Proceedings of the Symposium on Global and Environmental Monitoring: Proceedings of the Symposium on Global and Environmental Monitoring

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Multivolume work

Persistent identifier:: 856665355

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts ; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856665355

Language:: English

Document type:: Multivolume work

Volume

Persistent identifier:: 856669164

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Scope:: XIV, 912 Seiten

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856669164

Illustration:: Illustrationen, Diagramme, Karten

Signature of the source:: ZS 312(28,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [WA-2 AGRICULTURE]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: CROP YIELD FORECASTING--STATE OF THE ART. Galen F. Hart

Document type:: Multivolume work

Structure type:: Chapter

187 CROP YIELD FORECASTING--STATE OF THE ART Galen F. Hart Agricultural Research Service U.S. Department of Agriculture Beltsville, Maryland ABSTRACT Forecasting grain crop yield or production has been routinely done by government and agri-business for decades, particularly in the United States. The United States commodity markets are quite sensitive to changes in yield and production prospects as reflected in sometimes dramatic shifts in future grain prices. Methods of forecasting vary from expert opinion to detailed mathematical models of plant growth processes. This full range of sophistication and approach is found to have value depending on the decisions to be made. Three broad categories of yield forecast modelling are described. The traditional method uses expert opinion, trends and comparative data to forecast the future. A second category uses plant measurements. Samples are located in selected producer fields and measurements, both destructive and non-destruetive, are made as the season progresses concluding with crop cutting just prior to producer harvest. A five year history of these relationships is regressed to create models for using current season measurements to project end - of-season yield. Lastly, there are models that attempt to mathematically represent the plant growth process including grain development. Extensive and sometimes complex mathematical structures are used in this modelling. Initial conditions, environmental information, current data (e.g. temperature and moisture) and state variables are combined to simulate daily plant growth. Then, by assuming a set of future conditions, these models are used to forecast yield. Refinements to improve performance are being made in all the yield modeling categories. Examples are given in the operational use of models. An intriguing area of research is the incorporation of spectral data in models and a review is provided of the current status of these efforts. KEY WORDS: Vegetation Indices, Estimation, Production Variability INTRODUCTION Over the past decade, there have been no break throughs in the ability to forecast crop grain yield either at the field level or for large areas. Clearly, however, progress has been made in understanding what causes change in yield potential. Operational large area yield forecasting procedures have changed little except for refinement. However, some notable events have occurred. The demand for more accurate forecasts and estimates of grain production worldwide has increased both from the economic and humanitarian points of view. Trade balance and human rights are matters of concern. Inexpensive personal computing has allowed more complex modelling to become a tool for individual decision making rather than a power reserved only for well financed agri-business. This paper represents what is believed to be state-of-the-art in the United States. Since most operational and proprietary systems are not documented in the literature, this paper cannot fully represent the breadth of the subj ect. OPERATIONAL STATE OF THE ART The National Agricultural Statistical Service, U.S. Department of Agriculture - Domestic Forecasting and Estimating Program The yield forecasting and estimating Program of the National Agricultural Statistics Service (NASS) is by far the largest program of its kind (USDA, 1983). It was developed primarily to replace or supplement a very large opinion survey system that had been used by the Service for decades. Monthly growing season forecasts by this organization are used inconjunction with other information by the commodity trade business to establish future prices for grains. An objective quantitative approach would provide estimates and forecasts of known precision. Such a forecasting and estimating program started in the early 1960's and now includes corn, cotton, potatoes, rice, soybeans and three categories of wheat (winter, spring and durum). Twenty-seven states are included in the program and over 9,000 crop samples are measured. The information collected is objective because trained field personnel randomly locate, in farmers' fields, units to be sampled. Field personnel then count and measure attributes of the growing crop. No survey opinions are used. In the states involved in the surveys, the total land area in a crop has an opportunity to be selected in the sample for measurement. This is

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