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					Precision Farming
Challenges and Opportunities
for Atlantic Canada
Edited by:

David A. Lobb



Copies available from:

Eastern Canada Soil and Water Conservation Centre
RR#4 Grand Falls NB E0J 1M0
tel: 506-475-4040; fax: 506-475-4030
ccse-swcc@cuslm.ca

June, 1997
Preface

This document is the proceedings of the precision farming workshop held on February 18th and 19th, 1997
at the CP Prince Edward Hotel in Charlottetown, Prince Edward Island. The meeting was held in
conjunction with the APASCC Atlantic Committee on Agricultural Engineering, the Northeast Potato
Technology Forum, and the Potato School. The Eastern Canada Soil and Water Conservation Centre
organized the workshop with the cooperation with the APASCC Atlantic Committee on Soil and Climate.
The Canada - New Brunswick Green Plan provided financial support.

The workshop focused on the challenges and opportunities of site-specific soil and crop management in
potato production systems. The specific objectives were: 1) to provide researchers, consultants, extension
staff and industry leaders with a knowledge of the basic principles and current issues of precision farming;
and 2) to begin the assessment of the most cost effective and environmentally effective application of
precision farming techniques.

Workshop discussion groups were asked to: 1) identify strengths and weaknesses of precision farming as it
applies to potatoes and crops in general in Atlantic Canada; and 2) identify where regional energies should
be focused to ensure the most effective implementation of precision farming. Discussion groups were
organized to address the following topics: A) delivery and adoption; B) hardware and software; C) soils
and fertility information; and D) assessing economic and environmental benefits.

The organizers wish to express there appreciation to Dr. John MacLeod and Richard Veinot for moderating
the workshop sessions; Jack van Roestel, Jean-Louis Daigle, Kevin Sibley, Charlie Coles, Gary Patterson,
David Lobb, Brian Sanderson, and Chuck Everett for leading the discussion groups; and Dr. Rob Gordon
for his synthesis of the two days of presentations and discussions. A special thanks goes out to the oral and
poster presenters for their efforts and all the participants who made the event a great success.



                                                                                           David A. Lobb
                                                                              Soil Conservation Specialist
                                                       Eastern Canada Soil and Water Conservation Centre
                                                                                    University of Moncton
                                                      Table of Contents

Precision farming: connecting the pieces                   ............................................. 1
   Doug Mackay

Site specific management of potatoes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
    Dr. R. Colin McKenzie

The Ontario site specific cropping management project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
   Doug Aspinall

Application of precision farming to potato production in Quebec . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
   Dr. Régis Simard, Dr. MichelC. Nolin and A.N. Cambouris

Development of a yield monitor for potato harvester . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
   Justin Larouche and Dr. Bernard Panneton

Développement d’un capteur de rendement de pomme de terre . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
   Justin Larouche et Dr. Bernard Panneton

Application of precision farming to potato production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
   Ray P. Carmichael

Precision agriculture: managing soil variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
   Gary Patterson

Field Scale variability of climate in Atlantic Canada . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
    George Read

On-the-go soil nitrate measurement and control system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
   Dr. Kevin J. Sibley

Workshop Group A: Delivery and adoption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
  Jack van Roestel

Workshop Group B: Hardware and software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
  Dr. Kevin Sibley

Workshop Group C: Soils and fertility information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
  Gary Patterson

Workshop Group D: Assessing economic and environmental benefits                                   . . . . . . . . . . . . . . . . . . . . . 22
  David Lobb

Workshop wrap-up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
  Dr. Robert Gordon
                          Precision farming: connecting the pieces
                                      Doug Mackay, Project Engineer
                              Alberta Agriculture, Food and Rural Development
                              201 7000 - 113 St., Edmonton, Alberta T6H 5T6
                                   tel: 403-427-4184; fax: 403-422-7755
                                          mackay@agric.gov.ab.ca


Precision farming is a rapidly developing management tool in agriculture today. Just what is precision
farming and what is the technology behind it? Computers, satellites, sensors, and controllers are the
backbone technology that has allowed precision farming to come of age. Precision farming can be defined
as managing variability at the sub-field level to best utilize resources and minimize environment impact.

The tools of precision farming:

GPS, or the Global Positioning System, is a constellation of 24 satellites operated by the US Department of
Defence which transmit precise time and orbit information. A receiver on the earth can determine its
location to within 100 m using signals from these satellites and be as accurate as 1 cm using differential
GPS. GPS is used for topographic surveying or in conjunction with other sensors to provide georeferenced
(x, y coordinate) maps of yield, salinity, or anything else which can be measured and you would want to
map.

GIS, or Geographic Information System, are used for handling large amounts of georeferenced data. Maps
can be viewed in layers to see the relationships between them. A GIS is basically a database of information
which has x and y coordinates attached to that data.

Yield Monitors allow instantaneous yield data to be collected from very small areas of the field to see the
variation of yield. They may also collect moisture and possibly protein data as well. There are yield
monitors available for most grain and bulk crop harvesters.

Variable Rate Application of fertilizer, seed, and pesticides can be done using controllers to vary the rate
on the go. This can be computer controlled according to a prescription map or varied manually.

Crop Scouting and Remote Sensing is important to note problems in the crop and make records of
sloughs, field boundaries, rocks, etc.

Guidance and Navigation system using differential GPS can be used for parallel tracking while spraying
or swathing. The GPS navigation system can be used to return to a point with known coordinates to spot
spray, soil sample at the same location, or return to a rock to pick it.

The tools of precision farming are much like pieces of a puzzle which are interconnected to form a
complete system. These can be a powerful management tool which can be increase net returns by using
resources more effectively.




                                                                                                               1
                             Site specific management of potatoes
                                 Dr. R. Colin McKenzie, Research Scientist
             Alberta Agriculture, Food and Rural Development, Crop Diversification Centre - South
                                        SS#4 Brooks, Alberta T1R 1E6
                                     tel: 403-362-1300; fax: 403-362-1306
                                           mckenzie@agric.gov.ab.ca

Traditionally, individual fields of many crops have been managed as one unit. Variability of fertility and
soil within the field were not taken into account. As a result, crop yields and quality varied within a field.
In Alberta, irrigated potato fields may yield more than 50 t ha-1. However, these high average yields from a
specific field are made up of even higher yields in some areas of the field and much lower yields in other
portions. The technology now exists to manage small portions of a field individually with the objective of
producing uniform optimum yield and quality.

