Hog supply: connect farrowing cohorts and market inventory
A hog inventory report contains stocks, completed-period flows and future intentions. Adding them as if they were the same kind of count can create an apparent supply forecast that double counts animals or ignores timing. The research task is to follow a defined cohort from actual farrowings through subsequent stages, while keeping observed head counts separate from expected values.
The deliverable is a cohort ledger and a separate inventory bridge. It identifies each observation date, stage boundary and mortality assumption. It does not recommend veterinary treatment or forecast animal health. Aggregate agricultural statistics inform the industry backdrop; they do not establish a particular producer’s actual output or a guaranteed future market supply.
Locate stocks, flows and intentions in the release
USDA NASS’s March 2026 Hogs and Pigs release uses weaning language in its period-production highlights, alongside point-in-time inventory and future farrowing intentions. Locate the opening inventory paragraphs and the subsequent weaning and farrowing-intention paragraphs. They concern different dates and evidence states; that headline language does not make every NASS productivity measure a direct weaning-time observation.
NASS’s Hogs and Pigs inclusion definitions, pages 1–2, define pigs saved per litter as pig crop divided by farrowings. Crop excludes non-slaughter deaths through the publication’s reference date, including post-weaning deaths, and includes qualifying pigs still owned, sold or slaughtered. Consequently, saved is neither an unadjusted weaning count nor the count still held. Keep that statistical boundary separate from the fictional producer’s direct observations below.
Inventory is a count at the stated date. Farrowings and pigs weaned concern activity over a period. Intentions are forward-looking survey responses rather than completed births. A breeding inventory is also not the number of sows that actually farrow during the next quarter. Preserve the release’s population, geography, period and revision status before doing any cohort arithmetic.
Use company disclosures for company conclusions. If those disclosures lack actual farrowings or subsequent cohort survival, a national average can support an explicitly labelled scenario but cannot be silently entered as the producer’s measured performance. The source defines the industry evidence; it does not endorse this worksheet or its fictional assumptions.
Rebuild a fictional cohort step by step
Assume a fictional producer records one thousand actual farrowings in a defined quarter and counts each litter’s live animals at that litter’s actual weaning time. These direct producer records show an average of eleven animals per litter, summing to 11,000 observed weaning entrants. This is an invented company weaning-time measurement, not NASS pigs saved. The following survival factors cover only stages after each animal’s recorded weaning point; earlier losses are not charged again.
For the later stages, assume a 97% survival factor followed by a separate 98% survival factor. These are hypothetical conditional rates for non-overlapping stages after the starting measurement. Expected survivors after the first stage are 11,000 × 97% = 10,670. Expected survivors after both stages are 10,670 × 98% = 10,456.6.
| Stage | Calculation | Evidence status |
|---|---|---|
| Actual farrowings | 1,000 | Assumed observed count for the case |
| Directly observed weaning entrants | 1,000 × 11 = 11,000 | Fictional producer’s litter-by-litter records |
| First later stage | 11,000 × 97% = 10,670 | Scenario expectation |
| Second later stage | 10,670 × 98% = 10,456.6 | Scenario expectation |
The decimal is a mathematical expectation across uncertain outcomes, not a claim that an actual farm has six-tenths of an animal. Do not round it to 10,457 and relabel the result measured head count. A planning document may display an approximate whole-number expectation if it retains the scenario label and underlying calculation.
Combined survival is 97% × 98% = 95.06%. Adding the assumed mortality percentages, three plus two, would produce a slightly different result because the second stage applies to survivors of the first. Expected cumulative loss is 11,000 − 10,456.6 = 543.4, not an observed death count.
Close the cohort with actual observations
Suppose later records show 10,700 animals entering the second stage and 10,460 completing it, with no purchases, transfers or other movements in this teaching case. Observed first-stage loss is three hundred, and observed second-stage loss is 240. The final count is an integer measured in the case, distinct from the earlier expected 10,456.6.
Actual first-stage survival is 10,700 ÷ 11,000, approximately 97.27%. Actual second-stage survival is 10,460 ÷ 10,700, approximately 97.76%. Overall survival is 10,460 ÷ 11,000, approximately 95.09%. These rates use different denominators; they should reproduce the actual stage ledger before being compared with the planning assumptions.
The small difference between expected and observed totals does not establish why performance changed. If transfers, sales, purchases or count revisions exist, they need separate lines. Do not assign every missing animal to mortality merely because the first version of the worksheet included only survival factors.
