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Based on the Essential Science Indicators (ESI) database, this study analyzed 734 top papers in the subject category of Agronomy from 2009 to 2019, which include 730 highly cited papers and seven hot papers in the field. All papers written in English, were from 3,540 authors; 1,225 organizations; and 91 countries/territories, published in 47 journals and book series in the field of Agronomy. The journals with the highest impact factor (IF) were Industrial Crops and Products, Plant and Soil, Agricultural and Forest Meteorology, Pest Management Science, and Field Crops Research. The countries and regions with the highest IF were United States, People's Republic of China, France, Germany, and Australia. The organizations with the highest average IF were INRA, University of Western Australia, Chinese Academy of Science, China Agriculture University, and University of California-Davis. This work is useful for students identifying graduate schools and researchers selecting journals. [ABSTRACT FROM AUTHOR]

This paper analyzes the effects of climatic variables on food crop yields at the prefectural level from 1996 to 2016 in Togo. Using the panel‐corrected standard errors method and panel data from departments in charge of agricultural statistics and meteorology, the results show that meteorological variables have various effects on food crops yields, but the negative effects are dominant. In addition, adaptation strategies through agricultural land reallocation have not enabled farmers to improve food crops yields. There is an urgent need for public authorities to implement actions to strengthen farmers' resilience through practice adoption and cultural innovations for adaptation to climate change. [ABSTRACT FROM AUTHOR]

This paper examines the impact of climate change on the decision of farmers to engage or not to engage in livestock activities and also on the choice of different livestock species in Kenya. To this end, cross-sectional household level data supplemented by long-term averages of climate data are used. The probit model is employed to derive the response of the probability of engaging in livestock activities to climate change. Probit and multivariate probit methods are employed to model the choice of different livestock species. Atmosphere–ocean global circulation models are used to project the impact of different climate scenarios on the probability of engaging in livestock activities and also of adopting different livestock species according to variations in climate. The results suggest that farmers adapt livestock management decisions to climate change. At low levels of temperature increase, the probability of engaging in livestock activities falls, but at higher levels of climate change, the probability rises. The results further show that as it gets hotter, farmers change their livestock choices from dairy cattle and sheep to beef cattle and goats. [ABSTRACT FROM AUTHOR]

This paper reports the development of a neurocomputingbased model for estimating the potential evapotranspiration over Gangetic West Bengal, India during the summer monsoon months of June, July and August. An artificial neural network is implemented in the form of multilayer perceptron to generate the model. Three weather variables, surface temperature, vapour pressure and rainfall are used as the independent variables in generating the model. The performance of the model is judged statistically against nonlinear regression in the form of asymptotic regression. The study reveals that an artificial neural network is more efficient than the regression approach to estimate the potential evapotranspiration in the summer monsoon months. Furthermore, it is established that the artificial neural network and nonlinear regression have almost equal efficiency in the previously mentioned estimation in the month of June. However, in July and August the higher values of correlation and Willmotts indices, and lower values of estimation error, indicate that the artificial neural network is more reliable than the nonlinear regression approach. Since evapotranspiration is one of the basic components of the hydrological cycle and is essential for estimating irrigation water requirement, an efficient estimation procedure may help in agrometeorological modelling and irrigation scheduling in the summer monsoon months, which are of high importance for agriculture in the study zone. Copyright © 2009 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The article offers the authors' insights regarding the weather index insurance (WII) program in Mexico, which is one of the largest in the world. The authors state that the WII makes disincentives to invest in non-insured crops that leads to overspecialization and monoculture. Furthermore , they suggest that the threshold of WII be recalibrated to adjust for the creation of drought-resistant seeds.

