of forbs and brush. The main hypothesis tested was that abundance of grassland bird and plant species will be positively related to the increase in frequency of maintenance events including burn, chemical, and mechanical maintenance. The related sub-hypothesis is that bird and plant communities will also respond to other abiotic and biotic factors or characteristics of the sampling units. In 45 sampling units within 9 forest preserves in Kane County, IL. Plant species percent cover, bird species abundance, soil moisture, and vegetation structure were measured. Data including burn regime, chemical and mechanical maintenance, grassland age, and seeding/planting frequencies were obtained from the Forest Preserve District of Kane County. Other variables analyzed including percent fragmentation, grassland size, and proportion of neighboring habitat type (forest, wetland, and shrubland) or land use (developed or agriculture) were determined using ArcMap. Eighteen environmental/management factors were analyzed against 156 plant species and 42 bird species across the 45 sampling units using canonical correspondence analysis (CCA). CCA axes explained 45.1% and 51.5% of total variation in plant and bird species distributions, respectively. This study’s two hypotheses were partially supported because both grassland plant and bird species responded positively to mechanical maintenance of brush and forbs, prescribed burning, and other environmental factors. Top environmental/management factors that influenced plant species distribution were hours of mechanical forb maintenance; followed by proportion of neighboring forest, wetland, and shrubland within a 400m radius; hours of mechanical brush maintenance; vegetation height; and grassland size within a 200m radius. Top factors that influenced bird species distribution were soil moisture followed by grassland size within both 200m and 400m radii, percent fragmentation both 200m and 400m radii, hours of mechanical forb maintenance, and proportion of neighboring agriculture cover within a 400m radius. Ten factors, three of which were maintenance activities, influenced both plant and bird species distributions. Results of both plant and bird species CCAs suggest that there is no single dominating environmental gradient that influences distribution. Moreover, additional environmental factors not included in this study may influence both grassland plant and bird species distributions. This study highlights the importance of conducting observational analyses on management sites to determine what major factors influence species presence and how management decisions can best be used to have the largest benefit upon the community as a whole

Warm-Season Grass Monocultures and Mixtures for Sustainable Bioenergy Feedstock Production in the Midwest, USA

Biomass yield and adaptability to a broad range of environments are important characteristics of dedicated energy crops for sustainable bioenergy feedstock production. In addition to yield potential, the role of species diversity on ecosystem services is also growing in importance as we seek to develop sustainable feedstock production systems. The objective of this study was to compare the biomass yield potential of the commercially available germplasm of native warm-season grasses in monocultures and in blends (mixture of different cultivars of the same species) or mixtures of different species across an environmental gradient (temperature and precipitation) in the Midwest, USA. Warm-season grasses including switchgrass (Panicum virgatum L.), big bluestem (Andropogon gerardii Vitman), indiangrass (Sorghastrum nutans[L.] Nash), sideoats grama (Bouteloua curtipendula [Michx.] Torr.) and Miscanthus × giganteus (Greef and Deu.) were planted in 2009. Biomass was annually harvested from 2010 through 2015 for Urbana, IL and Mead, NE but only in 2010 and 2011 for Ames, IA. The effect of species in monocultures and mixtures (or blends) on biomass yields was significant for all locations. In monocultures, the annual biomass yields averaged over a 6-year period were 11.12 Mg ha−1 and 10.98 Mg ha−1 at Urbana and Mead, respectively, while the annual biomass yield averaged over a 2-year period was 7.99 Mg ha−1 at Ames, IA. Also, the annual biomass yields averaged across the different mixtures and blends at each location were 10.25 Mg ha−1, 9.88 Mg ha−1, and 7.64 Mg ha−1 at Urbana, Mead, and Ames, respectively. At all locations, M. × giganteus and ‘Kanlow N1’ produced the highest biomass yield in monocultures while mixtures containing switchgrass and big bluestem had the greatest mixture yield. The results from this multi-environment study suggest mixtures of different species provided no yield advantage over monocultures for bioenergy feedstocks in Illinois and Nebraska and both systems consistently produced biomass as long as April–July precipitation was near or above the average precipitation (300 mm) of the regions

