Location: Pasture Systems & Watershed Management Research
2022 Annual Report
Objectives
Objective 1: Develop novel, and improve existing, pasture and crop management strategies to improve agricultural productivity and environmental sustainability in integrated crop-pasture-livestock systems. Sub-objectives include:
Sub-objective 1.A. Develop cover crop management strategies to enhance plant and animal productivity and soil health.
Sub-objective 1.B. Evaluate plant and animal performance using alternative forages to extend the grazing season to compensate for periods of low perennial cool-season pasture production.
Sub-objective 1.C. Evaluate soil health benefits achieved when a confinement dairy is converted to grazing-based forage production.
Objective 2: Incorporate novel and existing management strategies into farm- and landscape-scale agricultural planning tools to foster sustainable intensification. Sub-objectives include:
Sub-objective 2.A. Quantify the effects of managed riparian grazing on water quality, invasive species, grazing behavior, and plant and animal productivity.
Sub-objective 2.B. Develop precision management strategies for perennial forage and biomass crops to increase production and profitability and minimize environmental impacts.
Sub-objective 2.C. Synthesize the results of farming system and statistical modeling to develop adaptive decision support tools and to quantify the regional consequences of incorporating the novel practices evaluated in other sub-objectives into integrated crop-pasture-livestock systems.
Approach
Agriculture in the Northeastern U.S. contributes greatly to the regional economy, but is constrained by complex topography, soils, hydrology, and land use patterns, and now faces challenges due to climate change. Strategies for sustainable intensification of characteristic small farms must incorporate crop, pasture, livestock, and biomass production to efficiently use the diverse resources available. Such integration has the potential to not only increase production, but also to improve nutrient cycling, carbon storage, and soil health. This integration and optimization require improved production systems, precision management, and new tools for assessment and decision-making. At the field scale, integrative strategies will result in more efficient utilization of cropland in space and time through cover crops and interseeding. These practices can improve soil health and water quality, while also providing additional forage and increasing crop yields. Conversion from annual to perennial crops benefits soil health and mitigates climate change. At the farm scale, managed grazing of riparian areas increases forage availability and reduces invasive plants without impacting water quality. Precision agriculture techniques adapted to this region improve targeting of management practices and reduce unnecessary inputs. Simulation modeling synthesizes new knowledge of farm and regional effects of these practices on production and ecosystem services and extrapolates these effects to future climates to better plan adaptation efforts. Results at all scales will be integrated into an adaptive decision support system. Explicit guidance on management strategies for sustainable intensification of diverse farms in the northeastern U.S. will benefit farmers through increased production efficiency, will contribute to the prosperity of rural communities, and will improve environmental quality across the entire region.
We will collaborate with larger USDA-led research networks, including the Long-Term Agroecological Research network (LTAR), Conservation Effects Assessment Project (CEAP), and Dairy Agroecosystems Working Group (DAWG). Such networking provides expertise and data on outcomes from management strategies for integrated crop-pasture-livestock systems that will be used to complete the objectives of this project.
With an emphasis on sustainable intensification in accord with climate predictions, our research must be approached not just on individual farms, but at landscape and regional scales. Because of the impossibility of performing experiments on multiple farms across the entire northeastern US, modeling is required to extrapolate on-farm research to a wider area, and to facilitate the development of broadly applicable decision support tools and management recommendations. To meet this objective, we will combine both on-farm studies and modeling. Outcomes of this research will support farmers directly through management strategies and decision support tools, and will provide scientifically-valid data to federal and state programs aimed at improving nutrient management, conservation, and resource use efficiency.
Progress Report
Under Sub-Objective 1.A, Plant species mixtures were established, and species biomass yield and quality measurements, hyperspectral, and Light Detection and Ranging (LiDAR) data collected (1.A.1). We also developed artificial intelligence models to extend the spatial and temporal coverage and to extend spectral range of unmanned aerial system (UAS) imagery, providing a complementary role for both UAS and satellite imagery in precision management strategies for perennial forage and biomass crops. Interseeded corn project was not grazed with beef cattle in fall 2020 due to late plantings and harvests. However, forage yield and quality and soil data were collected. Soybeans were planted in May 2022 to rotate crops and control weeds. Annual ryegrass will be planted in Aug/Sept 2022. (1.A.2).
Under Sub-Objective 1.B, warm-season grasses (teff, pearl millet, sorghum-sudangrass) were planted as monocultures or interseeded into previously established orchardgrass pastures in June 2022. These species will be monitored for biomass productivity and persistence during the remainder of the 2022 growing season (1.B.1). Sub-objective 1.B.2 has been delayed. Due to restrictions as a result of the COVID pandemic and FY22 Maximized Telework, the University of New Hampshire (UNH) was not able to conduct the research, and ARS personnel were not able to travel to UNH to assist with data collection. The future of this sub-objective is unknown, as cow availability in the future is uncertain due to commitments to other UNH research projects (including grant-funded projects).
Under Sub-Objective 1.C, due to travel restrictions as a result of FY22 Maximized Telework, ARS personnel were not able to travel to UNH to collect the soil samples (1.C.1). Samples are scheduled to be collected in Fall 2022.
Under Sub-Objective 2.A, the riparian grazing sub-objective is on indefinite delay. FY22 Maximized Telework prevented ARS researchers from traveling to potential farms to identify a suitable site for this research. In addition, the lead investigator on this project took another position (at another location) within ARS and is no longer able to lead this project. Commitments by other researchers and current vacancies prevent anyone else from taking the lead on this project (2.A.1).
