The assessment of soil variables relative importance for cereal yield prediction under rainfed cropping system in Morocco
In this study, Explainable Artificial Intelligence (XAI) techniques were applied to identify the most important factors influencing crop yield prediction, with a focus on strategies for sustainable agriculture. Using permutation importance and residual plot analysis, the results showed that nitrogen...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
|
| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525001832 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850145408152502272 |
|---|---|
| author | Oumnia Ennaji Abdellah Hamma Leonardus Vergütz Achraf El Allali |
| author_facet | Oumnia Ennaji Abdellah Hamma Leonardus Vergütz Achraf El Allali |
| author_sort | Oumnia Ennaji |
| collection | DOAJ |
| description | In this study, Explainable Artificial Intelligence (XAI) techniques were applied to identify the most important factors influencing crop yield prediction, with a focus on strategies for sustainable agriculture. Using permutation importance and residual plot analysis, the results showed that nitrogen (N) content, Bandera variety and potassium oxide (K2O) are the most important traits influencing yield prediction. Extreme Gradient Boosting (XGB) was used to predict yield using a large Moroccan national cereal dataset spanning 3 seasons. Residual Plots Analysis, Partial Dependent Plots (PDP), Permutation Importance (PI) and SHapley Additive ExPlanations (SHap) were used to select the features that influence yield prediction. The results indicate that optimizing soil nitrogen and potassium oxide levels together with strategic selection of crop varieties can significantly increase productivity. Residual analysis of the eXtreme Gradient Boosting (XGB) model confirmed its high predictive accuracy. This study underlines the value of XAI methods in improving the interpretability of predictive models. The insights gained can contribute to better soil management and informed crop selection, ultimately reducing yield losses under environmental stress. By increasing the resilience of agricultural systems, we aim to contribute to sustainable and data-driven farming practices and, in particular, address some of Morocco's unique agricultural challenges. |
| format | Article |
| id | doaj-art-07b32bacd5c24f11ac5aa4d67433cf1b |
| institution | OA Journals |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-07b32bacd5c24f11ac5aa4d67433cf1b2025-08-20T02:28:07ZengElsevierSmart Agricultural Technology2772-37552025-08-011110095010.1016/j.atech.2025.100950The assessment of soil variables relative importance for cereal yield prediction under rainfed cropping system in MoroccoOumnia Ennaji0Abdellah Hamma1Leonardus Vergütz2Achraf El Allali3College of Computing, Mohammed VI Polytechnic University, Lot 660, Ben Guerir, 43150, Morocco; College of Agriculture and Environmental Sciences, Mohammed VI Polytechnic University, Lot 660, Ben Guerir, 43150, Morocco; Corresponding author at: College of Computing, Mohammed VI Polytechnic University, Lot 660, Ben Guerir, 43150, Morocco.College of Agriculture and Environmental Sciences, Mohammed VI Polytechnic University, Lot 660, Ben Guerir, 43150, MoroccoCollege of Agriculture and Environmental Sciences, Mohammed VI Polytechnic University, Lot 660, Ben Guerir, 43150, MoroccoCollege of Computing, Mohammed VI Polytechnic University, Lot 660, Ben Guerir, 43150, Morocco; Principal corresponding author.In this study, Explainable Artificial Intelligence (XAI) techniques were applied to identify the most important factors influencing crop yield prediction, with a focus on strategies for sustainable agriculture. Using permutation importance and residual plot analysis, the results showed that nitrogen (N) content, Bandera variety and potassium oxide (K2O) are the most important traits influencing yield prediction. Extreme Gradient Boosting (XGB) was used to predict yield using a large Moroccan national cereal dataset spanning 3 seasons. Residual Plots Analysis, Partial Dependent Plots (PDP), Permutation Importance (PI) and SHapley Additive ExPlanations (SHap) were used to select the features that influence yield prediction. The results indicate that optimizing soil nitrogen and potassium oxide levels together with strategic selection of crop varieties can significantly increase productivity. Residual analysis of the eXtreme Gradient Boosting (XGB) model confirmed its high predictive accuracy. This study underlines the value of XAI methods in improving the interpretability of predictive models. The insights gained can contribute to better soil management and informed crop selection, ultimately reducing yield losses under environmental stress. By increasing the resilience of agricultural systems, we aim to contribute to sustainable and data-driven farming practices and, in particular, address some of Morocco's unique agricultural challenges.http://www.sciencedirect.com/science/article/pii/S2772375525001832Machine learningYield predictionGrain productionExplainable artificial intelligence |
| spellingShingle | Oumnia Ennaji Abdellah Hamma Leonardus Vergütz Achraf El Allali The assessment of soil variables relative importance for cereal yield prediction under rainfed cropping system in Morocco Smart Agricultural Technology Machine learning Yield prediction Grain production Explainable artificial intelligence |
| title | The assessment of soil variables relative importance for cereal yield prediction under rainfed cropping system in Morocco |
| title_full | The assessment of soil variables relative importance for cereal yield prediction under rainfed cropping system in Morocco |
| title_fullStr | The assessment of soil variables relative importance for cereal yield prediction under rainfed cropping system in Morocco |
| title_full_unstemmed | The assessment of soil variables relative importance for cereal yield prediction under rainfed cropping system in Morocco |
| title_short | The assessment of soil variables relative importance for cereal yield prediction under rainfed cropping system in Morocco |
| title_sort | assessment of soil variables relative importance for cereal yield prediction under rainfed cropping system in morocco |
| topic | Machine learning Yield prediction Grain production Explainable artificial intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525001832 |
| work_keys_str_mv | AT oumniaennaji theassessmentofsoilvariablesrelativeimportanceforcerealyieldpredictionunderrainfedcroppingsysteminmorocco AT abdellahhamma theassessmentofsoilvariablesrelativeimportanceforcerealyieldpredictionunderrainfedcroppingsysteminmorocco AT leonardusvergutz theassessmentofsoilvariablesrelativeimportanceforcerealyieldpredictionunderrainfedcroppingsysteminmorocco AT achrafelallali theassessmentofsoilvariablesrelativeimportanceforcerealyieldpredictionunderrainfedcroppingsysteminmorocco AT oumniaennaji assessmentofsoilvariablesrelativeimportanceforcerealyieldpredictionunderrainfedcroppingsysteminmorocco AT abdellahhamma assessmentofsoilvariablesrelativeimportanceforcerealyieldpredictionunderrainfedcroppingsysteminmorocco AT leonardusvergutz assessmentofsoilvariablesrelativeimportanceforcerealyieldpredictionunderrainfedcroppingsysteminmorocco AT achrafelallali assessmentofsoilvariablesrelativeimportanceforcerealyieldpredictionunderrainfedcroppingsysteminmorocco |