In the USA, Global Positioning System (GPS) technology has interested farmers as a method to increase
profits by optimizing fertilizer applications. In Western Europe, it has been used to avoid environmental
contamination. Several groups in the USA have developed yield monitors for potato harvesters which can
be used with global positioning technology to develop yield maps. In Alberta potatoes are often grown on
coarse textured soils which have a low nutrient holding capacity and high field variability. Under small
plot conditions traditional research does not describe this variability. Consequently, current management of
potatoes does not account for this variability.

In 1996 an Alberta Agriculture Food and Rural Development team started a precision agriculture project
with potatoes. The project received support from Alberta Agriculture Research Institute, Alberta Potato
Growers, Agrium, Potash and Phosphate Institute of Canada, Viridian and Westco.

Objectives and Progress

C   To measure the variability of the yield of potatoes by using a yield monitor
C   To determine the effect of soil type, landscape position, nutrient level, fertility treatments, disease and
    weeds on the yield of potatoes.
C   To determine variability of yield of the preceding crop and relate this to field variability of potato
    production.
C   To evaluate the use of digital image analysis to detect nutrient deficiencies and diseases.
C   To measure the financial and environmental benefits of site specific management of potatoes.
C   To measure a relationship between soil and crop characteristics and processing quality.

Two potato fields were monitored during 1996. Each field consisted of half of a centre pivot (about 26 ha).
The Hays field had hummocky topography. The average clay content in the 0 to 0.60 m depth varied from
5 to 50%. The Grassy Lake field was gently sloping and the average clay content varied from 9 to 25 %
for the 0 - 0.60 m depth.

Forty neutron meter access tubes were set out at the Hays field and 8 access tubes were set out at the
Grassy Lake field. Soil moisture readings were taken weekly from June 1 until September 9, 1996. Potato
petiole tissue samples were taken near each neutron meter access tube on 3 dates.

A yield monitor was used on two potato combines and yields were recorded and positioned with differential
GPS. Due to wet weather and delayed harvest, part of the harvest of the two fields occurred at the same

                                                                                                                  2
time. This meant not all of the harvest was yield mapped. This was the first year the yield monitor has
been marketed. Difficulties were encountered and yield data collected was not uniformly reliable.

Fertilizer rates and additional fertilizer application by fertigation were set by the farmer. Each farmer had
his own soil and plant tissue testing program. Portions of both fields showed deficiencies of both tissue
nitrate and tissue phosphorus. The analysis values obtained at each of the three sampling times were
ranked according to nutrient content and grouped into quarters (Table 1). The upper and lower quarter of
the samples taken differ greatly from the mean. This indicates it should be advantageous to divide the field
into different units and make fertilizer applications according to soil or tissue test levels within these units.


Table 1. Petiole analysis from site specific potato fields.

                                             NO3-N%                                        P%

                                July 3-4     July 30       Aug. 20          July 3-4      July 30      Aug. 20

 Adequate level                  2.4-1.6     1.8-1.2       1.6-1.0          0.60-0.22    0.48-0.18       0.30-
                                                                                                         0.10

 Grassy Lake
 Mean upper quarter†              1.38*       0.49*        0.163*             0.55         0.34         0.18
 Mean                             0.92*       0.27*        0.076*             0.49         0.22         0.13
 Mean lower quarter               0.31*       0.02*        0.008*             0.38         0.14*        0.085*


 Hays
 Mean upper quarter               2.35        1.37          0.62*             0.48         0.20         0.077*
 Mean                             2.04        0.98          0.35*             0.42         0.14*        0.070*
 Mean lower quarter               1.67        0.57*         0.10*             0.34         0.11*        0.062*

† analysis values were ranked and divided into quarters.
*Tissue levels were deficient for that maturity stage.


Petiole phosphorus (% P) (Table 1) was adequate on both sites on July 3 and 4. It declined on the Hays
site and by July 30 most of the field was deficient and by August 20 all of the field was deficient. The
Hays site had a low level of soil phosphorus (35 kg ha-1) but received 107 kg ha-1 P205 fertilizer. The
Grassy Lake site remained near adequate level on July 30 and August 20. This site had a moderately high
level of soil phosphorus (67 kg ha-1).

Irrigation and precipitation maps of both fields indicate an uneven distribution of water by the pivots with a
higher application near the center and less to the outside. The Hays field showed excess water in the 50-
100 cm depth in areas near the pivot centre. The Grassy Lake field was deficient in water on the coarse
textured south end throughout the season.




                                                                                                                 3
Conclusions

The potato fields examined had a great deal of variability in soil texture. This will make water and
nitrogen management difficult. Both fields did not receive uniform water applications. Tissue phosphorus
declined rapidly and became deficient on one field but not on the other. Most farmers are monitoring tissue
nitrogen but not tissue phosphorus.

Yield monitoring was successful on only portions of the field. Changes in the design of the yield monitor
should improve its performance. Current yield monitors need to be attended all the time.

Improved yield monitoring will permit doing analysis between yield and quality measurements versus soil
and crop factors. This analysis can include digital values of remote sensing data. Quality is the major
concern for the potatoes produced for processing. Data on the variability of quality will be related to soil
and crop characteristics in 1997. Measurements of nitrogen movement below the root zone will permit
determining some of the environmental impact of potato production.




                                                                                                               4
                The Ontario site specific cropping management project
                             Doug Aspinall, Resource Management Specialist
                            Ontario Ministry of Agriculture, Food and Rural Affairs
                              One Stone Road West, Guelph, Ontario N1G 4Y2
                                    tel: 519-826-3575; fax: 519-826-3259


Background

C   1983    - conflict between research and on-farm research results
C   1985    - Tillage-2000 a 5-year, on-farm, field scale, research and demonstration project
              (conventional vs. conservation tillage comparisons)
C   1989    - Partners in Nitrogen Project, the Ontario soil nitrate test
C   1993    - delta yield concept
C   1995    - Site-Specific Management Project


