Rehabilitation of Barbarine cull ewes using rosemary residues and linseed: effect on weight gain,carcass characteristics and meat quality

The cull ewes represent an important part of sheep flock. However, this category of animal is often submitted to under nutrition leading to poor BW and skeletal carcasses. Their rehabilitation using a high energy diet can be an alternative to improve their body condition. The objective of this experiment was to study the BW gain and carcass characteristics of Barbarine cull ewes using rosemary (Rosmarinus officinalis L.) distillation residues (RR) and extruded linseed. For this, 28 ewes above 6 years old and 33±0.5 kg of BW were divided into four groups: CCC was fed 500 g of barley-straw with concentrate, RCC received 300 g of straw and 200 g of RR as basal diet with concentrate; whereas two other groups received the experimental concentrate, containing 10% of linseed, with 500 g of straw for CLC and 300 g of straw plus 200 g of RR for RLC group. At the end of experiment (90 days), all animals were slaughtered. For all ewes, the daily concentrate intake averaged 700 g; the average daily gain was 131 g and the slaughter BW 43.4 kg without significant difference between groups. Neither basal diet nor concentrate type did affect the carcass’ weight, yield and composition. In addition, the organ’s proportions were similar for all groups. The RR intake slightly improved muscle’s protein content (P=0.03) and tended to decrease initial pH (P=0.06) and to increase meat redness (P=0.06), whereas linseed concentrate had no effect on meat color and its chemical composition. The subcutaneous fat color and firmness score relived a good quality trade for carcasses from all diets, in spite of higher yellowness and lower firmness recorded for linseed diet (P<0.05), which were moderately improved by rosemary combination with linseed. To conclude, the Barbarine cull ewes could gain up to 120 g/day in BW. The used diets permitted this BW gain without undesirable effects on carcass characteristics and meat quality. However, the study of meat fatty acid profile and antioxidant status should continue.     

A predictive model for freshwater bioassessment (Mondego River,Portugal)

We sampled macroinvertebrates at 75 locations in the Mondego river catchment, Central Portugal, and developed a predictive model for water quality assessment of this basin, based on the Reference Condition Approach. Sampling was done from June to September 2001. Fifty-five sites were identified as “Reference sites” and 20 sites were used as “Test sites” to test the model. At each site we also measured 40 habitat variables to characterize water physics and chemistry, habitat type, land use, stream hydrology and geographic location. Macroinvertebrates were generally identified to species or genus level; a total of 207 taxa were found. By Unweighted Pair Group Method with Arithmetic mean (UPGMA) clustering and analysis of species contribution to similarities percentage (SIMPER), two groups of reference sites were established. Using Discriminant Analysis (stepwise forward), four variables correctly predicted 78% of the reference sites to the appropriate group: stream order, pool quality, substrate quality and current velocity. Test sites’ environmental quality was established from their relative distance to reference sites, in MDS ordination space, using a series of bands (BEAST methodology). The model performed well at upstream sites, but at downstream sites it was compromised by the lack of reference sites. As with the English RIVPACS predictive model, the Mondego model should be continually improved with the addition of new reference sites. The adaptation of the Mondego model methodology to the Water Framework Directive is possible and would consist mainly of the integration of the WFD typology and increasing the number of ellipses that define quality bands. Handling editor: K. Martens     

Effects of grazing intensity and grazing exclusion on litter decomposition in the temperate steppe of Nei Mongol,China

