Modelling of lucerne (Medicago sativa L.) for livestock production in diverse environments
Andrew P. Smith A G , Andrew D. Moore B , Suzanne P. Boschma C , Richard C. Hayes D , Zhongnan Nie E and Keith G. Pembleton F
+ Author Affiliations
- Author Affiliations
A Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, Vic. 3010, Australia. Formerly: CSIRO Agriculture, PMB 2, Glen Osmond, SA 5064, Australia.
B CSIRO Agriculture, GPO Box 1600, Canberra, ACT 2601, Australia.
C NSW Department of Primary Industries, Tamworth Agricultural Institute, 4 Marsden Park Road, Calala, NSW 2340, Australia.
D E. H. Graham Centre for Agricultural Innovation (an alliance between NSW Department of Primary Industries and Charles Sturt University), Pine Gully Road, Wagga Wagga, NSW 2650, Australia.
E Department of Economic Development, Jobs, Transport and Resources, Private Bag 105, Hamilton, Vic. 3300, Australia.
F Institute for Agriculture and the Environment, University of Southern Queensland, Toowoomba, Qld 4350, Australia.
G Corresponding author. Email: andrew.smith@unimelb.edu.au
Crop and Pasture Science 68(1) 74-91 https://doi.org/10.1071/CP16176
Submitted: 13 May 2016 Accepted: 18 January 2017 Published: 8 February 2017
Abstract
Several models exist to predict lucerne (Medicago sativa L.) dry matter production; however, most do not adequately represent the ecophysiology of the species to predict daily growth rates across the range of environments in which it is grown. Since it was developed in the late 1990s, the GRAZPLAN pasture growth model has not been updated to reflect modern genotypes and has not been widely validated across the range of climates and farming systems in which lucerne is grown in modern times. Therefore, the capacity of GRAZPLAN to predict lucerne growth and development was assessed. This was done by re-estimating values for some key parameters based on information in the scientific literature. The improved GRAZPLAN model was also assessed for its capacity to reflect differences in the growth and physiology of lucerne genotypes with different winter activity. Modifications were made to GRAZPLAN to improve its capacity to reflect changes in phenology due to environmental triggers such as short photoperiods, declining low temperatures, defoliation and water stress. Changes were also made to the parameter governing the effect of vapour pressure on the biomass-transpiration ratio and therefore biomass accumulation. Other developments included the representation of root development and partitioning of canopy structure, notably the ratio leaf : stem dry matter. Data from replicated field experiments across Australia were identified for model validation. These data were broadly representative of the range of climate zones, soil types and farming systems in which lucerne is used for livestock grazing. Validation of predicted lucerne growth rates was comprehensive owing to plentiful data. Across a range of climate zones, soils and farming systems, there was an overall improvement in the capacity to simulate pasture dry matter production, with a reduction in the mean prediction error of 0.33 and the root-mean-square deviation of 9.6 kg/ha.day. Validation of other parts of the model was restricted because information relating to plant roots, soil water, plant morphology and phenology was limited. This study has highlighted the predictive power, versatility and robust nature of GRAZPLAN to predict the growth, development and nutritive value of perennial species such as lucerne.
Additional keywords: agroecosystems, alfalfa, biophysical model, grazing systems.
References
Angus JF, Peoples MB (2012) Nitrogen from Australian dryland pastures. Crop & Pasture Science 63, 746–758.| Nitrogen from Australian dryland pastures.Crossref | GoogleScholarGoogle Scholar |
Barrett PD, Laidlaw AS, Mayne CS (2005) GrazeGro: a European herbage growth model to predict pasture production in perennial ryegrass swards for decision support. European Journal of Agronomy 23, 37–56.
| GrazeGro: a European herbage growth model to predict pasture production in perennial ryegrass swards for decision support.Crossref | GoogleScholarGoogle Scholar |
Bristow KL (1992) Prediction of daily mean vapor density from daily minimum air-temperature. Agricultural and Forest Meteorology 59, 309–317.