Project Description

C   Initiated in 1995 by OMAFRA and the University of Guelph.
C   Has a 5 year time-frame.
C   The project is focused on nitrogen.
C   Preliminary research has indicated that an increase in N efficiency is possible.
C   One goal is to increase net return/ha by $25 by achieving more efficient use of nitrogen.
C   Will examine the possibility of using maps of within field variability of soil conditions and crop yield to
    vary N management on-the-go.
C   27 fields across the province have been selected to participate in the project, plot size varies from 6 to
    20 hectares.
C   Corn/soybean/winter wheat is the dominant rotation, a corn/soybean rotation is common on the ridge
    till fields, about a third of the fields receive manure on a regular basis. A few of the field include
    underseeded wheat. Other crops included in the rotation on some plots are canola and white beans.
C   The dominant tillage system on all plots is no-till, 4 of the plots are ridge till, tillage in the form of
    plowing, soil saving or a light discing is often used following a wheat crop in preparation for corn.
C   Soil textures across the plots ranges from clay to loams/silt loams to sands. Landscapes range from
    flat to hummocky to hilly.
C   Agronomic data includes: crop inputs, field history, machinery used, operational dates, population,
    field scouting.
C   All of the fields are harvested with yield monitors and a Global Positioning System (GPS). This data is
    used to create yield maps. A yield map is a spatially referenced, graphical representation of crop yield.
    Yield mapping leads to site specific management because yield mapping identifies problem areas within
    the field. The value of a yield map is in its interpretation. The goal is to assign causes to the variation
    observed on the map and then identify management opportunities for improvement.
C   Field characterization includes soil sampling for pH, texture, P, K, organic matter, NO3, NH4, plough
    layer thickness, bulk density, elevation, slope classes. All sample points have been georeferenced.
    Samples for fertility and textural analysis were collected from a 30 by 30 m grid pattern over the whole
    plot, nitrate samples were collected from check strips every 10 m. In addition, soil profile descriptions
    have been made at selected slope positions within each field.


                                                                                                             5
C   Elevation data was obtained for each plot using a high resolution GPS unit. This data is used to create
    3 dimensional maps of the elevation and to classify and map slope positions in each field.
C   The main objective is to evaluate the delta yield concept:

    Delta yield (ÄY) = * Yield with fertilizer applied - Yield without fertilizer applied *

C   Within each field 3 check strips are installed when corn or wheat is grown. Nitrogen is not applied in
    these areas. The yield in these strips is compared to the fertilized areas on each side of the check strips.
    Those areas within the fertilized strips where the delta yield is greater than 0 indicates a response to N
    fertilizer. If delta yield is equal to 0 or less than 0 then there is no response to N fertilizer. The delta
    yield can be mapped. Because there is a strong relationship between delta yield and the most economic
    rate of nitrogen (MERN) a recommendation function was developed to predict the amount of N
    fertilizer required. An N recommendation map can be created. This project is examining the
    usefulness of using ÄY to predict the spatial pattern of MERN within fields.


Common causes of in-field variability

C   Drainage (soil moisture surpluses and deficits;
    temporally and spatially variable; related to hydrology and soil degradation)
C   Hybrid/variety (crop sensitivity)
C   Insect, disease and weed problems (related to drainage, pesticide management)
C   Crop rotation (crop selection)
C   Tillage (timing, systems) (related to weeds and soil degradation)
C   Soil compaction (induced and exposed by soil loss)
C   pH (related to drainage and soil loss)
C   Herbicide drift, selection and rates (effective timing and management)
C   Condition of subsoil (depth to, compactness, permeability)
C   Fertility placement
C   Soil fertility levels and balance
C   Plant population


Information tools

C   Crop yield maps
C   Performance maps (operations, economics, environment impacts)
C   Field history (fence lines, woodlots, buildings, feedlots, etc.)
C   Field records (operations, dates, rates, observations, yields)
C   Soil survey maps (soil types, soil phases, etc.)
C   Topographic maps (elevation, surface and subsurface drainage)
C   Features maps (tile lines, surface drains, terraces, etc.)
C   Air photos (colour & black and white)
C   Satellite imagery
C   Soil properties maps (fertility, organic matter, etc.)
C   Variable inputs maps




                                                                                                              6
Steps towards precision farming

C   Eliminate unwanted variability in managing inputs
    · equipment selection, maintenance and operation
C   Identify crop variability
    · field observations and yield maps
C   Assess economic and environmental impacts of crop variability
C   Identify the probable causes of crop variability
C   Manage the field to stop the increase in field variability and
    where possible manage to reduce the field variability
    · soil conservation and restoration practices
C   Use variable management to increase the efficiency of inputs
    · tillage, yield goals, plant density, fertilizer, lime, pesticides, irrigation




                                                                                      7
          Application of precision farming to potato production in Quebec
                    Dr. Régis R. Simard1, Dr. Michel C. Nolin1 and A.N. Cambouris2
              1
              Agriculture and Agri-Food Canada, 2560 Hochelaga Blvd., Ste-Foy, Quebec G1V 2J3
                           tel: 418-657-7980; fax: 418-648-2402; simardr@em.agr.ca
                                         2
                                          CRH, Laval University, Quebec


The potato production in Quebec occupies around 18,000 ha and is 4th in order of national production. The
mean marketable yield is 23 t ha-1 and the production cost is between $3,200 and 3,600 per hectare. The
province imports 35 % of its fresh consumption, 10 % of its chips, 35 % of its fries and 33 % of the seed
potatoes. There are many reasons to these large needs , the main one being the lack of availability of
varieties suitable for the ever changing markets. The potato crop usually get a uniform rate of fertilizer
application. However large yield variability is normally encountered in a given field. This may be
ascribed to differences in soil fertility, texture or other factors such as localized pest infestation or weeds.
The knowledge of this variability may be integrated through the new technologies such as the use of
satellite positioning systems (SPS) to vary fertilizer and pesticides application to reach optima yields and
protect the soil and water resources.

Precision farming entered the province of Quebec four years ago through SPS systems and yield monitors
on grain combines. The current feeling can be described as a Afever@. Feature articles in popular magazines
and electronic media coverage is highly frequent. Numerous conferences are organized on the subject.
This Afever@ might result in greater hope than reality and caution is necessary. More practically, the
mapping of soil for variable rate application (VRA) of lime was initiated in 1995 and potato yield monitors
have been used in the field in 1996.

Precision farming in potato production aims at improving yields, tuber quality and profitability, to optimize
fertilizers and pesticide inputs and to reduce environmental risk of the production. The VRA will have
particular impact on the zones of low soil fertility. The improvement in quality will come by better
coupling of N use and maturity and limitation of K excess impact on tuber specific gravity. The
improvement in profitability will originate from yield increases from areas of higher fertilizer needs and in
decreasing areas of over fertilization because of limited yield potential. The environmental benefits will
come from better adjustments of N application and localized pesticide use.

