Two new trial sources: LG Seeds + AgriGold plot reports (+2,307 cross-vendor yield trials)

Adds the **first non-Syngenta trial coverage** to the corpus:

| Source | Docs | Publisher | URL pattern |
|---|---|---|---|
| lg_plot_reports | 1,304 | LG Seeds (AgReliant) | lgseeds.com/performance/{crop} JSON XHR |
| agrigold_plot_reports | 1,003 | AgriGold (AgReliant) | agrigold.com/{crop}/performance/{crop}-yield-results |

Total trial coverage now: gh_plot_reports (4,299) + agripro_trials (14) +
lg_plot_reports (1,304) + agrigold_plot_reports (1,003) = 6,620 trial docs.

**Both scrapers follow the gh_plot_reports template** — same RateLimitedSession
primitive, same TrialResult/PlotReport dataclass shape, same data_type="trial"
sidecar convention. The trial chunker (`rag/chunk.py:_render_trial_chunk`) is
extended to recognize both new sources; they share `_render_gh_plot_chunk`
since their sidecars are structurally identical (just different brand label).

**LG specifics:**
- POST `/performance/{crop}/GetPlots/` returns sparse listing (id, year, lat/lng)
- GET `/performance/{crop}/GetPlotData/?PlotId=X&IsSilage=Y` returns full detail
  with state, cooperator, planting/harvest dates, and **top-5 hybrids** (LG +
  competitors). Top-5 is what LG publishes publicly; not the full ranking.
- 4 crops: corn (963), soybeans (287), sorghum (10), silage (50) — first
  alfalfa absent because LG doesn't run alfalfa plots; that's variety-only data.
- 301 gotcha: www.lgseeds.com redirects to lgseeds.com which drops POST body,
  so the scraper hits the apex host directly.

**AgriGold specifics:**
- Listing: GET `/{crop}/performance/{crop}-yield-results?harvestYear={year}`
  (server-rendered HTML, ~1MB; 408 corn plots in 2025 alone)
- Detail: GET `/{crop_url}/performance/{slug}/{plot_id}` returns the **full
  ranking** (not just top-5) plus rich plot management metadata: tillage,
  previous crop, fungicide, herbicide, insecticide, irrigation, soil type,
  row width, population. Most metadata-rich of the three trial sources.
- Soybean URL slug is singular: `/soybeans/performance/soybean-yield-results/`
- Columns: Rank | Brand | Product | Trait | Ck | H20 (moisture) | Test Wt. |
  Yield | Adj Yield (check-adjusted)
- 2 crops: corn (849) + soybeans (157)

**Indexer needs no changes** — `rag/index.py` auto-discovers any directory
under corpus/ and routes by data_type. Both new sources flow into the
existing trial collection and surface via `search_trials`.

Years scraped: 2024+2025 (matching gh_plot_reports baseline). 2023 is
available via `--include-2023` on either scraper for future backfill.
This commit is contained in:
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"""AgriGold plot-report scraper — cross-vendor yield trials.
AgriGold publishes detailed cross-vendor yield trials at
``agrigold.com/{crop}/performance/{crop}-yield-results``. Each
``plot-id`` is a single head-to-head trial site comparing AgriGold
hybrids against competitors (DEKALB, Pioneer, Dairyland, etc.) on
yield, moisture, test weight, and an "Adj Yield" check-adjusted
yield.
This is the THIRD ``data_type: "trial"`` source in the corpus
(after ``gh_plot_reports`` and ``lg_plot_reports``) — same shape
(per-site cross-vendor), different vendor (AgReliant Genetics /
AgriGold), and the **most metadata-rich** of the three. AgriGold's
detail page includes tillage, previous crop, fungicide, herbicide,
insecticide, irrigation, soil type — fields the others don't
publish.
Listing URLs (one per crop, year-filterable):
/corn/performance/corn-yield-results?harvestYear={year}
/soybeans/performance/soybean-yield-results?harvestYear={year}
Detail URL:
/<crop_url>/performance/<crop_url>-yield-results/{plot_id}
(For soybeans the URL is ``/soybeans/performance/soybean-yield-results/{id}``
- note the singular "soybean" in the path.)
Plot counts by harvest year (corn):
2025: 408, 2024: 441, 2023: 583, plus 2022 + 2026 (likely sparse)
Initial scrape: 2024+2025 (matching gh_plot_reports baseline).
Output:
corpus/agrigold_plot_reports/<source_key>.md LLM-visible body
corpus/agrigold_plot_reports/<source_key>.json sidecar metadata
source_key convention: ``agpr-<crop>-<year>-<plot_id>``
e.g. ``agpr-corn-2025-145926``.
CLI:
python -m scrape.sources.agrigold_plot_reports --limit 5
python -m scrape.sources.agrigold_plot_reports --crop corn --year 2025
python -m scrape.sources.agrigold_plot_reports --include-2023 --force
"""
from __future__ import annotations
import argparse
import json
import logging
import os
import random
import re
import sys
import threading
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
from dataclasses import dataclass, field
from datetime import datetime, timezone
from pathlib import Path
from typing import Any
import requests
from bs4 import BeautifulSoup
SCRAPER_VERSION = "0.1.0"
USER_AGENT = "seed-mcp-scraper/0.1 (+https://drawbar.example/contact)"
BASE = "https://www.agrigold.com"
REPO_ROOT = Path(__file__).resolve().parents[2]
CORPUS_ROOT = Path(os.environ.get("CORPUS_ROOT") or REPO_ROOT / "corpus")
CORPUS_DIR = CORPUS_ROOT / "agrigold_plot_reports"
REQ_INTERVAL_SEC = 0.5 # AgriGold pages are HEAVY (~50KB detail, 1MB+ listing)
DEFAULT_WORKERS = 4
log = logging.getLogger("scrape.agrigold_plot_reports")
# Crop → (URL segment, listing URL slug, detail URL slug)
# Corn: /corn/performance/corn-yield-results[/{id}]
# Soybeans: /soybeans/performance/soybean-yield-results[/{id}] (singular "soybean")
CROPS: dict[str, tuple[str, str]] = {
"corn": ("corn", "corn-yield-results"),
"soybeans": ("soybeans", "soybean-yield-results"),
}
# --------------------------------------------------------------------- HTTP
class RateLimitedSession:
"""Thread-safe rate-limited requests.Session wrapper.
