Pasture Growth Curves Research Articles

Pasture growth curve impacts the economic merit of extended lactation: a simulation study

Voluntarily increasing the calving interval beyond 12-months (extended lactation; EL) has possible benefits in reducing labour requirements, herd replacement rate, and surplus calf numbers. With limited information on feed balance and economic effects with EL, reductions in profitability were hypothesised. A MS Excel spreadsheet model was developed to compare calving systems: spring calving annually (Base), two herds between spring and autumn calving with 18-month calving intervals (EL18), or two herds calving in alternating springs with 24-month intervals (EL24). Weekly feed balances using pasture growth curves for four regions (Northland, Waikato, Canterbury, Southland) and partial budget analysis informed estimations of supplement costs and net income (NI). The EL24 system had lower supplement costs (-$240 /ha) and higher NI (+$439 /ha) than the Base system for Northland only. The EL18 systemalways had the highest annual milk production and total revenue, but highest supplement costs and lowest NI in the four regions. Replacement, calving, and mating costs were lower for EL systems than Base; however, costs for milking and repairs and maintenance were higher. This preliminary study indicates there is merit in investigating EL systems, particularly for Northland. Further data from EL systems are needed to increase confidence in the system’s potential.

GROWTH CURVE OF A TROPICAL MIXED-GRASS PASTURE IN A HOT AND HUMID CLIMATE

<p><strong>Background. </strong>Pasture growth rate (PGR, kg/ha/day) depends on climatic and management practices. However, studies on the influence of the environment on pasture production and productivity of dry matter are scarce in tropical, hot, and humid regions of México. <strong>Objective.</strong> To estimate the pasture growth curve using time and climatic variables. <strong>Methodology.</strong> We related, through nonlinear models, the accumulated growth of a pasture composed of native grasses mixed with exotic grasses, using time and the variables temperature and day length as independent variables, the latter integrated into a single variable called thermal photo units (PTU). We estimated the daily growth rates of five divisions; from these, the forage yields for ten days until completing 29 periods. The best-fit models had the largest coefficients of determination and the lowest Akaike’s information criterion. <strong>Results.</strong> The model that best described the relationship between cumulative yield (Y) and cumulative growing days (X) was reciprocal-quadratic: y = x/(0.097535 – 0.0000881x + 0.0000006810x<sup>2</sup>) with R<sup>2</sup><sub>Adj,</sub> of 0.9988 and an AIC<sub>C</sub> of 222.6. The model that best described the relationship between the accumulated performance and the accumulated PTU was rational: y = (- 317.8 + 1.594x + 0.00001307x<sup>2</sup>)/(1 + 0.001059x + 0.00000001964x<sup>2</sup>), with R<sup>2</sup><sub>Adjt</sub>.=0.9985 and AIC<sub>C</sub>=233.4. Likewise, a two-segment model showed a close fit. The logarithmic model described the first segment: y<sub>1</sub> = -2268 + 417.2*(ln(x)), when y<sub>2</sub> =1079.3e<sup>0.00003932X</sup>, if x > 8415; with R<sup>2</sup><sub>Adj.</sub> = 0.9975 and AICc = 245.1. The value 8415 PTU was when the first derivative of both models coincided. <strong>Implications.</strong> The information generated is useful because it allows grazing system adjustment concerning the correct stocking rate application and designing more efficient grazing rotations. <strong>Conclusions. </strong>The conversion of growth rates to accumulated yield for ten-day periods produced a smooth curve that allowed fitting high-precision nonlinear models to predict forage accumulation from time and climatic variables.</p>

Feedbase intervention in a cow-calf system in the flooding pampas of Argentina: 2. Estimation of the marginal value of additional feed

