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3 edition of For[e]casting of crop yields from meteorological data in the EC countries found in the catalog.

For[e]casting of crop yields from meteorological data in the EC countries

H. Hanus

For[e]casting of crop yields from meteorological data in the EC countries

by H. Hanus

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  • 15 Currently reading

Published by Statistical Office of the European Communities in Luxembourg .
Written in English

    Places:
  • European Economic Community countries.,
  • European Economic Community countries
    • Subjects:
    • Agricultural estimating and reporting -- European Economic Community countries.,
    • Crops and climate -- European Economic Community countries.,
    • Crop yields -- European Economic Community countries -- Forecasting -- Statistical methods.

    • Edition Notes

      Bibliography: p. 33.

      Other titlesForcasting of crop yields from meteorological data in the EC countries.
      StatementH. Hanus.
      SeriesAgricultural statistical studies ;, no. 21
      Classifications
      LC ClassificationsS494.5.E8 H36 1978
      The Physical Object
      Paginationvii, 54 p. :
      Number of Pages54
      ID Numbers
      Open LibraryOL2959983M
      ISBN 109282505359
      LC Control Number84203752

        Moreover, these reference yields will be needed for each crop each year due to the effect of weather conditions, pests, etc. on crop yields. . Measurement. The units by which the yield of a crop is usually measured today are kilograms per hectare or bushels per acre.. Long-term cereal yields in the United Kingdom were some kg/ha in Medieval times, jumping to kg/ha in the Industrial Revolution, and jumping again to kg/ha in the Green Revolution. Each technological advance increasing the crop yield also reduces the.

      Weather Anomalies, Crop Yields, and based on data from Table QT-P30 of the Census summary file 3 (). 2. requires that US production losses are offset by increases in other countries, e.g., Canada or Northern Russia to keep price levels constant. In case there is no such offset, we include. different from that experienced during the years used to construct the yield model. Although the use of weather data to forecast and estimate crop yields has been investigated, no generally-applicable technique for incorporating the effects of weather into the objective yield .

      The crop yield gap is estimated as the difference between average simulated yield potential (Yp, crop production without water stress) or water-limited yield potential (Yw, rainfed crop production with water stress) minus the average on-farm actual the GYGA project, the finest spatial resolution at which yield gap is determined is at the reference weather station (RWS) buffer as. I suggest use of CropSyst to predict crop yields by generating weather conditions for a number of points using ClimGen. However, a lot of data is to be collected for sample points as different.


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For[e]casting of crop yields from meteorological data in the EC countries by H. Hanus Download PDF EPUB FB2

For[e]casting of crop yields from meteorological data in the EC countries. Luxembourg: Statistical Office of the European Communities, (OCoLC) Material Type: Government publication, International government publication: Document Type: Book: All Authors /. Crop simulation models can be used to estimate impact of current and future climates on crop yields and food security, but require long-term historical daily weather data to obtain robust simulations.

In many regions where crops are grown, daily weather data are not by: In the chart we see the average yields in key cereal crops (wheat, barley and oats) in Chile from This figure is based on the combination of two datasets: data from is based on figures in Engler and del Pozo (), which has been combined with UN Food and Agricultural Organization statistics from onwards.

2 Also shown on this figure are specific technological. The successful application of weather and climate information needs to integrate three components: data, analysis and users. Therefore, the ultimate goal of any application is to serve the needs of the users.

A solid foundation of data is a prerequisite for successful agricultural meteorological applications (see Chapters 1 and 2).File Size: KB. Crop monitoring and forecasting: Analysis of meteorological and climatic data allows to provide near real-time information about the crop state, in quality and quantity, with the possibility of early warning on alarm/alert situations so that timely interventions can be planned and undertaken.

developing countries alike; the applications of crop weather modelling cover the spectrum of scales form the farmer’s field to entire countries, from individuals to governments.

The majority of current model uses involve some kind of forecast of yields based essentially on weather and management.

In most cases, the purpose is a better. Crop yield forecasts and crop production estimates are necessary at EU and Member State level to provide the EU’s Common Agricultural Policy (CAP) decision makers with timely information for rapid decision-making during the growing season.

