No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
GRAZPLAN: Decision support systems for Australian grazing enterprises. III. Pasture growth and soil moisture submodels, and the GrassGro DSS
Abstract
This paper specifies the pasture growth module of a model for simulating grazing systems for ruminants and the soil moisture budget that drives pasture growth. Both modules operate at a daily time step. The pasture growth module is quite general in structure but recognises four functional groups of pasture plants: annual and perennial species are distinguished, as are grasses and forbs. Shoot tissue is classified as live, senescing, standing dead, or litter, and also according to its dry matter digestibility, thus enabling integration with diet selection and feed intake models.The phenological development of pasture plants is modelled, with the transitions between each stage governed by environmental variables (day length, temperature and soil moisture). Prereproductive and postreproductive phenostages of vernalisation and ‘summer dormancy’, respectively, are modelled in the appropriate cultivars. Functions predicting net primary production in response to light intercepted, mean daytime temperature, and available soil moisture, and also the process of maturation, are common to all functional groups. The model's treatment of the allocation of assimilate has a similarly general form. Seed and seedling dynamics are modelled for annual species only.GrassGro is a discrete computer package, developed for Microsoft Windows,™ that combines the pasture growth module with a module for predicting the intake of herbage of ruminants and their productivity. This decision support system enables users to analyse simplified grazing systems in terms of pasture and animal production, gross margins, and year-to-year variability for any specified pasture cultivar, or combination of cultivars, at any specified site. The package may also be used to simulate forward from current pasture and animal conditions, for assessing the probability distribution of production outcomes, given the historical variability of weather conditions over the specified forward period.
... The GrassGro Ò biophysical model (Moore et al., 1997;version 3.3.10) was used to simulate scenarios for Grass-fed and Grainfinished Angus beef enterprises representative of those in the New England region of New South Wales. ...
... Details of the two systems are outlined in Table 1. The GrassGro Ò model is a ruminant grazing model that has been developed for extensive livestock systems of southern Australia (Moore et al., 1997;Makony et al., 2010). GrassGro Ò is comprised of components that describe the biophysical (climate, soils and land management units (paddocks), pastures, livestock), managerial (e.g., stocking rate, soil fertility, pasture grazing rotations and animal reproductive management) and financial subsystems, which form the 'farm system' under consideration. ...
... Body protein needed to be estimated for each livestock class (steer, heifer, CFA cow) and is based on empty BW (EBW). The EBW was derived from the GrassGro Ò output, generalised from Feeding Standards for Australian Livestock, Ruminants (Moore et al., 1997;Standing Committee on Agriculture, 1990). ...
Ruminant red meat production systems around the world often include a grain feeding phase. The role of red meat in the food system is therefore often discussed in terms of the food vs feed debate, as well as invoking the comparatively poor feed conversion efficiency of ruminants and climate impacts from enteric methane. The concept of net protein contribution (NPC) incorporates the quality attributes of protein produced by livestock systems into estimates of the efficiency of production systems. We applied the NPC method to two Australian beef supply chains, i) Grass-fed and ii) Grain-finished beef, using an established model of ruminant grazing systems (GrassGro®) and these are reflective of beef production systems in other countries. The beef supply chains evaluated did not compete with humans for protein. The Grain-finished beef supply chain, while positively contributing to human protein requirements (NPC value 1.96), had markedly lower NPC values than the Grass-fed system (NPC value 1 597). However, Grass-fed beef production systems have a higher methane intensity than the Grain-finished supply chain. The two examples of pasture-based beef production systems examined provide a positive net protein contribution to human food supply, even with extended periods of finishing on grain-based diets. This is achieved by ruminant grazing on pastures converting low-quality forage into high value human edible protein. The efficiency of protein production varies according to the system design, and other considerations such as land use and enteric methane production are elements that need consideration in the overall assessment of the production footprint.
... Farm production output was generated, taking into account weather variability, using the GrassGro decision support tool [17], which is used to assist decision-making in Australian sheep and beef enterprises. The whole farm risk analysis model (Figure 1a) was built using production outputs generated by GrassGro and historical prices and costs for the same period. ...
... The variability in the production of grazing systems is driven by altering weather conditions annually, and was predicted through simulation using GrassGro version 3.3.9 [17]. Using a representative case study approach [21] the model was set up to replicate a 1000 Hectare (ha) Merino ewe farm near Tarcutta (Figure 2), south-western New South Wales (NSW), and calibrated using a field experiment conducted between 2006 and 2010, which is previously reported in Robertson et al. [22]. ...
... Using a representative case study approach [21] the model was set up to replicate a 1000 Hectare (ha) Merino ewe farm near Tarcutta (Figure 2), south-western New South Wales (NSW), and calibrated using a field experiment conducted between 2006 and 2010, which is previously reported in Robertson et al. [22]. GrassGro is a mechanistic biophysical model that allows farmers and natural resource managers to focus on the biophysical interactions within a farming system [17]. It quantifies pasture and animal production at daily time-steps. ...
The resilience and profitability of livestock production in many countries can be impacted by shocks, such as drought and market shifts, especially under high debt levels. For farmers to remain profitable through such uncertainty, there is a need to understand and predict a farming business’s ability to withstand and recover from such shocks. This research demonstrates the use of biophysical modelling linked with copula and Monte Carlo simulation techniques to predict the risks faced by a typical wool and meat lamb enterprise in South-Eastern Australia, given the financial impacts of different debt levels on a farming business’s profitability and growth in net wealth. The study tested five starting gearing scenarios, i.e., debt to equity (D:E) ratios to define a farm’s financial risk profiles, given weather and price variations over time. Farms with higher gearing are increasingly worse off, highlighting the implications of debt accumulating over time due to drought shocks. In addition to business risk, financial risk should be included in the analyses and planning of farm production to identify optimal management strategies better. The methods described in this paper enable the extension of production simulation to include the farmer’s management information to determine financial risk profiles and guide decision making for improved business resilience.
... A range of models are currently used to comprehensively represent grazing systems. In Australia and New Zealand, these include the SGS Pasture Model [4], DairyMod, [5], GRAZPLAN [6], GrassGro [7], DairyNZ whole-farm model [8] and further abroad, other models [9,10] with similar objectives. The component models forming the foundation of these grazing systems models are often complex representations of specific processes such as the flow of nutrients, energy or water throughout plants, animals and soils. ...
... This was also the period when there was an under prediction of sheep weight. There were few data points (7)(8) of these pasture quality measurements however, which made it difficult to be conclusive about the effects. The legume (sub-clover) dry matter predictions were in close agreement with the observed data (Figure 6b). ...
The performance of farming systems models for grazed grasslands are seldom evaluated against comprehensive field data. The aim of this study was to evaluate the capacity of a daily time step, grazing systems simulation model—the SGS (Sustainable Grazing Systems) Pasture Model—to simulate production and aspects of sustainability. This was completed by evaluating temporal changes in soil water balance, some major nitrogen (N) fluxes, as well as plant and animal production using data from two large scale experimental sites with grazing sheep. The simulations were broadly in agreement with the measurements. In cases where divergence occurred the reasons were apparent and could be explained by reference to the model structure or aspects of the field data. In particular, the simulations showed good agreement with the observed soil water, but poorer agreement with the volumes of runoff. The simulated N in leachate and soil inorganic N were less in agreement with the measured data. The model outputs were sensitive to symbiotic biological fixation by subterranean (sub) clover and mineralisation of soil organic matter, which were not measured. Similarly, there were variable results for the simulation of animal growth and production. The complexities of simulating grazing systems and comparing field observations to simulated values are discussed.
... Several models represent the timing of onset of green-up and brown-down only as a function of temperature or day of year (e.g., Hurley Pasture Model, Johnson and Thornley (1983); DAY-CENT, Parton et al. (1993); CABLE, Zhang (2004)). Some models also account for the impact of soil moisture availability on growth via a prescribed linear function based on a minimum threshold of soil moisture availability, while still assuming a constant senescence rate (GrassGro, Moore et al. (1997); ORCHIDEE, Friedlingstein et al. (1999) and Krinner et al. (2005)). However, the observed change in grassland foliage cover in PAHCE was more abrupt than model predictions suggesting that the possibility of nonlinear responses of grassland foliage phenology to soil moisture availability (e.g., Paschalis et al., 2020). ...
... Plants can also retranslocate foliar nutrients and carbohydrates from leaves under drought, which could result in increased brown-down (e.g., Gonzalez-Dugo et al., 2012). Although foliage carbon allocation, translocation and senescence may all relate to plant water regulation, previous models generally assume a fixed rate of senescence (e.g., Moore et al., 1997;Friedlingstein et al., 1999;Krinner et al., 2005;Johnson et al., 2008;Haverd et al., 2016). The senescence rates and their sensitivities to soil moisture availability quantified in this study are important first steps in modelling brown-down under variable rainfall. ...
Grassland responses to rainfall are characterised by leaf phenology, with greening and browning being highly sensitive to soil moisture. However, this process is represented overly simplistically in most vegetation models, limiting their capacity to predict grassland responses to global change factors. We derive functions representing grassland phenological responses to soil water content (SWC), by fitting an empirical model to greenness data. Data were obtained from fixed cameras (phenocams) monitoring phenology at several grassland experiments in Sydney, Australia. The data-model synthesis showed that the sensitivity of growth to SWC exhibited a concave-down response in most species. For senescence, we found a strong nonlinear increase in senescence rate with declining SWC. Both findings contradict common assumptions of growth and senescence in vegetation models. Incorporating nonlinear responses in the empirical model reduced the error in cover predictions by 7%. Model evaluation against data from drought treatments indicated that differential sensitivity of phenology to SWC helps explain differences among species’ responses to variable rainfall. Our work provides a new methodology, and new evidence, to support the development of improved representations of grassland phenology for vegetation models.
... [1] HP model Thornley el al. (1989) [2] GEM Hunt et al. (1991) [3] GRAZPLAN Moore et al. (1997) [4] PaSim Riedo et al. (1998) [5] Lingra Schapendonck et al. (1998) [6] - [7] SGS Johnson et al. (2008) [8] GRASSMIND Taubert et al. (2012) [9] GEMINI Plant growth depends on species functional traits, soil profile and atmospheric conditions. The model is then divided in three sections: Atmosphere, Plant and Soil (Fig. A.1). ...
... [1] HP model Thornley el al. (1989) [2] GEM Hunt et al. (1991) [3] GRAZPLAN Moore et al. (1997) [4] PaSim Riedo et al. (1998) [5] Lingra Schapendonck et al. (1998) [6] - [7] SGS Johnson et al. (2008) [8] GRASSMIND Taubert et al. (2012) [9] GEMINI ...
Les communautés végétales constituent des systèmes complexes au sein desquels de nombreuses espèces, pouvant présenter une large variété de traits fonctionnels, interagissent entre elles et avec leur environnement. En raison de la quantité et de la diversité de ces interactions les mécanismes qui gouvernent les dynamiques des ces communautés sont encore mal connus. Les approches basées sur la modélisation permettent de relier de manière mécaniste les processus gouvernant les dynamiques des individus ou des populations aux dynamiques des communautés qu'ils forment. L'objectif de cette thèse était de développer de telles approches et de les mettre en oeuvre pour étudier les mécanismes sous-jacents aux dynamiques des communautés. Nous avons ainsi développé deux approches de modélisation. La première s'appuie sur un cadre de modélisation stochastique permettant de relier les dynamiques de populations aux dynamiques des communautés en tenant compte des interactions intra- et interspécifiques et de l'impact des variations environnementale et démographique. Cette approche peut-être aisément appliquée à des systèmes réels et permet de caractériser les populations végétales à l'aide d'un petit nombre de paramètres démographiques. Cependant nos travaux suggèrent qu'il n'existe pas de relation simple entre ces paramètres et les traits fonctionnels des espèces, qui gouvernent pourtant leur réponse aux facteurs externes. La seconde approche a été développée pour dépasser cette limite et s'appuie sur le modèle individu-centré Nemossos qui représente de manière explicite le lien entre le fonctionnement des individus et les dynamiques de la communauté qu'ils forment. Afin d'assurer un grand potentiel d'application à Nemossos, nous avons apportés une grande attention au compromis entre réalisme et coût de paramétrisation. Nemossos a ainsi pu être entièrement paramétré à partir de valeur de traits issues de la littérature , son réalisme a été démontré, et il a été utilisé pour mener des expériences de simulations numériques sur l'importance de la variabilité temporelle des conditions environnementales pour la coexistence d'espèces fonctionnellement différentes. La complémentarité des deux approches nous a permis de proposer des éléments de réponse à divers questions fondamentales de l'écologie des communautés incluant le rôle de la compétition dans les dynamiques des communautés, l'effet du filtrage environnemental sur leur composition fonctionnelle ou encore les mécanismes favorisant la coexistence des espèces végétales. Ici ces approches ont été utilisées séparément mais leur couplage peut offrir des perspectives intéressantes telles que l'étude du lien entre le fonctionnement des plantes et les dynamiques des populations. Par ailleurs chacune des approches peut être utilisée dans une grande variété d'expériences de simulation susceptible d'améliorer notre compréhension des mécanismes gouvernant les communautés végétales.
... The CSIRO Division of Plant Industry in Australia developed the GrassGro Decision Support System in 1997 (Moore et al., 1997). It runs on a daily time step combining the pasture growth module with a module for predicting the intake of herbage of ruminants (sheep and cattle) and their productivity. ...
... The user can test management options against a wide range of seasons to achieve a more profitable and sustainable utilisation of grasslands. The CSIRO Division of Plant Industry (Moore et al., 1997) has successfully used the model in Australia. ...
A large part (69%) of South Africa’s surface is suitable for grazing resulting in livestock farming being the largest agricultural sector in the country. Rangelands are an important resource for a stock farmer as it provides a cheap food source for the livestock midst it is in a good condition. In order to feed an ever-increasing population, better rangeland management practices are needed to ensure food security. Adaptation strategies should address climate variability and change, which is already suspected to be the main cause for variable crop yields and rangeland production. It is therefore imperative to investigate what the effect of climate change will be on rangeland production in the long run. Thus, the main aim of this study was to assess the historical and future rangeland production in the Bloemfontein area of South Africa, which falls within the dry Themeda-Cymbopogon veld type and is deemed representative of the central grassland biome.
Observed climate data was sourced from the South African Weather Service (SAWS) station at Bloemfontein Airport for the historical base period (1980/81 – 2009/10). Simulated climate data was also obtained for the base and three future periods (i.e. current period (2010/11 – 2039/40), near future (2040/41 – 2069/70) and distant future (2070/71 – 2098/99)) from five Global Climate Models (GCMs) using two Representative Concentration Pathways (RCPs). Here RCP 4.5 and 8.5 respectively represented intermediate and high greenhouse gas emission pathways. Measured rangeland production data was obtained from the Sydenham Experimental Farm outside Bloemfontein for the historical base period. PUTU VELD (PV) was used to simulate rangeland production for the base and future time periods. Inputs included rainfall (mm), minimum and maximum temperature (°C), sunshine hours (h) and evapotranspiration (mm.d-1) at daily intervals, where the latter was estimated using the Hargreaves-Samani method. PV outputs included maximum dry matter production (DMPmax), the date of occurrence of DMPmax (Dtp) and the number of moisture stress days (MSD).
Results showed a weak positive trend in measured DMPmax over the historical base period. It should be stressed that the results of this study should not be interpreted or extrapolated outside the context of this document since the validation of PV over the historical base period yielded poor results (R2 = 0.28), revealing possible serious
vii
overfitting issues. PV was also found to generally underestimate DMPmax when using GCM data as input when compared to runs employing SAWS data. Dtp showed a weak negative trend, implying a tendency for Dtp to occur slightly earlier in the season with time, while MSD revealed weak linear trends over the base period. Using 3-month running means of the Niño 3.4 anomalies as predictor of standardized DMPmax showed real promise as approximately 17.5% of the variation in DMPmax could be explained by the variation in the July-August-September (JAS) Niño 3.4.
