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Use of proximal sensing and vegetation indexes to detect the inefficient spatial allocation of drip irrigation in a spot area of tomato field crop

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Abstract

Hyperspectral vegetation indexes (VIs) were used to detect stressed crop areas in drip irrigated tomato subjected to waterlogging. The crop was quite uniform throughout the field until the beginning of flowering, as confirmed by spectroradiometric readings and agronomic traits. From 78 days after transplanting (DAT) (42 days before harvest), a spot area of 500 m2 showed increasing excess soil moisture due to topsoil depression, which induced evident waterlogging. Leaves first yellowed (90 DAT) and eventually plants died (100 DAT). The plants surrounding this spot area were affected in their physiological, spectroradiometric and productive responses. Regressions among spectral VIs and crop yield, and photosynthesis (A) and stomatal conductance (gs) were highly significant. The best relationships were found with Soil-Adjusted Vegetation Index, Optimized Soil-Adjusted Vegetation Index, Transformed Soil-Adjusted Vegetation Index, Structure Intensive Pigment Index and Normalized Difference Vegetation Index. Maps of photosynthesis and VIs were roughly similar to the spatial distribution of crop yield. Spectroradiometry was proved efficient as early warning tool for detecting over-irrigation at the field scale. Proximal sensing techniques may contribute to improve (i) irrigation efficiency, with positive effects on tomato crop productivity and water saving, and (ii) the accuracy of remote sensing surveys aimed at estimating tomato crop yield.

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References

  • Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration: Guidelines for computing crop requirements Irrigation and Drainage Paper, 56. (p. 300). Rome: FAO.

  • Araki, T., Kitano, M., Hamakoga, M., & Eguchi, H. (1998). Analysis of growth, water balance and respiration of tomato fruits under water deficit by using multiple chamber system. Biotronics, 27, 61–68.

    Google Scholar 

  • ARSIA. (2010). Prova di confronto varietale pomodoro da industria. L’Informatore Agrario, 2, 30–35.

    Google Scholar 

  • Baret, F., Guyot, G., & Major, D. (1989). TSAVI: A vegetation index, which minimizes soil brightness effects on LAI and APAR estimation. Proceedings of 12th Canadian Symposium on Remote Sensing and IGARSS’89, Vancouver, Canada, 10–14 July 1989 (pp. 1355–1358).

  • Basso, B., Cammarano, D., & De Vita, P. (2004). Remotely sensed vegetation indices: Theory and applications for crop management. Rivista Italiana di Agrometeorologia, 1, 36–53.

    Google Scholar 

  • Bongiovanni, R., & Lowenberg-Deboer, J. (2004). Precision agriculture and sustainability. Precision Agriculture, 5, 359–387.

    Article  Google Scholar 

  • Bradford, K. J., & Hsiao, T. C. (1982). Stomatal behavior and water relations of waterlogged tomato plants. Plant Physiology, 70, 1508–1513.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Clay, D. A., Kim, K., Chang, J., Clay, S. A., & Dalsted, K. (2006). Characterizing water and nitrogen stress in corn using remote sensing. Agronomy Journal, 98, 579–587.

    Article  Google Scholar 

  • Clevers, J. G. P. W. (1989). The application of a weighted infrared-red vegetation index for estimating leaf area index by correcting soil moisture. Remote Sensing of Environment, 29, 25–37.

    Article  Google Scholar 

  • Daughtry, C. S. T., Walthall, C. L., Kim, M. S., Brown de Colstoun, E., & McMurtrey, J. E, I. I. I. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74, 229–285.

    Article  Google Scholar 

  • Davies, W. J., Bacon, M. A., Thompson, D. S., Sobeih, W., & Gonzalez-Rodriguez, L. (2000). Regulation of leaf and fruit growth in plants growing in drying soil: Exploitation of plants’ chemical signaling efficiency of water use in agriculture. Journal of Experimental Botany, 51, 1617–1626.

    Article  CAS  PubMed  Google Scholar 

  • Delhomme, J. P. (1978). Kriging in the hydrosciences. Advances in Water Resources, 1, 251–266.