Aims Grazing intensity and grazing exclusion affect ecosystem carbon cycling by changing the plant community and soil micro-environment in grassland ecosystems. The aims of this study were: 1) to determine the effects of grazing intensity and grazing exclusion on litter decomposition in the temperate grasslands of Nei Mongol; 2) to compare the difference between above-ground and below-ground litter decomposition; 3) to identify the effects of precipitation on litter production and decomposition. Methods We measured litter production, quality, decomposition rates and soil nutrient contents during the growing season in 2011 and 2012 in four plots, i.e. light grazing, heavy grazing, light grazing exclusion and heavy grazing exclusion. Quadrate surveys and litter bags were used to measure litter production and decomposition rates. All data were analyzed with ANOVA and Pearson’s correlation procedures in SPSS. Important findings Litter production and decomposition rates differed greatly among four plots. During the two years of our study, above-ground litter production and decomposition in heavy-grazing plots were faster than those in light-grazing plots. In the dry year, below-ground litter production and decomposition in light-grazing plots were faster than those in heavy-grazing plots, which is opposite to the findings in the wet year. Short-term grazing exclusion could promote litter production, and the exclusion of light-grazing could increase litter decomposition and nutrient cycling. In contrast, heavy-grazing exclusion decreased litter decomposition. Thus, grazing exclusion is beneficial to the restoration of the light-grazing grasslands, and more human management measures are needed during the restoration of heavy-grazing grasslands. Precipitation increased litter production and decomposition, and below-ground litter was more vulnerable to the inter-annual change of precipitation than above-ground litter. Compared to the light-grazing grasslands, heavy-grazing grasslands had higher sensitivity to precipitation. The above-ground litter decomposition was strongly positively correlated with the litter N content (R² = 0.489, p < 0.01) and strongly negatively correlated with the soil total N content (R² = 0.450, p < 0.01), but it was not significantly correlated with C:N and lignin:N. Below-ground litter decomposition was negatively correlated with the litter C (R² = 0.263, p < 0.01), C:N (R² = 0.349, p < 0.01) and cellulose content (R² = 0.460, p < 0.01). Our results will provide a theoretical basis for ecosystem restoration and the research of carbon cycling.     

Assessing water scarcity by simultaneously considering environmental flow requirements,water quantity,and water quality

Water scarcity is a widespread problem in many parts of the world. Most previous methods of water scarcity assessment only considered water quantity, and ignored water quality. In addition, the environmental flow requirement (EFR) was commonly not explicitly considered in the assessment. In this study, we developed an approach to assess water scarcity by considering both water quantity and quality, while at the same time explicitly considering EFR. We applied this quantity–quality-EFR (QQE) approach for the Huangqihai River Basin in Inner Mongolia, China. We found that to keep the river ecosystem health at a “good” level (i.e., suitable for swimming, fishing, and aquaculture), 26% of the total blue water resources should be allocated to meet the EFR. When such a “good” level is maintained, the quantity- and quality-based water scarcity indicators were 1.3 and 14.2, respectively; both were above the threshold of 1.0. The QQE water scarcity indicator thus can be expressed as 1.3(26%)|14.2, indicating that the basin was suffering from scarcity problems related to both water quantity and water quality for a given rate of EFR. The current water consumption has resulted in degradation of the basin's river ecosystems, and the EFR cannot be met in 3 months of a year. To reverse this situation, future policies should aim to reduce water use and pollution discharge, meet the EFR for maintaining healthy river ecosystems, and substantially improve pollution treatment.     

RNA‐seq analysis reveals new candidate genes for drip loss in a Pietrain × Duroc × Landrace × Yorkshire population

Drip loss, one of the most important meat quality traits, is characterized by low heritability. To date, the genetic factors affecting the drip loss trait have not been clearly elucidated. The objective of this study was to identify critical candidate genes affecting drip loss. First, we generated a Pietrain × Duroc × Landrace × Yorkshire commercial pig population and obtained phenotypic values for the drip loss trait. Furthermore, we constructed two RNA libraries from pooled samples of longissimus dorsi muscles with the highest (H group) and lowest (L group) drip loss and identified the differentially expressed genes (DEGs) between these extreme phenotypes using RNA‐seq technology. In total, 25 883 genes were detected in the H and L group libraries, and none was specifically expressed in only one library. Comparative analysis of gene expression levels found that 150 genes were differentially expressed, of which 127 were upregulated and 23 were downregulated in the H group relative to the L group. In addition, 68 drip loss quantitative trait loci (QTL) overlapping with 63 DEGs were identified, and these QTL were distributed mainly on chromosomes 1, 2, 5 and 6. Interestingly, the triadin (TRDN) gene, which is involved in muscle contraction and fat deposition, and the myostatin (MSTN) gene, which has a role in muscle growth, were localized to more than two drip loss QTL, suggesting that both are critical candidate genes responsible for drip loss.     

