Assessing Biogeography of Coffee Rust Risk in Brazil as Affected by the El Niño Southern Oscillation.

The El Niño Southern Oscillation (ENSO) is an oceanic-atmospheric phenomenon influencing worldwide weather and climate. Its occurrence is determined by the sea surface temperature (SST) anomaly of the 3.4 Niño region in the Pacific Ocean (5°N-5°S, 120°-170°W). El Niño (EN), Neutral (NT), and La Niña (LN) are the three possible phases of ENSO, respectively, for warm, normal, and cold SST anomaly. As in other regions around the world, weather in Brazil is influenced by ENSO phases. The country is the major coffee producer in the world, and production is strongly influenced by weather conditions, which affect plant yield, harvest quality, and interactions with pests and diseases. Coffee leaf rust (CLR), caused by the fungus Hemileia vastatrix, is a major cause of coffee yield and quality losses in Brazil, and requires fungicide spray applications every season. Because CLR is highly influenced by weather conditions, it is possible to use weather variables to simulate its progress during the cropping cycle. Therefore, the aims of this study were to estimate CLR infection rate based on a validated empirical model, which has daily minimum air temperature and relative humidity as inputs, and to assess the extent of ENSO influence on the annual risk of this disease at 45 sites in Brazil. Cumulative infection rates (CIR) were estimated daily from October to June of each growing season and location, based on the prevailing ENSO phase. Differences between the extreme phases (EN-LN) were assessed by the Two-One-Sided-Tests (TOST) method. Analysis of data from eight sites, located mainly in Paraná State, provided evidence of CIR differences between EN and LN phases (G1). Evidence of no difference of CIR between EN and LN was found in 18 sites (G2), whereas 19 sites showed no evidence of differences (G3) due to relatively large variation of CIR within the same ENSO phase. The G1 sites are located mostly in Southern Brazil, where ENSO exerts a well-defined influence on rainfall regime. In contrast, the G2 sites are mainly in Minas Gerais State, which is characterized as a transition region for ENSO influence on rainfall. The G3 sites are located between the northern region of Minas Gerais State and southern region of Bahia State, which is characterized by a subhumid climate that is usually very dry during winter, and where rainfall can vary up to 300% from one year to another, influencing relative humidity and resulting in a high CIR variability. Therefore, ENSO had a well-defined influence on CIR only in Paraná State, a region with minor importance for coffee production in Brazil. No ENSO influence was found in more northerly zones where the majority of Brazilian coffee is produced. This is the first evidence of ENSO-linked regional impact on the risk of coffee rust.

Sensitivity and requirement of improvements of four soybean crop simulation models for climate change studies in Southern Brazil.

Crop growth models have many uncertainties that affect the yield response to climate change. Based on that, the aim of this study was to evaluate the sensitivity of crop models to systematic changes in climate for simulating soybean attainable yield in Southern Brazil. Four crop models were used to simulate yields: AQUACROP, MONICA, DSSAT, and APSIM, as well as their ensemble. The simulations were performed considering changes of air temperature (0, + 1.5, + 3.0, + 4.5, and + 6.0 °C), [CO2] (380, 480, 580, 680, and 780 ppm), rainfall (- 30, - 15, 0, + 15, and + 30%), and solar radiation (- 15, 0, + 15), applied to daily values. The baseline climate was from 1961 to 2014, totalizing 53 crop seasons. The crop models simulated a reduction of attainable yield with temperature increase, reaching 2000 kg ha-1 for the ensemble at + 6 °C, mainly due to shorter crop cycle. For rainfall, the yield had a higher rate of reduction when it was diminished than when rainfall was increased. The crop models increased yield variability when solar radiation was changed from - 15 to + 15%, whereas [CO2] rise resulted in yield gains, following an asymptotic response, with a mean increase of 31% from 380 to 680 ppm. The models used require further attention to improvements in optimal and maximum cardinal temperature for development rate; runoff, water infiltration, deep drainage, and dynamic of root growth; photosynthesis parameters related to soil water availability; and energy balance of soil-plant system to define leaf temperature under elevated CO2.

Electronic leaf wetness duration sensor: why it should be painted.

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.