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A methodology to develop algorithms that predict nitrogen fertilizer needs in maize based on chlorophyll measurements: a case study in Central Mexico

Published online by Cambridge University Press:  17 August 2015

L. TORRES-DORANTE*
Affiliation:
Research Centre Hanninghof, Yara International ASA, 48249 Duelmen, Germany
R. PAREDES-MELESIO
Affiliation:
Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP), Campo Experimental Bajío, 38010 Celaya, Guanajuato, México
A. LINK
Affiliation:
Research Centre Hanninghof, Yara International ASA, 48249 Duelmen, Germany
J. LAMMEL
Affiliation:
Research Centre Hanninghof, Yara International ASA, 48249 Duelmen, Germany
*
*To whom all correspondence should be addressed. Email: [email protected]

Summary

Identifying and applying the optimum fertilizer nitrogen (N) rate is a permanent challenge for farmers. Prediction of fertilizer N requirement, based on crop chlorophyll measurements (CMs), relies on a strong relationship between fertilizer N supply and leaf chlorophyll concentration at a given crop growth stage. A methodological approach is described, aiming to develop an algorithm that uses CM inputs to derive the economically optimum fertilizer N rate for top-dressing, without using a reference plot for data normalization. The method was tested on maize (Zea mays L. cvar Jabali) at experimental and farmer sites in the central (‘Bajío’) region of Mexico over 3 years (2010–12). Increasing fertilizer N supply at planting significantly influenced chlorophyll concentration at the seventh unfolded maize leaf stage (GS 17 on the Zadoks scale). Maize grain yields increased with increasing total fertilizer N supply and fitted quadratic models, which allowed economically optimum fertilizer N rates (Nopt) to be calculated. The Nopt ranged from 160 to 300 kg N/ha and corresponding grain yields ranged from 7·7 to 14 t/ha. Grouped data analysis (sites–years) confirmed a highly significant relationship between the Nopt and the chlorophyll concentration at GS 17, which could be described by a linear model: Nopt = 513·3–0·58 × CM. This model predicted the top-dressing Nopt within a fertilizer N management regime adapted to local maize cropping systems and led to similar grain yields across test sites compared with the same parameters calculated based on grain yield response trials. The current approach is variety-specific, so development of so-called correction factors accounting for variety-related differences in chlorophyll concentration is described. The results demonstrated the feasibility of the proposed algorithms to support decision-making on the optimum fertilizer N rate to apply in maize production systems with one top-dressing application.

Type
Crops and Soils Research Papers
Copyright
Copyright © Cambridge University Press 2015 

