Coffee Rust Forecast Systems: Development of a Warning Platform in a Minas Gerais State, Brazil
Abstract
:1. Introduction
2. Methodology to Construct Forecast Models and Interface
2.1. Phase 1: Validation of the Forecast System from 2018 to 2020
2.1.1. Sampling of Environmental Variables
2.1.2. Forecasting Systems and Spraying Methodology
- y = Rust incidence forecast;
- Tavg30 = Average of mean temperatures to 30 days before rust incidence;
- DP45 = Days with precipitation to 45 days before rust incidence: precipitation > 0 mm;
- RHavg60 = Average of mean relative humidity to 60 days before rust incidence;
- NIH60 = Average of mean number of isolation hours to 60 days before rust incidence.
- I(%) = Coffee rust incidence;
- NLL = Number of lesioned leaves;
- NTL = Number of total leaves sampled on the coffee tree.
2.1.3. Development of the Interface to View Phytosanitary Warnings or Coffee Rust Forecast
2.2. Phase 2: Adjustment of Models with Data Collected in Five Different Counties in the State of Minas Gerais, Brazil
- Average/accumulated values of 2–4, 4–7, 7–15 and 15–30 Days Before Rust Incidence (DBRI) including the day of assessment of twelve meteorological variables collected from meteorological stations from October 2018 to August 2019;
- Average/accumulated values of eleven meteorological variables collected from October 2018 to January 2020 at the meteorological stations fifteen days before the disease assessments, including the assessment day, according to the methodology of Pinto et al. [18] and Oliveira [50]. The adjustment of the models was performed with two data sets in and out of the harvest period (June, July and August). Regression equations were also adjusted, excluding the environmental variables Insolation Hours, Wind Speed, Wetting and Dew Point Temperature, to obtain fitted models with few variables, also in and out of the harvest period;
- Average/accumulated values delayed from 15 to 45 DBRI, including the assessment day from October 2018 to January 2020, of ten meteorological variables, four of which were collected from the meteorological stations and six elaborated from these data. Initially, the best variables were selected to adjust the models. In this case, Pearson’s correlation was performed between the variables and disease incidence. The analyses used significant variables and others with a correlation greater than 0.6. Afterward, the following variables were also calculated: Average of maximum, mean and minimum temperatures; Average of temperatures with leaf moisture from 6 p.m. to 9 a.m.; Average of temperatures with leaf moisture from 6 p.m. to 6 a.m.; Number of hours a day with temperature ≥ 18 °C and <26 °C, and ≥15 °C and <26 °C; Precipitation; Number of days with precipitation; Number of hours of precipitation from 6 p.m. to 9 a.m.. In addition, models with all these variables were fitted data in and out of the harvest period from June to August. In this case, the following variables were also excluded from the analysis: insolation hours, dew point temperature, wind speed and duration of the moisture period, which are variables obtained only from complete meteorological stations, rarely found throughout the coffee-producing areas in the state of Minas Gerais, Brazil.
- y = Disease incidence, in percentage;
- x1, x2, xp = Environmental variables;
- β0 = Regression constant;
- β1, β2,…,βp = Partial regression parameters or coefficients;
- ε = Independent random errors.
- L (θ) = Estimate of the maximum likelihood function;
- p = Number of parameters of the evaluated model.
2.3. Phase 3: Expansion of the Warning System with New Models
3. Results
3.1. Phase 1: Validation of the Models
3.2. Phase 2: Adjustment of Forecasting Models in Five Different Counties in the State of Minas Gerais, Brazil
3.3. Phase 3: Expansion of the Warning System
4. Discussion
5. Conclusions
6. Final Considerations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Municipality | Location | Altitude (m) | Latitude (S) | Longitude (W) |
---|---|---|---|---|
Carmo do Rio Claro | Fazenda Boa Esperança | 796 | 21.004600 | 46.022900 |
