Shortened Photoperiod Enhances Protein and Fat Energy Deposition in Growing Pigs

Simple Summary

Pigs that spent 18 h a day in darkness kept significantly more feed calories in their bodies as muscle and fat than pigs under long bright days, even though both groups ate the same high-fiber ration. Blood tests showed the “long-night” pigs had lower “bad-fat” levels and higher hunger-hormone levels, explaining the better growth. Fiber digestion did not change. Giving growing pigs longer nights is a simple, cost-free way for farmers to turn more feed into pork and reduce waste.

Abstract

This study examined how different photoperiods affect net energy partitioning and explored the mechanisms via blood biochemistry, gut microbiota, and fecal metabolites. Twelve healthy crossbred pigs (47.7 ± 7.5 kg) were randomly allocated to two groups and subjected to a self-controlled crossover design. Following an 8-day baseline under a normal photoperiod (12L:12D, 12 h light:12 h dark), pigs were assigned to two photoperiod treatment groups: prolonged photoperiod (18L:6D, 18 h light:6 h dark; P group) and shortened photoperiod (6L:18D, 6 h light:18 h dark; S group). Measurements during the baseline (12L:12D) and treatment phases are designated as N1/P (for the P group) and N2/S (for the S group), respectively. The treatment periods were interspersed with the baseline 12L:12D photoperiod and repeated six times. It was observed that, compared to N2, shortened photoperiod (S) had significantly higher net energy deposition, net energy for protein deposition, and net energy for fat deposition (p < 0.05). Compared with N2, plasma low-density lipoprotein in short photoperiod decreased (p < 0.05), and gastric inhibitory peptides increased (p < 0.05). Compared to the prolonged photoperiod, the levels of ghrelin and apolipoprotein A-IV were higher in the shortened photoperiod (p < 0.05). A shortened photoperiod decreased fecal acetic acid compared to N2 (p < 0.05) and decreased propionic acids compared to P (p < 0.05). The significance test of differences between microbial groups showed that there were different microorganisms among the different groups. The results indicated that shortening the photoperiod significantly altered the energy allocation in growing pigs.

1. Introduction

Feed, drinking water, temperature, lighting, and ventilation are critical factors for maintaining normal production and animal welfare in large-scale pig farming, as they significantly influence production efficiency and economic returns. In production practice, factors such as feed, drinking water, temperature, and ventilation are given significant attention. However, little research has been conducted on the optimization of light conditions to enhance the production performance of pigs. The biological clock is a physiological mechanism that has evolved in animals to synchronize their internal processes with the Earth’s rotation, resulting in a cycle of light and dark. This mechanism regulates various physiological activities in animals, including sleep, wakefulness, and eating, and causes rhythmic changes in nutritional and energy requirements in response to the light–dark cycle. The regulation of glucose metabolism, lipid metabolism, and hormone secretion in the mammalian liver is governed by the biological clock. This clock is physically situated in the suprachiasmatic nucleus of the hypothalamus, and its functioning is influenced by the light and dark cycles of the external environment [1]. The retinohypothalamic tract receives the light signal and transmits it to the suprachiasmatic nucleus of the hypothalamus. This synchronization between the endogenous central biological clock and the external environment allows for the transmission of time signals to peripheral tissues through nerve and humoral pathways. This coordination contributes to the rhythmic oscillation of the peripheral biological clock and the maintenance of the body’s physiological steady state [2]. Recent studies have shown that the composition, quantity, colonization and functional activities of intestinal flora have significant circadian rhythms, which are closely related to various physiological functions under the regulation of the biological clock, and affect the host’s immune function, growth and development, and health. For example, the structure of the gut microbiota can influence the fat deposition of the host, and the increase in the relative abundance of Proteobacteria in the gut microbiota is a result of long photoperiods and is significantly related to body weight and fat content [3]. Increased body weight is associated with increased relative abundance of Firmicutes and decreased relative abundance of Bacteroidetes [4]. Reversing the day and night treatment of male mice once a week for 22 weeks resulted in an increase in the relative amount of Firmicutes in the feces [5].

At present, the study on light factors in pigs is mainly focused on the effects of light time on reproductive performance of sows and on feed intake and immune function of piglets, so the study on the effect of light on net energy distribution is very limited. In addition, light can affect the circadian rhythm of the animal body, which will affect the activity of intestinal microorganisms, and thus may also affect the utilization of fiber in the diet. Based on this, this study aimed to investigate the effects of different photoperiods on net energy allocation and fiber utilization in growing pigs, and to explore the possible internal mechanisms.

2. Materials and Methods

2.1. Animals and Treatments

This study utilized a self-controlled crossover design. A total of 12 Duroc × Landrace × Yorkshire crossbred healthy growing barrows with an initial body weight of 47.7 ± 7.5 kg were randomly divided into two experimental groups. The trial consisted of 6 repeated periods. Each period included: (1) an 8-day baseline photoperiod (12L:12D, 12 h light:12 h dark, serving as the normal photoperiod control); (2) 1-day fasting; (3) 1-day recovery; (4) 8-day experimental photoperiod treatment; and (5) 1-day fasting. The two experimental groups were: the prolonged photoperiod group (18L:6D, 18 h light:6 h dark; P group) and the shortened photoperiod group (6L:18D, 6 h light:18 h dark; S group).

To distinguish baseline measurements within each treatment, data collected during the 12L:12D baseline phase in the P group are denoted as N1 (normal photoperiod within the prolonged photoperiod group), and those in the S group as N2 (normal photoperiod within the shortened photoperiod group). P denotes the 18L:6D treatment phase, and S denotes the 6L:18D treatment phase. Thus, comparisons between N1 and P represent within-subject contrasts for the prolonged photoperiod group, while comparisons between N2 and S represent within-subject contrasts for the shortened photoperiod group.

During the experiment, the growing pigs were fed a fiber diet with 12% wheat bran content, and before the official period began, the growing pigs adapted to the diet for 10 days. During the experiment, all feces and urine were collected every two days, and the growing pigs were weighed before and after normal light, prolonged light, and shortened light, and before and after a hunger strike. In this experiment, the basic diet of corn–soybean meal was prepared according to the nutrient requirements of swine in China [6] (GB/T 39235-2020) and fed as powder. The formula of the diet and the calculated nutritional level of the diet are shown in

Table 1. Experimental diet composition and nutrients (as-fed basis, %).

Items	Basal Diet
Ingredients
Corn	63.00
Soybean meal	20.25
Wheat bran	12.00
Soybean oil	1.07
Premix 1	1.00
Lysine 2	0.35
Methionine	0.08
Threonine	0.16
Tryptophan	0.04
Limestone	0.80
Calcium hydrophosphate	0.90
Salt	0.35
Total	100.00
Calculated nutrient levels
Net energy, MJ/kg	10.63
Crude protein	16.51
Calcium	0.66
Phosphorus	0.29
SID Lys 3	0.98
SID Met	0.32
SID Thr	0.64
SID Trp	0.18
Measured value of nutritional level
Gross energy, MJ/kg	16.19
Net energy, MJ/kg	12.12
Crude protein, %	17.98
Neutral detergent fiber, %	15.68
Acid detergent fiber, %	5.43

¹ Provided per kilogram of premix: vitamin A, 1000–1300 Kilo International Units (KIU); vitamin D3, 270–1000 KIU; vitamin E, ≥2700 IU; vitamin K3, ≥290 mg; vitamin B1, ≥140 mg; vitamin B2, ≥540 mg; vitamin B6, ≥270 mg; vitamin B12, ≥1.8 mg; nicotinamide, ≥3200 mg; pantothenic acid, ≥1600 mg; folic acid, ≥60 mg; biotin, ≥4 mg; Fe, 14–150 g; Cu, 2–5 g; Zn, 8–16 g; Mn, 3–16 g; I, 60–250 mg; Se, 40–99 mg; and water, ≤10%. ² Lysine was added as lysine hydrochloride. ³ SID Lys = standardized ileal digestible lysine; SID Met = standardized ileal digestible methionine; SID Thr = standardized ileal digestible threonine; SID Trp = standardized ileal digestible tryptophan.

.

