A Dataset on Corn Silage in China Used to Establish a Prediction Model Showing Variation in Nutrient Composition

Zhang, Shu; Hao, Jian; Wang, Donghai; Luo, Chenglong; Lu, Na; Guo, Xiaocen; Liu, Yanfang; Zhang, Zixiao; Li, Shengli

doi:https://doi.org/10.1155/2023/7860822

Journal of Spectroscopy

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Abstract Introduction Materials and Methods Results and Discussion Conclusions Data Availability Conflicts of Interest Authors’ Contributions Acknowledgments Supplementary Materials References Copyright Related Articles

Research Article | Open Access

Volume 2023 | Article ID 7860822 | https://doi.org/10.1155/2023/7860822

A Dataset on Corn Silage in China Used to Establish a Prediction Model Showing Variation in Nutrient Composition

Shu Zhang,¹Jian Hao,²Donghai Wang,¹Chenglong Luo,³Na Lu,⁴Xiaocen Guo,⁴Yanfang Liu,⁵Zixiao Zhang,⁴and Shengli Li³

Academic Editor: Sarfaraz Ahmed Mahesar

Received06 Apr 2023

Revised30 May 2023

Accepted08 Jun 2023

Published01 Jul 2023

Abstract

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.

1. Introduction

The Chinese dairy industry has developed rapidly over the recent decades and needs to provide milk products for a quarter of the world’s population. To adapt to intensive systems, it further requires sustainable supplementation of feedstuffs. A sequence of studies indicates that the nutrient variation of feedstuffs contributes to variation in dairy production [1, 2]. For example, a 13.1% crude protein (CP) diet significantly reduced milk yield to 16.2% CP of the total mixed ration (TMR) in dairy cows [3]. The increase in ether extract (EE) improved the milk fat percentage of dairy cows [4]. The content of starch (28.5%) owned higher dry matter intake (DMI) and milk yield (MY) than 24% [5]. Thus, it is truly important to detect the nutrient composition before they are utilized.

Whole plant corn silage (WPCS) is the main ingredient in dairy TMR under most dietary regimes, especially for high-yield cows. The corn silage percentage utilized in TMR has contributed to 42% [6]. The extensive use of it could attribute to a high and stable production in conjunction with high contents of total digestible nutrients and metabolizable energy [6–9]. With the exception of genotype and harvest maturity, the yield and nutritional quality of WPSC are highly influenced by environmental conditions [10–12]. For example, high growing temperatures can reduce the digestibility of corn silage because of a substantial increase in lignin content in stovers and a decrease in starch content in the cobs [13, 14]. Moreover, previous studies have reported that precipitation was one of the most influential abiotic factors for plant productivity [15], and drought stress generally contributed to delays in plant growth and development by decreasing cell elongation and reducing photosynthesis [16]. Furthermore, soil moisture and growing temperature were highly related to DM yields because they affected the canopy and anatomical development of maize crops [17]. Above all, it is necessary for dairy farms to detect the nutritional quality of roughage delivered from different frown regions before they are formulated and fed, which provides fundamental information to satisfy the exact nutrient requirements.

Traditional wet chemical analysis requires considerable human, material, and financial resources, and the reagents used would result in environmental pollution [18]. Therefore, the exploration of real-time, efficient, and environmentally friendly techniques has attracted a widespread interest. As a fast, simple, noninvasive, and economical technology, Fourier-transform infrared (ATR-FTIR) spectroscopy can complement or replace existing techniques [19–22]. Using the ATR-FTIR technique, previous studies [23, 24] constructed prediction models for dry matter (DM), CP, neutral detergent fiber (NDF), and acid detergent fiber (ADF) contents in plants. These experiments have predominantly implemented traditional linear regression methods, such as partial least squares regression (PLSR), to build a prediction model between the nutrient contents and spectroscopy information of feedstuffs. But nowadays, the application of artificial neural networks (ANNs) could bring significant improvements in the development of models because of their ability to build complicated and potentially nonlinear relations without any prior assumptions about the underlying data-generating process [25]. A backpropagation artificial neural network (BP-ANN) is the most representative and extensively exploited ANN using the error backpropagation algorithm [26–29]. However, to the authors’ knowledge, there is limited information on the application of ATR-FTIR spectroscopy along with PLS and BP-ANN methods to predict the nutrient content of WPCS collected from various grown regions in China.

