Generating digital models of nutrients available to a crop over the course of the crop's development based on weather and soil data

Patent metadata

Field	Value
Publication number	US-11068625-B2
Application number	US-201615351344-A
Country	US
Kind code	B2
Filing date	Nov 14, 2016
Priority date	Jul 15, 2015
Publication date	Jul 20, 2021
Grant date	Jul 20, 2021

Abstract

A system for generating digital models of nitrogen availability based on field data, weather forecast data, and models of water flow, temperature, and crop uptake of nitrogen and water is provided. In an embodiment, field data and forecast data are received by an agricultural intelligence computing system. Based on the received data, the agricultural intelligence computing system models changes in temperature of different soil layers, moisture content of different soil layers, and loss of nitrogen and water to the soil through crop uptake, leaching, denitrification, volatilization, and evapotranspiration. The agricultural intelligence computing system creates a digital model of nitrogen availability based on the temperature, moisture content, and loss models. The agricultural intelligence computing system may then send nitrogen availability data to a field manager computing device and/or use the nitrogen availability data to create notifications, recommendations, agronomic models, and/or control parameters for an application controller.

First claim

What is claimed is:

1. A method comprising:
receiving over a network at an agricultural intelligence computing system comprising one or more processors and digital memory, electronic digital data comprising a plurality of values representing crop data, soil data, and weather data for one or more fields;
using digitally programmed logic of the agricultural intelligence computing system, creating and storing in computer memory a first digital model of temperature of soil in the one or more fields over a particular period of time based, at least in part, on the plurality of values representing crop data, soil data, and weather data;
using digitally programmed logic of the agricultural intelligence computing system, creating and storing in the computer memory a second digital model of water flow through the one or more fields over the particular period of time based, at least in part, on the plurality of values representing crop data, soil data, and weather data;
using digitally programmed logic of the agricultural intelligence computing system, creating and storing a third digital model of an uptake of water of one or more crops on the one or more fields over the particular period of time based, at least in part, on the second digital model of water flow through the one or more fields and the plurality of values representing crop data, soil data, and weather data;
using digitally programmed logic of the agricultural intelligence computing system, creating and storing a fourth digital model of an uptake of a nutrient of the one or more crops on the one or more fields over the particular period of time based, at least in part, on the plurality of values representing crop data, soil data, and weather data;
using digitally programmed logic of the agricultural intelligence computing system, creating a fifth digital model of nutrient availability in the one or more fields over the particular period of time based, at least in part on the first digital model of temperature of soil, the second digital model of water flow, the third digital model of the uptake of water of the one or more crops, the fourth digital model of the uptake of the nutrient of the one or more crops, and the plurality of values representing crop data, soil data, and weather data for the one or more fields;
wherein the fifth digital model of nutrient availability in the one or more fields models an amount of the nutrient in the soil that the crop is capable of accessing and utilizing over a particular period in time by:
computing an initial nutrient availability at an initial point in time comprising a portion of the nutrients in the soil that the crop is capable of accessing over the particular period of time as a function of initial nutrient levels in the soil, initial soil composition, and initial soil moisture;
computing uptake of the nutrient by the one or more crops over the particular period of time using the fourth digital model;
computing nutrient loss over the particular period of time to the atmosphere using one or more of soil data, temperature computed using the first digital model, or soil moisture computed using one or more of the second digital model or the third digital model;
computing nutrient loss over the particular period of time through leaching, using soil moisture computed using one or more of the second digital model or the third digital model;
computing a difference between the initial nutrient availability and the nutrient loss to the atmosphere and through leaching before the uptake of the nutrient by the one or more crops over the particular period of time after the initial point in time;

sending, over a network to a field manager computing device from the agricultural intelligence computing system, nutrient availability data indicating an availability of the nutrient in the one or more fields based on the fifth digital model of nutrient availability in the one or more fields;
creating one or more watering recommendations based, at least in part, on the second digital model of water flow through the one or more fields and the plurality of values representing crop data, soil data and weather data;
generating instructions for an application controller based on the one or more watering recommendations and sending the instructions to the application controller;
wherein the instructions cause the application controller to control an operating parameter of an agricultural vehicle to implement the one or more watering recommendations.

2. The method of claim 1 wherein the hydrology module creating and storing the second digital model of water flow through the one or more fields includes, for each given time step of a plurality of time steps comprises:
creating an initial estimation of water flow into the one or more fields based, at least in part, on a plurality of values representing moisture content in the one or more fields;
updating the plurality of values representing moisture content in the one or more fields.

3. The method of claim 2 wherein the hydrology module dynamically alters a size of each time step of the plurality of time steps based, at least in part, on one or more of moisture content, hydrologic gradient, or soil type of the one or more fields.

4. The method of claim 1 wherein the hydrology module creating and storing the second digital model of water flow through the one or more fields includes determining one or more shape parameters for a hydrology curve by creating a plurality of curves that fit known parameters and selecting a particular parameter for the hydrology curve that minimizes a sum of square differences between a wilting point of the hydrology curve and a field capacity of the hydrology curve.

5. The method of claim 1 further comprising:
the hydrology module, using digitally programmed logic, determining that the second digital model of water flow through the one or more fields contains one or more unrealistic moisture profiles;
the hydrology module, based on the determining, homogenizing moisture content over a soil profile of the one or more fields.

6. The method of claim 1 further comprising:
the hydrology module creating one or more watering recommendations based, at least in part, on the second digital model of water flow through the one or more fields and the plurality of values representing crop data, soil data, and weather data for one or more fields;
using a mobile device interface module, sending the one or more watering recommendations to a field manager computing device.

7. The method of claim 1 :
wherein the plurality of values representing crop data, soil data, and weather data for one or more fields include input data received from a field manager computing device comprising crop type, soil type, soil composition, and tillage methods applied to the one or more fields;
wherein the fertility advisor module of the agricultural intelligence computing system creating and displaying the fifth digital model of nutrient availability includes:
determining an effect on nutrient availability in soil of the one or more fields from the soil composition; and
determining an effect on nutrient availability in the soil of the one or more fields from the tillage methods applied to the one or more fields.

8. The method of claim 1 further comprising:
using the fertility advisor module, creating one or more stabilizer recommendations based, at least in part, on the fifth digital model of nutrient availability in the one or more fields and the plurality of values representing crop data, soil data, and weather data for one or more fields;
using a mobile device interface module, sending the one or more stabilizer recommendations to a field manager computing device.

9. The method of claim 1 furthe

Assignees

• Climate Corp

Inventors

• Gates John
• De Gryze Steven

Classifications

• G06F30/20 Primary

  Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) · CPC title

• A01G25/167

  Control by humidity of the soil itself or of devices simulating soil or of the atmosphere; Soil humidity sensors · CPC title

• A01C21/007 Primary

  Determining fertilization requirements · CPC title

• Y02A40/10

  in agriculture · CPC title

• G06N5/04

  Inference or reasoning models · CPC title