SITE-SPECIFIC AGRICULTURE BY MEANS OF BIO-INSPIRED MODELS FOR UNDER-RESEARCHED TROPICAL FRUIT SPECIES IN COLOMBIA

1. 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

2. 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
publicly-available environmental databases
Site-Specific Crop Production (SSCP)
2

3. 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
3

4. • 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:
4

5. Methods
Collecting farmers’ production experiences
Participatory research
• Consultative mode
• Collaborative mode
Guide form based on a calendar77

6. Collecting Farmers’ production experiences
Calendars developed to capture harvest events
Cropping events
88

7. Mostly estimates physical soil properties:
Texture, Drainage, Effective soil depth, Structure, Colour
Collecting Farmers’ production experiences
Soil information
9

8. 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

9. 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/ Inputs
dependent
/output
(known)
…
11

10. 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
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
… … … … … … … … … … … … …
Obs3000 0.04 15 3 235 0.6 … 85 1 1 1 1 0
L 1
Analytical approaches
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11. SSCP = (Participatory & Operational research ) + publicly-available environmental data +
analytical approaches + farmers’ production experiences
Crop Departments
Geo-
referenced
Cropping
events
Production
Variety and
number of
plants
RASTA Complete plots
No of
farms
weekly
periods
No of
farms
No of
farms
No of
farms
No of
farms
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
14

12. Results - Andean blackberry
Scatter plot displaying MLP predicted yield versus real Andean blackberry yield, using only the
validation dataset1715
R² = 0.892
-0.2
0.3
0.8
1.3
1.8
-0.2 0.3 0.8 1.3 1.8
Predictedyield(kg/plant/week)
Real yield (kg/plant/week)
Predicted
Supervised models - Non-linear regression
Coefficient of determination= 0.89
Histogram displaying yield data distribution of Andean blackberry
(Kg/plant/week)
Numberofobservations

13. 0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
EffDepth
TempAvg_1
Na_un_chical
Na_un_cusba
TempAvg_0
TempAvg_2
TempAvg_3
ExtDrain
PrecAcc_1
Trmm_3
Nar-Cal
Cal_riosu_zr
Srtm
Slope
PrecAcc_0
Trmm_2
Na_un_cusal
Trmm_0
PrecAcc_3
TempRang_0
TempRang_2
AB_Thorn_N
Na_un_lajac
PrecAcc_2
Trmm_1
IntDrain
TempRang_3
TempRang_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
%Sensitivity
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
16
Effective soil depth
Temperature averages
Geographic location

14. 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
17
Andean blackberry yieldKohonen map – 6 clusters
(a) (b)

15. 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
18
Effective soil depth
Unsupervised model - Visualization – component planes - SOM

16. 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
19
Unsupervised model - Visualization – component planes - SOM

17. 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
20
Nariño - La Union – Chical Alto Nariño - La Union – Cusillo bajo

18. 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
provided by each approach
MLP
Robust regression
0.2877 0.3545 0.4214 0.4883 0.5552 0.6221 0.6889 0.7558 0.8227
0
2
4
6
8
10
12
14
16
18
20
22
24
26
NumberofobservationsNumberofobservations
Numberofobservations
Supervised modelling

19. Results - Lulo
The Sensitivity Matrix
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
%Sensitivity
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
22
Sensitivity distribution of the model with respect to the inputs
Effective soil depth
Temperature averages
Slope

20. (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 - Lulo
Unsupervised model - Clustering – component planes - SOM
23
U-Matrix Kohonen map – 3 clusters

21. Results - Lulo
Clustering – 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)
Standard
Error
%
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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22. Variable ranges HEC
Slope (degrees) EffDepth (cm) TempAvg_0
( C)
5-14 21-40 15 -16.5 1
8-15 32-69 15 -18.9 2
13-24 40-67 15.8 -19 3
HEC 3 yielded 41 g/plant/week
more fruit than average
Results - Lulo
-30.00
-20.00
-10.00
0.00
10.00
20.00
30.00
40.00
50.00
1 2 3
Luloyield(g/plant/week)
Effects of clusters of environmental
conditions
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23. 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
-80.00
-60.00
-40.00
-20.00
0.00
20.00
40.00
60.00
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 21
1 2 3
Luloyield(g/plant/week)
Effects of farms across clusters of environmental conditions
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

24. Conclusions
27
• 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

25. 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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26. 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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27. 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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28. Questions