This content originally appeared on HackerNoon and was authored by gautam-sihag
In Part 1, we built the framework: 375 H3 Res 10 cells across a 562-hectare Iowa corn field, Moran's I = 0.807, confirming strong spatial structure, and 12 K-Means management zones that cut within-zone soil-nitrogen CV by 66.8%. The zones are built. The cells are assigned. Each row in our DataFrame represents a ~1.5-hectare prescription unit with aggregated soil N, organic matter, pest counts, and mean yield.
Now let's open the hood and look at what those zones actually contain, because the numbers are more interesting than the methodology.
What the 12 Zones Look Like Agronomically
Here's the full zone table. Every number in it comes from the simulation dataset directly.
| Zone | Area(ha) | Soil N (kg/ha) | Soil n CV | Pest Total | Yield (t/ha) | VRT N(kg/ha) | |----|----|----|----|----|----|----| | 0 | 42.0 | 48.1 | 0.0043 | 2,252 | 13.28 | 221.0 | | 1 | 67.5 | 53.2 | 0.0055 | 6,751 | 13.49 | 218.4 | | 2 | 42.0 | 45.1 | 0.0081 | 7,286 | 12.91 | 222.5 | | 3 | 24.0 | 56.0 | 0.0097 | 6,108 | 13.33 | 217.0 | | 4 | 48.0 | 50.0 | 0.0057 | 4,113 | 13.31 | 220.0 | | 5 | 64.5 | 52.2 | 0.0056 | 7,478 | 13.40 | 218.9 | | 6 | 49.5 | 54.4 | 0.0067 | 8,183 | 13.42 | 217.8 | | 7 | 48.0 | 46.2 | 0.0067 | 6,416 | 13.03 | 221.9 | | 8 | 12.0 | 43.4 | 0.0097 | 1,167 | 12.92 | 223.3 | | 9 | 54.0 | 49.0 | 0.0051 | 4,670 | 13.20 | 220.5 | | 10 | 49.5 | 51.0 | 0.0066 | 10,750 | 13.20 | 219.5 | | 11 | 61.5 | 47.2 | 0.0052 | 6,341 | 13.09 | 221.4 |
Three zones stand out and demand immediate attention.
- Zones 3 and 6 - over-fertilization candidates: Soil nitrogen at 56.0 and 54.4 kg/ha, respectively, both well above the field mean of 50 kg/ha. Under a uniform 220 kg/ha baseline, these zones receive the same nitrogen as every depleted cell in the field. The asymmetric VRT prescription (more on this later) pulls them back to 217.0 and 217.8 kg/ha, a modest but real reduction, grounded in the actual soil reading.
- Zone 8 - the quiet under-performer: 12 hectares, just 8 cells. Lowest soil N on the field (43.4 kg/ha), lowest yield (12.92 t/ha), and notably low pest pressure (1,167 counts, pest is not the problem here). This zone is either chronically depleted soil or a drainage issue that suppresses nutrient availability. It won't be fixed by VRT nitrogen alone. An example resolution for this zone: reach out and request a soil scientist for detailed analysis before next season. Uniform application has been masking it for years.
- Zone 10 - the pest emergency: 10,750 aggregate pest counts across 33 cells. Highest in the field by a factor of 1.3x over the next worst zone. Yield is mid-table at 13.20 t/ha, without intervention, which will drop further. This zone gets a targeted treatment flag, and its k-ring neighbors get a containment buffer. We will quantify that below.
The Prescription Trap
Before writing the VRT nitrogen formula, it's worth understanding why the most intuitive implementation produces near-zero benefit. \n The natural instinct is symmetric adjustment: if soil nitrogen is above the field mean, reduce the application rate; if it's below, increase it.
# The broken version - looks right, does nothing
mean_N = agg['soil_N'].mean()
agg['vrt_N_wrong'] = 220 + (-0.5 * (agg['soil_N'] - mean_N))
This feels balanced. High-N cells get less, low-N cells get more. The problem is the math:
# Why symmetric VRT saves nothing:
#
# Σ adjustment(h) = -0.5 x Σ(soil_N[h] - mean_N)
# = -0.5 x (Σ soil_N[h] - n x mean_N)
# = -0.5 x (n x mean_N - n x mean_N)
# = 0
#
# The positive adjustments (low-N cells) cancel the negative ones (high-N cells).
# Total nitrogen applied = uniform baseline. Every time.
The adjustments are anti-symmetric around the mean. They cancel to zero across the whole field. Total nitrogen applied is identical to the uniform baseline. This means we built the entire pipeline: sensors, aggregation, zoning, prescription, and applied exactly the same fertilizer as if we had done nothing.
