Location: Pasture Systems & Watershed Management Research
2024 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., random forest was used to predict nitrogen and biomass of cover crops from UAS imagery (1.A.1). Cereal rye was interseeded into corn in June 2023. Cattle were grazed in Fall 2023 after corn grain harvest but not in Spring 2024 due to lack of spring regrowth of cereal rye. This project is part of the Long-Term Agroecosytems Research (LTAR) Common Experiment and will continue in Fiscal Year 25. Manuscript is in preparation (summarizing data from 2019-2022) for submission to LTAR Special Sections in the Journal of Environmental Quality (1.A.2).
Under Sub-objective 1.B., Year 3 (final year) of the warm-season grasses (teff, pearl millet, sorghum-sudangrass) were planted as monocultures or interseeded into previously established orchardgrass pastures in June 2023. These species were monitored for biomass productivity and persistence during the remainder of the 2023 growing season. Data is currently being summarized (1.B.1). Due to restrictions during the COVID pandemic and Fiscal Year 22 Maximized Telework, Sub-objective 1.B.2 has modified. The University of New Hampshire (UNH) was not able to conduct the original grazing research, and USDA-ARS personnel from University Park, Pennsylvania, were not able to travel to UNH to assist with data collection. Rather a study was conducted to evaluate the effects of red clover silage or alfalfa silage (a similar forage with different enzyme composition) fed to dairy cows on milk production and nutrient utilization. Results were mixed and additional research is needed to further determine the efficacy of using red clover silage to improve production efficiency in dairy cows to reduce nitrogen losses to the environment and improve milk production.
Under Sub-objective 1.C, pandemic travel restrictions and FY22 Maximized Telework prevented travel to the University of New Hampshire (UNH). Additionally, complications with the 2010 baseline dataset have been discovered and on-site meetings with UNH researchers are needed to develop a new plan for sampling to evaluate soil health in the future (1.C.1).
Under Sub-objective 2.A, the riparian grazing sub-objective is on indefinite delay. Fiscal Year 22 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, (2.B.1) Model results showed that there was a good relationship between EVI from Landsat imagery and spatial maps of Miscanthus biomass yields, supporting the potential application of using satellite imagery to better understand yields across biophysical gradients.
Under Sub-objective 2.C, further refinement and testing of the forage production models continued, along with assessment under climate change scenarios. Comparison of these results with field data and with machine learning models previously developed revealed shortcomings in machine learning models of species abundance necessary to understand agricultural production potentials, so novel machine learning approaches were developed and implemented. Because of this need for additional tool development, the maps were not ready for public release on the planned timeline (2.C.1). However, these results are being finalized now, and will form a component of NRCS Conservation Effects Assessment Project (CEAP) Grazinglands outputs and tool development. Relevant components, including weather data, floral phenology, species distributions, and cropping systems, as well as economic valuations, have been incorporated into a decision support tool developed in collaboration with university partners, and collaboration continues with NRCS to further develop and refine online decision support tools for assessing pasture conservation needs based on environmental factors and management (2.C.2)
Give-year Summary:
Substantial progress was made in improving pasture and crop management strategies for enhanced animal and forage management in integrated crop-pasture-livestock systems. Annual forages were evaluated (in monoculture and interseeded into permanent pastures) to fill gaps in forage production in traditional cool-season perennial grass pastures. Results showed that monoculture seeding of annual forages can improve overall forage productivity on the farm but requires greater land area than interseeding into existing pastures. However, the interseeded forages were inconsistent in forage yield and were greatly dependent on weather patterns. In addition, research with cover crop forages containing secondary compounds such as tannins in legumes or glucosinolates in brassicas were shown to reduce methane production in grazing cattle while providing ecosystem services in forage cropping systems. Lastly, adding chicory to an orchardgrass pasture delayed orchardgrass maturity and improved pasture nutrition during the spring when wet field conditions make optimal harvesting a challenge. Although the chicory died out after the first winter, seed costs were reduced, and yield and nutrition of the pasture were increased with the addition of chicory to orchardgrass compared to planting orchardgrass alone or in a mixture with white clover.
The project also made substantial progress in incorporating novel and existing management strategies into farm- and landscape-scale agricultural planning tools to foster sustainable intensification. An inventory of energy use on a commercial Miscanthus farm found that harvest consumed the most energy and satellites could be used to understand factors contributing to yield variation across fields. This research provided valuable insights for the bioenergy industry to promote practices which reduce the environmental impact of farming practices. Additionally, nitrous oxide emissions from agricultural soils were found to be much lower than assumed. Machine learning models were developed to characterize soil nitrous oxide emissions, intensive site measurements and remotely available data. This information provided improved tools to enhance the management of soil fertility and water quality and reduce nitrous oxide emissions on farms. Finally, a novel online tool for assessing pasture conservation needs based on environmental factors and management was developed and is being used by the USDA-Natural Resources Conservation Service.
