Dry Rot Caused by the Complex Colletotrichum falcatum and Thielaviopsis paradoxa Emerges as a Key Stalk Disorder in Newly Expanded Sugarcane Plantations from Northwestern São Paulo, Brazil
Abstract
:1. Introduction
2. Materials and Methods
2.1. Survey of Sugarcane Stalk-Related Disorders
2.2. Mapping and Estimates Related to the Field Prevalence and Distribution of Three Stalk-Associated Disorders in Sugarcane Plantations
2.2.1. Mapping the Frequency Field Distribution of Sugarcane Stalk-Associated Disorders in Northwestern São Paulo
2.2.2. Estimating the Prevalence and Distribution of Sugarcane Stalk-Associated Disorders
- (1)
- TL = (Estimated yield–Actual yield)/total area sampled, where TL is total yield loss in the area in t·ha−1;
- (2)
- RL = [(Estimated yield–Actual yield)/Estimated yield], where RL is the relative yield loss in each area as a percentage;
- (3)
- rrTL = TL × [(I.rr)/(I.rr + I.sib + I.dr)], where rrTL is the yield loss associated with the incidence of the red rot disease (t·ha−1), I.rr = incidence of the red rot disease (%), I.sib = incidence of the spittlebug-induced budding (%), and I.dr = incidence of the stalk internal dry rot (%);
- (4)
- rrRL = RL × [(I.rr)/(I.rr + I.sib + I.dr)], where rrRL is the relative yield loss associated with the red rot disease (%);
- (5)
- sbiTL and (6) drTL (the yield losses associated with spittlebug-induced budding or stalk internal dry rot) were calculated in a similar manner to (3), while (7) sbiRL and (8) drRL (the corresponding relative yield losses) were calculated in a similar manner to (4).
2.3. Predicting the Yield Losses in Northwestern São Paulo Sugarcane Fields on the Basis of Each Independent Stalk-Related Disorder
BootstrapRandomX <- function(dat=mydata, mod.formula=formula (TL~rrTL + sbiTL + drTL)) {dat.boot <- dat[sample(x = nrow(dat), size = nrow(dat), replace=T),] boot.lm <- lm(mod.formula, data=dat.boot) coef(boot.lm)} N = 5000 vector.boot <- t(replicate(N, BootstrapRandomX())) apply(vector.boot, MARGIN = 2, sd) t(apply(vector.boot, MARGIN = 2, quantile, probs=c(0.025, 0.975))) |
# Building the model–Linear model: model <- lm(y~x, data = train.data) # For a quadric or cubic polynomial, the model would be: model <- lm(y~poly(x, 2, raw = TRUE), data = train.data) # for quadratic model <- lm(y~poly(x, 3, raw = TRUE), data = train.data) # for cubic # Making predictions: predictions <- model %>% predict(test.data) # Model performance data.frame(RMSE = RMSE(predictions, test.data$y), + R2 = R2(predictions, test.data$y)) # Visualizing the data: p1<-ggplot(train.data, aes(x, y)) + geom_point() + stat_smooth(method = lm, formula = y~x) p1 # For a quadric or cubic polynomial, the code would be: stat_smooth(method = lm, formula = y~poly(x, 2, raw = TRUE)) # for quadratic stat_smooth(method = lm, formula = y~poly(x, 3, raw = TRUE)) # for cubic # Adding predictions: pred.int <- predict(model, interval = “prediction”) datalwrupr <- cbind(train.data, pred.int) # Regression line plus confidence intervals: p2 <- ggplot(datalwrupr, aes(x, y)) + geom_point() + coord_cartesian(xlim = c(0, 45), ylim = c(-10,45))+ stat_smooth(method = lm, formula = y~x) + # For a quadric or cubic polynomial, the code would be: stat_smooth(method = lm, formula = y~poly(x, 2, raw = TRUE)) + # for quadratic stat_smooth(method = lm, formula = y~poly(x, 3, raw = TRUE)) + # for cubic # Adding prediction intervals to the regression line: geom_line(aes(y = lwr), color = “red”, linetype = “dashed”)+ geom_line(aes(y = upr), color = “red”, linetype = “dashed”)+ theme (panel.background = element_rect(fill = “white”, color = “black”), panel.grid.minor = element_line(color = “gray50”), panel.spacing = unit(5, “lines”)) p2 |
2.4. Detection of Pathogens Associated with Sugarcane Stalk Internal Dry Rot
3. Results
3.1. Mapping the Frequency Field Distribution of Sugarcane Stalk-Associated Disorders in Northwestern São Paulo
3.2. Estimating the Prevalence and Distribution of Sugarcane Stalk-Associated Disorders
3.3. Predicting Yield Losses in Northwestern São Paulo Sugarcane Fields on the Basis of Each Independent Stalk-Related Disorder
3.4. Detection of Pathogens Associated with Sugarcane Stalk Internal Dry Rot
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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County and Sugarcane Variety | Cropped Area Surveyed (ha) | Stalk-Related Disorder Incidence (%) | Total Losses in the Areas (t) | Yield Losses (TL) (t·ha−1) | Relative Yield Losses (RL) (%) | Yield Losses Associated with the Stalk Disorders (t·ha−1) | Relative Yield Losses Associated with the Stalk Disorders (%) | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Red Rot (I.rr) | Spittlebug (I.sib) | Dry Rot (I.dr) | Red Rot (rrTL) | Spittlebug (sbiTL) | Dry Rot (drTL) | Red Rot (rrRL) | Spittlebug (sbiRL) | Dry Rot (drRL) | |||||
Castilho, SP81-3250 | 151.32 | 12.68 | 11.00 | 25.00 | 1938.62 | 12.81 | 19.71 | 3.34 | 2.89 | 6.58 | 5.14 | 4.45 | 10.12 |
258.26 | 30.16 | 12.34 | 0.00 | 8427.94 | 32.63 | 42.04 | 23.16 | 9.47 | 0.00 | 29.83 | 12.21 | 0.00 | |
333.79 | 3.20 | 4.93 | 30.55 | 7499.61 | 22.47 | 34.57 | 1.86 | 2.87 | 17.74 | 2.86 | 4.41 | 27.30 | |
Ilha Solteira, CTC4 | 78.42 | 0.00 | 12.00 | 39.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
95.27 | 16.00 | 24.00 | 32.00 | 1875.56 | 19.69 | 23.27 | 4.37 | 6.56 | 8.75 | 5.17 | 7.76 | 10.34 | |
Ilha Solteira, SP81-3250 | 216.10 | 2.42 | 1.41 | 18.39 | 6438.41 | 29.79 | 49.66 | 3.25 | 1.89 | 24.66 | 5.41 | 3.15 | 41.10 |
420.46 | 30.91 | 7.50 | 2.94 | 7988.70 | 19.00 | 25.33 | 14.20 | 3.44 | 1.35 | 18.94 | 4.59 | 1.80 | |
Itapura, SP81-3250 | 47.95 | 0.39 | 2.54 | 13.00 | 792.72 | 16.53 | 15.03 | 0.41 | 2.64 | 13.49 | 0.37 | 2.40 | 12.26 |
104.93 | 2.51 | 4.57 | 20.78 | 753.62 | 7.18 | 11.97 | 0.65 | 1.18 | 5.36 | 1.08 | 1.96 | 8.93 | |
157.28 | 0.00 | 4.48 | 27.00 | 2332.28 | 14.83 | 19.77 | 0.00 | 2.11 | 12.72 | 0.00 | 2.81 | 16.96 | |
