Canadian Agricultural Engineering
Pasternak, H., Israel Agricultural Research, Organization, Bet-Dagan, Isr, Israel Agricultural Research Organization, Bet-Dagan, Isr
Peiper, U.M., Israel Agricultural Research, Organization, Bet-Dagan, Isr, Israel Agricultural Research Organization, Bet-Dagan, Isr
Putter, J., Israel Agricultural Research, Organization, Bet-Dagan, Isr, Israel Agricultural Research Organization, Bet-Dagan, Isr
The coefficient of variation (CV) of the distances (x) between successive seeds is an accepted criterion of seeding uniformity. The standard method of seeder evaluation estimates CV by the corresponding coefficient of variation in the sample run. In this paper, we propose to estimate CV using the regression of the seed location on its serial number in the sample run. It is shown that this method gives more accurate estimates of the mean and variance of x, and therefore also of CV and hence may reduce the costs of measuring seeding uniformity. A more appropriate approach to the problem of detecting missing and multiple seedings is also discussed. This approach is based on taking into consideration the distance pattern of all the sample run in the process of elimination suspected seedings rather than the suspected distances.
פותח על ידי קלירמאש פתרונות בע"מ -
הספר "אוצר וולקני"
אודות
תנאי שימוש
METHOD OF EVALUATING SEEDING UNIFORMITY.
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Pasternak, H., Israel Agricultural Research, Organization, Bet-Dagan, Isr, Israel Agricultural Research Organization, Bet-Dagan, Isr
Peiper, U.M., Israel Agricultural Research, Organization, Bet-Dagan, Isr, Israel Agricultural Research Organization, Bet-Dagan, Isr
Putter, J., Israel Agricultural Research, Organization, Bet-Dagan, Isr, Israel Agricultural Research Organization, Bet-Dagan, Isr
METHOD OF EVALUATING SEEDING UNIFORMITY.
The coefficient of variation (CV) of the distances (x) between successive seeds is an accepted criterion of seeding uniformity. The standard method of seeder evaluation estimates CV by the corresponding coefficient of variation in the sample run. In this paper, we propose to estimate CV using the regression of the seed location on its serial number in the sample run. It is shown that this method gives more accurate estimates of the mean and variance of x, and therefore also of CV and hence may reduce the costs of measuring seeding uniformity. A more appropriate approach to the problem of detecting missing and multiple seedings is also discussed. This approach is based on taking into consideration the distance pattern of all the sample run in the process of elimination suspected seedings rather than the suspected distances.
Scientific Publication