Evaluation of Energy Digestibility and Prediction of Digestible and Metabolisable Energy in Sunflower Seed Meal Fed to Growing Pigs

Abstract

The experiment was conducted to determine the chemical composition, digestible energy (DE), metabolisable energy (ME) and the apparent total tract digestibility (ATTD) in sunflower seed meal (SFSM) and to develop reasonable prediction equations for estimating energy digestibility based on chemical composition of these meals for growing pigs. Ten SFSMs were collected and 66 crossbred barrows (initial body weight=36.43±5.25 kg) were randomly allocated to one of eleven diets. The basal diet was based on corn and soybean meal which contained 82.2% corn, 14.8% de-hulled soybean meal and 3% premix. The test diets contained 30% SFSM at the expense of corn and soybean meals. The experiment comprised a 7-d adaptation period followed by a 5-d collection period of faecal matter and urine. On a dry matter (DM) basis, the content of crude protein, neutral and acid detergent fibre, and gross energy (GE) ranged from 29.33 to 39.09%, 38.15 to 55.40%, 24.59 to 37.34% and 18.65 to 21.50 MJ/kg, respectively. The concentration of DE and ME ranged from 10.51 to 12.47 MJ/kg and 10.26 to 12.16 MJ/kg of DM, respectively. The DE values and ATTD of GE among the ten SFSMs significantly differed (P<0.05). The ATTD of GE ranged from 52.86 to 63.90%. The best models for predicting DE and ME were also established. In conclusion, the chemical composition of SFSM was variable (coeffiecient of variance>10%) among the ten SFSMs (DM). The results of DE, ME and prediction equations could be applied to evaluate the energy digestibility of SFSM in growing pigs.

Key Words:

• Sunflower seed meal
• Chemical composition
• Energy value
• Growing pigs
• Prediction equations

Introduction

Sunflower seed meal (SFSM) is a major potential feed ingredient in many countries (Senkoylu and Dale, 1999). Together with cotton seed meal and canola meal, SFSM is frequently used as an alternative to soybean meal (Rose et al., 1972; Leeson et al., 1987; Barros et al., 2002). Energy is meaningful to calculate the utilisation of nutrient requirements (Whittemore et al., 2001) and represents the greatest proportion of feed cost (Noblet and Van Milgen, 2004). Sunflower seed meal is a by-product of the oil extraction of sunflowers and contained on average 17.1 MJ/kg gross energy (GE) (National Research Council, 2012). Different processing methods such as different temperature, pressure or processing time result in a variation of chemical composition which in turn may lead to variation of DE or ME in SFSM (Parrado et al., 1991).

The traditional method to evaluate the energy value of feed ingredients is based on DE and ME content. Some predicting models were established for DE and ME of complete diet (Noblet and Perez, 1993) and ingredients (Kim et al., 2009; Anderson et al., 2012). Therefore, it is important to estimate the variation of nutrient digestibility of SFSM in order to be better used in feed industry. The objectives of this study were to evaluate the concentration of DE, ME and apparent total tract digestibility (ATTD) and to develop prediction equations for estimating energy digestibility based on chemical composition of SFSM for growing pigs.

Materials and methods

The Institutional Animal Care and Use Commitee at China Agricultural University (Beijing, China) approved the protocol for the experiment. Ten SFSM samples were obtained from five provinces in China. The chemical composition of the samples were analysed (Table 1).

Experiment design

Sixty-six crossbred (Duroc×Landrace× Yorkshire) barrows [initial body weight (BW), 36.43±5.25 kg] were allotted to one of eleven diets according to a completed randomised design. The basal diet was contained 82.2% corn and 14.8% de-hulled soybean meal and the test diets contained 30% SFSM at the expense of corn and soybean meals (Table 2). Each diet was ground through a 2.5 mm screen (hammer mill) and fed to six replicate pigs. The diet was fed as a mash. All pigs were individually housed in stainless metabolism cages (1.44×0.66×1.22 m³) with a feeder and a nipple drinker. The environmental temperature was controlled at 22±2°C.

