scholarly journals Consideration of Latent Infections Improves the Prediction of Botrytis Bunch Rot Severity in Vineyards

Plant Disease ◽  
2020 ◽  
Vol 104 (5) ◽  
pp. 1291-1297 ◽  
Author(s):  
Giorgia Fedele ◽  
Elisa González-Domínguez ◽  
Laurent Delière ◽  
Ana M. Díez-Navajas ◽  
Vittorio Rossi
Keyword(s):  
Decision Making ◽  
Mechanistic Model ◽  
False Negative ◽  
Growing Season ◽  
Weather Data ◽  
Growth Stages ◽  
Latent Infections ◽  
Vine Growth ◽  
Model Predictions ◽  
Bunch Rot

The current study validated a mechanistic model for Botrytis cinerea on grapevine with data from 23 independent Botrytis bunch rot (BBR) epidemics (combinations of vineyards × year) that occurred between 1997 and 2018 in Italy, France, and Spain. The model was operated for each vineyard by using weather data and vine growth stages to anticipate, at any day of the vine-growing season, the disease severity (DS) at harvest (severe, DS ≥ 15%; intermediate, 5 < DS < 15%; and mild, DS ≤ 5%). To determine the ability of the model to account for latent infections, postharvest incubation assays were also conducted using mature berries without symptoms or signs of BBR. The model correctly classified the severity of 15 of 23 epidemics (65% of epidemics) when the classification was based on field assessments of BBR severity; when the model was operated to include BBR severity after incubation assays, its ability to correctly predict BBR severity increased from 65% to >87%. This result showed that the model correctly accounts for latent infections, which is important because latent infections can substantially increase DS. The model was sensitive and specific, with the false-positive and false-negative proportion of model predictions equal to 0.24 and 0, respectively. Therefore, the model may be considered a reliable tool for decision-making for BBR control in vineyards.

Analysis on Coupling Degree among Crop Evapotranspiration, Effective Precipitation and Water Requirement of Corn on Southern Kerqin Sandy Land in China

2013 ◽  
Vol 742 ◽  
pp. 331-336
Author(s):  
Ning Yang ◽  
Zhan Xiang Sun ◽  
Jia Ming Zheng ◽  
Dao Cai Chi
Keyword(s):  
Crop Coefficient ◽  
Growing Season ◽  
Water Requirement ◽  
Weather Data ◽  
Growth Stages ◽  
Sandy Land ◽  
Crop Evapotranspiration ◽  
Irrigation Frequency ◽  
Agricultural Water Management ◽  
Kerqin Sandy Land

Historical characteristics of crop evapotranspiration and irrigation requirement were the bases for determining the irrigation quota in local areas. Based on the trials of two years, crop evapotranspiration and crop coefficient of corn at monthly growth stages were determined and tested by soil water balance method in Fuxin of the southern kerqin sandy land. Using the weather data in Fuxin and Chaoyang from 1953 to 2009, estimated the coupling degree between crop water requirement of λ and apply irrigation water (ETaw) in growing season under three hydrologic years (P=25%,50%and75%).Under the humid year (25%),λ (0.756) in Fuxin was more suitable than Chaoyang (0.694),ETaw(95.5mm) in Fuxin was lower than Chaoyang (148.7mm);under the normal year (50%),λ(0.622) in Fuxin was close to Chaoyang (0.647),ETaw(180.4mm) in Fuxin was higher than Chaoyang (154mm); under droughty year (75%), λ (0.574) in Fuxin was also more suitable than Chaoyang (0.523),ETaw (204.8mm) in Fuxin was also lower than Chaoyang (245mm). The monthly change of λ and ETaw were sharping and influenced irrigation frequency in the growing season of humid and droughty year. The method and results can be further applied to agricultural water management study and guide irrigation in other same regions.

Analyzing Temperature and Precipitation Influences on Yield Distributions of Canola and Spring Wheat in Saskatchewan

2017 ◽  
Vol 56 (4) ◽  
pp. 897-913 ◽  
Author(s):  
Ting Meng ◽  
Richard Carew ◽  
Wojciech J. Florkowski ◽  
Anna M. Klepacka
Keyword(s):  
Crop Yield ◽  
Spring Wheat ◽  
Climate Model ◽  
Crop Yields ◽  
Wheat Yield ◽  
Growing Season ◽  
Weather Data ◽  
Growth Stages ◽  
Yield Distribution ◽  
The Impact

