Skip to main content

Advertisement

SpringerLink
Log in

Spatial interpretation of plant parameters in winter wheat

  • Published:
Precision Agriculture Aims and scope Submit manuscript

Abstract

A methodology is described for the spatial interpretation of plant parameters (SIOPP), which was used to diagnose the nutritional status of winter wheat. The data used in this study were collected in 2010 throughout the monitoring of two fields (52 and 38 ha) with uniform and conventional agricultural management, located in the Czech Republic. The survey was carried out at BBCH 30 phenological stage in a regular sampling grid with 150 m of distance between grid points (27 and 18 samples). The plant height and the chlorophyll concentration (Yara N-Tester) were recorded. Plant and soil samples were taken to analyse the nutrient concentrations (N, P, K, Mg, Ca, and S). A crop development index (CDI) was developed combining plant height and N-Tester values to quantify the growth of the plant (biomass and vigour). The relationship between this index and the concentration of nutrients were studied and confirmed by cross-validation and spatial analysis; the aim was to determine the factors that limit plant growth. The method revealed the limiting factors in field #1 were potassium, calcium (pH problems) and nitrogen (in descending order of relevance). In field #2, CDI was only related to the soil moisture. In all cases, it was found that the spatial variability of the indices and the limiting factors followed a pattern result of the combination of the gradients in climate, topography and soils of each field. This allowed the interpolation of the maps for variable-rate application using only 0.5 samples per hectare arranged in regular mesh, which was insufficient for the use of geostatistics. All diagnoses were consistent with the crop yield, the soil sampling and the DRIS diagnoses. The results showed that if leaf analyses are complemented with a few additional measures, instantaneous and with a minimal cost, it is possible to deduce the diagnosis using statistical and spatial analysis.

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

Abbreviations

CDI:

Crop development index

CV:

Coefficient of variation

DRIS:

Diagnosis and recommendation integrated system

NBI:

Nutrient balance index

P value:

Statistical significance

R2 :

Coefficient of determination

RMSE:

Root mean square error

SF:

Spatial factors (latitude, longitude and variables derived from digital elevation model)

SIOPP:

Spatial interpretation of plant parameters

References

  • Ahrens, T. D., & University, S. (2009). Improving regional nitrogen use efficiency. Opportunities and constraints. Stanford: Stanford University.

    Google Scholar 

  • Amundson, R., & Koehler, F. (1987). Utilization of DRIS for diagnosis of nutrient deficiencies in winter wheat. Agronomy Journal, 79(3), 472–476.

    Article  CAS  Google Scholar 

  • Bailey, J. S., Beattie, J. A. M., & Kilpatrick, D. J. (1997). The diagnosis and recommendation integrated system (DRIS) for diagnosing the nutrient status of grassland swards: I. Model establishment. Plant and Soil, 197(1), 127–135. doi:10.1023/a:1004236521744.

    Article  CAS  Google Scholar 

  • Barker, A. V., & Pilbeam, D. J. (2007). Handbook of plant nutrition (pp. 21–50). Boca Raton: CRC.

    Google Scholar 

  • Basso, B., Ritchie, J. T., Pierce, F. J., Braga, R. P., & Jones, J. W. (2001). Spatial validation of crop models for precision agriculture. Agricultural Systems, 68(2), 97–112. doi:10.1016/s0308-521x(00)00063-9.

    Article  Google Scholar 

  • Beaufils, E. R. (1973). Diagnosis and recommendation integrated system (DRIS). Soil Science Bulletin, 1, 1–132.

    Google Scholar 

  • Beverly, R. B. (1991). A practical guide to the diagnosis and recommendation integrated system (DRIS). Athens: Cornell University.

    Google Scholar 

  • Byamukama, E., Tatineni, S., Hein, G. L., Graybosch, R. A., Baenziger, P. S., French, R., et al. (2012). Effects of single and double infections of winter wheat by Triticum mosaic virus and wheat streak mosaic virus on yield determinants. Plant Disease, 96(6), 859–864. doi:10.1094/PDIS-11-11-0957-RE.

    Article  Google Scholar 

  • Cao, Q., Cui, Z., Chen, X., Khosla, R., Dao, T. H., & Miao, Y. (2012). Quantifying spatial variability of indigenous nitrogen supply for precision nitrogen management in small scale farming. Precision Agriculture, 13(1), 45–61. doi:10.1007/s11119-011-9244-3.