With respect to the future periods, the results showed that on average DMPmax will decrease slightly over time under RCP 4.5, while it will increase under RCP 8.5. In terms of grazing capacity, both RCPs revealed that more land will be needed per animal for sustainable farming. The Dtp showed a general shift to later in the growing season under both RCPs. It was also noted that although both RCPs had more MSDs when compared to the base period, there were larger differences observed under RCP 8.5.
It was suggested that active monitoring and good rangeland improvement techniques be utilised by livestock farmers to ensure a good rangeland condition with adequate food supply for livestock. Future work should focus on evaluating other rangeland production models for this region.
Keywords: climate change, global climate models, PUTU VELD, rangeland production model, Themeda-Cymbopogon veld
... Pasture API is a project funded by the CSIRO that aims to provide a platform to bring together sources of spatiotemporal input data for modelling extensive livestock grazing systems of southern Australia, to help address many of the constraints outlined above. Specifically, we focus on the implementation of a platform to increase simplicity and efficiency of use of the GrazPlan ruminant grazing models (Moore et al. 1997). This paper describes the processes that were put in place to produce a digital platform that enables ruminant livestock production to be simulated and outcomes forecast for any chosen location across southern Australia. ...
... The project used the GrazPlan Pasture and Ruminant models (Moore et al. 1997) for simulating livestock grazing scenarios, implemented in the GrassGro TM software application. These simulations are run on a daily time step, and we used the tactical modelling function to simulate probabilistic outcomes over a selected forecast period based on 30 years of historical data; 1 January 1989 to 31 December 2018. ...
Extensive ruminant livestock production is complex, and it is difficult and time-consuming to obtain quantitative information for decision making (e.g. biomass and quality of forage). Increasingly data from historic records, seasonal forecasts, or near real-time data from remote or on-farm sensors. However, there is limited capacity to compile, integrate and analyse these data. In some cases, there are decision support tools available with such analytical capability, but the time required to learn and apply these to gain the benefits is significant, and a major disincentive to their widespread adoption. Based on the need for simple, entry level, decision support for the livestock industry we have developed the Pasture API (Application Programming Interface) platform. The aim of the Pasture API was to build a seasonal pasture forecast system that can provide a forecast to specific locations up to 6 months into the future for anywhere in Australia and made available to a variety of client software packages. The platform is able to forecast a wide range of simulated data outputs including pasture biomass, ground cover, supplementary feeding and livestock growth. The Pasture API application required the integration of a number of both new and existing analytical
capabilities, which are summarised below and described in more detail in this paper.
1) Ruminant grazing simulation engine: The GrazPlan biophysical pasture and ruminant nutrition model (adapted from GrassGroTM software) was repurposed for use as the modelling engine in a backend service infrastructure.
2) Flexible tactical grazing scenarios: The GrazPlan models were incorporated into a new software application for batch processing of tactical grazing scenarios. This application was called GGTactical.
3) Dynamic platform for connecting the pasture simulation engine with data streams: We used CSIRO’s Senaps platform to connect the GGTactical application with a range of spatiotemporal data
streams so that simulation scenario workflows could be implemented.
4) Demonstration interface: A demonstration website (Pasture Tracker) was built to interact with the Pasture API application and implement workflow’s based on location and livestock enterprise details.
Currently the software is hosted internally by CSIRO, with the intention that a version become publicly available in the near future. We demonstrated that Pasture API is able to replicate simulation of a livestock grazing scenario, as can be done with more complex modelling software, such as GrassGroTM. Key production metrics such as net primary productivity (NPP) of pasture, supplementary feeding, ground cover and liveweight of stock were charted. These were reported both as historic percentile values across a season, the now-cast (current) value, and a probabilistic forecast for a predefined period of time (e.g. 3 or 6 months).
To compare the effects on forecasts for various input data streams that were available, a sensitivity analyses was conducted. This provided information about the suitability of more generic data streams (e.g. national soils database) for forecasting, comparing forecast outcomes for those where local data were available.
The Pasture API project demonstrates the ability to create easy to use, yet powerful, decision support systems for the livestock industry. This is a novel integrating technology that we expect to continue to
develop to make use of the many sources of sensor and archive data that are collected within livestock businesses. This information is expected to increase the precision across a range of interventions,
including; stocking density, timing of paddock rotations, and supplementary feeding. In the future we expect to increase the use of local data streams and refine the data delivery processes to produce the site specific information sought by the industry for decision making.
... Asimismo, un reciente estudio regional reportado por Laulhe (2015) demostró la capacidad de DSSAT para simular el rendimiento de MS de festuca en dos localidades del sur de Buenos Aires. Sin embargo, la literatura internacional demuestra que los estudios de modelación en sistemas forrajeros se han centrado principalmente en el SE de Australia (Moore et al., 1997;Pembleton et al., 2013;Islam et al., 2015) y Nueva ...
... Esto permitiría simular la EUP a partir de sus componentes fundamentales, captura y eficiencia de uso, y, de esta manera, analizar los posibles efectos del clima sobre ambos componentes. [Moore et al., 1997]; ...
The growing demand for beef and dairy products requires technological options to improve the productivity and resource use efficiency of forages crops with less environmental impact. Livestock production systems based on forage crop sequences (FCS) could be more productive and efficient than those based on perennial pastures (PP). However, there are many questions about the FCS implementation related to the system stability in the long term and about the root-derived soil organic carbon in these systems. The main objective of this thesis was to provide original knowledge about the main ecophysiological aspects determining forage supply in livestock systems based on the use of FCS and PP. The study was conducted in three steps (i) above-ground dry matter yield (AGDM) and precipitation use efficiency (PUE) were analyzed in Rafaela, Pergamino, General Villegas and Trenque Lauquen, (ii) in Balcarce were evaluated AGDM, below-ground dry matter yield (BGDM), PUE (i.e. water capture [WC] * water use efficiency [WUE]), radiation productivity (RP, i.e. radiation capture [RC] * radiation use efficiency [RUE]) and soil carbon (C) variations in different organic matter fractions. Finally, (iii) Agricultural Production Systems Simulator (APSIM) was calibrated and validated to analyze the accumulated annual precipitation, AGDM and PUE variability using a long-term climate database (30 years). In general, the AGDM was higher for the FCS than the PP treatments, although more variable in the long term. Below-ground dry matter yield was similar for both treatments. Likewise, there was a greater association between the contribution of C and BGDM in sub-surface horizons below than 0,15 m soil depth. The PP treatments shown higher RC and similar WC than the FCS treatments. However, FCS shown higher RUE and WUE, which led to higher RP and PUE. In turn, the PP treatments shown lower inter-annual variability of PUE than FCS in the long term. The multi-environmental analysis on the impacts of different forage cropping systems on PUE, as well as on the soil C variations, provide key knowledge and information to develop management strategies to increase the sustainable productivity of livestock systems in the Argentinean Pampas.
... Hammer et al. (1995);Moore et al. (1997);; Dolling et al. (2005); Peake, Whitbread, et al. (2008); Huth, Banabas, Nelson, and Webb (2014). Similarly, the data of the current study have been employed for a validity check of the wheat and sorghum modules in the APSIM model. ...
With the rapid global trend towards mechanized, continuous and dense cropping
systems that provide agricultural efficiency to meet consumer demand, soil
compaction has become a recognized problem. Soil compaction under modern
machines has had immense impact on productive land‘s physical, chemical and
biological properties, including soil-water storage capacity, fertiliser use efficiency,
and plant root architecture. As a result, farms are experiencing substantially reduced
crop yields and economic returns. The percentage of soil compaction increases with
increased soil clay fraction. Numerous investigations have been conducted to
evaluate the technical, economic and soil-crop efficiency of compaction mitigation
strategies, but deep tillage has not received sufficient consideration, particularly in
relation to high clay content soils.
This study was conducted to technically and economically evaluate a range of deep
ripping systems, and study the effect of tillage on soil and crop grown on cohesive
soils. A series of field experiments were conducted to parametrise a soil tillage force
prediction model, previously developed by Godwin and O‘Dogherty (2007) and the
Agricultural Productions Systems sIMulator (APSIM) developed by the Agricultural
Production Systems Research Unit in Australia (Holzworth et al., 2014; Keating et
al., 2003). The behaviour of soil physical properties, power requirements of ripping
operations and cost, and agronomic and economic performance of sorghum and
wheat were assessed at the University of Southern Queensland‘s research ground in
Toowoomba, Queensland (Australia) over two consecutive seasons (2015-16 and
2016-17). The work was conducted by replicating the soil conditions commonly
found in non-controlled or ‗random‘ traffic farming systems, referred to as RTF.
Sorghum was also grown at a commercial farm located in Evanslea near
Toowoomba, under controlled traffic (CTF) conditions (a farm system based on a
permanent lanes for machinery traffic) during the 2018 summer crop season.
The soil types at the two sites are Red Ferrosol (69.1% clay, 10.0% silt, and 20.9%
sand) and Black Vertosol (64.8% clay, 23.4% silt, and 11.8% sand). Three levels of
deep ripping depth, namely, Deep Ripping 1 (D1= 0-0.3 m), Deep Ripping 2 (D2= 0-
0.6 m), and Control (C= no ripping) were applied using a Barrow single tine ripper at
the Ag plot site - USQ, and a Tilco eight-tine ripper was used at the Evanslea site.
The tillage operations were performed at 2.7 km/h. A predetermined optimum N
fertiliser rate was applied after sorghum and wheat sowing at the Ag plot site. The
field experiments were conducted according to the randomized complete block
design (RCBD). The Statistical Package for Social Scientists (SPSS) software was
utilized to analyse the significance of the differences between the variables at the
probability level of 5% as the least significant difference (LSD).
The statistical analysis results showed that the D2 treatment significantly reduced
soil bulk density and soil strength by up to 5% and 24% for Red Ferrosol soil, and by
up to 6% and 40% for Black Vertosol soil respectively, and increased water content
compared with the D1 and C treatments. Overall results showed that D2 was superior in ameliorating the properties of both soils. In both soils, energy requirement results showed that tillage draft force and tractor power requirements were dependent on tillage depth, but for both tillage treatments, energy consumption was slightly lower for the CTF system (Evanslea site) than the RTF system at Ag plot site.
Crop performance results showed that at the Ag plot site, the grain and biomass
yields were highest by up to 19% for sorghum and by up to 30% for wheat when the
D2 treatment was applied, compared to the D1 and C treated crop yield components. Also, the grain and biomass yields were highest for fertilised soil by up to 10% for sorghum and by up to 16% and 25% for wheat respectively, in comparison with the non-fertilised treatments soils yield. Fertilising of D2 treated soil produced the highest significant yield of sorghum grain (5360 kg/ha), biomass (13269 kg/ha),
wheat grain (2419 kg/ha), and biomass (5960 kg/ha) compared to the yield of the
other treatment interactions. However, at Evanslea site, the D1 treatment showed
significantly higher yield and yield components for sorghum compared with C
practice (by up to 17% higher yield), and no differences were observed for treatment
D2. Economically, the D1 treatment required the lowest total operational cost at both
sites, which was estimated at AUD125/ha and AUD25.8/ha at the Ag plot and
Evanslea sites, respectively. These results compare to AUD139.3/ha (Ag plot) and
AUD30.8/ha (Evanslea) for the D2 ripping system. With regard to economic returns,
at the Ag plot site, D2 yielded the highest sorghum gross benefit (AUD1422/ha) and
net benefit (AUD1122/ha), wheat gross benefit (AUD590/ha) and net benefit (AUD482.3/ha), 2017 season gross benefit (AUD 2011.7/ha) and 2017 season net
benefit (AUD 1604.7/ha), compared to D1 and C soil benefits. The economic
fertiliser application at this site achieved the highest gross benefit for sorghum
(AUD1384.2/ha), wheat (AUD555.6/ha), and 2017 season (AUD1939.8/ha)
respectively, in comparison with the non-fertilised soils‘ total return. Also, fertilised
D2 treated soil resulted in the highest sorghum gross benefit (AUD1512.9/ha) and
net benefit (AUD1170.3/ha), wheat gross benefit (AUD633.7/ha) and net benefit
(AUD492.4/ha), 2017 season gross benefit (AUD2146.6/ha), and net benefit
(AUD1662.7/ha) compared to other interactions‘ benefits. At the Evanslea site, D1
significantly increased sorghum gross benefit and net benefit by up to 17%
(AUD2277.9/ha) and by up to 20% (AUD1825.5/ha), respectively compared to C
benefits, and no differences were observed with treatment D2.
The average of APSIM derived results for the long-term (1980-2017) at the Ag plot
site showed that the D2 treatment reported consistently higher grain sorghum (4192
kg/ha), biomass (11454 kg/ha), wheat grain (3783 kg/ha), and biomass (10623
kg/ha), compared to the D1 and C treatments‘ yields under the same long-term
conditions. However, at the Evanslea site, for long-term (1980-2018), APSIM
simulation showed that D1 treatment increased the yield of sorghum grain and
biomass significantly by up to 10% (5823 kg/ha) and 11% (12171 kg/ha),
respectively compared to C treatment‘s production, but these increases were found
not significant with the D2 yields‘ components. APSIM model simulation of field
experiment conditions during 2017 season at the Ag plot site showed that the D2
treatment also had the highest significant yield of sorghum grain (5284 kg/ha),
biomass (12488 kg/ha), wheat grain (2341 kg/ha) and biomass (6081 kg/ha)
compared to the C and D1 crop yields. Similarly, APSIM model simulation of field
experiment circumstances during the 2018 season at the Evanslea site showed that
the D1 treatment produced the highest yield of sorghum grain (7129 kg/ha), biomass
(13364 kg/ha) yields, compared to the C and D1 crop yields.
Overall, both the long and short-term model outputs were in good agreement with
experimental data, suggesting beneficial effects of deep tillage in improving cereal
crops‘ productivity in this region. Moreover, in comparison with the study findings,
the model prediction error rate was ±7, which indicates that the developed model
approach is valid and calibrated during this study.
Results derived from the G&O soil tillage mechanics model under the Ag plot and
Evanslea soil conditions showed that the required tractive force increases with the
increasing operation working depth. Furthermore, the D1 was superior, requiring the
lowest draft force at Ag plot (7.48 kN) and Evanslea (19.65 kN) soils, compared to
the D2 required forces which were 43.28 kN and 41.41kN at both sites, respectively.
In general, the model values were in line with the experiments' draft forces and when compared with the study readings, the model prediction error rate was ±8, which indicates that it is also valid and calibrated during this study.
Finally, the study provides conclusions and recommendations that contribute to crop production improvement in the face of recurrent and increasing challenges, as well as emphasizing the necessity of correct management and cultivation of economically important crops after the application of deep ripping to produce accurate results that serve decision-making in the agricultural sector.
... Forecasts based on the full probability distribution have been promoted as a complement or alternative to categorical probability formats for the USA (Barnston et al., 2000;Hartmann et al., 2002;National Academy of Sciences, 2006), and were adopted alongside the tercile convention by NOAA's Climate Prediction Center in the 1990s (Barnston et al., 2000). Within agriculture, POE graphs had been widely used in weather-driven agricultural modeling applications agricultural decision support tools by the mid-2000s, particularly in Australia and the USA (e.g., Moore et al., 1997;Paz et al., 2007;Breuer et al., 2008;Carberry et al., 2009;Hochman et al., 2009). Yet the tercile convention, and other probabilistic categorical formats (e.g., probability of exceeding median) have dominated forecast information generated by global, regional and national climate institutions. ...
