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Food Security

, Volume 7, Issue 2, pp 365–373 | Cite as

The role of technology transfer to improve fertiliser use efficiency

  • Miles Grafton
  • Ian Yule
Original Paper

Abstract

Over the past 80 years, crop yields under developed agriculture have increased rapidly. However, in many countries the rate of increase has slowed significantly. In some areas of the world such as Europe, nutrient inputs have been significantly reduced and yields have stagnated, while in other parts of the world the amount of nutrients applied have been constant with moderate yield increases. In either case, farmers are seeking to improve nutrient use efficiency. The potential to increase yields is significant. The greatest potential exists in areas where yields are low and limited by a readily remedied limiting factor; a situation often encountered in developing nations where nutrient deficiencies persist. Yield response to nutrient application where no other limiting factor exists, is invariably a diminishing return curve, showing strong increases in yield with initial additions of nutrients then plateauing with increasing applications. Remote sensing by satellite and wireless communications offer an opportunity to identify limiting factors in crops globally and provide timely management information to farm managers to control inputs or adjust practices. In the developing world, the use of remote sensed data may be a required focus area, where services are provided and information dispersed, to assist farmers. For example climate and surface temperature monitoring could improve fertiliser efficacy by scheduling applications when soil moisture conditions allow uptake of nutrient, whilst also preventing applications prior to deluges, which would cause fertiliser run off to waterways. This paper explores the use of remote sensing technologies to improve efficiency of fertiliser use.

Keywords

Remote sensing Nutrient management budget Nutrient losses 

Notes

Acknowledgments

The authors would like to acknowledge the work of Dr. Reddy Pullinagari and Dr. Gabor Kereszturi in their work in correlating hyperspectral data to plant tissue wet chemistry.This paper was part of a workshop sponsored by the OECD Co-operative Research Programme on Biological Resource Management for Sustainable Agricultural Systems. 

