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Proximal fluorescence sensing of potassium responsive crops to develop improved predictions of biomass, yield and grain quality of wheat and barley

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Abstract

Precision nutrient management requires accurate assessment of crop nutrient status. This is common for assessing N status, but much less so for other nutrients. Because fluorescence can indicate crop stress, the robustness of different fluorescence indices was assessed to predict crop nutrient status (K, Mg and Ca). The hypothesis was that crop nutrition limitations, especially K, can be detected using fluorescence proximal sensing to quantify crop response with a high degree of spatial resolution. A factorial experiment was imposed with four treatment factors: crop, K fertilizer rate, lime and row management. The soil at the experimental site was K deficient and the crop variables showed significant treatment effects (e.g. yield, protein). Fluorescence sensing identified a significant positive K response for three chlorophyll related indices (SFR_G, SFR_R and CHL), but not for FLAV; while wheat was significantly different from barley. Using a k-fold cross-validation method promising predictive relationships were found. The strongest predictions were for SFR_R to predict crop biomass, for SFR_G to predict crop K content of inter-row wheat, for CHL to predict crop Ca content of inter-row wheat and for FLAV with barley grain protein in the windrow treatment. The fluorescence indices produced more significant crop variable predictions than measuring NDVI using an active sensor. This study illustrates the utility of fluorescence sensing to measure chlorophyll related signals for capturing the nutritional status of barley and wheat crops. These results show encouraging potential to rapidly detect crop nutrient status for non-N nutrients using fluorescence sensing.

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References

  • Adams, F. (1984). Soil acidity and liming. Madison, USA: American Society of Agronomy.

    Google Scholar 

  • Agati, G., Foschi, L., Grossi, N., Guglielminetti, L., Cerovic, Z. G., & Volterrani, M. (2013). Fluorescence-based versus reflectance proximal sensing of nitrogen content in Paspalum vaginatum and Zoysia matrella turfgrasses. European Journal of Agronomy, 45, 39–51.

    Article  CAS  Google Scholar 

  • Agati, G., Foschi, L., Grossi, N., & Volterrani, M. (2015). In field non-invasive sensing of the nitrogen status in hybrid bermudagrass (Cynodon dactylon × C. transvaalensis Burtt Davy) by a fluorescence-based method. European Journal of Agronomy, 63, 89–96.

    Article  CAS  Google Scholar 

  • Armstrong, D. (1998). Potassium for agriculture. Better Crops with Plant Food, 82, 4–5.

    Google Scholar 

  • Barmeier, G., Hofer, K., & Schmidhalter, U. (2017). Mid-season prediction of grain yield and protein content of spring barley cultivars using high-throughput spectral sensing. European Journal of Agronomy, 90, 108–116.

    Article  Google Scholar 

  • Bell, M. J., Moody, P. W., Harch, G. R., Compton, B., & Want, P. S. (2009). Fate of potassium fertilisers applied to clay soils under rainfed grain cropping in south-east Queensland, Australia. Australian Journal of Soil Research, 47, 60–73.

    Article  CAS  Google Scholar 

  • Brennan, R. F., & Bell, M. J. (2013). Soil potassium—crop response calibration relationships and criteria for field crops grown in Australia. Crop and Pasture Science, 64, 514–522.

    Article  CAS  Google Scholar 

  • Brennan, R. F., Bolland, M. D. A., & Bowden, J. W. (2004). Potassium deficiency, and molybdenum deficiency and aluminium toxicity due to soil acidification, have become problems for cropping sandy soils in south-western Australia. Australian Journal of Experimental Agriculture, 44, 1031–1039.

    Article  CAS  Google Scholar 

  • Cakmak, I. (2005). The role of potassium in alleviating detrimental effects of abiotic stresses in plants. Journal of Plant Nutrition and Soil Science, 168, 521–530.

    Article  CAS  Google Scholar 

  • Cammarano, D., Fitzgerald, G. J., Casa, R., & Basso, B. (2014). Assessing the robustness of vegetation indices to estimate wheat N in Mediterranean environments. Remote Sensing, 6, 2827–2844.

    Article  Google Scholar 

  • Consonni, V., Ballabio, D., & Todeschini, R. (2010). Evaluation of model predictive ability by external validation techniques. Journal of Chemometrics, 24, 194–201.

    Article  CAS  Google Scholar 

  • Gee, G. W., & Or, D. (2002). Particle-size analysis. In J. H. Dane & G. Clarke Topp (Eds.), Methods of soil analysis. Part 4. Physical methods (pp. 255–293). Madison, WI: SSSA.

    Google Scholar 

  • Ghozlen, N. B., Cerovic, Z. G., Germain, C., Toutain, S., & Latouche, G. (2010). Non-destructive optical monitoring of grape maturation by proximal sensing. Sensors, 10, 10040–10068.

    Article  PubMed  Google Scholar 

  • Gillman, G. P., & Sumpter, E. A. (1986). Modification to the compulsive exchange method for measuring exchange characteristics of soils. Australian Journal of Soil Research, 24, 61–66.