AbstractThe IPCC indicates that global mean temperature increases of 2°C or more above preindustrial levels negatively affect such crops as wheat. Canadian climate model projections show warmer temperatures and variable rainfall will likely affect Saskatchewan’s canola and spring wheat production. Drier weather will have the greatest impact. The major climate change challenges will be summer water availability, greater drought frequencies, and crop adaptation. This study investigates the impact of precipitation and temperature changes on canola and spring wheat yield distributions using Environment Canada weather data and Statistics Canada crop yield and planted area for 20 crop districts over the 1987–2010 period. The moment-based methods (full- and partial-moment-based approaches) are employed to characterize and estimate asymmetric relationships between climate variables and the higher-order moments of crop yields. A stochastic production function and the focus on crop yield’s elasticity imply choosing the natural logarithm function as the mean function transformation prior to higher-moment function estimation. Results show that average crop yields are positively associated with the growing season degree-days and pregrowing season precipitation, while they are negatively affected by extremely high temperatures in the growing season. The climate measures have asymmetric effects on the higher moments of crop yield distribution along with stronger effects of changing temperatures than precipitation on yield distribution. Higher temperatures tend to decrease wheat yields, confirming earlier Saskatchewan studies. This study finds pregrowing season precipitation and precipitation in the early plant growth stages particularly relevant in providing opportunities to develop new crop varieties and agronomic practices to mitigate climate changes.

scholarly journals Prediction of disease risk using sitespecific estimates of weather variables

2007 ◽  
Vol 60 ◽  
pp. 128-132 ◽  
Author(s):  
K.S. Kim ◽  
R.M. Beresford ◽  
W.R. Henshall
Keyword(s):  
Spatial Interpolation ◽  
Disease Risk ◽  
Interpolation Method ◽  
Voronoi Tessellation ◽  
Growing Season ◽  
Weather Data ◽  
Weather Variables ◽  
Risk Models ◽  
Bunch Rot ◽  
Spatial Interpolation Method

To improve the implementation of weatherbased disease risk models a spatial interpolation method was investigated to provide weather estimates for specific sites Two sites in the HortResearch horticultural weather station network one in Marlborough and one in Hawkes Bay were selected as validation sites Interpolated weather data were estimated for these sites from November to March in 200304 and 200405 using actual weather data from nearby stations that were selected as natural neighbours using the geometrical technique Voronoi tessellation Wetness duration was also estimated using interpolated weather data as inputs to an empirical wetness model Air temperature estimates were comparable to actual measurements but wetness duration was overestimated When interpolated and actual data were used as inputs to the grape botrytis model Bacchus predicted risks were comparable to each other for short periods rather than the whole growing season This suggests that risk of botrytis bunch rot could be predicted reliably at a specific site using the spatial interpolation method

The nature of metacognitive inefficiency in perceptual decision making

2020 ◽  
Author(s):  
Medha Shekhar ◽  
Dobromir Rahnev
Keyword(s):  
Decision Making ◽  
Mechanistic Model ◽  
Perceptual Decision Making ◽  
Large Dataset ◽  
Substantial Body ◽  
Confidence Levels ◽  
Continuous Scale ◽  
Competing Models ◽  
Metacognitive Ability ◽  
Confidence Ratings

Humans have the metacognitive ability to judge the accuracy of their own decisions via confidence ratings. A substantial body of research has demonstrated that human metacognition is fallible but it remains unclear how metacognitive inefficiency should be incorporated into a mechanistic model of confidence generation. Here we show that, contrary to what is typically assumed, metacognitive inefficiency depends on the level of confidence. We found that, across five different datasets and four different measures of metacognition, metacognitive ability decreased with higher confidence ratings. To understand the nature of this effect, we collected a large dataset of 20 subjects completing 2,800 trials each and providing confidence ratings on a continuous scale. The results demonstrated a robustly nonlinear zROC curve with downward curvature, despite a decades-old assumption of linearity. This pattern of results was reproduced by a new mechanistic model of confidence generation, which assumes the existence of lognormally-distributed metacognitive noise. The model outperformed competing models either lacking metacognitive noise altogether or featuring Gaussian metacognitive noise. Further, the model could generate a measure of metacognitive ability which was independent of confidence levels. These findings establish an empirically-validated model of confidence generation, have significant implications about measures of metacognitive ability, and begin to reveal the underlying nature of metacognitive inefficiency.

scholarly journals Development of an Online Tool for Tracking Soil Nitrogen to Improve the Environmental Performance of Maize Production

2021 ◽  
Vol 13 (10) ◽  
pp. 5649
Author(s):  
Giovani Preza-Fontes ◽  
Junming Wang ◽  
Muhammad Umar ◽  
Meilan Qi ◽  
Kamaljit Banger ◽  
...  
Keyword(s):  
Real Time ◽  
N Uptake ◽  
Growing Season ◽  
Weather Data ◽  
N Availability ◽  
Soil N ◽  
N Losses ◽  
Online Tool ◽  
Support Tool ◽  
Soil N Availability

