Academic literature on the topic 'Herbage production modelling'

Data from a long-term grazing experiment were used to develop relationships suitable for modelling between-year variation in pasture and animal production. The experiment was conducted near Townsville in North Queensland and consisted of a factorial combination of 2 stocking rates (0.6, 1.2 steers/ha) x 2 pasture types (native pasture, native pasture plus Stylosanthes hamata cv. Verano) x 2 superphosphate rates (nil, 300 kg/ha.year). Cattle were weighed monthly, and the herbage presentation yield and stylo content estimated annually. Relationships between annual liveweight gain (LWG) and a range of climate-derived parameters including the number of 'green days' were calculated from a water balance and pasture growth model. When a different intercept was allowed for each pasture and stocking rate combination, there was a strong relationship between annual LWG and the number of 'green days' (R2 = 0.78). In a more general relationship, LWG was related to green days, stylo content, and utilisation rate (R2 = 0.58). No relationships was found that would enable reliable prediction of between-year variation in the pasture attributes of stylo content and the water use efficiency of herbage growth. The times of the year when daily rates of LWG changed could be predicted satisfactorily, but rate of gain within each phase varied considerably from year to year. Differences in LWG between stocking rates occurred when there would have been restricted amounts of new green feed, or of stylo in the late wet season.

AbstractFeeding fodder beet (FB) to dairy cows in early lactation has recently been adopted by New Zealand dairy producers despite limited definition of feeding and grazing management practices that may prevent acute and sub-acute ruminal acidosis (SARA). This modelling study aimed to characterize changes of rumen pH, milk production and total discomfort from FB and define practical feeding strategies of a mixed herbage and FB diet. The deterministic, dynamic and mechanistic model MINDY was used to compare a factorial arrangement of FB allowance (FBA), herbage allowance (HA) and time of allocation. The FBA were 0, 2, 4 or 7 kg dry matter (DM)/cow/day (0FB, 2FB, 4FB and 7FB, respectively) and HA were 18, 24 or 48 kg DM/cow/day above ground. All combinations were offered either in the morning or afternoon or split across two equal meals. Milk production from 2FB diets was similar to 0FB but declined by 4 and 16% when FB increased to 4 and 7 kg DM, respectively. MINDY predicted that 7FB would result in SARA and that rumen conditions were sub-optimal even at moderate FBA (pH < 5.6 for 160 and 90 min/day, 7FB and 4FB respectively). Pareto front analysis identified the best compromise between high milk production and low total discomfort was achieved by splitting the 2FB diet into two equal meals fed each day with 48 kg DM herbage. However, due to low milk response and high risk of acidosis, it is concluded that FB is a poor supplement for lactating dairy cows.

Grazing-system experiments address complex interactions among animals, pastures, soils, climate and management. As part of the national EverGraze program, a grazing-system experiment was designed to determine how the intensity of grazing management, from continuous grazing (P01) to flexible 4- and 20-paddock rotational systems (P04 and P20), influences the profitability and sustainability of a Merino ewe, terminal sire, lamb production system grazed on heterogeneous native pastures. When implementing such an experiment, it is important to understand and characterise landscape variability, and include this in the design of the experiment. A second challenge for grazing-system research is to operate experimental systems with sufficient flexibility to adequately represent commercial production systems and maintain even utilisation across treatments. The present paper addresses the following two issues: (1) the process used to characterise the potential productivity of variable native pastures and the results of this characterisation; and (2) the development of flexible systems that adequately represent commercial production within an experiment. This was undertaken with input from a project-steering committee called the EverGraze Regional Group, comprising producers, extension staff and private consultants. Prior to the commencement, the site was mapped into three production zones, namely, high (HPZ), medium (MPZ) and low (LPZ), by visually estimating green herbage mass in late spring and marking boundaries between zones with a GPS. The production zones represented differences in soil properties (gravel, pH and available P) and pasture composition, and were used to balance potential production among plots within the same replication. Grazing-system options were evaluated using the sustainable grazing systems pasture model to help choose an appropriate starting stocking rate. The initial stocking rate chosen for the spring-lambing systems was 5.4 ewes/ha. The modelling predicted large variations in feed availability and quality over summer among years; flexible management criteria were therefore developed, including variable sale time for lambs, to utilise the greater feed supply in better seasons. Minimum-pasture benchmarks (>0.8 t DM/ha standing herbage mass and >80% ground cover) and variable green herbage-mass targets were designed to sustain high levels of livestock production and prevent pasture degradation. Criteria for adjusting ewe numbers were developed, but were constrained to pre-joining (March), scanning (July) and post-weaning (December), being consistent with commercial practices. The experiment incorporated flexible management rules as these were considered integral to the successful management of commercial grazing systems.