| Prediction of daily mean vapor density from daily minimum air-temperature.Crossref | GoogleScholarGoogle Scholar |
Brown HE, Moot DJ, Teixeira EI (2005) The components of lucerne (Medicago sativa) leaf area index respond to temperature and photoperiod in a temperate environment. European Journal of Agronomy 23, 348–358.
| The components of lucerne (Medicago sativa) leaf area index respond to temperature and photoperiod in a temperate environment.Crossref | GoogleScholarGoogle Scholar |
Brown HE, Moot DJ, Teixeira EI (2006) Radiation use efficiency and biomass partitioning of lucerne (Medicago sativa) in a temperate climate. European Journal of Agronomy 25, 319–327.
| Radiation use efficiency and biomass partitioning of lucerne (Medicago sativa) in a temperate climate.Crossref | GoogleScholarGoogle Scholar |
Brown HE, Jamieson PD, Moot DJ (2012) Predicting the transpiration of lucerne. European Journal of Agronomy 43, 9–17.
| Predicting the transpiration of lucerne.Crossref | GoogleScholarGoogle Scholar |
Bula RJ, Smith D, Hodgson HJ (1956) Cold resistance in alfalfa at two diverse latitudes. Agronomy Journal 48, 153–156.
| Cold resistance in alfalfa at two diverse latitudes.Crossref | GoogleScholarGoogle Scholar |
Carter PR, Sheaffer CC (1983) Alfalfa response to soil water deficits. I. Growth, forage quality, yield, water use, and water-use efficiency. Crop Science 23, 669–675.
| Alfalfa response to soil water deficits. I. Growth, forage quality, yield, water use, and water-use efficiency.Crossref | GoogleScholarGoogle Scholar |
Castonguay Y, Laberge S, Brummer EC, Volenec JJ (2006) Alfalfa winter hardiness: a research retrospective and integrated perspective. Advances in Agronomy 90, 203–265.
| Alfalfa winter hardiness: a research retrospective and integrated perspective.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXivFyrtb4%3D&md5=135e716a8c38789ac9bc3cab8ae2246aCAS |
Cayley JWD, Hannah MC, Kearney GA, Clark SG (1998) Effects of phosphorus fertiliser and rate of stocking on the seasonal pasture production of perennial ryegrass-subterranean clover pasture. Australian Journal of Agricultural Research 49, 233–248.
| Effects of phosphorus fertiliser and rate of stocking on the seasonal pasture production of perennial ryegrass-subterranean clover pasture.Crossref | GoogleScholarGoogle Scholar |
Chen W, Shen YY, Robertson MJ, Probert ME, Bellotti WD (2008) Simulation analysis of lucerne–wheat crop rotation on the Loess Plateau of Northern China. Field Crops Research 108, 179–187.
| Simulation analysis of lucerne–wheat crop rotation on the Loess Plateau of Northern China.Crossref | GoogleScholarGoogle Scholar |
Christian KR (1977) Effects of the environment on the growth of alfalfa. Advances in Agronomy 29, 183–227.
| Effects of the environment on the growth of alfalfa.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaE1cXktl2gtrY%3D&md5=be5d3be30abe731407d1cacfb5ba891bCAS |
Cullen BR, Eckard RJ, Callow MN, Johnson IR, Chapman DF, Rawnsley RP, Garcia SC, White TA, Snow VO (2008) Simulating pasture growth rates in Australian and New Zealand grazing systems. Australian Journal of Agricultural Research 59, 761–768.
| Simulating pasture growth rates in Australian and New Zealand grazing systems.Crossref | GoogleScholarGoogle Scholar |
Dalal RC, Weston EJ, Strong WM, Lehane KJ, Cooper JE, Wildermuth GB, King AJ, Holmes CJ (2004) Sustaining productivity of a Vertosol at Warra, Queensland, with fertilisers, no-tillage or legumes. 7. Yield, nitrogen and disease-break benefits from lucerne in a two-year lucerne–wheat rotation. Australian Journal of Experimental Agriculture 44, 607–616.