Mirrors the primitive in gh_plot_reports.py.
"""
_lock = threading.Lock()
_last_global: float = 0.0
_global_interval: float = REQ_INTERVAL_SEC
def __init__(self, interval: float = REQ_INTERVAL_SEC) -> None:
self.s = requests.Session()
self.s.headers["User-Agent"] = USER_AGENT
with RateLimitedSession._lock:
if interval > RateLimitedSession._global_interval:
RateLimitedSession._global_interval = interval
def _wait(self) -> None:
with RateLimitedSession._lock:
delta = time.monotonic() - RateLimitedSession._last_global
if delta < RateLimitedSession._global_interval:
time.sleep(RateLimitedSession._global_interval - delta)
RateLimitedSession._last_global = time.monotonic()
def request(
self,
method: str,
url: str,
*,
max_retries: int = 4,
timeout: float = 30.0,
**kw: Any,
) -> requests.Response:
last_exc: Exception | None = None
for attempt in range(max_retries):
self._wait()
try:
resp = self.s.request(method, url, timeout=timeout, **kw)
except requests.RequestException as exc:
last_exc = exc
backoff = min(30.0, (2 ** attempt) + random.random())
log.warning("network error on %s %s: %s — retry in %.1fs",
method, url, exc, backoff)
time.sleep(backoff)
continue
if resp.status_code == 429 or 500 <= resp.status_code < 600:
ra = resp.headers.get("Retry-After")
backoff = float(ra) if (ra and ra.isdigit()) else min(30.0, (2 ** attempt) + random.random())
log.warning("HTTP %d on %s %s — retry in %.1fs",
resp.status_code, method, url, backoff)
time.sleep(backoff)
continue
return resp
if last_exc:
raise last_exc
return resp # type: ignore[return-value]
def get(self, url: str, **kw: Any) -> requests.Response:
return self.request("GET", url, **kw)
# --------------------------------------------------------------------- model
@dataclass
class TrialResult:
rank: int | None = None
brand: str = ""
product: str = ""
traits: str = ""
# Columns: Ck, H20 (moisture %), Test Wt., Yield, Adj Yield
metrics: dict[str, float | str | None] = field(default_factory=dict)
@dataclass
class PlotReport:
source_key: str
source_url: str
crop: str # "corn" / "soybeans"
year: int
plot_id: str
city: str | None = None
state_abbrev: str | None = None
county: str | None = None
cooperator: str | None = None
plot_average: float | None = None # whole-plot mean yield
# Plot management details (AgriGold publishes more of these
# than GH or LG — useful for agronomic comparison queries).
planted_date: str | None = None # ISO date
harvested_date: str | None = None # ISO date
population: int | None = None
fungicide: str | None = None
soil_type: str | None = None
tillage: str | None = None
herbicide: str | None = None
insecticide: str | None = None
row_width_in: str | None = None # kept as string ("30.0\"")
num_rows: int | None = None
previous_crop: str | None = None
irrigation: str | None = None
results: list[TrialResult] = field(default_factory=list)
# --------------------------------------------------------------------- discovery
def discover_plots(
http: RateLimitedSession,
*,
crops: set[str],
years: set[int],
) -> list[tuple[str, int, str]]:
"""Walk the listing pages per (crop, year). Returns
``[(crop, year, plot_id), ...]``."""
out: list[tuple[str, int, str]] = []
for crop in sorted(crops):
if crop not in CROPS:
log.warning("unknown crop %r, skipping", crop)
continue
url_seg, listing_slug = CROPS[crop]
for year in sorted(years):
url = f"{BASE}/{url_seg}/performance/{listing_slug}?harvestYear={year}"
log.info("GET %s", url)
r = http.get(url)
r.raise_for_status()
# data-plotid="123456" appears 5x per plot. Dedupe.
ids = set(re.findall(r'data-plotid="(\d+)"', r.text))
log.info(" %s %d: %d unique plot ids", crop, year, len(ids))
for pid in sorted(ids):
out.append((crop, year, pid))
return out
# --------------------------------------------------------------------- helpers
def source_key_for(crop: str, year: int, plot_id: str) -> str:
return f"agpr-{crop}-{year}-{plot_id}"
# State abbrev (city, ST format) regex — e.g. "Erie, IL", "Cottage Hill , KS"
_CITY_STATE_RE = re.compile(r"^(.*?)\s*,\s*([A-Z]{2})\s*$")
def _parse_int(s: str | None) -> int | None:
if not s:
return None
s = re.sub(r"[,$]", "", str(s).strip())
try:
return int(s)
except ValueError:
return None
def _parse_float(s: str | None) -> float | None:
if not s:
return None
s = re.sub(r"[,$]", "", str(s).strip())
try:
return float(s)
except ValueError:
return None
def _parse_date_slash(s: str | None) -> str | None:
"""``05/10/25`` → ``2025-05-10``. 2-digit year → 20xx."""
if not s:
return None
s = s.strip()
m = re.match(r"^(\d{1,2})/(\d{1,2})/(\d{2,4})$", s)
if not m:
return None
mo, dy, yr = m.group(1), m.group(2), m.group(3)
if len(yr) == 2:
yr = "20" + yr
try:
return f"{int(yr):04d}-{int(mo):02d}-{int(dy):02d}"
except ValueError:
return None
def _detail_pairs(soup: BeautifulSoup, container_class: str) -> dict[str, str]:
"""Pull ``.detail-item`` label/value pairs from a container."""
