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SITE-SPECIFIC CROP PRODUCTION BASED ON FARMERS’ PRODUCTION EXPERIENCES IN COLOMBIA. CASE STUDIES ON ANDEAN BLACKBERRY
(Rubus glaucus Benth) AND LULO (Solanum quitoense Lam)
Daniel Ricardo Jiménez Rodas
Farmers’ production experiences
Principles of participatory and
operational research
Modern information technology
SSCP
Environmental characterization of the production system
Analysis of the Observations to optimize the system
Kg/tplant Temperature Age
Observations made by farmers according to their particular circumstances
- 3 0 . 1
3 0 . 5
M e a n a n n u a l
t e m p e r a t u r e ( º C )
0
1 2 0 8 4
A n n u a l
p r e c i p i t a t i o n ( m m )
publicly-available environmental databases
Site-Specific Crop Production (SSCP)
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Objectives
The objectives of this thesis are to:
• Demonstrate that the principles of operational and participatory research can be applied to Andean blackberry and lulo, and provide growers with insights into how yield varies
• Evaluate modelling methodologies developed for sugarcane, to determine their suitability as tools for modelling Andean blackberry and lulo yield
• Use these methods to identify the conditions that are most suitable for the production of Andean blackberry and lulo
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• Modern information technology can be used to combine information on farmers’ production experiences with publicly-available environmental databases
• Principles of operational and participatory research facilitate the task of collecting, characterizing and interpreting cropping events that occur under a wide range of conditions
The hypotheses that this research seeks to verify are:
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Methods
Collecting farmers’ production experiences
Participatory research • Consultative mode• Collaborative mode
Guide form based on a calendar77
Collecting Farmers’ production experiences
Calendars developed to capture harvest events
Cropping events
88
Mostly estimates physical soil properties:Texture, Drainage, Effective soil depth, Structure, Colour
Collecting Farmers’ production experiences
Soil information
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- 3 0 .1
3 0 .5
M e a n a n n u a l
t e m p e r a t u r e ( º C )
0
1 2 0 8 4
A n n u a l
p r e c i p i t a t i o n ( m m )
SRTM : The Shuttle Radar Topography Mission (high-resolution topographical and landscape information )
WorldClim: Monthly data (precipitation, temperature)
TRMM : Tropical Rainfall Measuring Mission
Publicly-available environmental databases
1010
Analytical approaches
V1 V2 V3 V4 V5 … V60 L 2 L 3 L 4 L 5 … Kg/plot
Obs 1 0.1 18 3 312 0.3 … 89 0 1 0 1 0 … 2.39
Obs 2 0.2 15 4 526 0.1 … 52 1 0 0 0 1 … 30.35
Obs 3 0.6 14 1 489 0.2 … 64 0 1 1 1 1 … 42.25
Obs 4 0.05 19 2 523 0.5 … 13 0 0 0 0 1 … 52.50
Obs 5 0.4 13 3 214 0.6 … 57 1 1 1 1 1 …
Obs 6 0.8 12 4 265 0.4 … 24 1 1 0 1 0 … 82.25
Obs 7 0.2 15 1 236 0.8 … 26 0 0 1 0 0 … 89.28
Obs 8 0.1 17 3 541 0.1 … 35 0 1 1 1 0 … 125.0
Obs9 0.6 16 2 845 0.3 … 51 0 0 1 1 0 … 142.8
Obs10 0.1 18 1 126 0.1 … 43 1 1 0 0 1 … 150.0
… … … … … … … … … … … … … … …
Obs3000 0.04 15 3 235 0.6 … 85 1 1 1 1 0 … 180
70.52
L 1
Supervised models
Independent variables/ Inputsdependent/output(known)
…
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12
L 1
Observations close to each other in the multidimensional/input are located close in the output/visualization layer - clustering and visualization tool
Unsupervised models
Unsupervised models
V1 V2 V3 V4 V5 … V60 L 2 L 3 L 4 L 5
Obs 1 0.1 18 3 312 0.3 … 89 0 1 0 1 0
Obs 2 0.2 15 4 526 0.1 … 52 1 0 0 0 1
Obs 3 0.6 14 1 489 0.2 … 64 0 1 1 1 1
Obs 4 0.05 19 2 523 0.5 … 13 0 0 0 0 1
Obs 5 0.4 13 3 214 0.6 … 57 1 1 1 1 1
Obs 6 0.8 12 4 265 0.4 … 24 1 1 0 1 0
Obs 7 0.2 15 1 236 0.8 … 26 0 0 1 0 0
Obs 8 0.1 17 3 541 0.1 … 35 0 1 1 1 0
Obs9 0.6 16 2 845 0.3 … 51 0 0 1 1 0
Obs10 0.1 18 1 126 0.1 … 43 1 1 0 0 1
… … … … … … … … … … … … …
Obs30000.04 15 3 235 0.6 … 85 1 1 1 1 0
L 1
Analytical approaches…………………………………………………………………………
…………
…………
………………………………
SSCP = (Participatory & Operational research ) + publicly-available environmental data + analytical approaches + farmers’ production experiences
Crop Departments Geo-referenced
Cropping events Production
Variety and number of
plantsRASTA Complete plots
No offarms
weeklyperiods
No offarms
No offarms
No offarms
No offarms
Andean blackberry
Caldas, Nariño
75 488 35 34 20 20
Lulo Nariño, Others
111 254 54 43 21 21
Total 186 742 89 77 41 41
Results
