Use of soil and vegetation spectroradiometry to investigate crop water use efficiency of a drip irrigated tomato

Abstract An agronomic research was conducted in Tuscany (Central Italy) to evaluate the effects of an advanced irrigation system on the water use efficiency (WUE) of a tomato crop and to investigate the ability of soil and vegetation spectroradiometry to detect and map WUE. Irrigation was applied following an innovative approach based on CropSense system. Soil water content was monitored at four soil depths (10, 20, 30 and 50 cm) by a probe. Rainfall during the crop cycle reached 162 mm and irrigation water applied with a drip system amounted to 207 mm, distributed with 16 irrigation events. Tomato yield varied from 7.10 to 14.4 kg m −2 , with a WUE ranging from 19.1 to 38.9 kg m −3 . The irrigation system allowed a high yield levels and a low depth of water applied, as compared to seasonal ET crop estimated with Hargraves’ formula and with the literature data on irrigated tomato. Measurements were carried out on geo-referenced points to gather information on crop (crop yield, eighteen Vegetation indices, leaf area index) and on soil (spectroradiometric and traditional analysis). Eight VIs, out of nineteen ones analyzed, showed a significant relationship with georeferenced yield data; PVI maps seemed able to return the best response, before harvesting, to improve the knowledge of the area of cultivation and irrigation system. CropSense irrigation system reduced seasonal irrigation volumes. Some vegetation indexes were significantly correlated to tomato yield and well identify, a posteriori, crop area with low WUE; spectroradiometry can be a valuable tool to improve irrigated tomato field management.

[1]  A. Gitelson,et al.  Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation , 1994 .

[2]  R. D. Jackson,et al.  Multidate spectral reflectance as predictors of yield in water stressed wheat and barley , 1981 .

[3]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[4]  Maria Manuela Chaves,et al.  Deficit Irrigation as a Strategy to Save Water： Physiology and Potential Application to Horticulture , 2007 .

[5]  Luis S. Pereira,et al.  Irrigation management under water scarcity , 2002 .

[6]  Moon S. Kim,et al.  The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation (A par) , 1994 .

[7]  James L. Petersen,et al.  Effect of timing of a deficit-irrigation allocation on corn evapotranspiration, yield, water use efficiency and dry mass , 2009 .

[8]  K. J. Fisher,et al.  Growth, Yield, and Postharvest Attributes of Glasshouse Tomatoes Produced under Deficit Irrigation , 1996 .

[9]  R. Escadafal,et al.  Statistical analysis of soil colour and spectroradiometric data for hyperspectral remote sensing of soil properties (example in a southern Italy Mediterranean ecosystem) , 2001 .

[10]  M. Kheder,et al.  Effect of conventional and organic production systems on the yield and quality of field tomato cultivars grown in Tunisia. , 2009 .

[11]  Alfonso Calera,et al.  Earth Observation products for operational irrigation management in the context of the PLEIADeS project , 2010 .

[12]  H. Lilliefors.  On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown , 1967 .

[13]  W. Kruskal,et al.  Use of Ranks in One-Criterion Variance Analysis , 1952 .

[14]  R. Jackson.  Remote sensing of biotic and abiotic plant stress , 1986 .

[15]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[16]  F. G. Viets,et al.  Fertilizers And The Efficient Use Of Water , 1962 .

[17]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[18]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[19]  E. C. Stegman,et al.  Irrigation Scheduling by the Checkbook Method , 1988 .

[20]  J. Flexas,et al.  Prospects for crop production under drought: research priorities and future directions , 2005 .

[21]  R. Huguenin,et al.  Intelligent information extraction from reflectance spectra Absorption band positions. [application to laboratory and earth-based telescope spectra , 1986 .

[22]  T. Spreen,et al.  Deficit Irrigation of Micro‐Irrigated Tomato Affects Yield, Fruit Quality, and Disease Severity , 1996 .

[23]  Simona Consoli,et al.  Remote sensing to estimate ET-fluxes and the performance of an irrigation district in southern Italy , 2006 .

[24]  F. J. Pierce,et al.  Remote Sensing of Canopy Dynamics and Biophysical Variables Estimation of Corn in Michigan , 2005 .

[25]  Arturo Alvino,et al.  Proximal sensing and vegetation indices for site-specific evaluation on an irrigated crop tomato , 2014 .

