Nutritional Characterization of Hay Produced in Campania Region: Analysis by the near Infrared Spectroscopy (NIRS) Technology

Zicarelli, Fabio; Sarubbi, Fiorella; Iommelli, Piera; Grossi, Micaela; Lotito, Daria; Lombardi, Pietro; Tudisco, Raffaella; Infascelli, F.; Musco, Nadia

doi:10.3390/ani12213035

Cited by 7 publications

(5 citation statements)

References 40 publications

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“…Excluding the PUA group, protein level (CP) seemed, on average, lower than those reported by [42], who evaluated samples of meadows and grasslands located in north-central Apennine (Italy), while ADL seemed to be similar. The content of CP of our sample was slightly higher for all types of hays and especially for prevalent alfalfa hays (PUA and PRA) with respect to those reported by Zicarelli et al in 2022 [43], which considered different altitude areas of Campania Region. The different protein content of hays could be affected by various factors, firstly the different climate conditions and endogenous characteristics between the Regions.…”

Section: Plos Onecontrasting

confidence: 66%

Discriminant analysis as a tool to classify farm hay in dairy farms

Dal Prà,

Bozzi,

Parrini

et al. 2023

PLoS ONE

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Hay is one of the primary constituents of ruminant feed, and rapid classification systems of nutritional value are essential. A reliable approach to evaluating hay quality is a combination of visual combined inspection by NIRS analysis. The analysis was carried out on 1,639 samples of hay collected from 2016 to 2021 in northern Italy. Discriminant analysis (DAPC) on five hay types (FOM, forage mixtures; APG, first alfalfa cutting with prevalence of graminaceous >50%; PRA, prevailing alfalfa >50%; PUA, purity alfalfa >95%; and PEM, permanent meadows) was performed by ex-ante visual inspection categorization and NIRS analysis. This study aimed to provide a complementary method to differentiate hay types and classify unknown samples. Two scenarios were used: i) all data were used for model training, and the discriminant functions were extracted based on all samples; ii) the assignment of each group was assessed without samples belonging to the training set group. DAPC model resulted in an overall assignment success rate of 66%; precisely, the success was 84, 79, 69, 37, and 27% for PUA, FOM, PRA, APG, and PEM, respectively. In the second scenario, three groups showed percentages of posterior assignment probability higher than 70% to only one group: PUA with PRA (~ 99%), PRA with PUA (~71%), and PEM with FOM (~75%). Discriminant analysis can be successfully used to differentiate hay types and could also be used to assess factors related to hay quality in addition to NIRS analysis.

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Section: Plos Onecontrasting

confidence: 66%

Discriminant analysis as a tool to classify farm hay in dairy farms

Dal Prà,

Bozzi,

Parrini

et al. 2023

PLoS ONE

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“…However, the higher gas production recorded for diets rich in concentrates compared to forages reveals that the higher content of highly fermentable constituents in concentrates are rapidly fermented. For four diets out of five (2,3,4,5), the trends of the curves (Figure 1) of the gas production as a function of time were very similar, showing a good activity of the fecal micro population during the first hours of incubation. The different trend of the gas production curve relating to Diet 1 can be attributed, at least in part, to the lower content of cereals for which the microorganisms took more time to develop and give rise to an adequate fermentation process with the relative production of gas.…”

Section: In Vitro Fermentationmentioning

confidence: 86%

“…The determination of digestibility represents the first step for the evaluation of feed net energy; in horses, digestibility can be determined in vivo by the ingesta–excreta balance or by the marker method [ 1 ]. In addition, feed digestibility can be estimated by chemical composition parameters [ 2 ], by NIRS method [ 3 , 4 ] by the “in sacco” method [ 5 ], and by the in vitro pepsin-cellulase technique [ 6 ]. These in vitro techniques arouse particular interest because they are not expensive and are easy to perform.…”

Section: Introductionmentioning

confidence: 99%

Forage:Concentrate Ratio Effects on In Vivo Digestibility and In Vitro Degradability of Horse’s Diet

