Estimating seasonal fragrant rice production in Thailand: A review article

Article Sidebar

PDF
Published: Sep 15, 2019
Keywords:
KDML105 rice variety Agro-informatics agricultural resource databases CSM-CERES-Rice model ORYZA2000 model

Main Article Content

Kaeomuangmoon, T.
Center for Agricultural Resource System Research, Faculty of Agriculture, Chiang Mai University, Chiang Mai, 50200, Thailand
Jintrawet, A.
Department of Plant and Soil Science and Center for Agricultural Resource System Research, Faculty of Agriculture, Chiang Mai University, Chiang Mai, 50200, Thailand
Katzfey, J.
Post Retirement Fellow, Climate Science Centre, CSIRO, Aspendale, 3195, Australia

Abstract

The article is reviewed the current and existing methods of fragrant rice crop yield and production forecasts in Thailand and their potential application for seasonal prediction.  While several government agencies have carried out the forecast task, however, the further research is needed in order to minimize risk and maximize efficiency of agricultural resources.  Incorporation of emerging methods could lead to a new strategy to forecast yields and production as well as to provide timely and reliable forecasts, by adopting integrated agro-informatic tools.  The manual crop cutting, end-of-season farmer surveys, remote sensing, spatial databases and decision support system tools and simulation models were reviewed.  We concluded that the spatial databases and climate and crop simulation models provided the opportunities to establish an inclusive and integrated platform for farmers, government agencies and private firms to participate in the important task of seasonal forecasting for fragrant rice production systems in Thailand.

Article Details

How to Cite
Kaeomuangmoon, T., Jintrawet, A., & Katzfey, J. (2019). Estimating seasonal fragrant rice production in Thailand: A review article. International Journal of Agricultural Technology, 15(5), 707–722. retrieved from https://li04.tci-thaijo.org/index.php/IJAT/article/view/8290
Issue
Vol. 15 No. 5 (2019): September
Section
Original Study
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

References

Amiri, E., Rezaei, M., Rezaei, E. E. and Bannayan, M. (2014). Evaluation of CERES-Rice, Aquacrop and Oryza2000 Models in Simulation of Rice Yield Response to Different Irrigation and Nitrogen Management Strategies. Journal of Plant Nutrition, 37:1749-1769.

Arora, V. K. (2006). Application of a Rice Growth and Water Balance Model in an Irrigated Semi-arid Subtropical Environment. Agricultural Water Management, 83: 51-57.

Baigorria, G. A., Chelliah, M., Mo, K. C., Romero, C. C., Jones, J. W., O’Brien, J. J. and Higgins, R. W. (2010). Forecasting Cotton Yield in the Southeastern United States using Coupled Global Circulation Models. Agronomy Journal, 102:187-196.

Bannayan, M., Kazuhiko, K., Han-Yong, K., Mark, L., Masumi, O. and Shu, M. (2005). Modeling the interactive effects of atmospheric CO2 and N on rice growth and yield. Field Crops Research, 93:14.

Basso, B., Liu, L. and Ritchie, J. T. (2016). A comprehensive review of the CERES-Wheat, -Maize and -Rice Models’ Performances. Advances in Agronomy, 136:27-132.

Bouman, B. A. M. and van Laar, H. H. (2006). Description and evaluation of the rice growth model ORYZA2000 under nitrogen-limited conditions. Agricultural Systems, 87:249-273.

Bouman, B. A. M. (2001). Water-efficient management strategies in rice production. Int. Rice Res. Notes. 26.

Bridhikitti, A. and Overcamp, T. J. (2012). Estimation of Southeast Asian rice paddy areas with different ecosystems from moderate-resolution satellite imagery. Agriculture, Ecosystems & Environment, 146:113-120.

Brown, J.N., Hochman, Z., Holzworth, D. and Horan, H. (2018). Seasonal Climate Forecasts Provide More Definitive and Accurate Crop Yield Predictions. Agricultural and Forest Meteorology, 260: 247-254.

