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Agronomy
Special Issues
Recent Advances in Spectral Technology Applications in Agriculture
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Recent Advances in Spectral Technology Applications in Agriculture

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (25 June 2023) | Viewed by 1840

Special Issue Editors

Dr. Carlos Marcelo Di Bella

E-Mail Website
Guest Editor
1. IFEVA, Universidad de Buenos Aires, CONICET, Facultad de Agronomía, Buenos Aires C1417DSE, Argentina
2. Department of Quantitative Methods and Information Systems, School of Agriculture, University of Buenos Aires (UBA), Av. San Martín 4453, Buenos Aires C1417DSE, Argentina
Interests: agriculture; livestock; natural resources; remote sensing; GIS
Dr. Pablo Baldassini

E-Mail Website
Guest Editor
Department of Quantitative Methods and Information Systems, School of Agriculture, University of Buenos Aires (UBA), Av. San Martín 4453, Buenos Aires C1417DSE, Argentina
Interests: regional ecosystem analysis; remote sensing; simulation models

Special Issue Information

Dear Colleagues,

Currently, there is a need to continue generating tools and technology applications based on remote sensing data, with the aim of offering researchers, technicians, public institution users, and agricultural traders updated information for decision making. These tools and technologies, based on the use and integration of different spatially information sources, may be used in the agricultural sector, and in the management of natural resources/soil/water, contributing to the improvement of agricultural productivity, efficiency in the use of resources and adaptation to climate change effects. This Special Issue invites authors to participate via the submission of original papers related to recent advances and future perspectives in the potential development of new technological applications related to: (1) describing land use and land cover changes at different temporal and spatial scales, (2) the monitoring and estimation of both forage and crop production, (3) precision agriculture, and (4) remote sensing and machine learning applications based on satellite images and UAVs, among others.

Dr. Carlos Marcelo Di Bella
Dr. Pablo Baldassini
Guest Editors

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

  • pheno-spectral information
  • yield estimation
  • crops

Published Papers (1 paper)

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Research

21 pages, 8346 KiB  
Article
County Scale Corn Yield Estimation Based on Multi-Source Data in Liaoning Province
by Ge Qu, Yanmin Shuai, Congying Shao, Xiuyuan Peng and Jiapeng Huang
Agronomy 2023, 13(5), 1428; https://doi.org/10.3390/agronomy13051428 - 22 May 2023
Viewed by 1327
Abstract
Corn as a dominant and productive cereal crop has been recognized as indispensable to the global food system and industrial raw materials. China’s corn consumption reached 2.82 × 108 t in 2021, but its production was only 2.65 × 108 t, [...] Read more.
Corn as a dominant and productive cereal crop has been recognized as indispensable to the global food system and industrial raw materials. China’s corn consumption reached 2.82 × 108 t in 2021, but its production was only 2.65 × 108 t, and China’s corn industry is still in short supply. Timely and reliable corn yield estimation at a large scale is imperative and prerequisite to prevent climate risk and meet the growing demand for corn. While crop growth models are well suited to simulate yield formation, they lack the ability to provide fast and accurate estimates of large-scale yields, owing to the sheer quantity of data they require for parameterization. This study was conducted in the typical rain-fed corn belt, Liaoning province, to evaluate the applicability of our modeling practices. We developed the factors using climate data and MCD43A4 production, and built a county-level corn yield estimation model based on correlation analysis and corn growth mechanisms. We used corn yield data from the county between 2007 and 2017, leaving out 2017 for verification. The results show that our model, with an R2 (the Coefficient of Determination) of 0.82 and an RMSE (Root Mean Square Error) of 279.33 kg/hm2, significantly improved estimation accuracy compared to only using historical records and climate data. Our model’s R2 was 0.34 higher than the trend yield estimation model and 0.27 higher than the climate yield estimation model. Additionally, RMSE was reduced by 300–400 kg/hm2 compared to the other two models. The improvement in performance achieved by adding remote sensing information to the model was due to the inclusion of variables such as monitored corn growth state, which corrected the model predictions. Our work demonstrates a simple, scalable, and accurate method for timely estimation of corn yield at the county level with publicly available multiple-source data, which can potentially be employed in situations with sparse ground data for estimating crop yields. Full article
(This article belongs to the Special Issue Recent Advances in Spectral Technology Applications in Agriculture)
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