Browsing by Browse by FOR 2020 "300205 Agricultural production systems simulation"

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Open Access
Report
Accelerating precision agriculture to decision agriculture: Enabling digital agriculture in Australia
(Cotton Research and Development Corporation (CRDC), 2017)
Leonard, Emma
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Rainbow, Rohan
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Laurie, A
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Lamb, David
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Llewellyn, R
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Perrett, Ed
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Sanderson, Jay
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Skinner, Andrew
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Stollery, T
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Wiseman, Leanne
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Wood, G
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Zhang, Airong
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Trindal, Jane
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Baker, I
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Barry, Simon
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Darragh, L
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Darnell, Ross
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George, A
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Heath, Richard
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Jakku, Emma
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Australian Government, Department of Agriculture and Water Reources
The aim of the project was to benchmark Australian producers' needs, perceived risks and benefits, and expectations associated with digital agriculture and big data context. Such understanding will inform strategies aimed at 1) better utilising agricultural data to enhance productivity and profitability, and 2) better capitalising on the opportunities created by digital agriculture and big data. In consultation with P2D project members and participating RDCs, CSIRO designed the survey questionnaire and conducted a survey of 1000 producers across 17 agricultural industries during the period of 7 March to 18 April 2017. The sampling specifications for each industry was defined in consultation with relevant participating RDCs. The study investigated producers' needs, perceived risks and benefits, and expectations from three aspects: telecommunication infrastructure, the status of current data collection, and data sharing and concerns in the big data context.
3670 1
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Thesis Doctoral
Adoption and impact of improved cassava varieties: Evidence from Ghana
(2017)
Kondo, Kodjo
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Fleming, Euan
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Cacho, Oscar J
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Villano, Renato
Cassava is an important tropical root crop for food security and national economies. In Ghana, the roots are used in popular local cuisines as well as in brewery, bakery, confectionery and plywood industries. A number of high-yielding and disease-resistant varieties are released and promoted to increase productivity and improve rural welfare. The study used a sequential mixed-method approach to identify, among drivers and impediments, the dissemination mechanism with highest impact on the adoption of improved cassava varieties (ICVs) and its intensity. The analyses helped estimate the impact of ICV adoption on productivity and households' livelihood, and to provide evidence of technological, managerial, and environmental gaps between adopters and non-adopters. Data were collected in 2014 from 608 randomly selected cassava-producing households in 14 communities in six districts of the Ashanti and Brong-Ahafo regions. Summary statistics reveal a 25 percent ICV adoption rate. Econometric analyses indicate significant and positive effects on the likelihood of households' ICV adoption for group members, the number of varieties planted, the number of livestock owned and information received mostly through innovation platforms (IPs). Impediments to ICV adoption include the location in the Ashanti region, household size, distances to the nearest tarred road and market, and grey-skin colour of ICVs. Results from propensity score matching and instrumental variable approaches indicate positive impacts of ICV adoption on cassava and whole-farm productivities and on per-capita annual crop income. Adopters appear to incur lower total annual per-capita expenditures and expenditures on food than non-adopters but spend more on children’s education. Bias-corrected stochastic output distance functions and stochastic metafrontier production functions showed strong evidence of technological, managerial, and environmental gaps between adopters and non-adopters in both cassava and whole-farm production. In both cases, adopters were found to operate on higher frontiers and to be more efficient than non-adopters. Adopters also appear to operate in a more favourable 2 production environment than non-adopters. The study provides strong evidence of inefficiency in cassava production for both ICV adopters and non-adopters. Findings imply that policy measures could be taken to increase the 25 percent ICV adoption rate through the establishment of IPs, focusing on households in Brong-Ahafo and those who are group members that integrate livestock-farming with cassava production. ICV adoption is expected to lead to increased productivity through technological change and enhanced efficiency. Moreover, the adoption of ICVs has the potential to increase crop incomes, food security and result in higher investment in children’s education, especially for female-headed households.
2522
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Journal Article
The AusBeef model for beef production: I. Description and evaluation
(Cambridge University Press, 2017-11)
Dougherty, H C
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Kebreab, E
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Evered, M
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Little, B A
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Ingham, A B
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Hegarty, R S
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Pacheco, D
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McPhee, M J
As demand for animal products, such as meat and milk, increases, and concern over environmental impact grows, mechanistic models can be useful tools to better represent and understand ruminant systems and evaluate mitigation options to reduce greenhouse gas emissions without compromising productivity. The objectives of the present study were to describe the representation of processes for growth and enteric methane (CH₄) production in AusBeef, a whole-animal, dynamic, mechanistic model for beef production; evaluate AusBeef for its ability to predict daily methane production (DMP, g/day), gross energy intake (GEI, MJ/day) and methane yield (MJ CH₄/MJ GEI) using an independent data set; and to compare AusBeef estimates to those from the empirical equations featured in the current National Academies of Sciences, Engineering and Medicine (NASEM, 2016) beef cattle requirements for growth and the Ruminant Nutrition System (RNS), a dynamic, mechanistic model of Tedeschi & Fox, 2016. AusBeef incorporates a unique fermentation stoichiometry that represents four microbial groups: protozoa, amylolytic bacteria, cellulolytic bacteria and lactate-utilizing bacteria. AusBeef also accounts for the effects of ruminal pH on microbial degradation of feed particles. Methane emissions are calculated from net ruminal hydrogen balance, which is defined as the difference between inputs from fermentation and outputs due to microbial use and biohydrogenation. AusBeef performed similarly to the NASEM empirical model in terms of prediction accuracy and error decomposition, and with less root mean square predicted error (RMSPE) than the RNS mechanistic model when expressed as a percentage of the observed mean (RMSPE, %), and the majority of error was non-systematic. For DMP, RMSPE for AusBeef, NASEM and RNS were 24·0, 19·8 and 50·0 g/day for the full data set (n = 35); 25·6, 18·2 and 56·2 g/day for forage diets (n = 19); and 21·8, 21·5 and 41·5 g/day for mixed diets (n = 16), respectively. Concordance correlation coefficients (CCC) were highest for GEI, with all models having CCC > 0·66, and higher CCC for forage diets than mixed, while CCC were lowest for MY, particularly forage diets. Systematic error increased for all models on forage diets, largely due to an increase in error due to mean bias, and while all models performed well for mixed diets, further refinements are required to improve the prediction of CH₄ on forage diets.
1652 12
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Journal Article
The AusBeef model for beef production: II. sensitivity analysis
(Cambridge University Press, 2017-11)
Dougherty, H C
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Kebreab, E
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Evered, M
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Little, B A
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Ingham, A B
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Nolan, J V
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Hegarty, R S
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Pacheco, D
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McPhee, M J
The present study evaluated the behaviour of the AusBeef model for beef production as part of a 2 × 2 study simulating performance on forage-based and concentrate-based diets from Oceania and North America for four methane (CH₄)-relevant outputs of interest. Three sensitivity analysis methods, one local and two global, were conducted. Different patterns of sensitivity were observed between forage-based and concentrate-based diets, but patterns were consistent within diet types. For the local analysis, 36, 196, 47 and 8 out of 305 model parameters had normalized sensitivities of 0, >0, >0·01 and >0·1 across all diets and outputs, respectively. No parameters had a normalized local sensitivity >1 across all diets and outputs. However, daily CH₄ production had the greatest number of parameters with normalized local sensitivities >1 for each individual diet. Parameters that were highly sensitive for global and local analyses across the range of diets and outputs examined included terms involved in microbial growth, volatile fatty acid (VFA) yields, maximum absorption rates and their inhibition due to pH effects and particle exit rates. Global sensitivity analysis I showed the high sensitivity of forage-based diets to lipid entering the rumen, which may be a result of the use of a feedlot-optimized model to represent high-forage diets and warrants further investigation. Global sensitivity analysis II showed that when all parameter values were simultaneously varied within ±10% of initial value, >96% of output values were within ±20% of the baseline, which decreased to >50% when parameter value boundaries were expanded to ±25% of their original values, giving a range for robustness of model outputs with regards to potential different ‘true’ parameter values. There were output-specific differences in sensitivity, where outputs that had greater maximum local sensitivities displayed greater degrees of non-linear interaction in global sensitivity analysis I and less variance in output values for global sensitivity analysis II. For outputs with less interaction, such as the acetate : propionate ratio and microbial protein production, the single most sensitive term in global sensitivity analysis I contributed more to the overall total-order sensitivity than for outputs with more interaction, with an average of 49, 33, 15 and 14% of total-order sensitivity for microbial protein production, acetate : propionate ratio, CH₄ production and energy from absorbed VFAs, respectively. Future studies should include data collection for highly sensitive parameters reported in the present study to improve overall model accuracy.
