Items where Subject is "Statistical software"

C

Collard, B., Mace, E., McPhail, M., Wenzl, P., Cakir, M., Fox, G., Poulsen, D. and Jordan, D. (2009) How accurate are the marker orders in crop linkage maps generated from large marker datasets? Crop & Pasture Science, 60 (4). pp. 362-372.

Courtney, A. J., Campbell, A. B., Quinn, R., O'Neill, M. F., Campbell, M. J., Shen, J. and Emery, M. (2016) TrackMapper Rises. Project Report. Department of Agriculture and Fisheries, State of Queensland.

D

Dahanayaka, B. A., Snyman, L., Vaghefi, N. and Martin, A. (2022) Using a Hybrid Mapping Population to Identify Genomic Regions of Pyrenophora teres Associated With Virulence. Frontiers in Plant Science, 13 . ISSN 1664-462X

Durrington, G., Brider, J., Holzworth, D., Hammer, G. L. and Wu, A. (2022) CropGen: A novel tool for optimising sorghum crop design. In: TropAg 2022 International Agriculture Conference, 31 October - 2 November 2022, Brisbane, Australia.

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Ergashev, A. (2019) Real Statistics for Policy-Makers: Exercises in the Queensland Context. Manual. State of Queensland.

F

Forknall, C. R., Verbyla, A. P., Nazarathy, Y., Yousif, A., Osama, S., Jones, S. H., Kerr, E., Schulz, B. L., Fox, G. P. and Kelly, A. M. (2024) Covariance Clustering: Modelling Covariance in Designed Experiments When the Number of Variables is Greater than Experimental Units. Journal of Agricultural, Biological and Environmental Statistics, 29 . pp. 232-256. ISSN 1537-2693

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George-Jaeggli, B., Zhi, X., Massey-Reed, S. R., Potgieter, A. B., Hunt, C. H., Watson, J., Chapman, S. C., Laws, K., Borrell, A., Tao, Y., Mace, E. S., Jordan, D. R., Van Oosterom, E. J., Hammer, G. L. and Wu, A. (2022) Deriving radiation use efficiency from hyperspectral sensing for enhanced sorghum production. In: TropAg 2022 International Agriculture Conference, 31 October - 2 November 2022, Brisbane, Australia.

Goswami, S. (2022) Using data to create value: Interactive market intelligence for export growth. In: TropAg 2022 International Agriculture Conference, 31 October - 2 November 2022, Brisbane, Australia.

H

Hamilton, J. and Banney, S. (2011) Preliminary investigation into the development of an electronic forage budget and land condition application, for use on existing hand-held devices, for the northern grazing industry. Project Report. Meat & Livestock Australia Limited.

Holzworth, D.P., Huth, N.I. and de Voil, P.G. (2010) Simplifying environmental model reuse. Environmental Modelling and Software, 25 (2). pp. 269-275. ISSN 13648152 (ISSN)

Hutchison, W. J., Keyes, T. J., Crowell, H. L., Serizay, J., Soneson, C., Davis, E. S., Sato, N., Moses, L., Tarlinton, B. and The tidyomics, C. (2024) The tidyomics ecosystem: enhancing omic data analyses. Nature Methods, 21 (7). pp. 1166-1170. ISSN 1548-7105

I

Innes, D. J., Dillon, N. L., Smyth, H., Karan, M., Holton, T. A., Bally, I. S.E. and Dietzgen, R. G. (2015) Mangomics: Information Systems Supporting Advanced Mango Breeding. In: Genomics and Proteomics. Apple Academic Press. ISBN 978-1-77188-114-2

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Keller, B., Russo, T., Rembold, F., Chauhan, Y., Battilani, P., Wenndt, A. and Connett, M. (2022) The potential for aflatoxin predictive risk modelling in sub-Saharan Africa: a review. World Mycotoxin Journal, 15 (2). pp. 101-118. ISSN 1875-0710

Kerr, D. V., Cowan, R. T. and Chaseling, J. (1999) DAIRYPRO—a knowledge-based decision support system for strategic planning on sub-tropical dairy farms. I. System description. Agricultural Systems, 59 (3). pp. 245-255. ISSN 0308-521X

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Mayer, D. G., Kinghorn, B. P. and Archer, A. A. (2005) Differential evolution – an easy and efficient evolutionary algorithm for model optimisation. Agricultural Systems, 83 (3). pp. 315-328. ISSN 0308-521X

Merz, T., Hrabar, S., Kendoul, F. and Jeffery, M. (2016) Unmanned helicopter system for miconia weed surveys. In: 20th Australasian Weeds Conference.

