Harvest Timing of Standing Corn Using Near-Infrared Reflectance Spectroscopy.

Harvesting corn at the proper maturity is important for managing its nutritive value as livestock feed. Standing whole-plant moisture content is commonly utilized as a surrogate for corn maturity. However, sampling whole plants is time consuming and requires equipment not commonly found on farms. This study evaluated three methods of estimating standing moisture content. The most convenient and accurate approach involved predicting ear moisture using handheld near-infrared reflectance spectrometers and applying a previously established relationship to estimate whole-plant moisture from the ear moisture. The ear moisture model was developed using a partial least squares regression model in the 2021 growing season utilizing reference data from 610 corn plants. Ear moisture contents ranged from 26 to 80 %w.b., corresponding to a whole-plant moisture range of 55 to 81 %w.b. The model was evaluated with a validation dataset of 330 plants collected in a subsequent growing year. The model could predict whole-plant moisture in 2022 plants with a standard error of prediction of 2.7 and an R2P of 0.88. Additionally, the transfer of calibrations between three spectrometers was evaluated. This revealed significant spectrometer-to-spectrometer differences that could be mitigated by including more than one spectrometer in the calibration dataset. While this result shows promise for the method, further work should be conducted to establish calibration stability in a larger geographical region.

Practical Considerations for Using the NeoSpectra-Scanner Handheld Near-Infrared Reflectance Spectrometer to Predict the Nutritive Value of Undried Ensiled Forage.

Prediction models of different types of forage were developed using a dataset of near-infrared reflectance spectra collected by three handheld NeoSpectra-Scanners and laboratory reference values for neutral detergent fiber (NDF), in vitro digestibility (IVTD), neutral detergent fiber digestibility (NDFD), acid detergent fiber (ADF), acid detergent lignin (ADL), crude protein (CP), Ash, and moisture content (MO) from a total of 555 undried ensiled corn, grass, and alfalfa samples. Data analyses and results of models developed in this study indicated that the scanning method significantly impacted the accuracy of the prediction of forage constituents, and using the NEO instrument with the sliding method improved calibration model performance (p < 0.05) for nearly all constituents. In general, poorer-performing models were more impacted by instrument-to-instrument variability. The exception, however, was moisture content (p = 0.02), where the validation set with an independent instrument resulted in an RMSEP of 2.39 compared to 1.44 where the same instruments were used for both calibration and validation. Validation model performance for NDF, IVTD, NDFD, ADL, ADF, Ash, CP, and moisture content were 4.18, 3.86, 6.14, 1.10, 2.75, 1.42, 2.71, and 1.67 for alfalfa-grass silage samples and 3.22, 2.21, 4.55, 0.38, 2.07, 0.50, 0.51, and 1.62 for corn silage, respectively. Based on the results of this study, the handheld spectrometer would be useful for predicting moisture content in undried and unground alfalfa-grass (R2 = 0.97) and corn (R2 = 0.93) forage samples.

Evaluating the Feasibility of a Low-Field Nuclear Magnetic Resonance (NMR) Sensor for Manure Nutrient Prediction.

Livestock manure is typically applied to fertilize crops, however the accurate determination of manure nutrient composition through a reliable method is important to optimize manure application rates that maximize crop yields and prevent environmental contamination. Existing laboratory methods can be time consuming, expensive, and generally the results are not provided prior to manure application. In this study, the evaluation of a low-field nuclear magnetic resonance (NMR) sensor designated for manure nutrient prediction was assessed. Twenty dairy manure samples were analyzed for total solid (TS), total nitrogen (TN), ammoniacal nitrogen (NH4-N), and total phosphorus (TP) in a certified laboratory and in parallel using the NMR analyzer. The linear regression of NMR prediction versus lab measurements for TS had an R2 value of 0.86 for samples with TS < 8%, and values of 0.94 and 0.98 for TN and NH4-N, respectively, indicating good correlations between NMR prediction and lab measurements. The TP prediction of NMR for all samples agreed with the lab analysis with R2 greater than 0.87. The intra- and inter-sample variations of TP measured by NMR were significantly larger than other parameters suggesting less robustness in TP prediction. The results of this study indicate low-field NMR is a rapid method that has a potential to be utilized as an alternative to laboratory analysis of manure nutrients, however, further investigation is needed before wide application for on farm analysis.

The Relative Performance of a Benchtop Scanning Monochromator and Handheld Fourier Transform Near-Infrared Reflectance Spectrometer in Predicting Forage Nutritive Value.

