Determination of Upland Rice Cultivar Coefficient Specific Parameters for DSSAT (Version 4.7)-CERES-Rice Crop Simulation Model and Evaluation of the Crop Model under Different Temperature Treatments conditions

To develop basis for strategic or arranged decision making towards crop yield improvement in Thailand, a new method in which crop models could be used is essential. Therefore, the objective of this study was to measure cultivar specific parameters by using DSSAT (v4.7) Cropping Simulation Model (CSM) with five upland rice genotypes namely Dawk Pa-yawm, Mai Tahk, Bow Leb Nahng, Dawk Kha 50 and Dawk Kahm. Experiment was laid out in a Completely Randomized Design (CRD) with split plot design. Results showed that five upland rice genotypes had significantly affected each other by different temperature treatments (28˚C, 30˚C, 32˚C) with grain yield, tops weight, harvest index, flowering, and maturity date. At the same time, all the phenological traits had highly significant variation with the genotypes. The cultivar specific parameters obtained by using a temperature tolerant cultivar (Basma-ti 385) with five upland genotypes involved in the DSSAT4.7-CSM. Model evaluation results indicated that utilizing the estimated cultivar coefficient parameters, model simulated well with varying temperature treatments as indicated by the agreement index (d-statistic) closer to unity. Hence, it was estimated that model calibration and evaluation was realistic in the limits of test cropping seasons and that CSM fitted with cultivar specific parameters can be used in lustrated by the portions given in this document. 40 60, 60 - 80 cm before planting. Soil classes, organic carbon (%), silt, clay (%), soil texture, soil


Introduction
Crop simulation models have been utilized globally as an effective or planned research for decision support tools in crop productivity or resources management. Several crop models used from long time to assist crop management practices with exploring physiological processes under different environments [1].
The Decision Support Systems for Agrotechnology Transfer (DSSAT) is a popular package software comprises crop simulation models (CSM) for over 42 different crops including rice. The DSSAT software consists of: 1) data base management system for soil, weather, genetic coefficients, and management inputs; 2) crop simulation models (CSM); 3) series of utility programs; 4) series of weather generation programs; 5) strategy evaluation program to evaluate options including choice of variety, planting date, plant population density, row spacing, soil type, irrigation, fertilizer application, initial conditions on yields, water stress in the vegetative or reproductive stages of development, and net returns.
DSSAT-CSM models simulate growth, development, and yield of crops as a function of the soil-plant-atmosphere-management dynamics [2].
Crop modelling can also be useful to understand the scientist, researchers define research priorities. Using a model to estimate the importance and the effect of certain parameters, a researcher can observe which factors should be more studied in future research, thus increasing the understanding of the system. Crop Simulation Models (CSM) are tools of systems that define procedures of crop growth and development as an act of weather, soil conditions, crop management and help in solving problems related to crop production [3].
CSM was used to simulate grain yield, biomass, and water balance in rice crop [4]. For dynamic crop simulation model accurate estimation of crop cultivar coefficients is the main point into use (for research as well as decision making) for improvement, identification and consequently narrowing gaps in our knowledge over crops and biophysical aspects for improved agricultural productivity.
Rice (Oryza sativa L.) rice is one of the most important crops and it represents a staple food for over half of the world's population, with a global production of more than 700 million tons per year and a harvested area reaching 165 million ha [5]. In 2050 world's population growth will be 10 billion and the demand for rice will grow faster than other crops [6]. There are already many challenges to attaining higher productivity of rice. Climate change e.g., high temperature and its Upland rice suffers severely from irregular environmental factors, e.g., air temperature, drought, and precipitation [7]. Temperature is attributed by its impact on crop yield, due to expansion under excess heat stress conditions that greatly influences the growth duration and yield of the rice plant [8]. The temperature rise is one of issues of climate change that has the effect of rice production in Thailand, especially to the development and growing of upland rice plants reported by [9]. In the growing season the mean temperature, temperature sum, ranges, distribution pattern and diurnal changes or a combination of these, highly correlated with grain yields had a significant issue [10]. Due to global warming and climatic risk, the current rice productions in Thailand are in danger. To fulfil the increase rice demand of ever-growing population pressures, an estimation of likely impact is vital for planning strategies.
For estimating cultivar coefficients, numerous methods have been recognized.
However, these methods need key knowledge regarding a specific crop cultivar such as planting dates, flowering dates, physiological maturity dates and final grain yield, which in most cases are not available. Genetic coefficient calculator (GENECALC) which is a sub module in the Decision Support System for Agrotechnology Transfer (DSSAT v4.7) was used to determine cultivar coefficients for new peanut lines in Thailand from standard varietal trials reported by [11]. Some researchers used generalized possibility uncertainty estimation (GLUE) method to estimate maize cultivar coefficients [12]. Hence, DSSAT v4.7 has GLUE module for estimating crop cultivar coefficients [13]. All the above-mentioned approaches

Daily Weather Model
Maximum and minimum air temperatures, precipitation, rainfall, and solar radiation (derived from sunshine hour data) were collected from the weather station of Kho Hong Agro meteorological office, Hat Yai, Thailand.

