“Connecting data streams to make irrigation science easier to implement” by Justin Gibson at the 2023 Water for Food Global Conference. A recording of the presentation can be found on the conference playlist: https://youtube.com/playlist?list=PLSBeKOIXsg3JNyPowwJj6NDSpx4vlnCYj.

1. Connecting data streams to make
irrigation science easier to implement
Justin Gibson

2. 2
• Adoption and challenges of irrigation science
being implemented in the field
• Opportunities and where we can fit in as an
irrigation company
• Connecting data streams to make
irrigation science easier to implement
• Case study examples
• Future outlook / Summary
Overview

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• Wide range of approaches used on-farm when
making the decision on when to irrigate
• Group irrigation scheduling approaches into two
categories:
• Qualitative: Condition of crop, feel of soil,
personal calendar
• Quantitative: Soil moisture probe, ET
reports, computer simulation
• Gap exists between established quantitative
approaches and use in-field
• USDA IWMS 2018: 33% of growers using a
quantitative approach in US
• Question: why and what can we do as an
irrigation company to help?
Irrigation Decision Making

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Feedback from the field on why:
• Qualitative approaches often seen as sufficient
• Water managers tend to make trips to fields for
reasons other than irrigation scheduling (checking for
pests, crop stage, etc.)
• While in the field, qualitative approaches are
inexpensive and perceived as reliable
• Quantitative approaches can be challenging to
apply/utilize over medium or larger operations
• In-field sensors: large upfront cost with unclear ROI
• Checkbook: struggle with managing data (precipitation,
irrigation as-applied, evapotranspiration) at field level
Challenges of implementing irrigation science

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On-Farm data is growing:
• Geospatial machinery data: as-planted, irrigation
as-applied, agrichemical as-applied, yield maps
• In-field sensing has an increasing number of
offerings: weather stations, soil moisture probes,
cameras
• Digital soil maps: texture, elevation, pH, CEC
• Remote sensing: Landsat, MODIS, Sentinel,
Planet, EarthDaily Agro, Hydrosat
On-Farm data is becoming more interconnected:
• APIs allow for readily available data to be used in
agronomic recommendation products
Data Streams
Irrigation center pivot monitoring device
Sentinel-2 NDVI imagery

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• We offer a platform where growers can
monitor and control their irrigation equipment
• As an irrigation company, we can help
support quantitative irrigation scheduling
methods by connecting and hosting on-
farm data streams on our platform
• One step further where we create digital
agronomic products:
• Simple: weather stations turning off pivots
following significant rainfall
• More advanced: make irrigation
recommendations using data streams as
inputs to drive a crop model
Monitor/Control Platform

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• We offer an irrigation scheduling tool: FieldNET Advisor
• Ingests on-farm data to automatically determine planting
date, seed product type, irrigation application, weather
station data, digital soil maps
• Allows us to run a crop model at the subfield scale to
predict evapotranspiration, soil moisture, and irrigation
needs on a daily basis
• We then automatically create irrigation plans using
irrigation recommendations from our model
• Hypothesis: providing data in real-time at the decision
point can lead to a ROI for the grower in terms of time,
water savings, and/or yield
Irrigation Scheduling Tool

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Case Study Trial Overview
• Field trials comprised of paired experimental
fields:
• Control: grower schedules irrigation in one
field
• Trial: follows irrigation recommendations
from the model over the season
• Same seed product, planting date, soil type,
management, and located near each other
• At the end of season yield maps are collected
• ROI is calculated in terms of yield and irrigation
costs
• Walk through three examples from 2020

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Case Study #1 - Design
• Paired fields located in Southwestern
Nebraska adjacent to each other
• Flat fields with loamy fine sand
• Both fields were planted (May 7th,
2020) and harvested (Sep 15th, 2020)
on the same dates.

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Case Study #1 - Results
Yield maps for trial field (left) and control (right)

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Case Study #2 - Design
• Paired fields located close to each
other in Northeast Nebraska
• Loamy soils with minor slopes
• Fields planted one day apart (Apr.
21st / Apr. 22nd) and harvested (Sep
7th, 2020) on the same dates.

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Case Study #2 - Results

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Case Study #3 - Design
• Paired fields located in South Central
Nebraska adjacent to each other
• Split between Corn and Soybean
• Loamy soils in flat fields
• Both fields were planted and
harvested on the same dates w/
different dates for Corn and Soybean

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Case Study #3

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• Yields were comparable between paired sites
• Lowest ROI observed when model recommendations
followed grower practice
• Still returned value when considering reducing time spent for
irrigation scheduling
• Highest ROI observed from energy savings due to reduced
irrigation application
• Future work could focus on larger efforts interleaving
subfield irrigation management control/treatment sectors
providing more observations for each field
• Results should be contextualized with grower practice (hand
feel, soil moisture probe, etc.)
Case Studies Summary

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Future Outlook – Irrigation Tool
• Continued feedback that model setup represents a
significant adoption friction point
• Addressing with automated as-planted import
• Integrating remote sensing can also reduce model input
requirements
• Further increases confidence when observations of
the crop are included in the model
• Continue to validate model states and fluxes (soil
moisture, evapotranspiration)

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Future Outlook – Case Studies
• Tracking irrigation scheduling behavior at a larger scale
and over multiple years critical for understanding value
• We would need a partner to help coordinate this
kind of effort
• Potential to contribute to larger ESG collaborations
• Our role: support equipment and deliver irrigation
recommendations
• Longer term vision of repeating in different
environments, cropping systems, and geographies

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Summary
• Gap exists between established quantitative approaches
and use in-field
• We can help support quantitative irrigation
scheduling methods by connecting and hosting data
streams on our platform
• Demonstrated providing data in real-time at the
decision point can lead to a positive ROI in terms of
time, water savings, and/or yield (limited
observations)
• Future work could focus on larger scale case
studies in ESG

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USDA, IWMS 2018