CropSyst作物模型在松嫩平原典型黑土区的校正和验证

通过对CropSyst作物模拟模型进行修订和验证，应用该模型对松嫩平原黑土区主要作物的生产潜力进行了模拟，并对作物生产力模拟的有效方法进行了初步探索。模拟结果表明，对于主要作物的经济产量、全生育期蒸散量、收获时的地上生物量，模拟值与实测值较为接近。模拟值和实测值的均方根误差RMSE为3.59%(小麦地上生物量)～8.02%(小麦产量)，模拟性能指数EF最小为0.76(玉米蒸散量)，最大为0.90(小麦产量)。     

Best management practices of tillage and nitrogen fertilization in Mediterranean rainfed conditions: Combining field and modelling approaches

In this work, appropriate management practices for crop production under the variable climate conditions of the Mediterranean region, in particular rainfall, were tested with the use of a modelling system applied to long-term (i.e. 18 years) field data. The calibration of the CropSyst model was performed using data collected from 1996 to 1999 at three different Mediterranean locations (i.e., HYP-Guissona, MYP-Agramunt and LYP-Candasnos, i.e. high, medium and low yield potential, respectively) within a degree of yield potential. The model simulated reasonably well barley growth and yield to different tillage and N fertilization strategies.Simulations of barley performance over 50 years with generated weather data showed that yields were often greater and never smaller under no-tillage compared to conventional tillage with a mean increase of 36%, 63% and 18% for HYP-Guissona, MYP-Agramunt and LYP-Candasnos. In MYP-Agramunt, the long-term data showed a 40% increase in grain yields when using no-tillage compared to conventional tillage, as an average of 18 years.The model also predicted that greater N applications in no-tillage were appropriate to take advantage of additional water supply. Taking into account the limited amount of soil water available, overall N fertilizer applications could be reduced to about half of the traditional rate applied by the farmers without yield loss. The 50-yr simulation, confirmed by the long-term experimental data, identified no-tillage as the most appropriate tillage practice for the rainfed Mediterranean areas. Also, N fertilization must be reduced significantly when tillage is used or when increasing aridity. Our work demonstrates the usefulness of the combination of long-term field experimentation and modelling as a tool to identify the best agricultural management practices. It also highlights the importance of posterior analysis with long-term observed field data to determine the performance of simulation results.     

CropSyst, a cropping systems simulation model

CropSyst is a multi-year, multi-crop, daily time step cropping systems simulation model developed to serve as an analytical tool to study the effect of climate, soils, and management on cropping systems productivity and the environment. CropSyst simulates the soil water and nitrogen budgets, crop growth and development, crop yield, residue production and decomposition, soil erosion by water, and salinity. The development of CropSyst started in the early 1990s, evolving to a suite of programs including a cropping systems simulator (CropSyst), a weather generator (ClimGen), GIS-CropSyst cooperator program (ArcCS), a watershed model (CropSyst Watershed), and several miscellaneous utility programs. CropSyst and associated programs can be downloaded free of charge over the Internet. One key feature of CropSyst is the implementation of a generic crop simulator that enables the simulation of both yearly and multi-year crops and crop rotations via a single set of parameters. Simulations can last a fraction of a year to hundreds of years. The model has been evaluated in many world locations by comparing model estimates to data collected in field experiments. CropSyst has been applied to perform risk and economic analyses of scenarios involving different cropping systems, management options, and soil and climatic conditions. An extensive list of references related to model development, evaluation, and application is provided.     

Simulating water use and N response of winter wheat in the irrigated floodplains of Northwest Uzbekistan

The crop-soil simulation model CropSyst was used to simulate growth, water- and N-uptakes of irrigated winter wheat (Triticum aestivum L. cv. Kupava) in Khorezm, in the dry lands of northwest Uzbekistan, Central Asia. CropSyst was calibrated using the findings of field experiments of 2005/06 and 2006/07 and validated for the 2007/08 season. A relative root mean squared error of 11% proved the accuracy between simulated and observed aboveground biomass and grain yield in 2007/08. Scenario analyses showed that N-leaching was high and ranged from 63 to 106 kg ha⁻¹ when irrigated between 749 and 869 mm during the first two cropping seasons. The simulated N-leaching was lowest and ranged from 7 to 15 kg ha⁻¹ when irrigation was only 148–395 mm during 2007/08. The considerable N losses during leaching and high N-uptakes by wheat together resulted in a negative N-balance even during applications of 180 and 240 kg ha⁻¹ of N-fertilizer. N scarcity in the N-balance was reduced with increasing N-fertilizer amounts and ranged from −29 to −153 kg N ha⁻¹ in 2005/06 and 2006/07. Despite a common shallow groundwater table in the region during some time of the year, scenario analysis revealed that only full irrigation water (580 mm) and N supply according to crop demand (180 kg ha⁻¹) guaranteed high grain yields, unless the water table is permanently shallow to overcome irrigation deficits. Limited irrigation and N application (40% and 55% of ‘optimal’, respectively) in combination with a groundwater table below 3 m resulted in a 55% yield decline. The CropSyst wheat model proved a robust tool for assessing the influence of water and N dynamics under conditions of varying irrigation and shallow groundwater tables. It thus has potential as a decision support not only in northwest Uzbekistan, but also in comparable regions of Central Asia.     

