Using Block Kriging as a Spatial Smooth Interpolator to Address Missing Values and Reduce Variability in Maize Field Yield Data
Block Kriging (a spatial interpolation method) and log<sub>10</sub> transformation were compared for their effectiveness in reducing relative variance (coefficient of variance: CV) and estimate mean values in all harvested maize plants grown in three randomly taken field plots and for ha...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/13/7/1685 |
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| author | Thomas M. Koutsos Georgios C. Menexes Ilias G. Eleftherohorinos Thomas K. Alexandridis |
| author_facet | Thomas M. Koutsos Georgios C. Menexes Ilias G. Eleftherohorinos Thomas K. Alexandridis |
| author_sort | Thomas M. Koutsos |
| collection | DOAJ |
| description | Block Kriging (a spatial interpolation method) and log<sub>10</sub> transformation were compared for their effectiveness in reducing relative variance (coefficient of variance: CV) and estimate mean values in all harvested maize plants grown in three randomly taken field plots and for harvested plants after removing the “edge or margin” ones. The results showed that log<sub>10</sub> transformation reduced CVs of all harvested original fresh weight (FW) plant data in the three plots from 35.6–41.6% (original data) to 6.0–7.5%, while the respective CVs due to Block Kriging were reduced to 14.5–19.9%. The back-log<sub>10</sub>-transformed means of all harvested FW plant data were reduced by 6.8–9.4%, while the respective reduction for plants excluding the margin ones was 1.3–8.3%. The Block Kriging means for all harvested FW plant data were reduced only by 0.3–0.4%, while the respective means of the harvested plants excluding margin ones were increased by 0.4–4.3%. These findings strongly suggest that Block Kriging should be preferred over the log<sub>10</sub> transformation method (used so far by agroscientists) as it managed to effectively reduce variability in crop data and estimate missing values that provide more precise and reliable estimates of corn yield for farmers. |
| first_indexed | 2024-03-11T01:23:46Z |
| format | Article |
| id | doaj.art-a65d3001c37043acae39502555766699 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-11T01:23:46Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-a65d3001c37043acae395025557666992023-11-18T17:54:38ZengMDPI AGAgronomy2073-43952023-06-01137168510.3390/agronomy13071685Using Block Kriging as a Spatial Smooth Interpolator to Address Missing Values and Reduce Variability in Maize Field Yield DataThomas M. Koutsos0Georgios C. Menexes1Ilias G. Eleftherohorinos2Thomas K. Alexandridis3Department of Hydraulics, Soil Science and Agricultural Engineering, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceDepartment of Field Crops and Ecology, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceDepartment of Field Crops and Ecology, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceDepartment of Hydraulics, Soil Science and Agricultural Engineering, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceBlock Kriging (a spatial interpolation method) and log<sub>10</sub> transformation were compared for their effectiveness in reducing relative variance (coefficient of variance: CV) and estimate mean values in all harvested maize plants grown in three randomly taken field plots and for harvested plants after removing the “edge or margin” ones. The results showed that log<sub>10</sub> transformation reduced CVs of all harvested original fresh weight (FW) plant data in the three plots from 35.6–41.6% (original data) to 6.0–7.5%, while the respective CVs due to Block Kriging were reduced to 14.5–19.9%. The back-log<sub>10</sub>-transformed means of all harvested FW plant data were reduced by 6.8–9.4%, while the respective reduction for plants excluding the margin ones was 1.3–8.3%. The Block Kriging means for all harvested FW plant data were reduced only by 0.3–0.4%, while the respective means of the harvested plants excluding margin ones were increased by 0.4–4.3%. These findings strongly suggest that Block Kriging should be preferred over the log<sub>10</sub> transformation method (used so far by agroscientists) as it managed to effectively reduce variability in crop data and estimate missing values that provide more precise and reliable estimates of corn yield for farmers.https://www.mdpi.com/2073-4395/13/7/1685agricultural experimentationdata variabilityedge effectsgeoinformatics |
| spellingShingle | Thomas M. Koutsos Georgios C. Menexes Ilias G. Eleftherohorinos Thomas K. Alexandridis Using Block Kriging as a Spatial Smooth Interpolator to Address Missing Values and Reduce Variability in Maize Field Yield Data Agronomy agricultural experimentation data variability edge effects geoinformatics |
| title | Using Block Kriging as a Spatial Smooth Interpolator to Address Missing Values and Reduce Variability in Maize Field Yield Data |
| title_full | Using Block Kriging as a Spatial Smooth Interpolator to Address Missing Values and Reduce Variability in Maize Field Yield Data |
| title_fullStr | Using Block Kriging as a Spatial Smooth Interpolator to Address Missing Values and Reduce Variability in Maize Field Yield Data |
| title_full_unstemmed | Using Block Kriging as a Spatial Smooth Interpolator to Address Missing Values and Reduce Variability in Maize Field Yield Data |
| title_short | Using Block Kriging as a Spatial Smooth Interpolator to Address Missing Values and Reduce Variability in Maize Field Yield Data |
| title_sort | using block kriging as a spatial smooth interpolator to address missing values and reduce variability in maize field yield data |
| topic | agricultural experimentation data variability edge effects geoinformatics |
| url | https://www.mdpi.com/2073-4395/13/7/1685 |
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