Under Sub-Objective 2.B., high resolution biomass yield data over three years from several thousand acres of Miscanthus planted on marginal lands in northeastern Ohio was combined with Sentinel-2 imagery, and biophysical factors, such as climate, topography and soils and machine learning models were developed to characterize the spatial and temporal yield variation of Miscanthus (2.B.1).
Under Sub-Objective 2.C, preliminary evaluation and a change in project and NRCS priorities led to a reassessment of model implementations, and a shift away from the APEX model for use in developing pasture tools described in 2.C.1. Instead, a suite of pasture-specific models developed for similar climates and management regimes was identified and parameterized for use with the chosen crops. These models were linked with climate change projections, species distribution models developed under previous milestones, to produce maps of potential future agricultural scenarios for the northeastern United States, and consequences for ecosystem services including production, soil erosion, and pollination services (2.C.2).
Accomplishments
1. Effect of sward structure on grazing behavior. Cool-season perennial grasses are the primary component of pastures grazed by dairy cows, but little is known on how sward structure influences grazing behavior. ARS researchers at University Park, Pennsylvania, and Madison, Wisconsin, evaluated which grass characteristics had the most influence on grazing behavior, including time spent grazing and number of bites per day, of cattle to better understand how productivity of grazing cattle can be improved. Results found that grass species and sward structure had minimal impact on grazing behavior, with sward height and mass being the most important traits and that dairy cows are quite adaptable at altering their grazing behavior to result in similar patterns of behavior across cool-season grass species if forage availability and nutritional quality are maintained at high levels.
2. Forages to reduce enteric methane emissions in grazing cows. Compounds in plants, such as condensed tannins, may reduce methane production in cattle, but may have negative impacts on ruminal fermentation. Supplementation of diets high in tannins with oilseeds may maintain fermentation while further reducing methane. ARS researchers at University Park, Pennsylvania, evaluated three oilseeds (soybean, sunflower and canola) plus a combination of the three oilseeds to evaluate effects on ruminal fermentation and methane production in a continuous culture fermentor system. Results found that addition of canola, sunflower and the mix of the three oilseeds reduced methane more than soybean, without decreasing nutrient digestibility.
3. Annual forages to extend the grazing season. Fall forage production in the northern United States is complicated late autumn weather. Perennial cool-season forages are less productive in the fall than in spring, making annual forages useful for supplementing production, but little is known about them. ARS researchers at University Park, Pennsylvania, in collaboration with the University of New Hampshire, conducted a 2-year study to assess the forage mass and quality of six annual forage species (canola, forage radish, oats, spring triticale, spring wheat, and sunn hemp) not typically grown for fall harvest. Findings from this work indicated that careful management of harvest timing in the fall in critical to maintaining a balance between forage mass and nutritive value, with most species declining in nutritive value rapidly following a killing frost.
Review Publications
Billman, E.D., Roca-Fernandez, A.I., Dillard, L., Soder, K.J. 2022. Supplementation of Oilseeds to an Herbage Diet High in Condensed Tannins Affects Methane Production with Minimal Impact on Ruminal Fermentation in Continuous Culture. Journal of Animal Science. 8(3):109. https://doi.org/10.3390/fermentation8030109.
Raynor, E.J., Hillhouse, H.L., Debinski, D.M., Miller, J.R., Schacht, W.H. 2021. Recoupling cross-scale interactions in tall fescue-invaded tallgrass prairie. Landscape Ecology. 1-17. https://doi.org/10.1007/s10980-021-01348-x.
Billman, E.D., Alexandre De Souza, I., Smith, R.G., Soder, K.J., Warren, N.D., Andrade Teixeira, F., Brito, A.F. 2021. Winter annual forage mass–nutritive value trade-offs are affected by harvest timing. Crop, Forage & Turfgrass Management. Article e20113. https://doi.org/10.1002/cft2.20113.
Meinen, R.J., Spiegal, S.A., Kleinman, P.J., Flynn, K.C., Goslee, S.C., Mikesell, R.E., Church, C., Bryant, R.B., Boggess, M.V. 2022. Opportunities to implement manureshed management in the Iowa, North Carolina, and Pennsylvania swine industry. Journal of Environmental Quality. 51(4):510-520. https://doi.org/10.1002/jeq2.20340.
Browning, D.M., Russell, E.S., Ponce-Campos, G.E., Kaplan, N.E., Richardson, A.D., Seyednasrollah, B., Spiegal, S.A., Saliendra, N.Z., Alfieri, J.G., Baker, J.M., Bernacchi, C.J., Bestelmeyer, B.T., Bosch, D.D., Boughton, E.H., Boughton, R.K., Clark, P., Flerchinger, G.N., Gomez-Casanovas, N., Goslee, S.C., Haddad, N., Hoover, D.L., Jaradat, A.A., Mauritz, M., Miller, G.R., McCarty, G.W., Sadler, J., Saha, A., Scott, R.L., Suyker, A., Tweedie, C., Wood, J., Zhang, X., Taylor, S.D. 2021. Monitoring agroecosystem productivity and phenology at a national scale: A metric assessment framework. Ecological Indicators. 131. Article 108147. https://doi.org/10.1016/j.ecolind.2021.108147.
Soder, K.J., Brink, G.E., Raynor, E.J., Casler, M.D. 2022. Relationship between temperate grass sward characteristics and grazing behavior of dairy heifers. Agronomy. 12(7):1584. https://doi.org/10.3390/agronomy12071584.