\ The fix is asymmetric, reducing N only in cells where soil N exceeds the field mean. Hold the base rate everywhere else. The net effect is always less than or equal to the uniform total.
def asymmetric_vrt(soil_N_cell, mean_N,
R_base=220.0, R_min=170.0, alpha=4.0):
"""
Asymmetric VRT prescription.
R_base: uniform baseline (kg N/ha)
R_min: agronomic floor - ISU MRTN lower bound for Iowa corn
alpha: reduction per unit soil-N above mean (kg N / kg soil-N)
High-N cells: rate is reduced, floored at R_min.
Low-N cells: rate stays at R_base. Never increased above baseline.
"""
if soil_N_cell > mean_N:
return max(R_min, R_base - alpha * (soil_N_cell - mean_N))
return R_base
agg['vrt_N'] = agg['soil_N'].apply(asymmetric_vrt, args=(mean_N,))
area_ha = 1.503
uniform_kg = 220.0 * len(agg) * area_ha
vrt_kg = (agg['vrt_N'] * area_ha).sum()
saved_kg = uniform_kg - vrt_kg
N_cost = 0.65 # USD/kg
print(f"Uniform total N: {uniform_kg:>10,.0f} kg")
print(f"VRT total N: {vrt_kg:>10,.0f} kg")
print(f"N saved: {saved_kg:>10,.0f} kg ({100*saved_kg/uniform_kg:.1f}%)")
print(f"Cost saving: ${saved_kg * N_cost:>9,.0f}")
# Uniform total N: 123,750 kg
# VRT total N: 120,633 kg
# N saved: 3,117 kg (2.5%)
# Cost saving: $2,026
The 2.5% reduction is modest here because the simulation's soil-N gradient is narrow, field range 43-57 kg/ha, mean 50. On real Iowa fields with broader variability, particularly those with historical manure application creating high-N hotspots, the same formulation delivers 5-15% reduction. Right-skewed soil-N distributions, more common in real fields than simulated ones, favor larger savings because the tail of high-N cells is longer. \n \n The ISU Extension MRTN research for Iowa corn establishes Rmin at roughly 170 kg/ha (152 lb N/acre) as the lower edge of the profitable range for corn-after-corn. That's the agronomic floor. Going below it risks genuine yield loss from nitrogen deficiency - which is why the `max(Rmin, …)` guard has been introduced.
\
Where the Real Money Was: Pest Targeting
VRT nitrogen saved $2,026. Pest targeting is a different order of magnitude.
The pest economic threshold approach is a graph operation on the H3 adjacency network. Flag cells above the threshold, expand to immediate neighbors as a containment buffer, intersect with active farm cells, and spray exactly that set.
ET = 200 # economic threshold: pest counts per H3 cell
HOP_K = 1 # containment buffer: trigger cells + 1 hexagonal hop
trigger_cells = set(agg.loc[agg['pest'] > ET, 'h3_r10'])
treatment_set = set()
for cell in trigger_cells:
treatment_set.update(h3.k_ring(cell, HOP_K))
active_cells = set(agg['h3_r10'])
treatment_set = treatment_set & active_cells # stay inside the farm
agg['treat'] = agg['h3_r10'].isin(treatment_set).astype(int)
n_treat = agg['treat'].sum()
area_treat_ha = n_treat * 1.503
pesticide_cost = 18.0 # USD/ha
print(f"Cells flagged for treatment: {n_treat} of {len(agg)} ({100*n_treat/len(agg):.1f}%)")
print(f"Treatment area: {area_treat_ha:.1f} ha")
print(f"Targeted spray cost: ${area_treat_ha * pesticide_cost:,.0f}")
print(f"Full-field spray cost: ${len(agg)*1.503 * pesticide_cost:,.0f}")
print(f"Pesticide saving: ${(len(agg)*1.503 - area_treat_ha) * pesticide_cost:,.0f}")
# Cells flagged for treatment: 104 of 375 (27.7%)
# Treatment area: 156.4 ha
# Targeted spray cost: $2,815
# Full-field spray cost: $10,125
# Pesticide saving: $7,310
27.7% of the field gets treated. The other 72.3%, i.e., 406 hectares, doesn't need it.
The yield gap tells the revenue protection story.
- Average yield in flagged zones: 12.87 t/ha.
- Average yield in clean zones: 13.37 t/ha.
- Difference: -0.34 t/ha in pest-affected cells.
