SITE-SPECIFIC CROP PRODUCTION BASED ON FARMERS’ PRODUCTIONEXPERIENCES IN COLOMBIA. CASE STUDIES ON ANDEAN BLACKBERRY(Rubus...
Farmers’ productionexperiencesPrinciples ofparticipatory andoperationalresearchModerninformationtechnologySSCPEnvironmenta...
ObjectivesThe objectives of this thesis are to:• Demonstrate that the principles of operational and participatoryresearch ...
• Modern information technology can be used to combine information onfarmers’ production experiences with publicly-availab...
MethodsCollecting farmers’ production experiencesParticipatory research• Consultative mode• Collaborative modeGuide form b...
Collecting Farmers’ production experiencesCalendars developed to capture harvest eventsCropping events88
Mostly estimates physical soil properties:Texture, Drainage, Effective soil depth, Structure, ColourCollecting Farmers’ pr...
SRTM : The Shuttle Radar Topography Mission (high-resolution topographical and landscape information )WorldClim: Monthly d...
Analytical approachesV1 V2 V3 V4 V5 … V60 L 2 L 3 L 4 L 5 … Kg/plotObs 1 0.1 18 3 312 0.3 … 89 0 1 0 1 0 … 2.39Obs 2 0.2 1...
12L 1Observations close to each other in themultidimensional/input are locatedclose in the output/visualization layer -clu...
SSCP = (Participatory & Operational research ) + publicly-available environmental data +analytical approaches + farmers’ p...
Results - Andean blackberryScatter plot displaying MLP predicted yield versus real Andean blackberry yield, using only the...
00.010.020.030.040.050.060.070.08EffDepthTempAvg_1Na_un_chicalNa_un_cusbaTempAvg_0TempAvg_2TempAvg_3ExtDrainPrecAcc_1Trmm_...
Results - Andean blackberry(a) Kohonen map displaying the resultant 6 clusters and their labels according to yield values ...
Results - Andean blackberryComponent plane of effective soil depth. The scale bar (right) indicates the range value in cm ...
Results - Andean blackberryComponents planes of the temperature averages. In all figures, the scale bar (right)indicates th...
Results - Andean blackberryComponent planes of the specifics geographic areas Nariño–La Union–Chical alto (left) and Nariño...
Results - LuloDistribution of R2 obtained with each modelRegression R2(mean)Confidenceinterval (95%)Robust (linear) 0.65 0...
Results - LuloThe Sensitivity Matrix00.020.040.060.080.10.120.140.160.18%SensitivityJiménez, D., Cock, J., Jarvis, A., Gar...
(a) U-matrix displaying the distance among prototypes. The scale bar (right) indicates the values ofdistance. The upper si...
Results - LuloClustering – component planes - SOMA mixed model with the categorical variables of three HECs, location and ...
Variable ranges HECSlope (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 -...
Results - LuloFarm 7 and 9 in HEC 3. Farm 7 produced 68 g/plant/week less than average, whilstfarm 9 produced 51 g/plant/w...
Conclusions27• Most suitable environmental conditions for producing Andean blackberry are: Average temperature between 16...
Conclusions• Key role of farmers (186 registered information on 742 cropping events)• Analytical approaches explained more...
Limitations of the research• Quality of the data collected• Information on management practices• Black-box / traditional m...
Contributions• Use of farmers’ production experiences (commercial data) for understandingvariability• To turn farmers day-...
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SITE-SPECIFIC AGRICULTURE BY MEANS OF BIO-INSPIRED MODELS FOR UNDER-RESEARCHED TROPICAL FRUIT SPECIES IN COLOMBIA

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  • the consultative mode, farmers collected information on their own. In the collaborative mode, as farmers participated and suggested ways to make the tools developed by the researchers easier-to-use.
