Identification of the associations of cow feed efficiency with feeding behaviour and milk production is important for supporting recommendations of strategies that optimise milk yield. The objective ...of this study was to identify associations between measures of feed efficiency, feed intake, feeding rate, rumination time, feeding time, and milk production using data collected from 26 dairy cows during a 3 month period in 2018. Cows averaged (mean ± standard deviation) 2.2 ± 1.7 lactations, 128 ± 40 days in milk, 27.5 ± 5.5 kg/day milk, 1.95 ± 0.69 kg feed/1 kg milk—the measure used to express feed conversion ratio (FCR), 575 ± 72 min/day rumination time, and 264 ± 67 min/day feeding time during the observation period. The coefficient of variation for rumination time (min/d) was 12.5%. A mixed linear model was selected for analyses. The most feed inefficient cows with the highest FCR (≥2.6 kg feed/1 kg milk) showed the lowest milk yield (24.8 kg/day), highest feed intake (78.8 kg), highest feeding rate (0.26 kg/min) and BCS (3.35 point). However, the relative milk yield (milk yield per 100 kg of body weight) was the highest (4.01 kg/day) in the most efficient group with the lowest FCR (≤1.4 kg feed/1 kg milk). Our study showed that the most efficient cows with the lowest FCR (≤1.4 kg feed/1 kg milk) had the highest rumination time (597 min/day; p < 0.05), feeding time (298 min/day; p < 0.05), rumination/activity ratio (4.39; p < 0.05) and rumination/feeding ratio (2.04; p < 0.05). Less active cows (activity time 164 min/day; p < 0.05) were the most efficient cows with the lowest FCR (≤1.4 kg feed/1 kg milk). The behavioural differences observed in this study provide new insight into the association of feed behaviour and feed efficiency with milk performance. Incorporating feeding behaviour into the dry matter intake model can improve its accuracy in the future and benefit breeding programmes.
The objective of this study was to evaluate main indicators of milk production on total 60 commercial dairy herds from the Czech Republic during a 9-yr period (2006-2014). Breakeven points and ...sensitivity analysis were used and associations of age at first calving (AFC), milk yield (MY) and calving interval (CI) were analyzed. Lowest AFC≤749 d showed the highest fertility, the lowest death rate of calves and lowest profitability without subsidies -2.49 %. Highest MY≥9,000 kg showed the highest fertility, lowest AFC, lowest feed costs and total costs 8.58 CZK (0.32 EUR) per L of milk and subsequent highest profitability 2.37 %. The analysis of fertility showed that herds with the lowest CI (≤389 d) achieved lowest cow depreciation costs 0.71 CZK (0.03 EUR) per L of milk, highest total costs 9.72 CZK (0.36 EUR) per L of milk and highest profitability 1.29 %. Breakeven points for the price of milk ranged between 7.81 and 8.75 CZK (0.29 and 0.32 EUR) per L in yr 2007 and 2014. Increase in input prices should adversely affect the increase the price of milk. The increase of price of milk by 1 % in 2014 will cause an increase in profit of CZK 745 (27.6 EUR) per cow per year.