    Article  CAS  Google Scholar 

  • Gilmour, A. R., Gogel, B. J., Cullis, B. R., & Thompson, R. (2009). ASReml user guide. VSNI http://www.vsni.co.uk.

  • Gremigni, P., Wong, M., Edwards, N., Harris, D., & Hamblin, J. (2001). Potassium nutrition effects on seed alkaloid concentrations, yield and mineral content of lupins (Lupinus angustifolius). Plant and Soil, 234, 131–142.

    Article  CAS  Google Scholar 

  • Hafeez, A., Ali, S., Ma, X., Tung, S. A., Shah, A. N., Liu, A., et al. (2018). Potassium to nitrogen ratio favors photosynthesis in late-planted cotton at high planting density. Industrial Crops and Products, 124, 369–381.

    Article  CAS  Google Scholar 

  • Harrell, F. (2001). Regression modeling strategies: With applications to linear models, logistic and ordinal regression, and survival analysis. New York, NY, USA: Springer.

    Book  Google Scholar 

  • Heanes, D. L. (1984). Determination of total organic-C in soils by an improved chromic acid digestion and spectrophotometric procedure. Communications in Soil Science Plant Analysis, 15, 1191–1213.

    Article  CAS  Google Scholar 

  • IUSS Working Group WRB. (2014). World reference base for soil resources 2014. Rome: FAO.

    Google Scholar 

  • Kenward, M. G., & Roger, J. H. (1997). The precision of fixed effects estimates from restricted maximum likelihood. Biometrics, 53, 983–997.

    Article  CAS  PubMed  Google Scholar 

  • Liu, M., Zhang, A., Chen, X., Jin, R., Li, H., & Tang, Z. (2017). The effect of potassium deficiency on growth and physiology in sweetpotato [Ipomoea batatas (L.) Lam.] during early growth. HortScience, 52, 1020–1028.

    Article  CAS  Google Scholar 

  • Lobell, D. B., Asner, G. P., Ortiz-Monasterio, J. I., & Benning, T. L. (2003). Remote sensing of regional crop production in the Yaqui Valley, Mexico: Estimates and uncertainties. Agriculture, Ecosystems & Environment, 94, 205–220.

    Article  Google Scholar 

  • Longchamps, L., & Khosla, R. (2014). Early detection of nitrogen variability in maize using fluorescence. Agronomy Journal, 106, 511–518.

    Article  CAS  Google Scholar 

  • Maher, J. M., & Martin, J. J. (1987). Soils and landforms of south-western Victoria. Part 1. Inventory of soils and their associated landscapes. Research Report No. 40, Department of Agriculture and Rural Affairs, East Melbourne.

  • McDonald, R. C., Isbell, R. F., Speight, J. G., Walker, J., & Hopkins, M. S. (1990). Australian soil and land survey: Field handbook (2nd ed.). Melbourne: Inkata Press.

    Google Scholar 

  • Padilla, F. M., Peña-Fleitas, M. T., Gallardo, M., & Thompson, R. B. (2016). Proximal optical sensing of cucumber crop N status using chlorophyll fluorescence indices. European Journal of Agronomy, 73, 83–97.

    Article  CAS  Google Scholar 

  • Pan, Y., Lu, Z., Lu, J., Li, X., Cong, R., & Ren, T. (2017). Effects of low sink demand on leaf photosynthesis under potassium deficiency. Plant Physiology and Biochemistry, 113, 110–121.

    Article  CAS  PubMed  Google Scholar 

  • Perry, E. M., Nuttall, J. G., Wallace, A. J., & Fitzgerald, G. J. (2017). In-field methods for rapid detection of frost damage in Australian dryland wheat during the reproductive and grain-filling phase. Crop and Pasture Science, 68, 516–526.

    Article  Google Scholar 

  • Peteinatos, G. G., Korsaeth, A., Berge, T. W., & Gerhards, R. (2016). Using optical sensors to identify water deprivation, nitrogen shortage, weed presence and fungal infection in wheat. Agriculture, 6, 24.

    Article  Google Scholar 

  • Scanlan, C. A., Bell, R. W., & Brennan, R. F. (2015). Simulating wheat growth response to potassium availability under field conditions in sandy soils. II. Effect of subsurface potassium on grain yield response to potassium fertiliser. Field Crops Research, 178, 125–134.

    Article  Google Scholar 

  • Severtson, D., Callow, N., Flower, K., Neuhaus, A., Olejnik, M., & Nansen, C. (2016). Unmanned aerial vehicle canopy reflectance data detects potassium deficiency and green peach aphid susceptibility in canola. Precision Agriculture, 17, 659–677.

    Article  Google Scholar 

  • Söderström, M., Börjesson, T., Pettersson, C.-G., Nissen, K., & Hagner, O. (2010). Prediction of protein content in malting barley using proximal and remote sensing. Precision Agriculture, 11, 587–599.