  • easy to learn methodology (Laboratory-based analysis of)….Which change less with time compared to chemical properties that change with each fertilizer application
  • TRMM contrasts with WorldClim which gives long term averages of rainfall at a particular time of year at a particular site… TRMM is an estimate of the actual rainfall at a given site over a given period of time, TRMM is snapshot views (18 km) and Worldclim layers
  • h yields are obtained when effective soil depth is greater than around 65 cm (cluster 2). Low yields were also found on soils with depths greater than 65 cm (clusters 3, 4 and 6) suggesting that other soil factors not included in the analysis were affecting productivity, presumably soil characteristics such as presence of rock fragments, soil structure or salinity and sodicity. As it was aforementioned, in this study there is absence of soil variables that were difficult to measure by means of RASTA and therefore were not integrated into the model. Without having these data it is not possible to draw firm conclusions on the factors that might affect yield in soil depth deeper than 65 cm.
  • Combination of factors
  • Were in general agreement
  • Altough: as farmers do not have the habit of recording data on crop production
  • Quality: As might be expected, the farmers’ data contained errors, such as: values of plant distances out of the range, or yields in different units such as boxes, bulk, handfull. To the extent possible, these were corrected, for example converting boxes, which are usually a standard size, to kilograms Solution ICTs tech parameters.. Website that can be accessed by farmers to enter their data directly, with interactive data checkinExtrapolated: the approach offers an adequate methodology to obtain more accurate information about the suitable conditions for growing under-researched crops in the tropics. Black-box models … are the assumptions a restriction to apply simpler models… not clear… we did Black-models as apparently were required according to the data obtained. May be that parametric & non-parametric approaches would give the same results, this may be the case here...Management: anagement practices, such as fertilizer and pesticide applications, which are likely to affect yield, were not recorded by the farmers. They are noy use to… Since they were so receptive to the RASTA methodology, further training might be useful to obtain this important information.
  • An approach unique by its ability to generate large datasets that capture the true spatial and temporal scale of commercial agricultureProvides…which is relevant for making agronomic decisions to increase production on a farm-by-farm basisMore than…is the basis of a research theme and extension program Provides a sound….that may be geographical distant from each other or separated in time by climate change
  • Presentation3

    1. 1. SITE-SPECIFIC CROP PRODUCTION BASED ON FARMERS’ PRODUCTIONEXPERIENCES IN COLOMBIA. CASE STUDIES ON ANDEAN BLACKBERRY(Rubus glaucus Benth) AND LULO (Solanum quitoense Lam)Daniel Ricardo Jiménez Rodas
    2. 2. Farmers’ productionexperiencesPrinciples ofparticipatory andoperationalresearchModerninformationtechnologySSCPEnvironmental characterization of the productionsystemAnalysis of the Observations to optimize the systemKg/tplant Temperature AgeObservations made by farmers according to theirparticular circumstancespublicly-available environmental databasesSite-Specific Crop Production (SSCP)2
    3. 3. ObjectivesThe objectives of this thesis are to:• Demonstrate that the principles of operational and participatoryresearch can be applied to Andean blackberry and lulo, and providegrowers with insights into how yield varies• Evaluate modelling methodologies developed for sugarcane, todetermine their suitability as tools for modelling Andean blackberry andlulo yield• Use these methods to identify the conditions that are most suitable forthe production of Andean blackberry and lulo3