out: dict[str, str] = {}
container = soup.find("div", class_=container_class)
if container is None:
return out
for item in container.find_all("div", class_="detail-item"):
label_el = item.find("div", class_="label")
value_el = item.find("div", class_="value")
if label_el is None or value_el is None:
continue
label = label_el.get_text(" ", strip=True)
value = value_el.get_text(" ", strip=True)
if label and value:
out[label] = value
return out
# --------------------------------------------------------------------- detail
def fetch_plot_detail(
http: RateLimitedSession,
*,
crop: str,
year: int,
plot_id: str,
) -> PlotReport | None:
url_seg, listing_slug = CROPS[crop]
detail_url = f"{BASE}/{url_seg}/performance/{listing_slug}/{plot_id}"
r = http.get(detail_url)
if r.status_code == 404:
return None
r.raise_for_status()
soup = BeautifulSoup(r.text, "html.parser")
prod = PlotReport(
source_key=source_key_for(crop, year, plot_id),
source_url=detail_url,
crop=crop,
year=year,
plot_id=str(plot_id),
)
# Header block: plot-average + city/state + county/cooperator
header = soup.find("div", class_="details-header")
if header is not None:
avg_el = header.find("div", class_="plot-average")
if avg_el is not None:
# Strip the inner "Plot Average" label and parse the leading number
label_inside = avg_el.find("div", class_="label")
if label_inside is not None:
label_inside.extract()
avg_text = avg_el.get_text(" ", strip=True)
prod.plot_average = _parse_float(avg_text)
cs_el = header.find("div", class_="city-state")
if cs_el is not None:
cs = cs_el.get_text(" ", strip=True)
m = _CITY_STATE_RE.match(cs)
if m:
prod.city = m.group(1).strip()
prod.state_abbrev = m.group(2).strip().lower()
else:
prod.city = cs.strip()
county_el = header.find("div", class_="county")
if county_el is not None:
prod.county = county_el.get_text(" ", strip=True)
coop_el = header.find("div", class_="coorperator") # site's typo, not ours
if coop_el is not None:
prod.cooperator = coop_el.get_text(" ", strip=True)
# Plot Details: two columns of (label, value) pairs.
details = _detail_pairs(soup, "plot-details")
prod.planted_date = _parse_date_slash(details.get("Planting Date"))
prod.harvested_date = _parse_date_slash(details.get("Harvest Date"))
prod.population = _parse_int(details.get("Planting Population"))
prod.fungicide = details.get("Fungicide") or None
prod.soil_type = details.get("Soil Type") or None
prod.tillage = details.get("Tillage") or None
prod.herbicide = details.get("Herbicide") or None
prod.insecticide = details.get("Insecticide") or None
prod.row_width_in = details.get("Row Width") or None
prod.num_rows = _parse_int(details.get("Number Of Rows"))
prod.previous_crop = details.get("Previous Crop") or None
prod.irrigation = details.get("Irrigation") or None
# Comparison table
table = soup.find("table", class_="plot-rows")
if table is None:
return prod
# Header cells — already known to be Rank, Brand, Product, Trait,
# Ck, H20, Test Wt., Yield, Adj Yield. Read defensively from DOM
# in case it shifts.
header_cells = []
thead = table.find("thead")
if thead is not None:
for th in thead.find_all("th"):
wrap = th.find("div", class_="th-wrapper")
txt = (wrap.get_text(" ", strip=True) if wrap else th.get_text(" ", strip=True)).strip()
header_cells.append(txt)
# Map header position → key
def find_col(*names: str) -> int | None:
for n in names:
for i, h in enumerate(header_cells):
if h.lower() == n.lower():
return i
return None
i_rank = find_col("Rank")
i_brand = find_col("Brand")
i_product = find_col("Product")
i_trait = find_col("Trait", "Traits")
skip = {i_rank, i_brand, i_product, i_trait}
# Anything else is a metric column
metric_cols: list[tuple[str, int]] = []
for i, h in enumerate(header_cells):
if i in skip:
continue
if h:
metric_cols.append((h, i))
tbody = table.find("tbody")
if tbody is None:
return prod
for row in tbody.find_all("tr"):
cls = row.get("class") or []
# Skip CK AVERAGE and PLOT AVERAGE summary rows
if "check-averages" in cls or "plot-averages" in cls:
continue
cells = [c.get_text(" ", strip=True) for c in row.find_all("td")]
if len(cells) < 4:
continue
def cell(i: int | None) -> str:
return cells[i] if i is not None and 0 <= i < len(cells) else ""
metrics: dict[str, float | str | None] = {}
for name, idx in metric_cols:
raw = cell(idx).strip()
if not raw or raw == "-":
metrics[name] = None
else:
f = _parse_float(raw)
metrics[name] = f if f is not None else raw
result = TrialResult(
rank=_parse_int(cell(i_rank)),
brand=cell(i_brand).strip(),
product=cell(i_product).strip(),
traits=cell(i_trait).strip(),
metrics=metrics,
)
if result.brand or result.product or any(v is not None for v in metrics.values()):
prod.results.append(result)
return prod
# --------------------------------------------------------------------- render
def render_markdown(p: PlotReport) -> str:
crop_label = {"corn": "Corn", "soybeans": "Soybean"}.get(p.crop, p.crop.title())
where = ", ".join(x for x in (p.city, p.state_abbrev.upper() if p.state_abbrev else None) if x) or "?"
head: list[str] = [
f"# {crop_label} yield trial — {where}, {p.year}",
"",
f"- **Source:** AgriGold plot report (cross-vendor head-to-head)",
f"- **Vendor:** AgReliant Genetics / AgriGold",
f"- **Crop:** {crop_label}",
]
if p.state_abbrev:
head.append(f"- **State:** {p.state_abbrev.upper()}")
if p.county:
head.append(f"- **County:** {p.county}")
if p.city:
head.append(f"- **City:** {p.city}")
head.append(f"- **Year:** {p.year}")
head.append(f"- **Plot ID:** {p.plot_id}")
if p.cooperator:
head.append(f"- **Cooperator:** {p.cooperator}")
if p.plot_average is not None:
unit = "BU/Ac" # AgriGold publishes BU/Ac for both corn and soy
head.append(f"- **Plot average:** {p.plot_average} {unit}")
if p.planted_date:
head.append(f"- **Planted:** {p.planted_date}")
if p.harvested_date:
head.append(f"- **Harvested:** {p.harvested_date}")
if p.population:
head.append(f"- **Population:** {p.population:,} seeds/acre")
if p.row_width_in:
head.append(f"- **Row width:** {p.row_width_in}")
if p.num_rows:
head.append(f"- **# Rows:** {p.num_rows}")
if p.soil_type:
head.append(f"- **Soil type:** {p.soil_type}")
if p.tillage:
head.append(f"- **Tillage:** {p.tillage}")
if p.previous_crop:
head.append(f"- **Previous crop:** {p.previous_crop}")
if p.irrigation:
head.append(f"- **Irrigation:** {p.irrigation}")
if p.fungicide and p.fungicide.upper() != "N/A":
head.append(f"- **Fungicide:** {p.fungicide}")
if p.herbicide and p.herbicide.upper() != "N/A":
head.append(f"- **Herbicide:** {p.herbicide}")
if p.insecticide and p.insecticide.upper() != "N/A":
head.append(f"- **Insecticide:** {p.insecticide}")
head.append(f"- **URL:** {p.source_url}")
head.append("")
head.append("---")
head.append("")
sections: list[str] = []
if p.results:
metric_keys: list[str] = []
seen: set[str] = set()
for r in p.results:
for k in r.metrics.keys():
if k not in seen:
seen.add(k)
metric_keys.append(k)
sections.append("## Results (by rank)")
sections.append("")
headers = ["Rank", "Brand", "Product", "Trait"] + metric_keys
sections.append("| " + " | ".join(headers) + " |")
sections.append("|" + "|".join(["---"] * len(headers)) + "|")
for r in p.results:
row = [
str(r.rank) if r.rank is not None else "-",
r.brand or "-",
r.product or "-",
r.traits or "-",
]
for k in metric_keys:
v = r.metrics.get(k)
if v is None:
row.append("-")
elif isinstance(v, (int, float)):
row.append(str(v))
else:
row.append(str(v))
sections.append("| " + " | ".join(row) + " |")
sections.append("")
