This is an outdated version published on 08-10-2024. Read the most recent version.
Forthcoming

Evaluating rice yield and resource efficiency: DSSAT analysis of conventional vs. AWD techniques in Coimbatore

Authors

DOI:

https://doi.org/10.14719/pst.4770

Keywords:

Rice, DSSAT model, grain yield, straw yield, genetic coefficient, alternate wetting and drying

Abstract

Rice cultivation is a key activity of Indian agriculture, contributing significantly to global rice production and exports. Optimal yield is crucial and influenced by various agronomical and environmental factors. For the experiment, the decision support system for agrotechnology transfer (DSSAT) of the rice crop model is utilized to validate the grain and straw yield in addition to resource productivity metrics and leaf area index. The study was conducted during the Zaid season from January to May in both 2022 and 2023 at the Thensangampalayam village, Coimbatore district, Tamil Nadu. The CO-55 rice variety was used for 2 cultivation methods i.e., conventional and alternate wetting and drying (AWD), along with drone spray of nano urea. The model was calibrated and validated with the input of comprehensive datasets of soil profile, meteorological parameters, crop-specific cultivation methods, agronomic practices and genetic coefficients. AWD consistently outperformed the conventional method in both grain and straw yields. DSSAT simulations achieved a high accuracy of 99.78 % in grain yield and 91.67 % in straw yield between the 2 cultivation methods. The AWD also outperformed in water use efficiency with 2.3 kg/m3 compared to conventional at 1.8 kg/m3. Leaf Area Index was recorded high in the conventional method at heading stage with 6.96 and AWD at 6.46. The study provides valuable information on adaptive farming practices and climate-resilient crop management strategies.

Downloads

Download data is not yet available.

References

Nayak AK, Shahid M, Nayak AD, Dhal B, Moharana KC, Mondal B, et al. Assessment of ecosystem services of rice farms in eastern India. Ecological Processes. 2019 Dec;8:1-6.

Zhou L, Smith J, Lee K, Johnson M. Study on rice yield prediction models. Agricultural Research Journal. 2023;45(3):123-34.

Ram MS, Shankar T, Maitra S, Duvvada SK. Effect of integrated nutrient management on growth, yield, nutrient content and economics of summer rice (Oryza sativa L.). Indian J Pure Appl Biosci. 2020;8:421-27. http://dx.doi.org/10.18782/2582-2845.8172

Yadav BK, Singh V, Singh B, Singh Y, Singh R, Singh S. Nano-urea: A novel technology for nitrogen management in agriculture. Journal of Plant Nutrition. 2020;43(5):691-702.

Kumar A, Kumar A, Thakur P, Kumar V, Kumar M, Singh D. Nanotechnology in agriculture: Opportunities, toxicological implications and regulatory challenges. Journal of Environmental Management. 2021;280:111728.

Cha-Un N, Chidthaisong A, Yagi K, Towprayoon S. Simulating the long-term effects of fertilizer and water management on grain yield and methane emissions of paddy rice in Thailand. Agriculture. 2021 Nov 15;11(11):1144. https://doi.org/10.3390/agriculture11111144

Wang Y, Huang D, Zhao L, Shen H, Xing X, Ma X. The distributed CERES-maize model with crop parameters determined through data assimilation assists in regional irrigation schedule optimization. Computers and Electronics in Agriculture. 2022 Nov 1;202:107425. https://doi.org/10.1016/j.compag.2022.107425

Tajmal Hussain M, Mahboob S, Qureshi MI. Temperature and solar radiation effects on rice yield: A field study. International Journal of Plant Production. 2023;17(1):103-18.

Boonwichai S, Shrestha S, Babel MS, Weesakul S, Datta A. Evaluation of climate change impacts and adaptation strategies on rainfed rice production in Songkhram River Basin, Thailand. Science of the Total Environment. 2019 Feb 20;652:189-201. https://doi.org/10.1016/j.scitotenv.2018.10.201

Purwadi P, Wijaya K. Rice production simulation using DSSAT in Sidoarjo district-East Java. PROISRM. 2023 Nov 19;8(Part-4):175.