1596 8
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Journal Article
Beef production simulation of nitrate and lipid supplements for pasture and rangeland fed enterprises
(Elsevier BV, 2019-03)
McPhee, Malcolm J
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Evered, Mark
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Andrews, Todd
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Pacheco, David
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Dougherty, Holland C
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Ingham, Aaron B
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Harden, Steven
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Crean, Jason
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Roche, Leslie
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Eastburn, Danny J
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Oltjen, James W
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Oddy, V Hutton
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Kebreab, Ermias
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Nolan, John V
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Hegarty, Roger S
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.
1741 6
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Journal Article
Carbon and blue water footprints of California sheep production
(American Society of Animal Science, 2019-02)
Dougherty, Holland C
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Oltjen, James W
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Mitloehner, Frank M
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DePeters, Edward J
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Pettey, Lee Allen
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Macon, Dan
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Finzel, Julie
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Rodrigues, Kimberly
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Kebreab, Ermias
While the environmental impacts of livestock production, such as greenhouse gas emissions and water usage, have been studied for a variety of US livestock production systems, the environmental impact of US sheep production is still unknown. A cradle-to-farm gate life cycle assessment (LCA) was conducted according to international standards (ISO 14040/44), analyzing the impacts of CS representing five different meat sheep production systems in California, and focusing on carbon footprint (carbon dioxide equivalents, CO₂e) and irrigated water usage (metric ton, MT). This study is the first to look specifically at the carbon footprint of the California sheep industry and consider both wool and meat production across the diverse sheep production systems within California. This study also explicitly examined the carbon footprint of hair sheep as compared with wooled sheep production. Data were derived from producer interviews and literature values, and California-specific emission factors were used wherever possible. Flock outputs studied included market lamb meat, breeding stock, 2-d-old lambs, cull adult meat, and wool. Four different methane prediction models were examined, including the current IPCC tier 1 and 2 equations, and an additional sensitivity analysis was conducted to examine the effect of a fixed vs. flexible coefficient of gain (k_g) in mature ewes on carbon footprint per ewe. Mass, economic, and protein mass allocation were used to examine the impact of allocation method on carbon footprint and water usage, while sensitivity analyses were used to examine the impact of ewe replacement rate (% of ewe flock per year) and lamb crop (lambs born per ewe bred) on carbon footprint per kilogram market lamb. The carbon footprint of market lamb production ranged from 13.9 to 30.6 kg CO₂e/kg market lamb production on a mass basis, 10.4 to 18.1 kg CO₂e/kg market lamb on an economic basis, and 6.6 to 10.1 kg CO₂e/kg market lamb on a protein mass basis. Enteric methane (CH₄) production was the largest single source of emissions for all CS, averaging 72% of total emissions. Emissions from feed production averaged 22% in total, primarily from manure emissions credited to feed. Whole-ranch water usage ranged from 2.1 to 44.8 MT/kg market lamb, almost entirely from feed production. Overall results were in agreement with those from meat-focused sheep systems in the United Kingdom as well as beef raised under similar conditions in California.
1158 6
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Conference Publication
Carbon and blue water footprints of California sheep production
(American Society of Animal Science, 2018-12)
Dougherty, H
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Oltjen, J
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Mitloehner, F
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DePeters, E
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Pettey, L
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Macon, D
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Finzel, J
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Rodrigues, K
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Kebreab, E
While the environmental impacts of livestock production have been studied for a variety of livestock production systems, information is still lacking for US sheep production. A cradle-to-farm gate life cycle assessment was conducted according to international standards (ISO 14040/44), analyzing the impacts of five different meat sheep production systems in California, and focusing on carbon footprint (carbon dioxide equivalents, CO2sub>2e) and irrigated water usage (MT). This study is the first to look at the carbon footprint of the California sheep industry and to consider both wool and meat production across the diverse sheep production systems within California. This study also explicitly examined the carbon foot-print of hair sheep as compared with wooled sheep production. Data were derived from producer interviews and literature values, and California-specific emission factors were used wherever possible. The carbon footprint of market lamb production ranged from 13.9 to 30.6 kg CO₂e/kg market lamb production on a mass basis, 10.4 to 18.1 on an economic basis, and 6.59 to 10.1 on a protein mass basis. Whole-ranch water usage ranged from 2.06 to 44.8 MT/kg market lamb, almost entirely from feed production, and four of five case studies used irrigated pasture for at least part of the year. Enteric methane (CH₄) production was the largest single source of emissions for all case studies, averaging 72% of total emissions. Emissions from manure credited to feed or from feed production averaged 22% in total. Sensitivity analysis showed that carbon footprint per kg market lamb increased as ewe replacement rate increased and decreased as lambs born/ewe bred increased. These results provide a proactive benchmark for the previously-unknown environmental impacts of current sheep production systems in California, which could be used to spur research into other US sheep production systems.
1637 3
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Conference Publication
A Case Study Assessment of the Carbon Footprint of Sheep Production Systems in California
(2017)
Dougherty, H C
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Oltjen, J W
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Mitloehner, F M
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Rodrigues, K A
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Kebreab, E
•California is the nation's leading producer of market sheep
•Most sheep in CA spend at least part of their life on rangeland, as part of a wide variety of diverse systems
•The environmental impact of these systems is unknown
•Life cycle assessment (LCA) and carbon footprint calculations can be used to measure the efficiency and environmental impacts of livestock production
•California-specific LCAs have been conducted (e.g. Stackhouse-Lawson et al. (2012) for beef production and DeLonge(2016) for wool)
•The environmental impact of sheep meat production in other countries has been studied, but not for the US
•Reducing carbon footprint is highly correlated with increasing production efficiency and profitability
•A case study based method will allow for the calculation and comparison of the carbon footprints of different production system types
•Baseline resource use and carbon footprint data from different sectors of the California sheep industry would allow producers to assess current environmental impacts
•This data would help producers improve production efficiency and reduce environmental impact while maintaining the health of native rangelands
1655 6
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Journal Article
Climate change adaptation options in rainfed upland cropping systems in the wet tropics: A case study of smallholder farms in North-West Cambodia
(Elsevier BV, 2016)
Touch, Van
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Martin, Robert John
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Scott, Jeannette Fiona
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Cowie, Annette
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Liu, De Li
While climate change is confirmed to have serious impacts on agricultural production in many regions worldwide, researchers have proposed various measures that farmers can apply to cope with and adapt to those changes. However, it is often the case that not every adaptation measure would be practical and adoptable in a specific region. Farmers may have their own ways of managing and adapting to climate change that need to be taken into account when considering interventions. This study aimed to engage with farmers to: (1) better understand small-holder knowledge, attitudes and practices in relation to perceived or expected climate change; and (2) document cropping practices, climate change perceptions, constraints to crop production, and coping and adaptation options with existing climate variability and expected climate change. This study was conducted in 2015 in Sala Krau village near Pailin (12°52′N, 102°45′E) and Samlout (12°39′N, 102°36′E) of North-West Cambodia. The methods used were a combination of focus group discussions and one-on-one interviews where 132 farming households were randomly selected. We found that farmers were conscious of changes in climate over recent years, and had a good understanding of likely future changes. While farmers are aware of some practices that can be modified to minimize risk and cope with anticipated changes, they are reluctant to apply them. Furthermore; there are no government agricultural extension services provided at the village level and farmers have relied on each other and other actors in the value chain network for information to support their decision-making. There is a lack of knowledge of the principles of conservation agriculture that urgently require agricultural extension services in the region to build farmer ability to better cope and adapt to climate change.
