A modified delta yield approach for estimation of economic optimal nitrogen rate (EONR) for wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.)
DOI:
https://doi.org/10.23986/afsci.63668Keywords:
barley , economic optimal nitrogen rate , prediction, soil N supply, wheat , yield potentialAbstract
A Swedish field trial database was mined for information on economic optimal nitrogen rate (EONR). A total of 100 trials with wheat (Triticum aestivum L.) and 47 trials with barley (Hordeum vulgare L.) were used to parameterise prediction models for EONR based on yields in plots with no nitrogen (N) fertilisation, intended to reflect N mineralisation, and yields in plots with a high N rate, aimed as a proxy variable for yield potential, i.e. a modification of the delta yield (∆Y) approach. When the prediction models were applied to new sites and years, predictions had mean absolute error (MAE) = 10 kg N ha-1 for wheat and = 9 kg N ha-1 for barley. Performing modified ∆Y experiments can complement current N fertilisation trials with many rates, in order to improve the spatial representation of EONR estimations. Moreover, ∆Y experiments can potentially be used for in-season EONR estimation, in which case the accuracy of the EONR predictions depends also on the uncertainty in yield predictions made at the time of supplementary fertilisation.
References
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Wang, L., Tian, Y., Yao, X., Zhu, Y. & Cao, W. 2014. Predicting grain yield and protein content in wheat by fusing multi-sensor and multi-temporal remote-sensing images. Field Crops Research 164: 178–188. https://doi.org/10.1016/j.fcr.2014.05.001
Wetterlind, J., Stenberg, B. & Jonsson, A. 2008. Near infrared reflectance spectroscopy compared with soil clay and organic matter content for estimating within-field variation in N uptake in cereals. Plant and Soil 302: 317–327. https://doi.org/10.1007/s11104-007-9489-9
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Algerbo, P.-A., Mattson, L. & Thylén, L. 2003. Skörderelaterad kvävegödsling-teknik, metodik och erfarenheter. JTI-institutet för jordbruks- och miljöteknik. Report 311. http://www.vaxteko.nu/html/sll/jti/rapp_lantbr_industri/JRL311/JRL311.PDF. Accessed 7 October 2017.
Blackmore, S. 2003. The role of yield maps in precision farming. Doctoral thesis. Cranfield, UK: Cranfield University. 170 p. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.460.682&rep=rep1&type=pdf. Accessed 7 October 2017.
Bullock, D. G. & Bullock, D. S. 1994. Quadratic and quadratic-plus-plateau models for predicting optimal nitrogen rate of corn: A comparison. Agronomy Journal 86: 191–195. https://doi.org/10.2134/agronj1994.00021962008600010033x
Delin, S. & Lindén, B. 2002. Relations between net nitrogen mineralization and soil characteristics within an arable field. Acta Agriculturae Scandinavica Section B–Soil and Plant Science 52: 8. https://doi.org/10.1080/090647102321089819
Delin, S. & Stenberg, M. 2014. Effect of nitrogen fertilisation on nitrate leaching in relation to grain yield response on loamy sand in Sweden. European Journal of Agronomy 52: 291–296. https://doi.org/10.1016/j.eja.2013.08.007
Engström, L. & Piikki, K. 2016. Skördeprognos med hjälp av YARA N-sensor. Department of Soil and Environment, Swedish University of Agricultural Sciences. POS technical report 39. https://pub.epsilon.slu.se/13480/. Accessed 7 October 2017.
Fixen, P.E. 2006. Use of Yield Goals for Providing N Rate Suggestions: General Concept. Proceedings, North Central Extension-Industry Soil Fertility Conference, Des Moines, IA, 7-8 November 2006. International Plant Nutrition Institute, Norcross, GA, USA. 22: 57–66.
Hastie T., Tibshirani R. & Friedman J. 2009. The elements of statistical learning: data mining Inference and prediction. New York, USA: Springer Science + Business Media. 746 p.
Kachanoski, R., Fairchild, G. & Beauchamp, E. 1996. Yield indices for corn response to applied fertilizer: Application in site specific crop management. In: Robert, P., Rust, R., and Larson, W. (eds.) Proc. Third International Conference on Precision Agriculture, Minneapolis. p. 425–432.