268.32 | 2.06 | 7.02 | 16.00 | 7394.32 | 27.56 | 22.96 | 2.26 | 7.71 | 17.58 | 1.89 | 6.42 | 14.65 | |
325.07 | 4.51 | 2.17 | 15.72 | 8025.85 | 24.69 | 32.92 | 4.97 | 2.39 | 17.33 | 6.63 | 3.19 | 23.10 | |
405.36 | 5.46 | 13.29 | 8.36 | 7821.90 | 19.30 | 25.73 | 3.88 | 9.46 | 5.95 | 5.18 | 12.61 | 7.94 | |
Pereira Barreto, SP81-3250 | 15.20 | 6.32 | 4.79 | 42.15 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
21.00 | 4.22 | 0.00 | 17.62 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | |
66.00 | 7.03 | 31.62 | 3.99 | 999.78 | 15.15 | 21.64 | 2.50 | 11.23 | 1.42 | 3.57 | 16.05 | 2.03 | |
127.79 | 10.25 | 15.47 | 24.17 | 2599.85 | 20.34 | 29.06 | 4.18 | 6.31 | 9.86 | 5.97 | 9.01 | 14.08 | |
131.56 | 5.79 | 18.47 | 2.97 | 2912.99 | 22.14 | 29.52 | 4.71 | 15.02 | 2.42 | 6.28 | 20.02 | 3.22 | |
117.51 | 10.83 | 2.45 | 22.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | |
S.A.Aracanguá, SP81-3250 | 25.07 | 0.66 | 2.96 | 15.46 | 104.87 | 4.18 | 7.61 | 0.14 | 0.65 | 3.39 | 0.26 | 1.18 | 6.16 |
33.90 | 1.34 | 0.81 | 16.94 | 141.96 | 4.19 | 7.61 | 0.29 | 0.18 | 3.72 | 0.54 | 0.32 | 6.76 | |
75.03 | 0.00 | 2.36 | 21.89 | 1033.95 | 13.78 | 25.06 | 0.00 | 1.34 | 12.44 | 0.00 | 2.44 | 22.62 | |
123.88 | 0.33 | 1.95 | 17.21 | 2449.37 | 19.77 | 32.95 | 0.33 | 1.98 | 17.47 | 0.55 | 3.29 | 29.11 | |
139.26 | 1.55 | 8.11 | 32.69 | 4419.05 | 31.73 | 45.33 | 1.16 | 6.07 | 24.50 | 1.66 | 8.68 | 35.00 | |
169.20 | 4.98 | 4.76 | 22.60 | 2574.46 | 15.22 | 23.41 | 2.34 | 2.24 | 10.64 | 3.60 | 3.44 | 16.36 | |
195.33 | 5.09 | 10.81 | 38.87 | 2058.17 | 10.54 | 14.05 | 0.98 | 2.08 | 7.48 | 1.30 | 2.77 | 9.97 | |
221.59 | 5.12 | 17.33 | 38.29 | 9862.19 | 44.51 | 59.34 | 3.75 | 12.70 | 28.05 | 5.00 | 16.93 | 37.41 | |
Sud Mennucci, SP81-3250 | 12.07 | 2.10 | 0.00 | 13.12 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
32.21 | 0.23 | 4.63 | 17.36 | 133.34 | 4.14 | 6.37 | 0.04 | 0.86 | 3.23 | 0.07 | 1.33 | 4.98 | |
44.43 | 5.65 | 1.00 | 15.95 | 961.54 | 21.64 | 36.07 | 5.41 | 0.96 | 15.27 | 9.02 | 1.60 | 25.46 | |
46.67 | 5.30 | 8.52 | 13.33 | 417.58 | 8.95 | 14.91 | 1.75 | 2.81 | 4.39 | 2.91 | 4.68 | 7.32 | |
47.77 | 5.78 | 0.00 | 10.87 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | |
66.45 | 0.00 | 2.38 | 2.98 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | |
278.39 | 5.26 | 6.31 | 25.10 | 311.85 | 1.12 | 10.05 | 0.16 | 0.19 | 0.77 | 1.44 | 1.73 | 6.88 | |
Mean | 142.73 | 5.83 | 7.41 | 19.54 | 2713.84 | 14.23 | 20.17 | 2.65 | 3.45 | 8.13 | 3.67 | 4.69 | 11.82 |
95% CI | 37.68 | 2.43 | 2.42 | 3.68 | 1044.87 | 3.83 | 5.22 | 1.51 | 1.34 | 2.72 | 1.98 | 1.76 | 3.93 |
Total | 4852.84 | - | - | - | 92,270.48 | 483.88 | - | 90.09 | 117.23 | 276.56 | - | - | - |