975 3
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Conference Publication
CropMate™ a web based decision support tool helping farmers make agronomic decisions using historic and forecast weather and climate data
(Australian Society of Agronomy Inc, 2012)
McIntosh, Graeme
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Herridge, David
The NSW Department of Primary Industries (DPI) launched an interactive website called CropMate™ in February 2011 in conjunction with the Grains Research and Development Corporation (GRDC). The website provides grain producers, advisors and agronomists in eastern Australia an online 'one-stop-shop' access to soil and crop management decision support tools linked to Bureau of Meteorology (BoM) weather and climate information and 100 years of rainfall data of the Queensland Climate Change Centre of Excellence. It is designed to provide timely and accurate climate and agronomic information to assist users make informed planning and management decisions about grain cropping during the crop management cycle. This paper provides an overview of CropMate™. The program is divided into 5 sections, which are aligned to different parts of the cropping cycle. The pre-season planning pages analyse historic climate data and seasonal forecasts and provide the latest information on the influences on climate such as ocean temperatures. Decision support tools such as 'CropChooser' and 'VarietyChooser' are also located in the pre-season planning pages. The sowing pages contain recent rainfall and temperature analyses, synoptic charts as well as a decision tool to help decide which variety of a particular crop to plant. Spraying pages interpret current and historic data into spraying advice. Tracking the season pages uses climate and agronomic data to support nitrogen topdressing decisions and difficult low rainfall questions such as salvaging crops for hay, silage or grain. The harvest page can collate the nutrient use of the crop just harvested.
1228
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Open Access
Conference Publication
Crude protein-content of fat-free muscle and viscera in sheep
(2020)
Dougherty, Holland
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Evered, Mark
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Oltjen, James
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Oddy, Hutton
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Hegarty, Roger
The nutritional effects of variation in feed supply and subsequent compensatory gain can play a significant role in cattle and beef production, due to their effects on carcass quality and feed costs. A system that predicts changes in fat and protein content of muscle and viscera in animals of different life stages and nutritional histories could therefore assist management to optimize performance and reduce costs of feed. A method has been developed to simultaneously estimate body composition and nutrient requirements of ruminants. This method estimates body composition from the difference in energy balance derived from ME intake and heat production from ME intake and protein content of muscle and viscera. Information on protein content of fat free mass in viscera and muscle (all non-viscera components of the body) is required by this new method. Data from the literature was combined with unpublished data from a study conducted in growing lambs. This experiment tested the effects of ad libitum intake of diets of varying energy density and added RUP on performance and carcass composition of lambs that had been previously restricted or unrestricted prior to a 12-13 week finishing phase, and both carcass and viscera components were chemically analysed on an individual basis. On a fat-free basis, muscle crude protein averaged 20.8%, with no effect of nutritional history or current diet; these numbers are in agreement with literature values, which lie between the range of 20-24% and which do not appear to vary substantially with age in postweaning sheep. In the same sheep, crude protein of viscera was affected by past and current nutritional state, and ranged from 15.3 to 16.2 % crude protein on a fat-free basis, averaging 15.7%. These values are within range of the limited literature data available on visceral composition. This data is use to parameterize functions describing growth of viscera and changes in heat production over time. This contributes to the method we have developed to estimate nutritional effects on body composition.
2157 3
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Open Access
Conference Publication
Description and evaluation of the AusBeef model of beef production
(California Grain & Feed Association, 2017)
Dougherty, H C
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Kebreab, E
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Evered, M
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Little, B A
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Ingham, A B
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Hegarty, R S
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Pacheco, D
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McPhee, M J
As demand for animal products such as meat and milk increases, and concern over environmental impact grows, mechanistic models can be useful tools to better represent and understand ruminant systems and evaluate mitigation options to reduce greenhouse gas emissions without compromising productivity. AusBeef is a whole-animal, dynamic, mechanistic model of beef production that calculates methane emissions from net ruminal hydrogen balance. AusBeef incorporates a unique fermentation stoichiometry that represents four different microbial groups, as well as the effects of ruminal pH on microbial degradation of feed. The objectives of this study were to evaluate the performance of the AusBeef model of beef production with regard to predicting daily methane production (DMP, g/d), dry matter intake (DMI, kg/d), gross energy intake (GEI, MJ/d) and methane yield (MY, %GEI), using independent data derived from the literature. AusBeef predictions were compared for the full dataset (n=37) as well as for high-forage diets (n=21) and mixed diets (n=16) using a root mean square predicted error expressed as a percentage of the observed mean (RMSPE%). AusBeef predicted DMP with RMSPE% of 26.6, 30.1, and 21.3% for the full dataset, high-forage, and mixed diets, respectively. AusBeef predicted MY, DMI, and GEI with a RMSPE% of 38.5, 8.91, and 9.86% for the full dataset, respectively. There were prediction differences between forage and mixed diets with a RMSPE% of 9.32 and 8.43% for DMI; 6.38 and 11.1% for GEI and 41.7 and 28.4% for MY. AusBeef prediction errors for DMI ranged from -18 to +42%, with AusBeef underpredicting DMI 76% of the time. AusBeef underpredicted methane emissions 65% of the time, with prediction error ranging from - 51 to +59%, and underpredicted GEI 90% of the time, with prediction error ranging from -1 to +30%. Further studies are required to improve the prediction of methane on forage only diets.
1824 4
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Journal Article
Determining the critical period for broadleaf weed control in high-yielding cotton using mungbean as a mimic weed
(Cambridge University Press, 2020-10)
Charles, Graham W
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Sindel, Brian M
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Cowie, Annette L
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Knox, Oliver G G
Research using the critical period for weed control (CPWC) has shown that high-yielding cotton crops are very sensitive to competition from grasses and large broadleaf weeds, but the CPWC has not been defined for smaller broadleaf weeds in Australian cotton. Field studies were conducted over five seasons from 2003 to 2015 to determine the CPWC for smaller broadleaf weeds, using mungbean as a mimic weed. Mungbean was planted at densities of 1, 3, 6, 15, 30, and 60 plants m⁻² with or after cotton emergence and added and removed at approximately 0, 150, 300, 450, 600, 750, and 900 degree days of crop growth (GDD). Mungbean competed strongly with cotton, with season-long interference; 60 mungbean plants m⁻² resulted in an 84% reduction in cotton yield. A dynamic CPWC function was developed for densities of 1 to 60 mungbean plants m⁻² using extended Gompertz and exponential curves including weed density as a covariate. Using a 1% yield-loss threshold, the CPWC defined by these curves extended for the full growing season of the crop at all weed densities. The minimum yield loss from a single weed control input was 35% at the highest weed density of 60 mungbean plants m⁻². The relationship for the critical time of weed removal was further improved by substituting weed biomass for weed density in the relationship.