Sample collection

Pigs were weighed at the beginning of the experiment and the feed allowance supplied each day was recorded. Two equal meals were fed at 08:00 and 15:30 at a rate of 4% of individual BW (Adeola, 2001).

The experiment period consists of a seven-day adaption period and five-day total faeces and urine collection period (Song et al., 2003). Faeces were collected in plastic bags and urine was collected in buckets under the metabolism crates which contained 10 mL of 6 N HCl for every 1000 mL of urine. The faeces and urine were stored, thawed and mixed for chemical analysis as described by Zhang et al. (2013).

Chemical analyses

The ten SFSMs were analysed for dry matter (DM), crude protein (CP) (AOAC, 2000), Kjeldahl N (Thiex et al., 2002). Crude protein was calculated as N×6.25. Crude fibre (CF), ash, calcium (Ca), total phosphorus (TP) (AOAC, 2000) and ether extract (EE) (Thiex et al., 2003) were also analysed. Neutral (NDF) and acid detergent fibre (ADF) were determined using fibre bags and an analyser (Fibre Analyzer; Ankom Technology, Macedon, NY, USA) following an adaptation procedure described by Van Soest et al. (1991). The GE in SFSM, diet, faeces and urine samples were measured by an Automatic Adiabatic Oxygen Bomb Calorimeter (Parr 6300 Calorimeter; Parr Instrument Company, Moline, IL, USA). The GEs of SFSM, diet and faeces were determined by putting them in the calorimeter. Still, the GE of urine was determined by injecting 4 mL samples into 2 filter paper in a special crucible which were dried for 8 h in 65°C drying oven and then determined for energy.

Calculations

The energy lost in faeces and urine was determined for each diet, and DE and ME of ten different SFSM diets were calculated. The DE and ME in the basal diet was then divided by 0.97 to calculate the DE and ME in the energy-contributing ingredient according to Gottlob et al. (2006). The DE and ME values provided by each sample were calculated by subtracting the DE and ME values provided by the basal energy-contributing ingredients (Adeola, 2001). The formulae of DE and ME are below:

DE of the diet=(GE in feed intake-GE in faeces)/weight of feed intake ME of the diet=(GE in feed intake-GE in faeces-GE in urine)/weight of feed intake DE correction (ME) of the diet=DE (ME) of the diet/0.97 DE correction (ME) of the ingredient=[correction DE (ME) of the diet-(100%-X%)×correction DE (ME) of basal diet]/X%

0.97 means the percentage of energy-contributing ingredient in the diet; X% means the percentage of ingredient aimed to be determined in the energy-contributing ingredient.

Table 1. Analysed chemical composition of sunflower seed meal.

Item	SFSM°							Min				Max	Mean	CV
	1	2	3	4	5	6	7	8	9	10
DM, %	90.91	91.37	92.51	90.76	92.15	92.91	91.27	91.85	90.79	90.31	90.31	92.91	91.48	0.92
CP, %DM	34.91	38.00	29.33	32.02	31.68	29.47	35.19	34.63	39.09	30.87	29.33	39.09	33.52	10.13
EE, %DM	2.19	2.08	5.23	0.93	1.23	3.47	2.02	0.88	1.73	11.33	0.88	11.33	3.11	101.99
NDF, %DM	41.65	38.15	55.40	45.93	47.32	43.97	40.96	42.67	40.72	51.90	38.15	55.40	44.87	12.01
ADF, %DM	25.85	24.59	37.34	31.31	31.42	28.04	25.89	27.99	26.07	30.52	24.59	37.34	28.90	13.28
CF, %DM	21.83	23.11	36.42	30.54	29.87	27.25	21.46	25.66	22.47	33.69	21.46	36.42	27.23	19.31
Ash, %DM	6.86	7.15	5.45	6.32	6.82	7.75	8.30	6.40	6.76	6.71	5.45	8.30	6.85	11.37
Ca, %DM	0.27	0.32	0.17	0.34	0.33	0.45	0.44	0.30	0.30	0.31	0.17	0.45	0.32	25.07
P, %DM	1.04	1.15	0.63	0.81	0.94	0.84	1.02	0.98	1.06	0.92	0.63	1.15	0.94	15.89
GE, MJ/kg	19.17	19.52	19.88	19.08	19.00	19.51	19.02	18.65	19.36	21.50	18.65	21.50	19.47	4.07

SFSM, sunflower seed meal; CV, coefficient of variation; DM, dry matter; CP, crude protein; EE, ether extract; NDF, neutral detergent fibre; ADF, acid detergent fibre; CF, crude fibre; Ca, calcium; P, phosphorus; GE, gross energy.