| Sustaining productivity of a Vertosol at Warra, Queensland, with fertilisers, no-tillage or legumes. 7. Yield, nitrogen and disease-break benefits from lucerne in a two-year lucerne–wheat rotation.Crossref | GoogleScholarGoogle Scholar |
Dalgliesh NP, Foale MA, McCown RL (2009) Re-inventing model-based decision support with Australian dryland farmers. 2. Pragmatic provision of soil information for paddock-specific simulation and farmer decision making. Crop & Pasture Science 60, 1031–1043.
| Re-inventing model-based decision support with Australian dryland farmers. 2. Pragmatic provision of soil information for paddock-specific simulation and farmer decision making.Crossref | GoogleScholarGoogle Scholar |
Dolling PJ (2001) Water use and drainage under phalaris, annual pasture, and crops on a duplex soil in Western Australia. Australian Journal of Agricultural Research 52, 305–316.
| Water use and drainage under phalaris, annual pasture, and crops on a duplex soil in Western Australia.Crossref | GoogleScholarGoogle Scholar |
Dolling PJ, Latta RA, Ward PR, Robertson MJ, Asseng S (2005a) Soil water extraction and biomass production by lucerne in the south of Western Australia. Australian Journal of Agricultural Research 56, 389–404.
| Soil water extraction and biomass production by lucerne in the south of Western Australia.Crossref | GoogleScholarGoogle Scholar |
Dolling PJ, Robertson MJ, Asseng S, Ward PR, Latta RA (2005b) Simulating lucerne growth and water use on diverse soil types in a Mediterranean-type environment. Australian Journal of Agricultural Research 56, 503–515.
| Simulating lucerne growth and water use on diverse soil types in a Mediterranean-type environment.Crossref | GoogleScholarGoogle Scholar |
Dolling PJ, Lyons AM, Latta RA (2011) Optimal plant densities of lucerne (Medicago sativa) for pasture production and soil water extraction in mixed pastures in south-western Australia. Plant and Soil 348, 315–327.
| Optimal plant densities of lucerne (Medicago sativa) for pasture production and soil water extraction in mixed pastures in south-western Australia.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXht1Gnt7zF&md5=91b3e1f884ac7ad55e7e0cc4267658acCAS |
Donald G, Burge S, Allan C (2012) Southern Australian feed-base pasture audit. In ‘Capturing opportunities and overcoming obstacles in Australian agronomy. Proceedings 16th Australian Agronomy Conference’. 14–18 October 2012, Armidale, NSW. (Ed. I Yunusa) (The Regional Institute: Gosford, NSW) Available at: http://www.regional.org.au/au/asa/2012/pastures/7945_donaldg.htm
Donnelly JR, Freer M, Salmon L, Moore AD, Simpson RJ, Dove H, Bolger TP (2002) Evolution of the GRAZPLAN decision support tools and adoption by the grazing industry in temperate Australia. Agricultural Systems 74, 115–139.
Fick GW (1984) Simple simulation models for yield prediction applied to alfalfa in the Northeast. Agronomy Journal 76, 235–239.
| Simple simulation models for yield prediction applied to alfalfa in the Northeast.Crossref | GoogleScholarGoogle Scholar |
Fick GW, Onstad DW (1988) Statistical models for predicting alfalfa herbage quality from morphological or weather data. Journal of Production Agriculture 1, 160–166.
| Statistical models for predicting alfalfa herbage quality from morphological or weather data.Crossref | GoogleScholarGoogle Scholar |
Freer M, Moore AD, Donnelly JR (1997) GRAZPLAN: Decision support systems for Australian grazing enterprises–II. The animal biology model for feed intake, production and reproduction and the GrazFeed DSS. Agricultural Systems 54, 77–126.