Accomplishments
1. Red seaweed to reduce methane in dairy cows. There is a growing interest in utilizing seaweed in dairy cow diets for reducing methane, but large-scale adoption is limited. ARS researchers at University Park, Pennsylvani, a collaborated with researchers at the University of New Hampshire and University of Maine to assess barriers to adoption of seaweed through 1) a survey sent to organic dairy farmers in Maine to assess barriers and drivers, and 2) a case study conducted on an organic dairy in Maine. Results of this research showed that farmer receptiveness to feeding seaweed that reduces methane will not only be dependent on purchase price, but also on co-benefits (such as improved milk production) and simplicity of integration into existing feed practices.
3. High-quality forages for organic dairy farms. Producing high-quality forages is challenging for organic dairy farms due to heavy reliance on forage-based rations, including grazing. ARS researchers at University Park, Pennsylvania, collaborated with researchers at the University of Vermont, the University of New Hampshire, and ARS researchers at Madison, Wisconsin, to conduct a nationwide survey of organic dairy farmers to self-describe current organic forage production practices, climate impacts, and needs for research, information, education, and outreach. Survey result showed that forage research and educational activities should focus on climate change resilience, improve forage quality, enhance economic returns from soil fertility amendments and pasture renovation, and introduce new forages and forage mixtures that suit economical, agronomical, and environmental needs of organic dairy farms.
4. Modeling nitrous oxide emissions with remotely sensed variables using machine learning. Nitrous oxide (N2O) is the largest greenhouse gas source from crop production and difficult to characterize at the farm level. We compared two sources of data to develop machine learning models to characterize soil N2O emissions, intensive site measurements and remotely available data. We found that the machine learning model built on remotely sensed variables performed as well as when direct site level measurements were available. This finding supports the potential of using remotely sensed data to build machine learning models to characterize soil N2O emissions without the need for intensive soil measurements for entity level assessments and could greatly reduce the requirements to verify the greenhouse gas credits from implementation of climate smart mitigation strategies.
Review Publications
Hatungimana, E., Darby, H.M., Soder, K.J., Ziegler, S.E., Brito, A.F., Kucek, L.K., Riday, H., Brummer, C. 2024. Assessing forage research and education needs of organic dairy farms in the United States. Renewable Agriculture and Food Systems. 39:1-10. https://doi.org/10.1017/S1742170523000455.
Lange, M.J., Silva, L.H., Zambon, M.A., Soder, K.J., Brito, A. 2024. Feeding alfalfa- or red clover-grass mixture baleage: Effect on milk yield and composition, ruminal fermentation and microbiota taxa relative abundance, and nutrient utilization in dairy cows. Journal of Dairy Science. 107(4):2066-2086. https://doi.org/10.3168/jds.2023-23836.
Jaramillo, D.M., Soder, K.J., Blount, A., Dubeux, J., Harrison, S. 2024. Nutritive value and forage accumulation of a black oat germplasm in northeastern United States. Agrosystems, Geosciences & Environment. https://doi.org/10.1002/agg2.20484.
Kammerer, M., Iverson, A.L., Li, K., Goslee, S.C. 2024. Not just crop or forest: an integrated land cover map for agricultural and natural areas. Earth System Science Data. 11(1):137. https://doi.org/10.1038/s41597-024-02979-w.
Gardezi, M., Abuayyash, H., Adler, P.R., Alvez, J.P., Anjum, R., Raju Badireddy, A., Brugler, S., Carcamo, P., Clay, D., Dadkhah, A., Emery, M., Faulkner, J.W., Joshi, B., Joshi, D.R., Hameed Khan, A., Koliba, C., Kumari, S., Mcmaine, J., Merrill, S., Mitra, S., Musayev, S., Oikonomou, P.D., Pinder, G., Prutzer, E., Rathore, J., Ricketts, T., Rizzo, D.M., Ryan, B.E., Sahraei, M., Schroth, A.W., Turnbull, S., Zia, A. 2024. The role of living labs in cultivating inclusive and responsible innovation in precision agriculture. Agricultural Systems. 216:103908. https://doi.org/10.1016/j.agsy.2024.103908.