°Sources of sunflower seed meal: 1, 4 and 8 were from Xinjiang; 2, 7 and 9 were from Hebei; 3 was from Liaoning; 5 was from Shanxi; 6 and 10 were from Inner Mongolia.

Table 2. Ingredient composition of experimental diets (as-fed basis).

	Basal diet	SFSM diets
Ingredients, %
Corn	82.2	57.54
Soybean meal	14.8	10.36
SFSM	-	29.1
Dicalcium phosphate	1.1	1.1
Limestone	0.9	0.9
Sodium chloride	0.3	0.3
Choline chloride	0.2	0.2
Mineral and vitamin premix^°	0.5	0.5
Calculated compositon, %
CP	13.88	21.31

SFSM, sunflower seed meal; CP, crude protein.

°Premix provided the following per kg of complete diets: vitamin A, 5512 U; vitamin D₃, 2200 U; vitamin E, 30 U; vitamin K₃, 2.2 mg; vitamin B₁₂, 27.6 μg; riboflavin, 4 mg; pantothenic acid, 14 mg; niacin, 30 mg; choline chloride, 400 mg; folacin, 0.7 mg; vitamin B₁, 1.5 mg; vitamin B₆, 3 mg; biotin, 44 μg; Mn, 40 mg (MnO); Fe, 75 mg (FeSO₄•H₂O); Zn, 75 mg (ZnO); Cu, 10 mg (CuSO₄•5H₂O); I, 0.3 mg (KI); Se, 0.3 mg (Na₂SeO₃).

Table 3. Energy concentration and apparent total tract digestibility of energy of sunflower seed meal.

Item	SFSM°						Mean					SEM	P
	1	2	3	4	5	6	7	8	9	10
DE, MJ/kg as-fed basis	11.07^ab	11.40^a	9.72^c	10.10^bc	9.69^c	9.87^bc	10.95^abc	9.87^bc	11.15^ab	10.83^abc	10.40	0.42	0.01
ME, MJ/kg as-fed basis	10.51	11.11	9.52	9.52	9.45	9.74	10.26	9.74	10.93	10.43	10.11	0.46	0.14
DE, MJ/kg DM	12.17^a	12.47^a	10.51^c	11.13^abc	10.52^c	10.62^bc	11.99^ab	10.74^bc	12.28^a	11.99^ab	11.36	0.46	<0.01
ME, MJ/kg DM	11.56	12.16	10.30	10.49	10.26	10.48	11.24	10.61	12.04	11.55	11.06	0.50	0.07
ATTD of GE, %	63.52^a	63.90^a	52.86^b	58.32^ab	55.36^b	54.44^b	63.04^a	57.60^ab	63.45^a	55.78^b	58.68	2.38	<0.01

SFSM, sunflower seed meal; DE, digestible energy; ME, metabolisable energy; ATTD, apparent total tract digestibility; GE, gross energy.

°Sources of sunflower seed meal: 1, 4 and 8 were from Xinjiang; 2, 7 and 9 were from Hebei; 3 was from Liaoning; 5 was from Shanxi; 6 and 10 were from Inner Mongolia.

^a-cMeans within a row without a common superscript letter are significantly different (P<0.05).

Table 4. Correlation coefficients between chemical composition and digestible and metabolisable energy of sunflower seed meal.

