Modeling impacts of climate-induced yield variability and adaptations on wheat and maize in a sub-tropical monsoon climate - using fuzzy logic
Abstract Climate change is causing more frequent and extraordinary extreme weather events that are already negatively affecting crop production. There is a need for improved climate risk assessment by developing smart adaptation strategies for sustainable future crop production. This study aims to a...
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-09820-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849235444697399296 |
|---|---|
| author | Md. Abdul Kaium Md. Sharif Ahmed Muhammad Habib-Ur-Rahman Md. Saidul Islam Yeasmin Akter Ratry Md Mostofa Uddin Helal Muhammad Ali Fardoush Siddquy Most. Moslema Haque Ahsan Raza Fatma Mansour Majed Alotaibi Ayman El Sabagh Reimund P. Roetter |
| author_facet | Md. Abdul Kaium Md. Sharif Ahmed Muhammad Habib-Ur-Rahman Md. Saidul Islam Yeasmin Akter Ratry Md Mostofa Uddin Helal Muhammad Ali Fardoush Siddquy Most. Moslema Haque Ahsan Raza Fatma Mansour Majed Alotaibi Ayman El Sabagh Reimund P. Roetter |
| author_sort | Md. Abdul Kaium |
| collection | DOAJ |
| description | Abstract Climate change is causing more frequent and extraordinary extreme weather events that are already negatively affecting crop production. There is a need for improved climate risk assessment by developing smart adaptation strategies for sustainable future crop production. This study aims to assess yield impacts of extreme temperatures and rainfall variability on wheat, and winter and summer season-planted maize in northwestern Bangladesh. Utilizing a machine learning approach, future yield patterns were predicted for these crops under various climate change scenarios. Additionally, the study developed adaptation strategies focusing on prediction of optimum sowing windows for wheat and maize to minimize climate risk-related yield losses jeopardizing food security. A fuzzy logical model was applied, incorporating a set of fuzzy rules to estimate the probable yields of wheat and maize (winter and summer growing seasons). Key climatic variables (temperature and rainfall) were added as model inputs, enabling the model to handle uncertainty and nonlinear interactions in the climate–yield relationship. Findings demonstrated that climate change has significant negative impacts at the different phenological stages of both wheat and maize (winter and summer seasons), with yield levels generally showing notable declines. Only small variations in optimal temperature and rainfall patterns affected crop yields significantly. Moreover, maize summer yield was consistently lower than maize winter as the temperature prevails high during the maize summer season (April to July). The study found that the wheat crop, maize winter, and maize summer have as optimal planting windows November 1–7, November 1–10, and February 20 - March 7, respectively. Such adaptation would ensure maximum yield and effective reduction of climate change risks. Outcomes of this study contribute to multiple Sustainable Development Goals (SDGs), especially three; zero hunger (SDG2), climate action (SDG13), and life on land (SDG14). These adaptations identified in this study can support policymakers and stakeholders to combat the impact of extreme climate – and achieving optimal yield. The approach is also applicable to other regions of the country and similar monsoon climates. |
| format | Article |
| id | doaj-art-2e9627adcf4f4b5bb3ffd5c1bde42d03 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-2e9627adcf4f4b5bb3ffd5c1bde42d032025-08-20T04:02:46ZengNature PortfolioScientific Reports2045-23222025-07-0115112010.1038/s41598-025-09820-3Modeling impacts of climate-induced yield variability and adaptations on wheat and maize in a sub-tropical monsoon climate - using fuzzy logicMd. Abdul Kaium0Md. Sharif Ahmed1Muhammad Habib-Ur-Rahman2Md. Saidul Islam3Yeasmin Akter Ratry4Md Mostofa Uddin Helal5Muhammad Ali Fardoush Siddquy6Most. Moslema Haque7Ahsan Raza8Fatma Mansour9Majed Alotaibi10Ayman El Sabagh11Reimund P. Roetter12Department of Crop Science and Technology, University of RajshahiDepartment of Crop Science and Technology, University of RajshahiDepartment of Crop Sciences, Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), University of GoettingenDepartment of Crop Science and Technology, University of RajshahiDepartment of Mathematics, University of RajshahiState Key Laboratory of Sustainable Dryland Agriculture, Institute of Wheat Research, College of Agriculture, Shanxi Agricultural UniversityGraduate School of Bioresources, Mie UniversityDepartment of Crop Science and Technology, University of RajshahiLeibniz Centre for Agricultural Landscape Research (ZALF)Department of Economics, Business and Economics Faculty, Siirt UniversityPlant Production Department, College of Food and Agricultural Sciences, King Saud UniversityFaculty of Agriculture, Department of Field Crops, Siirt UniversityDepartment of Crop Sciences, Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), University of GoettingenAbstract Climate change is causing more frequent and extraordinary extreme weather events that are already negatively affecting crop production. There is a need for improved climate risk assessment by developing smart adaptation strategies for sustainable future crop production. This study aims to assess yield impacts of extreme temperatures and rainfall variability on wheat, and winter and summer season-planted