Dataset Information

A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate.

ABSTRACT: Historically crop models have been used to evaluate crop yield responses to nitrogen (N) rates after harvest when it is too late for the farmers to make in-season adjustments. We hypothesize that the use of a crop model as an in-season forecast tool will improve current N decision-making. To explore this, we used the Agricultural Production Systems sIMulator (APSIM) calibrated with long-term experimental data for central Iowa, USA (16-years in continuous corn and 15-years in soybean-corn rotation) combined with actual weather data up to a specific crop stage and historical weather data thereafter. The objectives were to: (1) evaluate the accuracy and uncertainty of corn yield and economic optimum N rate (EONR) predictions at four forecast times (planting time, 6th and 12th leaf, and silking phenological stages); (2) determine whether the use of analogous historical weather years based on precipitation and temperature patterns as opposed to using a 35-year dataset could improve the accuracy of the forecast; and (3) quantify the value added by the crop model in predicting annual EONR and yields using the site-mean EONR and the yield at the EONR to benchmark predicted values. Results indicated that the mean corn yield predictions at planting time (R2 = 0.77) using 35-years of historical weather was close to the observed and predicted yield at maturity (R2 = 0.81). Across all forecasting times, the EONR predictions were more accurate in corn-corn than soybean-corn rotation (relative root mean square error, RRMSE, of 25 vs. 45%, respectively). At planting time, the APSIM model predicted the direction of optimum N rates (above, below or at average site-mean EONR) in 62% of the cases examined (n = 31) with an average error range of ±38 kg N ha-1 (22% of the average N rate). Across all forecast times, prediction error of EONR was about three times higher than yield predictions. The use of the 35-year weather record was better than using selected historical weather years to forecast (RRMSE was on average 3% lower). Overall, the proposed approach of using the crop model as a forecasting tool could improve year-to-year predictability of corn yields and optimum N rates. Further improvements in modeling and set-up protocols are needed toward more accurate forecast, especially for extreme weather years with the most significant economic and environmental cost.

SUBMITTER: Puntel LA

PROVIDER: S-EPMC5909184 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate.

Puntel Laila A LA Sawyer John E JE Barker Daniel W DW Thorburn Peter J PJ Castellano Michael J MJ Moore Kenneth J KJ VanLoocke Andrew A Heaton Emily A EA Archontoulis Sotirios V SV

Frontiers in plant science 20180413

Historically crop models have been used to evaluate crop yield responses to nitrogen (N) rates after harvest when it is too late for the farmers to make in-season adjustments. We hypothesize that the use of a crop model as an in-season forecast tool will improve current N decision-making. To explore this, we used the Agricultural Production Systems sIMulator (APSIM) calibrated with long-term experimental data for central Iowa, USA (16-years in continuous corn and 15-years in soybean-corn rotatio ...[more]

PMID: 29706974

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Project description: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.

Project description:Introduction The application of controlled-release nitrogen fertilizer (CRN) has become an important production method to achieve high crop yield and ecological safety. However, the rate of urea-blended CRN for rice is usually determined by conventional urea, and the actual rate is still unclear. Methods A five-year field experiment was carried out in the Chaohu watershed in the Yangtze River Delta to study rice yield, N fertilizer utilization efficiency (NUE), ammonia (NH3) volatilization and economic benefit under the four urea-blended CRN treatments with a 4:3:3 ratio applied at one time (60, 120, 180, 240 kg/hm2, CRN60, CRN120, CRN180, CRN240), four conventional N fertilizer treatments (N60, N120, N180, N240) and a control without N fertilizer (N0). Results and Discussion The results showed that the N released from the blended CRNs could well satisfy the N demand of rice growth. Similar to the conventional N fertilizer treatments, a quadratic equation was used to model the relationship between rice yield and N rate under the blended CRN treatments. The blended CRN treatments increased rice yield by 0.9-8.2% and NUE by 6.9-14.8%, respectively, compared with the conventional N fertilizer treatments at the same N application rate. The increase in NUE in response to applied blended CRN was related to the reduction in NH3 volatilization. Based on the quadratic equation, the five-year average NUE under the blended CRN treatment was 42.0% when rice yield reached the maximum, which was 28.9% higher than that under the conventional N fertilizer treatment. Among all treatments, CRN180 had the highest yield and net benefit in 2019. Considering the yield output, environmental loss, labor and fertilizer costs, the optimum economic N rate under the blended CRN treatment in the Chaohu watershed was 180-214 kg/hm2, compared with 212-278 kg/hm2 under the conventional N fertilizer treatment. The findings suggest that blended CRN improved rice yield, NUE and economic income while decreasing NH3 volatilization and negative environmental outcomes.

Project description:Autism is the fastest growing developmental disorder in the world today. The prevalence of autism in the US has risen from 1 in 2500 in 1970 to 1 in 88 children today. People with autism present with repetitive movements and with social and communication impairments. These impairments can range from mild to profound. The estimated total lifetime societal cost of caring for one individual with autism is $3.2 million US dollars. With the rapid growth in this disorder and the great expense of caring for those with autism, it is imperative for both individuals and society that techniques be developed to model and understand autism. There is increasing evidence that those individuals diagnosed with autism present with highly diverse set of abnormalities affecting multiple systems of the body. To this date, little to no work has been done using a whole body systems biology approach to model the characteristics of this disorder. Identification and modelling of these systems might lead to new and improved treatment protocols, better diagnosis and treatment of the affected systems, which might lead to improved quality of life by themselves, and, in addition, might also help the core symptoms of autism due to the potential interconnections between the brain and nervous system with all these other systems being modeled. This paper first reviews research which shows that autism impacts many systems in the body, including the metabolic, mitochondrial, immunological, gastrointestinal and the neurological. These systems interact in complex and highly interdependent ways. Many of these disturbances have effects in most of the systems of the body. In particular, clinical evidence exists for increased oxidative stress, inflammation, and immune and mitochondrial dysfunction which can affect almost every cell in the body. Three promising research areas are discussed, hierarchical, subgroup analysis and modeling over time. This paper reviews some of the systems disturbed in autism and suggests several systems biology research areas. Autism poses a rich test bed for systems biology modeling techniques.

Dataset Information

A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate.

Publications

A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate.

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