Summary of the number of Andean blackberry and lulo growers who recorded information via calendars
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Results - Andean blackberry
Scatter plot displaying MLP predicted yield versus real Andean blackberry yield, using only the validation dataset1715
-0.2 0.3 0.8 1.3 1.8-0.2
0.3
0.8
1.3
1.8
f(x) = 0.892655122481665 x + 0.0157451798619761R² = 0.891999243225333
Predicted
Real yield (kg/plant/week)
Pred
icte
d yi
eld
(kg/
plan
t/w
eek)
Supervised models - Non-linear regressionCoefficient of determination= 0.89
Histogram displaying yield data distribution of Andean blackberry (Kg/plant/week)
Num
ber o
f obs
erva
tions
Eff
Dep
th
Tem
pAvg
_1
Na_
un_c
hica
l
Na_
un_c
usba
Tem
pAvg
_0
Tem
pAvg
_2
Tem
pAvg
_3
Ext
Dra
in
Pre
cAcc
_1
Trm
m_3
Nar
-Cal
Cal
_rio
su_z
r
Srt
m
Slo
pe
Pre
cAcc
_0
Trm
m_2
Na_
un_c
usal
Trm
m_0
Pre
cAcc
_3
Tem
pRan
g_0
Tem
pRan
g_2
AB
_Tho
rn_N
Na_
un_l
ajac
Pre
cAcc
_2
Trm
m_1
IntD
rain
Tem
pRan
g_3
Tem
pRan
g_1
12 20 3 5 17 23 26 11 22 16 2 7 8 9 19 15 4 13 28 18 24 1 6 25 14 10 27 21
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
% S
ensi
tivity
Sensitivity distribution of the model with respect to the inputs
Jiménez, D., Cock, J., Satizábal, F., Barreto, M., Pérez-Uribe, A., Jarvis, A. and Van Damme, P., 2009. Computers and Electronics in Agriculture. 69 (2): 198–208
Sensitivity Matrix Results - Andean blackberry
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- 3 0 .1
3 0 .5
M e a n a n n u a lt e m p e r a t u r e ( º C )
0
1 2 0 8 4
A n n u a l p r e c i p i t a t i o n ( m m )
Effective soil depth
Temperature averages
Geographic location
Results - Andean blackberry
(a) Kohonen map displaying the resultant 6 clusters and their labels according to yield values (b) Component plane of Andean blackberry yield, the scale bar (right) indicates the range value of productivity in kg/plant/week The upper side exhibits high values of yield, whereas the lower displays low values
Unsupervised model - Visualization – component planes - SOM
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Andean blackberry yieldKohonen map – 6 clusters
(a) (b)
Results - Andean blackberry
Component plane of effective soil depth. The scale bar (right) indicates the range value in cm of soil depth: the upper side of the scale exhibits high values, whereas the lower displays low values
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Effective soil depth
Unsupervised model - Visualization – component planes - SOM
Results - Andean blackberry
Components planes of the temperature averages. In all figures, the scale bar (right) indicates the range value in ◦C of temperature. The upper side exhibits high values, whereas the lower displays low values
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Unsupervised model - Visualization – component planes - SOM
Results - Andean blackberry
Component planes of the specifics geographic areas Nariño–La Union–Chical alto (left) and Nariño–La union–Cusillo bajo (right). The highest values indicate presence and the lowest absence as they are categorical variables
Visualization – component planes - SOM
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Nariño - La Union – Chical Alto Nariño - La Union – Cusillo bajo
Results - Lulo
Distribution of R2 obtained with each model
Regression R2
(mean)Confidence
interval (95%)
Robust (linear) 0.65 0.63 - 0.66
MLP (non-linear) 0.69 0.67 - 0.70
Both models explained more than 60% of variability in Lulo production
2321
Histogram displaying yield data distribution of lulo (g/plant/week)
R2 pr ovide d by e ach appr oach
MLP
Robus t regres s ion
0.2877 0.3545 0.4214 0.4883 0.5552 0.6221 0.6889 0.7558 0.82270
2
4
6
8
10
12
14
16
18
20
22
24
26
Nu
mb
er o
f o
bse
rvat
ion
sN
umbe
r of o
bser
vatio
ns
Num
ber o
f obs
erva
tions
Supervised modelling
Results - LuloThe Sensitivity Matrix
effD
epth
tem
pAvg
_0slo
pe
Na_un
_chi
cal
srtm
Na_un
_jac
trmm
_2
Na_ca
_san
Na_un
_ba
Tem
pRan
g_1
Tem
pRan
g_0
trmm
_1
int_
extD
rain
Tem
pRan
g_2
trmm
_0
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
% S
ensi
tivity
Jiménez, D., Cock, J., Jarvis, A., Garcia, J., Satizábal, H.F., Van Damme, Pérez-Uribe, A., and Barreto, M., 2010. Interpretation of Commercial Production Information: A case study of lulo, an under-researched Andean fruit. Agricultural Systems. 104 (3): 258-270
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Sensitivity distribution of the model with respect to the inputs
Effective soil depth
Temperature averages
Slope