| GRAZPLAN: Decision support systems for Australian grazing enterprises–II. The animal biology model for feed intake, production and reproduction and the GrazFeed DSS.Crossref | GoogleScholarGoogle Scholar |
Halim RA, Buxton DR, Hattendorf MJ, Carlson RE (1989) Water-stress effects on alfalfa forage quality after adjustment for maturity differences. Agronomy Journal 81, 189–194.
| Water-stress effects on alfalfa forage quality after adjustment for maturity differences.Crossref | GoogleScholarGoogle Scholar |
Hanley F, Ridgman WJ, Jarvis RH (1964) The of leys and their management on the yield of succeeding wheat crops on Heavy land. The Journal of Agricultural Science 62, 47–54.
| The of leys and their management on the yield of succeeding wheat crops on Heavy land.Crossref | GoogleScholarGoogle Scholar |
Hattendorf MJ, Carlson RE, Halim RA, Buxton DR (1988) Crop water-stress index and yield of water-deficit-stressed alfalfa. Agronomy Journal 80, 871–875.
| Crop water-stress index and yield of water-deficit-stressed alfalfa.Crossref | GoogleScholarGoogle Scholar |
Hayes RC, Dear BS, Li GD, Virgona JM, Conyers MK, Hackney BF, Tidd J (2010) Perennial pastures for recharge control in temperate drought-prone environments. Part 1: productivity, persistence and herbage quality of key species. New Zealand Journal of Agricultural Research 53, 283–302.
| Perennial pastures for recharge control in temperate drought-prone environments. Part 1: productivity, persistence and herbage quality of key species.Crossref | GoogleScholarGoogle Scholar |
Hodgson HJ (1964) Effect of photoperiod on development of cold resistance in alfalfa. Crop Science 4, 302–305.
| Effect of photoperiod on development of cold resistance in alfalfa.Crossref | GoogleScholarGoogle Scholar |
Holzworth DP, Huth NI, deVoil PG, Zurcher EJ, Herrmann NI, McLean G, Chenu K, van Oosterom EJ, Snow V, Murphy C, Moore AD, Brown H, Whish JPM, Verrall S, Fainges J, Bell LW, Peake AS, Poulton PL, Hochman Z, Thorburn PJ, Gaydon DS, Dalgliesh NP, Rodriguez D, Cox H, Chapman S, Doherty A, Teixeira E, Sharp J, Cichota R, Vogeler I, Li FY, Wang EL, Hammer GL, Robertson MJ, Dimes JP, Whitbread AM, Hunt J, van Rees H, McClelland T, Carberry PS, Hargreaves JNG, MacLeod N, McDonald C, Harsdorf J, Wedgwood S, Keating BA (2014) APSIM—Evolution towards a new generation of agricultural systems simulation. Environmental Modelling & Software 62, 327–350.
| APSIM—Evolution towards a new generation of agricultural systems simulation.Crossref | GoogleScholarGoogle Scholar |
Humphries AW, Latta RA, Auricht GC, Bellotti WD (2004) Over-cropping lucerne with wheat: effect of lucerne winter activity on total plant production and water use of the mixture, and wheat yield and quality. Australian Journal of Agricultural Research 55, 839–848.
| Over-cropping lucerne with wheat: effect of lucerne winter activity on total plant production and water use of the mixture, and wheat yield and quality.Crossref | GoogleScholarGoogle Scholar |
Isbell RF (2002) ‘The Australian Soil Classification.’ (CSIRO Publishing: Melbourne)
Jeffrey SJ, Carter JO, Moodie KB, Beswick AR (2001) Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environmental Modelling & Software 16, 309–330.
| Using spatial interpolation to construct a comprehensive archive of Australian climate data.Crossref | GoogleScholarGoogle Scholar |
Kanneganti VR, Rotz CA, Walgenbach RP (1998) Modeling freezing injury in alfalfa to calculate forage yield: I. Model development and sensitivity analysis. Agronomy Journal 90, 687–697.
| Modeling freezing injury in alfalfa to calculate forage yield: I. Model development and sensitivity analysis.Crossref | GoogleScholarGoogle Scholar |
Khaiti M, Lemaire G (1992) Dynamics of shoot and root growth of lucerne after seeding and after cutting. European Journal of Agronomy 1, 241–247.
