Determination of fibre and protein content in heterogeneous pastures using field spectroscopy and ultrasonic sward height measurements

Computers and Electronics in Agriculture

Volumn 123, Issue , 2016, Pages 256-263

  Safari, Hanieh ^a   Fricke, Thomas ^a   Wachendorf, Michael ^a  

^a UNIVERSITY OF KASSEL   (Germany)

Author keywords

Field spectroscopy; Forage quality; Proximal remote sensing; Sensor combination; Ultrasonic sensor

Indexed keywords

CALIBRATION; FORECASTING; PROTEINS; REFLECTION; REMOTE SENSING; ULTRASONIC SENSORS; VEGETATION;

CONVENTIONAL TECHNIQUES; FIELD SPECTROSCOPY; FORAGE QUALITY; HYPERSPECTRAL ANALYSIS; HYPERSPECTRAL REFLECTANCE; MODIFIED PARTIAL LEAST SQUARES; SENSOR COMBINATIONS; STANDARD ERROR OF PREDICTION;

AGRICULTURE;

BIOCHEMICAL COMPOSITION; CANOPY REFLECTANCE; DIET; FEEDING; FOOD QUALITY; FORAGE; LIVESTOCK; LIVESTOCK FARMING; MEASUREMENT METHOD; PROTEIN; REMOTE SENSING; SENSOR; SPECTROSCOPY; STOCKING DENSITY; SWARD; ULTRASONICS; VEGETATION INDEX;

ANIMALIA;

EID: 85008697501     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2016.03.002     Document Type: Article

References (35)