2.2. Animal Feeding Management and Sample Collection

The temperature and humidity of the open circuit breathing and circulating calorimeter chambers used in the experiment were set at 22 °C, 75%, and the wind speed was set at 0.1~0.3 m/s. The growing pigs were placed in special metabolic cages, and each metabolic cage was equipped with a duckbill drinking fountain and a trough of suitable height. The diet was fed in the form of powder, which kept the pig house clean and hygienic. The experimental pigs had been immunized according to the regulations (the pigs were inoculated with erysipelas-Streptococcus suis bivalent vaccine (inactivated vaccine), porcine circovirus type 2-mycoplasma hyopneumoniae bivalent vaccine, porcine circovirus type 2 monovalent inactivated vaccine, and hog cholera live vaccine), and their growth was observed and recorded at any time during the experiment.

During the experiment, all feces were collected once every two days. Prior to collection, the collection containers were thoroughly cleaned and dried to prevent any contamination or interference with the samples. During collection, meticulous efforts were made to ensure that all feces produced by the animals within the specified time-frame were included. Each feces sample was immediately weighed using a high-precision electronic balance with an accuracy of ±0.1 g. After weighing, the sample was carefully transferred into a pre-labeled, sterile plastic container. The container was then tightly sealed to prevent any moisture loss or contamination from the external environment. Subsequently, the feces samples were promptly stored in a dedicated cold storage unit maintained at a constant temperature of −20 °C. To facilitate easy identification and tracking, detailed records were kept for each sample, including the date of collection, the animal identification number, and the weight of the sample. Before initiating the urine collection process, the urine collection bucket was first cleaned with a mild detergent, rinsed thoroughly with distilled water, and then dried. To maintain the acidity of the urine and prevent nitrogen loss due to ammonia volatilization, 10 mL of 6 mol/L hydrochloric acid was precisely measured using a graduated pipette and added to the urine collection bucket. The volume of the added hydrochloric acid was carefully recorded and deducted when calculating the daily urine output to ensure accurate measurement of the actual urine volume. When it was time to collect urine, a four-layer sterile gauze was used for filtration. The gauze was placed over a clean, funnel-shaped device that was positioned above the urine collection bottle (Suzhou Ketone Bio-Pharma Co., Ltd., Suzhou, China). The urine was slowly poured through the gauze to remove any large particles or debris. To ensure a consistent and representative sub-sample, a fixed ratio of 5% of the filtered urine was taken. This was achieved by using a graduated cylinder to measure the total volume of the filtered urine and then calculating 5% of that volume. The calculated volume of urine was then carefully transferred into a pre-labeled, sterile urine collection bottle. The bottle was immediately sealed and placed in a cold storage unit at −20 °C for temporary storage. After the end of the fecal urine collection period, the urine in the collection bottle was thoroughly mixed. This was done by gently inverting the bottle several times to ensure a homogeneous mixture. Then, a 50 mL sample was taken using a sterile centrifuge tube (Yeasen Biotechnology (Shanghai) Co., Ltd., Shanghai, China). The sample was transferred into a 50 mL centrifuge tube that had been pre—cooled to −20 °C. The tube was tightly capped and stored in the −20 °C cold storage until it was ready for testing. Throughout the entire process, strict aseptic techniques were followed to prevent any contamination of the samples, which could potentially affect the experimental results.

In the open breathing cycle calorimetric chamber (Beijing SDL Technology Co., Ltd., Beijing, China) for pigs, the daily oxygen consumption and carbon dioxide and methane production of pigs were measured to calculate the total heat production of pigs. To avoid the influence of activity-induced heat production on the measurement of fasting heat production, the gas data used for fasting heat production were collected from 22:30 pm to 06:30 the next day, and the oxygen consumption, as well as carbon dioxide and methane production, of the eight hours were corrected to 24 h for calculation. The growing pigs were weighed on days 1, 8, 9, 11, 18, and 19 of the formal experiment. These time points corresponded to the periods before and after the onset of the normal photoperiod, before and after the fasting period under the normal photoperiod, before and after the onset of the prolonged/shortened photoperiod, and before and after the fasting period under the prolonged/shortened photoperiod. Throughout the experiment, the amount of feed provided, the leftovers, and the amount scattered were recorded daily. The illumination system is shown in Figure 1a.

Considering that each photoperiod phase (baseline and treatment) was followed by a mandatory 1-day fasting period for heat production measurements, blood and fresh feces samples were collected on the 5th and 15th day of each trial (i.e., day 5 of the 12L:12D baseline and day 5 of the 18L:6D or 6L:18D treatment, respectively) to minimize handling stress and avoid interference with fasting protocols. Blood was collected from the left jugular vein of growing pigs, and collected by a heparin sodium anticoagulation tube (Beijing Solarbio Science & Technology Co., Ltd., Beijing, China). After successful blood collection, the whole blood was slightly inverted in the anticoagulation tube 5–8 times, centrifuged at 1800× g for 10 min at room temperature, transferred to a 1.5 mL centrifuge tube, then centrifuged at 1300× g for 2 min, and the supernatant was collected.

2.3. Test Indicators and Methods

The dry matter, crude protein, neutral detergent fiber, acid detergent fiber and total energy in diet and feces, urine nitrogen and total energy in urine samples, the concentrations of oxygen, carbon dioxide and methane and the gas exchange capacity in the air and open breathing cycle calorimeter chamber, the plasma biochemical index, the microbial flora, and short chain volatile fatty acids in feces are measured in this study.

Moisture-absorbing water, crude protein and neutral detergent fiber were determined according to the national standards of the People’s Republic of China (PRC) GB/T 6435-2014 [7], GB/T 6432-1994 [8], and GB/T 20806-2006 [9], respectively.

Acid detergent fiber refers to the agricultural industry standard NY/T 1459-2007 [10] of the People’s Republic of China. Total energy was measured according to the method of international standard ISO 9831:1998 [11], and it was measured by an oxygen bomb calorimeter (Model 6400, Parr Company, Moline, IL, USA).

Oxygen was determined by a cis-magnetic oxygen analyzer (Oxymat 6E, Siemens, Munich, Germany). CO₂ and CH₄ were measured by an infrared analyzer (Ultramat 6E, Siemens, Munich, Germany), and the exhaust flow was measured by a mass flowmeter (Alicat, Tucson, AZ, USA). The two rooms share a gas analyzer, and the indoor and outdoor gases are detected every 5 min.

The concentration of cortisol (COR) in plasma was detected by radioimmunoassay. Glucose (GLU), triglyceride (TG), blood urea nitrogen (BUN), low-density lipoprotein (LDL), total cholesterol (TC), and high-density lipoprotein (HDL) in plasma were detected by an automatic biochemical detector (Zhejiang Pushkang Biotechnology Co., Ltd., Zhejiang, China). The plasma ghrelin, glucagon-like peptide-1 (GLP-1), peptide YY(PYY), leptin (LEP), gastric inhibitory peptides (GIPs), Apolipoprotein A-IV(ApoA-IV), and melatonin (MT) were detected by enzyme-linked immunosorbent assay (Shanghai Enzyme-linked Biotechnology Co., Ltd., Shanghai, China).

A high-throughput sequencing method was used to analyze the microbial community diversity of collected fecal samples in this study, and the steps were as follows:

• DNA extraction and quality assessment: Total genomic DNA was extracted using the E.Z.N.A.^® Soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA). DNA purity and concentration were assessed using a micro-spectrophotometer (NanoDrop™ 2000, Thermo Scientific Inc., Waltham, MA, USA; software version 1.6), and DNA integrity was verified by 1% agarose gel electrophoresis (5 V/cm, 20 min).
• PCR amplification: The V3–V4 hypervariable region of the bacterial 16S rRNA gene was amplified using primer pairs 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) [12] on an ABI GeneAmp^® 9700 PCR thermocycler (Applied Biosystems, Foster City, CA, USA; software version 3.12). PCR conditions were as follows: initial denaturation at 95 °C for 3 min, followed by 27–30 cycles (optimized per sample) of 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 45 s, with a final extension at 72 °C for 10 min.
• Library preparation and sequencing: PCR products were purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA), quantified using a Quantus™ Fluorometer (Promega, Madison, WI, USA), and pooled in equimolar ratios according to sequencing depth requirements. Sequencing libraries were constructed using the NEXTFLEX^® Rapid DNA-Seq Kit (Bioo Scientific, Austin, TX, USA). Paired-end sequencing was performed on the MiSeq PE300 platform (2 × 250 bp), generating a total of 1,684,913 raw reads.
• Quality filtering parameters: Raw reads were processed using the QIIME2 (v2023.2) pipeline with the DADA2 plugin (v1.20) according to the following criteria: (i) truncation of forward reads at 290 bp and reverse reads at 260 bp based on quality score profiles; (ii) filtering reads with ambiguous bases (N > 0) or expected error rate > 2 (maxEE = 2); (iii) paired-end merging with minimum overlap of 20 bp; and (iv) chimera removal using the consensus method. After quality control, a total of 1,459,506 high-quality sequences were obtained across 36 fecal samples, corresponding to 602,623,548 base pairs (mean ± SD: 40,542 ± 5537 sequences per sample; range: 33,240–59,319 sequences; average length: 413 bp; range: 210–525 bp).
• Rarefaction curves based on the Shannon diversity index were generated using the Majorbio Cloud platform (https://cloud.majorbio.com, accessed on 22 September 2022) to assess sequencing depth adequacy. All curves reached saturation at less than 5000 reads per sample, indicating that the observed bacterial diversity was adequately captured. Given that the actual sequencing depth averaged 40,542 ± 5537 reads per sample (minimum 33,240 reads), the sequencing coverage was sufficient for robust diversity analysis.
• Taxonomic assignment of ASV representative sequences was performed using the RDP Classifier (version 2.2) against the SILVA 138.1 SSU rRNA database (https://www.arb-silva.de, accessed on 28 September 2022) with a confidence threshold of 0.7 (70%). ASVs annotated as chloroplast or mitochondrial sequences were removed from the dataset. The final ASV table was normalized to relative abundance for downstream analysis.

