Towards Large Reasoning Models for Agriculture | Paper Citation Record

Observation fdcfbed6-eee2-46fd-b713-ec7eb7842f51 · outbound

This paper cites The State of Food and Agriculture 2024 – Value-driven transformation of agrifood systems, 2024.

Towards Large Reasoning Models for Agriculture The State of Food and Agriculture 2024 – Value-driven transformation of agrifood systems, 2024

Reference 1

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Observation 37af4a03-f8b7-4a72-a03f-c1fdeb6ff6a0 · outbound

This paper cites Employment in agriculture (% of total employment) (modeled ILO estimate), 2024.

Towards Large Reasoning Models for Agriculture Employment in agriculture (% of total employment) (modeled ILO estimate), 2024

Reference 2

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Observation 94e6f36f-f55f-4648-b08e-48fda7a6e5fa · outbound

This paper cites Agriculture, forestry and fishing, value added (% of GDP), 2024.

Towards Large Reasoning Models for Agriculture Agriculture, forestry and fishing, value added (% of GDP), 2024

Reference 3

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Observation c32c233d-6922-40d8-aad1-874f7108ee0d · outbound

This paper cites ShizishanGPT: An Agricultural Large Language Model Integrating Tools and Resources.

Towards Large Reasoning Models for Agriculture ShizishanGPT: An Agricultural Large Language Model Integrating Tools and Resources

Reference 4

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Observation 7679f040-3c4a-4bfa-8b60-c231994a3fef · outbound

This paper cites AgroLLM: Connecting Farmers and Agricultural Practices through Large Language Models for Enhanced Knowledge Transfer and Practical Application.

Towards Large Reasoning Models for Agriculture AgroLLM: Connecting Farmers and Agricultural Practices through Large Language Models for Enhanced Knowledge Transfer and Practical Application

Reference 5

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Observation 234a4acd-b974-4a30-a156-22c108c5ff34 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning, 2025.

Towards Large Reasoning Models for Agriculture DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning, 2025

Reference 6

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Observation 22639d26-a1d1-48be-bcef-574e3fe14809 · outbound

This paper cites Qwen3 Technical Report.

Towards Large Reasoning Models for Agriculture Qwen3 Technical Report

Reference 7

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Observation 8f7f2d7c-931d-4b5d-8720-938891dce9f1 · outbound

This paper cites Sky-T1: Train your own O1 preview model within $450, 2025.

Towards Large Reasoning Models for Agriculture Sky-T1: Train your own O1 preview model within $450, 2025

Reference 8

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Observation 1895035f-cad9-47a6-99fc-5147c5f8ac65 · outbound

This paper cites LiveBench: A Challenging, Contamination-Limited LLM Benchmark.

Towards Large Reasoning Models for Agriculture LiveBench: A Challenging, Contamination-Limited LLM Benchmark

Reference 9

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Observation 719341f9-f3f5-46f7-a3e0-95baa7e145ef · outbound

This paper cites GPQA: A Graduate-Level Google-Proof Q&A Benchmark.

Towards Large Reasoning Models for Agriculture GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 10

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Observation 867ecd1c-4183-40da-ba24-f6dbdded4bc4 · outbound

This paper cites Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering.

Towards Large Reasoning Models for Agriculture Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering

Reference 11

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Observation 14986d75-c587-4854-902e-983bca56523d · outbound

This paper cites AgXQA: A benchmark for advanced Agricultural Extension question answering.

Towards Large Reasoning Models for Agriculture AgXQA: A benchmark for advanced Agricultural Extension question answering

Reference 12

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Observation 20557a58-3627-4737-a5b9-9a17574fe71f · outbound

This paper cites AgriBench: A Hierarchical Agriculture Benchmark for Multimodal Large Language Models.

Towards Large Reasoning Models for Agriculture AgriBench: A Hierarchical Agriculture Benchmark for Multimodal Large Language Models

Reference 13

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Observation 6dda9778-9cf8-48e4-976b-9b123fd7f67d · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Towards Large Reasoning Models for Agriculture DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 14

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Observation e0281919-e6f0-4179-bec3-645d9d885ea5 · outbound

This paper cites QwQ-32B: Embracing the Power of Reinforcement Learning, March 2025.

Towards Large Reasoning Models for Agriculture QwQ-32B: Embracing the Power of Reinforcement Learning, March 2025

Reference 15

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Observation beea082a-ec33-425b-81d7-1f796587e221 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Towards Large Reasoning Models for Agriculture LLaMA: Open and Efficient Foundation Language Models

Reference 16

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This paper cites Bespoke-Stratos: The unreasonable effectiveness of rea- soning distillation, 2025.