Freshwater nitrogen (N) pollution is a significant sustainability concern in agriculture. In the U.S. Midwest, large precipitation events during winter and spring are a major driver of N losses. Uncertainty about the fate of applied N early in the growing season can prompt farmers to make additional N applications, increasing the risk of environmental N losses. New tools are needed to provide real-time estimates of soil inorganic N status for corn (Zea mays L.) production, especially considering projected increases in precipitation and N losses due to climate change. In this study, we describe the initial stages of developing an online tool for tracking soil N, which included, (i) implementing a network of field trials to monitor changes in soil N concentration during the winter and early growing season, (ii) calibrating and validating a process-based model for soil and crop N cycling, and (iii) developing a user-friendly and publicly available online decision support tool that could potentially assist N fertilizer management. The online tool can estimate real-time soil N availability by simulating corn growth, crop N uptake, soil organic matter mineralization, and N losses from assimilated soil data (from USDA gSSURGO soil database), hourly weather data (from National Weather Service Real-Time Mesoscale Analysis), and user-entered crop management information that is readily available for farmers. The assimilated data have a resolution of 2.5 km. Given limitations in prediction accuracy, however, we acknowledge that further work is needed to improve model performance, which is also critical for enabling adoption by potential users, such as agricultural producers, fertilizer industry, and researchers. We discuss the strengths and limitations of attempting to provide rapid and cost-effective estimates of soil N availability to support in-season N management decisions, specifically related to the need for supplemental N application. If barriers to adoption are overcome to facilitate broader use by farmers, such tools could balance the need for ensuring sufficient soil N supply while decreasing the risk of N losses, and helping increase N use efficiency, reduce pollution, and increase profits.

scholarly journals Protective Netting Improves Leaf-level Photosynthetic Light Use Efficiency in ‘Honeycrisp’ Apple Under Heat Stress

HortScience ◽  
2018 ◽  
Vol 53 (10) ◽  
pp. 1416-1422 ◽  
Author(s):  
Giverson Mupambi ◽  
Stefano Musacchi ◽  
Sara Serra ◽  
Lee A. Kalcsits ◽  
Desmond R. Layne ◽  
...  
Keyword(s):  
Heat Stress ◽  
Solar Radiation ◽  
Leaf Gas Exchange ◽  
Growing Season ◽  
High Light ◽  
Weather Data ◽  
Light Use Efficiency ◽  
Ambient Temperatures ◽  
Leaf Level ◽  
Use Efficiency

Globally, apple production often occurs in semiarid climates characterized by high summer temperatures and solar radiation. Heat stress events occur regularly during the growing season in these regions. For example, in the semiarid eastern half of Washington State, historic weather data show that, on average, 33% of the days during the growing season exceed 30 °C. To mediate some of the effects of heat stress, protective netting (PN) can be used to reduce the occurrence of fruit sunburn. However, the impacts of reduced solar radiation in a high light environment on light-use efficiency and photosynthesis are poorly understood. We sought to understand the ecophysiological response of apple (Malus domestica Borkh. cv. Honeycrisp) under blue photoselective PN during days with low (26.6 °C), moderate (33.7 °C), or high (38.1 °C) ambient temperatures. Two treatments were evaluated; an uncovered control and blue photoselective PN. Maximum photochemical efficiency of PSII, or photosystem II (Fv/Fm) was significantly greater at all measurement times under blue photoselective PN compared with the control on days with high ambient temperatures. Fv/Fm dropped below 0.79, which is considered the threshold for stress, at 1000 hr in the control and at 1200 hr under blue photoselective PN on a day with high ambient temperature. On days with low or moderate ambient temperatures, Fv/Fm was significantly greater under blue photoselective PN at 1400 hr, which coincided with the peak in solar radiation. ‘Honeycrisp’ apple exhibited dynamic photoinhibition as shown by the diurnal decline in Fv/Fm. Quantum photosynthetic yield of PSII (ΦPSII) was also generally greater under blue photoselective PN compared with the control for days with moderate or high ambient temperatures. Photochemical reflectance index (ΔPRI), the difference in reflectance between a stress-responsive and nonstress-responsive wavelength, was greater under PN compared with the control on the day with high ambient temperatures, with no differences observed under low or moderate ambient temperatures. Leaf gas exchange did not show noticeable improvement under blue photoselective netting when compared with the control despite the improvement in leaf-level photosynthetic light use efficiency. In conclusion, PN reduced incoming solar radiation, improved leaf-level photosynthetic light use efficiency, and reduced the symptoms of photoinhibition in a high-light, arid environment.

