Assessment of yield monitoring equipment for dry matter and yield of corn silage and alfalfa/grass

Assessment of yield monitoring equipment for dry matter and yield of corn silage and alfalfa/grass Whole farm evaluations have shown that accurate yield data are difficult to collect for alfalfa (Medicago sativa L.) and grass mixtures and corn (Zea mays L.) silage fields. Additionally, on-farm research, a recommended tool for adaptive management, is hindered by lack of practical ways to collect yield data. Recently, forage yield monitors have become available on self-propelled forage harvesters (SPFHs), but precision and accuracy of this technology are unknown. The objective of this project was to evaluate accuracy of yield and moisture sensing components of forage yield monitors for use in alfalfa/grass and corn silage. Moisture content, mass flow weights, total area harvested and total dry yield per hectare were measured on 11 farms in 2013; forage samples were collected for truck loads, analyzed for dry matter content, and compared to monitor-registered dry matter. Truck weights were used to compare monitor-derived yield to actual yield on two farms for alfalfa/grass and three farms for corn silage. Moisture sensors estimated crop moisture content within 3.7 % DM for alfalfa/grass and 3.0 % DM for corn silage of the oven dry value. Flow sensors estimated alfalfa/grass yield to ±0.5 and ±1.1 Mg DM/ha for corn silage. When calibrations are done regularly, forage yield monitors can provide an accurate and precise measure of dry yield for adaptive management. It is concluded that this technology can be used when plots are large and large treatment-driven yield differences are expected. Precision Agriculture Springer Journals

Assessment of yield monitoring equipment for dry matter and yield of corn silage and alfalfa/grass

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Springer US
Copyright © 2016 by Springer Science+Business Media New York
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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