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

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

Combining Crop Models and Remote Sensing for Yield Prediction: Concepts, Applications and Challenges for Heterogeneous Smallholder Environments

JRC and CCAFS jointly organized a workshop on June 13-14, 2012 in Ispra, Italy with the aim to advance the state-of-knowledge of data assimilation for crop yield forecasting in general, to address challenges and needs for successful applications of data assimilation in forecasting crop yields in heterogeneous, smallholder environments, and to enhance collaboration and exchange of knowledge among data assimilation and crop forecasting groups. The workshop showed that advances made in crop science are widely applicable to crop forecasting. The presentations of the participants approached the challenge from many sides, leading to ideas for improvement that can be implemented in real-time, operational crop yield forecasting. When applied, this knowledge has the potential to benefit the livelihoods of smallholder farmers in the developing world.JRC.H.4-Monitoring Agricultural Resource

Biophysics and vegetation cover change: A process-based evaluation framework for confronting land surface models with satellite observations

This is the final version. Available on open access from Copernicus Publications via the DOI in this recordLand use and land cover change (LULCC) alter the biophysical properties of the Earth's surface. The associated changes in vegetation cover can perturb the local surface energy balance, which in turn can affect the local climate. The sign and magnitude of this change in climate depends on the specific vegetation transition, its timing and its location, as well as on the background climate. Land surface models (LSMs) can be used to simulate such land-climate interactions and study their impact in past and future climates, but their capacity to model biophysical effects accurately across the globe remain unclear due to the complexity of the phenomena. Here we present a framework to evaluate the performance of such models with respect to a dedicated dataset derived from satellite remote sensing observations. Idealized simulations from four LSMs (JULES, ORCHIDEE, JSBACH and CLM) are combined with satellite observations to analyse the changes in radiative and turbulent fluxes caused by 15 specific vegetation cover transitions across geographic, seasonal and climatic gradients. The seasonal variation in net radiation associated with land cover change is the process that models capture best, whereas LSMs perform poorly when simulating spatial and climatic gradients of variation in latent, sensible and ground heat fluxes induced by land cover transitions. We expect that this analysis will help identify model limitations and prioritize efforts in model development as well as inform where consensus between model and observations is already met, ultimately helping to improve the robustness and consistency of model simulations to better inform land-based mitigation and adaptation policies.The study was funded by the FP7 LUC4C project (grant no. 603542

MARS Bulletin Vol. 20, No. 11

BulletinJRC.H.4-Monitoring Agricultural Resource

Assessing agriculture vulnerabilities for the design of effective measures for adaptation to climate change (AVEMAC project)

This final report of the AVEMAC study presents an assessment of the potential vulnerability of European agriculture to changing climatic conditions in the coming decades. The analysis is based on weather data generated from two contrasting realizations of the A1B emission scenario of the Intergovernmental Panel on Climate Change (IPCC) for the time horizons 2020 and 2030. These two realizations (obtained from two different general circulation models, downscaled using regional climate models and biascorrected) represent the warmest and coldest realizations of the A1B scenario over Europe as estimated by the ENSEMBLES project. The future weather data fed two types of analyses. The first analysis consisted in computing static agro-meteorological indicators as proxies of potential vulnerabilities of agricultural systems, expressed as changes in the classification of agricultural areas in Europe under climate constraints. The second analysis relied on biophysical modelling to characterize crop specific plant responses derived from crop growth simulations at different production levels (potential production, water-limited production, and production limited by diseases). Assessing the importance of vulnerability to climate change requires not only the localisation of relative yield changes, but also the analysis of the impact of the change on the acreage affected. Consequently, the simulation results of the impact assessment on crops were further processed to estimate the potential changes in production at sub-national (NUTS2) level. This was achieved by relating the simulation results to farm typologies in order to identify which types of systems are likely to be affected by reductions in production. The analyses of this study must be considered as a first step only, since they have neither included adaptation strategies that the farmer can take in response to changes in climate, nor a bio-economic evaluation of estimated vulnerabilities. Therefore, the main aspects and the requirements for a possible future integrated analysis at EU27 level to address climate change and agriculture with the target of providing policy support are also presented in this report. Eventually the results of this study shall help the formulation of appropriate policy options and the development of adequate policy instruments to support the adaptation to climate change of the EU agricultural sector.JRC.H.4-Monitoring Agricultural Resource

MARS Bulletin Vol.21 No. 13

MARS Bulletin Vol.21 No. 13 CAMPAIGN REVIEWJRC.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. 10

MARS Bulletin Vol.21 No. 10JRC.H.4-Monitoring Agricultural Resource

Identification of multiple root disease resistant wheat germplasm against cereal nematodes and dryland root rot and their validation in regions of economic importance

História da literatura portuguesa coordenada por Giulia Lanciani - primeiras páginas de um total pp. 7-108)História literária do século XVIII portuguêsGoverno de Portuga