A method for determining national crop yields during the growing season may be accomplished using a server computer system that receives, via a network, agricultural data records that are used to forecast a national crop yield for a particular year. Within the server computer system weather index calculation instructions receive one or more agricultural data records that represent observed agricultural data points for a specific geo-location at a specific time. The observed agricultural data points include, but are not limited to, observed temperature records, soil moisture records, and precipitation records for the specific geo-location. The weather index calculation instructions then calculate one or more weather index values from the one or more agricultural data records. Weather index values represent crop stress that may affect the crop yield of particular plants. The weather index values are then compiled into one or more geo-specific weather indices for one or more geo-locations over a specified period of time. Each of the geo-specific weather indices may contain weather index values representing various types of stress events on crop including, but not limited to, flood stress, drought stress, early drought stress, daytime heat stress, and nighttime heat stress. Within the server computer system, weather indices aggregation instructions aggregate the one or more geo-specific weather indices to create one or more aggregated weather index data series, where each aggregated weather index data series contains weather index values for a specific calculated weather index for one or more specific geo-locations. Each weather index data series represents a specific geographic area from a subset of the one or more geo-specific weather indices. Crop yield estimating instructions, in the server computer system, then select representative features from each aggregated weather index data series within each of the one or more aggregated weather index data series. The crop yield estimating instructions then creates a covariate matrix for each specific geographic area in computer memory of the server computer system. The covariate matrix contains the representative features selected from the one or more aggregate weather index data series. The crop yield for the specific geographic area for the specific year is calculated using linear regression instructions to calculate the specific geographic area crop yield from the covariate matrix that represents the specific geographic area for that specific year. The parameters of the linear regression instructions include regression parameters that are calculated using distribution generation instructions and an error term that is calculated using the distribution generation instructions where a mean parameter for the error term is zero and the variance parameter is a geographic area specific bias coefficient. After determining crop yields for the specific geographic area representing the one or more aggregated weather index data series, the crop estimating instructions determine a national crop yield for the specific year by using the distribution generation instructions to calculate the national crop yield for a specific year from the sum of the crop yields for the specific geographic areas for the specific year nationally adjusted using national yield adjustment instructions. In an embodiment, the crop yield may refer to a specific crop yield such as corn yield.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: using weather index calculation instructions in a server computer system, receiving one or more agricultural data records that represent observed agricultural data points for a specific geo-location at a specific time, wherein the observed agricultural data points include at least one of an observed temperature record, soil moisture record, and precipitation record; using the weather index calculation instructions, calculating weather index values from the one or more agricultural data records to create one or more geo-specific weather indices, wherein each of the geo-specific weather indices includes one or more calculated weather index values for a specific geo-location over a specified period of time; using weather indices aggregation instructions in the server computer system, aggregating the one or more geo-specific weather indices to create one or more aggregated weather indices that represent specific geographic area, from a subset of the one or more geo-specific weather indices; using crop yield estimating instructions in the server computer system, selecting representative features from the one or more aggregated weather indices and creating, for each specific geographic area, a covariate matrix in computer memory comprising the representative features selected from the one or more aggregated weather indices; using the crop yield estimating instructions, determining a geographic area specific crop yield for a specific year by using linear regression instructions to calculate the geographic area specific crop yield from the covariate matrix that represents the specific geographic area for that specific year, wherein one or more regression parameters for the linear regression instructions for the specific geographic area are calculated by using distribution generation instructions and wherein an error term in the linear regression instructions for the specific geographic area is calculated by using the distribution generation instructions, wherein a mean parameter is zero and a variance parameter is a geographic area specific bias coefficient; using the crop yield estimating instructions, determining a national crop yield for the specific year by using the distribution generation instructions to calculate the national crop yield for the specific year as a sum of the geographic area specific crop yields for the specific year and nationally adjusted using national yield adjustment instructions in the server computer system. 2. The method of claim 1, further comprising, using the national yield adjustment instructions in the server computer system: calculating a mean parameter for the distribution generation instructions wherein the mean parameter comprises a sum of each geographic area specific crop yield multiplied by a specific weighted value that corresponds to each geographic area, multiplied by a first national bias coefficient, and added to a second national bias coefficient; and calculating a variance parameter for the distribution generation instructions wherein the variance parameter comprises a sum of each bias coefficient for each geographic area, squared, multiplied by the specific weighted values for each geographic area, multiplied by the first national bias coefficient, squared, and added to a third national bias coefficient, squared. 