Item	CP	EE	NDF	ADF	CF	Ash	Ca	P	GE	DE	ME
CP	1.00
EE	-0.45	1.00
NDF	-0.80^**	0.64^*	1.00
ADF	-0.77^**	0.34	0.94^**	1.00
CF	-0.81^**	0.57	0.95^**	0.93^**	1.00
Ash	0.26	-0.15	-0.62	-0.71^*	-0.65^*	1.00
Ca	0.03	-0.19	-0.49	-0.53	-0.43	0.89^**	1.00
P	0.85^**	-0.29	-0.84^**	-0.91^**	-0.84^**	0.54	0.30	1.00
GE	-0.34	0.97^**	0.56	0.28	0.54	-0.16	-0.19	-0.21	1.00
DE	0.74^*	0.12	-0.54	-0.69^*	-0.59	0.36	0.07	0.74^*	0.23	1.00
ME	0.78^**	0.14	-0.55	-0.70^*	-0.58	0.30	<0.01	0.77^**	0.26	0.96^**	1.00

CP, crude protein; EE, ether extract; NDF, neutral detergent fibre; ADF, acid detergent fibre; CF, crude fibre; Ca, calcium; P, phosphorus; GE, gross energy; DE, digestible energy; ME, metabolisable energy.

*P<0.05;

**P<0.01.

Table 5. Linear regression equations for prediction of energy content (MJ/kg DM) based on the chemical composition (% of DM) of sunflower seed meal fed to growing pigs.

No.	Linear regression equations	R^2	RSD^2	P
1	DE=-4.90+0.14 CP-0.08 CF+0.71 GE	0.89	0.27	<0.01
2	DE=2.5-0.15 NDF+0.80 GE	0.71	0.44	0.01
3	DE=-6.89+0.56 GE+0.22 CP	0.82	0.34	<0.01
4	DE=15.67-0.15 ADF	0.48	0.58	0.03
5	DE=5.5+0.18 CP	0.55	0.54	0.01
6	DE=7.28-0.17 ADF+0.47 GE	0.68	0.46	0.02
7	ME=1.14+0.87 DE	0.93	0.20	<0.01
8	ME=4.59-0.18 NDF-0.35 Ash+0.87 GE	0.85	0.28	<0.01
9	ME=-4.90-0.05 NDF+0.66 GE+0.16 CP	0.96	0.14	<0.01
10	ME=14.93-0.13 ADF	0.49	0.52	0.02
11	ME=5.43+0.17 CP	0.62	0.45	<0.01
12	ME=2.33-0.14 NDF+0.77 GE	0.77	0.35	<0.01
13	ME=6.81-0.16 ADF+0.46 GE	0.72	0.38	0.01

RSD, residual standard deviation; DE, digestible energy; CP, crude protein; CF, crude fibre; GE, gross energy; NDF, neutral detergent fibre; ADF, acid detergent fibre; ME, metabolisable energy. Regression equations were developed based on stepwise regression analyses.

Statistical analyses

All data were processed using SAS (SAS Inst. Inc., Cary, NC, USA). Individual pig was considered as the experimental unit. CORR procedure, simple and multiple regression analyses (stepwise regression analysis) were conducted to study the relationship among chemical composition and energy content. For selecting the energy prediction equations, the residual standard deviation (RSD) was used as the selection criterion. A smaller RSD was proposed to indicate a better fit. Date were also analysed using the Proc-GLM procedure of SAS. In all analyses, the differences were considered significant if P<0.05.

Results and discussion

The chemical composition for ten SFSMs is shown in Table 1. Obvious coefficient of variation (CV>10%) were almost observed in all chemical compositions (CP, EE, NDF, ADF, CF, Ash, Ca and P). On a DM basis, the content of CP, NDF, ADF and GE ranged from 29.33 to 39.09%, 38.15 to 55.40%, 24.59 to 37.34% and 18.65 to 21.50 MJ/kg, respectively. The average concentration of GE in SFSM was higher than what published by the National Research Council (2012) (19.5 vs 17.1 MJ/kg).