# Compact summary line for embedder signal — top 5 by Yield.
primary = "Yield" if "Yield" in metric_keys else (metric_keys[0] if metric_keys else None)
if primary:
top = sorted(
(r for r in p.results if isinstance(r.metrics.get(primary), (int, float))),
key=lambda r: -r.metrics[primary], # type: ignore[operator]
)[:5]
if top:
bits = [f"{r.product} ({r.brand}) {r.metrics[primary]}" for r in top]
sections.append(f"Top 5 by {primary}: " + ", ".join(bits) + ".")
sections.append("")
return "\n".join(head) + "\n".join(sections)
# --------------------------------------------------------------------- write
def write_plot(p: PlotReport, body_md: str) -> None:
CORPUS_DIR.mkdir(parents=True, exist_ok=True)
md_path = CORPUS_DIR / f"{p.source_key}.md"
json_path = CORPUS_DIR / f"{p.source_key}.json"
md_path.write_text(body_md, encoding="utf-8")
sidecar = {
"source": "agrigold_plot_reports",
"source_key": p.source_key,
"data_type": "trial",
"vendor": "AgReliant Genetics",
"brand": "AgriGold",
"crop": p.crop,
"state": p.state_abbrev.upper() if p.state_abbrev else None,
"state_abbrev": p.state_abbrev,
"city": p.city,
"county": p.county,
"year": p.year,
"plot_id": p.plot_id,
"cooperator": p.cooperator,
"plot_average": p.plot_average,
"planted_date": p.planted_date,
"harvested_date": p.harvested_date,
"population_seeds_per_acre": p.population,
"row_width": p.row_width_in,
"num_rows": p.num_rows,
"soil_type": p.soil_type,
"tillage": p.tillage,
"previous_crop": p.previous_crop,
"irrigation": p.irrigation,
"fungicide": p.fungicide,
"herbicide": p.herbicide,
"insecticide": p.insecticide,
"results": [
{
"rank": r.rank,
"brand": r.brand,
"product": r.product,
"traits": r.traits,
"metrics": r.metrics,
}
for r in p.results
],
"n_results": len(p.results),
"source_urls": [p.source_url],
"fetched_at": datetime.now(timezone.utc).isoformat(),
"scraper_version": SCRAPER_VERSION,
}
json_path.write_text(
json.dumps(sidecar, indent=2, ensure_ascii=False) + "\n",
encoding="utf-8",
)
# --------------------------------------------------------------------- pipeline
def process_plot(
http: RateLimitedSession,
*,
crop: str,
year: int,
plot_id: str,
force: bool,
) -> tuple[str, PlotReport | None]:
sk = source_key_for(crop, year, plot_id)
md_path = CORPUS_DIR / f"{sk}.md"
if md_path.exists() and not force:
return "skipped", None
try:
p = fetch_plot_detail(http, crop=crop, year=year, plot_id=plot_id)
except Exception as exc: # noqa: BLE001
log.error("detail fetch failed for %s/%s: %s", crop, plot_id, exc)
return "failed", None
if p is None:
return "missing", None
body = render_markdown(p)
write_plot(p, body)
return "written", p
def run(
*,
limit: int | None,
force: bool,
only_crop: str | None,
only_year: int | None,
include_2023: bool,
workers: int = DEFAULT_WORKERS,
) -> int:
CORPUS_DIR.mkdir(parents=True, exist_ok=True)
crops = {only_crop} if only_crop else set(CROPS.keys())
if only_year:
years = {only_year}
elif include_2023:
years = {2023, 2024, 2025}
else:
years = {2024, 2025}
discovery_http = RateLimitedSession()
targets = discover_plots(discovery_http, crops=crops, years=years)
log.info("discovered %d total plot targets", len(targets))
if limit is not None:
targets = targets[:limit]
counts = {"written": 0, "skipped": 0, "missing": 0, "failed": 0}
counts_lock = threading.Lock()
processed_counter = {"n": 0}
total = len(targets)
thread_local = threading.local()
def _session() -> RateLimitedSession:
s = getattr(thread_local, "session", None)
if s is None:
s = RateLimitedSession()
thread_local.session = s
return s
def _worker(target: tuple[str, int, str]) -> tuple[str, Any]:
crop, year, plot_id = target
return process_plot(
_session(), crop=crop, year=year, plot_id=plot_id, force=force,
)
log.info(
"dispatching %d plots across %d workers (shared rate limiter %.2f sec/req)",
total, workers, REQ_INTERVAL_SEC,
)
with ThreadPoolExecutor(max_workers=workers) as pool:
futures = {pool.submit(_worker, t): t for t in targets}
for fut in as_completed(futures):
target = futures[fut]
crop, year, plot_id = target
try:
status, p = fut.result()
except Exception as exc: # noqa: BLE001
log.error("worker failed for %s/%s/%s: %s", crop, year, plot_id, exc)
status, p = "failed", None
with counts_lock:
counts[status] = counts.get(status, 0) + 1
processed_counter["n"] += 1
n = processed_counter["n"]
if (p is not None and n <= 5) or n % 100 == 0 or status == "failed":
log.info(
"[%d/%d] %s %s | results=%d state=%s",
n, total,
source_key_for(crop, year, plot_id), status,
len(p.results) if p else 0,
(p.state_abbrev.upper() if p and p.state_abbrev else "-"),
)
log.info(
"done: processed=%d written=%d skipped=%d missing=%d failed=%d (of %d candidates)",
processed_counter["n"], counts["written"], counts["skipped"],
counts["missing"], counts["failed"], total,
)
return 0 if counts["failed"] == 0 else 1
# --------------------------------------------------------------------- CLI
def _build_argparser() -> argparse.ArgumentParser:
p = argparse.ArgumentParser(
prog="scrape.sources.agrigold_plot_reports",
description="Scrape AgriGold cross-vendor plot reports (yield trials).",
)
p.add_argument("--limit", type=int, default=None,
help="Stop after processing N plots (default: all).")
