Jonghan Ko

Parameterization of EPIC crop model for simulation of cotton growth in South Texas

Parameterization in crop simulation modelling is a general procedure to calibrate a crop model to explore the best fit for a certain regional environment of interest. The parameters of radiation use efficiency (RUE) and light interception coefficient (k) of cotton (Gossypium hirsutum) for different cultivars were estimated under various irrigation conditions in South Texas in 2006 and 2007. A calibration procedure was then performed for determination of RUE using the environmental policy impact calculator (EPIC) crop model (Williams et al. 1984). This was carried out using data sets obtained separately from the data for parameter estimation. The estimates of k and RUE were 0·63 and 2·5 g/MJ, respectively, which were determined based on the field experiment and variation of simulated lint yield. When the parameters were used with EPIC to simulate the variability in lint yields, a correlation coefficient of 0·86 and root mean square error (RMSE) of 0·22 t/ha were obtained, presenting no significant differences (paired t-test: P= 0·282) between simulation and measurement. The results demonstrate that an appropriate estimate of the model parameters including RUE is essential in order to make crop models reproduce field conditions properly in simulating crop growth, yield and other variables.

The Value of ENSO Forecast Information to Dual-Purpose Winter Wheat Production in the U.S. Southern High Plains

The value of El Nino-Southern Oscillation (ENSO) forecast information to southern high plains winter wheat and cattle-grazing production systems was estimated here by simulation. Although previous work has calculated average forecast value, the approach here was to estimate probabilities of the value of single forecasts from value distributions associated with categorical ENSO forecast conditions. A simple ENSO- phase forecast systems value was compared with that of an ideal forecast method that exactly predicted the tercile category of regional November-March precipitation. Simulations were conducted for four price sce- narios with wheat prices that randomly varied about a historical (

Simulation and mapping of rice growth and yield based on remote sensing

3.22 per bushel) and elevated (

Potential impacts on climate change on paddy rice yield in mountainous highland terrains

7.00 per bushel) mean and with returns on live weight gain that are consistent with the grain producer leasing pas- turage or owning cattle. In the simulations at

Application of GOCI-derived vegetation index profiles to estimation of paddy rice yield using the GRAMI rice model

3.22 per bushel, the best practices for specific forecast con- ditions varied with cattle-ownership conditions. However, the ENSO-phase systems value distributions were comparable to that of the perfect forecast system; thus more-accurate regional precipitation forecasts may not lead to more forecast value at the farm level. In the simulations at

Monitoring canopy growth and grain yield of paddy rice in South Korea by using the GRAMI model and high spatial resolution imagery

7.00 per bushel, even perfect categorical forecasts produced only minor profit effects, a result that is attributed here to an increased profit margin rather than to increased wheat value. Under both wheat-price conditions, however, the best no-forecast baseline practices are also shown to have value relative to an arbitrarily chosen management practice. Thus, following practices optimized to climatic conditions and current price and cost conditions might increase profits when no forecast information is available.

Soil water availability and capacity of nitrogen accumulation influence variations of intrinsic water use efficiency in rice

Abstract. The GRAMI crop growth model uses remote sensing data and thus has the potential to produce maps of crop growth and yield. A pixel-based crop information delivery system (CIDS) to simulate and map rice (Oryza sativa) growth and yield was developed using GRAMI. The GRAMI-rice model was parameterized using field data obtained at Chonnam National University, Gwangju, Republic of Korea, in 2011 and 2012. The model was separately validated using field data obtained at the same research site in 2009 and 2010. The model was then integrated into the CIDS to produce two-dimensional (2-D) maps of crop growth and yield. Simulated values of rice growth and yield agreed well with the corresponding measurements in both parameterization and evaluation. The simulated yields were in statistical agreement with the corresponding measured yields according to paired t tests (p=0.415 for parameterization and p=0.939 for validation). The CIDS accurately produced 2-D maps of rice growth and yield. The GRAMI-rice CIDS has simple input requirements and will be useful for regional rice growth monitoring and yield mapping projects.