Ion chromatography (IC) was used to determine the concentration of short chain fatty acids (SCFAs) in the feces of growing pigs. The specific determination steps are as follows:

a. Pretreatment of fresh feces sample: Weigh about 0.5 g of feces and put it in a 10 mL centrifuge tube, then add 8 mL of deionized water. After ultrasonic cracking for 30 min, centrifuge at 8000 rpm for 10 min. After centrifugation, the supernatant of the sample was diluted 50 times with ultrapure water, filtered through a 0.22 μm filter membrane, and finally, 25 μL of liquid was taken for ion chromatography determination.

b. Loading the sample on the computer: The chromatographic column is ICS-3000 (250 mm × 4 mm), the flow rate of potassium hydroxide is set at 1.0 mL/min, and the gradient separation conditions are 0–5 min, 0.8–1.5 mM; 5–10 min, 1.5–2.5 mM and 10–15 min, 2.5 mM, respectively.

2.4. Calculations

(1)

The volume of gas discharged in each period (V) is as follows:

$$\mathrm{V}=\mathrm{time}\left(\min \right) \times \mathrm{gas} \mathrm{flow} \mathrm{rate}(\mathrm{L}/\min )$$

Convert the exhausted gas volume to the standard state (0 °C, 1013 hPa) according to the following formula:

$$\mathrm{Standard} \mathrm{volume}\left(\mathrm{SV}\right) = \mathrm{V} \times (\mathrm{P} - \mathrm{Pw})/1013 \times 273/(273 + \mathrm{t})$$

$$\mathrm{Pw}=\mathrm{RH}/100\times (3.999+0.45547\mathrm{T}+0.001708{\mathrm{T}}^2+0.000469{\mathrm{T}}^3)$$

SV is the standard volume of exhaust gas (0 °C, 1013 HPA). V is the actual volume of gas discharged. P is the air pressure in the breathing thermowell. Pw is water vapor pressure. T is the temperature in the breathing thermowell. RH is the relative humidity in the breath calorimeter.

(2)

Calculate animal oxygen consumption and carbon dioxide production according to the following formula:

$${\mathrm{V}}_{\mathrm{c}{\mathrm{o}}_2,\mathrm{production}}=\left({\mathrm{C}}_{\mathrm{c}{\mathrm{o}}_2,\mathrm{b}}-{\mathrm{C}}_{\mathrm{c}{\mathrm{o}}_2,\mathrm{a}}\right) \times {\mathrm{SV}}_{\mathrm{chamber}}+{\mathrm{C}}_{\mathrm{c}{\mathrm{o}}_2,\mathrm{air}} \times {\mathrm{SV}}_{\mathrm{exhaust}}$$

$${\mathrm{V}}_{{\mathrm{o}}_2,\mathrm{consumption}}=\left({\mathrm{C}}_{{\mathrm{o}}_2,\mathrm{a}}-{\mathrm{C}}_{{\mathrm{o}}_2,\mathrm{b}}\right) \times {\mathrm{SV}}_{\mathrm{chamber}}+\left({\mathrm{C}}_{{\mathrm{o}}_2,\mathrm{air}}-{\mathrm{C}}_{{\mathrm{o}}_2,\mathrm{a}}\right) \times {\mathrm{SV}}_{\mathrm{exhaust}}$$

Cco_2,a and Cco_2,b are the carbon dioxide concentration at the beginning and end of breathing (%). CO_2,a and CO_2,b are the oxygen concentrations at the beginning and end of breathing (%). SV_chamber is the net use standard volume of the chamber. SV_exhaust is the standard volume of exhaust gas.

(3)

Calculate the heat production according to the following formula [13]:

$$\mathrm{Heat} \mathrm{production}\left(\mathrm{kJ}\right)=16.1753 \times { \mathrm{O}}_2\left(\mathrm{L}\right)+5.0208 \times \mathrm{C}{\mathrm{O}}_2\left(\mathrm{L}\right) - 2.1673 \times \mathrm{C}{\mathrm{H}}_4\left(\mathrm{L}\right) - 5.9873 \times \mathrm{N}(\mathrm{urine} \mathrm{nitrogen}, \mathrm{g})$$

(4)

Dietary digestibility and metabolizable energy of pigs refer to the formula of Adeola [14]:

$$\begin{array}{c}\mathrm{Apparent} \mathrm{digestible} \mathrm{energy} \mathrm{of} \mathrm{diet}\left(\mathrm{MJ}/\mathrm{kg}, \mathrm{dry} \mathrm{matter} \mathrm{basis}\right)=\\ (\mathrm{total} \mathrm{energy} \mathrm{of} \mathrm{intake}, \mathrm{MJ-total} \mathrm{energy} \mathrm{of} \mathrm{defecation}, \mathrm{MJ})/\mathrm{dry} \mathrm{matter} \mathrm{intake}, \mathrm{kg}\\ \mathrm{Dietary} \mathrm{apparent} \mathrm{metabolizable} \mathrm{energy} (\mathrm{MJ}/\mathrm{kg}, \mathrm{dry} \mathrm{matter} \mathrm{basis})=\\ (\mathrm{total} \mathrm{energy} \mathrm{ingested}, \mathrm{MJ-total} \mathrm{energy} \mathrm{of} \mathrm{defecation}, \mathrm{MJ-}\\ \mathrm{urinary} \mathrm{energy}, \mathrm{MJ-methane} \mathrm{energy}, \mathrm{MJ})/\mathrm{dry} \mathrm{matter} \mathrm{intake}, \mathrm{kg}\end{array}$$

(5)

The formula of Noblet [15] is used for calculating the net energy of the pig diet:

$$\begin{array}{c}\mathrm{Net} \mathrm{energy}(\mathrm{MJ}/\mathrm{kg}, \mathrm{dry} \mathrm{matter} \mathrm{basis})=(\mathrm{metaboli}\mathrm{zable} \mathrm{energy} \mathrm{intake}, \mathrm{MJ-}\\ \mathrm{total} \mathrm{heat} \mathrm{production}, \mathrm{MJ} +\mathrm{heat} \mathrm{production} \mathrm{of} \mathrm{fasting}, \mathrm{MJ})/\mathrm{dry} \mathrm{matter} \mathrm{intake}, \mathrm{kg}\end{array}$$

2.5. Statistical Analysis

This study employed a replicated crossover design with baseline reset periods. Each of the six experimental periods was physiologically independent, initiated by an 8-day return to baseline photoperiod (12L:12D) to minimize carryover effects. Consequently, data were analyzed as within-period paired comparisons (baseline vs. treatment), not as continuous longitudinal trajectories. The fasting days interspersed between phases further ensured metabolic clearance. This approach is standard for open-circuit respirometry studies in pigs, where controlling for individual metabolic variation outweighs concerns about repeated sampling, provided that baseline conditions are re-established between periods.