Advanced manufacturing techniques have enabled low-cost, on-chip spectrometers. Little research exists, however, on their performance relative to the state of technology systems. The present study compares the utility of a benchtop FOSS NIRSystems 6500 (FOSS) to a handheld NeoSpectra-Scanner (NEO) to develop models that predict the composition of dried and ground grass, and alfalfa forages. Mixed-species prediction models were developed for several forage constituents, and performance was assessed using an independent dataset. Prediction models developed with spectra from the FOSS instrument had a standard error of prediction (SEP, % DM) of 1.4, 1.8, 3.3, 1.0, 0.42, and 1.3, for neutral detergent fiber (NDF), true in vitro digestibility (IVTD), neutral detergent fiber digestibility (NDFD), acid detergent fiber (ADF), acid detergent lignin (ADL), and crude protein (CP), respectively. The R2P for these models ranged from 0.90 to 0.97. Models developed with the NEO resulted in an average increase in SEP of 0.14 and an average decrease in R2P of 0.002.

Extraction, composition, and functional properties of dried alfalfa (Medicago sativa L.) leaf protein.

BACKGROUND: Alfalfa is considered a potential feedstock for biofuels; co-products with value-added uses would enhance process viability. This work evaluated dried alfalfa leaves for protein production and describes the functional properties of the protein. RESULTS: Dried alfalfa leaves contained 260 g kg-1 dry basis (DB) crude protein, with albumins being the major fraction (260 g kg-1 of total protein). Alkali solubilization for 2 h at 50 °C, acid precipitation, dialysis, and freeze-drying produced a protein concentrate (600 g kg-1 DB crude protein). Alfalfa leaf protein concentrate showed moderate solubility (maximum 500 g kg-1 soluble protein from pH 5.5 to 10), excellent emulsifying properties (activity 158-219 m2 g-1 protein, stability 17-49 min) and minimal loss of solubility during heating at pH ≥ 7.0. CONCLUSIONS: It is technically feasible to extract protein with desirable emulsifying and heat stability properties from dried alfalfa leaves; however, the dried form may not be a practical starting material for protein production, given the difficulty of achieving high yields and high-purity protein product. © 2016 Society of Chemical Industry.

Improving ethanol production from alfalfa stems via ambient-temperature acid pretreatment and washing.

The concept of co-production of liquid fuel (ethanol) along with animal feed on farm was proposed, and the strategy of using ambient-temperature acid pretreatment, ensiling and washing to improve ethanol production from alfalfa stems was investigated. Alfalfa stems were separated and pretreated with sulfuric acid at ambient-temperature after harvest, and following ensiling, after which the ensiled stems were subjected to simultaneous saccharification and fermentation (SSF) for ethanol production. Ethanol yield was improved by ambient-temperature sulfuric acid pretreatment before ensiling, and by washing before SSF. It was theorized that the acid pretreatment at ambient temperature partially degraded hemicellulose, and altered cell wall structure, resulted in improved cellulose accessibility, whereas washing removed soluble ash in substrates which could inhibit the SSF. The pH of stored alfalfa stems can be used to predict the ethanol yield, with a correlation coefficient of +0.83 for washed alfalfa stems.

Optimizing on-farm pretreatment of perennial grasses for fuel ethanol production.

Switchgrass (Panicum virgatum L.) and reed canarygrass (Phalaris arundinacea L.) were pretreated under ambient temperature and pressure with sulfuric acid and calcium hydroxide in separate experiments. Chemical loadings from 0 to 100g (kg DM)(-1) and durations of anaerobic storage from 0 to 180days were investigated by way of a central composite design at two moisture contents (40% or 60% w.b.). Pretreated and untreated samples were fermented to ethanol by Saccharomyces cerevisiae D5A in the presence of a commercially available cellulase (Celluclast 1.5L) and beta-glucosidase (Novozyme 188). Xylose levels were also measured following fermentation because xylose is not metabolized by S. cerevisiae. After sulfuric acid pretreatment and anaerobic storage, conversion of cell wall glucose to ethanol for reed canarygrass ranged from 22% to 83% whereas switchgrass conversions ranged from 16% to 46%. Pretreatment duration had a positive effect on conversion but was mitigated with increased chemical loadings. Conversions after calcium hydroxide pretreatment and anaerobic storage ranged from 21% to 55% and 18% to 54% for reed canarygrass and switchgrass, respectively. The efficacy of lime pretreatment was found to be highly dependent on moisture content. Moreover, pretreatment duration was only found to be significant for reed canarygrass. Although significant levels of acetate and lactate were observed in the biomass after storage, S. cerevisiae was not found to be inhibited at a 10% solids loading.