Technologically Innovative Organic / Sustainable Farming Past and Future Research By Hala Chaoui.

Technologically Innovative Organic / Sustainable Farming

Past and Future Research

By Hala Chaoui

OUTLINEWaste management research driven by the search for

a sustainable form of processed waste, and a paradigm shift

• Past research– Master’s in Plant and Soil Sc. Earthworm casts – RA. Disease suppression in earthworm casts– PhD in Agricultural and Biological Engineering. Modeling the effect

of electric fields on earthworms– Postdoc projects

• Odor setback distance models• Instrumentation and programming (in yield monitors)• Compost biofilters for dairy manure• Effect of mixing on biodigester yield• Ammonia emission modeling• Advising undergraduate thesis• Managing lab

– Outreach and innovation

OUTLINEWaste management research driven by the search for

a sustainable form of processed waste, and a paradigm shift

• Planned future research– Goal, develop technologically advanced organic farming– Principles and ideas for future research– Ideas on funding sources– Principles in guiding graduate students– Vision of paradigm shift in organic production

• Conclusion

Past Research

Past Research > Master’s

• Master’s thesis, U Maine– comparing earthworms casts, compost and synthetic fertilizer – soil respiration; BOD, biomass-C– plant nutrient uptake, mineralization rate, salinity– slow release, higher yields in earthworm casts– new topic, cited 13 times, 8th in Soil Biol. and Biochem., 2003. – Chaoui et al. 2003. Effects of earthworm casts and compost on soil microbial activity and plant nutrient availability. Soil Biology

& Biochemistry 35. 295-302

• Research Assistant, OSU– suppression of damping off diseases in earthworm casts– Chaoui et al. 2002. Suppression of the plant parasitic diseases: Pythium (damping off), Rhizoctonia (root rot) and Verticillium

(wilt) by vermicompost. Proceedings Brighton Crop Protection

Past Research > PhD

• PhD – Shift to engineering– Thesis

• goal: more efficient earthworms separation in vermicomposting

• design model for effectiveness of electric fields on separating earthworms from organic media

• in second review in Biosystems Engineering Journal

– Classes on molecular biology techniques, waste management, FE certificate, Autocad, microchip programming, ArcGIS

– Biological and Agricultural Engineers work at the interface of science and engineering

– Chaoui et al. 2006. Testing a model of the effectiveness of an electric field at repelling earthworms. ASABE Paper No. 067010

– Chaoui et al. 2005. Modeling the effectiveness of an electric field at repelling earthworms. ASAE Paper No. 054153

Modeling the Effectiveness of an Electric Field at Repelling Earthworms

Advisor Dr. Harold Keener

• V = f(i, D, R, d, s) = f(current, diameter and resistivity, electrode depth and spacing)

• Dimensional analysis:– Length unit; cm, i in amps, R in Ohms

• Electric field efficiency is a function of V:

• Electric field efficiency is measured as:

)sd/(DRikV 2 ⋅⋅⋅⋅=

0T

0Vf nn

nnw

−

−=ε

determined experimentally

Past Research > PhD > PhD thesis > Theory

Run

Vector of 1’s used to find C

ithreshold measured at following i between

electrodes

D2R electrodes depth

electrodes spacing

1 1 low (5 mA) low (E. foetida) high (7.5 cm) high (2.8 cm) 2 1 low (5 mA) low (E. foetida) high (7.5 cm) Low (1.4 cm) 3 1 low (5 mA) low (E. foetida) low (5 cm) Low (1.4 cm) 4 1 high (12 mA) low (E. foetida) high (7.5 cm) High (2.8 cm) 5 1 high (12 mA) high (E. hortensis) high (7.5 cm) High (2.8 cm)

In the model…

Other treatments: 2, 12, 24, 35 mA, e. foetida, 7.5 cm depth, 2.8 cm spacing

Past Research > PhD > PhD thesis > Methods Experimental design

An electric field

_

+

_

+

An electric fieldin the soil

_

+

_

+

Electric field diffusion

Field repels earthworms

An electric fieldin the soil repels earthworms

_

+

_

+

Electric field diffusion

Field repels earthworms

E. foetida

E. hortensis

Past Research > PhD > PhD thesis > Methods

• Experimental set up: soil slabs made of soil and earthworms• Reproducible systematic method