All data were first tested for normality using descriptive statistics in SPSS 21.0 (IBM Corp., Armonk, NY, USA). The normality of all data was assessed using the Shapiro–Wilk test. To account for the replicated crossover structure, the statistical model was set as

$$Y_{ijk}=\mu +{\tau }_{d(i,j)}+{\pi }_j+{\gamma }_i+{\epsilon }_{ijk} {\gamma }_i~N\left(0, {\sigma }_{\gamma }^2\right), {\epsilon }_{ijk}~N\left(0, {\sigma }_{\epsilon }^2\right)$$

where ${\tau }_{d(i,j)}$, the photoperiod treatment (N1, N2, P, S), and ${\pi }_j$, the experimental period (1–6, blocking factor), were treated as fixed effects, and ${\gamma }_i$, the individual pig, was treated as a random effect to account for between-subject variation.

Three specific comparisons were conducted:

(1)

Within-group paired comparisons: Paired-samples t-tests were performed for N1 vs. P (prolonged group baseline vs. treatment) and N2 vs. S (shortened group baseline vs. treatment) to assess treatment effects relative to baseline.

(2)

Between-group independent comparisons: Independent-samples t-tests were used for P vs. S (treatment comparison).

Significance was set at p < 0.05, with trends considered at 0.05 ≤ p < 0.10.

Microbiome and metabolite analysis. Bioinformatic analysis of gut microbiota was performed using the Majorbio Cloud platform (https://cloud.majorbio.com, accessed on 20 November 2022). The Kruskal–Wallis H test was used to assess group differences in α-diversity indices, followed by Games_Howell post hoc test for pairwise comparisons if they were significant. Differential microbial communities were analyzed using the Wilcoxon signed-rank test with bootstrap confidence interval estimation (1000 iterations) for paired comparisons (N1 vs. P; N2 vs. S) or the Wilcoxon rank-sum test for independent comparison (P vs. S). Benjamini–Hochberg false discovery rate (FDR) correction was applied to all pairwise comparisons, with significant differential taxa defined as p < 0.05. Prior to differential analysis, taxa unclassified at the phylum level and those with a mean relative abundance < 0.01% across all samples were removed to reduce noise from low-abundance features. Spearman correlation analysis was used to examine associations between microorganisms and volatile fatty acids. For short-chain fatty acids and blood biochemical indicators, normality was tested using descriptive statistics, followed by paired-samples t-tests (N1 vs. P; N2 vs. S) or independent-samples t-tests (P vs. S) as appropriate. Pearson correlation analysis was conducted to assess relationships between energy allocation and plasma biochemical indicators.

3. Results

3.1. Effects of Different Photoperiods on Energy Distribution and Fiber Digestibility of Growing Pigs

Compared to the normal photoperiod group, the prolonged photoperiod group and the shortened photoperiod group significantly increased the feed intake (p < 0.05), energy intake (p < 0.05), fecal energy (p < 0.05), metabolizable energy intake (p < 0.05), and net energy intake (p < 0.05) of growing pigs, the prolonged photoperiod group significantly reduced total heat production (p < 0.05). Additionally, the prolonged photoperiod group showed a trend of increasing net energy deposition (p = 0.068) and net energy for fat deposition (p = 0.052). Compared to the normal photoperiod group, the shortened photoperiod group significantly increased the net energy deposition, net energy for fat deposition and net energy for protein deposition (p < 0.05) (

Table 2. Effects of different photoperiods on energy distribution and fiber digestibility of growing pigs ¹.

					p-Value
Items	N1	N2	P	S	N1 vs. P	N2 vs. S	P vs. S
Energy balance, MJ/d
Feed intake	2.01 ± 0.22	1.87 ± 0.27	2.36 ± 0.17	2.32 ± 0.16	<0.001	<0.001	0.424
Energy intake	32.59 ± 3.52	30.23 ± 4.29	38.24 ± 2.75	37.62 ± 2.53	<0.001	<0.001	0.655
Feces energy	4.42 ± 1.21	4.76 ± 1.55	5.32 ± 0.97	5.57 ± 1.02	0.004	0.009	0.458
Urine energy	0.55 ± 0.14	0.62 ± 0.12	0.79 ± 0.31	0.60 ± 0.28	0.170	0.904	0.283
Methane energy	0.07 ± 0.04	0.07 ± 0.05	0.07 ± 0.05	0.08 ± 0.04	0.370	0.197	0.737
ME intake	27.62 ± 2.40	24.86 ± 2.01	32.12 ± 2.16	31.45 ± 1.93	0.043	0.004	0.586
NE intake	24.82 ± 2.69	21.73 ± 4.10	28.95 ± 2.51	27.79 ± 2.25	<0.001	<0.001	0.328
THP	6.87 ± 0.70	8.86 ± 0.92	6.74 ± 0.75	8.87 ± 0.75	0.037	0.939	<0.001
RQ	1.13 ± 0.05	1.14 ± 0.03	1.14 ± 0.07	1.17 ± 0.06	0.778	0.211	0.459
Energy efficiency, %	89.99 ± 2.83	86.93 ± 3.34	90.08 ± 2.94	88.47 ± 5.28	0.957	0.599	0.526
Energy deposition/metabolic weight, MJ∙ kg BW-0.6
NE deposition	1.78 ± 0.19	1.42 ± 0.20	2.05 ± 0.32	1.92 ± 0.30	0.068	0.016	0.458
REp	0.57 ± 0.06	0.56 ± 0.06	0.61 ± 0.13	0.68 ± 0.09	0.400	0.006	0.312
REf	1.21 ± 0.14	0.87 ± 0.22	1.44 ± 0.21	1.23 ± 0.22	0.052	0.036	0.127
ATTD, %
Dry matter	86.58 ± 3.13	84.49 ± 4.74	86.25 ± 2.16	85.38 ± 2.41	0.645	0.326	0.193
NDF	63.32 ± 9.74	57.31 ±12.98	60.46 ± 8.19	56.56 ± 6.72	0.272	0.795	0.078
ADF	59.51 ±12.33	53.22 ±14.91	57.05 ± 8.85	53.18 ± 7.93	0.433	0.990	0.117
Crude protein	88.00 ± 2.99	85.23 ± 5.00	87.90 ± 2.24	86.75 ± 1.98	0.880	0.146	0.066
Energy	86.47 ± 3.20	84.20 ± 4.94	86.08 ± 2.32	85.20 ± 2.54	0.602	0.288	0.218

¹ Data are presented as mean ± SD. 12L:12D = normal photoperiod (12 h light:12 h dark, baseline); 18L:6D = prolonged photoperiod (18 h light:6 h dark); 6L:18D = shortened photoperiod (6 h light:18 h dark). N1, baseline phase (12L:12D) under normal photoperiod conditions within the prolonged photoperiod group (P group); N2, baseline phase (12L:12D) under normal photoperiod conditions within the shortened photoperiod group (S group); P, treatment phase (18L:6D) in the prolonged photoperiod group; S, treatment phase (6L:18D) in the shortened photoperiod group. Abbreviations: THP = total heat production; REp = retained energy as protein; REf = retained energy as fat; RQ = respiratory quotient. ATTD = apparent total tract nutrient digestibility; and NDF = neutral detergent fiber; ADF = acid detergent fiber.

).

To demonstrate the consistency of the feeding response across the replicated crossover periods, individual period data for average feed intake are presented in Figure 1c. Feed intake remained stable across the six baseline phases (N1 and N2) and consistently increased during both photoperiod treatments (P and S) relative to their respective baselines in each period (Figure 1c).

The heat production (p < 0.05) of shortened photoperiod significantly increased compared to prolonged photoperiod. The NDF digestibility rate (p = 0.078) and crude protein digestibility rate (p = 0.066) of prolonged photoperiod showed an increasing trend compared to the shortened photoperiod (Table 2).

3.2. Variation in Heat Production of Growing Pigs with Prolonged and Shortened Photoperiod

From 19:00 to 24:00, normal photoperiod treatment is in the dark period, while prolonged photoperiod treatment is in the illumination period (Figure 2). At this time, the heat production of the pigs under prolonged photoperiod treatment is lower than that of the normal photoperiod treatment, and both treatments show a decreasing trend in heat production.

From 12:00 to 19:00, the normal photoperiod is in the illumination period, and the heat production shows an increasing trend. Shortened photoperiod is in the dark period, and the heat production shows a decreasing trend (Figure 2).

