dairy cow – Livestock and Poultry Environmental Learning Community

Purpose

Manure is rich in essential elements, including nitrogen (N), phosphorus (P), and potassium (K), for plant growth. Although applying manure as a fertilizer at agronomic rates can restore organic matter and nutrients to the soil, over-application of manure may contribute to environmental issues such as eutrophication and water contamination. Manure nutrient prediction and variable rate application are promising new technologies to reduce the risk of over-application, however, the variability in manure nutrient concentrations and the time-lag caused by traditional chemical analysis of manure composition make precise nutrient application difficult to achieve.

Near-infrared (NIR) spectroscopy is a high-energy vibrational spectroscopy performed in the wavelength range between 750 to 2500 nm and has been proven to accurately determine total solid (TS), organic matter (OM), total nitrogen (TN), and ammoniacal nitrogen (NH4-N) of animal manure in several previous studies. A low-field nuclear magnetic resonance (NMR) device that is designed based on the absorption and emissions of energy in the radio frequency range of the electromagnetic spectrum is another potential method for predicting manure nutrients accurately. The main purpose of this manure sensing project was to determine if the NIR and NMR sensing techniques can provide robust prediction of manure nutrients and, therefore, improve the precision of field application.

What Did We D

We investigated NIR spectroscopy with reflectance and transflectance modes to predict micronutrients in dairy manure. In this study, 20 dairy manure samples were collected and spiked by dissolving a specific amount of ammonium chloride (NH4Cl) or Arginine to achieve incremental NH4-N and organic nitrogen (Org-N) concentrations, respectively. Each raw sample was spiked at four levels which were 1.25, 1.5, 2, and 4 times the NH4-N or Org-N concentrations of the raw manure as analyzed by a certified lab. All samples were scanned and analyzed using a NIR with a reflectance head sensor and a transflectance probe of three different optical path lengths. NIR calibration models were developed using partial least square regression analysis and the coefficient of determination (R2) and root mean square error (RMSE) were calculated to evaluate the models.

The accuracy and precision of a low-field NMR designated for manure nutrient prediction was assessed. Twenty dairy manure samples were collected and analyzed for TS, TN, NH4-N, and total phosphorus (TP) in a certified laboratory and using the NMR analyzer. Runtimes of 15 min to 90 min were tested to investigate their effects on accuracy and precision of NMR.

What Have We Learned

For the NIR study, the transflectance probe yielded calibrations that had higher R2 and RMSE for TS, ash, and particle size (PS), and reflectance sensor improved the accuracy of NH4-N and Org-N predictions. NIR sensors have the potential to predict N concentrations without being affected by the TS, ash content, and PS of the dairy manure.

The NMR predictions of TS, NH4-N, and TN were accurate for samples with relativley low TS, but not well correlated to the lab measurements for high TS samples. TP predicted by NMR was not affected by TS levels and the TP prediction was not precise and robust. The effects of runtime on the accuracy and precision of NMR prediction were not consistent.

Future Plans

Additional work is needed to improve the accuracy and precision of NIR calibration models. The procedure of spiking method in manure analysis using NIR techniques needs to be enhanced in order to be widely applied for preparing manure samples for NIR calibrations. Finally, further investigation of the methodology with other manure constituents such as P and K and conducting online variable rate application of organic fertilizer using NIR sensing system are needed to evaluate the potential effects of reducing the overall system variability.

Additional work to improve NMR prediction includes recalibrating the system based on specific manure samples and improving the accuracy and precision of TP prediction.

Authors

Xiaoyu Feng, Research Associate, University of Wisconsin-Madison
xfeng43@wisc.edu

Additional Authors

-Rebecca Larson, Associate Professor and Extension Specialist, University of Wisconsin-Madison; Matthew Digman, Assistant Professor, University of Wisconsin-Madison;
-Joseph Sanford, Assistant Professor, University of Wisconsin- Platteville

Additional Informaion

Feng, X.Y., R.A. Larson, and M. Digman. 2022. Evaluating the Feasibility of a Low-Field Nuclear Magnetic Resonance (NMR) Sensor for Manure Nutrient Prediction. Sensors 22(7):2438. https://doi.org/10.3390/s22072438

Feng, X.Y., R.A. Larson, and M. Digman. 2022. Evaluation of Near-Infrared Reflectance and Transflectance Sensing System for Predicting Manure Nutrients. Remote Sensing 14(4): 963. https://doi.org/10.3390/rs14040963

Acknowledgements

Support for this project was provided by the Wisconsin Dairy Innovation Hub.