p.add_argument("--force", action="store_true",
help="Re-fetch even if the markdown file already exists.")
p.add_argument("--crop", default=None,
choices=tuple(CROPS.keys()),
help="Limit to one crop.")
p.add_argument("--year", type=int, default=None,
choices=(2022, 2023, 2024, 2025, 2026),
help="Limit to one year.")
p.add_argument("--include-2023", action="store_true",
help="Include 2023 plot reports (default: 2024-2025 only).")
p.add_argument("--workers", type=int, default=DEFAULT_WORKERS,
help=f"Concurrent worker threads (default {DEFAULT_WORKERS}, "
f"all share a global {REQ_INTERVAL_SEC}-sec rate limiter).")
p.add_argument("--log-level", default=os.environ.get("LOG_LEVEL", "INFO"))
return p
def main(argv: list[str] | None = None) -> int:
args = _build_argparser().parse_args(argv)
logging.basicConfig(
level=args.log_level.upper(),
format="%(asctime)s %(levelname)s %(name)s %(message)s",
stream=sys.stderr,
)
return run(
limit=args.limit,
force=args.force,
only_crop=args.crop,
only_year=args.year,
include_2023=args.include_2023,
workers=args.workers,
)
if __name__ == "__main__":
sys.exit(main())
+690
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"""LG Seeds plot-report scraper — cross-vendor yield trials.
LG Seeds publishes its plot data via a JSON XHR API behind
``lgseeds.com/performance/{crop}``. Each plot is a head-to-head
yield trial at a specific state/year/site, with the **top 5
performing hybrids** shown — sometimes all LG products, sometimes a
mix of LG + competitors (e.g. "212-02VT2PRIB" from another brand
shows up as a competitor entry with no productId).
This is the SECOND ``data_type: "trial"`` source in the corpus
after ``gh_plot_reports`` — same shape (per-site cross-vendor
yield), different vendor (AgReliant Genetics / LG Seeds).
Sources:
POST /performance/{crop}/GetPlots/ body={seedType,traits:[],products:[]}
→ list of {id, lat, lng, year, avg, ...} (sparse; state=null until detail)
GET /performance/{crop}/GetPlotData/?PlotId=<id>&IsSilage=<bool>
{id, lat, lng, avg, year, city, state, salesperson, cooperator,
plantingDate, harvestDate, top: [{name, avg, productId, crop, variety}]}
Crops supported by LG's portal: corn, soybeans, sorghum, silage.
Plot counts (across all years 2023-2025):
Crop 2023 2024 2025 Total
Corn 553 487 476 1,516
Soybeans 198 134 153 485
Sorghum 9 6 4 19
Silage 29 26 24 79
Initial scrape: 2024+2025 (matching gh_plot_reports baseline).
2023 is older but still informative; defer to ``--include-2023``.
Output:
corpus/lg_plot_reports/<source_key>.md LLM-visible body
corpus/lg_plot_reports/<source_key>.json sidecar metadata
source_key convention: ``lgpr-<crop>-<year>-<plot_id>``
e.g. ``lgpr-corn-2025-146257``. State isn't in the source_key
because the discovery endpoint returns ``state: null`` — it's only
filled in by the detail call. The full state name lives in the
sidecar so filters work.
CLI:
python -m scrape.sources.lg_plot_reports --limit 5
python -m scrape.sources.lg_plot_reports --crop corn --year 2025
python -m scrape.sources.lg_plot_reports --include-2023 --force
"""
from __future__ import annotations
import argparse
import json
import logging
import os
import random
import re
import sys
import threading
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
from dataclasses import dataclass, field
from datetime import datetime, timezone
from pathlib import Path
from typing import Any
import requests
SCRAPER_VERSION = "0.1.0"
USER_AGENT = "seed-mcp-scraper/0.1 (+https://drawbar.example/contact)"
# Note: lgseeds.com 301-redirects www→apex for the POST endpoints,
# which curl follows but turns the POST into a GET. Hit the apex
# host directly.
BASE = "https://lgseeds.com"
REPO_ROOT = Path(__file__).resolve().parents[2]
CORPUS_ROOT = Path(os.environ.get("CORPUS_ROOT") or REPO_ROOT / "corpus")
CORPUS_DIR = CORPUS_ROOT / "lg_plot_reports"
REQ_INTERVAL_SEC = 0.25
DEFAULT_WORKERS = 4
log = logging.getLogger("scrape.lg_plot_reports")
# Crop → (URL segment, seedType payload, IsSilage flag for detail call)
CROPS: dict[str, tuple[str, str, bool]] = {
"corn": ("corn", "Corn", False),
"soybeans": ("soybeans", "Soybeans", False),
"sorghum": ("sorghum", "Sorghum", False),
"silage": ("silage", "Silage", True),
}
# State abbrev → full name. Used for sidecar normalization when
# detail returns a 2-letter state code.
STATE_NAMES = {
"al": "Alabama", "ak": "Alaska", "az": "Arizona", "ar": "Arkansas",
"ca": "California", "co": "Colorado", "ct": "Connecticut",
"de": "Delaware", "fl": "Florida", "ga": "Georgia", "hi": "Hawaii",
"id": "Idaho", "il": "Illinois", "in": "Indiana", "ia": "Iowa",
"ks": "Kansas", "ky": "Kentucky", "la": "Louisiana", "me": "Maine",
"md": "Maryland", "ma": "Massachusetts", "mi": "Michigan",
"mn": "Minnesota", "ms": "Mississippi", "mo": "Missouri",
"mt": "Montana", "ne": "Nebraska", "nv": "Nevada", "nh": "New Hampshire",
"nj": "New Jersey", "nm": "New Mexico", "ny": "New York",
"nc": "North Carolina", "nd": "North Dakota", "oh": "Ohio",
"ok": "Oklahoma", "or": "Oregon", "pa": "Pennsylvania",
"ri": "Rhode Island", "sc": "South Carolina", "sd": "South Dakota",
"tn": "Tennessee", "tx": "Texas", "ut": "Utah", "vt": "Vermont",
"va": "Virginia", "wa": "Washington", "wv": "West Virginia",
"wi": "Wisconsin", "wy": "Wyoming",
}
# --------------------------------------------------------------------- HTTP
class RateLimitedSession:
"""Thread-safe rate-limited requests.Session wrapper.
