Skip to main content
SpringerLink
Log in

Systematic analysis of site-specific yield distributions resulting from nitrogen management and climatic variability interactions

  • Published:
Precision Agriculture Aims and scope Submit manuscript

Abstract

At the plot level, crop simulation models such as STICS have the potential to evaluate risk associated with management practices. In nitrogen (N) management, however, the decision-making process is complex because the decision has to be taken without any knowledge of future weather conditions. The objective of this paper is to present a general methodology for assessing yield variability linked to climatic uncertainty and variable N rate strategies. The STICS model was coupled with the LARS-Weather Generator. The Pearson system and coefficients were used to characterise the shape of yield distribution. Alternatives to classical statistical tests were proposed for assessing the normality of distributions and conducting comparisons (namely, the Jarque–Bera and Wilcoxon tests, respectively). Finally, the focus was put on the probability risk assessment, which remains a key point within the decision process. The simulation results showed that, based on current N application practice among Belgian farmers (60-60-60 kgN ha−1), yield distribution was very highly significantly non-normal, with the highest degree of asymmetry characterised by a skewness value of −1.02. They showed that this strategy gave the greatest probability (60 %) of achieving yields that were superior to the mean (10.5 t ha−1) of the distribution.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Arslan, S., & Colvin, T. (2002). Grain Yield Mapping: Yield Sensing, Yield Reconstruction, and Errors. Precision Agriculture, 3(2), 135–154.

    Article  Google Scholar 

  • Basso, B., Bertocco, M., Sartori, L., & Martin, E. C. (2007). Analyzing the effects of climate variability on spatial pattern of yield in a maize–wheat–soybean rotation. European Journal of Agronomy, 26(2), 82–89.

    Article  Google Scholar 

  • Basso, B., Fiorentino, C., Cammarano, D., Cafiero, G., & Dardanelli, J. (2012a). Analysis of rainfall distribution on spatial and temporal patterns of wheat yield in Mediterranean environment. European Journal of Agronomy, 41, 52–65.

    Article  Google Scholar 

  • Basso, B., & Ritchie, J. T. (2005). Impact of compost, manure and inorganic fertilizer on nitrate leaching and yield for a 6-year maize–alfalfa rotation in Michigan. Agriculture, Ecosystems & Environment, 108(4), 329–341.

    Article  Google Scholar 

  • Basso, B., Ritchie, J. T., Cammarano, D., & Sartori, L. (2011a). A strategic and tactical management approach to select optimal N fertilizer rates for wheat in a spatially variable field. European Journal of Agronomy, 35(4), 215–222. doi:10.1016/j.eja.2011.06.004.

    Article  Google Scholar 

  • Basso, B., Sartori, L., Bertocco, M., Cammarano, D., Martin, E. C., & Grace, P. R. (2011b). Economic and environmental evaluation of site-specific tillage in a maize crop in NE Italy. European Journal of Agronomy, 35(2), 83–92. doi:10.1016/j.eja.2011.04.002.

    Article  Google Scholar 

  • Basso, B., Sartori, L., Cammarano, D., Fiorentino, C., Grace, P. R., Fountas, S., et al. (2012b). Environmental and economic evaluation of N fertilizer rates in a maize crop in Italy: A spatial and temporal analysis using crop models. Biosystems Engineering, 113(2), 103–111. doi:10.1016/j.biosystemseng.2012.06.012.

    Article  Google Scholar 

  • Beaudoin, N., Launay, M., Sauboua, E., Ponsardin, G., & Mary, B. (2008). Evaluation of the soil crop model STICS over 8 years against the ‘on farm’ database of Bruyères catchment. European Journal of Agronomy, 29, 46–57.

    Article  Google Scholar 

  • Binder, J., Graeff, S., Link, J., Claupein, W., Liu, M., Dai, M., et al. (2008). Model-Based Approach to Quantify Production Potentials of Summer Maize and Spring Maize in the North China Plain. Agrononomy Journal, 100(3), 862–873.

    Article  Google Scholar 

  • Brisson, N., Gary, C., Justes, E., Roche, R., Mary, B., Ripoche, D., et al. (2003). An overview of the crop model stics. European Journal of Agronomy, 18(3–4), 309–332.

    Article  Google Scholar 

  • Brisson, N., Launay, M., Mary, B., & Beaudoin, N (Eds.). (2009). Conceptual basis, formalisations and parameterization of the STICS crop model, Versailles, FRA : Editions Quae. http://prodinra.inra.fr/record/28387 (Last accessed 09-15-2014).

