Core Ideas Corn silage and grain yield monitors collect yield data of relevance to farmers. Evaluation of quality of yield monitor data is essential, especially for silage. A data cleaning protocol, consistent across fields, farms, and years, is needed. Semi‐automation is needed for quick and consistent processing of whole‐farm data. Yield monitor data are being used for a variety of purposes including conducting on‐farm studies, assessing nutrient balances, determining yield potential, and creating management zones. However, standardization of raw data processing is needed to obtain comparable data across fields, farms, and years. Our objective was to evaluate the impact of data cleaning protocols on corn (Zea mays L.) grain and silage yield data at the whole field (with and without headlands) and within field (soil map unit) scales. Corn silage data from 145 fields (three farms) and grain data from 88 fields (three farms) were processed. Comparisons were made to evaluate yields among three levels of cleaning: (i) none; (ii) automated cleaning (“Auto”) with filter settings derived for 10 fields per farm; and (iii) automated cleaning with manual inspection for unrepresentative patterns, after the automated cleaning step was completed (“Auto+”). The Auto+ cleaning process was conducted separately by three individuals to evaluate person‐to‐person differences. Spatial Management System software was used to read raw data and transfer to Ag Leader format. Yield Editor software was used to clean data (Auto and Auto+). Results showed the necessity of data cleaning, especially for corn silage. However, considering less than 5% deviation between methods at three spatial scales, the Auto and Auto+ cleaning resulted in similar output, as long as (i) each field or subfield included at least 100 harvester measurement points, and (ii) a moisture filter was applied for corn silage data.

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Use of agricultural equipment on corn (Zea mays L.) fields can contribute to soil compaction, especially on headland (HL) areas where wheel traffic is more intense than on non‐headland (NHL) areas. Better decisions about HL management (investment to improve production potential, discontinue, or plant another crop) can be made when the HL contribution to field and farm yield is known. We quantified yield differences between HL and NHL areas, at field‐, and at farm‐scale using corn grain and silage yield data from 4,145 fields (∼20,000 ha) across 63 farms in New York. Further, we quantified the yield impact of HL areas across years from four farms with 8–11 yr of yield records. Per field and farm “potential production gain” was determined as the potential gain in production if HL yield could be increased to equal NHL yield. Yields per hectare were 14% (grain) and 16% (silage) lower in the HL areas. Production gain per field averaged 4% for both grain and silage, reflecting the smaller proportion of HL per field. For about 70% of the fields potential production gain was <5%, vs. 5–20% potential production gain for about 25% of the fields. Small, low‐yielding fields had the highest potential production gain (>20%). Production gains across years ranged from 1 to 7% (grain) and 0.4 to 6% (silage), independent of growing season precipitation. We conclude potential production gains are sufficiently large to warrant headland management, but management should be directed to fields with the greatest potential for yield increase.

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