The rainy season is of high importance for livelihoods in the Southern Peruvian Andes (SPA), especially for agriculture, which is mainly rain fed and one of the main income sources in the region. Therefore, knowledge and predictions of the rainy season such as its onset and ending are crucial for planning purposes. However, such information is currently not readily available for the local population. Moreover, an evaluation of existing rainy season indices shows that they are not optimally suited for the SPA and may not be directly applicable in a forecasting context. Therefore, we develop a new index, named Climandes index, which is tailored to the SPA and designed to be of use for operational monitoring and forecasting purposes. Using this index, we analyse the climatology and trends of the rainy season in the SPA. We find that the rainy season starts roughly between September and January with durations between 3 and 8 months. Both onset and duration show a pronounced northeast‐southwest gradient, regions closer to the Amazon Basin have a considerably longer rainy season. The inter‐annual variability of the onset is very high, that is, 2–5 months depending on the station, while the end of the rainy season shows a much lower variability (i.e., 1.5–3 months). The spatial patterns of total precipitation amount and dry spells within the rainy season are only weakly related to its timing. Trends in rainy season characteristics since 1965 are mostly weak and not significant, but generally indicate a tendency towards a shortening of the rainy season in the whole study area due to a later onset and an increase in precipitation sums during the rainy season in the northwestern study area. [ABSTRACT FROM AUTHOR]

Crop insurance programs rely on conditional predictive distributions of loss random variables (e.g., yield, revenue, loss costs, etc.) to determine probabilities and magnitudes of loss. The loss variables may be related to stochastic variables that are not known at the time the policy is priced. Such is the case for weather; weather is stochastic, realizations are not known when the crop insurance policy is sold, and there is often additional historical information on weather relative to the loss variable itself. We provide a Bayesian methodology for incorporating historical weather information in crop insurance rating. We apply the method in empirical applications to county‐level U.S. corn yields and loss cost ratios in the Midwest. The historical weather‐conditioned distributions differ from those based on shorter samples. In the yield distribution setting, additional temporal weather information leads to economic gains relative to other rating approaches; the magnitude of these gains increases with the amount of historical weather information included in the analysis. [ABSTRACT FROM AUTHOR]

All over the world, farming communities need to adapt to a changing climate. At the same time, they are confronted by the necessity to increase food availability for a growing population, but also to ensure a sustainable use of natural resources. In this process, agriculture is not just the fiend responsible for an increase in greenhouse gas emissions, nor the victim of rising temperatures and extreme weather events. Agriculture is also an ally in climate change mitigation and adaptation because the selection of ad hoc crop varieties and livestock can lower the environmental impact of farming and the implementation of better management practices can promote soil conservation. Whether agriculture will have a positive or a negative impact on climate change adaptation and mitigation will depend on the rural policies implemented, but it will also be contingent on an adequate understanding of the interconnections existing between weather, climate, and farming. This review explores such interconnections by focusing on the history of agricultural meteorology, which is the research field that studies the impact of weather and climate on crops, livestock, farming operations, and plant and animal pests and diseases. The article discusses stakeholders, institutions, and main developments in agricultural meteorology, and describes how the agenda of agricultural meteorology has shifted over time. At the beginning of the 20th century, research in agricultural meteorology focused on increasing the profitability of farming. In the 21st century, instead, the main goal of agricultural meteorology is to ensure food security and guarantee sustainability. This article is categorized under: Climate, History, Society, Culture > Disciplinary Perspectives [ABSTRACT FROM AUTHOR]