We describe an innovative forecast presentation that aims to overcome obstacles to using seasonal climate forecasts for decision making, trace factors that influenced how seasonal forecast conventions have evolved, and describe a workshop process for training and supporting farmers in sub-Saharan Africa to use probabilistic seasonal forecasts. Mainstreaming seasonal climate forecasts through Regional Climate Outlook Forums (RCOFs) was an important milestone in the development of climate services. Most RCOFs and National Meteorological Services (NMS) adopted a subjective process to arrive at a consensus among different sources of prediction, and express the forecast as probabilities that rainfall in the upcoming season will fall in “below-normal,” “normal” or “above-normal” historical tercile categories. The Flexible Forecast is an online presentation that rectifies the main criticisms of the tercile convention by presenting downscaled forecasts as full probability distributions in probability-of-exceedance format along with the historical climate distribution. A map view provides seasonal forecast quantities, anomalies, or probabilities of experiencing above or below a user-selected threshold in amount or percentile, at the spatial resolution of the underlying gridded data (typically 4 to 5 km). We discuss factors that contributed to the persistence of the tercile convention, and milestones that paved the way to adopting seasonal forecast methods and formats that better align with user needs. The experience of adopting the new flexible forecast presentation regionally and at a national level in Eastern Africa illustrates the challenges and how they can be overcome. We also describe a seasonal forecast training and planning workshop process that has been piloted with smallholder farmers in several African countries. Beginning with participants' collective memory of past seasonal climate variations, the process leads them incrementally to understand the forecast presented in probability-of-exceedance format, and apply it to their seasonal planning decisions.
... [ [65][66][67][68] Software GHG emissions Software and models to assess the GHG emissions of the farm based on the management and provision of recommendations on how to reduce them. There is a growing interest in assessing the GHG emissions of farming activities. ...
Grasslands are of key importance for the provision of ecosystem services (ES). Suitable management is essential to guarantee their persistence and functionality. There is a growing interest in innovations such as new technologies aimed at facilitating and improving the management of grasslands while increasing their provision of ES. The uptake of innovations by farmers is a complex process, and relevant socio-economic or technological factors that are crucial to farmers are often overlooked. This information can be useful for increasing the adoption of these innovations through the design of public policies to facilitate them. This paper analyses the relevance of the main innovations that can be applied to the management of the grasslands of Dehesa farms for the farmers and the factors that might affect this relevance. Through questionaries, we gathered information on the relevance that farmers give to the selected innovations and analysed it by cumulative link models. The results show that innovations aimed at increasing the biomass production of grasslands and resilience such as the use of seed mixtures and the use of forage drought-resistant species are considered highly relevant by Dehesa farmers. However, high-tech innovations such as GPS collars were poorly rated which could denote low applicability to the context of Dehesas or the existence of barriers hindering the adoption but also a need for further development and better information on their potential. Characteristics of the farmer and farm such as age, education level, and stocking rate seem to be related to the relevance given to some of the innovations. These results provide insightful information for the implementation and research of relevant grassland-related innovations in the context of Mediterranean Dehesa/Montado systems, as well as for the design of policies supporting them.
... Simulation models have been used to explore the value other crops to fill feed gaps when grown in a range of environments (Bell, 2008;Bell et al., 2018;Martin and Magne, 2015). While several simulation models are available to predict growth and nutritive value of a range of forages and pastures grown in dairy (e.g., DairyMod; Johnson et al., 2008), temperate pasture-livestock (e.g., GrassGro; Moore, Donnelly and Freer, 1997) and rangeland grazing systems (e.g., GRASP;McKeon et al., 2000;Rickert, Stuth and McKeon, 2000), only DairyMod has the capacity to simulate forage brassica crops, and this is limited to spring-summer sown crops grown in temperate environments. The Agricultural Production Systems Simulator (APSIM; Holzworth et al., 2014) is a well-known cropping systems model that is widely used for simulating broadacre crops across a range of environments and has become an important decision-support tool in cropping systems, and crop-livestock systems where it can be linked with other forage and livestock simulation models via a shared binary protocol (Moore et al., 2007). ...
Forage brassicas have historically been used in high rainfall/irrigated temperate livestock systems, but there is increasing interest in diverse forage brassicas in drier mixed crop-livestock farming systems. Computer-based modelling is an important decision support tool used in agriculture to explore the adaptability of crops to different climates and agronomic management practices, but existing modelling tools for forage brassicas are limited to temperate environments. We parameterised the APSIM (Agricultural Production Systems Simulator) model for four forage brassica genotypes, including three diverse forage rape cultivars and a raphanobrassica. The model was calibrated using two experiments with repeated measures of biomass components, nutritive value, and leaf and canopy development. We then tested the model extensively using data from a diverse set of environments within Australian and New Zealand (23 sites across four agro-climatic zones). Model predictions of biomass were good for all the genotypes (NSE > 0.60, Nash-Sutcliffe efficiency; RMSE ~1.5 t DM/ha, root mean square error). Predictions of metabolisable energy yield were satisfactory for all genotypes (NSE 0.43–0.73; RMSE ~17.8 GJ ME/ha) but forage dry matter digestibility (DMD) were poorly predicted due to the small variation in observed data. Our robust and widely tested model can be confidently used to predict forage productivity of common and new forage brassicas across a wide range of production environments and agronomic management practices. This model will enable future work to develop a better understanding of the potential value of these important forage crops for livestock production systems.
... There are many approaches to evaluate farm management, ranging from simple field experiments, benchmarking (Kahan 2013) and gross margins (DPI 2020) to more complex system modelling (Kingwell and Pannell 1987;Moore et al. 1997). Benchmarking can provide general I insights about farm management by comparing productivity of different businesses. ...
Sheep stocking rate influences farm profit significantly; however determining the optimal stocking rate is a difficult task. In this paper, we address this challenge through three main steps. First, we review the definition of stocking rate; second, we examine prior research relevant to the review topic and highlight the factors that need to be considered when determining the optimal stocking rate; and third, we make recommendations for improvements in research on establishing the optimal sheep stocking rate. Inconsistency in the definition of stocking rate can lead to miscommunication among researchers, advisers and farmers. If 10 dry sheep equivalents (DSE)/ha is optimal for one flock, it may not be optimal for another flock because the DSE measure does not fully capture the nuances of different patterns of nutritional requirements among sheep classes and feed availabilities and their respective prices and costs. The optimal stocking rate occurs when the marginal economic benefit of an additional animal equals its marginal cost. Determining this point requires an understanding of the quantity and quality of feed available throughout the year, the optimal liveweight profile throughout the year, the impact of seasonal variation, the impact of labour availability, the cost of alternative feeds, prices of livestock and livestock products, the risk preferences of the farmer, and any emission policies relating to greenhouse gases. Farmers tend to use their own judgement to set their stocking rates, with the aim of maximising utility. However, the complexities listed make it a challenging task. Thus, researchers have used various simulation and programming models to aid decision-making over optimal stocking rates, but most farmers continue to rely on their own personal judgement. Moreover, often a focus of this modelling is for sheep systems in eastern Australia. Generalising this research across Australia is difficult due to differences in climatic conditions and markets across Australia. Often farmers are unaware of the profits they are foregoing when choosing either an overly conservative or excessive stocking rate. Our research has shown that foregone income of up to AUD50 per hectare can occur when a stocking rate 30% below or above the optimum is selected. Thus, despite the complexities that underpin the stocking rate decision, we believe that there are potential rewards from further research on the optimisation of stocking rates.
... Commonwealth Scientific and Industrial Research Organisation's (CSIRO) AusFarm TM model (version No. 1.4.13) was used for this study as it enables integration of the GrassGro TM Moore et al. 1997) and APSIM (Keating et al. 2003;McCowan et al. 1995) modules, while providing additional functionality to enable tailoring of the model to be representative of the farming system and associated management decision making. AusFarm operates on a daily timestep where in addition to simulating biophysical performance, modules can be built to represent the management structure and decisions in complex mixed farms. ...
This paper is part of a broader study that uses AusFarm, a complex biophysical model, to model a mixed irrigation farming system in the Murray Irrigation Area and Districts (Murray Irrigation Limited (MIL) post 1995). The study is unique is that the biophysical model has been coupled to an economic module that includes an irrigation sequence model and pricing function. The study has enabled the examination of water market tools that irrigators have available to them in the MIL ('temporary water' and 'carry over'), and the importance of these tool in managing future risk under changing climates. This paper shows that a combination of water market tools will be an important component in managing risk under changing climate, at least under a mid-range emissions scenario with modest reductions in irrigation water availability. For example, the modelling in this paper shows that having both the ability to carry over water to the following water year, and access to temporary water markets increases the likelihood of sowing opportunities and yield gains for the summer irrigated crop for both historical and projected climate periods. Access to these tools will become crucial as irrigators manage future water supply and demand risk under the impact of climate change and changes to water policy.
... Models already exist that could be adapted to provide seasonal and yearly predictions of vegetation production in the Nature Reserves (e.g. Moore et al. 1997;Hill et al. 2004). However, the predictions from these models would have to be tested with local data. ...
Populations of macropods are higher than estimated pre-European densities in many parts of Australia. To achieve appropriate densities of macropods in the Australian Capital Territory's nature reserves, multi-tenure kangaroo management units are used to tailor management of kangaroos and total grazing pressure to achieve conservation objectives. An adaptive management framework is recommended that monitors the state of the ground-layer vegetation and alters the cull accordingly. This case study may provide insights for kangaroo management in other temperate areas of Australia.
... Moore et al., 2007) using six plant models (annual pasture legumes; Table 1) × seven sites (Fig. 1). The GrassGro™ model is a ruminant grazing model that has been developed for extensive livestock systems of southern Australia (Moore et al., 1997;Mokany et al., 2010). GrassGro™ is comprised of components that describe the biophysical (climate, soils and land management units (paddocks), pastures, livestock), managerial (e.g., stocking rate, soil fertility, pasture grazing rotations and animal reproductive management) and financial subsystems, which form the 'farm system' under consideration. ...
CONTEXT
The nutritive value of annual pasture legumes changes with phenology during each season, with a progressive decrease in feeding value as plants accumulate structural carbohydrates and nutrients are diluted. However, these changes differ widely among annual legume species, affecting the pattern of nutrient availability and production in a grazing enterprise.
OBJECTIVE
This paper set out to examine the implications of differences in the nutritive value of annual pasture legumes in relation to their economic value for mixed farming systems of southern Australia.
METHODS
Calibrated GrassGro™ plant models with data generated from field experiments to represent Dry Matter Digestibility (DMD) profiles of six annual legume species; Medicago truncatula Gaertn. (barrel medic), B. pelecinus L. (biserrula), Trifolium spumosum L. (bladder clover), Ornithopus sativus Brot. (French serradella), Medicago littoralis Rhode ex Loisel. (strand medic) and Trifolium subterraneum L. (subterranean clover). The plant models were used in a modelling study to support a self-replacing Merino sheep enterprise, which was simulated at seven locations across southern Australia, where annual pasture species are heavily relied on for livestock production.
RESULTS AND CONCLUSIONS
Modelled values of DMD of green material, averaged across all phenological stages, ranged from approximately 62% (French serradella) to 73% (bladder clover), with little variation due to location. Pastures with higher nutritive value had a reduced requirement for supplementary feeding of sheep. Variation in nutritive value had a greater effect on supplementary feeding costs and gross margins than differences in biomass production among the annual legume species. Differences in the levels of supplementary feeding required in systems with the different legumes was most pronounced during the period from May to July (late autumn to winter). For sheep enterprises compared at the same stocking rate, the total supplementary feed required was 18% lower with bladder clover pastures, and 9% lower in subterranean clover pastures, compared with an average of the remaining four pasture species.
SIGNIFICANCE
This is the first study to compare the effects of variation in nutritive value among the annual pasture legumes developed for the soils and climatic conditions of southern Australia. Our results demonstrated that the magnitude of differences in nutritive value among annual pasture legumes are economically important and that the establishment of higher nutritive value legumes will reduce supplementary feeding costs in extensive grazing systems, in some cases even where their biomass production is lower.
... The livestock sub-models are based on many of the equations described by the modified Technical Paper Freer et al. (2012) and Freer et al. (2007), both of which superseded the preceding publication, Freer et al. (1997). These publications represent a revised version of the original report by SCA (1990) and fundamentally describes the functions used in the GrazPlan suite of decision support tools Moore et al., 1997), which have been broadly applied and shown to adequately predict ruminant livestock performance under diverse environments. This was required to ensure there were adequate feedback mechanisms between the selective grazing by livestock and changes in botanical composition, grassland quantity and growth. ...
A model developed as part of the ACIAR projects LPS/2008/048 Sustainable Livestock Grazing Systems on Chinese Temperate Grasslands & ADP/2012/107 Strengthening incentives for improved grassland management in China and Mongolia
... Atmospheric carbon dioxide concentrations were assumed to be 350 ppm for Baseline, 380 ppm for Recent, 450 ppm for 2030 scenarios and 530 ppm for 2050 scenarios. Pasture growth patterns were simulated using the Grassgro biophysical model (Moore et al. 1997), which has been previously applied Table 1 Long-term average rainfall (mm) and temperature (°C) statistics for the Baseline and Recent scenarios, together with rainfall (percentage) and temperature (°C) changes relative to Baseline (1986Baseline ( -2005 for the future projections at Violet Town, Victoria. XX-YY (2021) to the analysis of climate change impacts in southern Australia (e.g., ). ...
In recent decades Australia has experienced warmer temperatures and, in southern Australia declining rainfall, and climate change projections indicate that these trends are likely to continue. In southern Australia pasture growth patterns have changed with increased winter production but a contraction of the spring growing season and increased inter-annual variability of production. A range of options have been investigated to adapt farm businesses to the changing climate including feedbase, livestock management and diversification. The challenge for adaptation research is to better understand impacts and adaptation options for increases in extreme climate events, such as heatwaves, drought and intense rainfall events.
... The latest technological advances in Geographical Information Systems, Remote Sensing, Decision Support Systems, and web-based applications allow more robust, precise, and sustainable interventions in agriculture in terms of where to farm and what crops are most suitable for cultivation. DSS in agriculture is driven by computer-based data systems that aim to solve unstructured problems and improve the performance of decision-makers [39,40,41,42]. GIS serves as a tool for input, storage and retrieval, manipulation, and analysis and output of spatial data while RS provides information on various spatial criteria/factors being considered [43]. ...
Sub-optimal land can be defined as land that naturally has low productivity due to internal (intrinsic) factors such as parent material, physical, chemical, and biological properties of soil, and external factors such as rainfall and extreme temperatures. For that reason, it is necessary to map foreknow the various types of sub-optimal dry land uses and their size in the district Aceh Besar. Administration maps, land use maps, slope maps, and soil type maps would later be overlaid and digitized on the screen to obtain the map and the area of research to be carried out. The slope gradient was limited to only 25% due to looking conditions that allow it to be used in the management business agriculture. The forest area was 19,136.65 ha, the dry land agricultural area was 89,472.15 ha, area open land of 1,070.75 ha, and scrub area of 58,840.87 ha. It can be observed that land use in the Aceh Besar district is dominated by Dryland farming.
