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A review of Precision Agriculture as an aid to Nutrient Management in Intensive Grassland Areas in North West Europe

Published online by Cambridge University Press:  01 June 2017

S. Higgins ,
J. Schellberg  and
J. S. Bailey
S. Higgins*
Affiliation:
Agri-Food and Biosciences Institute, Newforge Lane, Belfast, BT9 5PX, UK
J. Schellberg
Affiliation:
Institute of Crop Science and Resource Conservation, University of Bonn, Katzenburgweg 5 D 53115, Bonn, Germany
J. S. Bailey
Affiliation:
Agri-Food and Biosciences Institute, Newforge Lane, Belfast, BT9 5PX, UK
*
†E-mail: Suzanne.Higgins@afbini.gov.uk
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Abstract

Great technological advances have been made in Precision Agriculture (PA) in the past decade, yet adoption of PA in intensive grassland areas in North West Europe is low. This is despite the fact that in these areas the market structures are suitable and there are highly developed agricultural and food industries offering great potential for the application of new technology. Specific inefficiencies in plant nutrient management in soil exist, which are not only limiting grass yields but are also causing environmental deterioration. Soil nutrient management efficiency could be greatly improved using PA techniques, but the complexity of grassland systems, coupled with a lack of calibration of sensors specific to grassland, together with local barriers, appear to be the reasons why PA adoption is poor in these areas. This paper reviews new and existing technology including soil and crop sensors, navigation devices, remote sensing and unmanned aerial vehicles. The suitability and readiness of these technologies for adoption in grassland areas is discussed, along with data interpretation issues, future perspectives and research opportunities.

Keywords

Grasslandnutrient managementenvironment
Type
Precision Pasture
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 782 - 786
Copyright
© The Animal Consortium 2017 