Water resources management and agriculture planning models require a statistical synthesis of some rainfall features, in particular those representing dry atmospheric conditions. The bigger the basin, the more important these features become, as is the case of the La Plata Basin LPB.This paper focuses on the precipitation variability in the large LPB in South America, analysing the number of months per year with low rainfall and the sequences of months with low rainfall, their theoretical distributions and stability, which are needed as input for the models mentioned above.Long time series are used to analyse the lowfrequency variability and the relative importance of decadal variability. Changes are evident in the number of months per year with low rainfall, with a decrease of about 20 in the period after 1970.Theoretical distribution models binomial and geometric are fitted to these empirical distributions, and the regional variability of the fitting parameters is shown. In practically the entire region, the goodnessoffit of the two theoretical models considered is statistically satisfactory.The temporal variability of the parameters of the theoretical binomial p and geometric α distributions is analysed, in excluding subperiods of 10 and 5 years, respectively. The results show lowfrequency variability overlapped on a decadal variability, with low homogeneous regional behaviour.The distribution models have proven to be efficient for frequency adjustments of the rainfall properties studied. These results are an acceptable and necessary input to decision models in LPB. They also make it possible to infer effects of climate change. Copyright © 2008 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Agriculture (arguably the backbone of India's economy) is highly dependent on the spatial and temporal distribution of monsoon rainfall. This paper presents an analysis of crop–climate relationships for India, using historic production statistics for major crops (rice, wheat, sorghum, groundnut and sugarcane) and for aggregate food grain, cereal, pulses and oilseed production. Correlation analysis provides an indication of the influence of monsoon rainfall and some of its potential predictors (Pacific and Indian Ocean sea‐surface temperatures, Darwin sea‐level pressure) on crop production. All‐India annual total production (except sorghum and sugarcane), and production in the monsoon (except sorghum) and post‐monsoon seasons (except rice and sorghum) were significantly correlated to all‐India summer monsoon rainfall. Monsoon season crops (except sorghum) were strongly associated with the three potential monsoon predictors. Results using state‐level crop production statistics and subdivisional monsoon rainfall were generally consistent with the all‐India results, but demonstrated some surprising spatial variations. Whereas the impact of subdivisional monsoon rainfall is strong in most of the country, the influence of concurrent predictors related to El Niño–southern oscillation and the Indian Ocean sea‐surface temperatures at a long lead time seem greatest in the western to central peninsula. Copyright © 2004 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

From 1872 onwards the diary is dominated by this type of descriptive note. gl Instrumental data Daily weather observations of temperature, pressure, wind direction and rainfall were taken at the site for 8 years 1864-1871. The Glendooen diary is an important weather diary from the nineteenth century but it is unusual that a greater number of such diaries for this period have not been unearthed in Ireland. Glendooen temperature observations We developed an eight-year time series of monthly mean temperature at Glendooen from 1864-1871 using the 0900h temperature observations from the diary. We present an overview of a weather diary for Glendooen, Co. Donegal in Ireland for the years 1846-1875. [Extracted from the article]

We study potential impacts of future climate change on U.S. agricultural productivity using county‐level yield and weather data from 1950 to 2015. To account for adaptation of production to different weather conditions, it is crucial to allow for both spatial and temporal variation in the production process mapping weather to crop yields. We present a new panel data estimation technique, called mean observation OLS (MO‐OLS) that allows for spatial and temporal heterogeneity in all regression parameters (intercepts and slopes). Both forms of heterogeneity are important: We find strong evidence that production function parameters adapt to local climate, and also that sensitivity of yield to high temperature declined from 1950–89. We use our estimates to project corn yields to 2100 using 19 climate models and three greenhouse gas emission scenarios. We predict unmitigated climate change will greatly reduce yield. Our mean prediction (over climate models) is that adaptation alone can mitigate 36% of the damage, while emissions reductions consistent with the Paris targets would mitigate 76%. [ABSTRACT FROM AUTHOR]

To evaluate the effects of type of micaceous mineral and degree of weathering on the ability to adsorb and desorb radiocaesium (RCs) ions, various levels of weathering were induced (K‐depletion) in three types of micaceous minerals: biotite, muscovite and illite. The ability to adsorb RCs was represented by RCs interception potential (RIP) and a desorption ratio was determined by the amount of 137Cs desorbed from the minerals by 1 M ammonium acetate (137Cs desorption ratio). The RIP values were in the order of illite > biotite > muscovite. The RIP values for biotite gradually increased with the release of up to ~50% of the K+ from interlayer sites but then decreased with the further release of K+, up to ~100%, which was associated with the formation of a fully expanded interlayer: vermiculite. The change from increasing to decreasing RIP due to the weathering of the biotite strongly indicated that the selective adsorption sites for RCs are not located in an expanded interlayer but in intermediate sites between non‐expanded and expanded interlayers: frayed edge sites (FES). In contrast, for both the muscovite and illite, the RIP only increased. The 137Cs desorption ratio, <7% for all the samples, tended to decrease with increasing degrees of weathering and did not differ greatly by mineral type. These results contribute to a more accurate prediction of the risk of RCs transfer from soils to crops and the understanding of the weathering processes of micaceous minerals in soils. Highlights: Radiocaesium adsorption and desorption was investigated for gradually K‐removed biotite, muscovite and illite.Vermiculitization of biotite via K removal firstly increased then decreased the frayed edge sites (FES).Adsorption was influenced by di/trioctahedral character and charge localization, and desorption by degree of weathering.These results lead to better understanding of the weathering processes of micaceous minerals and fate of RCs in soils. [ABSTRACT FROM AUTHOR]