The objective of this study was to evaluate the between-region differences in nutritive components and rumen degradation of WPCS and to develop rapid and efficient models for predicting nutritional concentrations of WPCS based on ATR-FTIR spectroscopy technology combined with PLSR and BP-ANN algorithms. Simultaneously, a better prediction performance model was selected for further applications.

2. Materials and Methods

2.1. Sample Preparation and Chemical Measurements

The Zhengdan 958 cultivar of WPCS was selected for this study from eight different areas of China, and the location information is shown in Table 1. In each area, three plots were selected, and 20 plants from each plot, 10–15 cm above the ground, were harvested at the kernel maturity stage of the half milk line. The exterior 1 m area of each plot was excluded from sampling to ensure uniformity in the plants being sampled. After harvesting, the corn material from the entire plant was chopped into 2 cm sections and immediately transported to the laboratory. Here, they were prepared by vacuum sealing the inoculated plant material into polyethylene bags (25 × 30 cm). They were then stored in the dark at an ambient temperature until analysis. The filling, compression, and sealing processes were the same for all twenty-four bags.

After 60 d of fermentation, the polyethylene bags were opened, and the samples were collected for the measurement of nutrient concentrations and digestibility. A total of twenty-four subsamples from all the individual bags were dried in a forced-air oven at 65°C for 48 h to determine DM. They were then ground in a Wiley mill (Model no. 2; Arthur H. Thomas Co., Philadelphia, PA) to pass through a 1 mm screen to analyze the chemical composition or through a 4 mm screen to detect the in situ nutritive disappearance. The crude protein (CP) was measured using the 988.05 method of the Association of Official Analytical Chemists [30]. Neutral detergent fiber (NDF) and acid detergent fiber (ADF) analyses were performed in an ANKOM 200 fiber analyzer (ANKOM Technologies, Macedon, USA) using thermostable α-amylase [31]. EE was obtained using an automatic extractor (ANKOM XT101; ANKOM Technology Corp., Macedon, NY, USA). Ash was determined by combustion at 600°C for 6 h in a furnace according to method no. 924.05 [32]. The starch content was analyzed using a total starch assay kit (Megazyme, Bray, Ireland; method no. 996.11) based on the AOAC method [32].

2.2. Animals and Digestible Measurements

Three healthy Holstein dairy cows (139 ± 15 days in milk, 2.50 ± 0.50 parity) fixed with permanent rumen fistula from the experimental base of China Agricultural University were used for the in situ incubation study. The trial procedure was submitted to the Experimental Animal Welfare and Animal Ethics Committee of China Agricultural University (approval no. CAU2021009−2). The animals were fed TMR with a forage-to-concentrate ratio of 60: 40, twice daily at 07:00 h and 21:00 h. The TMR components and nutrient levels are shown in Table 2. Subsamples (ca.7 g) were randomly incubated in sealed nylon bags (10 × 20 cm, pore size 40 μm) in the rumen of fistulated cows for 6, 24, 30, and 48 h, using the “gradual in/all out” schedule. Starch digestibility after 6 h of incubation and NDF digestibility after 30 h of incubation were associated with the value and quality of feedstuff [33, 34]. Three replicate bags per sample from individual cows were used at each incubation time point. After incubation, all the nylon bags were removed from the rumen, washed with cold running tap water six times, and then dried to constant weight in forced air at 65°C. The dried residues of the replicate bags of each sample were pooled and mixed according to the incubation time, ground, and stored in sealed plastic bags for further analysis. The rumen degradation characteristics were calculated using the following formula [35, 36]:

2.3. Sample Preparation, ATR-FTIR Spectra Analysis, and Model Building

To establish stable and precious predictive models of nutritional components (DM, CP, EE, ash, NDF, ADF, starch, Ca, and P), 974 WPCS samples (43 cultivars) were collected from more than 200 dairy farms located in Beijing, Tianjin, Ningxia, Inner Mongolia, Shandong, Heilongjiang, and some other sites. The relative information of these samples is shown in Table 3. The physical parameters of fresh plants, including whole-plant height and weight, kernel number, ear number, and weight, were measured immediately at harvest, and the emergence rate was calculated later. All the samples selected were chopped into small particles (1−2 cm) and transported to the laboratory, where they were ground through a 1.0 mm screen for chemical analysis, or a 0.25 mm screen for molecular spectral analysis.