Use of hot KCl-NH4-N to estimate fertilizer N requirements

1997 ◽  
Vol 77 (2) ◽  
pp. 161-166 ◽  
Author(s):  
C. A. Campbell ◽  
Y. W. Jamel ◽  
A. Jalil ◽  
J. Schoenau
Keyword(s):  
N Mineralization ◽  
Growth Models ◽  
Growing Season ◽  
Order Rate Constant ◽  
Weather Data ◽  
N Availability ◽  
Fertilizer N ◽  
Available N ◽  
Mineralizable N ◽  
Potentially Mineralizable N

We need an easy-to-use chemical index for estimating the amount of N that becomes available during the growing season, to improve N use efficiency. This paper discusses how producers may, in future, use crop growth models that incorporate indices of soil N availability, to make more accurate, risk-sensitive estimates of fertilizer N requirements. In a previous study, we developed an equation, using 42 diverse Saskatchewan soils, that related potentially mineralizable N (N0) to NH4N extracted with hot 2 M KCl (X), (i.e., N0 = 37.7 + 7.7X, r2 = 0.78). We also established that the first order rate constant (k) for N mineralization at 35°C is indeed a constant for arable prairie soils (k = 0.067 wk−1). We modified the N submodel of CERES-wheat to include k and N0 (values of N0 were derived from the hot KCl test). With long-term weather data (precipitation and temperature) as input, this model was used to estimate probable N mineralization during a growing season and yield of wheat (grown on fallow or stubble), in response to fertilizer N rates at Swift Current. The model output indicated that the amount of N mineralized in a growing season for wheat on fallow was similar to that for wheat on stubble, as we hypothesized. Further the model indicated that rate of fertilizer N had only minimal effect on N mineralized. We concluded that, despite the importance of knowing the Nmin capability of a soil, it is available water, initial levels of available N and rate of fertilizer N that are the main determinants of yield in this semiarid environment. The theoretical approach we have proposed must be validated under field conditions before it can be adopted for use. Key words: N mineralization, Hot KCl-NH4-N, potentially mineralizable N, CERES-wheat model

scholarly journals Digital Link—A

2021 ◽  
pp. 115-142
Author(s):  
Jean Walrand
Keyword(s):  
Decision Making ◽  
Decision Problem ◽  
False Negative ◽  
Main Tool ◽  
Maximum Likelihood Estimates ◽  
Modulation Scheme ◽  
Bayes Rule ◽  
Binary Symmetric Channel ◽  
Symmetric Channel ◽  
Complex Modulation

AbstractIn a digital link, a transmitter converts bits into signals and a receiver converts the signals it receives into bits. The receiver faces a decision problem that we study in Sect. 7.1. The main tool is Bayes’ Rule. The key notions are maximum a posteriori and maximum likelihood estimates. Transmission systems use codes to reduce the number of bits they need to transmit. Section 7.2 explains the Huffman codes that minimize the expected number of bits needed to transmit symbols; the idea is to use fewer bits for more likely symbols. Section 7.3 explores a commonly used model of a communication channel: the binary symmetric channel. It explains how to calculate the probability of errors. Section 7.4 studies a more complex modulation scheme employed by most smartphones and computers: QAM. Section 7.5 is devoted to a central problem in decision making: how to infer which situation is in force from observations. Does a test reveal the presence of a disease; how to balance the probability of false positive and that of false negative? The main result of that section is the Neyman–Pearson Theorem that the section illustrates with many examples.

Sensor-Based Decision Making in Uncertain Context

2017 ◽  
pp. 234-257
Author(s):  
Eric Villeneuve ◽  
François Pérès ◽  
Cedrik Beler ◽  
Vicente González-Prida
Keyword(s):  
Decision Making ◽  
Network Architecture ◽  
Measurement Errors ◽  
False Negative ◽  
Decision Makers ◽  
Organizational Network ◽  
Decision Aiding ◽  
External Impact ◽  
Information Collecting ◽  
Embedded Intelligence

Decision makers, whether human or computer, using sensor networks to instrument information collecting necessary for decision, often face difficulties in assessing confidence granted to signals transmitted and received in the network. Several organizational (network architecture or nature, distance between sensors ...), internal (sensor reliability or accuracy ...) or external (impact of environment ...) factors can lead to measurement errors (false alarm, non-detection by misinterpretation of the analyzed signals, false-negative …). A system-embedded intelligence is then necessary, to compare the information received for the purpose of decision aiding based on margin of errors converted in confidence intervals. In this chapter, the authors present four complementary approaches to quantify the interpretation of signals exchanged in a network of sensors in the presence of uncertainty.

Sign in / Sign up

Export Citation Format

Share Document