Utilizing spatial statistical models for implementing agronomic trials

1. A system comprising:

one or more processors;

a memory storing instructions which, when executed by the one or more processors, causes performing:

using a spatial statistical model and first yield data for a first portion of an agronomic field comprising a first treatment, computing a yield value for a second portion of the agronomic field comprising a second treatment, the yield value indicating an agronomic yield for the second portion of the agronomic field if the second portion of the agronomic field had received the first treatment instead of the second treatment;

based on the computed yield value and second yield data for the second portion of the agronomic field, selecting the second treatment; and

generating one or more scripts which, when executed by an application controller, cause the application controller to control an operating parameter of an agricultural implement to apply the second treatment.

2. The system of claim 1 , comprising generating a prescription map based on the spatial statistical model and including the second treatment.

3. The system of claim 1 , wherein the first treatment and the second treatment comprise one or more of a particular seeding population, hybrid type, pesticide application, or nutrient application.

4. The system of claim 1 , wherein the spatial statistical model is configured to compute the yield value as a function of a spatially correlated Gaussian process.

5. The system of claim 1 , wherein the spatial statistical model is configured to model yield as a function of one or more of percentage of organic matter, pH, cation exchange capacity, elevation, soil type, or nutrient levels.

6. The system of claim 1 , wherein selecting the second treatment comprises: computing an upper threshold based on the computed yield value; and

determining that a yield of the second yield data is greater than the computed yield value and, in response, selecting the second treatment.

7. A system comprising:

one or more processors;

a memory storing instructions which, when executed by the one or more processors, causes performing:

for each of a plurality of particular portions of an agronomic field comprising a first treatment, performing:

using a spatial statistical model and yield data for a separate portion of the agronomic field, computing a yield value for the particular portion of the agronomic field;

using the yield value and a portion of the yield data corresponding to the particular portion of the agronomic field, computing an average statistical deviation value for the particular portion of the agronomic field;

based on the average statistical deviation values for each of the plurality of particular portions of the agronomic field, selecting one or more of the plurality of particular portions of the agronomic field as trial portions of the agronomic field; and

generating one or more scripts which, when executed by an application controller, cause the application controller to control an operating parameter of an agricultural implement to apply a second treatment to the trial portions of the agronomic field.

8. The system of claim 7 , comprising generating a prescription map based on the spatial statistical model, wherein the prescription map comprises the second treatment that is different from the first treatment in the trial portions.

9. The system of claim 7 , wherein the first treatment and the second treatment comprise one or more of a particular seeding population, hybrid type, pesticide application, or nutrient application.

10. The system of claim 7 , wherein the spatial statistical model is configured to compute the yield values as a function of a spatially correlated Gaussian process.

11. The system of claim 7 , wherein the spatial statistical model is configured to model yield as a function of one or more of percentage of organic matter, pH, cation exchange capacity, elevation, soil type, or nutrient levels.

12. The system of claim 7 , wherein selecting one or more of the plurality of particular portions of the agronomic field as trial portions of the agronomic field comprises selecting one or more portions with a lowest average statistical deviation of the plurality of particular portions of the agronomic field.

13. A computer-implemented method comprising:

using a spatial statistical model and first yield data for a first portion of an agronomic field comprising a first treatment, computing a yield value for a second portion of the agronomic field comprising a second treatment, the yield value indicating an agronomic yield for the second portion of the agronomic field if the second portion of the agronomic field had received the first treatment instead of the second treatment;

based on the computed yield value and second yield data for the second portion of the agronomic field, selecting the second treatment; and

generating one or more scripts which, when executed by an application controller, cause the application controller to control an operating parameter of an agricultural implement to apply the second treatment.

14. The computer-implemented method of claim 13 , comprising generating a prescription map based on the spatial statistical model and including the second treatment.

15. The computer-implemented method of claim 13 , wherein the first treatment and the second treatment comprise one or more of a particular seeding population, hybrid type, pesticide application, or nutrient application.

16. The computer-implemented method of claim 13 , wherein the spatial statistical model is configured to compute the yield value as a function of a spatially correlated Gaussian process.

17. The computer-implemented method of claim 13 , wherein the spatial statistical model is configured to model yield as a function of one or more of percentage of organic matter, pH, cation-exchange capacity, elevation, soil type, or nutrient levels.

18. The computer-implemented method of claim 13 , wherein selecting the second treatment comprises:

computing an upper threshold based on the computed yield value; and

determining that a yield of the second yield data is greater than the computed yield value and, in response, selecting the second treatment.

19. A computer-implemented method comprising:

for each of a plurality of particular portions of an agronomic field comprising a first treatment, performing:

using a spatial statistical model and yield data for a separate portion of the agronomic field, computing a yield value for the particular portion of the agronomic field;

using the yield value and a portion of the yield data corresponding to the particular portion of the agronomic field, computing an average statistical deviation value for the particular portion of the agronomic field;

based on the average statistical deviation values for each of the plurality of particular portions of the agronomic field, selecting one or more of the plurality of particular portions of the agronomic field as trial portions of the agronomic field; and

generating one or more scripts which, when executed by an application controller, cause the application controller to control an operating parameter of an agricultural implement to apply a second treatment to the trial portions of the agronomic field.

20. The computer-implemented method of claim 19 , comprising generating a prescription map based on the spatial statistical model, wherein the prescription map comprises the second treatment that is different from the first treatment in the trial portions.

21. The computer-implemented method of claim 19 , wherein the first treatment and the second treatment comprise one or more of a particular seeding population, hybrid type, pesticide application, or nutrient application.

22. The computer-implemented method of claim 19 , wherein the spatial statistical model is configured to compute the yield values as a function of a spatially correlated Gaussian process.

23. The computer-implemented method of claim 19 , wherein the spatial statistical model is configured to model yield as a function of one or more of percentage of organic matter, pH, cation-exchange capacity, elevation, soil type, or nutrient levels.

24. The computer-implemented method of claim 19 , wherein selecting one or more of the plurality of particular portions of the agronomic field as trial portions of the agronomic field comprises selecting one or more portions with a lowest average statistical deviation of the plurality of particular portions of the agronomic field.