Cassava mosaic disease

Cassava mosaic in Uganda. Photo courtesy A. Baudoin, Virgina Tech, Blacksburg, VA.

Cassava is a primary food source for more than 250 million Africans (about 40% of the continent's population). Its starchy root is a substantial portion of the diet of nearly 600 million people worldwide. In fact, cassava is relatively easy to grow in drought conditions and its root can stay in the ground for up to two years.

Cassava mosaic disease (CMD) is the most important disease of cassava in Africa and the Indian subcontinent. The disease is caused by cassava mosaic viruses of the genus Begomovirus (family Geminiviridae) that are transmitted by the whitefly, Bemisia tabaci (Gennadius), (Homoptera, Aleyrodidae). For more on this insect, please refer to the January 2007 APSnet.org feature.

Three approaches to control the spread of the cassava mosaic virus are commonly considered. The first is the use of insecticides, with potential impacts on non-target organisms. The second is the use of cultivars with cassava mosaic virus resistance. In general, breeding resistant cultivars takes several years. The third method is to intercrop cassava with other crops such as corn or cowpea.

Fondong et al. (2002) studied the spread of cassava mosaic disease, both in cassava alone and cassava intercropped with corn, cowpea, or both corn and cowpea. After a two-year experiment, they found that the disease progress curves in the three intercropped treatments had a similar shape and were less sigmoid than the curve for cassava grown alone.

Below is a comparison of curves for disease progress under monocropping and intercropping illustrated using R.

## Load data into vector
weeksAfterPlantingM <- c(  4,  6,  8, 10, 12, 14, 16,
                          18, 20, 22, 24, 26, 28);
diseaseIncidenceForCassavaM <- c(0.04, 0.11, 0.18, 0.32, 0.53,
                                 0.64, 0.70, 0.73, 0.75, 0.77,
                                 0.78, 0.79, 0.79);
diseaseIncidenceForCassavaAndMaizeM <-
                               c(0.03, 0.10, 0.17, 0.27, 0.36,
                                 0.43, 0.47, 0.50, 0.51, 0.60,
                                 0.60, 0.61, 0.61);
diseaseIncidenceForCassavaAndCowpeaM <-
                               c(0.03, 0.09, 0.17, 0.27, 0.35,
                                 0.42, 0.43, 0.43, 0.44, 0.55,
                                 0.56, 0.5700, 0.57);
diseaseIncidenceForCassavaAndMaizeAndCowpeaM <-
                               c(0.0200, 0.08, 0.16, 0.27, 0.35,
                                 0.41, 0.41, 0.42, 0.42, 0.52,
                                 0.53, 0.54, 0.55);

## Set default values for drawing a plot
default_type = 'o';
default_pch = 22;
default_xlim = c(4, 28);
default_ylim = c(0, 1);
default_xlab = 'Weeks After Planting';
default_ylab = 'Disease Incidence (%)';
default_color1 = 'orange';
default_color2 = 'blue';
default_color3 = 'green';
default_color4 = 'black';
default_lty_for_actual_data = 1;
default_lty_for_fitted_data = 2;
default_lwd_for_actual_data = 2;
default_lwd_for_fitted_data = 1;

## Plot disease incidence (y) vs. weeks after planting
# Plot disease incidence for cassava alone
plot(
     weeksAfterPlantingM, diseaseIncidenceForCassavaM,
     type = default_type,
     pch = default_pch,
     lwd = default_lwd_for_actual_data,
     xlim = default_xlim,
     ylim = default_ylim,
     xlab = default_xlab,
     ylab = default_ylab,
     col = default_color1 
);
title(main = 'Cassava mosaic disease progress');

## Draw a fitted curve given monomolecular model
# Note: intercept and slope have been already
#    estimated by Fondong et al. (2002)
esti_intercept = -0.21; 
esti_slope = 0.08;
temp = esti_intercept + esti_slope * weeksAfterPlantingM;
yM = (exp(temp) - 1) / exp(temp);
lines(
      weeksAfterPlantingM, yM,
      col = default_color1,
      lwd = default_lwd_for_fitted_data,
      lty = default_lty_for_fitted_data);

# Plot disease incidence for cassava intercropped with maize
lines(
      weeksAfterPlantingM,
      diseaseIncidenceForCassavaAndMaizeM,
      type = default_type,
      lwd = default_lwd_for_actual_data,
      col = default_color2
      );