Temporal variability in the availability of forage reduces the production and economic performance of livestock systems. The marginal value of feed (MVF, the possible gross economic benefit of additional feed on offer during an annual cycle), was assessed under the expected variability of climate and prices in a cow-calf operation from the Flooding Pampas, Argentina. Herbage mass accumulation (HMA) was simulated on a daily basis over 20 different years with DairyMod, grouped by month and season and where the HMA was equal or below 50% of its long-term average, it was tagged as “Dry”. Typical monthly pasture growth rates were synthetically depicted for average years (Average), or with dry autumn (D-Au), winter (D-Wi), spring (D-Sp) or summer (D-Su) conditions. These pasture growth curves were incorporated into whole-farm scenarios which were modelled with SIMUGAN, a bio-economic whole-farm model. Farm scenarios were baseline (unchanged HMA) or with additional 10% of the annual HMA. This additional feed was either evenly distributed across each month of the year (all year), or the full amount provided in one of the four seasons. These scenarios were repeated in a factorial design across a range of stocking rates (SR; 0.9–1.3cows/ha) on an average year or years including one dry season (D-Au, D-Wi, D-Sp orD-Su). SIMUGAN results were fed to an ad-hoc built model to calculate production and market risk profiles. In years with average HMA, MVF were always below 0.05US$/kg DM but the presence of a dry season caused significantly higher MVF. Years with dry autumn presented the highest economic responses when the extra feed was fed during autumn or winter. MVF analyses showed a positive impact of additional forage only above 1.1head/ha and this increased with SR, whereas MVF at the low SR were mostly negative due to extra hay making costs. At 1.1 and 1.2head/ha, allocating additional feed in autumn produced a higher return (0.04 and 0.08US$/kg DM) than feed provided at other times of the year (averaging 0.02 and 0.05US$/kg DM). Otherwise, at 1.3 SR extra feed in winter always had the highest MVF (up to 0.19US$/kg DM). Bio-physical variables of livestock demand and seasonality of pasture growth were the main drivers of MVF variability. Overall, the framework developed by integrating forage, livestock and economic models “in a series” effectively identified the economic feasibility of changes to the farm feed-base under different climatic and livestock management conditions.

Potential benefits of diverse pasture swards for sheep and beef farming

To investigate the potential use of diverse pasture swards to reduce nitrate leaching from intensive sheep and beef farms while maintaining economic viability, an integrated modelling assessment was conducted for the Canterbury region, New Zealand. The biophysical Agriculture Production Systems Simulator (APSIM) was used to obtain pasture growth curves for simple and diverse pastures over 10 different years, and the whole-farm system models FARMAX® and OVERSEER® were used to examine feed flow, nutrient balance, profitability, and nitrate leaching. The N leaching estimates obtained from OVERSEER® nutrient budget model (Overseer) were compared to estimates from APSIM. Five farm scenarios were explored, including three different proportions of the flat farm area under simple and diverse pastures (100% simple, 100% diverse, 50/50), two different stocking policies (without and with adjustment of livestock numbers), and three different years (an average, a best and a worst year, based on annual pasture yields). In the average year pasture growth was similar for the simple and diverse pasture swards, with annual pasture yields of 8.98 and 9.23tDM/ha. The simple pasture had slightly higher growth in winter due to lower sensitivity to cold temperatures, whereas the diverse pasture showed higher growth during summer, which is frequently prone to water limitations. However, in the best year modelled pasture growth as well as profit were higher for the simple compared with the diverse pasture sward. Pasture N concentrations ranged from 2.5 to 3.5% of dry matter (DM) in the simple pasture, and from 2.2 to 3.1% in the diverse pasture sward, mainly due to a lower proportion of legumes. For the average year, having a diverse pasture on 50% of the farm area without changing the stocking policy of the farm, increased farm profit by 16%, due to the sale of surplus pasture.The total farm N leaching values predicted by APSIM, based on excretal N amounts obtained from Overseer, showed that the use of diverse pastures on 100% of the flat area decreased N leaching in an average year by 35%. In the worst year however, N leaching under the diverse pasture was slightly higher, and in the best year it decreased by 14% compared with the simple pasture. Corresponding reductions in N leaching estimated from Overseer were 6, 5 and 13% for the diverse pasture, primarily due to the lower N concentration of the diverse pasture. In contrast to APSIM, Overseer does not take into account the higher uptake of N of diverse pastures from the urine patches compared to simple pastures, which is mainly due to increased pasture growth in summer of the species included in the diverse pasture. This potential of diverse pastures to decrease N leaching needs to be evaluated via experimental studies, which should also include other aspects such as pasture persistence, and address the need for accurate model parameterisation. The modelling work also provided guidance for model refinement.