County, Sample Size (= N Fields) | Mean Cropped Area Surveyed (ha) | Stalk-Related Disorder Incidence (%) | Total Losses in the Areas (t) | Yield Losses (TL) (t·ha−1) | Relative Yield Losses (RL) (%) | Yield Losses Associated with the Disorders (t·ha−1) | Relative Yield Losses Associated with the Disorders (%) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Red Rot (I.rr) * | Spittlebug (I.sib) | Dry Rot (I.dr) | Red Rot–rrTL * | Spittlebug (sbiTL) | Dry Rot (drTL) | Red Rot (rrRL) * | Spittlebug (sbiRL) | Dry Rot (drRL) | ||||||||
Castilho, N = 3 | 247.79 | 15.35 | a | 9.42 | 18.52 | 5955.39 | 22.64 | 32.11 | 9.45 | a | 5.08 | 8.11 | 12.61 | a | 7.02 | 12.47 |
Ilha Solteira, N = 4 | 202.56 | 12.33 | a | 11.23 | 23.08 | 4075.67 | 17.12 | 24.57 | 5.46 | a | 2.97 | 8.69 | 7.38 | a | 3.87 | 13.31 |
Itapura, N = 6 | 218.15 | 2.49 | b | 5.68 | 16.81 | 4520.12 | 18.35 | 21.40 | 2.03 | b | 4.25 | 12.07 | 2.52 | b | 4.90 | 13.97 |
Pereira Barreto, N = 6 | 79.84 | 7.41 | b | 12.13 | 18.82 | 1085.44 | 9.61 | 13.37 | 1.90 | b | 5.43 | 2.28 | 2.64 | b | 7.51 | 3.22 |
S.A.Aracanguá, N = 8 | 122.91 | 2.38 | b | 6.13 | 25.49 | 2830.50 | 17.99 | 26.92 | 1.12 | b | 3.41 | 13.46 | 1.61 | b | 4.88 | 20.42 |
Sud Mennucci, N = 7 | 75.43 | 3.47 | b | 3.26 | 14.10 | 260.62 | 5.12 | 9.63 | 1.05 | b | 0.69 | 3.38 | 1.92 | b | 1.33 | 6.38 |
Mean | 142.73 | 5.83 | 7.98 | 19.54 | 2713.84 | 14.23 | 20.17 | 2.65 | 3.45 | 8.13 | 3.67 | 4.69 | 11.82 | |||
95% CI | 37.68 | 2.43 | 2.55 | 3.68 | 1044.87 | 3.83 | 5.22 | 1.51 | 1.34 | 2.72 | 1.98 | 1.76 | 3.93 |
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Silva, T.C.; Moreira, S.I.; Felizardo, L.M.; Martins, R.M.; Suzuki, A.N.; Faria, G.A.; Montanari, R.; Ceresini, P.C. Dry Rot Caused by the Complex Colletotrichum falcatum and Thielaviopsis paradoxa Emerges as a Key Stalk Disorder in Newly Expanded Sugarcane Plantations from Northwestern São Paulo, Brazil. Agronomy 2023, 13, 2729. https://doi.org/10.3390/agronomy13112729
Silva TC, Moreira SI, Felizardo LM, Martins RM, Suzuki AN, Faria GA, Montanari R, Ceresini PC. Dry Rot Caused by the Complex Colletotrichum falcatum and Thielaviopsis paradoxa Emerges as a Key Stalk Disorder in Newly Expanded Sugarcane Plantations from Northwestern São Paulo, Brazil. Agronomy. 2023; 13(11):2729. https://doi.org/10.3390/agronomy13112729
Chicago/Turabian StyleSilva, Tatiane Carla, Silvino Intra Moreira, Lucas Menezes Felizardo, Renan Milani Martins, Aline Namie Suzuki, Gláucia Amorim Faria, Rafael Montanari, and Paulo Cezar Ceresini. 2023. "Dry Rot Caused by the Complex Colletotrichum falcatum and Thielaviopsis paradoxa Emerges as a Key Stalk Disorder in Newly Expanded Sugarcane Plantations from Northwestern São Paulo, Brazil" Agronomy 13, no. 11: 2729. https://doi.org/10.3390/agronomy13112729
APA StyleSilva, T. C., Moreira, S. I., Felizardo, L. M., Martins, R. M., Suzuki, A. N., Faria, G. A., Montanari, R., & Ceresini, P. C. (2023). Dry Rot Caused by the Complex Colletotrichum falcatum and Thielaviopsis paradoxa Emerges as a Key Stalk Disorder in Newly Expanded Sugarcane Plantations from Northwestern São Paulo, Brazil. Agronomy, 13(11), 2729. https://doi.org/10.3390/agronomy13112729