    Article  Google Scholar 

  • Song, X., Yang, G., Yang, C., Wang, J., & Cui, B. (2017). Spatial variability analysis of within-field winter wheat nitrogen and grain quality using canopy fluorescence sensor measurements. Remote Sensing, 9, 237.

    Article  Google Scholar 

  • Srinivasarao, C., Shanker, A. K., Kundu, S., & Reddy, S. (2016). Chlorophyll fluorescence induction kinetics and yield responses in rainfed crops with variable potassium nutrition in K deficient semi-arid alfisols. Journal of Photochemistry and Photobiology B: Biology, 160, 86–95.

    Article  CAS  Google Scholar 

  • Tremblay, N., Wang, Z., & Cerovic, Z. G. (2012). Sensing crop nitrogen status with fluorescence indicators: A review. Agronomy for Sustainable Development, 32, 451–464.

    Article  CAS  Google Scholar 

  • Walkley, A., & Black, I. A. (1934). An examination of Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Science, 37, 29–38.

    Article  CAS  Google Scholar 

  • Wallach, D., Makowski, D., Jones, J. W., & Brun, F. (2013). Working with dynamic crop models: Methods, tools and examples for agriculture and environment. London, UK: Academic Press.

    Google Scholar 

  • Wang, X.-G., Zhao, X.-H., Jiang, C.-J., Li, C.-H., Cong, S., Wu, D., et al. (2015). Effects of potassium deficiency on photosynthesis and photoprotection mechanisms in soybean (Glycine max (L.) Merr.). Journal of Integrative Agriculture, 14, 856–863.

    Article  CAS  Google Scholar 

  • Yang, K., Ryu, Y., Dechant, B., Berry, J. A., Hwang, Y., Jiang, C., et al. (2018). Sun-induced chlorophyll fluorescence is more strongly related to absorbed light than to photosynthesis at half-hourly resolution in a rice paddy. Remote Sensing of Environment, 216, 658–673.

    Article  Google Scholar 

  • Yu, K., Leufen, G., Hunsche, M., Noga, G., Chen, X., & Bareth, G. (2013). Investigation of leaf diseases and estimation of chlorophyll concentration in seven barley varieties using fluorescence and hyperspectral indices. Remote Sensing, 6, 64–86.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Zarcinas, B. A., Cartwright, B., & Spouncer, L. R. (1987). Nitric acid digestion and multi-element analysis of plant material by inductively coupled plasma spectrometry. Communications in Soil Science and Plant Analysis, 18, 131–146.

    Article  CAS  Google Scholar 

  • Zecha, C. W., Link, J., & Claupein, W. (2017). Fluorescence and reflectance sensor comparison in winter wheat. Agriculture, 7, 78. https://doi.org/10.3390/agriculture7090078.

    Article  CAS  Google Scholar 

  • Zhao, X., Du, Q., Zhao, Y., Wang, H., Li, Y., Wang, X., et al. (2016). Effects of different potassium stress on leaf photosynthesis and chlorophyll fluorescence in maize (Zea mays L.) at seedling stage. Agricultural Sciences, 7, 44.

    Article  CAS  Google Scholar 

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Acknowledgements

The authors are very grateful to the farmer Mr. Rob Veale for providing access to and use of his field to conduct these experiments. Corrine Celestina and Megan Beveridge (Southern Farming Systems) undertook important tasks at sowing, harvest and general crop management. We also must thank Mr. Ed Hilsdon (Landmark) for his constant support and helpful agronomic advice. Financial support for this work was provided through a grant from the Grains Research and Development Corporation, Australia and with significant in-kind support from the NSW Department of Primary Industries (NSW DPI). In addition, we acknowledge the encouragement of Dr. Rob Norton and the financial support received from the International Plant Nutrition Institute (IPNI).

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Authors and Affiliations

  1. James Hutton Institute, Dundee, DD2 5DA, UK

    Jonathan Eddison Holland & Davide Cammarano

  2. Grains Innovation Park, Department of Economic Development Jobs, Transport and Resources, 110 Natimuk Road, Horsham, VIC, 3400, Australia

    Glenn J. Fitzgerald

  3. Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Creswick, VIC, Australia

    Glenn J. Fitzgerald

  4. Department of Economic Development Jobs, Transport and Resources, Cnr Midland Highway and Taylors Street, Epsom, VIC, 3551, Australia

    Eileen M. Perry

  5. Faculty of the Melbourne School of Engineering, The University of Melbourne, Parkville, VIC, Australia

    Eileen M. Perry

  6. NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW, 2650, Australia

    Graeme Poile & Mark Kenneth Conyers

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  1. Jonathan Eddison Holland
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  2. Davide Cammarano
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Correspondence to Jonathan Eddison Holland.

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Holland, J.E., Cammarano, D., Fitzgerald, G.J. et al. Proximal fluorescence sensing of potassium responsive crops to develop improved predictions of biomass, yield and grain quality of wheat and barley. Precision Agric 20, 379–397 (2019). https://doi.org/10.1007/s11119-018-09629-3

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  • DOI: https://doi.org/10.1007/s11119-018-09629-3

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