Climate change has a significant impact on agrometeorological conditions. We analysed the winter wheat overwintering indices for the northern winter wheat production areas of China (NWPA), which include the percentage of extreme low‐temperature (daily minimum temperature ≤−22 °C) years, the first day of the overwintering period (FDO), sowing date (SD) and precipitation before winter (PBW). Then, we compared the differences of the four indices between the forecast period 2021–2050 and the baseline period 1961–1990 under the RCP4.5 scenario, which were based on the 0.5° × 0.5° grid daily meteorological data output provided by the Inter‐Sectoral Impact Model Intercomparison Project. The results showed that the occurrence of extreme cold winters decreases the NWPA, which will result in the northern boundary of winter wheat moving north by 1–2 latitudes and in an increase in the potentially safe overwintering areas by 8.5%. The FDO will be delayed in 2021–2050 by an average of 7.7 days. Climate warming will change the meteorological conditions for winter wheat overwintering in the Huang‐Huai winter wheat area, especially near the southern boundary of the winter wheat planting area, and the areas and years with no obvious overwintering periods will experience an increase by 50% and 56%, respectively. The SD will be postponed by 8.6–9.0 days. The PBW will decrease as the accumulated temperature increases, especially in the Huang‐Huai winter wheat area. In the future, the possible northern boundary for winter wheat planting may move northward, and the northernmost tip may reach 46°N. South of the Huang‐Huai region, winter wheat planting may change from a semi‐winter to a spring variety. These findings will provide a reference for generating wheat planting and management strategies in China. Climate change has significantly influenced the winter wheat planting in northern China. In 2021–2050, climate warming would decrease the occurrence of extreme cold winters in the regions near northern planting boundary, which would result in the possible northern boundary (red and green lines) moving northward, and the northernmost tip may reach 46°N. However, south of the Huang‐Huai region (black oval areas), wheat planting may change from a semi‐winter to a spring variety due to temperature increase and precipitation reduction. [ABSTRACT FROM AUTHOR]

Agrometeorological models have been applied to crop yield estimations, providing information for management. A limiting factor of these models is the input data, which come mostly from surface meteorological stations (SMS). We propose the use of gridded weather data provided by the European Center for Medium-Range Weather Forecasts (ECMWF) and the NASA as a viable and innovative alternative for coffee (Coffea arabica L.) yield estimation in Brazil. We made modifications in the coeffi- cients of the model proposed by Santos and Camargo, regarding the penalties for extreme temperatures and those related to different gridded data sources. The accuracy, measured by the mean absolute percentage error (MAPE), was 23.76, 24.61, and 22% for the calibrations with ECMWF, NASA, and SMS data, respectively. These high levels of MAPE were the result of the high biennality of the crop. The average tendency, measured by the systematic root mean square error, was an overestimation (or underestimation) of ±465 kg ha-1 by the ECMWF, ±411 kg ha-1 by NASA, and ±653 by the SMS models. The mean precision, measured by the nonsystematic root mean square error, was 186, 190, and 280 kg ha-1 for the ECMWF, NASA, and SMS models, respectively. These results indicate that coffee yield for São Paulo and Minas Gerais can be calibrated and estimated with the ECMWF and NASA gridded data. [ABSTRACT FROM AUTHOR]

In third world countries like Indonesia, higher education is expected to contribute to the development of applied sciences that tackle pressing problems. Adaptation to and mitigation of climate change are such fields. Applied sciences work better in problem solving when approached in an interdisciplinary way. Winarto and Stigter have decades of experience in applied agricultural sciences but from different main fields: anthropology and physics/(micro)meteorology/(micro)climatology. They provide a welcome complementarity in their dialogue-based interactions with farmers on how climate change has necessitated livelihood/resilience changes for them. They developed and established climatological services for farmers, consisting of seven initial climate services for agriculture, bringing farmers closer to their ecosystems that are currently threatened by climate change. They established what they call 'agrometeorological learning,' which is about changes of beliefs, attitudes, behaviors, and goals of farmers due to new meteorological and climatologic knowledge they acquire, which considerably help increase farmers resiliencies. [ABSTRACT FROM AUTHOR]

