CSIRO PUBLISHING
Animal Production Science, 2009, 49, 797–806
www.publish.csiro.au/journals/an
Sacrificial grazing of wheat crops: identifying tactics and opportunities in Western Australia’s grainbelt using simulation approaches Lindsay W. Bell A,C, John N. G. Hargreaves A, Roger A. Lawes B and Michael J. Robertson B A
CSIRO Sustainable Ecosystems/APSRU, PO Box 102, Toowoomba, Qld 4350, Australia. CSIRO Sustainable Ecosystems, Private Bag 5, PO Wembley, WA 6913, Australia. C Corresponding author. Email:
[email protected] B
Abstract. Failing grain crops are sometimes sacrificed for grazing by mixed farmers, a decision involving a complex range of factors. This simulation study used two APSIM (Agricultural Production Systems Simulator)-based approaches to investigate the circumstances under which more revenue might be obtained by sacrificing a wheat crop for grazing rather than harvesting it for grain in Western Australia’s grainbelt. First, we developed a simple partial budget calculation to estimate and compare revenue from grain or grazing alternatives using data for grain yield and standing biomass at flowering. This was simulated for a factorial of soil types and locations varying in mean annual rainfall. We then simulated wheat quality and livestock production on spring wheat grazed at different stages of crop development and at a range of stocking rates. Dynamic simulations of grazing showed that livestock production increased as grazing was delayed; stocking rate had little impact at this time. Grazing earlier necessitated lighter stocking rates but surprisingly had little benefit for animal performance. Partial budgets showed that under average commodity prices, grazing a wheat crop could be more profitable 40–75% of the time on poorer soil types in lower rainfall environments. In these situations, by tactically grazing when grain yield is below a critical level economic returns could be increased by more than A$50/ha in 30–40% of years and over the long term average revenues could be increased by A$30/ha.year. This critical grain yield ranged from 1.3 to 1.7 t/ha on shallow gravel soil and 1.9 to 2.2 t/ha on a deep sand. In higher rainfall environments and on better soil types grazing was rarely a better option unless livestock prices were high relative to grain. This approach, combining crop simulation with partial budgets, was useful for developing simple management rules for a complex system. Overall, the findings of this study suggest that making tactical use of a wheat crop for forage in situations with low grain yield prospects is a major opportunity to increase profitability and help respond to climate variability in mixed farms in many areas of the Western Australian wheatbelt. Additional keywords: APSIM, economics, feed quality, livestock production, modelling.
Introduction In mixed crop-livestock farming systems of Australia, livestock utilise cereal grain crops for feed both during the season and crop stubbles after harvest. Long-season dual-purpose cereals may be grazed early in the crop’s life, at Zadok’s growth stage 30, allowing sufficient crop regrowth to produce grain if seasonal conditions permit (Virgona et al. 2006; Kelman and Dove 2007). Alternatively, later in the season any cereals may be grazed or cut for hay as the crop approaches maturity if desired. In this instance, grain production is sacrificed. The decision to graze a cereal crop is influenced by many factors, such as the availability of feed on-farm, weed populations and the likely prospect of obtaining a profitable grain crop (Herbert 2006), all of which are influenced by soil and seasonal conditions. To date, no formal framework has been developed to evaluate the costs and benefits associated with the sacrificial grazing of grain crops. Furthermore, certain agro-environments may favour grazing over grain production and these need to be elucidated to develop an actionable management strategy for the industry. CSIRO 2009
Grazing cereals is complicated further by the desire to make best use of the forage available to maximise animal performance. In a cereal crop, as in other forages, nutritional quality will decline as the crop approaches maturity, due to an increasing proportion of lower quality stem and reduced quality of herbage components (particularly stem). For example, wheat early in its growth at floral initiation has high quality [~80% dry matter digestibility (DMD), with leaf and stem having similar quality] (Walker et al. 1990; Kelman and Dove 2007). At flowering, wheat quality is reduced (68–72% DMD for leaf and 61–70% DMD for stem) and as grain fill begins and maturity approaches, the quality of stem and leaf continues to decline, particularly the stem component which falls to 48–53% DMD by crop maturity (Walker et al. 1990; Akin et al. 1995; Fohner 2002). Meanwhile, there is an increasing proportion of high quality grain in the crop biomass. As the crop develops phenologically, final grain yield is more predictable, leading to a management dilemma – grazing the crop early in the season when nutritional quality of the forage is greatest but uncertainty of grain yield is