3.3. Effects of Different Photoperiods on Plasma Biochemical Indexes of Growing Pigs

The levels of glucagon-like peptide-1 in the plasma of the prolonged photoperiod showed an increasing tendency compared to normal photoperiod (p = 0.054). Compared to the normal photoperiod, the shortened photoperiod significantly decreased the level of low-density lipoprotein in the plasma (p < 0.05) and significantly increased the level of gastric inhibitory peptides (GIPs) (p < 0.05). Compared to the prolonged photoperiod, the shortened photoperiod significantly increased the levels of ghrelin (p < 0.05) and apolipoprotein A-IV (ApoA-IV (p < 0.05) (

Table 3. Effects of different photoperiods on plasma biochemical indexes of growing pigs ¹.

					p-Value
Items	N1	N2	P	S	N1 vs. P	N2 vs. S	P vs. S
Routine blood test
GLU, mmol/L	5.55 ± 0.95	5.01 ± 0.62	5.43 ± 0.35	5.74 ± 0.97	0.813	0.127	0.479
TC, mmol/L	2.15 ± 0.16	2.14 ± 0.32	2.02 ± 0.47	2.03 ± 0.23	0.592	0.159	0.951
TG, mmol/L	0.24 ± 0.13	0.30 ± 0.12	0.33 ± 0.27	0.21 ± 0.07	0.482	0.211	0.313
HDL, mmol/L	0.82 ± 0.11	0.82 ± 0.15	0.84 ± 0.22	0.90 ± 0.07	0.845	0.110	0.541
LDL, mmol/L	1.44 ± 0.13	1.57 ± 0.22	1.35 ± 0.28	1.43 ± 0.13	0.518	0.033	0.533
ApoA-Ⅳ, ug/mL	19.37 ± 1.27	19.92 ± 1.41	18.85 ± 0.87	20.12 ± 0.94	0.132	0.778	0.036
BUN, mmol/L	3.22 ± 0.47	3.23 ± 0.68	2.92 ± 0.53	3.72 ± 0.87	0.283	0.299	0.083
Hormone
GLP-1, pmol/L	1.39 ± 0.12	1.57 ± 0.18	1.47 ± 0.08	1.52 ± 0.07	0.054	0.576	0.250
Ghrelin, pg/mL	64.57 ± 4.22	66.42 ± 4.70	62.84 ± 2.90	67.07 ± 3.13	0.132	0.778	0.036
PYY, pmol/mL	2.20 ± 0.10	2.29 ± 0.12	2.20 ± 0.16	2.30 ± 0.16	0.921	0.844	0.293
COR, ng/mL	52.98 ± 26.55	36.46 ± 13.84	62.34 ± 26.33	77.47 ± 55.79	0.253	0.192	0.561
GIP, pg/mL	77.69 ± 5.52	80.38 ± 4.93	79.27 ± 6.91	82.90 ± 4.11	0.608	0.010	0.296
LEP, ng/mL	1.88 ± 0.17	1.89 ± 0.13	1.82 ± 0.10	1.88 ± 0.07	0.226	0.661	0.323
MT, pg/mL	283.12 ± 20.46	294.04 ± 21.39	274.44 ± 11.96	294.50 ± 26.84	0.269	0.976	0.125

¹ Data are presented as mean ± SD. 12L:12D = normal photoperiod (12 h light:12 h dark, baseline); 18L:6D = prolonged photoperiod (18 h light:6 h dark); 6L:18D = shortened photoperiod (6 h light:18 h dark). N1, baseline phase (12L:12D) under normal photoperiod conditions within the prolonged photoperiod group (P group); N2, baseline phase (12L:12D) under normal photoperiod conditions within the shortened photoperiod group (S group); P, treatment phase (18L:6D) in the prolonged photoperiod group; S, treatment phase (6L:18D) in the shortened photoperiod group. Abbreviations: GLU = glucose; TC = total cholesterol; TG = triglyceride; HDL = high-density lipoprotein; LDL = low-density lipoprotein; ApoA-Ⅳ = apolipoprotein A-Ⅳ; BUN = blood urea nitrogen; GLP-1 = glucagon-like peptide-1; PYY = peptide YY; COR = cortisol; GIPs = gastric inhibitory peptides; LEP = leptin; MT = melatonin.

).

3.4. Effects of Different Photoperiods on Rectal Microflora of Growing Pigs

3.4.1. Composition of Microorganisms in Feces of Growing Pigs at the Phylum Level

At the Phylum level, the microorganisms in pig manure are mainly composed of Firmicutes, Bacteroidota, Spirochaetota, Actinobacteriota and Cyanobacteria (Figure 3b).

3.4.2. Alpha Diversity Analysis on the Feces Microbes of Growing Pigs in Different Photoperiod Group

The group comparison of α-diversity, as measured by the Sobs and Shannon index, showed no significant differences among groups (p = 0.1062 (Sobs), p = 0.907 (Shannon); Figure 3c,d).

3.4.3. PCoA Analysis on the Feces Microbes of Growing Pigs in Different Photoperiod Group

Principal Coordinate Analysis (PCoA) did not reveal any significant clustering in the P group and the S group (R = 0.17037, p = 0.952 in the P group and R = 0.04815, p = 0.588 in the S group; see Figure 3e,f).

3.4.4. Differential Microorganisms in Growing Pig Feces in Different Groups

In the P group, a prolonged photoperiod significantly increased the relative abundance of Fibrobacter (p < 0.05), while significantly decreasing the relative abundances of norank_f__norank_o_Coriobacteriales, Lachnospiraceae_NK4A136_group, Prevotellaceae_UCG-003, and Frisingicoccus (p < 0.05). In the S group, a shortened photoperiod significantly elevated the relative abundance of Ruminococcus (p < 0.05). Compared to the prolonged photoperiod, the shortened photoperiod significantly enhanced the relative abundances of Rikenellaceae_RC9_gut_group and Mogibacterium, while significantly reducing those of Campylobacter, Fibrobacter, Quinella, and Mitsuokella (p < 0.05) (Figure 3h–j).

3.5. Effects of Different Photoperiods on Volatile Fatty Acids in Growing Pig Manure

Compared with the normal photoperiod, the level of acetic acid in pig manure with shortened photoperiod treatment significantly decreased (p < 0.05). Compared with prolonged photoperiod, the level of acetic acid and the total short chain fatty acids (SCFAs) in pig manure in shortened photoperiod tended to decrease; the level of propionic acid in pig manure in shortened photoperiod significantly decreased (p < 0.05) (

Table 4. Effects of different photoperiods on volatile fatty acids in growing pig manure ¹.

					p-Value
Items	N1	N2	P	S	N1 vs. P	N2 vs. S	P vs. S
Acetic acid	5.05 ± 0.71	4.90 ± 1.14	5.36 ± 1.27	4.04 ± 0.87	0.642	0.048	0.062
Propionic acid	2.85 ± 1.12	2.52 ± 0.95	3.06 ± 0.79	2.14 ± 0.47	0.628	0.330	0.035
Isobutyric acid	0.36 ± 0.06	0.36 ± 0.18	0.39 ± 0.12	0.32 ± 0.11	0.619	0.650	0.318
Butyric acid	1.48 ± 0.38	1.71 ± 0.86	1.54 ± 0.39	1.31 ± 0.45	0.844	0.243	0.364
Isovaleric acid	0.61 ± 0.16	0.61 ± 0.16	0.69 ± 0.25	0.69 ± 0.25	0.609	0.609	0.189
Valeric acid	0.35 ± 0.11	0.40 ± 0.19	0.39 ± 0.12	0.34 ± 0.10	0.688	0.449	0.478
Total SCFAs	10.69 ± 2.19	10.47 ± 3.26	11.43 ± 2.41	8.68 ± 1.90	0.655	0.138	0.053

¹ Data are presented as mean ± SD. 12L:12D = normal photoperiod (12 h light:12 h dark, baseline); 18L:6D = prolonged photoperiod (18 h light:6 h dark); 6L:18D = shortened photoperiod (6 h light:18 h dark). N1, baseline phase (12L:12D) under normal photoperiod conditions within the prolonged photoperiod group (P group); N2, baseline phase (12L:12D) under normal photoperiod conditions within the shortened photoperiod group (S group); P, treatment phase (18L:6D) in the prolonged photoperiod group; S, treatment phase (6L:18D) in the shortened photoperiod group. Abbreviations: SCFAs = short chain fatty acids.

).