1221 3
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Open Access
Journal Article
Domestic and trade impacts of foot-and-mouth disease on the Australian beef industry
(Wiley-Blackwell Publishing Asia, 2012)
Tozer, Peter
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Marsh, Thomas L
Australia is the sixth largest producer of beef and the second largest exporter of beef. Average beef exports from Australia are approximately 65 per cent of the total amount of beef produced, about 1.3 million tonnes. Australia is particularly vulnerable to diseases that are not endemic to the country and could close or disrupt its export markets for beef. In this study, we construct a bioeconomic optimisation model of the Australian beef industry that captures production and consumption decisions, domestically and internationally, and the impacts on the beef industry of a potentially catastrophic disease, foot-and-mouth disease (FMD). This study analyses localised to large-scale outbreaks and suggests that changes in economic surplus because of FMD range from a positive net gain of $57 million to a net loss of $1.7 billion, with impacts on producers and consumers varying depending on the location of the outbreak, control levels and the nature of any trade ban.
864 2
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Report
Evaluating the feasibility of developing a model to better manage nematode infections of sheep
(Meat and Livestock Australia Limited, 2014)
Laurenson, Yan
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Meat and Livestock Australia (MLA)
This study evaluates the feasibility of developing (or accessing) a sheep nematode epidemiology model for Australian conditions. Following consultation with animal health experts, such a model would need to predict the impact of integrated parasite control strategies (nutrition, grazing management, anthelmintic treatment strategies and selective breeding for resistance) upon productive traits, parasitological traits and the emergence of anthelmintic resistance. Seven existing nematode epidemiology models were reviewed to evaluate their suitability for Australian conditions in their current form, or after customisation. Whilst individually these models were found to be incapable of evaluating integrated parasite control strategies, a composite of these models could achieve this aim. The best functions from the models reviewed were identified and the initial outline of a composite model is consequently proposed. Access to such a model for industry advice, educational or research purposes can be facilitated via its inclusion in the WormBoss website following development of a user friendly interface. Further, providing open-access to the model source code will inform researchers of underlying assumptions, allow for thorough review, remove reliance upon an individual, and facilitate further development. Finally, the potential pathway and cost of developing a validated sheep nematode epidemiology model and advice tool is considered.
2333
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Conference Publication
Evaluation of AusBeef Methane Predictions from Beef Cattle
(CSIRO Publishing, 2016)
Pacheco, David
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Evered, Mark
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Little, Bryce
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Ingham, Aaron
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Dougherty, Holland
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Kebreab, Ermias
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Hegarty, Roger
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McPhee, Malcolm
The AusBEEF model (version 1.2) has been evaluated against data from 5 experiments (12 experimental means) with beef cattle which had methane emissions measured in respiration chambers. The dataset included a range of animal ages, gender and diet types (perennial ryegrass ranging from vegetative to mature, mixes of ryegrass with maize silage or palm kernel, maize silage alone, and forage rape). The predictions of the model for absolute methane emissions (g/d) and relative methane emissions (yield per unit of DM intake: yCH4 and as a percentage of gross energy intake: CH4%GEI) were evaluated against the observed values. Irrespective of the unit used for methane emissions, AusBEEF mean predictions were slightly (~5%) greater than the observed data. The agreement between observed and predicted values was good for absolute methane emissions (concordance correlation coefficient 0.86) and moderate for yield measurements (CCC 0.48 and 0.58 for yCH4 and CH4%GEI, respectively). For yield measurements, a systematic slope bias accounted for ~40% of the mean square prediction error (MSPE). The ratio of the root MSPE to standard deviation of the observed values (RSR), was used to assess the model predictions in context to the inherent variability of the observed data. Based on the RSR, AusBeef predicted absolute methane emissions very well (RSR=0.5), but prediction of relative methane emissions could be improved (RSR 1.0 for yCH4 and CH4%GEI, respectively). AusBEEF predictions were correctly ranked for forage rape and mixes of pasture and supplements. However, the predictions were not as for diets of 100% pasture. AusBeef predicted live weight losses for most experiments in the database, in contrast with the observed data. These results suggest that improvements in the representation of digestive processes may be required in AusBEEF if accurate predictions of both methane and animal performance are to be obtained, particularly for forage-only diets.
1852 2
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Journal Article
Identifying risk-efficient strategies using stochastic frontier analysis and simulation: An application to irrigated cropping in Australia
(Elsevier BV, 2014)
Power, Brendan
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Cacho, Oscar J
In irrigated cropping, as with any other industry, profit and risk are inter-dependent. An increase in profit would normally coincide with an increase in risk, and this means that risk can be traded for profit. It is desirable to manage a farm so that it achieves the maximum possible profit for the desired level of risk. This paper identifies risk-efficient cropping strategies that allocate land and water between crop enterprises for a case study of an irrigated farm in Southern Queensland, Australia. This is achieved by applying stochastic frontier analysis to the output of a simulation experiment. The simulation experiment involved changes to the levels of business risk by systematically varying the crop sowing rules in a bioeconomic model of the case study farm. This model utilises the multi-field capability of the process based Agricultural Production System Simulator (APSIM) and is parameterised using data collected from interviews with a collaborating farmer. We found sowing rules that increased the farm area sown to cotton caused the greatest increase in risk-efficiency. Increasing maize area also improved risk-efficiency but to a lesser extent than cotton. Sowing rules that increased the areas sown to wheat reduced the risk-efficiency of the farm business. Sowing rules were identified that had the potential to improve the expected farm profit by ca. $50,000 Annually, without significantly increasing risk. The concept of the shadow price of risk is discussed and an expression is derived from the estimated frontier equation that quantifies the trade-off between profit and risk.
1331
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Journal Article
Improvement to the prediction of the USLE 'K' factor
(Elsevier BV, 2014)
Shabani, Farzin
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Kumar, Lalit
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Esmaeili, Atefeh
In the Universal Soil Loss Equation (USLE), the soil erodibility factor ('K') corresponds to the collective effects of the detachment susceptibility of soil and the sediment transportability as well as the amount and rate of runoff under a given rainfall erosivity. Based on the USLE equation, 'K' is sensitive to the particle size distribution ('M'), the percentage of organic matter (%'OM'), soil structure ('Z'), and soil permeability ('perm'). This study evaluated the sensitivity of 'K' to lime content (%'lime') in the soil and slope (%'slope') of the site. Although the effects of the slope factor ('S') on the amount of soil loss ('A') have been independently taken into account in the USLE, our results and other studies showed that 'K' is highly sensitive to other factors including %'lime' and %'slope'. To evaluate the appropriateness of the USLE nomograph and other methods for estimating 'K' and to develop a 'K' estimation method for limy soils, a set of 'K' values were measured in northern Iran using standard plots and natural precipitation events, for four different land uses (forest, rangeland, irrigated farming, and dry farming) and three slope categories (3-8%, 8-18% and 18-40%). Results indicated that there was considerable association between 'K' and soil properties including the contents of sand, silt, very fine sand, organic matter and particularly lime, as well as slope inclination. A strong linear relationship was observed between the 'K' values estimated from our model and the measured 'K' was observed (adjusted 'R'² = 0.89), indicating that considering lime and slope gives a better estimate of 'K'.
