Application of DSSAT Crop Simulation Model to Estimate Rice Yield in Keonjhar District of Odisha (India) under Changing Climatic Conditions

Ray, Mrinmoy; Roul, PK; Baliarsingh, A

doi:10.20546/ijcmas.2018.704.075

Cited by 11 publications

(6 citation statements)

References 15 publications

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“…The lowest grain yield (2.40t ha -1 ) was obtained from Surjomoni with 0 kg N ha -1 (Table 3). On the other hand, BRRI dhan 29 showed highest grain production using 150 kg N/ha which is confirmed with the findings of (Ray et al, 2018;Osmel et al, 2020). As a result, the study revealed optimum use of nitrogen level influences all the parameters of different rice varieties as well as productivity.…”

Section: Effect Of Interaction Between Variety and Levels Of Nitrogen...supporting

confidence: 85%

Performance of Three Boro Rice Varieties Under Different Levels of Nitrogen Application

Razib¹,

Sarker²,

Sultana³

et al. 2023

Res. Agric. Livest. Fish.

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A field exper iment was carr ied out to study the effect of levels of nitrogen on the performance of three Boro rice varieties e.g. cv. BRRI dhan28, cv BRRI dhan29 and cv. Surjomoni. The experiment comprised four levels of nitrogen viz. 0 kg N ha-1, 60 kg N ha-1, 120 kg N ha-1 and 180 kg N ha- 1 . The experiment was laid out in a randomized complete block design with three replications. The highest grain yield (4.23 t ha-1) was obtained from cv. BRRI dhan29 because of production of the highest number of both total tillers m-2 and effective tillers m-2. BRRI dhan28 produced higher grain yield (3.39 t ha-1) compared to that of Surjomoni (3.07 t ha-1). Application of 120 kg N ha-1 produced the highest grain yield (4.23 t ha-1) because of the production of the highest number of both total tillers m- ' and effective tillers m-2 in the t reatment . Appl icat ion of 180 kg N ha-1 produced the second-highest grain yield (3.70 t ha-1) and 0 kg N ha-1 produced the lowest grain yield (2.76 t ha-1). Interaction between variety and levels of nitrogen significantly influenced all parameters studied except plant height, total tillers hill -1, effective tillers hill-1, non-effective tillers hill-1, length of panicle, number of sterile spikelets panicle-1 and 1000-grain weight. The highest grain yield (4.60 t ha-1) was obtained from BRRI dhan29 with the application of 120 kg N ha-1 and the lowest grain yield (2.40 t ha- 1 ) was obtained f rom the treatment combination of Surjomoni and 0 kg N ha-1. Rice variety BRRI dhan29 can be provided with 120 kg N ha-1 to get the highest and economic production of the crop. Vol. 9, No. 3, December 2022: 267-278

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Section: Effect Of Interaction Between Variety and Levels Of Nitrogen...supporting

confidence: 85%

Performance of Three Boro Rice Varieties Under Different Levels of Nitrogen Application

Razib¹,

Sarker²,

Sultana³

et al. 2023

Res. Agric. Livest. Fish.

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“…In fact this study provides an insight into the complex issue of evaluation and model performance. M.Ray (2018) [20] showed a good match was between observed and simulated grain yield of Swarna variety with a RMSE of 0.817 t/ha and a normalized RMSE (RMSEn) of 14.943% in the field trial performed in Orissa, India. An index of agreement for grain yield closer to 1 (0.869) also revealed that the model performed well in predicting the yield.…”

Section: G4mentioning

confidence: 89%

“…[16] 335. 31 [23] 175 kg/ha (TDK8) 4.67 Saythong Vilayvong (2014) [23] 84 kg/ha (TDK11) 2.67 KaushikSar (2017) [24] 266.72 kg/ha 1.46 M. Ray (2018) [20] 0.817 T/ha 14.94 Ravikant Chandrvavanshi (2019) [27] 0.35% NT Son (2016) [28] 11.7% Hasan (BR22) [17] 26.02% (Rajshahi) 9.90% (Barishal) Ranjit K Jha(2020) [29] 2.73 0.62 Kadiyala (2014) [35] 0.57T/ha 10.30 0.97 Anthesis Days (AD)…”

Section: Phenologymentioning

confidence: 99%

Evaluation of DSSAT model (CERES rice) on rice production: A review

Goswami¹,

Dutta²

2020

Int. J. Chem. Stud.

