Based on qualitative and quantitative melissopalynological analyses, 19 Chinese honeys were classified by botanical origin to determine their floral sources. The honey samples were collected during 2010–2011 from the central region of Shanxi Province, North China. A diverse spectrum of 61 pollen types from 37 families was identified. Fourteen samples were classified as unifloral, whereas the remaining samples were multifloral. Bee-favoured families (occurring in more than 50% of the samples) included Caprifoliaceae (found in 10 samples), Laminaceae (10), Brassicaceae (12), Rosaceae (12), Moraceae (13), Rhamnaceae (15), Asteraceae (17), and Fabaceae (19). In the unifloral honeys, the predominant pollen types were Ziziphus jujuba (in 5 samples), Robinia pseudoacacia (3), Vitex negundo var. heterophylla (2), Sophora japonica (1), Ailanthus altissima (1), Asteraceae type (1), and Fabaceae type (1). The absolute pollen count (i.e., the number of pollen grains per 10 g honey sample) suggested that 13 samples belonged to Group I (<20,000 pollen grains), 4 to Group II (20,000–100,000), and 2 to Group III (100,000–500,000). The dominance of unifloral honeys without toxic pollen grains and the low value of the HDE/P ratio (i.e., honey dew elements/pollen grains from nectariferous plants) indicated that the honey samples are of good quality and suitable for human consumption.

Rapid and non-destructive estimation of plant water status is essential for adjusting field practices and irrigation schemes of winter wheat. The objective of this study was to find new combination spectral indices based on canopy reflectance for the estimation of plant water status. Two experiments with different irrigation regimes were conducted in 2015–2016 and 2016–2017. The canopy spectra were collected at different growth stages of winter wheat. The raw and derivative reflectance of canopy spectra showed obvious responses to the change of plant water status. Except for equivalent water thickness (EWT), other water metrics had good relationships with new combination spectral indices (R²>0.7). An acceptable model of canopy water content (CWC) was established with the best spectral index (RVI (1605, 1712)). Models of leaf water content (LWC) and plant water content (PWC) had better performances. Optimal spectral index of LWC was FDRVI (687, 531), having R², RMSE and RPD of 0.77, 2.181 and 2.09; R², RMSE and RPD of 0.87, 2.652 and 2.34 for calibration and validation, respectively. And PWC could be well estimated with FDDVI (688, 532) (R², RMSE and RPD of 0.79, 3.136 and 2.21; R², RMSE and RPD of 0.83, 3.702 and 2.18 for calibration and validation, respectively). Comparing the performances of estimation models, the new combination spectral indices FDRVI (687, 531) based on canopy reflectance improved the accuracy of estimation of plant water status. Besides, based on FDRVI (687, 531), LWC was the optimal water metrics for plant water status estimation.

Nitrogen fertilizer is one of the key elements to increase the yield and significance of winter wheat. The experiment was established in the split zone design and was repeated three times. The nitrogen application level is set to 4 treatments, 75, 150, 225 and 300 kg ha⁻¹ are arranged in the main plot, and different nitrogen application ratios are arranged in the sub-plots, respectively 5:5 (50%+50%) and 6: 4 (60%) + 40%). Nitrogen fertilizer was applied before sowing, jointing stage, flowering stage and filling stage. The experimental plot is 12 m2 (3 m × 4 m). The results showed that under the conditions of 225 kg/hm2 nitrogen application and 60%+40% nitrogen application rate, the yield of Jintai 182 was the highest compared with other treatment groups. With the increase of nitrogen application rate, the number of ears, grains per ear, thousand-grain weight and grain yield all increase first and then decrease. Each factor reached the highest 225 N kg / hm2, 417.17, 30.74, 40.96 g and 6182.11 kg / hm2. Compared with 75 kg/hm2 topdressing fertilizer, 225 kg/hm2 is a more suitable nitrogen fertilizer application rate for winter wheat. Within a reasonable range of nitrogen fertilizer application, there is a significant positive correlation between nitrogen content and winter wheat yield. By studying the amount of nitrogen fertilizer and a reasonable ratio of base fertilizer to topdressing, the utilization rate of nitrogen fertilizer can be maximized and excessive application of nitrogen fertilizer can be avoided.

It is of merit to study the appropriate amount of dietary fiber to add to free-range chickens’ feed to improve their microbial diversity and gut health in times of plant fiber deprivation. Lignocellulose is a useful source of dietary fiber, and its positive effects on the growth performance and laying performance of chickens has already been proven. However, few researchers have researched the effects of adding it on the gut microbiota of chickens. In this research, we added three different levels of eubiotic lignocellulose (0%, 2%, and 4%) to the feed of caged and free-range Bian chickens from September to November, aiming to observe the effects of added dietary fiber and different rearing systems on the gut microbial diversity and gut health of chickens, as well as to determine an appropriate amount of lignocellulose. The results showed that adding dietary fiber increased the thickness of the cecum mucus layer and the abundance of Akkermansia and Faecalibacterium in caged chickens, and 4% lignocellulose was appropriate. In addition, adding lignocellulose increased the microbial diversity and the abundance of the butyrate-producing bacteria Faecalibacterium and Roseburia in fee-range chickens. The α-diversity and the length of the small intestine with 2% lignocellulose in free-range chickens were better than with 2% lignocellulose in caged chickens. Maybe it is necessary to add dietary fiber to the feed of free-range chickens when plant fibers are lacking, and 2% lignocellulose was found to be appropriate in this experiment. In addition, compared with caged chickens, the free-range chickens had a longer small intestine and a lower glucagon like peptide-1 (GLP-1) level. The significant difference of GLP-1 levels was mainly driven by energy rather than short chain fatty acids (SCFAs). There was no interaction between added dietary fiber and the rearing system on SCFAs, cecum inner mucus layer, and GLP-1.

The saturation problem associated with the use of normalized difference vegetation index for crop variable estimation is well known. However, its physical mechanism is not systemically explored. The wavebands computing the vegetation indices also suffered saturation when the leaf area index and aboveground biomass reached to 2.5 and 1 kg m⁻², respectively. We thought that the saturation might be not only referred to normalized difference vegetation index, but also to certain wavebands. Furthermore, the performances of seven different vegetation indices were assessed on overcoming the saturation. The findings will improve our understanding of the spectral saturation.