1389
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Journal Article
Integration of energy and protein transactions in the body to build new tools for predicting performance and body composition of ruminants
(CSIRO Publishing, 2019)
Oddy, V H
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Dougherty, H C
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Oltjen, J W
Increased market pressure to improve meat yield and quality require improved methods of predicting body composition in growing animals. Current systems of animal nutrition based on nutrient supply and animal characteristics predict animal growth from nutrient inputs, but, as of yet, do not accurately predict body composition. The present paper explores the evidence and data required to support an existing model of the effects of energy intake on visceral and muscle protein mass and energy expenditure to predict heat production, growth and body composition of sheep. While parameters of the model related to energetic costs of protein in muscle and viscera can be supported by independent studies, parameters associated with energetic costs of protein gain, particularly in viscera, are harder to reconcile with independent measurements. The range of available data on systematic changes in visceral organ mass over time in response to feed intake is limited, which may constrain generalisation of the parameters of the model with regard to the wide range of production situations faced by the sheep and cattle industries. However, sufficient data exist in the literature to test, and if required, revise the current framework.
1426 5
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Conference Publication
Life cycle assessment of sheep meat and wool production in Northern California
(American Society of Animal Science, 2017-08-01)
Dougherty, H C
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Oltjen, J W
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Mitloehner, F M
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Rodrigues, K A
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Kebreab, E
A partial life cycle assessment (LCA) of a sheep production system in California was conducted to better understand the environmental impacts of sheep production in the United States. This cradle-to-farm-gate LCA analyzed emissions from sheep-lamb, stocker, and finishing stages of lamb production within the same market chain. Our objective was to calculate the carbon footprint associated with commercial sheep production in northern California and to compare the impact of allocation methods for 1 kg of live weight lamb (LWT), 1 kg cull adult (LWT), and 1 kg of greasy wool at the farm gate. Primary data was collected from on-farm records wherever possible and secondary data from published literature. Whole-system emissions totaled 474.7 Mt carbon dioxide equivalent (CO₂e), of which 42.2% were from animal emissions, 52.6% from feed production and transport, and 5.2% due to animal transport and on-farm operations. Enteric methane was responsible for 34% of total emissions. The sheep–lamb, backgrounding, and feedlot phases were responsible for 86.1, 4.18, and 9.72%, of overall emissions, respectively. Emissions were allocated 100% to meat or between lamb, wool, and cull adult meat on a mass basis. The production system studied for this analysis focused on producing market lambs, with final live weights of 56.4 kg and carcass yield grade 2. Cull adults averaged 54.4 and 68 kg for ewes and rams, respectively, and whole-farm wool production was 2.05 Mt greasy wool. When all emissions were allocated to lamb production, carbon footprints were 28.6 kg CO₂e/kg LWT. When emissions were allocated on a mass basis between lamb, wool, and cull adult meat, 65, 27, and 8% were allocated to lamb, cull adults, and wool, respectively. Carbon footprints were 19 kg CO₂e/kg lamb, 8.0 kg CO₂e/kg cull adult, and 2.5 CO₂e/kg wool. These values highlight the importance of meat production to Californian sheep producers, compared with wool-focused systems found in Australia and the United Kingdom. Whilst lamb has a higher carbon footprint compared with beef regardless of allocation method, coproduct allocation methods also play a significant role in assigning environmental impacts. This analysis is a first step in assessing the overall impact of small ruminant supply chains in the country and identifying aspects that contribute to environmental impacts of production. The results provide baseline data on emissions from sheep production that may be useful in future efforts by the California lamb industry.
1684 5
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Journal Article
Live animal predictions of carcass components and marble score in beef cattle: model development and evaluation
(Cambridge University Press, 2020-08)
McPhee, M J
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Walmsley, B J
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Dougherty, H C
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McKiernan, W A
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Oddy, V H
Until recently, beef carcass payment grids were predominantly based on weight and fatness categories with some adjustment for age, defined as number of adult teeth, to determine the price received by Australian beef producers for slaughter cattle. With the introduction of the Meat Standards Australia (MSA) grading system, the beef industry has moved towards payments that account for intramuscular fat (IMF) content (marble score (MarbSc)) and MSA grades. The possibility of a payment system based on lean meat yield (LMY, %) has also been raised. The BeefSpecs suite of tools has been developed to assist producers to meet current market specifications, specifically P8-rump fat and hot standard carcass weight (HCW). A series of equations have now been developed to partition empty body fat and fat-free weight into carcass fat-free mass (FFM) and fat mass (FM) and then into flesh FFM (FleshFFM) and flesh FM (FleshFM) to predict carcass components from live cattle assessments. These components then predict denuded lean (kg) and finally LMY (%) that contribute to emerging market specifications. The equations, along with the MarbSc equation, are described and then evaluated using two independent datasets. The decomposition of evaluation datasets demonstrates that error in prediction of HCW (kg), bone weight (BoneWt, kg), FleshFFM (kg), FleshFM (kg), MarbSc and chemical IMF percentage (ChemIMF%) is shown to be largely random error (%) in evaluation dataset 1, though error for ChemIMF% was primarily slope bias (%) in evaluation dataset 1, and BoneWt had substantial mean bias (%) in evaluation dataset 2. High modelling efficiencies of 0.97 and 0.95 for predicting HCW for evaluation datasets 1 and 2, respectively, suggest a high level of accuracy and precision in the prediction of HCW. The new outputs of the model are then described as to their role in estimating MSA index scores. The modelling system to partition chemical components of the empty body into carcass components is not dependent on the base modelling system used to derive empty body FFM and FM. This can be considered a general process that could be used with any appropriate model of body composition.
2010 11
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Conference Publication
Local and global sensitivity analysis of methane production relevant parameters in the AusBeef rumen model of beef production
(Canadian Journal of Animal Science, 2016-09-16)
Dougherty, H C
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Kebreab, E
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Evered, M
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Little, B A
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Ingham, A B
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Nolan, J V
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Hegarty, R S
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Pacheco, D
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McPhee, M J
Sensitivity analysis of mechanistic models allows for identification of influential model parameters as well as evaluation of model behaviour respective to current understanding of the biological processes involved in the model. Local sensitivity analysis, which looks at the effect of changing one parameter at a time on model outputs, is important for identifying internal parameters to which the model is highly sensitive for a given space of input and output. Global sensitivity analysis, which varies multiple parameters at a time across a given space, is important for identifying parameter interactions and downstream effects, as well as checking the mechanistic validity of the model with known biological response patterns.
1918 3
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Book
Managing Legume and Fertiliser N for Northern Grains Cropping
(Grains Research and Development Corporation (GRDC), 2013)
Herridge, David
The purpose for writing this manual was twofold. First, it was to provide an update of current information on fertiliser and legume nitrogen (N) in broadacre cropping in Australia's northern grains region, with particular emphasis on the legume N. Second, the manual was written to provide instructions, underpinning technical information and background science for 'NBudget' - the web-based (CropMate™) calculator for estimating the fertiliser N requirements of cereal and oilseed crops and dinitrogen (N₂) fixation by legumes. The manual's target audience is likely to be the private and government agronomists, consultants and advisers who work with farmers to make decisions about N, rather than the farmers themselves. The manual may also provide useful material for tertiary-level education and training. Data and concepts that underpin the manual and calculator were sourced from the many published and unpublished experiments conducted primarily by the farming systems and plant nutrition programs of the NSW and Queensland government agencies during the past 30 years. I have interpreted and reported not only the data but also the knowledge and insight of the Australian and international scientists who have worked and published in the fields of soil and plant N.