Latta RA, Lyons A (2006) The performance of lucerne–wheat rotations on Western Australian duplex soils. Australian Journal of Agricultural Research 57, 335–346.
| The performance of lucerne–wheat rotations on Western Australian duplex soils.Crossref | GoogleScholarGoogle Scholar |
Li GD, Nie ZN, Boschma SP, Dear BS, Lodge GM, Hayes RC, Clark B, Hughes SJ, Humphries AW (2010) Persistence and productivity of Medicago sativa subspecies sativa, caerulea, falcata and varia accessions at three intermittently dry sites in south-eastern Australia. Crop & Pasture Science 61, 645–658.
| Persistence and productivity of Medicago sativa subspecies sativa, caerulea, falcata and varia accessions at three intermittently dry sites in south-eastern Australia.Crossref | GoogleScholarGoogle Scholar |
Lodge GM (1985) Effects of grazing and haycutting on the yield and persistence of dryland phid-resistant lucerne cultivars at Tamworth, New South Wales. Australian Journal of Experimental Agriculture –148.
Lodge GM (1991) Management practices and other factors contributing to the decline in persistence of grazed lucerne in temperate Australia: a review. Australian Journal of Experimental Agriculture 31, 713–724.
| Management practices and other factors contributing to the decline in persistence of grazed lucerne in temperate Australia: a review.Crossref | GoogleScholarGoogle Scholar |
Major DJ, Hanna MR, Beasley BW (1991) Photoperiod response characteristics of alfalfa (Medicago sativa L.) cultivars. Canadian Journal of Plant Science 71, 87–93.
| Photoperiod response characteristics of alfalfa (Medicago sativa L.) cultivars.Crossref | GoogleScholarGoogle Scholar |
McCallum MH, Kirkegaard JA, Green TW, Cresswell HP, Davies SL, Angus JF, Peoples MB (2004) Improved subsoil macroporosity following perennial pastures. Australian Journal of Experimental Agriculture 44, 299–307.
| Improved subsoil macroporosity following perennial pastures.Crossref | GoogleScholarGoogle Scholar |
McKenzie JS, Paquin R, Duke SH (1988) Cold and heat tolerance. In ‘Alfalfa and alfalfa improvement’. Agronomy Monograph No. 29. (Eds AA Hanson, DK Barnes, RR Hill Jr) pp. 259–302. (American Society of Agronomy, Crop Science Society of America, Soil Science Society of America: Madison, WI, USA)
Moore AD (2014) The case for and against perennial forages in the Australian sheep–wheat zone: modelling livestock production, business risk and environmental interactions. Animal Production Science 54, 2029–2041.
Moore AD, Donnelly JR, Freer M (1997) GRAZPLAN: Decision support systems for Australian grazing enterprises. III. Pasture growth and soil moisture submodels, and the GrassGro DSS. Agricultural Systems 55, 535–582.
| GRAZPLAN: Decision support systems for Australian grazing enterprises. III. Pasture growth and soil moisture submodels, and the GrassGro DSS.Crossref | GoogleScholarGoogle Scholar |
Moore AD, Holzworth DP, Herrmann NI, Brown HE, de Voil PG, Snow VO, Zurcher EJ, Huth NI (2014) Modelling the manager: Representing rule-based management in farming systems simulation models. Environmental Modelling & Software 62, 399–410.