3.6. Correlation Analysis Between Microorganisms and Volatile Fatty Acids, Between Volatile Fatty Acids and Plasma Biochemical Indicators, and Between Plasma Biochemical Indicators and Energy Distribution

3.6.1. Correlation Analysis Between Microorganisms and Volatile Fatty Acids (Figure 4a)

In normal photoperiod treatment, Parabacteroides was significantly negatively correlated with isovaleric acid, isobutyric acid and valeric acid. In prolonged photoperiod treatment, NK4A214_group was significantly positively correlated with propionic acid; Christensenellaceae_R-7_group was significantly positively correlated with butyric acid, isobutyric acid, valeric acid and isovaleric acid; UCG-002 was significantly positively correlated with butyric acid, isobutyric acid, valeric acid, and isovaleric acid; Norank_f_norank_o_clostridia_UCG-014 was significantly positively correlated with isobutyric acid; Solobacterium was significantly positively correlated with butyric acid; Lachnospiraceae was significantly positively correlated with acetic acid and propionic acid; Norank_f_oscillospilaceae was significantly positively correlated with propionic acid and butyric acid. Phascolarctobacterium was significantly negatively correlated with isobutyric acid and isovaleric acid; Terrisporobacter was significantly negatively correlated with propionic acid, butyric acid, and valeric acid; Clostridium_sensu_stricto_1 strict_1 was significantly negatively correlated with acetic acid, propionic acid, butyric acid, and valeric acid; Treponema was significantly negatively correlated with acetic acid, propionic acid, butyric acid, valeric acid, and isobutyric acid; Rikenellaceae_RC9_gut_group was significantly negatively correlated with acetic acid and butyric acid; Family_XIII_AD3011_group was significantly negatively correlated with propionic acid and butyric acid; and Norank_f__Eggerthellaceae was significantly negatively correlated with propionic acid.

Figure 4. (a) Correlation analysis between microorganisms and volatile fatty acids. C2, acetic acid; C3, propionic acid; C4, butyric acid; i−C4, isobutyric acid; C5, valeric acid; i−C5, isovaleric acid. (b) Correlation analysis between short chain fatty acids and plasma indicators among samples. Glu, glucose; Tc, total cholesterol; TG, triglyceride; HDL, high density lipoprotein; LDL, low density lipoprotein; APOA−Ⅳ, apolipoprotein A−Ⅳ; BUN, blood urea nitrogen; GLP−1, glucagon−like peptide−1; PYY, peptide YY; Cor, cortisol; GIPs, gastric inhibitory peptides; LEP, leptin; MT, melatonin; C2, acetic acid; C3, propionic acid; C4, butyric acid; i−C4, isobutyric acid; C5, valeric acid; i−C5. (c) Correlation analysis between plasma biochemical indicators and energy distribution. Glu, glucose; Tc, total cholesterol; TG, triglyceride; HDL, high density lipoprotein; LDL, low density lipoprotein; APOA−Ⅳ, apolipoprotein A−Ⅳ; BUN, blood urea nitrogen; GLP−1, glucagon−like peptide−1; PYY, peptide YY; Cor, cortisol; GIPs, gastric inhibitory peptides; LEP, leptin; MT, melatonin; NE, net energy deposition; Protein, net energy deposition as protein; Fat, net energy deposition as fat. The R values are displayed in different colors in the figure. If the p-value is less than 0.05, it is marked with *; if the p-value is less than 0.01, it is marked with **; and if the p-value is less than 0.01, it is marked with ***. The legend on the right is the color interval of different R values.

Figure 4. (a) Correlation analysis between microorganisms and volatile fatty acids. C2, acetic acid; C3, propionic acid; C4, butyric acid; i−C4, isobutyric acid; C5, valeric acid; i−C5, isovaleric acid. (b) Correlation analysis between short chain fatty acids and plasma indicators among samples. Glu, glucose; Tc, total cholesterol; TG, triglyceride; HDL, high density lipoprotein; LDL, low density lipoprotein; APOA−Ⅳ, apolipoprotein A−Ⅳ; BUN, blood urea nitrogen; GLP−1, glucagon−like peptide−1; PYY, peptide YY; Cor, cortisol; GIPs, gastric inhibitory peptides; LEP, leptin; MT, melatonin; C2, acetic acid; C3, propionic acid; C4, butyric acid; i−C4, isobutyric acid; C5, valeric acid; i−C5. (c) Correlation analysis between plasma biochemical indicators and energy distribution. Glu, glucose; Tc, total cholesterol; TG, triglyceride; HDL, high density lipoprotein; LDL, low density lipoprotein; APOA−Ⅳ, apolipoprotein A−Ⅳ; BUN, blood urea nitrogen; GLP−1, glucagon−like peptide−1; PYY, peptide YY; Cor, cortisol; GIPs, gastric inhibitory peptides; LEP, leptin; MT, melatonin; NE, net energy deposition; Protein, net energy deposition as protein; Fat, net energy deposition as fat. The R values are displayed in different colors in the figure. If the p-value is less than 0.05, it is marked with *; if the p-value is less than 0.01, it is marked with **; and if the p-value is less than 0.01, it is marked with ***. The legend on the right is the color interval of different R values.

In shortened photoperiod treatment, Oribacterium was significantly positively correlated with propionic acid; Norank _ f _ norank _ o _ clostridia _ UCG-014 was significantly positively correlated with propionic acid; Subdoligranulum was significantly positively correlated with propionic acid and butyric acid; Coprococcus was significantly positively correlated with acetic acid, propionic acid, and butyric acid; Eubacterium_hallii_group was significantly positively correlated with acetic acid, propionic acid, and butyric acid; Norank _ f _ norank _ o __ RF39 was significantly positively correlated with butyric acid; Bifidobacterium was significantly positively correlated with propionic acid; Norank _ f __ bacteroidales _ RF16 _ group was significantly positively correlated with isovaleric acid; Solubacterium was significantly positively correlated with isobutyric acid and isovaleric acid; Mogibacterium was significantly positively correlated with isobutyric acid. Phascolarctobacterium was significantly negatively correlated with isobutyric acid and isovaleric acid; Terrisporobacter was significantly negatively correlated with propionic acid, butyric acid, and valeric acid; Clostridium_sensu_stricto_1 was significantly negatively correlated with acetic acid, propionic acid, butyric acid, and valeric acid; Treponema was significantly negatively correlated with acetic acid, propionic acid, butyric acid, valeric acid, and isobutyric acid; Rikenellaceae_RC9_gut_group was significantly negatively correlated with acetic acid and butyric acid; and Family_XIII_AD3011_group was significantly negatively correlated with propionic acid and butyric acid.

3.6.2. Correlation Analysis Between Volatile Fatty Acids and Plasma Biochemical Indicators (Figure 4b)

In the normal photoperiod treatment, acetic acid, isobutyric acid, valeric acid, and isovaleric acid were significantly negatively correlated with ghrelin; isobutyric acid was significantly negatively correlated with melatonin (MT); acetic acid, isobutyric acid, and isovaleric acid were significantly negatively correlated with apolipoprotein A-IV (ApoA-IV); and acetic acid and isobutyric acid were significantly negatively correlated with glucagon-like peptide-1 (GLP-1). In the shortened photoperiod treatment, butyric acid and isobutyric acid were significantly negatively correlated with GLP-1.3.6.3. Correlation Analysis Between Plasma Biochemical Indicators and Energy Distribution (Figure 4c).

In normal photoperiod treatment, BUN and protein were significantly positive, and GLP-1 was significantly negative, with NE and net energy deposition for fat. In the prolonged photoperiod treatment, TC was significantly positive with net energy deposition as fat, and GIPs were significantly negative with NE and net energy deposition as fat. In the shortened photoperiod treatment, ghrelin was significantly positively correlated with NE and net energy deposition as protein, PYY was significantly positively correlated with NE and net energy deposition as protein, LEP as significantly positively correlated with NE, MT was significantly positively correlated with NE and net energy deposition as protein, ApoA-Ⅳ was significantly positively correlated with NE and net energy deposition as protein, and GLP-1 was significantly positively correlated with NE and net energy deposition as protein. TG was negative with NE and Protein.

4. Discussion

Lighting factors, such as the photoperiod, illumination intensity, and illumination wavelength, influence the growth and reproductive performances of pigs in pig production. This experiment aimed to investigate the impact of adjusting the duration of light exposure for growing pigs, under the condition of fixed feeding time, on their energy and metabolism. Specifically, we aimed to understand how this change in the periodicity of light signals received by growing pigs might affect their energy and metabolism, as the exact effects of such modifications are still unclear.