The concentration of DE and ME ranged from 10.51 to 12.47 MJ/kg and 10.26 to 12.16 MJ/kg of DM, respectively (Table 3). The DE values and ATTD of GE among the ten SFSM samples significantly differed (P<0.05). The difference between the highest and lowest values of the ten SFSMs for DE and ME was 1.96 MJ/kg and 1.90 MJ/kg of DM, respectively. The ATTD of GE ranged from 52.86 to 63.90%.

The concentration of CP had a positive correlation with DE and ME (P<0.05), while ADF content negatively correlated with DE (P<0.05) (Table 4).

Several equations were developed to predict the energy content following a stepwise regression procedure (Table 5). The best single predictor for DE value was the CP content (R²=0.55). The accuracy of the equations was improved with the inclusion of either GE or CF factors. The best model for prediction of DE was DE=-4.90+0.14 CP-0.08 CF+0.71 GE (R²=0.89; RSD=0.27). The two best linear regression equations for predicting ME value were ME=1.14+0.87 DE (R²=0.93; RSD=0.20) and ME=-4.90-0.05 NDF+0.66GE +0.16CP (R²=0.96; RSD=0.14).

The results of CP, EE, NDF, ADF, CF, Ash, Ca and P concentration of SFSM varied greatly (CV>10%), possibly due to the different regions where the sunflower grew. The average concentration of CP (sources 1, 2 and 9) was higher than sources 3, 5 and 6 probably because of the improvement of breeding (Robertson, 1972). The concentrations of GE among the ten SFSMs varied from 18.65 to 21.50 MJ/kg and were related to the extraction process and the amount of residual oil left (Dinusson, 1990). Although the production process for most SFSMs was similar using pre-pressing extraction method (Fick and Miller, 1997), small differences in temperature, pressure and time might lead to the changes in chemical concentration in SFSM (Clandinin and Robblee, 1950). The NDF content of the ten SFSMs was higher than the data reported by González-Vega and Stein (2012), but the average concentration of ADF was lower than the value reported in National Research Council (2012). The reason was not clear depending on the previous papers, but the different grown places may had a role in these difference in chemical composition.

The range of DE (from 10.51 to 12.47 MJ/kg DM) and ME (from 10.30 to 12.16 MJ/kg DM) content in the ten SFSM sources was related to the differences in CP and cell well fractions levels among these meals (Noblet and Peres, 1993). The average DE and ME contents of sources 3 and 10 (10.51 and 11.99 MJ/kg DM for DE; 10.30 and 11.55 MJ/kg DM for ME, respectively) were higher than the values published by the National Research Council (2012), may be because of the higher content of EE (Ren et al., 2011). It has been reported that the presence of fibre could increase the endogenous secretions of CP and that fat associated with the increasing microbial mass could lead to a decrease in digestion of CP and fat (Sauer and Ozimek, 1986; Noblet and Perez, 1993). Sunflower seed meals with lower fibre concentration (sources 1, 2, 7 and 9) showed higher ATTD of GE (P<0.05) than those with higher fibre content (sources 3, 5, 6 and 10) (Noblet and Shi, 1993).

Noblet and Perez (1993) developed prediction equations based on 114 complete feeds which cannot be used to for individual ingredient. So, the prediction equations of valuable energy in ingredients are essential. However, little research has been done to estimate the DE and ME contents based on the chemical composition of SFSM. Therefore, the prediction equation should be established based on the measured data of SFSM. The results showed that CP content positively correlated with DE and ME variation (Table 5). The reason may be that SFSM was mainly used as a protein source which can provide energy or promote metabolism. However, the concentration of ADF had significantly negative correlation with DE and ME (P<0.05) because the insoluble fibre can be hardly digested through the intestine.

Conclusions

The energy digestibility of SFSM has great variation resulting from differences in chemical composition. The concentration of CP was the factor mostly affected in the equations established in this experiment. Crude fibre, ADF and GE can significantly improve the accuracy of the prediction equations of DE and ME.

Acknowledgments

this research was financially supported by the National Natural Science Foundation of China (31372316), Special Public Sector Fund in Agriculture (200903006) and National Key Technology Support Program (2012BAD51G01).