We have initiated a project in 1996 to investigate the economic and agronomic efficiency of variable rate
application of P, K and lime on the potato crop. The study is carried out at the Joseph-Rhéaume research
farm of Laval University in Sainte-Croix de Lotbinière in Quebec. Soil samples were collected on a 15 m
grid from the 0-20 and 20-40 cm layers and analyzed for pH, Mehlich 3 extractable nutrients,
KCl-extractable N and particle size analysis. A soil survey was also carried out according to a 30 m grid.
The surveys were carried out using a HERMEStd digital general positioning system (DGPS) distributed in
Quebec by Innotag. The system receives its signals from the satellites via its antenna and corrects with
signals from the Canadian Coast Guard. The data is integrated to produce georeferenced map with the
Surfer 6.0 software. The spring soil test values were highly variable (Table 1). Maps of pH showed that
liming was unnecessary. The P and K maps derived from 106 sampling sites showed that 2 levels of P and
3 levels of K could be combined in the VRA treatment. Rates of 190 and 215 kg P2O5 ha-1 were combined
with rates of 160 to 240 kg K2O ha-1. The detailed soil map (1:3000) of this 2 ha field showed that 3 soil
orders and four soil series were present. The Neubois loam (Humic Gleysol) has a high CEC, is rich in Al
and is highly erodible. The Le Bras humic loam (Humic Gleysol) is located in a small depression is very
rich in organic matter and P but has imperfect drainage and high Al content. The Valère sandy loam

                                                                                                               8
(Humo-Ferric Podzol) has low CEC and nutrient availability whereas the Sainte-Croix sandy clay loam
(Sombric Brunisol) has high pH.

Three treatments were imposed in the field. Strips of 6 rows and 300 m long received either VRA
application ( 6 combinations, a constant rate (CR) of 175 kg N, 215 kg P2O5 and K2O ha-1, and a control
(CT, no-fertilizer). The CR treatment resulted in 42 % of the areas overfertilized with P and 23 % with K
whereas 17 % of the area was under-fertilized with K. The VRA resulted in a $28 saving in fertilizers.
Potatoes (cv. Jemseg) were planted on May 27, the hilling and second fertilizer application was made in
July 6 and the harvest on September 13. Tissues were sampled on July 10th.

The marketable yield of the VRA (34 t ha-1) treatment was not significantly different from the CR treatment
but was larger than in CT (18 t ha-1). The VRA had also equivalent yields of Canada #1 small, Canada #1
and Canada #1 large tuber categories. The yield map indicated much lower yields in the block # 4 which
had the lowest amounts of extractable Mn and Cu. There was no significant difference in foliage nutrient
contents. However the VRA had higher N and P concentrations than the other treatments but less K than in
CR. Calculations of the compositional nutrient diagnostic (CND) index showed that VRA had the best
nutrient equilibrium among treatments but that K was the yield most limiting soil nutrient. The Neubois
loam had the highest marketable yield (33.6 t ha-1) and the lowest was associated with the Le Bras (25.8 t
ha-1) because of poor drainage which cause tuber rotting in the soil. The VRA resulted in more uniformity
in soil pH and extractable P and K at harvest than CR or CT.

The results of the first year at this highly variable site indicate that the VRA, in which less fertilizer was
applied, gave comparable potato yields as CR. Potassium was the most limiting nutrient. The fertilizer
savings the first year were almost equal to the commercial cost of mapping the site. These results are
interesting since nutrient mapping is only necessary every 3-4 years. The fertilizers treatments will be more
widely varied next year. A longer evaluation is necessary before conclusions be drawn on the agronomic
and economic benefits of precision fertilizer application in potato production. The interest of the farming
community is there, data must be summarized quickly before large investments be made.




                                                                                                            9
Table 1. Statistical summary of the soil test values at the Sainte-Croix site.

                                                            Soil series: Neubois
                                                Drainage: moderately good to imperfect (n=24)

             Clay   Silt         Sand    SOM      pH       pH        P      K        Ca         Mg    Fe    Mn      Cu     Zn     A1
                           (%)                    H2O     CaCl2             (kg ha-1)                              (ppm)

 Mean        16.0   52.0          32.0    6.2     6.50     5.65     90      80        3817      70    311   11      0.88   1.63   1727
 Std. Dev.    1.4    3.0           3.7    1.6     0.21     0.15     26      12        321       19     11    2      0.16   0.20    145
 Minimum     13.0   47.0          27.0    2.5     6.20     5.32     27      58        3184      43    289    8      0.59   1.33   1482
 Maximum     19.0   56.0          39.0    8.2     6.90     5.83     134     103       4390      111   328   15      1.24   1.99   2038



                                                            Soil series: Le Bras
                                                         Drainage: imperfect (n=12)

             Clay   Silt         Sand    SOM      pH       pH        P      K        Ca         Mg    Fe    Mn      Cu     Zn     A1
                           (%)                    H2O     CaCl2             (kg ha-1)                              (ppm)

 Mean        15.1   38.3          46.8    7.8     6.3      5.55     167     123       3844      97    222   13      0.96   1.96   1683
 Std. Dev.   1.3    3.0            3.2    0.9     0.15      0.1      27     46        408       13     7     1      0.17   0.22    83
 Minimum     13.5   33.5          42.0    6.5     6.2      5.41     121     75        3096      78    213   12      0.77   1.53   1556
 Maximum     17.2   43.5          52.1    9.2     6.6      5.72     192     216       4441      113   232   14      1.26   2.19   1796



                                                            Soil series: Valère
                                                Drainage: moderately good to imperfect (n=24)

             Clay   Silt         Sand    SOM      pH       pH        P      K        Ca         Mg    Fe    Mn      Cu     Zn     A1
                           (%)                    H2O     CaCl2             (kg ha-1)                              (ppm)

 Mean        16.9   17.9          64.9    4.5     6.39     5.42     88      131       3684      93    371   13.0    0.72   1.64   1398
 Std. Dev.   3.8    3.6            2.0    1.0     0.15     0.14     38      47        358       19    104   3.5     0.19   0.27    175
 Minimum     11.2   13.6          61.2    2.9     6.13     5.19     44      68        2934      54    232   8.5     0.54   1.11   1052
 Maximum     24.1   24.6          67.6    6.1     5.72     5.67     167     249       4116      118   602   20.8    1.18   2.07   1645



                                                          Soil series: Sainte-Croix
                                                Drainage: moderately good to imperfect (n=12)

             Clay   Silt         Sand    SOM      pH       pH        P      K        Ca         Mg    Fe    Mn      Cu     Zn     A1
                           (%)                    H2O     CaCl2             (kg ha-1)                              (ppm)