Some forecasting techniques have been tested with crop models using various statistical analyses for generating future scenarios of yield (Y). Forecasting, however, can be achieved by simply using regression analysis and carefully selecting independent variables (IVs) with time displacement relative to the dependent variable. The early forecasting of Y is the vanguard of agronomic modeling, promoting improvements in planning, allowing more rational strategic decisions, and increasing food and economic security. Climatic variables are the most important factors controlling the yield and quality of coffee (Coffea arabica L.). We calibrated and tested agrometeorological models to forecast the annual Y of coffee for six traditional producing regions in the state of Minas Gerais, Brazil. We used multiple linear regressions, selecting IVs to maximize the period between the forecast of Y and the harvest for each locality. The IVs were monthly meteorological variables from 1997 to 2014: air temperature, rainfall, potential evapotranspiration, soil water storage, water deficit, and water surplus. The IVs were selected by testing all possible combinations in the domain and avoiding multicollinearity. The agrometeorological models were accurate for all regions, and the earliest forecasts were 6 and 5 mo before harvest for the producing locations of Guaxupé and Coromandel, respectively. The models for yield forecasting for Guaxupé included the water deficit in July and October and July precipitation for the high-yield season and the water deficit in April and September and October precipitation for the low-yield season. The models for yield forecasting for Coromandel included the November water surplus and February and September precipitation for the high-yield season and precipitation for January, April, and October for the low-yield season. [ABSTRACT FROM AUTHOR]

Sugarcane is a multipurpose crop whose components may be used, in addition to sugar production, for various energy carriers or end-products (electricity, liquid biofuels and heat) which enhance its economic potential. For many years, plant breeders and agronomists have focused on increasing sucrose yields per hectare and millers on increasing recoverable sucrose per ton of sugarcane in sugar mills. Attempting to exploit the energy potential of sugarcane more fully, calls for a more holistic approach focusing on both sucrose and lignocellulosic components of sugarcane biomass, and gaining some insight into the management practices required to optimize sugarcane cropping systems in these respects. Such options include genotype selection, harvest date with respect to the crop's growing cycle, crop type (plant crop vs. ratoon crops) and harvesting systems (mechanical vs. manual). The effects of these factors are strongly modulated by climate and soil properties, and these interactions are overall poorly known. Here, we set out to examine sugarcane infield management × environmental interactions with respect to (i) sugarcane yield and partitioning of the aboveground biomass; and (ii) sugarcane milling products (recoverable sucrose yield and amounts of coproducts) and their derived energy carriers. Three Saccharum cv. cultivars (R570, R579 and R585) were planted in three locations on La Reunion Island with contrasting management practices and climatological conditions. Quality characteristics of the samples were assessed by conventional and near infrared spectroscopy methods. Product, coproducts and potential energy production were measured and computed using transfer equations and a mill-operating model. Yields and quality characteristics from cultivars and harvesting systems were affected differently by environmental factors - low temperature and radiation, and water stress. The current study also provides valuable information on how combinations between environments, genotypes and practices affect yield and partitioning of the aboveground biomass, and food and energy production. [ABSTRACT FROM AUTHOR]

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A 17 year (1990-2006) meteorological and soil hydrology database has been developed for the Walnut Gulch Experimental Watershed in southeastern Arizona. Data have been acquired at three automated weather stations, 5 soil profile trench sites, and 19 locations dispersed across the watershed colocated with recording rain gauges. Meteorological elements measured at the weather stations include air temperature, relative humidity, wind speed, wind direction, barometric pressure, solar radiation, photosynthetically active radiation, and net radiation. Soil hydrology properties measured at the weather stations, trench sites, and rain gauges include soil moisture, soil temperature, soil heat flux, and soil surface temperature. Data are available at http://www.tucson.ars.ag.gov/dap. [ABSTRACT FROM AUTHOR]