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References

REFERENCES

Abendroth, L. J., Elmore, R. W., Boyer, M. J. & Marlay, S. K. (2011). Corn Growth and Development. Special Report No. 48. Ames, Iowa: Iowa State University of Science and Technology Cooperative Extension Service.Google Scholar
Brentrup, F. & Palliere, C. (2008). GHG emissions and energy efficiency in European nitrogen fertiliser production and use. In Proceedings of the International Fertiliser Society, Vol. 369. York, UK: International Fertiliser Society.Google Scholar
Bullock, D. & Anderson, D. (1998). Evaluation of the Minolta SPAD-502 chlorophyll meter for nitrogen management in corn. Journal of Plant Nutrition 21, 741755.Google Scholar
Cantarella, H. & Montezano, Z. (2010). Nitrogêno e enxofre. In Boas Práticas para uso Eficiente de Fertilizantes: Nutrientes (Eds Prochnow, L. I., Casarin, V. & Stipp, S. R.), pp. 546. Piracicaba, Brazil: International Plant Nutrition Institute.Google Scholar
Cassman, K. G., Dobermann, A. & Walters, D. T. (2002). Agroecosystems, nitrogen-use efficiency, and nitrogen management. Ambio 31, 132140.CrossRefGoogle ScholarPubMed
Cerrato, M. E. & Blackmer, A. M. (1990). Comparison of models for describing corn yield response to nitrogen fertilizer. Agronomy Journal 82, 138143.CrossRefGoogle Scholar
Chapman, S. C. & Barreto, H. J. (1997). Using a chlorophyll meter to estimate specific leaf nitrogen of tropical maize during vegetative growth. Agronomy Journal 89, 557562.CrossRefGoogle Scholar
Dobermann, A. R. (2007). Nutrient use efficiency – measurements and management. In Fertilizer Best Management Practices: General Principles, Strategy for their Adoption and Voluntary Initiatives vs Regulations, pp. 128. Paris, France: International Fertilizer Association.Google Scholar
Espinosa, J. & García, J. (2009). Tools to improve nutrient use efficiency in corn in tropical Latin America. In Proceedings of the Symposium on Nutrient Use Efficiency (Eds F. García & J. Espinosa), pp. 47–54. San José, Costa Rica: International Plant Nutrition Institute (IPNI).Google Scholar
Gianquinto, G., Goffart, J. P., Olivier, M., Guarda, G., Colauzzi, M., Dalla Costa, L., Delle Vedove, G., Vos, J. & Mackerron, D. K. L. (2004). The use of hand-held chlorophyll meters as a tool to assess the nitrogen status and to guide nitrogen fertilization of potato crop. Potato Research 47, 3580.Google Scholar
Grahmann, K., Verhulst, N., Buerkert, A., Ortiz-Monasterio, I. & Govaerts, B. (2013). Nitrogen use efficiency and optimization of nitrogen fertilization in conservation agriculture. CAB Reviews 8, 119.Google Scholar
IUSS Working Group WRB (2006). World Reference Base for Soil Resources 2006. World Soil Resources Reports No. 103. Rome: FAO.Google Scholar
Kindred, D., Berry, P., Burch, O. & Sylvester-Bradley, R. (2008). Effects of nitrogen fertiliser use on greenhouse gas emissions and land use change. In Effects of Climate Change on Plants: Implications for Agriculture (Eds Halford, N., Jones, H. & Lawlor, D.), pp. 5356. Aspects of Applied Biology 88. Wellesbourne, Warwick, UK: Association of Applied Biologists.Google Scholar
Lammel, J., Wollring, J. & Reusch, S. (2001). Tractor based remote sensing for variable nitrogen fertilizer application. In Plant Nutrition – Food Security and Sustainability of Agro-ecosystems through Basic and Applied Research (Eds Horst, W. J., Schenk, M. K., Buerkert, A., Claassen, N., Flessa, H., Frommer, W. B., Goldbach, H., Olfs, H-W., Roemheld, V., Sattelmacher, B., Schmidhalter, U., Schubert, S., Wiren, N. v. & Wittenmayer, L.), pp. 694695. The Netherlands: Kluwer Academic Publishers.Google Scholar
Lory, J. A. & Scharf, P. C. (2003). Yield goal versus delta yield for predicting fertilizer nitrogen need in corn. Agronomy Journal 95, 994999.Google Scholar
Markwell, J., Osterman, J. C. & Mitchell, J. L. (1995). Calibration of the Minolta SPAD-502 leaf chlorophyll meter. Photosynthesis Research 46, 467472.CrossRefGoogle ScholarPubMed
Olfs, H-W. (2009). Improved precision of arable nitrogen application: requirements, technologies and implementation. In Proceedings, Vol. 662. York, UK: International Fertiliser Society.Google Scholar
Olfs, H. W., Blankenau, K., Brentrup, F., Jasper, J., Link, A. & Lammel, J. (2005). Soil and plant-based nitrogen-fertilizer recommendations in arable farming. Journal of Plant Nutrition and Soil Science 168, 414431.CrossRefGoogle Scholar
Piekkielek, W. P. & Fox, R. H. (1992). Use of a chlorophyll meter to predict sidedress nitrogen requirements for maize. Agronomy Journal 84, 5965.CrossRefGoogle Scholar
Rashid, M. T., Voroney, P. & Parkin, G. (2005). Predicting nitrogen fertilizer requirements for corn by chlorophyll meter under different N availability conditions. Canadian Journal of Soil Science 85, 149159.Google Scholar
Raun, W. N., Solie, J. B., Stone, M. L., Martin, K. L., Freeman, K. W., Mullen, R. W., Zhang, H., Schepers, J. S. & Johnson, G. V. (2005). Optical sensor-based algorithm for crop nitrogen fertilization. Communications in Soil Science and Plant Analysis 36, 27592781.CrossRefGoogle Scholar
R Core Team (2013). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. URL http://www.R-project.org/.Google Scholar
Samborski, S. M., Tremblay, N. & Fallon, E. (2009). Strategies to make use of plant sensors-based diagnostic information for nitrogen recommendations. Agronomy Journal 101, 800816.Google Scholar
Scharf, P. C. (2001). Soil and plant tests to predict optimum nitrogen rates for corn. Journal of Plant Nutrition 24, 805826.CrossRefGoogle Scholar
Scharf, P. C., Brouder, S. M. & Hoeft, R. G. (2006). Chlorophyll meter readings can predict nitrogen need and yield response of corn in the north central USA. Agronomy Journal 98, 655665.Google Scholar
Takebe, M. & Yoneyama, T. (1989). Measurement of leaf colour scores and its implication to nitrogen nutrition of rice plants. Japan Agricultural Research Quarterly 23, 8693.Google Scholar
Torres-Dorante, L. O. & Link, A. (2010). Best management principles and techniques to optimise nutrient use efficiency. In Proceedings, Vol. 683. York, UK: International Fertiliser Society.Google Scholar
Varvel, G. E., Schepers, J. S. & Francis, D. D. (1997). Chlorophyll meter and stalk nitrate techniques as complementary indices for residual nitrogen. Journal of Production Agriculture 10, 147151.Google Scholar
Varvel, G. E., Wilhelm, W. W., Shanahan, J. F. & Schepers, J. S. (2007). An algorithm for corn nitrogen recommendations using a chlorophyll meter based sufficiency index. Agronomy Journal 99, 701706.Google Scholar
Wood, C. W., Reeves, D. W. & Himelrick, D. G. (1993). Relationships between chlorophyll meter readings and leaf chlorophyll concentration, N status and crop yield: a review. Proceedings of the Agronomy Society of New Zealand 23, 19.Google Scholar
Zadoks, J. C., Chang, T. T. & Konzak, C. F. (1974). A decimal code for the growth stages of cereals. Weed Research 14, 415421.Google Scholar
Zhang, J., Blackmer, A. M., Ellsworth, J. W. & Koehler, K. J. (2008). Sensitivity of chlorophyll meters for diagnosing nitrogen deficiencies of corn in production agriculture. Agronomy Journal 100, 543550.Google Scholar
Ziadi, N., Brassard, M., Bélanger, G., Claessens, A., Tremblay, N., Cambouris, A. N., Nolin, M. C. & Parent, L-E. (2008). Chlorophyll measurements and nitrogen nutrition index for the evaluation of corn nitrogen status. Agronomy Journal 100, 12641273.Google Scholar