Modeling seed dormancy release and germination for predicting Avena fatua L. field emergence: A genetic algorithm approach

Authors
Blanco, Anibal Manuel; Chantre Balacca, Guillermo Ruben; Lodovichi, Mariela Victoria; Bandoni, Jose Alberto; Lopez, Ricardo Luis; Vigna, Mario Raul; Gigon, Ramon; Sabbatini, Mario Ricardo
Publication Year
2014
Language
English
Format
article
Status
Published version
Description
Avena fatua is a cosmopolite weed species which produces severe yield losses in small-grain production systems in temperate and semiarid climates. In the semiarid region of Argentina, A. fatua field emergence patterns show great year-to-year variability mainly due to the effect of highly unpredictable precipitation regimes as well as a complex seedbank dormancy behavior regulated by both, genetic and environmental factors. Previously developed models for the same agroecological system based on Non-Linear Regression techniques (NLR) and Artificial Neural Networks (ANN) were either unable to accurately predict field emergence or lacked explanatory power. The main objective of the present work is to develop a simple (i.e. parsimonious) model for A. fatua field emergence prediction for the semiarid region of Argentina based on the disaggregation of the dormancy release phase from the germination/pre-emergence growth processes, using easy accessible soil microclimate derived indices as input variables and observed cumulative field emergence data as output variable. The parsimony and predictive capability of the newly developed model were compared with NLR and ANN approaches developed by the same authors for the same agroecological system. Specifically, dormancy release was modeled as a logistic function of an after-ripening thermal-time index while germination/pre-emergence growth was represented by a logistic distribution of hydrothermal-time accumulation. A total of 528 input/output data pairs corresponding to 11 years of data collection were used in this study. Due to its implementation simplicity and good convergence features, a Genetic Algorithm (GA) was adopted to solve the resulting optimization problem consisting on the minimization of the Mean Square Error (MSE) between training data and experimentally obtained field emergence data. The newly developed GA based approach resulted in a significantly more parsimonious model (BIC = −1.54) compared to ANN (BIC = −1.36) and NLR (BIC = −1.32) models. Model evaluation with independent data also showed a better predictive capacity of the GA approach (RMSE = 0.07) compared to NLR (RMSE = 0.19) and ANN (RMSE = 0.11) alternatives. These outcomes suggest the potential applicability of the proposed predictive tool in weed management decision support systems design.
EEA Bordenave
Fil: Blanco, Anibal Manuel. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnol.conicet - Bahia Blanca. Planta Piloto de Ingenieria Quimica (i). Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Chantre Balacca, Guillermo Ruben. Universidad Nacional del Sur. Departamento de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida; Argentina
Fil: Lodovichi, Mariela Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida; Argentina. Universidad Nacional del Sur. Departamento de Agronomía; Argentina
Fil: Bandoni, Jose Alberto. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnol.conicet - Bahia Blanca. Planta Piloto de Ingenieria Quimica (i). Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: López, Ricardo Luis. INTA. Estación Experimental Agropecuaria Bordenave; Argentina
Fil: Vigna, Mario Raúl. INTA. Estación Experimental Agropecuaria Bordenave; Argentina
Fil: Gigón, Ramón. INTA. Chacra Experimental Integrada Barrow; Argentina
Fil: Sabbatini, Mario Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida; Argentina. Universidad Nacional del Sur. Departamento de Agronomía; Argentina
Source
Ecological Modelling 272 : 293-300 (January 2014)
Subject
Avena Fatua
Malezas
Genética
Dormición
Germinación
Weeds
Genetics
Dormancy
Germination
Access level
Restricted access
License
Repository
INTA Digital (INTA)
Institution
Instituto Nacional de Tecnología Agropecuaria
OAI Identifier
oai:localhost:20.500.12123/2886