The future of farming and agronomy

Stephanie Race and David Firman

CAMBRIDGEUNIVERSITY FARM

Past vs. Future

Until now …

Limited use of sophisticated decision support

systems for crop forecasting and analysis.

Systems have been in existence for some time

but have not been widely adopted.

This is set to change – facilitated by

Improved understanding of factors affecting

yield formation, water use and nutrient demand

Availability of effective low cost IT solutions

Realisation of the value by beneficiaries

Incentives to change are commercially relevant

Connection between users and developers

Benefits of monitoring potato crop growth and water use

1. Real time knowledge of current crop status

2. Allocation of resources (soils, water, fertilizer, people)

3. Advance warning (shortfalls, surpluses, soil water)

4. Reduction in labour costs to monitor crops

5. ‘Gap analysis’ – achieved yield vs. potential yield

6. ‘Scenario analysis’ – testing new agronomies

Process of Yield Formation

Incident radiation

Absorbed radiation

1. Ground cover

Total DM yield

Tuber DM yieldHaulm DM yield

3. Partitioning of DM

Tuber FW yield

4. Tuber DM concentration

2. Radiation use efficiency (RUE)

Marketable yield

5. Number of tubers and size distribution

On 18 July

Model predicted:

61 t/ha on 29 August

with mu = 58.0 mm

Achieved:

64 t/ha on 25 August

with mu = 59.6 mm

Initial forecasts issued after each crop had been sampled for the first time

Date of initial

yield forecast

Initial yield

forecast

(t/ha)

Yield at final

sampling

(t/ha)

Retrospective

modelled

yield (t/ha)

2010 Mean (n=24) 8 August 47.0 52.8 51.1

Lower quartile 17 July 45.0 48.5 47.3

Upper quartile 2 August 50.3 58.1 56.1

2011 Mean (n=86) 12 July 58.8 59.2 59.3

Lower quartile 28 June 56.0 52.7 54.8

Upper quartile 25 July 62.0 64.4 64.2

Within-season PepsiCo I-Crop Forecasts 2010 & 2011

Actual water usage by crop ETact

Water OutputsDrainage(Run-off)

Meteorological data

Incident radiationMax-min temperature

Max-min humidityWind run

Reference ET0

Ground cover

Water InputsRainfallIrrigation

(Capillary Rise)(Run-on)

Potential ETp

Soil moisture deficit

Root length

Water Balance in Crop -Soil System

Markies, UK, 2011, 69 t/ha, well-scheduled

Actual:Potential water use = 91%

Saturna, UK, 2010, 47 t/ha, poorly-scheduled

Actual:Potential water use = 81%

Hermes, Spain, 2011, 59 t/ha, poorly-scheduledActual:Potential water use = 74 %

Current “Technology”

Technology to aid crop modelling

Alternative solutions

1.Apps for smart-phones

2.Remote sensing using satellite data,

aircraft, & drone platforms

Current ‘technology’

Smart Phones

Smart Phone features include:

•High Quality Camera•Ability to associate text to picture•GPS enabled •Email/Webserver connection

These features enable:

•Capture usable images of crop canopy•Capture field and varietal information•Record field location•Recognise if returning to nearby location•Easy dispatch of images & data for analysis

CanopyCheck - Image Capture

Ground Cover Results

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1 May 31 May 30 Jun 30 Jul 29 Aug 28 Sep 28 Oct

Tub

er F

W y

ield

(t/h

a)

Grid (6808 %/days, 11.25 TJ/ha)Phone (6776 % days, 11.22 TJ/ha)

Comparison of model yield using ground cover from g rid or IPhone with sampled yield, Saturna 2011

Sampled yield (1 S.E.)

Using remote sensing data to drive crop models

160 acre field

65 ha

650 000 m2

Ground cover grid is c. 0.75 m2

With three replicate measurements (2.25m2) only 0.00035 % is sampled

Inputs to models – Measurement of Ground cover

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Variation in ground cover (based on Landsat NDVI), FTC Colorado Field 43 3 July 2011

70 – 85 % 55 – 70 %40 – 55 %25 – 40 %

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Variation in ground cover (based on Landsat NDVI), FTC Colorado Field 43 10 July 2011

70 – 85 % 55 – 70 %40 – 55 %25 – 40 %

Space Age Agronomy: From Science to Field Practice

• Grow more for less. Increase yields, lower cost of production.

• Know risk & predict crop supply. Verify acres planted, forecast yield in-season.

• Increase profit per acre. Inform multiyear hedge decisions.

• Monitor variety performance. Location specific crop production history.

• Close crop yield gaps. Know crop potential relative to historical production.

• Monitor environmental impact. Provide scientific data for compliance.

Scale Observations to Inform Grower Decision Suppor t

• Crop Production

• GHGs/Carbon

Footprint

• Air, Water &

Soil Quality

• Nutrient

Management

• Water

Management

• Pesticide

Management

Results:

• Predict in-season yield, inform

irrigation vs. management by

“walking around”.

