Predictive modeling of soil profiles for precision agriculture: a case study in safflower cultivation environments
Abstract Evaluating high-throughput soil profile information is essential in safflower precision agriculture, as it facilitates efficient resource management and design of an experiment that promotes sustainable production. We collected soil from representative target environments (TE) of safflower...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-83551-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841559753377972224 |
|---|---|
| author | Megha Sharma Shailendra Goel Ani A. Elias |
| author_facet | Megha Sharma Shailendra Goel Ani A. Elias |
| author_sort | Megha Sharma |
| collection | DOAJ |
| description | Abstract Evaluating high-throughput soil profile information is essential in safflower precision agriculture, as it facilitates efficient resource management and design of an experiment that promotes sustainable production. We collected soil from representative target environments (TE) of safflower cultivation and evaluated 14 soil physio-chemical features for constructing fine-resolution maps. The robustness, versatility, and predictive ability of two statistical learning models in correctly classifying the soil profile to clusters were tested. Calcium, sand, soil organic carbon, phosphorous, potassium, and sodium were found to be most influential in classifying the representative TE. Random Forest model was found to be the best performing with average prediction accuracy above 85% in all test settings which reached 100% in some. The optimal training population size for prediction was found to be 70–80%. The spatial distribution of sodium in Delhi was found to be aligned with the low yield of safflower emphasizing the importance of fine-resolution soil mapping to design a field experiment and optimize the nutrient supply. Fine-resolution mapping not only enhance soil management strategies but also support government initiatives such as soil health cards, delineation of cultivable land, and risk assessments in crop-growing areas. |
| format | Article |
| id | doaj-art-90e372ee79334a60bf0fd2f9c750487a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-90e372ee79334a60bf0fd2f9c750487a2025-01-05T12:15:19ZengNature PortfolioScientific Reports2045-23222025-01-0115111710.1038/s41598-024-83551-9Predictive modeling of soil profiles for precision agriculture: a case study in safflower cultivation environmentsMegha Sharma0Shailendra Goel1Ani A. Elias2Department of Botany, University of DelhiDepartment of Botany, University of DelhiICFRE-Institute of Forest Genetics and Tree BreedingAbstract Evaluating high-throughput soil profile information is essential in safflower precision agriculture, as it facilitates efficient resource management and design of an experiment that promotes sustainable production. We collected soil from representative target environments (TE) of safflower cultivation and evaluated 14 soil physio-chemical features for constructing fine-resolution maps. The robustness, versatility, and predictive ability of two statistical learning models in correctly classifying the soil profile to clusters were tested. Calcium, sand, soil organic carbon, phosphorous, potassium, and sodium were found to be most influential in classifying the representative TE. Random Forest model was found to be the best performing with average prediction accuracy above 85% in all test settings which reached 100% in some. The optimal training population size for prediction was found to be 70–80%. The spatial distribution of sodium in Delhi was found to be aligned with the low yield of safflower emphasizing the importance of fine-resolution soil mapping to design a field experiment and optimize the nutrient supply. Fine-resolution mapping not only enhance soil management strategies but also support government initiatives such as soil health cards, delineation of cultivable land, and risk assessments in crop-growing areas.https://doi.org/10.1038/s41598-024-83551-9Fine-resolution soil mapSoil prediction modelRandom forestSelf-organizing mapSafflowerPrecision agriculture |
| spellingShingle | Megha Sharma Shailendra Goel Ani A. Elias Predictive modeling of soil profiles for precision agriculture: a case study in safflower cultivation environments Scientific Reports Fine-resolution soil map Soil prediction model Random forest Self-organizing map Safflower Precision agriculture |
| title | Predictive modeling of soil profiles for precision agriculture: a case study in safflower cultivation environments |
| title_full | Predictive modeling of soil profiles for precision agriculture: a case study in safflower cultivation environments |
| title_fullStr | Predictive modeling of soil profiles for precision agriculture: a case study in safflower cultivation environments |
| title_full_unstemmed | Predictive modeling of soil profiles for precision agriculture: a case study in safflower cultivation environments |
| title_short | Predictive modeling of soil profiles for precision agriculture: a case study in safflower cultivation environments |
| title_sort | predictive modeling of soil profiles for precision agriculture a case study in safflower cultivation environments |
| topic | Fine-resolution soil map Soil prediction model Random forest Self-organizing map Safflower Precision agriculture |
| url | https://doi.org/10.1038/s41598-024-83551-9 |
| work_keys_str_mv | AT meghasharma predictivemodelingofsoilprofilesforprecisionagricultureacasestudyinsafflowercultivationenvironments AT shailendragoel predictivemodelingofsoilprofilesforprecisionagricultureacasestudyinsafflowercultivationenvironments AT aniaelias predictivemodelingofsoilprofilesforprecisionagricultureacasestudyinsafflowercultivationenvironments |