Predicting field weed emergence with empirical models and soft computing techniques
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José Luis González-Andújar | Guillermo R. Chantre | C. M. Morvillo | Antonio M. Blanco | Frank Forcella | J. González-Andújar | F. Forcella | G. Chantre | C. Morvillo | A. M. Blanco
[1] F. Forcella,et al. Modeling seedling emergence , 2000 .
[2] M. Kropff,et al. Modelling field emergence patterns in arable weeds. , 2000, The New phytologist.
[3] C. Swanton,et al. Simulation of Chenopodium album seedling emergence , 2000, Weed Science.
[4] N. Colbach,et al. Modeling the dynamics and emergence of a multispecies weed seed bank with species traits , 2012 .
[5] Santosh Kumar Das,et al. On Soft Computing Techniques in Various Areas , 2013 .
[6] J. González-Andújar,et al. Predicting weed emergence in maize crops under two contrasting climatic conditions , 2009 .
[7] A. M. Blanco,et al. Modeling Avena fatua seedling emergence dynamics: An artificial neural network approach , 2012 .
[8] Andrea Onofri,et al. A new method for the analysis of germination and emergence data of weed species , 2010 .
[9] W. Bond,et al. Modelling the effect of weed‐seed distribution in the soil profile on seedling emergence , 1996 .
[10] F. Anctil,et al. A neural network experiment on the site-specific simulation of potato tuber growth in Eastern Canada , 2010 .
[11] R. J. Gummerson. The Effect of Constant Temperatures and Osmotic Potentials on the Germination of Sugar Beet , 1986 .
[12] K. Bradford,et al. Hydrothermal time analysis of tomato seed germination at suboptimal temperature and reduced water potential , 1994, Seed Science Research.
[13] J. González-Andújar,et al. Modeling Bromus diandrus Seedling Emergence Using Nonparametric Estimation , 2013 .
[14] F. Forcella,et al. Maximizing Efficacy and Economics of Mechanical Weed Control in Row Crops Through Forecasts of Weed Emergence , 1999, Expanding the Context of Weed Management.
[15] Gade Pandu Rangaiah,et al. Stochastic global optimization : techniques and applications in chemical engineering , 2010 .
[16] J. Cardina,et al. Seed Burial Physical Environment Explains Departures from Regional Hydrothermal Model of Giant Ragweed (Ambrosia trifida) Seedling Emergence in U.S. Midwest , 2013, Weed Science.
[17] E. Kebreab,et al. Modelling the effects of water stress and temperature on germination rate of Orobanche aegyptiaca seeds. , 1999 .
[18] Zailin Huo,et al. Simulation for response of crop yield to soil moisture and salinity with artificial neural network , 2011 .
[19] A. M. Blanco,et al. Modeling seed dormancy release and germination for predicting Avena fatua L. field emergence: A genetic algorithm approach , 2014 .
[20] J. González-Andújar,et al. Computing statistical indices for hydrothermal times using weed emergence data , 2011, The Journal of Agricultural Science.
[21] Ramiz M. Aliguliyev,et al. An Optimization Model and DPSO-EDA for Document Summarization , 2011 .
[22] K. Spokas,et al. Software Tools for Weed Seed Germination Modeling , 2009, Weed Science.
[23] C. Ghersa. Plant phenology and the management of crop-weed interactions. , 2000 .
[24] José Luis González-Andújar,et al. Comparison of fitting weed seedling emergence models with nonlinear regression and genetic algorithm , 2009 .
[25] J. Recasens,et al. Emergence of field pennycress (Thlaspi arvense L.): Comparison of two accessions and modelling , 2015 .
[26] B. White,et al. A bioeconomic model for analysis of integrated weed management strategies for annual barnyardgrass (Echinochloa crus-galli complex) in Philippine rice farming systems , 2012 .
[27] A. M. Blanco,et al. Operational planning of herbicide-based weed management , 2013 .
[28] F. Forcella. Air-Propelled Abrasive Grit for Postemergence In-Row Weed Control in Field Corn , 2012, Weed Technology.
[29] M. Mesgaran,et al. Experimental design and parameter estimation for threshold models in seed germination , 2014 .
[30] A. M. Blanco,et al. A comparative study between non-linear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence , 2013, The Journal of Agricultural Science.
[31] A. Grundy. Predicting weed emergence: a review of approaches and future challenges , 2003 .
[32] J. Lindquist,et al. Predicting Emergence of 23 Summer Annual Weed Species , 2014, Weed Science.
[33] Kenneth Holmström,et al. A review of the parameter estimation problem of fitting positive exponential sums to empirical data , 2002, Appl. Math. Comput..
[34] Sovan Lek,et al. Artificial neural networks as a tool in ecological modelling, an introduction , 1999 .
[35] J. González-Andújar,et al. A hydrothermal seedling emergence model for Conyza bonariensis , 2013 .
[36] J. J. Kells,et al. Effect of Glyphosate Application Timing and Row Spacing on Corn (Zea mays) and Soybean (Glycine max) Yields1 , 2004, Weed Technology.
[37] J. González-Andújar,et al. Development and evaluation of a model for predicting Lolium rigidum emergence in winter cereal crops in the Mediterranean area , 2013 .
[38] M. Mcgiffen,et al. Emergence Prediction of Common Groundsel (Senecio Vulgaris) , 2008, Weed Science.
[39] J. Khazaei,et al. Artificial neural network modelling of common lambsquarters biomass production response to corn population and planting pattern. , 2007, Pakistan journal of biological sciences : PJBS.
[40] J. Wiersma,et al. An Emergence Model for Wild Oat (Avena fatua) , 2007, Weed Science.
[41] Piero P. Bonissone,et al. Soft computing: the convergence of emerging reasoning technologies , 1997, Soft Comput..
[42] K. Spokas,et al. A Hydrothermal Seedling Emergence Model for Giant Ragweed (Ambrosia trifida) , 2008, Weed Science.
[43] Andrea Onofri,et al. The cure model: an improved way to describe seed germination? , 2011 .
[44] Kent J. Bradford,et al. Applications of hydrothermal time to quantifying and modeling seed germination and dormancy , 2002, Weed Science.
[45] Leo Breiman,et al. Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001 .
[46] S Forrest,et al. Genetic algorithms , 1996, CSUR.