    Article  Google Scholar 

  • Cheng, F., Yu, J., & Xiong, H. (2010). Facial expression recognition in JAFFE dataset based on Gaussian process classification. IEEE Transactions on Neural Networks, 21(10), 1685–1690. doi:10.1109/tnn.2010.2064176.

    Article  PubMed  Google Scholar 

  • Colla, G., Rouphael, Y., Cardarelli, M., Temperini, O., Rea, E., Salerno, A. et al. (2008). Influence of grafting on yield and fruit quality of pepper (Capsicum annuum L.) grown under greenhouse conditions. In D. I. Leskovar (Ed.), (Vol. 782, pp. 359–363).

  • de Sá, J. P. M. (2007). Applied statistics using SPSS. New York: Springer.

    Google Scholar 

  • Draper, N. R., & Smith, H. (1981). Applied regression analysis (Vol. 1). New York: Wiley.

    Google Scholar 

  • Escobar-Gutiérrez, A. J., & Combe, L. (2012). Senescence in field-grown maize: From flowering to harvest. Field Crops Research, 134, 47–58. doi:10.1016/j.fcr.2012.04.013.

    Article  Google Scholar 

  • Fraisse, C. W., Sudduth, K. A., & Kitchen, N. R. (2001). Delineation of site-specific management zones by unsupervised classification of topographic attributes and soil electrical conductivity. Transactions of the American Society of Agricultural Engineers, 44(1), 155–166.

    Article  Google Scholar 

  • Gholizadeh, A., Amin, M. S. M., Anuar, A. R., & Aimrun, W. (2009). Evaluation of leaf total nitrogen content for nitrogen management in a Malaysian paddy field by using soil plant analysis development chlorophyll meter. American Journal of Agricultural and Biological Science, 4(4), 278–282. doi:10.3844/ajabssp.2009.278.282.

    Article  Google Scholar 

  • Guastaferro, F., Castrignanò, A., de Benedetto, D., Sollitto, D., Troccoli, A., & Cafarelli, B. (2010). A comparison of different algorithms for the delineation of management zones. Precision Agriculture, 11(6), 600–620. doi:10.1007/s11119-010-9183-4.

    Article  Google Scholar 

  • Guisan, A., & Zimmermann, N. E. (2000). Predictive habitat distribution models in ecology. Ecological Modelling, 135(2–3), 147–186. doi:10.1016/s0304-3800(00)00354-9.

    Article  Google Scholar 

  • Guo, J., Chen, L., Wang, X., Chen, T., Ma, W., Meng, Z., et al. (2010). The effect of precision variable fertilization on wheat based on prescription map. Sensor Letters, 8(1), 173–177. doi:10.1166/s1. 2010.1222.

    Article  CAS  Google Scholar 

  • Hocking, R. R. (2003). Methods and applications of linear models: Regression and the analysis of variance (Vol. 427). New York: Wiley.

    Book  Google Scholar 

  • Ivanova, A., & Tsenov, N. (2011). Winter wheat productivity under favorable and drought environments. An overall effect. Bulgarian Journal of Agricultural Science, 17(6), 777–781.

    Google Scholar 

  • Jewell, C. P., Keeling, M. J., & Roberts, G. O. (2009). Predicting undetected infections during the 2007 foot-and-mouth disease outbreak. Journal of the Royal Society Interface, 6(41), 1145–1151. doi:10.1098/rsif.2008.0433.

    Article  CAS  PubMed Central  Google Scholar 

  • Jhanji, S., & Sekhon, N. K. (2011). Chlorophyll meter (SPAD-502), an effective and reliable tool for estimating chlorophyll content-How? National Academy Science Letters, 34(11–12), 407–412.

    Google Scholar 

  • Jones, G., Gée, C., Villette, S., & Truchetet, F. (2010). Validation of a crop field modeling to simulate agronomic images. Optics Express, 18(10), 10694–10703. doi:10.1364/oe.18.010694.

    Article  PubMed  Google Scholar 

  • Kravchenko, A. N., & Bullock, D. G. (2000). Correlation of corn and soybean grain yield with topography and soil properties. Agronomy Journal, 92(1), 75–83.

    Article  Google Scholar 

  • Kun, Y., Haipeng, W., Guojun, D., Sanqing, H., Yanbin, Z. & Jing, X. (2011). Determining the repeat number of cross-validation. In Paper presented at the 4th international conference on biomedical engineering and informatics (BMEI) 2011, 15–17 Oct.

