Towards a Low-Cost Comprehensive Process for On-Farm Precision Experimentation and Analysis

Abstract

Few mechanisms turn field-specific ecological data into management recommendations for crop production with appropriate uncertainty. Precision agriculture is mainly deployed for machine efficiencies and soil-based zonal management, and the traditional paradigm of small plot research fails to unite agronomic research and effective management under farmers’ unique field constraints. This work assesses the use of on-farm experiments applied with precision agriculture technologies and open-source data to gain local knowledge of the spatiotemporal variability in agroeconomic performance on the subfield scale to accelerate learning and overcome the bias inherent in traditional research approaches. The on-farm precision experimentation methodology is an approach to improve farmers’ abilities to make site-specific agronomic input decisions by simulating a distribution of economic outcomes for the producer using field-specific crop response models that account for spatiotemporal uncertainty in crop responses. The methodology is the basis of a decision support system that includes a six-step cyclical process that engages precision agriculture technology to apply experiments, gather field-specific data, incorporate modern data management and analytical approaches, and generate management recommendations as probabilities of outcomes. The quantification of variability in crop response to inputs and drawing on historic knowledge about the field and economic constraints up to the time a decision is required allows for probabilistic inference that a future management scenario will outcompete another in terms of production, economics, and sustainability. The proposed methodology represents advancement over other approaches by comparing management strategies and providing the probability that each will increase producer profits over their previous input management on the field scale.

Spatial and temporal variability of corn response to nitrogen and seed rates

“…Recently, Hegedus et al. (2023) proposed a low‐cost framework to incorporate OFPE results and weather uncertainty into decision support.…”

“…As a result, the inclusion of this feature into predictive models or into management zone delineation algorithms without addressing its temporal variability could have variable results. Recently, Hegedus et al (2023) proposed a low-cost framework to incorporate OFPE results and weather uncertainty into decision support.…”

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

“…Intensive developments in the field of precision agriculture open the possibilities for conducting of the on-farm experiments to compare the different agronomic practices or to test the conclusions from a small-scale field experiments with advanced possibilities in data collection with a spatial resolution such as the yield monitor data throughout the standard machineries Paccioretti et al. (2021) ; Hegedus et al. (2023) .…”

“…In contrast to small-scale field trials, the large-scale field or onfarm trials are more variable and less precise but more representative when compared with a standard agronomic practice in a given production region Piepho et al (2011). Intensive developments in the field of precision agriculture open the possibilities for conducting of the on-farm experiments to compare the different agronomic practices or to test the conclusions from a small-scale field experiments with advanced possibilities in data collection with a spatial resolution such as the yield monitor data throughout the standard machineries Paccioretti et al (2021); Hegedus et al (2023).…”

“…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).…”