2157 1
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Conference Publication
A method for estimating the target for protein energy retention in sheep
(2020)
Dougherty, Holland
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Oddy, Hutton
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Evered, Mark
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Oltjen, James
Target protein mass at maturity is a common "attractor" used in animal models to derive components of animal growth. This target muscle protein at maturity, M*, is used as a driver of a model of animal growth and body composition with pools representing muscle and visceral protein; where viscera is heart, lungs, liver, kidneys, reticulorumen and gastrointestinal tract; and muscle is non-visceral protein. This M* term then drives changes in protein mass and heat production, based on literature data stating that heat production scales linearly with protein mass but not liveweight. This led us to adopt a modelling approach where energy utilisation is directly related to protein content of the animal, and energy not lost as heat or deposited as protein is fat. To maintain continuity with existing feeding systems we estimate M* from Standard Reference Weight (SRW) as follows: M* (kJ) = SRW * SHRINK * (1-FMAT) * (MUSC) * (CPM)* 23800. Where SRW is standard reference weight (kg), SHRINK is the ratio of empty body to live weight (0.86), FMAT is proportion of fat in the empty body at maturity (0.30), MUSC is the proportion of empty body protein that is in muscle (0.85), CPM is the crude protein content of fat-free muscle at maturity (0.21), and 23800 is the energetic content (kJ) of a kilogram of crude protein. Values for SHRINK, FMAT, MUSC and CPM were derived from a synthesis of our own experimental data and the literature. For sheep, these values show M* to be M* (kJ) = SRW * 0.86* (1-0.3) * 0.85 * 0.21 *23800 = SRW * 2557. This method allows for use of existing knowledge regarding standard reference weight and other parameters in estimating target muscle mass at maturity, as part of a model of body composition and performance in ruminants.
1891 4
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Journal Article
A mixed-effects regression modeling approach for evaluating paddy soil productivity
(American Society of Agronomy, Inc, 2017)
Zou, Ganghua
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Li, Yong
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Huang, Tieping
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Liu, De Li
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Herridge, David
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Wu, Jinshui
Soil productivity (SP) is a description of the soil's inherent capacity for crop production and approximates the long-term average crop yield. Knowledge of the key driving factors of SP is essential for short-term soil management and long-term agricultural sustainability. Representative 50-cm intact soil profiles from high-, moderate-, and low-yielding paddy fields with long rice (Oryza sativa L.)-production histories were collected in southern China. Each profile was stratified into 10 layers at 5-cm intervals. Multiple linear (MLM) and mixed-effects (MEM) regression models were developed from the basic soil properties, with the MEM using four different combinations of soil depth of sampling, to evaluate paddy SP. Soil cation exchange capacity (CEC), Ca2+, K+, available potassium (AVK), pH, and clay content were correlated with SP (n = 60, r = 0.25-0.59, p < 0.05), while soil organic C and N contents were poorly related to SP (r = 0.03-0.07, p > 0.05). A MEM with three fixed effects [log (AVK), CEC, and pH] and two random effects [log (Na+) and clay] with two-layer stratification (0-20 and 20-50 cm) best estimated SP (n = 12, R2 = 0.96, p < 0.001). We concluded that the combination of soil stratification and mixed effects could make SP assessment in paddy fields more efficient.
1447 1
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Open Access
Journal Article
Modelling the consequences of targeted selective treatment strategies on performance and emergence of anthelmintic resistance amongst grazing calves
(Elsevier Ltd, 2016)
Berk, Zoe
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Laurenson, Yan
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Forbes, Andrew B
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Kyriazakis, Ilias
The development of anthelmintic resistance by helminths can be slowed by maintaining refugia on pasture or in untreated hosts. Targeted selective treatments (TST) may achieve this through the treatment only of individuals that would benefit most from anthelmintic, according to certain criteria. However TST consequences on cattle are uncertain, mainly due to difficulties of comparison between alternative strategies. We developed a mathematical model to compare: 1) the most 'beneficial' indicator for treatment selection and 2) the method of selection of calves exposed to Ostertagia ostertagi, i.e. treating a fixed percentage of the population with the lowest (or highest) indicator values versus treating individuals who exceed (or are below) a given indicator threshold. The indicators evaluated were average daily gain (ADG), faecal egg counts (FEC), plasma pepsinogen, combined FEC and plasma pepsinogen, versus random selection of individuals. Treatment success was assessed in terms of benefit per R (BPR), the ratio of average benefit in weight gain to change in frequency of resistance alleles R (relative to an untreated population). The optimal indicator in terms of BPR for fixed percentages of calves treated was plasma pepsinogen and the worst ADG; in the latter case treatment was applied to some individuals who were not in need of treatment. The reverse was found when calves were treated according to threshold criteria, with ADG being the best target indicator for treatment. This was also the most beneficial strategy overall, with a significantly higher BPR value than any other strategy, but its degree of success depended on the chosen threshold of the indicator. The study shows strong support for TST, with all strategies showing improvements on calves treated selectively, compared with whole-herd treatment at 3, 8, 13 weeks post-turnout. The developed model appeared capable of assessing the consequences of other TST strategies on calf populations.
1044 3
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Journal Article
Opinion: Models Help Us See The Big Picture of Sustainable Agriculture
(University of California, Agriculture and Natural Resources, 2017)
Dougherty, Holland C
As human population and per-capita income increase, demand for meat has also increased. At the same time, millions of people worldwide are food insecure, and with the environmental impacts of existing food production systems already under public and regulatory pressure, the big challenges for today’s animal scientists are how do we make sure people have access to affordable, nutritious food now while minimizing the environmental impacts, both now and in the future? How do we calculate the impacts of what farmers are already doing, and see how different management strategies affect economics and the environment? These are the questions my work, and that of my colleagues in modeling of sustainable agriculture, are trying to answer.
1083 5
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Conference Publication
Overview of the Methane Prediction Module in the AusBeef Rumen Model
(CSIRO Publishing, 2014)
Dougherty, H C
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Kebreab, E
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Little, B A
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Ingham, A B
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Hegarty, R S
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Pacheco, D
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McPhee, M J
Improved livestock nutrition modelling can enable better description and prediction of the physiological and environmental effects of specific production processes, thereby improving productivity and efficiency. Although there are several models of livestock methane emissions (e.g., Ellis et al.2007) few mechanistic whole-animal models exist. The AusBeef rumen model, initially proposed and developed by Nagorcka and Zurcher (2002), is one of the few whole-animal dynamic and mechanistic models that can be used to predict both productivity and enteric fermentation from ruminants.
1826 3
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Journal Article
Review: Modeling production and environmental impacts of small ruminants - Incorporation of existing ruminant modeling techniques, and future directions for research and extension
(Elsevier Inc, 2019-02)
Dougherty, H C
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Ahmadi, A
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Oltjen PAS, J W
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Mitloehner, F M
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Kebreab, E
Mathematical models are a useful part of extension and researcher collaboration with producers and policymakers. Although many models of large ruminant production exist, with a wide variety of objectives and users, the range available to small ruminant producers is limited. This review summarizes the current state of models available to small ruminant research, identifies data gaps that could be filled to improve representation of current small ruminant production practices, and suggests a framework that could be used to develop a suite of models to improve small ruminant research at the research and consultant levels and research and teaching levels and to improve system-level assessment of environmental impacts.
