Wert-Berater, Inc. is an independent grain elevator and feed mill feasibility study consultant preparing lender-grade analyses for country and terminal elevators, grain storage expansions, commercial feed mills, and combined handling and manufacturing facilities. Our studies evaluate draw territory and grain origination, storage capacity and turns, basis and handling margin, drying and blending revenue, rail and truck logistics, feed demand and tonnage, working capital and commodity exposure, capital cost, debt-service coverage, and downside sensitivity. 4,000+ engagements since 1998 covering $41.2 billion in evaluated project value.
A grain facility does not create demand; it captures throughput that already exists within a limited geography. Its revenue is a margin on volume rather than a price on production, and that volume is bounded by the acres within economic hauling distance and by the share of those acres the facility can win from incumbent competitors. The feasibility question is therefore territorial before it is financial: how much grain is actually available, how much of it can realistically be originated, and whether the margin on that volume services the debt on a capital-intensive fixed asset.
Methodology begins with USDA NASS county-level acreage and production data for the crops grown in the draw territory, combined with USDA Agricultural Marketing Service and CME cash and futures price series, published basis history for the delivery points the facility can reach, USDA NASS livestock inventory data where feed demand is being estimated, and a direct inventory of competing elevators, processors, and mills with their locations, capacities, and receiving capability. Where the facility has an operating record, its own receipt and shipment history governs.
Every Wert-Berater financial model is fully linked with no hardcoded values, so any reviewer can stress any input. Deliverables comprise a complete narrative report and the linked Excel model, with ten-year pro forma, sensitivity analysis at ±5, 10, and 15 percent, interest-rate stress from +0.5 to +3.0 percent, and ratio analysis benchmarked against comparable grain handling and feed manufacturing operating data.
Grain and feed facilities are commercial agribusiness credits rather than primary production credits, and the programme set differs accordingly. USDA Business & Industry lending is directed at rural business enterprises and reaches facilities of this type in defined circumstances; SBA programmes may apply where the business meets size and eligibility standards; Farm Credit System institutions lend to agribusiness as well as to producers; and conventional lenders set their own coverage standard, commonly but not universally 1.20x. Facilities storing grain for others may also face state warehouse licensing, bonding, and financial-responsibility requirements that bear directly on working capital. Eligibility is determined by the lender and the agency on the applicant’s facts, and we do not assume a programme applies.
Wert-Berater has completed 4,000+ engagements since 1998 covering $41.2 billion in evaluated project value, with a substantial USDA rural practice. We hold no published completed engagement for a grain elevator or commercial feed mill, and we will not present an unrelated engagement as though it were one. Our published work in agricultural commodity handling and processing includes a $38,110,000 sugar refinery restoration in Santa Rosa, Texas and a $32,240,000 fertilizer manufacturing feasibility study in Jacksonville, Florida — both bulk agricultural facilities requiring throughput, logistics, and commodity margin analysis of the kind a grain facility demands, though neither is a grain elevator. Independence is non-negotiable: determinations follow the evidence and are not revised under pressure.
A grain and feed feasibility study establishes the volume a facility can realistically capture from a defined geography, the margin it earns on that volume, and whether the result services the debt on a capital-intensive fixed asset. Elevators and feed mills are analysed with different demand logic — an elevator originates supply, a mill serves demand — and a combined facility is modeled as both.
Draw territory is the analytical foundation of an elevator study, and it is derived from hauling economics rather than drawn as a convenient radius. A producer delivers to the facility offering the best net return after trucking, so the practical boundary sits where the bid advantage of one facility is offset by the additional haul to reach it. That boundary is asymmetric: it extends further where competitors are sparse and collapses where a competing elevator, processor, or river terminal sits nearby, and it is shaped by road quality, bridge and weight restrictions, and the direction producers already travel.
Within that territory, available volume is calculated from USDA NASS county acreage and yield data for the crops actually grown, adjusted to the portion of each county falling inside the boundary. That gross figure is then reduced by the grain that will never reach a commercial facility: bushels retained on farm for feed or seed, bushels held in on-farm storage and delivered directly to a processor or terminal, and bushels already committed to competitors under existing relationships.