10.1071/AN09014
1836-0939/09/100797
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high, or delaying the grazing decision until there is greater confidence in the grain yield prediction of the crop. The question central to this paper is, ‘What are the circumstances under which it is more profitable to sacrifice a crop to grazing than to harvest it for grain?’ This question is of particular interest in Western Australia given the often poor and highly variable soils in the cereal-growing areas (Oliver et al. 2006) and the dependence on in-season rainfall of varying reliability. We developed a simple economic tool that captured the dominant processes and used this to evaluate the costs and benefits of grazing cereals across a diverse array of agroenvironments. We then investigated complexity of timing and intensity of grazing a wheat crop by combining grazing and plant growth modules from the APSIM (Agricultural Production Systems Simulator; Keating et al. 2003) and GRAZPLAN (Donnelly et al. 2002) simulation models that capture most of the animal-crop interactions. This simulation analysis shows that if a grain crop with lower grain harvest prospects could be sufficiently utilised by grazing livestock then sacrificial grazing may often be a more profitable option, especially in marginal cropping areas with lower rainfall and poor soils. Materials and method Before the simulation experiments were conducted, a validation exercise was conducted to evaluate the ability of the wheat module (APSIM-Wheat version 5.4) to simulate biomass at flowering across a range of locations, seasons and soil types. We then used long-term APSIM simulations of spring wheat biomass at flowering and associated crop yield at maturity to compare alternative tactics of sacrificial grazing and grain harvesting. Simulations were conducted across a range of locations and soil types for the cereal-livestock zone of Western Australia (Table 1). The simulation outputs were used in a partial budgeting approach to compare the value of grain production v. livestock production from grazing, with assumptions on the utilisation of forage by animals and its conversion to liveweight gain (LWG). An in-depth simulation exercise was also conducted with a dynamic livestock production module (GRAZPLAN) coupled to the wheat module (APSIM), to explore the sensitivity of livestock
production to timing and intensity of grazing. However, while we are confident in the processes simulated using this approach, there has been limited validation of animal performance predictions, so care should be exercised with interpretation from this data. Validation of biomass and yield simulation in APSIM Simulation of grain yield and livestock production requires accurate predictions of both grain yield and biomass. Past validation efforts of APSIM-Wheat have concentrated on predictive performance for grain yield (e.g. Asseng et al. 1998; Oliver et al. 2006). Here we present additional validations for biomass. A variety of studies where wheat biomass and yield have been measured were simulated to assess the performance of the wheat module in Western Australia and elsewhere. First, simulations were compared with wheat biomass and grain yield measured during vegetative growth and at maturity at several on-farm experimental sites near Buntine, Western Australia from 2002 to 2005 (Oliver et al. 2006). Data for 28 combinations of soil type, nitrogen (N) rate, sowing date and growing season were tested. Second, validation was conducted on data from past wheat experiments in Western Australia reported in Asseng et al. (1998). This included experimental data for 56 circumstances from five locations, with a variety of soil types, N rates, sowing times and several seasons. Finally, a variety of experiments from outside Western Australia (Queensland and New Zealand) were also included, covering two locations and experiments involving N rate, water and residue treatments covering several seasons (a total of 33 circumstances) (Wang et al. 2003). Partial budget comparisons of grain and grazing options A partial budget, like a gross margin, was used to compare the relative profitability of options to either graze or harvest the crop, but because many of the costs for growing the crop would be the same they were ignored (they would be included in a complete gross margin). The simple partial budgeting approach estimated the income from grain and grazing options using long-term APSIM simulation outputs of grain yield and standing biomass at flowering (Eqns 1, 2). This partial budget captured many of the dominant processes yet was simple enough to be used to