# Draw a fitted curve given monomolecular model
esti_intercept = -0.19; 
esti_slope = 0.05;
temp = esti_intercept + esti_slope * weeksAfterPlantingM;
yM = (exp(temp) - 1) / exp(temp);
lines(
      weeksAfterPlantingM, yM,
      col = default_color2,
      lwd = default_lwd_for_fitted_data,
      lty = default_lty_for_fitted_data
);

# Plot disease incidence for cassava intercropped with cowpea
lines(
      weeksAfterPlantingM,
      diseaseIncidenceForCassavaAndCowpeaM,
      type = default_type,
      col = default_color3,
      lwd = default_lwd_for_actual_data
);

# Draw a fitted curve given monomolecular model
esti_intercept = -0.15; 
esti_slope = 0.05;
temp = esti_intercept + esti_slope * weeksAfterPlantingM;
yM = (exp(temp) - 1) / exp(temp);
lines(
      weeksAfterPlantingM, yM,
      col = default_color3,
      lwd = default_lwd_for_fitted_data,
      lty = default_lty_for_fitted_data
);

# Plot disease incidence for cassava intercropped
#    with maize and cowpea
lines(
      weeksAfterPlantingM,
      diseaseIncidenceForCassavaAndMaizeAndCowpeaM,
      type = default_type,
      col = default_color4,
      lwd = default_lwd_for_actual_data
);

# Draw a fitted curve given monomolecular model
esti_intercept = -0.19; 
esti_slope = 0.04;
temp = esti_intercept + esti_slope * weeksAfterPlantingM;
yM = (exp(temp) - 1) / exp(temp);
lines(
      weeksAfterPlantingM, yM,
      col = default_color4,
      lwd = default_lwd_for_fitted_data,
      lty = default_lty_for_fitted_data
);

## Add legend to the graph
legend(
      'topleft',
       c(
          'Cassava',
          'Cassava+maize',
          'Cassava+cowpea',
          'Cassava+maize+cowpea'
        ),
       pch = c(22),
       lty = 1,
       col = c('orange','blue','green','black'),
       title = 'Cropping Type',
       inset = 0.01
)

Output

Note that the dashed lines represent fitted curves using a monomolecular model. How well does that model fit the data?

Modeling the whitefly population over time.

Since the virus is vectored by whiteflies, it is useful to model the whitefly populations as well.

Below is the code to model the whitefly population changes over time.

## Load data into vectors
weeksAfterPlantingM <-
    c(4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16);
WFForCassavaM <-
    c(4, 7, 14, 33, 28, 39, 24, 14, 6, 10, 8, 7, 4);
WFForCassavaAndMaizeM <-
    c(3, 4, 6, 13, 8, 12, 7, 3, 3, 4, 3, 3, 4);
WFForCassavaAndCowpeaM <-
    c(2, 4, 5, 6, 8, 10, 16, 4, 4, 6, 4, 4, 3);
WFForCassavaAndMaizeAndCowpeaM <-
    c(2, 3, 7, 6, 8, 13, 10, 4, 4, 9, 8, 7, 6);

## Set default values for drawing a plot
default_type = 'o';
default_pch = 22;
default_xlim = c(4, 17);
default_ylim = c(0, 45);
default_xlab = 'Weeks after planting';
default_ylab = 'Number of adult whiteflies';
default_color1 = 'orange';
default_color2 = 'blue';
default_color3 = 'green';
default_color4 = 'black';
default_lty_for_actual_data = 1;
default_lty_for_fitted_data = 2;
default_lwd_for_actual_data = 2;
default_lwd_for_fitted_data = 1;

## Plot insect population vs. weeks after planting plot
# Plot insect populations for cassava alone
plot(
     weeksAfterPlantingM, WFForCassavaM,
     type = default_type,
     pch = default_pch,
     lwd = default_lwd_for_actual_data,
     xlim = default_xlim,
     ylim = default_ylim,
     xlab = default_xlab,
     ylab = default_ylab,
     col = default_color1
);
title(
   main=
     'Mean number of adult whiteflies on the leaves of cassava'
);

# Plot insect populations for cassava intercropped with maize
lines(
      weeksAfterPlantingM, WFForCassavaAndMaizeM,
      type = default_type,
      lwd = default_lwd_for_actual_data,
      col = default_color2
);