_

+

prescribed i

2 x prescribed i

pres

crib

ed i

+

_

Past Research > PhD > PhD thesis > Methods

• Develop model for the electric field’s effectiveness

• Test model (soil porosity, moisture, salinity)

• Find t100% , mortality, test AC vs. DC

Past Research > PhD > PhD thesis > Results

Verifying treatment effect (of inputs in model)

0.0

0.2

0.4

0.6

0.8

1.0

5mA, E. foetida, 7.5

cm, 2.8 cm


cm, 2.8 cm


cm, 1.4 cm5mA, E. foetida, 5

cm, 1.4 cm

12mA, E. hortensis,7.5 cm, 2.8 cm

Effectiveness of electric fielda

b

a

b

c

Past Research > PhD > PhD thesis > Results

y = 1 . 4 4 5 8 e- 0 . 3 7 3 9 x

R2

= 1

y = 0 . 3 1 9 9 L n ( x ) + 0 . 2 9 4 6

R2

= 0 . 9 1 7 4

y = 0 . 3 0 4 8 L n ( x ) + 0 . 2 8 9 4

R2

= 0 . 9 6 6 8

0 . 0

0 . 2

0 . 4

0 . 6

0 . 8

1 . 0

0 2 4 6 8

( i R D2

/ d s ) i n V o l t s

Ef

E f , s = 1 . 4 , E . f o e t i d a o n l y

E f , s = 2 . 8 , b e f o r e m o r t a l i t y o c c u r s

E f , s = 2 . 8 , a f t e r m o r t a l i t y o c c u r s , E . f o e t i d a o n l y

E f , s = 2 . 8 , b e f o r e m o r t a l i t y m o r t a l i t y o c c u r s , E . f o e t i d a o n l y

M o d e l o f E f , s = 2 . 8 , a f t e r m o r t a l i t y o c c u r s , f o r E . f o e t i d a o n l y

M o d e l o f E f , s = 2 . 8 , b e f o r e m o r t a l i t y o c c u r s , f o r E . f o e t i d a o n l y

M o d e l o f E f f o r s = 2 8 , b e f o r e m o r t a l i t y o c c u r s

Past Research > PhD > PhD thesis > ResultsExperimental model

Past Research > PhD > PhD thesis > Results Relevance of soil properties (test the model)

Ef vs. iRD2/ds using i threshold

y = 0.3048Ln(x) - 0.3383

R2 = 0.9668

0.0

0.2

0.4

0.6

0.8

1.0

0 5 10 15 20 25 30 35 40 45 50 55 60 65(iRD2/ds) in Volts

Ef

Ef vs. i threshold, s=2.8, before mortality occurs

5 mA e. foetida

12 mA e. foetida

5 mA e. hortensis

Log. (Ef vs. i threshold, s=2.8, before mortality occurs)

Past Research > Postdocs > Penn State > cost / benefit analysis template

• Univ. of Florida (3 months)– Algorithm to process GPS and load cells data from a

citrus yield monitor– Derive spatial yield map from GPS data, using arc GIS

– Ehsani , R., Chaoui, H., Grejner-Brzezinska, D and Sullivan, M. A method of evaluating the performance of RTK GPS receivers used in Agriculture. 2006. Proceedings of the World Congress on Agricultural Engineering, 2006 and Proceedings of the Automation Technology for Off Road Equipment Conference.

Past Research > Postdocs

• OSU, on air quality (3 months)– Models for setback distance from animal

facilities, review and sensitivity analysis. – Study published in ASABE air quality

symposium, Colorado Sept 2007

– Chaoui & Brugger. 2007. A review and sensitivity analysis of odor setback distance models. International Symposium on Air Quality and Waste Management for Agriculture. September 15-19, 2007 in Broomfield, Colorado

Past Research > Postdocs > Penn State

• Postdoc at Penn State University– Evaluating compost biofilters to mitigate ammonia and greenhouse gases

• ASABE AIM proceedings, June 2007

– Exploratory experiment on the effect of mixing on biogas yield in biodigesters• ‘Progress in Biogas’ conference in Stuttgart, September 2007