Management Practices
Plant density, planting date, irrigation, weeding, plant row spacing, sowing depth, amount and types of fertilizers, insecticide application was done whenever necessary.

Plant Profile Data
Sowing date, emergence date, flowering date, physiological maturity date, panicle initiation (when 50% and 100% of the crop had reached those stages), planting density, plant height, tops weight (grain weight), harvest index and grain yield per genotype, i.e., grain yield per area of production.     Critical photoperiod or the longest day length (in hours) at which the development occurs at a maximum rate.

P2R (Photoperiodism Coefficients)
Extent to which phasic development leading to panicle initiation is delayed (expressed as GDD) for each hour increase in photoperiod above P2O.

P5 (Grain filling duration coefficient)
Time period in GDD) from beginning of grain filling (3 to 4 days after flowering) to physiological maturity with a base temperature of 9˚C.

G1 (Spikelet number coefficient)
Potential spikelet number coefficient as estimated from the number of spikelets per gm of main culm dry weight (less lead blades and sheaths plus spikes) at anthesis.

G2 (Single grain weight)
Under ideal growing conditions, i.e., non limiting light, water, nutrients, and absence of pests and diseases.

G3 (Tillering coefficient)
A higher tillering cultivar would have coefficient greater than 1.0 G4 (Temperature tolerance coefficient) = Usually 1.0 for genotypes grown in normal environments.

G4 (Temperature tolerance coefficient)
Usually, 1.0 for genotypes grown in normal environments. G4 for japonica type rice growing in a warmer environment would be 1.0 or greater. Likewise, the G4 value for Indica type rice in very cool environments or season would be less than 1.0.

Statistical Analysis
The analysis of variance (ANOVA) to evaluate cultivars growth and develop-

Model Calibration
Calibration is the process of adjusting some model parameters to local environmental conditions and obtains genetic coefficients for new cultivar used in modeling study [15]. According to [16]

Weather Conditions
From Figure 1 and

Soil Condition
Different soil layers of the study site with surroundings contain sandy clay loam texture and were highly acidic. Special management e.g., irrigation due to higher clay contents and drainage system should be adopted for heavy rainfall. As the soil was acidic, phosphorus fixation was required to improve soil pH, phosphorus fertilizers were added.

Analysis of Variance Result of Yield and Yield Attributes
Although ANOVA results (Table 4) for yield contributing traits using least significant differences (LSD) test (P < 0.05 and P < 0.01) revealed that some traits showed highly significant differences with temperatures and genotypes. Grain yield, tops weight, harvest index, flowering date, and maturity date was significantly affected by temperatures. Non-significant difference for tillers number, and leaf area index occurred possibly due to the optimum input of temperatures at the early stage. Whereas all the phenological traits had shown highly significant   Table 5 showed that both grain yield at harvesting period and tops weight at

CERES-Rice Model Calibration
Experiment for estimation cultivar coefficient was used for model calibration.
Data of observed and simulated days to anthesis, physiological maturity, grain yield, by product and tops weight were collected for all the genotypes (Table   6(a) and

Model Evaluation
The cultivar specific parameters found from experiments were used to evaluate CSM-CERES-Rice for simulating different temperature treatments under rainfed upland condition. The model simulated well the average number of anthesis to maturity days with high degree of agreement as indicated by the agreement index (d-statistics) ( Table 8). This is an indication that the model calibration and resulting cultivar specific parameters were reasonably estimated. Generally, there were significant different (p < 0.05) between observed and simulated data at all temperature treatments and in all variables. Especially simulated yield decreased as temperature increased in both model simulation and experimental observations. This suggests that the CERES-Rice model is sensitive to climatic variables such as temperature. Furthermore t-test showed significant difference (P < 0.05) between simulated and observed yields at all temperature treatments (Table 9).
For model evaluation, a comparison was made for the five upland rice genotypes between simulated and observed grain yield at 28˚C, 30˚C and 32˚C temperature treatments (Table 7). In this study all the genotypes had shown a close agreement between observed and simulated values of grain yield data. Results showed that Mai Tahk and Bow Leb Nahng were the best cultivars due to