Towards Large Reasoning Models for Agriculture Bespoke-Stratos: The unreasonable effectiveness of rea- soning distillation, 2025

Reference 17

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Observation c63227eb-9cad-4316-b319-7a312b08cab5 · outbound

This paper cites HuatuoGPT-o1: Towards medical complex reasoning with LLMs,.

Towards Large Reasoning Models for Agriculture HuatuoGPT-o1: Towards medical complex reasoning with LLMs,

Reference 18

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Observation b4e69439-a18f-4dcf-990f-54898720e0ae · outbound

This paper cites Open Thoughts, January 2025.

Towards Large Reasoning Models for Agriculture Open Thoughts, January 2025

Reference 19

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Observation 609cacb6-0019-4b3f-93ac-ff452f29b9d1 · outbound

This paper cites Dolphin-R1, January 2025.

Towards Large Reasoning Models for Agriculture Dolphin-R1, January 2025

Reference 20

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Observation 53854164-5d8a-4f07-9ed5-5f41c8b7930e · outbound

This paper cites reasoning-v1-20m, January 2025.

Towards Large Reasoning Models for Agriculture reasoning-v1-20m, January 2025

Reference 21

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Observation d1f55fa7-ca24-4fcf-9ebf-0a514cf2d56e · outbound

This paper cites From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline.

Towards Large Reasoning Models for Agriculture From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline

Reference 22

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Observation afb4849f-1502-448b-86cd-96e33b840f07 · outbound

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Towards Large Reasoning Models for Agriculture Unresolved cited work

Reference 23

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Observation 61173a17-d8e3-4b63-a62a-280568797167 · outbound

This paper cites LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code.

Towards Large Reasoning Models for Agriculture LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 24

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Observation 7f4aa9d2-33be-4bf6-8def-c628972511ad · outbound

This paper cites MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding.

Towards Large Reasoning Models for Agriculture MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding

Reference 25

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Observation 4457ddbc-cf62-45a7-bc22-44e191c38f93 · outbound

This paper cites LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models.

Towards Large Reasoning Models for Agriculture LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models

Reference 26

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Observation 31906c74-7dfc-4b8f-b607-751f897f362e · outbound

This paper cites Large language models and agricultural extension services.

Towards Large Reasoning Models for Agriculture Large language models and agricultural extension services

Reference 27

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Observation b6d48c81-2279-4d30-ae23-1c90277597a3 · outbound

This paper cites agriculture-qa-english-only, 2025.

Towards Large Reasoning Models for Agriculture agriculture-qa-english-only, 2025

Reference 28

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Observation 0d103b60-efe5-4780-ba66-3cf91b36c0a7 · outbound

This paper cites AgMMU: A Comprehensive Agricultural Multimodal Understanding Benchmark.

Towards Large Reasoning Models for Agriculture AgMMU: A Comprehensive Agricultural Multimodal Understanding Benchmark

Reference 29

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Observation c1bd91ab-3adb-4d73-9f69-851a67c12fa9 · outbound

This paper cites Leveraging Vision Language Models for Specialized Agricultural Tasks.

Towards Large Reasoning Models for Agriculture Leveraging Vision Language Models for Specialized Agricultural Tasks

Reference 30

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Towards Large Reasoning Models for Agriculture Unresolved cited work

Reference 31

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Observation c466df1d-ac21-44c7-89d4-79900472966e · outbound

This paper cites Label Studio: Data labeling software, 2020-2025.

Towards Large Reasoning Models for Agriculture Label Studio: Data labeling software, 2020-2025

Reference 32

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Observation 934ffd7b-4604-4766-802b-a12986f35cfa · outbound

This paper cites The FACTS Grounding Leaderboard: Benchmarking LLMs' Ability to Ground Responses to Long-Form Input.

Towards Large Reasoning Models for Agriculture The FACTS Grounding Leaderboard: Benchmarking LLMs' Ability to Ground Responses to Long-Form Input

Reference 33

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Towards Large Reasoning Models for Agriculture Unresolved cited work

Reference 44

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Observation 99d92270-09ac-4a58-a6df-6a9a38d5f7c7 · outbound

This paper cites Additional Tips • Weather Monitoring: Use apps like FarmWise to track rainfall and adjust plans.

