The study was conducted at Mountain Research Centre for Field Crops, SKUAST-Kashmir, Larnoo, Anantnag, J&K (75.331oN, 33.644oE) at an altitude of 2280 msl. The location is characterized by cold temperate conditions with moderate summers and severe winters. The rice crop is grown between April to October with the maximum temperatures falling in the range of 25-30oC. The precipitation follows the even distribution at 75-100 mm per month during active growth phase and then decreases sharply near maturity in September (Figs. 1-6). The soil texture is clay loam to clay with moderate fertility status and slightly acidic pH devoid of any hazardous salt accumulation.
Record of observations
The observations on the variety K-332 were recorded on a uniformly maintained experimental plot of 225 m2 area for a period of 20 years starting from the year 2000 to 2019. The traits recorded were plant height (cm), days to 50% anthesis, number of tillers per m2, number of spikelets per panicle, days to physiological maturity and grain yield per plot (q/ha). The observations on yield recorded during the year 2000 were used to calculate the genetic coefficients of the rice variety K-332 and for subsequent calibration and validation of the CERES model. Validation of the CERES model was carried out with the yearly crop data recorded from 2001 to 2019. Days to flowering was counted from the date of sowing up to anthers dehiscence stage of the terminal spikelets of half of the plant population on plot basis and physiological maturity stage was marked on the day when caryopses of 90 percent of the grains from sampled secondary and tertiary panicles were fully developed in size and were hard, free from greenish tint. The single panicle from the primary tiller was harvested at maturity from thirty plants from random corners of the plot, the number of spikelets was counted and expressed as spikelets per panicle. Height of such thirty plants was recorded from ground level to the tip of the longest leaf and averaged to get the plant height (cm). Grain yield was calculated from the weight of grains harvested from the whole plot and air dried to about 14 percent moisture. Information on texture (clay and silt %), depth, surface albedo, soil organic carbon, fertility factor, and pH was included in soil data sub-module as per the outlined procedure. Soil drainage and hydraulic characteristics were computed through the sub-module (Gijsman et al. 2002, 2007 and Ritchieet al. 1989).
The Crop model
The DSSAT CERES v 4.7crop model required daily record of observations on total incoming solar radiation (MJ/m²-day), maximum and minimum air temperature (ºC), rainfall (mm), information on soil parameters (texture, depth, organic carbon, pH and bulk density ) and crop management data (planting date, planting density, row spacing, planting depth, crop variety, irrigation and fertilizer practices) as input (Hoogenboom et al. 2019 and Jones et al. 2003). Genetic information about rice crop were defined in rice cultivar file of the software. The simulation model integrated the effects of soil, crop phenotype, weather and management options, and allowed "what if" virtual simulation experiments. The model also provided for evaluation of crop model outputs with experimental data to facilitate comparison of simulated outcomes with observed results.
Model calibration and validation
The DSSAT-CERES v 4.7 rice model was calibrated with average end-of-season data on days to anthesis, days to physiological maturity and grain yield obtained from field experiments carried under optimum crop and soil management in 2000 (Supplementary Table S1). The Genetic coefficients for K-332 rice variety were estimated by running genetic coefficient calculator(GENCALC) module in the DSSAT v 4.7.The GENCALC for K-332 was run with a set of cultivar coefficients available in rice cultivar file after adding the K-332 rice cultivar to the file. The genetic coefficients were increased and decreased through an interactive procedure in the cultivar file, to get a best suitable set of genetic coefficients as per Hunt et al. (1993).
For comparing the impact of projected climate change scenario on phenology and yield of K-332 with that of other four high altitude rice varieties (Koshihikari, Mushk Budji, Kamad and Shalimar Rice 5) at MCRS, Larnoo and five low altitude rice varieties (Jhelum. Shalimar Rice-1, Shalimar Rice-2, Shalimar Rice-3 and Shalimar Rice-4) at MRCFC Khudwani the model was calibrated as per the recommended procedure (Hunt et al. 1993) for each of the nine varieties using the required crop data, recorded during 2019 from experimental plots of these varieties, maintained under optimum management. The simulation performance of the model was validated with crop data for these varieties, obtained during 2020. Subsequently, the temperature in the weather module of the model was increased as per the IPCC climate change projections and effect of every modification on crop performance was simulated and compared with base crop data for the year 2019 (Table 1 and Supplementary Table S2).
The observed and model simulated data was subjected to following statistical analysis for elucidating the performance of the simulation.
Coefficient of Determination
Where, Xi: Observed data; Yi: Simulated Data
R2 was used as a measure of goodness of fit for the observed and simulated data and ranges from 0-1.
Mean absolute error
MAE takes the units of y - x and is used as measure of accuracy to compare the output of the same variables. MAE >= 0
Modified index of agreement
Modified modelling efficiency
EF1 (-∞ to 1.0) is based on sum of absolute values of deviation
Climate change projections
Climate change projections for South Asia made by Intergovernmental Panel on Climate Change (IPCC) using representative concentration pathways (RCPs) under the Coupled Model Inter-comparison Project 5 (CMIP5) were used for the assessment of impact of climate change on rice crop performance (Table 1). The RCPs consider four 21st century pathways of greenhouse gas (GHG) emissions, their atmospheric concentrations, air pollutant emissions and land use. The RCPs rely on Integrated Assessment Models (IAMs) and multiple climate simulation models for making future climate projections. The RCPs include a strict mitigation scenario (RCP2.6), two intermediate scenarios (RCP4.5 and RCP6.0), and one scenario (RCP8.5) with very high GHG emissions. (IPCC, 2014)
Simulation of spikelets per panicle and plant height
The traits plant height and spikelets per panicle were not directly included in the crop model. Therefore, a separate analysis was carried out to estimate the effect of climate change on these two parameters. The simple genotypic correlations between the traits plant height, spikelets per panicle, days to anthesis and number of tillers as against grain yield were mined from the previous studies. Here the data from eleven such studies published in rated journals was used to create a correlation matrix among the four above given traits. Match pair analysis was carried out among the correlation coefficients from all the eleven studies and two arrays from the present study. Only the match pair analysis which involves the comparison with expected and observed values from the present study have been discussed.