Moot DJ, Robertson MJ, Pollock KM (2001) Validation of the APSIM-Lucerne model for phenological development in a cool-temperate climate. In ‘Proceedings 10th Australian Agronomy Conference’. Hobart, Tas. (Eds B Rowe, D Donaghy, N Mendham) (Australian Society of Agronomy, The Regional Institute: Gosford, NSW) Available at: www.regional.org.au/au/asa/2001/6/d/moot.htm
Moot DJ, Hargreaves J, Brown HE, Teixeira EI (2015) Calibration of the APSIM-Lucerne model for ‘Grasslands Kaituna’ lucerne crops grown in New Zealand. New Zealand Journal of Agricultural Research 58, 190–202.
| Calibration of the APSIM-Lucerne model for ‘Grasslands Kaituna’ lucerne crops grown in New Zealand.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2MXmsl2msb8%3D&md5=04f72571d2aceb0b2e0148c515b91315CAS |
Ojeda JJ, Pembleton KG, Islam MR, Agnusdei MG, Garcia SC (2016) Evaluation of the agricultural production systems simulator simulating lucerne and annual ryegrass dry matter yield in the Argentine Pampas and south-eastern Australia. Agricultural Systems 143, 61–75.
| Evaluation of the agricultural production systems simulator simulating lucerne and annual ryegrass dry matter yield in the Argentine Pampas and south-eastern Australia.Crossref | GoogleScholarGoogle Scholar |
Paquin R, Pelletier H (1980) Influence of the environment on acclimatization of lucerne (Medicago media Pers.) to cold and its resistance to frost. Influence de l’environnement sur l’acclimatation au froid de la luzerne (Medicago media Pers.) et sa resistance au gel. Canadian Journal of Plant Science 60, 1351–1366.
| Influence of the environment on acclimatization of lucerne (Medicago media Pers.) to cold and its resistance to frost. Influence de l’environnement sur l’acclimatation au froid de la luzerne (Medicago media Pers.) et sa resistance au gel.Crossref | GoogleScholarGoogle Scholar |
Pembleton KG, Smith RS, Rawnsley RP, Donaghy DJ, Humphries AW (2010) Genotype by environment interactions of lucerne (Medicago sativa L.) in a cool temperate climate. Crop & Pasture Science 61, 493–502.
Pembleton KG, Rawnsley RP, Donaghy DJ (2011) Yield and water-use efficiency of contrasting lucerne genotypes grown in a cool temperate environment. Crop & Pasture Science 62, 610–623.
| Yield and water-use efficiency of contrasting lucerne genotypes grown in a cool temperate environment.Crossref | GoogleScholarGoogle Scholar |
Robertson MJ (2006) ‘Lucerne prospects: drivers for widespread adoption of lucerne for profit and salinity management.’ (CRC for Plant-based Management of Dryland Salinity, University of Western Australia: Perth, W. Aust.)
Robertson MJ, Carberry PS, Huth NI, Turpin JE, Probert ME, Poulton PL, Bell M, Wright GC, Yeates SJ, Brinsmead RB (2002) Simulation of growth and development of diverse legume species in APSIM. Australian Journal of Agricultural Research 53, 429–446.
| Simulation of growth and development of diverse legume species in APSIM.Crossref | GoogleScholarGoogle Scholar |
Schonhorst MH, Davis RL, Carter AS (1957) Response of alfalfa varieties to temperatures and daylengths. Agronomy Journal 49, 142–143.
| Response of alfalfa varieties to temperatures and daylengths.Crossref | GoogleScholarGoogle Scholar |
Shih SC, Jung GA, Shelton DC (1967) Effects of temperature and photoperiod on metabolic changes in alfalfa in relation to cold hardiness. Crop Science 7, 385–389.
| Effects of temperature and photoperiod on metabolic changes in alfalfa in relation to cold hardiness.Crossref | GoogleScholarGoogle Scholar |
Sim RE (2014) Water extraction and use of seedling and established dryland lucerne crops. PhD Thesis, Lincoln University, New Zealand.