Test a boundary: intentions versus actual farrowings
Assume the producer initially intended 1,100 farrowings but actually recorded one thousand. Applying an assumed eleven directly measured weaning entrants per litter and both later survival assumptions to the intention gives 11,502.26 expected survivors. Applying them to actual farrowings gives 10,456.6. The difference, 1,045.66, comes from the starting farrowing assumption, not improved or deteriorated survival.
A separate NASS-definition boundary case has a fictional pig crop of 11,000: nine thousand still owned, 1,500 sold and five hundred slaughtered at the reference date. Only the nine thousand still held can enter a new remaining-cohort scenario. Applying 97% and 98% solely to future, non-overlapping stages after that date gives an expected 8,555.4. Applying them to all 11,000 would include departed animals and could charge already excluded deaths twice. This separate example does not redefine the main case’s direct weaning count.
This scenario shows why an intention update is not a revised mortality outcome. The inputs should retain their dates and evidence states. Do not combine actual farrowings for one quarter with intentions for another and call the total one completed cohort. If the release’s periods change, rebuild the timeline rather than infer an immediate change in market-ready animals.
Timing remains a separate constraint. Post-weaning animals are not automatically ready for market at the reporting date. A cohort can cross calendar quarters, and total inventory includes different weight or production stages. Use stage-specific timing evidence before assigning the scenario’s eventual survivors to a particular month’s supply.
Reconcile the inventory separately
For a distinct fictional post-weaning inventory example, opening stock is ten thousand animals, newly weaned entrants are three thousand and purchases are five hundred. This inventory excludes animals before weaning. Deaths are two hundred and marketings are 2,800. With no other movements, ending stock is 10,000 + 3,000 + 500 − 200 − 2,800 = 10,500.
This bridge describes the whole defined inventory, not the earlier one-thousand-farrowing cohort. Do not add its ending stock to the cohort’s expected survivors: animals could overlap. Nor does 10,500 ending inventory establish that all animals are ready for sale. The composition and age or weight distribution remain necessary.
Apply the verification sheet
| Input | Obtain | Check |
|---|---|---|
| Starting cohort | Actual farrowing and litter records | Same period and producer population |
| Weaning entrants | Direct producer records at each litter’s weaning | Not substituted with NASS saved |
| Later survival | Stage records or explicit assumptions | Conditional, non-overlapping denominators |
| Movements | Transfers, purchases, sales and revisions | Mortality not used as a residual plug |
| Timing | Stage dates and relevant distribution | Eventual survivors not called immediate supply |
| Industry comparison | Dated NASS release and revision | Aggregate context distinct from company evidence |
When two sources disagree, compare census date, reporting period, geographical scope, stage definition and preliminary versus revised status. Retain the source vintage and recompute only the affected cohort. Averaging a national estimate with a company’s head count does not resolve a scope conflict.
If farrowings are missing, breeding-stock counts alone cannot reproduce the cohort. Show the absent productivity or timing input instead of inventing a conversion. If survival is unavailable, report a range of clearly labelled scenarios and leave actual market supply unknown. Do not use more decimal places to imply greater biological certainty.
Update with actual farrowings, revised direct weaning counts, stage counts, movement records and changed timing. Keep revisions to NASS saved as a separate industry series with its own reference date. Preserve the original intention-based case to evaluate forecast error. The final conclusion should identify what was counted, what was derived and what remains an expectation, without converting uncertain cohort arithmetic into a guaranteed supply or price prediction.
Attribute scenario changes without inventing health causes
Keep the initial intention-based case, the actual-farrowing case and the observed closing cohort as separate versions. The difference between the first two concerns the starting activity assumption; the difference between expected and observed later-stage counts concerns the combined subsequent outcome and any recorded movements. Neither difference alone identifies a biological cause.
Preserve cohort identifiers across transfers. If a stage-end count already excludes transferred animals, using the original cohort as the next stage’s denominator can turn transfers into apparent mortality. Reconcile starting count plus documented additions less documented exits before interpreting a survival percentage.
Company revenue remains another bridge. Even when a producer’s head count follows the national direction, selling weight, timing, product mix and contract prices may differ. Do not multiply a cohort expectation by an industry spot price and call it actual sales. If the research requires a commercial sensitivity, state the weight, sale date and price assumptions separately. This preserves the useful distinction between eventual animal numbers, the reporting-date inventory and the volume actually sold during a financial period.
FAQ
Is 10,456.6 a real animal count?
No. It is the scenario’s expected value, while observed head counts must be recorded separately.
Can breeding inventory directly substitute for quarterly farrowings?
No. Stock and period activity require different evidence and timing.