It is well established that crop production is inherently vulnerable to variations in the weather and climate. More recently the influence of vegetation on the state of the atmosphere has been recognized. The seasonal growth of crops can influence the atmosphere and have local impacts on the weather, which in turn affects the rate of seasonal crop growth and development. Considering the coupled nature of the crop–climate system, and the fact that a significant proportion of land is devoted to the cultivation of crops, important interactions may be missed when studying crops and the climate system in isolation, particularly in the context of land use and climate change. To represent the two-way interactions between seasonal crop growth and atmospheric variability, we integrate a crop model developed specifically to operate at large spatial scales (General Large Area Model for annual crops) into the land surface component of a global climate model (GCM; HadAM3). In the new coupled crop–climate model, the simulated environment (atmosphere and soil states) influences growth and development of the crop, while simultaneously the temporal variations in crop leaf area and height across its growing season alter the characteristics of the land surface that are important determinants of surface fluxes of heat and moisture, as well as other aspects of the land-surface hydrological cycle. The coupled model realistically simulates the seasonal growth of a summer annual crop in response to the GCM's simulated weather and climate. The model also reproduces the observed relationship between seasonal rainfall and crop yield. The integration of a large-scale single crop model into a GCM, as described here, represents a first step towards the development of fully coupled crop and climate models. Future development priorities and challenges related to coupling crop and climate models are discussed. [ABSTRACT FROM AUTHOR]

Taking into consideration the fact that in recent years weather conditions were sufficient to feed cattle on pasture until late autumn or early winter, an experiment was carried out during the years 2000 and 2003 on a low input pasture to show the effect of the pre-utilization date (June, July, August) and the harvest date in the autumn/winter period (November, December, January) on the accumulation of the following secondary metabolites formed by fungi: ergosterol (ERG), ochratoxin A (OA) and zearalenone (ZEA) in the autumn-saved herbage. The highest levels of concentration of these metabolites were 470.3, 1.63 and 34.9 ng g−1 respectively. Lower ERG levels were influenced by later pre-utilization, which is a logical consequence of the shorter period of time for field toxigenic fungi development. Pre-utilization in August is strongly recommended as the concentration of ochratoxin A (OA) in the autumn-saved herbage in January was the lowest (not much changed in comparison with earlier harvest). Later harvest was also affected by higher ZEA concentrations in the analysed samples, which indicates that, under conditions of prolonged pasture utilization, field toxigenic fungi (producing ZEA) increased their population and had proper weather conditions stimulating the biosynthesis of the toxin. The later suggests that the final harvest of autumn-saved herbage should be made rather in December than in January. Higher levels of humidity influenced higher concentrations of ERG and ZEA in the autumn-saved herbage. [ABSTRACT FROM AUTHOR]

Based on existing physical theories and models, a dynamic model estimating the concentration of Venturia inaequalis ascospores in the orchard air and their deposition on apple leaves was elaborated. The model produces two main outputs: number of ascospores deposited per leaf and proportion of ascospores discharged from pseudothecia deposited onto the leaves. The model has a relatively simple structure, and computations are based on few algorithms, which are implemented on an electronic data sheet of common use. Nevertheless, it preserves the accuracy of more complex physical models reasonably well. The model includes the effect of meteorological conditions and horticultural characteristics, and thus provides information for each type of orchard. Few input variables are required: wind speed and rainfall rate can be measured in standard meteorological stations; horticultural characteristics of the orchard can be determined for each type of orchard. The model produces conservative estimates of ascospore deposition, because it assumes a complete retention of the spores deposited by rainfall and does not consider either deposition on stems and flowers or the spatial distribution of plant surfaces. After further validation under orchard conditions, the model will be used to obtain better estimates of scab infection risk in current scab control strategies. [ABSTRACT FROM AUTHOR]

Diaporthe helianthi is the causal agent of a severe sunflower disease but, in Italy, disease outbreaks are sporadic with no significant losses. The present work investigates the role of meteorological conditions on the potential development of D. helianthi epidemics in Italy, using the French model Asphodel, which simulates the effect of air temperature, relative humidity and rainfall on ascospore maturation and dispersal, infection establishment, disease onset and severity during the period of host susceptibility. Meteorological data measured in eight stations distributed from north to south Italy, over a 5-year period (1995–99), was used as model input. Results showed that meteorological conditions in Italy are frequently favourable for D. helianthi infections on sunflower, and severe epidemics are possible. Therefore, climatic conditions are not a limiting factor for disease development in the Italian sunflower-growing areas. The lack of disease epidemics in Italy may be related to differences in the pathogen populations compared with the French ones. [ABSTRACT FROM AUTHOR]