• Continuous fact-based insights vs.

field-based spot tests.

• Monitor environmental outcomes.

Growing More on Less Land

Yield in 2011:

46.5 t/ha

Total land

cultivated in

2011:

127,500 ha

Total Annual

Production

(2011) :

6mn tons

Over the years, yield per hectare has consistently increased in spite of a drop in the

number of potato growers and the land area cultivated

Source: Potato Council Report & FAO Commodities by Country Statistics Website

Forecast Crop Potential - Land Capacity

What has the field historically produced?

What is the season’s anticipated production level?

How can we close yield gaps?

Set Production Targets based upon:

Land Capacity

Crop Potential

Sequestration Benefits

Monitor & Report Progress Against Productivity & Sustainability Goals

Monitoring Yields: Know Land Capacity vs. Producti on

Provisioned at the Field, Farm and Watershed Scale

Retrospective to Real Time

Key elements:

•Monitoring

•Modeling

•Forecasting

•Local to Global

Scientific Results

b. c.

Dynamic Climate Data

What’s planted

Soil Type & Texture

Elevation Data

Historical Yield

Water

Fertilizer

Indicator of Growth & Crop Type

Temperature air/soil

PET

Soil water content

Plant biomass

Soil Nitrate

Predicted yield

Harvest date

Greenhouse gases

EVI Light Use Efficiency

Grower Data

Scaling Observations to Inform Field Decision Suppo rt

pla

nti

ng

emer

gen

ce

imag

e d

ate

Monitoring the Progression of Canopy Through the Se ason

Monitoring Crop Growth Across Multiple Seasons

Raw Data to Actionable Intelligence

Plant Physiological

CUF Crop Model

Plant/Soil/GHG

Interactions Model

Federated Data:

Imagery, climate,

soils, ground

observations

RS Imagery NDVI Time-Series

•Irrigation

•Yield

•N Uptake

•Leaching

•GHG Emissions

•Irrigation

•Yield

•N Uptake

•Leaching

•GHG Emissions

Model Execution Grower Decision SupportData Capture

1. Ground Cover is measured by NDVI

2. In-Season Irrigation Scheduling

3. In-Season Yield Monitoring

4. GHG Emissions Monitoring

• Baseline: Model Calibration

• Goal Setting: Evaluate Practices Over Time

• Verification & Reporting: Track Progress

1

2

3

4

Inform Practice Changes that Increase Yields & Redu ce Emissions

Decision Support

• Yield Forecasting

• Irrigation

• N Use Efficiency

• GHG’s *

Potato CropModel

GHG Emissions

Model

Field GHG Flux

Measurements

Yield SampleMeasurements

Remote Sensing

Image Time-Series

MeteorologySoils

IrrigationFertilizer

• Soil Organic Matter

• Soil C, N; Crop Residue

In-Season Data Inputs

Start of Season Data Inputs

Calibrate Models & Verify Results

Decision Support for Crop Intelligence

Analytics Decisions Supported

Field Production Capacity / Crop Potential What is the capacity of a field relative to its historical production?

Crop Fertility / N Use Efficiency What is the nutrient status at each growth stage of the crop?

Irrigation / Water Use Efficiency What is the water status at each growth stage of the crop?

Canopy Maturity How is canopy maturity progressing relative to expected crop growth stage?

Yield Performance What is the performance of in-season forecasted yield vs. planned yield?

Greenhouse Gases What are the emissions and sinks for a given field? N2O emissions? NH3, NO?

Water Quality, Runoff & Erosion What is the fate of nitrates that can leach into water bodies adjacent to field?

Monitor In-Season Yield, Plant Water & Fertility St atus

The FutureAdoption of tools that bridge scientific research to field practice.

Utilize advanced technology platforms for:

– Data capture, model development & extension, calibration - for scientists.

– Decision support to optimize production - for field agronomists, farm managers.

– Reporting for supply forecasting & sustainability - for food processors, retailers.

Scale observations of ground cover across catchment, region, country, continents.

Provide real time feedback on irrigation, nutrient uptake and leaching at the field scale

for in-season decisions to increase yields at a lower cost of production.

Capture dynamic data on soils and meteorology, utilizing in-situ measurements that

describe the spatial distribution of weather, water, soil nutrients in relation to crop

demand each day throughout the season.

Display data in easy to use formats for growers to interpret and use daily.

Acknowledgements

• CUF Agronomy Research Group

• Marc Allison

• Mark Stalham

• Earth Analytics Group

• Dr. Mutlu Ozdogan, Assistant Professor, Nelson Institute for Environmental

Studies, University of Wisconsin Madison

• Kiley Stuker, Farm Manager, Paramount Farms, Bancroft Wisconsin

• Cambridge University Potato Growers Association

• FTC

• Landmark

• PepsiCo

• NASA