Evaluation of the RZWQM-CERES-Maize hybrid model for maize production

The root zone water quality model (RZWQM) was developed primarily for water quality research with a generic plant growth module primarily serving as a sink for plant nitrogen and water uptake. In this study, we coupled the CERES-Maize Version 3.5 crop growth model with RZWQM to provide RZWQM users with the option for selecting a more comprehensive plant growth model. In the hybrid model, RZWQM supplied CERES with daily soil water and nitrogen contents, soil temperature, and potential evapotranspiration, in addition to daily weather data. CERES-Maize supplied RZWQM with daily water and nitrogen uptake, and other plant growth variables (e.g., root distribution and leaf area index). The RZWQM-CERES hybrid model was evaluated with two well-documented experimental datasets distributed with DSSAT (Decision Support System for Agrotechnology Transfer) Version 3.5, which had various nitrogen and irrigation treatments. Simulation results were compared to the original DSSAT-CERES-Maize model. Both models used the same plant cultivar coefficients and the same soil parameters as distributed with DSSAT Version 3.5. The hybrid model provided similar maize prediction in terms of yield, biomass and leaf area index, as the DSSAT-CERES model when the same soil and crop parameters were used. No overall differences were found between the two models based on the paired t test, suggesting successful coupling of the two models. The hybrid model offers RZWQM users access to a rigorous new plant growth model and provides CERES-Maize users with a tool to address soil and water quality issues under different cropping systems.     

Influence of likelihood function choice for estimating crop model parameters using the generalized likelihood uncertainty estimation method

Proper estimation of model parameters is required for ensuring accurate model predictions and good model-based decisions. The generalized likelihood uncertainty estimation (GLUE) method is a Bayesian Monte Carlo parameter estimation technique that makes use of a likelihood function to measure the closeness-of-fit of modeled and observed data. Various likelihood functions and methods of combining likelihood values have been used in previous studies. This research was conducted to determine the effects of using previously reported likelihood functions in a GLUE procedure for estimating parameters in a widely-used crop simulation model. A factorial computer experiment was conducted with synthetic measurement data to compare four likelihood functions and three methods of combining likelihood values using the CERES-Maize model of the Decision Support System for Agrotechnology Transfer (DSSAT). The procedure used an arbitrarily-selected parameter set as the known “true parameter set” and the CERES-Maize model to generate true output values. Then synthetic observations of crop variables were randomly generated (four replicates) by using the simulated true output values (dry yield, anthesis date, maturity date, leaf nitrogen concentration, soil nitrate concentration, and soil moisture) and adding a random observation error based on the variances of corresponding field measurements. The environmental conditions were obtained from a sweet corn (Zea mays L.) experiment conducted in 2005 in northern Florida. Results showed that the method of combining likelihood values had a strong influence on parameter estimates. The combination method based on the product of the likelihoods associated with each set of observations reduced the uncertainties in posterior distributions of parameter estimates most significantly. It was also found that the likelihood function based on Gaussian probability density function was the best among those tested. This combination accurately estimated the true parameter values, suggesting that it can be used when estimating CERES-Maize model parameters for real experiments.     

Evaluation of Nitrogen Fertilization Patterns Using DSSAT for Enhancing Grain Yield and Nitrogen Use Efficiency in Rice

Temporal variation of rice growth and nitrogen (N) uptake generally follow a sigmoid curve and may respond positively to the N-fertilizer application at critical growth stages. In this study, it was hypothesized that the amount of N-fertilizer applied at critical growth stages possibly follows a geometric pattern such as line, parabola, and sinusoidal to attain maximum yield and nitrogen use efficiency. To test and identify the best pattern, short-term modeling-field testing-long-term modeling strategy was followed. The patterns with the highest simulated yield and nitrogen use efficiency from short-term modeling were tested in the field. Finally, long-term evaluation of N-fertilization patterns was performed using 25 years of historical weather data, resulting in the line pattern with 14% more yield and 25% less NO₃^? leaching in comparison to the conventional N-Fertilization pattern. Therefore, line pattern may be adopted to enhance the yield and nitrogen use efficiency in rice.     

