Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Puntel LA

PROVIDER: S-EPMC5104953 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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

Puntel Laila A LA Sawyer John E JE Barker Daniel W DW Dietzel Ranae R Poffenbarger Hanna H Castellano Michael J MJ Moore Kenneth J KJ Thorburn Peter P Archontoulis Sotirios V SV

Frontiers in plant science 20161111

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</s ...[more]

PMID: 27891133

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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:The soybean cyst nematode (SCN), Heterodera glycines Ichinohe (Phylum Nematoda), is a major pathogen of soybean. It causes substantial yield losses worldwide and is difficult to control because the cyst protects the eggs which can remain viable for nearly a decade. Crop rotation with non-host crops and use of biocontrol organisms such as fungi and bacteria offer promising approaches, but remain hampered by lack of knowledge of the biology of nematode parasitic organisms. We used a high-throughput metabarcoding approach to characterize fungal communities associated with the SCN cyst, a microenvironment in soil that may harbor both nematode parasites and plant pathogens. SCN cysts were collected from a long-term crop rotation experiment in Southeastern Minnesota at three time points over two growing seasons to characterize diversity of fungi inhabiting cysts and to examine how crop rotation and seasonal variation affects fungal communities. A majority of fungi in cysts belonged to Ascomycota and Basidiomycota, but the presence of several early diverging fungal subphyla thought to be primarily plant and litter associated, including Mortierellomycotina and Glomeromycotina (e.g., arbuscular mycorrhizal fungi), suggests a possible role as nematode egg parasites. Species richness varied by both crop rotation and season and was higher in early years of crop rotation and in fall at the end of the growing season. Crop rotation and season also impacted fungal community composition and identified several classes of fungi, including Eurotiomycetes, Sordariomycetes, and Orbiliomycetes (e.g., nematode trapping fungi), with higher relative abundance in early soybean rotations. The relative abundance of several genera was correlated with increasing years of soybean. Fungal communities also varied by season and were most divergent at midseason. The percentage of OTUs assigned to Mortierellomycotina_cls_Incertae_sedis and Sordariomycetes increased at midseason, while Orbiliomycetes decreased at midseason, and Glomeromycetes increased in fall. Ecological guilds of fungi containing an animal-pathogen lifestyle, as well as potential egg-parasitic taxa previously isolated from parasitized SCN eggs, increased at midseason. The animal pathogen guilds included known (e.g., Pochonia chlamydosporia) and new candidate biocontrol organisms. This research advances knowledge of the ecology of nematophagous fungi in agroecosystems and their use as biocontrol agents of the SCN.

Dataset Information

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

Publications

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

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