 Mean        34.9   15.7          59.9    7.1     6.62     5.74     111     140       5501      168   459   23      1.04   2.12   1124
 Std. Dev.   1.6    2.4            2.8    1.3     0.56     0.57     40      30        1603      41    88    12      0.45   0.5    124
 Minimum     21.1   13.8          55.1    5.0     6.13     5.18     61      94        3878      125   350   11      0.58   1.41   986
 Maximum     26.8   20.9          63.6    8.6     7.7      6.78     180     198       8821      236   610   47      1.84   2.93   1325




                                                                                                                                   10
                  Development of a yield monitor for potato harvester
                              Justin Larouche1 and Dr. Bernard Panneton2
                                       1
                                         Technical Director, Innotag Inc.
                       3125, rue Bernard-Pilon, St-Mathieu-de-Beloeil, Quebec J3G 4S5
                            tel: 514-464-7427, 1-800-363-8727; fax: 514-464-0874;
                                             innotag@generation.net
                    2
                     Centre de R&D en Horticulture, Agriculture et Agroalimentaire Canada
                           430 Boul. Gouin, St-Jean-sur-Richelieu, Quebec J3B 3E6
                                tel: 514-346-4494, poste 205; fax: 514-346-7740;
                                              pannetonb@em.agr.ca




Identification of the need

Today, on the market, a certain number of grain yield monitors are available. Those systems integrate a
grain yield sensor, a humidity sensor, and a speed sensor. They can work as a stand alone unit or in
conjunction with Global Positioning System (GPS) to allow farmers to map yield and see variations within
every field on the PC.

Because of the cost related to potato production, yield mapping provides means for better management on
the farm which can result in a more efficient use of inputs such as fertilizers and pesticides. Yield mapping
is an essential step in the precision farming approach. There is a need to develop new instruments for
measuring yield for root crops.


Objective of the project

The main objective of the project was to develop a yield monitoring system for potato production that can
be integrated to the technology of grain yield monitoring. Comparison between traditional measuring
techniques and yield monitoring data were conducted to evaluate the precision and limitations of such a
system. Area, yield as total tonnage and on spot yield in tonnage per unit area were the main elements
tested during the project.


Description of the system

The potato yield monitor was designed to be integrated to the existing and proven technology of grain yield
monitoring. The Ceres 2 Yield Monitoring system of RDS Technology, Gloucester UK, was selected as a
suitable platform. A measuring system based on weighing wheels supporting a portion of a conveyor on a
potato harvester was developed and implemented. The signal from the load cells of the weighing wheels
was combined to the signal of a magnetic pick up measuring the speed of the conveyor to produce an
electric signal that is compatible with the existing Ceres 2 data processing unit.

The Ceres derives the yield data (mass per unit area) and stores the values for further use and for display to
the operator. When used in conjunction with the Hermes data logger/DGPS receiver, all the data required
to draw a yield map are recorded on standard 3-1/2" floppy disk and can be viewed with the RDS
PLOT/PLAN software.


                                                                                                           11
Results and economic and societal benefits

During the summer 1996, 3 Yield Monitoring Systems were installed on 2 Grimme DL1700 and 1 Hill
Machine equipped with a cleaning table. The results of the project and the limitations of such systems can
be summarized as below:

C   the measuring system is based on the premise that the weighing wheels supporting a portion of a
    conveyor is a reliable means to measure yield on potato harvester;
C   each harvester model requires specific engineering and adaptation of the load cells for proper operation
    and ease of calibration;
C   area measured are within 99 % accuracy; and
C   yield as total tonnage and as on spot tonnage per area in each field were within less than 5 % error.

In the short term, some of the impacts expected from the development and implementation of Potato Yield
Monitor are presented below:

C   supply potato farmer with a reliable and easy to use system to measure yield in each field during the
    harvest;
C   allow the measurement and mapping of variations of potato yield;
C   better management of the land based using precision farming which can minimize the adverse effect of
    fertilizers and pesticides by optimizing the use of these inputs to where they are needed and in the
    amount needed; and
C   give farmer the tools to evaluate management practices with the objective of sustainable agriculture.




                                                                                                          12
           Développement d’un capteur de rendement de pomme de terre
                              Justin Larouche1 and Dr. Bernard Panneton2
                                       1
                                          Technical Director, Innotag Inc.
                       3125, rue Bernard-Pilon, St-Mathieu-de-Beloeil, Quebec J3G 4S5
                             tel: 514-464-7427, 1-800-363-8727; fax: 514-464-0874
                                             innotag@generation.net
                    2
                     Centre de R&D en Horticulture, Agriculture et Agroalimentaire Canada
                           430 Boul. Gouin, St-Jean-sur-Richelieu, Quebec J3B 3E6
                                 tel: 514-346-4494, poste 205; fax: 514-346-7740
                                              pannetonb@em.agr.ca


Problématique

Actuellement, il existe divers capteurs de rendement pour les céréales. Ces unités incluent un capteur de
rendement de grain, un capteur d’humidité, un capteur de vitesse d’avancement et de superficie. À ce type
de capteur, s’ajoute un système de positionnement par satellite (DGPS) qui permet au producteur de
visualiser sur PC les variations de rendement de ses champs de céréales.

Étant donné l’importance des coûts de production dans la pomme de terre, il y a un grand besoin pour un
capteur de rendement dans cette culture. La cartographie de rendement est essentielle en agriculture de
précision et, pour plusieurs experts, est considérée comme la première étape à une approche raisonnée de
suivi et de diagnostic. Pour établir des cartes de rendement, le producteur doit avoir à sa disposition des
appareils appropriés sans quoi il devrait appliquer une méthodologie demandant beaucoup de travail
manuel.


Objectifs du projet

L’objectif principal du projet était de mettre au point un capteur de rendement pour la pomme de terre
s’intégrant à la technologie existante du capteur de rendement de céréales. De plus ce projet avait comme
objectif de vérifier la précision du capteur de rendement en le comparant à des méthodes conventionnelles
de mesurage. Les éléments qui ont fait l’objet d’évaluation furent la superficie et le rendement total et en
tonnes par unité de surface.


Description du système

Le développement du capteur de rendement de pomme de terre a été effectué à partir de la technololgie
existante et bien éprouvée du capteur de rendement de céréales. Le système Ceres 2 de RDS Technology
(firme britanique) a été retenu dans le cadre de ce projet.

Un système de mesure basé sur le principe de balance avec roues portantes d’un convoyeur fut adapté aux
arracheuses à pomme de terre et mis au point. Le signal d’une paire de cellules de charge avec roues
portantes fut combiné au magnétique d’un capteur de vitesse de convoyeur afin de produire un signal
compatible avec le capteur de rendement déjà employé dans les céréales.




