Comparison of haymaking strategies for cow-calf systems in the Salado Region of Argentina using a simulation model. 1. Effect of herbage mass at cutting and cow stocking rate under a rigid system of management

A simulation model was used to compare the long‐term performance of cow‐calf farm systems under different haymaking strategies on a 100‐ha farm. In the simulation, farm management was based on that which had been developed on Reserva 6, an experimental cow‐calf farm established in 1966 at Instituto Nacional de Tecnología Agropecuaria‐Balcarce Experimental Station, Argentina, where different technologies, including haymaking, have been adapted and applied in order to increase productivity of cow‐calf systems in the Salado Region of Argentina. The management of the system is based on a restricted mating season (2 months), early weaning (5–7 months of age) and forage conservation. The simulations showed that the effect of using hay with respect to the strategy without hay, in terms of calf liveweight (LW) production per hectare, was greatest at the cow numbers that maximized production (290–320 cows) with a proportionate increase of 0.25. On the other hand, the advantage of using hay was smallest when the herbage mass at cutting for hay was 6 t dry matter (DM) ha^?1, particularly when more than 0.50 of the farm area was allocated to haymaking. The differences among the haymaking policies increased with cow numbers, especially at high herbage masses at cutting for hay. The analysis also suggested that the LW production per hectare of cow‐calf farms would be maximized by harvesting 0.40–0.50 of the total farm area and aiming to cut hay at a herbage mass of 4 t DM ha^?1 and with medium quality.     

Comparison of haymaking strategies for cow-calf systems in the Salado Region of Argentina using a simulation model. 3. Exploratory risk assessment

Risk and cost estimates, calculated using a weather‐driven farm simulation model, were used to evaluate the impact of different haymaking strategies in dryland cow‐calf systems such as those in the Salado region of Argentina. Based on a 100‐ha farm, each strategy was simulated using twenty sequences of 50 years of random weather. A risk‐efficiency methodology was used, using calf live weight sold and considering the economic value of the live weight produced per year minus the cost of haymaking, as a simple measure of profit. The analysis highlighted both production and risk advantages in using hay, especially when a flexible hay management approach was implemented, although some haymaking strategies performed worse than not using hay. When calf live weight sold was considered alone, the risk‐efficient set included strategies with large proportions of the areas being harvested (0.42–1.05; paddocks could be cut more that once a year in some of the strategies) and high proportions of hay not being used. The amount of hay for maximizing risk efficiency for profit was notably lower than that for maximizing cattle production. When haymaking costs were included, the proportion of area harvested for risk‐efficient profit ranged from 0.28 to 0.52, depending on the target stocking rate.     

Assessment of regional variation in climate on the management of dairy cow systems in Ireland using a simulation model

A dairy system simulator, Dairy_sim, was designed to assess the interactions between climate and management in spring‐calving milk production systems based on the grazing of grass pastures. The simulator comprises three main components: a grass herbage growth model, an intake and grazing behaviour model, and a nutrient demand model. The simulator was initially parameterized using the Irish National Dairy Blueprint. Sensitivity analysis indicated that the simulator was most sensitive to stocking rate, milk output per cow and nitrogen fertilizer inputs, but less sensitive to other variables. Field data from four grazing systems were used to test the simulator and it was concluded that Dairy_sim was suitable for evaluating the interaction of climate and management for rotational grazing dairy systems based on perennial ryegrass pastures with Friesian cows. The simulator, Dairy_sim, was then used to evaluate the effects of the regional climates of Ireland on system management. The results indicated that, between regions, herbage production at the same input of nitrogen may vary proportionally by 0·10 and that the length of the grazing season may vary by 0·25. It was concluded that the simulator could be a useful tool for developing region‐specific dairy production blueprints.     

Comparison of outputs of a biophysical simulation model for pasture growth and composition with measured data under dryland and irrigated conditions in New Zealand

Simulation models have a role in predicting and understanding the consequences of weather and management perturbations to biophysical systems. One such model is EcoMod – a biophysical simulation model of a pastoral ecosystem. It comprises a range of sub-models that form the main components of pastoral ecosystems – namely plants, soils and ruminant livestock. These component models are integrated to simulate the complex interactions occurring within such systems. The purpose of this study was to test how well EcoMod simulated the changes in growth rate and plant composition of dryland and irrigated pasture production in a temperate climate through comparison with a long-term (1966–2003) data set measured in New Zealand. The general behaviour of the modelled pastures was similar to those of the observed pastures. There was also close agreement between the measured and modelled total annual and monthly growth rates of dry matter (DM), particularly after modifying some of the parameters of plant growth to better represent the characteristics of dominant species present under dryland and irrigation pastures. There was greater discrepancy between measured and modelled pasture composition but in the case of the simulation of the dryland pasture, the proportional differences in species composition were much greater than the absolute differences in species composition. In addition to comparing the outputs of EcoMod to data from actual pasture production, model testing issues and potential sources of error between model outputs and data were also examined.     