Modeling daily soil water dynamics during vertical drainage using the incoming flow concept

Crop management models require simulation of daily soil water dynamics. The objective of this study was to develop a model to simulate the daily soil water dynamics during vertical drainage with reasonable accuracy using the incoming flow concept. The execution of this model, which has been developed based on the conservation of mass law, consists of two steps. First, calculating the potential daily change of soil water content (Δθp) for each soil layer in the profile assuming each one receives no water from the above layer. Then, calculating the actual daily change of soil water (Δθa) for each soil layer in the profile by adjusting Δθp using the incoming water flow, which can be defined as the amount of drainage water that reaches a layer in a soil profile from the above layer. The model was compared with the Suleiman and Ritchie [Suleiman, A.A., Ritchie, J.T., 2004. Modifications to the DSSAT vertical drainage model for more accurate soil water dynamics estimation. Soil Sci. 169 (11), 745–757] vertical drainage model (SRVDM) and HYDRUS-1D for diverse soils and was tested using drainage experimental data of a Eutric Regosol in Bekkevoort, Belgium and a sandy soil in Georgia, U.S. The difference in Δθp between the new model and HYDRUS-1D for diverse soils ranged from − 0.01 to 0.016 m³ m^− 3 for the first day and from − 0.005 to − 0.025 m³ m^− 3 for the second day while the difference in Δθp between the SRVDM and HYDRUS-1D for these soils ranged from 0.014 to 0.062 m³ m^− 3 for the first day and from − 0.01 to 0.026 m³ m^− 3 for the second day. The relative maximum absolute errors in Δθa between the new model and HYDRUS-1D was 10% while the relative maximum absolute errors in Δθa between the SRVDM and HYDRUS-1D was 112%. In the experiments, the root mean square difference of the soil water content for the new model was lower than that for the SRVDM at the different soil depths. These results indicated that the new model outperformed the SRVDM in simulating Δθp and Δθa for diverse soil. It can be concluded that the new model was robust and reasonably accurate for diverse soils at different soil depths. The implementation of such model will improve the accuracy and applicability of regional soil water dynamics simulation and will reduce considerably the computational time and the required inputs.     

洛阳孟津冬小麦生产潜力长周期定量模拟与评价

以洛阳孟津地区气象数据库、土壤数据库、作物数据库和多年田间试验数据库为基础, 应用DSSAT作物生长模型估算了当地冬小麦光温生产潜力和光温水生产潜力, 并对节水潜力及途径进行了分析.结果表明, 洛阳孟津地区46年冬小麦光温生产潜力为7 571～10 965 kg·hm-2, 平均9 209 kg·hm-2, 此值可作为当地补灌区产量的上限参考值; 光温水生产潜力为3 957～7 450 kg·hm-2, 平均5 510 kg·hm-2, 仅占光温生产潜力的59.8%, 此值可作为雨养冬小麦产量的上限参考值.冬小麦生育期内平均降水量为226.29 mm, 不及需水量的一半, 平均水分亏缺量占生育期降水量比例高达72.5%, 平均水分满足率为66.4%, 生育期水分亏缺成为影响洛阳孟津地区旱作冬小麦生产发展的首要障碍.不同土壤类型及不同降雨年型冬小麦潜在水分利用效率变幅较大, 波动范围为12.06～22.94 kg·hm-2·mm-1, 46年均值为16.94 kg·hm-2·mm-1.近8年旱作冬小麦现实水分利用效率仅占潜在水分利用效率的58.5%, 平均光温水生产潜力开发度为60.5%.结果表明, 洛阳孟津地区冬小麦节水潜力较大, 采用合理的节水农业措施, 加强农田建设, 改善地力, 增强土壤储水蓄水能力, 高效利用降水资源, 提高水分利用效率是今后提高冬小麦产量的重要途径.     

黄土高原地区秋粮作物生产潜力模拟研究

应用 DSSAT3中的 CERES-谷子模型、CROPGRO-大豆模型和 SU BSTOR-马铃薯模型分别模拟研究了黄土高原 2 8个地点谷子、大豆和马铃薯的光温生产潜力 (TPP)和气候生产潜力 (CPP) ,获得了各点 6～ 15年各作物的产量潜力值 ,并统计计算了研究时段内各作物生产潜力的平均值、标准差、最高值、最低值和水分满足率 (WCR)。     

The Potential Effect of Climate Change and Elevated Air Carbon Dioxide on Agricultural Crop Production in Central and Southeastern Europe

SUMMARY

The vulnerability and adaptation of major agricultural crops to different soils in Austria and Bulgaria under a changing climate and elevated air CO₂ were investigated. Several incremental and transient GCM climate change scenarios were created and applied. Warming will decrease the crop-growing duration of the selected crops in the regions of interest. All GCM scenarios, including the climate change effect only, projected reductions in grain yield of winter wheat and spring barley, caused by a shorter crop-growing period. However, when the direct effect of an increased CO₂ level was assumed, most GCM climate change scenarios projected an increase in wheat and barley yield and especially in soybean yield. An increased level of CO₂ alone had no significant impact on the simulated maize yield reductions under climate change.     

