Multilevel systematic sampling to estimate total fruit number for yield forecasts

Multilevel systematic sampling to estimate total fruit number for yield forecasts Early forecasting of fruit orchard yield is important for market planning and for growers and exporters to plan labour, bins, storage and purchase of packing materials. Large variations in tree yield pose a challenge for accurate yield estimation. We evaluated a three-level systematic sampling procedure for unbiased estimation of fruit number for yield forecasts. In the Spring of 2009 we estimated the total number of fruit in several rows of each of 14 commercial fruit orchards growing apple (11 groves), kiwifruit (two groves), and table grapes (one grove) in central Chile. Survey times were 10–100 min for apples (depending on vigour), 85 min for the table grapes, and 85 and 150 min for the kiwifruit. During harvest in the Fall, the fruit were counted to obtain the true number. Yields ranged from lows of several thousand (grape bunches), to highs of more than 40 000 fruit (apples, kiwifruit). Absolute true errors (defined as the absolute difference between the estimate and the true value, divided by the true value) were less than 5% in six orchards, between 5 and 10% in a further five orchards and 13% in one orchard. In two apple orchards we obtained absolute true errors of about 20%. Error analysis based on systematic sub-sampling across each sampling stage was used to determine how to distribute sampling effort to achieve a total coefficient of error of 10%. We discuss the extension of the procedure for yield estimation at the full orchard scale for any target precision. Precision Agriculture Springer Journals

Multilevel systematic sampling to estimate total fruit number for yield forecasts

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Springer US
Copyright © 2011 by Springer Science+Business Media, LLC
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
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