ATR-FTIR spectra were acquired using a Fourier-transform spectrometer (FOSS-DS-2500, FOSS Analytical SA, DK 3400 HillerØed, Denmark). Two grams of each crushed WPCS powder was placed into a glass sample. During each scanning procedure, the ATR-FTIR spectra were recorded with a wavelength in the range of 800–2500 nm at 1 nm intervals, and 32 scans at a resolution of 8 cm⁻¹ were taken per side and averaged into a single spectrum. Each sample was scanned three times, and the average value was used for spectrum analysis. The spectral absorbance values were obtained as log 1/R, where R is the sample reflectance. The raw ATR-FTIR spectra of the 974 samples are shown in Figure 1.

Raw spectra measured using the ATR-FTIR spectrometer included noise and extra background information in addition to sample information. Therefore, preprocessing of spectral data before calibration of a reliable, accurate, and stable model was necessary. In the current study, mean centering was applied to the spectral preprocess. A principal component analysis (PCA) model was used to detect outliers and reduce the dimensions of spectral data in the WPCS samples through principal components and scores (PCs) [27].

The PLSR algorithm implemented in Unscrambler X 10.4 software (CAMO Software AS, Oslo, Norway) was used to establish a predictable model. A three-layer structure (input, hidden, and output layers) BP-ANN implemented in MATLAB R2019a (MathWorks Inc., Natick, MA, USA) was used as another predictable model [37]. To assess the efficiency of the multivariate calibration models, two statistical parameters, root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP), were calculated according to the following equations (27):where and are the predicted and measured values (nutrient content of the WPCS), respectively.

The correlation coefficients for calibration (R²c) and prediction (R²p) are generally used to evaluate the correlation between the results:where is the average measurement of the WPCS samples and n denotes the number of WPCS samples in the dataset. A model with high R² and low RMSEC demonstrated superior performance [30]. The model may be used for crude prediction if 0.66 ≤ R2 ≤ 0.81, more accurate prediction if 0.82 ≤ R2 ≤ 0.90, or normal analysis if R2 ≥ 0.91 [31].

2.4. Statistical Analysis

Data on nutritional components and rumen degradation kinetic parameters were analyzed using one-way ANOVA in SAS 9.2 software (SAS Institute, Cary, NC, USA). The Duncan method was used to analyze the multiple comparisons based on the following model:where Y_ijk represents the nutritional components and real-time degradable rate, is the overall average, and T_i represents the different growing regions of alfalfa hay, D_j is the random effect, and e_ijk is the model error.

For all the statistical analyses, a significant difference was declared at , whereas a tendency was identified at .

3. Results and Discussion

3.1. Effects of Nutritional Contents and Rumen Degradation of WPCS Grown in Different Regions

As the main roughage source of ruminant feedstuffs, nutritional content and rumen degradability have attracted widespread attention. According to Table 4, except for CP, the nutrient components of WPCS, including DM, NDF, ADF, EE, ash, and starch of WPCS, varied considerably in different regions. WPCS grown in Wuxi had the highest DM content (93.89%), whereas Jinan (92.48%) had the lowest. Meanwhile, the highest NDF (47.19%) and ADF (26.77%) concentrations of WPCS were observed when they were cultivated in Jinan. The city of Ningxia had the highest EE (3.32%), while Liaoning represented the opposite condition (2.33%). These results imply that climate conditions such as precipitation and growing temperature can affect internal nutrient accumulation in WPSC [38]. Higher soil pH accelerates the deposition of fatty acids in plants [39]. This means that the different EE contents of WPCS may be the result of soil salinity.

The WPCS from Jinan and Ningxia had higher ash content than that from Lanzhou and Durbert. This result may have contributed to the discrepancy in smooth harvesting ground. More soil was taken into the feedstuffs, and higher ash content was detected when ground flatness was poor. Starch is one of the main factors that influence cow milking performance [40–42]. The results related to starch content in our study have verified that alfalfa hay grown in northeast China may have a greater milking quality.

Figure 2 shows that the DM degradability of WPCS planted in Bayannur was substantially higher than that in Jinan. The starch content of WPCS from Bayannur was also the highest after 6 h in the rumen. The difference in rumen degradation among various regions could be explained by the effective area of rumen microbial invasion to feed and the protein structure [36, 37]. The passage rate of digestation through the foreign stomachs is triggered by particle size, rumen washout, rumen wall distension, or papillae tactile signals that also occur in the different results [43]. Sugar digestibility may be another reason that led to the discrepancy [44], and it is worth investigating in the future. In the current study, the 6 h and 48 h rumen degradation of NDF and ADF in WPCS from different regions reflected their various nutritional uses [45].

