Recipient Organization
SOUTH DAKOTA STATE UNIVERSITY
PO BOX 2275A
BROOKINGS,SD 57007
Performing Department
Animal Science
Non Technical Summary
Reducing nitrogen loss from agricultural production is a currently difficult, but essential, aspect of meeting growing consumer demands for food produced in an environmentally sustainable manner. Demand for food, including red meat products such as beef, is expected to double by 2050. Conserving nitrogen is important to beef production because lack of nitrogen severely limits the growth of plants such as grasses and clovers that provide nutrients to grazing animals like beef cattle. The transfer of nitrogen between soil, plants, animals, and back into the soil is commonly called the nitrogen cycle or the nitrogen cycling process. Unfortunately, during this process nitrogen losses occur very easily, and this presents a significant complication for beef livestock producers with goals of improving the environmental footprint of their operations. Common mechanisms of nitrogen loss include: runoff of nitrates into surface or groundwater, ammonia volatilization, and conversion of fixed forms of nitrogen into nitrogen gas that is lost to the atmosphere.Agriculture practices that can help beef producers more effectively manage nitrogen may include: 1) different types of grazing strategies, 2) the use of plants called "legumes" that utilize specific bacteria to fix nitrogen from the air, thereby adding nitrogen to the plant tissues and soil, and 3) supplemental feeding of legume plants that have unique nitrogen binding compounds. These compounds include tannins, which enable soils hold onto nitrogen so it can be used for plants instead of being lost into the environment. Hence the use of these strategies may help producers meet growing consumer demands for beef products by improving nitrogen retention in their grazing systems. Without such strategies it is unlikely that substantial gains in beef production will be achieved. Decreased nitrogen losses may also help mitigate environmental damage to waterways and the ozone layer. Therefore, there is critical need to evaluate the impact of different grazing strategies, legumes incorporation, and plants that produce tannins on the nitrogen cycling process. Field experiments to evaluate the effectiveness of alternative livestock grazing strategies, tannins, and supplementation in reducing nitrogen loss would be costly and time intensive, especially if they were to be conducted across many locations. Mathematical computer simulation modeling provides a way for researchers to ask "what if" questions about new strategies to improve nitrogen cycling, without incurring the high costs of field experiments. Computers increase the types of "what if" questions that can be asked, as they can process many more factors than is possible for the human mind. Therefore, a dynamic grazing system (DSG) sub-model designed to simulate nitrogen cycling provides scientists a research tool to test different scenarios and determine which strategies will result in the desired outcomes based on their goals (optimal nitrogen cycling).This project will collect and organize a broad range of currently available data. This includes soil, plant, climate, and beef cattle characteristics that exist in Western South Dakota rangeland grazing systems. This data will enable us to develop a model of nitrogen cycling for rangeland cattle grazing systems. The model will be calibrated and then tested (ground truthed) using nitrogen cycling data that was not used to calibrate the model. However, for dynamic models it is more important that the relationships between variables, that is the processes, are correct rather than only relying on a statistical fit. For example, if nitrogen is added to a pasture through fertilization the model should be able to capture an increase in plant quality and growth. This will provide confidence that the model results are reliable and that the model is able to accurately evaluate different scenarios.The model will then be made into an online decision support tool for producers and land managers. This tool will be freely accessible through the internet, with clear and easy to follow instructions. Producers will be able to intuitively learn how to use the model and evaluate their own operations. The producer may choose to evaluate differences in nitrogen under different grazing types, plant communities, and supplementation of tannins. The model will allow producers to customize cattle grazing duration, stocking rates, and location to more accurately represent their operation. This model will help guide livestock producer decisions, leading to more effective management that results in improved nitrogen cycling and consequently translates to gains in soil, plant, and animal productivity.
Animal Health Component
50%
Research Effort Categories
Basic
(N/A)
Applied
50%
Developmental
50%
Goals / Objectives
Quantify environmental and economic effects of forage- and pasture-based management strategies and climate change on N-use efficiency by ruminant animals, N cycling in herbage and soils, aquatic N losses, and GHG and other pollutant emissions from grassland agro-ecosystems. (AR, GA, KY, MI, NE, TN, UT, WA, TN). Specific objectives: (i) Investigate effects of management strategies that alter spatiotemporal distribution of soil N pools, grazing and nutritive value of forage on ruminant performance, protein metabolism, and N harvest efficiency; (ii) Evaluate environmental and economic effects of management strategies and climate change on herbage mass and accumulation, nutritive value, botanical composition, and N use efficiency across growing seasons and pasture landscapes; (iii) Determine N pool and cycling (soil, plant, atmosphere, and water), N-use efficiency and biological activity, and economic responses to management strategies in forage-based ruminant production systems with or without forage legumes across variable soil environments and climatic conditions; (iv) Determine the impact of legumes on the GHG footprint and economic returns of livestock production systems.
Assess the efficacy of secondary plant metabolites in legume species for increasing N retention and improving N-cycling in forage-livestock systems. (AR, KY, MO, UT). Specific objectives: (i) evaluate effects of legumes containing tannins or other secondary plant metabolites on N partitioning in fecal and urine excretions; (ii) Determine soluble phenolic effects on forage legume protein fractionations and N availability; (iii) Evaluate effects of tannin or other secondary plant metabolites and their concentrations on soil N availability in mixed legume/grass swards.
Assess the efficacy of supplementation practices including alternative feedstuffs or feed additives on N-use efficiency in ruminant animals in forage-based livestock production systems (AR, KY, NE) and how these practices affect composition of feces and urine and rumen microbial efficiency.