1120 5
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Journal Article
Selection of optimum cropping pattern for maximum return through computer programming
(G - Science Implementation & Publication, 2005-07-20)
Rahman, M M
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Roy, A P
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Rabbani, M A
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Mandal, A K
Computer software based on different operations of production such as land preparation, tillage (P.T/Tractor) operation, sowing, transplanting, weeding, fertilizer application, threshing, cleaning etc. was developed for the selection of optimum cropping pattern for maximum return (SOCPMR). The program was designed in order to optimize the cropping pattern from different cropping patterns. A study had been under taken to address the issue of labour cost, material cost and cost of products and byproducts to individual cropping pattern. Optimum cropping pattern was considered with regard to cost of productions among the seven different patterns. Maximum profit was achieved from pattern no. six and that pattern was T.aman-Boro-BARIpea-2.
362 99
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Thesis Masters Research
Selection strategies to improve the production potential of layer chicken in Thailand
(University of New England, 2016-04-30)
Tongsiri, Siriporn
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Jeyaruban, M Gilbert
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van der Werf, Julius H
This thesis explores the options to genetically improve the performances of the layer chicken produced by the Department of Livestock Development for small scale commercial layer operations in Thailand. To fulfill this objective, the genetic parameters were estimated for five economically important traits; age at first egg (AFE), body weight at first egg (BWT), egg weight at first egg (EWFE), total number of eggs up to 17 weeks of lay (EN) and average egg weight at the 17th week of lay (EW). A total of 11,195 hen records from 652 sires and 3,892 dams were used to estimate genetic parameters for the five traits from two purebred lines, Rhode Island Red (RIR), White Plymouth Rock (WPR) and two hybrid lines, RC and WC, generated by crossing RIR and WPR to a commercial strain.
4197 752
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Journal Article
A stochastic model to investigate the effects of control strategies on calves exposed to 'Ostertagia ostertagi'
(Cambridge University Press, 2016)
Berk, Zoe
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Laurenson, Yan
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Forbes, Andrew B
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Kyriazakis, Ilias
Predicting the effectiveness of parasite control strategies requires accounting for the responses of individual hosts and the epidemiology of parasite supra- and infra-populations. The first objective was to develop a stochastic model that predicted the parasitological interactions within a group of first season grazing calves challenged by 'Ostertagia ostertagi', by considering phenotypic variation amongst the calves and variation in parasite infra-population. Model behaviour was assessed using variations in parasite supra-population and calf stocking rate. The model showed the initial pasture infection level to have little impact on parasitological output traits, such as worm burdens and FEC, or overall performance of calves, whereas increasing stocking rate had a disproportionately large effect on both parasitological and performance traits. Model predictions were compared with published data taken from experiments on common control strategies, such as reducing stocking rates, the 'dose and move' strategy and strategic treatment with anthelmintic at specific times. Model predictions showed in most cases reasonable agreement with observations, supporting model robustness. The stochastic model developed is flexible, with the potential to predict the consequences of other nematode control strategies, such as targeted selective treatments on groups of grazing calves.
1070 3
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Thesis Doctoral
Structure and Function of a Central West Plains Grazed Grassland Hill Slope
(University of New England, 2020-02-07)
Taylor, David Arthur
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Reid, Nicholas
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Tighe, Matthew
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Hacker, Ron
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Tongway, David
The central west plains (CWP) grasslands of New South Wales have adapted over millions of years to climate and rainfall variability. Understanding how these grasslands function offers insight into the response of agricultural production systems attuned to the unique characteristics of the CWP. This thesis examines the structure and function of CWP native grazed grasslands addressing the hypothesis that these grasslands have an underlying hill slope patch structure that results in higher biomass production. Water from rainfall events when redistributed through this mosaic (structure) of resource patches shedding (source) and resource capturing patches (sink), results in greater growth than would otherwise occur if rainfall were evenly distributed. The research was conducted at Myola, Trundle (average annual rainfall 490 mm), on the CWP of NSW. The principal research approach was the use of a chronosequence substitution of spacefor-time with six grassland-monitoring sites. Each site had five permanent 30-m transects and represented a different period of recovery from cropping, 4 to 40 years. Data was collected at the end of the winter and summer growing seasons over a 4-year period commencing in 2010.

Chapter 3 addressed whether CWP grasslands are patchy and, if so, what patch types can be distinguished and what soil features best discriminate between these patches? Decision tree partitioning, based on the eleven Soil Surface Assessment (SSA) indicators developed by Tongway and Hindley (2004), was undertaken to aggregate the 15 a priori patch types into six a posteriori patch types – bare sealed (Bs), annual (Als), crusted perennial (Cp), perennial (P), high cover and high surface roughness (CovR) and low resistance to disturbance (Lrd). These patch types were significantly different in five of the 11 SSA indicators – crusting, perenniality, litter score, biological soil crust (BSC) cover and surface roughness. These five SSA indicators were used in a framework for the rapid field assessment of the six different patch types.

Chapter 4 examined how the six patch types differ from each other in terms of Landscape Function Analysis indices, infiltration, soil moisture, nutrient availability and soil microbial characteristics, plant species presence and biomass production. Large and significant differences were found between the different patch types in biomass production and species composition. Biomass production in CovR (6235 ± 263 kg/ha) and Lrd (6683 ± 93 kg/ha) patch types was nearly double that of other patch types. Patches also had significant differences in sorptivity, infiltration, depth of wetting, hydrophobicity, BSC composition and abundance, nutrient availability, patch size and patch position in the landscape. It was evident that CovR and Lrd patch types behave as sink patches most of the time, as do P patches some of the time depending on rainfall. Patches immediately downslope of CovR patches had a lower wetting front depth and less topsoil moisture, which suggested that CovR behave as sink patches. Bs, Cp and Als patches had significantly less depth of wetting and topsoil moisture than patches immediately downslope, providing evidence that these patches behave as source patches.

Chapter 5 examined the spatial structure and juxtaposition of patch types and related this to biomass production. The chapter also looked at the relationship between the dominant late post-disturbance recovery patch types (P and CovR) and how patch structure and rain event characteristics – amount, timing, duration and intensity – interact to influence biomass production. The monitoring site transects were examined for patch structure (i.e. the changing proportion of Bs, Als, Cp, P, CovR and Lrd patches) at different topographic positions down the hill slope. Biomass production was highest when the hill slope contained 10–30% source patches, 50–80% P patches and 30–50% CovR patches. Patch structure varied spatially by both hill slope position and site disturbance history. Bs and Als patch types were more abundant in early post-crop recovery sites while CovR patches type were significantly more frequent in sites 20 and 40 years post-crop than at 4 and 6 years post-crop. Patch type juxtaposition was clearly defined. For example, CovR patches were nested within larger P patches and were preceded and followed by P patches on 89% and 92% of occasions, respectively. CovR biomass decreased from 6900 kg/ha in quadrats near to upslope source patches (Bs, Als or Cp) to 5600 kg/ha in quadrats 16 m from these upslope patch types. CovR patch biomass was highest when transect P patch proportion was in the range of 40–80%. A generalised linear model found high rainfall-intensity best explained CovR patch type soil moisture at both 30 and 75 cm depth but rainfall event duration provided a better explanation of soil moisture at 30 and 75 cm depth in other patch types. Mean subsoil moisture (75 cm depth) carryover from winter to spring was nearly 45% greater in CovR than any other patch types. Mean patch type aggregate seasonal rainfall use efficiency (RUE) was 9.1 kg/ha/mm. CovR patch type RUE was 14.0 kg/ha/mm in winter and 20.4 kg/ha/mm in summer. There was a summer season rainfall threshold of about 70 mm, below which no biomass production occurred. It was concluded that biomass production of CWP grazed grasslands is influenced by spatial patch structure and rainfall characteristics – amount, duration, intensity frequency and timing. Individual patches types form a mosaic of source and sink patches and the resulting redistribution of rainfall results in large and significant differences in patch type biomass production. Spatial patch structure affects the amount and location of CWP grassland biomass.