In modern commercial pig production, lighting is a key environmental management factor. The Chinese national standard for environmental parameters and management in swine housing (GB/T 17824.3-2008) [16] recommends a photoperiod of 8 to 12 h of light per day for growing–finishing pigs. In this study, the prolonged (18L:6D) and shortened (6L:18D) photoperiod treatments were deliberately set above and below this common production range, respectively. This design allows us to investigate the physiological mechanisms and limits of how pronounced variations in the light–dark cycle affect energy partitioning in growing pigs, providing theoretical insights that may inform more refined management strategies.

The results of this experiment showed that shortening the photoperiod significantly improved the net energy deposition, net energy for protein deposition, and net energy for fat deposition in growing pigs, thereby enhancing the net energy production of growing pigs.

Previous research showed that, compared to daytime feeding, the circadian rhythm disruption induced by nighttime feeding increased energy deposition in pigs, and this energy was stored as body fat [17], which may be caused by the unsynchronization of the endogenous circadian clock and feeding time. Martelli et al. [18] found that proper extension of photoperiod can improve the production performance of finishing pigs. When growing–finishing pigs were given 10 h light and 14 h light, respectively, the daily gain and feed conversion efficiency of growing–finishing pigs in the 14 h light group were higher than those in the 10 h light group when the light intensity was 70 Lux. Subsequent research [19] explored the influence of photoperiod at 8 h and 16 h, respectively, when the illumination intensity was reduced to 40 Lux. The results showed that extending the illumination to 16 h improved the production performance of growing pigs and had no significant effect on meat quality and subcutaneous fatty acid composition under the condition of reducing the light intensity. The research results showed that the duration of light was increased, even at a low light intensity, as long as animals were given a proper dark rest period. In the experiment of studying the influence of light duration on piglets, it was found that in the second week of the experiment (2 weeks in total), the light duration of 23 h significantly increased the average daily feed intake and average daily weight gain of piglets, and the total heat production, total energy deposition, protein energy deposition and fat energy deposition were significantly higher than those of the control group (8 h light and 16 h darkness), which indicated that prolonging the light duration was of great significance for improving the feed intake and production performance of weaned piglets [1].

Most research suggests that prolonging the photoperiod improves swine production performance. However, our results indicate that while a prolonged photoperiod (18L:6D) showed only a tendency to increase net energy for fat deposition, a shortened photoperiod (6L:18D) significantly enhanced net energy deposition overall, as well as its partition into both protein and fat. We propose that several methodological and contextual factors specific to our study may explain this divergence from the prevailing literature.

First, the experiment was conducted within open-circuit respiration chambers. This confined, controlled environment, coupled with an abrupt transition to an extended dark phase (18 h), may have promoted greater rest and reduced chronic stress compared to conventional housing, thereby creating conditions more favorable to anabolic processes.

Second, the dietary composition (12% wheat bran as a fiber source) likely played a role. The interaction between this high-fiber diet and the photoperiod-induced shifts in gut microbiota we observed may have altered energy harvesting and partitioning in a way that differs from studies using standard, lower-fiber diets.

Third, animal-specific factors must be considered. The use of growing barrows (Duroc × Landrace × Yorkshire) at 47 kg may reflect a metabolic sensitivity to light cues of a specific breed or stage.

Finally, our experimental protocol itself was distinctive. The within-animal, “self-controlled” design, involving a sudden shift from a normal (12L:12D) to a shortened photoperiod (6L:18D), likely elicited a pronounced neuroendocrine response (evidenced by significant rises in ghrelin and ApoA-IV) that might be attenuated under less dramatic or more gradual lighting transitions.

Therefore, our findings do not contradict the established principle that photoperiod regulates swine physiology. Instead, they highlight that the direction and magnitude of this regulation are highly context-dependent, modulated by environmental constraints, diet, animal factors, and experimental design. Future research should aim to disentangle these interactions under varied production settings.

It is important to note a related implication of our self-controlled method. While it effectively minimized confounding from different respiration chambers, it also resulted in significantly higher feed intake during the later (treatment) periods compared to the initial normal photoperiod baseline, consistent with normal growth. To account for this, all energy deposition results were normalized to metabolic body weight. Nonetheless, inherent differences in metabolic rate between earlier and later experimental phases may remain a contributing factor alongside the photoperiod manipulation itself.

Studies have shown that circadian rhythm disturbances can alter the gut microbiota [20]. Since the gut microbiota can ferment and utilize dietary fiber, this experiment tested the hypothesis that changes in the light–dark cycle would have an impact on the digestion and utilization of fiber. However, contrary to our initial hypothesis, the alteration of the light–dark cycle did not lead to significant changes in the apparent total tract digestibility of NDF, ADF, or crude protein. This indicates that, under the conditions of this experiment, the utilization of dietary fiber remained stable, although different photoperiod changes produced different microorganisms among the groups. This resilience in digestive function may be attributed to several factors.

First, the level of dietary fiber used in this study (15.68% NDF, 5.43% ADF) may not have been sufficiently high to stress the fermentative capacity of the microbial community. At this moderate inclusion level, the gut microbiome likely possesses considerable functional redundancy, whereby different microbial taxa can perform similar metabolic roles, thereby buffering the ecosystem against compositional changes and maintaining overall fermentation output.

Second, the 8-day duration of each photoperiod treatment is not sufficient for inducing detectable microbial population shifts. Thus, it may not allow for a full functional adaptation or re-organization of the microbial network dedicated to complex carbohydrate breakdown. Although specific fiber-degrading taxa (Fibrobacterota) showed changes in relative abundance, the collective activity of the microbial consortia responsible for fiber degradation appears to have been preserved.

Therefore, our results suggest that short-term photoperiod manipulation alters the taxonomic landscape of the gut microbiota without necessarily disrupting its core metabolic function related to fiber fermentation, at least when a moderate-fiber diet is provided. Future studies employing diets with higher fiber loads, longer adaptation periods, or direct measurements of microbial enzymatic activity would be valuable to determine the thresholds at which photoperiod changes begin to impact functional outcomes in nutrient digestion.

However, the results of this experiment indicate that the changes in the light cycle did not have a significant impact on the digestion and utilization of fiber, which is contrary to the expected hypothesis. The reason for this may be that the changes in the light cycle under the experimental conditions were not sufficient to cause an impact on the microbial community or that the regulation of microbial communities in the intestines, including those associated with fiber degradation, is not consistent with the effect of light (the change in light cycle may increase the abundance of some microorganisms related to fiber decomposition, but also decrease the abundance of other microorganisms related to fiber decomposition). The combined effect does not significantly affect the utilization of fiber in the diet. At the same time, we acknowledge that the experimental period (8 days per photoperiod treatment) may not have been sufficient to fully capture the long-term adaptive responses of microbial communities. Future investigations employing extended adaptation periods (>3–4 weeks) and multi-omics approaches (metatranscriptomics, metabolomics) would help clarify the temporal dynamics of microbial adaptation and its functional implications for nutrient utilization and host metabolism.

Glucagon-like peptide-1 (GLP-1) is a hormone secreted by intestinal cells and is one of the main components of insulin secretion. The main function of GLP-1 is to promote insulin secretion by pancreatic β cells and inhibit the secretion of glucose-raising hormones by pancreatic α cells, thereby delaying gastric emptying, suppressing appetite, and maintaining blood sugar stability. It is mainly used for the treatment of diabetes [21]. In this experiment, compared with the normal photoperiod treatment, the prolonged photoperiod treatment showed an increasing trend in the level of GLP-1 in the plasma of growing pigs, and the diversity of intestinal microbiota in the prolonged photoperiod treatment was reduced. Intestinal secretory cells are mainly located at the distal end of the ileum and colon, directly in contact with nutrients and intestinal microorganisms [22]. Therefore, intestinal microbiota may directly or indirectly affect the secretion of GLP-1. In addition, the secretion of GLP-1 is also indirectly influenced by short-chain fatty acids (SCFAs). Acetate, propionate, butyrate, and other SCFAs produced by the fermentation of dietary fibers act as ligands for gastric inhibitory polypeptide release and can promote the secretion of GLP-1 by enteroendocrine cells [23,24]. Some studies have shown that fermentation products of carbohydrates in the intestine can promote the differentiation of enteroendocrine L cells [25]. The results of this experiment showed that the overall level of SCFAs in the prolonged photoperiod group tended to be higher compared to the normal light group. Therefore, the changes in the intestinal microbiota caused by prolonged photoperiod increased the concentration of SCFAs in the intestine, promoted the differentiation of enteroendocrine L cells, and increased the level of GLP-1.