The authors are solely responsible for the content of these proceedings. The technical information does not necessarily reflect the official position of the sponsoring agencies or institutions represented by planning committee members, and inclusion and distribution herein does not constitute an endorsement of views expressed by the same. Printed materials included herein are not refereed publications. Citations should appear as follows. EXAMPLE: Authors. 2022. Title of presentation. Waste to Worth. Oregon, OH. April 18-22, 2022. URL of this page. Accessed on: today’s date.

What Did We Do?

Most of our research work was relative to dairy cows. We realized several studies by different experimental approaches. First of all, we have been running numerous experiments under climate chamber conditions followed by a number of field studies. To reach more precise objectives, we also performed several in vitro studies on selected cell populations. In the last few years we have been also building and exploring multi-year datasets and measuring the impact of air temperature and relative humidity on performances and health in intensively managed dairy cows/pigs. Finally, we have been working on bioclimate, namely temperature humidity index (THI), characterization of selected geographic areas both retrospectively and in terms of scenario (Figure 1).

climate graph for lactera proceedings paper

JJA anomolies 2021-2030 vs CiNo

JJA anomolies 2031-2040 vs CliNo

Figure 1. Regional distribution of Mediterranean summer (JJA, June-July-August) temperature humidity index anomalies versus CliNo (Climate Normal, 1971-2000 period) for the four decades 2011-2020, 2021-2030, 2031-2040, and 2041-2050 (Segnalini et al., in press)

What Have We Learned?

We have learned that the ability of dairy cows to breed, grow, and lactate to their maximal genetic potential, and their capacity to survive and keep healthy is dramatically influenced by climate, meteorological events and biological environment and their interactions. Climate and meteorological features affect animals both indirectly and directly. Indirect effects include those exerted on quality and quantity of crops and pastures and on survival of pathogens and/or their vectors. The direct effects of air temperature on animals depend on their ability to maintain a normal body temperature under unfavourable thermal conditions. A series of studies carried out at Mediterranean level, one of the hot spot in the context of global warming, pointed out a constant increase for livestock of the risk to suffer from heat stress related conditions. Climate change is imposing a growing attention to adaptation measures, which may help farm animals to face with conditions of environmental warmth. These may include set up of meteorological warning systems, revision of health maintenance strategies, correction of feeding plans, shade, sprinkling, air movement, active cooling, genetic selection, and others.

Authors

Alessandro Nardone, Professor, Dipartimento per la Innovazione nei sistemi Biologici, Agroalimentari e Forestali (DIBAF), Università degli Studi della Tuscia, Viterbo, Italy nardone@unitus.it

Nicola Lacetera, Professor, Dipartimento di scienze e tecnologie per l’Agricoltura, le Foreste, la Natura e l’Energia (DAFNE), Università degli Studi della Tuscia, Viterbo, Italy

Acknowledgements

We gratefully acknowledge National (CNR, MIUR, MIPAF) and International (UE) funding bodies, and Umberto Bernabucci, Bruno Ronchi, Andrea Vitali, Maria Segnalini, Alessio Valentini, Patrizia Morera, Loredana Basiricò, M. Stella Ranieri and others in quality of co-authors of the numerous peer-reviewed papers we published in this field during the last 20 years.

The authors are solely responsible for the content of these proceedings. The technical information does not necessarily reflect the official position of the sponsoring agencies or institutions represented by planning committee members, and inclusion and distribution herein does not constitute an endorsement of views expressed by the same. Printed materials included herein are not refereed publications. Citations should appear as follows. EXAMPLE: Authors. 2013. Title of presentation. Waste to Worth: Spreading Science and Solutions. Denver, CO. April 1-5, 2013. URL of this page. Accessed on: today’s date.