\
corn_price_usd_t = 175.0
yield_treat = agg.loc[agg['treat']==1, 'yield_mean'].mean()
yield_clean = agg.loc[agg['treat']==0, 'yield_mean'].mean()
yield_gap = yield_clean - yield_treat
revenue_at_risk = yield_gap * area_treat_ha * corn_price_usd_t
print(f"Yield gap (pest vs clean): {yield_gap:.3f} t/ha")
print(f"Revenue protected: ${revenue_at_risk:,.0f}")
# Yield gap (pest vs clean): 0.343 t/ha
# Revenue protected: $9,975
$9,975 in revenue protection across 156 ha, against a targeted spray cost of $2,815. The ISU corn rootworm monitoring network puts economic-level pest pressure at 15-45% yield loss across affected areas; our 0.34 t/ha gap represents roughly 2.6% of field mean yield, moderate pressure well short of full EIL, consistent with a mixed field where hot-spots are spatially bounded rather than field-wide.
The full-field spray counterfactual spends USD 10,125 to protect the same crop and sprays 406 hectares unnecessarily. The H3 framework's ability to isolate the pest cluster spatially and contain it with a k-ring buffer is where precision agriculture pays its most direct dividend.
Exporting Prescription: For a Real VRT Controller
Once prescriptions are set, convert H3 cells to polygons and export to a shapefile. Most ISO 11783 / ISOXML VRT controllers and all major FMIS platforms (John Deere Operations Center, Trimble Ag Software, Climate FieldView) accept shapefiles with an application-rate attribute column.
import geopandas as gpd
from shapely.geometry import Polygon
def h3_to_polygon(h3_index):
boundary = h3.h3_to_geo_boundary(h3_index)
return Polygon([(lon, lat) for lat, lon in boundary])
gdf = gpd.GeoDataFrame(
agg[['h3_r10','zone','soil_N','vrt_N','treat','yield_mean']],
geometry = agg['h3_r10'].apply(h3_to_polygon),
crs='EPSG:4326'
)
# Area-weight edge cells that are only partially inside the field boundary
# field_poly = our farm boundary as a Shapely Polygon
# gdf['omega'] = gdf.geometry.apply(
# lambda c: c.intersection(field_poly).area / c.area
# )
# gdf['vrt_N_eff'] = gdf['vrt_N'] * gdf['omega']
gdf.to_file('vrt_prescription.shp')
print(f"Exported {len(gdf)} cells | N range: "
f"{gdf['vrt_N'].min():.0f}-{gdf['vrt_N'].max():.0f} kg/ha")
# Exported 375 cells | N range: 170-220 kg/ha
The commented-out area-weighting block matters for real fields. Cells on the farm boundary are only partially inside the field polygon; without weighting, we over-apply to the overlap. With it, the effective rate scales by the fraction of the cell that's actually farmable. For a 562-ha field with an irregular boundary, 15-20% of cells typically need this correction. \n For controllers that expectlb N/acre rather than kg N/ha, we multiply by 0.892.
Pulling Real Soil Data: SSURGO and NASS APIs
Everything above got executed on simulated soil values. Next steps: How to replace them with real USDA open data at no cost.
SSURGO - soil organic matter and yield potential by map unit:
import requests, time, numpy as np
def ssurgo_om(lat, lon, timeout=15):
"""Organic Matter (%) at (lat, lon), top 20 cm, via Soil Data Access API."""
sql = f"""
SELECT AVG(ch.om_r) AS om_pct
FROM mapunit mu
INNER JOIN component co ON mu.mukey = co.mukey
INNER JOIN chorizon ch ON co.cokey = ch.cokey
WHERE mu.mukey = (
SELECT TOP 1 mukey
FROM SDA_Get_Mukey_from_intersection_with_WktWgs84(
'POINT ({lon} {lat})')
) AND ch.hzdept_r <= 20 AND co.majcompflag = 'Yes'
"""
try:
resp = requests.post(
"https://SDMDataAccess.sc.egov.usda.gov/Tabular/post.rest",
data={"query": sql, "FORMAT": "JSON+COLUMNNAME"},
timeout=timeout
)
val = resp.json().get("Table", [[None],[None]])[1][0]
return float(val) if val else np.nan
except Exception:
return np.nan
# Sample H3 centroids: respect ~30 req/min rate limit
for cell in agg['h3_r10']:
lat, lon = h3.h3_to_geo(cell)
agg.loc[agg['h3_r10']==cell, 'SOM_real'] = ssurgo_om(lat, lon)
time.sleep(0.5)
SSURGO resolution is ~1:24,000 map scale; it captures between-field variation reliably but smooths within-field gradients. Use it as a prior for the prescription; supplement with grid soil sampling on fields larger than ~40 ha where the within-field gradient is agronomically significant.
def nass_yield(state='IA', county='STORY', year=2023, api_key='YOUR_KEY'):
"""County corn yield (t/ha) from NASS QuickStats API."""
