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APPLYING SIMULATION TO IMPROVE RICE VARIETIES IN REDUCING THE ON-FARM YIELD GAP IN CAMBODIAN LOWLAND RICE ECOSYSTEMS

Published online by Cambridge University Press:  10 September 2014

P. L. POULTON ,
T. VESNA ,
N. P. DALGLIESH  and
V. SENG
P. L. POULTON*
Affiliation:
CSIRO Agriculture Flagship, Toowoomba, Queensland, Australia
T. VESNA
Affiliation:
Cambodian Agricultural Research and Development Institute (CARDI), Phnom Penh, Cambodia
N. P. DALGLIESH
Affiliation:
CSIRO Agriculture Flagship, Toowoomba, Queensland, Australia
V. SENG
Affiliation:
Cambodian Agricultural Research and Development Institute (CARDI), Phnom Penh, Cambodia
*
§Corresponding author. Email: [email protected]
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Summary

Achieving export growth in rice production from variable rainfed lowland rice ecosystems is at risk if depending on conventional breeding or genetic development alone. Sustained, long-term production requires building adaption capacity of smallholder farmers to better manage the challenges of seasonal climate variability and future climate change. Better understanding of the risks and constraints that farmers face in managing their current cropping system helps develop strategies for improving rice production in Cambodia. System models are now considered valuable assessment tools for evaluating cropping systems performance worldwide but require validation at the local level. This paper presents an evaluation of the APSIM-Oryza model for 15 Cambodian rice varieties under recommended practice. Data from a field experiment in 2011, conducted in a non-limiting water and nutrient environment, are used to calibrate varietal-specific coefficients and model input parameters. An independent dataset is then used to validate the model performance for a ‘real-world’ situation using on-farm data for six rice varieties planted in 54 farmer fields on 32 farms in two villages of Southeastern Cambodia. From this analysis, the APSIM-Oryza model is shown to be an acceptable tool for exploring the mismatch between current on-farm yields and potential production through yield gap analysis and the exploration of cropping system options for smallholder farmers to increase production, adapt to seasonal climate variability and be prepared for potential climate changes.

Type
Research Article
Information
Experimental Agriculture , Volume 51 , Issue 2 , April 2015 , pp. 264 - 284
Copyright
Copyright © Cambridge University Press 2014 

