RESUMEN
The application of Near Infrared (NIR) spectroscopy for analyzing wet feed directly on farms is increasingly recognized for its role in supporting harvest-time decisions and refining the precision of animal feeding practices. This study aims to evaluate the accuracy of NIR spectroscopy calibrations for both undried, unprocessed samples and dried, ground samples. Additionally, it investigates the influence of the bases of reference data (wet vs. dry basis) on the predictive capabilities of the NIR analysis. The study utilized 492 Corn Whole Plant (CWP) and 405 High Moisture Corn (HMC) samples, sourced from various farms across Italy. Spectral data were acquired from both undried, unground and dried, ground samples using laboratory bench NIR instruments, covering a spectral range of 1100 to 2498 nm. The reference chemical composition of these samples was analyzed and presented in two formats: on a wet matter basis and on a dry matter basis. The study revealed that calibrations based on undried samples generally exhibited lower predictive accuracy for most traits, with the exception of Dry Matter (DM). Notably, the decline in predictive performance was more pronounced in highly moist products like CWP, where the average error increased by 60-70%. Conversely, this reduction in accuracy was relatively contained (10-15%) in drier samples such as HMC. The Standard Error of Cross-Validation (SECV) values for DMres, Ash, CP, and EE were notably low, at 0.39, 0.30, 0.29, 0.21% for CWP and 0.49, 0.14, 0.25, 0.14% for HMC, respectively. These results align with previous studies, indicating the reliability of NIR spectroscopy in diverse moisture contexts. The study attributes this variance to the interference caused by water in 'as is' samples, where the spectral features predominantly reflect water content, thereby obscuring the spectral signatures of other nutrients. In terms of calibration development strategies, the study concludes that there is no significant difference in predictive performance between undried calibrations based on either 'dry matter' or 'as is' basis. This finding emphasizes the potential of NIR spectroscopy in diverse moisture contexts, although with varying degrees of accuracy contingent upon the moisture content of the analyzed samples. Overall, this research provides valuable insights into the calibration strategies of NIR spectroscopy and its practical applications in agricultural settings, particularly for on-farm forage analysis.
Asunto(s)
Alimentación Animal , Espectroscopía Infrarroja Corta , Zea mays , Espectroscopía Infrarroja Corta/métodos , Calibración , Zea mays/química , Alimentación Animal/análisis , Agua/análisis , Agua/química , DesecaciónRESUMEN
The techniques used to assess pH in silages vary greatly. The aim of this study was to evaluate the effects of water-to-sample ratio, extraction procedure, and standing time on pH determination. Silage samples (n = 20 for each silage) were chosen to represent diverse crops (corn, elephant grass, sugarcane, and forage peanut) to have a varied ensilability index and thus a wide range in final pH. Three water-to-sample ratios and 2 extraction procedures were used to measure pH at 0, 5, 10, 15, 30, 60, and 120 min of standing time. The ratios (undried silage to water) were 9:60, 25:100, and 30:270. The samples with the first 2 ratios were manually extracted, using a glass beaker and a glass stirring rod. The samples with the 30:270 ratio were extracted by using a stomacher blender for 4 min at 200 rpm. An electrode was used to perform pH readings. Dry matter (DM), water-soluble carbohydrates, and lactic acid concentrations were determined. The experimental design was completely randomized using a mixed repeated-measures model. Mean separation was performed using the Tukey test at P < 0.10 using the MIXED procedure of SAS (SAS Institute Inc., Cary, NC). The DM concentrations ranged from 24.5 to 40.2, 15.8 to 25.9, 26.9 to 30.6, and 17.8 to 21.4% for corn, elephant grass, sugarcane, and forage peanut silages, respectively. The lactic acid concentrations ranged from 2.9 to 10.1, 1.8 to 4.4, 0.7 to 11.4, and 0.3 to 1.4% of DM for corn, elephant grass, sugarcane, and forage peanut silages, respectively. The pH values measured by the 9:60 method were greater than other techniques at any standing times. The pH values from the 25:100 and 30:270 methods did not differ for elephant grass and forage peanut silages at any standing times. However, the 30:270 method had greater pH values for corn and sugarcane silages than the 25:100 technique at any standing times. The pH values measured by the 30:270 method did not vary for any of the silages according to standing time. Nevertheless, the pH values of the 25:100 method were greater from 0 to 10 min than for other standing times for all silages. Thus, for this method, there was pH stabilization from 15 min of standing time on. Overall, the 25:100 and 30:270 methods are recommended for assessment of silage pH. Fifteen minutes of standing time should be used for the 25:100 method but the pH reading can be performed immediately after mixing for the 30:270 technique.
Asunto(s)
Ácido Láctico/análisis , Ensilaje/análisis , Animales , Grano Comestible , Fermentación , Concentración de Iones de Hidrógeno , Saccharum , Zea maysRESUMEN
Over the last 25 years, whole-plant corn silage has become an important and popular feedstuff for dairy production. Copious research has been dedicated to the development and evaluation of alternatives to enhance the nutritive value of whole-plant corn silage. These efforts have been aimed at manipulating the physical and chemical characteristics of whole-plant corn silage in an effort to maximize dairy profitability. Results from this review indicate that optimization of harvest maturity, kernel processing, theoretical length of cut, and cutting height improve or maintain the nutritive value and milk production of lactating dairy cows. Technological advancements have been developed and made available to dairy producers and corn growers desiring to enhance fiber and starch digestibility of whole-plant corn silage. Future research should be directed toward further assessment of new processors available in the market and the development of assessment methods for optimization of crop processor settings, harvest efficiency, and nutritional modeling.