Influence of the Ratio of Sheep and Cow Milk on the Composition and Yield Efficiency of Lećevački Cheese

Lećevački cheese is a traditional Croatian hard cheese, which belongs to the group of hard Mediterranean cheeses produced from mixed milk (cow and sheep). The aim of this research was to determine the influence of different ratios and compositions of sheep milk on the composition and yield of Lećevački cheese. A total of 15 batches of Lećevački cheese were selected containing different ratios of sheep and cow milk from the regular production of a dairy plant. The ratio of sheep milk was as follows: up to 39%, from 40 to 44%, and from 45 to 50%. For each ratio, five batches were randomly selected. A higher ratio of sheep milk caused a noticeable increase in fat, protein, lactose, and total solids content, while the content of solids-not-fat significantly (p < 0.05) increased. A similar trend was found for casein content (p < 0.1). The highest ratio of sheep milk in mixed milk increased (p < 0.05) the protein content by almost 1%. However, the results showed that it is not reasonable to increase the sheep milk ratio in mixed milk above 44% (v/v) because it causes a higher (p < 0.01) moisture content in the cheese, as well as a lower fat content (p < 0.01) and fat recovery (p = 0.07) during the manufacturing of Lećevački cheese.

Sequential Enzymatic Digestion of Different Cartilage Tissues: A Rapid and High-Efficiency Protocol for Chondrocyte Isolation, and Its Application in Cartilage Tissue Engineering

Objective The classic chondrocyte isolation protocol is a 1-step enzymatic digestion protocol in which cartilage samples are digested in collagenase solution for a single, long period. However, this method usually results in incomplete cartilage dissociation and low chondrocyte quality. In this study, we aimed to develop a rapid, high-efficiency, and flexible chondrocyte isolation protocol for cartilage tissue engineering. Design Cartilage tissues harvested from rabbit ear, rib, septum, and articulation were minced and subjected to enzymatic digestion using the classic protocol or the newly developed sequential protocol. In the classic protocol, cartilage fragments were subjected to one 12-hour digestion. In the sequential protocol, cartilage fragments were sequentially subjected to 2-hour first digestion, followed by two 3-hour digestions. The collected cells were then subjected to analyses of cell-yield efficiency, viability, proliferation, phenotype, and cartilage matrix synthesis capacity Results Overall, the sequential protocol exhibited higher cell-yield efficiency than the classic protocol for the 4 cartilage types. The cells harvested from the second and third digestions demonstrated higher cell viability, more proliferative activity, a better chondrocyte phenotype, and a higher cartilage-specific matrix synthesis ability than those harvested from the first digestion and after the classic 1-step protocol. Conclusions The sequential protocol is a rapid, flexible, high-efficiency chondrocyte isolation protocol for different cartilage tissues. We recommend using this protocol for chondrocyte isolation, and in particular, the cells obtained after the subsequent 3-hour sequential digestions should be used for chondrocyte-based therapy.

Bioconversion of Lignocellulosic Biomass into Value Added Products under Anaerobic Conditions: Insight into Proteomic Studies

Production of biofuels and other value-added products from lignocellulose breakdown requires the coordinated metabolic activity of varied microorganisms. The increasing global demand for biofuels encourages the development and optimization of production strategies. Optimization in turn requires a thorough understanding of the microbial mechanisms and metabolic pathways behind the formation of each product of interest. Hydrolysis of lignocellulosic biomass is a bottleneck in its industrial use and often affects yield efficiency. The accessibility of the biomass to the microorganisms is the key to the release of sugars that are then taken up as substrates and subsequently transformed into the desired products. While the effects of different metabolic intermediates in the overall production of biofuel and other relevant products have been studied, the role of proteins and their activity under anaerobic conditions has not been widely explored. Shifts in enzyme production may inform the state of the microorganisms involved; thus, acquiring insights into the protein production and enzyme activity could be an effective resource to optimize production strategies. The application of proteomic analysis is currently a promising strategy in this area. This review deals on the aspects of enzymes and proteomics of bioprocesses of biofuels production using lignocellulosic biomass as substrate.

Effect of plant bio-regulators on photosynthesis, growth and yield efficiency of pear trained on Y-trellis system

‘Patharnakh’ and ‘Punjab Beauty’ are major pear cultivars grown under the subtropical conditions of northwestern India. These pear cultivars tend to put up profuse vegetative growth under the high density planting that leads to excessive shading of bearing zone of canopy. To facilitate light penetration into inner plant canopy, there is a need to contrive an efficient approach for vegetative growth management. The foliar applications of varying concentrations of plant bio-regulators viz. Prohexadione calcium (Pro-Ca) (100, 200, 400 mg L-1) and Paclobutrazol (PBZ) (100, 250, 500 mg L-1) were done at 10 days after full bloom (DAFB) ‘Patharnakh’ and ‘Punjab Beauty’ pear plants trained on the Y-trellis training system. Shoot length, leaf area index and trunk cross-sectional areas (TCSA) were greatly reduced by Pro-Ca200 and 400 mg L-1 concentrations. Treatments enhanced light penetration and photosynthesis. Fruit weight improved with plant bio-regulator treatments resulting in increased yield efficiency of pear plants. This study exhibited a strong positive correlation between light availability and yield efficiency of pear plants.

