For centuries, farmers relied on almanacs and passed-down wisdom. Today, agricultural science is driven by data, technology, and access to a global repository of scientific knowledge. Whether you're a student researching sustainable practices, a scientist developing drought-resistant crops, or an innovator exploring vertical farming, the journey to discovery begins with knowing where to look. This article unveils the rich landscape of agricultural information available through Rutgers University Libraries, your gateway to shaping the future of food and farming.
| Database Name | Key Features & Subject Coverage |
|---|---|
| Agricultural Science Collection (ProQuest) 1 | A comprehensive resource with over 1,000 journals; also includes all records from the AGRICOLA database. Covers agronomy, animal science, biotechnology, food science, forestry, soil science, and more. |
| AGRICOLA (ProQuest) 4 | Contains millions of bibliographic records from the U.S. Department of Agriculture (USDA) National Agricultural Library. A cornerstone database for all agriculture-related topics. |
| CAB Abstracts 4 | A premier database in the applied life sciences, offering extensive coverage of agriculture, animal and veterinary sciences, environmental sciences, human health, food, nutrition, and plant sciences. |
| Web of Science / Scopus 4 | Multidisciplinary databases that allow you to track article citations, discover how research has been built upon, and find the most influential papers across the sciences, including agriculture. |
| Google Scholar 4 | A free, web-based search tool that can be a useful supplement to other databases, especially when accessed from a Rutgers IP address for enhanced full-text linking. |
Database Usage Statistics
Research Tips
- Use specific keywords related to your research topic
- Combine database searches with manual reference checking
- Set up alerts for new publications in your field
- Utilize advanced search features and filters
- Consult with subject librarians for specialized guidance
From Question to Crop: The Anatomy of an On-Farm Experiment
Imagine you're a farmer or researcher trying to determine if a new biological fungicide works better than your current standard. How do you find out? The answer lies in well-designed on-farm experiments 3 .
The Blueprint: Designing a Valid Experiment
A robust experiment is built on three core principles: replication, randomization, and control .
Selecting Treatments and Controls
The individual options you test are called "treatments." A crucial treatment is a "control"—plots that receive no treatment at all. This provides a baseline to measure if treatments actually work .
The Power of Replication
Applying each treatment to only one plot is risky. Replication means applying each treatment to multiple plots across the field. This helps average out random variations .
Ensuring Fairness with Randomization
You must randomize the placement of treatments within each replication. This ensures every treatment has an equal chance to perform in varying field conditions .
A Step-by-Step Experiment: Fungicide Efficacy Trial
Objective: To evaluate the efficacy of a new biological fungicide ("Bio-Fungicide A") compared to a standard synthetic fungicide and an untreated control in reducing black spot disease on roses.
Methodology:
- Plot Design: The field is divided into three blocks (replications), with each block containing three plots.
- Treatment Application: Within each block, the three treatments are assigned to plots completely at random.
- Data Collection: After a set period, disease severity is recorded as the number of diseased spots per plant.
Experimental Design Visualization
Results and Analysis:
The collected data might look something like this:
| Table 1: Raw Data - Disease Spots per Plant | |||
|---|---|---|---|
| Block | Untreated Control | Standard Fungicide | Bio-Fungicide A |
| 1 | 26 | 15 | 18 |
| 2 | 21 | 18 | 16 |
| 3 | 25 | 21 | 20 |
To make sense of this, researchers calculate the average (mean) number of spots for each treatment across all blocks.
| Table 2: Average Disease Severity per Treatment | |
|---|---|
| Treatment | Average Number of Disease Spots per Plant |
| Untreated Control | 24.0 |
| Standard Fungicide | 18.0 |
| Bio-Fungicide A | 18.0 |
A statistical analysis, such as Analysis of Variance (ANOVA), would then be used to determine if the differences between these averages are statistically significant or likely due to random chance 3 . In this case, both fungicides show a clear reduction in disease compared to the control.
The Scientist's Toolkit: Essential Research Materials
Agricultural research relies on a diverse array of tools and reagents. The table below outlines some key materials used in modern experiments, from the field to the lab.
| Item | Function in Research |
|---|---|
| Biostimulants (e.g., BIOZ portfolio) | Used to enhance plant nutrition, soil health, and crop resilience to abiotic stress. A key focus in regenerative agriculture research 2 . |
| Nitrogen-Fixing Microorganisms | Soil additives tested as natural fertilizers to improve plant growth and reduce the need for synthetic inputs 7 . |
| RNA-based Crop Inputs | A cutting-edge biotechnology tool used to modulate gene expression in plants, offering an environmentally sustainable alternative to traditional chemical treatments for enhancing resilience 2 . |
| Chlorine & Peracetic Acid | Common disinfectants studied for their ability to reduce foodborne pathogens, like Salmonella, on fresh produce, crucial for food safety research 7 . |
| AI-Aided Computer Vision | A non-invasive tool for real-time monitoring of traits like animal health in aquaculture or plant growth, generating large datasets for analysis 7 . |
Biotechnology
Advanced tools for genetic modification and crop improvement
EmergingLaboratory Analysis
Precise measurement and analysis of agricultural samples
EstablishedAutomation
AI and robotics for efficient agricultural processes
GrowingThe Rise of AI and Precision Agriculture
Artificial Intelligence is now central to precision agriculture, where algorithms analyze weather, soil, and crop data to provide actionable insights for planting, irrigation, and pest management 2 5 . Platforms like Farmonaut are making this technology accessible, using satellite data to help farmers monitor crop health 5 .
Regenerative Agriculture and Carbon Utilization
There is a major shift from sustainable to regenerative agriculture—a set of practices that actively restore soil health and biodiversity. Technology plays a key role, with AI-driven remote sensing used to measure and verify carbon sequestration in soil, allowing farmers to participate in carbon credit markets 2 .
Breakthroughs in Agricultural Biotechnology
Advances in synthetic biology and microbiome-based inputs are creating highly resilient crops. Researchers are using AI-powered breeding platforms to develop seeds that can withstand droughts, floods, and soil degradation caused by climate change 2 .
Key Biotechnology Areas
- Gene Editing High
- Microbiome Research Medium
- Synthetic Biology Medium
- Biofortification High
Rutgers Agricultural Resources
To dive deeper into these local innovations, the Rutgers Libraries guide points to invaluable university-specific resources like the New Jersey Agricultural Experiment Station (NJAES), Rutgers Cooperative Extension, and the Rutgers Plant & Pest Advisory, which delivers timely, science-based management advice to New Jersey's growers 6 .
NJAES
Research & Innovation
Cooperative Extension
Community Outreach
Plant & Pest Advisory
Timely Information
Shape the Future of Agriculture
The journey to answering agriculture's most pressing questions—from improving local crop yields to ensuring global food security—begins with access to information. The databases, digital tools, and expert guidance available through Rutgers Libraries provide the fertile ground from which new discoveries and sustainable solutions can grow.
Whether you're starting a research paper or planning a field experiment, you now have the map to navigate this rich and vital landscape.