How well can APSIM simulate nitrogen uptake and nitrogen fixation of legume crops?

Chen, Chao; Lawes, Roger; Fletcher, Andrew; Oliver, Yvette; Robertson, Michael; Bell, Michael J.; Wang, Enli

doi:10.1016/j.fcr.2015.12.007

Cited by 31 publications

(21 citation statements)

References 80 publications

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“…After calibration, the soybean residue C:N ratio increased to reasonable values (around 30; Supplementary Figure S2D ), the simulation of the annual N fixation decreased to realistic estimates (around 180 kg N ha -1 year -1 , Salvagiotti et al, 2008; Christianson et al, 2012), while the model maintained good performance in terms of N fixation and yield response to prior-year corn N fertilization ( Figure 4 ; Supplementary Figures S2G and S3 ). We believe these changes improve the representation of N fixation (Chen et al, 2016) and soybean residue in the model. However, more experimental work is needed to verify these changes and improve the simulation of soybean rotation effects in APSIM.…”

Section: Discussionmentioning

confidence: 97%

Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation

et al. 2016

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Improved prediction of optimal N fertilizer rates for corn (Zea mays L.) can reduce N losses and increase profits. We tested the ability of the Agricultural Production Systems sIMulator (APSIM) to simulate corn and soybean (Glycine max L.) yields, the economic optimum N rate (EONR) using a 16-year field-experiment dataset from central Iowa, USA that included two crop sequences (continuous corn and soybean-corn) and five N fertilizer rates (0, 67, 134, 201, and 268 kg N ha-1) applied to corn. Our objectives were to: (a) quantify model prediction accuracy before and after calibration, and report calibration steps; (b) compare crop model-based techniques in estimating optimal N rate for corn; and (c) utilize the calibrated model to explain factors causing year to year variability in yield and optimal N. Results indicated that the model simulated well long-term crop yields response to N (relative root mean square error, RRMSE of 19.6% before and 12.3% after calibration), which provided strong evidence that important soil and crop processes were accounted for in the model. The prediction of EONR was more complex and had greater uncertainty than the prediction of crop yield (RRMSE of 44.5% before and 36.6% after calibration). For long-term site mean EONR predictions, both calibrated and uncalibrated versions can be used as the 16-year mean differences in EONR’s were within the historical N rate error range (40–50 kg N ha-1). However, for accurate year-by-year simulation of EONR the calibrated version should be used. Model analysis revealed that higher EONR values in years with above normal spring precipitation were caused by an exponential increase in N loss (denitrification and leaching) with precipitation. We concluded that long-term experimental data were valuable in testing and refining APSIM predictions. The model can be used as a tool to assist N management guidelines in the US Midwest and we identified five avenues on how the model can add value toward agronomic, economic, and environmental sustainability.

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Section: Discussionmentioning

confidence: 97%

Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation

et al. 2016

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“…In the present study, GM with lower N application increased soil pH and nutrient availability and C inputs as compared to sole N application and no N application treatments (Figure 3). Cultivation of leguminous crops as cover crop in fallow period of rice-based cropping system enhance the soil nutrient availability and fix the atmospheric N 2 by symbiotic association with root nodules [70], and GM easily decomposed in soil and enhance the SOC and nutrient availability [28,71]. Many studies of long-term experiments reported the soil acidification due to inorganic N fertilization in different soil and cropping systems [65,72].…”

Section: Discussionmentioning

confidence: 99%

Substitution of Inorganic Nitrogen Fertilizer with Green Manure (GM) Increased Yield Stability by Improving C Input and Nitrogen Recovery Efficiency in Rice Based Cropping System