... APSIM-Soybean belongs to the PLANT family of crop modules, which simulates crop growth on a daily time-step per unit area in response to climate, soil water supply, and soil nitrogen (Robertson et al., 2002). The integrated AusFarm Pasture module recognizes four functional groups of forage plants-annuals, perennials, grasses, and forbs-and models phenological development according to daily environmental variables (Moore et al., 1997). Model configuration for soil and production parameters for the integrated system were based on the moderately grazed treatments, where sward heights were maintained between 20 and 30 cm, and from the specialized control system (Supplementary Tables 1, 2). ...
Integrated crop–livestock systems are a form of sustainable intensification of agriculture
that rely on synergistic relationships between plant and animal system elements to
bolster critical agroecosystem processes, with potential impacts on resilience to weather
anomalies. We simulated productivity dynamics in an integrated cover crop grazing
agroecosystem typical of southern Brazil to gain a better understanding of the impacts of
livestock integration on system performance, including future productivity and resilience
under climate change. Long-term historical simulations in APSIM showed that the
integrated system resulted in greater system-wide productivity than a specialized control
system in 77% of simulated years. Although soybean yields were typically lower in
the integrated system, the additional forage and livestock production increased total
system outputs. Under simulated future climate conditions [representative concentration
pathway 8.5 (RCP8.5) scenario from 2020 to 2060], integrated system productivity
exceeded specialized system productivity in 95% of years despite declines in average
soybean yield and aboveground cover crop biomass production. While the integrated
system provided a productivity buffer against chronic climate stress, its resilience
to annual weather anomalies depended on disturbance type and timing. This study
demonstrates the utility of process-based models for exploring biophysical proxies for
resilience, as well as the potential advantages of livestock integration into cropland as a
sustainable intensification strategy.
... Because of the complexity of diverse agricultural pastures, functional-structural plant modeling represents an important tool to synthesize and integrate knowledge and to recognize research problems (Evers et al., 2019). This approach emerged from single species or growth forms, continuing with models that predicted the behavior of simple mixes considering each species separately, which represented a great complexity of inputs and outputs for highly diverse pastures (Moore et al., 1997). More recently, modeling efforts have focused on functional-structural approaches under the assumption that diverse pasture functioning can be explained by the mean value of biological attributes (i.e., functional traits) of its constituent forage groups (Jouven et al., 2006). ...
Pasture-based production systems represent a significant sustainable supplier of animal source foods worldwide. For such systems, mounting evidence highlights the importance of plant diversity on the proper functioning of soils, plants and animals. A diversity of forages and biochemicals –primary and secondary compounds- at appropriate doses and sequences of ingestion, may lead to benefits to the animal and their environment that are greater than grazing monocultures and the isolated effects of single chemicals. Here we review the importance of plant and phytochemical diversity on animal nutrition, welfare, health, and environmental impact while exploring some novel ideas about pasture design and management based on the biochemical complexity of traditional and non-traditional forage sources. Such effort will require an integration and synthesis on the morphology, ecophysiology, and biochemistry of traditional and non-traditional forage species, as well as on the foraging behavior of livestock grazing diverse pasturelands. Thus, the challenge ahead entails selecting the “right” species combination, spatial aggregation, distribution and management of the forage resource such that productivity and stability of plant communities and ecological services provided by grazing are enhanced. We conclude that there is strong experimental support for replacing simple traditional agricultural pastures of reduced phytochemical diversity with multiple arrays of complementary forage species that enable ruminants to select a diet in benefit of their nutrition, health and welfare, whilst reducing the negative environmental impacts caused by livestock production systems.
... Most critically, these tools assist producers to better understand the likely responses to additional inputs, especially feed and stocking rates. Other industry models include GrassGro and Grazfeed developed by the CSIRO (Moore et al. 1997), the Cornell Net Energy and Protein System (CNCPS 2012) and various feeding standards including texts such as NRC for Beef Cattle (1996). The application of feeding standards and understanding basic nutrition will assist producers to make sound decisions about the use of supplementary feeds and to feed with confidence (Chapter 16). ...
The Chapter describes the beef market in Australia
... A range of existing biophysical models was tailored to the case studies to generate these inputs. APSIM ) was used to describe northern grains, sugar and cotton; AusFarm (Moore et al. 1997;Moore et al. 2007;Moore et al. 2014) for southern beef and lamb; GRASP (Littleboy and McKeon 1997) for northern beef; and yield potential algorithms (French and Schultz 1984) for southern grains. Further details are contained in the Supporting Information (see 'Biophysical modelling' section for each case study). ...
Seasonal climate forecasts (forecasts) aim to reduce climate‐related productivity risk by helping farmers make decisions that minimise losses in poor years and maximise profits in good years. Most Australian forecast valuations have focused on fertiliser decisions to wheat operations, and few assessments have evaluated the benefit of incremental improvements of forecast skill. These gaps have limited our understanding of forecast value to the broader agriculture sector and the benefit of investments to improve forecast skill. To address these gaps, we consistently assessed forecast value for seven Australian case studies (southern grains, northern grains, southern beef, northern beef, lamb, cotton, and sugar). We implemented a three‐stage methodology which consisted of engagement with industry practitioners; modelling production under different climatic and environmental conditions; and economic modelling to evaluate forecast value for eleven levels of forecast skill. Our results show that forecast value was often low and highly variable. Value was found to vary based on forecast attributes (forecast skill, resolution and state), industry application and prevailing conditions (environmental and market). This is the first Australian valuation study where the same methodological approach was applied across multiple industries, incremental improvements in skill were valued, and prevailing conditions were explicitly evaluated for impact on value.
... The TOC occurs in forage-based CPR systems due to exploitation by users via livestock overgrazing. These forage-based CPR problems become increasingly difficult to manage due to feedback between forage resources, animal performance and economic outcomes, and environmental factors such as temperature and rainfall that regulate forage growth [8]. Trade-offs exist between forage productivity and animal performance based on grazing intensity [9][10][11][12], as well as other factors such as diet selection, nutrient intake and growth stage [13][14][15][16][17][18][19][20][21]. ...
There exist common-pool resource systems where it is difficult to prevent prospective beneficiaries from receiving profits from the use or harvest of shared resources, and they are often subject to continual utilization, leading to resource degradation and economic erosion (a behavior known as the 'tragedy of the commons'). Nigerian nomadic grazing systems currently undergoing the tragedy of the commons pose a great challenge to agrarian communities, herders and political stability throughout the country due to violent conflicts and property destruction as herders migrate in search of forage resources for livestock. We modeled these dynamics in order to better understand the Nigerian grazing lands, with the objective of identifying potential leverage points capable of reversing overgrazing-induced forage degradation, in order to ensure a sustainable livestock production sector. Model what-if experiments (crop restrictions, crop marketing and increased labor costs) were run, resulting in partial solutions that were effective only in the short-term or limited in geographic-scope. A sustainable solution should include a combination of strategies, as the impact of one strategy alone cannot effectively resolve these Nigerian grazing issues (e.g., collaboration between farmers, herdsmen and government stakeholders to increase market integration via crop market expansion while simultaneously providing forage regeneration time for grazing lands). The resulting model could be used by Nigerian policy-makers to evaluate the long-term effects of decisions which were previously unexplored.
... GrassGro simulates pasture growth based on soil information and historical daily climatic data (Moore et al. 1997). Output from a GrassGro simulation of a merino wether enterprise was used as a basis for the analysis. ...
Early warning of drought would generate major economic gains to the wool and other agricultural industries by permitting better decision-making before and during adverse seasons. An individual wool producer's success in managing drought lies in taking early action to avoid price discounts on culled stock and price premiums on fodder, while keeping remaining stock in a productive condition. The GrassGro decision support tool was used to investigate the value of a climatic prediction rule to trigger management changes in a wool enterprise. The rule is that 'Poor spring conditions tend to follow winters with low available soil moisture and a negative or falling Southern Oscillation Index phase'. Simulations of typical wool production systems were conducted for Rutherglen and Colbinabbin in Victoria, for each year climatic data was available (99 and 90 years respectively). Estimates of returns from using the rule to trigger the following three management actions were made: i. Fodder purchase, ii. Destocking, and iii. A combined strategy. Analysis was conducted under different stock and grain price scenarios and comparisons were made to a 'perfect knowledge' scenario. For Rutherglen an average net gain was estimated from using the rule for various scenarios for each strategy. Results were less favorable for Colbinabbin. However negative cashflow was estimated for many years at both sites. While this reduces the attractiveness of using the rule rigidly, more flexible use is expected to be more beneficial. For example farmers should be on heightened alert when the rule is triggered, but not necessarily commit to significant changes. The rule may also contribute to reduced stress on farm families and enhance environmental benefits.
... For each climate scenario, we simulate pasture growth using GrassGroÒ (version 3.2.6) (Moore et al. 1997) and crop yields using APSIM (Agricultural Production Systems Simulator, version 7.5) (Keating et al. 2003). These process-based models use soil data and daily weather data to drive detailed projections of the growth of crop and pasture plant biomass and its conversion into saleable products. ...
Agricultural research on climate change generally follows two themes: (i) impact and adaptation or (ii) mitigation and emissions. Despite both being simultaneously relevant to future agricultural systems, the two are usually studied separately. By contrast, this study jointly compares the potential impacts of climate change and the effects of mitigation policy on farming systems in the central region of Western Australia’s grainbelt, using the results of several biophysical models integrated into a whole‐farm bioeconomic model. In particular, we focus on the potential for interactions between climate impacts and mitigation activities. Results suggest that, in the study area, farm profitability is much more sensitive to changes in climate than to a mitigation policy involving a carbon price on agricultural emissions. Climate change reduces the profitability of agricultural production and, as a result, reduces the opportunity cost of reforesting land for carbon sequestration. Nonetheless, the financial attractiveness of reforestation does not necessarily improve because climate change also reduces tree growth and, therefore, the income from sequestration. Consequently, at least for the study area, climate change has the potential to reduce the amount of abatement obtainable from sequestration – a result potentially relevant to the debate about the desirability of sequestration as a mitigation option.
... from https://academic.oup.com/jas/advance-article-abstract/doi/10.1093/jas/skz092/5382308 by guest on 18 decision support systems (WFDSS) use a multi-objective modeling approach in which independent DSS are systematically and harmoniously integrated into a highly aggregated platform to simulate specific operations within the boundary of a farm, ranch, or basin. As shown inFigure 3, several WFDSS have been developed for ruminant production, including the Agricultural Production Systems Simulator (APSIM) (Moore et al., 2007), Australian Dairy Grazing Systems (DairyMod) (Johnson et al., 2008), DairyNZ Whole Farm Model, Discrete Event Simulation Environment (DIESE) (Martin-Clouaire and Clouaire, 2009), EcoMod (Johnson et al., 2008), Farm Assessment Tool (FASSET)(Berntsen et al., 2003), GRAZE(Loewer, 1998), GRAZPLANMoore et al., 1997), Great Plains Framework for Agricultural Resource Management (GPFARM)(Andales et al., 2003), Hurley Pasture Model (HPM)(Thornley, 1998), Integrated Farm System Model (IFSM)(Rotz et al., 1999;Rotz et al., 2005), LINCFARM, Pasture Simulation (PaSim)(Graux et al., 2011), PROGRASS, Sustainable Grazing Systems (SGS)(Johnson et al., 2003), and Whole Farm Model (WFM). ...
This paper outlines typical terminology for modeling and highlights key historical and forthcoming aspects of mathematical modeling. Mathematical models (MM) are mental conceptualizations, enclosed in a virtual domain, whose purpose is to translate real-life situations into mathematical formulations to describe existing patterns or forecast future behaviors in real-life situations. The appropriateness of the virtual representation of real-life situations through MM depends on the modeler’s ability to synthesize essential concepts and associate their interrelationships with measured data. The development of MM paralleled the evolution of digital computing. The scientific community has only slightly accepted and used MM, in part because scientists are trained in experimental research and not systems thinking. The scientific advancements in ruminant production have been tangible but incipient because we are still learning how to connect experimental research data and concepts through MM, a process that is still obscure to many scientists. Our inability to ask the right questions and to define the boundaries of our problem when developing models might have limited the breadth and depth of MM in agriculture. Artificial intelligence (AI) has been developed in tandem with the need to analyze big data using high-performance computing. However, the emergence of AI, a computational technology that is data-intensive and requires less systems thinking of how things are interrelated, may further reduce the interest in mechanistic, conceptual MM. AI might provide, however, a paradigm shift in MM, including nutrition modeling, by creating novel opportunities to understand the underlying mechanisms when integrating large amounts of quantifiable data. Associating AI with mechanistic models may eventually lead to the development of hybrid mechanistic machine-learning modeling. Modelers must learn how to integrate powerful data-driven tools and knowledge-driven approaches into functional models that are sustainable and resilient. The successful future of MM might rely on the development of redesigned models that can integrate existing technological advancements in data analytics to take advantage of accumulated scientific knowledge. However, the next evolution may require the creation of novel technologies for data gathering and analyses and the rethinking of innovative MM concepts rather than spending resources in collecting futile data or amending old technologies.
... Recent technological advancements in Geographic Information System (GIS), Remote Sensing (RS), Decision Support System (DSS) and web-based application have allowed more powerful, precise and sustainable intervention in agriculture in terms of where to farm and which crop is the most suitable. DSS in agriculture is data driven computer-based systems that aims to solve unstructured problems and improve the performance of decision makers (De La Rosa, Mayol, Diaz-Pereira, Fernandez, & De La Rosa, 2004;Jones, 1993;McCown, 2002;Moore, Donnelly, & Freer, 1997). GIS serves as a tool for input, storage and retrieval, manipulation and analysis and output of spatial data while RS provides the information about the various spatial criteria/ factors under consideration (Malczewski, 2004). ...
Agricultural land suitability analysis (ALSA) for crop production is one of the key tools for ensuring sustainable agriculture and for attaining the current global food security goal in line with the Sustainability Development Goals (SDGs) of United Nations. Although some review studies addressed land suitability, few of them specifically focused on land suitability analysis for agriculture. Furthermore, previous reviews have not reflected on the impact of climate change on future land suitability and how this can be addressed or integrated into ALSA methods. In the context of global environmental changes and sustainable agriculture debate, we showed from the current review that ALSA is a worldwide land use planning approach. We reported from the reviewed articles 69 frequently used factors in ALSA. These factors were further categorized in climatic conditions (16), nutrients and favorable soils (34 of soil and landscape), water availability in the root zone (8 for hydrology and irrigation) and socioeconomic and technical requirements (11). Also, in getting a complete view of crop's ecosystems and factors that can explain and improve yield, inherent local socioeconomic factors should be considered. We showed that this aspect has been often omitted in most of the ALSA modeling with only 38% of the total reviewed article using socioeconomic factors. Also, only 30% of the studies included uncertainty and sensitivity analysis in their modeling process. We found limited inclusions of climate change in the application of the ALSA. We emphasize that incorporating current and future climate change projections in ALSA is the way forward for sustainable or optimum agriculture and food security. To this end, qualitative and quantitative approaches must be integrated into a unique ALSA system (Hybrid Land Evaluation System-HLES) to improve the land evaluation approach.
... Preliminary simulations were conducted in GrassGro® ( Moore et al., 1997) and then imported into AusFarm® a biophysical whole-farm systems model (Herrmann, 2013). The rumen model of AusBeef ( Nagorcka et al., 2000;Nagorcka and Zurcher, 2002;Nagorcka 2004aNagorcka , 2004bDougherty et al., 2017a) was re-engineered into modular C ++ code and linked with AusFarm®. ...