Mumford, M. H., Forknall, C. R., Rodriguez, D., Eyre, J. X. and Kelly, A. M. (2023) Incorporating environmental covariates to explore genotype × environment × management (G × E × M) interactions: A one-stage predictive model. Field Crops Research, 304 . p. 109133. ISSN 0378-4290

Munroe, S., Guerin, G., Saleeba, T., Martín-Forés, I., Blanco-Martin, B., Sparrow, B. and Tokmakoff, A. (2021) ausplotsR: An R package for rapid extraction and analysis of vegetation and soil data collected by Australia's Terrestrial Ecosystem Research Network. Journal of Vegetation Science, 32 (3). e13046. ISSN 1100-9233

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O'Hallaran, J. (2019) Challenges and opportunities for PA adoption in vegetables. In: TropAg 2019 International Tropical Agriculture Conference - Shaping the Science of Tomorrow, 11 - 13 November 2019, Brisbane, Australia.

O'Hallaran, J. (2019) Using precision information systems for advanced decision making in vegetables. In: TropAg 2019 International Tropical Agriculture Conference - Shaping the Science of Tomorrow, 11 - 13 November 2019, Brisbane, Australia.

Ovenden, J., Street, R., Peel, D., Peel, S., Courtney, T., Podlich, H., Basford, K. and Dichmont, C. (2004) A new data source for fisheries resource assessment: genetic estimates of the effective number of spawners. Final Report to the Fisheries Research and Development Corporation. Project Report. QO 04010. Department of Primary Industries & Fisheries. Queensland..

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Patane, P., Nothard, B., Thompson, M., Olayemi, M. and Stringer, J. (2024) Development of the decision-support tool ‘Harvest Mate’: agronomic algorithms. Zuckerindustrie, 149 (7-8). pp. 516-525.

Phan, T. D., Smart, J. C. R., Stewart-Koster, B., Sahin, O., Hadwen, W. L., Dinh, L. T., Tahmasbian, I. and Capon, S. J. (2019) Applications of Bayesian Networks as Decision Support Tools for Water Resource Management under Climate Change and Socio-Economic Stressors: A Critical Appraisal. Water, 11 (12). p. 2642. ISSN 2073-4441

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Robson, A., Abbott, C., Lamb, D. and Bramley, R. (2012) Developing sugar cane yield prediction algorithms from satellite imagery. In: 34th Annual Conference Australian Society of Sugar Cane Technologists, Cairns.

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Seyoum, S., Chauhan, Y., Rachaputi, R., Fekybelu, S. and Prasanna, B. (2017) Characterising production environments for maize in eastern and southern Africa using the APSIM Model. Agricultural and Forest Meteorology, 247 . pp. 445-453. ISSN 0168-1923

Srivastava, S. K., Lewis, T., Behrendorff, L. and Phinn, S. (2020) Spatial databases and techniques to assist with prescribed fire management in the south-east Queensland bioregion. International Journal of Wildland Fire, 30 (2). pp. 90-111. ISSN 1448-5516

Stone, G., Zhang, B., Carter, J., Fraser, G., Whish, G., Paton, C. and McKeon, G. (2021) An online system for calculating and delivering long-term carrying capacity information for Queensland grazing properties. Part 1: background and development. The Rangeland Journal, 43 (3). pp. 143-157.

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Van Sprang, C. (2019) Using precision information technologies to understand crop variability. In: TropAg 2019 International Tropical Agriculture Conference - Shaping the Science of Tomorrow, 11 - 13 November 2019, Brisbane, Australia.

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Wang, E., Robertson, M. J., Hammer, G. L., Carberry, P. S., Holzworth, D., Meinke, H., Chapman, S. C., Hargreaves, J. N. G., Huth, N. I. and McLean, G. (2002) Development of a generic crop model template in the cropping system model APSIM. European Journal of Agronomy, 18 (1). pp. 121-140. ISSN 1161-0301

Wang, M., Thorp, G., Hofman, H., White, N., Wherritt, E. and Hanan, J. (2016) Pattern-oriented modelling of plant architecture: A new approach for constructing functional-structural plant models. In: IEEE International Conference on Functional-Structural Plant Growth Modeling, Simulation, Visualization and Applications (FSPMA), 7-11 Nov. 2016, Qingdao, China.

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Zhang, B., Fraser, G., Carter, J., Stone, G., Irvine, S., Whish, G., Willcocks, J. and McKeon, G. (2021) An online system for calculating and delivering long-term carrying capacity information for Queensland grazing properties. Part 2: modelling and outputs. The Rangeland Journal, 43 (3). pp. 159-172.