{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T19:46:23Z","timestamp":1773085583337,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,25]],"date-time":"2023-03-25T00:00:00Z","timestamp":1679702400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"Texas A&amp;M University","doi-asserted-by":"publisher","award":["1543957"],"award-info":[{"award-number":["1543957"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Many processes concerning below-ground plant performance are not fully understood, such as spatial and temporal dynamics and their relation to environmental factors. Accounting for these spatial patterns is very important as they may be used to adjust for the estimation of cassava fresh root yield masked by field heterogeneity. The yield of cassava is an important characteristic that every breeder seeks to maintain in their germplasm. Ground-Penetrating Radar (GPR) has proven to be an effective tool for studying the below-ground characteristics of developing plants, but it has not yet been explored with respect to its utility in normalizing spatial heterogeneity in agricultural field experiments. In this study, the use of GPR for this purpose was evaluated in a cassava field trial conducted in Momil, Colombia. Using the signal amplitude of the GPR radargram from each field plot, we constructed a spatial plot error structure using the variance of the signal amplitude and developed GPR-based autoregressive (AR) models for fresh root yield adjustment. The comparison of the models was based on the average standard error (SE) of the Best Linear Unbiased Estimator (BLUE) and through majority voting (MV) with respect to the SE of the genotype across the models. Our results show that the GPR-based AR model outperformed the other models, yielding an SE of 9.57 and an MV score of 88.33%, while the AR1 \u00d7 AR1 and IID models had SEs of 10.15 and 10.56% and MV scores of 17.37 and 0.00%, respectively. Our results suggest that GPR can serve a dual purpose in non-destructive yield estimation and field spatial heterogeneity normalization in global root and tuber crop programs, presenting a great potential for adoption in many applications.<\/jats:p>","DOI":"10.3390\/rs15071771","type":"journal-article","created":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T02:18:27Z","timestamp":1679883507000},"page":"1771","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Yield Adjustment Using GPR-Derived Spatial Covariance Structure in Cassava Field: A Preliminary Investigation"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9756-5432","authenticated-orcid":false,"given":"Afolabi","family":"Agbona","sequence":"first","affiliation":[{"name":"Molecular & Environmental Plant Sciences, Texas A&M University, College Station, TX 77843, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3973-6547","authenticated-orcid":false,"given":"Osval A.","family":"Montesinos-Lopez","sequence":"additional","affiliation":[{"name":"Facultad de Telem\u00e1tica, Universidad de Colima, Colima 28040, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6453-0316","authenticated-orcid":false,"given":"Mark E.","family":"Everett","sequence":"additional","affiliation":[{"name":"Department of Geology & Geophysics, Texas A&M University, College Station, TX 77843, USA"}]},{"given":"Henry","family":"Ruiz-Guzman","sequence":"additional","affiliation":[{"name":"Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA"}]},{"given":"Dirk B.","family":"Hays","sequence":"additional","affiliation":[{"name":"Molecular & Environmental Plant Sciences, Texas A&M University, College Station, TX 77843, USA"},{"name":"Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hardie, M. (2020). Review of novel and emerging proximal soil moisture sensors for use in agriculture. Sensors, 20.","DOI":"10.3390\/s20236934"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Abbas, F., Afzaal, H., Farooque, A.A., and Tang, S. (2020). Crop yield prediction through proximal sensing and machine learning algorithms. Agronomy, 10.","DOI":"10.3390\/agronomy10071046"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"12","DOI":"10.2225\/vol7-issue1-fulltext-9","article-title":"Cassava and the future of starch","volume":"7","author":"Tonukari","year":"2004","journal-title":"Electron. J. Biotechnol."