maize in northwestern Bangladesh. Utilizing a machine learning approach, future yield patterns were predicted for these crops under various climate change scenarios. Additionally, the study developed adaptation strategies focusing on prediction of optimum sowing windows for wheat and maize to minimize climate risk-related yield losses jeopardizing food security. A fuzzy logical model was applied, incorporating a set of fuzzy rules to estimate the probable yields of wheat and maize (winter and summer growing seasons). Key climatic variables (temperature and rainfall) were added as model inputs, enabling the model to handle uncertainty and nonlinear interactions in the climate–yield relationship. Findings demonstrated that climate change has significant negative impacts at the different phenological stages of both wheat and maize (winter and summer seasons), with yield levels generally showing notable declines. Only small variations in optimal temperature and rainfall patterns affected crop yields significantly. Moreover, maize summer yield was consistently lower than maize winter as the temperature prevails high during the maize summer season (April to July). The study found that the wheat crop, maize winter, and maize summer have as optimal planting windows November 1–7, November 1–10, and February 20 - March 7, respectively. Such adaptation would ensure maximum yield and effective reduction of climate change risks. Outcomes of this study contribute to multiple Sustainable Development Goals (SDGs), especially three; zero hunger (SDG2), climate action (SDG13), and life on land (SDG14). These adaptations identified in this study can support policymakers and stakeholders to combat the impact of extreme climate – and achieving optimal yield. The approach is also applicable to other regions of the country and similar monsoon climates.https://doi.org/10.1038/s41598-025-09820-3Adaptation, elevated temperaturePhenological developmentRainfall variabilitySub-tropical monsoon climate |
| spellingShingle | Md. Abdul Kaium Md. Sharif Ahmed Muhammad Habib-Ur-Rahman Md. Saidul Islam Yeasmin Akter Ratry Md Mostofa Uddin Helal Muhammad Ali Fardoush Siddquy Most. Moslema Haque Ahsan Raza Fatma Mansour Majed Alotaibi Ayman El Sabagh Reimund P. Roetter Modeling impacts of climate-induced yield variability and adaptations on wheat and maize in a sub-tropical monsoon climate - using fuzzy logic Scientific Reports Adaptation, elevated temperature Phenological development Rainfall variability Sub-tropical monsoon climate |
| title | Modeling impacts of climate-induced yield variability and adaptations on wheat and maize in a sub-tropical monsoon climate - using fuzzy logic |
| title_full | Modeling impacts of climate-induced yield variability and adaptations on wheat and maize in a sub-tropical monsoon climate - using fuzzy logic |
| title_fullStr | Modeling impacts of climate-induced yield variability and adaptations on wheat and maize in a sub-tropical monsoon climate - using fuzzy logic |
| title_full_unstemmed | Modeling impacts of climate-induced yield variability and adaptations on wheat and maize in a sub-tropical monsoon climate - using fuzzy logic |
| title_short | Modeling impacts of climate-induced yield variability and adaptations on wheat and maize in a sub-tropical monsoon climate - using fuzzy logic |
| title_sort | modeling impacts of climate induced yield variability and adaptations on wheat and maize in a sub tropical monsoon climate using fuzzy logic |
| topic | Adaptation, elevated temperature Phenological development Rainfall variability Sub-tropical monsoon climate |
| url | https://doi.org/10.1038/s41598-025-09820-3 |
| work_keys_str_mv | AT mdabdulkaium modelingimpactsofclimateinducedyieldvariabilityandadaptationsonwheatandmaizeinasubtropicalmonsoonclimateusingfuzzylogic AT mdsharifahmed modelingimpactsofclimateinducedyieldvariabilityandadaptationsonwheatandmaizeinasubtropicalmonsoonclimateusingfuzzylogic AT muhammadhabiburrahman modelingimpactsofclimateinducedyieldvariabilityandadaptationsonwheatandmaizeinasubtropicalmonsoonclimateusingfuzzylogic AT mdsaidulislam modelingimpactsofclimateinducedyieldvariabilityandadaptationsonwheatandmaizeinasubtropicalmonsoonclimateusingfuzzylogic AT yeasminakterratry modelingimpactsofclimateinducedyieldvariabilityandadaptationsonwheatandmaizeinasubtropicalmonsoonclimateusingfuzzylogic AT mdmostofauddinhelal modelingimpactsofclimateinducedyieldvariabilityandadaptationsonwheatandmaizeinasubtropicalmonsoonclimateusingfuzzylogic AT muhammadalifardoushsiddquy modelingimpactsofclimateinducedyieldvariabilityandadaptationsonwheatandmaizeinasubtropicalmonsoonclimateusingfuzzylogic AT mostmoslemahaque modelingimpactsofclimateinducedyieldvariabilityandadaptationsonwheatandmaizeinasubtropicalmonsoonclimateusingfuzzylogic AT ahsanraza modelingimpactsofclimateinducedyieldvariabilityandadaptationsonwheatandmaizeinasubtropicalmonsoonclimateusingfuzzylogic AT fatmamansour modelingimpactsofclimateinducedyieldvariabilityandadaptationsonwheatandmaizeinasubtropicalmonsoonclimateusingfuzzylogic AT majedalotaibi modelingimpactsofclimateinducedyieldvariabilityandadaptationsonwheatandmaizeinasubtropicalmonsoonclimateusingfuzzylogic AT aymanelsabagh modelingimpactsofclimateinducedyieldvariabilityandadaptationsonwheatandmaizeinasubtropicalmonsoonclimateusingfuzzylogic AT reimundproetter modelingimpactsofclimateinducedyieldvariabilityandadaptationsonwheatandmaizeinasubtropicalmonsoonclimateusingfuzzylogic |