CGMS Version 9.2 - User Manual and Technical Documentation

Detailed information on the installation and use of the new CGMS version 9.2JRC.G.3-Monitoring agricultural resource

ADAPTATION OF WOFOST MODEL FROM CGMS TO ROMANIAN CONDITIONS

This preliminary study is an inventory of the main resources and difficulties in adaptation of the Crop Growth Monitoring System (CGMS) used by Agri4cast unit of IPSC from Joint Research Centre (JRC) - Ispra of European Commission to conditions of Romania.In contrast with the original model calibrated mainly with statistical average yields at national level, for local calibration of the model the statistical yields at lower administrative units (macroregion or county) must be used. In addition, for winter crops, the start of simulation in the new system will be in the autumn of the previous year. The start of simulation (and emergence day) in the genuine system is 1st of January of the current year and the existing calibration was meant to provide a compensation system for this technical physiological delay.Proposed approach provides a better initialisation of the water balance (emergence occurs after start of simulation), as well as a better account for impact of wintering conditions, but obviously a new calibration for all cultivar dependent parameters is necessary. For the preoperational run, the localized model will use the weather data available till the last day available and the missing data from the rest of the year will be replaced either by the daily values of the long term averages or by the values from a year considered similar with the current one.Proposed adaptations permit a better use of information available on local scale and the localized model may be the core of a regional system for crop monitoring and in the same degree as the original system it can be used as tool for specific researches, such as studying the impact of climate changes

Extraction of phenological parameters from temporally smothed vegetation indices

Within the MARS Crop Yield Forecasting System (MCYFS; Royer and Genovese, 2004) of the European Commission vegetation indicators like NDVI, SAVI and fAPAR are operationally derived for daily, decadal and monthly time steps. Besides low resolution sensors as SPOT-VGT and NOAA-AVHRR, medium resolution data from TERRA/AQUA-MODIS or ENVISAT-MERIS are used at pan-European level. In case of available time-series, esp. NOAA AVHRR (since 1981) and SPOT-VGT (since 1998) difference values of the indicators (e.g. relative or absolute differences) and frequency analysis of the indicators (e.g. position in historical range or distribution) are calculated. The exploitation of the data is performed at full resolution, at grid level of the MCYFS or regional unmixed means (C-indicators) are used. Therefore a database has been set-up in order to provide the indicators based on a weighted average for each CORINE land cover class within an area of interest. The study aims to develop a strategy for an optimal use of the different sensors and thus derived indicators at different aggregation levels for the ingestion into the MCYFS. As a first step smoothing algorithms have to be applied to the time series to diminish noise effects. Therefore, existing methods as simple sliding windows, piecewise linear regression or fitting of polynomial functions are employed and compared. Thereafter the time-series analysis is performed with the aim to establish relationships between indicators profile features and the crop phenology.JRC.DDG.H.4-Monitoring agricultural resource

Enhanced processing of 1-km spatial resolution fAPAR time series for sugarcane yield forecasting and monitoring

A processing of remotely-sensed Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) time series at 1-km spatial resolution is established to estimate sugarcane yield over the state of São Paulo, Brazil. It includes selecting adequate time series according to the signal spatial purity, using thermal time instead of calendar time and smoothing temporally the irregularly sampled observations. A systematic construction of various metrics and their capacity to predict yield is explored to identify the best performance, and see how timely the yield forecast can be made. The resulting dataset not only reveals a strong spatio-temporal structure, but is also capable of detecting both absolute changes in biomass accumulation and changes in its inter-annual variability. Sugarcane yield can thus be estimated with a RMSE of 1.5 t/ha (or 2%) without taking into account the strong linear trend in yield increase witnessed in the past decade. Including the trend reduces the error to 0.6 t/ha, correctly predicting whether the yield in a given year is above or below the trend in 90% of cases. The methodological framework presented here could be applied beyond the specific case of sugarcane in São Paulo, namely to other crops in other agro-ecological landscapes, to enhance current systems for monitoring agriculture or forecasting yield using remote sensing.JRC.H.4-Monitoring Agricultural Resource