Construction of an unmanned aerial vehicle remote sensing system for crop monitoring

Crop models are suitable tools to assess the potential impacts of climate change on crop productivity. While the associated assessment reports have been focused on major rice production regions, there is little information on how climate change will impact the future rice crop production in mountainous highland regions. This study investigated effects of climate change on yield of paddy rice (Oryza sativa) in mountainous highland terrains of Korea using the CERES-Rice 4.0 crop model. The model was first calibrated and validated based on observed data and then applied to simulations for the future projections of rice yield in a typical mountainous terrain which borders North and South Korea, the Haean Basin in Kangwon Province, Republic of Korea. Rice yield in the highland terrain was projected to increase by 2050 and 2100 primarily due to elevated CO2 concentration. This effect of CO2 fertilization on yield (+10.9% in 2050 and +20.0% in 2100) was also responsible for increases in water-use efficiency and nitrogen-use efficiency. With management options, such as planting date shift and increasing nitrogen application, additional yield gains were predicted in response to the future climate in this area. We also found that improving genetic traits should be another option to get further yield increases. All in all, climate change in mountainous highland areas should positively influence on paddy rice productivity.

Global warming likely reduces crop yield and water availability of the dryland cropping systems in the U.S. Central Great Plains

Feasibility study of the GOCI satellite for rice yield simulation with GRAMI model.Advantages of GOCI with high temporal resolution to simulate rice growth and development.Method to calculate rice phenology based on the BRDF-adjusted reflectance with GOCI and MODIS. In this paper, satellite remote sensing was used as the input parameter of the GRAMI rice model to evaluate its applicability for simulating paddy rice crop condition and yield assessment at the field scale. Especially, the worlds first Geostationary Ocean Color Imager (GOCI), which provides better temporal resolution than does MODIS, was applied to evaluate the estimation of intuitive paddy rice growth and development and to examine the feasibility for vegetation index profiles of the GRAMI rice model. Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data at 500-m resolution were used as reference data to validate the quality of the crop growth and development data derived from GOCI. Field measurements of paddy rice at Chonnam National University, Gwangju, South Korea, were performed to determine initial parameters of the GRAMI rice model, which is used to optimize biophysical processes in the soil-crop-atmosphere system. For angular-dependent vegetation products, daily rolling time series of vegetation indices of GOCI and MODIS were estimated using semi-empirical BRDF modeling based on 16-day composite procedures. The observed temporal variation in GOCI vegetation indices (VIs) based on BAR (bidirectional reflectance distribution function adjusted reflectance) showed a similar growing pattern to the simulated VIs of the crop model, but MODIS showed a difference between measured and simulated VIs during the cloudy monsoon season. The rice yields predicted by integrating satellite data and the GRAMI rice model were compared with field measurements and showed reasonable agreement with reference to paddy rice productivity in the study area.

Application of an unmanned aerial system for monitoring paddy productivity using the GRAMI-rice model

Monitoring crop conditions and forecasting crop yields are both important for assessing crop production and for determining appropriate agricultural management practices; however, remote sensing is limited by the resolution, timing, and coverage of satellite images, and crop modeling is limited in its application at regional scales. To resolve these issues, the Gramineae (GRAMI)-rice model, which utilizes remote sensing data, was used in an effort to combine the complementary techniques of remote sensing and crop modeling. The model was then investigated for its capability to monitor canopy growth and estimate the grain yield of rice (Oryza sativa), at both the field and the regional scales, by using remote sensing images with high spatial resolution. The field scale investigation was performed using unmanned aerial vehicle (UAV) images, and the regional-scale investigation was performed using RapidEye satellite images. Simulated grain yields at the field scale were not significantly different (p = 0.45, p = 0.27, and p = 0.52) from the corresponding measured grain yields according to paired t-tests (α = 0.05). The model’s projections of grain yield at the regional scale represented the spatial grain yield variation of the corresponding field conditions to within ±1 standard deviation. Therefore, based on mapping the growth and grain yield of rice at both field and regional scales of interest within coverages of a UAV or the RapidEye satellite, our results demonstrate the applicability of the GRAMI-rice model to the monitoring and prediction of rice growth and grain yield at different spatial scales. In addition, the GRAMI-rice model is capable of reproducing seasonal variations in rice growth and grain yield at different spatial scales.