Putting fear in its place: remapping of hippocampal place cells during fear conditioning

We recorded hippocampal place cells in two spatial environments: a training environment in which rats underwent fear conditioning and a neutral control environment. Fear conditioning caused many place cells to alter ( or remap) their preferred firing locations in the training environment, whereas most cells remained stable in the control environment. This finding indicates that aversive reinforcement can induce place cell remapping even when the environment itself remains unchanged. Furthermore, contextual fear conditioning caused significantly more remapping of place cells than auditory fear conditioning, suggesting that place cell remapping was related to the rat's learned fear of the environment. These results suggest that one possible function of place cell remapping may be to generate new spatial representations of a single environment, which could help the animal to discriminate among different motivational contexts within that environment

Cumulative dairy cow genetic change from selection and crossbreeding over the last 2 decades in New Zealand closely aligns to model-based predictions published in 2000

A deterministic model was developed in 1998 to evaluate the concurrent effects of selection and crossbreeding on the rate of genetic gain and increases in productivity of New Zealand dairy cattle over the ensuing 25-yr period. The predictions of today's breed composition of the national dairy herd and genetic trends for body weight and lactation yields of milk, fat, and protein are compared with today's actual values. Selection was assumed to use an index that included live weight and lactation yields of milk, fat, and protein. Mating strategies involving the Holstein-Friesian (F), Jersey (J), and Ayrshire (A) breeds were evaluated. Effects of heterosis and age were included to calculate phenotypic live weight and yields of milk, fat, and protein per cow. At the time the model was developed, New Zealand had an across-breed evaluation system, but only straightbred bulls were used after progeny testing based on records of straightbred and crossbred daughters. The model predicted that if crossbred cows and bulls could be considered as bull parents, faster rates of genetic gain may result because of increased selection intensities in the cow to breed bull selection pathway. This scenario transpired, and the best bulls and cows for farm profit were used regardless of breed. Under that mating strategy for the 2018 birthyear, the model predicted the national breed composition would be 11% F, 34% J, 52% F×J, and 2% A; the actual breed composition was 36% F, 9% J, 53% F×J, and 1% A. The model-predicted annual genetic gains would be 16.7 L of milk, 1.2 kg of fat, 1.5 kg of protein, and −0.7 kg of body weight; the realized annual improvements were 13.6 L of milk, 1.31 kg of fat, 1.17 kg of protein, and −0.36 kg of body weight. Predicted long-term responses to selection can closely mirror realized improvements, confirming the value of modeling to inform animal breeding decision making

Optimization of Profit for Pasture-Based Beef Cattle and Sheep Farming Using Linear Programming: Model Development and Evaluation

A linear programming optimization tool is useful to assist farmers with optimizing resource allocation and profitability. This study developed a linear programming profit optimization model with a silage supplement scenario. Utilizable kilograms of pasture dry matter (kg DM) of the total pasture mass was derived using minimum and maximum pasture mass available for beef cattle and sheep and herbage utilization percentage. Daily metabolizable energy (MJ ME/head) requirements for the various activities of beef cattle and sheep were estimated and then converted to kg DM/head on a bi-monthly basis. Linear programming was employed to identify the optimum carrying capacity of beef cattle and sheep, the most profitable slaughtering ages of beef cattle, the number of prime lambs (sold to meat processing plants), and sold store lambs (sold to other farmers for finishing). Gross farm revenue (GFR) and farm earnings before tax (EBT) per hectare and per stock unit, as well as total farm expenditure (TFE), were calculated and compared to the average value of Taranaki-Manawatu North Island intensive finishing sheep and beef Class 5 farming using Beef and Lamb New Zealand (B+LNZ) data. The modeled farm ran 46% more stock units (a stock unit consumed 550 kg DM/year) than the average value of Class 5 farms. At this stocking rate, 83% of the total feed supplied for each species was consumed, and pasture supplied 95% and 98% of beef cattle and sheep feed demands respectively. More than 70% of beef cattle were finished before the second winter. This enabled the optimized system to return 53% and 188% higher GFR/ha and EBT/ha, respectively, compared to the average values for a Class 5 farm. This paper did not address risk, such as pasture growth and price fluctuations. To understand this, several additional scenarios could be examined using this model. Further studies to include alternative herbages and crops for feed supply during summer and winter are required to expand the applicability of the model for different sheep and beef cattle farm systems.falsearticle-number: 52