},{"key":"ref_4","unstructured":"Chiona, M., Ntawuruhunga, P., Mukuka, I., Chalwe, A., Phiri, N., Chikoti, P., and Simwambana, M. (2016). Growing Cassava: Training Manual for Extension & Farmers in Zambia, International Institute of Tropical Agriculture (IITA)."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1007\/s12231-018-9421-7","article-title":"Cassava Trait Preferences of Men and Women Farmers in Nigeria: Implications for Breeding","volume":"72","author":"Teeken","year":"2018","journal-title":"Econ. Bot."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Agbona, A., Teare, B., Ruiz-Guzman, H., Dobreva, I.D., Everett, M.E., Adams, T., Montesinos-Lopez, O.A., Kulakow, P.A., and Hays, D.B. (2021). Prediction of root biomass in cassava based on ground penetrating radar phenomics. Remote Sens., 13.","DOI":"10.3390\/rs13234908"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1186\/s13007-017-0216-0","article-title":"Ground penetrating radar: A case study for estimating root bulking rate in cassava (Manihot esculenta Crantz)","volume":"13","author":"Delgado","year":"2017","journal-title":"Plant Methods"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1534\/g3.117.300323","article-title":"Improving genomic prediction in cassava field experiments using spatial analysis","volume":"8","author":"Elias","year":"2018","journal-title":"G3 Genes Genomes Genet."},{"key":"ref_9","first-page":"23","article-title":"M\u00e9thode statistique pour des exp\u00e9riences sur champ","volume":"30","author":"Papadakis","year":"1937","journal-title":"Bull. Inst. Am\u00e9l. Plantes Salonique"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1017\/S0021859600083283","article-title":"Relative accuracy of a neighbour method for field trials","volume":"111","author":"Lill","year":"1988","journal-title":"J. Agric. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1449","DOI":"10.2307\/2532398","article-title":"Spatial Analysis of Field Experiments\u2014An Extension to Two Dimensions","volume":"47","author":"Cullis","year":"1991","journal-title":"Biometrics"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"269","DOI":"10.2307\/1400446","article-title":"Accounting for Natural and Extraneous Variation in the Analysis of Field Experiments","volume":"2","author":"Gilmour","year":"1997","journal-title":"J. Agric. Biol. Environ. Stat."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Rado\u010daj, D., Jug, I., Vukadinovi\u0107, V., Juri\u0161i\u0107, M., and Ga\u0161parovi\u0107, M. (2021). The effect of soil sampling density and spatial autocorrelation on interpolation accuracy of chemical soil properties in arable cropland. Agronomy, 11.","DOI":"10.3390\/agronomy11122430"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"984963","DOI":"10.3389\/fsoil.2022.984963","article-title":"Spatial distribution as a key factor for evaluation of soil attributes prediction at field level using online near-infrared spectroscopy","volume":"2","author":"Molin","year":"2022","journal-title":"Front. Soil Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s40808-016-0160-4","article-title":"Spatial analysis of soil properties using GIS based geostatistics models","volume":"2","author":"Shit","year":"2016","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1139\/cjss-2019-0163","article-title":"Spatial variation of soil quality indicators as a function of land use and topography","volume":"100","author":"Kiani","year":"2020","journal-title":"Can. J. Soil Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"116","DOI":"10.3389\/fenvs.2019.00116","article-title":"Small-scale spatial variability of soil chemical and biochemical properties in a rewetted degraded Peatland","volume":"7","author":"Negassa","year":"2019","journal-title":"Front. Environ. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.compag.2004.11.002","article-title":"Characterizing soil spatial variability with apparent soil electrical conductivity: I. Survey protocols","volume":"46","author":"Corwin","year":"2005","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, S.L., and Liang, W.L. (2019). Spatial-temporal soil water dynamics beneath a tree monitored by tensiometer-time domain reflectometry probes. Water, 11.","DOI":"10.3390\/w11081662"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/S0022-1694(02)00239-1","article-title":"Mapping spatial variation in surface soil water content: Comparison of ground-penetrating radar and time domain reflectometry","volume":"269","author":"Huisman","year":"2002","journal-title":"J. Hydrol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.jhydrol.2007.04.013","article-title":"Mapping the spatial variation of soil water content at the field scale with different ground penetrating radar techniques","volume":"340","author":"Huisman","year":"2007","journal-title":"J. Hydrol."