Numerical prediction of suspended sediment concentrations in the Ariake Sea, Japan, using a time-dependent sediment resuspension and deposition model

The Ariake Sea, a typical semi-closed bay located in Kyushu Island, western Japan, has a maximum tidal range of 6 m in spring tide, and its vast tidal flats are composed of both sandy bottom areas and muddy bottom areas. In this study, two-dimensional depth-averaged finite difference numerical models have been developed for predicting the tidal flow velocity and suspended sediment concentration in the Ariake Sea. In the suspended sediment transportation model, a time-dependent sediment resuspension and deposition process was modeled as a response to the tidal flow by considering the presence of cohesive bottom sediment. The shifting-particles method, known as an operator-splitting technique, was used for time integration of the convective-dispersion equation. The model results were compared with in situ measurements of suspended sediment concentration and tidal flow velocity at several points in Isahaya Bay—located at the western part of the Ariake Sea—and also with the estimated suspended sediment concentration distribution via a Landsat Thematic Mapper image. The results indicated that the models were able to predict the measured values for suspended sediment concentration and tidal flow velocity, as well as the spatial distribution pattern of the suspended sediment concentration as estimated by the Landsat Thematic Mapper image.     

Evaluation of the GrazeIn model of grass dry‐matter intake and milk production prediction for dairy cows in temperate grass‐based production systems. 2 – Animal characteristics

This study evaluated the prediction accuracy of grass dry‐matter intake (GDMI) and milk yield predicted by the GrazeIn model using a large database representing 8787 per cow GDMI measurements. In this study, the animal input variables (age, parity, week of lactation, potential peak milk yield, milk fat content, milk protein content, bodyweight, body condition score (BCS), week of conception, BCS at calving and calf birth weight) were investigated. The mean actual GDMI of the database was 15·9 kg DM per cow d^?1 and GrazeIn predicted a mean GDMI for the database of 15·5 kg DM per cow d^?1. The mean bias was ?0·4 kg DM per cow d^?1. GrazeIn predicted GDMI for the total database with an RPE of 15·5% at cow level. The mean actual daily milk yield of the database was 21·3 kg per cow d^?1 and GrazeIn predicted a daily milk yield for the database of 22·2 kg per cow d^?1. The mean bias was +0·9 kg per cow d^?1. GrazeIn predicted milk yield for the total database with an RPE of 16·7% at cow level. From the evaluation, GrazeIn predicted milk yield of all cows in late lactation with a larger level of error than in early and mid‐lactation. This error appears to be due to the persistency of the lactation curve used by the model, which results in a higher predicted milk yield in late lactation compared with the actual milk yield.     

Evaluation of the GrazeIn model of grass dry‐matter intake and milk production prediction for dairy cows in temperate grass‐based production systems. 1–Sward characteristics and grazing management factors

This study evaluated the prediction accuracy of grass dry‐matter intake (GDMI) and milk yield predicted by the model GrazeIn using a database representing 522 grazing herds. The GrazeIn input variables under consideration were fill value (FV), grass energy content [Unité Fourragère Lait (UFL)], grass protein value [true protein absorbable in the small intestine when rumen fermen energy is limiting microbial protein synthesis in the rumen (PDIE)], pre‐grazing herbage mass (PGHM), daily herbage allowance (DHA) and concentrate supplementation. GrazeIn was evaluated using the relative prediction error (RPE). The mean actual GDMI and milk yields of grazing herds in the database ranged from 9·9–22·0 kg DM per cow d^?1 and 8·9–41·8 kg per cow d^?1, respectively. The accuracy of predictions for the total database estimated by RPE was 12·2 and 12·8% for GDMI and milk yield, respectively. The mean bias (predicted minus actual) for GDMI was ?0·3 kg DM per cow d^?1 and for milk yield was +0·9 kg per cow d^?1. GrazeIn predicted GDMI with a level of error <13·4% RPE for spring, summer and autumn. GrazeIn predicted milk yield in autumn (RPE = 17·6%) with a larger error in comparison with spring (RPE = 10·4%) and summer (RPE = 11·0%). Future studies should focus on the adaptation of GrazeIn to correct and improve the prediction of milk yield in autumn.