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References

REFERENCES

Almekinders, C. J. M. and Elings, A. (2001). Collaboration of farmers and breeders: participatory crop improvement in perspective. Euphytica 122:425–438.CrossRefGoogle Scholar
Bautista, E. U. (1999). Drum Seeder Technology for Rice Farmers. PCARRD Book Series No. 169.Google Scholar
Bhatia, V. S., Singh, P., Wani, S. P., Chauhan, G. S., Kesava Rao, A. V. R., Mishra, A. K. and Srinivas, K. (2008). Analysis of potential yields and yield gaps of rainfed soybean in India using CROPGRO-soybean model. Agricultural and Forest Meteorology 148:1252–1265.CrossRefGoogle Scholar
Bindraban, P. S., Stoorvogel, J. J., Jansen, D. M., Vlaming, J. and Groot, J. J. R. (2000). Land quality indicators for sustainable land management: proposed method for yield gap and soil nutrient balance. Agriculture, Ecosystems & Environment 81 (2):103–112.Google Scholar
Bingxin, Y. and Xinshen, D. (2011, March). Cambodia's agricultural strategy: future development options for the rice sector. Policy Discussion Paper, International Food Policy Research Insitute, Washington.Google Scholar
Boote, K. J., Jones, J. W. and Pickering, N. B. (1996). Potential uses and limitations of crop models. Agronomy Journal 88 (5):704–716.Google Scholar
Bouman, B. and Vanlaar, H. (2006). Description and evaluation of the rice growth model ORYZA2000 under nitrogen-limited conditions. Agricultural Systems 87 (3):249–273.CrossRefGoogle Scholar
Carberry, P. S., Hochman, Z. and McCown, R. L. (2002). The FARMSCAPE approach to decision support: farmers', advisers', researchers', monitoring, simulation, communication and performance evaluation. Agricultural Systems 74:141–177.CrossRefGoogle Scholar
Carberry, P., Gladwin, C. and Twomlow, S. (2004). Linking simulation modelling to participatory research in smallholder farming systems. In Modelling Nutrient Management in Tropical Cropping Systems. ACIAR Proceedings No. 114, ACIAR, Canberra, 32–46 (Eds. Delve, R. J. and Probert, M. E.).Google Scholar
Carberry, P. S., Hochman, Z., Hunt, J. R., Dalgliesh, N. P., McCown, R. L., Whish, J. P. M., Robertson, M. J., Foale, M. A., Poulton, P. L. and Van Rees, H. (2009). Re-inventing model-based decision support with Australian dryland farmers. 3. Relevance of APSIM to commercial crops. Crop and Pasture Science 60:1044–1056.Google Scholar
Dalgliesh, N. P. and Foale, M. A. (1998). Soil Matters – Monitoring Soil Water and Nitrogen in Dryland Farming. Toowoomba, QLD, Australia: Agricultural Production Systems Research Unit. ISBN: 0-643-06375-7.Google Scholar
Donald, C. M. and Hamblin, J. (1976). The biological yield and harvest index of cereals as agronomic and plant breeding criteria. Advances in Agronomy 28:361–405.CrossRefGoogle Scholar
Dunn, B. and Beecher, H.. (1994). Green manuring legume pasture for aerial-sown rice. Australian Journal of Experimental Agriculture 34:967–975.CrossRefGoogle Scholar
Fukai, S. (1999). Phenology in rainfed lowland rice. Field Crops Research 64:51–60.CrossRefGoogle Scholar
Gaydon, D. S., Probert, M. E., Buresh, R. J., Meinke, H., Suriadi, A., Dobermann, A., Bouman, B.et al. (2012). Rice in cropping systems – modelling transitions between flooded and non-flooded soil environments. European Journal of Agronomy 39:9–24.Google Scholar
Hay, R. K. M. (1995). Harvest index: a review of its use in plant breeding and crop physiology. Annals of Applied Biology 126:197–216.CrossRefGoogle Scholar
Horie, T., Ohnishi, M., Angus, J. F., Lewin, L. G., Tsukaguchi, T. and Matano, T. (1997). Physiological characteristics of high-yielding rice inferred from cross-location experiments. Field Crops Research 52:55–67.Google Scholar
Islam, M. S., Peng, S., Vispera, R. M., Sultan, M., Bhuyia, U., Hossain, S. M. Altaf and Julfiquar, A. W. (2010). Comparative study on yield and yield attributes of hybrid, inbred and NPT rice genotypes in a tropic irrigated ecosystem. Bangladesh Journal of Agricultural Research 35 (2):343–353.Google Scholar
Keating, B. A., Carberry, P. S., Hammer, G. L., Probert, M. E., Robertson, M. J., Holzworth, D., Huth, N. I. and Hargreaves, J. N. G. (2003). An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy 18:267–288.Google Scholar
