Besides the use of maize grain as food and feed, maize stover can be a profitable by-product for cellulosic ethanol production, whereas the whole plant can be used for silage production. However, ...yield is reduced by pest damages, stem corn borers being one of the most important yield constraints. Overall, cell wall composition is key in determining the quality of maize biomass, as well as pest resistance. This study aims to evaluate the composition of the four cell wall fractions (cellulose, hemicellulose, lignin and hydroxycinnamates) in diverse maize genotypes and to understand how this composition influences the resistance to pests, ethanol capacity and digestibility. The following results can be highlighted: (i) pests' resistant materials may show cell walls with low p-coumaric acid and low hemicellulose content; (ii) inbred lines showing cell walls with high cellulose content and high diferulate cross-linking may present higher performance for ethanol production; (iii) and inbreds with enhanced digestibility may have cell walls poor in neutral detergent fibre and diferulates, combined with a lignin polymer composition richer in G subunits.
This study aimed to evaluate the predictive ability of NIRS for the estimation of the chemical composition and organic matter digestibility (OMD) of total mixed rations (TMR) for dairy cows. ...Moreover, herein, empirical equations based on chemical parameters were developed for calculating the OMD. Samples were collected from Galician dairy farms and were scanned in duplicate a Foss NIRSystem 6500 monochromator (1100-2500 nm). The predictive ability of NIRS models was evaluated according to the coefficient of determination in external validation (
2
). High
2
(equal to or higher than 0.90) was shown in predicting chemical composition, however, the estimation of OMD was acceptable (
2
= 0.83). The ability of the NIRS models for estimating the chemical composition was considered excellent, with the ratio performance deviation of external validation (
) higher than 3.0, allowing for quantitative predictions. The estimation of the OMD value by NIRS was considered acceptable (
= 2.5), and led to reduction in the standard error of external validation, in comparison of the best empirical model based on the chemical composition of samples (from ± 2.06% to ± 1.89%). Therefore, this study demonstrated that the NIRS is an effective technology for the rapid and precise nutritional evaluation of TMR in Galicia.
Forage feedstock is the greatest source of energy for livestock. Unfortunately, less than 50% of their fiber content is actually digested and assimilated by the ruminant animals. This recalcitrance ...is mainly due to the high concentration of plant cell wall material and to the limited digestion of the fiber by the microorganisms. A Genome-Wide Association Study (GWAS) was carried out in order to identify Single Nucleotide Polymorphisms (SNPs) associated with forage digestibility traits in a maize Multi-Parent Advanced Generation Intercross (MAGIC) population. We identified seven SNPs, corresponding to five Quantitative Trait Loci (QTL), associated to digestibility of the organic matter, 11 SNPs, clustered in eight QTLs, associated to Neutral Detergent Fiber (NDF) content and eight SNPs corresponding with four QTL associated with Acid Detergent Fiber (ADF). Candidate genes under the QTL for digestibility of the organic matter could be the ones involved in pectin degradation or phenylpropanoid pathway. Transcription factor genes were also proposed for the fiber QTL identified, in addition to genes induced by oxidative stress, or a gene involved in lignin modifications. Nevertheless, for the improvement of the traits under study, and based on the moderate heritability value and low percentage of the phenotypic variability explained by each QTL, a genomic selection strategy using markers evenly distributed across the whole genome is proposed.
In the present work it is studied the predictive ability of NIRS for the estimation of chemical composition (n=171) and organic matter digestibility (n=71) of whole plants forage sorghum and ...morphological components, being developed empirical equations based on chemical parameters to estimate the organic matter digestibility (OMD) values and compared the predictive ability of empirical models vs. NIRS equations. The predictive ability of NIRS models for estimating the OMD and chemical composition showed high reliability, according to the coefficient of determination in external validation (r²≥0.90), whilst the ratio of the standard deviation of the original data to standard error of external validation (RPD) values were higher than 3.0 for all parameters studied. Applying NIRS models to the prediction of OMD of whole plants and morphological components of forage sorghum led to the reduction in the standard error of external validation, in comparison of the best empirical model based on the chemical composition of samples (from ±3.9 to ±1.9%). It is concluded that the NIRS equations developed in the present work are valuable tools for the fast and accurate assessment of the nutritive value of the whole plant and components of forage sorghum.
El objetivo del presente trabajo fue evaluar la capacidad de predicción de las ecuaciones de calibración desarrolladas mediante NIRS (espectroscopía de reflectancia en el infrarrojo cercano) sobre ...muestras secas y molidas, para estimar la calidad fermentativa de ensilados de girasol. Un total de 52 muestras de ensilados procedentes de diferentes ensayos de silos de laboratorio realizados en el CIAM (Centro de Investigacións Agrarias de Mabegondo), cuyo espectro NIRS se registró sobre muestras secas en estufa y molidas. Las muestras en estado fresco fueron analizadas por métodos de referencia. Se determinó el pH, ácido láctico, ácido acético, etanol, nitrógeno amoniacal y nitrógeno soluble. Las calibraciones NIRS fueron desarrolladas utilizando regresión por mínimos cuadrados parciales modificada, realizando la regresión entre los datos espectrales y los de referencia. La capacidad predictiva de las ecuaciones obtenidas osciló entre excelente y buena, mostrando coeficientes de determinación de validación cruzada (r2vc) iguales o superiores a 0.88. Los valores del índice RPD para todos los parámetros estudiados fueron iguales o superiores a 3.0, por lo tanto, las ecuaciones de calibración obtenidas sobre muestras secas y molidas pueden utilizarse satisfactoriamente para predecir la calidad fermentativa de ensilados de girasol en análisis de rutina.