    Article  Google Scholar 

  • Dell’Amico, J., Torrecillas, A., Rodríguez, P., Morales, D., & Sánchez-Blanco, M. J. (2001). Differences in the effects of flooding the soil early and late in the photoperiod on the water relations of pot-grown tomato plants. Plant Science, 160, 481–487.

    Article  PubMed  Google Scholar 

  • Dreleimanis, A. (1962). Quantities gasometric determination of calcite and dolomite by using Chittick apparatus. Journal of Sedimentary Petrology, 32, 20–29.

    Google Scholar 

  • Elwadie, M. E., Pierce, F. J., & Qi, J. (2005). Remote sensing of canopy dynamics and biophysical variables estimation of corn in Michigan. Agronomy Journal, 97, 99–105.

    Article  Google Scholar 

  • Gamon, J., Penuelas, J., & Field, C. (1992). A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment, 41, 35–44.

    Article  Google Scholar 

  • Garatuza-Payan, J., & Watts, C. (2005). The use of remote sensing for estimating ET of irrigated wheat and cotton in Northwest Mexico. Irrigation and Drainage Systems, 19, 301–320.

    Article  Google Scholar 

  • Gardner, W. H. (1986). Water content. In: A. Klute (Ed.), Methods of soil analysis. Part 1—Physical and mineralogical methods (2nd ed., pp. 493–544), SSSA Book Series No. 5. Madison, WI: SSSA and ASA.

  • Gianquinto, G., Orsini, F., Fecondini, M., Mezzetti, M., Sambo, P., & Bona, S. (2011). A methodological approach for defining spectral indices for assessing tomato nitrogen status and yield. European Journal of Agronomy, 35, 135–143.

    Article  Google Scholar 

  • Giorio, G., Stigliani, A. L., & D’Ambrosio, C. (2007). Agronomic performance and transcriptional analysis of carotenoid biosynthesis in fruits of transgenic HighCaro and control tomato lines under field conditions. Transgenic Research, 16, 15–28.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Gitelson, A. A., Gritz, Y., & Merzlyak, M. N. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 160, 271–282.

    Article  CAS  PubMed  Google Scholar 

  • Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58, 289–298.

    Article  Google Scholar 

  • Gitelson, A., & Merzlyak, M. N. (1994). Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves: Spectral features and relation to chlorophyll estimation. Journal of Plant Physiology, 143, 286–292.

    Article  CAS  Google Scholar 

  • Gitelson, A. A., & Merzlyak, M. N. (1997). Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing, 18, 291–298.

    Article  Google Scholar 

  • González-Dugo, M. P., & Mateos, L. (2008). Spectral vegetation indices for benchmarking water productivity of irrigated cotton and sugarbeet crops. Agricultural Water Management, 95, 48–58.

    Article  Google Scholar 

  • Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J., & Strachan, I. B. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 90, 337–352.

    Article  Google Scholar 

  • Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., & Dextraze, L. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81, 416–426.

    Article  Google Scholar 

  • Hesse, P. R. (1971). A Textbook of soil chemical analysis (p. 520). London: John Murray Publishers.

    Google Scholar 

  • Holbrook, N. M., Shashidhar, V. R., James, R. A., & Munns, R. (2002). Stomatal control in tomato with ABA-deficient roots: Response of grafted plants to soil drying. Journal of Experimental Botany, 53, 1503–1514.

    Article  CAS  PubMed  Google Scholar 

  • Horchani, F., Aloui, A., Brouquisse, R., & Aschi-Smiti, S. (2008). Physiological responses of tomato plants (Solanum lycopersicum) as affected by root hypoxia. Journal of Agronomy and Crop Science, 194, 297–303.

    Article  CAS  Google Scholar 

  • Horchani, F., Khayati, H., Raymond, P., Brouquisse, R., & Aschi-Smiti, S. (2009). Contrasted effects of prolonged root hypoxia on tomato root and fruit (Solanum lycopersicum) metabolism. Juornal of Agronomy and Crop Science, 195, 313–318.