et al. 2019

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A long-term field experiment was carried out (since 2008) for evaluating the effects of different substitution rates of inorganic nitrogen (N) fertilizer by green manure (GM) on yield stability and N balance under double rice cropping system. Treatments included, (1) N0 (no N fertilizer and no green manure); (2) N100 (recommended rate of N fertilizer and no green manure); (3) N100-M (recommended rate of N fertilizer and green manure); (4) N80-M (80% of recommended N fertilizer and green manure); (5) N60-M (60% of recommended N fertilizer and green manure); and (6) M (green manure without N fertilization). Results showed that, among all treatments, annual crop yield under N80-M treatment was highest. Crop yield did not show significant differences between N100-M and N80-M treatments. Substitution of different N fertilizer rates by GM reduced the yield variability index. Compared to the N0 treatment, yield variability index of early rice under N100-M, N80-M, and N60-M treatments was decreased by 11%, 26%, and 36%, respectively. Compared to the N0 treatment, yield variability index of late rice was decreased by 12%, 38%, 49%, 47%, and 24% under the N100, N100-M, N80-M, N60-M, and M treatments, respectively. During period of 2009–2013 and 2014–2018, nitrogen recovery efficiency (NRE) was highest under N80-M treatment and N balance was highest under N100 treatment. NRE of all treatments with GM was increased over the time from 2009–2013 to 2014–2018. All treatments with GM showed increasing trend of SOC over the years. Substitution of N fertilizer by GM also increased C inputs and soil C:N ratio compared to the N100 and N0 treatments. Boosted regression model indicated that C input, N uptake and AN were most influencing factors of crop yield. Thus, we concluded that N fertilization rates should be reduced by 20% under GM rotation to attain high yield stability of double rice cropping system through increasing NRE and C inputs.

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“…We first used the calculated parameters to run the model, and then we refined the parameter values to get a better match between the simulated and measured values. Based on the agroecosystem experimental observations and other research results (Asseng et al, ; Chen et al, , 2010b, ; Holzworth et al, ; Liu & Tian, ; Mohanty et al, ), the thermal time between the various crop growth stages could be estimated. Genetic coefficients were determined after obtaining a close match between the observed and predicted values for LAI, total biomasses, grain yield, time to reach physiological maturity, etc.…”

Section: The Dlemmentioning

confidence: 99%

Improving Representation of Crop Growth and Yield in the Dynamic Land Ecosystem Model and Its Application to China

Zhang

Tian

Yang

et al. 2018

J Adv Model Earth Syst

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To accurately assess the roles of agriculture in securing food security and maintaining environmental sustainability, it is essential to improve the representation of crop growth, development, and yield formation in global land models that traditionally focus on energy, water, carbon, and nitrogen exchanges between land and the atmosphere. In this study, a process‐based agricultural module has been coupled with the Dynamic Land Ecosystem Model (DLEM‐AG2.0) for assessing how multiple environmental factors (climate change, atmospheric CO2 concentration, tropospheric O3, and nitrogen deposition) and human activities (land use/cover change, nitrogen fertilizer use, and irrigation) have affected the crop growth, development, yield, carbon (C), nitrogen (N), and water cycles in agroecosystems. Here we describe the model structure for simulating crop growth, development, and yield formation in the DLEM‐AG2.0, and then we validate the model using field observations and a national yield survey for three major crops (wheat, maize, and rice) in China during 1980–2012. Results show that the DLEM‐AG2.0 is capable of simulating the dynamic processes of phenological development, leaf growth expansion, biomass accumulation, biomass allocation, and yield formation for wheat, maize, and rice with normalized root mean square errors of the simulations of less than 20%. Our model‐based yield estimation for the three major crops at the national scale for the period 1980–2012 is generally consistent with the national yield survey in China. The crop representation in the DLEM‐AG2.0 is flexible for extrapolating to a global scale after rigorous testing with both site‐specific and regional observations. Further advancement of agricultural modeling within the global land modeling framework will require consideration of human perception and behavior for adapting and mitigating global change.

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How well can APSIM simulate nitrogen uptake and nitrogen fixation of legume crops?

Cited by 31 publications

References 80 publications

Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation

Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation

Substitution of Inorganic Nitrogen Fertilizer with Green Manure (GM) Increased Yield Stability by Improving C Input and Nitrogen Recovery Efficiency in Rice Based Cropping System

Improving Representation of Crop Growth and Yield in the Dynamic Land Ecosystem Model and Its Application to China

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