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Soybean rust (SBR), caused by the fungus Phakopsora pachyrhizi, is the most damaging disease of soybean in Brazil. Effective management is achieved by means of calendar-timed sprays of fungicide mixtures, which do not explicitly consider weather-associated disease risk. Two rain-based action thresholds of disease severity values (DSV50 and DSV80) were proposed and compared with two leaf wetness duration-temperature thresholds of daily values of infection probability (DVIP6 and DVIP9) and with a calendar program, with regard to performance and profitability. An unsprayed check treatment plot was included for calculating relative control. Disease severity and yield data were obtained from 29 experiments conducted at six sites across four states in Brazil during the 2012-13, 2014-15, and 2015-16 growing seasons, which represented different growing regions and climatic conditions. The less conservative rainfall action threshold (DSV80) resulted in fewer fungicide sprays compared with the other treatments, and the more conservative one (DSV50) resulted in fewer sprays than the DVIP thresholds. Yield was generally higher with the increase in spray number, but the economic analysis showed no significant differences in the risk of not offsetting the costs of fungicide sprays regardless of the system. Therefore, based on the simplicity and the profitability of the rain-based model, the system is a good candidate for incorporating into the management of SBR in soybean production fields in Brazil.

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The low availability of high-quality meteorological data resulted in the development of synthetic meteorological data generated by satellite or data interpolation, which are available in grids with varying spatio-temporal resolution. Among these different data sources, NASA/POWER and DailyGridded databases have been applied for crop yield simulations. The objective of this study was to evaluate the performance of these two datasets, in different time scales (daily, 10-day, monthly, and annual), as input data for estimating potential (YP) and attainable (YA) maize yields, using the FAO Agroecological Zone crop simulation model (FAO-AEZ), properly calibrated and validated. For that, daily weather data from ten Brazilian locations were collected and compared to the data extracted from NASA/POWER and DailyGridded systems and later applied to estimate the potential and attainable maize yields. DailyGridded data showed a better performance than NASA/POWER for all weather variables and time scales, with confidence index (C) ranging from 0.52 to 0.99 for the former and from 0.09 and 0.99 for the latter. As a consequence of that, DailyGridded data was better than NASA/POWER to estimate maize yields with estimates close to those obtained with observed data, with a lower mean absolute errors (< 30 kg ha-1) and a higher confidence index (C = 0.99).

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The original article was published with errors found in the "Material and Methods" section, as well as the "Results and Discussion" section and Tables 5 and 6.

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Leaf wetness duration (LWD) plays a key role in disease development and is often used as an input in disease-warning systems. LWD is often estimated using mathematical models, since measurement by sensors is rarely available and/or reliable. A strawberry disease-warning system called "Strawberry Advisory System" (SAS) is used by growers in Florida, USA, in deciding when to spray their strawberry fields to control anthracnose and Botrytis fruit rot. Currently, SAS is implemented at six locations, where reliable LWD sensors are deployed. A robust LWD model would facilitate SAS expansion from Florida to other regions where reliable LW sensors are not available. The objective of this study was to evaluate the use of mathematical models to estimate LWD and time of spray recommendations in comparison to on site LWD measurements. Specific objectives were to (i) compare model estimated and observed LWD and resulting differences in timing and number of fungicide spray recommendations, (ii) evaluate the effects of weather station sensors precision on LWD models performance, and (iii) compare LWD models performance across four states in the USA. The LWD models evaluated were the classification and regression tree (CART), dew point depression (DPD), number of hours with relative humidity equal or greater than 90 % (NHRH ≥90 %), and Penman-Monteith (P-M). P-M model was expected to have the lowest errors, since it is a physically based and thus portable model. Indeed, the P-M model estimated LWD most accurately (MAE <2 h) at a weather station with high precision sensors but was the least accurate when lower precision sensors of relative humidity and estimated net radiation (based on solar radiation and temperature) were used (MAE = 3.7 h). The CART model was the most robust for estimating LWD and for advising growers on fungicide-spray timing for anthracnose and Botrytis fruit rot control and is therefore the model we recommend for expanding the strawberry disease warning beyond Florida, to other locations where weather stations may be deployed with lower precision sensors, and net radiation observations are not available.