– Evaluating models for ammonia emissions from animal waste • In progress – planned, Transactions of ASABE

• Managed bio-processing lab

• Co-developed wiki website online

• Co-advisor in undergraduate thesis – Effect of biofilters on manure stack temperatures

– Mentored in data collection, processing

– Data analysis, organized writing


• Pooling ideas for technologically advanced organic farming– organized a conference session on ‘Innovative Technologies for

Organic Farming’, 2005 to 2007, ASABE – vice-president of ecological engineering committee at ASABE, officer

for past 2 years– Bio Ag Engineering.net website

Claus Sorensen Research Centre Bygholm, Denmark

Adrian BowyerBath University, UK


• Outreach through professional website and lab wiki site– Lit review on vermicomposting– Excel programs for optimized feed mix composition– Excel program for separation of means in statistics– Template for cost / benefit analysis – Excel macro and programs for filtering and processing sensors data

Past Research > Postdocs > Penn State > Biofilters








– Mentored in efficient data processing

– data analysis, organized writing

Past Research > Postdocs > Penn State > Biofilters

• Rationale for evaluating the effect of biofilters of gaseous emissions from stacked dairy manure– Stacked manure emits NH3 , N2O , CO2, and CH4, H2O– GWP: methane = 23, nitrous oxide = 296. – NH3 causes acidification and eutrophication– Chaoui et al. 2007. The effect of compost and earthworms casts biofilters on dairy manure

stack emissions. ASABE Annual International Meeting. Minneapolis, Minnesota.

Past Research > Postdocs > Penn State > Biofilters > Methods

Treatment Blanket thickness Irrigation ofblanket Type of material in blanket

1 2.5 cm yes compost

2 5 cm yes compost

3 2.5 cm no compost

4 5 cm no compost

5 2.5 cm yes vermicompost

6 5 cm yes vermicompost

7 2.5 cm no vermicompost8 5 cm no vermicompost9 1 layer moistened Curlex

10 1 layer moistened Bio-Net SC150 (North American Green)11 1 layer dry Curlex

Control no blanket

Past Research > Postdocs > Penn State > Biofilters > Experimental design

Are emission rates of NH3 , N2O , CO2, and CH4, H2O affected by:Biofilters? Biofilter filling, thickness, moisture content, respiration levels?By time, ambient and manure temperatures?

Random Complete Blocked Design - Pseudo-replication in time (weekly) 3 seasons



2 5 cm yes compost3 2.5 cm no compost4 5 cm no compost





Control no blanket




2 5 cm yes compost

3 2.5 cm no compost

4 5 cm no compost





Control no blanket


Weekly NH3, N2O, CH4, CO2, water vapor

Temperature at 3 depthsData recorded and logged hourly

Biofilter respiration levels, BOD assayMoisture content of biofilter

Photoacoustic sensor / Flux Chamber

Excel program to extract relevant data continuous data emissions

Pedersen (2001) equation: to derive gas flux rates from gas build up rate


Mean CH4 emission in kg / m2 . hr

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

wet compost 5 cmearthworm casts 5 cm

compost 2.5 cm compost 5 cm

wet earthworm casts 5 cmearthworm casts 2.5 cm

wet earthworm casts 2.5 cm

wet compost 2.5 cm

dry Curlexwet bioCurlex

wet Curlex

control (no filter)

Mean CH

4 emission in kg / m

2 . hr

a aa aa a a

a

b

abab

ab

No significant effect of time, a significant effect of treatment (p=0.00) and a treatment x time effect (p=0.04)

Past Research > Postdocs > Penn State > Biofilters > Results

NH3 emissions differed significantly due to filler type, p = 0.03N2O, CH4, CO2, H2O: filler type had no significant effect

NH3 emission rate in kg / hr.m2 vs. compost or casts

-0.000002

0

0.000002

0.000004

0.000006

0.000008

0.00001

0.000012

0.000014

0.000016

earthw orm casts compostNH3 emission rate in kg / hr.m

2


NH3 emission rate in kg / hr.m2 vs. biofilter thickness

-0.000005

0

0.000005

0.00001

0.000015

0.00002

2.5 cm 5 cmNH3 emission rate in kg / hr.m

2

Only N2O (p=0.02) and NH3 (p=0.01) emission rates were significantly affected by biofilter thickness

