In northern Italy, there has been a tendency in recent years to advance maize sowing from mid-April to mid- March. This may lead to many agronomic advantages but can strongly influence weed flora composition, density, time of emergence, and crop and weed relative growth. Altering the maize sowing date thus requires a better understanding of the competitive relationship between crop and weeds. This study evaluates an existing model expressing crop yield as a function of maximum yield of the weed-free crop, weed competitive load and time of weed emergence and removal. The model can predict the yield loss caused by mixed infestations with a single set of parameters. The aim of this study was to validate this model with independent data from experiments on early and traditionally sown maize, in support of the idea that the model is robust enough to be used as a prediction tool for forecasting yield losses in a variety of conditions created by different sowing dates. In traditional sowing, the plot of expected versus observed yields showed that the model gives a good fit. However, for early sowing, the model accuracy in predicting crop yields was improved when the curve intercepts were shifted by 60 growing degree days (d C). This shift corresponded to the difference between the onset of weed emergence in traditional and early sowing, which suggests that weeds may be less competitive when maize is sown earlier.
Validation of a model relating yield loss to weed time of emergence and removal in traditional and early-sown maize
A BERTI;S OTTO;G ZANIN
2010
Abstract
In northern Italy, there has been a tendency in recent years to advance maize sowing from mid-April to mid- March. This may lead to many agronomic advantages but can strongly influence weed flora composition, density, time of emergence, and crop and weed relative growth. Altering the maize sowing date thus requires a better understanding of the competitive relationship between crop and weeds. This study evaluates an existing model expressing crop yield as a function of maximum yield of the weed-free crop, weed competitive load and time of weed emergence and removal. The model can predict the yield loss caused by mixed infestations with a single set of parameters. The aim of this study was to validate this model with independent data from experiments on early and traditionally sown maize, in support of the idea that the model is robust enough to be used as a prediction tool for forecasting yield losses in a variety of conditions created by different sowing dates. In traditional sowing, the plot of expected versus observed yields showed that the model gives a good fit. However, for early sowing, the model accuracy in predicting crop yields was improved when the curve intercepts were shifted by 60 growing degree days (d C). This shift corresponded to the difference between the onset of weed emergence in traditional and early sowing, which suggests that weeds may be less competitive when maize is sown earlier.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.