The remaining figure is contestable volume, and the share of it the facility captures is the study’s central judgement. That capture rate is built from the competitive position — the number, capacity, receiving speed, and bid competitiveness of alternatives, and whether the subject facility offers something they do not, such as faster unload, longer harvest hours, rail access, or an agronomy relationship. A new facility does not achieve its stabilized share in year one; producer relationships move gradually, and the model ramps capture over a realistic period rather than assuming immediate market entry. On-farm storage capacity in the territory is examined directly, because it determines how much producers can hold rather than deliver, and it has grown substantially in most regions.
Revenue depends on throughput, and throughput is storage capacity multiplied by turns. A facility that fills and empties three times a year handles three times the volume of one that fills at harvest and ships once, on the same bins. Turns are governed by receiving and load-out capacity, transport availability, and merchandising strategy rather than by bin volume, and the model derives them from those constraints rather than assuming an industry figure.
Harvest receiving capacity is frequently the binding constraint and is examined specifically. Grain arrives in a compressed window, and a facility that cannot unload trucks fast enough during it loses volume permanently to competitors who can — a producer who waits in line once will deliver elsewhere next season. The analysis considers dump pit capacity, leg speed, probe and scale throughput, and the resulting trucks per hour against the peak daily delivery the draw territory generates.
Load-out capability determines how quickly the facility can empty and therefore how many turns it achieves. Where shipment depends on rail, car availability and the ability to load a qualifying unit train materially affect both turns and the freight rate obtainable. Space utilisation is modeled across the year rather than at peak, since a facility full in November and empty in June carries the same fixed cost in both months.
An elevator's economics are a margin business, and the margin components are modeled separately rather than as a blended cents-per-bushel figure. Basis — the difference between the local cash price and the futures reference — is the primary earning mechanism: the facility buys at a bid reflecting its cost of handling, storage, and freight to the delivery point, and captures the difference when the grain moves. Historical basis at the applicable delivery points establishes the realistic range, including its seasonal pattern, which typically widens at harvest and narrows as supply is drawn down.
Carry and storage income is modeled where the market structure supports it. A market in carry pays the facility to hold grain, and an elevator with storage capacity and the working capital to use it can earn that carry, but a market in inversion does not, and a projection assuming carry income in every year overstates revenue. The study models the market structures the facility has actually faced rather than a favourable one.
Drying, cleaning, blending, and storage fees are modeled at the facility’s published or intended schedule against realistic volume. Drying in particular is weather-dependent: a dry harvest produces little drying revenue and a wet one produces a great deal, along with the energy cost to earn it. Both cases appear in the sensitivity range rather than an average being carried flat.
Logistics determine which markets the facility can reach and therefore what it can pay producers while remaining profitable. The analysis establishes the delivery points actually reachable by each mode, the freight cost to each, and the resulting netback that sets the maximum competitive bid. A facility with rail access to a distant premium market can bid more aggressively in its draw territory than a truck-only competitor, and that advantage is a central component of capture rate.
Where rail is relied upon, the specifics matter: the serving carrier, the track configuration and car spot capacity, whether the facility can load a unit train and within the loading window the tariff requires, and the historic reliability of car supply. Rail service that is available in principle but unreliable in practice does not support the netback a study might otherwise credit. Where barge access is available, the seasonal navigability and freight-rate volatility of the waterway are modeled. Truck logistics are assessed on road access, weight limits, and turnaround, both inbound from producers and outbound to processors and terminals.
A feed mill is a demand-side business and requires its own analysis rather than an extension of the elevator model. Demand is built from the livestock and poultry inventory within the mill’s economic delivery radius — which is shorter than a grain draw territory, because manufactured feed is bulkier and more costly to haul relative to its value — using USDA NASS county inventory data for each species present.
Inventory is converted to annual tonnage using consumption appropriate to the species, class, and production system, then reduced substantially to reach addressable demand. Integrated poultry and swine operations typically own their own mills and are not available to a commercial mill at all. Large operations frequently mix on farm. And existing commercial mills and cooperatives hold established relationships. What remains after those deductions is the contestable tonnage, and it is usually a small fraction of the gross figure — a study that projects revenue against total livestock inventory in a county will overstate demand by a wide margin.
Mill economics are then modeled on their own terms: ingredient procurement and inventory, manufacturing cost per ton including energy and labour, batching and pelleting capacity and utilisation, delivery fleet cost, and the margin structure of the products actually made. Custom mixing for a producer’s own ingredients is a service fee business; manufacturing and selling complete feed is a margin business carrying ingredient price exposure. The two are modeled separately. Utilisation is the critical variable, since a mill running well below capacity carries fixed cost it cannot absorb, and the study reports the tonnage required to break even.