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Rice is the staple food for over half of the world population. Crop models potentially offer a means to readily explore management options to increase yield, and to determine trade-off between yield, resource-use efficiency and environmental outcomes. This paper reviews the performance of CERES-Rice model in different regions of the world in relation to their potential application towards increasing resource use efficiency and yield of rice. In this article, the CERES-Rice model evaluation by using the simulated and observed values on crop phenology (anthesis, physiological maturity) and final grain yield mainly over Asian countries by different authors has been compiled and described. Mainly the model was evaluated based on different statistical measures such as RMSE and D-index. Several datasets for the prediction of grain yield and phenological period across different parts of Asia were examined. This particular model predicted those with high-accuracy (nRMSE1-5% for anthesis and 1-4% for physiological maturity days). For various data sets for grain yield, the nRMSE varied between 0.05-5.00 percent with error percentage of 2-5%. The model sometimes over-estimated or under-estimated the values of grain yield, especially under water stress conditions.

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“…Process-based models usually use data that can be obtained from remote sensing such as LAI and NDVI by adding non -remote sensing parameters such as phenology, meteorology, geohydrology, land, and chemical levels such as CO2 and NO2. With this it is concluded that the process-based model not only estimates rice production results, but is also able to determine the duration of plant growth stages, the production of dry materials and partitions, the dynamics of the root system, the effects of groundwater content and soil nitrogen in photosynthesis, carbon balance, and water availability [39].…”

Section: Process-based Modelmentioning

confidence: 99%

“…The process-based model is presented in module form. Some examples of these modules are ORYZA [36], The World Food Study (WOFOST) [37], Simulation Model for Rice-Weather Relations (SIMRIW) [38], and the Ceres model from DSSAT [39]. Process-based models usually use data that can be obtained from remote sensing such as LAI and NDVI by adding non -remote sensing parameters such as phenology, meteorology, geohydrology, land, and chemical levels such as CO2 and NO2.…”

Section: Process-based Modelmentioning

confidence: 99%

Application remote sensing for rice yield estimation: a methodology review

Dairina,

Murti,

Harini

2024

Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet

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Precision agriculture is an integrated farming system based on information and production, to increase the efficiency, productivity and profitability of agricultural production. The application of remote sensing for monitoring rice growth and calculating rice yields is considered more effective than conventional calculation methods. This study aims to review the application of remote sensing for mapping paddy fields and forecasting rice production. The review includes rice characteristics that can be sensed through remote sensing images, types of images, rice yield estimation models, and remote sensing analysis approaches. The study shows that optical and radar images are capable of mapping paddy fields and providing information on rice production by relying on the phenological of rice. Empirical, process-based, and semiempirical estimation models provide information with different levels of accuracy and scale. The spatial ecological approach is able to examine the relationship between the suitability of paddy fields and production yields, while the spectral approach relies on the transformation of the vegetation index with production yields. Research on rice yield estimation is suggested to go to the field to test the accuracy of mapping paddy fields and interview farmers to obtain data on rice production.

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Application of DSSAT Crop Simulation Model to Estimate Rice Yield in Keonjhar District of Odisha (India) under Changing Climatic Conditions

Cited by 11 publications

References 15 publications

Performance of Three Boro Rice Varieties Under Different Levels of Nitrogen Application

Performance of Three Boro Rice Varieties Under Different Levels of Nitrogen Application

Evaluation of DSSAT model (CERES rice) on rice production: A review

Application remote sensing for rice yield estimation: a methodology review

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