},{"key":"ref_22","first-page":"358","article-title":"Use of Ground Penetrating Radar to study spatial variability and soil stratigraphy","volume":"39","author":"Campos","year":"2019","journal-title":"Eng. Agric."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"269","DOI":"10.5194\/tc-4-269-2010","article-title":"Multi-channel ground-penetrating radar to explore spatial variations in thaw depth and moisture content in the active layer of a permafrost site","volume":"4","author":"Gerhards","year":"2010","journal-title":"Cryosphere"},{"key":"ref_24","unstructured":"Redman, D., Galagedara, L., and Parkin, G. (2013). 2003 ASAE Annual Meeting, American Society of Agricultural and Biological Engineers."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"De Benedetto, D., Barca, E., Castellini, M., Popolizio, S., Lacolla, G., and Stellacci, A.M. (2022). Prediction of Soil Organic Carbon at Field Scale by Regression Kriging and Multivariate Adaptive Regression Splines Using Geophysical Covariates. Land, 11.","DOI":"10.3390\/land11030381"},{"key":"ref_26","unstructured":"Fedorova, L.L., Sokolov, K.O., Savvin, D.V., and Kulyandin, G.A. (July, January 30). Analysis of Variance Amplitudes of Signals for Detecting Structural Permafrost Heterogeneities by Ground Penetrating Radar. Proceedings of the 15th International Conference on Ground Penetrating Radar, Brussels, Belgium."},{"key":"ref_27","first-page":"1366","article-title":"Non-destructive testing for the analysis of moisture in the masonry arch bridge of Lubians (Spain)","volume":"20","author":"Solla","year":"2013","journal-title":"Struct. Control Health Monit."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"107662","DOI":"10.1016\/j.measurement.2020.107662","article-title":"GPR laboratory tests and numerical models to characterize cracks in cement concrete specimens, exemplifying damage in rigid pavement","volume":"158","author":"Rasol","year":"2020","journal-title":"Meas. J. Int. Meas. Confed."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1023\/A:1020657129590","article-title":"GPR\u2014History, Trends, and Future Developments","volume":"3","author":"Annan","year":"2002","journal-title":"Subsurf. Sens. Technol. Appl."},{"key":"ref_30","unstructured":"Turpin, N., Stapleton, L., Perret, E., Van Der Heide, C.M., Garrod, G., Brouwer, F., Voltr, V., and Cairol, D. (2010). Environmental and Agricultural Modelling: Integrated Approaches for Policy Impact Assessment, Springer."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5754","DOI":"10.3390\/rs6065754","article-title":"3D Ground Penetrating Radar to Detect Tree Roots and Estimate Root Biomass in the Field","volume":"6","author":"Zhu","year":"2014","journal-title":"Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Dobreva, I.D., Ruiz-Guzman, H.A., Barrios-Perez, I., Adams, T., Teare, B.L., Payton, P., Everett, M.E., Burow, M.D., and Hays, D.B. (2021). Thresholding Analysis and Feature Extraction from 3D Ground Penetrating Radar Data for Noninvasive Assessment of Peanut Yield. Remote Sens., 13.","DOI":"10.3390\/rs13101896"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.1093\/treephys\/21.17.1269","article-title":"Use of ground-penetrating radar to study tree roots in the southeastern United States","volume":"21","author":"Butnor","year":"2001","journal-title":"Tree Physiol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1007\/s11104-017-3531-3","article-title":"Ground penetrating radar (GPR) detects fine roots of agricultural crops in the field","volume":"423","author":"Liu","year":"2018","journal-title":"Plant Soil"},{"key":"ref_35","unstructured":"Jol, H.M. (2009). Ground Penetrating Radar Theory and Applications, Elsevier. [1st ed.]."