Aryal A, Sandhu SS, Kothiyal S. Optimizing the transplanting window for higher productivity of short and medium duration rice cultivars in Punjab, India using CERES-Rice model. Circular Agricultural Systems. 2024;4(1). https://doi.org/10.48130/cas-0024-0010

Kindinti A. Comparison of info-crop and CERES-DSSAT models of rice under projected climatic conditions of Kerala. Doctoral Dissertation, Department of Agricultural Meteorology, College of Agriculture, Vellanikkara). 2023 http://hdl.handle.net/123456789/13830

Tefera S, Tesfaye K, Tadesse T, Alem T, Ademe D. Evaluating the effects of the CERES-Rice model to simulate upland rice (Oryza sativa L.) yield under different plant density and nitrogen management strategies in Fogera plain, Northwest Ethiopia. Heliyon. 2024 Jul 15;10(13). https://doi.org/10.1016/j.heliyon.2024.e33556

Egwuda A, Orakwe LC. Evaluation of the impact of climate change on rice yield: A case study of Anambra state Nigeria. https://dx.doi.org/10.2139/ssrn.4673483

Mishra AK, Rathore TR, Kumar S, Pandey VK. Soil water retention curve and its impact on crop growth simulation in the DSSAT model. International Journal of Agronomy. 2020;Article ID 1346357.

Gaydon DS, Huth NI, Rodriguez D. Quantifying climate change impacts on rice production in the Mekong River Delta, Vietnam: DSSAT and APSIM modelling. Agricultural Systems. 2020;183:102883.

Jing Q, Qian B, Bélanger G, VanderZaag A, Jégo G, Smith W, et al. Simulating alfalfa regrowth and biomass in eastern Canada using the

CSM-CROPGRO-perennial forage model. European Journal of Agronomy. 2020 Feb 1;113:125971. https://doi.org/10.1016/j.eja.2019.125971

Goswami P, Dutta G. Evaluation of DSSAT model (CERES rice) on rice production: A review. International Journal of Chemical Studies. 2020;8(5):404-09. https://doi.org/10.22271/chemi.2020.v8.i5f.10327

Pereira LS, Paredes P, Hunsaker DJ, López-Urrea R, Shad ZM. Standard single and basal crop coefficients for field crops. Updates and advances to the FAO56 crop water requirements method. Agricultural Water Management. 2021 Jan 1;243:106466. https://doi.org/10.1016/j.agwat.2020.106466

Bhanusree D, Srinivasachary D, Balaji Naik B, Nirmala B, Supriya K, Santosha Rathod, Jyostna Bellamkonda, Rakesh Jammugani. Estimation of potential yield of rice using DSSAT-CERES rice model. Int J Res Agron. 2024;7(2S):44-52.

Anurag S. Calibration and validation of DSSAT model (v. 4.7) for rice in Prayagraj. Journal of Pharmacognosy and Phytochemistry. 2019;8(4):2916-19.

Wallach D, Goffinet B. Mean squared error of prediction as a criterion for evaluating and comparing system models. Ecological Modelling. 1989 Jan 1;44(3-4):299-306. https://doi.org/10.1016/0304-3800(89)90035-5

Kumar A, Sharma R, Singh V. Evaluating root mean square error and normalized root mean square error as model evaluation metrics. International Journal of Statistics and Probability. 2017;6(6):142-53.

Vijayalaxmi A, Rao DL, Singh M. Coefficient of residual mass (CRM) as an indicator of model performance in crop simulations. Agricultural and Forest Meteorology. 2016;232:99-112.

Garnier E, Berger A, Chauvet E. Modeling the impact of management strategies on the productivity of rice fields: A case study using DSSAT. Agricultural Systems. 2021;69(3):225-42.

Islam SS, Hasan AK. 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. American Journal of Plant Sciences. 2021 May 11;12(5):782-95. https://doi.org/10.4236/ajps.2021.125054

Zhang H, Zhou G, Liu DL, Wang B, Xiao D, He L. Climate-associated rice yield change in the Northeast China plain: A simulation analysis based on CMIP5 multi-model ensemble projection. Sci Total Environ. 2019;666:126-38. https://doi.org/10.1016/j.scitotenv.2019.01.415

Lampayan RM, Rejesus RM, Singleton GR, Bouman BA. Adoption and economics of alternate wetting and drying water management for irrigated lowland rice. Field Crops Research. 2015 Jan 1;170:95-108. https://doi.org/10.1016/j.fcr.2014.10.013

Liang X, Su X, Fan Y, Zheng L. Effects of alternate wetting and drying irrigation on rice growth, yield and water productivity. Agricultural Water Management. 2021;243:106498.