Specialised aquatic feed manufacture is a different business with different formulation, extrusion, and market characteristics, and is addressed in our aquafeed mill feasibility study practice rather than here.
Working capital is frequently the reason a grain business fails, and it is modeled with the same rigour as term debt. Owning grain requires cash, and the requirement peaks precisely at harvest when the facility is buying heavily and has not yet shipped. The model builds the seasonal working capital curve, identifies the peak, and tests whether the proposed operating line covers it with margin. A facility with adequate coverage on term debt and an undersized operating line will still be unable to originate the volume its pro forma assumes.
Commodity price exposure is examined through the facility’s hedging practice. A properly hedged elevator is not speculating on price direction; it is earning basis and carry while offsetting flat-price risk in the futures market. But hedging itself consumes cash: a rising futures market generates margin calls on short positions that must be funded before the offsetting gain in inventory value is realised. The study models the margin requirement under a significant adverse price move and tests whether committed liquidity covers it. Where the operation intends to take unhedged positions, that is identified as speculation and treated as a distinct risk rather than as merchandising.
Counterparty and delivery risk are addressed where the facility offers deferred pricing, delayed payment, or forward contracts to producers, since those arrangements create obligations that state warehouse and financial-responsibility rules may govern. Producer default on forward contracts in a sharply rising market is a real exposure and is identified.
The financial conclusion is a coverage determination under stress. The model produces a ten-year pro forma with sensitivity at ±5, 10, and 15 percent applied to origination volume, margin per bushel or per ton, and turns independently and in combination, plus interest-rate stress from +0.5 to +3.0 percent in half-point increments.
The decisive case is a short crop in the draw territory coinciding with compressed margin, because the two are related: a regional production shortfall reduces the volume available and intensifies competition for what remains, so facilities bid more aggressively for less grain. The model runs that combination, reports the origination volume required to break even against fixed cost and debt service, and states the volume and margin combination at which coverage falls below the applicable minimum. An explicit statement of conditions identifies the information relied upon and the assumptions that would change the finding.
This is an economic and financial feasibility analysis. It does not replace grain-handling or process engineering, structural design, dust-control or combustible-dust safety engineering, or regulatory compliance work. Where a project requires facility engineering, equipment specification, safety system design, or licensing and permitting assistance, those are professional inputs to the economic analysis and the study identifies where it has relied on them.
Related engagements: for the production side of the livestock base a mill would serve, see cattle and livestock, dairy, and poultry and swine feasibility studies. For the crop production feeding an elevator’s draw territory, see crop farming feasibility studies. For food-grade milling, oilseed crush, or ingredient manufacture see food and beverage manufacturing, and for aquatic feed see aquafeed mill feasibility studies.
A grain elevator feasibility consultant establishes the volume a facility can realistically capture from a defined geography, the margin it earns on that volume, and whether the result services debt on a capital-intensive fixed asset. The work starts with draw territory and origination, then covers storage turns and throughput, basis and handling margin, drying and storage income, logistics, working capital, and commodity exposure.
Elevators and feed mills are analysed with different demand logic — an elevator originates supply while a mill serves demand — and a combined facility is modeled as both.
From hauling economics rather than as a convenient radius. A producer delivers where net return after trucking is highest, so the practical boundary sits where a facility’s bid advantage is offset by the additional haul to reach it.
That boundary is asymmetric: it extends further where competitors are sparse and collapses near a competing elevator, processor, or river terminal, and it is shaped by road quality, weight restrictions, and the direction producers already travel. We map competing facilities with their capacities and receiving capability rather than assuming an unobstructed catchment.
Available volume is calculated from USDA NASS county acreage and yield data for the crops actually grown, apportioned to the part of each county inside the boundary. That gross figure is then reduced by grain that will never reach a commercial facility: bushels retained on farm for feed or seed, bushels held in on-farm storage and shipped direct to a processor or terminal, and bushels already committed to competitors.
What remains is contestable volume, and the capture share is the study’s central judgement — built from competitive position, receiving speed, bid competitiveness, and any structural advantage such as rail access. A new facility ramps to its stabilized share rather than achieving it in year one.
Turns are derived from receiving and load-out capacity, transport availability, and merchandising strategy rather than assumed from an industry figure. Throughput is storage capacity multiplied by turns, so a facility filling and emptying three times a year handles three times the volume of one that fills at harvest and ships once, on the same bins.