Exploring climate change impacts and adaptations of extensive pastoral agriculture systems by combining biophysical simulation and farm system models

Climate change has the potential to affect the productivity of agricultural enterprises with the ability to adapt varying among farm systems. We modelled the impacts and tested the effects of potential adaptations to climate change for two New Zealand sheep and beef grazing enterprises located in regions that have contrasting climate change projections. For both enterprises we examined six management systems varying in farming intensity and the tactics used to respond to changes in feed supply and demand. The effects of the impacts and adaptations were determined by examining the economic viability of the systems. We modelled pasture growth over two 20-year periods centred on 1990 and 2040 using downscaled climate projections and a pasture simulation model; the resultant pasture growth curves were used in a farm system model to both determine impacts and test the benefits of adaptation. For both regions there were only slight increases in total annual pasture growth but marked changes in seasonality that required changes in the management system. For one region there were clear benefits of climate change which were accentuated by adaptation while in the other climate change had no positive impacts and adaptation was not effective. Two caveats are noteworthy: first, although adaptations used for the 2040 management systems were not outside the biologically feasible options achievable today, they did involve changes in reproductive efficiency and animal growth rates that are only currently achieved on the highest performing farms. Secondly, some of the adaptations assumed and depended on stock and feed movement between farms and regions, however we were not able to assess the possibility of synchronous, widespread droughts in multiple regions; should these increase then our adaptations would be challenging and could also put pressure on current agricultural infrastructure.

Linking Land Use Capability classes and APSIM to estimate pasture growth for regional land use planning

Investigation of land-use and management changes at regional scales require the linkage of farm-system models with land-resource information, which for pastoral systems includes forage supply. The New Zealand Land Resource Inventory (NZLRI) and associated Land Use Capability (LUC) database include estimates of the potential stock-carrying capacity across the country, which can be used to derive estimates of average annual pasture yields. Farm system models and decision support tools, however, require information on the seasonal patterns of pasture growth. To generate such pasture growth curves (PGCs), the Agricultural Production Systems Simulator (APSIM) was used, with generic soil profiles based on descriptions of LUC classes, to generate PGCs for three regions of New Zealand. Simulated annual pasture yields were similar to the estimates of annual potential pasture yield in the NZLRI spatial database, and they provided information on inter-annual variability. Simulated PGCs generally agreed well with measured long-term patterns of seasonal pasture growth. The approach can be used to obtain spatially discrete estimates of seasonal pasture growth patterns across New Zealand for use in farm system models and for assessing the impact of management practices and climate change on the regional sustainability.

Deriving pasture growth patterns for Land Use Capability Classes in different regions of New Zealand

Abstract Farm system models are increasingly being used to assess the implications of land use and practice changes on profitability and environmental impacts. Exploring implications beyond individual farms requires the linkage of such models to land resource information, which for pastoral systems includes forage supply. The New Zealand Land Resource Inventory (LRI) and associated Land Use Capability (LUC) database includes estimates of the potential stock carrying capacity across the country, which can be used to derive annual, but not seasonal, patterns of pasture growth. The Agricultural Production Systems Simulator (APSIM) was used, with generic soil profiles based on descriptions of LUC classes, to generate pasture growth curves (PGCs) in three regions of the country. The simulated pasture yields were similar to the estimates in the LRI spatial database, and varied with LUC Class within and across regions. The simulated PGCs also agreed well with measured data. The approach can be used to obtain spatially discrete estimates of seasonal pasture growth patterns across New Zealand, enabling investigation of land use and management changes at regional scales. Key words: APSIM modelling, pasture growth curve (PGC), year to year variability, farm system analysis

Increasing home-grown forage consumption and profit in non-irrigated dairy systems. 1. Rationale, systems design and management

The profitability of dairy businesses in southern Australia is closely related to the amount of feed consumed from perennial ryegrass-dominant pasture. Historically, the dairy industry has relied on improvements in pasture productivity and utilisation to support profitable increases in stocking rate and milk production per hectare. However, doubts surround the extent to which the industry can continue to rely on perennial ryegrass technology to provide the necessary productivity improvements required into the future. This paper describes the design and management of a dairy systems experiment at Terang in south-west Victoria (780-mm average annual rainfall) conducted over four lactations (June 2005–March 2009) to compare the production and profitability of two forage base options for non-irrigated dairy farms. These options were represented by two self-contained farmlets each milking 36 mixed-age, autumn-calving Holstein-Friesian cows at peak: (1) well managed perennial ryegrass pasture (‘Ryegrass Max’, or ‘RM’); and (2) perennial ryegrass plus complementary forages (‘CF’) including 15% of farmlet area under double cropping with annual species (winter cereal grown for silage followed by summer brassica for grazing on the same land) and an average of 25% of farmlet area in perennial pasture based on tall fescue for improved late spring–early summer feed supply. The design of these systems was informed by farming systems models (DairyMod, UDDER and Redsky), which were used to estimate the effects of introducing different forage options on farm profitability. The design of the CF system was selected based on modelled profitability increases assuming that all forage components could be managed to optimise forage production and be effectively integrated to optimise milk production per cow. Using the historical ‘average’ pasture growth curve for the Terang district and a mean milk price of $3.71 per kg milk solids, the models estimated that the return on assets of the RM and CF systems would be 9.4 and 15.0%, respectively. The objectives of the experiment described here were to test whether or not such differences in profitability could be achieved in practice, and to determine the risks associated with including complementary forages on a substantial proportion of the effective farm area. Key results of the experiment are presented in subsequent papers.

