Harvester-mounted yield monitor systems are increasingly used to document corn (Zea mays L.) yield. The three most commonly used spatial estimation methods to convert point data gathered with yield monitors to regular, grid-based, raster maps include nearest neighbor (NN), inverse distance weighting (IDW) and kriging. Seven spatial estimation methods (NN, IDW using 10, 20, 30 and all data points and kriging with exponential and Matérn covariance functions) were evaluated to determine the method that most accurately captures intra-field spatial variability of corn silage and corn grain yield in New York. Yield monitor data from two dairy farms and two grain operations were cleaned using Yield Editor prior to spatial analyses. The dataset included 7–10 years of data per farm for a combined 7484 ha (245 fields) of silage and 6971 ha (253 fields) of grain. Data were split into training (80%) and cross-validation datasets (remaining 20% of the data). Normalized root mean squared error (NRMSE) was used to evaluate the accuracy of the spatial estimation methods. Kriging with the Matérn covariance function resulted in the most accurate corn silage and grain yield raster maps both at the farm and field level. There were statistically significant differences in NRMSE between kriging with the Matérn isotropic covariance function and all other models for both corn silage and grain, regardless of field size, year when data were obtained or farm that supplied the data. These results are beneficial to ensure accurate and precise spatial mapping of yield products toward optimized corn growth management.

On-farm experimentation (OFE) allows farmers to improve crop management over time. The randomized complete blocks design (RCBD) with field-length strips as individual plots is commonly used, but it requires advanced planning and has limited statistical power when only three to four replications are implemented. Harvester-mounted yield monitor systems generate high resolution data (1-s intervals), allowing for development of more meaningful, easily implementable OFE designs. Here we explored statistical frameworks to quantify the effect of a single treatment strip using georeferenced yield monitor data and yield stability-based management zones. Nitrogen-rich single treatment strips per field were implemented in 2018 and 2019 on three fields each on two farms in central New York. Least squares and generalized least squares approaches were evaluated for estimating treatment effects (assuming independence) versus spatial covariance for estimating standard errors. The analysis showed that estimates of treatment effects using the generalized least squares approach are unstable due to over-emphasis on certain data points, while assuming independence leads to underestimation of standard errors. We concluded that the least squares approach should be used to estimate treatment effects, while spatial covariance should be assumed when estimating standard errors for evaluation of zone-based treatment effects using the single-strip spatial evaluation approach.

Harvester-mounted yield monitor sensors are expensive and require calibration and data cleaning. Therefore, we evaluated six vegetation indices (VI) from unmanned aerial system (Quantix™ Mapper) imagery for corn (Zea mays L.) yield prediction. A field trial was conducted with N sidedress treatments applied at four growth stages (V4, V6, V8, or V10) compared against zero-N and N-rich controls. Normalized difference vegetation index (NDVI) and enhanced vegetation index 2 (EVI2), based on flights at R4, resulted in the most accurate yield estimations, as long as sidedressing was performed before V6. Yield estimations based on earlier flights were less accurate. Estimations were most accurate when imagery from both N-rich and zero-N control plots were included, but elimination of the zero-N data only slightly reduced the accuracy. Use of a ratio approach (VITrt/VIN-rich and YieldTrt/YieldN-rich) enables the extension of findings across fields and only slightly reduced the model performance. Finally, a smaller plot size (9 or 75 m2 compared to 150 m2) resulted in a slightly reduced model performance. We concluded that accurate yield estimates can be obtained using NDVI and EVI2, as long as there is an N-rich strip in the field, sidedressing is performed prior to V6, and sensing takes place at R3 or R4.

Abstract 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.