Sim RE, Moot DJ, Brown HE, Teixeira EI (2015) Sowing date affected shoot and root biomass accumulation of lucerne during establishment and subsequent regrowth season. European Journal of Agronomy 68, 69–77.
| Sowing date affected shoot and root biomass accumulation of lucerne during establishment and subsequent regrowth season.Crossref | GoogleScholarGoogle Scholar |
Stöckle CO, Donatelli M, Nelson R (2003) CropSyst, a cropping systems simulation model. European Journal of Agronomy 18, 289–307.
| CropSyst, a cropping systems simulation model.Crossref | GoogleScholarGoogle Scholar |
Tanner, CB, Sinclair, TR (1983) Efficient water use in crop production: research or re-search? Limitations to efficient water use in crop production. In ‘Limitations to efficient water use in crop production’. (Eds HM Taylor, WR Jordan, TR Sinclair) pp. 1–27. (American Society of Agronomy, Inc., Crop Science Society of America, Inc., Soil Science Society of America, Inc.: Madison, WI)
Teixeira EI, Moot DJ, Brown HE, Fletcher AL (2007) The dynamics of lucerne (Medicago sativa L.) yield components in response to defoliation frequency. European Journal of Agronomy 26, 394
Teixeira EI, Moot DJ, Brown HE (2008) Defoliation frequency and season affected radiation use efficiency and dry matter partitioning to roots of lucerne (Medicago sativa L.) crops. European Journal of Agronomy 28, 103–111.
| Defoliation frequency and season affected radiation use efficiency and dry matter partitioning to roots of lucerne (Medicago sativa L.) crops.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXhtlyrsbbL&md5=9e900ec6102fb54dc3079186f47a85dbCAS |
Teixeira EI, Moot DJ, Brown HE (2009) Modelling seasonality of dry matter partitioning and root maintenance respiration in lucerne (Medicago sativa L.) crops. Crop & Pasture Science 60, 778–784.
| Modelling seasonality of dry matter partitioning and root maintenance respiration in lucerne (Medicago sativa L.) crops.Crossref | GoogleScholarGoogle Scholar |
Teixeira E, Hargreaves J, Brown H, Moot D (2010) Simulating perennial crops with the APSIM plant module—a study with lucerne. In ‘Food security from sustainable agriculture. Proceedings 15th Agronomy Conference’. 15–18 November 2010, Lincoln, New Zealand. (Eds HH Dove, RA Culvenor) (Australian Society of Agronomy) Available at: www.regional.org.au/au/asa/2010/farming-systems/simulation-decision-support/7074_teixeiraei.htm
Teixeira EI, Brown HE, Meenken ED, Moot DJ (2011) Growth and phenological development patterns differ between seedling and regrowth lucerne crops (Medicago sativa L.). European Journal of Agronomy 35, 47–55.
| Growth and phenological development patterns differ between seedling and regrowth lucerne crops (Medicago sativa L.).Crossref | GoogleScholarGoogle Scholar |
Teuber LR, Brick MA (1988) Morphology and anatomy. In ‘Alfalfa and alfalfa improvement’. (Eds AA Hanson, DK Barnes, RR Hill) (American Society of Agronomy: Madison, WI, USA)
Warn LK, Webb Ware J, Salmon L, Donnelly JR, Alcock G (2006) Analysis of the Profitability of Sheep Wool and Meat Enterprises in Southern Australia. Final Report for Project 1.2.6. Sheep CRC. Available at: www.sheepcrc.org.au/files/pages/articles/analysis-of-the-profitability-of-sheep-wool-and-meat-enterprises-in-southern-australia2006/Analysis_of_Profitability_of_sheepmeat_and_wool.pdf
Woodward FI, Sheehy JE (1979) Microclimate, photosynthesis and growth of lucerne (Medicago sativa L.). 2. Canopy structure and growth. Annals of Botany 44, 709–717.
Zahid MS (2009) Lucerne performance on duplex soil under Mediterranean climate: Field measurement and simulation modelling. PhD Thesis, The University of Adelaide, SA, Australia.
