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Agriculture is vulnerable to seasonal, annual, and long-term variations in
climate as well as to short-term weather changes. Impact of climate change on crops
has become a top focus for research during the past ten years. Unfavorable weather
and climatic conditions might influence the growth and development of rice crop.
Although irrigation systems and better crop varieties have been developed, climate
continue to have high impact on rice production. Researchers can forecast future needs
based on climate change by using an appropriate model to simulate the characteristics
of a natural environmental system, that has been studied over a short time span. Crop
growth models are formulated to overcome crop production variability issues in
agricultural meteorology (Rauff and Bello, 2015). A general dynamic crop model
called InfoCrop has been developed to provide an integrated assessment of how
weather, crop variety, pests, soil, management strategies, and soil nitrogen and organic
carbon dynamics in both aerobic and anaerobic environments affect crop growth and
production (Aggarwal et al., 2006). A Decision Support System for Agrotechnology
transfer (DSSAT) was created by an international team of scientists with integrated
meteorological, soil, and crop data bases, strategy evaluation programmes, and cropsoil simulation models (Jones et al., 1998). It includes the Cropping System Model
(CSM)-Crop Environment Resource Synthesis (CERES)-Rice model, which simulates
the growth, development, and yield of rice crops based on interactions between soil,
water, weather, atmosphere, plants, and crop management (Jones et al., 2003).
The present investigation “Comparison of InfoCrop and CERES-DSSAT
models of rice under projected climatic conditions of Kerala” was carried out to assess
the impact of projected climate change on the performance of rice using InfoCrop and
CERES-DSSAT simulation models and to quantify the uncertainty in climate change
impact using GCM under various future climate scenarios (RCP 4.5 and 8.5). Short
duration variety, Jyothi and medium duration variety, Jaya were raised at Agricultural
Research Station, Mannuthy under KAU, Vellanikkara. The split plot design was used
with five dates of planting (June 5th, June 20th, July 5th, July 20th and August 5th) as
main plot treatments and two varieties as subplot treatments, with four replications.
Various observations like weather, phenological, biometric, yield and yield attributes
had been recorded to study the crop weather relationship. The crop weather analysis
has been carried out with SPSS software. The results indicated that duration of
phenophases had a negative correlation with the maximum temperature. A significant
variation in the biometric observations was also obtained. Plant height and drymatter
accumulation were found to be higher in Jyothi, when compared to Jaya. Highest yield
was found in July 20th planting of Jyothi and June 5th planting of Jaya. The crop
genetic coefficients that influence the growth and yield of rice in the InfoCrop and
CERES-DSSAT model were calibrated and validated, to achieve the best possible
agreement between the observed and simulated values. Predicted phenology and yield
of both rice varieties, Jyothi and Jaya, under different planting dates were reasonably
close to the observed values.
To study the impact of climate change on rice growth and yield, climate
change in Kerala had been estimated. The base climate (2021) has been compared with
three different future conditions like 2030 (near century), 2050 (mid century) and 2080
(end century) simulations for Thrissur district of Kerala under Representative
Concentration Pathway (RCP) 4.5 and 8.5. Weather data during the crop growth
period has been compared the base with future conditions. Under RCP 4.5 and 8.5, the
solar radiation expected to increase in all the future simulations. The maximum as well
as minimum temperature projected to increase by +2°C under RCP 4.5 and +3°C
under RCP 8.5 scenarios, by the end of the century. Analysis of growth phases and
yield of rice under RCP 4.5 and 8.5 scenarios for the 2030 (near century), 2050 (mid
century) and 2080 (end century) was done by using InfoCrop and CERES-DSSAT
models.
The rice varieties (Jyothi and Jaya) showed decrease in duration in the near,
mid and end century, compared to base period and the lowest duration was found in
August 5th planting under RCP 4.5 and 8.5 scenarios for InfoCrop as well as CERESDSSAT models. InfoCrop model predicted higher duration for June 20th planting
followed by July 5th planting and CERES-DSSAT model showed highest duration for
June 5th. InfoCrop model predicted higher yield in the near, mid and end century
compared to base period for both the varieties, in all the dates of planting under RCP
4.5 and 8.5 scenarios. Similarly, CERES-DSSAT model predicted higher yield for
Jyothi in all the dates of planting and for Jaya variety, in July 5th and July 20th
plantings, in both the scenarios