(a)

(b)

(c)

(d)

3.2. Establishment and Validation of the PLSR Model

The substantial variation in nutritional indices and rumen degradation indicated that it was necessary to evaluate the nutrients in roughage before they were priced, formulated, and used. However, traditional chemical methods not only consume human, material, and financial resources but also contribute to a potential environmental pollution caused by reagents [18], which deviates from dairy farming profits and is inconsistent with sustainable development. The conventional method resulted in some errors owing to different experimenters and instruments. Therefore, a rapid, efficient, and environment-friendly technique needs to be explored. ATR-FTIR technology has expanded considerably worldwide because of its ability for field or online applications and its simultaneous evaluation of large amounts of samples over relatively short timescales. Therefore, 43 WPCS cultivars from over 200 dairy farms located in five Chinese regions were collected to establish a model for predicting nutrients.

As shown in Table S1, a high variable coefficient (CV) was calculated, especially the contents of Ca (33.58%), ash (24.08%), and starch (23.64%), which were followed by ADF (16.25%), EE (16.07%), P (15.74%), NDF (14.41%), CP (12.62%), and DM (1.83%). This substantial range of variation demonstrated that the WPCS samples (n = 974) were broadly representative. The variations in rumen degradation and morphological characteristics are shown in Tables S2 and S3.

PLSR is the most commonly used regression method for quantitative analysis of the ATR-FTIR spectrum [46]. In this study, cross-validation was performed on the calibration set to select the optimal factors for the PLSR model [22]. With the growth of the factors, the ascensional range of the explained variance becomes relatively small. The closer the explained value is to 1, the higher the accuracy of the constructed model. However, a wide gap between the calibration and prediction sets would be observed if many factors contributed to overfitting [25]. Therefore, the selection of a strategic number of factors is more conducive to the establishment of an optimum model. All the WPCS was sorted randomly into N counterparts. Each part had similar numbers and accounted for approximately 5% of the total samples. Subsequently, one out of N was removed as the prediction set, and the remaining samples were used as the calibration set (for more details on the PLSR models, please refer to Xing et al. [47]). RMSE and R² were used as parameters to select the optimal calibration model, which was then applied to the prediction set. The smaller the RMSE and the bigger the R², the greater the prediction performance of the model [48].

A summary of the optional factor number of different nutrients in WPCS, in conjunction with the calibration and prediction results, is shown in Table 5 and Figure S1. The PLSR model developed showed excellent prediction performance for NDF, ADF, and starch of WPCS samples, with R²c of 0.910, 0.921, and 0.933 and R²p of 0.904, 0.916, and 0.929, respectively. Our results were partially similar to those of Werbos et al.[49], who constructed optimal prediction models for NDF and ADF. The reason for this similar phenomenon may be explained by the high contents of NDF and ADF. The existence of hydrogen-containing groups in them produced pronounced absorption peaks in the near-infrared region. ANKOM 2000i (ANKOM Technology, USA) was used for the measurement of NDF and ADF, and six parallel replicates ensured the accuracy of the analysis. However, He et al. [24] reported that the predictive performance of NDF and ADF contents was lower than that of other nutritional items. This may be related to the source and number of samples, in conjunction with the ATR-FTIR sensitivity as well as the chemical determination accuracy [50].

A strong performance for predicting DM and CP was displayed with R²c values of 0.836 and 0.903 and R²p values of 0.823 and 0.900, respectively. Then, EE and ash were tested according to values of R²c of 0.788 and 0.795, and R²p of 0.763 and 0.799, respectively. Anyway, the value of R² obtained in the current study is usable for screening and most applications according to Williams [51]. However, neither Ca nor P could be forecasted based on the available data because of the low values of R². A likely explanation for this is the lack of ATR-FTIR absorption features for minerals which may be related to water absorption bands [22]. It means that the potential limitations and drawbacks of ATR-FTIR technology, such as its inability to accurately assess certain nutrients, are not adequately addressed, and it is worth searching further.