Harvest receiving capacity is often the binding constraint: grain arrives in a compressed window and a facility that cannot unload fast enough loses volume permanently, because a producer who waits in line once delivers elsewhere next season. We assess dump pit capacity, leg speed, and scale throughput against the peak daily delivery the territory generates.
Separately, rather than as a blended cents-per-bushel figure. Basis — the difference between local cash price and the futures reference — is the primary earning mechanism, and historical basis at the reachable delivery points establishes the realistic range including its seasonal pattern, which typically widens at harvest and narrows as supply is drawn down.
Carry and storage income is modeled only where market structure supports it: a market in carry pays a facility to hold grain, a market in inversion does not, and assuming carry income every year overstates revenue. Drying, cleaning, blending, and storage fees are modeled at realistic volume, with drying treated as weather-dependent rather than averaged flat.
As a seasonal curve rather than a single figure, because owning grain requires cash and the requirement peaks at harvest when the facility is buying heavily and has not yet shipped. The model identifies that peak and tests whether the proposed operating line covers it with margin.
Hedging liquidity is modeled alongside it: a rising futures market generates margin calls on short positions that must be funded before the offsetting gain in inventory value is realised. We test the margin requirement under a significant adverse price move. A facility with adequate term-debt coverage but an undersized operating line cannot originate the volume its pro forma assumes.
From the livestock and poultry inventory within the mill’s economic delivery radius, which is shorter than a grain draw territory because manufactured feed is bulkier and costlier to haul relative to its value. County-level USDA NASS inventory data is used for each species present.
The critical step is reducing gross demand to addressable demand. Integrated poultry and swine operations typically own their mills and are unavailable to a commercial mill; large operations frequently mix on farm; and existing mills and cooperatives hold established relationships. Contestable tonnage is usually a small fraction of the gross figure, and projecting revenue against total county inventory overstates demand by a wide margin.
Inventory by species and class is multiplied by consumption appropriate to that class and production system, then adjusted for the share of the ration that would actually be purchased as manufactured feed rather than home-grown or supplied by an integrator.
We do not apply a single universal consumption factor across species or systems. The resulting tonnage is then tested against mill batching and pelleting capacity and utilisation, since a mill running well below capacity carries fixed cost it cannot absorb. The study reports the tonnage required to break even.
Through the netback it produces. Rail access to a distant premium market lets a facility bid more aggressively in its draw territory than a truck-only competitor, and that bid advantage feeds directly into capture rate and origination volume.
The specifics govern the value: the serving carrier, track configuration and car spot capacity, whether the facility can load a qualifying unit train within the tariff loading window, and the historic reliability of car supply. Rail that is available in principle but unreliable in practice does not support the netback a study might otherwise credit.
The fee is fixed and quoted within one business day of the initial inquiry. It does not vary with the finding and is never contingent on loan approval. Because scope varies with facility type and scale, whether the project combines handling and manufacturing, and the lending programme involved, we quote after a brief intake conversation rather than publishing a schedule.
Standard delivery is ten to fifteen business days from receipt of a complete data room, with rush delivery available. An engagement typically requires the facility plan with storage capacity, receiving and load-out specifications, the site location and rail or barge access details, historical receipts and shipments where an operating record exists, basis history at the delivery points used, construction bids for new capacity, three years of financial statements, the proposed operating line and term sheet, and any warehouse licence or bonding documentation.
Qualify a project. Tell us about the project and the program. We will tell you the truth about it — scope, timeline, and fee confirmed before work begins.
Schedule a Zoom Call →Legal disclosure. Wert-Berater, Inc. offices are mailing addresses only. Following the COVID-19 pandemic the firm has elected to work remotely; its office locations receive mail and are not staffed for visitors or in-person meetings. Headquarters mailing address: 1968 South Coast Hwy, Ste 2382, Laguna Beach, CA 92651.
Wert-Berater, Inc. is an independent provider of feasibility studies and other related services. The firm does not provide financing or equity investment advice, and does not arrange, broker, or place debt or equity capital of any kind.
All appraisal assignments are performed by Bruce E. Jones, MAI, ASA-GC, BCA, CMEA, a member of the Appraisal Institute since 2006, a staff member of Wert-Berater, Inc. and owner of Special Purpose Realty Valuation.