Proposed Method for Statistical Analysis of On-Farm Single Strip Treatment Trials

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.

Single‐Strip Spatial Evaluation Approach for Easier, More Meaningful On‐Farm Research

“…Accounting for variability is lost in the traditional approaches (RCBD and split‐plot) when a single yield value is collected per strip or plot. In the single‐strip approach, the yield population (yield values for each 6‐ by 6‐ft grid) in the treatment strip is compared with the yield population in the business‐as‐usual areas directly outside of the strip, and the effect of treatment is estimated using least square approach with spatial covariance for standard errors (Cho et al., 2021a).…”

“…The treatment strip can be placed in one field or multiple fields (the more the better) to evaluate whether a treatment change benefits yield in the zones of interest. Once the fields with strips have been harvested, the SSEA uses the field’s yield stability zones and the current‐season yields to evaluate the level of confidence that a certain increase in yield took place (Cho et al., 2021a). In this approach, the level of confidence of a treatment response in a given growing season is determined for each yield stability zone in the field(s) that the strip was placed in.…”

The digitalisation of agriculture

“…However, while the specialty crop industry as a whole has made significant progress in mechanisation (Huffman, 2012[152]), many cropsacross developed and developing countriesare still manually harvested. Rising opportunity costs of agricultural workalong with substantial agricultural productivity increases and an economic reorientation towards the provision of services, among other factorshas given rise to agricultural labour shortages on specialty crop farms in many OECD countries, either as an long-standing or temporary condition (Hertz and Zahniser, 2013[173]; Charlton and Taylor, 2016 [174]).…”

Assessing green manure impact on wheat productivity through Bayesian analysis of yield monitor data

“…Previously mentioned techniques for analysis small-scale experiments cannot be readily applied for data analysis of the onfarm experiments due to their complexity, heterogeneity and spatial scale variability. There are large number of methodological approaches for the analysis of on-farm experiments ranging from geospatial regression models to Bayesian statistical methods Kyveryga (2019); Cho et al (2021); Paccioretti et al (2021); Hegedus et al (2023).…”