                                                                                                              13
Ce signal fournit la mesure de rendement (masse par unité de surface) qui est affiché et enregistré sur le
moniteur de rendement Ceres 2. Lorsque l’enregistreur de données/DGPS Hermes est utilisé, ces données
de rendement et celles de position (latitude et longitude) sont enregistrées sur une disquette 3-1/2" standard
pour fins de cartographie de rendement avec le logiciel RDS Plot/Plan.


Résultat et impacts sur le milieu

Durant l’été 1996, 3 unités ont été installés sur 2 Grimme Dl 1700 ET 1 Hill Machine avec une table de
triage. Les résultats des essais réalises durant la récolte de même que les contraintes d’installation et
d’opération permettent de conclure que:

C   Le principe d’une paire de cellules de charge sur un convoyeur propre est un bon moyen pour mesurer
    le rendement de la pomme de terre lors de la récolte à même l’arrachage;
C   Les contraintes physiques de chaque machine sont très importantes à considérer pour permettre des
    mesures précises;
C   Les mesures de surface ont une précision de l’ordre de 1 %;
C   Les mesures de rendement ont une précision estimée à 5 % sur le tonnage total de même que sur le
    tonnage par unité de surface à l’intérieur de chaque champ.

À court terme, les impacts prévus du développement et de l’implantation du capteur de rendement de
pomme de terre sont résumés ci-dessous:

C   Fournir au producteur agricole un outil simple et fiable de mesure de rendement de la récolte;
C   Évaluer et cartographier les variations de rendement dans chaque champ;
C   Permettre d’évaluer et de choisir les variétés et les champs les mieux adaptés à une production
    optimum;
C   Sensibiliser les producteurs à ces nouvelles méthodes d’évaluation en vue de promouvoir une
    agriculture durable.




                                                                                                            14
                 Applications of precision farming to potato production
                                      Ray P. Carmichael, PAg, CAC
                                      Atlantic Agri-Food Associates Inc.
                                      R.R. 4, Centreville, NB EOJ 1HO
                                            tel/fax: 506- 276-3311
                                            raymond@nbnet.nb.ca


Atlantic Agri-Food Associates Inc. has successfully developed a georeferenced crop management (Precision
Farming) service for the potato industry of NB, PEI, NS and the state of Maine. In addition to radically
changing the approaches to crop production management, as precision farming technology becomes more
available, agricultural researchers must look at the possibility of using it to enhance their methods.
Agronomic research has traditionally been conducted on small uniform areas within fields, ignoring the
spatial variability of soils and landscapes. The average results from these small scale experiments are used
in management decisions applied to large fields assumed to be homogenous, which is not true. Soil
fertility, moisture, topography, and other yield-influencing factors often vary drastically across fields.
Yield mapping and other precision farming technologies now provide the tools to divide fields into smaller
units and apply variable rate inputs. There is a growing need for research conducted on a scale large
enough to encompass the spectrum of variation found within fields, to provide agronomic information for
precision farming.

Once the spatial variability is measured, optimizing management requires an understanding of what caused
the variability and a method to determine optimal management (remediation) over the field. Crop
simulation models (bench marks) are ultimately needed to help consultants, researchers and other farm
advisors determine the site specific management practices that optimize production or profit. However, the
effective use of these tools requires their evaluation or verification in the fields to be optimized, their
integration with other information tools such as GIS, geostatistics, remote sensing, and optimization
analysis.

Results to date, from grid soil sampling over 900 acres in rotation with potatoes, suggest that Site Specific
Crop Management techniques would result in an estimated 39 % improvement in fertilizer efficiency
compared to conventional Whole Field Management techniques. Based on standard New Brunswick
Department of Agriculture criteria, Site Specific Crop Management would recommend the application of
74 % more limestone, 133 % more P as well as additional K than in Whole Field Management techniques.

Initial application of these precision farming technologies to potato yield monitoring demonstrated a
significant variation in potato yield within any given field. Yields in excess of 500 cwt/ac were achieved
with the same inputs on the same field in the same year, as a field average yield of 200 cwt/ac.

Such a variation challenges the conventional approaches to defining production recommendations and
highlights the need for further adaptive research to support the development of site specific remediation
strategies.




                                                                                                             15
                     Precision agriculture: managing soil variability
                                              Gary Patterson
                                CRC Soils, Agriculture and Agri-food Canada
                                 PO Box 550, Truro, Nova Scotia B2N 5E3
                                   tel: 902-893-7430; fax: 902-893-0244
                                          gpatterson@es.nsac.ns.ca


A farm field is generally managed as though it contained only one soil. However, one soil per field is the
exception rather than the rule. Most fields contain two or more soils with different crop yield potentials
(Carr et al., 1991). The related variability has recently engendered an interest in what is now know as
“precision farming”.

Precision farming encompasses a set of management practices designed to respond to the underlying causes
of yield variability, and then to take correction measures (Blackmore, 1994). The process requires large
amounts of data (Yule et al., 1996) as follows:

C   yield maps prepared by using yield monitors and GPS on harvesters;
C   soil maps based upon existing soil surveys and/or on custom sampling; and
C   transient data maps of pest and disease infestation.

Yield maps based on a single year may be valuable but full benefits are derived over several years of
observation (Graham and Dawe, 1995). Depending upon individual objectives, precision farming
techniques can be adapted to maximizing profit, to reducing environmental impact, or both (Runge, 1992).
Integrating and interpreting large amounts of spatially referenced data require the use of a Geographic
Information System (Peterson et al., 1995) capable of handling geostatistical techniques such as krieging.

In some cases, the application of variable rate technology may involve very simple techniques such as field
reorientation. In other cases, the use of more sophisticated equipment to control pesticide or fertilizer
application rates may be justified.


References

Blackmore, S. 1994. Precision farming: an introduction. Outlook. 23: 275-280.
Carr, P.M., G.R. Carlson, J.S. Jacobsen, G.A. Nielsen, E.O. Skogley. 1991. Farming soils, not fields: a
    strategy for increasing fertilizer profitability. J. Prod. Agr. 4: 57-61.
Graham, R.M., A.F. Dawe. 1995. Yield mapping for precision farming - operating principles and
    equipment. J. Royal Agric. Soc. England. 156: 35-42.
Peterson, G.W., J.C. Bell, K. McSweeney, G.A. Nielsen, P.C. Robert. 1995. Geographic information
    systems in agronomy. Adv. Agron. 55: 67-111.
Runge, C.F. 1992. A policy perspective on the sustainability of production environments: toward a land
    theory of value. Quarterly J. Inter. Agr. 31:149-161.
Usery, E.L., S. Pocknee, B. Boydell. 1995. Precision farming data management using geographic
    information systems. Photogram. Eng. Remote Sensing. 61: 1383-1391.
Yule, I.J., P.J. Cain, E.J. Evans, C. Venus. 1996. A spatial inventory approach to farm planning.
    Comput. Electron. Agric. 14:151-161.



