RESUMEN
Kernel processing and theoretical length of cut (TLOC) of whole-plant corn silage (WPCS) can affect feed intake, digestibility, and performance of dairy cows. The objective of this study was to evaluate for lactating dairy cows the effects of kernel processing and TLOC of WPCS with vitreous endosperm. The treatments were a pull-type forage harvester without kernel processor set for a 6-mm TLOC (PT6) and a self-propelled forage harvester with kernel processor set for a 6-mm TLOC (SP6), 12-mm TLOC (SP12), and 18-mm TLOC (SP18). Processing scores of the WPCS were 32.1% (PT6), 53.9% (SP6), 49.0% (SP12), and 40.1% (SP18). Twenty-four Holstein cows (139 ± 63 d in milk) were blocked and assigned to six 4 × 4 Latin squares with 24-d periods (18 d of adaptation). Diets were formulated to contain 48.5% WPCS, 15.5% citrus pulp, 15.0% dry ground corn, 9.5% soybean meal, 6.8% low rumen degradability soybean meal, 1.8% calcium soap of palm fatty acids (FA), 1.7% mineral and vitamin mix, and 1% urea (dry matter basis). Nutrient composition of the diets (% of dry matter) was 16.5% crude protein, 28.9% neutral detergent fiber, and 25.4% starch. Three orthogonal contrasts were used to compare treatments: effect of kernel processing (PT6 vs. SP6) and effect of TLOC (particle size; SP6 vs. SP12 and SP12 vs. SP18). Cows fed SP6 produced 1.2 kg/d greater milk yield with no changes in dry matter intake, resulting in greater feed efficiency compared with PT6. Cows fed SP6 also produced more milk protein (+36 g/d), lactose (+61 g/d), and total solids (+94 g/d) than cows fed PT6. The mechanism for increased yield of milk and milk components involved greater kernel fragmentation, starch digestibility, and glucose availability for lactose synthesis by the mammary gland. However, cows fed SP6 had lower chewing time and tended to have greater levels of serum amyloid A compared with PT6. Milk yield was similar for SP6 and SP12, but SP12 cows tended to have less serum amyloid A with greater chewing time. Cows fed SP18 had lower total-tract starch digestibility and tended to have lower plasma glucose and produce less milk compared with cows fed SP12. Compared with PT6, feeding SP6 raised linear odd-chain FA concentration in milk. Similarly, a reduction of these same FA occurred for SP12 compared with SP6. Cows fed SP6 had greater proportion of milk C14:1 and C16:1 compared with PT6 and SP12. Lesser trans C18:1 followed by greater C18:0 concentrations were observed for SP12 and PT6 compared with SP6, which is an indication of more complete biohydrogenation in the rumen. Under the conditions of this study, the use of a self-propelled forage harvester with kernel processing set for a 12-mm TLOC is recommended for WPCS from hybrids with vitreous endosperm.
Asunto(s)
Bovinos/fisiología , Endospermo/metabolismo , Manipulación de Alimentos/métodos , Ensilaje/análisis , Zea mays/metabolismo , Animales , Fibras de la Dieta/metabolismo , Ingestión de Alimentos , Femenino , Lactancia/fisiología , Lactosa/metabolismo , Leche/química , Leche/metabolismo , Proteínas de la Leche/metabolismo , Tamaño de la Partícula , Rumen/metabolismo , Almidón/metabolismoRESUMEN
Efficient and robust evaluation of kernel processing from corn silage is an important indicator to a farmer to determine the quality of their harvested crop. Current methods are cumbersome to conduct and take between hours to days. We present the adoption of two deep learning-based methods for kernel processing prediction without the cumbersome step of separating kernels and stover before capturing images. The methods show that kernels can be detected both with bounding boxes and at pixel-level instance segmentation. Networks were trained on up to 1393 images containing just over 6907 manually annotated kernel instances. Both methods showed promising results despite the challenging setting, with an average precision at an intersection-over-union of 0.5 of 34.0% and 36.1% on the test set consisting of images from three different harvest seasons for the bounding-box and instance segmentation networks respectively. Additionally, analysis of the correlation between the Kernel Processing Score (KPS) of annotations against the KPS of model predictions showed a strong correlation, with the best performing at r(15) = 0.88, p = 0.00003. The adoption of deep learning-based object recognition approaches for kernel processing measurement has the potential to lower the quality assessment process to minutes, greatly aiding a farmer in the strenuous harvesting season.
RESUMEN
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.