Same primitive as gh_plot_reports — single global cadence across
every worker thread + retries on 429/5xx with backoff.
"""
_lock = threading.Lock()
_last_global: float = 0.0
_global_interval: float = REQ_INTERVAL_SEC
def __init__(self, interval: float = REQ_INTERVAL_SEC) -> None:
self.s = requests.Session()
self.s.headers["User-Agent"] = USER_AGENT
# XHR + JSON headers — required because the LG endpoints
# return the whole HTML page if not flagged as XHR.
self.s.headers["Accept"] = "application/json, text/plain, */*"
self.s.headers["X-Requested-With"] = "XMLHttpRequest"
with RateLimitedSession._lock:
if interval > RateLimitedSession._global_interval:
RateLimitedSession._global_interval = interval
def _wait(self) -> None:
with RateLimitedSession._lock:
delta = time.monotonic() - RateLimitedSession._last_global
if delta < RateLimitedSession._global_interval:
time.sleep(RateLimitedSession._global_interval - delta)
RateLimitedSession._last_global = time.monotonic()
def request(
self,
method: str,
url: str,
*,
max_retries: int = 4,
timeout: float = 30.0,
**kw: Any,
) -> requests.Response:
last_exc: Exception | None = None
for attempt in range(max_retries):
self._wait()
try:
resp = self.s.request(method, url, timeout=timeout, **kw)
except requests.RequestException as exc:
last_exc = exc
backoff = min(30.0, (2 ** attempt) + random.random())
log.warning("network error on %s %s: %s — retry in %.1fs",
method, url, exc, backoff)
time.sleep(backoff)
continue
if resp.status_code == 429 or 500 <= resp.status_code < 600:
ra = resp.headers.get("Retry-After")
backoff = float(ra) if (ra and ra.isdigit()) else min(30.0, (2 ** attempt) + random.random())
log.warning("HTTP %d on %s %s — retry in %.1fs",
resp.status_code, method, url, backoff)
time.sleep(backoff)
continue
return resp
if last_exc:
raise last_exc
return resp # type: ignore[return-value]
def get(self, url: str, **kw: Any) -> requests.Response:
return self.request("GET", url, **kw)
def post(self, url: str, **kw: Any) -> requests.Response:
return self.request("POST", url, **kw)
# --------------------------------------------------------------------- model
@dataclass
class TrialResult:
rank: int | None = None
brand: str = ""
product: str = ""
traits: str = ""
metrics: dict[str, float | str | None] = field(default_factory=dict)
@dataclass
class PlotReport:
source_key: str
source_url: str
crop: str # "corn" / "soybeans" / "sorghum" / "silage"
year: int
plot_id: str
lat: float | None = None
lng: float | None = None
plot_average: float | None = None # whole-plot mean (BU/Ac or T/Ac)
state_abbrev: str | None = None
state_name: str | None = None
city: str | None = None
cooperator: str | None = None
salesperson: str | None = None
planted_date: str | None = None # ISO date
harvested_date: str | None = None # ISO date
results: list[TrialResult] = field(default_factory=list)
# --------------------------------------------------------------------- discovery
def discover_plots(
http: RateLimitedSession,
*,
crops: set[str],
years: set[int],
) -> list[tuple[str, int, str]]:
"""Hit POST GetPlots per crop, filter by target years, return
``[(crop, year, plot_id), ...]``."""
out: list[tuple[str, int, str]] = []
for crop in sorted(crops):
if crop not in CROPS:
log.warning("unknown crop %r, skipping", crop)
continue
url_seg, seed_type, _is_silage = CROPS[crop]
url = f"{BASE}/performance/{url_seg}/GetPlots/"
log.info("POST %s seedType=%s", url, seed_type)
r = http.post(
url,
data=json.dumps({"seedType": seed_type, "traits": [], "products": []}),
headers={
"Content-Type": "application/json",
"Referer": f"{BASE}/performance/{url_seg}",
},
)
r.raise_for_status()
plots = r.json()
for p in plots:
try:
year = int(p["year"])
pid = str(p["id"])
except (KeyError, TypeError, ValueError):
continue
if years and year not in years:
continue
out.append((crop, year, pid))
log.info(" %s: %d plots after year filter", crop, sum(1 for c, _, _ in out if c == crop))
return out
# --------------------------------------------------------------------- helpers
def source_key_for(crop: str, year: int, plot_id: str) -> str:
return f"lgpr-{crop}-{year}-{plot_id}"
def _parse_iso_date(s: str | None) -> str | None:
"""``2025-05-19T00:00:00`` → ``2025-05-19``."""
if not s:
return None
s = s.strip()
m = re.match(r"^(\d{4}-\d{2}-\d{2})", s)
return m.group(1) if m else None
# Known seed brands that can appear as competitor entries. Used to
# split a single "name" string like "LG60C24VT4PRO" or
# "P1170AM" or "212-02VT2PRIB" into brand/product/traits.
# Ordered longest-first so multi-word brands match before substrings.
_BRAND_HINTS = (
"Golden Harvest", "WestBred", "AgriPro", "DEKALB", "Pioneer",
"Channel", "Asgrow", "Becks", "Beck's", "Brevant",
"Stine", "Renk", "Wyffels", "Croplan", "FS",
"Local Choice", "Mycogen", "AgriGold", "Hoegemeyer", "NK",
)
def _split_lg_top_entry(entry: dict) -> tuple[str, str, str]:
"""LG Seeds 'top' entries are flat — just a name like
``LG60C24VT4PRO`` (LG hybrid + trait stack) or ``P1170AM``
(Pioneer competitor) or ``212-02VT2PRIB`` (untagged competitor
code). We use the productId/variety/crop fields to detect LG
entries; otherwise best-effort brand inference.
