The potential of microwave and ultrasound was evaluated for the pretreatment of brewer's spent grain (BSG). Under optimal conditions of microwave and ultrasound pretreatments, reducing sugar yields per 1 g of pretreated BSG were 64.4 ± 7 mg and 39.9 ± 6 mg, respectively. Subsequently, the pretreated BSG was evaluated as a substrate for production of Xylanopectinolytic enzymes using fungi isolated from spoiled fruits. Out of twenty-nine (29) isolates recovered, Mucor sp. (AB1) isolated from Bramley apple (Malus domestica) produced xylanopectinolytic enzymes with higher specific activity, and was selected for further studies. The highest enzyme activity (137 U/g, and 67 U/g BSG, for pectinase and xylanase, respectively) was achieved in a medium that contained 15 g of BSG, at pH 6, temperature of 30 °C, supplemented with 1% xylan or pectin for inducing the production of xylanase or pectinase, respectively. The partially purified xylanopectinolytic enzymes were optimally active at 60 °C and pH 5.

Sequential heating processes are commonly applied to milk by the dairy industry as part of their microbiological control strategy. Often pasteurisation at 72 °C is followed by a sequential high heat treatment step of up to 125 °C; however, such severe heat treatment can lead to reduced protein quality. Nine temperature combinations (80–90 °C) were evaluated to assess microbial reduction and whey protein nitrogen index values during pilot scale milk processing. A total of 110 bacterial isolates were identified to species level by 16S rDNA sequencing, with Bacillus licheniformis identified as the dominant species. While the experimental treatments did not achieve microbial reductions comparable with the control heating process, the results of this study provide a benchmark for milk processors relative to the effects of sequential heat treatments on milk and their impact on the survival of both thermally resistant microbial populations and thermally labile milk components during processing.

This article explores the potential of a farm technology to simultaneously improve farm efficiency and provide wider environmental and social benefits. Identifying these ‘win-win-win’ strategies and encouraging their widespread adoption is critical to achieve sustainable intensification. Using a nationally representative sample of 296 Irish dairy farms from 2015, propensity score matching is applied to measure the impact of milk recording on a broad set of farm sustainability indicators. The findings reveal that the technology enhances economic sustainability by increasing dairy gross margin and milk yield per cow. Furthermore, social sustainability is improved through a reduction in milk bulk tank somatic cell count (an indicator of animal health and welfare status). Conversely, milk recording (as it is currently implemented) does not impact farm environmental sustainability, represented by greenhouse gas emission efficiency. While the study shows that milk recording is a ‘win-win’ strategy, ways of improving current levels of utilisation are discussed so that milk recording achieves its ‘win-win-win’ potential in the future.

Currently, nitrogen fertilizers are utilized to meet 48% of the total global food demand. The demand for nitrogen fertilizers is expected to grow as global populations continue to rise. The use of nitrogen fertilizers is associated with many negative environmental impacts and is a key source of greenhouse and harmful gas emissions. In recent years, urease and nitrification inhibitors have emerged as mitigation tools that are presently utilized in agriculture to prevent nitrogen losses and reduce greenhouse and harmful gas emissions that are associated with the use of nitrogen-based fertilizers. Both classes of inhibitor work by different mechanisms and have different physiochemical properties. Consequently, each class must be evaluated on its own merits. Although there are many benefits associated with the use of these inhibitors, little is known about their potential to enter the food chain, an event that may pose challenges to food safety. This phenomenon was highlighted when the nitrification inhibitor dicyandiamide was found as a residual contaminant in milk products in 2013. This comprehensive review aims to discuss the uses of inhibitor technologies in agriculture and their possible impacts on dairy product safety and quality, highlighting areas of concern with regards to the introduction of these inhibitor technologies into the dairy supply chain. Furthermore, this review discusses the benefits and challenges of inhibitor usage with a focus on EU regulations, as well as associated health concerns, chemical behavior, and analytical detection methods for these compounds within milk and environmental matrices.

Background: Carbohydrate-rich (CR) foods are essential parts of the Chinese diet. However, CR foods are often given less attention than animal-based foods. The objectives of this study were to analyze the carbon emissions caused by CR foods and to generate sustainable diets with low climate impact and adequate nutrients. Methods: Twelve common CR food consumption records from 4857 individuals were analyzed using K-means clustering algorithms. Furthermore, linear programming was used to generate optimized diets. Results: Total carbon emissions by CR foods was 683.38g CO2eq per day per capita, accounting for an annual total of 341.9Mt CO2eq. All individuals were ultimately divided into eight clusters, and none of the popular clusters were low carbon or nutrient sufficient. Optimized diets could reduce about 40% of carbon emissions compared to the average current diet. However, significant structural differences exist between the current diet and optimized diets. Conclusions: To reduce carbon emissions from the food chain, CR foods should be a research focus. Current Chinese diets need a big change to achieve positive environmental and health goals. The reduction of rice and wheat-based foods and an increase of bean foods were the focus of structural dietary change in CR food consumption.