Low-density lipoprotein (LDL) is a class of lipoproteins in plasma that constitutes the highest proportion of cholesterol. It is a complex of proteins, cholesterol, and phospholipids. The level of LDL is influenced by various factors such as age, gender, diet, exercise, and genetics. High-fat diet, lack of exercise, and psychological stress can increase LDL levels [26,27]. The research results of this experiment suggest that the shortened photoperiod treatment significantly reduced the level of LDL in the plasma. The potential reason for this might be that the growing pigs experienced a transition from 12 h of darkness to 18 h of darkness, which may have increased their sleep duration. At the same time, there was enough adaptation period to the respiratory calorimetry chamber before the prolonged dark period (during which the growing pigs had undergone 8 days of normal light exposure calorimetry test). These factors, to some extent, alleviated the anxiety and tension of the growing pigs in the confined experimental environment, thereby reducing the level of LDL in their plasma.

Gastric inhibitory polypeptide (GIP) is an important metabolic hormone in animals, mainly produced by K cells in the duodenum and jejunum [28,29]. One of its functions is promoting the accumulation of fat tissue [30,31]. The research results of this experiment indicate that the shortened photoperiod treatment significantly increased the level of GIP, consistent with the result of increased net energy for fat deposition.

Apolipoprotein A-IV is mainly synthesized by the small intestine and hypothalamus and is involved in the composition of high-density lipoprotein or exists in a free state in plasma [32]. It has complex functions, and many studies have shown that apolipoprotein A-IV has antioxidant and anti-inflammatory functions [33,34,35], playing an important role in the process of anti-atherosclerosis. Ghrelin is a peptide hormone produced in the stomach, which increases before meals and decreases after meals. When it is present at high levels, it induces satiety. The levels of apolipoprotein A-IV and ghrelin in the plasma of the shortened photoperiod treatment are significantly higher than those in the prolonged light group, indicating that, compared to the prolonged photoperiod treatment, the shortened photoperiod can promote the feeding of growing pigs and show good antioxidant and anti-inflammatory effects to some extent.

In this study, there was no significant difference in the levels of melatonin among the groups. Melatonin is a hormone secreted by the pineal gland and mainly regulates the sleep–wake cycle. Its secretion is regulated by the internal circadian clock and the external light–dark cycle of the body [36]. During the day, light signals stimulate the photosensitive cells on the retina, thereby inhibiting the secretion of melatonin by the pineal gland, and in dark conditions at night, the pineal gland begins to secrete melatonin [37]. In this experiment, plasma samples were collected at 9:00 in the morning. At this time, all treatment groups were in the illumination phase. The levels of melatonin in the plasma of growing pigs were low and showed no significant difference. It has been shown that increasing the duration of darkness can increase the secretion level of melatonin [38]. Due to the extension of the duration of darkness in the shortened photoperiod treatment, the secretion level of melatonin theoretically would increase. However, due to the short half-life of melatonin in the body, which is about 20–50 min [39], even with the extension of the duration of darkness in the shortened light group, the melatonin level in the body would not significantly exceed that of other treatments when blood was collected at 9:00 in the morning. Therefore, to investigate the influence of the light–dark cycle on melatonin secretion levels, the timing and frequency of blood collection should be appropriately set to understand the impact of the light–dark cycle on melatonin secretion levels and the phase of its secretion level changes over time.

In addition, compared to the shortened photoperiod group, the prolonged photoperiod group showed a trend of increasing neutral detergent fiber digestibility. Therefore, in this study, a differential analysis of the gut microbiota at the phylum level was conducted between the two groups. The results indicated that the prolonged photoperiod treatment significantly increased the relative abundance of the Fibrobacterota, which is an important group of cellulolytic bacteria in the intestines capable of degrading specialized cellulose polysaccharides. The increase in the relative abundance of this phylum is the reason for the improvement in neutral detergent fiber digestibility. However, the utilization of fiber is also influenced by other microbial groups, such as Actinobacteria, which utilize complex polysaccharides, and Fibrobacteres and Bacteroidales, which utilize lignin and fructooligosaccharides, respectively. Their relative abundance did not show significant changes. These are microbial communities that are associated with fiber degradation in the pig hindgut. Due to the large number and complex functions of microbial communities in the gut, their composition and quantity vary unpredictably under different light cycles. The response of fiber-degrading microbial communities to light exposure is not entirely positive. Therefore, the changed microbial communities were not enough to improve the degradation rate of fiber.

The circadian rhythm disorder may also be one of the reasons for the increase in net energy deposition in growing pigs. In the study of the effects of an extended lighting regime (16 h of light and 8 h of darkness) on growing and finishing pigs, it was found that, compared to the normal lighting group, extended lighting increased the concentrations of melatonin, insulin, and leptin in serum. It also significantly increased the feed intake and daily weight gain of finishing pigs, with no significant effect on most meat quality indicators [40]. The results of this study also showed that the prolonged photoperiod group significantly increased the daily feed intake of growing pigs. However, due to the characteristics of melatonin mentioned above and the timing of blood sample collection in this experiment, the level of melatonin in the plasma of the extended lighting group had no significant impact. It should be noted that this result cannot be used as evidence that the circadian rhythm has not changed.

Microbial metabolites mainly include volatile fatty acids, such as acetic acid, propionic acid, and butyric acid, which play important roles in regulating the metabolism and function of the host’s gastrointestinal tract [41]. Yuan et al. found that the level of isovaleric acid in the feces of the constant long-day group was significantly higher than that of the constant short-day group [42]. The results of this study also showed that the level of isovaleric acid in the feces of the prolonged photoperiod group was higher than that of the shortened photoperiod group. Studies have shown that acetic acid can stimulate the parasympathetic nervous system, thereby promoting the secretion of ghrelin and increasing feed intake, ultimately leading to obesity [43]. In this study, the level of acetic acid in the feces of the shortened photoperiod treatment was significantly lower than that of the normal photoperiod group, and the shortened photoperiod group increased the level of ghrelin in the plasma. The reason may be that the absorption of acetic acid in the feces of the shortened photoperiod group increased, activating the parasympathetic nervous system and stimulating the secretion of ghrelin, resulting in an increase in feed intake.

In this experiment, there is a significant positive correlation between the deposition (including the net energy deposition, net energy for protein deposition, and net energy for fat deposition) and several plasma biochemical indicators in the shortened photoperiod treatment. These indicators play important roles in regulating appetite, metabolism, and physiological rhythms. It indicates that under the experimental conditions of shortened photoperiod, animals may activate certain protective mechanisms to maintain normal physiological processes and energy metabolism in this environmental stress. In the shortened photoperiod treatment, net energy deposition, net energy for fat deposition, and net energy for protein deposition show a significant negative correlation with triglycerides. The relationship between energy deposition and triglycerides is determined by the metabolism and regulation of carbohydrates and fats by the body. The reason may be that hepatocytes preferentially convert glucose into glycogen to store energy, and use UDPG, an intermediate metabolite in glycogen synthesis, to inhibit the synthesis of triglycerides [44].

Beyond production metrics, the lighting regimen is a critical factor influencing animal welfare. Our findings, particularly under the shortened photoperiod (6L:18D), offer insights into potential welfare benefits. The significant reduction in plasma low-density lipoproteins (LDLs) observed in this group may reflect a lower state of metabolic stress. Furthermore, the elevated levels of ghrelin, a hormone stimulating appetite, alongside the provision of an extended, uninterrupted dark period (18 h), likely support more natural resting and behavioral patterns. In confined environments such as respiration chambers, this prolonged darkness could be particularly valuable in reducing chronic stress associated with constant human activity and artificial lighting. The concurrent improvement in net energy deposition and feed efficiency suggests that this welfare-positive lighting schedule does not come at the expense of productivity. Instead, it may promote a state where improved well-being and metabolic efficiency are synergistic. Future studies incorporating direct behavioral observations (e.g., resting and stereotypic behavior) and cortisol measurements would be valuable to directly correlate these physiological markers with welfare status.

5. Conclusions

Shortened photoperiod affects both the net energy retention and the partition of net energy in growing pigs; under the experimental conditions described, altering the light–dark cycle did not influence the utilization of dietary fiber when wheat bran was the main fiber source.

Different photoperiods modified the diversity and structure of the intestinal microbiota, microbial metabolites, and the secretion of hormones such as ghrelin; these changes are potential mechanisms by which light regimen influences net energy partitioning. However, the effects of photoperiod on the microbiota were not unidirectional, and their combined impact was insufficient to alter dietary fiber utilization in growing pigs.