N2O emission rate in kg / hr.m2 vs. biofilter thickness

0

0.000005

0.00001

0.000015

0.00002

0.000025

0.00003

0.000035

0.00004

0.000045

2.5 cm 5 cm

N2O emission rate in kg / hr.m

2


Past Research > Postdocs > Penn State > Biodigestion








– Mentored in data collection, processing


• Rationale for evaluating the effect of mixing on biodigesters– Less frequent mixing to prevent de-anchoring

anaerobic bacteria (Aldrich, 1993)– Less operational costs– Mixing: distribute microorganisms and heat, reduces

particle size, help release biogas – Is it optimal at intermediate (Smith et al, 1996) or

minimal levels (Stroot et al., 2001)– Does it have no effect (Karim et al., 2005)– A positive effect (model by Banister (1998))– A negative one (Stroot et al. (2001)

Past Research > Postdocs > Penn State > Biodigestion

• Experiment for evaluating the effect of mixing on biodigesters– 3 replicates mixed for 1, 2, 3 minutes / day, 17 days, at 30oC

– Stir bar, peripheral stirring plate, 1.04 m/s velocity

– Pressure sensors measure gas build up

– Gas composition (GC)

– Stabilization: volatile solids and BOD

Past Research > Postdocs > Penn State > Biodigestion > Methods

Past Research > Postdocs > Penn State > Biodigestion > Results

Past Research > Postdocs > Penn State > Biodigestion > Results

Chaoui & Richard. 2007. Effect of mixing frequency on biogas yield in biodigesters. International conference on progress in biogas. September 19, 2007 in Stuttgart, Germany.









– Mentored in data collection processing


• Models predict ammonia speciation and volatilization• Estimating pollution form animal facilities • Rationale: empirically evaluate models, verify missing inputs• Models for dissociation fraction of ammonia(l) from ammonium(l)

– Elzing and Monteny, 1997, Hashimoto, 1972.

• Henry’s law predict ammonia(g) based on ammonia(l)

– Henry’s constant = P(ammonia gas) / [ammonia(l)] [ammonia(g)] / [ammonia(l)]

Past Research > Postdocs > Penn State > Evaluating ammonia emission models

• f x H = [ammonia(l) / ammonium(l)] x [ammonia(g) / ammonia(l)]

= [ammonia(g)] / [ammonium(l)]


J ar 1, 50 ml of 5 g NH4-N/ l, maximum build up = 33.54 mg/ m3

0

5

10

15

20

25

30

35

40

3/12/200714:31

3/12/200714:38

3/12/200714:45

3/12/200714:52

3/12/200715:00

3/12/200715:07

time

mg N / m3

Incubate samples at prescribed temperature for 1 hour, measure for 10 minutes

• Evaluating mass transfer models, with wind velocity as an input


Run 2: NH3 emission rate in kg / hr.m2 vs. fan velocity in m/s

y = 0.0001xR2 = 0.3269

y = 9E-05x + 2E-05R2 = 0.3663

0

0.00002

0.00004

0.00006

0.00008

0.0001

0.00012

0.00014

0.00016

0 0.2 0.4 0.6 0.8 1 1.2

fan velocity in m/s

NH3 emission rate in kg / hr.m2

Future Research

• Background:– Trained in precision ag– Teaching assistant for precision ag class– ArcGIS work as a graduate assistant, analyzing spatial data– Developing program to filter data from citrus yield monitor data logger– Literature review on Zigbee routers for wireless signals– Workshop on using sensors in the Zigbee system– Develop online networking tool (2005) for innovative technologyie on

organic farming

• Using background for future research:– Draw on network to co-author proposal for wireless sensing /

precision ag use in organic animal production– Develop online database for exchange of weeding robots protocols

Future Research > Develop technologically advanced organic / sustainable farming

• Build on existing advances in precision ag and wireless sensing– Wireless animal guidance in free range pastures

• Creativity– “ The best way to never have a good idea, is to never have a bad idea”– Incremental innovations to transformative ideas – Sustainability: ecological, social and economic

