The objective of this study was to evaluate two different methods to estimate cotton leaf area (LA), based on leaf dimensions (length -L and width -W) and leaf dry mass (DM). Two cultivars, IAC 23 and Coodetec 401, were used. For leaf dimensions method, leaves were classified by age: young, heartshape, and mature. For each age class, a leaf shape factor (LSF) was obtained by simple linear regression between L*W and LA. For leaf dry mass method, leaves were classified in new and mature and a leaf dry mass factor (LDMF) was obtained by simple linear regression between DM and LA. LA estimates the two methods were compared to LA measured in an independent sample. Good accuracy was observed with both methods, but leaf dry mass method presented a better performance with r 2 ranging from 0.94 to 0.98 and regression slopes between 0.97 and 1.00, when regression line was forced thought the origin. In this case there is and advantage since leaf dry mass method is less time-consuming.
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.
The purpose of this study was to compare and evaluate the performance of electronic leaf wetness duration (LWD) sensors in measuring LWD in a cotton crop canopy when unpainted and painted sensors were used. LWD was measured with flat, printed-circuit wetness sensors, and the data were divided into two periods of 24 days: from 18 December 2001 to 10 January 2002, when the sensors were unpainted, and from 20 January to 13 February 2002, when the sensors were painted with white latex paint (two coats of paint). The data analysis included evaluating the coefficient of variation (CV%) among the six sensors for each day, and the relationship between the measured LWD (mean for the six sensors) and the number of hours with dew point depression under 2 degrees C, used as an indicator of dew presence. The results showed that the painting markedly reduced the CV% values. For the unpainted sensors the CV% was on average 67% against 9% for painted sensors. For the days without rainfall this reduction was greater. Comparing the sensor measurements to another estimator of LWD, in this case the number of hours with dew point depression under 2 degrees C, it was also observed that painting improved not only the precision of the sensors but also their sensitivity, because it increases the ability of the sensor to detect and measure the wetness promoted by small water droplets.
Disease-warning systems are decision support tools designed to help growers determine when to apply control measures to suppress crop diseases. Weather data are nearly ubiquitous inputs to warning systems. This contribution reviews ways in which weather data are gathered for use as inputs to disease-warning systems, and the associated logistical challenges. Grower-operated weather monitoring is contrasted with obtaining data from networks of weather stations, and the advantages and disadvantages of measuring vs. estimating weather data are discussed. Special emphasis is given to leaf wetness duration (LWD), not only because LWD data are inputs to many disease-warning systems but also because accurate data are uniquely challenging to obtain. It is concluded that there is no single "best" method to acquire weather data for use in disease-warning systems; instead, local, regional, and national circumstances are likely to influence which strategy is most successful. Key words: integrated pest management, site-specific weather data, disease forecasting, disease prediction, sustainable agriculture OBTENÇÃO DE DADOS METEOROLÓGICOS PARA SISTEMAS DE ALERTA FITOSSANITÁRIO: O CASO DA DURAÇÃO DO PERÍODO DE MOLHAMENTO FOLIARRESUMO: Os sistemas de alerta fitossanitário são ferramentas de suporte à decisão desenvolvidos para ajudar os agricultures a determinar o melhor momento da aplicação das medidas de controle para combater as doenças de plantas. As variáveis meteorológicas são dados de entrada quase que obrigatórios desses sistemas. Este trabalho apresenta uma revisão sobre os meios pelos quais as variáveis meteorológicas são coletadas para serem usadas como dados de entrada em sistemas de alerta fitossanitário e sobre os desafios associados à logística de obtenção desses dados. Essa revisão compara o monitoramento meteorológico ao nível do produtor, nas propriedades agrícolas, com aquele feito ao nível de redes de estações meteorológicas, assim como discute as vantagens e desvantagens entre medir e estimar tais variáveis meteorológicas. Especial ênfase é dada à duração do período de molhamento foliar (DPM), não somente pela sua importância como dado de entrada em diversos sistemas de alerta fitossanitário, mas também pelo desafio de se obter dados acurados dessa variável. Pode-se concluir, após ampla discussão do assunto, que não há um método único e melhor para se obter os dados meteorológicos para uso em sistemas de alerta fitossanitário; por outro lado, as circunstâncias a nível local, regional e nacional provavelmente influenciam a estratégia de maior sucesso. Palavras chave: manejo integrado de doenças, dados meteorológicos específicos do local, previsão de doenças, estimativa de doenças, agricultura sustentável
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