r = requests.get(
"https://quickstats.nass.usda.gov/api/api_GET/",
params={
"key": api_key, "commodity_desc": "CORN",
"statisticcat_desc": "YIELD", "unit_desc": "BU / ACRE",
"agg_level_desc": "COUNTY", "state_alpha": state,
"county_name": county, "year": str(year), "format": "JSON"
}, timeout=20
)
data = r.json().get("data", [])
return float(data[0]["Value"].replace(",","")) * 0.0628 if data else None
# Story County, Iowa 2023: ~201 bu/ac: 12.63 t/ha
# Use as field mean prior when no yield monitor data is available
yield_prior = nass_yield()
Register for a free NASS API key at `quickstats.nass.usda.gov/api`. The Story County 2023 benchmark (12.63 t/ha) sits slightly below our simulation mean of 13.24 t/ha, consistent with precision-managed, higher-input trial conditions outperforming the county average, which includes less-optimized fields.
Full open data reference:
| Layer | Source | Access | Notes | |----|----|----|----| | Soil OM, yield potential | USDA NRCS SSURGO | sdmdataaccess.sc.egov.usda.gov | Free REST API, map-unit scale | | CONUS soil N (1 km raster) | Smith et al. 2022, Ecosphere | Figshare - DOI 10.6084/m9.figshare.19397156.v1 | CC-BY | | County corn yield (Iowa) | USDA NASS QuickStats | quickstats.nass.usda.gov/api | Free API key, annual | | GPS yield monitor data | KBS LTER, Michigan State | EDI - DOI 10.6073/pasta/423c07d6ea3317c545beabb4b8e502c8 | Published tables, free | | N rate benchmarks (MRTN) | ISU Extension | crops.extension.iastate.edu/anr | Published tables, free | | NDVI time-series | Sentinel-2 via Google Earth | earthengine.google.com | Free tier, 10m, 5-day revisit |
\
The Full Economic Picture
Putting it all together for the 562.5-ha Iowa simulation:
| Item | Value | |----|----| | Total Area | 562.5 ha | | Revenue baseline (corn @ $175/t) | $1,281,031 | | N cost saving - asymmetric VRT | $2,026 | | Pesticide saving - targeted vs full-field spray | $7,310 | | Revenue protected - pest yield gap eliminated | $9,975 | | Total annual benefit | $19,311 | | Per hectare | $34.30/ha |
:::info $34/ha per year is meaningful in row-crop farming where net margins typically run $80 to $180/ha. This comes entirely from spatial analysis with no new seed variety, no additional passes, and no new equipment. The H3 framework reorganized existing data into decisions that existing equipment can execute.
:::
- The pest finding is the most important number in the table. Nitrogen VRT is the obvious target for variable-rate technology, and it saved $2,026. Targeted pest management - less visible, less talked about, but directly enabled by the same spatial zoning framework saved $7,310 in spray costs and protected another $9,975 in yield. The spatial framework made the pest clusters findable and the containment actionable.
- Zone 8, the quiet 12-hectare underperformer with soil N at 43.4 kg/ha and the field's lowest yield, doesn't appear in this economic table yet. It needs soil investigation, not a changed prescription rate. But it is findable now. A uniform grid masked it for years. The H3 zones surfaced it in the first aggregation pass.
Quick Recap: What the Two-Part Framework Delivers
Part 1: Built the foundation
- GPS points mapped to H3 Res 10 cells (~1.5 ha each)
- Moran's I as a spatial structure gate (I = 0.807)
- 12 KMeans management zones with 66.8% CV reduction
- Isolated-cell fix for operational legibility; SKATER for contiguity guarantees
Part 2: Delivered the findings
- Zone 8: Chronic underperformer undetectable under uniform management
- Zones 3 & 6: Fertilizer over-application targets
- Zone 10: pest emergency, highest burden by 30% over the next worst zone
- Asymmetric VRT: $2,026 N saving (and why the symmetric version saves nothing)
- Pest targeting: $7,310 spray saving + $9,975 revenue protection
- Total: ~$34/ha per year from spatial analysis alone
The full pipeline, GPS intake to ISOXML-ready shapefile, runs in under two minutes per field using Python and free USDA APIs. The H3 cell index is the only join key we need across every data layer.
This is Part 2 of a two-part series. Part 1 covers the H3 geometry, the MAUP problem, Moran's I as a deployment gate, and the full K-Means + SKATER zoning pipeline.
\
This content originally appeared on HackerNoon and was authored by gautam-sihag
gautam-sihag | Sciencx (2026-07-07T01:00:06+00:00) What 12 Farm Zones Revealed About a 562-Hectare Field (Part 2 of 2). Retrieved from https://www.scien.cx/2026/07/07/what-12-farm-zones-revealed-about-a-562-hectare-field-part-2-of-2/
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