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AgroMetShell Manual








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    Brochure, flyer, fact-sheet
    Strengthening agro-climatic monitoring, analysis, communication and use of data and information for decision-making and food security in the agricultural sector in the Lao People’s Democratic Republic
    SAMIS PROJECT / Component 1
    2019
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    The leaflet present the activities of the first component of the project “Strengthening Agro-climatic Monitoring and Information Systems (SAMIS) to improve adaptation to climate change and food security in Lao PDR”. The component, implemented in strict collaboration with the Department of Meteorology and Hydrology (DMH), Ministry of Natural Resources and Environment, is titled “Strengthening agro-climatic monitoring, analysis, communication and use of data and information for decision making in agriculture and food security”. The activities includes the installment of agro-meteorological stations, the setup of a Laboratory for agro-meteorological analysis and instrument calibration, the implementation of the Laos Climate Services in Agriculture (LaCSA) system for modelling and distribution of climate services to farmers, and the facilitation of a process to ensure national Standard Operation Procedure are followed.
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    Document
    An Introduction to the Art of Agrometereological Crop Yield Forecasting Using Multiple Regression
    Crop Yeald Forecasting and Agrometeorology Sub-Project UTF/BGD/029, ASIRP/DAE
    2001
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    It is suggested that the approach used by FAO and a number of developing countries for crop forecasting at the national level strikes a good compromise between input requirements and ease of validation. The article thus describes the FAO crop modelling and forecasting philosophy.
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    Document
    FAO-WMO Roving Seminar on Crop-Yield Weather Modelling. Lecture Notes and Exercises 2017
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    Crop-yield weather modelling refers to the techniques which can be used operationally to determine the likely effect of weather on yields. Although the incidence of weather conditions on yields is well established, its quantitative assessment is not always straightforward: time series analyses of agricultural statistics show that the inter-annual variability1 of crop yields can be roughly subdivided into 3 components : trend, direct weather factors and indirect weather effects (pests, diseases, weed competition, etc.).

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