Kropff, M. J., van Laar, H. H. and Mathews, R. B. (eds) (1994). ORYZA1, an ecophysiological model for irrigated rice production. In SARP Research Proceedings, International Rice Research Institute, Los Banos, Philippines and AB-DLO, TPE-WAU, Wageningen, the Netherlands, 110 pp.Google Scholar
Lobell, D. B., Cassman, K. G. and Field, C. B. (2009). Crop yield gaps: their importance, magnitudes, and causes. Annual Review of Environment and Resources 34:179–204.Google Scholar
Ministry of Agriculture Forestry and Fisheries, Cambodia (MAFF). (2011). Agricultural Statistics. Phnom Penh, Cambodia: Department of Planning, Statistics and International Cooperation.Google Scholar
Ntanos, D. and Koutroubas, S. (2002). Dry matter and N accumulation and translocation for Indica and Japonica rice under Mediterranean conditions. Field Crops Research 74 (1):93–101.Google Scholar
Ockerby, S. E. and Fukai, S. (2001). The management of rice grown on raised beds with continuous furrow irrigation. Field Crops Research 69:215–226.Google Scholar
Pathak, H., Ladha, J. K., Aggarwal, P. K., Peng, S., Das, S., Singh, Y., Singh, B., Kamra, S. K., Mishra, B., Sastri, A. S. R. A. S., Aggarwal, H. P., Das, D. K. and Gupta, R. K. (2003). Trends of climatic potential and on-farm yields of rice and wheat in the Indo-Gangetic Plains. Field Crops Research 80 (3):30.Google Scholar
Penning de Vries, F. W. T. (1977). Evaluation of simulation models in agriculture and biology; conclusion of a workshop. Agricultural System 1:99–107.Google Scholar
Probert, M. E., Keating, B. K, Thompson, J. P. and Parton, W. J. (1995). Modelling water, nitrogen, and crop yield for a long-term fallow management experiment. Australian Journal of Experimental Agriculture 35:941–950.Google Scholar
Sarom, M., Chaudhary, R. C., Javier, E., Makara, O., Sophany, S., Yadana, H., Hel, P. K., LeangHak, K., Sidhu, G. S., Sovith, S., Puthea, S., Sopheap, U. and Visarto, P. (2001). Description of Rice Varieties Released by Varietal Recommendation Committee of Cambodia (1990–2000). Phnom Penh, Cambodia: Cambodian Agricultural Research and Development Institute.Google Scholar
Sinclair, T. R. (1998). Historical changes in harvest index and crop nitrogen accumulation. Crop Science 38 (3):638–643.Google Scholar
Sing, U., Ladha, J. K., Castillo, E. G., Punzalan, G., Tirol-Padre, A. and Duqueza, M. (1998). Geotypic variation in nitrogen use efficiency in medium- and long-duration rice. Field Crops Research 58:35–53.Google Scholar
Swain, D. K., Herath, S., Bhaskar, B. C., Krishnan, P., Rao, K. S., Nayak, S. K. and Dash, R. N. (2007). Developing ORYZA 1N for Medium- and Long-Duration Rice. Agronomy Journal 99:428–440.Google Scholar
Timsina, J. and Humphreys, E. (2006). Performance of CERES-rice and CERES-wheat models in rice–wheat systems: a review. Agricultural Systems 90 (1–3):5–31.Google Scholar
Tsubo, M., Fukai, S., Tuong, T. P. and Ouk, M. (2007). A water balance model for rainfed lowland rice fields emphasising lateral water movement with a toposequence. Ecological Modelling 204:503–515.Google Scholar
United States Department of Agriculture (USDA). (2010). CAMBODIA: Future Growth Rate of Rice Production. Foreign Agricultural Service, Commodity Intelligence Report, January 26.Google Scholar
Van Ittersum, M. K., Cassman, K. G., Grassini, P., Wolf, J., Tittonell, P. and Hochman, Z. (2013). Yield gap analysis with local to global relevance – a review. Field Crops Research 143:4–17.Google Scholar
Whitbread, A. M., Robertson, M. J., Carberry, P. S. and Dimes, J. P. (2010). How farming systems simulation can aid the development of more sustainable smallholder farming systems in southern Africa. European Journal of Agronomy 32 (1):51–58.CrossRefGoogle Scholar
White, P. F., Oberthur, T. and Sovuthy, P. (eds). (1997). The Soils Used for Rice Production in Cambodia. A Manual for Their Identification and Management. International Rice Research Institute Report. ISBN 971-22-0094-9.Google Scholar
Yang, J. and Zhang, J.. (2010). Crop management techniques to enhance harvest index in rice. Journal of Experimental Botany, 61 (12):3177–3189.Google Scholar
Yoshida, S. (1972). Physiological aspects of grain yield. Annual Review of Plant Physiology 23:437–464.CrossRefGoogle Scholar