Long-term effects of dietary supplements on productivity, economics, and greenhouse gas (GHG) emissions of 2 beef enterprises were simulated, using AusBeef integrated with AusFarm®, across 30 years: Enterprise 1. Angus steers (1.5 head/ha) in New South Wales, Australia, grazing for 238 days/year, and Enterprise 2. British x Charolais steers (1.0 head/ha) in California, USA, grazing for 148 days/year. Simulation effects of 3 supplements with potential to reduce enteric methane (CH4) emissions were evaluated: (1) nitrate (NO3¯), (2) lipid, and (3) NO3¯ + lipid. All supplementation effects were evaluated against a baseline simulation (i.e., no supplement). Results on beef production, rumen products, GHG emissions, and enterprise gross margins are reported. Simulations indicated that supplementing steers with lipid alone relative to the baseline in Enterprises 1 and 2: increased final live weight (LW) by 68 and 25 kg, decreased emissions intensity (EI) by 69 and 49 g CH4/kg live weight gain (LWG), and decreased total GHG by 0.08 and 0.04 t CO2-e/ha/year, respectively. Supplementing steers with NO3¯ + lipid relative to the baseline: increased final LW by 70 and 30 kg, decreased EI by 89 and 77 g CH4/kg LWG, and decreased total GHG by 0.27 and 0.12 t CO2-e/ha/year for Enterprises 1 and 2 respectively. The most profitable mitigation strategy, across all years, for Enterprise 1 was the lipid supplement with a median gross margin of $AUD753/ha and for Enterprise 2 was the NO3¯ + lipid supplement with a median gross margin of $AUD224/ha. The NO3¯ supplement alone was the least preferred option across both enterprises, consistently delivering lower returns than other options across the entire probability range. The results indicate the potential economic benefit of lipid supplementation, either alone or in combination with NO3¯, as GHG mitigation strategies that increase profitability and inhibit methanogenesis for beef production across diverse environments.
... Decision-making regarding the adoption of pasture management can be aided by crop simulation models, which facilitate the understanding of plant growth according to environmental conditions and agronomic and management strategies. Many forage simulation models are available, such as GRASIM (Mohtar et al. 2000), SPUR (MacNeil et al. 1985), GrassGro (Moore et al. 1997), ELM (Steinhorst et al. 1978), DAFOSYM (Rotz et al. 1989), ALMANAC (Kiniry et al. 1992), CATIMO (Bonesmo and Bélanger 2002), CropSyst (Stöckle et al. 2003), Invernada (Barioni et al. 2012), LINGRA (Schapendonk et al. 1998), Hurley Pasture Model (Noy-Meir 1975, GRAZE (Smith and Loewer 1983), Whole farm model-Sheep version (Cacho et al. 1995) and Whole farm model-Dairy version (McCall and Bishop-Hurley 2003). Among these models, the CROPGRO module of the DSSAT (Decision Support System for Agrotechnology Transfer) system has been widely used to predict the growth and biomass accumulation of tropical forages under Brazilian climatic conditions (Cruz 2010;Pedreira et al. 2011;Lara et al. 2012;Pequeno et al. 2014). ...
Crop models are important tools for assisting farmers and crop consultants to make decisions about fertilisation, irrigation and harvest management, because they allow users to understand productivity from the view of integrated sensitivities of basic plant physiological processes. The first objective of this study was to evaluate the performance of the CSM-CROPGRO-Perennial Forage model (PFM) to simulate regrowth of Urochloa brizantha (Hochst. ex A.Rich.) R.D.Webster cv. Marandu under varying irrigation and nitrogen levels. The second objective was to evaluate the water-balance module of the model under soil and climatic conditions in the Cerrado biome of central-eastern Brazil. The experimental data for model evaluation were obtained from a field experiment conducted during 2015, 2016 and 2017, and included herbage production, plant-part composition and plant nitrogen (N) concentration. The results suggest that the model can be used to simulate growth of Marandu palisade grass adequately under different managements of irrigation and N fertilisation. The findings indicate also that the agreement between simulations and field-observed soil moisture shows good performance of the water-balance module of CSM-CROPGRO-PFM. The most important parameterisation required by the model was the determination and calibration of inputs such as the stable soil carbon pool (SOM3) for N mineralisation, which affected the N response, and the soil water-holding characteristics, which affected the irrigation response. The default parameterisation (species, ecotype, cultivar) of cv. Marandu in CSM-CROPGRO-PFM was sufficient for adequate performance of the model for this new environment and new crop management. However, minor modifications of species parameters were helpful to account for winter-kill of foliage.
... The environmental drivers that stress forage quality and quantity needed to sustain profitable range livestock production have been well documented. Three critical factors influence forage growth and quality: soil dynamics, rainfall and climate, each of which are measurable factors that can be used to estimate and manage forage production and quality (Aydin and Uzun, 2005;Bollig and Feller, 2014;Dillard et al., 2015;Dumont et al., 2015;Ludewig et al., 2015;Walter et al., 2012) and therefore stocking rates (Moore et al., 1997;Schmalz et al., 2013). Forage height, density and nutritional value are valuable indicators which can help rangeland managers to predict the forage selection and intake of large ruminants (Baker et al., 1992). ...
Sustainable ranching operations require access to adequate forage reserves and suitable means to market livestock, both of which are critical determinants of adaptive capacity (defined here as the ability to manipulate stocking rate). Ranch adaptive capacity is most relevant during times of forage shortages from drought. Unfortunately for island beef production systems, traditional adaptive measures used in continental systems are unavailable, such as transporting livestock to less affected areas, importing feed resources (cost prohibitive), intensive grazing practices or stockpiling forage (since forages mature too rapidly and are generally low quality), or destocking through cull cow sales (due to limited marketing and processing capacities). Located on an island of Hawaii, the case study ranch investigated here is challenged by each of these environmental and market constraints. The ranch resides on the leeward side of its island such that it receives minimal rainfall and forage productivity is similar to semi-arid rangelands in the western United States. The ranch's livestock management problem is compounded during drought, since island slaughter capacity is limited and there is no financially feasible means of marketing and transporting culled livestock off the island. Therefore, when forage is limited, managers are forced to retain ownership of culled mature cows, who are moved into a terminal herd to await the next available harvest or shipping availability. Terminal herds occupy areas with lower quality forages to conserve the most productive pastures for higher valued calves. This backlog of cull cows creates extended periods of stress on forage resources, since grazing pressure is not relieved as drought intensifies and increases operational expenses. A simulation model was created in an effort to identify key leverage points within the ranching operation that have the greatest impact on forage availability, herd size and net income. Upon completion of the model, sensitivity analyses were conducted to identify key drivers of model behaviors and several what-if scenarios were run based on questions provided by island ranch managers. Results showed that reducing terminal herd size through increased island processing capacity would not relieve forage pressure or eliminate the backlog of terminal cull cows, although net income was improved through greater cow sales. Several follow up tests were then run to evaluate changes to internal ranch decision making, which showed that reductions in heifer retention would provide a wider array of ecological and economic benefits. The ranch's ability to manipulate heifer retention rates, rather than cull cow rates, terminal herd shipping, or island processing capacity, was shown to be the critical aspect which drives ranch adaptive capacity.
... They also highlight some ongoing challenges in this shift towards agricultural analytics, the most important being the need for model development and application to maintain the stringent verification standards currently applied to models in the scientific literature-even when the automated equations in the cloud are self-learning to feed advanced farm decision making dashboards. (Moore et al. 1997) is a computer program that simulates pasture and animal production in response to weather and management. Variability in production and risk in the grazing systems modelled can be assessed to assist farmers in making informed decisions. ...
Infrared thermography (IRT) has potential for remote monitoring of extensively raised sheep. IRT has been shown to detect changes in interdigital temperature associated with foot lesions (Talukder 2015) and it is therefore reasonable to propose that it may be capable of detecting other sources of inflammation. Due to the insulating properties of wool, it is likely that a fleece length greater than 2mm may significantly impact on the ability of IRT to reliably identify areas of inflammation of the skin from a thermal image. It was hypothesised that IRT measurements on sheep with vaccination-related inflammation and at varying wool lengths may indicate whether this method can be used to detect other causes of skin inflammation, particularly with regards to inflammation associated with fly strike.
... Models of grazing systems, such as GRASP in northern Australia (McKeon et al., 1990) and DYNAMOF (Bowman et al., 1993(Bowman et al., , 1995 and GrazPlan Freer et al., 1997;Moore et al., 1997) in southern Australia, are of considerable value in determining appropriate long-term stocking rates, supplementary feeding and other strategies. In other words, they can be of fundamental importance in achieving sustainable grazing systems and in improving the management of climate variability per se. ...
... The climate, through weather and the timing of weather patterns, is one of the main factors because rainfall and temperature drive the productivity, profitability and environmental health of the system. Integrated models of croplivestock systems were constructed by linking the APSIM 7.7 soil water, soil nutrient cycling, crop and surface residue modelling components (Holzworth et al., 2014) to the GRAZPLAN pasture and ruminant simulation models (Donnelly et al., 2002;Moore et al., 1997) using the AusFarm modelling software (version 1.4.7). AusFarm is an agroecosystem modelling environment that couples APSIM and GRAZPLAN to model dynamic interactions between climate, soil, plants, and animals . ...
Mixed crop-livestock farming systems provide food for more than half of the world's population. These agricultural systems are predicted to be vulnerable to climate change and therefore require transformative adaptations. In collaboration with farmers in the wheatbelt of Western Australia (WA), a range of systemic and transformative adaptation options, e.g. land use change, were designed for the modelled climate change projected to occur in 2030 (0.4–1.4° increase in mean temperature). The effectiveness of the adaptation options was evaluated using coupled crop and livestock biophysical models within an economic and environmental framework at both the enterprise and farm scales. The relative changes in economic return and environmental variables in 2030 are presented in comparison with a baseline period (1970–2010). The analysis was performed on representative farm systems across a rainfall transect. Under the impact of projected climate change, the economic returns of the current farms without adaptation declined by between 2 and 47%, with a few exceptions where profit increased by up to 4%. When the adaptations were applied for 2030, profit increased at the high rainfall site in the range between 78 and 81% through a 25% increase in the size of livestock enterprise and adjustment in sowing dates, but such profit increases were associated with 6–10% increase in greenhouse gas (GHG) emissions. At the medium rainfall site, a 100% increase in stocking rate resulted in 5% growth in profit but with a 61–71% increase in GHG emissions and the increased likelihood of soil degradation. At the relatively low rainfall site, a 75% increase in livestock when associated with changes in crop management resulted in greater profitability and a smaller risk of soil erosion. This research identified that a shift toward a greater livestock enterprises (stocking rate and pasture area) could be a profitable and low-risk approach and may have most relevance in years with extremely low rainfall. If transformative adaptations are adopted then there will be an increased requirement for an emissions control policy due to livestock GHG emissions, while there would be also need for soil conservation strategies to be implemented during dry periods. The adoption rate analysis with producers suggests there would be a greater adoption rate for less intensified adaptations even if they are transformative. Overall the current systems would be more resilient with the adaptations, but there may be challenges in terms of environmental sustainability and in particular with soil conservation.
... ANPP & biomass consumption Moderate Pickup 1996 Explicit Soil stock with ET losses; single WUE 1 month n/a >1 yr. ANPP & biomass consumption Moderate Moore et al. 1997 Explicit Soil water balance; biomass turnover 1 day n/a 1 yr. ANPP & animal production High Thornley 1998 Explicit ET, soil-and plantwater potential 1 day 1/64 >1 yr. ...
This paper presents a simple soil-water (ecohydrologic) model developed in STELLA™ modeling environment. The model development was framed by previous work in both the rangeland science and ecohydrology disciplines and was calibrated to four locations of diverse soils and climate across Texas, USA. Overall, the model calibration procedure showed that the model is fairly well behaved compared to the available observed data at each location. Exploratory and sensitivity analyses showed some expected patterns of behavior that illustrate the model is sensitive to some extreme conditions and that the directional impacts of the changes followed a logical progression. However, there are several model components, particularly the biomass stock and the plant-soil related feedbacks on infiltration and runoff were not well parameterized and need to be improved before intended applications. Future work includes extending this model to include a feedback loop impact component [after Hayward and Boswell (2014), Model behavior and the concept of loop impact: A practical method. System Dynamics Review 30(1-2), 29-57] to better understand the water dynamics in semi-arid environments arising from climate and grazing management changes as well as developing a teaching tool for rangeland, soils, and/or modeling courses.
... Alternatively complex dynamic simulation models capture the interactions of livestock and forage feed supply over time (e.g. GRAZPLAN, Moore et al., 1997), but these are highly complex, require a large amount of input information, and so are difficult to specify to explore large numbers of feed-base combinations. There is a need for approaches to feedbase analysis of intermediate complexity that are easy to specify, but can integrate forage inputs from a range of potential elements of the feedbase with the demand of the whole livestock enterprise (Bell et al., 2008;Martin et al., 2011). ...
Highly variable climates induce large variability in the supply of forage for livestock and so farmers must manage their livestock systems to reduce the risk of feed gaps (i.e. periods when livestock feed demand exceeds forage supply). However, mixed crop-livestock farmers can utilise a range of feed sources on their farms to help mitigate these risks. This paper reports on the development and application of a simple whole-farm feed-energy balance calculator which is used to evaluate the frequency and magnitude of feed gaps. The calculator matches long-term simulations of variation in forage and metabolisable energy supply from diverse sources against energy demand for different livestock enterprises. Scenarios of increasing the diversity of forage sources in livestock systems is investigated for six locations selected to span Australia’s crop-livestock zone. We found that systems relying on only one feed source were prone to higher risk of feed gaps, and hence, would often have to reduce stocking rates to mitigate these risks or use supplementary feed. At all sites, by adding more feed sources to the farm feedbase the continuity of supply of both fresh and carry-over forage was improved, reducing the frequency and magnitude of feed deficits. However, there were diminishing returns from making the feedbase more complex, with combinations of two to three feed sources typically achieving the maximum benefits in terms of reducing the risk of feed gaps. Higher stocking rates could be maintained while limiting risk when combinations of other feed sources were introduced into the feedbase. For the same level of risk, a feedbase relying on a diversity of forage sources could support stocking rates 1.4 to 3 times higher than if they were using a single pasture source. This suggests that there is significant capacity to mitigate both risk of feed gaps at the same time as increasing ‘safe’ stocking rates through better integration of feed sources on mixed crop-livestock farms across diverse regions and climates.
... Other complex models such as GrazPLAN and Growth, Metabolism and Mortality (GMM) model [Owen-Smith, 2002] have been used to model herbivore population dynamics with detailed animal physiology. For example, GrazPLAN includes detailed animal and plant physiology, operating at a daily time step, but it can only be applied to sheep or cattle [Gill et al., 2010;King et al., 2012;Moore et al., 1997]. Similarly, the GMM model does not explicitly consider resource and climatic constraints on different age classes of herbivores [Owen-Smith, 2002]. ...
Mammalian herbivores are an essential component of grassland and savanna ecosystems, and with feedbacks to the climate system. To date, the response and feedbacks of mammalian herbivores to changes in both abiotic and biotic factors are poorly quantified and not adequately represented in the current global land modeling framework. In this study, we coupled herbivore population dynamics in a global land model (the Dynamic Land Ecosystem Model, DLEM3.0) to simulate populations of horses, cattle, sheep and goats, and their responses to changes in multiple environmental factors at the site level across different continents during 1980-2010. Simulated results show that the model is capable of reproducing observed herbivore populations across all sites for these animal groups. Our simulation results also indicate that during this period, climate extremes led to a maximum mortality of 27% of the total herbivores in Mongolia. Across all sites, herbivores reduced aboveground net primary productivity (ANPP) and heterotrophic respiration (Rh) by 14% and 15%, respectively (p < 0.05). With adequate parameterization, the model can be used for historical assessment and future prediction of mammalian herbivore populations and their relevant impacts on biogeochemical cycles. Our simulation results demonstrate a strong coupling between primary producers and consumers, indicating that inclusion of herbivores into the global land modeling framework is essential to better understand the potentially large effect of herbivores on carbon and water cycles in grassland and savanna ecosystems.
