Reviewed August 2026 against USDA ERS/ARMS, USDA ARS, NOAA, and the American Farm Bureau Federation.

Try it: Run your own numbers →

Agricultural and Forest Meteorology is an Elsevier-published international journal covering the interaction of weather and climate with crops, forests, and soils; its author guidelines require a structured manuscript (150–250 word abstract, explicit methods, open data statement) submitted through Elsevier’s Editorial Manager system. This article lays out those guidelines section by section, then gives you the US weather-station networks, adoption-rate data, and loss figures that the journal’s own research actually draws on — the part a generic AI summary skips.

If you searched “agricultural and forest meteorology,” “agriculture forest meteorology,” or any variant, you’re likely trying to do one of two things: submit a manuscript, or find current agrometeorological data to cite in one. Both are covered below.

Table of Contents

1. What the Journal Covers (Aims and Scope, Official)

Agricultural and Forest Meteorology publishes original research and review articles on the physical, biological, and biogeochemical processes governing the exchange of energy, mass, and momentum between the atmosphere and agricultural or forest ecosystems. In practice that spans: micrometeorology of crop and forest canopies, evapotranspiration modeling, drought and frost risk, remote sensing of vegetation, and the meteorological drivers of pest, disease, and wildfire risk. It sits under Elsevier’s environmental science and agricultural science portfolios, meaning submissions need a clear atmosphere-to-ecosystem mechanism, not just a weather correlation.

Papers combining field observation with modeling (crop models, land-surface models, machine-learning yield forecasts) are the journal’s mainstream, not an edge case — that combination now dominates the accepted-article mix in agrometeorology publishing generally.

2. Author Guidelines: Manuscript Structure

The structural requirements below reflect the standard Elsevier “Guide for Authors” format that Agricultural and Forest Meteorology and its sister journals use. Always cross-check the live version on the journal’s Elsevier page before submission, since section-order or reference-style details can update between volumes — the structure itself is stable, the specifics are not.

1. Abstract: 150–250 words. States objective, method, headline result, and significance in that order — reviewers read this first and decide whether to keep reading.
2. Introduction: Problem statement, a literature review that engages the most recent relevant work (not a historical survey), and explicit research objectives or hypotheses.
3. Materials and Methods: Full reproducibility detail — instrument models and calibration, site coordinates and years, model equations or code repository, statistical tests used.
4. Results and Discussion: Figures/tables carrying the primary evidence; discussion ties findings back to existing literature and states practical implications for agriculture or forestry management.
5. Conclusion: What was found, what it changes, and what the next open question is.
6. References: Journal-specific citation style, prioritizing recent, primary, peer-reviewed sources over reviews-of-reviews.

Non-negotiable compliance items

• 📂 Data availability statement — raw meteorological/experimental data deposited in a recognized repository (Zenodo, figshare) with a persistent identifier (DOI)
• 🛡️ Ethics and funding disclosure — field/lab ethics compliance, funding sources, conflicts of interest, and any land-access or research permits
• 📏 SI units throughout and consistent meteorological terminology (e.g., always distinguish potential vs. actual evapotranspiration)
• 🖼️ Figures with defined axes and units — unlabeled or ambiguous-unit figures are a common desk-rejection reason
• Try it: Run your own numbers

3. Guide for Authors: Review Articles Specifically

Review articles carry a different bar than original-research submissions. Where a research paper needs a novel dataset or experiment, a review needs a novel synthesis — a review that merely restates existing survey papers without a new organizing framework, a quantitative meta-comparison, or an identified research gap is the most common rejection pattern for this submission type.

• Scope discipline: a review claiming to cover “agrometeorology” broadly is too wide; reviewers want a bounded scope (e.g., “evapotranspiration modeling in temperate row crops, 2015–2026”)
• Explicit method for source selection: state your search strategy, databases, date range, and inclusion/exclusion criteria — treat it like a mini systematic-review protocol even if it isn’t a full PRISMA review
• Synthesis, not summary: group findings by mechanism or outcome, not paper-by-paper; a table comparing methodologies and their reported effect sizes across studies is stronger than sequential paragraph summaries
• Identify the gap: reviews without a “what’s still unknown” section rarely clear the bar aims-and-scope sets for this format

4. Where to Check Impact Factor and Acceptance Data

We are not going to invent an impact-factor number here. Elsevier and Clarivate update journal metrics (Impact Factor, CiteScore, acceptance rate) on their own schedules, and a figure printed in an article goes stale the moment the next Journal Citation Reports release lands. The correct method: check the journal’s official page directly on Elsevier’s ScienceDirect/Journal Insights portal, or Clarivate’s Journal Citation Reports if your institution has a subscription — both show the current-year figure, not a cached one.

