Management of soil salinity and crop yield using conditional probability maps
| dc.contributor.author | Eldeiry, Ahmed A., author | |
| dc.contributor.author | Garcia, Luis A., author | |
| dc.contributor.author | Colorado State University, publisher | |
| dc.date.accessioned | 2020-02-12T17:22:53Z | |
| dc.date.available | 2020-02-12T17:22:53Z | |
| dc.date.issued | 2012 | |
| dc.description | 2012 annual AGU hydrology days was held at Colorado State University on March 21 - March 23, 2012. | |
| dc.description | Includes bibliographical references. | |
| dc.description.abstract | Classifying irrigated fields into zones with different probabilities to reach a specific yield potential percentage (YP%) is imperative in the management of soil salinity and crop yield. Three nonlinear geostatistical models: Disjunctive Kriging (DK), Indicator Kriging (IK), and Probability Kriging (PK) are investigated in this study. Conditional probability (CP) maps generated by these models are used to classify two irrigated fields into zones with different probabilities to reach a specific YP% for a given crop. Soil salinity thresholds of alfalfa and corn were used as the conditions for applying the three models on the two datasets of two irrigated fields to generate CP maps for the two evaluated crops (alfalfa and corn). The objectives of this study are: 1) compare different CP maps generated using DK, IK, and PK; 2) compare the estimated YP% of alfalfa and corn under different soil salinity thresholds based on the CP maps generated by the three models; and 3) provide some guidance to growers to help them decide which crops to grow or whether some remediation actions need to be taken or not. The three models were applied on two datasets of soil salinity (316 and 163 data points) collected in two irrigated fields. These datasets were selected from a project conducted in the southeastern part of the Arkansas River Basin in Colorado where soil salinity impacts the crop productivity. Alfalfa and corn were selected because they are prevailing crops in the study area. Also, alfalfa represents a moderate tolerant crop while corn represents a moderate sensitive crop. The results of this study show that the DK, IK, and PK techniques give an accurate characterization and quantification of the different zones of the irrigated fields. The generated CP maps using DK are more accurate than those generated using IK and PK. The generated CP maps can be used to quantify and assess the productivity of different crops under different soil salinity thresholds. | |
| dc.format.medium | born digital | |
| dc.format.medium | proceedings (reports) | |
| dc.identifier.uri | https://hdl.handle.net/10217/201018 | |
| dc.identifier.uri | http://dx.doi.org/10.25675/10217/201018 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | Hydrology Days | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.title | Management of soil salinity and crop yield using conditional probability maps | |
| dc.title.alternative | Hydrology days 2012 | |
| dc.title.alternative | AGU hydrology days 2012 | |
| dc.type | Text |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- CONF_HydroDays_Eldeiry_2012.pdf
- Size:
- 1.21 MB
- Format:
- Adobe Portable Document Format