The current study was designed to investigate the chemical composition, amino acid content, and rumen degradation characteristics (nylon bag method) of six organic feeds to illustrate their feeding values. The feeds analyzed were: corn grain (CG), soybean cake (SC), wheat bran (WB), corn silage (CS), oat hay (OT), and alfalfa hay (AF). Our results showed that the contents of crude protein (CP) (47.46%) and ether extract (EE) (8.23%) in SC were highest. The contents of neutral detergent fiber (NDF) (65.00%) and acid detergent fiber (ADF) (39.16%) in OT were highest. The contents of total amino acid (TAA) (42.95%) and essential amino acid (EAA) (19.73%) in SC were highest. Among SC, WB, and CG, the effective degradation rate (ED) of dry matter (DM) is SC (87.89%) > WB (73.32%) > CG (64.25%); the ED value of CP is CG (82.37%) > WB (82.40%) > SC (60.47%). Among CS, OT, and AF, the effective degradation rate (ED) of DM is CS (72.68%) > OT (59.97%) > AF (58.89%); the ED value of CP is AF (76.46%) > CS (72.03%) > OT (71.99%). In conclusion, the chemical composition, amino acid content, and rumen degradation rate of SC and AF were better than those of the other four feeds.

The nutrition loss of silage is partly due to the heat production of silage. In this study, the amount of nutrition loss of silage was estimated by measuring the heat production of silage, and the concept of the accumulated temperature of fermentation was put forward. The laboratory measured the fermentation-accumulated temperature of whole-plant corn silage with different compaction densities. The samples were analyzed to establish a multiple linear regression model with nutrition loss. The results demonstrated a significant non-linear relationship between the whole-plant corn silage compaction density and fermentation-accumulated temperature. The multiple linear regression model between the accumulated-fermentation temperature and nutrition loss was significant under different densities. The amount of silage nutrition loss can be predicted by the fermentation-accumulated temperature.

It is important to assess the nutritional concentrations of forage before it can be used for tremendous improvements in the dairy industry. This improvement requires a rapid, accurate, and portable method for detecting nutrient values, instead of traditional laboratory analysis. Fourier‐transform infrared (ATR‐FTIR) spectroscopy technology was applied, and partial least squares regression (PLSR) and backpropagation artificial neural network (BP‐ANN) algorithms were used in the current study. The objective of this study was to estimate the discrepancy in nutritional content and rumen degradation in WPCS grown in various regions and to propose a novel analytical method for predicting the nutrient content of whole plant corn silage (WPCS). The Zhengdan 958 cultivar of WPCS was selected from eight different sites to compare the discrepancies in nutrients and rumen degradation. A total of 974 WPCS samples from 235 dairy farms scattered across five Chinese regions were collected, and nutritional indicators were modeled. As a result, substantial discrepancies in nutritional concentrations and rumen degradation of WPCS were found when they were cultivated in different growing regions. The WPCS in Wuxi showed 1.14% higher dry matter (DM) content than that in Jinan. Lanzhou had 11.57% and 8.25% lower neutral detergent fiber (NDF) and acid detergent fiber (ADF) concentrations than Jinan, respectively. The DM degradability of WPCS planted in Bayannur was considerably higher than that in Jinan (6 h degradability: Bayannur vs. Jinan = 49.85% vs. 33.96%), and the starch of WPCS from Bayannur (71.79%) was also the highest after 6 h in the rumen. The results indicated that the contents of NDF, ADF, and starch were estimated precisely based on ATR‐FTIR combined with PLSR or the BP‐ANN algorithm (R² ≥ 0.91). This was followed by crude protein (CP), DM (0.82 ≤ R² ≤ 0.90), ether extract (EE), and ash (0.66 ≤ R² ≤ 0.81). The BP‐ANN algorithm had a higher prediction performance than PLSR (R²_PLSR ≤ R²_BP-ANN; RMSE_PLSR ≥ RMSE_BP-ANN). The same WPCS cultivar grown in different regions had various nutrient concentrations and rumen degradation. ATR‐FTIR technology combined with the BP‐ANN algorithm could effectively evaluate the CP, NDF, ADF, and starch contents of WPCS.