Statistical models for the assessment of yield loss due to weeds

A study was undertaken to identify suitable functional models for assessing the effect of weeds on the yields of three major crops of Kerala Viz. Rice, Tapioca and Sesame and to estimate the loss in yield in these crops caused -by the major weeds. The data required for the study were gathered fr...

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Bibliografski detalji
Glavni autor: Priyalakshmi M
Daljnji autori: Prabhakaran P V ( Guide)
Format: Ph.D Thesis
Jezik:Undetermined
Izdano: Vellanikkara Department of Agricultural Statistics, College of Horticulture 2002
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245 |a Statistical models for the assessment of yield loss due to weeds 
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502 |b MSc 
520 3 |a A study was undertaken to identify suitable functional models for assessing the effect of weeds on the yields of three major crops of Kerala Viz. Rice, Tapioca and Sesame and to estimate the loss in yield in these crops caused -by the major weeds. The data required for the study were gathered from the available records of A.I.C.R.P on weed control . Multivariate techniques such as multiple linear regression analysis, step wise regression analysis and principal component analysis were used along with univariate techniques for the prediction of yield and yield loss. The study undoubtedly revealed the importance of weed in suppressing the potential yield of plants. The effect of weeds on crops depended on the type of management , crop and season. Crop loss estimates showed wide variation between seasons and locations. The estimate of loss ranged from 5.3% to 68.4% in rice, 3l.4% to 46.3% in sesame and '12.8% to 40.6% in tapioca. The percentage of avoidable loss' in different crops varied from 5.3% to 93.4%. Weed dry matter (W.D.M.) was found to be the most important weed character in , ' predicting crop yield and yield loss. Echinocloa was found to be one of the major weeds causing considerable havoc to rice crop. In general non linear mod~ls were more efficient than linear model in predicting crop yield. The cauchy function, reciprocal hyperbola, second order hyperbola and reciprocal straight line were adjudged to be the most prormsmg univariate functional models in des£ribing the yield-weed relation ship. Multivariate regression models were found to be more powerful in predicting crop yield than univariate models. In most of the cases the fitted statistical models described the proposed relation ship with satisfactorily high degree of precision.  
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