Kachanoski, R. 2009. Crop response to nitrogen fertilizer: The delta yield concept. Canadian Journal of Soil Science 89: 543–554. https://doi.org/10.4141/CJSS09003
Kyveryga, P., Blackmer, A. & Morris, T. 2007. Alternative benchmarks for economically optimal rates of nitrogen fertilization for corn. Agronomy Journal 99: 1057–1065. https://doi.org/10.2134/agronj2006.0340
Lord, E. & Mitchell, R. 1998. Effect of nitrogen inputs to cereals on nitrate leaching from sandy soils. Soil Use and Management 14: 78–83. https://doi.org/10.1111/j.1475-2743.1998.tb00619.x
Lory, J. & Scharf, P. 2003. Yield goal versus delta yield for predicting fertilizer nitrogen need in corn. Agronomy Journal 95: 994–999. https://doi.org/10.2134/agronj2003.0994
Montesinos-López, O., Montesinos-López, A., Crossa, J., los Campos, G., Alvarado, G., Suchismita, M., Rutkoski, J., Gonzalez‑Perez L. & Burgueno, J. 2017. Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data. Plant methods 13. https://doi.org/10.1186/s13007-016-0154-2
Nash, J. & Sutcliffe J. 1970. River flow forecasting through conceptual models: Part 1. A discussion of principles. Journal of Hydrology 10: 282–290. https://doi.org/10.1016/0022-1694(70)90255-6
Nel A. & Bloem A. 2006. The delta yield procedure for nitrogen fertilisation of maize in South Africa. South African Journal of Plant and Soil 23: 203–208. https://doi.org/10.1080/02571862.2006.10634755
Øvergaard, S., Isaksson, T. & Korsaeth, A. 2013. Prediction of wheat yield and protein using remote sensors on plots–Part I: Assessing near infrared model robustness for year and site variations. Journal of near Infrared Spectroscopy 21: 117–131. https://doi.org/10.1255/jnirs.1042
Piikki, K., Söderström, M., Eriksson, J., Muturi John, J., Ireri Muthee, P., Wetterlind, J. & Lund, E. 2016. Performance Evaluation of Proximal Sensors for Soil Assessment in Smallholder Farms in Embu County, Kenya. Sensors 16. https://doi.org/10.3390/s16111950
Rajsic, P. & Weersink, A. 2008. Do farmers waste fertilizer? A comparison of ex post optimal nitrogen rates and ex ante recommendations by model, site and year. Agricultural Systems 97: 56–67. https://doi.org/10.1016/j.agsy.2007.12.001
Raun, W., Solie, J. & Stone, M. 2011. Independence of yield potential and crop nitrogen response. Precision Agriculture 12: 508–518. https://doi.org/10.1007/s11119-010-9196-z
R Core Team 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. Accessed 8 October 2017.
Solie, J., Monroe, A., Raun, W. & Stone, M. 2012. Generalized algorithm for variable-rate nitrogen application in cereal grains. Agronomy Journal 104: 378–387. https://doi.org/10.2134/agronj2011.0249
Stenberg, M., Söderström, M., Gruvaeus, I., Bjurling, E., Gustafsson K., Krijger, A.-K., Stenberg, B. & Pettersson, C-G. 2009. Orsaker till skillnader mellan rekommenderade kvävegivor och de verkliga eller beräknat optimala i praktisk spannmålsodling. Hushållningssällskapet Skaraborg report 5. 58 pp. http://hushallningssallskapet.se/wp-content/uploads/2015/05/2009_5_kvave_spannmalsodling.pdf. Accessed 7 October 2017.
Söderström, M., Piikki, K., Stenberg, M., Stadig, H. & Martinsson, J. 2017. Producing nitrogen (N) uptake maps in winter wheat by combining proximal crop measurements with Sentinel-2 and DMC satellite images in a decision support system for farmers. Acta Agriculturae Scandinavica, Section B—Soil & Plant Science 67: 637–650.
Wang, L., Tian, Y., Yao, X., Zhu, Y. & Cao, W. 2014. Predicting grain yield and protein content in wheat by fusing multi-sensor and multi-temporal remote-sensing images. Field Crops Research 164: 178–188. https://doi.org/10.1016/j.fcr.2014.05.001
Wetterlind, J., Stenberg, B. & Jonsson, A. 2008. Near infrared reflectance spectroscopy compared with soil clay and organic matter content for estimating within-field variation in N uptake in cereals. Plant and Soil 302: 317–327. https://doi.org/10.1007/s11104-007-9489-9
Zadoks, J., Chang, T. & Konzak, C. 1974. A decimal code for the growth stages of cereals. Weed Research 14: 415–421. https://doi.org/10.1111/j.1365-3180.1974.tb01084.x
Zecha, C. W., Link, J. & Claupein, W. 2017. Fluorescence and Reflectance Sensor Comparison in Winter Wheat. Agriculture 7: 78–92. https://doi.org/10.3390/agriculture7090078
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How to Cite
A modified delta yield approach for estimation of economic optimal nitrogen rate (EONR) for wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.). (2017). Agricultural and Food Science, 26(4), 233–241. https://doi.org/10.23986/afsci.63668
Received 2017-05-11
Accepted 2017-11-18
Published 2017-12-27
Accepted 2017-11-18
Published 2017-12-27