Table 1. Percentage of years when grazing has greater value than grain production from wheat at eight locations in the Western Australian wheatbelt on three soils varying in plant available water-holding capacity (PAWC) (using standard price assumptions) Site
Dalwallinu Merredin Cunderdin Binnu Mingenew York Bakers Hill Badgingarra
Latitude
30.22S 31.48S 31.62S 28.04S 29.19S 31.91S 31.76S 30.34S
Longitude
116.78E 118.28E 117.22E 114.67E 115.67E 116.78E 116.48E 115.54E
Mean annual rainfall (mm)
Red loam (148 mm)
300 320 365 360 410 450 595 575
16 13 8 8 4 1 0 0
Soil type (PAWC) Deep sand Shallow gravel (90 mm) (40 mm) 51 53 40 32 19 16 3 0
74 75 70 56 38 48 32 23
Sacrificial grazing of wheat crops
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evaluate the costs and benefits of grazing a cereal crop across a diverse range of agro-environments. Grain value ð$=haÞ ¼ ðgrain yield · priceÞ harvesting cost ð1Þ Grazing value ð$=haÞ ¼ U · FCR · LWP · wheat biomass at flowering
ð2Þ
Grazing value was estimated using assumptions of 50% utilisation (U) of the wheat biomass at flowering (~100 days after sowing), a feed conversion rate (FCR) of 0.1 kg of LW/kg of DM intake, and a LW price (LWP) of A$1.6/kg LW [1998–2007 average for yearling trade steers (Meat and Livestock Australia’s National Livestock Reporting Service 2008)]. Grain value was calculated using a grain price of A$200/t [1997–2006 average wheat price (ABARE 2007)] and harvesting cost of A$35/ha. Other costs, such as transport, transaction and sowing costs were assumed equal and budgets assume that farms already have the necessary equipment and other livestock costs have already been expended. Sensitivity analysis of different commodity price scenarios was also investigated for the two enterprises. Long-term simulations of wheat yield and biomass To predict the variability in wheat yield and biomass at flowering across a wide variety of seasons, locations and soil types, the APSIM-Wheat module was run using the historical climate data obtained from the Silo Database (1889–2005) (Jeffrey et al. 2001). This was simulated for a full factorial of eight locations in the Western Australian wheatbelt (Table 1) and three soils representing poor, average and good soils of the region; a shallow gravel [40 mm plant available waterholding capacity (PAWC)], a deep sand (90 mm PAWC) and a red loam (150 mm PAWC). The eight locations were chosen to represent a range of long-term mean annual rainfall in the major grain-growing regions of Western Australia. Wheat production was simulated using spring wheat cultivar Wyalkatchem at 150 plants/m2 with row spacing of 250 mm and sowing depth of 40 mm. Wheat was sown between 15 May and 30 June after 15 mm of rain occurring over no more than 10 days. To represent a situation where a previous wheat crop had extracted all plant available water from the soil profile at harvest, on 1 January each year soil water was reset to wheat lower limit. Surface organic matter was also reset to 1000 kg/ha of wheat stubble on 1 January each year. Preliminary simulations showed that N availability to the crop could often restrict the yield on these soils, which further favoured grazing of a crop as an option. Hence, to avoid grain yield reductions due to N stress, 100 kg/ha of NO3-N was maintained in the top 60 cm of soil when the crop was growing. Dynamic simulations of livestock grazing wheat using APSIM-GRAZPLAN models Many of the complexities of predicting animal production from grazing a wheat crop were examined using recently developed capabilities in the APSIM modelling framework which enable crop quality, livestock nutrition and LWG to be simulated. This series of simulations was undertaken to examine the sensitivity of
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animal production to stocking rate and the phenological stage at grazing but the predicted animal performance is not validated against field data. Background to model developments The common modelling protocol enables the application of modules from APSIM and GRAZPLAN simulation frameworks to investigate mixed-farming enterprises that involve both crops and livestock in a common simulation setting (Moore et al. 2007). In this case, the GRAZPLANStock module version 1.2.1 (Freer et al. 1997) provided the functions of diet selection and intake, ruminant nutrition and production in the APSIM simulation framework, interacting with APSIM crop, soil water and soil N modules. The APSIM Stock Science Converter was used to translate incompatibilities and facilitate communication between the GRAZPLAN-Stock module and the herbage pool in the APSIM crop module. This converter must handle several two-way conversions. The stock can eat herbage from several sources within the field, such as multiple APSIM crops and surface organic matter (i.e. crop residues). This requires the herbage sources (plant components and crop modules) to be identified and the herbage stored into pools of digestibility classes. After the stock has eaten herbage from these pools, the