Buddhaboon, C. and Jintrawet, A. (2009). Impacts of Climate Change on Rice Production in Chi-Moon river basin. KKU Research Journal, 14: 611-625.

Calanca, P. (2014). Weather forecasting applications in agriculture. Reference Module in Food Science, from Encyclopedia of Agriculture and Food Systems, 437-449.

Cantelaube, P. and Terres, J. M. (2005). Seasonal weather forecasts for crop yield modelling in Europe. Tellus A: Dynamic Meteorology and Oceanography, 57:476-487.

Capa-Morocho, M., Ines, A. V. M., Baethgen, W. E., Rodríguez-Fonseca, B., Han, E. and Ruiz-Ramos, M. (2016). Crop yield outlooks in the Iberian Peninsula: Connecting seasonal climate forecasts with crop simulation models. Agricultural Systems, 149:75-87.

Challinor, A. (2009). Towards the development of adaptation options using climate and crop yield forecasting at seasonal to multi-decadal timescales. Environmental Science & Policy, 12:453-465.

Chotamonsak, C., Salathé, E. P. J., Kreasuwan, J. and Chantara, S. (2011). Projected climate change over Southeast Asia simulated using a WRF regional climate model. Atmospheric Science Letters, 12:213-219.

Chun, J. A., Li, S., Wang, Q., Lee, W. S., Lee, E. J., Horstmann, N., HPark, O., Veasna, T., Vanndy, L., Pros, K. and Vang, S. (2016). Assessing rice productivity and adaptation strategies for Southeast Asia under climate change through multi-scale crop modeling. Agricultural Systems.

DOAE (Department of Agricultural Extension) (2017). Rice Farmers’ Registration System. [Online]. Available: http://farmer.doae.go.th/ [1 April 2018]

FAO (Food and Agriculture Organization of the United Nations) (1982). Estimation of Crop Areas and Yields in Agricultural Statistics. Rome: FAO.

Fermont, A. M. and Benson, T. (2011). Estimating Yield of Food Crops Grown by Smallholder Farmers: A review in the Uganda context. IFPRI Discussion paper 01097.

GISTDA (Geo-Informatics and Space Technology Development Agency (Public Organization)) 2018. Planting Rice Monitoring System from Satellite in Thailand. [Online]. Available: http://rice.gistda.or.th [1 April 2018].

Grosz, B., Horváth, L., Gyöngyösi, A. Z., Weidinger, T. and André, K. (2015). Use of WRF result as meteorological input to DNDC model for greenhouse gas flux simulation. Atmospheric Environment, 122:230-235.

Hoogenboom, G., Porter, C. H., Shelia, V., Boote, K. J., Singh, U., White, J. W., Hunt, L. A., Ogoshi, R., Lizaso, J. I., Koo, J., Asseng, S., Singels, A., Moreno, L. P. and Jones, J. W. (2017). Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.7. DSSAT Foundation. Gainesville, Florida, USA. [Online]. Available: https://DSSAT.net. [1 April 2018].

Hoogenboom, G. (2000). Contribution of agrometeorology to the simulation of crop production and its applications. Agricultural and Forest Meteorology, 103:137-157.

Hunt, L. A. and Boote, K. J. (1998). Data for model operation, calibration, and evaluation. pp 9-39, In Tsuji, G. Y., Hoogenboom, G. and Thornton, P. K. Understanding options for Agricultural Production. Kluwer Academic.

Jäger, A. D., Onduru, D., van Wijk, M. S., Vlaming, J. and Gachini, G. N. (2001). Assessing sustainability of low-external-input farm management systems with the nutrient monitoring approach: a case study in Kenya. Agricultural Systems. 69:99-118.

Jintrawet, A. (2009). A Decision Support System to Study the Impact of Climate Change on Food Production Systems. KKU Research Journal, 14: 589-600.

