Preliminary Study of WOFOST Crop Simulation in Its Prospect for Soybean (Glycine max L.) Optimum Harvest Time and Yield Gap Analysis in East Java

F. R. Abadi, I. K. Tastra, B. S. Koentjoro


Optimum harvest time and yield gap information are important aspects of grain quality optimization and production development. The World Food Studies (WOFOST) crop simulation model was studied in its application for soybean optimum harvest time and yield gap analysis in East Java, Indonesia. Data inputs were local weather of solar irradiance and daily temperature, with given soybean varieties provided in the WOFOST simulation. The simulation result was validated with the actual data using homogeneity test of regression coefficient. Result showed that differences between simulation and actual yield were insignificant (α=0.05), for each tested locations and soybean varieties. The average potential yield was 1,716 kg ha-1, where the highest was obtained from S-France 904 variety located in Malang Regency. The optimum root mean square error was 49.42 kg ha-1 with correlation coefficient of 0.918. Meanwhile, the optimum harvest time and yield gap have corresponded to the actual data where harvest time was at the shortest in Blitar Regency using N-France 901 and N Spain 903 varieties, while the average yield gap was 33%. In conclusion, WOFOST simulation model has a prospect to be applied further using local soybean varieties followed by validation in the whole East Java region.


East Java; Harvest time; Soybean; WOFOST; Yield gap

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Balitkabi. (2016). Deskripsi varietas unggul: Aneka kacang dan umbi. Malang, ID: Balai Penelitian Tanaman Aneka Kacang dan Umbi. Retrieved from website

Bellocchi, G., Rivington, M., Donatelli, M., & Matthews, K. (2011). Validation of biophysical models: Issues and methodologies. In Lichtfouse. E., Hamelin. M., Navarrete. M., & Debaeke. P. (Eds.), Sustainable Agriculture (2nd ed., pp. 577–603). Dordrecht: Springer. crossref

Boogaard, H., Wolf, J., Supit, I., Niemeyer, S., & van Ittersum, M. (2013). A regional implementation of WOFOST for calculating yield gaps of autumn-sown wheat across the European Union. Field Crops Research, 143, 130–142. crossref

de Wit, A., Boogaard, H., van Diepen, K., van Kraalingen, D., Rötter, R., Supit, I., … van Ittersum, M. (2015). WOFOST developer’s response to article by Stella et al., Environmental Modelling & Software 59 (2014): 44-58. Environmental Modelling & Software, 73, 57–59. crossref

Eweys, O. A., Elwan, A. A., & Borham, T. I. (2017). Integrating WOFOST and Noah LSM for modeling maize production and soil moisture with sensitivity analysis, in the east of The Netherlands. Field Crops Research, 210, 147–161. crossref

Fischer, R. A. (2015). Definitions and determination of crop yield, yield gaps, and of rates of change. Field Crops Research, 182, 9–18. crossref

Gilardelli, C., Stella, T., Frasso, N., Cappelli, G., Bregaglio, S., Chiodini, M. E., … Confalonieri, R. (2016). WOFOST-GTC: A new model for the simulation of winter rapeseed production and oil quality. Field Crops Research, 197, 125–132. crossref

Grassini, P., Torrion, J. A., Yang, H. S., Rees, J., Andersen, D., Cassman, K. G., & Specht, J. E. (2015). Soybean yield gaps and water productivity in the western U.S. Corn Belt. Field Crops Research, 179, 150–163. crossref

Huang, J., Tian, L., Liang, S., Ma, H., Becker-Reshef, I., Huang, Y., … Wu, W. (2015). Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model. Agricultural and Forest Meteorology, 204, 106–121. crossref

Jin, M., Liu, X., Wu, L., & Liu, M. (2015). An improved assimilation method with stress factors incorporated in the WOFOST model for the efficient assessment of heavy metal stress levels in rice. International Journal of Applied Earth Observation and Geoinformation, 41, 118–129. crossref

Koentjoro, B. S., Sitanggang, I. S., & Makarim, A. K. (2015). Model simulasi dan visualisasi prediksi potensi hasil dan produksi kedelai di Jawa Timur. Jurnal Penelitian Pertanian Tanaman Pangan, 34(3), 195–201. crossref

Kroes, J. G., & Supit, I. (2011). Impact analysis of drought, water excess and salinity on grass production in The Netherlands using historical and future climate data. Agriculture, Ecosystems and Environment, 144(1), 370–381. crossref

Mourtzinis, S., Gaspar, A. P., Naeve, S. L., & Conley, S. P. (2017). Planting date, maturity, and temperature effects on soybean seed yield and composition. Agronomy Journal, 109(5), 2040–2049. crossref

Poerwoko, M. S. (2016). Breeding of the soybean varieties, aged maturity and resistant to rust disease. Agriculture and Agricultural Science Procedia, 9, 197–201. crossref

Pound, B., & Conroy, C. (2017). The innovation systems approach to agricultural research and development. In Sieglinde S. & Barry B. T. P. (Eds.), Agricultural Systems: Agroecology and Rural Innovation for Development (2nd ed., pp. 371–405). San Diego: Academic Press. crossref

Rattalino Edreira, J. I., Mourtzinis, S., Conley, S. P., Roth, A. C., Ciampitti, I. A., Licht, M. A., … Grassini, P. (2017). Assessing causes of yield gaps in agricultural areas with diversity in climate and soils. Agricultural and Forest Meteorology, 247, 170–180. crossref

Salmerón, M., & Purcell, L. C. (2016). Simplifying the prediction of phenology with the DSSAT-CROPGRO-soybean model based on relative maturity group and determinacy. Agricultural Systems, 148, 178–187. crossref

Setiyono, T. D., Cassman, K. G., Specht, J. E., Dobermann, A., Weiss, A., Yang, H., … De Bruin, J. L. (2010). Simulation of soybean growth and yield in near-optimal growth conditions. Field Crops Research, 119(1), 161–174. crossref

Smidt, E. R., Conley, S. P., Zhu, J., & Arriaga, F. J. (2016). Identifying field attributes that predict soybean yield using random forest analysis. Agronomy Journal, 108, 637–646. crossref

Statistics Indonesia. (2017). Luas panen, produksi dan produktivitas kedelai tahun 1993-2015 [Soybean harvested area, production and yield in 1993-2015]. Retrieved from website

Tastra, I. K., Erliana, G., & Fatah, G. S. A. (2012). Menuju swasembada kedelai melalui penerapan kebijakan yang sinergis. Iptek Tanaman Pangan, 7(1), 47–57. Retrieved from website

Tastra, I. K., Koentjoro, B. S., & Abadi, F. R. (2017). Pengembangan model simulasi potensi hasil kedelai berbasis web (Sucsoy.Ins). In Prosiding Seminar Hasil Penelitian Tanaman Aneka Kacang dan Umbi 2017 (pp. 296–313). Retrieved from PDF

Ugwuoke, P., & Okeke, C. (2012). Statistical assessment of average global and diffuse solar radiation on horizontal surfaces in tropical climate. International Journal of Renewable Energy Research, 2(2), 269–273. Retrieved from website

Wegerer, R., Popp, M., Hu, X., & Purcell, L. (2015). Soybean maturity group selection: Irrigation and nitrogen fixation effects on returns. Field Crops Research, 180, 1–9. crossref

Zhang, L., Van Der Werf, W., Cao, W., Li, B., Pan, X., & Spiertz, J. H. J. (2008). Development and validation of SUCROS-cotton: A potential crop growth simulation model for cotton. NJAS - Wageningen Journal of Life Sciences, 58(1–2), 59–83. crossref



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