• cost / benefit analysis ･ creative design can reduce cost of technology

• Teams – Research specialists, students, faculty, for diverse perspectives– Focus-groups to better select research

• Communication – put results in accessible terms, stakeholder– combine producers with academic inputs (ASABE session)

Future Research > Principles and ideas

• Combine biological and agricultural engineering – Strong collaborations– Grow into that area– Training: protein and enzymatic assays, SDS-Page method

• Engineering design– Designing a system, not single process

• vermicomposting system

– Optimize a combination existing processes • co-processes• multi-waste streams • excel program for optimized feedstock• result: complex but easy to operate system

• Organization in experiments– Experimental design, hypothesis– Streamlining data collection and processing – Industry-like efficiency in labs – Enhances career of graduates, increases credibility of lab

Future Research > Principles and ideas

Future Research > Principles and ideas > designing systems

Vision of what’s next: free range fitted with technologyTools: waste bioprocessing techniques, instrumentation, electrical engineering, precision ag and programming

A free-range fitted with engineering designs to process wasteGoal: - social acceptability, no odor - pro-pig environment - ecologically sound, lucrative - not at the expense of increased labor

Porous soil inoculated with earthworms processes some of the pre-decomposed waste

Wireless soil sensors indicate when soil reaches its capacity in P nitrate

Control station: computerized, feedback loops based on wireless communication, prevents long work hours

Solar powered Feeder Ant - An autonomous mobile unit feeding outdoor pigs (Jorgensen et al. 2007). Position output by control station

Mobile barn, guided by wireless input from control

station

Automated mobile “lids” detect gaseous emissions from, and cover waste. They’re fitted with waste degrading technologies, and powered by renewable energy. They collect waste as well, into a central waste processing station.

GPS or LPS receivers, RF modules and and Remote controlled-locomotion will

be used to input / ouput locations

GPS and RF module - fitted pig collars.

Vision of what’s next: free range fitted with technologyTools: waste bioprocessing techniques, instrumentation, electrical engineering, precision ag and programming

Article by L. Hamilton in The New Farm, Jan 2003 on organic dairy milk. A mobile milking parlor follows the

cows as they rotate in the pasture. Organic Pastures Dairy Company, Fresno CA

Paradigm shift in animal husbandry

Eggmobile, such as used in Perry Winkle Farms - Debbie Roos, North Carolina Cooperative extension, Chatham county

• Ideas on funding source, grant proposals– NRI and agstar at USDA– Industry

– Co-authored grant proposal in PhD; received 86% approval rate

– Patent and royalties– Cater to mainstream in R&D, technologically advanced

and convenient micro-gardens

– Envisioned grant proposal: design model for optimizing a multi process / multi waste stream bio-processing system

Future Research > Funding Sources

• Principles in guiding graduate students– Make room for creative thinking– Identify a problem, be inspired, envision a solution, test it

using scientific method– Literature reviews and planning; 80% of effort. Organized

data collection and recording – Demystify statistical analysis and experimental design – Meticulous lab methods, not micro-manage – Streamline data processing, replace tedious tasks with

programs (for data filtering), leaves time for intelligent work

– Practical engineering skills. Autocad, machine shops. Design and print parts with a 3D printer

Future Research > Graduate Students

• Paradigm shift in organic productions– from inert technologies, to dynamic ones on which we can

‘download’ new protocols (like weed recognition for a machine vision system)

– use of wireless sensors, feedback loops– becoming more socially, ecologically sound– more humane environment for animals, and less labor-dependent

for plants and animal production

• Increased efficiency of small scale - organic swine productions – modular technologies, – the UN FAO report: Organic Agriculture and Food Security (2007):

organic agriculture can address local and global food security challenges.

Future Research > Future Vision

Conclusion• Goal

– Transform organic plant and animal production – Integrate innovative technologies in existing systems

• How I will work with others– Communicate with stakeholders– Collaborate with agricultural and biological engineers

• Tools– Science + engineering background– Easily take on new subjects

Thank You

Technologically Innovative Organic / Sustainable Farming Past and Future Research By Hala Chaoui.

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Transcript of Technologically Innovative Organic / Sustainable Farming Past and Future Research By Hala Chaoui.