Returns ``(brand, product, traits)`` so the chunk renderer can
display the LG vs. competitor breakdown clearly.
"""
name = (entry.get("name") or "").strip()
if not name:
return ("", "", "")
# LG entry: productId + variety are populated
if entry.get("productId") and entry.get("variety"):
product = str(entry["variety"]).strip()
# The 'name' field includes the trait stack as a suffix
# appended to the variety code (e.g. variety "LG60C24",
# name "LG60C24VT4PRO" → traits "VT4PRO").
traits = ""
if name.upper().startswith(product.upper()):
traits = name[len(product):].strip(" -")
return ("LG Seeds", product, traits)
# Competitor entry — try to identify the brand from the name prefix.
upper = name.upper()
if upper.startswith("P") and re.match(r"^P\d", upper):
# Pioneer code pattern "P1170AM"
return ("Pioneer", name, "")
for brand in _BRAND_HINTS:
if upper.startswith(brand.upper()):
rest = name[len(brand):].strip()
return (brand, rest or name, "")
# No brand match — return whole name as product, brand unknown
return ("Unknown", name, "")
# --------------------------------------------------------------------- detail
def fetch_plot_detail(
http: RateLimitedSession,
*,
crop: str,
plot_id: str,
) -> PlotReport | None:
url_seg, _seed_type, is_silage = CROPS[crop]
detail_url = f"{BASE}/performance/{url_seg}/GetPlotData/"
r = http.get(
detail_url,
params={"PlotId": plot_id, "IsSilage": "true" if is_silage else "false"},
headers={"Referer": f"{BASE}/performance/{url_seg}"},
)
if r.status_code == 404:
return None
r.raise_for_status()
try:
d = r.json()
except json.JSONDecodeError:
log.error("non-JSON response for plot %s (%s)", plot_id, crop)
return None
if not d:
return None
year = int(d.get("year")) if d.get("year") else 0
state_abbrev = (d.get("state") or "").strip().lower() or None
state_name = STATE_NAMES.get(state_abbrev, state_abbrev.upper()) if state_abbrev else None
prod = PlotReport(
source_key=source_key_for(crop, year, plot_id),
source_url=f"{BASE}/performance/{url_seg}#plot-{plot_id}",
crop=crop,
year=year,
plot_id=str(plot_id),
lat=d.get("lat"),
lng=d.get("lng"),
plot_average=d.get("avg"),
state_abbrev=state_abbrev,
state_name=state_name,
city=d.get("city"),
cooperator=d.get("cooperator"),
salesperson=d.get("salesperson"),
planted_date=_parse_iso_date(d.get("plantingDate")),
harvested_date=_parse_iso_date(d.get("harvestDate")),
)
# Choose the primary metric label per crop.
metric_label = "Ton/Acre" if crop == "silage" else "Yield"
top = d.get("top") or []
for i, entry in enumerate(top, start=1):
brand, product, traits = _split_lg_top_entry(entry)
avg = entry.get("avg")
# The "name" sometimes deduplicates with the same product
# but a different trait suffix — keep both rows but tag the
# variety with its raw name in the traits field for trace.
metrics: dict[str, float | str | None] = {}
if isinstance(avg, (int, float)):
metrics[metric_label] = float(avg)
prod.results.append(TrialResult(
rank=i,
brand=brand,
product=product,
traits=traits,
metrics=metrics,
))
return prod
# --------------------------------------------------------------------- render
def render_markdown(p: PlotReport) -> str:
crop_label = {
"corn": "Corn", "soybeans": "Soybean",
"sorghum": "Sorghum", "silage": "Silage",
}.get(p.crop, p.crop.title())
where = ", ".join(x for x in (p.city, p.state_name) if x) or "?"
head: list[str] = [
f"# {crop_label} yield trial — {where}, {p.year}",
"",
f"- **Source:** LG Seeds plot report (top-5 cross-vendor)",
f"- **Vendor:** AgReliant Genetics / LG Seeds",
f"- **Crop:** {crop_label}",
]
if p.state_name:
head.append(f"- **State:** {p.state_name}" + (f" ({p.state_abbrev.upper()})" if p.state_abbrev else ""))
if p.city:
head.append(f"- **City:** {p.city}")
head.append(f"- **Year:** {p.year}")
head.append(f"- **Plot ID:** {p.plot_id}")
if p.cooperator:
head.append(f"- **Cooperator:** {p.cooperator}")
if p.salesperson and p.salesperson != p.cooperator:
head.append(f"- **Salesperson:** {p.salesperson}")
if p.planted_date:
head.append(f"- **Planted:** {p.planted_date}")
if p.harvested_date:
head.append(f"- **Harvested:** {p.harvested_date}")
if p.plot_average is not None:
unit = "T/Ac" if p.crop == "silage" else "BU/Ac"
head.append(f"- **Plot average:** {p.plot_average} {unit}")
if p.lat and p.lng:
head.append(f"- **Coordinates:** {p.lat:.5f}, {p.lng:.5f}")
head.append(f"- **URL:** {p.source_url}")
head.append("")
head.append("---")
head.append("")
sections: list[str] = []
if p.results:
metric_keys: list[str] = []
seen: set[str] = set()
for r in p.results:
for k in r.metrics.keys():
if k not in seen:
seen.add(k)
metric_keys.append(k)
sections.append("## Top 5 results (by yield rank)")
sections.append("")
headers = ["Rank", "Brand", "Product", "Traits"] + metric_keys
sections.append("| " + " | ".join(headers) + " |")
sections.append("|" + "|".join(["---"] * len(headers)) + "|")
for r in p.results:
row = [
str(r.rank) if r.rank is not None else "-",
r.brand or "-",
r.product or "-",
r.traits or "-",
]
for k in metric_keys:
v = r.metrics.get(k)
if v is None:
row.append("-")
elif isinstance(v, (int, float)):
row.append(str(v))
else:
row.append(str(v))
sections.append("| " + " | ".join(row) + " |")
sections.append("")
# Compact summary line — useful BM25/embedder signal.
primary_label = metric_keys[0] if metric_keys else "Yield"
summary_bits = []
for r in p.results:
v = r.metrics.get(primary_label)
if isinstance(v, (int, float)):
summary_bits.append(f"{r.product} ({r.brand}) {v}")
if summary_bits:
sections.append(f"Top 5 by {primary_label}: " + ", ".join(summary_bits) + ".")