El objetivo fue evaluar fechas de corte y del uso de aditivos sobre la calidad del ensilado de la planta entera de girasol. La variedad forrajera (Rumbosol-91) se cosechó en las semanas 1, 3 y 5 ...post-floración (F1, F2 y F3, respectivamente) y tratada con los siguientes aditivos: 1) 1.5 × 105 ufc de inoculante g-1 de forraje, a base de bacterias lácticas homofermentativas Enterococcus faecium, Pediococcus pentosaceus y Lactobacillus plantarum (INOC), 2) 3 ml kg-1 de forraje de una solución al 85% de ácido fórmico (FORM) y 3) sin aditivo (Testigo); siguiendo un diseño factorial 3x3 con cinco repeticiones. La producción de efluente y las pérdidas totales de materia seca (MS) se redujeron, desde 282 y 134 g kg-1 en F+1 hasta 96 y 87 g kg-1 en F+5 como resultado del alto contenido de humedad del forraje próxima a la floración. El análisis NIRS de las muestras de ensilaje mostró que los contenidos de proteína, fibra y digestibilidad descendían significativamente con la madurez de la planta; la rápida acumulación de aceite en la MS hizo que la concentración energética fuese superior en el estado fenológico más avanzado. La calidad fermentativa de los ensilajes fue satisfactoria, independientemente del momento de corte y del uso de aditivo. Se concluye que es preferible el corte de la planta a las cinco semanas post-floración, donde se espera una fermentación aceptable sin necesidad de conservantes.
Abstract Nowadays, in the bioethanol production process, improving the simplicity and yield of cell wall saccharification procedure represent the main technical hurdles to overcome. This work ...evaluated the application of a rapid and cost-effective technology such as near -infrared spectroscopy (NIRS) for easily predict saccharification efficiency from corn stover biomass. Calibration process focussing on the number of samples and the genetic background of the maize inbred lines were tested; while Modified Partial Least Squares Regression (MPLS) and Multiple Linear Regression (MLR) were assessed in predictions. The predictive capacity of the NIRS models was mainly determined by the coefficient of determination (r 2 ev) and the index of prediction to deviation (RPDev) in external validation. Overall, we could check a better efficiency of the NIRS calibration process for saccharification using larger number of observations (1500 sample set) and genetic backgrounds; while MPLS regression provided better prediction statistics (r 2 ev = 0.80; RPDev = 2.21) compared to MLR (r 2 ev = 0.68; RPDev = 1.75). These results indicate that NIRS could be successfully implemented as a large-phenotyping tool in order to test the saccharification potential of corn biomass.
The effect of leaf removal treatments around vine cluster zones on must quality were tested during 2003 and 2004 in Rías Baixas (Spain) in two Albariño vineyards infected by GLRaV-3. As expected, the ...main virus damage was decreased sugar content (2.1 Brix in 2003 and 0.9 Brix in 2004) in the musts compared with leafroll-free plants. Leaf removal improved must quality by decreasing titratable acidity by between 0.5 and 1.9 g L⁻¹ of tartaric acid, depending on the experiment; it also increased the grape sugar content an average of 1 Brix. In vineyards with high incidence of GLRaV-3, partial defoliation at veraison, or 2 to 3 weeks later, had an improved must quality, counteracting the negative impact of the virus. This process is recommended to avoid penalties in wineries during years with poor ripening conditions.
This technical note sought to examine the ability of near-infrared reflectance spectroscopy (NIRS) to predict the chemical content and organic matter digestibility (OMD) of whole plants and the ...morphological components of forage sunflower. Empirical models for the prediction of OMD values from chemical components were developed, and their predictive ability vs. NIRS models was assessed. The total set of samples (n=147) was composed of whole plants (n=14) and morphological components (n=133) from different experiments performed at Galicia (Spain) and were scanned using a Foss NIR System 6500 instrument. The reference values of OMD corresponded to in vitro determinations (n=112 samples) from laboratory incubation tests using rumen fluid. The predictive capacity of the NIRS models was assessed by the coefficient of determination value in external validation (r2 ), showing good to excellent quality prediction of OMD and chemical components with values of r2 ≥0.88. However, the estimation of lignin did not show predictive utility (r2 =0.40). Using the NIRS models to predict the OMD of whole plants and morphological components of forage sunflower led to a decrease in the standard error in external validation, in contrast to the best empirical equation through the chemical components of samples (from ±8.25 to ±3.23%). This technical note showed that NIRS is a suitable technology, providing a rapid assessment of forage sunflower. However, these results should be considered preliminary, as they are based on a limited number of samples, and it is desirable to improve the performance of NIRS equations by increasing the dataset in future works.
In the present work it is studied the predictive ability of NIRS for the estimation of chemical composition (n=171) and organic matter digestibility (n=71) of whole plants forage sorghum and ...morphological components, being developed empirical equations based on chemical parameters to estimate the organic matter digestibility (OMD) values and compared the predictive ability of empirical models vs. NIRS equations. The predictive ability of NIRS models for estimating the OMD and chemical composition showed high reliability, according to the coefficient of determination in external validation (r²≥0.90), whilst the ratio of the standard deviation of the original data to standard error of external validation (RPD) values were higher than 3.0 for all parameters studied. Applying NIRS models to the prediction of OMD of whole plants and morphological components of forage sorghum led to the reduction in the standard error of external validation, in comparison of the best empirical model based on the chemical composition of samples (from ±3.9 to ±1.9%). It is concluded that the NIRS equations developed in the present work are valuable tools for the fast and accurate assessment of the nutritive value of the whole plant and components of forage sorghum.
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