Strategy of Soybean Management (Glycine max L.) to Cope with Extreme Climate Using CropSyst© Model

Aminah Aminah, Ambo Ala, Yunus Musa, Rusnadi Padjung, Kaimuddin Kaimuddin

Abstract


This research was carried out to verify the CropSyst© plant model from experimental data in a soybean field and to predict planting time along with its potential yield. The researches were divided into two stages. First stage was a calibration for model on field from June to September 2015. Second stage was the application of the model. The required data models included climatic, soil and crop’s genetic data. There were relationship between the obtained data in field and the simulation from CropSyst© model which was indicated by 0.679 of Efficiency Index (EF) value. This meant that the CropSyst© model was well used. In case of Relative Root Mean Square Error (RRMSE), it was shown at 2.68 %. RRMSE value described that there was a 2.68 % error prediction between simulation and actual production. In conclusion, CropSyst© can be used to predict the suitable planting time for soybean and as the result, the suitable planting time for soybean on the dry land is the end of rainy season (2 June 2015). Tanggamus variety is the most resistant variety based on slow planting time, because the decreased percentage of production was lower (8.3 %) than Wilis (26.3 %) and Anjasmoro (43.0 %).


Keywords


calibration; CropSyst©; Efficiency Index; RRMSE; soybean

Full Text:

PDF

References


Aminah, Jusoff, K., Hadijah, St., Nuraeni, Reta, Marliana, S. P., .... & Nonci, M. (2013). Increasing soybean (Glycine max L.) drought resistance with osmolit sorbitol. Modern Applied Science, 7(9), 78-85. http://dx.doi.org/10.5539/mas.v7n9p78

Bellocchi, G., Ashman, M., Shevtsova, L., Donatelli, M., Smith, P., Romanenkov, V., .... & Dailey, G. (2000). Using cropsyst and sundial to simulate soil organic matter dynamics at two sites in Eastern Europe. Paper presented at Proceedings of the 3rd ICS – ESA Congress, Hamburg, Germany (pp. 1-3). Retrieved from http://www.cracin.it/sipeaa/tools/CropSyst/CS_lassee.pdf

Donatelli, M., Bregaglio, S., Confalonieri, R., de Mascellis, R., & Acutis, M. (2014). A generic framework for evaluating hybrid models by reuse and composition – A case study on soil temperature simulation. Environmental Modelling & Software, 62, 478–486. http://doi.org/10.1016/j.envsoft.2014.04.011

Evett, S. R., & Tolk, J. A. (2009). Introduction: Can water use efficiency be modeled well enough to impact crop management? Agronomy Journal, 101(3), 423–425. http://doi.org/10.2134/agronj2009.0038xs

Hong, J,-K., Sung, C.-H., Kim, D.-K., Yun, H.-T., Jung, W., & Kim, K.-D. (2012). Differential effect of delayed planting on soybean cultivars varying in susceptibility to bacterial pustule and wildfire in Korea. Crop Protection, 42, 244-249. https://doi.org/10.1016/j.cropro.2012.07.014

Hu, M., & Wiatrak, P. (2011). Effect of planting date on soybean growth, yield, and grain quality: Review. Agronomy Journal, 104(3), 785–790. http://doi.org/10.2134/agronj2011.0382

Kaimuddin, K., Kamaluddin, A., & Sasmono, M. S. (2013). Analisis tingkat kerentanan dan adaptasi terhadap perubahan iklim berbasis ekosistem padi di provinsi Sulawesi Selatan [Analysis of vulnerability and adaptation to climate change with rice-based ecosystem in South Sulawesi]. Retrieved from http://balitbangda.sulselprov.go.id/artikel-analisis-tingkat-kerentanan-dan-adaptasi-terhadap-perubahan-iklim-berbasis-ekosistem-padi-di-provins.html

Palosuo, T., Kersebaum, K. C., Angulo, C., Hlavinka, P., Moriondo, M., Olesen, J. E., … Rötter, R. (2011). Simulation of winter wheat yield and its variability in different climates of Europe: A comparison of eight crop growth models. European Journal of Agronomy, 35(3), 103–114. http://doi.org/10.1016/j.eja.2011.05.001

Radovanović, S., & Šovljanski, A. (2013). CropSyst model and model testing for use in Serbia. Paper presented at Technical Workshop on Crop Yield Forecast in SEE, Skopje, Macedonia 30 – 31 May. Republic Hydrometeorological Service of Serbia. Retrieved from http://ies-webarchive-ext.jrc.it/mars/mars/content/download/3244/16290/file/24_Sovljanski_Serbia.pdf

Rotter, R. P., Palosuo, T., Kersebaum, K. C., Angulo, C., Bindi, M., Ewert, F., … Trnka, M. (2012). Simulation of spring barley yield in different climatic zones of Northern and Central Europe: A comparison of nine crop models. Field Crops Research, 133, 23–36. http://doi.org/10.1016/j.fcr.2012.03.016

Singh, A. K., Tripathy, R., & Chopra, U. K. (2008). Evaluation of CERES-Wheat and CropSyst models for water–nitrogen interactions in wheat crop. Agricultural Water Management, 95(7), 776-786. https://doi.org/10.1016/j.agwat.2008.02.006

Statistics Indonesia. (2016). Tanaman pangan [Crop plants]. Retrieved from https://bps.go.id/Subjek/view/id/53#subjekViewTab3|accordion-daftar-subjek1

Stöckle, C. O., Kemanian, A. R., Nelson, R. L., Adam, J. C., Sommer, R., & Carlson, B. (2014). CropSyst model evolution: From field to regional to global scales and from research to decision support systems. Environmental Modelling & Software, 62, 361–369. http://doi.org/10.1016/j.envsoft.2014.09.006

Stöckle, C., Higgins, S., Kemanian, A., Nelson, R., Huggins, D., Marcos, J., & Collins, H. (2012). Carbon storage and nitrous oxide emissions of cropping systems in eastern Washington: A simulation study. Journal of Soil and Water Conservation, 67(5), 365–377. http://doi.org/10.2489/jswc.67.5.365

Wijayanto, Y. (2010). Site specific nitrogen management simulated by cropsyst model under different inputs of nitrogen fertilizer. Journal of Tropical Soils, 15(3), 229-235. Retrieved from http://journal.unila.ac.id/index.php/tropicalsoil/article/viewFile/113/pdf




DOI: http://doi.org/10.17503/agrivita.v39i3.1020

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