    4. 4. • Modern information technology can be used to combine information onfarmers’ production experiences with publicly-available environmentaldatabases• Principles of operational and participatory research facilitate the task ofcollecting, characterizing and interpreting cropping events that occurunder a wide range of conditionsThe hypotheses that this research seeks to verify are:4
    5. 5. MethodsCollecting farmers’ production experiencesParticipatory research• Consultative mode• Collaborative modeGuide form based on a calendar77
    6. 6. Collecting Farmers’ production experiencesCalendars developed to capture harvest eventsCropping events88
    7. 7. Mostly estimates physical soil properties:Texture, Drainage, Effective soil depth, Structure, ColourCollecting Farmers’ production experiencesSoil information9
    8. 8. SRTM : The Shuttle Radar Topography Mission (high-resolution topographical and landscape information )WorldClim: Monthly data (precipitation, temperature)TRMM : TropicalRainfall MeasuringMissionPublicly-available environmental databases1010
    9. 9. Analytical approachesV1 V2 V3 V4 V5 … V60 L 2 L 3 L 4 L 5 … Kg/plotObs 1 0.1 18 3 312 0.3 … 89 0 1 0 1 0 … 2.39Obs 2 0.2 15 4 526 0.1 … 52 1 0 0 0 1 … 30.35Obs 3 0.6 14 1 489 0.2 … 64 0 1 1 1 1 … 42.25Obs 4 0.05 19 2 523 0.5 … 13 0 0 0 0 1 … 52.50Obs 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 … 82.25Obs 7 0.2 15 1 236 0.8 … 26 0 0 1 0 0 … 89.28Obs 8 0.1 17 3 541 0.1 … 35 0 1 1 1 0 … 125.0Obs9 0.6 16 2 845 0.3 … 51 0 0 1 1 0 … 142.8Obs10 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 … 18070.52L 1Supervised modelsIndependent variables/ Inputsdependent/output(known)…11
    10. 10. 12L 1Observations close to each other in themultidimensional/input are locatedclose in the output/visualization layer -clustering and visualization toolUnsupervisedmodelsV1 V2 V3 V4 V5 … V60 L 2 L 3 L 4 L 5Obs 1 0.1 18 3 312 0.3 … 89 0 1 0 1 0Obs 2 0.2 15 4 526 0.1 … 52 1 0 0 0 1Obs 3 0.6 14 1 489 0.2 … 64 0 1 1 1 1Obs 4 0.05 19 2 523 0.5 … 13 0 0 0 0 1Obs 5 0.4 13 3 214 0.6 … 57 1 1 1 1 1Obs 6 0.8 12 4 265 0.4 … 24 1 1 0 1 0Obs 7 0.2 15 1 236 0.8 … 26 0 0 1 0 0Obs 8 0.1 17 3 541 0.1 … 35 0 1 1 1 0Obs9 0.6 16 2 845 0.3 … 51 0 0 1 1 0Obs10 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 0L 1Analytical approaches………………………………………………………………………………………………………………………………
    11. 11. SSCP = (Participatory & Operational research ) + publicly-available environmental data +analytical approaches + farmers’ production experiencesCrop DepartmentsGeo-referencedCroppingeventsProductionVariety andnumber ofplantsRASTA Complete plotsNo offarmsweeklyperiodsNo offarmsNo offarmsNo offarmsNo offarmsAndeanblackberryCaldas, Nariño 75 488 35 34 20 20Lulo Nariño,Others111 254 54 43 21 21Total 186 742 89 77 41 41ResultsSummary of the number of Andean blackberry and lulo growers who recorded information via calendars14
    12. 12. Results - Andean blackberryScatter plot displaying MLP predicted yield versus real Andean blackberry yield, using only thevalidation dataset1715R² = 0.892-0.20.30.81.31.8-0.2 0.3 0.8 1.3 1.8Predictedyield(kg/plant/week)Real yield (kg/plant/week)PredictedSupervised models - Non-linear regressionCoefficient of determination= 0.89Histogram displaying yield data distribution of Andean blackberry(Kg/plant/week)Numberofobservations
    13. 13. 00.010.020.030.040.050.060.070.08EffDepthTempAvg_1Na_un_chicalNa_un_cusbaTempAvg_0TempAvg_2TempAvg_3ExtDrainPrecAcc_1Trmm_3Nar-CalCal_riosu_zrSrtmSlopePrecAcc_0Trmm_2Na_un_cusalTrmm_0PrecAcc_3TempRang_0TempRang_2AB_Thorn_NNa_un_lajacPrecAcc_2Trmm_1IntDrainTempRang_3TempRang_112 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%SensitivitySensitivity distribution of the model with respect to the inputsJiménez, D., Cock, J., Satizábal, F., Barreto, M., Pérez-Uribe, A., Jarvis, A. and Van Damme, P., 2009. Computers andElectronics in Agriculture. 69 (2): 198–208Sensitivity MatrixResults - Andean blackberry16Effective soil depthTemperature averagesGeographic location
