Characterization of Temporal Attribute of Common rust Epidemics of Maize in Ethiopia

The low yield is attributed to a number of factors such as Biotic (Diseases, insect pests, and weeds), abiotic (moisture, soil fertility, etc). Among biotic factors, foliar diseases such as turcicum leaf blight (Exserohilumtrurcicum) and common rust (Puccinia sorghiSchw) are generally among the important constraints in tropical maize production [4]. Especially common rust caused by Puccinia sorghiSchw is a destructive disease in the major maize growing highland areas and causes from 1261% yield loss in disease epidemic years [5].


Introduction
Maize (Zea mays L.) is one of the popular crops grown in the world, ranking second to wheat and followed by rice [1]. It occupies an important position in the world economy as food, feed, and industrial grain crop. It is a staple food for several million people in the developing world where they derive their protein and calorie requirements from it.
Maize is among the leading cereal crops selected to achieve food self-sufficiency in Ethiopia [2]. Although, improved cultivars have been largely included in the national extension package, the national average yield of maize is only 3.45tons/ha [3], which is far below the world average of 5.5tons/ha. The low yield is attributed to a number of factors such as Biotic (Diseases, insect pests, and weeds), abiotic (moisture, soil fertility, etc). Among biotic factors, foliar diseases such as turcicum leaf blight (Exserohilumtrurcicum) and common rust (Puccinia sorghiSchw) are generally among the important constraints in tropical maize production [4]. Especially common rust caused by Puccinia sorghiSchw is a destructive disease in Agricultural Research & Technology: Open Access Journal research center for two consecutive years (2014-2015 main cropping seasons). Six germ plasms/lines/that constitute Resistant [2] and moderately resistant [4] were used. The treatment was arranged following a randomized complete block design (RCBD) with three replications. The plots were tract or ploughed and disc harrowed twice before planting. The experimental unit measured 4.5x4.5m with 6 maize rows planted at a spacing of 75X30cm. All plots were planted by hand with two seeds per hole. Inorganic fertilizer (Dap & Urea) and all agronomic practices were applied based on the area recommendations.

Inoculation and disease assessment
Rust inoculum was collected from naturally infected leaves showing large number of pustules. Collection of rust uredospore was done by lightly tapping the leaves in to a cup or a suitable container. The spores were dried and kept in tightly sealed glass jars and stored at minus 20 °C. Maize plants under field conditions were inoculated first time at around 6-8 leaf stage and it repeated within 2 weeks. Rust spore suspension @60,000spores/ml was prepared and applied in the whorl using hand atomizer. To avoid spores clump together on the upper surface of the water, the spore suspension was agitated (stirred) continuously and tween 20 was added in the solutions. One week after inoculation, plots were assessed for disease severity using a 1 to 5 scale [7]. Disease assessment commenced 7 days after inoculation. Six assessments were made at 7 day intervals from four central tag maize plants with visual observations.

Modeling temporal disease spread and data analysis
Rust symptoms were observed on each test plant by visual assessment on the leaves. Plants will be assessed for six weeks. Disease data was subjected to ANOVA. Severity data was used to compute areas under disease progress curves (AUDPC), as well as tested to check Logistic, Monomolecular and Gompertz models. Areas under disease progress curves (AUDPC) were computed according to Campbell & Madden [6]. The formulae for computing AUDPC is given as a.
The progress of the infection is constrained by the amount of tissue that remains to be infected; and if it were not so constrained, the extent of infection would exhibit exponential growth. b.
There is a single model parameter r, which is the apparent infection rate. It can be calculated analytically using the formula Where: r is the apparent infection rate, t1 is the time of the first measurement, t2 is the time of the second measurement, x1 is the proportion of infection measured at time t1, x2 is the proportion of infection measured at time t2 Fitting of the data to growth curve models of Logistic and Gompertz are performed to characterize the polycyclic nature of epidemics. The slope of the curve, (r) depicts rate of disease increase over time, and y, the theoretical estimates of initial amount of epidemic y0 (y-axis intercept). The Logistic model was given by Computation of y and r (apparent rate of infection) was performed over time. Data was also fitted to the Gompertz model (Berger 1981) as described for the logistic models expect that the linearized formula for the Gompertz model may be different, i.e. it is given by These models are selected because of their common usage and suitability for different fungal diseases. In these models y is proportion of disease severity at time (t) due to inoculums application and background infection and r is the slope. The appropriateness of each model was evaluated on the basis of coefficient of determination (R2) and plot of residuals. As appropriate, data was subjected to ANOVA and if significant differences were found, means were compared using Fisher's Protected Least Significant Differences(LSD)at P<0.05.

Result and Discussion
There was statically variation among six maize lines in mean infection rates of rust disease at Ambo, in 2014. Relatively higher mean infection rate of rust disease was recorded on maize line 3, 2 and 5 even though line 2 and 5 was statically at par with line 1 and 4 ( Table 1). The least infection rate of rust was recorded on maize line 6 though statically the same with line 4 and 1. There were no significant differences among six maize lines in AUDPC and severity of rust disease at Ambo, in 2014 (Table 1). There was statically variation among maize lines in mean area under disease progress curve (AUDPC) and mean severity of common leaf rust at Ambo, in 2015 as shown below Table 2. The highest mean AUDPC and severity of rust disease was recorded on maize line 4, but these parameters did not varied on maize line 1, 2, 3, and 5 ( Table 2). The list mean AUDPC and severity of rust disease was recorded on maize line 6 ( Table 2). However, there was no significant variation among six maize lines in infection rate of rust disease at Ambo in 2015 (Table 2) (Table 3). at Ambo in the same growing season. Whereas, rust disease was not fitted to any models on [POOL9Ac7-SR (BC2)] FS59-4-1-2-1-1-1-#-#-#-# (maize line 6) and this is due to R-square was below 0.5 (50%) on the mentioned maize line as shown Table 3.   (Table 4).

Conclusion and Recommendation
Rust disease is among devastating foliar fungal diseases of maize in Ethiopia. Temporal epidemiology was studied at Ambo using six maize lines. There was significant difference among the six lines in rust disease apparent infection rates while AUDPC and severity did not differ in 2014 at Ambo. There were significant variations among maize lines in rust AUDPC and severity while apparent infection rates did not varied in 2015 at Ambo.

Rust disease was fitted to Gompertz on maize and
Monomolecular on maize line 1, 2, 3 and 5 at Ambo in 2014 growing season. Rust disease was not described by any models on maize line 6 at Ambo in the same growing seasons. Rust disease was fitted to Monomolecular model on maize line 3, 4 and 6 whereas it was not described by any models on maize line 1, 2 and 5 in 2015 at Ambo. Except on maize line 3, rust disease was not described by the same models on the same maize lines in 2014 and 2015 growing seasons at Ambo. Therefore, additional studies involving more maize lines and locations are recommended to adequately explain epiphytotic of rust disease and to recommend resistant lines for breeding programs. Race analysis should be done in order to check race variability. Moreover, disease intensities should be correlated with environmental factors.