3. The method of claim 1, wherein calculating weather index values from the one or more agricultural data records to create one or more geo-specific weather indices includes calculating a flood stress index as a sum of daily water runoff above a specific threshold value over a specific time period. 4. The method of claim 1, wherein calculating weather index values from the one or more agricultural data records to create one or more geo-specific weather indices includes calculating an early drought stress index as a sum of daily precipitation minus runoff, using daily precipitation not to exceed 2.5 inches (625 mm) over a specific time period. 5. The method of claim 1, wherein calculating weather index values from the one or more agricultural data records to create one or more geo-specific weather indices includes calculating a drought stress index as a sum of a fraction of soil moisture below a specified threshold over a specific time period. 6. The method of claim 1, wherein calculating weather index values from the one or more agricultural data records to create one or more geo-specific weather indices includes calculating a daytime heat stress index as a sum of a number of degrees that a maximum daily temperature is above a specified threshold over a specific time period. 7. The method of claim 1, wherein calculating weather index values from the one or more agricultural data records to create one or more geo-specific weather indices includes calculating a nighttime heat stress index as a sum of a number of degrees that a minimum daily temperature is above a specified threshold over a specific time period. 8. The method of claim 1, wherein selecting the representative features from the one or more aggregated weather indices comprises selecting, for each type of weather index within the one or more aggregated weather indices, one of: a median value for each type of weather index from the aggregated weather indices, or a mean value for each type of weather index from the aggregated weather indices. 9. The method of claim 1, wherein the specific geographic area represents a specific state in a country. 10. The method of claim 1, wherein determining a geographic area specific crop yield for a specific year using linear regression instructions comprises using distribution generation instructions to calculate the one or more regression parameters for the linear regression instructions, wherein the one or more regression parameters are based upon observed agricultural data points from multiple measured geographic areas when the observed agricultural data points are captured on or before a specified observation date. 11. The method of claim 1, wherein determining a geographic area specific crop yield for a specific year using linear regression instructions comprises using distribution generation instructions to calculate the one or more regression parameters for the linear regression instructions, wherein the one or more regression parameters are based upon observed agricultural data points from the specific geographic area when the observed agricultural data points that include observed agricultural data points captured after a specified observation date. 12. The method of claim 1, wherein determining the national crop yield for the specific year by using the distribution generation instructions comprises using independent and identically distributed random variables. 13. The method of claim 1, wherein determining the national crop yield for the specific year further comprises determining a prediction interval associated with the national crop yield, wherein the prediction interval is range of values that measures a level of certainty associated with the national crop yield. 14. The method of claim 1, wherein determining the national crop yield for the specific year further comprises determining a distribution set associated with the national crop yield, wherein the distribution set measures a level of certainty associated with the national crop yield. 15. A data processing system comprising: a memory; one or more processors coupled to the memory; weather index calculation instructions stored in memory, executed by the one or more processors, and configured to cause the one or more processors to receive one or more agricultural data records that represent observed agricultural data points for a specific geo-location at a specific time, wherein the observed agricultural data points include at least one of an observed temperature record, soil moisture record, and precipitation record; the weather index calculation instructions stored in memory, executed by the one or more processors, and configured to cause the one or more processors to calculate weather index values from the one or more agricultural data records to create one or more geo-specific weather indices, wherein each of the geo-specific weather indices includes one or more calculated weather index values for a specific geo-location over a specified period of time; weather indices aggregation instructions stored in memory, executed by the one or more processors, and configured to cause the one or more processors to aggregate the one or more geo-specific weather indices to create one or more aggregated weather indices that represent specific geographic area, from a subset of the one or more geo-specific weather indices; crop yield estimating instructions stored in memory, executed by the one or more processors, and configured to cause the one or more processors to select representative features from the one or more aggregated weather indices and creating, for each specific geographic area, a covariate matrix in computer memory comprising the representative features selected from the one or more aggregated weather indices; the crop yield estimating instructions stored in memory, executed by the one or more processors, and configured to cause the one or more processors to determine a geographic area specific crop yield for a specific year by using linear regression instructions to calculate the geographic area specific crop yield from the covariate matrix that represents the specific geographic area for that specific year, wherein one or more regression parameters for the linear regression instructions for the specific geographic area are calculated by using distribution generation instructions and wherein an error term in the linear regression instructions for the specific geographic area is calculated by using the distribution generation instructions, wherein a mean parameter is zero and a variance parameter is a geographic area specific bias coefficient; the crop yield estimating instructions stored in memory, executed by the one or more processors, and configured to cause the one or more processors to determine a national crop yield for the specific year by using the distribution generation instructions to calculate the national crop yield for the specific year as a sum of the geographic area specific crop yields for the specific year and nationally adjusted using national yield adjustment instructions stored in memory. 