The journal’s specific acceptance/rejection rate is not published by Elsevier as a standing public metric — per our research, that number isn’t centrally available; if you need it for a funding application or tenure file, the editorial office is the direct contact point, reachable through the journal’s Elsevier homepage.

5. The US Agrometeorology Data Researchers Cite

This is the part most author-guidelines pages skip entirely: what agrometeorological infrastructure and data actually exists in the US for researchers to cite as methods or context. Two federal networks anchor almost all US-based agrometeorology papers.

USDA Soil Climate Analysis Network (SCAN)

Operated by USDA’s Agricultural Research Service, SCAN comprised over 200 stations monitoring agricultural weather across the US as of 2024, each instrumented for soil moisture, soil temperature, precipitation, and wind — the standard station specification cited in irrigation and drought-risk studies (USDA ARS, 2024).

NOAA US Climate Reference Network (USCRN)

NOAA had commissioned 137 climate stations under USCRN as of 2023, purpose-built for long-term climate-quality measurement rather than short-term forecasting, making them the reference dataset of choice when a paper needs a trend baseline rather than a real-time reading (NOAA USCRN, 2023).

For a manuscript’s Materials and Methods section, citing which network your station data came from — and its instrumentation spec — is exactly the kind of reproducibility detail reviewers check first.

6. Comparison: Journal Requirement vs. How to Satisfy It

The table below maps each author-guideline requirement to a concrete way of meeting it — the kind of structured reference an AI-generated summary won’t hand you because it requires knowing what reviewers actually flag.

7. Precision Agriculture Adoption — the Numbers Behind the Papers

If your manuscript discusses technology adoption, decision-support systems, or the practical uptake of agrometeorological data, USDA’s Agricultural Resource Management Survey (ARMS) is the standard citation. As of the 2023 survey cycle: 27% of US farms used precision agriculture practices, but adoption is sharply size-stratified — 70% of large US farms used GPS guidance systems, against a much lower share for small and midsize operations (USDA ERS/ARMS, 2023).

The US precision farming market itself was valued at $4.37 billion as of 2025, per Precedence Research — a figure worth citing in any Introduction section that needs to establish commercial-scale relevance rather than just research interest (Precedence Research, 2025).

ARMS data refreshes on its own survey cycle, not annually on a fixed date — for the current adoption percentages, pull the latest respondent data file directly from USDA ERS rather than citing last cycle’s number as if it were current: USDA ERS ARMS data products.

8. Crop Loss and Drought-Risk Data for the Submission Cycle

Papers framing agrometeorology’s stakes for a US audience should lead with loss data, not generic climate-risk language. NOAA recorded 27 billion-dollar weather and climate disasters in the US for 2024, and the American Farm Bureau Federation’s analysis put total US crop and rangeland losses from extreme weather and wildfires at $20.3 billion for that year (NOAA / American Farm Bureau Federation, 2024).

For drought-specific risk, a 1991–2020 study in IOP Science’s environmental research literature quantified crop-specific hectarage facing increased drought-related yield-loss risk in the US: 11.9 million hectares of corn (56% of average corn acreage), 9.2 million hectares of soybean (45% of average soybean acreage), and 7.9 million hectares of winter wheat (62% of average wheat acreage) (IOP Science, 2024).

These three figures — disaster count, dollar loss, and hectares at drought risk by crop — are the kind of concrete numbers that belong in a paper’s Introduction to establish stakes, replacing vaguer language about “increasing climate variability.”

9. Farmonaut’s Role: Turning Field Data into the Datasets This Research Needs

Author guidelines increasingly expect field-verified, geolocated data — not just station averages — behind agrometeorological claims. Farmonaut’s satellite and AI tools generate exactly that kind of dataset for researchers and practitioners working alongside published station networks like SCAN and USCRN:

• 🛰️ Satellite monitoring for NDVI, soil moisture, and crop/forest structural health at field resolution
• ⚡ Jeevn AI Decision Support — field-level meteorological forecasts and management strategies (see Jeevn AI: Smart Farming with Satellite & AI Insights)
• 🔗 Blockchain traceability for supply-chain and provenance data (Traceability Product Details)
• 🚛 Fleet management for logistics and resource-allocation datasets (Learn about Fleet Optimization)
• 🌱 Large-scale farm management tools for institutional and government-scale data collection: Farmonaut Large-Scale Farm Management

Our API lets researchers pull satellite and AI-derived variables directly into a reproducible pipeline rather than manual export: Farmonaut API and the API Developer Docs. That matters for the data-transparency requirement above — an API-sourced dataset with a documented endpoint and version is easier to describe reproducibly in a Methods section than a manually screenshotted dashboard.