Towards Large Reasoning Models for Agriculture Additional Tips • Weather Monitoring: Use apps like FarmWise to track rainfall and adjust plans

Reference 46

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Observation 18f3e439-0baa-499e-93c0-143d66685e56 · outbound

This paper cites Spinach’s shallow roots benefit from consistent moisture.

Towards Large Reasoning Models for Agriculture Spinach’s shallow roots benefit from consistent moisture

Reference 47

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Observation b8630f92-7d8c-4ca4-b06c-f2065115df2c · outbound

This paper cites Avoid stem contact to prevent rot.

Towards Large Reasoning Models for Agriculture Avoid stem contact to prevent rot

Reference 48

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Observation a3a8ec82-24cd-4c71-ada9-34e139e33200 · outbound

This paper cites Raised beds can help manage moisture but monitor for drying.

Towards Large Reasoning Models for Agriculture Raised beds can help manage moisture but monitor for drying

Reference 49

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Observation beceaafd-6701-4cee-9946-f4e70f03a6ef · outbound

This paper cites Ensure 4-6 hours of sunlight daily to maintain growth without stress.

Towards Large Reasoning Models for Agriculture Ensure 4-6 hours of sunlight daily to maintain growth without stress

Reference 50

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Observation 2b596b4b-700b-40b0-a157-5c0144841cff · outbound

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Towards Large Reasoning Models for Agriculture Unresolved cited work

Reference 51

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Observation 924526b5-1037-4163-bddc-71f4eed5580b · outbound

This paper cites Use row covers to extend seasons and reduce evaporation.

Towards Large Reasoning Models for Agriculture Use row covers to extend seasons and reduce evaporation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:59.847980Z

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Observation 3f747adc-1980-45a0-a4e2-d7e5074a4591 · outbound

This paper cites Watch for wilting or bolting, which signal stress.

Towards Large Reasoning Models for Agriculture Watch for wilting or bolting, which signal stress

Reference 53

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Observation be33edfb-1f07-45a8-b2da-69879f7ea5b0 · outbound

This paper cites Use slow-release options if necessary.

Towards Large Reasoning Models for Agriculture Use slow-release options if necessary

Reference 54

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Observation 4579f698-3bda-4ba1-9271-7eea4f5bc554 · outbound

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Towards Large Reasoning Models for Agriculture Unresolved cited work

Reference 55

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Observation e7fea6c4-8ece-4d7e-ae08-1a3ef51845f7 · outbound

This paper cites Har- vest leaves promptly to encourage growth.

Towards Large Reasoning Models for Agriculture Har- vest leaves promptly to encourage growth

Reference 56

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Observation 9f0f6605-9860-445a-a0a6-43a94b6e9c2c · outbound

This paper cites Enhances biodiversity without competing heavily with straw- berries.

Towards Large Reasoning Models for Agriculture Enhances biodiversity without competing heavily with straw- berries

Reference 57

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Observation 5fef0155-3361-4d6f-838f-43b5bdb31a0d · outbound

This paper cites Fast-growing and easy to terminate.

Towards Large Reasoning Models for Agriculture Fast-growing and easy to terminate

Reference 58

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Observation 5dca3fd2-565a-4e0b-944b-309bef8f9dfb · outbound

This paper cites • Timing: Plant in late summer/fall post-harvest.

Towards Large Reasoning Models for Agriculture • Timing: Plant in late summer/fall post-harvest

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:58.648817Z

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Observation 2c2ede82-8d84-4258-93b8-6cc7c371fdcb · outbound

This paper cites • Add Organic Matter : Mix compost or aged manure into soil to improve structure (if soil isn’t fully waterlogged).

Towards Large Reasoning Models for Agriculture • Add Organic Matter : Mix compost or aged manure into soil to improve structure (if soil isn’t fully waterlogged)

Reference 60

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This paper cites • Pesticides: Use slug bait (iron phosphate) and insecticidal soap for aphids/s- lugs.

Towards Large Reasoning Models for Agriculture • Pesticides: Use slug bait (iron phosphate) and insecticidal soap for aphids/s- lugs

Reference 61

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Observation c4c6be07-180b-459c-ab97-79f23243149c · outbound

This paper cites • Foliar Spray: Use a liquid fertilizer (e.g., seaweed extract) for quick nutrient uptake.

Towards Large Reasoning Models for Agriculture • Foliar Spray: Use a liquid fertilizer (e.g., seaweed extract) for quick nutrient uptake

Reference 62

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Observation 9d2ea9b1-d478-49ed-b1a1-df85b912d7fa · outbound

This paper cites During Your Absence.