Monte Santo de Minas | Sítio Bela Vista | 915 | 21.181100 | 46.965600 |
Nova Resende | Sítio São João | 1184 | 21.104300 | 46.410400 |
Rio Paranaíba | Fazenda Caetés e Olhos D’água | 1129 | 19.226100 | 46.219200 |
Serra do Salitre | Fazenda Cachoeira do Campo | 1200 | 19.163300 | 46.589200 |
Treatments | Trade Name | Dose per Hectare | Active Ingredient | Concentration of Active Ingredient | Chemical Group |
---|---|---|---|---|---|
1. Control | - | - | - | - | - |
2. Standard farm | Verdadero® and Opera® (with 2 pulverization) or Priori Xtra, depends of the location | 1 kg + 1.5 L + 0.75 L | Cyproconazole + ThiamethoxamPyraclostrobin + Epoxiconazole and Azoxystrobin + Cyproconazole | 300 g kg−1 + 300 g kg−1e133 g L−1+ 50 g L−1 and 200 g L−1+ 80 g L−1 | Triazole + Neonicotinoid and Strobirulin |
3. IHARA | Fusão® and Spirit® | 1.5 L + 2 L | Tebuconazole + Metominostrobin and Flutriafol + Dinotefuran | 165 g L−1 + 110 g L−1 and 273 g L−1 + 87.5 g L−1 | Triazole + Strobirulin + Neonicotinoid |
4. DSS 1 1 | Fusão® | 1.5 L | Tebuconazole+ Metominostrobin | 165 g L−1 + 110 g L−1 | Triazole + Strobirulin |
5. DSS 2 1 | Fusão® | 1.5 L | Tebuconazole+ Metominostrobin | 165 g L−1 + 110 g L−1 | Triazole + Strobirulin |
Variables | Description |
---|---|
Tavg 1 | Average of mean temperatures |
Tmax 1 | Average of maximum temperatures |
Tmin 1 | Average of minimum temperatures |
RHavg 1 | Average of mean relative humidity |
RHmin 1 | Average of minimum relative humidity |
RHmax 1 | Average of maximum relative humidity |
WS 1 | Windy speed |
IH 1 | Insolation hours |
DPT 1 | Dew point temperature |
LT 1 | Leaf temperature |
WH 2 | Wetness hours |
P 2 | Precipitation |
TavgLW(6 p.m.–9 a.m.) 1 | Average temperatures with leaf wetness from 6 p.m. to 9 a.m. |
TavgLW(6 p.m.–6 a.m.) 1 | Average temperatures with leaf wetness from 6 p.m. to 6 a.m. |
NHDT(≥18 °C, <26 °C) 2 | Number of hours of the day with temperature ≥18 °C and <26 °C |
NHDT(≥15 °C, <26 °C) 2 | Number of hours of the day with temperature ≥15 °C and <26 °C |
NDP 2 | number of days with precipitation |
NHP(6 p.m.–9 a.m.) 2 | number of hours of precipitation from 6 p.m. to 9 a.m. |
Municipality | Altitude (m) | Latitude (S) | Longitude (W) |
---|---|---|---|
Alfenas 1 | 827 | 21.41373 | 45.97055 |
Alpinópolis 1 | 935 | 20.84751 | 46.37944 |
Cabo Verde 1 | 940 | 21.45448 | 46.41182 |
Caconde 2 | 830 | 21.53722 | 46.63796 |
Campestre 1 | 1082 | 21.69692 | 46.25357 |
Campos Gerais 1 | 900 | 21.24417 | 45.75556 |
Coromandel 1 | 962 | 18.47386 | 47.21385 |
Guaxupé 1 | 870 | 21.28687 | 46.69303 |
Monte Carmelo 1 | 912 | 18.75402 | 47.51819 |
São José do Rio Pardo 2 | 755 | 21.63306 | 46.89889 |
Municipality | Model 1 | Equation 2 |
---|---|---|
Carmo do Rio Claro | 15–30 DBRI | Y = −304.78667634 *** + 13.16506156 Tmax *** − 13.91295446 Tmin *** − 0.07066871 p + 3.42680986 RHmin *** |
Nova Resende | 15–30 DBRI | Y = 18.3098934 *** − 1.3387465 Tavg ** + 1.2618530 Tmin ** − 1.0714839 IH ** − 0.2205387 WH ** |
Model | Determination Coefficient 1 | AIC | Standard Deviation | Sum of Squared of Errors |
---|---|---|---|---|
Carmo do Rio Claro 15–30 DBRI | 0.67 *** | 4.11 | 8.95 | 155.71 |
Nova Resende 15–30 DBRI | 0.56 *** | 0.76 | 1.24 | 1.71 |
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Pozza, E.A.; Santos, É.R.d.; Gaspar, N.A.; Vilela, X.M.d.S.; Alves, M.d.C.; Colares, M.R.N. Coffee Rust Forecast Systems: Development of a Warning Platform in a Minas Gerais State, Brazil. Agronomy 2021, 11, 2284. https://doi.org/10.3390/agronomy11112284
Pozza EA, Santos ÉRd, Gaspar NA, Vilela XMdS, Alves MdC, Colares MRN. Coffee Rust Forecast Systems: Development of a Warning Platform in a Minas Gerais State, Brazil. Agronomy. 2021; 11(11):2284. https://doi.org/10.3390/agronomy11112284
Chicago/Turabian StylePozza, Edson Ampélio, Éder Ribeiro dos Santos, Nilva Alice Gaspar, Ximena Maira de Souza Vilela, Marcelo de Carvalho Alves, and Mário Roberto Nogueira Colares. 2021. "Coffee Rust Forecast Systems: Development of a Warning Platform in a Minas Gerais State, Brazil" Agronomy 11, no. 11: 2284. https://doi.org/10.3390/agronomy11112284