Firenze University Press - Università degli Studi di Firenze - Agricultural Meteorology and Climatology is an introductory textbook for meteorology and climatology courses at faculties of agriculture and for agrometeorology and agroclimatology courses at faculties whose curricula include these subjects. Additionally, this book may be a useful source of information for practicing agronomists and all those interested in different aspects of weather and climate impacts on agriculture. In times when scientific knowledge and practical experience increase exponentially, it is not a simple matter to prepare a textbook. Therefore we decided not to constrain Agricultural Meteorology and Climatology by its binding pages. Only a part of it is a conventional textbook. The other part includes numerical examples (easy-to-edit worksheets) and recommended additional reading available on-line in digital form. To keep the reader's attention, the book is divided into three sections: Basics, Applications and Agrometeorological Measurements with Numerical Examples.

The radiation transfer within the forest canopy plays crucial role in energy balance and turbulent transfer processes. Objective of this study is to suggest a new relation for vertical profile of photosynthetically active radiation (PAR) in case of horizontally uniform forest canopy. It is based on (i) the Lambert‐Beer law relationship and (ii) new parameterization of leaf area density (LAD) profile. We have supposed that absorption coefficient μ varies with height and depends on LAD distribution. To check validity of the relation proposed, we have compared calculated values with the observations using data sets assimilated during Anglo‐Brazilian Amazonian Climate Observation Study experiment at two observational sites located in Reserva Jaru and Reserva Ducke (Brazil) with different types of forest. Among all available measurements, 615 profiles observed between 08 and 18 local mean time for 72 days at 2 locations were selected. For comparison study, two more profiles based on constant‐ and variable‐LAD approximation were introduced. Obtained results indicate that suggested relation: (i) well reproduces PAR profile within the forest in comparison with observations and (ii) shows better agreement with observations in comparison with two other profiles used in this study.

A probabilistic crop forecast based on ensembles of crop model output estimates, presented here, offers an ensemble of possible realizations and probabilistic forecasts of green water components, crop yield and green water footprints (WFs) on seasonal scales for selected summer crops. The present paper presents results of an ongoing study related to the application of ensemble forecasting concepts in crop production. Seasonal forecasting of crop water use indicators (evapotranspiration (ET), water productivity, green WF) and yield of rainfed summer crops (maize, spring barley and sunflower), was performed using the AquaCrop model and ensemble weather forecast, provided by The European Centre for Medium-range Weather Forecast. The ensemble of estimates obtained was tested with observation-based simulations to assess the ability of seasonal weather forecasts to ensure that accuracy of the simulation results was the same as for those obtained using observed weather data. Best results are obtained for ensemble forecast for yield, ET, water productivity and green WF for sunflower in Novi Sad (Serbia) and maize in Groß-Enzersdorf (Austria) – average root mean square error (2006–2014) was <10% of observation-based values of selected variables. For variables yielding a probability distribution, capacity to reflect the distribution from which their outcomes will be drawn was tested using an Ignorance score. Average Ignorance score, for all locations, crops and variables varied from 1.49 (spring barley ET in Groß-Enzersdorf) to 3.35 (sunflower water productivity in Groß-Enzersdorf).

This study was designed to better understand vegetation’s impact on air maximum (T_max), minimum (T_min), and daily temperature range (DTR), as well as seasonality and variability. We selected a flat, under synoptic-scale, northern Serbian region with an operational network of automated weather stations (AWS) for the study. Data were collected directly from the eighteen AWSs placed in the orchard canopy during 2013–2018. Meteorological data, plant phenological data in the form of the BBCH scale, and orchards’ soil characteristics data were collected. Environmental factors influencing the temperature were classified as static (slow or unchangeable) and dynamic (fast-changing). The impact of both factors on maximum, minimum, and daily temperature range and its variability were analyzed. Results show that static factors (like soil texture) affect the annual variation of T_max, T_min, and DTR rather than its variability over the season. The dynamic factors, mainly coming from the plant’s phenology, substantially affected the seasonal variability of these variables. Studies like this suffer from missing data and sparse spatial coverage by the AWS network. Therefore, the alternatives of orchard micrometeorological data, nearest climatological station, and ERA5-Land reanalysis data are tested. Both data sets showcased limitations in their applicability, while reanalysis data deviated more from the in-situ measurements, both seasonally and regionally.

A probabilistic crop forecast based on ensembles of crop model output (CMO) estimates offers a myriad of possible realizations and probabilistic forecasts of green water components (precipitation and evapotranspiration), crop yields and green water footprints (GWFs) on monthly or seasonal scales. The present paper presents part of the results of an ongoing study related to the application of ensemble forecasting concepts for agricultural production. The methodology used to produce the ensemble CMO using the ensemble seasonal weather forecasts as the crop model input meteorological data without the perturbation of initial soil or crop conditions is presented and tested for accuracy, as are its results. The selected case study is for winter wheat growth in Austria and Serbia during the 2006–2014 period modelled with the SIRIUS crop model. The historical seasonal forecasts for a 6-month period (1 March-31 August) were collected for the period 2006–2014 and were assimilated from the European Centre for Medium-range Weather Forecast and the Meteorological Archival and Retrieval System. The seasonal ensemble forecasting results obtained for winter wheat phenology dynamics, yield and GWF showed a narrow range of estimates. These results indicate that the use of seasonal weather forecasting in agriculture and its applications for probabilistic crop forecasting can optimize field operations (e.g., soil cultivation, plant protection, fertilizing, irrigation) and takes advantage of the predictions of crop development and yield a few weeks or months in advance.

Vegetation is a climate modifier: It is a primary modifier, such as the Amazon rain forest, or secondary modifier, such as the agricultural fields of Pannonian lowlands in Central Europe. At periods of winter crop spring renewal and the start of the orchard growing season, enhanced evapotranspiration shifts energy balance partitions from sensible toward latent heat flux. This surface flux alteration converges into the boundary layer, and it can be detected in the daily variations of air temperature and humidity as well as daily temperature range records. The time series of micrometeorological measurements and phenological observations in dominant plant canopies conducted by Forecasting and Reporting Service for Plant Protection of the Republic of Serbia (PIS) are explored to select indices that best record the signatures of plant growth stages in temperature and humidity daily variations. From the timing of extreme values and inflection points of relative humidity (R1 and R2) and normalized daily temperature range (DTR/T_d), we identified the following stages: (a) start of flowering (orchard)/spring start of the growing season (crop), (b) full bloom (orchard)/development (crop), (c) maximum LAI reached/yield formation (orchard and crop), and (d) start of dormancy (orchard)/leaf drying (crop). The average day of year (DOY) for dominant plants corresponds to the timing obtained from climatological time series recorded on a representative climate station.