Systems and methods utilizing real data-driven models for predicting and optimizing crop production

A method for optimizing operations of a farming enterprise, comprising:

• identifying first value data for a plurality of isolated factors contributing to crop production returns;
• identifying second value data for a plurality of interaction factors contributing to crop production returns;
• sending the first value data and the second value data to a remote computing environment;
• causing an optimization analysis to be performed by the remote computing environment using the first value data and the second value data, to identify at least one optimization factor to be increased or decreased in order to maximize the crop production returns;
• and
• increasing or decreasing the farming enterprise's allocation of resources to the at least one optimization factor.

The method of claim 10, wherein the optimization analysis is performed using a model defined by:

• RT=9.424e−01+2.801e−02X1−8.737e−2X4−6.225e−02X6−3.589e−02X7−1.447e−01X11−5.173e−02X14+2.082e−01X24−4.223e−03X1*X18+1.505e−02X4*X18+9.248e−05X4*X191.238e−02X4*X22+6.140e−03X14*X18−9.953e−03X8*X24,
• wherein X1 represents value of primary product grain;
• X4 represents a fertilizer value;
• X6 represents value of custom services;
• X7 represents value of fuel, lube, and electricity;
• X11 represents value of hired labor;
• X14 represents opportunity cost of land;
• X24 represents value of operating capital;
• X18 represents price of the crop;
• X19 represents enterprise size;
• X22 represents variable cost expenses;
• and X8 represents value of repairs.

A system for generating predictions of crop returns, comprising:

• a communications connection;
• at least one processor coupled to the communications connection;
• and
• a memory device having stored thereon a set of computer-readable instructions which, when executed by the at least one processor, cause the at least one processor to:
• identify a set of initial factors contributing to returns from production of a given crop in a given geography;
• obtain data for the initial factors and historic returns from production of the given crop in the given geography, and assess statistical reliability of the production returns data;
• assess linearity of correlation between each of the set of initial factors and historic returns;
• assess multicollinearity of each of the set of initial factors and historic returns;
• transform the historic returns data, and fit the initial factors to the transformed historic returns data, to employ a step-by-step backward elimination model selection, to select significant contributing factors and interactions of factors to form a predictive model;
• using the predictive model, process agricultural data, operational cost data, and economic data for the given crop for a given farming enterprise growing the given crop within the given geography;
• and
• return to a user a prediction of production returns for the given crop under the farming enterprise's supplied data.