  • Brisson, N., Mary, B., Ripoche, D., Jeuffroy, M. H., Ruget, F., Nicoullaud, B., et al. (1998). STICS: a generic model for the simulation of crops and their water and nitrogen balances. I. Theory and parameterization applied to wheat and corn. Agronomie, 18(5–6), 311–346.

    Article  Google Scholar 

  • Brisson, N., Ruget, F., Gate, P., Lorgeau, J., Nicoulaud, B., Tayo, X., et al. (2002). STICS: a generic model for simulating crops and their water and nitrogen balances. II. Model validation for wheat and maize. Agronomie, 22, 69–82.

    Article  Google Scholar 

  • Constantin, J., Beaudoin, N., Launay, M., Duval, J., & Mary, B. (2012). Long-term nitrogen dynamics in various catch crop scenarios: Test and simulations with STICS model in a temperate climate. Agriculture, Ecosystems & Environment, 147, 36–46.

    Article  CAS  Google Scholar 

  • Constantin, J., Beaudoin, N., Laurent, F., Cohan, J.-P., Duyme, F., & Mary, B. (2011). Cumulative effects of catch crops on nitrogen uptake, leaching and net mineralization. Plant and Soil, 341(1–2), 137–154.

    Article  CAS  Google Scholar 

  • Day, R. H. (1965). Probability Distributions of Field Crop Yields. Journal of Farm Economics, 47(3), 713–741.

    Article  Google Scholar 

  • Delin, S., Lindén, B., & Berglund, K. (2005). Yield and protein response to fertilizer nitrogen in different parts of a cereal field: potential of site-specific fertilization. European Journal of Agronomy, 22(3), 325–336.

    Article  Google Scholar 

  • Du, X., Hennessy, D., & Yu, C. (2012). Testing Day’s Conjecture that More Nitrogen Decreases Crop Yield Skewness. American Journal of Agricultural Economics, 94(1), 225–237.

    Article  Google Scholar 

  • Dumont, B., Basso, B., Leemans, V., Bodson, B., Destain, J. P., & Destain, M. F. (2013). Yield variability linked to climate uncertainty and nitrogen fertilisation. In J. Stafford (Ed.), Precision agriculture’13 Proceedings of the 9 th European Confernce on Precision Agriculture (pp. 427–434). The Netherlands: Wageningen Academic Publishers.

    Google Scholar 

  • Dumont, B., Leemans, V., Ferrandis, S., Bodson, B., Destain, J.P., & Destain, M. F., (2014a). Assessing the potential of an algorithm based on mean climatic data to predict wheat yield. Precision Agriculture, 15(3), 255–272.

  • Dumont, B., Leemans, V., Mansouri, M., Bodson, B., Destain, J., & Destain, M. (2014b). Parameter optimisation of the STICS crop model, with an accelerated formal MCMC approach (DREAM algorithm). Environmental Modelling and Software, 52, 121–135.

    Article  Google Scholar 

  • EC-Council Directive, 1991. Council Directive 91/676/EEC concerning the protection of waters against pollution caused by nitrates from agricultural sources.

  • Eickhout, B., Bouwman, A. F., & van Zeijts, H. (2006). The role of nitrogen in world food production and environmental sustainability. Agriculture, Ecosystems & Environment, 116(1–2), 4–14.

    Article  CAS  Google Scholar 

  • Ewert, F., van Ittersum, M. K., Heckelei, T., Therond, O., Bezlepkina, I., & Andersen, E. (2011). Scale changes and model linking methods for integrated assessment of agri-environmental systems. Agriculture, Ecosystems & Environment, 142(1–2), 6–17.

    Article  Google Scholar 

  • Guillaume, S., Bergez, J. E., Wallach, D., & Justes, E. (2011). Methodological comparison of calibration procedures for durum wheat parameters in the STICS model. European Journal of Agronomy, 35, 115–126.

    Article  Google Scholar 

  • Hennessy, D. A. (2009a). Crop Yield Skewness and the Normal Distribution. Journal of Agricultural and Resource Economics, 34(1), 34–52.

    Google Scholar 

  • Hennessy, D. A. (2009b). Crop Yield Skewness Under Law of the Minimum Technology. American Journal of Agricultural Economics, 91(1), 197–208.