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References

Akyildiz, IF, Su, W, Sankarasubramaniam, Y and Cayirci, E 2002. Wireless sensor networks: a survey. Computer Networks 38, 393–422.Google Scholar
Ali, I, Cawkwell, F, Dwyer, E, Barrett, B and Green, S 2016. Satellite remote sensing of grasslands: from observation to management – a review. Journal of Plant Ecology 9, 649–671.Google Scholar
Aqeel-ur-Rehman, Abbasi AZ, Islam, N and Shaikh, ZA 2014. A review of wireless sensors and networks’ applications in agriculture. Computer Standards & Interfaces 36, 263–270.CrossRefGoogle Scholar
Bailey, JS, Wang, K, Jordan, C and Higgins, A 2001. Use of precision agriculture technology to investigate spatial variability in nitrogen yields in cut grassland. Chemosphere 42, 131–140.Google Scholar
Barrett, B, Nitze, I, Green, S and Cawkwell, F 2014. Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches. Remote Sensing of Environment 152, 109–124.Google Scholar
Bowman, K 2008. Economic and environmental analysis of converting to Controlled Traffic Farming. Proc 6th Australian Controlled Traffic Farming Conference, Dubbo, NSW. P 61-69. Australian Controlled Traffic Farming Association.Google Scholar
Bramley, RGV and White, RE 1991. An analysis of the variability in the activity of nitrifiers in a soil under pasture. I. Spatially dependent variability and optimum sampling strategy. Australian Journal of Soil Research 29, 95–108.Google Scholar
Chen, X, Vierling, L, Rowell, E and DeFelice, T 2004. Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest. Remote Sensing of Environment 91, 14–26.Google Scholar
Craighead, M and Yule, I Opportunities for increased profitability from precision agriculture. [Online]. Available: http://www.nutrientsolutions.co.nz/wp-content/uploads/2015/05/ Precision_Agriculture-initial_NZ_arable_studies.pdf (Retrieved 8 Dec 2016).Google Scholar
Cuttle, SP 1992. Spatial variability and the use of ceramic cup samplers to measure nitrate leaching from pastures. Aspects of Applied Biology 30, 71–74.Google Scholar
Diacono, M, Rubino, P and Montemurro, F 2013. Precision nitrogen management of wheat. A review. Agronomy for Sustainable Development 33, 219–241.Google Scholar
EIP-Agri 2015. EIP-AGRI Focus Group. Precision Farming Final Report. November 2015.Google Scholar
Feilhauer, H, Thonfeld, F, Faude, U, He, KS, Rocchini, D and Schmidtlein, S 2013. Assessing floristic composition with multispectral sensors – a comparison based on monotemporal and multiseasonal field spectra. International Journal of Applied Earth Observation Geoinform 21, 218–229.CrossRefGoogle Scholar
Flynn, ES, Dougherty, CT and Wendroth, O 2008. Assessment of pasture biomass with the normalized difference vegetation index from active ground-based sensors. Agronomy Journal 100, 114.Google Scholar
Fowler, RA 2000. The lowdown on LIDAR. Earth Observation Magazine 19 (http://www.eomonline.com/Common/Archives/2000mar/00mar_fowler.html).Google Scholar
Garcia-Sanchez, AJ, Garcia-Sanchez, F and Garcia-Haro, J 2011. Wireless sensor network deployment for integrating video-surveillance and data-monitoring in precision agriculture over distributed crops. Computers and Electronics in Agriculture 75, 288–303.Google Scholar
Gebhardt, S and Kühbauch, W 2007. A new algorithm for automatic Rumex obtusifolius detection in digital images using colour and texture features and the influence of image resolution. Precision Agriculture 8, 1–13.Google Scholar
Gebhardt, S, Schellberg, J, Lock, R and Kühbauch, W 2006. Identification of broad-leaved dock (Rumex obtusifolius L.) on grassland by means of digital image processing. Precision Agriculture 7, 165–178.Google Scholar
Hedley, C 2014. The role of precision agriculture for improved nutrient management on farms. Mini-review. Journal of Science Food and Agriculture 95, 12–19.Google Scholar
Huang, YB, Thomson, SJ, Hoffmann, WC, Lan, YB and Fritz, BK 2013. Development and prospect of unmanned aerial vehicle technologies for agricultural production management. International Journal of Agricultural and Biological Engineering 6, 1–10.Google Scholar
Jordan, C, Shi, Z, Bailey, JS and Higgins, AJ 2003. Sampling strategies for mapping ‘within-field’ variability in the dry matter yield and mineral nutrient status of forage grass crops in cool temperate climes. Precision Agriculture 4, 69–86.CrossRefGoogle Scholar
Kasischke, ES, Mekack, JM and Dobson, MC 1997. The use of imaging radars for ecological applications - A review. Remote Sensing of Environment 59, 141–156.Google Scholar
Lafferty, S, Commins, P and Walsh, JA 1999. Irish agriculture in transition: a census atlas of agriculture in the Republic of Ireland: Teagasc. Dublin, Ireland.Google Scholar
Liu, Y, Dorigo, W, Parinussa, R, De Jeu, R, Wagner, W, McCabe, M, Evans, J and van Dijk, A 2012. Trend-preserving blending of passive and active microwave soil moisture retrievals. Remote Sensing of Environment 123, 280–297.CrossRefGoogle Scholar
Maselli, F, Barbati, A, Chiesi, M, Chirici, G and Corona, P 2006. Use of remotely sensed and ancillary data for estimating forest gross primary productivity in Italy. Remote Sensing of Environment 100, 563–575.CrossRefGoogle Scholar
McCormick, S 2005. The Nature and Causes of Spatial Variability in Forage Grass Production in Cool Temperate Climes. PhD Thesis, Queen’s University Belfast.McCormick, S. Jordan, C. Bailey, J.S. 2009. Within and between-field spatial variation in soil phosphors in permanent grassland. Precision Agriculture 10, 262-276.Google Scholar
Miao, Y, Stewart, BA and Zhang, F 2011. Long-term experiments for sustainable nutrient management in China. A review. Agronomy for Sustainable Development 31, 397–414.Google Scholar
Pedrera-Parrilla, A, Brevik, EC, Giráldez, JV and Vanderlinden, K 2016. Temporal stability of electrical conductivity in a sandy soil. International Agrophysics 30, 349–357.Google Scholar
Pierce, LE, Bergen, KM, Dobson, MC and Ulaby, FT 1998. Multitemporal land-cover classification using SIR-C/X-SAR imagery. Remote Sensing of Environment 64, 20–33.Google Scholar
Pinar, A and Curran, PJ 1996. Technical Note Grass chlorophyll and the reflectance red edge. International Journal of Remote Sensing 17, 351–357.Google Scholar
Ramoelo, A, Skidmore, AK, Cho, MA, Schlerf, M, Mathieu, R and Heitonig, IMA 2012. Regional estimation of savanna grass nitrogen using the red-edge band of the spaceborne RapidEye sensor. International Journal of Applied Earth Observation and Geoinformation 19, 151–162.Google Scholar
Ranson, KJ and Sun, GQ 1994. Northern Forest Classification Using Temporal Multifrequency and Multipolarimetric Sar Images. Remote Sensing Of Environment 47, 142–153.Google Scholar
Rhoades, JA, Manteghi, NA, Shouse, PJ and Alves, WJ 1989. Soil electrical conductivity and soil salinity: new formulations and calibrations. Soil Science Society of America Journal 53, 433–439.Google Scholar
Roelofsen, HD, van Bodegom, PM, Kooistra, L, van Amerongen, JJ and White, JM 2015. An evaluation of remote sensing derived soil pH and average spring groundwater table for ecological assessments. International Journal of Applied Earth Observation and Geoinformation 43, 149–159.Google Scholar
Schellberg, J and Lock, R 2009. A site-specific slurry application technique on grassland and on arable crops. Bioresource Technology 100, 280–286.Google Scholar
Schuster, C, Förster, M and Kleinschmit, B 2012. Testing the red edge channel for improving land-use classifications based on high-resolution multi-spectral satellite data. International Journal of Remote Sensing 33, 5583–5599.Google Scholar
Tullber, JN, Yule, DF and McGarry, D 2007. Controlled traffic farming— from research to adoption in Australia. Soil and Tillage Research 97, 272–281.CrossRefGoogle Scholar
West, CP, Mallarino, AP, Wedin, WF and Marx, DB 1989. Spatial variability of soil chemical properties in grazed pastures. Soil Science Society of America Journal 53 (3), 784–789.Google Scholar
Zhang, C and Kovacs, JM 2012. The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture 13, 693–712.Google Scholar