Managing mixed wheat–sheep farms with a seasonal forecast

Seasonal rainfall forecasts have been shown to have significant skill in many parts of the world. In this study, seasonal forecasts were used together with a crop simulation model and a simple pasture growth curve to inform several decisions on year-to-year farm management, including on land-allocations in a mixed wheat–sheep farming system. In seasons where “above-median” rainfall was forecast, N fertiliser applications in cropping were increased to support the higher grain yield potential, sheep stocking rates were increased to take advantage of higher pasture growth and unused pasture land was made available for cropping. In seasons where “below–median” rainfall was forecast, N fertiliser applications in cropping were reduced to minimise costs, and traditional conservative sheep stocking rates were used. Application of the Predictive Ocean Atmosphere Model for Australia (POAMA) seasonal rainfall forecast yielded additional profit of up to A$66ha−1 per annum, equivalent to A$200,000y−1 for an average size farm in Western Australia. The largest benefit from applying a forecast in wheat–sheep farms comes from the increase of more profitable cropping during “above-median” rainfall seasons. This is well above the benefit of previous single-commodity forecast applications, and therefore has widespread potential to improve decision making on mixed crop–livestock farms. With the projected decline of rainfall in the Australian and other rain-fed crop–livestock regions of the world, skilful seasonal forecasting systems will become increasingly valuable as they will assist farm managers to capture the benefits in the declining number of potentially high-production seasons and minimise the input costs in the increasing number of low-production seasons.

Effects of weather variability on sheep and beef farming in northern Southland, New Zealand: A modelling analysis

Inter-annual weather variation has profound effects on pasture production and pastoral farm performance. However, the relationship between the variation in herbage and animal production and farm economic and environmental (nitrate leaching and greenhouse gas emission) outcomes are not clearly quantified, especially with varying farm management strategies applied under variable weather scenarios. We used a pasture model (APSIM-AgPasture), corroborated by measured pasture growth data, to generate pasture growth curves for a typical sheep and beef farm in northern Southland for the past 39 years, and grouped them into five weather scenarios. We fed these curves into a farm system model (Farmax) to examine the changes in farm performance with various management adaptations. We found that farm economic and environmental outcomes did not respond proportionally to pasture production; compared with the variation range in herbage production, the variation range in animal production was smaller, but the variation range in farm profitability was larger. Environmental efficiency (nitrate leaching and greenhouse gas emissions per unit of animal products) was high in wet years. Our results highlight the importance of considering the nonproportional changes among pasture production, animal production, profitability, and environmental outcomes of pastoral farming when selecting farm management strategies and tactics to adapt to weather variations, and indicate the necessity of incorporating these non-linear relationships in the development of farm management decision support tools. Key words: Climate variability, pasture herbage production, animal production, production efficiency, environmental efficiency, management adaptation

Interannual variation in pasture growth rate in Australian and New Zealand dairy regions and its consequences for system management

The profitability of dairy farms in Australia and New Zealand is closely related to the amount of pasture dry matter consumed per hectare per year. There is variability in the pasture growth curve within years (seasonal variation) and between years (interannual variation) in all dairy regions in both countries. Therefore, the biological efficiency of production systems depends on the accuracy and timeliness of the many strategic and tactical decisions that influence the balance between feed supply and demand over an annual cycle. In the case of interannual variation, decisions are made with only limited quantitative information on the range of possible pasture growth outcomes. To address this limitation, we used the biophysical simulation model ‘DairyMod’ to estimate mean monthly herbage accumulation rates of annual or perennial ryegrass-based pastures in 100 years (1907–2006) for five Australian sites (Kyabram in northern Victoria, Terang in south-west Victoria, Ellinbank in Gippsland, Elliott in north-west Tasmania and Vasse in south-west Western Australia) and in 35 years (1972–2006) for three sites in New Zealand (Hamilton in the Waikato, Palmerston North in the Manawatu and Winchmore in Canterbury). The aim was to evaluate whether or not a probabilistic approach to the analysis of pasture growth could provide useful information to support decision making. For the one site where annual ryegrass was simulated, Vasse, the difference between the 25th and 75th percentile years was 20 kg DM/ha.day or less in all months when pasture growth occurred. Irrigation at Kyabram and Winchmore also resulted in a narrow range of growth rates in most months. For non-irrigated sites, the 25th–75th percentile range was narrow (10–15 kg DM/ha.day) from May or June through to September or October, because plant available soil water was adequate to support perennial ryegrass growth, and the main source of interannual variability was variation in temperature. Outside of these months, however, variability in growth was large. There was a positive relationship between total annual herbage accumulation rate and mean stocking for four southern Australian regions (northern Victoria, south-west Victoria, Gippsland and Tasmania), but there was evidence of a negative relationship between the co-efficient of variation in pasture growth and stocking rate. The latter suggests that farmers do account for risk in pasture supply in their stocking rate decisions. However, for the one New Zealand region included in this analysis, Waikato, stocking rate was much higher than would be expected based on the variability in pasture growth, indicating that farmers in this region have well defined decision rules for coping with feed deficits or surpluses. Model predictions such as those presented here are one source of information that can support farm management decision making, but should always be coupled with published data, direct experience, and other relevant information to analyse risk for individual farm businesses.

