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

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.

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

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.

Patterns of linkage disequilibrium and association mapping in diploid alfalfa (M. sativa L.).

Association mapping enables the detection of marker-trait associations in unstructured populations by taking advantage of historical linkage disequilibrium (LD) that exists between a marker and the true causative polymorphism of the trait phenotype. Our first objective was to understand the pattern of LD decay in the diploid alfalfa genome. We used 89 highly polymorphic SSR loci in 374 unimproved diploid alfalfa (Medicago sativa L.) genotypes from 120 accessions to infer chromosome-wide patterns of LD. We also sequenced four lignin biosynthesis candidate genes (caffeoyl-CoA 3-O-methyltransferase (CCoAoMT), ferulate-5-hydroxylase (F5H), caffeic acid-O-methyltransferase (COMT), and phenylalanine amonialyase (PAL 1)) to identify single nucleotide polymorphisms (SNPs) and infer within gene estimates of LD. As the second objective of this study, we conducted association mapping for cell wall components and agronomic traits using the SSR markers and SNPs from the four candidate genes. We found very little LD among SSR markers implying limited value for genomewide association studies. In contrast, within gene LD decayed within 300 bp below an r (2) of 0.2 in three of four candidate genes. We identified one SSR and two highly significant SNPs associated with biomass yield. Based on our results, focusing association mapping on candidate gene sequences will be necessary until a dense set of genome-wide markers is available for alfalfa.

Comparison of common lignin methods and modifications on forage and lignocellulosic biomass materials.

BACKGROUND: A variety of methods have been developed for estimating lignin concentration within plant materials. The objective of this study was to compare the lignin concentrations produced by six methods on a diverse population of forage and biomass materials and to examine the relationship between these concentrations and the portions of these materials that are available for utilisation by livestock or for ethanol conversion. RESULTS: Several methods produced lignin concentrations that were highly correlated with the digestibility of the forages, but there were few relationships between these methods and the available carbohydrate of the biomass materials. The use of Na2SO3 during preparation of residues for hydrolysis resulted in reduced lignin concentrations and decreased correlation with digestibility of forage materials, particularly the warm-season grasses. CONCLUSION: There were several methods that were well suited for predicting the digestible portion of forage materials, with the acid detergent lignin and Klason lignin method giving the highest correlation across the three types of forage. The continued use of Na2SO3 during preparation of Van Soest fibres needs to be evaluated owing to its ability to reduce lignin concentrations and effectiveness in predicting the utilisation of feedstuffs and feedstocks. Because there was little correlation between the lignin concentration and the biomass materials, there is a need to examine alternative or develop new methods to estimate lignin concentrations that may be used to predict the availability of carbohydrates for ethanol conversion.

Comparison of gas chromotography, spectrophotometry and near infrared spectroscopy to quantify prussic acid potential in forages.

BACKGROUND: Sorghum [Sorghum bicolor (L.) Moench] has been shown to contain the cyanogenic glycoside dhurrin, which is responsible for the disorder known as prussic acid poisoning in livestock. The current standard method for estimating hydrogen cyanide (HCN) uses spectrophotometry to measure the aglycone, p-hydroxybenzaldehyde (p-HB), after hydrolysis. Errors may occur due to the inability of this method to solely estimate the absorbance of p-HB at a given wavelength. The objective of this study was to compare the use of gas chromatography (GC) and near infrared spectroscopy (NIRS) methods, along with a spectrophotometry method to estimate the potential for prussic acid (HCNp) of sorghum and sudangrasses over three stages maturities. RESULTS: It was shown that the GC produced higher HCNp estimates than the spectrophotometer for the grain sorghums, but lower concentrations for the sudangrass. Based on what is known about the analytical process of each method, the GC data is likely closer to the true HCNp concentrations of the forages. Both the GC and spectrophotometry methods yielded robust equations with the NIRS method; however, using GC as the calibration method resulted in more accurate and repeatable estimates. CONCLUSION: The HCNp values obtained from using the GC quantification method are believed to be closer to the actual values of the forage, and that use of this method will provide a more accurate and easily automated means of quantifying prussic acid.

The impact of enzyme characteristics on corn stover fiber degradation and acid production during ensiled storage.

Ensilage can be used to store lignocellulosic biomass before industrial bioprocessing. This study investigated the impacts of seven commercial enzyme mixtures derived from Aspergillus niger, Trichoderma reesei, and T. longibrachiatum. Treatments included three size grades of corn stover, two enzyme levels (1.67 and 5 IU/g dry matter based on hemicellulase), and various ratios of cellulase to hemicellulase (C:H). The highest C:H ratio tested, 2.38, derived from T. reesei, resulted in the most effective fermentation, with lactic acid as the dominant product. Enzymatic activity during storage may complement industrial pretreatment; creating synergies that could reduce total bioconversion costs.

Ensiling corn stover: effect of feedstock preservation on particleboard performance.

Ensilage is a truncated solid-state fermentation in which anaerobically produced organic acids accumulate to reduce pH and limit microbial activity. Ensilage can be used to both preserve and pretreat biomass feedstock for further downstream conversion into chemicals, fuels, and/or fiber products. This study examined the ensilage of enzyme-treated corn stover as a feedstock for particleboard manufacturing. Corn stover at three different particle size ranges (<100, <10, and <5 mm) was ensiled with and without a commercial enzyme mixture having a cellulase:hemicellulase ratio of 2.54:1, applied at a hemicellulase rate of 1670 IU/kg dry mass. Triplicate 20 L mini-silos were destructively sampled and analyzed on days 0, 1, 7, 21, 63, and 189. Analysis included produced organic acids and water-soluble carbohydrates, fiber fractions, pH, and microorganisms, including Lactobacillus spp. and clostridia were monitored. On days 0, 21, and 189, the triplicate samples were mixed evenly and assembled into particleboard using 10% ISU 2 resin, a soy-based adhesive. Particleboard panels were subjected to industry standard tests for modulus of rupture (MOR), modulus of elasticity (MOE), internal bonding strength (IB), thickness swell (TS), and water absorption at 2 h boiling and 24 h soaking. Enzyme addition did improve the ensilage process, as indicated by sustained lower pH (P < 0.0001), higher water-soluble carbohydrates (P < 0.05), and increased lactic acid production (P < 0.0001). The middle particle size range (<10 mm) demonstrated the most promising results during the ensilage process. Compared with fresh stover, the ensilage process did increase IB of stover particleboard by 33% (P < 0.05) and decrease water adsorption at 2 h boiling and 24 h soaking significantly (P < 0.05). Particleboard panels produced from substrate ensiled with enzymes showed a significant reduction in water adsorption of 12% at 2 h boiling testing. On the basis of these results, ensilage can be used as a long-term feedstock preservation method for particleboard production from corn stover. Enzyme-amended ensilage not only improved stover preservation but also enhanced the properties of particleboard products.