Rumen Development of Artificially-Reared Lambs Exposed to Three Different Rearing Regimens

(c) The Author/sPublishe

Optimization of Profit for Pasture-Based Beef Cattle and Sheep Farming Using Linear Programming: Young Beef Cattle Production in New Zealand

In New Zealand, surplus dairy-origin calves not needed as replacement or for beef cattle farms requirements for finishing are commercially slaughtered within two weeks of age. This system has perceived ethical issues which can potentially negatively affect the dairy industry. Therefore, a young beef cattle production system to maximize the use of excess calves within the land size constraint is considered as an alternative to a traditional 18 to 33-months slaughtering system. The current study examined the effects of young beef cattle production with slaughter ages at 8 to 14 months on pasture utilization, farm profitability and selling policy on class 5, intensive finishing sheep and beef cattle farms in New Zealand. A linear programming model that had previously been developed for this farm class (optimized traditional beef cattle system) was modified to include a young beef cattle slaughter system and identified the carrying capacity for young and traditional beef cattle and the selling policy required to optimize pasture utilization and farm profitability. Systems with young beef cattle slaughtered at 8, 10, 12 or 14-months of age were simulated without (Scenario I) or with (Scenario II) decreasing the number of traditional beef cattle. Daily per head energy demand for maintenance and live weight change was estimated and converted to kg DM/head on a bimonthly basis. Carcasses from young beef cattle were processed as one class under manufacturing beef price (NZ$4.50). The modified young and traditional beef cattle slaughtering system maintained an extra 6% and 35% beef cattle in Scenario I and Scenario II respectively, and finished 90% and 84% of traditional beef cattle before the second winter. Pasture supplied 98% of the feed demand for the beef cattle activities and 79–83% of that was consumed. Mixed young and traditional beef cattle finishing scenarios returned 2% less gross farm revenue per hectare (GFR/ha). However, earnings before tax per hectare (ETB/ha) in Scenario I and Scenario II were 15–25% greater than that of the optimized traditional beef cattle system, respectively. Young beef cattle production increased pasture utilization and farm profitability and increased selling options for finished beef cattle. Therefore, the young beef cattle system is a viable option for farmers and will help to reduce the need to slaughter calves within two weeks of age

Growth and Body Composition of Artificially-Reared Lambs Exposed to Three Different Rearing Regimens.

(c) The Author/sThis study was designed to investigate the influence of pellet fibre level, milk replacer composition and age at weaning on growth and body composition of lambs reared artificially. Romney ram lambs were randomly allocated to one of three rearing treatments; HFP57: commercial milk replacer to 57 days of age, and high fibre concentrate pellets; HFP42: commercial milk replacer with early weaning at 42 days of age, and high fibre concentrate pellets; LFP42: high protein milk replacer from 2-16 days of age followed by commercial milk replacer with early weaning at 42 days of age, and low fibre concentrate pellets. Lambs were slaughtered at 57 days of age. Overall average daily liveweight gain of lambs did not differ (p > 0.05) between treatments. Dressing out percentage, carcass weight, empty small intestine and omental fat were higher (p < 0.05) in HFP57 than in both HFP42 and LFP42 lambs. HFP42 and LFP42 lambs had heavier (p < 0.05) empty rumen weights. Whole body protein content was higher (p < 0.05) in HFP42 lambs compared to both HFP57 and LFP42 lambs. Fat content and daily fat deposition were greater (p < 0.05) in HFP57 lambs than HFP42 and LFP42 lambs. Weaning lambs at 42 days of age with provision of either low or high fibre concentrate pellets, resulted in similar growth rates, reduced whole body fat deposition and was a more cost-effective rearing regimen.Published onlin