... Research in farm simulation and modelling has evolved since the mid-1990s to cover more than one on-farm enterprise; examples include, GPFARM [24], GRAZPLAN [25], and EcoMod [26] (Table 1). In the case of animal models, several important gaps have been highlighted including the need for a more mechanistic representation of the control of feed intake [27]. ...
Smart farming envisages the harnessing of Information and Communication Technologies as an enabler of more efficient, productive, and profitable farming enterprises. Such technologies do not suffice on their own; rather they must be judiciously combined to deliver meaningful information in near real-time. Decision-support tools incorporating models of disparate farming activities, either on their own or in combination with other models, offer one popular approach; exemplars include GPFARM, APSIM, GRAZPLAN amongst many others. Such models tend to be generic in nature and their adoption by individual farmers is minimal. Smart technologies offer an opportunity to remedy this situation; farm-specific models that can reflect near real-time events become tractable using such technologies. Research on the development, and application of farm-specific models is at a very early stage. This paper thus presents an overview of models within the farming enterprise; it then reviews the state-of the art in smart technologies that promise to enable a new generation of enterprise-specific models that will underpin future smart farming enterprises.
The structure and stability of grassland ecosystems have a significant impact on biodiversity, material cycling and productivity for ecosystem services. However, the issue of the structure and stability of grassland ecosystems has not been systematically reviewed. Based on the Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) databases, we used the systematic-review method and screened 133 papers to describe and analyze the frontiers of research into the structure and stability of grassland ecosystems. The research results showed that: (1) The number of articles about the structure and stability of grassland ecosystems is gradually increasing, and the research themes are becoming increasingly diverse. (2) There is a high degree of consistency between the study area and the spatial distribution of grassland. (3) Based on the changes in ecosystem patterns and their interrelationships with ecosystem processes, we reviewed the research progress and landmark results on the structure, stability, structure–stability relationship and their influencing factors of grassland ecosystems; among them, the study of structure is the main research focus (51.12%), followed by the study of the influencing factors of structure and stability (37.57%). (4) Key scientific questions on structural optimization, stability enhancement and harmonizing the relationship between structure and stability are explored. (5) Based on the background of karst desertification control (KDC) and its geographical characteristics, three insights are proposed to optimize the spatial allocation, enhance the stability of grassland for rocky desertification control and coordinate the regulation mechanism of grassland structure and stability. This study provided some references for grassland managers and relevant policy makers to optimize the structure and enhance the stability of grassland ecosystems. It also provided important insights to enhance the service capacity of grassland ecosystems in KDC.
This study reports on the herbage production and quality of a range of pasture legume species during spring, at three sites with moderate to strongly acidic soils in southern NSW, Australia. These data have enabled prediction of livestock production when legumes are conserved as silage or hay. Total herbage production, its timing and quality differed significantly between species, which generally was not predicted by the traditional metric of time to flowering. Trifolium incarnatum L., T. michelianum L., Biserrula pelecinus L., Ornithopus sativus Brot. and Hedysarum coronarium L. all maintained growth rates exceeding 200 kg DM/ha/d for 4 weeks over spring, while T. vesiculosum Savi. maintained this for 6 weeks, with a peak growth rate of 561 kg DM/ha/d. Herbage quality of all species declined over time and was greatest in species with a rapid increase in stem‐to‐leaf ratio or with weak stems, resulting in lodging. The modelling inferred that maximizing potential livestock production via fodder conservation required earlier cutting. Only the indeterminate species B. pelecinus and Ornithopus spp. increased potential liveweight gain per hectare through delayed harvesting from (probable) silage to hay‐making time.
Context
Mixed crop-livestock farms are important production systems worldwide and dominate Australia's broadacre agricultural regions. While integration of crops and livestock can offer many benefits, these are often intertwined and are hard to quantify explicitly.
Objective
This paper set out to examine specifically the financial risk and return implications from operating a farm portfolio involving segregated crop and livestock enterprises considering both production variability and commodity price variability.
Methods
Crop and livestock production from representative systems were simulated over 40 years at six locations spanning Australia's crop-livestock zone using coupled biophysical production simulation models, APSIM for cropping enterprises and GRAZPLAN for livestock enterprises. Time series of varying prices and costs for the same period were derived using historical data. Annual gross margins for each enterprise and different proportional land allocations to the farm were calculated either allowing both production and price to vary or keeping either constant at average values.
Results and conclusions
At all locations the livestock enterprise had less downside risk than the cropping enterprises (as measured by conditional value at risk). Across both enterprises, the impact of production variability was greater than price variability at 5 of the 6 locations. Annual gross margins from the modelled crop and livestock enterprises were not well correlated with each other. Hence, even in the absence of biophysical interactions, the risk-efficient frontier included mixtures of crops and livestock enterprises at most sites. At two sites, a mix of 20–40% crop resulted in the lowest downside risk, while at other sites there was a clear trade-off between maximising farm returns and minimising risk across a range of crop-livestock mixtures.
Significance
This is the first study to explicitly quantify and show across a diversity of environments in Australia's mixed farming systems that operating a mixture of even segregated crop and livestock enterprises in a farming business can help farmers optimise their risk-return trade-off. Similar risk mitigation benefits may be achieved through crop-livestock systems in other agricultural regions exposed to high climate and price variability.
In contrast to a pure cropping system, integrated crop-livestock systems offer a potential for sustainable intensification of pasture-based savanna systems by maintaining vital ecosystem functions (ESF) while providing stable crop yields in a changing environment. In all variations of the mentioned systems, vegetation and animals interact and influence ESFs in different ways and to different extents. However, to date, there is no comprehensive model available to simulate impacts of large-scale savanna land use change (LUC) on food provision and ESFs. We developed a catalogue of required functional model skills for savanna LUC simulation and analysed existing models against this catalogue by scoring. Based on the model scoring, we discuss challenges and opportunities of different model development pathways. Further steps of model integration and coupling are required to simulate interactions with socio-economic decision-making and LUC effects on wildlife.
Context Mating ewe lambs at ~7 months of age is viewed as a way to increase the profit of sheep farms in south-west Victoria, Australia. For a successful mating and high reproductive rate, ewe lambs need to be of >40 kg liveweight and condition score 3 at mating. The region has a temperate Mediterranean climate, and as such, dry summer pastures do not provide adequate nutrition for the weight gain required over summer and autumn if ewe lambs are to be mated early. There is limited economic information on the whole-farm benefits and risks associated with different feeding strategies for meeting the feed requirements of mating ewe lambs. Aims The aims were to test, for a prime-lamb system, whether profit would be increased by the mating of ewe lambs and whether there would be a reduction in whole-farm business risk. We hypothesised that different forage systems would offer profit and risk advantages over current dry-pasture and supplement systems for growing out ewe lambs. Method The biophysical and economic characteristics of a prime-lamb case-study farm were modelled to examine how six different pasture and forage systems for mating ewe lambs would perform under varying seasonal, price and cost conditions. Systems 1 and 2 were based on perennial ryegrass and subterranean clover pastures. System 1 compared lambing at 2 years of age, and System 2 lambing at 1 year of age. The other four systems simulated the use of different forages on a portion of the farm to grow out the ewe lambs for lambing at 1 year of age: System 3, spring-sown forage brassica rape; System 4, spring-sown winter-type canola; and System 5, lucerne; System 6, as for System 4 but at a higher lamb marking rate. Results and conclusions Lambing at 1 year of age increased profit and reduced business risk compared with lambing at 2 years of age. Use of spring-sown canola or lucerne forage for ewe-lamb mating provided the best returns on capital relative to the risk involved. Use of spring-sown canola reduced variability of annual returns, in part because of the diversification of income received from both lamb and canola. Implications The results of this modelling study indicate that some feed systems can increase farm profit and reduce business risk.
Grazing land models can assess the provisioning and trade-offs among ecosystem services attributable to grazing management strategies. We reviewed 12 grazing land models used for evaluating forage and animal (meat and milk) production, soil C sequestration, greenhouse gas emission, and nitrogen leaching, under both current and projected climate conditions. Given the spatial and temporal variability that characterizes most rangelands and pastures in which animal, plant, and soil interact, none of the models currently have the capability to simulate a full suite of ecosystem services provided by grazing lands at different spatial scales and across multiple locations. A large number of model applications have focused on topics such as environmental impacts of grazing land management and sustainability of ecosystems. Additional model components are needed to address the spatial and temporal dynamics of animal foraging behavior and interactions with biophysical and ecological processes on grazing lands and their impacts on animal performance. In addition to identified knowledge gaps in simulating biophysical processes in grazing land ecosystems, our review suggests further improvements that could increase adoption of these models as decision support tools. Grazing land models need to increase user-friendliness by utilizing available big data to minimize model parameterization so that multiple models can be used to reduce simulation uncertainty. Efforts need to reduce inconsistencies among grazing land models in simulated ecosystem services and grazing management effects by carefully examining the underlying biophysical and ecological processes and their interactions in each model. Learning experiences among modelers, experimentalists, and stakeholders need to be strengthened by co-developing modeling objectives, approaches, and interpretation of simulation results.
Global crop production is affected by seasonal and climatic variations in temperature, rainfall patterns or intensity and the occurrence of abiotic and biotic stresses. Climate change can alter pest and pathogen populations as well as pathogen complexes that pose an enormous risk to crop yields and future food security. Crop simulation models have been validated as an important tool for the development of more resilient agricultural systems and improved decision making for growers. The Agricultural Production Systems Simulator (APSIM) is a software tool that enables sub-models to be incorporated for simulation of production in diverse agricultural systems. Modification of APSIM to incorporate epidemiological disease model for crop growth and yield under different disease intensities has few attempts in the UK or elsewhere. The overall aim of this project is to model disease impact on wheat for improved food security in two different agro-ecological zones. The incidence of wheat diseases between 2009 and 2014 in two different agro-ecological zones, UK and Oman were compared. Most of the fields surveyed in Oman and UK were found to have at least one disease. Leaf spot was the most prevalent foliar disease found in Omani fields while Septoria was the most common foliar disease in the UK. Fusarium followed by eyespot and ear blight represents the most common diseases of stem and ears in UK winter wheat between 2009 and 2014. However, in Omani wheat Fusarium causing stem base and loose smut of ears were the most common. Eyespot was not found in Omani winter wheat and this may relate to the high temperature during winter in Oman. This study discussed the first work on the occurrence of fungal diseases and their pathogens in Oman and the influence of agronomy factors. Large numbers of pathogenic fungi causing symptoms were found to be prevalent in wheat fields in Oman. Isolation from six symptomatic wheat varieties resulted in 36 different fungal species. Alternaria alternata was the most frequently isolated pathogen followed by Bipolaris sorokiniana, Setosphaeria rostrata, and Fusarium equiseti. Results also showed some agronomic practices influenced disease incidence. Mechanical sowing method and time of urea application were found to influence leaf spot disease. An investigation into the recovery of treatment cost for eyespot control through yield and the effect of fungicide treatment on risk showed that all fungicides apart from (epoxiconazole) Opus at 1 L ha-1 were found to be worth the costs, either under high disease pressure (inoculated sites) or naturally infected sites. For the risk averse manger fungicide treatment would be worth the cost as it would reduce the higher level of disease and consequently minimise associated yield losses. In this work, disease models were built to predict the disease development and yield loss in relation to crop phenology using results from previous literature on conditions favouring sporulation, infection and disease development and severity. Analysis of 461 data sets showed that climatic conditions and agronomic factors significantly influenced disease development either positively or negatively in all models. The application of a range of fungicides at GS31/32 reduced disease significantly at GS39 in comparison to epoxiconazole alone. Disease severity at GS39 decreased yield only slightly by 2.2% whilst only (prothioconazole) Proline 275 increased yield significantly with almost 30% yield increase. The performance of the APSIM wheat model to simulate phenology, leaf area index, biomass and grain yield of two winter wheat varieties (Okley and Cashel) was evaluated under UK conditions and the previously developed eyespot disease were linked with APSIM. Generally, APSIM poorly predicted the phenology, LAI, biomass and yield of winter wheat grown under UK conditions. The linked eyespot disease models with APSIM simulated an adequate level of disease predication at GS12/13 (9.6%), GS31/32 (1.3%) and GS39 (12%). Overall, the link between eyespot epidemiological disease models and crop growth model has successfully provided the basis for further development of the model and enhance crop growth simulation. Moreover identification of main diseases threatening wheat production in Oman can help to plan for future research, to assess the economic importance and to contrast environment models for yield loss.
This study was conducted to determine whether circulating concentrations of blood isoprostanes can be used as an effective biomarker in lambs to predict degradation of color and/or lipid stability in meat. Lambs ( = 84) were fed diets of either lucerne pasture, annual ryegrass pasture, a commercial feedlot pellet, or a combination of annual ryegrass and feedlot pellet for 8 wk, including a 2-wk adaptation period. Blood isoprostane concentration at wk 0, 4, 6 or 8 of feeding was determined. Blood isoprostane concentration for each animal was then correlated with muscle biochemical components that impact color and/or lipid oxidative status during retail display. This included lipid oxidation levels in muscle assessed by thiobarbituric acid reactive substances and meat redness determined by a HunterLab colorimetric spectrometer. Lambs that consumed the commercial feedlot pellet had a lower muscle vitamin E level (< 0.01) and a greater level of -6 PUFA ( < 0.001) compared with lambs finished on annual ryegrass or lucerne. Lipid oxidation levels were greatest for lambs finished on the feedlot ration, lowest in lambs finished on the ryegrass diet, and intermediate for lambs finished on lucerne and ryegrass-feedlot combination ( < 0.01). After 8 wk of feeding, blood isoprostane concentration was positively correlated with lipid oxidation of meat displayed for 72 h in simulated retail conditions ( < 0.01). There was a negative linear relationship between isoprostane concentration and muscle vitamin E concentration ( = 0.07), lipid oxidation and muscle vitamin E concentration ( < 0.01) but a positive linear relationship between isoprostane concentration and muscle -6 PUFA ( < 0.001) or lipid oxidation and muscle -6 PUFA concentration ( < 0.001). Blood isoprostane concentration and lipid oxidation in meat were influenced by muscle vitamin E and -6 PUFA but not by -3 PUFA. There was no significant relationship observed between blood isoprostane concentration at 0, 4, 6 or 8 wk feeding vs. overall meat color (redness of meat) at 0 and 72 h of display, stored under simulated retail conditions. The results indicate that circulating blood isoprostane concentration can be a useful tool to predict the oxidative status of postmortem meat. Future work will examine the impact of this relationship on meat flavor/aroma deterioration post farm.
Assessments of grazing systems are often constrained by the decisions regarding the management of the grazing systems, including stocking rate, and also the seasonal conditions that occur during the assessment period. These constraints have led to sometimes conflicting results about comparisons of grazing management systems. This paper examines 1-, 4- and 20-paddock (1P, 4P and 20P) grazing management systems to determine how the intensity of grazing management on native pastures influences the financial performance of sheep production systems. The performance of the grazing systems, as part of the Orange EverGraze research experiment, was initially examined using the biophysical data over the 4 years of the experiment and then a more detailed analysis over a longer timeframe was undertaken using the AusFarm simulation modelling software. Flexible management strategies to optimise ewe numbers, sale time of lambs, and adjust ewe numbers based on season, were also assessed to determine which management systems are the most profitable and sustainable. There was higher profit for the 20P grazing system than the 1P system during the experiment. However, when stocking rates were held constant at optimum levels and systems were simulated over 40 years, there was no difference between grazing systems. Modelling strategies used to vary stocking rates showed that flexible management options are better based on optimising ewe numbers and the sale time of lambs rather than changing ewe numbers between years. The sustainability of modelled systems was also assessed using frequency of events where the average herbage mass (0.8 t DM/ha) or ground cover (80%) in autumn dropped below levels that are associated with degradation. Degradation events occurred more so with increasing ewe number than lamb sale time. Overall, the most sustainable systems, when considering profitability and environmental issues, had a stocking rate of 4.2 ewes per ha, with lambs sold in February (2 or 18). Higher stocking rates (5.3 ewes/ha) would need to be run for more intensive grazing management to have higher profitability.