                                                                                                             16
                   Field scale variability of climate in Atlantic Canada
                                      George Read, Agroclimatologist
           Land Resources Branch, New Brunswick Department of Agriculture and Rural Development
                            PO Box 6000, Fredericton, New Brunswick E3B 5H1
                                    tel: 506-453-2109; fax: 506-457-7267
                                              gread@gov.nb.ca


Climatic variation is an important component of agricultural production. Often, as a result of improved
measurement techniques, variation becomes evident which was previously not known or clear. Variability
is seen in space and time (where and when). We often deal with climate variation on a large area or
regional basis, however significant differences do occur in space, and certainly with time, at the field scale,
be it year-to-year, season-to-season, month-to-month, etc.

Regional factors such as latitude, longitude, topography, weather system patterns and marine influences
help define and determine the probable climate of a large area. The Atlantic region has significant
variability in several agroclimatic parameters that are important to viable agriculture.

Farm scale variability has been documented in several studies world wide with a few examples here in
Atlantic Canada. Influential factors at this scale include, topography, soil, vegetation within and nearby
fields, and nearness to modifying water bodies. Variability in intercepted radiation, temperatures in both
space and time, precipitation, evaporation and wind, all significantly impact crop and animal production.

Dealing with variation in time is an essential component of farming and, in particular, precision farming.
Evaluation of soil factors, both physical and chemical, crop characteristics and pest management are
dependant on climate variability between locations and between time scales. Risk management must deal
with how the situation currently being examined fits into the long term likelihood of it recurring.

Precision farming offers some tools that hold promise of understanding climate variability.

C   Improved delineation of differences in crop performance.
C   High priority on characterizing topography which has the potential to explain a large percent of climate
    variability on a farm.
C   Using the combined tools of GPS, GIS and remotely sensed data to more accurately map climate
    variability in space and time and, therefore, permit more meaningful interpretation of soil, fertility,
    water and crop impacts.




                                                                                                             17
                 On-the-go soil nitrate measurement and control system
                                  Dr. Kevin J. Sibley, Associate Professor
                   Department of Agricultural Engineering, Nova Scotia Agricultural College
                                 PO Box 550, Truro, Nova Scotia B2N 5E3
                                   tel: 902-893-6710; fax: 902-893-1859
                                            ksibley@ae.nsac.ns.ca

Nitrate, a key form of nitrogen for plants, is leachable in some soil types if present in quantities larger than
required by the growing crop. Nitrate is being found in increasing concentrations in drinking water, rivers,
and lakes. Efficient nitrogen management, including more accurate nitrogen fertilizer recommendations and
placement, could help minimize the contribution by agriculture to the nitrate contamination problem.

Direct nitrate measurements made in the field during fertilizer application would ensure that only the
amount needed by the plants is applied. Using this concept, fertilizer application rate could be adjusted on-
the-go in direct response to the concentration of available nitrate in the soil. This process requires an
automated method of sampling the soil and nitrate level determination, interpretation of the measured
nitrate level which considers the needs of the plants, and automated control of a variable rate fertilizer
spreader.

An on-the-go soil nitrate measurement and control system, developed at the Nova Scotia Agricultural
College by Agricultural Engineers Kevin Sibley and John Adsett, is a tractor-mounted system designed to
provide rapid on-the-go electrochemical analysis of the nitrate content of agricultural topsoils and to
provide on-the-go direct control and adjustment of a fertilizer spreader’s application rate. The system
collects a soil sample, combines the sample with a nitrate extractant solution, measures the nitrate
concentration of the soil/extractant mixture, and produces a control signal for a fertilizer spreader.

Reliable soil nitrate measurements and be obtained in as little time as six seconds, as shown in the figure
below for samples taken in a silty clay loam soil.




Figure 1.

Currently, USA, Canadian, and International patents are pending. Applied Microelectronics Inc., Halifax,
NS, has purchased the intellectual property rights. A commercialization and world-wide distribution
agreement between Applied Microelectronics and Greenland b.v., Holland, the world’s largest fertilizer
spreader manufacturer under the VICON brand name, is under negotiation.

                                                                                                              18
                            Workshop Group A:      Delivery and adoption
                                         Rapporteur: Jack van Roestel


Two questions were addressed to focus the discussion: Will precision farming be useful to Maritime
farmers? How do government researchers/extensionists get involved in the evaluation and delivery
process?

Before this technology can be delivered and possibly adopted by Maritime farmers, it's important that
researchers and extension workers are involved with innovative farmers and industry representatives in
assessing this technology. There is a need to partner between these individuals to check the reliability of
the equipment (i.e. the consistency of the satellite signal, the convenience and reliability of the yield
monitors and data software/collectors). This equipment assessment is crucial to ensure data soundness,
and should be tackled in the following ways:

C   Assess and possibly modify "on-the-go" yield monitors.
C   Develop a framework for "benchmark" information collection of key yield-determinant factors. It was
    felt that government should not be involved directly with the collection of this data. However,
    governments could do research to determine the intensity, sampling technique and frequency that this
    information would need to be collected.

Once researchers and extension workers went through the equipment evaluation and data collection stage,
then there would be a need to work with groups of innovative farmers under "Precision Farming Clubs" to
interpret data/make management adjustments/and monitor results over a long term for both
REPEATABILITY and PROFITABILITY.

While this process is going on, it's very important to maintain good communication with all the Maritime
Precision Farming partners along with innovators in other parts of the continent.




                                                                                                              19
                           Workshop Group B:      Hardware and software
                                           Rapporteur: Kevin Sibley


Issues

C   Limitations of software packages - there are various levels of “GIS” capability, and the level which is
    sufficient for farmers needs is questioned.
C   Compatibility between equipment suppliers’ products.
C   Assessment of accuracy and reliability, particularly for yield - the level of accuracy which can be
    attained and the level which is needed must be established.
C   Guidance systems - are they necessary?
C   Cost effectiveness of the systems needs to be demonstrated for conditions in Atlantic Canada.
C   Crop rotation extends the experience timeline for the evaluation of technologies and can delay full
    implementation of the technology. Sound management decisions will have to be made based on a
    historical database specific to the individual farm.
C   Equipment selection - how do you know what is good equipment and what is not?


