Pemodelan Keparahan Penyakit Blas pada Tanaman Padi di Kabupaten Subang
2018
Zulaika, Zulaika | Soekarno, Bonny Poernomo | Nurmansyah, Ali
indonésien. Modelling on Rice Blast Disease Severity in Subang DistrictBlast disease (Pyricularia oryzae) is a major diseases of rice in Indonesia. Research related to modelling of blast disease severity is limited. Therefore, this study aimed to design a statistical model on rice blast disease infestation on the rice paddy and to asses a correlation between the disease severity and infected seed level. The models were constructed based on multiple linier regression analyses. The study was conducted by observing the disease severity, collecting information about cultivation technique and weather conditions. The result of regression analysis showed severity modeling on influencing factors is Y = -67.17 + 5.51X1 – 10.54X2 + 13.26X3 + 8.51X4 + 2.29X5 + 1.32X6 + 8.47X7 + 0.31X8 + 4.53X9 (p-value <0.0001, R2 = 0.85). Nitrogen application and plant ages had significant effect on disease severity. The addition of N fertilizer increased the severity of blast disease by 8.47%. Increasing the daily life of plants increases the disease severity by 0.31%. The correlation (r) of pathogen infection after harvesting was strongly influenced by infection before planting with correlation value of 0.78. This means that the severity of seed disease after harvesting is influenced by 78% infection of pathogenic seed before planting. The result of regression analysis showed Y = 5.98 + 2.41X (p-value = 0.0076, R2 = 0.61). An increase of 1% pathogens carried by seed before planting will be increasing disease severity by 2.41% after harvesting. The results of this study can be used as a reference in preparing preventive control measure and reduce the risk of pathogen carried by seeds that act as a source of initial inoculum.
Afficher plus [+] Moins [-]Mots clés AGROVOC
Informations bibliographiques
Cette notice bibliographique a été fournie par IPB University in The City of Bogor (formerly Bogor Agricultural University)
Découvrez la collection de ce fournisseur de données dans AGRIS