    Article  CAS  Google Scholar 

  • Huete, A. R. (1988). A soil adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295–309.

    Article  Google Scholar 

  • Ierna, A., & Mauromicale, G. (2012). Tuber yield and irrigation water productivity in early potatoes as affected by irrigation regime. Agricultural Water Management, 115, 276–284.

    Article  Google Scholar 

  • Indorante, S. P. L. (1990). Enzyme studies II. The measurement and importance of the hydrogen ion concentration in enzyme reactions. Comptes rendus des travaux du laboratoire Carlsberg, 8, 1.

    Google Scholar 

  • Ji, L., & Peters, A. J. (2007). Performance evaluation of spectral vegetation indices using a statistical sensitivity function. Remote Sensing of Environment, 106, 59–65.

    Article  Google Scholar 

  • Johnson, R. W., Dixon, M. A., & Lee, D. R. (1992). Water relations of the tomato during fruit growth. Plant, Cell and Environment, 15, 947–953.

    Article  Google Scholar 

  • Jones, H. G. (2004). Irrigation scheduling: Advantages and pitfalls of plant-based methods. Journal of Experimental Botany, 55, 2427–2436.

    Article  CAS  PubMed  Google Scholar 

  • Karlen, D. L., Sojka, R. E., & Robbins, M. L. (2008). Influence of excess soil-water and n rates on leaf diffusive resistance and storage quality of tomato fruit. Soil Science and Plant Analysis, 14, 699–708.

    Article  Google Scholar 

  • Kim, M. S., Daughtry, C. S. T., Chappelle, E. W., McMurtrey III, J. E., & Walthall, C. L. (1994). The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation (Apar). Proceedings of the 6th Symposium on Physical Measurements and Signatures in Remote Sensing, January 17–21, 1994, Val d’Isere, France (pp. 299–306).

  • Koller, M., & Upadhyay, S. K. (2005). Prediction of processing tomato yield using a crop growth model and remotely sensed aerial image. Transactions of the ASAE, 48, 2335.

    Article  Google Scholar 

  • Lati, R. N., Filin, S., & Eizenberg, H. (2013). Plant growth parameter estimation from sparse 3D reconstruction based on highly-textured feature points. Precision Agriculture, 14, 586–605.

    Article  Google Scholar 

  • le Maire, G., Francois, C., Soudani, K., Berveiller, D., Pontailler, J. Y., & Breda, N. (2008). Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass. Remote Sensing of Environment, 112, 3846–3864.

    Article  Google Scholar 

  • Li, A. N., Liang, S. L., Wang, A. S., & Qin, J. (2007). Estimating crop yield from multitemporal satellite data using multivariate regression and neural network. Photogrammetric Engineering and Remote Sensing, 73, 1149–1157.

    Article  Google Scholar 

  • Ma, B. L., Morrison, M. J., & Dwyer, L. M. (1996). Canopy light reflectance and field greenness to assess nitrogen fertilization and yield of maize. Agronomy Journal, 88, 915–920.

    Article  Google Scholar 

  • Machado, R. M. A., & Oliveira, M. D. G. (2005). Tomato root distribution, yield and fruit quality under different subsurface drip irrigation regimes and depths. Irrigation Science, 24, 15–24.

    Article  Google Scholar 

  • Major, D. J., Baret, F., & Guyot, G. (1990). A ratio vegetation index adjusted for soil brightness. International Journal of Remote Sensing, 11, 727–740.

    Article  Google Scholar 

  • Marino, S., Basso, B., Leone, A. P., & Alvino, A. (2013). Agronomic traits and vegetation indices of two onion hybrids. Scientia Horticulturae, 155, 56–64.

    Article  Google Scholar 

  • Marino, S., Cocozza, C., Tognetti, R., & Alvino, A. (2014). Effects of inefficient spatial allocation of irrigation water on fruit yield, leaf physiology and spectral reflectance in a tomato crop. ISHS Symposium, July, 16–20, 2012, Geisenheim, Germany. Acta Horticulturae, 1038, 185–192.