(a) U-matrix displaying the distance among prototypes. The scale bar (right) indicates the values of distance. The upper side exhibits high distances, whilst the lower displays low distances; (b) Kohonen map displaying the 3 clusters obtained after using the K-means algorithm and the Davies–Bouldin index
The three most relevant variables were used to train a Kohonen map and identify clusters of Homogeneous Environmental Conditions (HECs)
Results - LuloUnsupervised model - Clustering – component planes - SOM
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U-Matrix Kohonen map – 3 clusters
Results - LuloClustering – component planes - SOM
A mixed model with the categorical variables of three HECs, location and farmer explained more than 80% of variation in lulo yield
Parameters Estimate (g/plant/week)
StandardError
%of total variance
Model including categorical variables of 3 HECs, location and farm
HEC 1.85 2.01 61.2%
Location 0.07 0.20 2.5%
Site-Farm 0.57 0.21 19.0%
Error 0.52 0.04 17.3%
Total 100.0%
Variance components of the mixed model estimations
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Variable ranges HEC
Slope (degrees) EffDepth (cm) TempAvg_0 (°C)
5-14 21-40 15 -16.5 18-15 32-69 15 -18.9 213-24 40-67 15.8 -19 3
HEC 3 yielded 41 g/plant/week more fruit than average
Results - Lulo
1 2 3
-30.00
-20.00
-10.00
0.00
10.00
20.00
30.00
40.00
50.00
Effects of clusters of environmental condi-tions
Lu
lo y
ield
(g
/pla
nt/
we
ek
)
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Results - Lulo
Farm 7 and 9 in HEC 3. Farm 7 produced 68 g/plant/week less than average, whilst farm 9 produced 51 g/plant/week more than average
1 2 3 4 5 8 17 5 6 8 10 11 12 13 15 16 17 19 20 7 9 14 18 19 20 211 2 3
-80.00
-60.00
-40.00
-20.00
0.00
20.00
40.00
60.00
Effects of farms across clusters of environmental conditions
Lu
lo y
ield
(g
/pla
nt/
we
ek
)
1 2 3
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Jiménez, D., Cock, J., Jarvis, A., Garcia, J., Satizábal, H.F., Van Damme, Pérez-Uribe, A., and Barreto, M., 2010. Interpretation of Commercial Production Information: A case study of lulo, an under-researched Andean fruit. Agricultural Systems. 104 (3): 258-270
Conclusions
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• Most suitable environmental conditions for producing Andean blackberry are: Average temperature between 16 and 18 °C Minimal effective soil depth between 40 and 65 cm
• Most suitable environmental conditions for producing lulo are: Average temperature between 15.8 and 19°C Effective soil depth between 40 and 67 cm Slope between 13 and 24 degrees
• Farmers who properly manage their fields were identified
• Yield differences Andean blackberry – localities Lulo - yield gap between farms in similar environmental conditions
Conclusions
• Key role of farmers (186 registered information on 742 cropping events)
• Analytical approaches explained more than 80% of variability for both crops
• Farmers’ production experiences and publicly-available environmental data can be analysed as long as it is possible to collect sufficient data on how the growers manage their crop, and how much they produce
• The biggest challenge is not the analysis of information… rather the collection of data
• The data collection and the analysis seem to be promising tools to develop a SSCP for other crops or regions where there is neither information on climate nor on soils
• This is the first time that this methodology has been implemented for under-researched crops in general and in Colombia in particular
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Limitations of the research
• Quality of the data collected
• Information on management practices
• Black-box / traditional models? In some cases in general agreement
• HECs constructed under the assumption of environmental variables that are constant over the time
• The results found here cannot be extrapolated outside the ranges of the variable values appearing in the collected datasets
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Contributions
• Use of farmers’ production experiences (commercial data) for understanding variability
• To turn farmers' day-to-day activities into experiments
• Introduction of novel analytical approaches in LAC for analyzing information
• Provides scientific evidence on the factors that drive productivity for highly under-researched fruits
• First formal research study that evidences the yield gap between farmers under similar climatic conditions in Colombia
• More than 3000 farmers in Colombia are willing to increase productivity and taking benefit of this doctoral research
• Provides a sound basis for transferring technology between localities and farms
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Questions