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Everett, M.E. (2013). Near-Surface Applied Geophysics, Cambridge University Press.","DOI":"10.1017\/CBO9781139088435"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2525","DOI":"10.1109\/TAP.2005.852292","article-title":"Design of a resistively loaded vee dipole for ultrawide-band ground-penetrating radar applications","volume":"53","author":"Kim","year":"2005","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_38","unstructured":"Nuzzo, L., Alli, G., Guidi, R., Cortesi, N., Sarri, A., Manacorda, G., Ingegneria, I.D.S., and Sistemi, D. (July, January 30). A new densely-sampled Ground Penetrating Radar array for landmine detection. Proceedings of the 15th International Conference on Ground Penetrating Radar, Brussels, Belgium."},{"key":"ref_39","unstructured":"Cropphenomics (2023, January 18). Crop Phenomics LLC, College Station, TX, USA. Available online: https:\/\/cropphenomics.com."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1029\/WR016i003p00574","article-title":"Electromagnetic determination of soil water content: Measurements in coaxial transmission lines","volume":"16","author":"Topp","year":"1980","journal-title":"Water Resour. Res."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Tsai, P.J., Lin, M.L., Chu, C.M., and Perng, C.H. (2009). Spatial autocorrelation analysis of health care hotspots in Taiwan in 2006. BMC Public Health, 9.","DOI":"10.1186\/1471-2458-9-464"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1007\/s11749-018-0599-x","article-title":"Comparing implementations of global and local indicators of spatial association","volume":"27","author":"Bivand","year":"2018","journal-title":"Test"},{"key":"ref_43","unstructured":"Paula, M. (2023, January 10). Geospatial Health Data: Modelling and Visualization with R-INLA and Shiny. Available online: https:\/\/www.paulamoraga.com\/book-geospatial\/sec-arealdatatheory.html."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Montesinos L\u00f3pez, O.A., Montesinos L\u00f3pez, A., and Crossa, J. (2022). Multivariate Statistical Machine Learning Methods for Genomic Prediction, Springer Nature.","DOI":"10.1007\/978-3-030-89010-0"},{"key":"ref_45","unstructured":"Pinheiro, J., Bates, D., and R Core Team (2022, December 10). nlme: Linear and Nonlinear Mixed Effects Models. Available online: https:\/\/cran.r-project.org\/package=nlme."},{"key":"ref_46","unstructured":"Rodriguez, L.S., and Munoz, F. (2016). IUFRO Genomics and Forest Tree Genetics, Hindustan Aeronautics Limited."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Simpson, T.W. (1998). Comparison of Response Surface and Kriging Models in the Multidisciplinary Design of an Aerospike Nozzle, NASA ICASE Rep.","DOI":"10.2514\/6.1998-4755"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v019.i04","article-title":"spBayes: An R Package for Univariate and Multivariate Hierarchical Point-referenced Spatial Models","volume":"19","author":"Finley","year":"2007","journal-title":"J. Stat. Softw."},{"key":"ref_49","unstructured":"R Core Team (2014). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1080\/03610927808827599","article-title":"Further Analysis of the Data by Anaike\u2019s Information Criterion and the Finite Corrections","volume":"7","author":"Sugiura","year":"1978","journal-title":"Commun. Stat. Theory Methods"},{"key":"ref_51","unstructured":"Shekhar, S., and Xiong, H. (2023, February 13). Encyclopedia of GIS. Available online: https:\/\/books.google.com\/books?id=6q2lOfLnwkAC&printsec=frontcover&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"469","DOI":"10.3389\/fpls.2014.00469","article-title":"Belowground plant development measured with magnetic resonance imaging (MRI): Exploiting the potential for non-invasive trait quantification using sugar beet as a proxy","volume":"5","author":"Metzner","year":"2014","journal-title":"Front. Plant Sci."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Williamson, H.F., and Leonelli, S. (2022). Towards Responsible Plant Data Linkage: Data Challenges for Agricultural Research and Development, Springer.","DOI":"10.1007\/978-3-031-13276-6"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/7\/1771\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:03:17Z","timestamp":1760122997000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/7\/1771"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,25]]},"references-count":53,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["rs15071771"],"URL":"https:\/\/doi.org\/10.3390\/rs15071771","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,25]]}}}