1. Aliah Baharom, S.N., Shibusawa, S., Kodaira, M., Kanda, R., Multiple-depth mapping of soil properties using a visible and near infrared real-time soil sensor for a paddy field. Eng. Agric., Environ. Food 8:1 (2015), 13–17, 10.1016/j.eaef.2015.01.002.
2. Bakker, J.P., De Leeuw, J., Van Wieren, S.E., Micro-patterns in grassland vegetation created and sustained by sheep-grazing. Vegetatio 55:3 (1984), 153–161.
3. Biewer, S., Fricke, Th., Wachendorf, M., Development of canopy reflectance models to predict forage quality of legume-grass mixtures. Crop Sci., 49(5), 2009, 1917, 10.2135/cropsci2008.11.0653.
4. Cammarano, D., Fitzgerald, G., Casa, R., Basso, B., Assessing the robustness of vegetation indices to estimate wheat N in mediterranean environments. Remote Sens. 6:4 (2014), 2827–2844, 10.3390/rs6042827.
5. Chang, C.W., Laird, D.A., Mausbach, M.J., Hurburgh, C.R., Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties. Soil Sci. Soc. Am. J., 65(2), 2001, 480, 10.2136/sssaj2001.652480x.
6. Deaville, E.R., Flinn, P.C., Near-infrared (NIR) spectroscopy: an alternative approach for the estimation of forage quality and voluntary intake. Givens, D.I., Owen, E., Axford, R.F.E., Omed, H.M., (eds.) Forage Evaluation in Ruminant Nutrition, 2000, CABI, Wallingford, 301–320.
7. Diaconis, P., Efron, B., Computer-intensive methods in statistics. Sci. Am. 248:5 (1983), 96–108.
8. Fricke, T., Richter, F., Wachendorf, M., Assessment of forage mass from grassland swards by height measurement using an ultrasonic sensor. Comput. Electron. Agric. 79 (2011), 142–152, 10.1016/j.compag.2011.09.005.
9. Fricke, T., Wachendorf, M., Combining ultrasonic sward height and spectral signatures to assess the biomass of legume–grass swards. Comput. Electron. Agric. 99 (2013), 236–247, 10.1016/j.compag.2013.10.004.
10. Guo, X., Wilmshurst, J.F., Li, Z., Comparison of laboratory and field remote sensing methods to measure forage quality. Int. J. Environ. Res. Public Health 7:9 (2010), 3513–3530, 10.3390/ijerph7093513.
11. Hobbs, N.T., Swift, D.M., Grazing in herds: when are nutritional benefits realized?. Am. Nat., 1988, 760–764.
12. Hofmann, M., Kowarsch, N., Bonn, S., Isselstein, J., Management for biodiversity and consequences for grassland productivity. Grassland Sci., Eur.(6), 2001, 113–116.
13. 2 flux measurements in rice. Remote Sens. Environ. 112:1 (2008), 156–172, 10.1016/j.rse.2007.04.011.
14. Jacquemoud, S., Baret, F., A model of leaf optical properties spectra. Remote Sens. Environ. 34:2 (1990), 75–91, 10.1016/0034-4257(90)90100-Z.
15. Li, F., Mistele, B., Hu, Y., Chen, X., Schmidhalter, U., Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression. Eur. J. Agron. 52 (2014), 198–209, 10.1016/j.eja.2013.09.006.
16. Li, X., Zhang, Y., Bao, Y., Luo, J., Jin, X., Xu, X., et al. Exploring the best hyperspectral features for LAI estimation using partial least squares regression. Remote Sens. 6:7 (2014), 6221–6241, 10.3390/rs6076221.
17. Milchunas, D.G., Varnamkhasti, A.S., Lauenroth, W.K., Goetz, H., Forage quality in relation to long-term grazing history, current-year defoliation, and water resource. Oecologia 101:3 (1995), 366–374.
18. Mutanga, O., Skidmore, A.K., Narrow band vegetation indices overcome the saturation problem in biomass estimation. Int. J. Remote Sens. 25:19 (2004), 3999–4014, 10.1080/01431160310001654923.
19. Nelder, J.A., The statistics of linear models: back to basics. Stat. Comput. 4:4 (1994), 221–234, 10.1007/BF00156745.
20. Park, R.S., Agnew, R.E., Gordon, F.J., Steen, R.W.J., The use of near infrared reflectance spectroscopy (NIRS) on undried samples of grass silage to predict chemical composition and digestibility parameters. Anim. Feed Sci. Technol. 72:1–2 (1998), 155–167, 10.1016/S0377-8401(97)00175-2.
21. Pavlů, V., Hejcman, M., Pavlů, L., Gaisler, J., Nežerková, P., Effect of continuous grazing on forage quality, quantity and animal performance. Agric. Ecosyst. Environ. 113:1–4 (2006), 349–355, 10.1016/j.agee.2005.10.010.
22. Pepperl, Fuchs, 2010. Sensors for the Factory Automation – Overview Standard Sensors. < http://www.pepperl-fuchs.us/usa/downloads_USA/Sensing-your-needs-2010-01-EN.pdf>.
23. Pittman, J.J., Arnall, D.B., Interrante, S.M., Moffet, C.A., Butler, T.J., Estimation of biomass and canopy height in bermudagrass, alfalfa, and wheat using ultrasonic, laser, and spectral sensors. Sensors (Basel, Switzerland) 15:2 (2015), 2920–2943, 10.3390/s150202920.
24. Pullanagari, R.R., Yule, I.J., Tuohy, M.P., Hedley, M.J., Dynes, R.A., King, W.M., In-field hyperspectral proximal sensing for estimating quality parameters of mixed pasture. Precision Agric. 13:3 (2012), 351–369, 10.1007/s11119-011-9251-4.
25. R Development Core Team, R: A Language and Environment for Statistical Computing. 2013, the R Foundation for Statistical Computing, Vienna, Austria 3-900051-07-0 Available online at http://www.R-project.org/.
26. Reddersen, B., Fricke, Th., Wachendorf, M., A multi-sensor approach for predicting biomass of extensively managed grassland. Comput. Electron. Agric. 109 (2014), 247–260, 10.1016/j.compag.2014.10.011.
27. Rhodes, B.D., Sharrow, S.H., Effect of grazing by sheep on the quantity and quality of forage available to big game in Oregon's coast range. J. Range Manage., 43(3), 1990, 235, 10.2307/3898680.
28. Starks, P.J., Coleman, S.W., Phillips, W.A., Determination of forage chemical composition using remote sensing. Rangeland Ecol. Manage. 57:6 (2004), 635–640, 10.2111/1551-5028(2004)057[0635:DOFCCU]2.0.CO;2.
29. Starks, P.J., Zhao, D., Phillips, W.A., Coleman, S.W., Herbage mass, nutritive value and canopy spectral reflectance of bermudagrass pastures. Grass Forage Sci. 61:2 (2006), 101–111, 10.1111/j.1365-2494.2006.00514.x.
30. Summers, C.G., Putnam, D.H., 2008. Irrigated Alfalfa management for Mediterranean and desert zones. University of California, Agriculture and Natural Resources, 3512, Oakland, Calif. (Chapter 16).
31. Thenkabail, P.S., Smith, R.B., Pauw, E., Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sens. Environ. 71:2 (2000), 158–182, 10.1016/S0034-4257(99)00067-X.
32. Thenkabail, P.S., Hyperspectral Remote Sensing of Vegetation. 2012, CRC Press, Boca Raton.
33. Tobias, R.D., 1995. An introduction to partial least squares regression. In: Proc. Ann. SAS Users Group Int. Conf., 20th, Orlando, FL, pp. 2–5.
34. Wrage, N., Şahin Demirbağ, N., Hofmann, M., Isselstein, J., Vegetation height of patch more important for phytodiversity than that of paddock. Agric., Ecosyst. Environ. 155 (2012), 111–116, 10.1016/j.agee.2012.04.008.
35. Zhao, D., Starks, P.J., Brown, M.A., Phillips, W.A., Coleman, S.W., Assessment of forage biomass and quality parameters of bermudagrass using proximal sensing of pasture canopy reflectance. Grassland Sci. 53:1 (2007), 39–49, 10.1111/j.1744-697X.2007.00072.x.