Cilj ove studije je vrednovanje glavnih pokazatelja proizvodnje mlijeka kod ukupno šezdeset stada komercijalno uzgajanih muznih krava u Republici Češkoj u razdoblju od devet godina (2006.- 2014.). ...Korištene su točke rentabilnosti i analiza osjetljivosti te je analizirana povezanost između starosti prvog teljenja (SPT), prinosa mlijeka (PM) i intervala između teljenja (IT). Kod najnižeg SPT≤749 d utvrđena je povezanost s najvišom plodnošću, najnižom smrtnošću teladi i najnižom rentabilnosti bez subvencija -2,49 %. Kod najvećeg PM≥9000 kg utvrđena je povezanost s najvišom plodnošću, najnižim SPT, najnižim troškovima za stočnu hranu i ukupnim troškovima 8,58 Kč (0,32 EUR) po litri mlijeka i stoga s najvišom rentabilnosti 2,37 %. Analiza plodnosti pokazala je da su stada s najnižim IT (≤389 d) postigla najniži pad vrijednosti krava u iznosu od 0,71 Kč (0,03 EUR) po litri mlijeka, najviše ukupne troškove 9,72 Kč (0,36 EUR) po litri mlijeka i najvišu rentabilnost 1,29 %. Točke rentabilnosti cijene mlijeka kretale su se između 7,81 i 8,75 Kč (0,29 i 0,32 EUR) po litri mlijeka u razdoblju od 2007. do 2014. Povećanje ulaznih cijena trebalo bi imati negativan utjecaj na povećanje cijene mlijeka. Povećanje cijene mlijeka za 1 % u 2014. godini uzrokovat će povećanje dobiti za 745 Kč (27,6 EUR) po kravi godišnje.
Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state ...of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.
This article identifies the essential technologies and considerations for the development of an Automated Cow Monitoring System (ACMS) which uses 3D camera technology for the assessment of Body ...Condition Score (BCS). We present a comparison of a range of common techniques at the different developmental stages of Computer Vision including data pre-processing and the implementation of Deep Learning for both 2D and 3D data formats commonly captured by 3D cameras. This research focuses on attaining better reliability from one deployment of an ACMS to the next and proposes a Geometric Deep Learning (GDL) approach and evaluating model performance for robustness from one farm to another in the presence of background, farm, herd, camera pose and cow pose variabilities.
The objective of this study was to calculate the breeding values (BVs) of traits missing in a selection index. Different traits can be evaluated within the breeding programs of given countries. The ...BV of a trait can be calculated based on genetic correlations with other traits. Similarly, the BV of a missing trait can be calculated for imported bulls. Two methods of calculation were used. Method A was based on a regression of BVs. Method B was based on performing a de-regression of BVs and their retroactive calculation. Both of these methods were tested using a Czech and a Canadian database of BVs for Holstein bulls. The Czech database of Holstein bulls contained 766 bulls and the Canadian database 851. Two calculations were performed for bulls with low reliability of estimated BVs, the first calculation with their genetic correlation matrix and the second with a genetic correlation matrix created from a set of bulls with high reliability of BVs. These newly calculated BVs (CBVs) were then compared with the national BVs (NBVs) using correlation coefficients. The highest correlations were achieved with high reliability bulls when all traits were included into the calculation (34 evaluated traits). The correlations of these bulls averaged 0.82, with an average standard deviation of 0.19. The lowest correlations were found when low reliability bulls were included and the genetic correlation matrix from the high reliability bulls was applied. That average correlation was 0.74 and standard deviation 0.25. When only 15 traits were evaluated in the model, the average correlation for all sets was 0.68 with standard deviation of 0.28. These results show that calculating the BV of a missing trait is possible using both methods. Method B was slightly more accurate in its prediction.
Fine-Grained Change Detection and Regression Analysis are essential in many applications of Artificial Intelligence. In practice, this task is often challenging owing to the lack of reliable ground ...truth information and complexity arising from interactions between the many underlying factors affecting a system. Therefore, developing a framework which can represent the relatedness and reliability of multiple sources of information becomes critical. In this paper, we investigate how techniques in multi-task metric learning can be applied for the regression of fine-grained change in real data. The key idea is that if we incorporate the incremental change in a metric of interest between specific instances of an individual object as one of the tasks in a multi-task metric learning framework, then interpreting that dimension will allow the user to be alerted to fine-grained change invariant to what the overall metric is generalised to be. The techniques investigated are specifically tailored for handling heterogeneous data sources, i.e. the input data for each of the tasks might contain missing values, the scale and resolution of the values is not consistent across tasks and the data contains non-independent and identically distributed (non-IID) instances. We present the results of our initial experimental implementations of this idea and discuss related research in this domain which may offer direction for further research.
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