Abstract
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.
Highlights
Applied agricultural research traditionally has been conducted in research stations with findings presented to farmers by extension staff or staff from other development organizations [1]
For estimation of treatment effects, we explore two frameworks, namely least squares (LS) and generalized least squares with spatial covariance (GLS), while for estimation of standard errors, we explore two frameworks, that of assuming independence (Independence) and assuming spatial covariance (Spatial)
For estimation of treatment effects, we explored two frameworks, namely least squares (LS) and generalized least squares with spatial covariance (GLS), while for estimation of standard errors, we explored two frameworks, that of assuming independence (Independence) and assuming spatial covariance (Spatial)
Summary
Applied agricultural research traditionally has been conducted in research stations with findings presented to farmers by extension staff or staff from other development organizations [1]. On-farm experiments (OFE), allow for more seamless transfer of research findings because the research is conducted in an environment relevant to the farmer in terms of soil types, management, weather, etc., often resulting in more adoptable and sustainable solutions for farmers [1,2]. OFE partnerships between farmers and industry or university researchers have expanded, because the approach has been shown to improve farmers’ crop and land management with increased productivity [3]. The most prevalent research design for OFE is the randomized complete block design (RCBD) with field-length
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