Jintrawet, A. and Chinvanno, S. (2011). Assessing Impacts of ECHAM4 GCM Climate Change Data on Main Season Rice Production Systems in Thailand. APN Science Bulletin, 1:29-34.

Jintrawet, A. and Kaeomuangmoon, T. (2016). A Decision Support System for Seasonal Rice Yield Forecast 3-6 Months (DSS-SRY4cast). A Research Final Report, Proj ID: RDG58A0013, submitted to The Thailand Research Fund (TRF). Bangkok, Thailand.

Jintrawet, A., Chitnucha B., Jerasorn S. and Archevarahuprok, B. (2017). Developing and Applying Climate Information for Supporting Adaptation in South East AsiaThailand Case Study: Impact of Projected Climate Change on Rice Production Systems. A case study report prepared under the Asian Development Bank (ADB) TA-8359 REG. (http://www.rccap.org/wp-content/uploads/2017/04/Thailand-FS-170622-web.pdf).

Johnson, M. D., Hsieh, W.W., Cannon, A. J., Davidson, A. and Bédard, F. (2016). Crop yield forecasting on the canadian prairies by remotely sensed vegetation indices and machine learning methods. Agricultural and Forest Meteorology, 218-219: 74-84.

Jones, J. W., Hansen, J. W., Royce, F. S. and Messina, C. D. (2000). Potential benefits of climate forecasting to agriculture. Agriculture, Ecosystems & Environment, 82:169-184.

Jones, J. W., Hoogenboom, G., Porter, C. H., Boote, K. J., Batchelor, W. D., Hunt, L. A., Wilkens, P. W., Singh, U., Gijsman, A. J. and Ritchie, J. T. (2003). The DSSAT cropping system model. Europ. J. Agronomy, 18:235-265.

Katzfey, J. J, McGregor, J. L. and Suppiah, R. (2014). High-resolution climate projections for Vietnam. Technical Report, CSIRO, Australia, pp.266.

Katzfey, J., Nguyen, K., McGregor, J. L., Hoffmann, P., Ramasamy, S., Nguyen, H. V., Khiem, M. V., Nguyen, T. V., Truong, K. B., Vu, T. V., Nguyen, H. T., Thuc, T., Phong, D. H., Nguyen, B. T., Tan, O. V., Trung, N. Q., Thanh, N. D. and Long, T. T. (2016). High-resolution simulations for Vietnam-methodology and evaluation of current climate. Asia-Pacific Journal of Atmospheric Science, 52: 91. https://doi.org/10.1007/s13143-016-0011-2.

Kioutsioukis, I., de Meij, A., Jakobs, H., Katragkou, E. and Kazantzidis, A. (2016). High resolution WRF ensemble forecasting for irrigation: Multi-variable evaluation. Atmospheric Research, 167:156-174.

Koide, N., Robertson, A. W., Ines, A. V. M., Qian, J.-H., DeWitt, D. G. and Lucero, A. (2013). Prediction of rice production in the philippines using seasonal climate forecasts. Journal of Applied Meteorology and Climatology, 52:552-569.

Li, T., Angeles, O., Marcaida III, M., Manalo, E., Manalili, M. P., Radanielson, A. and Mohanty, S. (2017). From ORYZA2000 to ORYZA (v3): An improved simulation model for rice in drought and nitrogen-deficient environments. Agricultural and Forest Meteorology, 237-238: 246-256.

Mauget, S., Leiker, G. and Nair, S. (2013). A web application for cotton irrigation management on the U.S. Southern High Plains. Part I: Crop yield modeling and profit analysis. Computers and Electronics in Agriculture, 99:248-257.

Mitkalaya, A., Thianthai, P. and Nuangjumnong, P. (2013). Geo-informatics for Rice Monitoring and Yield Estimation in Thailand.

Mkhabela, M. S., Mkhabela, M.S. and Mashinini, N. N. (2005). Early maize yield forecasting in the four agro-ecological regions of Swaziland using NDVI data derived fom NOAA's-AVHRR. Agricultural and Forest Meteorology, 129:1-9.