  • Li, X., Li, Y., Liu, J., & Zhang, Z. (2005). Application DRIS on winter wheat fertilization in Guanzhong area. Plant Nutrition and Fertilizer Science, 11(2), 174–178.

    Google Scholar 

  • Li, B., Sanderlin, R. S., Melanson, R. A., & Yu, Q. (2011). Spatio-temporal analysis of a plant disease in a non-uniform crop: A Monte Carlo approach. Journal of Applied Statistics, 38(1), 175–182. doi:10.1080/02664760903301150.

    Article  CAS  Google Scholar 

  • Lukas, V., Neudert, L., & Kren, J. (2009a). Mapping of soil conditions in precision agriculture. Acta Agrophysica, 13(2), 393–405.

    Google Scholar 

  • Lukas, V., Neudert, L. & Kren, J. (2009b). Use of aerial imaging and electrical conductivity for spatial variability mapping of soil condition. In Paper presented at the JIAC2009: Book of abstracts, Wageningen.

  • Ma, S., Zhang, X., Duan, A., Yang, S., & Sun, J. (2012). Regulated deficit irrigation effect of winter wheat under different fertilization treatments. Transactions of the Chinese Society of Agricultural Engineering, 28(6), 139–143. doi:10.3969/j.issn.1002-6819.2012.06.023.

    Google Scholar 

  • Marschner, H. (1995). Mineral nutrition of higher plants. London: Academic Press.

    Google Scholar 

  • Martinez, W. L., & Martinez, A. R. (2001). Computational statistics handbook with MATLAB (2nd ed.). New York: Taylor & Francis.

    Book  Google Scholar 

  • Miao, Y., Stewart, B. A., & Zhang, F. (2011). Long-term experiments for sustainable nutrient management in China. A review. Agronomy for Sustainable Development, 31(2), 397–414. doi:10.1051/agro/2010034.

    Article  Google Scholar 

  • Míša, P., & Křen, J. (2001). Measurement of sustainability of model arable farming systems. Rostlinna Vyroba, 47(7), 301–308.

    Google Scholar 

  • Muñoz, J., & Felicísimo, A. M. (2004). Comparison of statistical methods commonly used in predictive modelling. Journal of Vegetation Science, 15(2), 285–292.

    Article  Google Scholar 

  • Niu, G., Rodriguez, D. S., Rodriguez, L., & Mackay, W. (2007). Effect of water stress on growth and flower yield of big bend bluebonnet. HortTechnology, 17(4), 557–560.

    Google Scholar 

  • Ogaja, C. A., & Engineers, A. S. (2011). Applied GPS for engineers and project managers. New York: American Society of Civil Engineers.

    Book  Google Scholar 

  • Oliver, A. (2010). Geostatistical applications for precision agriculture. New York: Springer.

    Book  Google Scholar 

  • Ormsby, T., Napoleon, E. J., & Burke, R. (2010). Getting to know ArcGIS: Desktop. Redlands: ESRI Incorporated.

    Google Scholar 

  • Patil, R. H., Laegdsmand, M., Olesen, J. E., & Porter, J. R. (2010). Growth and yield response of winter wheat to soil warming and rainfall patterns. Journal of Agricultural Science, 148(5), 553–566. doi:10.1017/s0021859610000419.

    Article  Google Scholar 

  • Persson, A., Pilesjö, P., & Eklundh, L. (2005). Spatial influence of topographical factors on yield of potato (Solanum tuberosum L.) in central Sweden. Precision Agriculture, 6(4), 341–357. doi:10.1007/s11119-005-2323-6.

    Article  Google Scholar 

  • Price, M. H. (2012). Mastering ArcGIS. Atlanta: McGraw-Hill.

    Google Scholar 

  • Rasmussen, P. E. (1996). Effect of nitrogen, sulfur, and phosphorus sufficiency on nutrient content in white winter wheat. Communications in Soil Science and Plant Analysis, 27(3–4), 585–596.

    Article  CAS  Google Scholar 

  • Rasmussen, P. E., & Douglas, C. L, Jr. (1992). The influence of tillage and cropping-intensity on cereal response to nitrogen, sulfur, and phosphorus. Fertilizer Research, 31(1), 15–19. doi:10.1007/bf01064223.

    Article  CAS  Google Scholar 

  • Rawlings, J. O., Pantula, S. G., & Dickey, D. A. (1998). Applied regression analysis: A research tool. New York: Springer.