Impact of warm‐season grass management on feedstock production on marginal farmland in Central Illinois

The production of dedicated energy crops on marginally productive cropland is projected to play an important role in reaching the US Billion Ton goal. This study aimed to evaluate warm‐season grasses for biomass production potential under different harvest timings (summer [H1], after killing frost [H2], or alternating between two [H3]) and nitrogen (N) fertilizer rates (0, 56, and 112 kg N/ha) on a wet marginal land across multiple production years. Six feedstocks were evaluated including Miscanthus x giganteus, two switchgrass cultivars (Panicum virgatum L.), prairie cordgrass (Spartina pectinata Link), and two polycultures including a mixture of big bluestem (Andropogon gerardii Vitman), indiangrass (Sorghastrum nutans), and sideoats grama (Bouteloua curtipendula [Michx.] Torr.), and a mixture of big bluestem and prairie cordgrass. Across four production years, harvest timing and feedstock type played an important role in biomass production. Miscanthus x giganteus produced the greatest biomass (18.7 Mg/ha), followed by the switchgrass cultivar “Liberty” (14.7 Mg/ha). Harvest in H1 tended to increase yield irrespective of feedstock; the exception being M. x giganteus that had significantly lower biomass when harvested in H1 when compared to H2 and H3. The advantage H1 harvest had over H2 for all feedstocks declined over time, suggesting H2 or H3 would provide greater and more sustainable biomass production for the observed feedstocks. The N application rate played an important role mainly for M. x giganteus where 112 kg N/ ha yielded more biomass than no N. Other feedstocks occasionally showed a slight, but statistically insignificant increase in biomass yield with increasing N rate. This study showed the potential of producing feedstocks for bioenergy on wet marginal land; however, more research on tissue and soil nutrient dynamics under different N rates and harvest regimes will be important in understanding stand longevity for feedstocks grown under these conditions

Willow buffers in agricultural systems : linking bioenergy production and ecosystem services

The production and consumption of food, energy, and water are inextricably linked. With agricultural systems contributing high levels of nutrients into ground and surface water systems, agriculture poses both human health and environmental risks downstream of these non-point sources. This project was designed to develop and prove concepts supporting multifunctional landscapes, which address multiple problems regarding food, energy, and water. The benefits of multifunctional landscapes over simplified landscapes (i.e., crop monocultures) include the ability to address these multiple societal challenges simultaneously by incorporating components that can perform multiple services

Warm-Season Grass Monocultures and Mixtures for Sustainable Bioenergy Feedstock Production in the Midwest, USA

Biomass yield and adaptability to a broad range of environments are important characteristics of dedicated energy crops for sustainable bioenergy feedstock production. In addition to yield potential, the role of species diversity on ecosystem services is also growing in importance as we seek to develop sustainable feedstock production systems. The objective of this study was to compare the biomass yield potential of the commercially available germplasm of native warm-season grasses in monocultures and in blends (mixture of different cultivars of the same species) or mixtures of different species across an environmental gradient (temperature and precipitation) in the Midwest, USA. Warm-season grasses including switchgrass (Panicum virgatum L.), big bluestem (Andropogon gerardii Vitman), indiangrass (Sorghastrum nutans[L.] Nash), sideoats grama (Bouteloua curtipendula [Michx.] Torr.) and Miscanthus × giganteus (Greef and Deu.) were planted in 2009. Biomass was annually harvested from 2010 through 2015 for Urbana, IL and Mead, NE but only in 2010 and 2011 for Ames, IA. The effect of species in monocultures and mixtures (or blends) on biomass yields was significant for all locations. In monocultures, the annual biomass yields averaged over a 6-year period were 11.12 Mg ha−1 and 10.98 Mg ha−1 at Urbana and Mead, respectively, while the annual biomass yield averaged over a 2-year period was 7.99 Mg ha−1 at Ames, IA. Also, the annual biomass yields averaged across the different mixtures and blends at each location were 10.25 Mg ha−1, 9.88 Mg ha−1, and 7.64 Mg ha−1 at Urbana, Mead, and Ames, respectively. At all locations, M. × giganteus and ‘Kanlow N1’ produced the highest biomass yield in monocultures while mixtures containing switchgrass and big bluestem had the greatest mixture yield. The results from this multi-environment study suggest mixtures of different species provided no yield advantage over monocultures for bioenergy feedstocks in Illinois and Nebraska and both systems consistently produced biomass as long as April–July precipitation was near or above the average precipitation (300 mm) of the regions.This article is published as Lee, Moon-Sub, Rob Mitchell, Emily Heaton, Colleen Zumpf, and D. K. Lee. "Warm-Season Grass Monocultures and Mixtures for Sustainable Bioenergy Feedstock Production in the Midwest, USA." BioEnergy Research (2018). doi: 10.1007/s12155-018-9947-7.</p

Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy

A sustainable bioeconomy would require growing high-yielding bioenergy crops on marginal agricultural areas with minimal inputs. To determine the cost competitiveness and environmental sustainability of such production systems, reliably estimating biomass yield is critical. However, because marginal areas are often small and spread across the landscape, yield estimation using traditional approaches is costly and time-consuming. This paper demonstrates the (1) initial investigation of optical remote sensing for predicting perennial bioenergy grass yields at harvest using a linear regression model with the green normalized difference vegetation index (GNDVI) derived from Sentinel-2 imagery and (2) evaluation of the model’s performance using data from five U.S. Midwest field sites. The linear regression model using midsummer GNDVI predicted yields at harvest with R2 as high as 0.879 and a mean absolute error and root mean squared error as low as 0.539 Mg/ha and 0.616 Mg/ha, respectively, except for the establishment year. Perennial bioenergy grass yields may be predicted 152 days before the harvest date on average, except for the establishment year. The green spectral band showed a greater contribution for predicting yields than the red band, which is indicative of increased chlorophyll content during the early growing season. Although additional testing is warranted, this study showed a great promise for a remote sensing approach for forecasting perennial bioenergy grass yields to support critical economic and logistical decisions of bioeconomy stakeholders.This article is published as Hamada, Yuki, Colleen R. Zumpf, Jules F. Cacho, DoKyoung Lee, Cheng-Hsien Lin, Arvid Boe, Emily Heaton, Robert Mitchell, and Maria Cristina Negri. "Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy." Land 10, no. 11 (2021): 1221. doi:10.3390/land10111221. Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted

Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy

A sustainable bioeconomy would require growing high-yielding bioenergy crops on marginal agricultural areas with minimal inputs. To determine the cost competitiveness and environmental sustainability of such production systems, reliably estimating biomass yield is critical. However, because marginal areas are often small and spread across the landscape, yield estimation using traditional approaches is costly and time-consuming. This paper demonstrates the (1) initial investigation of optical remote sensing for predicting perennial bioenergy grass yields at harvest using a linear regression model with the green normalized difference vegetation index (GNDVI) derived from Sentinel-2 imagery and (2) evaluation of the model’s performance using data from five U.S. Midwest field sites. The linear regression model using midsummer GNDVI predicted yields at harvest with R2 as high as 0.879 and a mean absolute error and root mean squared error as low as 0.539 Mg/ha and 0.616 Mg/ha, respectively, except for the establishment year. Perennial bioenergy grass yields may be predicted 152 days before the harvest date on average, except for the establishment year. The green spectral band showed a greater contribution for predicting yields than the red band, which is indicative of increased chlorophyll content during the early growing season. Although additional testing is warranted, this study showed a great promise for a remote sensing approach for forecasting perennial bioenergy grass yields to support critical economic and logistical decisions of bioeconomy stakeholders

Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy

A sustainable bioeconomy would require growing high-yielding bioenergy crops on marginal agricultural areas with minimal inputs. To determine the cost competitiveness and environmental sustainability of such production systems, reliably estimating biomass yield is critical. However, because marginal areas are often small and spread across the landscape, yield estimation using traditional approaches is costly and time-consuming. This paper demonstrates the (1) initial investigation of optical remote sensing for predicting perennial bioenergy grass yields at harvest using a linear regression model with the green normalized difference vegetation index (GNDVI) derived from Sentinel-2 imagery and (2) evaluation of the model’s performance using data from five U.S. Midwest field sites. The linear regression model using midsummer GNDVI predicted yields at harvest with R2 as high as 0.879 and a mean absolute error and root mean squared error as low as 0.539 Mg/ha and 0.616 Mg/ha, respectively, except for the establishment year. Perennial bioenergy grass yields may be predicted 152 days before the harvest date on average, except for the establishment year. The green spectral band showed a greater contribution for predicting yields than the red band, which is indicative of increased chlorophyll content during the early growing season. Although additional testing is warranted, this study showed a great promise for a remote sensing approach for forecasting perennial bioenergy grass yields to support critical economic and logistical decisions of bioeconomy stakeholders

Use of the Assessment of Caregiver Experience with Neuromuscular Disease (ACEND) in Spinal Muscular Atrophy

Background: Spinal muscular atrophy (SMA) has a remarkable impact on function and participation. Subsequently, the caregivers of individuals with SMA are impacted as well. Providers and the SMA community should be aware of the presence of and likely expectations for the existence of caregiver burden. Methods: The Assessment of Caregiver Experience with Neuromuscular Disease (ACEND) quantifies caregivers’ perceptions of function and quality of life pertaining to time, finance and emotion. Analyses were conducted among SMA types and ambulatory and ventilatory status. Participants with SMA had varying ranges of function and were on pharmaceutical treatment. Total ACEND score, longitudinal change in total ACEND score, total quality of life (QOL) score, change in total QOL score and subdomains for QOL, including time, emotion and finance, were all explored. Results: Overall, the ACEND demonstrated discriminant validity and some observed trends. Total ACEND scores improved for caregivers of those with SMA 2, remained stable longitudinally for caregivers of those with SMA 1 and 3 and were not influenced by ventilation status. The caregivers of individuals with SMA 1 had the lowest total quality of life (QOL) score, as did the caregivers of non-ambulatory individuals and those requiring assisted ventilation. Longitudinally, there were no changes in total QOL between caregivers of individuals with different SMA types or ambulatory or ventilation status. There were some differences in emotional needs, but no differences in financial impact between the caregivers of individuals with different types of SMA or ambulatory and ventilatory status. Conclusions: With this information enlightening the presence of caregiver burden and expected changes in burden with pharmaceutical treatment, providers, third party payors and the SMA community at large can better assist, equip and empower those providing the necessary assistance to enable the lives of those with SMA

Multi-site evaluation of stratified and balanced sampling of soil organic carbon stocks in agricultural fields

Estimating soil organic carbon (SOC) stocks in agricultural fields is essential for environmental and agronomic research, management, and policy. Stratified sampling is a classic strategy for estimating mean soil properties, and has recently been codified in SOC monitoring protocols. However, for the specific task of estimating the SOC stock of an agricultural field, concrete guidance is needed for which covariates to stratify on and how much stratification can improve estimation efficiency. It is also unknown how stratified sampling of SOC stocks compares to modern alternatives, notably doubly balanced sampling. To address these gaps, we collected high-density (average of 7 samples ha−1) and deep (average of 75 cm) measurements of SOC stocks at eight commercial fields under maize-soybean production in two US Midwestern states. We combined these measurements with a Bayesian geostatistical model to evaluate stratified and balanced sampling strategies that use a set of readily-available geographic, topographic, spectroscopic, and soil survey data. We examined the number of samples needed to achieve a given level of SOC stock estimation accuracy. While stratified sampling using these variables enables an average sample size reduction of 17% (95% CI, 11% to 23%) compared to simple random sampling, doubly balanced sampling is consistently more efficient, reducing sample sizes by 32% (95% CI, 25% to 37%). The data most important to these efficiency gains are a remotely-sensed SOC index, SSURGO estimates of SOC stocks, and the topographic wetness index. We conclude that in order to meet the urgent challenge of climate change, SOC stocks in agricultural fields could be more efficiently estimated by taking advantage of this readily-available data, especially with doubly balanced sampling