Evaluation of CERES-Wheat and CropSyst models for water–nitrogen interactions in wheat crop

Crop simulation models can provide an alternative, less time-consuming and inexpensive means of determining the optimum crop N and irrigation requirements under varied soil and climatic conditions. In this context, two dynamic mechanistic models (CERES (Crop Environment REsource Synthesis)-Wheat and CropSyst (Cropping Systems Simulation Model)) were validated for predicting growth and yield of wheat (Triticum aestivum L) under different nitrogen and water management conditions. Their potential as N and water management tool was evaluated for New Delhi representing semi-arid irrigated ecosystems in the Indo-Gangetic Plains. The field experiment was carried out on a silty clay loam soil at the Research Farm of the Indian Agricultural Research Institute, New Delhi, India during 2000–2001 to collect the input data for the calibration and validation of both the models on wheat crop (variety HD 2687). The models were evaluated for three water regimes [I4 (4 irrigations within the growing season), I3 (3 irrigations within the growing season) and I2 (2 irrigations within the growing season)] and five N treatments (N₀, N₆₀, N₉₀, N₁₂₀ and N₁₅₀). Both the models were calibrated using data obtained from the treatments receiving maximum nitrogen and irrigations, i.e., N₁₅₀ and I4 treatments. The models were then validated against other water and nitrogen treatments. For performance evaluation, in addition to coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE) and Wilmot's index of agreement (IoA) were estimated. Both CERES-Wheat and CropSyst provided very satisfactory estimates for the emergence, flowering and physiological maturity dates. For CERES-Wheat overall prediction (pooled result of the three water regimes) of grain yield was satisfactory with significant R² values (0.88). The model, however, under estimated the biomass under all water regimes and N levels except for N₀ level, under which biomass was overpredicted. CropSyst predicted yield and biomass of wheat more closely than CERES-Wheat. The combined RMSE for the three water regimes between predicted and observed grain yield was 0.36 Mg ha⁻¹ for CropSyst as compared to 0.63 Mg ha⁻¹ for CERES-Wheat. Similarly, RMSE between observed and predicted biomass by CropSyst was 1.27 Mg ha⁻¹ as compared to 1.94 Mg ha⁻¹ between observed and predicted biomass by CERES-Wheat. Wilmot's index of agreement (IoA) also indicated that CropSyst model is more appropriate than CERES-Wheat in predicting growth and yield of wheat under different N and irrigation application situations in this study.     

Soil texture, climate and management effects on plant growth, grain yield and water use by rainfed maize–wheat cropping system: Field and simulation study

In sub-mountain tract of Punjab state of India, maize (Zea mays, L.) and wheat (Triticum aestivum L.) crops are grown as rainfed having low crop and water productivity. To enhance that, proper understanding of the factors (soil type, climate, management practices and their interactions) affecting it is a pre-requisite. The present study aims to assess the effects of tillage, date of sowing, and irrigation practices on the rainfed maize–wheat cropping system involving combined approach of field study and simulation. Field experiments comprising 18 treatments (three dates of sowing as main, three tillage systems as subplot and two irrigation regimes as the sub-subplot) were conducted for two years (2004–2006) and simulations were made for 15 years using CropSyst model. Field and simulated results showed that grain yields of maize and wheat crops were more in early July planted maize and early November planted wheat on silt loam soil. Different statistical parameters (root mean square error, coefficient of residual mass, model efficiency, coefficient of correlation and paired t-test) indicated that CropSyst model did fair job to simulate biomass production and grain yield for maize–wheat cropping system under varying soil texture, date of planting and irrigation regimes.