LINEAR MODELS FOR THE PREDICTION OF LEAF RUST AND LEAF SPOT DISEASES OF MULBERRY
V GUNASEKHAR, V P GUPTA and K SRIKANTASWAMY
Mulberry Pathology Laboratory, Central Sericultural Research and Training Institute, Sriramprura, Mysore-570 008, India
Background and Objectives
Mulberry (Morus alba L.), is perennial plant and cultivated as monocrop for its leaf to rear silkworm. Leaf rust (Cerotelium fici ) and leaf spot (Cercospora moricola) are the major epidemic diseases and cause 10-20% leaf yield loss during rainy and winter seasons . This leads to a shortage of quality leaf for late-age rearing of silkworm, which results in severe economic loss to farmers. Hence, it is proposed to investigate the incidence of the diseases and possible prediction models for prognosis for need-based control measures.
Materials and methods
A fixed-plot survey was conducted during 1994-96. In each garden, 25 plants were selected, five each at four corners and five at the middle; in each plant five branches were tagged for disease assessment. Incidence and development of disease was recorded at 7-day intervals starting from pruning for 60-70 days following standard assessment keys in 0-4 grade scale. Minimum and maximum temperature, rainfall and relative humidity (r.h.) were obtained from the Meteorological Department. Before subjecting the data to least-square regression, the logistic transformations were applied to the disease proportions to linearize the data of per cent disease severity. Regression models were applied for analysing the effect of parameters for the progress of the disease .
Results and discussion
Disease progress curves for both diseases showed similar trend in all epidemics. Leaf rust initiates 30-35 days after pruning (DAP) and leaf spot 25-30 DAP during rainy and winter seasons. Multiple regressions analysis revealed that the minimum and maximum temperature were negatively correlated and rainfall, r.h. and age of the leaf were positively correlated with disease progress. The linear models developed were verified with actual data. Therefore, the prediction models can be used to predict the disease severity and progress in advance for need-based prophylactic measures for disease control.
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