Joint multiple quantitative trait loci mapping for allometries of body compositions and metabolic traits to body weights in broiler

In order to map quantitative trait loci (QTLs) for allometries of body compositions and metabolic traits in chicken, we phenotypically characterize the allometric growths of multiple body components and metabolic traits relative to BWs using joint allometric scaling models and then establish random regression models (RRMs) to fit genetic effects of markers and minor polygenes derived from the pedigree on the allometric scalings. Prior to statistically inferring the QTLs for the allometric scalings by solving the RRMs, the LASSO technique is adopted to rapidly shrink most of marker genetic effects to zero. Computer simulation analysis confirms the reliability and adaptability of the so-called LASSO-RRM mapping method. In the F₂ population constructed by multiple families, we formulate two joint allometric scaling models of body compositions and metabolic traits, in which six of nine body compositions are tested as significant, while six of eight metabolic traits are as significant. For body compositions, a total of 14 QTLs, of which 9 dominant, were detected to be associated with the allometric scalings of drumstick, fat, heart, shank, liver and spleen to BWs; while for metabolic traits, a total of 19 QTLs also including 9 dominant be responsible for the allometries of T4, IGFI, IGFII, GLC, INS, IGR to BWs. The detectable QTLs or highly linked markers can be used to regulate relative growths of the body components and metabolic traits to BWs in marker-assisted breeding of chickens.     

Arsenic Bioaccumulation in Freshwater Fishes

The arsenic ambient water quality criterion (AWQC) for protection of human health via ingestion of aquatic organisms is currently 0.14 μ g/L. This AWQC is derived using a bioconcentration factor (BCF) of 44, which is a consumption-weighted average based on two data points for oysters and fish that was proposed by the U.S. Environmental Protection Agency in 1980 for broad application to freshwater and marine environments. This BCF is based on the assumption that bioaccumulation is a simple linear function of the exposure concentration. In the nearly quarter of a century since this BCF was promulgated, there have been additions to the arsenic bioaccumulation database and a broader scientific understanding of bioaccumulation mechanisms and how they can be applied to estimating tissue concentrations in aquatic organisms. From this database, we identified 12 studies of arsenic bioaccumulation in freshwater fishes in order to explore differences in laboratory-generated BCFs and field-generated bioaccumulation factors (BAFs) and to assess their relationship to arsenic concentrations in water. Our analysis indicates that arsenic concentrations in tissue and arsenic BAFs may be power functions of arsenic concentration in water. A power function indicates that the highest BCF values may occur at low background levels and may decrease as environmental concentrations increase above the ambient range.     

Identification of soil quality indicators for assessing the effect of different tillage practices through a soil quality index in a semi-arid environment

Tillage is known to potentially affect soil quality in various ways. In this study, a soil quality index (SQI) was developed by quantifying several soil attributes either sensitive or insensitive to physical disturbance, using factor analysis as a dimension reduction technique, in order to discriminate different tillage systems. Soil properties including physical (MWD), chemical (pH, organic C, total N, available P and POM contents) and microbial (MBC, MBN, PCM, PNM and three enzymes) parameters were measured to establish a minimum data set (MDS) for the assessment of overall SQI. The soil attributes were determined on samples (0–20 cm depth) collected under moldboard (MP) and disk (DP) plows as conventional tillage (CT), and rotary (RP) and chisel (CP) plows as reduced tillage (RT) systems with a similar plant C input rate and cover crop over a period of six years (2005–2011) in a semi-arid calcareous soil (Calcixerepts) from Central Iran. Results indicated a clear difference in soil quality among the tillage systems with a significant increase of SQI under RT over time, particularly under CP practices. Although RT improved most soil microbial attributes, not all attributes contributed to SQI because of their close interrelationship. The final SQI consisted only of geometric mean of microbial activity (GMA, the square root of the product of PCM and PNM) and geometric mean of enzyme activity (GME, the cube root of the product of enzyme activities). Soil GME and GMA were found to be as key indicators contributing 55% and 36% to SQI, respectively. Therefore, the GME and GMA were the most important indicators effectively discriminating tillage systems, and could be used to monitor the enhancement of soil quality under RT in this semiarid environment. The influence of tillage year on SQI was greater than that of tillage practices. In conclusion, RT systems were characterized by a higher value of SQI, suggesting a good recovery of soil capacity and functions after abandoning CT in the studied area. Smallholder farmers should therefore be aware of the potential for high soil quality in future as a result of continuing RT systems, especially with surface tillage using CP practices.