Improving estimation of in-season crop water use and health of wheat genotypes on sodic soils using spatial interpolation techniques and multi-component metrics

Abstract Crop water use can be a useful indicator of in-season crop conditions including health and greenness on sodic soils. However, in-field manual measurements of crop water use can be tedious, time and labour-intensive and may also exhibit large spatial variability, and inefficient for surveying large areas. Here we propose a novel approach to estimate in-field crop water use of wheat using spatial interpolation techniques at critical crop development stages (tillering and close to flowering) on a moderately sodic and a highly sodic site in southern Queensland, Australia. The six spatial interpolation techniques; ordinary kriging, empirical Bayes kriging, inverse distance weighting, spline, local polynomial interpolation, and radial basis function were employed on ground-measured crop water use data and compared to accurately estimate the spatial distribution of crop water use. Initially, the comparison was made using root mean square error, mean absolute error, and coefficient of determination values using cross-validation. For a more clear comparison and to account for complexities of site-specific crop water use distribution, the standardized, multi-component model performance efficiency metrics, i.e. Kling-Gupta efficiency and spatial efficiency were adopted and tested over cross-validation techniques. Results showed that Kling-Gupta efficiency was slightly better at selecting the optimum interpolation models for crop water use estimation at a field scale than the spatial efficiency metric. Spline and local polynomial interpolation were relatively better estimators of crop water use for both the sites at tillering, while the radial basis function and spline were superior close to flowering. The estimated crop water use was positively and closely associated with seasonal GreenSeeker® normalized difference vegetation index data (R2 = 0.49 and 0.39 at tillering; and R2 = 0.71 and 0.62 at close to flowering, for the moderately sodic and highly sodic sites, respectively). The research improves our understanding of selecting appropriate spatial interpolation methods for in-season crop water use estimation that could help detect changes in crop health due to in-field and seasonal variations of crop water use in a sodic soil environment.

[1]  Z. Shi,et al.  Improved Prediction and Reduction of Sampling Density for Soil Salinity by Different Geostatistical Methods , 2007 .

[2]  A. Konopka,et al.  FIELD-SCALE VARIABILITY OF SOIL PROPERTIES IN CENTRAL IOWA SOILS , 1994 .

[3]  Z. Hochman,et al.  Climate trends account for stalled wheat yields in Australia since 1990 , 2017, Global change biology.

[4]  H. K. Poshtmasari.  Comparison of interpolation methods for estimating pH and EC in agricultural fields of Golestan province (north of Iran) , 2012 .

[5]  P. Krause,et al.  COMPARISON OF DIFFERENT EFFICIENCY CRITERIA FOR HYDROLOGICAL MODEL ASSESSMENT , 2005 .

[6]  A. Shukla,et al.  Spatial Distribution of Surface Soil Acidity, Electrical Conductivity, Soil Organic Carbon Content and Exchangeable Potassium, Calcium and Magnesium in Some Cropped Acid Soils of India , 2015 .

[7]  P. R. Day,et al.  Particle Fractionation and Particle‐Size Analysis , 2015 .

[8]  Alex B. McBratney,et al.  Comparison of several spatial prediction methods for soil pH , 1987 .

[9]  G. Metternicht,et al.  Testing the performance of spatial interpolation techniques for mapping soil properties , 2006 .

[10]  Robert J Ferl,et al.  Utilization of single‐image normalized difference vegetation index (SI‐NDVI) for early plant stress detection , 2018, Applications in plant sciences.

[11]  Ming Ye,et al.  Towards a comprehensive assessment of model structural adequacy , 2012 .

[12]  R. Mirzaei,et al.  Comparison of interpolation methods for the estimation of groundwater contamination in Andimeshk-Shush Plain, Southwest of Iran , 2016, Environmental Science and Pollution Research.

[13]  High subsoil chloride concentrations reduce soil water extraction and crop yield on Vertosols in north-eastern Australia , 2008 .

[14]  P. Shit,et al.  Comparison of GIS-based interpolation methods for spatial distribution of soil organic carbon (SOC) , 2016 .