16. The data processing system of claim 15, further comprising the national yield adjustment instructions stored in memory, executed by the one or more processors, and configured to cause: calculating a mean parameter for the distribution generation instructions wherein the mean parameter comprises a sum of each geographic area specific crop yield multiplied by a specific weighted value that corresponds to each geographic area, multiplied by a first national bias coefficient, and added to a second national bias coefficient; and calculating a variance parameter for the distribution generation instructions wherein the variance parameter comprises a sum of each bias coefficient for each geographic area, squared, multiplied by the specific weighted values for each geographic area, multiplied by the first national bias coefficient, squared, and added to a third national bias coefficient, squared. 17. The data processing system of claim 15, further comprising instructions stored in memory, executed by the one or more processors, and configured to cause calculating weather index values from the one or more agricultural data records to create one or more geo-specific weather indices includes calculating a flood stress index as a sum of daily water runoff above a specific threshold value over a specific time period. 18. The data processing system of claim 15, further comprising instructions stored in memory, executed by the one or more processors, and configured to cause calculating weather index values from the one or more agricultural data records to create one or more geo-specific weather indices includes calculating an early drought stress index as a sum of daily precipitation minus runoff, using daily precipitation not to exceed 2.5 inches (625 mm) over a specific time period. 19. The data processing system of claim 15, further comprising instructions stored in memory, executed by the one or more processors, and configured to cause calculating weather index values from the one or more agricultural data records to create one or more geo-specific weather indices includes calculating a drought stress index as a sum of a fraction of soil moisture below a specified threshold over a specific time period. 20. The data processing system of claim 15, further comprising instructions stored in memory, executed by the one or more processors, and configured to cause calculating weather index values from the one or more agricultural data records to create one or more geo-specific weather indices includes calculating a daytime heat stress index as a sum of a number of degrees that a maximum daily temperature is above a specified threshold over a specific time period. 21. The data processing system of claim 15, further comprising instructions stored in memory, executed by the one or more processors, and configured to cause calculating weather index values from the one or more agricultural data records to create one or more geo-specific weather indices includes calculating a nighttime heat stress index as a sum of a number of degrees that a minimum daily temperature is above a specified threshold over a specific time period. 22. The data processing system of claim 15, further comprising instructions stored in memory, executed by the one or more processors, and configured to cause selecting the representative features from the one or more aggregated weather indices comprises selecting, for each type of weather index within the one or more aggregated weather indices, one of: a median value for each type of weather index from the aggregated weather indices, or a mean value for each type of weather index from the aggregated weather indices. 23. The data processing system of claim 15, further comprising instructions stored in memory, executed by the one or more processors, and configured to cause the specific geographic area represents a specific state in a country. 24. The data processing system of claim 15, further comprising instructions stored in memory, executed by the one or more processors, and configured to cause determining a geographic area specific crop yield for a specific year using linear regression instructions comprises using distribution generation instructions to calculate the one or more regression parameters for the linear regression instructions, wherein the one or more regression parameters are based upon observed agricultural data points from multiple measured geographic areas when the observed agricultural data points are captured on or before a specified observation date. 25. The data processing system of claim 15, further comprising instructions stored in memory, executed by the one or more processors, and configured to cause determining a geographic area specific crop yield for a specific year using linear regression instructions comprises using distribution generation instructions to calculate the one or more regression parameters for the linear regression instructions, wherein the one or more regression parameters are based upon observed agricultural data points from the specific geographic area when the observed agricultural data points that include observed agricultural data points captured after a specified observation date. 26. The data processing system of claim 15, further comprising instructions stored in memory, executed by the one or more processors, and configured to cause determining the national crop yield for the specific year by using the distribution generation instructions comprises using independent and identically distributed random variables. 27. The data processing system of claim 15, further comprising instructions stored in memory, executed by the one or more processors, and configured to cause determining the national crop yield for the specific year further comprises determining a prediction interval associated with the national crop yield, wherein the prediction interval is range of values that measures a level of certainty associated with the national crop yield. 28. The data processing system of claim 15, further comprising instructions stored in memory, executed by the one or more processors, and configured to cause determining the national crop yield for the specific year further comprises determining a distribution set associated with the national crop yield, wherein the distribution set measures a level of certainty associated with the national crop yield.