an issue that arises in almost all lines of insurance. Adequate training of loss adjusters is a critical component needed in the development of a sound insurance program. It is also important that any conflicts of interests between loss adjusters and the insured agents be identified and remedied.

Actuarial considerations must, of course, make a distinction between measuring all peril risk and measuring the risks associated with a specific peril. It is often argued that it is easier to measure the risks associated with a specific peril than to attempt to measure all risks, seen and unseen, that might be expected to cause yield losses. This may be true in cases where exogenous information about risks is available. For example, in the case of hail, historical weather records may allow actuaries to accurately measure the risks associated with hail damages in any particular area. Specific peril lines of insurance may also be provided against events that are very low probability and non-systemic. For example, fire may be a rare event and may not affect large numbers of producers. In such cases, adequate measures of risk may be obtained from considering a large, crosssectional pool of producers.

Specific peril insurance may also rely on historical yield data to measure risks. In such a case, actuaries must be able to identify the exact causes of loss associated with historical yield shortfalls. The methods discussed below for detrending yields can be extended in a straightforward manner to “condition” on non-insured causes of loss. For example, consider a case where factors associated with yield shortfalls can be segmented into a single hazard that is covered (e.g., hail), denoted by **_X,_** and hazards that are not covered, denoted as **_Z_** . One would run a regression of yields on **_Z_** , thereby conditioning out the effects of those factors affecting yields that are not covered by the insurance. The residuals from such a regression represent yield shocks that are associated with the covered factor **_X_** , as well as random, unmeasured factors. Thus, the overall approach to modeling the risks associated with a specific peril from historical yield data is no different than what is commonly undertaken to detrend data. The only distinction involves the choice of conditioning factors included in the regression.

# **_2.3.  Unit Insured_**

Agricultural insurance is an integral part of risk management. It deals with residual risks that cannot be prevented through cost-effective preventive measures. Field diversification is a standard risk management technique that allows farmers to reduce risk. On the demand side, farmers would prefer to cover their assets at risk by purchasing an “umbrella” contract that guarantees a minimum revenue at the farm level. However, premium subsidies and information asymmetries would give them incentives to insure each field, plot or crop separately. On the supply side, such an umbrella coverage would correspond to the primary objective of crop insurance, i.e., stabilizing the agricultural revenue at the farm level.

Indemnity payments received on a disaggregated set of units are at least as large as what would be received if all the units were aggregated. Intuitively, if yields are not perfectly correlated across individual units, it is likely that shortfalls for some units may be offset by higher than expected yields on others, such that the aggregate yield does not qualify for a payment while the individual units would qualify if insured individually.

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