The distributions of individual plant age and biomass of Chamaecrista rotundifolia cv. Wynn and a mix of Stylosanthes scabra cvv. Seca and Fitzroy in grazed grass–legume pastures were examined to determine their effect on seed production. The effects of enhanced soil water conditions and severe defoliation on seed production were assessed in ungrazed plots. These experiments were part of a larger study to develop a demographic model of perennial forage legumes. The distribution of individual plant age and biomass was highly skewed towards a large number of young/small plants, with fewer old/large plants. Lack of seed set when stem length was less than approximately 200 mm, and in most small plants (<2 g), resulted in older/larger plants contributing far more to seed production and, to a lesser extent, legume biomass, than they did to legume plant numbers. C. rotundifolia seed production was linearly related to individual plant biomass but was highly varaiable and was greatly reduced in swards containing >3000 kg/ha of grass. Using log-transformed data, plant biomass accounted for 74% of the variation in seed production (SP), but together with grass biomass accounted for 91% of the variation [ln(SP) = 6.01 + 0.91*ln(BIOMASS) – 0.28*ln(GRASS BIOMASS), P < 0.001]. Total legume biomass accounted for only 44% of the variation in seed production. S. scabra herbage allowance (kg legume/head) had a major impact on seed production. Total legume biomass and individual plant biomass alone accounted for less than 40% of the variation in seed production. Using herbage allowance (HA) as well as individual plant biomass improved the prediction of seed production (SP) to account for 74% of the variation [ln(SP) = 0.11 + 1.14*ln(BIOMASS) + 0.24*ln(HA), P < 0.001]. Enhanced soil water conditions increased the biomass of individual plants of both species and increased the seed production per gram of plant in S. scabra but not in C. rotundifolia. Severe defoliation in early summer or autumn can greatly reduce or even eliminate seed production by some plants by removal of flowers, reducing individual plant biomass, or allowing insufficient time for plants to reach minimum stem lengths. The different factors affecting seed production in the 2 species highlight the complexity of legume seed set in grazed pasture systems, and some implications for grazing management and modelling are discussed.

In the context of dairy grazing systems, pasture mixtures including tall fescue, lucerne and plantain have been identified by animal modelling as having potential to both improve milk production and reduce urinary nitrogen excretion. A grazed paddock-scale trial was established in the Waikato in September 2015 to test this in two short-term grazing trials including these species. This paper presents the pasture production, botanical composition and nutritive value data generated from four pasture mixtures sown in spring 2015 and sampled until autumn 2017 (18 months). The pasture mixtures represented a comparison between perennial ryegrass and tall fescue, with and without the herb narrow-leaved plantain. The inclusion of plantain in grass-lucerne mixtures had a positive effect on firstyear herbage dry matter (DM) production, by ~2.6 t DM/ha/year in ryegrass-based pastures and ~1.6 t DM/ ha/year in tall fescue-based pastures. Where plantain was included, the proportion of grass was reduced by more than half from autumn 2016 through to summer 2016-2017, while the proportion of lucerne was reduced to a lesser degree. The proportion of plantain was 35-70% through most of the first year, declining to

Ecological studies often suggest that natural grasslands with high species diversity will grow more biomass and leach less nitrogen (N). If this diversity effect also applies to fertilised and irrigated pastures with controlled removal of herbage, it might be exploited to design pastures that can assist the dairy industry to maintain production while reducing N leaching losses. The purpose of this study was to test whether pasture mixtures with a high functional diversity in ryegrass traits will confer on the system higher water- and N-use efficiency. The hypothesis was tested using a process-based model in which pasture mixtures were created with varying levels of diversity in ryegrass traits likely to affect pasture growth. Those traits were: the winter- or summer-dominance of growth, the ability of the plant to intercept radiation at low pasture mass, and rooting depth. Pasture production, leaching and water- and N-use efficiency were simulated for management typical of a dairy pasture. We found that the performance of the diverse ryegrass–clover mixtures was more strongly associated with the performance of the individual components than with the diversity across the components. Diverse pasture mixtures may confer other benefits, e.g. pest or disease resistance and pasture persistence. The testing here was within a selection of ryegrasses, and the greater possible diversity across species may produce different effects. However, these results suggest that highly performing pastures under fertilised and irrigated grazed conditions are best constructed by selecting components that perform well individually than by deliberately introducing diversity between components.

Pasture-based dairy farms are a complex system involving interactions between soils, pastures, forage crops, and livestock as well as the economic and social aspects of the business. Consequently, biophysical and farm systems models are becoming important tools to study pasture-based dairy systems. However, there is currently a paucity of modelling tools available for the simulation of one key component of the system—forage crops. This study evaluated the accuracy of the Agricultural Production Systems Simulator (APSIM) in simulating dry matter (DM) yield, phenology, and herbage nutritive characteristics of forage crops grown in the dairy regions of south-eastern Australia. Simulation results were compared with data for forage wheat (Triticum aestivum L.), oats (Avena sativa L.), forage rape (Brassica napus L.), forage sorghum (Sorghum bicolor (L.) Moench), and maize (Zea mays L.) collated from previous field research and demonstration activities undertaken across the dairy regions of south-eastern Australia. This study showed that APSIM adequately predicted the DM yield of forage crops, as evidenced by the range of values for the coefficient of determination (0.58–0.95), correlation coefficient (0.76–0.94), and bias correction factor (0.97–1.00). Crop phenology for maize, forage wheat, and oats was predicted with similar accuracy to forage crop DM yield, whereas the phenology of forage rape and forage sorghum was poorly predicted (R2 values 0.38 and 0.80, correlation coefficient 0.62 and –0.90, and bias correction factors 0.67 and 0.28, respectively). Herbage nutritive characteristics for all crop species were poorly predicted. While the selection of a model to explore an aspect of agricultural production will depend on the specific problem being addressed, the performance of APSIM in simulating forage crop DM yield and, in many cases, crop phenology, coupled with its ease of use, open access, and science-based mechanistic methods of simulating agricultural and crop processes, makes it an ideal model for exploring the influence of management and environment on forage crops grown on dairy farms in south-eastern Australia. Potential future model developments and improvements are discussed in the context of the results of this validation analysis.