Early Performance of Several Prunus Interspecific Hybrid Rootstocks for Redhaven Peach in Southern Ontario

A multi-year orchard experiment was established to measure the performance of Rootpac®-R, Rootpac®-20, Rootpac®-40, and Rootpac®-70 rootstocks using ‘Redhaven’ peach (Prunus persica var. persica) as the scion, and compared with the Bailey peach seedling rootstock, the current industry standard. Tree survival after five years was 79% on Rootpac-40, whereas the remaining rootstocks showed no tree mortality. Tree vigour and canopy height and width were influenced by rootstock genotype beginning the year of planting in 2016. For the first five years of production, Rootpac-70 consistently produced the largest trees based on TCSA and by year five, all rootstocks produced trees with similar TCSAs, except for Rootpac-70, which was 38% larger than Bailey. By year five, cumulative yields were greatest on Rootpac-70, which were 10% higher than Bailey; cumulative yields of Rootpac-R, Rootpac-20, and Rootpac-30 were 98%, 89%, and 84% that of Bailey, respectively. Cumulative yield efficiency was significantly influenced by rootstock although the magnitude of the differences was small and likely of insignificant commercial importance. Rootpac-40 consistently produced the largest fruit. These results are only reflective of the orchard establishment years and additional data are required before peach producers can make fully informed decisions concerning the rootstocks evaluated in this study for their orchard systems. However, at this juncture, all the Rootpac rootstocks evaluated in this study are likely to impart excessive vigour to be used in a higher density system and offer little advantage over Bailey.

Effects of Par on Plant Spacing in Relation to Yield Efficiency in Peach

Effects of PAR in relation with plant spacing in peach cultivar ‘Shan-i-Punjab’ during the year 2013-14 and 2014-15 were evaluated. The trees were trained to 4 different training systems and each system consisted of 2 spacings viz., 5 × 3 m and 5 × 2 m. Daily PAR was recorded at morning, midday and evening and it was found that irrespective of planting distance maximum light interception was recorded at midday hours. Plants spaced at 5 × 3m received more light inside the canopy during full day which directly influenced number of fruits per tree, number of picking, fruiting density, yield efficiency, relative pattern of fruit maturity whereas, fruit yield per hectare was maximum in 5 × 2 m. Bangladesh J. Bot. 50(3): 701-707, 2021 (September)

Rootstock Affects the Fruit Quality of ‘Early Bigi’ Sweet Cherries

The cherry rootstock influences the performance of the scion cultivar. It has an effect on cherry fruit quality, tree growth, yield and yield efficiency and floral and foliar nutrition. In this work, the influence of Saint Lucie 64 and Maxma 60 rootstocks on the fruit quality traits of cv. Early Bigi was evaluated. For this, several parameters, namely fruit weight (FW) and size (FS), soluble solids content (SSC), pH, titratable acidity (TA), flesh firmness (FF), epidermis rupture force (ERF), color and sensory profile, were assessed. Results showed that the fruits from trees on Saint Lucie 64 presented higher FF and ERF values and, consequently, better texture. On the other hand, fruits from trees on Maxma 60 showed sweeter cherries (higher SSC). Moreover, these trees presented the darkest cherries (lower values of L*, a*, b*, C* and hue°) and the highest SSC. Therefore, although the trees on Saint Lucie 64 produced firmer cherries, it was those with the Maxma 60 rootstock that produced sweeter and darker fruits. In conclusion, both scion–rootstock combinations proved to be good options for the region of Resende.

Design of ANN Based Machine Learning Method for Crop Prediction

In agriculture, crop yield estimation is critical. Remote sensing is being used in farming systems to increase yield efficiency and lower operating costs. Remote sensing-based strategies, on the other hand, necessitate extensive processing, necessitating the use of machine learning models for crop yield prediction. Descriptive analytics is a form of analytics that is used to accurately estimate crop yields. This paper discusses the most recent research on machine learning-based strategies for efficient crop yield prediction. In general, the training model's accuracy should be higher, and the error rate should be low. As a result, significant effort is being put forward to propose a machine learning technique that will provide high precision in crop yield prediction.