[15]  A. Apan,et al.  UAV-Thermal imaging and agglomerative hierarchical clustering techniques to evaluate and rank physiological performance of wheat genotypes on sodic soil , 2021 .

[16]  X. Chen,et al.  Analysis and comparison of spatial interpolation methods for temperature data in Xinjiang Uygur Autonomous Region, China , 2011 .

[17]  M. Mkhabela,et al.  Early maize yield forecasting in the four agro-ecological regions of Swaziland using NDVI data derived from NOAA's-AVHRR , 2005 .

[18]  Jin Li,et al.  Spatial interpolation methods applied in the environmental sciences: A review , 2014, Environ. Model. Softw..

[19]  Y. Lü,et al.  Comparison of Four Spatial Interpolation Methods for Estimating Soil Moisture in a Complex Terrain Catchment , 2013, PloS one.

[20]  Zhi-biao Chen,et al.  Spatial Distribution of Soil Nutrients and Their Response to Land Use in Eroded Area of South China , 2011 .

[21]  R. Dalal,et al.  Genetic Diversity in Barley and Wheat for Tolerance to Soil Constraints , 2016 .

[22]  R. Routley,et al.  Subsoil constraints to grain production in the cropping soils of the north-eastern region of Australia: an overview , 2006 .

[23]  G. Gilat,et al.  Method for smooth approximation of data , 1977 .

[24]  N. Lam.  Spatial Interpolation Methods: A Review , 1983 .

[25]  M. Nazarifar,et al.  COMPARISON OF SPATIAL INTERPOLATION METHODS FOR WHEAT WATER REQUIREMENT AND ITS TEMPORAL DISTRIBUTION IN HAMEDAN PROVINCE (IRAN) , 2015 .

[26]  Deepesh Machiwal,et al.  Comparison of ordinary and Bayesian kriging techniques in depicting rainfall variability in arid and semi-arid regions of north-west India , 2017, Environmental Earth Sciences.

[27]  Taha Gorji,et al.  Interpolation Approaches for Characterizing Spatial Variability of Soil Properties in Tuz Lake Basin of Turkey , 2017 .

[28]  R. M. Lark,et al.  Estimating variograms of soil properties by the method‐of‐moments and maximum likelihood , 2000 .

[29]  T. Mueller,et al.  Map Quality for Ordinary Kriging and Inverse Distance Weighted Interpolation , 2004 .

[30]  P. Burrough,et al.  Principles of geographical information systems , 1998 .

[31]  Y. Niu,et al.  Geostatistical interpolation model selection based on ArcGIS and spatio-temporal variability analysis of groundwater level in piedmont plains, northwest China , 2016, SpringerPlus.

[32]  P. L. Poulton,et al.  An electromagnetic induction method for monitoring variation in soil moisture in agroforestry systems , 2007 .

[33]  A. Apan,et al.  Evaluation of water status of wheat genotypes to aid prediction of yield on sodic soils using UAV-thermal imaging and machine learning , 2021 .

[34]  Jim E. Freer,et al.  Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores , 2019, Hydrology and Earth System Sciences.

[35]  Raj Khosla,et al.  Interpolation type and data computation of crop yield maps is important for precision crop production , 2016 .

[36]  George P. Petropoulos,et al.  GIS and Remote Sensing Aided Information for Soil Moisture Estimation: A Comparative Study of Interpolation Techniques , 2019, Resources.

[37]  Fred C. Collins,et al.  A comparison of spatial interpolation techniques in temperature estimation , 1995 .

[38]  I. Hussain,et al.  Distribution of Total Dissolved Solids in Drinking Water by Means of Bayesian Kriging and Gaussian Spatial Predictive Process , 2014, Water Quality, Exposure and Health.

[39]  Yun Xu,et al.  On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning , 2018, Journal of Analysis and Testing.

[40]  Julian Koch,et al.  The SPAtial EFficiency metric (SPAEF): multiple-component evaluation of spatial patterns for optimization of hydrological models , 2018 .