Towards Large Reasoning Models for Agriculture During Your Absence

Reference 63

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This paper cites Provide clear instructions for emergencies (e.g., reapplying fungi- cides).

Towards Large Reasoning Models for Agriculture Provide clear instructions for emergencies (e.g., reapplying fungi- cides)

Reference 64

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Towards Large Reasoning Models for Agriculture Unresolved cited work

Reference 65

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Observation 459cd43a-a256-43f8-a3d9-9681156589eb · outbound

This paper cites Long-Term Strategies (Post-Travel).

Towards Large Reasoning Models for Agriculture Long-Term Strategies (Post-Travel)

Reference 66

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Towards Large Reasoning Models for Agriculture Unresolved cited work

Reference 67

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Towards Large Reasoning Models for Agriculture Unresolved cited work

Reference 68

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Towards Large Reasoning Models for Agriculture Unresolved cited work

Reference 69

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Towards Large Reasoning Models for Agriculture Unresolved cited work

Reference 70

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Observation e8038bdc-0c56-4a87-aed0-ad1c2f7a798b · outbound

This paper cites Additional Tips • Weather Monitoring: Use apps like FarmWise to track rainfall and adjust plans.

Towards Large Reasoning Models for Agriculture Additional Tips • Weather Monitoring: Use apps like FarmWise to track rainfall and adjust plans

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:58.341573Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 53271b8d-7343-4c9d-9691-5744d0a4298e · outbound

This paper cites Plant in late summer to establish before frost; it may overwinter in milder areas.

Towards Large Reasoning Models for Agriculture Plant in late summer to establish before frost; it may overwinter in milder areas

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:58.174089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5bfffb6d-fcd6-434a-a584-c77d84344dac · outbound

This paper cites Plant in late summer post-lettuce harvest.

Towards Large Reasoning Models for Agriculture Plant in late summer post-lettuce harvest

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:58.046720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e82c25c8-895f-403f-b900-7be7321290e4 · outbound

This paper cites Ensure planting 6–8 weeks before frost for adequate growth.

Towards Large Reasoning Models for Agriculture Ensure planting 6–8 weeks before frost for adequate growth

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:57.922858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0e4441b2-f2dd-4648-90a5-aa1cfd0e4044 · outbound

This paper cites Alaska Biodiversity Blend.

Towards Large Reasoning Models for Agriculture Alaska Biodiversity Blend

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:57.815250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 98f35b13-bfbb-48bf-8a83-a27cdfec7504 · outbound

This paper cites lodged plants).

Towards Large Reasoning Models for Agriculture lodged plants)

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:57.708982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a37e6860-6862-48a3-8d1a-89de25bc0a7f · outbound

This paper cites If replant- ing by early June is feasible, use a maturity group suited to your remaining growing season.

Towards Large Reasoning Models for Agriculture If replant- ing by early June is feasible, use a maturity group suited to your remaining growing season

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:57.437766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b0d0086c-7bf5-48ee-944e-2177eec81309 · outbound

This paper cites Balance sulfur with gypsum or other amendments if tests indicate excess.

Towards Large Reasoning Models for Agriculture Balance sulfur with gypsum or other amendments if tests indicate excess

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:57.047961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b4a639be-1a0a-4dca-a7c2-c94d2cad1d9f · outbound

This paper cites • Chemical Applications: – Apply fungicides (e.g., strobilurins) preventively if hail caused plant wounds.

Towards Large Reasoning Models for Agriculture • Chemical Applications: – Apply fungicides (e.g., strobilurins) preventively if hail caused plant wounds

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:56.781752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3fc68954-5336-4f58-b700-9c4ea03db05f · outbound

This paper cites Adjust irrigation sched- ules to avoid drought stress, especially in shallow soils.

Towards Large Reasoning Models for Agriculture Adjust irrigation sched- ules to avoid drought stress, especially in shallow soils

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:56.557074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0219c955-6b9b-4f80-8bc9-0428600ca9e8 · outbound

This paper cites • Nitrogen Boost: If root nodules are damaged, a small N application (20–30 lbs/acre) may aid recovery.

Towards Large Reasoning Models for Agriculture • Nitrogen Boost: If root nodules are damaged, a small N application (20–30 lbs/acre) may aid recovery

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:56.316331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0da34aad-7b8b-4d3f-a9ec-9449080f6bf1 · outbound

This paper cites Repair irrigation systems, storage units, or fences impacted by the tornado.