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Postbloom fruit drop (PFD) causes lesions on the petals of citrus flowers and induces fruit abscission causing severe damage to production when the flowering period coincides with intense rainfall. The aims of this study were to develop a phenological-climatological model for citrus PFD occurrence and, together with weather data series from several locations, to determine and map the agro-climatic favorability of PFD occurrence in the state of São Paulo, Southern Brazil. A phenological flowering model was developed to identify when citrus flowering occurs. The flowering starts after when a temperature below 10 °C in the months of June or July is reached followed by cumulative rainfall within 5 days of at least 20 mm, and then 96 °C days. Between the beginning of flowering and its peak, 147 °C days are required, and between the peak and its end, approximately 229 °C days, being 206 °C days from the peak to the moment when flowers remaining are about 50 % of total. The relationship between PFD incidence and accumulated rainfall during the critical period (between flowering peak and 50 % of flowers remaining) was adjusted by the Gompertz model (R (2) = 0.99, p < 0.05). After its validation, this model was used to estimate PFD incidence for 29 locations in the state, from 1993 to 2013, which allowed to map the PFD climatic favorability for the state through a Geographical Information System using linear models based on latitude, longitude, and altitude. The obtained map showed a trend of PFD incidence increasing from the northwest of the state of São Paulo towards the south and the coastal region, with medium to very high favorability in the center of the state. The results of this study can be used by growers as a guide for disease control planning as well as for defining the regions where the climatic conditions are likely to escape this disease.

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The effect of weather variables on sugarcane ripening is a process still not completely understood, despite its huge impact on the quality of raw material for the sugar energy industry. The aim of the present study was to evaluate the influence of weather variables on sugarcane ripening in southern Brazil, propose empirical models for estimating total recoverable sugar (TRS) content, and evaluate the performance of these models with experimental and commercial independent data from different regions. A field experiment was carried out in Piracicaba, in the state of São Paulo, Brazil, considering eight sugarcane cultivars planted monthly, from March to October 2002. In 2003, at the harvest, 12 months later, samples were collected to evaluate TRS (kg t(-1)). TRS and weather variables (air temperature, solar radiation, relative humidity, and rainfall) were analyzed using descriptive and multivariate statistical analysis to understand their interactions. From these correlations, variables were selected to generate empirical models for estimating TRS, according to the cultivar groups and their ripening characteristics (early, mid, and late). These models were evaluated by residual analysis and regression analysis with independent experimental data from two other locations in the same years and with independent commercial data from six different locations from 2005 to 2010. The best performances were found with exponential models which considered cumulative rainfall during the 120 days before harvest as an independent variable (R (2) adj ranging from 0.92 to 0.95). Independent evaluations revealed that our models were capable of estimating TRS with reasonable to high precision (R (2) adj ranging from 0.66 to 0.99) and accuracy (D index ranging from 0.90 to 0.99), and with low mean absolute percentage errors (MAPE ≤ 5 %), even in regions with different climatic conditions.

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As jatropha (Jatropha curcas L.) is a recent crop in Brazil, the studies for defining its suitability for different regions are not yet available, even considering the promises about this plant as of high potential for marginal zones where poor soils and dry climate occur. Based on that, the present study had as objective to characterize the climatic conditions of jatropha's center of origin in Central America for establishing its climatic requirements and to develop the agro-climatic zoning for this crop for some Brazilian regions where, according to the literature, it would be suitable. For classifying the climatic conditions of the jatropha's center of origin, climate data from 123 weather stations located in Mexico (93) and in Guatemala (30) were used. These data were input for Thornthwaite and Mather's climatological water balance for determining the annual water deficiency (WD) and water surplus (WS) of each location, considering a soil water-holding capacity (SWHC) of 100 mm. Mean annual temperature (T m), WD, and WS data were organized in histograms for defining the limits of suitability for jatropha cultivation. The results showed that the suitable range of T m for jatropha cultivation is between 23 and 27 °C. T m between 15 and 22.9 °C and between 27.1 and 28 °C were classified as marginal by thermal deficiency and excess, respectively. T m below 15 °C and above 28 °C were considered as unsuitable for jatropha cultivation, respectively, by risk of frosts and physiological disturbs. For WD, suitability for rain-fed jatropha cultivation was considered when its value is below 360 mm, while between 361 and 720 mm is considered as marginal and over 720 mm unsuitable. The same order of suitability was also defined for WS, with the following limits: suitable for WS up to 1,200 mm; marginal for WS between 1,201 and 2,400 mm, and unsuitable for WS above 2,400 mm. For the crop zoning, the criteria previously defined were applied to 1,814 climate stations in the following Brazilian regions: Northeast (NE) region and the states of Goiás (GO), Tocantins (TO), and Minas Gerais (MG). The suitability maps were generated by crossing the crop climate requirements with the interpolated climate conditions of the selected regions. The maps showed that only 22.65% of the areas in the NE region are suitable for jatropha as a rain-fed crop. The other areas of the region are classified as marginal (62.61%) and unsuitable (14.74%). In the states of GO and TO, the majority of the areas (47.78%) is classified as suitable, and in the state of MG, 33.92% of the territory has suitability for the crop. These results prove that jatropha cannot be cultivated everywhere and will require, as any other crop, minimum climatic conditions to have sustainable performance and high yields.

