ArticleAgricultural systems2017
Toward a new generation of agricultural system data, models, and knowledge products: State of agricultural systems science.
Article in Agricultural systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it, 431 citations in OpenAlex.
- Pooled it
- Harnessing microRNA regulatory networks for engineering climate resilience and nutritional enhancement in millets.Stress biology · 2026Review
- The "Agriculture and Diet: Value Added for Nutrition, Translation, and Adaptation in a Global Ecology" (ADVANTAGE) Project: An Overview of an Integrated Approach to Address a Complex Challenge.Advances in nutrition (Bethesda, Md.) · 2026Review
- The Ruminant Farm Systems (RuFaS) model is a platform to support future research and actions for sustainable dairy farming.JDS communications · 2025Review
- Mitigating crop modeling uncertainties through machine learning in drylands.Scientific reports · 2025Article
- Key Challenges in Plant Microbiome Research in the Next Decade.Microorganisms · 2025Review
- A multi model ensemble reveals net climate benefits from regenerative practices in US Midwest croplands.Scientific reports · 2025Article
- Assessing the capability of electrical capacitance tomography for monitoring chopped maize mass flow rates in field forage harvesters.Scientific reports · 2025Article
- Data quality challenges of AIGC application in smart agriculture.Frontiers in artificial intelligence · 2025Review
- Article
- A systematic review of food-environment interactions in catchment models.Discover agriculture · 2025Review
- Fertilization for Growth or Feeding the Weeds? A Deep Dive into Nitrogen's Role in Rice Dynamics in Ecuador.Life (Basel, Switzerland) · 2024Article
- Crop water productivity assessment and planting structure optimization in typical arid irrigation district using dynamic Bayesian network.Scientific reports · 2024Article
- Multi-year aboveground data of minirhizotron facilities in Selhausen.Scientific data · 2024Article
- Biowelding 3D-Printed Biodigital Brick of Seashell-Based Biocomposite byBiomimetics (Basel, Switzerland) · 2023Article
- Evaluating the effect of climate change on rice production in Indonesia using multimodelling approach.Heliyon · 2023Review
- Challenges and opportunities in remote sensing-based crop monitoring: a review.National science review · 2023Review
- Timing and intensity of heat and drought stress determine wheat yield losses in Germany.PloS one · 2023Article
- An Open Soil Health Assessment Framework Facilitating Sustainable Soil Management.Environmental science & technology · 2022Article
- Non-Destructive Monitoring of Crop Fresh Weight and Leaf Area with a Simple Formula and a Convolutional Neural Network.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 11 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We review the current state of agricultural systems science, focusing in particular on the capabilities and limitations of agricultural systems models. We discuss the state of models relative to five different Use Cases spanning field, farm, landscape, regional, and global spatial scales and engaging questions in past, current, and future time periods. Contributions from multiple disciplines have made major advances relevant to a wide range of agricultural system model applications at various spatial and temporal scales. Although current agricultural systems models have features that are needed for the Use Cases, we found that all of them have limitations and need to be improved. We identified common limitations across all Use Cases, namely 1) a scarcity of data for developing, evaluating, and applying agricultural system models and 2) inadequate knowledge systems that effectively communicate model results to society. We argue that these limitations are greater obstacles to progress than gaps in conceptual theory or available methods for using system models. New initiatives on open data show promise for addressing the data problem, but there also needs to be a cultural change among agricultural researchers to ensure that data for addressing the range of Use Cases are available for future model improvements and applications. We conclude that multiple platforms and multiple models are needed for model applications for different purposes. The Use Cases provide a useful framework for considering capabilities and limitations of existing models and data.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.