    Book  Google Scholar 

  • Rengel, Z. (1998). Nutrient use in crop production. London: CRC.

    Google Scholar 

  • Reuter, D., Robinson, B., Mader, P., & Tlustos, P. (1998). Plant analysis: An interpretation manual. Biologia Plantarum, 41(2), 317.

    Article  Google Scholar 

  • Schepers, J. S., & Holland, K. H. (2012). Evidence of dependence between crop vigor and yield. Precision Agriculture, 13(2), 276–284. doi:10.1007/s11119-012-9258-5.

    Article  Google Scholar 

  • Schroder, P., Pfadenhauer, J., & Munch, J. (2011). Perspectives for agroecosystem management. Balancing environmental and socio-economic demands. Amsterdam: Elsevier.

    Google Scholar 

  • Shi, W., Liu, J., Du, Z., & Yue, T. (2012). Development of a surface modeling method for mapping soil properties. Journal of Geographical Sciences, 22(4), 752–760. doi:10.1007/s11442-012-0960-z.

    Article  Google Scholar 

  • Tetley, L., & Calcutt, D. (2012). Electronic navigation systems. New York: Taylor & Francis.

    Google Scholar 

  • Vieira, S. R., & Gonzalez, A. P. (2003). Analysis of the spatial variability of crop yield and soil properties in small agricultural plots. Bragantia, 62(1), 127–138.

    Article  Google Scholar 

  • Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining: Practical machine learning tools and techniques. Amsterdam: Elsevier.

    Google Scholar 

  • Yang, C. C., Prasher, S. O., Lacroix, R., & Kim, S. H. (2004). Application of multivariate adaptive regression spline (MARS) to simulate soil temperature. Transactions of the American Society of Agricultural Engineers, 47(3), 881–887.

    Article  Google Scholar 

  • Yenish, J. P., & Young, F. L. (2004). Winter wheat competition against jointed goatgrass (Aegilops cylindrica) as influenced by wheat plant height, seeding rate, and seed size. Weed Science, 52(6), 996–1001. doi:10.1614/ws-04-006r.

    Article  CAS  Google Scholar 

  • Zadoks, J. C., Chang, T. T., & Konzak, C. F. (1974). A decimal code for the growth stages of cereals. Weed Research, 14(6), 415–421. doi:10.1111/j.1365-3180.1974.tb01084.x.

    Article  Google Scholar 

  • Zbíral, J. (2005). Plant analysis: Integrated work procedures. Brno: ÚKZÚZ (Central Institute for Supervising and Testing in Agriculture).

    Google Scholar 

  • Zhang, X., Shi, L., Jia, X., Seielstad, G., & Helgason, C. (2010). Zone mapping application for precision-farming: A decision support tool for variable rate application. Precision Agriculture, 11(2), 103–114. doi:10.1007/s11119-009-9130-4.

    Article  CAS  Google Scholar 

  • Zhang, L., Zhou, Z., Zhang, G., Meng, Y., Chen, B., & Wang, Y. (2012). Monitoring the leaf water content and specific leaf weight of cotton (Gossypium hirsutum L.) in saline soil using leaf spectral reflectance. European Journal of Agronomy, 41, 103–117. doi:10.1016/j.eja.2012.04.003.

    Article  CAS  Google Scholar 

Download references

Acknowledgments

This study was supported by research project NAZV QI111A133 “Improvement of cereal variety potential realization using temporal and spatial analysis of stand spectral characteristic” and OPVK project CZ. 1.07/2.4.00/31.0213: “HYDAP: New remote sensing technologies in research, education and application to support regional development”. I would like to thank J. S. Schepers for his enormous patience, surpassed only by his knowledge.

Author information

Authors and Affiliations

  1. Department of Agrosystems and Bioclimatology, Mendel University in Brno, Zemedelska 1, 613 00, Brno, Czech Republic

    F. Rodriguez-Moreno, V. Lukas, L. Neudert & T. Dryšlová

Authors
  1. F. Rodriguez-Moreno
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. Lukas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. L. Neudert
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. T. Dryšlová
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to F. Rodriguez-Moreno.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rodriguez-Moreno, F., Lukas, V., Neudert, L. et al. Spatial interpretation of plant parameters in winter wheat. Precision Agric 15, 447–465 (2014). https://doi.org/10.1007/s11119-013-9340-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11119-013-9340-7

Keywords

Search

Navigation