Yield Adjustment Using GPR-Derived Spatial Covariance Structure in Cassava Field: A Preliminary Investigation

被引:2
作者
Agbona, Afolabi [1 ]
Montesinos-Lopez, Osval A. [2 ]
Everett, Mark E. [3 ]
Ruiz-Guzman, Henry [4 ]
Hays, Dirk B. [1 ,4 ]
机构
[1] Texas A&M Univ, Mol & Environm Plant Sci, College Stn, TX 77843 USA
[2] Univ Colima, Fac Telematica, Colima 28040, Mexico
[3] Texas A&M Univ, Dept Geol & Geophys, College Stn, TX 77843 USA
[4] Texas A&M Univ, Dept Soil & Crop Sci, College Stn, TX 77843 USA
关键词
ground-penetrating radar; cassava; heterogeneity; amplitude; variance; spatial; fresh yield; GROUND-PENETRATING RADAR; SOIL-WATER CONTENT; TREE ROOTS; VARIABILITY;
D O I
10.3390/rs15071771
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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 x 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.
引用
收藏
页数:17
相关论文
共 53 条
[41]  
Shekhar S., Encyclopedia of GIS
[42]   Spatial analysis of soil properties using GIS based geostatistics models [J].
Shit P.K. ;
Bhunia G.S. ;
Maiti R. .
Modeling Earth Systems and Environment, 2016, 2 (2)
[43]  
Simpson T.W., 1998, COMP RESPONSE SURFAC
[44]   Non-destructive testing for the analysis of moisture in the masonry arch bridge of Lubians (Spain) [J].
Solla, Mercedes ;
Laguecla, Susana ;
Riveiro, Belen ;
Lorenzo, Henrique .
STRUCTURAL CONTROL & HEALTH MONITORING, 2013, 20 (11) :1366-1376
[45]   FURTHER ANALYSIS OF DATA BY AKAIKES INFORMATION CRITERION AND FINITE CORRECTIONS [J].
SUGIURA, N .
COMMUNICATIONS IN STATISTICS PART A-THEORY AND METHODS, 1978, 7 (01) :13-26
[46]   Cassava Trait Preferences of Men and Women Farmers in Nigeria: Implications for Breeding [J].
Teeken, Bela ;
Olaosebikan, Olamide ;
Haleegoah, Joyce ;
Oladejo, Elizabeth ;
Madu, Tessy ;
Bello, Abolore ;
Parkes, Elizabeth ;
Egesi, Chiedozie ;
Kulakow, Peter ;
Kirscht, Holger ;
Tufan, Hale Ann .
ECONOMIC BOTANY, 2018, 72 (03) :263-277
[47]  
Tonukari NJ, 2004, ELECTRON J BIOTECHN, V7, P5, DOI 10.2225/vol7-issue1-fulltext-i02
[48]   ELECTROMAGNETIC DETERMINATION OF SOIL-WATER CONTENT - MEASUREMENTS IN COAXIAL TRANSMISSION-LINES [J].
TOPP, GC ;
DAVIS, JL ;
ANNAN, AP .
WATER RESOURCES RESEARCH, 1980, 16 (03) :574-582
[49]   Spatial autocorrelation analysis of health care hotspots in Taiwan in 2006 [J].
Tsai, Pui-Jen ;
Lin, Men-Lung ;
Chu, Chien-Min ;
Perng, Cheng-Hwang .
BMC PUBLIC HEALTH, 2009, 9
[50]  
Turpin N, 2010, ENVIRONMENTAL AND AGRICULTURAL MODELLING: INTEGRATED APPROACHES FOR POLICY IMPACT ASSESSMENT, P11, DOI 10.1007/978-90-481-3619-3_2