[26]  A. Gitelson,et al.  Signature Analysis of Leaf Reflectance Spectra: Algorithm Development for Remote Sensing of Chlorophyll , 1996 .

[27]  A. Gitelson,et al.  Non‐destructive optical detection of pigment changes during leaf senescence and fruit ripening , 1999 .

[28]  Michele Perniola,et al.  Yield response factor to water (Ky) and water use efficiency of Carthamus tinctorius L. and Solanum melongena L , 2007 .

[29]  S. Tarantola,et al.  Detecting vegetation leaf water content using reflectance in the optical domain , 2001 .

[30]  D. May,et al.  MAJOR CALIFORNIA PROCESSING TOMATO CULTIVARS RESPOND DIFFERENTLY IN YIELD AND FRUIT QUALITY TO VARIOUS LEVELS OF MOISTURE STRESS , 1999 .

[31]  S. K. Upadhyaya,et al.  PREDICTION OF PROCESSING TOMATO YIELD USING A CROP GROWTH MODEL AND REMOTELY SENSED AERIAL IMAGES , 2005 .

[32]  J. Peñuelas,et al.  Estimation of plant water concentration by the reflectance Water Index WI (R900/R970) , 1997 .

[33]  A. Huete,et al.  A Modified Soil Adjusted Vegetation Index , 1994 .

[34]  R. D'Agostino,et al.  A Suggestion for Using Powerful and Informative Tests of Normality , 1990 .

[35]  F. Baret,et al.  A ratio vegetation index adjusted for soil brightness , 1990 .

[36]  Adriana Lucia Stigliani,et al.  Agronomic performance and transcriptional analysis of carotenoid biosynthesis in fruits of transgenic HighCaro and control tomato lines under field conditions , 2006, Transgenic Research.

[37]  Robert G. Evans,et al.  Irrigation of fruit trees and vines: an introduction , 2005, Irrigation Science.

[38]  G. J. Hoffman,et al.  Engineering systems to enhance irrigation performance , 2004, Irrigation Science.

[39]  C. Kirda,et al.  Deficit irrigation scheduling based on plant growth stages showing water stress tolerance , 2002 .

[40]  S. Shapiro,et al.  An Analysis of Variance Test for Normality (Complete Samples) , 1965 .

[41]  T. L. Lyon,et al.  The Nature and Properties of Soils , 1930 .

[42]  J. Peñuelas,et al.  The reflectance at the 950–970 nm region as an indicator of plant water status , 1993 .

[43]  Kenneth G. Hubbard,et al.  Determination of Semivariogram Models to Krige Hourly and Daily Solar Irradiance in Western Nebraska , 2001 .

[44]  R. Jackson,et al.  Interpreting vegetation indices , 1991 .

[45]  Sergio Gómez,et al.  Solving Non-Uniqueness in Agglomerative Hierarchical Clustering Using Multidendrograms , 2006, J. Classif..

[46]  Stefan Sommer,et al.  Multivariate Analysis of Laboratory Spectra for the Assessment of Soil Development and Soil Degradation in the Southern Apennines (Italy) , 2000 .

[47]  H. Kirnak,et al.  Effects of Different Irrigation Regimes and Mulches on Yield and Macronutrition Levels of Drip-Irrigated Cucumber Under Open Field Conditions , 2006 .

[48]  W. Raun,et al.  In-Season Prediction of Corn Grain Yield Potential Using Normalized Difference Vegetation Index , 2006 .

[49]  S. Raine,et al.  Soil–water and solute movement under precision irrigation: knowledge gaps for managing sustainable root zones , 2007, Irrigation Science.

[50]  John R. Miller,et al.  Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .

[51]  Ranga B. Myneni,et al.  Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS , 2006 .

[52]  Siva Kumar Balasundram,et al.  Spatial variability of orange spotting disease in oil palm. , 2012 .

[53]  Z. Samani,et al.  Estimating Potential Evapotranspiration , 1982 .

[54]  B. Ma,et al.  Canopy Light Reflectance and Field Greenness to Assess Nitrogen Fertilization and Yield of Maize , 1996 .

[55]  Stefano Bona,et al.  A methodological approach for defining spectral indices for assessing tomato nitrogen status and yield , 2011 .

[56]  F. Baret,et al.  TSAVI: A Vegetation Index Which Minimizes Soil Brightness Effects On LAI And APAR Estimation , 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.

