Several studies have compared C3 and C4 species for response to water and temperature regimes. Little comparative information exists, however, on growth responses of C3 and C4 species to irradiance regime. The objective of this study was to determine adaptive responses of C3 and C4 grasses to irradiance regime. Three C3 and two C4 perennial forage grasses were field—established near Ames, IA, and grown under 37, 70, and 100% of full sunlight by use of polypropylene shade cloths. Morphology and growth measurements were conducted three times at ⊄21‐d intervals each year for 2 yr. Net leaf CO2 exchange rate (CER) was measured for one C3, and one C4 grass. Specific leaf weight increased and leaf‐area ratio decreased with increasing irradiance similarly in all five species. Responses of herbage yield, shoot dry weight, and crop growth rate to irradiance were two to three times greater for C4 grasses than for C3 grasses. Responses of CER to irradiance were greater for the C4 grass than for the C3 grass. Morphological adaptive responses were similar for C3 and C4 grasses, but responses closely related to photosynthesis (e.g., CER, growth rate, and herbage yield) were affected more in C4 than in C3 species.

The ability of empirical models to enhance accuracy of site-specific estimates of leaf wetness duration (LWD) was assessed for 15 sites in Iowa, Nebraska, and Illinois during May to September of 1997, 1998, and 1999. Enhanced estimation of LWD was obtained by applying a 0.3-m height correction to SkyBit wind-speed estimates for input to the classification and regression tree/stepwise linear discriminant (CART/SLD) model (CART/SLD/Wind model), compared to either a proprietary model (SkyBit wetness) or to the CART/SLD model using wind speed estimates for a 10-m height. The CART/SLD/Wind model estimated LWD more accurately than the other models during dew-eligible (20:00 to 9:00) as well as dew-ineligible (10:00 to 19:00) periods, and for the period 20:00 to 9:00 regardless of rain events. Improvement of LWD estimation accuracy was ascribed to both the hierarchical structure of decision-making in the CART procedure and wind speed correction. Accuracy of the CART/SLD/Wind model identifying hours as wet or dry varied little among the 15 sites, suggesting that this model may be desirable for estimating LWD from site-specific data throughout the midwestern United States.

Response of electronic, printed-circuit wetness sensors was compared to visual observations of free water on processing-tomato leaflets during 13 dew-onset and 11 dew-dryoff events. Deployment angle and painting of the sensor surface significantly (P < 0.01) influenced the mean absolute time difference between observation of the first wet or dry leaflet at the top of the tomato canopy and the start of sensor response (kΩ) to dew onset or dryoff, respectively. Compass orientation of painted sensors deployed at 45° to horizontal had no significant effect on response to dew onset or dryoff. For sensors deployed at 45° during dew onset, mean absolute time difference between the first observed wet leaflet and the start of unpainted sensor response was 4.00 h, compared to 0.58 and 1.09 h for sensors with three and nine coats of paint, respectively. At deployment angles of 30 or 0°, paint coating had a lesser influence on time differences between visual observation and sensor response to dew onset. During dew dryoff, absolute time differences between visual confirmation of the first dry leaflet and the start of sensor response were ≤1.03 h for all sensors. Trends were similar when the visual observation criterion was 50% wet or dry leaflets during dew onset or dryoff, respectively, rather than first wet or dry leaflet. Standard deviation of sensor response during dew onset was generally larger for unpainted sensors than for sensors with three coats of paint, especially when deployed at a 45° angle. The apparent temperature of unpainted sensors at 0 or 30° deployment angles decreased much more rapidly during the period preceding dew onset than for painted sensors at the same deployment angles, whose apparent temperatures cooled at rates similar to those of tomato leaflets positioned at these angles. The results indicate that deployment angle can significantly affect accuracy and precision of dew-duration measurements by unpainted, but not painted, electronic wetness sensors.

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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

Weather and crop yields in the Midwest have exhibited wide fluctuations during the past 10 yr. Sea surface temperatures (SST, El Niño and its counterpart, La Niña) have been related to or blamed for these weather abnormalities via teleconnections. Yield, weather, and El Niño related (southern oscillation index — SO) data beginning in 1900 were assembled to determine if significant relationships could be ascertained. Midwestern weather and corn (Zea mays L.) yield data were coded relative to the SO. The SO groupings were <−8.0 (El Niño like, low phase), <0.8 (La Niña like, high phase), and in between. Yields were grouped >10% or <10% relative to expected, or in between. Corn yields exhibited wide variation when the SO was in between, indicating that weather factors other than SO influenced corn yield during those oceanic conditions. However, when summer SO was in the high phase (low phase), there was a statistical tendency for corn yields to be lower (higher) than expected, respectively for all Corn Belt states studied, except Missouri. The low (high) phase of the SO is generally related to El Niño (La Niña). During the low (high) phase of the SO, much of the Corn Belt received more (less) rainfall in July, August, and September. At the same time, high temperatures—or heat stress—were generally lower (higher) during the low (high) phase of the SO. Both high (but not excessive) precipitation and lower temperatures are associated with good corn yield. If El Niño forecasts improve as is expected, Midwestern corn yield forecasts should similarly improve. Research Question In recent years, much has been said about temperatures in the Pacific Ocean and weather in the USA. The recent warm ocean event (El Niño) started in 1991 and continued until early 1995. This event was unusually long when compared with similar events during this century. During this same time, great yield variations have been experienced in the Midwest. We examined the statistical relationships between this ocean phenomenon and both Midwestern corn yields and weather. Literature Summary It has been shown that sea surface temperature (SST) anomalies can be associated with both positive and negative weather anomalies throughout the world. This includes both temperature and precipitation variations. The oceanic anomalies have been related to air pressure differences between the central Pacific Ocean (Tahiti) and Australia (Darwin). This difference is expressed as the Southern Oscillation (SO). The SO influences both winds and ocean currents, and they, in turn, produce equatorial SST anomalies in the Pacific. In fact, these SST anomalies can then influence pressure and winds in this area by changing the fluxes of heat and moisture to the atmosphere. The latter affects solar radiation and other atmospheric variables by producing clouds and possible precipitation. El Niño and La Niña are termed warm and cold events, respectively. The El Niño is combined with the low (negative) phase of the SO and termed ENSO in meteorological literature. La Niña generally c...