Items where Subject is "Statistical software"

C

Collard, B., Mace, E. S., 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. https://doi.org/10.1071/CP08099

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 . https://doi.org/10.3389/fpls.2022.925107

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.

E

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. https://doi.org/10.1007/s13253-023-00574-x

Fraser, G., Carter, J., Stone, G., Irvine, S., Whish, G., Willcocks, J., McKeon, G. and Zhang, B. (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. https://doi.org/10.1071/RJ20088

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Gautam, D., Mawardi, Z., Elliott, L., Loewensteiner, D., Whiteside, T. and Brooks, S. J. (2025) Detection of Invasive Species (Siam Weed) Using Drone-Based Imaging and YOLO Deep Learning Model. Remote Sensing, 17 (1). p. 120. https://doi.org/10.3390/rs17010120

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.

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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. https://doi.org/10.1016/j.envsoft.2008.10.018

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. https://doi.org/10.1038/s41592-024-02299-2

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. https://doi.org/10.1201/b18597-12

J

Jones, P.N. and Carberry, P.S. (1994) A technique to develop and validate simulation models. Agricultural Systems, 46 (4). pp. 427-442. https://doi.org/10.1016/0308-521X(94)90105-O

K

Keller, B., Russo, T., Rembold, F., Chauhan, Y. S., 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. https://doi.org/10.3920/WMJ2021.2683

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. https://doi.org/10.1016/S0308-521X(99)00007-4

M

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. https://doi.org/10.1016/j.agsy.2004.05.002

McCown, R.L., Hammer, G. L., Hargreaves, J. N.G., Holzworth, D.P. and Freebairn, D.M. (1996) APSIM: a novel software system for model development, model testing and simulation in agricultural systems research. Agricultural Systems, 50 (3). pp. 255-271. https://doi.org/10.1016/0308-521X(94)00055-V

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. https://doi.org/10.1016/j.fcr.2023.109133

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. https://doi.org/10.1111/jvs.13046

O

O'Halloran, 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'Halloran, 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. https://doi.org/10.36961/si31757

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. https://doi.org/10.3390/w11122642

R

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. S., 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. https://doi.org/10.1016/j.agrformet.2017.08.023

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. https://doi.org/10.1071/WF19105

Stone, G., Carter, J., Fraser, G., Whish, G., Paton, C., McKeon, G. and Zhang, B. (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. https://doi.org/10.1071/RJ20084

V

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.

W

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. https://doi.org/10.1016/S1161-0301(02)00100-4

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. https://doi.org/10.1109/FSPMA.2016.7818308