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Agronomy
Special Issues
Predictive Modeling to Aid Agronomic Decision Making
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Predictive Modeling to Aid Agronomic Decision Making

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Farming Sustainability".

Deadline for manuscript submissions: closed (1 June 2021) | Viewed by 7059

Special Issue Editor

Dr. Peter Kyveryga

E-Mail Website1 Website2
Guest Editor
Iowa Soybean Association, Ankeny, IA, USA
Interests: predictive modeling; analytical approaches; agronomic decision-making process; communication of predictive modeling results

Special Issue Information

Dear Colleagues,

To produce crops economically and sustainably, agronomists and farmers should consider a range of interactive environmental factors, including soil, climatic, field management, and production equipment. Available data collection technologies (remote and proximal sensing, precision agriculture, and others) allow us to characterize these factors at much finer scales and with greater frequency than ever before. At the same time, new analytical tools and methodologies that can handle this surge of agronomic data are becoming more common. Predictive modeling, a general umbrella term for a variety of analytical methods, is often touted as the ultimate way to make sense of these data and better understand agronomic processes and interactions among various factors. While the concept of predictive modeling is not new, there is a growing interest among students, researchers, practical agronomists, farmers, and environmentalists in how to use new predictive analytical tools to generate new information out of complexity to develop or simplify and communicate complex agronomic decisions.

We invite original articles that are focused on agronomic topics related to:

  • Methodologies including process-based, statistical, machine learning, data fusion, and hybrid approaches for predictive modeling;
  • Decision aid tools based on predictive modeling;
  • Researcher- and practitioner-friendly communication of predictive modeling results;
  • Quanitfying the economic and environmental impact of predictive modeling;
  • Practical examples of using predicive modeling to improve soil productivity, crop production, production economics, soil health, and environmental stewardship;
  • Integreated and interdisplinary approaches for predictive modeling and decision making.

Original thinking and new ideas are welcomed. Studies, analyses, and examples should follow the common scientific rigor, including replications in time or space and appropriate statistical analyses.

Consideration of manuscripts for this Special Issue will begin immediately and continue until February 2021. 

Dr. Peter Kyveryga
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • predictive modeling
  • analytical approaches
  • agronomic decision-making process
  • communication of predictive modeling results

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Published Papers (2 papers)

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Research

16 pages, 2008 KiB  
Article
Proposed Method for Statistical Analysis of On-Farm Single Strip Treatment Trials
by Jason B. Cho, Joseph Guinness, Tulsi Kharel, Ángel Maresma, Karl J. Czymmek, Jan van Aardt and Quirine M. Ketterings
Agronomy 2021, 11(10), 2042; https://doi.org/10.3390/agronomy11102042 - 12 Oct 2021
Cited by 8 | Viewed by 3383
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Predictive Modeling to Aid Agronomic Decision Making)
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24 pages, 1677 KiB  
Article
Simultaneous Calibration of Grapevine Phenology and Yield with a Soil–Plant–Atmosphere System Model Using the Frequentist Method
by Chenyao Yang, Christoph Menz, Helder Fraga, Samuel Reis, Nelson Machado, Aureliano C. Malheiro and João A. Santos
Agronomy 2021, 11(8), 1659; https://doi.org/10.3390/agronomy11081659 - 20 Aug 2021
Cited by 13 | Viewed by 2925
Abstract
Reliable estimations of parameter values and associated uncertainties are crucial for crop model applications in agro-environmental research. However, estimating many parameters simultaneously for different types of response variables is difficult. This becomes more complicated for grapevines with different phenotypes between varieties and training [...] Read more.
Reliable estimations of parameter values and associated uncertainties are crucial for crop model applications in agro-environmental research. However, estimating many parameters simultaneously for different types of response variables is difficult. This becomes more complicated for grapevines with different phenotypes between varieties and training systems. Our study aims to evaluate how a standard least square approach can be used to calibrate a complex grapevine model for simulating both the phenology (flowering and harvest date) and yield of four different variety–training systems in the Douro Demarcated Region, northern Portugal. An objective function is defined to search for the best-fit parameters that result in the minimum value of the unweighted sum of the normalized Root Mean Squared Error (nRMSE) of the studied variables. Parameter uncertainties are estimated as how a given parameter value can determine the total prediction variability caused by variations in the other parameter combinations. The results indicate that the best-estimated parameters show a satisfactory predictive performance, with a mean bias of −2 to 4 days for phenology and −232 to 159 kg/ha for yield. The corresponding variance in the observed data was generally well reproduced, except for one occasion. These parameters are a good trade-off to achieve results close to the best possible fit of each response variable. No parameter combinations can achieve minimum errors simultaneously for phenology and yield, where the best fit to one variable can lead to a poor fit to another. The proposed parameter uncertainty analysis is particularly useful to select the best-fit parameter values when several choices with equal performance occur. A global sensitivity analysis is applied where the fruit-setting parameters are identified as key determinants for yield simulations. Overall, the approach (including uncertainty analysis) is relatively simple and straightforward without specific pre-conditions (e.g., model continuity), which can be easily applied for other models and crops. However, a challenge has been identified, which is associated with the appropriate assumption of the model errors, where a combination of various calibration approaches might be essential to have a more robust parameter estimation. Full article
(This article belongs to the Special Issue Predictive Modeling to Aid Agronomic Decision Making)
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