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YIELD PREDICTIONS FOR 1965 WHEAT, OATS, AND BARLEY IN MANITOBA 1
Author(s) -
YEH MARTIN H.
Publication year - 1965
Publication title -
canadian journal of agricultural economics/revue canadienne d'agroeconomie
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.505
H-Index - 37
eISSN - 1744-7976
pISSN - 0008-3976
DOI - 10.1111/j.1744-7976.1965.tb02363.x
Subject(s) - yield (engineering) , seeding , grain yield , environmental science , aggregate (composite) , margin (machine learning) , agricultural engineering , work (physics) , winter wheat , mathematics , resource (disambiguation) , statistics , agronomy , computer science , engineering , mechanical engineering , computer network , materials science , machine learning , metallurgy , composite material , biology
This is an aggregate study based on the time series data, 1921 to 1964, covering the entire Province of Manitoba on a district‐by‐district basis. The study has attempted to include three parts. First it has examined grain yield fluctuations and has selected the most significant factors influencing yields. These include resource inputs, technology, weather and chance. Second, the study has developed a method by which the influence of each factor on grain yields may be measured. Third, it has shown that grain yields can be forecast within an acceptable margin of error, based on the past pattern of weather cycles, the current trend of technical advance and a monthly assessment of rainfall and temperature during seeding, growing and harvesting periods in any given year. The findings from this study show that the predicted yields for each of the grain crops were reasonably close to the actual yields, demonstrating an adequate model used and the efficiency of the analytical technique employed. Additional work on this topic is necessary, firstly to explore other possible methods of analysis and, secondly, to extend the analysis to cover the other two prairie provinces on a district‐by‐district basis, in order to be able to provide more information regarding the crop predictions which may be useful to farmers, distributors, businessmen, and governments involved in decision making.