The objective of the current study was to examine phenotypic fertility performance and survival, and to gain insight into underlying factors that may contribute to greater fertility performance in 2 divergent genetic groups (GG) of Holstein-Friesian, selected using the Irish Economic Breeding Index (EBI). The GG were evaluated across 3 spring calving pasture-based feeding treatments (FT) over 4 yr. The 2 divergent GG were (1) high EBI; representative of the top 5% nationally (elite), and (2) EBI representative of the national average (NA). In each year, 90 elite and 45 NA cows were randomly allocated to 1 of 3 FT: control, lower grass allowance, and high concentrate. No interaction between GG and FT was observed for any of the measures of fertility investigated. The elite cows achieved significantly greater pregnancy rate to first service (+14.9 percentage points), and significantly greater pregnancy rates after 21, 42, and 84 d of breeding (+17.3, +15.2, and +9.6 percentage points, respectively) compared with NA. The number of services per cow was fewer for elite (1.57) compared with NA (1.80). The interval from mating start date to pregnancy was significantly shorter for elite cows compared with NA. The elite cows maintained greater mean body condition score than NA throughout the study (2.91 vs. 2.72), and had greater body condition score at calving, artificial insemination, and drying off compared with NA. The elite cows had greater mean circulating concentrations of insulin-like growth factor-1 compared with NA. No significant effect was observed of GG on commencement of luteal activity, or progesterone profile variables. Greater survival to the start of fifth lactation was observed for elite cows. The elite cows were 43% less likely to be culled than NA by the beginning of the fifth lactation. The results highlight the success of the Economic Breeding Index to deliver reproductive performance and longevity consistent with industry targets across a range of seasonal pasture-based FT. The results also clearly demonstrate the potential of appropriate genetic selection to reverse negative fertility trends incurred during previous decades of selection for milk production alone.

This report contains the maps and statistics for this project.

Could we be missing a trick here? Could silvopasture be a design solution to the environmental challenges facing farming? Can it be the ideal mechanism to combine agriculture, forestry and ecology with very positive outcomes for farmers? Well -designed silvopasture can help increase profits and productivity, animal, and soil health, diversify the farm business, buffer against increasingly variable weather, drought and flood risks while benefiting the environment, the water cycle and the carbon cycle.

The net benefit from investing in any technology is a function of the cost of implementation and the expected return in revenue. The objective of the present study was to quantify, using deterministic equations, the net monetary benefit from investing in genotyping of commercial females. Three case studies were presented reflecting dairy cows, beef cows and ewes based on Irish population parameters; sensitivity analyses were also performed. Parameters considered in the sensitivity analyses included the accuracy of genomic evaluations, replacement rate, proportion of female selection candidates retained as replacements, the cost of genotyping, the sire parentage error rate and the age of the female when it first gave birth. Results were presented as an annualised monetary net benefit over the lifetime of an individual, after discounting for the timing of expressions. In the base scenarios, the net benefit was greatest for dairy, followed by beef and then sheep. The net benefit improved as the reliability of the genomic evaluations improved and, in fact, a negative net benefit of genotyping was less frequent when the reliability of the genomic evaluations was high. The impact of a 10% point increase in genomic reliability was, however, greatest in sheep, followed by beef and then dairy. The net benefit of genotyping female selection candidates reduced as replacement rate increased. As genotyping costs increased, the net benefit reduced irrespective of the percentage of selection candidates kept, the replacement rate or even the population considered. Nonetheless, the association between the genotyping cost and the net benefit of genotyping differed by the percentage of selection candidates kept. Across all replacement rates evaluated, retaining 25% of the selection candidates resulted in the greatest net benefit when genotyping cost was low but the lowest net benefit when genotyping cost was high. Genotyping breakeven cost was non-linearly associated with the percentage of selection candidates retained, reaching a maximum when 50% of selection candidates were retained, irrespective of replacement rate, genomic reliability or the population. The genotyping breakeven cost was also non-linearly associated with replacement rate. The approaches outlined within provide the back-end framework for a decision support tool to quantify the net benefit of genotyping, once parameterised by the relevant population metrics.

Lactoferrin (LF) is a glycoprotein naturally present in milk. Its content varies throughout lactation, but also with mastitis; therefore it is a potential additional indicator of udder health beyond somatic cell count. Condequently, there is an interest in quantifying this biomolecule routinely. First prediction equations proposed in the literature to predict the content in milk using milk mid-infrared spectrometry were built using partial least square regression (PLSR) due to the limited size of the data set. Thanks to a large data set, the current study aimed to test 4 different machine learning algorithms using a large data set comprising 6,619 records collected across different herds, breeds, and countries. The first algorithm was a PLSR, as used in past investigations. The second and third algorithms used partial least square (PLS) factors combined with a linear and polynomial support vector regression (PLS + SVR). The fourth algorithm also used PLS factors, but included in an artificial neural network with 1 hidden layer (PLS + ANN). The training and validation sets comprised 5,541 and 836 records, respectively. Even if the calibration prediction performances were the best for PLS + polynomial SVR, their validation prediction performances were the worst. The 3 other algorithms had similar validation performances. Indeed, the validation root mean squared error (RMSE) ranged between 162.17 and 166.75 mg/L of milk. However, the lower standard deviation of cross-validation RMSE and the better normality of the residual distribution observed for PLS + ANN suggest that this modeling was more suitable to predict the LF content in milk from milk mid-infrared spectra (R2v = 0.60 and validation RMSE = 162.17 mg/L of milk). This PLS +ANN model was then applied to almost 6 million spectral records. The predicted LF showed the expected relationships with milk yield, somatic cell score, somatic cell count, and stage of lactation. The model tended to underestimate high LF values (higher than 600 mg/L of milk). However, if the prediction threshold was set to 500 mg/L, 82% of samples from the validation having a content of LF higher than 600 mg/L were detected. Future research should aim to increase the number of those extremely high LF records in the calibration set.