Zicarelli

Tudisco

Lotito

et al. 2023

Animals

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Determination of digestibility represents the first step for the evaluation of the net energy content of feed for livestock animals. The aim of this study was to evaluate the in vivo digestibility and in vitro degradability of five diets characterized by different forage/concentrate ratios (F:C) in horses. The in vitro degradability was determined by the Gas Production Technique (GPT), using as an inoculum source the feces of the same subjects used for the in vivo test. Five diets consisting of poliphyte hay, straw and grains of barley and oats with a different F:C ratio [90/10 (Diet 1); 78/22 (Diet 2); 68/32 (Diet 3); 60/40 (Diet 4); 50/50 (Diet 5) were formulated and administered in succession, starting with Diet 1. In the in vivo results, no significant differences emerged, despite the different F:C content. In in vitro fermentation, four diets out of the five (2, 3, 4, 5) presented a similar trend of the curve of gas production, showing good activity of the fecal micro population during the first hours of incubation. An important correlation between gas and Volatile Fatty Acid (VFA) were found, suggesting that the processes linked to the micro population deriving from the horse’s caecum follow metabolic pathways whose products can be modeled in the same way as for the rumen. The GPT could represent the correct method for studying the nutritional characteristics of feed for horses, using feces as the source of inoculum, even if further investigations must be performed to improve the technique.

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“…To measure dry weight, plants were transferred into paper bags, dried in the sun for 2 days, and then transferred to the oven for further drying at 65 ± 2 °C until a constant weight was obtained. After dry weight measurements, stem and leaves were separated and ground in a grinder machine, and data for fodder quality traits such as crude fat (CF %), crude protein (CP %), neutral detergent fiber (NDF %), acid detergent fiber (ADF %) [37], and moisture (Mois %) were recorded separately for both stems and leaves using a near-infrared (NIR) instrument (AgriNIR™) [38]. ) represent plant residues after harvesting.…”

Section: Measurements Of Morphological and Quality Parametersmentioning

confidence: 99%

Multi-Trait Selection of Quinoa Ideotypes at Different Levels of Cutting and Spacing

et al. 2023

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Climate change has affected the food supply chain and raised serious food concerns for humans and animals worldwide. The present investigation aimed to assess the effect of environmental factors along with three different levels of cutting (i.e., cutting 1, 2, and 3 at the vegetative, budding, and flowering stages, respectively) and spacing (i.e., 21, 23, and 26 cm) on quinoa biomass and quality to select the most suitable accessions. This experiment was repeated for two years using a split–split plot experimental design. The cutting × genotype × year and cutting × space × genotype interactions were significant for most quinoa morphological traits (except for leaf area and intermodal distance), where the maximum growth in number of leaves/plant (NoL), plant height (PH), fresh weight (FW), number of branches/plant (Br), and dry weight (DW) were observed during the second growing season. Cutting and spacing levels also showed significant effects on morphological and quality traits of quinoa. Among the different levels of cutting and spacing, cutting level 3 and spacing level 2 were more effective across both years at gaining maximum biomass and quality traits such as crude fat (CF) and crude protein (CP). According to the MGIDI, only two accessions (R3 and R9) fared better in both growing seasons, and selected accessions had positive morphological and quality traits. There were moderately significant negative correlations between PH, NoL, LA, FW, and DW and anti-quality traits such as neutral detergent fiber (NDF) and acid detergent fiber (ADF), indicating that an increase in biomass decreased the concentrations of ADF and NDF in both stem and leaves. A comparison with oat accessions (G3 and G7) revealed that quinoa has higher CP and CF and lower NDF than oats in both stems and leaves (except for ADF). In conclusion, the combination of cutting level 3 and spacing level 2 (23 cm) is more suitable to obtain high-quality quinoa forage with maximum biomass production. Furthermore, the MGIDI is a useful tool for breeders to select genotypes based on their mean performance, stability, and desired traits.

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Nutritional Characterization of Hay Produced in Campania Region: Analysis by the near Infrared Spectroscopy (NIRS) Technology

Cited by 7 publications

References 40 publications

Discriminant analysis as a tool to classify farm hay in dairy farms

Discriminant analysis as a tool to classify farm hay in dairy farms

Forage:Concentrate Ratio Effects on In Vivo Digestibility and In Vitro Degradability of Horse’s Diet

Multi-Trait Selection of Quinoa Ideotypes at Different Levels of Cutting and Spacing

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