High Nature Value farming systems cover a large proportion of the agricultural land in marginal and mountain areas of Europe. These large areas face environmental, economic and social challenges and formulating policies that support all these aspects is difficult. Although farmers play an important role in maintaining the ecological diversity of these areas, their differing management styles are often not recognised when land use policies are formulated. This paper examines these issues using an optimisation model based on an extensive livestock farm in Western Scotland, where four farmers' management styles are combined with a series of six alternative future land use scenarios, to provide a more realistic and robust insight of policy impacts on land use and habitat, labour and farm income. The management styles derived from a typology that was based on a composite of both available resources and attitudinal components. The six alternative scenarios encompassed competitive land use diversification options (woodland and wild deer shooting), abandonment of native pasture for agriculture, no support, high market prices for livestock products, and increased animal efficiency. Although diversification via forestry was found to be potentially central to increasing farming incomes, farmers' reticence to adopt forestry or any diversification was a major constraint. This case study also reinforced that managing livestock on these HNV farming systems was not economical unless support subsidies were in place. The only scenario which could enhance the HNV biodiversity value on farms was one with high market prices, resulting in the most varied land use (sheep, cattle and forestry). All others scenarios meant an increase in afforestation (which displaced livestock), an increase in livestock grazing or abandonment of the land, none of which would maintain biodiversity in these areas. Very few scenarios were able to increase on-farm labour demand and although greater flexibility in farm labour was found to be essential, labour scarcity in these marginal mountain areas remained a problem. In conclusion, this case study reinforced that farmers' management style and motivation do play a major role on how they respond to policies, and unless this role is acknowledged by policy-makers, these European HNV areas may not be targeted properly for the most desired outcomes and sustainability.
A model is presented for calculating the daily evaporation rate from a
crop surface. It applies to a row crop canopy situation in which the
soil water supply to the plant roots is not limited and the crop has not
come into an advanced stage of maturation or senescence. The crop
evaporation rate is calculated by adding the soil surface and plant
surface components (each of these requiring daily numbers for the leaf
area index), the potential evaporation, the rainfall, and the net
radiation above the canopy. The evaporation from the soil surface
Es is calculated in two stages: (1) the constant rate stage
in which Es is limited only by the supply of energy to the
surface and (2) the falling rate stage in which water movement to the
evaporating sites near the surface is controlled by the hydraulic
properties of the soil. The evaporation from the plant surfaces
Ep is predicted by using an empirical relation based on local
data, which shows how Ep is related to Eo through
the leaf area index. The model was used to obtain the total evaporation
rate E = Es + Ep of a developing grain sorghum
(Sorghum bicolor L.) canopy in central Texas. The results agreed well
with values for E measured directly with a weighing lysimeter.
The EPIC plant growth model was developed to estimate soil productivity as affected by erosion throughout the U.S. Since soil productivity is expressed in terms of crop yield, the model must be capable of simulating crop yields realistically for soils with a wide range of erosion damage. Also, simulation of many crops is required because of the wide variety grown in the U.S. EPIC simulates all crops with one crop growth model using unique parameter values for each crop. The processes simulated include leaf interception of solar radiation; conversion to biomass; division of biomass into roots, aboveground mass, and economic yield; root growth; water use; and nutrient uptake. The model has been tested throughout the U.S. and in several foreign countries.
Sites on several farms in each district were selected and extensive data were collected including hydraulic conductivity, moisture retention curves and mechanical impedance to penetration. Chemical, textural, and mineralogical analyses were also carried out. -from Authors
The two primary producer submodels described in this chapter, plant phenology and carbon flow, are components of the grassland ecosystem model, ELM. The objectives of these submodels are to simulate biologically realistic phenology and growth responses to conditions varying from drought to irrigation and fertilization. These submodels simulate five primary producer groups and interact extensively with each other and the other ELM submodels.
The phenology submodel simulates phenological change in each producer group. The rate of phenologic change is determined from maximum air temperature, insolation, soilwater potential, soil temperature, day length, and shoot weight.
The carbon submodel simulates dynamics of live shoots, dead shoots, live roots, seeds, and crowns for each producer group. One litter and three dead-root state variables are represented for all producers. The processes modeled for each producer group are growth, respiration, and death of shoots, roots, and crowns, gross photosynthesis, seed production and germination, and the fall of standing dead shoots.
Comparison between observed data and simulations suggest the model to be most sensitive to the belowground system, the fall of standing dead, and the relationship between phenology and environment, and that these areas need additional refinement.
This chapter discusses the Mediterranean annual-type pasture that has potential scope for marked and rapid seasonal changes in botanical composition and chemical composition. Most species of the annual-type pasture appear to have originated from the Mediterranean Basin. Mediterranean annuals are principally self-fertilized. The pasture communities are characterized, by either resistant shrubs or numerous Mediterranean annual plants. Relatively, little has been published on variation in botanical composition, due to general climatic conditions, within any geographic region. Some of the most spectacular effects on botanical composition and on the total yield, at least in pastures sown to subterranean clover, have resulted from the use of trace elements. One of the drawbacks of heavily grazed annual-type pastures is the inefficient use of soil water resources (and probably nutrient resources), consequent on restrictions to plant root development and extension. The incorporation of deep-rooted perennials, such as Hyperrhenia hirta or Eragrostis curuula, into the ecosystem, might offer a solution in some areas, especially, if the perennials were protected from grazing at “critical” periods in their life cycle.
A simulation model for abiotic variables influencing grassland ecosystems including water flow and temperature profile submodels is presented. The water-flow submodel treats flow in the plant canopy and soil, while the temperature submodel includes solar radiation, canopy air temperature, and soil temperature. The atmospheric driving variables are either daily weather observations or stochastic weather-simulator results. A preliminary validation of the model has been performed.
Four basic models exist for the control of shoot: root ratio (S: R): (a) allometric models, proposing a fixed ratio of shoot growth rate to root growth rate; (b) functional equilibrium models, based on the ratio of shoot activity to root activity; (c) the Thornley model, based on carbon and nitrogen uptake and transport, (d) hormone models, generally suggesting the root produces a hormone that controls the shoot and vice versa. Models (a) and (b) are empirical, and therefore provide no test of the processes operating.
Ontogenetic changes in S: R for fibrous-rooted herbs could be fitted by a modified Thornley model. Ontogenetic effects must be excluded in judging other effects.
Responses of S: R to deficits of water, major inorganic nutrients, light and carbon dioxide, and to defoliation and root pruning, usually conform to Thornley's model. With current knowledge Thornley's model cannot usefully be applied to minor nutrients, nutrient toxicity or temperature differences. S: R changes at reproduction usually conform to Thornley's model if it is assumed that young reproductive structures are a strong sink, but this begs the question of what determines sink strength. There are apparent exceptions to most of these responses, which should be studied further.
Phytohormones can influence S: R, but may not be the control operating in the normal, intact plant. Most of the available evidence is compatible with a source-sink model of Thornley's type, and therefore does not demand a hormonal theory of S: R control. There is a need for more critical tests.
An experiment was conducted in a glasshouse to study the effects of withholding water during flowering on seed production in subterranean clover. Two strains, Northam A (long duration of flowering) and Geraldton (shorter duration) were grown as swards in boxes, defoliated weekly until flowering, and subjected to the following watering regimes: T1, a control; T2, water withheld over the whole of the flowering period; T3 and T4, water withheld for short periods only, during flowering. Regular determinations were made of soil water, leaf water potential and inflorescence number. Seed yield and some of its components were measured in all treatments. The prolonged stress (T2) reduced seed yield by about 80% in both strains. Rates of inflorescence production, duration of flowering and individual seed weight were also reduced. The shorter stress treatments (T3, T4) had no effect on seed yield in the longer-flowering cultivar Northam A, but in cv. Geraldton, T4 caused a marked reduction in seed yield. The practical implications of this differential strain response are discussed.
Three experiments are described in which the effects of constant temperatures ranging from 15 to 80°C, followed by diurnally fluctuating temperatures of 60/15°C were compared on seed softening in three cultivars of T. subterraneum. Constant temperature treatments alone produced relatively few soft seeds, but rapid seed softening ensued within a few days of subsequent fluctuating temperature treatment. The higher and the longer the constant temperature pretreatment, the more soft seeds were obtained. The rate at which this preconditioning of the seed took place was highest in the early stages of exposure to constant temperatures. Differences between cultivars and between temperature treatments were established mainly during this early period of constant temperature treatment. Rates of change of temperature in the 60/15°C fluctuation treatment were important in the final seed softening process: the slower the rate of temperature change the more soft seeds were produced.
It is suggested that two distinct temperature dependent processes are involved in seed softening. The first appears to be a thermal degradation process which probably results in weakening of the strophiolar region. Fluctuating temperatures bring about the second stage of softening in which the strophiole is rendered permeable to water.
Two efficient finite difference methods for solving Richards' equation
in one dimension are presented, and their use in a range of soils and
conditions is investigated. Large time steps are possible when the
mass-conserving mixed form of Richards' equation is combined with an
implicit iterative scheme, while a hyperbolic sine transform for the
matric potential allows large spatial increments even in dry,
inhomogeneous soil. Infiltration in a range of soils can be simulated in
a few seconds on a personal computer with errors of only a few percent
in the amount and distribution of soil water. One of the methods adds
points to the space grid as an infiltration or redistribution front
advances, thus gaining considerably in efficiency over the other fixed
grid method for infiltration problems. In 17-s computing, this advancing
front method simulated infiltration, redistribution, and drainage for 50
days in an inhomogeneous soil with nonuniform initial conditions. Only
16 space and 21 time steps were needed for the simulation, which
included early ponding with the development and dissipation of a perched
water table.
The advent of generic pasture growth models, where biological responses are represented mechanistically rather than by empirical regressions, provides plant breeders with powerful new tools for assessing the potential impact of their breeding objectives on the profitability of grazing systems. The use of the GRAZPLAN computer models for evaluating breeding strategies is illustrated with case studies for enhanced winter growth and reduced maturation rate, and the benefit of increasing the legume content of a pasture. These examples show that the effect of pasture improvement on profitability will usually depend on whether grazing management is adjusted to take advantage of the improvement.
The interaction of vernalization and high temperature promotion on the flowering time of three Medicago truncatula cultivars is examined. A previous model describing these processes is modified to account for the hypothesis that temperature promotion of flowering is subsequent to full vernalization. Both models are calibrated to minimize errors in predicting flowering dates from field observations at Condobolin and Tamworth in New South Wales. The calibrated models are then tested on data from other seasons and locations. The implications of the modified model are discussed with regard to an ideotype and to improved efficiency in agronomic evaluation of genetic material.
Summer dormancy of field-grown plants of five strains of Phalaris tuberosa L. was investigated at various times during the summer by transferring them to trays with moist sand kept at room temperature (20–25°C). The rate of appearance of new shoots was used to measure differences in dormancy Levels. Differences between strains were significant in most experiments. They were largest in January, and decreased subsequently. The average level of dormancy was high in January, low in February, and increased again after mid February without reaching the original level. Development of new shoots was retarded by high temperatures. It is suggested that in the early stages dormancy is mainly a continuation of inhibition of bud growth by elongated tillers, whereas in the later part of summer, soil temperature and soil moisture are the main factors which control the development of new shoots.
Hard seeds of subterranean clover (Trifolium subterraneum L.) of the Geraldton and Bacchus Marsh strains, and of West Australian blue lupin (Lupinus varius L.), were subjected to various daily fluctuating temperatures within the normal summer environmental range (15–75°C). The main factor determining the rate of softening of the hard seeds was the maxinlum temperature of the fluctuation. Provided the temperature changed by some 15°C , the amplitude of the fluctuation did not appear to be a critical factor. The softening of hard seeds of any particular species did not commence until the amplitude of the temperature fluctuation, or the maximum temperature, reached a certain level, which in turn varied with the species. Beyond this level the rate of softening increased with increasing fluctuations to a point where the rate became very rapid, and thereafter wider fluctuations or higher maximum temperatures did not give significant increases.
The density, productivity, flowering characteristics, and seed reserves of 14 lines (10 cultivars and 4 experimental lines) of subterranean clover were observed over 5 years (1983-87) on a red earth soil at Wagga Wagga, New South Wales. Plant density increased from 149-318 plants/m2:in 1983 to 1975-13925 plants/m2 in 1987. Herbage yields of all cultivars during autumn-winter were similar in most years except in July 1985 when Seaton Park was superior. Cultivars in the midseason or later flowering groups were more productive in late spring and better able to utilise the extended growing seasons that occur periodically in this environment. The mean time from emergence to 5% flowering of all cultivars was 168 days with March germination in 1985 but decreased to 135 days with May germination in 1986. The number of days to flowering at Wagga Wagga was highly correlated with maturity ranking at Perth (r2 = 0.92 in 1985 and r2 = 0.93 in 1986). In the first year, average seed set was 295 kg seed/ha, but by summer of the fourth year the seed pool ranged from 124 kg/ha for Clare to 1190 kg/ha for Nungarin, the earliest flowering cultivar. The quantity of hard seed that carried over to the next year varied significantly between cultivars, with Enfield, Woogenellup, and Clare having the least, and Nungarin, Northam, Dalkeith, and Daliak the most. Seed set was related to maturity ranking only in 1984, although root disease probably affected seed yields in 1985-86. The proportion of hard seed that carried over was much higher than expected, particularly in soft-seeded cultivars. The newly released cultivar Junee was well adapted to the environment; it was later maturing than the recommended cultivar Seaton Park but was able to maintain high seed reserves. Karridale, another new cultivar, maintained higher seed reserves than the older Mount Barker.
An attempt is made to model a developing pasture in a manner requiring no experimental data on dependent variables. The term “pasture” here includes all forms of uniform vegetation which can be characterized primarily by leaves and roots. The model is based on the concept of limiting values. At any time at a particular level in the canopy, growth is determined by one of three parameters (radiation available, carbon dioxide available, or the capacity of individual leaves to carry out the photosynthetic conversion) which are calculable from the defined above-crop external conditions and the parameters of the plant which have been selected as independent. Because the model does not rely on field data, it is possible to use simple evolutionary constraints to enable the pasture to generate its own architecture as the photo-synthesized material becomes available. Soil water status is included, so that the effect of water deficit on the growth pattern of both leaves and roots can be investigated. The trials of the model so far have not been true simulations in the sense that the leaves and roots have been identified with a particular crop. However, the most obviously useful agrometeorological concepts have been incorporated, so that for a particular simulation attempt it is only necessary to devise suitable physical descriptions of leaf shape and orientation which at the same time enable calculation of the necessary dependent variables (for instance, the vertical mass transfer coefficient and the radiation flux). Illustrative trials of the model compare well (in those aspects where comparison is possible without direct simulation) with real pastures, and reveal behaviour-patterns which may help to direct experimental research in the field.
