Charles R. Hurburgh

SINGLE-KERNEL MAIZE ANALYSIS BY NEAR-INFRARED HYPERSPECTRAL IMAGING

The objectives of this research were: (1) to develop a technique for creating calibrations to predict the constituent concentrations of single maize kernels from near-infrared (NIR) hyperspectral image data, and (2) to evaluate the feasibility of an NIR hyperspectral imaging spectrometer as a tool for the quality analysis of single maize kernels. Single kernels of maize were analyzed by hyperspectral transmittance in the range of 750 to 1090 nm. The transmittance data were standardized using an opal glass transmission standard and converted to optical absorbance units. Partial least squares (PLS) regression and principal components regression (PCR) were used to develop predictive calibrations for moisture and oil content using the standardized absorbance spectra. Standard normal variate, detrending, multiplicative scatter correction, wavelength selection by genetic algorithm, and no preprocessing were compared for their effect on model predictive performance. The moisture calibration achieved a best standard error of cross-validation (SECV) of 1.20%, with relative performance determinant (RPD) of 2.74. The best oil calibration achieved an SECV of 1.38%, with an RPD of only 1.45. The performance and subsequent analysis of the oil calibration reveal the need for improved methods of single-seed reference analysis.

Influence of soil moisture on near-infrared reflectance spectroscopic measurement of soil properties

Near-infrared reflectance spectroscopy (NIRS), a nondestructive analytical technique, may someday be used to rapidly and simultaneously quantify several soil properties in agricultural fields. The objectives of this study were to examine the influence of moisture content on the accuracy of NIRS analysis of soil properties and to assess the robustness of a NIRS multivariate calibration technique. Four hundred agricultural soil samples (<2 mm) from Iowa and Minnesota were studied at two moisture levels: moist and air-dried. The soil properties tested included total C, organic C, inorganic C, total N, CEC, pH, texture, moisture, and potentially mineralizable N. About 70% of the Iowa samples were selected for the calibration set, and the rest of the Iowa samples and all of the Minnesota samples were assigned to validation set I and validation set II, respectively. Calibrations were based on partial least-squares regression (PLSR), using the first differentials of log (1/R) for the 1100 to 2500-nm spectral range. The results for the calibration set and validation set I indicated that NIRS-PLSR was able to predict many soil properties (total C, organic C, inorganic C, total N, CEC, % clay, and moisture) with reasonable accuracy for both the air-dried (R2 > 0.76) and moist (R2 > 0.74) soils. The results for validation set II showed that NIRS-PLSR was able to predict some properties of soils (total C, organic C, total N, and moisture content) from a different geographic region, but other soil properties in validation set II were not accurately predicted. Although NIRS-PLSR predictions are slightly more accurate for air-dried soils than for moist soils, the results indicate that the NIRS-PLSR technique can be used for analysis of field moist samples with acceptable accuracy as long as diverse soil samples from the same region are included in the calibration database.

Protein and oil patterns in U.S. and world soybean markets

The Japan Oilseed Processors Association provided yearly data showing the average protein and oil content of imported soybeans from the U.S. (No. 2 Yellow and IOM grades), Brazil, Argentina, and the Peoples Republic of China. Throughout the years 1972–1988, U.S. No. 2 soybeans contained about 1–1.5% less oil than Brazilian soybeans. Recently, the protein content of U.S. soybeans has also fallen behind that of Brazil. U.S. IOM soybeans, a designation based on seed size, contained about 1.5% more protein and about 0.5% less oil than U.S. No. 2 soybeans. Surveys of U.S. soybeans in the years 1986, 1987, and 1988 showed consistent state and regional differences in protein and oil content. Soybeans from northern and western soybean-growing states (North Dakota, South Dakota, Minnesota, Iowa, Wisconsin) contained 1.5–2% less protein and 0.2–0.5% more oil than soybeans from southern states (Texas, Arkansas, Louisiana, Mississippi, Tennessee, Kentucky, Alabama, Georgia, South Carolina, North Carolina). State and regional differences in composition represented differences of up to 25 cents per bushel in Estimated Processed Value for one set of soybean meal and oil prices.

NEAR-INFRARED SENSING OF MANURE NUTRIENTS

The effectiveness of near-infrared (NIR) technology for quickly analyzing the nutrient content of three types of animal manure was evaluated. Swine lagoon effluent, liquid swine pit manure, and solid beef feedlot manure were tested. An NIRSystems 6500 scanning monochromator unit was calibrated against wet chemistry data. Total solids (TS), total Kjeldahl nitrogen (TKN), ammonia nitrogen (NH3-N), total phosphorus (P), and potassium (K) were measured. Correlation coefficients (r) ranged from 0.688 to 0.976, Ratios of data range:standard error of prediction (SEP) varied from 7.0 to 13.6 for the various chemical constituents and manure sources. Based on the individual ratios we conclude that NIR techniques will allow us to predict TS, TKN, NH3-N, and K in all three manure types. Further work will be required before P is predictable.

Estimating the processed value of soybeans

Interest in marketing soybeans on the basis of protein and oil content is increasing. Producers, breeders, handlers and buyers of soybeans need a method of evaluating soybean lots of different composition. A model is presented that predicts, given soybean composition and processing conditions, the yield of crude soybean oil and soybean meal from the processing of soybeans in a solvent extraction plant. From these yields, an estimated processed value (EPV) was calculated. For one set of price conditions, the EPV of typical soybeans had a range of

Potential economic losses to the US corn industry from aflatoxin contamination

0.93 per bushel if premiums were paid for meal protein in excess of specifications and a range of

Evaluation of Spectral Pretreatments, Partial Least Squares, Least Squares Support Vector Machines and Locally Weighted Regression for Quantitative Spectroscopic Analysis of Soils

0.53 per bushel if meal protein premiums were not paid. Trading rules established by the National Oilseed Processors Association for domestic meal markets have a significant effect on the value and composition of soybean meal.

Compositional, Physical, and Wet-Milling Properties of Accessions Used in Germplasm Enhancement of Maize Project

ABSTRACT Mycotoxins, toxins produced by fungi that colonise food crops, can pose a heavy economic burden to the US corn industry. In terms of economic burden, aflatoxins are the most problematic mycotoxins in US agriculture. Estimates of their market impacts are important in determining the benefits of implementing mitigation strategies within the US corn industry, and the value of strategies to mitigate mycotoxin problems. Additionally, climate change may cause increases in aflatoxin contamination in corn, greatly affecting the economy of the US Midwest and all sectors in the United States and worldwide that rely upon its corn production. We propose two separate models for estimating the potential market loss to the corn industry from aflatoxin contamination, in the case of potential near-future climate scenarios (based on aflatoxin levels in Midwest corn in warm summers in the last decade). One model uses the probability of acceptance based on operating characteristic (OC) curves for aflatoxin sampling and testing, while the other employs partial equilibrium economic analysis, assuming no Type 1 or Type 2 errors, to estimate losses due to proportions of lots above the US Food and Drug Administration (USFDA) aflatoxin action levels. We estimate that aflatoxin contamination could cause losses to the corn industry ranging from US

Identification and segregation of high-value soybeans at a country elevator

52.1 million to US

Measurement of Single Soybean Seed Attributes by Near-Infrared Technologies. A Comparative Study

1.68 billion annually in the United States, if climate change causes more regular aflatoxin contamination in the Corn Belt as was experienced in years such as 2012. The wide range represents the natural variability in aflatoxin contamination from year to year in US corn, with higher losses representative of warmer years.