    Article  Google Scholar 

  • Marouelli, W. A., & Silva, W. L. C. (2007). Water tension thresholds for processing tomatoes under drip irrigation in Central Brazil. Irrigation Science, 25, 41–418.

    Article  Google Scholar 

  • Matheron, G. (1965). Les variables regionalisees et leur estimation: Une application de la theorie de fonctions aleatoires aux sciences de la nature. Paris: Masson et Cie.

    Google Scholar 

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

    Article  Google Scholar 

  • Merino, G. G., Jones, D., Stooksbury, D. E., & Hubbard, K. G. (2001). Determination of semivariogram models to krige hourly and daily solar irradiance in western Nebraska. Journal of Applied Meteorology, 40, 1085–1094.

    Article  Google Scholar 

  • Merzlyak, M. N., Gitelson, A. A., Chivkunova, O. B., & Rakitin, V. Y. (1999). Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiologia Plantarum, 106, 135–141.

    Article  CAS  Google Scholar 

  • Nguyen, H. T., & Byun-Woo, L. (2006). Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression. European Journal of Agronomy, 24, 349–356.

    Article  Google Scholar 

  • Patanè, C., Tringali, S., & Sortino, O. (2011). Effects of deficit irrigation on biomass, yield, water productivity and fruit quality of processing tomato under semi-arid Mediterranean climate conditions. Scientia Horticulturae, 129, 590–596.

    Article  Google Scholar 

  • Peet, M. M., & Willits, D. H. (1995). Role of excess water in tomato fruit cracking. Horticultural Science, 30, 65–68.

    Google Scholar 

  • Peñuelas, J., & Filella, I. (1998). Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends in Plant Science, 3, 151–156.

    Article  Google Scholar 

  • Peñuelas, J., Filella, I., Briel, C., Serrano, L., & Savé, R. (1993). The reflectance at the 950–970 nm region as an indicator of plant water status. International Journal of Remote Sensing, 14, 1887–1905.

    Article  Google Scholar 

  • Peñuelas, J., Filella, I., & Gamon, J. A. (1995). Assessment of photosynthetic radiation-use efficiency with spectral reflectance. New Phytologist, 131, 291–296.

    Article  Google Scholar 

  • Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., & Sorooshian, S. (1994). A modified soil adjusted vegetation index. Remote Sensing of Environment, 48, 119–126.

    Article  Google Scholar 

  • Riahi, A., Hdider, C., Sanaa, M., Tarchoun, N., Ben Kheder, M., & Guezal, I. (2009). Effect of conventional and organic production systems on the yield and quality of field tomato cultivars grown in Tunisia. Journal of the Science of Food and Agriculture, 89, 2275–2282.

    Article  CAS  Google Scholar 

  • Richardson, A. J., & Wiegand, C. L. (1977). Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, 43, 1541–1552.

    Google Scholar 

  • Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55, 95–107.

    Article  Google Scholar 

  • Rouse, J. W., Haas, Jr., R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the great plains with ERTS. Proceedings of ERTS-1 Symposium, Greenbelt, MD, 10–15 December 1973 (3rd ed., Vol. 1, pp. 309–317). Washington, DC: NASA NASA SP-351.

  • Sairam, R. K., Kumutha, D., Ezhilmathi, K., Deshmukh, P. S., & Srivastava, G. C. (2008). Physiology and biochemistry of waterlogging tolerance in plants. Biologia Plantarum, 52, 401–412.

    Article  CAS  Google Scholar 

  • Savitzky, A., & Golay, M. J. E. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36, 1627–1639.

    Article  CAS  Google Scholar 

  • Scholberg, J. M. S., McNeal, B. L., Jones, J. W., Boote, K. J., Stanley, C. D., & Obreza, T. A. (2000). Growth and canopy characteristics of field-grown tomato. Agronomy Journal, 92, 152–159.

    Article  Google Scholar 

  • Selvaraja, S., Balasundram, S. K., Vadamalai, G., & Husni, M. H. A. (2012). Spatial variability of orange spotting disease in oil palm. Journal of Biological Sciences, 12, 232–238.