# Plot insect populations for cassava intercropped with cowpea
lines(
      weeksAfterPlantingM, WFForCassavaAndCowpeaM,
      type = default_type,
      col = default_color3,
      lwd = default_lwd_for_actual_data
);

# Plot insect populations for cassava intercropped
#    with maize and cowpea
lines(
      weeksAfterPlantingM,
      WFForCassavaAndMaizeAndCowpeaM,
      type = default_type,
      col = default_color4,
      lwd = default_lwd_for_actual_data
);

## Add legend to the graph
legend(
      'topright',
       c(
          'Cassava',
          'Cassava+maize',
          'Cassava+cowpea',
          'Cassava+maize+cowpea'
        ),
       pch = 22,
       lty = 1,
       col = c(
         'orange',

         'blue',
         'green',
         'black'
        ),
       title = 'Cropping Type',
       inset = 0.05
)

Output

Note that there were more whiteflies on cassava grown alone than on cassava in the other cropping systems.

Intercropping effect on disease severity

The effect of intercropping on disease severity was examined. However, no evidence was found that intercropping affected disease severity.

## Load data into vector
weeksAfterPlantingM <-
    c(4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28);
diseaseSevForCassavaM <-
    c(2.0, 3.0, 3.9, 3.6, 4.1, 4.4, 4.5,
      3.7, 3.3, 2.7, 2.3, 2.7, 2.8);
diseaseSevForCassavaAndMaizeM <-
    c(2.3, 2.6, 3.7, 3.3, 3.7, 4.0, 4.5,
      3.6, 3.3, 2.8, 2.3, 2.7, 2.7);
diseaseSevForCassavaAndCowpeaM <-
    c(1.9, 2.6, 3.3, 3.2, 3.4, 3.6, 4.5,
      3.9, 3.8, 3.1, 2.6, 2.8, 2.7);
diseaseSevForCassavaAndMaizeAndCowpeaM <-
    c(2.5, 2.6, 3.2, 3.2, 2.8, 3.6, 4.5,
      3.9, 3.9, 3.4, 2.7, 2.7, 2.7);

## Set default values for drawing a plot
default_type = 'o';
default_pch = 22;
default_xlim = c(4, 33);
default_ylim = c(1.0, 5);
default_xlab = 'Weeks after planting';
default_ylab = 'Disease Severity';
default_color1 = 'orange';
default_color2 = 'blue';
default_color3 = 'green';
default_color4 = 'black';
default_lty_for_actual_data = 1;
default_lty_for_fitted_data = 2;
default_lwd_for_actual_data = 2;
default_lwd_for_fitted_data = 1;

## Plot disease severity (y) vs. weeks after planting
# Plot disease severity for cassava alone
plot(
     weeksAfterPlantingM, diseaseSevForCassavaM,
     type = default_type,
     pch = default_pch,
     lwd = default_lwd_for_actual_data,
     xlim = default_xlim,
     ylim = default_ylim,
     xlab = default_xlab,
     ylab = default_ylab,
     col = default_color1
);
title(main = 'Disease symptom severity');

# Plot disease severity for cassava intercropped
#    with maize
lines(
      weeksAfterPlantingM,
      diseaseSevForCassavaAndMaizeM,
      type = default_type,
      lwd = default_lwd_for_actual_data,
      col = default_color2
);

# Plot disease severity for cassava intercropped
#    with cowpea
lines(
      weeksAfterPlantingM,
      diseaseSevForCassavaAndCowpeaM,
      type = default_type,
      col = default_color3,
      lwd = default_lwd_for_actual_data
);

# Plot disease severity for cassava intercropped
#    with maize and cowpea
lines(
      weeksAfterPlantingM,
      diseaseSevForCassavaAndMaizeAndCowpeaM,
      type = default_type,
      col = default_color4,
      lwd = default_lwd_for_actual_data
);

## Add legend to the graph
legend(
      'bottomright',
       c(
          'Cassava',
          'Cassava+maize',
          'Cassava+cowpea',
          'Cassava+maize+cowpea'
        ),
       pch = 22,
       lty = 1,
       col = c(
          'orange',
          'blue',
          'green',
          'black'
       ),
       title = 'CroppingType',
       inset = 0.05
)

Output

Based on these results, Fondong et al. (2002) concluded that intercropping decreased the number of whiteflies and reduced the disease incidence, whereas intercropping did not affect disease severity. Reducing the number of whiteflies may also slow spread of the disease to adjacent areas.

Conclusions