Forage-based livestock systems are complex, and interactions among animals, plants and the environment exist at several levels of complexity, which can be evaluated using computer modelling. Despite the importance of grasslands for livestock production in Brazil, tools to assist producers to make decisions in forage–livestock systems are scarce. The objective of this research was to use the CROPGRO-Perennial Forage model to simulate the irrigated and rainfed growth of Marandu palisade grass (Brachiaria brizantha (A. Rich.) Stapf. cv. Marandu), the most widely grown forage in Brazil, by using parameters previously calibrated for the tall-growing cv. Xaraes of the same species, under non-limiting water conditions. The model was calibrated for the irrigated experiment and then tested against independent data of the rainfed experiment. Data used to calibrate the model included forage production, plant-part composition, leaf photosynthesis, leaf area index, specific leaf area, light interception and plant nitrogen (N) concentration from a field experiment conducted during 2011–13 in Piracicaba, SP, Brazil. Agronomic and morpho-physiological differences between the two grasses, such as maximum leaf photosynthesis, N concentration and temperature effect on growth rate, were considered in the calibration. Under rainfed conditions, the simulations using the Penman–Monteith FAO 56 method gave a more realistic water stress response than the Priestley and Taylor method. After model parameterisation, the mean simulated herbage yield was 4582 and 5249 kg ha–1 for 28 days and 42 days irrigated, and 4158 and 4735 kg ha–1 for 28 days and 42 days rainfed, respectively. The root-mean-square error ranged from 464 to 526 kg ha–1 and the D-statistic from 0.907 to 0.962. The simulated/observed ratios ranged from 0.977 to 1.001. These results suggest that the CROPGRO-Perennial Forage model can be used to simulate growth of Marandu palisade grass adequately under irrigated and rainfed conditions.

The management and sustainable use of Central Australian rangelands for livestock production and conservation requires improved knowledge of the temporal and spatial distribution of primary production in this region. To provide such information, this thesis investigated methods that could rapidly and efficiently estimate regional herbage biomass production in these arid landscapes. Two different approaches were examined, using (1) ground-based or (2) satellite-based data sources. Soil moisture and herbage growth data were collected over several growth seasons and five landscape types in Central Australia, and the data used to develop a model of soil moisture balance and herbage production for the region. The model has few parameters and only requires inputs of rainfall and potential evaporation to predict daily soil moisture and plant growth. Moisture loss in the 0-500 mm soil profile was modelled using a negative exponential function that depends on available soil moisture and is driven by potential evaporation. The growth of herbage, whilst soil moisture is above wilting point, is a linear function of actual evapotranspiration, with the decay of plant material represented by a logistic curve through time. Soil moisture, herbage biomass and species composition assessments made at hectare and square kilometre scales at four locations within Central Australia were examined to determine if a small sample area could be used to accurately describe the soil and plant conditions at a landscape scale. Moisture levels of the 0-200 and 0-500 mm soil profiles from nine samples were analysed for the beginning and conclusion of a growth season, whilst herbage biomass and species composition from 50 samples were compared at the end of the growth season. Results suggest that mean soil moisture levels determined in a 1 ha area are comparable with mean values in the surrounding 1 km2 area. Herbage biomass and species richness for a square kilometre can be assessed at a hectare site for some landscape types, but a larger sampling area (> 1 ha) is recommended for most rangeland assessments. Satellite data (NOAA-11) were examined for their potential application in assessing primary productivity in Central Australia. Several image correction techniques were tested to minimise the adverse effects of atmospheric contamination and illumination. Two measures of atmospheric moisture: (1) radiosonde data and (2) temperature differences between bands 4 and 5 of the NOAA satellite (split-window) were used to explain variations in NOAA-11 normalised difference vegetation index (NDVI) on inert desert sites. The splitwindow approach provided the best single factor relationship (r2=0.63) and, when combined with scattering angle (illumination) effects, up to 81% of the variation in NDVI data could be explained. Field measurements of herbage biomass were correlated with four growth indices derived from NOAA-11 NDVI data. The influence of preflight and sensor degradation calibrations of Bands 1 and 2, and atmospheric correction techniques were also tested. Correlations between temporal sums of NDVI and herbage biomass data were relatively poor (r2<0.42) and unsuitable for herbage assessment in Central Australia. However, correlations between atmospherically corrected and background-adjusted maximum NDVI data and observed herbage biomass were strong (r2=0.91), that will allow primary production in the arid rangelands of Central Australia to be assessed rapidly and efficiently using remotely-sensed information.