Disseminate research results through coordinated extension/education activities, including extension publications, university course material, and national, regional and state conferences on legume establishment, interseeding and management of grass-legume mixtures, and N cycling and use efficiency. (AR, GA, KY, MI, MO, NE, OH, UT, WA).
Project Methods
Objective 1. Quantify environmental and economic effects of forage- and pasture-based management strategies and climate change on N-use efficiency by ruminant animals, N cycling in herbage and soils, aquatic N losses, and GHG and other pollutant emissions from grassland agro-ecosystems. (AR, GA, KY, MI, NE, TN, UT, WA, SD, TN). Specific task: (v) develop a model to estimate differences in herbage mass and nutritive value and nitrogen pools from different pasture-based management strategies and climate scenarios.South Dakota research will seek to develop a dynamic grazing system (DSG) sub-model to evaluate differences in nitrogen cycling resulting from different management strategies, secondary compounds, and feed supplementation. The central hypothesis is that differences in total nitrogen retained in forage-based livestock systems exist under different grazing management strategies, plant secondary metabolite contributions, and supplementation.Evaluating the effects of pasture management strategies, climate, secondary compounds, and use of feed supplementation and additives on nitrogen cycling in forage-based livestock grazing systems is difficult, as grazing operations are diverse, and nitrogen is easily transformed or lost. Simulation modeling is a tool that helps to identify and test viable strategies to improve nitrogen cycling goals, reveal short and long-term unexpected consequences, and minimize risk. Simulation modeling enables multiple scenarios to be analyzed that are otherwise economically infeasible, such as evaluating nitrogen cycling in ranches across the United States with different grazing practices, secondary compounds, or supplementation/additives.Differences in nitrogen pools (g/kg) will be determined by developing a sub-model for the Dynamic Grazing System model, capable of simulating multiple grazing management strategies (e.g., livestock integration), plant secondary metabolite contributions to soil dynamics (e.g., proteins and tannins), and animal contributions (e.g., manure-N, urine-N).The DGS sub-model will be developed in a visually based dynamic modeling software. The methods include a five-step modeling process: 1) problem articulation, 2) dynamic hypothesis formulation, 3) mathematical formulation, 4) model testing, and 5) policy design and evaluation. Steps 1-2 will be completed from existing literature and from available data. A diverse set of data will be required to simulate N-cycling for grazing livestock systems which include: 1) large ruminant nutrition coefficients such as changes to ruminally degradable proteins, recycled nitrogen, passage rate, and fecal/urinary nitrogen, 2) yellow sweet clover growth, nutrient, and secondary metabolite concentration data, 3) SDSU Mesonet climate data (i.e., minimum and maximum temperature, precipitation, windspeed, solar radiation), and 4) SSURGO soil data (i.e., soil texture, slope, pH, and bulk density).Steps 3-4 will include parameterizing variables with mathematical equations and statistical analysis to assess model accuracy, precision, and structural adequacy. We will use existing forage, supplements, manure, urine, soil N (g/kg dry matter basis) to: 1) calculate a mass balance and ensure the model is following the law of conservation of mass (i.e., avoid mathematical errors that inappropriately create or reduce N), and 2) calibrate the model to these datasets and perform measures of accuracy (e.g., mean bias), precision (R2), and screening for systematic errors (e.g., Thiel's inequality statistics, root mean square error decomposition). Calibration will ensure that parameters and model estimates reflect what occurs to N-cycling in reality.Step 5 will use sensitivity analysis (Monte-Carlo; n = 50 to 10,000) and optimization to identify how grazing strategies, secondary plant metabolites, and/or supplementation impact nitrogen cycling in forage-based livestock systems. Simulations will be compared using a t-test to determine mean differences. We will simulate continuous, rotational, and adaptive multi-paddock grazing management strategies using beef cattle (Bos taurus). Grazing treatments will be simulated for a native grass species composition found in Western South Dakota. Specifically this will include cool season (C3) green needlegrass (Nassella viridula) and western wheatgrass (Pascopyrum smithii) and warm season (C4) blue grama (Bouteloua gracilis) and buffalo grass (Buchloe dactyloides).Objective 2. Assess the efficacy of secondary plant metabolites in legume species for increasing N retention and improving N-cycling in forage-livestock systems. (AR, KY, MO, SD, UT). Specific task: (iv) estimate differences in nitrogen cycling in forage-livestock systems from simulating different secondary plant metabolite contribution rates to the soil.This grass composition will be simulated with and without interseeded alfalfa (Medicago sativa), a nitrogen fixing legume, and yellow sweet clover (Melilotus officinalis) which is a legume that contains secondary plant metabolites (i.e., tannins and phenols).Objective 3. Assess the efficacy of supplementation practices including alternative feedstuffs or feed additives on N-use efficiency in ruminant animals in forage-based livestock production systems (AR, KY, NE, SD) and how these practices affect composition of feces and urine and rumen microbial efficiency.Specific task: using modeling, estimate the differences in N-use efficiency in ruminant animals under various supplementation practices and feed additives.These three grazing scenarios will include no supplementation versus supplementation of yellow sweet clover hay during winter months to assess the impact of this legumes secondary plant metabolites on the N-cycle.Objective 4. Disseminate research results through coordinated extension/education activities, including extension publications, university course material, and national, regional and state conferences on legume establishment, interseeding and management of grass-legume mixtures, and N cycling and use efficiency. (AR, GA, KY, MI, MO, NE, OH, SD, UT, WA).The model will be made available online using Python and Program R for Statistical Computing open-source platforms. Additionally, training videos, manuals, extension publications and journal articles will be developed and disseminated through SDSU Extension and professional societies (e.g., the System Dynamics Society, The American Society for Animal Scientist, and the Society for Rangeland Management).