[41]  M. McLaurin,et al.  Applicability of the Normalized Difference Vegetation Index in Index-Based Crop Insurance Design , 2011 .

[42]  Juliane Mai,et al.  Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model , 2017 .

[43]  J. Seyedmohammadi,et al.  Spatial variation modelling of groundwater electrical conductivity using geostatistics and GIS , 2016, Modeling Earth Systems and Environment.

[44]  Xiao-jun Ruan,et al.  Convergence in $$s_{2}$$s2-quasicontinuous posets , 2016, SpringerPlus.

[45]  Saffet Erdogan,et al.  A comparision of interpolation methods for producing digital elevation models at the field scale , 2009 .

[46]  C. T. Haan,et al.  Statistical Methods In Hydrology , 1977 .

[47]  M. Pringle,et al.  Electromagnetic induction sensing of soil identifies constraints to the crop yields of north-eastern Australia , 2011 .

[48]  Yunfeng Xie,et al.  Spatial distribution of soil heavy metal pollution estimated by different interpolation methods: accuracy and uncertainty analysis. , 2011, Chemosphere.

[49]  Mostafa Emadi,et al.  Comparison of spatial interpolation techniques for mapping soil pH and salinity in agricultural coastal areas, northern Iran , 2014 .

[50]  P. Rengasamy.  World salinization with emphasis on Australia. , 2006, Journal of experimental botany.

[51]  Hoshin Vijai Gupta,et al.  On the development of regionalization relationships for lumped watershed models: The impact of ignoring sub-basin scale variability , 2009 .

[52]  UAV-Thermal Imaging: A Robust Technology to Evaluate in-field Crop Water Stress and Yield Variation of Wheat Genotypes , 2020, 2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS).

[53]  E. Bettenay.  Salt affected soils in Australia , 1986 .

[54]  M. S. Rodrigues,et al.  Spatial interpolation techniques for site-specific irrigation management in a mango orchard , 2018, Comunicata Scientiae.

[55]  Joann Mossa,et al.  Comparison of different spatial interpolation methods for historical hydrographic data of the lowermost Mississippi River , 2019, Ann. GIS.

[56]  G.B.M. Heuvelink,et al.  A disposition of interpolation techniques , 2010 .

[57]  H. Mitásová,et al.  Interpolation by regularized spline with tension: I. Theory and implementation , 1993 .

[58]  P. Thakur,et al.  Assessment of land use land cover change impact on hydrological regime of a basin , 2017, Environmental Earth Sciences.

[59]  Yi-Hwa Wu,et al.  Accuracy and Effort of Interpolation and Sampling: Can GIS Help Lower Field Costs? , 2014, ISPRS Int. J. Geo Inf..

[60]  Lixin Li,et al.  A Spatiotemporal Interpolation Method Using Radial Basis Functions for Geospatiotemporal Big Data , 2014, 2014 Fifth International Conference on Computing for Geospatial Research and Application.

[61]  M. Flaherty,et al.  Unpacking healthcare waste management at rural village health clinics in the Ntcheu District (Malawi) , 2019, Environmental Monitoring and Assessment.

[62]  G. Owens,et al.  Dataset characteristics influence the performance of different interpolation methods for soil salinity spatial mapping , 2019, Environmental Monitoring and Assessment.

[63]  Patrick Willems,et al.  Spatial and temporal rainfall variability in mountainous areas: A case study from the south Ecuadorian Andes , 2006 .

[64]  Kaishan Song,et al.  Spatial distribution of soil organic carbon and analysis of related factors in croplands of the black soil region, Northeast China , 2006 .

[65]  B. Tucker.  A proposed new reagent for the measurement of cation exchange properties of carbonate soils , 1985 .

[66]  Lars Eklundh,et al.  Estimating relations between AVHRR NDVI and rainfall in East Africa at 10-day and monthly time scales , 1998 .

[67]  R. Webster,et al.  Geostatistics for Environmental Scientists , 2001 .