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Colletotrichum gossypii var. cephalosporioides, the fungus that causes ramulosis disease of cotton, is widespread in Brazil and can cause severe yield loss. Because weather conditions greatly affect disease development, the objective of this work was to develop weather-based models to assess disease favorability. Latent period, incidence, and severity of ramulosis symptoms were evaluated in controlled environment experiments using factorial combinations of temperature (15, 20, 25, 30, and 35 degrees C) and leaf wetness duration (0, 4, 8, 16, 32, and 64 h after inoculation). Severity was modeled as an exponential function of leaf wetness duration and temperature. At the optimum temperature of disease development, 27 degrees C, average latent period was 10 days. Maximum ramulosis severity occurred from 20 to 30 degrees C, with sharp decreases at lower and higher temperatures. Ramulosis severity increased as wetness periods were increased from 4 to 32 h. In field experiments at Piracicaba, São Paulo State, Brazil, cotton plots were inoculated (10(5) conidia ml(-1)) and ramulosis severity was evaluated weekly. The model obtained from the controlled environment study was used to generate a disease favorability index for comparison with disease progress rate in the field. Hourly measurements of solar radiation, temperature, relative humidity, leaf wetness duration, rainfall, and wind speed were also evaluated as possible explanatory variables. Both the disease favorability model and a model based on rainfall explained ramulosis growth rate well, with R(2) of 0.89 and 0.91, respectively. They are proposed as models of ramulosis development rate on cotton in Brazil, and weather-disease relationships revealed by this work can form the basis of a warning system for ramulosis development.

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In general, leaf wetness duration (LWD) is a key parameter influencing plant disease epidemiology, since it provides the free water required by pathogens to infect foliar tissue. LWD is used as an input in many disease warning systems, which help growers to decide the best time to spray their crops against diseases. Since there is no observation standard either for sensor or exposure, LWD measurement is often problematic. To assess the performance of electronic sensors, LWD measurements obtained with painted cylindrical and flat plate sensors were compared under different field conditions in Elora, Ontario, Canada, and in Piracicaba, São Paulo, Brazil. The sensors were tested in four different crop environments--mowed turfgrass, maize, soybean, and tomatoes--during the summer of 2003 and 2004 in Elora and during the winter of 2005 in Piracicaba. Flat plate sensors were deployed facing north and at 45 degrees to horizontal, and cylindrical sensors were deployed horizontally. At the turfgrass site, both sensors were installed 30 cm above the ground, while at the crop fields, the sensors were installed at the top and inside the canopy (except for maize, with a sensor only at the top). Considering the flat plate sensor as a reference (Sentelhas et al. Operational exposure of leaf wetness sensors. Agric For Meteorol 126:59-72, 2004a), the results in the more humid climate at Elora showed that the cylindrical sensor overestimated LWD by 1.1-4.2 h, depending on the crop and canopy position. The main cause of the overestimation was the accumulation of big water drops along the bottom of the cylindrical sensors, which required much more energy and, consequently, time to evaporate. The overall difference between sensors when evaporating wetness formed during the night was around 1.6 h. Cylindrical sensors also detected wetness earlier than did flat plates--around 0.6 h. Agreement between plate and cylinder sensors was much better in the drier climate at Piracicaba. These results allow us to caution that cylindrical sensors may overestimate wetness for operational LWD measurements in humid climates and that the effect of other protocols for angling or positioning this sensor should be investigated for different crops.

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The spatial variability of leaf wetness duration (LWD) was evaluated in four different height-structure crop canopies: apple, coffee, maize, and grape. LWD measurements were made using painted flat plate, printed-circuit wetness sensors deployed in different positions above and inside the crops, with inclination angles ranging from 30 to 45 degrees. For apple trees, the sensors were installed in 12 east-west positions: 4 at each of the top (3.3 m), middle (2.1 m), and bottom (1.1 m) levels. For young coffee plants (80 cm tall), four sensors were installed close to the leaves at heights of 20, 40, 60, and 80 cm. For the maize and grape crops, LWD sensors were installed in two positions, one just below the canopy top and another inside the canopy. Adjacent to each experiment, LWD was measured above nearby mowed turfgrass with the same kind of flat plate sensor, deployed at 30 cm and between 30 and 45 degrees. We found average LWD varied by canopy position for apple and maize (P<0.05). In these cases, LWD was longer at the top, particularly when dew was the source of wetness. For grapes, cultivated in a hedgerow system and for young coffee plants, average LWD did not differ between the top and inside the canopy. The comparison by geometric mean regression analysis between crop and turfgrass LWD measurements showed that sensors at 30 cm over turfgrass provided quite accurate estimates of LWD at the top of the crops, despite large differences in crop height and structure, but poorer estimates for wetness within leaf canopies.

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