Estimating plant biomass in agroecosystems using a drop-plate meter
Reason for doing the work Plant biomass is a commonly used metric to assess agricultural health and productivity. Removing plant material is the most accurate method to estimate plant biomass, but this approach is time consuming, labor intensive, and destructive. Previous attempts to use indirect me...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2023-08-01
|
| Series: | PeerJ |
| Subjects: | |
| Online Access: | https://peerj.com/articles/15740.pdf |
| _version_ | 1797418995804012544 |
|---|---|
| author | Stephen M. Robertson Ryan B. Schmid Jonathan G. Lundgren |
| author_facet | Stephen M. Robertson Ryan B. Schmid Jonathan G. Lundgren |
| author_sort | Stephen M. Robertson |
| collection | DOAJ |
| description | Reason for doing the work Plant biomass is a commonly used metric to assess agricultural health and productivity. Removing plant material is the most accurate method to estimate plant biomass, but this approach is time consuming, labor intensive, and destructive. Previous attempts to use indirect methods to estimate plant biomass have been limited in breadth and/or have added complexity in data collection and/or modeling. A cost-effective, quick, accurate, and easy to use and understand approach is desirable for use by scientists and growers. Objectives An indirect method for estimating plant biomass using a drop-plate meter was explored for use in broad array of crop systems. Methods Drop-plate data collected by more than 20 individuals from 16 crop types on 312 farms across 15 states were used to generate models to estimate plant biomass among and within crop types. Results A linear model using data from all crop types explained approximately 67% of the variation in plant biomass overall. This model performed differently among crop types and stand heights, which was owed to differences among sample sizes and farming between annual and perennial systems. Comparatively, the model using the combined dataset explained more variance in biomass than models generated with commodity specific data, with the exception of wheat. Conclusions The drop-plate approach described here was inexpensive, quick, simple, and easy to interpret, and the model generated was robust to error and accurate across multiple crop types. The methods met all expectations for a broad-use approach to estimating plant biomass and are recommended for use across all agroecosystems included in this study. While it may be useful in crops beyond those included, validation is suggested before application. |
| first_indexed | 2024-03-09T06:42:07Z |
| format | Article |
| id | doaj.art-39058f80adfe4834af194b67ef14349f |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:42:07Z |
| publishDate | 2023-08-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-39058f80adfe4834af194b67ef14349f2023-12-03T10:48:35ZengPeerJ Inc.PeerJ2167-83592023-08-0111e1574010.7717/peerj.15740Estimating plant biomass in agroecosystems using a drop-plate meterStephen M. RobertsonRyan B. SchmidJonathan G. LundgrenReason for doing the work Plant biomass is a commonly used metric to assess agricultural health and productivity. Removing plant material is the most accurate method to estimate plant biomass, but this approach is time consuming, labor intensive, and destructive. Previous attempts to use indirect methods to estimate plant biomass have been limited in breadth and/or have added complexity in data collection and/or modeling. A cost-effective, quick, accurate, and easy to use and understand approach is desirable for use by scientists and growers. Objectives An indirect method for estimating plant biomass using a drop-plate meter was explored for use in broad array of crop systems. Methods Drop-plate data collected by more than 20 individuals from 16 crop types on 312 farms across 15 states were used to generate models to estimate plant biomass among and within crop types. Results A linear model using data from all crop types explained approximately 67% of the variation in plant biomass overall. This model performed differently among crop types and stand heights, which was owed to differences among sample sizes and farming between annual and perennial systems. Comparatively, the model using the combined dataset explained more variance in biomass than models generated with commodity specific data, with the exception of wheat. Conclusions The drop-plate approach described here was inexpensive, quick, simple, and easy to interpret, and the model generated was robust to error and accurate across multiple crop types. The methods met all expectations for a broad-use approach to estimating plant biomass and are recommended for use across all agroecosystems included in this study. While it may be useful in crops beyond those included, validation is suggested before application.https://peerj.com/articles/15740.pdfRegenerative agricultureFalling-plate meterPrimary productivityFalling discRotational grazingPlant community |
| spellingShingle | Stephen M. Robertson Ryan B. Schmid Jonathan G. Lundgren Estimating plant biomass in agroecosystems using a drop-plate meter PeerJ Regenerative agriculture Falling-plate meter Primary productivity Falling disc Rotational grazing Plant community |
| title | Estimating plant biomass in agroecosystems using a drop-plate meter |
| title_full | Estimating plant biomass in agroecosystems using a drop-plate meter |
| title_fullStr | Estimating plant biomass in agroecosystems using a drop-plate meter |
| title_full_unstemmed | Estimating plant biomass in agroecosystems using a drop-plate meter |
| title_short | Estimating plant biomass in agroecosystems using a drop-plate meter |
| title_sort | estimating plant biomass in agroecosystems using a drop plate meter |
| topic | Regenerative agriculture Falling-plate meter Primary productivity Falling disc Rotational grazing Plant community |
| url | https://peerj.com/articles/15740.pdf |
| work_keys_str_mv | AT stephenmrobertson estimatingplantbiomassinagroecosystemsusingadropplatemeter AT ryanbschmid estimatingplantbiomassinagroecosystemsusingadropplatemeter AT jonathanglundgren estimatingplantbiomassinagroecosystemsusingadropplatemeter |