Estimates of crop production are also useful in relation to trade, development policies and humanitarian assistance linked to food security. I.e. yield statistics of the last five years are taken and ranked. The trimmed average is the average of the three centre years. This trimmed average will be used as yield forecast when all other methods (prediction models such as CGMS level 3, trend analysis, scenario analysis) do not lead to satisfactory results or when there is a lack of.

The JRC developed its Crop Yield Forecasting System (MCYFS) in The system monitors crop growth, including the short-term effects of meteorological events on crop production, and provides seasonal yield forecasts of key European crops.

The weather monitoring component was the first part that became operational. The resource consists of consolidated and coherent future daily weather data for Europe on a 25x25 km grid designed for crop modelling. The dataset is based on three time horizons (, and ), each represented by 30 synthetic years created using the weather generator ClimGen and the statistical distribution of meteorological variables.

Note: Yields are forecast for crops with more than ha per country; figures are rounded to kg Sources: data come from EUROSTAT Eurobase (last update: 03/10/) and EES (last update: 20/09/) yields come from MARS CROP YIELD FORECASTING SYSTEM (CGMS output up to 20/10/) CROP YIELD FORECAST.

VOL No (). Palm and Dagnelie () fitted various time trend functions to national yield series ( -1) of several crops for 9 EU member states. Regressions were executed for the period prior to and a forecast for was made.

This procedure was repeated for successive years up till 53 regions where the major crops are grown are not necessarily the same regions that are studied in 54 the literature.

Supplementary Table 1 presents data on area harvested fortogether with the 55 percentage of data points in the meta-analysis (by crop and region) that come from those countries.

yield is estimated by dividing crop production by crop area. Under this approach, crop production estimates are obtained using farmers recall/diary method. The former approach is widely practiced in most of the countries. Crop area Estimated crop area is one of the two major components of estimated crop production.

To estimate crop production. Highlights A protocol is proposed for estimating crop yield potential at national scales. Robust estimates of rainfed yield potential required >15 years of weather data. Yield potential of crops in diverse systems was highly sensitive to sowing date.

Diverse cropping systems required >40% coverage of crop area for stable estimates. Crop yield plateaus were observed at 75–85% of. insecure countries and globally. The candidate will contribute to the work of the group in the use of meteorological and Earth Observation (EO) data for crop yield forecasting in Africa.

In particular, the candidate will support the Food and Nutrition project in the development and testing of machine learning (ML) methods and artificial. Crop Yield: A crop yield is a measurement of the amount of agricultural production harvested per unit of land area.

Crop yield is the measurement most. R.H. Bromilow, in Encyclopedia of Soils in the Environment, Introduction. Pesticides are used worldwide and increase crop yield on average by 30% as well as improving crop quality. Modern organic (i.e., carbon-containing) pesticides have been used widely since the late s, and so there is over 50 years of experience of their behavior in the environment and their impact upon it.

sources, can also be used as primary data sources. • Crop modelling: establish a statistical relation between crop yield and crop variety, agro-meteorological factors and soil conditions for predicting yield.

• Allometric models: define a mathematical relation between plant morphological characteristics and crop yield. Based on August 1 conditions, Nebraska's corn production is forecast at a record billion bushels, up 1% from last year's production.

Soybean production in Nebraska is forecast at million bushels, up 8% from last year. with a predefined “nutrients” data store. hese compared results are supplied to controller 3 wherein the combination of the above results and the predefined data set present in the crop data store is compared.

Finally, the results are displayed in the form of bar graphs along with accuracy percentage. Fig Modular Diagram.productivity. Favorable weather conditions for dryland crop production, including a proper amount of heat and rainfall during the growing season, are critical factors de-termining yield outcomes.

There is a large body of existing literature on estimating the weather impacts on crop yields. Methodologies used in most of these studies, how.This is the motive to develop this system. Based on crop weather studies, crop yield forecast models are prepared for estimating yield much before actual harvest of the crops.

By use of empirical statistical models using correlation and regression technique crops yield .