Towards Large Reasoning Models for Agriculture Repair irrigation systems, storage units, or fences impacted by the tornado

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:56.146288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 874f5588-6198-4b75-9a97-1ee93c234c7f · outbound

This paper cites Contact your provider promptly to discuss replanting compensation or loss coverage.

Towards Large Reasoning Models for Agriculture Contact your provider promptly to discuss replanting compensation or loss coverage

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:55.974603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a4183ffb-4e53-4c88-a4f3-d7ab038516cf · outbound

This paper cites • Diversification: Consider crop rotation or insurance add-ons for extreme weather resilience.

Towards Large Reasoning Models for Agriculture • Diversification: Consider crop rotation or insurance add-ons for extreme weather resilience

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:55.827999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 23f1c6fe-d67d-48f4-a34a-f838eb3c0169 · outbound

This paper cites This occurs during the milky or dough stages of grain development.

Towards Large Reasoning Models for Agriculture This occurs during the milky or dough stages of grain development

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:55.671440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e9b0544e-1fa7-4e71-9610-d1044e305fb8 · outbound

This paper cites • Indirect Impact: Heavy infestations can lead to significant economic losses due to compromised seed viability and marketability.

Towards Large Reasoning Models for Agriculture • Indirect Impact: Heavy infestations can lead to significant economic losses due to compromised seed viability and marketability

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:55.481813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 39d8bbc9-ec9e-4391-9e40-d8a65c4eb0b9 · outbound

This paper cites Farmers should monitor wheat heads for bugs and damaged kernels, particularly during grain fill.

Towards Large Reasoning Models for Agriculture Farmers should monitor wheat heads for bugs and damaged kernels, particularly during grain fill

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:55.301017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation aefa0258-ded8-4f95-ae9e-83ae653a0db7 · outbound

This paper cites Examples include Pendimethalin or DCPA (Dacthal), which inhibit weed germination.

Towards Large Reasoning Models for Agriculture Examples include Pendimethalin or DCPA (Dacthal), which inhibit weed germination

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:55.121602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 453f2933-5d7f-49f0-b653-133a8ca8564d · outbound

This paper cites • Stale Seedbed Technique: (a) Prepare the seedbed 2–3 weeks before planting lettuce.

Towards Large Reasoning Models for Agriculture • Stale Seedbed Technique: (a) Prepare the seedbed 2–3 weeks before planting lettuce

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:54.968373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation dc514377-fa1c-4c32-aeaa-b7c0cf3877d2 · outbound

This paper cites Avoid deep plowing, which may bring buried seeds to the surface.

Towards Large Reasoning Models for Agriculture Avoid deep plowing, which may bring buried seeds to the surface

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:54.770774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c97d2a2a-4411-4f3a-8d92-8f2775fd25c5 · outbound

This paper cites Solar heat kills weed seeds and pathogens.

Towards Large Reasoning Models for Agriculture Solar heat kills weed seeds and pathogens

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:54.559452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 59212b99-6ddc-40f2-a4d0-475b7ee87f5a · outbound

This paper cites • Edge Management: Mow or herbicide field borders to prevent Wild safflower from encroaching.

Towards Large Reasoning Models for Agriculture • Edge Management: Mow or herbicide field borders to prevent Wild safflower from encroaching

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:54.408119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 76aa3d6a-b983-4291-bdfb-0a3b1479e17f · outbound

This paper cites Ideal for lettuce rows, as it warms soil and blocks light to weed seeds.

Towards Large Reasoning Models for Agriculture Ideal for lettuce rows, as it warms soil and blocks light to weed seeds

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:54.265715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 30223a0d-d7c7-47d0-be91-8b375e9451af · outbound

This paper cites Early detection simplifies control.

Towards Large Reasoning Models for Agriculture Early detection simplifies control

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:54.169881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e0b639ac-6e9e-424b-9bb7-bf64d17c2df1 · outbound

This paper cites Safety and Family Involvement • Herbicide Safety: Choose herbicides with low toxicity and follow re-entry intervals (REIs) to ensure safety for your kids.

Towards Large Reasoning Models for Agriculture Safety and Family Involvement • Herbicide Safety: Choose herbicides with low toxicity and follow re-entry intervals (REIs) to ensure safety for your kids

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:54.041354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3da17bb9-0c84-423d-8b37-c42d4e24c1ae · outbound

This paper cites an unresolved cited work.

Towards Large Reasoning Models for Agriculture Unresolved cited work

Reference 96

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:21:53.918315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bb2a81c2-a2aa-41df-8251-463aa354d66a · outbound

This paper cites an unresolved cited work.

