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 Agriculture

Volumn 16, Issue 6, 2015, Pages 613-629

  Marino, S ^a   Cocozza, C ^a   Tognetti, R ^a   Alvino, A ^a  

^a UNIVERSITY OF MOLISE   (Italy)

Author keywords

Crop spatial variability; Early warning; Over irrigation; Tomato yield loss; Vegetation indices

Indexed keywords

ALLOCATIVE EFFICIENCY; CROP DAMAGE; CROP YIELD; DRIP IRRIGATION; EARLY WARNING SYSTEM; FRUIT; SOIL MOISTURE; SPATIAL VARIATION; SPATIOTEMPORAL ANALYSIS; VEGETATION INDEX; WATERLOGGING;

LYCOPERSICON ESCULENTUM;

EID: 84947490745     PISSN: 13852256     EISSN: 15731618     Source Type: Journal    
DOI: 10.1007/s11119-015-9396-7     Document Type: Article

References (80)

1. 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.
2. 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.
3. ARSIA. (2010). Prova di confronto varietale pomodoro da industria. L’Informatore Agrario,2, 30–35.
4. 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).
5. 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.
6. Bongiovanni, R., & Lowenberg-Deboer, J. (2004). Precision agriculture and sustainability. Precision Agriculture,5, 359–387.
7. Bradford, K. J., & Hsiao, T. C. (1982). Stomatal behavior and water relations of waterlogged tomato plants. Plant Physiology,70, 1508–1513.
8. 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.
9. 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.
10. 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.
11. 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.
12. Delhomme, J. P. (1978). Kriging in the hydrosciences. Advances in Water Resources,1, 251–266.
13. 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.
14. Dreleimanis, A. (1962). Quantities gasometric determination of calcite and dolomite by using Chittick apparatus. Journal of Sedimentary Petrology,32, 20–29.
15. 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.
16. 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.
17. 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.
18. 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.
19. 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.
20. 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.
21. 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.
22. 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.
23. 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.
24. Gitelson, A. A., & Merzlyak, M. N. (1997). Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing,18, 291–298.
25. 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.
26. 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.
27. 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.
28. Hesse, P. R. (1971). A Textbook of soil chemical analysis (p. 520). London: John Murray Publishers.
29. 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.
30. 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.
31. 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.
32. Huete, A. R. (1988). A soil adjusted vegetation index (SAVI). Remote Sensing of Environment,25, 295–309.
33. 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.
34. 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.
35. Ji, L., & Peters, A. J. (2007). Performance evaluation of spectral vegetation indices using a statistical sensitivity function. Remote Sensing of Environment,106, 59–65.
36. 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.
37. Jones, H. G. (2004). Irrigation scheduling: Advantages and pitfalls of plant-based methods. Journal of Experimental Botany,55, 2427–2436.
38. 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.
39. 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).
40. 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.
41. 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.
42. 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.
43. 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.
44. 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.
45. 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.
46. Major, D. J., Baret, F., & Guyot, G. (1990). A ratio vegetation index adjusted for soil brightness. International Journal of Remote Sensing,11, 727–740.
47. Marino, S., Basso, B., Leone, A. P., & Alvino, A. (2013). Agronomic traits and vegetation indices of two onion hybrids. Scientia Horticulturae,155, 56–64.
48. 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.
49. 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.
50. 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.
51. McBratney, A., Whelan, B., Ancev, T., & Bouma, J. (2005). Future directions of precision agriculture. Precision Agriculture,6, 7–23.
52. 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.
53. 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.
54. 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.
55. 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.
56. Peet, M. M., & Willits, D. H. (1995). Role of excess water in tomato fruit cracking. Horticultural Science,30, 65–68.
57. Peñuelas, J., & Filella, I. (1998). Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends in Plant Science,3, 151–156.
58. 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.
59. Peñuelas, J., Filella, I., & Gamon, J. A. (1995). Assessment of photosynthetic radiation-use efficiency with spectral reflectance. New Phytologist,131, 291–296.
60. 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.
61. 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.
62. Richardson, A. J., & Wiegand, C. L. (1977). Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing,43, 1541–1552.
63. Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment,55, 95–107.
64. 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.
65. 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.
66. Savitzky, A., & Golay, M. J. E. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry,36, 1627–1639.
67. 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.
68. 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.
69. 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.
70. 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.
71. Tarantino, E., & Onofri, M. (1991). Determinazione dei coefficienti colturali mediante lisimetri. Bonifica,8, 119–136.
72. 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.
73. 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.
74. 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.
75. 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.
76. Van Alphen, B. J., & Stoorvogel, J. J. (2000). A methodology for precision nitrogen fertilization in highinput farming systems. Precision Agriculture,2, 319–332.
77. 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.
78. Webster, R., & Oliver, M. A. (2007). Geostatistics for environmental scientists (2nd ed., p. 315). England: John Wiley & Sons Ltd.
79. 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.
80. 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.