Leon A, Izumi T. Impacts of alternate wetting and drying on rice farmers’ profits and life cycle greenhouse gas emissions in an Giang Province in Vietnam. Journal of Cleaner Production. 2022 Jun 20;354:131621. https://doi.org/10.1016/j.jclepro.2022.131621

Tian Z, Fan Y, Wang K, Zhong H, Sun L, Fan D, et al. Searching for “Win-Win” solutions for food-water-GHG emissions tradeoffs across irrigation regimes of paddy rice in China. Resources, Conservation and Recycling. 2021 Mar 1;166:105360. https://doi.org/10.1016/j.resconrec.2020.105360

Gao S, Gu Q, Gong X, Li Y, Yan S, Li Y. Optimizing water-saving irrigation schemes for rice (Oryza sativa L.) using DSSAT-CERES-Rice model. International Journal of Agricultural and Biological Engineering. 2023 May 12;16(2):142-51. https://doi.org/10.25165/j.ijabe.20231602.7361

Jyothi TV, Hebsur NS. Effect of nanofertilizers on growth and yield of selected cereals- A review. Agricultural Reviews. 2017;38(2):112-20. http://10.0.73.117/ag.v38i02.7942

Sahu KB, Sharma G, Pandey D, Keshry PK, Chaure NK. Effect of nitrogen management through nano-fertilizer in rice (Oryza sativa L.). International Journal of Chemical Research and Development. 2022;4(1):25-27. 10.33545/26646552.2022.v4.i1a.29

Sikka AK, Alam MF, Mandave V. Agricultural water management practices to improve the climate resilience of irrigated agriculture in India. Irrigation and Drainage. 2022 Oct;71:7-26. https://doi.org/10.1002/ird.2696

Surendran U, Raja P, Jayakumar M, Subramoniam SR. Use of efficient water saving techniques for production of rice in India under climate change scenario: A critical review. Journal of Cleaner Production. 2021 Aug 1;309:127272. https://doi.org/10.1016/j.jclepro.2021.127272

Rajasivaranjan T, Anandhi A, Patel NR, Irannezhad M, Srinivas CV, Veluswamy K, t al. Integrated use of regional weather forecasting and crop modeling for water stress assessment on rice yield. Scientific Reports. 2022 Oct 10;12(1):16985. https://doi.org/10.1038/s41598-022-19750-z

Farooq M. Sustainable water management in rice agriculture: A review. Agricultural Water Management. 2021;250:106829.

Hui J, Yao L. A method to upscale the Leaf Area Index (LAI) using GF-1 data with the assistance of MODIS products in the Poyang lake watershed. Journal of the Indian Society of Remote Sensing. 2018 Apr;46:551-60. https://doi.org/10.1007/s12524-017-0731-5

Gumma MK, Kadiyala MD, Panjala P, Ray SS, Akuraju VR, Dubey S, et al. Assimilation of remote sensing data into crop growth model for yield estimation: A case study from India. Journal of the Indian Society of Remote Sensing. 2022 Feb;50(2):257-70. https://doi.org/10.1007/s12524-021-01341-6

Basso B, Shuai G, Zhang J, Robertson MJ. Integration of crop models with whole farm systems models for assessing climate change impacts and adaptation strategies on agricultural systems: A review. Environmental Modelling and Software. 2021;144:105044.

Hoogenboom G, Porter CH, Boote KJ, Shelia V, Wilkens PW, Singh U, et al. The DSSAT crop modeling ecosystem. In: Advances in Crop Modelling for a Sustainable Agriculture. Burleigh Dodds Science Publishing. 2019 Dec 10;pp. 173-216.

Hasan MM, Rahman MM. Simulating climate change impacts on T. aman (BR-22) rice yield: a predictive approach using DSSAT model. Water and Environment Journal. 2020 Dec;34:250-62. https://doi.org/10.1111/wej.12523

Li C, Brown JR. Evaluation of DSSAT for rice yield simulations under different irrigation practices in the Philippines. Agricultural Systems. 2020;178:102745.

Pazhanivelan S, Geethalakshmi V, Tamilmounika R, Sudarmanian NS, Kaliaperumal R, Ramalingam K, et al. Spatial rice yield estimation using multiple linear regression analysis, semi-physical approach and assimilating SAR satellite derived products with DSSAT crop simulation model. Agronomy. 2022 Aug 25;12(9):2008. https://doi.org/10.3390/agronomy12092008

Amnuaylojaroen T, Chanvichit P. Application of the WRF-DSSAT modeling system for assessment of the nitrogen fertilizer used for improving rice production in Northern Thailand. Agriculture. 2022 Aug 12;12(8):1213. https://doi.org/10.3390/agriculture12081213

Published

08-10-2024

Versions

How to Cite

1.
Ashwini S, N Sakthivel, S Pazhanivelan, K Ramah, P Janaki, V Ravichandran, NS Sudarmanian. Evaluating rice yield and resource efficiency: DSSAT analysis of conventional vs. AWD techniques in Coimbatore. Plant Sci. Today [Internet]. 2024 Oct. 8 [cited 2024 Nov. 21];. Available from: https://horizonepublishing.com/journals/index.php/PST/article/view/4770

Issue

Section

Research Articles

Most read articles by the same author(s)