CLIMA: a weather generator framework

Abstract: Weather generators (WG) can be defined as collections of models to estimate site specific weather data and derived variables. Their use spans from providing inputs to a variety of biophysical models to deriving weather indices. Also, using either global circulation models or local area models inputs, sets of parameters calculated from long term weather series specific to a site can be modified to reproduce via WG synthetic series representing climate change scenarios. Finally, models implemented in WG are used for estimating missing data and to perform quality control on data collected from sensors in weather stations. The models implemented in WG vary from purely empirical to physically based. There are several models to either estimate or to generate each weather variable, with different input requirements. New models are continuously being proposed, and, whether some models to estimate specific variables are commonly accepted as reference methods, the lack of some inputs requires at times using alternate approaches. Currently available WG are applications which implement a predefined set of modelling options, in software implementations which do not allow for independent extensions by third parties. The CLIMA weather generator is a component based application which consist of a set of reusable graphical user interface (GUI) components, and of a set of extensible model components. The latter are subdivided into six namespaces to estimate variables related to air temperature, rainfall, solar radiation, evapotranspiration, wind, and leaf wetness. The time characteristic of the variables estimated varies from a day to ten minutes. Another library allows estimating climatic indices from one year of daily data at the time. The current implementation consists of a total of more than 300 models. Components are usable either via the CLIMA GUI, or via custom developed applications in a client-server architecture. The architecture of components is based on the composite and strategy as keystone design patterns. Models are implemented as single approaches (simple strategies), and as composite models (composite strategies) which are associated to models of finer granularity. Another type of model unit is represented by context strategies, which implement logic to select within associated models. Finally, the GUI allows building composite models which can be saved as libraries, to be reused both within CLIMA for weather series generation, or independently by other applications. The components are implemented as .NET libraries. They implement the test of pre- and post-conditions, and a scalable tracing via .NET listeners. All variables and parameters are documented via a description, units, default, maximum, and minimum values. Components are extensible: new models can be added independently by third parties and detected by the CLIMA application, which can also use them for data generation via building new composite libraries. Each component is made available via a software development kit which includes the code of two sample projects, either to extend or to reuse the component. CLIMA and its model components are freely available for reuse in no-profit applications.JRC.DG.G.3-Monitoring agricultural resource

A dataset of future daily weather data for crop modelling over Europe derived from climate change scenarios

Coupled atmosphere-ocean general circulation models (AOGCMs, or just GCMs for short) simulate different realizations of possible future climates at global scale under contrasting scenarios of greenhouse gases emissions. While these datasets provide several meteorological variables as output, but two of the most important ones are air temperature at the Earth's surface and daily precipitation. GCMs outputs are spatially downscaled using different methodologies, but it is accepted that such data require further processing to be used in impact models, and particularly for crop simulation models. Daily values of solar radiation, wind, air humidity, and, at times, rainfall may have values which are not realistic, and/or the daily record of data may contain values of meteorological variables which are totally uncorrelated. Crop models are deterministic, but they are typicallyrun in a stochastic fashion by using a sample of possible weather time series that can be generated using stochastic weather generators. With their random variability, these multiple years of weather data can represent the time horizon of interest. GCMs estimate climate dynamics, hence providing unique time series for a given emission scenario; the multiplicity of years to evaluate a given time horizon is consequently not available from such outputs. Furthermore, if the time horizons of interest are very close (e.g. 2020 and 2030), averaging only the non-overlapping years of the GCM weather variables time series may not adequately represent the time horizon; this may lead to apparent inversions of trends, creating artefacts also in the impact model simulations. This paper presents a database of consolidated and coherent future daily weather data covering Europe with a 25 km grid, which is adequate for crop modelling in the near-future. Climate data are derived from the ENSEMBLES downscaling of the HadCM3, ECHAM5, and ETHZ realizations of the IPCC A1B emission scenario, using for HadCM3 two different regional models for downscaling. Solar radiation, wind and relative air humidity weather variables where either estimated or collected from historical series, and derived variables reference evapotranspiration and vapour pressure deficit were estimated from other variables, ensuring consistency within daily records. Synthetic time series data were also generated using the weather generator ClimGen. All data are made available upon request to the European Commission Joint Research Centre's MARS unit.JRC.H.7-Climate Risk Managemen

MARS Bulletin Vol 17 No 1

The annexed document is the template for the bulletin that will be issued on the 10th March. This bulletin covers meteorological analysis and crop yield forecasts for the period 21 November 2008 - 28 February 2009 (since the day after the last covered period, to the last day of the decade before)JRC.G.3-Monitoring agricultural resource

MARS Bulletin Vol. 20, No. 11

BulletinJRC.H.4-Monitoring Agricultural Resource

MARS Bulletin Vol.21 No. 11

MARS Bulletin Vol.21 No. 11JRC.H.4-Monitoring Agricultural Resource

MARS Bulletin Vol.21 No. 13

MARS Bulletin Vol.21 No. 13 CAMPAIGN REVIEWJRC.H.4-Monitoring Agricultural Resource