Direction

C   Multi-year experience / trials on farm.
C   Start with yield mapping - this at least gets the historical database up and running and can initially
    provide the most benefit to farmers.


Producer support very important

C   Proper installation.          ,
C   Proper calibration.           1 Very important for farmer’s confidence in using the technology.
C   Training / field support.     -
C   On-farm demonstrations.
C   Education / training seminars by those knowledgeable with the technology and its application.
C   Joint industry - government projects for evaluation /development.
C   Involve all disciplines in projects - engineers, agronomists, soil scientists, etc.
C   Development of variable rate technology specific to Atlantic Canada crop conditions will be required to
    enhance the overall benefit of precision farming technology.




                                                                                                             20
                       Workshop Group C:     Soils and fertility information
                                         Rapporteur: Gary Patterson


Several general farm management needs and specific research needs were identified.

Farm Management

C   Accounting by enterprise is important. The objective of precision farming is to maximize net income.
    Therefore, accounting practices must be more specific than a compilation at the whole farm level.
C   Crop scouting must be an integral part of precision agriculture. It is fruitless to attempt to correlate
    yields with soil fertility if factors like weed infestations, crop diseases, or damage due to wind are
    ignored.
C   Precision agriculture will most likely be applied to crops of high volume and/or high value.
C   Sampling methods must account for spatial as well as temporal variability. Soil pH, for example,
    varies across a field on any given day and this pattern often changes from one year to the next.


Research

C   Any research undertaking must be mutidisciplinary. It is virtually impossible for a group from a single
    discipline to have the depth and breadth of expertise required to collect and interpret the massive
    quantities of data generated by precision agriculture projects.
C   To be valuable, research should be carried out at the field scale, be multivariate in nature, and be long-
    term (> 5 years).
C   Soil moisture may be the single most important yield determinant. However, water content is difficult
    to measure either in space or in time and a method is required to monitor soil moisture cheaply and
    reliably.




                                                                                                           21
           Workshop Group D:     Assessing economic and environmental benefits
                                           Rapporteur: David Lobb


Economic

C   A conservative approach to the economics of precision farming is recommended
    · there are several elements of precision farming and the economics of each must be available for
        farmers to make appropriate management decisions
    · piece-wise and step-by-step approach to adoption (1st step/piece is yield mapping since this
        demonstrates the economic opportunities)
C   Economic advantages are scale dependent
    · need for custom services for smaller operations
C   The are economic benefits to standardizing and simplifying
    · data collection strategies (e.g. soil sampling and soil analysis)
C   Much more information is required to make appropriate economic decisions
    · demonstrate the economics
C   There is a need for better understanding of the relationships between total yield and marketable yield
    and their various controlling factors


Environmental

C   There are potential environmental benefits of Precision Farming w.r.t. Nutrient Management and Pest
    Management
    · much is currently being done with IPM
    · research is progressing to improve these techniques
C   Benefits can be achieved by optimizing crop production and improving input efficiency
    · reduce variability in yield potential
    · match inputs to variability in yield potential
    · isolating areas of unacceptable risk
C   Use information to better understand soil degradation and improve soil conservation practices




                                                                                                             22
                                         Workshop wrap-up
                                             Dr. Robert Gordon
                             Nova Scotia Department of Agriculture and Marketing
                                  PO Box 550, Truro, Nova Scotia B2N 5E3
                                    tel: 902-893-6561; fax; 902-893-0244
                                             rgordon@nsac.nc.ca


What is precision farming?

C   Precision farming is managing field variability to best utilize resources and minimize environmental
    impacts.
C   Fields without fences
C   Prescription farming
C   Site-specific farming
C   “Turning back the clock”
C   “The way our grandfathers did it”


Potential for precision farming

C   High input/value crops
C   Where the nature and sources of variation can be identified


Yield mapping

C   revolutionary, evolutionary, complex and perplexing
C   shows where and how much!
C   yield mapping - “.... collects the information from the most sophisticated sensor, the plant....”
C   “yield maps .... an integration of what happened”
C   “harvest much more data than anyone can interpret”


Information analysis

C   GINGO (Garbage IN Garbage Out)
C   “How do we make meaningful conclusions from so much data?”
C   “How do we interpret yield maps”
    · how to indentify sources of variation
C   Data versus information


Tools

C   On-farm field trails/research
    · requires cooperation of farmers, researchers and consultants
C   Farm level application ??


                                                                                                           23
                     Distribution List
David Lobb                      Marcel Michaud
Lise Ouellette                  Roger Godbout
                                Earnst Cuthberson
Gary Patterson                  David Frost
Ken Webb
Vernon Rodd                     Ed Woodrow
                                John Richards
Phil Warman                     David McKenzie
Gordon Brewster                 Hazen Scarth
                                Jan van de Hulst
Mike Langman                    Jeff Whalen
Rob Gordon
Jack van Roestel                Justin Larouche
Dennis Moerman                  Régis Simard

Richard Donald                  Doug MacKay
Angus Ells                      Colin McKenzie

Delmar Holmstrom                Doug Aspinall
Brian Sanderson                 Ivan O’Halloran
John MacLeod                    Len Senychyn
Martin Carter                   Peter Darbishire

Richard Veinot                  PEI Soil and Crop Improvement Association
Theresa Mellish                 PEI Federation of Agriculture
Barry Thompson                  Nova Scotion Soil and Crop Improvement Associations
Ron DeHaan                      Nova Scotia Federation of Agriculture
Graeme Linkletter               Nova Scotia Potato Marketing Board
Charles Coles                   Vegetables Nova Scotia
Peter Boswell                   New Brunswick Soil and Crop Improvement Associations
Gwen Vessey                     New Brunswick Federation of Agriculture
                                New Brunswick Potato Agency
Steve Howatt                    Atlantic Farmers Council
Margret Drake                   Atlantic Fertilizer Institute

Herb Rees
Lien Chow
Paul Milburn
Sherif Fahmy

Paul Smith
Clair Gartley
George Read
Petra Loro
Jean-Louis Daigle
Chuck Everett
Peter Scott
David Walker
Roger Theriault

Ghislain Pelletier
John Walsh
Gilles Moreau
Leigh Morrow

Ray Carmichael


                                                                                  24

				
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