appropriate amounts are removed from each of the herbage sources. Another set of conversions relate to the stock in the field, which include management and the return of organic and inorganic matter to the field through excreta. These conversions are independent of the herbage sources and here the Converter interacts with the appropriate APSIM modules, such as Soil N, Surface OM and Manager. The GRAZPLAN-Stock module requires herbage digestibility parameters for the grazed crop and pasture to be available from the modules providing the herbage. APSIM crop modules were not able to provide this until modified. To enable the dynamic simulation of herbage quality in APSIMWheat, we developed a record of stage-dependent digestibility parameters for green, senesced and dead material of each of the wheat plant parts; leaf, stem and grain (refer to Fig. 1). These data were collated from published sources of wheat quality at various phenological stages (Pearce et al. 1979, 1988; Mader and Horn 1986; Muldoon 1986; Helsel and Thomas 1987; Capper et al. 1988; Van Keuren and Underwood 1990; Wales et al. 1990; Walker et al. 1990; Akin et al. 1995; Ben-Ghedalia et al. 1995; Lippke et al. 2000; Rao et al. 2000; Fohner 2002; Kelman et al. 2006; Kelman and Dove 2007). The wheat module was also configured so that phenological development was not impacted by grazing. Potential faster regrowth after defoliation due to reallocation of stored carbohydrates was not simulated. These issues are not so important for a crop grazed late in its development with no expectation of further regrowth. We know that both these will effect wheat regrowth (Virgona et al. 2006), yet there are few documented studies on which to parameterise the physiology of the model to predict this effect accurately across a range of environments. The model simulated the impact of grazing by removing leaf and stem and, hence, photosynthetic area and radiation interception; DM production and N uptake change in response to this.
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(a) Leaf
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time (seven stages) and stocking intensity (eight intensities). Twelve-month-old Angus steers with a starting weight of 250 kg were used in all simulations to represent a system where yearling cattle could be sourced tactically from extensive northern pastoral regions. Stock began grazing from 50 days after sowing (DAS) (~floral initiation) to 120 DAS (~mid-grain fill) in seven 10-day increments, and at stocking rates of 2, 4, 6, 8, 10, 15, 20 and 30 head/ha. Stock was removed from the wheat when the average weight gain of animals fell below 0.2 kg/ha.day over 2 consecutive days. Due to the longer time required to prepare and run these simulations they were conducted for 100 years at only one site (Mingenew), but were repeated for three soil types described previously. Wheat production was simulated using the same crop management as described previously.
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APSIM-Wheat simulated wheat biomass and grain yield well in Western Australia and at other locations (Fig. 2). The model explained 89% of the variation in grain yield and 95% of the variation in standing biomass. There was, however, a slight tendency for the model to under-predict yield and biomass in high yielding situations. The root mean squared deviation for grain yield was 537 and 1270 kg/ha for biomass, which represented 18 and 17% of the mean observed values. Hence, biomass was simulated with similar accuracy to grain yield. Estimated value of grazing at flowering v. grain under average prices
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Wheat phenological stages Fig. 1. Average dry matter digestibility (DMD, g/g) of wheat plant components/pools simulated in the Agricultural Production Systems Simulator through its phenological development compared with measured values in the literature; (a) measured leaf DMD (*) compared with simulated green leaf (solid line) and senesced leaf (dotted line); (b) measured stem DMD (&) compared with simulated stem DMD; (c) measured ear (spike) DMD (~) compared with simulated DMD for grain (dashed line) and pod (solid line). Wheat phenological stages are: 3, emergence; 4, end of juvenile; 5, floral initiation; 6, flowering; 7, start of grain fill; 8, end of grain fill; 9, maturity; 10, harvest ripe; 11, after harvest.
Simulation design and treatments of livestock grazing wheat LWG, utilisation and feed conversion efficiency of livestock grazing a wheat crop was simulated in a factorial of grazing
The percentage of years that grazing a crop at flowering may be more profitable than harvesting grain varied considerably between locations, but was always highest on the soil with a low PAWC (Table 1). These soils store less water that can help crops convert biomass to grain yield in dry years. This effect was also greater in the lower rainfall locations (
