Using the DSSAT-CERES-Maize model to simulate crop yield and nitrogen cycling in fields under long-term continuous maize production

Liu, H. L.; Yang, Jingyi; Drury, C. F.; Reynolds, W. D.; Tan, C. S.; Bai, Yanchao; He, Ping; Jin, Jian; Hoogenboom, Gerrit

doi:10.1007/s10705-010-9396-y

Cited by 141 publications

(96 citation statements)

References 57 publications

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“…Here, the RMSE was just 25-29% (n-RMSE, Table 7) of mean measured net N mineralization. There are no comparable data in the literature for net N mineralization, but n-RMSE was even two to three times larger for simulated soil mineral N in the top layer (Liu et al 2011). Here, highest deviations were observed for soil group A, the typical loess soils (Fig.…”

Section: Evaluation Via Field Mineralization Measurementsmentioning

confidence: 70%

Field-specific simulations of net N mineralization based on digitally available soil and weather data. I. Temperature and soil water dependency of the rate coefficients

Heumann

Ringe

Böttcher

2011

Nutr Cycl Agroecosyst

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Including field-or even site-specific estimates of current net N mineralization into N fertilizer strategy is essential in order to further reduce N surpluses while maintaining crop yields, but adequate estimates are not available. Simulation models could account for many influencing factors, yet are not easily adjustable to different soil and site characteristics. Nowadays important input data for N mineralization models are digitally available. Thus, our objectives were (1) to experimentally determine specific temperature and soil water dependency functions for the rate coefficients of net N mineralization that could be allocated via digitally mapped data and (2) to find out the least necessary discrimination between soils. Specific and general functions for the rate coefficients of two organic N pools with first-order kinetics were derived using laboratory long-and short-term incubations from a broad variety of soils. Functions were evaluated using comparisons to field incubations of undisturbed soil columns from 27 sites. Interestingly, a differentiation between specific functions of not more than three soil groups was necessary for quite accurate simulations (r 2 = 0.87, P \ 0.001; RMSE = 23 kg N ha -1 , n-RMSE = 29%). The two criteria for grouping, soil texture (loess vs. sandy/loamy classes) and humus content class (applies only to temperature functions for sandy textures), can be taken from digital soil maps. Field studies, especially under suboptimal water contents, with plant cover and N-fertilization, will have to further prove the applicability of the derived functions. Pedotransfer functions for the pool sizes also based on digitally available data are needed for automatically calculating specific estimates of net N mineralization.

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Section: Evaluation Via Field Mineralization Measurementsmentioning

confidence: 70%

Field-specific simulations of net N mineralization based on digitally available soil and weather data. I. Temperature and soil water dependency of the rate coefficients

Heumann

Ringe

Böttcher

2011

Nutr Cycl Agroecosyst

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“…According to the results of the grain yield and GPC estimations, the inverted SVCNA method estimated grain yield and GPC better than the SVLAI method (Figure 3a-d). This is because CNA is determined by actual plant N concentration and the corresponding canopy biomass of each plant organ [32,35], thus, nutrient status and plant biomass are simultaneously considered in crop growth. The joint use of LAI and CNA as state variables also obtained good estimations of grain yield and GPC, and the RMSE values between the simulated and measured grain yield and GPC were the lowest.…”

Section: Discussionmentioning

confidence: 99%

“…The N concentration of different plant organs varies with the plant growth stage. The available N uptake from the soil depends on the soil NH4 + and NO3 − concentrations, soil water, and root growth [34,35]. The grain dry matter was derived from photosynthesis during the grain filling stage and re-translocation from the pre-stored dry matter, whereas the N accumulation of grain was derived from the direct root uptake during the grain filling stage and re-translocation from the pre-stored N uptake.…”

Section: Dssat-ceres Model Descriptionmentioning

confidence: 99%

Assimilation of Two Variables Derived from Hyperspectral Data into the DSSAT-CERES Model for Grain Yield and Quality Estimation

Wang

et al. 2015

Remote Sensing

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Abstract:The combination of remote sensing and crop growth models has become an effective tool for yield estimation and a potential method for grain quality estimation. In this study, two assimilation variables (derived from a hyperspectral sensor), called leaf area index (LAI) and canopy nitrogen accumulation (CNA), were jointly used to calibrate the sensitive parameters and initial states of the DSSAT-CERES crop model, to improve This study provides a more robust method for estimating the grain yield and protein content of winter wheat based on the integration of the DSSAT-CERES crop model and remote sensing data.

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“…Micro-fertilization APSIM [121][122][123], EPICSEAR [124], MSM [125], DSSAT [126][127][128][129][130][131] In general, the simulations of UPS for semi-arid climates are reported by only a few papers compared to the multitude of papers available on modelling/simulation. We found that 30 out of the 187 papers contained in this review specifically covered the modelling of at least one of the four selected UPS in semi-arid areas.…”

Section: Ups Models and Authors Country Cropsmentioning

confidence: 99%

Crop Upgrading Strategies and Modelling for Rainfed Cereals in a Semi-Arid Climate—A Review

Silungwe

Graef

Bellingrath-Kimura

et al. 2018

Water

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Spatiotemporal rainfall variability and low soil fertility are the primary crop production challenges facing poor farmers in semi-arid environments. However, there are few solutions for addressing these challenges. The literature provides several crop upgrading strategies (UPS) for improving crop yields, and biophysical models are used to simulate these strategies. However, the suitability of UPS is limited by systemization of their areas of application and the need to cope with the challenges faced by poor farmers. In this study, we reviewed 187 papers from peer-reviewed journals, conferences and reports that discuss UPS suitable for cereals and biophysical models used to assist in the selection of UPS in semi-arid areas. We found that four UPS were the most suitable, namely tied ridges, microdose fertilization, varying sowing dates, and field scattering. The DSSAT, APSIM and AquaCrop models adequately simulate these UPS. This work provides a systemization of crop UPS and models in semi-arid areas that can be applied by scientists and planners.

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Using the DSSAT-CERES-Maize model to simulate crop yield and nitrogen cycling in fields under long-term continuous maize production

Abstract: Simulation models, such as the DSSAT (Decision Support System for Agrotechnology Transfer) Crop System Models are often used to characterize, develop and assess field crop production practices.

Cited by 141 publications

References 57 publications

Field-specific simulations of net N mineralization based on digitally available soil and weather data. I. Temperature and soil water dependency of the rate coefficients

Field-specific simulations of net N mineralization based on digitally available soil and weather data. I. Temperature and soil water dependency of the rate coefficients

Assimilation of Two Variables Derived from Hyperspectral Data into the DSSAT-CERES Model for Grain Yield and Quality Estimation

Crop Upgrading Strategies and Modelling for Rainfed Cereals in a Semi-Arid Climate—A Review

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