References

  1. Anon (2014). Crop nutrient contents – yield unit changes. Access Database from United States Department of Agriculture. Downloaded from: http://www.plants.usda.gov/npk/Nutrientreport. Accessed 30 July 2014.
  2. Anon. Ministry of Agriculture and Forestry (2010). Pastoral Sector Overview, downloaded from www.MAF.govt.nz Accessed 17 July 2010.
  3. Bretherton, M.R. (2012). An Investigation into Repellency – Induced Runoff and its Consequences in a New Zealand Hill Country Pasture System. Unpublished Thesis, Massey University Palmerston North, New Zealand.Google Scholar
  4. Cooke, G.W. (1982). Fertilizing for maximum yield. – 3rd (Ed). Granada Publishing Limited – technical Books Division (pp. 278–308)St Albans, Herts AL2 2NF. Google Scholar
  5. Fageria, N.D., Virupax, C.B., Jones, C.A. (2011). Growth and Mineral Nutrition of Field Crops. – 3rd (Ed). CRC Press Taylor and Francis Group,(pp. 219–517) Boca Raton, FL. Google Scholar
  6. Grafton, M. C. E., Yule, I. J., & Manning, M. J. (2013). A review of the economic impact of high levels of variance in fertiliser spreading systems. Proceedings of the New Zealand Grassland Association, 75, 131–136.Google Scholar
  7. Haag, L., (2014). PA lessons learned, dreams dreamt, and plans made: some perspectives of a U.S. High Plains Dryland Researcher and Farmer. In Proceedings of the 17th Precision Agriculture Symposium of Australasia. 68–75.Google Scholar
  8. Heffer, P., (2013). Assessment of Fertilizer Use by Crop at the Global Level. International Fertilizer Association. Downloaded from: www.fertilizer.org Accessed 7 August 2014.
  9. Holland K.H., Lamb D.W., Schepers J.S., (2012). Radiometry of Proximal Active Optical Sensors (AOS) for Agricultural Sensing. Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of PP:1–10. DOI:  10.1109/jstars.2012.2198049.
  10. Jarvis, P. J., Boswell, C. C., Metherell, A. K., Davison, R. M., & Murphy, J. A. (2002). A nutrient budget for the Meat and Wool Economic Service of a New Zealand Class 1 high – country farm model. New Zealand Journal of Agricultural Research, 45, 1–15.CrossRefGoogle Scholar
  11. Kuri, F., Murwira, A., Murwira, K., & Masocha, M. (2014). Predicting maize yield in Zimbabwe using dry dekads derived from remotely sensed vegetation condition index. International Journal of Applied Earth Observation and Geoinformation, 33, 39–46.CrossRefGoogle Scholar
  12. Mackenzie, C., (2013). Utilising Variable Rate Feriliser Application to improve Farm Profit. In: Accurate and efficient use of nutrients on farms. (Eds. L.D. Currie and C.L. Christensen). http://flrc.massey.ac.nz/publications.html. Occasional Report No. 26. Fertilizer and Lime Research Centre, Massey University, Palmerston North, New Zealand. Pages 1.
  13. Mersmann, M., Walthers, S., and Sia, T., (2013). Fertilizer Application – Driving the Future with Innovations. In: Accurate and efficient use of nutrients on farms. (Eds. L.D. Currie and C.L. Christensen). http://flrc.massey.ac.nz/publications.html. Occasional Report No. 26. Fertiliser and Lime Research Centre, Massey University, Palmerston North, New Zealand. Pages 13.Google Scholar
  14. Pullanagari, R., Yule, I., Tuohy, M., Hedley, M., Dynes, R., & King, W. (2012). In-field hyperspectral proximal sensing for estimating quality parameters of mixed pasture. Precision Agriculture, 13(3), 351–369.CrossRefGoogle Scholar
  15. Pullanagari, R. R., Yule, I. J., Tuohy, M. P., Hedley, M. J., Dynes, R. A., & King, W. M. (2013). Proximal sensing of the seasonal variability of pasture nutritive value using multispectral radiometry. Grass and Forage Science, 68(1), 110–119.CrossRefGoogle Scholar
  16. Rahmann, M. M., Lamb, D. W., Stanley, J. N., & Trotter, M. G. (2014). Use of proximal sensors to evaluate at the sub-paddock scale a pasture growth-rate model based on light-use efficiency. Crop and Pasture Science, 65, 400–409.CrossRefGoogle Scholar
  17. Scott, D., Robertson, J. S., & Hoglund, J. H. (2006). Considerations in low fertiliser rate application in the high country. New Zealand Journal of Agricultural Research, 49, 59–65.CrossRefGoogle Scholar
  18. Solari, F., Shanahan, J., Ferguson, R., Schepers, J., & Gitelson, A. (2008). Active sensor reflectance measurements of corn nitrogen status and yield potential. Agronomy Journal, 100, 571.CrossRefGoogle Scholar
  19. Starks, P. J., Zhao, D., & Brown, M. A. (2008). Estimation of nitrogen concentration and in vitro dry matter digestibility of herbage of warm-season grass pastures from canopy hyperspectral reflectance measurements. Grass and Forage Science, 63, 168–178.CrossRefGoogle Scholar
  20. Torino, M. S., Ortiz, B. V., Fulton, J. P., Balkcom, K. S., & Wood, C. W. (2014). Evaluation of vegetation indices for early assessment of corn status and yield potential in the South Eastern United States. Agronomy Journal, 106(4), 1389–1401.CrossRefGoogle Scholar
  21. Wan, L., Zhang, B., Kemp, P., & Xianglin, L. (2009). Modelling the abundance of three key plant species in New Zealand hill-pasture using a decision tree approach. Ecological Modelling, 220, 1819–1825.CrossRefGoogle Scholar
  22. Xingha, L., Huanfeng, S., Liangpei, Z., Hongyan, Z., Quiandquiang, Y., & Gang, Y. (2014). Recovering qualitative remote sensing products contaminated by thick clouds and shadows using multispectral dictionary learning. IEEE Transactions of Geoscience and Remote Sensing, 52(11), 7086–7096.CrossRefGoogle Scholar
  23. Yule, I.J., and Grafton, M.C.E., (2010). Factors Affecting Fertiliser Application Uniformity. In: Farming’s Future minimising Footprints and maximising Margins. (Eds. L.D. Currie and C.L. Christensen). http://flrc.massey.ac.nz/publications.html. Occasional Report No. 23. Fertilizer and Lime Research Centre, Massey University, Palmerston North, New Zealand, 413–420.

Copyright information

© Springer Science+Business Media Dordrecht and International Society for Plant Pathology 2015

Authors and Affiliations

  1. 1.New Zealand Centre for Precision AgricultureMassey UniversityPalmerston NorthNew Zealand

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