    Article  Google Scholar 

  • Houlès, V., Mary, B., Guérif, M., Makowski, D., & Justes, E. (2004). Evaluation of the ability of the crop model STICS to recommend nitrogen fertilisation rates according to agro-environmental criteria. Agronomie, 24(6–7), 339–349.

    Article  Google Scholar 

  • Jamieson, P. D., Semenov, M. A., Brooking, I. R., & Francis, G. S. (1998). Sirius: a mechanistic model of wheat response to environmental variation. European Journal of Agronomy, 8(3–4), 161–179.

    Article  Google Scholar 

  • Just, R. E., & Weninger, Q. (1999). Are Crop Yields Normally Distributed? American Journal of Agricultural Economics, 81(2), 287–304.

    Article  Google Scholar 

  • Koundouri, P., & Kourogenis, N. (2011). On the Distribution of Crop Yields: Does the Central Limit Theorem Apply? American Journal of Agricultural Economics, 93(5), 1341–1357.

    Article  Google Scholar 

  • Lawless, C., & Semenov, M. A. (2005). Assessing lead-time for predicting wheat growth using a crop simulation model. Agricultural and Forest Meteorology, 135(1–4), 302–313.

    Article  Google Scholar 

  • Link, J., Batchelor, W., Graeff, S., & Claupein, W. (2008). Evaluation of current and model-based site-specific nitrogen applications on wheat (Triticum aestivum L.) yield and environmental quality. Precision Agriculture, 9(5), 251–267.

    Article  Google Scholar 

  • Loague, K., & Green, R. E. (1991). Statistical and graphical methods for evaluating solute transport models: Overview and application. Journal of Contaminant Hydrology, 7(1–2), 51–73.

    Article  CAS  Google Scholar 

  • McBratney, A., Whelan, B., Ancev, T., & Bouma, J. (2005). Future Directions of Precision Agriculture. Precision Agriculture, 6(1), 7–23.

    Article  Google Scholar 

  • Mosier, A. R., Bleken, M., Chaiwanakupt, P., Ellis, E. C., Freney, J., Howarth, R. B., et al. (2001). Policy implications of human-accelerated nitrogen cycling. Biogeochemistry, 52, 281–320.

    Article  CAS  Google Scholar 

  • Nonhebel, S. (1994). The effects of use of average instead of daily weather data in crop growth simulation models. Agricultural Systems, 44(4), 377–396.

    Article  Google Scholar 

  • Palosuo, T., Kersebaum, K. C., Angulo, C., Hlavinka, P., Moriondo, M., Olesen, J. E., et al. (2011). Simulation of winter wheat yield and its variability in different climates of Europe: A comparison of eight crop growth models. European Journal of Agronomy, 35(3), 103–114.

    Article  Google Scholar 

  • Pearson, K. (1894). Contributions to the Mathematical Theory of Evolution. Philosophical Transactions of the Royal Society of London., 185 (Full publication date: 1894/Copyright © 1894 The Royal Society), 71–110.

  • Porter, J. R., & Semenov, M. A. (1999). Climate variability and crop yields in Europe. Nature, 400(6746), 724. doi:10.1038/23385.

    Article  CAS  Google Scholar 

  • Porter, J. R., & Semenov, M. A. (2005). Crop responses to climatic variation. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1463), 2021–2035.

    Article  Google Scholar 

  • Racsko, P., Szeidl, L., & Semenov, M. (1991). A serial approach to local stochastic weather models. Ecological Modelling, 57(1–2), 27–41.

    Article  Google Scholar 

  • Recous, S., & Machet, J.-M. (1999). Short-term immobilisation and crop uptake of fertiliser nitrogen applied to winter wheat: effect of date of application in spring. Plant and Soil, 206(2), 137–149.

    Article  Google Scholar 

  • Riha, S. J., Wilks, D. S., & Simoens, P. (1996). Impact of temperature and precipitation variability on crop model predictions. Climatic Change, 32(3), 293–311.

    Article  Google Scholar 

  • Robertson, G. P., Paul, E. A., & Harwood, R. R. (2000). Greenhouse gases in intensive agriculture: Contributions of individual gases to the radiative forcing of the atmosphere. Science, 289, 1922–1925.

    Article  CAS  PubMed  Google Scholar 

  • Robertson, G. P., & Vitousek, P. M. (2009). Nitrogen in agriculture: balancing the cost of an essential resource. Annual Review of Environment and Resources, 34, 97–125.

    Article  Google Scholar 

  • Semenov, M. A., & Barrow, E. M. (1997). Use of a stochastic weather generator in the development of climate change scenarios. Climatic Change, 35(4), 397–414.