A mechanistic model called PLASMO was developed earlier to simulate grapevine downy mildew ( Plasmopara viticola) and has been applied in several viticultural areas of Italy since 1988 by the collaboration of several research institutions of Firenze. In this study, a new simulation model based on fuzzy logic has been developed for the same structure (biological cycle of P. viticola). This approach allows classical quantitative information to be used together with qualitative information. Vague concepts can also be handled. Agrometeorological data is used, with an hourly time step, starting from budbreak to the end of the growing season. Air temperature, relative humidity, rainfall and leaf wetness are required. The simulated processes are the growth of grapevine leaf area and the main phases of the biological cycle of the pathogen: incubation, sporulation, germination, spore survival and inoculation. The main epidemiological outputs are timing of infection events and disease intensity. The performance of the model is evaluated and the mechanistic and fuzzy logic approaches are compared. [ABSTRACT FROM AUTHOR]

A dynamic simulation model for the risk of Fusarium head blight on wheat was elaborated based on systems analysis. The model calculates a daily infection risk based on sporulation, spore dispersal and infection of host tissue of the four main species causing the disease ( Gibberella zeae, Fusarium culmorum, Gibberella avenacea, Monographella nivalis). Spore yield and dispersal are calculated as functions of temperature, rainfall and relative humidity, while the main factors affecting the infection rate are temperature, wetness and the host growth stage. The model also calculates a risk for mycotoxin production by G. zeae and F. culmorum in the infected head tissue. First validations against field data, collected in some wheat-growing areas in northern Italy and not used in model elaboration, produced satisfactory results. [ABSTRACT FROM AUTHOR]

Shows the processing of weather data in the Agrometeorological Measuring Net of Rheinland-Pfalz (AMN RP) in Rhineland, Germany. Aims of AMN RP; Standards for agrometeorological measuring stations; Modules for the administration and transfer of data; Steps in the final assembly of the weather data; Actions carried out at the weather stations of the AMN RP.

Presents the different decision aids used for forecasting apple scab outbreaks in Rhineland, Germany. Life cycle of apple scab; Biological parameters for dispersal of the pathogen; Meteorological parameters for the establishment of scab infection; Forecasting of apple scab on the basis of climatological conditions and ascospore release simulation.

Outlines the construction of a revised system for collecting meteorological data from automatic agrometeorological stations and connecting the data to site specific information and biological information in the Norwegian Crop Research Institute in Norway. Main user groups of meteorological data; Measurements of the meteorological data; Use of World Wide Web for the dissemination of information.

Describes the status of the warning services for plant protection in Italy. Methods for preparing warnings; Role of forecasting model as a tool to support warning preparation; Interactions among warning services, research institutes, farmers' associations and agrometeorological networks; Contents of warnings; Methods to disseminate warnings to farmers; Warning services for selected crops.

For more than 10 years, a forecasting model for Cydia pomonella in apple orchards, worked out by the French NPPO, has described the development of emergence cohorts in spring, and followed their development and descent, through the whole agricultural season. In homogeneous areas, this tool, still under development, can be used to define periods for effective treatment (ovicides, larvicides, mating disruption) and the tolerance for codling moth levels in various situations. Alternation of insecticides is provided for, so as to manage resistance phenomena. The reliability of the model is based on rigorous continuous biological checks on the forecasts made by the algorithm, on scientific surveillance by INRA and on regular collaboration between the advisers involved and the agrometeorological services. The model forms part of a wider decisional scheme, or expert system, in which the fruit grower and his adviser provide observations on the changes which cannot be provided by the model: installation of hail protection nets, night lights, fruit spreading. More parameters will be incorporated in future and the geographical validity of the model will be extended, particularly to more northerly areas. [ABSTRACT FROM AUTHOR]