Dataset Information

Modelling cereal root systems for water and nitrogen capture: towards an economic optimum.

ABSTRACT: A quantitative model of wheat root systems is developed that links the size and distribution of the root system to the capture of water and nitrogen (which are assumed to be evenly distributed with depth) during grain filling, and allows estimates of the economic consequences of this capture to be assessed. A particular feature of the model is its use of summarizing concepts, and reliance on only the minimum number of parameters (each with a clear biological meaning). The model is then used to provide an economic sensitivity analysis of possible target characteristics for manipulating root systems. These characteristics were: root distribution with depth, proportional dry matter partitioning to roots, resource capture coefficients, shoot dry weight at anthesis, specific root weight and water use efficiency. From the current estimates of parameters it is concluded that a larger investment by the crop in fine roots at depth in the soil, and less proliferation of roots in surface layers, would improve yields by accessing extra resources. The economic return on investment in roots for water capture was twice that of the same amount invested for nitrogen capture.

SUBMITTER: King J

PROVIDER: S-EPMC4244970 | biostudies-literature | 2003 Feb

REPOSITORIES: biostudies-literature

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Modelling cereal root systems for water and nitrogen capture: towards an economic optimum.

King John J Gay Alan A Sylvester-Bradley Roger R Bingham Ian I Foulkes John J Gregory Peter P Robinson David D

Annals of botany 20030201 3

A quantitative model of wheat root systems is developed that links the size and distribution of the root system to the capture of water and nitrogen (which are assumed to be evenly distributed with depth) during grain filling, and allows estimates of the economic consequences of this capture to be assessed. A particular feature of the model is its use of summarizing concepts, and reliance on only the minimum number of parameters (each with a clear biological meaning). The model is then used to p ...[more]

PMID: 12547691

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Project description: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.

Project description:IntroductionThe 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.MethodsA 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 discussionThe 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.

Dataset Information

Modelling cereal root systems for water and nitrogen capture: towards an economic optimum.

Publications

Modelling cereal root systems for water and nitrogen capture: towards an economic optimum.

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