[57]  Pietro Amenta,et al.  Prediction of Soil Properties with PLSR and vis-NIR Spectroscopy: Application to Mediterranean Soils from Southern Italy , 2012 .

[58]  F. Baret,et al.  Potentials and limits of vegetation indices for LAI and APAR assessment , 1991 .

[59]  Y. Daşgan,et al.  Yield response and N-fertiliser recovery of tomato grown under deficit irrigation , 2007 .

[60]  Saleh M. Ismail,et al.  Influence of single and multiple water application timings on yield and water use efficiency in tomato (var. First power) , 2008 .

[61]  J. Hair.  Multivariate data analysis , 1972 .

[62]  Öner Çetin,et al.  The effect of drip line spacing, irrigation regimes and planting geometries of tomato on yield, irrigation water use efficiency and net return , 2008 .

[63]  C. Patané,et al.  Effects of deficit irrigation on biomass, yield, water productivity and fruit quality of processing tomato under semi-arid Mediterranean climate conditions , 2011 .

[64]  C. Batchelor.  Improving water use efficiency as part of integrated catchment management , 1999 .

[65]  John R. Miller,et al.  Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture , 2004 .

[66]  J. Araus,et al.  Spectral vegetation indices as nondestructive tools for determining durum wheat yield. , 2000 .

[67]  F. Galgano,et al.  Processing tomato quality as affected by irrigation scheduling , 2009 .

[68]  Christopher B. Field,et al.  Remote sensing of the xanthophyll cycle and chlorophyll fluorescence in sunflower leaves and canopies , 1990, Oecologia.

[69]  M. Apan,et al.  Estimation of green bean yield, water deficiency and productivity using spectral indexes during the growing season , 2008 .

[70]  Albert Rango,et al.  Utilization of surface cover composition to improve the microwave determination of snow water equivalent in a mountain basin , 1991 .

[71]  A. J. Richardsons,et al.  DISTINGUISHING VEGETATION FROM SOIL BACKGROUND INFORMATION , 1977 .

[72]  Massimo Menenti,et al.  A field experiment on spectrometry of crop response to soil salinity , 2007 .

[73]  J. Clevers.  Application of a weighted infrared-red vegetation index for estimating leaf Area Index by Correcting for Soil Moisture , 1989 .

[74]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

[75]  M. S. Moran,et al.  Remote Sensing for Crop Management , 2003 .

[76]  B. Hanson,et al.  Effect of subsurface drip irrigation on processing tomato yield, water table depth, soil salinity, and profitability , 2004 .

[77]  J. Delhomme.  Kriging in the hydrosciences , 1978 .

[78]  J. Muller,et al.  Fe-Speciation in Kaolins: A Diffuse Reflectance Study , 1994 .

[79]  George H. Hargreaves,et al.  Reference Crop Evapotranspiration from Temperature , 1985 .

[80]  C. Field,et al.  A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency , 1992 .

[81]  J. Gamon,et al.  The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels , 1997, Oecologia.

[82]  David M. Sherman,et al.  Electronic spectra of Fe3+ oxides and oxide hydroxides in the near IR to near UV , 1985 .

[83]  T. Carlson,et al.  On the relation between NDVI, fractional vegetation cover, and leaf area index , 1997 .

[84]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

[85]  Bruno Basso,et al.  Agronomic traits and vegetation indices of two onion hybrids , 2013 .

[86]  H. Jones.  Irrigation scheduling: advantages and pitfalls of plant-based methods. , 2004, Journal of experimental botany.

[87]  Herman Bouwer,et al.  Integrated water management: emerging issues and challenges , 2000 .

[88]  Moon S. Kim,et al.  Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance , 2000 .

[89]  C. Stanghellini,et al.  Enhancing environmental quality in agricultural systems , 2003 .

[90]  R. Reese.  Geostatistics for Environmental Scientists , 2001 .

[91]  G. Matheron.  Les variables régionalisées et leur estimation : une application de la théorie de fonctions aléatoires aux sciences de la nature , 1965 .

[92]  Bruno Basso,et al.  Cultivar discrimination at different site elevations with remotely sensed vegetation indices , 2010 .

[93]  Wim G.M. Bastiaanssen,et al.  Twenty-five years modeling irrigated and drained soils: State of the art , 2007 .

[94]  J. Doorenbos,et al.  Guidelines for predicting crop water requirements , 1977 .