Future challenges + Ag Tech Requirements Tillage Dermot Forristal - - PowerPoint PPT Presentation

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Future challenges + Ag Tech Requirements Tillage Dermot Forristal - - PowerPoint PPT Presentation

Dec 14, 2022 527 likes •870 views

Future challenges + Ag Tech Requirements Tillage Dermot Forristal Teagasc CELUP Oak Park Crops Research Challenges in the crops sector Competition for land Profitability per ha Disease, Pest and Weed control E.g. loss of

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Future challenges + Ag Tech Requirements Tillage

Dermot Forristal

Teagasc CELUP Oak Park Crops Research

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Challenges in the crops sector

 Competition for land  Profitability per ha  Disease, Pest and Weed control

▶ E.g. loss of fungicide sensitivity / less new products ▶ IPM and cultural control

GHG emissions

Positives

▶ World’s highest yields ▶ Labour efficient

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Ag Tech Needs

‘Precise’ Management: measuring + responding to ‘variability’.  Fields: Spatial variability Machine control  Auto-steer  Auto ‘section-control’  Any automated function

More precise management

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‘SMART’

Measure Collect data Analyse Decision Sensors Data communications Research Algorithms Controllers

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Mesmerised by Yield Maps !

 Huge expectations generated  Blinded by ‘possibilities’

10t / ha 10t / ha 7t / ha 14t / ha Initial Assumption

  • All could yield 14t
  • At least 10t ?

Not That Simple!

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Advances in Precision Ag but!

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Variable rate application: Nitrogen

Applying N more accurately

 Huge scope as optimum varies hugely: 100 – 300 kg/ha  Cost, quality and environmental consequences !

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Crop Reflectance and N

 Measure crop biomass and N content – crop reflectance  Reflectance scanner (multi-spec):

▶ Visible and NIR wave bands

 Quite a bit of research since the 1970s!!

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Farmstar N sensing - France

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Yara N Sensor

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E bee drone with Sensor

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Does crop sensing work for N ?

 BUT, Does it work? 1% or 3-4% yield improvement.  Algorithms not region specific

▶ Some maximise protein ▶ Some optimise yield

 N is Not that simple

 What comes from the soil ?  What is crop yield potential  Weather and soil impact on both  Need to measure and predict these

 What’s needed to improve it: soil sensors, leaching

prediction, crop growth models etc all need development

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Precision Crop management

Crop sensing:

  • Nutrients
  • Development
  • Health / disease
  • Yield / Quality
  • Variability

Soil sensing:

  • Nutrients
  • Organic Carbon
  • Structure / texture
  • Microbiome
  • Moisture

Environment sensing:

  • Microclimate
  • Weather prediction

Data analytics Crop Models Decision Support Systems Supporting Research Tech transfer support Precision management response (spatially variable, real time or sequential)

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Machine Guidance, Autosteer and Control

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Machine Guidance: Steering, Headland systems

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97% full header vs 87% Not 10% performance improvement

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Does it Pay? (Getting Farmers to Adopt!)

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Auto-steer + Section Control

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Sprayer section control (avoids excess overlaps)

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Guidance and Section control

  • Benefits: - depends on field
  • 3m saving on headlands:

2.0% saving

  • Saving on short ground:

0.5%

  • No loss on tramlines:

4.0% Total saving 6.5%

  • Fungicide / Herbicide saving
  • Winter wheat:

€16.00 / ha

  • Spring Barley:

€8.76 / ha

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Guidance and sprayer control costs

Break even areas

  • W. wheat: 128 / 172ha
  • S. barley: 230 / 315ha
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Machine control (– does it pay?)

  • Control systems on all machines
  • Sprayers
  • Fert spreaders
  • Combines
  • Seeders
  • Slurry / Muck
  • Diet feeders
  • Ploughs
  • Balers / Foragers
  • Tractors
  • Etc, etc
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SMART can be simple and free ! Oilseed Rape N management

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Oilseed rape: Canopy Management

 Optimises N – Saves N  Optimises canopy size, pod number and yield.

It Works: Why?

 Good relationship between accumulated N and required N  Substantial research programme  Simple to operate  Free

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Farm Management Applications

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Farm management applications

 Around for decades.  SMART phones breathing new life  Management; Agronomy; Animal / Herd; Financial  Regulatory compliance: Cattle ID; Farm health; Pesticides etc; Nitrates etc

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Getting their hands on the Data!!

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Farm data !!!

 Data from:

▶ Reflectance sensors: Sattelite, Drone, Tractor mounted ▶ Soil sensors: Electrical conductivity, Tractor draught ▶ Soil Analysis: nutrients, pH, Carbon ▶ Yield mapping combine ▶ Input application: seeder, sprayer, fertiliser, manures ▶ Weather data: field level or region based ▶ Disease data; crop growth etc ▶ Financial data from farm at farm or field level

 Who collects, transmits, stores, analyses and uses data?

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Lots of players !

 Tractor / equipment manufacturers: JD, CLAAS  ‘Positioning’ companies: TRIMBLE; TOPCON  Breeders / Chemical companies  Traditional Farm management companies  New Data management Hubs 365FARMNET

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Conclusions

 Huge potential in crop systems and machines  Concepts are there and good; but delivery challenging  Seek simple opportunities  For the user: the technology must pay.  For the developer: the technology must pay!