Abstract
Predicting the effect of climate change on rice yield is crucial as global food demand rapidly increases with the human population. This study combined simulated daily weather data (MarkSim) and the CERES-Rice crop model from the Decision Support System for Agrotechnology Transfer (DSSAT) software to predict rice production for three planting seasons under four climate change scenarios (RCPs 2.6, 4.5, 6.0, and 8.5) for the years 2021 to 2050 in the Keduang subwatershed, Wonogiri Regency, Central Java, Indonesia. The CERES-Rice model was calibrated and validated for the local rice cultivar (Ciherang) with historical data using GenCalc software. The model evaluation indicated good performance with both calibration (coefficient of determination (R2) = 0.89, Nash–Sutcliffe efficiency (NSE) = 0.88) and validation (R2 = 0.87, NSE = 0.76). Our results suggest that the predicted changing rainfall patterns, rising temperature, and intensifying solar radiation under climate change can reduce the rice yield in all three growing seasons. Under RCP 8.5, the impact on rice yield in the second dry season may decrease by up to 11.77% in the 2050s. Relevant strategies associated with policies based on the results were provided for decision makers. Furthermore, to adapt the impact of climate change on rice production, a dynamic cropping calendar, modernization of irrigation systems, and integrated plant nutrient management should be developed for farming practices based on our results in the study area. Our study is not only the first assessment of the impact of climate change on the study site but also provides solutions under projected rice shortages that threaten regional food security.
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Claims 7
Claim · model-written summary
CERES-Rice calibration achieved R2 of 0.89 for local rice data.
- Taxon
- rice
Oryza sativae1 - Material
- Ciherange1
- Outcome
- coefficient of determination (R2)e0
- Value
- 0.89e0
- Qualifier
- calibratione0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The model evaluation indicated good performance with both calibration (coefficient of determination (R2) = 0.89, Nash–Sutcliffe efficiency (NSE) = 0.88) and validation (R2 = 0.87, NSE = 0.76).”
e1“The CERES-Rice model was calibrated and validated for the local rice cultivar (Ciherang) with historical data using GenCalc software.”
Claim · model-written summary
CERES-Rice calibration achieved NSE of 0.88.
- Taxon
- riceOryza sativae1
- Material
- Ciherange1
- Outcome
- Nash–Sutcliffe efficiency (NSE)e0
- Value
- 0.88e0
- Qualifier
- calibratione0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The model evaluation indicated good performance with both calibration (coefficient of determination (R2) = 0.89, Nash–Sutcliffe efficiency (NSE) = 0.88) and validation (R2 = 0.87, NSE = 0.76).”
e1“The CERES-Rice model was calibrated and validated for the local rice cultivar (Ciherang) with historical data using GenCalc software.”
Claim · model-written summary
CERES-Rice validation achieved R2 of 0.87.
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“validation (R2 = 0.87, NSE = 0.76)”
e1“The CERES-Rice model was calibrated and validated for the local rice cultivar (Ciherang) with historical data using GenCalc software.”
Claim · model-written summary
CERES-Rice validation achieved NSE of 0.76.
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“validation (R2 = 0.87, NSE = 0.76)”
e1“The CERES-Rice model was calibrated and validated for the local rice cultivar (Ciherang) with historical data using GenCalc software.”
Claim · model-written summary
Projected climate changes can reduce rice yield in all three growing seasons.
- Taxon
- riceOryza sativae0
- Treatment
- changing rainfall patterns, rising temperature, and intensifying solar radiation under climate changee0
- Outcome
- rice yielde0
- Direction
- reducee0
- Qualifier
- in all three growing seasonse0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Our results suggest that the predicted changing rainfall patterns, rising temperature, and intensifying solar radiation under climate change can reduce the rice yield in all three growing seasons.”
Claim · model-written summary
Under RCP 8.5, rice yield in the second dry season may decrease by up to 11.77% in the 2050s.
- Taxon
- riceOryza sativae0
- Treatment
- RCP 8.5e0
- Outcome
- rice yielde0
- Direction
- decreasee0
- Value
- 11.77%e0
- Qualifier
- second dry season may decrease by up to 11.77% in the 2050se0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Under RCP 8.5, the impact on rice yield in the second dry season may decrease by up to 11.77% in the 2050s.”
Claim · model-written summary
The authors recommend a dynamic cropping calendar, modernized irrigation, and integrated plant nutrient management to adapt rice production to climate change.
- Taxon
- riceOryza sativae0
- Outcome
- a dynamic cropping calendar, modernization of irrigation systems, and integrated plant nutrient managemente0
- Qualifier
- to adapt the impact of climate change on rice productione0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Furthermore, to adapt the impact of climate change on rice production, a dynamic cropping calendar, modernization of irrigation systems, and integrated plant nutrient management should be developed for farming practices based on our results in the study area.”
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