The FeedSmart "Cost of Supplements and Cost of Pasture" models

The "FeedSmart" extension programme, focused on improved feed planning for sheep and beef farmers includes two computer models to assist with decision making. The Cost of Pasture model requires the user to input a farm income and cost structure, and a pasture growth curve. It calculates the costs of growing pasture on a dry matter (DM) basis. Keywords: cost of pasture, cost of supplements, cost, pasture, supplements

CHANGES IN MANAGEMENT TO ACHIEVE HIGHER MILKFAT PRODUCTION ON THE WEST COAST

Based on research findings and observation, a dairy farm pasture management system is presented that could result in an increased per hectare milkfat production and profitability. In autumn there should be a change away from milk production towards pasture management and cow condition by using strategic drying off of cows relevant to the individual farm situation. This is probably the most important decision made each year. As winter pasture production is inadequate to meet cow maintenance and pregnancy requirements, cow condition should be at the level required for calving by the start of winter. The use of supplements conserved from summer' surpluses to specifically 'in-' crease cow condition in winter is of marginal economic value. They should be used primarily in autumn to produce extra pasture to be available for later use in winter. The matching of correct drying-off date, calving date and stocking rate to the pasture growth curve for the region, and an appreciation of correct feeding levels at the different stages of lactation are important.

The disc pasture meter : Possible applications in grazing management

Abstract The disc meter is a simple inexpensive instrument which may be used to make rapid yield estimates of standing forage. Linear regression relationships between meter reading and pasture dry matter yield are usually fairly good, but these may be affected by a number of different factors. The meter should therefore be calibrated for each specific set of conditions in which it is to be used. Estimation of pasture yield with this instrument provides an objective basis on which to adjust animal numbers in grazing trials and enables pasture intake by grazing animals to be estimated in rotational grazing systems. It may also be useful for quantitatively describing pasture‐animal relationships and for constructing pasture growth curves.

Environment-dependent logistic equations applied to natural pasture growth curves

Logistic equations, in which the relative growth rates are assumed to be functions of daily radiation, temperature, and soil moisture, are fitted to the growth curves of two natural pastures in Israel. Two functional forms for R, the relative growth rate, are considered, R as a product of a maximum relative growth rate and sigmoid reduction factors due to the environmental variables gives better results than R as a linear function of environmental variables. The best model at one site contains reduction factors due to radiation and soil moisture. At the other site the best model contains reduction factors due to all three environmental variables. In all cases the best soil moisture reduction factor is nearly a step function. The maximum relative growth rates are 9.4%/day for the first site and 10%/day for the second site.

Pasture growth curves and grazing management

Growth curves, published by Brougham (1956), of pastures growing in winter at Palmerston North, New Zealand, were analysed. The mathematical models were examined by computer runs in which the various parameters were varied within likely limits. The results obtained suggest that the following principles may be applied to the management of pastures similar to those studied. The average rate of regrowth increases to a maximum after removal of grazing animals, but this maximum is not sharply defined. The optimum number of subdivisions in a rotational grazing system is probably less than ten, unless considerations other than pasture growth are important. The optimum length of intervals between grazings, and of grazing periods, for a given level of subdivision, are not sharply defined. Following dry autumns, management should generally aim at maximum pasture production rather than most rapid relief of present stress, provided this policy does not endanger animal or financial survival. Systems of grazing management may therefore vary within fairly wide limits, without serious loss of pasture production, provided pasture stability and animal welfare are not jeopardized.