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Afikim, Israel

Afimilk Inc. | Entity website

AfiFarm integratesinput from automatic sensors, a comprehensive cow data baseand automated operation devices. Afimilk Silent Herdsman (neck based sensors) and AfiAct II (leg based sensors) Operating 24/7, these automated cow monitoring and heat detection systems will become the most effective worker on the farm ...

Afimilk Inc. | Entity website

IDeal animal identification concepts Data integrity is a primary condition for reliance on automation systems for management decisions. In the hands of a good manager, a trustworthy system is a powerful management tool, yet systems allowing low quality data may cause more harm than good ...

Katz G.,Afimilk Inc. | Merin U.,Afimilk Inc. | Bezman D.,Afimilk Inc. | Lavie S.,Afimilk Inc. | And 2 more authors.
Journal of Dairy Science

Cheese was produced in a series of experiments from milk separated in real time during milking by using the Afilab MCS milk classification service (Afikim, Israel), which is installed on the milk line in every stall and sorts milk in real time into 2 target tanks: the A tank for cheese production (CM) and the B tank for fluid milk products (FM). The cheese milk was prepared in varying ratios ranging from ~10:90 to ~90:10 CM:FM by using this system. Cheese was made with corrected protein-to-fat ratio and without it, as well as from milk stored at 4°C for 1, 2, 3, 4, and 8 d before production. Cheese weight at 24 h increased along the separation cutoff level with no difference in moisture, and dry matter increased. The data compiled allowed a theoretical calculation of cheese yield and comparing it to the original van Slyke equation. Whenever the value of Afi-Cf, which is the optical measure of curd firmness obtained by the Afilab instrument, was used, a better predicted level of cheese yield was obtained. In addition, 27 bulk milk tanks with milk separated at a 50:50 CM:FM ratio resulted in cheese with a significantly higher fat and protein, dry matter, and weight at 24 h. Moreover, solids incorporated from the milk into the cheese were significantly higher in cheeses made of milk from A tanks. The influence of storage of milk up to 8 d before cheese making was tested. Gross milk composition did not change and no differences were found in cheese moisture, but dry matter and protein incorporated in the cheese dropped significantly along the storage time. These findings confirm that storage of milk for several days before processing is prone to physico-chemical deterioration processes, which result in loss of milk constituents to the whey and therefore reduced product yield. The study demonstrates that introducing the unknown parameters for calculating the predicted cheese yield, such as the empiric measured Afi-Cf properties, are more accurate and the increase in cheese yield is more than increasing just the protein level, the value that is being tested by the dairies, or even casein. © 2016 American Dairy Science Association. Source

Katz G.,Afimilk Inc. | Bezman D.,Afimilk Inc. | Lemberskiy-Kuzin L.,Afimilk Inc. | Merin U.,Afimilk Inc. | Leitner G.,Kimron Veterinary Institute
Precision Livestock Farming 2015 - Papers Presented at the 7th European Conference on Precision Livestock Farming, ECPLF 2015

Cheese making economics is maximizing yields and quality through efficient recovery of milk constituents and minimizing constituent's losses in the whey. Raw milk efficiency for cheese manufacturing is determined mainly by the level of milk constituents and its coagulation properties, i.e., rennet clotting time (RCT) and curd firmness (CF). These are influenced by many factors such as genetics, diet, lactation stage, parity, environment and health. These factors vary between cows, during lactation, between milking sessions and during a single milking of an individual cow. A new approach for in-farm control of bulk tank milk properties for cheese manufacturing is presented. Afilab-milk spectrometer, evaluating coagulation properties in real time during the milking, channeling each pull of milk into one of two different bulk tanks depending on predetermined required traits. The distinction of the milk and designation of tank is derived to from the dairy's required quotas for different products. The potential and optimization for commercial cheese production through employment of the Afilab real time milk channeling was studied for 9 months within a commercial dairy herd in Israel, totaling ∼100 Holstein cows. During the milking session, coagulation properties for each cow were analyzed in real time and the milk was separated into the two bulk milk tanks accordingly. At the dairy, several products were manufactured to compare yields, quality and texture between undesignated milk and product made from designated milk. Higher cheese yield achieved by processing milk separated in the farm based on its coagulation properties. Source

Leitner G.,Kimron Veterinary Institute | Lavon Y.,Israel Cattle Breeders Association | Matzrafi Z.,Ruppin Academic Center | Benun O.,Ruppin Academic Center | And 2 more authors.
International Dairy Journal

This study focused on the need for bulk milk tank somatic cell count (BMTSCC) thresholds and cut-off levels indicating a decrease in milk quality that consequently influences product quantity and quality. First, 226 ewes and 231 goat bulk tank milk samples were collected from different Israeli herds and coagulation properties were determined. Second, soft cheese was produced. No correlation of coagulation properties was found with BMTSCC for sheep milk up to 3264 × 103 and goat milk up to 6452 × 103 cells mL-1. Coagulation properties of goat milk with cell count higher than the latter resulted in a significant decrease in curd firmness. For breeds and management system in Israel, 2500 × 103 cells mL-1 is suggested as the cut-off level for sheep and 3500 × 103 cell mL-1 for goats. The cell count cut-off level and milk price according to BMTSCC should be tested and then determined for every breed and management and final dairy product. © 2015 Elsevier Ltd. Source

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