Chapter 6 studied changes in patch structure over time and examined the drivers of patch transition from a less productive patch type to a more productive patch type or vice versa. The research questions examined the long-term (>50-years recovery from disturbance) dynamics of sink patches and the influence of disturbance intensity and duration and seasonal influences. Species transitions were examined as the likely cause of patch transitions, and disturbance intensity and duration were examined as a likely influence on species composition. Substitution of space-for-time was used and specific patches followed over a 5-year period. Patch type progressed from Als dominance immediately following disturbance to dominance of P and CovR patch types in the range of 20–60% of each, respectively, depending on antecedent seasonal conditions. This progression was first evident in lower-slope positions and moved upslope over time. Over 60% of transitions from one patch type to another occurred at the edge of patches. Species growth and litter characteristics were more influential than rainfall event characteristics in explaining transitions. Changes in litter score were observed in a high proportion (60–90%) of patch types that had progressed to a more functional patch type (litter score trend increasing) or regressed to a less functional patch type (litter score trend decreasing). Extrapolation of patch transition probabilities derived from Bayesian Belief Network (BBN) analysis indicated that patch-type progression and regression resulted in a basin of attraction for patch composition, given Trundle rainfall characteristics, of 25% CovR, 47% P, 18% Cp and 10% Als, 15–20 years post-cropping disturbance. In the absence of grazing, patch type composition was similar after 30 years of recovery from disturbance regardless of disturbance type. However species composition in the 30-year post disturbance sites under grazing differed with disturbance history. This indicates that seed availability and disturbance history (grazing vs cropping and subsequent recovery) can modify species composition trajectory over time, but that succession in patch type composition follows a similar path regardless of differences in within-patch type species composition. Disturbance intensity (degree and duration) was a more important influence on recovery than disturbance type. In conclusion, grassland recovery from disturbance is characterised by progression from low litter cover patch types (Bs, Als, Cp) where surface sealing under raindrop impact restricts water infiltration to high litter cover patch types (P, CovR, Lrd) with higher water infiltration. In the absence of disturbance, CovR patch types in the range of 50– 80% dominate CWP grasslands. Patch progression in grasslands subject to grazing restricts CovR proportion to the range of 20–30%.

This study extended the understanding of grassland heterogeneity in general and the functioning of CWP grasslands in particular. Patch dynamics were explored – how different patches form and patch types transition over time forming the underlying patch mosaic driving grassland productivity, stability and resilience. The interaction of this patch mosaic and rainfall (amount, timing, duration and intensity) and the resulting effects on CWP grassland productivity were examined. In summary, CWP grassland hill slopes comprise a mosaic of sink and source patches, which differ in water infiltration, species composition and biomass production. These patches form in response to complex feedback dynamics between grass species, physical and biological crust formation, herbivore off take, plant–soil biology associations and nutrient availability, collectively called vegetation-driven spatial heterogeneity.

This research has provided the basis for the further examination of stability and resilience of CWP grasslands. Sufficient insight has been gained to develop and validate an agent-based model (ABM) of these grasslands. ABMs are used to examine emergent behaviour in complex adaptive systems, such as CWP grasslands, and can be used in simulated experiments, for example, to determine optimal patch structure for CWP grassland production stability.
1484 2
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Journal Article
Trigonometric correction factors renders the fAPAR-NDVI relationship from active optical reflectance sensors insensitive to solar elevation angle
(Elsevier BV, 2016)
Rahman, Muhammad Moshiur
;
Lamb, David
The normalized difference vegetation index (NDVI), derived from ground based or satellite borne, passive sensors is often used to estimate the fraction of absorbed photosynthetically active radiation (fAPAR) of a plant canopy. It is well documented that the measured NDVI from passive sensors is affected by the sun and/or view geometry due to the non-Lambertian properties of plant canopies. Despite this the fAPAR-NDVI relationships are often found to be independent of the solar elevation angle (ᶿs) because the ᶿs-dependent absorption of the Red wavelengths within the canopy, which dominates the fAPAR, cancels out the ᶿs-dependency of the NIR scattering which dominates the NDVI measurement. Active optical sensors (AOS), which have their own illuminating light source measure NDVI (NDVI AOS) without any interference of solar geometry. However as fAPAR of a plant canopy does change with solar elevation angle (ᶿs), the fAPAR-NDVIAOS relationship too changes with varying ᶿs. The objective of this study was to explore a correction factor which can eliminate the ᶿs-dependency in fAPAR-NDVIAOS relationship. Data were collected using LightScout quantum bar and CropCircle™ for Tall fescue ('Festuca arundinacea' var. Fletcher) at ᶿs ranging from 40° to 80°. A ᶿs-dependent vegetation index, NDVI*AOS that introduces simple trigonometric correction factors to the measured Red and NIR irradiance for nadir-viewing active optical sensor provides a fAPAR-NDVI relationship that is independent of ᶿs. When the solar elevation angle is introduced this way into the NDVIAOS the fAPAR can then be calculated from the NDVIAOS for any solar elevation angle within the range of 40-80°.
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Journal Article
Which is the best phenotypic trait for use in a targeted selective treatment strategy for growing lambs in temperate climates?
(Elsevier BV, 2016)
Laurenson, Yan
;
Kahn, Lewis
;
Bishop, Stephen C
;
Kyriazakis, Ilias
Targeted selective treatment (TST) requires the ability to identify the animals for which anthelmintic treatment will result in the greatest benefit to the entire flock. Various phenotypic traits have previously been suggested as determinant criteria for TST; however, the weight gain benefit and impact on anthelmintic efficacy for each determinant criterion is expected to be dependent upon the level of nematode challenge and the timing of anthelmintic treatment. A mathematical model was used to simulate a population of 10,000 parasitologically naïve Scottish Blackface lambs (with heritable variation in host-parasite interactions) grazing on medium-quality pasture (grazing density = 30 lambs/ha, crude protein = 140 g/kg DM, metabolisable energy = 10 MJ/kg DM) with an initial larval contamination of 1000, 3000 or 5000 Teladorsagia circumcincta L₃/kg DM. Anthelmintic drenches were administered to 0, 50 or 100% of the population on a single occasion. The day of anthelmintic treatment was independently modelled for every day within the 121 day simulation. Where TST scenarios were simulated (50% treated), lambs were either chosen by random selection or according to highest faecal egg count (FEC, eggs/g DM faeces), lowest live weight (LW, kg) or lowest growth rate (kg/day). Average lamb empty body weight (kg) and the resistance (R) allele frequency amongst the parasite population on pasture were recorded at slaughter (day 121) for each scenario. Average weight gain benefit and increase in R allele frequency for each determinant criterion, level of initial larval contamination and day of anthelmintic treatment were calculated by comparison to a non-treated population. Determinant criteria were evaluated according to average weight gain benefit divided by increase in R allele frequency to determine the benefit per R. Whilst positive phenotypic correlations were predicted between worm burden and FEC; using LW as the determinant criterion provided the greatest benefit per R for all levels of initial larval contamination and day of anthelmintic treatment. Hence, LW was identified as the best determinant criterion for use in a TST regime. This study supports the use of TST strategies as benefit per R predictions for all determinant criteria were greater than those predicted for the 100% treatment group, representing an increased longterm productive benefit resulting from the maintenance of anthelmintic efficacy. Whilst not included in this study, the model could be extended to consider other parasite species and host breed parameters, variation in climatic influences on larval availability and grass growth, repeated anthelmintic treatments and variable proportional flock treatments.
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