The effects of weather on agriculture in recent years have become a major concern across the globe . Hence, the need for an effective weather risk management tool (  weather derivatives) for agricultural stakeholders . However, most of these stakeholders are unwilling to pay for the price of weather derivatives (WD) because of  product-design and geographical basis risks in the pricing models of WD. Using machine learning ensemble technique for crop yield forecasting and feature importance, the major major weather variable (average temperature) that affects crop yields are empirically determined. This variable (average temperature) is used as the underlying index for WD to eliminate product-design basis risks. A model with time-varying speed of mean reversion, seasonal mean,  local volatility that depends on the average temperature and time for the contract period is proposed. Based on this model , pricing models for futures, options on futures, and basket futures for cumulative average temperature and growing degree-days are presented. Pricing futures on baskets reduces geographical basis risk as buyer's have the opportunity to select the most appropriate weather stations with their desired weight preference. With these pricing models,  agricultural stakeholders can hedge their crops against the perils of  weather.
The effects of weather on agriculture in recent years have become a major global concern . Hence, the need for an effective weather risk management tool ( i.e., weather derivatives) that can hedge crop yields against weather uncertainties . However, most smallholder farmers and agricultural stakeholders are unwilling to pay for the price of weather derivatives (WD) because of the presence of basis risks ( product-design and geographical ) in the pricing models . To eliminate product-design basis risks, a machine learning ensemble technique was used to determine the relationship between maize yield and weather variables. The results revealed that the most significant weather variable that affected the yield of maize was average temperature. A mean-reverting model with a time-varying speed of mean reversion, seasonal mean, and local volatility that depended on the local average temperature was then proposed. The model was extended to a multi-dimensional model for different but correlated locations. Based on these average temperature models , pricing models for futures, options on futures, and basket futures for cumulative average temperature and growing degree-days are presented. Pricing futures on baskets reduces geographical basis risk , as buyers have the opportunity to select the most appropriate weather stations with their desired weight preference. With these pricing models, farmers and agricultural stakeholders can hedge their crops against the perils of extreme weather.
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concern across the globe
global concern
A
i.e.,
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for agricultural stakeholders
that can hedge crop yields against weather uncertainties
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of these
smallholder farmers and agricultural
A
the presence of basis risks (
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basis risks
)
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of WD. Using
. To eliminate product-design basis risks, a
R
for crop yield forecasting and feature importance, the major major weather variable (average temperature) that affects crop yields are empirically determined. This variable (average temperature) is used as the underlying index for WD to eliminate product-design basis risks. A model with
was used to determine the relationship between maize yield and weather variables. The results revealed that the most significant weather variable that affected the yield of maize was average temperature. A mean-reverting model with a
A
and
R
depends on the average temperature and time for the contract period is proposed. Based on this model
depended on the local average temperature was then proposed. The model was extended to a multi-dimensional model for different but correlated locations. Based on these average temperature models
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as buyer's
, as buyers
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farmers and
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extreme