    Article  Google Scholar 

  • Sorensen, S. P. L. (1909). Enzyme studies II. The measurement and importance of the hydrogen ion concentration in enzyme reactions. Comptes rendus des travaux du laboratoire Carlsberg, 8, 1.

    CAS  Google Scholar 

  • Stagakis, S., Markos, N., Sykioti, O., & Kyparissis, A. (2010). Monitoring canopy biophysical and biochemical parameters in ecosystem scale using satellite hyperspectral imagery: An application on a Phlomis fruticosa Mediterrannean ecosystem using multiangular CHRIS/PROBA observations. Remote Sensing of Environment, 114, 977–994.

    Article  Google Scholar 

  • Tarantino, E., & Onofri, M. (1991). Determinazione dei coefficienti colturali mediante lisimetri. Bonifica, 8, 119–136.

    Google Scholar 

  • Thenkabail, P. S., Smith, R. B., & De Pauw, E. (2000). Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment, 71, 158–182.

    Article  Google Scholar 

  • Tognetti, R., d’Andria, R., Sacchi, R., Lavini, A., Morelli, G., & Alvino, A. (2007). Deficit irrigation affects seasonal changes in leaf physiology and oil quality of Olea europaea L. (cultivars Frantoio and Leccino). Annals of Applied Biology, 150, 169–186.

    Article  Google Scholar 

  • Tognetti, R., Delfine, S., Sorella, P., & Alvino, A. (2002). Responses of sugarbeet to drip and low-pressure sprinkler irrigation systems: Root yield and sucrose accumulation. Agricoltura Mediterranea, 132, 1–8.

    Google Scholar 

  • Tognetti, R., Palladino, M., Minnocci, A., Delfine, S., & Alvino, A. (2003). The response of sugar beet to drip and low-pressure sprinkler irrigation in southern Italy. Agricultural Water Management, 60, 135–155.

    Article  Google Scholar 

  • Van Alphen, B. J., & Stoorvogel, J. J. (2000). A methodology for precision nitrogen fertilization in highinput farming systems. Precision Agriculture, 2, 319–332.

    Article  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 

  • Webster, R., & Oliver, M. A. (2007). Geostatistics for environmental scientists (2nd ed., p. 315). England: John Wiley & Sons Ltd.

    Book  Google Scholar 

  • Zarco-Tejada, P. J., Berjón, A., López-Lozano, R., Miller, J. R., Martín, P., Cachorro, V., et al. (2005). Assessing vineyard condition with hyperspectral indices: Leaf and Canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sensing of Environment, 99, 271–287.

    Article  Google Scholar 

  • Zhao, D., Reddya, K. R., Kakani, V. G., & Reddy, V. R. (2005). Nitrogen deficiency effects on plant growth, leaf photosynthesis and hyperspectral reflectance properties of sorghum. European Journal of Agronomy, 22, 391–403.

    Article  CAS  Google Scholar 

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Acknowledgments

This research has been supported by PRIN 2008 (Spatial and temporal relationships between N2O emissions, NO3 leaching and yield in production and bioenergetic cropping system—research units Nitrate leaching and precision irrigation in tomato crop). The authors are gratefully indebted to prof. Bruno Basso, project coordinator of the PRIN 2008, and to Dr. Giovanni Cafiero for his technical assistance.

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  1. Dipartimento Agricoltura, Ambiente e Alimenti, Università degli Studi del Molise, Via De Sanctis, 86100, Campobasso, Italy

    S. Marino & A. Alvino

  2. Dipartimento di Bioscienze e Territorio, Università degli Studi del Molise, Contrada Fonte Lappone, 86090, Pesche, Italy

    C. Cocozza & R. Tognetti

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Marino, S., Cocozza, C., Tognetti, R. et al. Use of proximal sensing and vegetation indexes to detect the inefficient spatial allocation of drip irrigation in a spot area of tomato field crop. Precision Agric 16, 613–629 (2015). https://doi.org/10.1007/s11119-015-9396-7

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