Application of Machine Learning Algorithms to Predict Body Condition Score from Liveweight Records of Mature Romney Ewes

Publishe

Predicting ewe body condition score using adjusted liveweight for conceptus and fleece weight, height at withers, and previous body condition score record

The relationship between ewe body condition score (BCS) and liveweight (LW) has been exploited previously to predict the former from LW, LW-change, and previous BCS records. It was hypothesized that if fleece weight and conceptus-free liveweight and LW-change, and in addition, height at withers were used, the accuracy of current approaches to predicting BCS would be enhanced. Ewes born in 2017 (n = 429) were followed from 8 mo to approximately 42 mo of age in New Zealand. Individual ewe data were collected on LW and BCS at different stages of the annual production cycle (i.e., prebreeding, at pregnancy diagnosis, prelambing, and weaning). Additionally, individual lambing dates, ewe fleece weight, and height at withers data were collected. Linear regression models were fitted to predict current BCS at each ewe age and stage of the annual production cycle using two LW-based models, namely, unadjusted for conceptus weight and fleece weight (LW alone1) and adjusted (LW alone2) models. Furthermore, another two models based on a combination of LW, LW-change, previous BCS, and height at withers (combined models), namely, unadjusted (combined1) and adjusted for conceptus and fleece weight (combined2), were fitted. Combined models gave more accurate (with lower root mean square error: RMSE) BCS predictions than models based on LW records alone. However, applying adjusted models did not improve BCS prediction accuracy (or reduce RMSE) or improve model goodness of fit (R2) (P > 0.05). Furthermore, in all models, both LW-alone and combined models a great proportion of variability in BCS, could not be accounted for (0.25 ≥ R2 ≥ 0.83) and there was substantial prediction error (0.33 BCS ≥ RMSE ≥ 0.49 BCS) across age groups and stages of the annual production cycle and over time (years). Therefore, using additional ewe data which allowed for the correction of LW for fleece and conceptus weight and using height at withers as an additional predictor did not improve model accuracy. In fact, the findings suggest that adjusting LW data for conceptus and fleece weight offer no additional value to the BCS prediction models based on LW. Therefore, additional research to identify alternative methodologies to account for individual animal variability is still needed

The Effect of Herbage Availability and Season of Year on the Rate of Liveweight Loss during Weighing of Fasting Ewe Lambs

Publishe

Agent-Based Modeling to Improve Beef Production from Dairy Cattle: Model Description and Evaluation

Agent-based modeling (ABM) enables an in silico representation of complex systems and captures agent behavior resulting from interaction with other agents and their environment. This study developed an ABM to represent a pasture-based beef cattle finishing systems in New Zealand (NZ) using attributes of the rearer, finisher, and processor, as well as specific attributes of dairy-origin beef cattle. The model was parameterized using values representing 1% of NZ dairy-origin cattle, and 10% of rearers and finishers in NZ. The cattle agent consisted of 32% Holstein-Friesian, 50% Holstein-Friesian–Jersey crossbred, and 8% Jersey, with the remainder being other breeds. Rearers and finishers repetitively and simultaneously interacted to determine the type and number of cattle populating the finishing system. Rearers brought in four-day-old spring-born calves and reared them until 60 calves (representing a full truck load) on average had a live weight of 100 kg before selling them on to finishers. Finishers mainly attained weaners from rearers, or directly from dairy farmers when weaner demand was higher than the supply from rearers. Fast-growing cattle were sent for slaughter before the second winter, and the remainder were sent before their third winter. The model finished a higher number of bulls than heifers and steers, although it was 4% lower than the industry reported value. Holstein-Friesian and Holstein-Friesian–Jersey-crossbred cattle dominated the dairy-origin beef finishing system. Jersey cattle account for less than 5% of total processed beef cattle. Further studies to include retailer and consumer perspectives and other decision alternatives for finishing farms would improve the applicability of the model for decision-making processes