What the Data for This Category Looks Like
Data for crop farming intelligent due diligence reports comes from four primary sources: national agricultural condition monitoring stations, regional meteorological observation stations, seed industry filing databases, and production ledgers submitted by farming entities. Data update frequencies vary across sources. Meteorological data updates hourly. Agricultural condition monitoring data updates monthly. Production ledger data syncs on a quarterly basis. Most documents are structured tables or standardized CSV files. They include fields such as plot code, planted variety, sown area, yield per unit area, pest and disease incidence rate, pesticide usage amount, and others. Standard industry units include mu, kg/mu, mm, ton, and additional supported units.
Constraints on Citation Traceability
The multi-source, decentralized nature of crop farming data and differences in update frequencies require accurate labeling of each data source's update time during traceability, to avoid using expired data. The industry-specific unit system, including mu, kg/mu, mm, and others, requires retaining original unit information during traceability to prevent unit conversion errors. Some production data comes from submissions by farming entities. Traceability must link to the corresponding entity filing identifier to ensure data sources are fully traceable. The fixed field layout of structured documents requires retaining original row numbers and column names during parsing, to support accurate location of original data.
Configuration Settings
| Config Item | Recommended Value | Rationale |
|---|---|---|
| Recall Count | Top 8-12 entries | Crop farming due diligence data is mostly structured entries. Too many recalls will increase traceability complexity, while too few will fail to cover core information |
| Similarity Threshold | 0.75-0.85 | Crop farming data fields have high professionality, so a certain matching flexibility must be retained while avoiding irrelevant content recalls |
PARSE_FILE_TIMEOUT_SECONDS | 300-600 seconds | Large production ledgers or meteorological data sets have large file sizes, requiring sufficient time to complete structured parsing and field mapping |
| Chunk Length | 800-1200 characters | Single data entries in crop farming due diligence reports are relatively long. Chunking must balance context completeness and traceability accuracy |
| Rearranged Return Count | Top 3-5 entries | Core traceability information is concentrated in the top few high-matching data entries. Rearranging can further improve traceability efficiency |
| Knowledge Base Update Sync Cycle | Configured according to data source update rhythm | Update cycles vary significantly across crop farming data sources, so matching the corresponding cycle is required to ensure data timeliness |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: When calling the due diligence application, the returned traceability content is unrelated to the crop farming production question. Cause: The similarity threshold was not set correctly. Threshold values outside the reasonable range caused low-matching irrelevant data to be recalled.
- Phenomenon: Unable to locate specific rows and fields of original data during traceability. Cause: Original row numbers and column names were not retained when parsing structured documents, destroying the accurate positioning foundation for data traceability.
- Phenomenon: Referenced production ledger data shows outdated information. Cause: The knowledge base update sync cycle was not configured according to data source update rhythm, and latest production ledger data was not synced in a timely manner.
How to Verify Proper Configuration
- Upload a standard crop farming due diligence document, check the parsed field display, and confirm that original row numbers and column names have been fully retained.
- Submit a query targeting a specific crop production scenario, check whether the returned traceability content labels the corresponding data source and update time.
- Adjust relevant matching configurations and run a test, observe changes in the relevance of recalled content, and confirm that the configuration values meet business scenario requirements.
- Trigger a manual knowledge base sync operation, verify that updated content can be traced normally in the application.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.