    Article  Google Scholar 

  • Semenov, M. A., & Barrow, E. M. (2002). LARS-WG - A stochastic weather generator for use in climate impact studies. User manual, version 3.0, August 2002. Tech. rep., Rothamsted Research, Harpenden, Hertfordshire, AL5 2JQ, UK.

  • Semenov, M. A., & Doblas-Reyes, F. J. (2007). Utility of dynamical seasonal forecasts in predicting crop yield. Climate Research, 34(1), 71–81.

    Article  Google Scholar 

  • Semenov, M., & Porter, J. (1995). Climatic variability and the modelling of crop yields. Agricultural and Forest Meteorology, 73(3–4), 265–283.

    Article  Google Scholar 

  • Smil, V. (1999). Nitrogen in crop production: An account of global flows. Global Biogeochemical Cycles, 13, 647–662.

    Article  CAS  Google Scholar 

  • Soltani, A., & Sinclair, T. R. (2012). Modeling Physiology of crop development, growth and yield.In Soltani & Sinclair (Eds.), CABI publishing, (pp. 336). doi:10.1079/9781845939700.0000.

  • Vandenberghe, C., Marcoen, J., Sohier, C., Degre, A., Hendrickx, C., Paulus, P., 2011. Developments in monitoring the effectiveness of the EU Nitrates Directive Action Programmes: Approach by Belgium, the Walloon region. In B. Fraters, et al. (Eds.), Developments in monitoring the effectiveness of the EU Nitrates Directive Action Programmes. Results of the second MonNO3 Workshop, 2009, (pp. 119–140). http://www.rivm.nl/bibliotheek/rapporten/680717019.pdf (Last accessed 09-15-2014).

  • Vrugt, J. A., Braak, C. J. F. T., Diks, C. G. H., Robinson, B. A., Hyman, J. M., & Higdon, D. (2009). Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling. International Journal of Nonlinear Sciences and Numerical Simulation, 10(3), 273–290.

    Article  Google Scholar 

  • Welsh, J. P., Wood, G. A., Godwin, R. J., Taylor, J. C., Earl, R., Blackmore, S., et al. (2003). Developing Strategies for Spatially Variable Nitrogen Application in Cereals. Part I: Winter Barley. Biosystems Engineering, 84(4), 481–494.

    Google Scholar 

  • Zadoks, J. C., Chang, T. T., & Konzak, C. F. (1974). A decimal code for the growth stages of cereals. Weed Research, 14, 415–421.

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the SPW (DGARNE – DGO-3) for its financial support for the project entitled ‘Suivi en temps réel de l’environnement d’une parcelle agricole par un réseau de microcapteurs en vue d’optimiser l’apport en engrais azotés’. The authors would also like to thank the OptimiSTICS team for allowing them to use the Matlab running code of the STICS model. The authors are very grateful to CRA-W, especially the Systèmes agraires, Territoire et Technologies de l’Information unit, for providing them with the Ernage station climatic database. The authors would thank Giles Collinet and Robert Oger, for their respective contribution to the field experiments and to the paper. Finally, the authors are thankful to the MACSUR knowledge hub within which the co-authors shared their experiences for this research.

Author information

Authors and Affiliations

  1. Precision Agriculture Unit, Department of Biosystems Engineering, ULg Gembloux Agro-Bio Tech, Passage des Déportés, 2, 5030, Gembloux, Belgium

    Benjamin Dumont, Vincent Leemans & Marie-France Destain

  2. Department of Geological Sciences, Michigan State University, East Lansing, MI, USA

    Bruno Basso

  3. Department of Agriculture, Bio-engineering and Chemistry, ULg Gembloux Agro-Bio Tech, Gembloux, Belgium

    Bernard Bodson

  4. Walloon Agricultural Research Center (CRA-W), ULg Gembloux Agro-Bio Tech, Gembloux, Belgium

    Jean-Pierre Destain

Authors
  1. Benjamin Dumont
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bruno Basso
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vincent Leemans
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bernard Bodson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jean-Pierre Destain
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Marie-France Destain
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Benjamin Dumont.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dumont, B., Basso, B., Leemans, V. et al. Systematic analysis of site-specific yield distributions resulting from nitrogen management and climatic variability interactions. Precision Agric 16, 361–384 (2015). https://doi.org/10.1007/s11119-014-9380-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11119-014-9380-7

Keywords

Search

Navigation