The ecological principle of homeostasis is the prime determinant of a self-stabilising model design to simulate grassland producers. Emphasis is given to interrelationships between analytical and conceptual components and to buffered interactions so as to maximise system balance and simulation realism. In result, model structure comprises a set of self-regulating producer subsystems: a metabolic carbon pool, a physiological time scale superimposed on Julian time, two ratio concepts for plant tissue masses, flowering control regulated by reserves, shoot phenology regulated by conditional responses to grazing, and overall herbage mass regulated by a set of shading interactions. As the links between subsystems are weaker than links within subsystems, model performance retains realism under a variety of system stresses.The difference equation model is deterministic and time-dependent with 1-day time steps and variable coefficients. It requires historical weather data and produces a wide range of tabular and graphical output options. It is intended for temperate grasslands and can be used to represent many growth forms of perennial plants within a single simulation. As the skeletal structure provides a comprehensive series of parameter sets, the model also admits a range of modified simulations by using parameter default values that negate various functions. Many of the basic functional relationships are described, or referenced from antecedent models.
A model called SWRRB (Simulator for Water Resources in Rural Basins) was developed for simulating hydrologic and related processes in rural basins. The objective in model development was to predict the effect of management decisions on water and sediment yields with reasonable accuracy for ungaged rural basins throughout the United States. The three major components of SWRRB are weather, hydrology, and sedimentation. Processes considered include surface runoff, percolation, return flow, evapotranspiration, pond and reservoir storage, and sedimentation. The SWRRB model was developed by modifying the CREAMS (Chemicals, Runoff, and Erosion from Agricultural Management Systems) daily rainfall hydrology model for application to large, complex, rural basins. The major changes were: (1) A return flow component was added; (2) the model was expanded to allow simultaneous computations on several sub-basins; (3) a reservoir storage component was added for use in determining the effects of farm ponds and other reservoirs on water and sediment yield; (4) a weather simulation model (precipitation, solar radiation, and temperature) was added to provide for longer-term simulations and more representative weather inputs, both temporally and spatially; (5) a better method was developed for predicting the peak runoff rate; and (6) a simple flood routing component was added. Besides water, SWRRB also simulates sediment yield using the Modified Universal Soil Loss Equation (MUSLE) and a sediment routing model. Tests with data from a 538km2 basin in Oklahoma and a 17.7km2 basin in Texas indicate that SWRRB is capable of simulating water and sediment yield realistically.
The rates of canopy and individual leaf photosynthesis, rates of growth of shoots and roots, and the extinction coefficient for light of eight temperate forage grasses were determined in the field during early autumn. Canopy gross photosynthesis was calculated as net photosynthesis plus dark respiration adjusted for temperature using a Q10 = 2. The relationships between canopy gross photosynthesis and light intensity were hyperbolic, and the initial slopes of these curves indicated that light was being utilized efficiently at low light intensities. The initial slope depended on the distribution of light in the canopy and the quantum efficiency of the individual leaves. The maximum rate of canopy gross photosynthesis reflected the maximum rate of individual leaf photosynthesis. Although the maximum rate of canopy gross photosynthesis was correlated with crop growth rate, there was no significant relationship between daily gross photosynthesis and crop growth rate. Indeed, daily gross photosynthesis varied by only 22 per cent, whereas the daily growth of shoots and roots varied by 120 per cent. This poor correlation is influenced by a negative correlation (P < 0.01) between the maximum rate of canopy gross photosynthesis and the initial slope of the curve relating canopy gross photosynthesis and light intensity. Difficulties in the interpretation of measurements of dark respiration appeared to confound attempts to relate daily net photosynthesis to crop growth rate, individual leaf photosynthesis, and the extinction coefficient for light.
Results of a grazing study involving Border Leicester x Merino ewes and their progeny are reported for a 4-year period 1969 to 1972. The study was conducted at Rutherglen in north-east Victoria. The 1260 ewes observed throughout the study were allotted to 42 treatment plots with 30 ewes per plot. Thirty of the plots were located on annual pasture (subterranean clover, barley grass) involving three replicates of ewes stocked at 7.4,9.9 and 12.4 ewes ha-l for ewes joined over 7-week periods to commence lambing about May 5, June 23 or August 8. Additionally, an extra three plots with spring lambing were grazed at 14.8 ewes ha-1. A further 12 plots contained lucerne on one third of the plot area. The ewes on these plots also lambed for a 7-week period commencing August 8, and provided three replicates of 7.4 9.9, 12.4 and 14.8 ewes ha-1. First services were concentrated early in the joining period in the spring-lambing ewes, there were few returns to services and few ewes failed to mate. On the other hand, among ewes joined to lamb in autumn, first services were distributed over the entire joining period, and many ewes returned to service or failed to mate. The mating pattern of ewes joined to lamb in winter were intermediate between the autumn and spring lambing groups. Although the liveweight of spring-lambing ewes was lighter at mating, the number of lambs born per 100 ewes joined was greater than for winter lambing, and these in turn were greater than for autumn lambing ewes. The response to an additional 1 kg liveweight at mating was 1.7, 2.0 and 2.8 additional lambs born per 100 ewes joined for autumn, winter and spring lambing, respectively. Wool production per hectare increased linearly with increase in stocking rate; it was marginally affected by time of lambing and was greatest where lucerne was provided. The number of lambs reared per ha increased linearly with increase in stocking rate, albeit there was some decline in carcase weight and a small decline in numbers of lambs reared per ewe. Lambs born in autumn and winter were generally slaughtered in prime condition at 31 kg liveweight, except in the drought year of 1972, but more lambs were produced from a winter lambing than from autumn lambing. When lambing was in August-September there was a slight increase in the numbers of lambs reared above that of winter lambing, but the proportion of lambs marketed at less than 31 kg was greatly increased. Where lucerne was provided to spring-lambing ewes the greatest weight of meat per hectare was produced, and few lambs were less than 31 kg. In descending order of magnitude, the major factors affecting carrying capacity were season, year, stocking rate and time of lambing. Seasonal shortcomings in nutrition were alleviated by the provision of supplementary feed. At the lighter stocking rates of 7.4 and 9.9 ewes ha-1 the amounts of supplements fed were very low, even in the drought year of 1972. These two lowest stocking rates could be maintained indefinitely without excessive use of supplementary feed. Discernible changes in botanical composition with partial loss of productive grass and clover species, reduction in pasture growth rate, increased use of supplementary feed and lowered wool production of ewes, occurred with spring lambing without lucerne, particularly at 12.4 and 14.8 ewes ha-1. Pasture composition and growth details were very similar with either autumn or winter lambing.
Possibly up to 20% of New Zealand beef production comes from bulls. Typically, dairy bull calves are purchased at about 12 weeks of age and slaughtered some 15 months later at around 400–420 kg. Profitability depends on the rapid attainment of target slaughter weights. Under New Zealand conditions, where stock are kept at pasture throughout the year, this means that good grazing management is of critical importance. Accordingly, a mathematical model of a bull beef system in New Zealand, describing herbage production and utilisation, has been constructed. Using the model, three specific issues have been addressed: (i) optimal stocking rates; (ii) the impact of feeding silage during periods of pasture shortage; and (iii) the most appropriate system of grazing management.Comparison of the simulated results with both experimental observations and recommended practices suggests that the model is capable of simulating the effects of weather, grazing management and stocking rate on livestock performance and pasture growth. However, before it can be confidently used to provide practical advice and to evaluate systems of bull beef production, further validation is required. In particular, the results from the model need to be tested over a longer run of years and for more sites. Sensitivity analysis also indicates that better estimates of some of the parameter values are required.
Effective transfer of new information and technology to farming practice is the major goal of the GRAZPLAN project. GRAZPLAN contains a suite of complementary decision support systems (DSS) that incorporate results from research on grazing systems and are now being released through a commercial partner as aids to extension. These computer packages are designed to be used in conjunction with local weather and farm data to test the relevance of different management procedures for individual farms. The main DSS, GrazPlan, can be used to evaluate and optimize long-term management decisions in relation to profitability and sustainability. It is quite general in its application and modular in structure. The Australia-wide database of daily weather records, which drives this program, is the basis for two smaller DSS, MetAccess and LambAlive, which are described in this paper. MetAccess is designed to display and analyse daily weather records and provide users with estimates of the probability of specified weather patterns within the range of data from a specified locality. LambAlive is designed to predict the risk of lamb deaths from bad weather for specified localities and flocks and enables the user to test different procedures that may reduce these losses.
This paper specifies the animal biology module of a model for simulating grazing systems for ruminants on pasture. The program predicts the intake of energy and protein, allowing for selective grazing and substitution by supplementary feeds, and estimates the use of the diet for maintenance and production, according to current feeding standards. Conception and death rates are predicted from the maturity and condition of the animals. The model is designed to be of general application to any type of sheep or cattle on any pasture.GrazFeed is a discrete package that uses the same procedures for predicting feed intake and productivity within a tactical decision support system. This is designed to help graziers to assess the feeding value of specified pastures and the need for the supplementary feeding of different classes of grazing animals.
A model for predicting diurnal changes in soil and air temperatures given the maximum and minimum temperatures has been developed. The model uses a truncated sine wave to predict daytime temperature changes and an exponential function to predict nighttime temperatures. The model is based upon hourly soil and air temperatures for 1977 at a shortgrass prairie site and is parameterized for 150-cm and 10-cm air temperatures and for soil-surface and 10-cm soil temperatures. The absolute mean error for the model ranged from a maximum of 2.64°C for the 10-cm air temperature to a minimum of 1.20°C for the 10-cm soil temperature. The model was also parameterized for hourly air temperature data for Denver, Colorado. Comparison of the model with other models showed that it did a superior job of fitting the data with a smaller number of parameters.
A plant growth model has been developed as a component of a general rangeland production and utilization model (SPUR). The carbon and nitrogen content of standing green, live roots, propagules, standing dead, litter, dead roots, soil organic matter and soil inorganic nitrogen are simulated. The model can simultaneously simulate up to seven plant species or species groups on a total of nine heterogeneous range sites. It incorporates processes which are common to C3 and C4 plants but does not consider plants with Crassulacean Acid Metabolism. When coupled with the other components of the SPUR model, the plant component allows testing of grazing management, environmental variation, and fertilizer application.
A simulation model was constructed of a self-replacing flock of Merino ewes grazing a predominantly Wimmera ryegrass and subterranean clover pasture in the Eppalock catchment of northern Victoria, Australia. The model was used to predict the likely physical, biological and economic consequences of changes in stocking rate and date of lambing.Routines for simulating the local climate, together with expected levels of pasture production, were based on available local data. The herbage produced was utilised for animal maintenance, growth, pregnancy, lactation and wool production. Predictions were made of the ovulation and fertilisation rates of the breeding ewes and the subsequent survival of embryos and lambs. Lamb growth rates were determined relative to their predicted intake levels of milk and herbage.The economic consequences of different combinations of ewe stocking rate and date of lambing were evaluated by simulating the cash flow of the property. Financial returns were obtained from the sale of wool, cast-for-age and culled ewes, and wether lambs.
Twelve pasture species were grown in the same aerial environment, but with five constant soil temperatures ranging from 5 to 35 °C, to determine the influence of root temperature on the weight of roots per unit weight of foliage (R/S ratio). This ratio varied by a factor of 2 to 8 within species.
Using maximum yield of foliage to indicate the optimum soil temperature for each species, it was found that the R/S ratio was lowest at the optimum soil temperature, and was progressively higher at soil temperatures above and below the optimum with only slight exceptions. This experimental manipulation of R/S ratios suggests that the partitioning of photosynthate is controlled by the relative rates of photosynthesis and root absorption, by inverse proportion:
Root mass x rate(abeorption) α Leaf mass x rate(photosynthesis).
When other environmental factors were uniform, the relative weight of roots tended to be proportional to the displacement of soil temperature from the optimum for each of the 12 species, the relationship being curvilinear.
The annual pattern of production of the perennial ryegrass crop is largely affected by the seasonal weather pattern and the change from the vegetative to the reproductive phase of growth.
Analysis of the relative importance of the factors governing the growth of the reproductive crop and the regrowth of the vegetative crop is complex. Often physiological variables are correlated as well as climatic variables, which influence the physiology and morphology of the crop.
Much experimental data has been collected, in the field and under controlled environments, concerning the growth of the grass crop. This information has been used to construct a mathematical model of the crop. In order to evaluate the problems of modelling the grass crop the model has been used to predict the net growth of the crop in terms of leaf, ‘stem’, root and dead matter using daily irradiance data. The influence of some crop characteristics on vegetative regrowth have been considered. The model has also been used to assess the relative importance, for yield, of some physiological characteristics of the reproductive grass crop.
The vegetative growth of a two-component plant consisting of root and shoot only is considered in terms of the transport and utilization of two required substrates, one providing carbon and the other providing nitrogen. The model provides a quantitative scheme for examining how root: shoot ratios depend upon the specific activities of root and shoot and hence environment. It has been shown that the total shoot activity is proportional to the total root activity in a plant undergoing steady-state growth.
A process-based model of the growth of a grazed subterranean clover pasture is described. The model predicts daily changes in the quantity and quality of seed, and green and dry vegetative biomass. Many of the functions describing processes were empirically derived. The model was calibrated against field data and then shown to give close agreement in predicting biomass of three other grazed pastures, two of which were in a different location.
Death and decay rates of per-ennial pasture as affected by season Simulation of Grazing Systems GRAZPLAN: decision support systems
- J W D Cayley
- P R Bird
- J F Chin
- K R Christian
- M Freer
- J R Donnelly
- J L Davidson
- J S Armstrong
Cayley, J. W. D, Bird, P. R. and Chin, J. F. (1980) Death and decay rates of per-ennial pasture as affected by season. Proc. Aust. Sot. Anim. Prod. 13, 469. Christian, K. R., Freer, M., Donnelly, J. R., Davidson, J. L. and Armstrong, J. S. (1978) Simulation of Grazing Systems. Centre for Agricultural Publishing and Documentation, Wageningen, The Netherlands. GRAZPLAN: decision support systems. III 581
Pasture production model
- G M Mckeon
- K G Rickert
- A J Ash
- D G Cooksley
- W J Scattini
McKeon, G. M., Rickert, K. G., Ash, A. J., Cooksley, D. G. and Scattini, W. J. (1982) Pasture production model. Proc. Aust. Sot. Anim. Prod. 14, 2OlL204.
Feeding Standards for Australian Livestock. Ruminants. Standing Com-mittee on Agriculture and The growth of perennial rye-grass: a model
- Csiro
- Melbourne
- J E Sheehy
- J M Cobby
- G J A Ryle
Springer, New York. SCA (1990) Feeding Standards for Australian Livestock. Ruminants. Standing Com-mittee on Agriculture and CSIRO, Melbourne. Sheehy, J. E., Cobby, J. M. and Ryle, G. J. A. (1979) The growth of perennial rye-grass: a model. Ann. Bot. 43, 335-354.
SIMTAG: a simulation model of wheat genotypes. Model doc-umentation. University of New Effect of constant temperature treatments followed by fluctu-ating temperatures on the softening of hard seeds of Trzfolium subterraneum L
- M Stapper
- England
- Icarda
- Armidale
- G B Taylor
Stapper, M. (1984) SIMTAG: a simulation model of wheat genotypes. Model doc-umentation. University of New England / ICARDA, Armidale. Taylor, G. B. (1981) Effect of constant temperature treatments followed by fluctu-ating temperatures on the softening of hard seeds of Trzfolium subterraneum L. Aust. J. Plant Physiol. 8, 547-558.
Death and decay rates of perennial pasture as affected by season
- Cayley
Pasture production model
- McKeon
SIMTAG: a simulation model of wheat genotypes
- Stapper