sections.append("")
return "\n".join(head) + "\n".join(sections)
# --------------------------------------------------------------------- write
def write_plot(p: PlotReport, body_md: str) -> None:
CORPUS_DIR.mkdir(parents=True, exist_ok=True)
md_path = CORPUS_DIR / f"{p.source_key}.md"
json_path = CORPUS_DIR / f"{p.source_key}.json"
md_path.write_text(body_md, encoding="utf-8")
sidecar = {
"source": "lg_plot_reports",
"source_key": p.source_key,
"data_type": "trial",
"vendor": "AgReliant Genetics",
"brand": "LG Seeds",
"crop": p.crop,
"state": p.state_name,
"state_abbrev": p.state_abbrev,
"city": p.city,
"year": p.year,
"plot_id": p.plot_id,
"lat": p.lat,
"lng": p.lng,
"cooperator": p.cooperator,
"salesperson": p.salesperson,
"planted_date": p.planted_date,
"harvested_date": p.harvested_date,
"plot_average": p.plot_average,
"results": [
{
"rank": r.rank,
"brand": r.brand,
"product": r.product,
"traits": r.traits,
"metrics": r.metrics,
}
for r in p.results
],
"n_results": len(p.results),
"source_urls": [p.source_url],
"fetched_at": datetime.now(timezone.utc).isoformat(),
"scraper_version": SCRAPER_VERSION,
}
json_path.write_text(
json.dumps(sidecar, indent=2, ensure_ascii=False) + "\n",
encoding="utf-8",
)
# --------------------------------------------------------------------- pipeline
def process_plot(
http: RateLimitedSession,
*,
crop: str,
year: int,
plot_id: str,
force: bool,
) -> tuple[str, PlotReport | None]:
sk = source_key_for(crop, year, plot_id)
md_path = CORPUS_DIR / f"{sk}.md"
if md_path.exists() and not force:
return "skipped", None
try:
p = fetch_plot_detail(http, crop=crop, plot_id=plot_id)
except Exception as exc: # noqa: BLE001
log.error("detail fetch failed for %s/%s: %s", crop, plot_id, exc)
return "failed", None
if p is None:
return "missing", None
body = render_markdown(p)
write_plot(p, body)
return "written", p
def run(
*,
limit: int | None,
force: bool,
only_crop: str | None,
only_year: int | None,
include_2023: bool,
workers: int = DEFAULT_WORKERS,
) -> int:
CORPUS_DIR.mkdir(parents=True, exist_ok=True)
crops = {only_crop} if only_crop else set(CROPS.keys())
if only_year:
years = {only_year}
elif include_2023:
years = {2023, 2024, 2025}
else:
years = {2024, 2025}
discovery_http = RateLimitedSession()
targets = discover_plots(discovery_http, crops=crops, years=years)
log.info("discovered %d total plot targets", len(targets))
if limit is not None:
targets = targets[:limit]
counts = {"written": 0, "skipped": 0, "missing": 0, "failed": 0}
counts_lock = threading.Lock()
processed_counter = {"n": 0}
total = len(targets)
thread_local = threading.local()
def _session() -> RateLimitedSession:
s = getattr(thread_local, "session", None)
if s is None:
s = RateLimitedSession()
thread_local.session = s
return s
def _worker(target: tuple[str, int, str]) -> tuple[str, Any]:
crop, year, plot_id = target
return process_plot(
_session(), crop=crop, year=year, plot_id=plot_id, force=force,
)
log.info(
"dispatching %d plots across %d workers (shared rate limiter %.2f sec/req)",
total, workers, REQ_INTERVAL_SEC,
)
with ThreadPoolExecutor(max_workers=workers) as pool:
futures = {pool.submit(_worker, t): t for t in targets}
for fut in as_completed(futures):
target = futures[fut]
crop, year, plot_id = target
try:
status, p = fut.result()
except Exception as exc: # noqa: BLE001
log.error("worker failed for %s/%s/%s: %s", crop, year, plot_id, exc)
status, p = "failed", None
with counts_lock:
counts[status] = counts.get(status, 0) + 1
processed_counter["n"] += 1
n = processed_counter["n"]
if (p is not None and n <= 5) or n % 100 == 0 or status == "failed":
log.info(
"[%d/%d] %s %s | results=%d state=%s",
n, total,
source_key_for(crop, year, plot_id), status,
len(p.results) if p else 0,
(p.state_abbrev.upper() if p and p.state_abbrev else "-"),
)
log.info(
"done: processed=%d written=%d skipped=%d missing=%d failed=%d (of %d candidates)",
processed_counter["n"], counts["written"], counts["skipped"],
counts["missing"], counts["failed"], total,
)
return 0 if counts["failed"] == 0 else 1
# --------------------------------------------------------------------- CLI
def _build_argparser() -> argparse.ArgumentParser:
p = argparse.ArgumentParser(
prog="scrape.sources.lg_plot_reports",
description="Scrape LG Seeds cross-vendor plot reports (yield trials).",
)
p.add_argument("--limit", type=int, default=None,
help="Stop after processing N plots (default: all).")
p.add_argument("--force", action="store_true",
help="Re-fetch even if the markdown file already exists.")
p.add_argument("--crop", default=None,
choices=tuple(CROPS.keys()),
help="Limit to one crop.")
p.add_argument("--year", type=int, default=None, choices=(2023, 2024, 2025),
help="Limit to one year.")
p.add_argument("--include-2023", action="store_true",
help="Include 2023 plot reports (default: 2024-2025 only).")
p.add_argument("--workers", type=int, default=DEFAULT_WORKERS,
help=f"Concurrent worker threads (default {DEFAULT_WORKERS}, "
f"all share a global {REQ_INTERVAL_SEC}-sec rate limiter).")
p.add_argument("--log-level", default=os.environ.get("LOG_LEVEL", "INFO"))
return p
def main(argv: list[str] | None = None) -> int:
args = _build_argparser().parse_args(argv)
logging.basicConfig(
level=args.log_level.upper(),
format="%(asctime)s %(levelname)s %(name)s %(message)s",
stream=sys.stderr,
)
return run(
limit=args.limit,
force=args.force,
only_crop=args.crop,
only_year=args.year,
include_2023=args.include_2023,
workers=args.workers,
)
if __name__ == "__main__":
sys.exit(main())