Towards Large Reasoning Models for Agriculture Unresolved cited work

Reference 97

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:21:53.807519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 681d973d-65bc-41f6-b2db-ed174b8178bf · outbound

This paper cites an unresolved cited work.

Towards Large Reasoning Models for Agriculture Unresolved cited work

Reference 98

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:21:53.681027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d9375ff4-4bd8-487c-b903-c4e6111b0383 · outbound

This paper cites an unresolved cited work.

Towards Large Reasoning Models for Agriculture Unresolved cited work

Reference 99

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:21:53.559769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 576f7f42-de03-44b4-8691-cd6ee7cc7747 · outbound

This paper cites Factual Accuracy.

Towards Large Reasoning Models for Agriculture Factual Accuracy

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:53.428805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 054028f4-1af8-44f9-82cc-3ab78138b197 · outbound

This paper cites Amend with lime (to raise pH) or sulfur (to lower pH) as needed.

Towards Large Reasoning Models for Agriculture Amend with lime (to raise pH) or sulfur (to lower pH) as needed

Reference 101

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:53.266429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ce86b86e-a19a-4ee1-941d-60df8237b763 · outbound

This paper cites Sanitation: Remove plant debris post-harvest to reduce disease carryover.

Towards Large Reasoning Models for Agriculture Sanitation: Remove plant debris post-harvest to reduce disease carryover

Reference 103

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:53.027650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 373e7113-2e21-4fac-b6c7-0350ffca9b9a · outbound

This paper cites IPM Strategies: Use row covers, handpick pests, apply neem oil or spinosad, and encourage beneficial insects (e.g., ladybugs).

Towards Large Reasoning Models for Agriculture IPM Strategies: Use row covers, handpick pests, apply neem oil or spinosad, and encourage beneficial insects (e.g., ladybugs)

Reference 104

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:52.848803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fe899f53-aef9-46cb-b93a-f67fcf76498b · outbound

This paper cites Avoid waterlogged soil.

Towards Large Reasoning Models for Agriculture Avoid waterlogged soil

Reference 105

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:52.713540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6eee4f76-cd14-4299-af46-c4b81af8999b · outbound

This paper cites Balanced Fertilization: Use a low-nitrogen, high-phosphorus/potassium fertilizer (e.g., 5-10-10) to prioritize tuber growth over foliage.

Towards Large Reasoning Models for Agriculture Balanced Fertilization: Use a low-nitrogen, high-phosphorus/potassium fertilizer (e.g., 5-10-10) to prioritize tuber growth over foliage

Reference 106

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:52.496577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c2c5bfc0-9f4c-4318-a5d0-8f1950397936 · outbound

This paper cites Mulch: Apply organic mulch to regulate soil temperature and moisture.

Towards Large Reasoning Models for Agriculture Mulch: Apply organic mulch to regulate soil temperature and moisture

Reference 107

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:52.377584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 23d08d98-0fb8-4637-bf23-77c68b457467 · outbound

This paper cites Avoid harvesting in wet conditions.

Towards Large Reasoning Models for Agriculture Avoid harvesting in wet conditions

Reference 108

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:52.135424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1e106fae-7da9-4c74-86ce-a8ed4b1f3ba5 · outbound

This paper cites Use shade cloth if extreme heat is forecasted.

Towards Large Reasoning Models for Agriculture Use shade cloth if extreme heat is forecasted

Reference 109

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:51.954116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation db396232-2d43-4fd5-8ab2-26a0bd588589 · outbound

This paper cites By systematically addressing these factors, you can optimize soil conditions, mitigate pest- s/diseases, and improve overall potato quality and yield in Missouri’s climate.

Towards Large Reasoning Models for Agriculture By systematically addressing these factors, you can optimize soil conditions, mitigate pest- s/diseases, and improve overall potato quality and yield in Missouri’s climate

Reference 110

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:51.811309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation da60face-53a3-4e0b-83d9-ad951a7093ea · outbound

This paper cites Amend with lime (to raise pH) or sulfur (to lower pH) as needed.

Towards Large Reasoning Models for Agriculture Amend with lime (to raise pH) or sulfur (to lower pH) as needed

Reference 111

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:51.630860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 98b8e21b-8f8f-43b5-b4bb-10bbe7c550fe · outbound

This paper cites Rotate with legumes (e.g., beans, peas) to fix nitrogen and break pest/disease cycles.

Towards Large Reasoning Models for Agriculture Rotate with legumes (e.g., beans, peas) to fix nitrogen and break pest/disease cycles

Reference 112

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:53.157555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers