Three essays on implications of on-farm precision experiments for N management

Gong, Aolin

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https://hdl.handle.net/2142/117643 Copy

Description

Title

Three essays on implications of on-farm precision experiments for N management

Author(s)

Gong, Aolin

Issue Date

2022-11-10

Director of Research (if dissertation) or Advisor (if thesis)

Mieno, Taro

Doctoral Committee Chair(s)

Bullock, David S

Committee Member(s)

• Michelson, Hope C
• Li, Bo
• Paulson, Nicholas

Department of Study

Agr & Consumer Economics

Discipline

Agricultural & Applied Econ

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• OFPE
• N Management
• Trial Design
• Monte Carlo Simulation
• Quantile Regression Forest

Abstract

On-Farm Precision Experimentation (OFPE) began near the end of the 20th century but has expanded rapidly over the past five years or so, with hundreds of trials recently conducted around the world. This dissertation investigates three topics of OFPE. as opposed. Compared to the smallplot trials or strip trials mostly used in previous research, OFPE covers more of the soil variation on the field, has more replicates of each treatment, and allows a spatially balanced allocation of treatment rates. Despite the obvious benefits of OFPE, the literature on how to design and manage these trials is scant, given the recent relevance of the approach. These chapters investigate how to improve the efficiency of trial designs for OFPE and revisit nitrogen management topics using OFPE's improved data from a variety of field and weather years. In the first chapter of this paper, I investigate the optimal experimental design, including the optimal plot length and optimal number of treatments, in order to gain a better understanding of how different experimental designs affect the effectiveness of trial data, generate better data for statistical analysis, and ultimately provide more cost-effective management recommendations. When alternative experimental designs are utilized, the variance patterns of trial treatments within a field are different, which influences the estimation of site-specific yield-to-input rate response functions. To determine the optimal trade-off between the reliability of data from a single plot and the variation in trial treatments in a trial field, I use Monte-Carlo simulations to compare the economic performance of simulated experimental trials with variable experimental design settings. In the second chapter, I assess the efficacy of Maximum Return to Nitrogen (MRTN) recommendation in guiding nitrogen application rates in the corn belt. The Midwest Land Grant Universities proposed MRTN recommendation in 2005. This is the first tool available to the public that considers economic outcome when recommending nitrogen application rate. However, MRTN adoption is low; farmers may continue to rely on retailer recommendations or past experience, in part because the nitrogen application rate suggested by the MRTN system is relatively low. Thus, I use both the ex-post economically optimal nitrogen rate (EONR) as well as the grower chosen rates to evaluate MRTN recommendations. The EONR is derived from forty-two OFPE conducted in Illinois and Ohio from 2016 to 2021. Results indicate that MRTN recommendations can be excessively high or insufficiently low across fields in the same region and throughout the same year. Moreover, grower-selected rates performed better than MRTN on some fields and in some regions. Consequently, farmers should not rely on MRTN recommendations to determine how much nitrogen to apply. In the third chapter, I assess whether EONR is appropriate for all farmers. The majority of optimal nitrogen rates used to recommend nitrogen application in the existing literature are EONR that maximize the expected conditional mean profit on a field, which may not be suitable for farmers who are not risk neutral. To estimate the optimal nitrogen rates for farmers with varying risk preferences, I use quantile regression forests (QRF) to show the probabilities of yield response at different levels of nitrogen rates and their economic implications. The data used in this chapter is the same as the previous one. In the majority of cases, the optimal nitrogen rates calculated for farmers with different risk preferences are identical, and excessive nitrogen should not be applied to mitigate yield loss-related profit risk. In other words, the entire yield response distribution is not necessary for nitrogen application guidance.

Graduation Semester

2022-12

Type of Resource

Thesis

Copyright and License Information

Copyright 2022 Aolin Gong

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