10. Calculator: Weather-Station Monitoring Density for Your Study Area

Before designing a field study, check whether your study area already has adequate federal station coverage or needs supplemental sensors — enter your area and desired station spacing below.

Assumptions: treats coverage as a simple grid-cell model (area ÷ spacing²), does not account for terrain, existing non-federal sensor networks, or crop-specific microclimate variability. Use it to scope a study design, not as a substitute for a formal network-density analysis.

11. Product Links for Climate-Smart Agrometeorological Practice

• 🌍 Carbon Footprinting: Quantify and monitor your agricultural or forestry carbon emissions — relevant for the sustainability-reporting angle many agrometeorology papers now include.
• 🔗 Traceability: Implement supply chain transparency from production to market.
• 💰 Crop Loan and Insurance: Satellite verification for agricultural finance.
• 🚛 Fleet Management: Deploy vehicles and agricultural assets across farm or forest operations.
• 🌲 Crop Plantation & Forest Advisory: Get tailored advisory via our app for soil, water, and crop/forest management.

12. FAQ: Agricultural and Forest Meteorology Author Guidelines

1. What is Agricultural and Forest Meteorology’s aims and scope, officially?
   The journal covers physical, biological, and biogeochemical exchange processes between the atmosphere and agricultural or forest ecosystems — micrometeorology, evapotranspiration, drought/frost risk, remote sensing, and weather-driven pest/disease/fire risk. Confirm the current wording on the journal’s official Elsevier page, as scope statements are periodically refined.
2. Where do I find the guide for authors (Elsevier)?
   On the journal’s official page hosted on Elsevier’s ScienceDirect/Journal Insights platform, under “Guide for Authors.” That page carries the current submission template, reference style, and word-count limits — check it directly rather than relying on a cached summary, since Elsevier updates these periodically.
3. What’s different about the guide for authors for review articles?
   Reviews need a bounded scope, an explicit source-selection method (databases, date range, inclusion criteria), and a synthesis framework rather than a paper-by-paper summary — plus an identified research gap.
4. What is the journal’s current impact factor?
   We’re not going to print a number here that goes stale by the next citation-report release. Check Elsevier’s Journal Insights page or Clarivate’s Journal Citation Reports directly for the current-year figure.
5. Is the acceptance/rejection rate published?
   Not as a standing public metric from Elsevier. Contact the journal’s editorial office through its official homepage if you need that figure for a grant or tenure application.
6. What US data sources should I cite for agrometeorology research?
   USDA’s SCAN network (200+ stations, 2024) and NOAA’s USCRN (137 stations, 2023) are the standard federal networks; USDA ERS/ARMS is standard for technology-adoption statistics; NOAA and the American Farm Bureau Federation are standard for weather-disaster loss figures.
7. Why does the article keep saying a number “as of” a specific year instead of just stating it?
   Because ARMS, NOAA disaster counts, and station-network totals all update on their own schedules. Citing a year-stamped figure with its source link lets you verify whether a newer number has since been published — a bare number does not.

13. Conclusion

Meeting Agricultural and Forest Meteorology‘s author guidelines comes down to five habits: bound your scope precisely, disclose data and ethics explicitly, cite methods reproducibly down to instrument and network, engage recent literature over review-of-reviews, and — for reviews specifically — synthesize rather than summarize. None of that changes with the calendar year.

What does change is the underlying data: ARMS adoption percentages, NOAA disaster counts, and station-network totals all refresh on their own cycles. The method here — go to USDA ERS, USDA ARS, and NOAA directly, cite the vintage, and note the refresh path — outlives any single year’s figures, which is the point of building a paper (or a page) around a method rather than a snapshot.

Farmonaut’s satellite and AI tools exist to make the field-data side of that method easier: geolocated, API-accessible, and reproducible enough to cite in a Methods section rather than described as a manual export.