NBACFS (National Bureau of Agricultural Commodity and Food Standards) (2017). Thai Agricultural Standard: Thai Hom Mali Rice. Ministry of Agriculture and Cooperatives, Bangkok, Thailand.

OAE (Office of Agricultural Economics) (2014). Agricultural Statistics of Thailand 2013. Ministry of Agriculture and Cooperatives, Bangkok, Thailand.

OAE (Office of Agricultural Economics) (2016). Agricultural Statistics of Thailand 2015. Ministry of Agriculture and Cooperatives, Bangkok, Thailand.

Ratjen, A. M. and Kage, H. (2015). Forecasting yield via reference- and scenario calculations. Computers and Electronics in Agriculture, 114:212-220.

Ritchie, I. J., Singh, U., Godwin, D. and Bowen, W. T. (1998). Cereal growth, development and yield. In Tsuji, G. Y., Hoogenboom, G. and Thornton, P. K. Understanding Options for Agricultural Production. Dordrecht, Boston, London, Kluwer Academic Publishers in cooporation with International Consortium for Agricultural Systems Applications, pp. 79-98.

Ritchie, J. T., Alocilja, E. C., Singh, U. and Uehara, G. (1987). IBSNAT and the CERES-rice model. Weather and rice. Proceedings of the International Workshop on the Impact of Weather Parameter on Growth and Yield of Rice, Manila, Philippines, International Rice Research Institute, Manila, Philippines, pp:271-281.

Sapkota, T. B., Jat, M. L., Jat, R. K., Kapoor, P. and Stirling, C. (2016). Yield estimation of food and non-food crops in smallholder production systems. Methods for measuring greenhouse gas balances and evaluating mitigation options in smallholder agriculture. Rosenstock, T. S., Rufino, M. C., Butterbach-Bahl, K., Wollenberg, E. and Richards, M. Springer International Publishing, 163-174.

Singh, U., Ritchie, J. T. and Godwin, D. C. (1989). A user's guide to CERES-Rice model v2.10. International Fertilizer Development Center, P.O. Box 2040, Muscle Shoals, Alabama 35662, USA, pp.131.

Supit, I. (1997). Predicting national wheat yields using a crop simulation and trend models. Agricultural and Forest Meteorology, 88:199-214.

Teng, P. S., Batchelor, W. D., Pinnschmidt, H. O. and Wilkerson, G. G. (1998). Simulation of pest effects on crops using coupled pest-crop models: The potential for decision support. In Tsuji, G.Y. et al. eds. 1998. Understanding Options for Agricultural Production. London: Kluwer Academic Publishers.

Thailand Rice Department (2018). iRiceZoning 2.5. [Online] Available: http://carsr.agri.cmu.ac.th:88/Research/2016/iRPZ_25/index.html [28 April 2019].

Timsina, J. and Humphreys, E. (2006). Applications of CERES-Rice and CERES-Wheat in Research, Policy and Climate Change Studies in Asia: A Review. International Journal of Agricultural Research, 1:202-225.

TREA (Thai Rice Exporters Association) (2016). Rice export quantity and value: 2015. [Online]. Available: http://www.thairiceexporters.or.th/statistic_2015.html [1 April 2018].

Tsuji, G. Y., Hoogenboom, G. and Thornton, P. J. (1998). Understanding options for agricultural production. Kluwer Academic Publishers in cooperation with ICASA, pp. 399.

Van, D. E. and Hervás, J. (2012). State of the art of national landslide databases in Europe and their potential for assessing landslide susceptibility, hazard and risk. Geomorphology, 139-140:545-558.

Yoshida, S. (1981). Fundamentals of rice crop science. Los Banos, Philippines, International Rice Research Institute, pp. 269.

Zhang, N., Wang, M. and Wang, N. (2002). Precision agriculture-a worldwide overview. Computers and Electronics in Agriculture, 36:113-132.