SOYGRO模型在大豆产量预测中的应用

本文概括地介绍了美国《农业技术推广决策支持系统（ＤＳＳＡＴ）》中模拟大豆生长,产量预测的大豆子模型（ＳＯＹＧＲＯ）及其应用。作者采集了ＫＡＮＳＡＳ州四个地区,７个年份,１２个品种的田间试验数据,早,晚２个播期,这,窄２种行距,共计１１０个组合。气象数据为：日最高,最低温度,降雨量和日照,及土壤剖面参数,通过ＤＳＳＡＴ３．０软件的子模块ＳＯＹＧＲＯ对大豆的产量和成熟期进行预测。结论是：利用ＳＯＹＧＲ     

基于DSSAT模型模拟气候变化对不同品种冬小麦产量潜力的影响

以石家庄地区为例分析了1955－2008年冬小麦不同生育阶段的积温、太阳辐射量和降雨量的变化趋势,并运用DSSAT模型模拟了不同年代主栽品种碧玛1号、泰山1号、济南13、冀麦30和石家庄8号的光温生产潜力和雨养产量,探讨了气候变化对冬小麦产量潜力的影响。结果表明：经过调试校正品种参数的模型模拟效果良好, 产量模拟值与实测值吻合度高;近50 a来冬小麦各品种光温生产潜力均随年份的推移呈现下降趋势,表明积温和太阳辐射量变化的减产效应;其中,拔节－抽穗期间积温的极显著增加趋势是影响20世纪90年代以前品种碧玛1号、泰山1号和济南13产量潜力的重要因素,而抽穗－成熟期间太阳辐射量的极显著减少趋势是影响近20 a品种冀麦30和石家庄8号产量潜力的重要因素;近50 a来冬小麦各品种雨养产量呈不显著增加趋势,返青-拔节期间降雨量的增加可以提高冬小麦雨养产量,并弥补生育后期降雨量减少引起的减产。     

Irrigation strategies to improve the water use efficiency of wheat-maize double cropping systems in North China Plain

Water is the most important limiting factor of wheat (Triticum aestivum L.) and maize (Zea mays L.) double cropping systems in the North China Plain (NCP). A two-year experiment with four irrigation levels based on crop growth stages was used to calibrate and validate RZWQM2, a hybrid model that combines the Root Zone Water Quality Model (RZWQM) and DSSAT4.0. The calibrated model was then used to investigate various irrigation strategies for high yield and water use efficiency (WUE) using weather data from 1961 to 1999. The model simulated soil moisture, crop yield, above-ground biomass and WUE in responses to irrigation schedules well, with root mean square errors (RMSEs) of 0.029 cm³ cm⁻³, 0.59 Mg ha⁻¹, 2.05 Mg ha⁻¹, and 0.19 kg m⁻³, respectively, for wheat; and 0.027 cm³ cm⁻³, 0.71 Mg ha⁻¹, 1.51 Mg ha⁻¹ and 0.35 kg m⁻³, respectively, for maize. WUE increased with the amount of irrigation applied during the dry growing season of 2001-2002, but was less sensitive to irrigation during the wet season of 2002-2003. Long-term simulation using weather data from 1961 to 1999 showed that initial soil water at planting was adequate (at 82% of crop available water) for wheat establishment due to the high rainfall during the previous maize season. Preseason irrigation for wheat commonly practiced by local farmers should be postponed to the most sensitive growth stage (stem extension) for higher yield and WUE in the area. Preseason irrigation for maize is needed in 40% of the years. With limited irrigation available (100, 150, 200, or 250 mm per year), 80% of the water allocated to the critical wheat growth stages and 20% applied at maize planting achieved the highest WUE and the least water drainage overall for the two crops.