3.3. Establishment and Validation of the BP-ANN Model

The BP algorithm was initially proposed by Werbos [52], and its application for the training of ANN was popularized by Niu et al. [53]. Working as neurons in the brain, the BP-ANN model is a powerful intelligent chemometric method for data processing [28]. The working principle of BP-ANN was introduced by Pérez−Marín et al. [29]. In this study, 974 WPCS samples were classified into calibration and prediction sets according to a ratio of 9:1. A total of 877 calibrations and 97 prediction set samples were obtained. Before BP training, some parameters were set as follows: 20 principal components were used as input layers because they explained more than 99% and close to 100% of the population variability. The transfer function of the hidden layer was transient, and the node number of the hidden layer was 6. The transfer function of the output layer used purelin, and the note number of the output layer was 1. The algorithm of LM (Levenberg–Marquardt) and ADAPT gradient descent momentum learning function were employed for model training; the training speed was 0.001.

The measured and predicted values of the nutrient content in the WPCS are shown in Figure S2. Table 6 shows the evaluation parameters of the BP-ANN model. These results indicate that the BP-ANN model exhibited excellent prediction performance for CP (R²c = 0.945; R²p = 0.927), NDF (R²c = 0.965; R²p = 0.935), ADF (R²c = 0.991; R²p = 0.975), and starch (R²c = 0.972; R²p = 0.944). The indicators of DM (R²c = 0.900; R²p = 0.845), EE (R²c = 0.886; R²p = 0.853), and ash (R²c = 0.902; R²p = 0.847) were also well predicted. However, poor prediction performance was observed for Ca (R²c = 0.730; R²p = 0.509) and P (R²c = 0.615; R²p = 0.453). The acquisition of successful prediction models, especially NDF, ADF, and starch, may be a result of large samples obtained from five Chinese regions that expressed an extensive geographical span. ATR-FTIR is a typical indirect analytical technique, and its veracity is strongly associated with the precision and accuracy of conventional chemical measurements. In addition, we need to continuously enlarge samples and upload data in the system to guarantee predictive accuracy.

3.4. Performance Evaluation of the PLSR and BP-ANN Multivariate Calibration Methods

The evaluation parameters for the comparison of the PLSR and BP-ANN models are shown in Table 7. The BP-ANN model exhibited more effective prediction performance for the nutrient content of WPCS than the PLSR model because of the higher R²c and R²p in conjunction with lower RMSEC and RMESP values. These were strongly influenced by the flexibility of the BP-ANN method. BP-ANN could determine the linear and nonlinear relationships between the ATR-FTIR spectrum data and the corresponding physicochemical attributes [28]. The use of BP-ANN reduced the training time and provided higher computational efficiency than the PLSR method.

4. Conclusions

In conclusion, the nutrient composition and rumen degradation of WPCS grown in different regions showed substantial discrepancies. Based on the representative data, ATR-FTIR technology is utilized and considered as an efficient and simple tool for predicting nutritional components of WPCS, which not only quickly optimizes feed formulation but also improves the productivity of the dairy industry. Furthermore, the application of the BP-ANN algorithm could contribute to marked improvements in the models developed and finally can supply a more rapid and reliable model because of its self-learning, self-organizing, strong fault-tolerating, and adapting high nonlinear computing abilities. Finally, extensive samples of WPCS were collected from different regions and dairy farms to improve the robustness and universality of the present study, which also enhanced the practical applicability of the models we explored.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

S.L. and Z.Z. conceptualized the study. S.Z. developed the methodology. D.W. developed the software. S.L. and S.Z. validated the data. S.Z. performed the formal analysis. S.Z., J.H., D.W., and C.L. investigated the data. J.H. collected the resources. N.L. curated the data. S.Z. wrote and prepared the original draft. Y.L. and X.G. wrote, reviewed, and edited the data. Y.L. visualized the study. S.L. supervised the study. Z.Z. administered the project. S.L. acquired the funds. All authors have read and agreed to the published version of the manuscript.

Acknowledgments

The authors would like to thank the China Agriculture Research System of MOF and MARA, in conjunction with the support of modern farming for this experiment. This research was supported by the Earmarked Fund for CARS36.

Supplementary Materials

Figure S1: distribution of predicted and measured nutrient contents of whole plant corn silage based on the PLSR model. Figure S2: distribution of predicted and measured nutrient contents of whole plant corn silage based on the BP-ANN model. Table S1: the nutrient contents and variation ranges of whole plant corn silage. Table S2: the rumen degradation and variation ranges of whole plant corn silage. Table S3: morphological measurements of different cultivars of whole plant corn silage. (Supplementary Materials)

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