    14. 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 ofproductivity in kg/plant/week The upper side exhibits high values of yield, whereas the lower displayslow valuesUnsupervised model - Visualization – component planes - SOM17Andean blackberry yieldKohonen map – 6 clusters(a) (b)
    15. 15. Results - Andean blackberryComponent 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 values18Effective soil depthUnsupervised model - Visualization – component planes - SOM
    16. 16. Results - Andean blackberryComponents 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 values19Unsupervised model - Visualization – component planes - SOM
    17. 17. Results - Andean blackberryComponent planes of the specifics geographic areas Nariño–La Union–Chical alto (left) and Nariño–Launion–Cusillo bajo (right). The highest values indicate presence and the lowest absence as they arecategorical variablesVisualization – component planes - SOM20Nariño - La Union – Chical Alto Nariño - La Union – Cusillo bajo
    18. 18. Results - LuloDistribution of R2 obtained with each modelRegression R2(mean)Confidenceinterval (95%)Robust (linear) 0.65 0.63 - 0.66MLP (non-linear) 0.69 0.67 - 0.70Both models explained more than 60% ofvariability in Lulo production2321Histogram displaying yield data distribution of lulo(g/plant/week)R2provided by each approachMLPRobust regression0.2877 0.3545 0.4214 0.4883 0.5552 0.6221 0.6889 0.7558 0.822702468101214161820222426NumberofobservationsNumberofobservationsNumberofobservationsSupervised modelling
    19. 19. Results - LuloThe Sensitivity Matrix00.020.040.060.080.10.120.140.160.18%SensitivityJiménez, 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): 258-27022Sensitivity distribution of the model with respect to the inputsEffective soil depthTemperature averagesSlope
    20. 20. (a) U-matrix displaying the distance among prototypes. The scale bar (right) indicates the values ofdistance. The upper side exhibits high distances, whilst the lower displays low distances; (b) Kohonenmap displaying the 3 clusters obtained after using the K-means algorithm and the Davies–Bouldin indexThe three most relevant variables were used to train a Kohonen map and identify clusters ofHomogeneous Environmental Conditions (HECs)Results - LuloUnsupervised model - Clustering – component planes - SOM23U-Matrix Kohonen map – 3 clusters
    21. 21. Results - LuloClustering – component planes - SOMA mixed model with the categorical variables of three HECs, location and farmerexplained more than 80% of variation in lulo yieldParameters Estimate(g/plant/week)StandardError%of total varianceModel including categorical variables of 3 HECs, location and farmHEC 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 estimations24
    22. 22. Variable ranges HECSlope (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 3HEC 3 yielded 41 g/plant/weekmore fruit than averageResults - Lulo-30.00-20.00-10.000.0010.0020.0030.0040.0050.001 2 3Luloyield(g/plant/week)Effects of clusters of environmentalconditions25
    23. 23. Results - LuloFarm 7 and 9 in HEC 3. Farm 7 produced 68 g/plant/week less than average, whilstfarm 9 produced 51 g/plant/week more than average-80.00-60.00-40.00-20.000.0020.0040.0060.001 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 3Luloyield(g/plant/week)Effects of farms across clusters of environmental conditions1 2 326Jiménez, D., Cock, J., Jarvis, A., Garcia, J., Satizábal, H.F., Van Damme, Pérez-Uribe, A., and Barreto, M., 2010. Interpretation of Commercial ProductionInformation: A case study of lulo, an under-researched Andean fruit. Agricultural Systems. 104 (3): 258-270
    24. 24. Conclusions27• 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. 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 canbe analysed as long as it is possible to collect sufficient data on how the growersmanage their crop, and how much they produce• The biggest challenge is not the analysis of information… rather the collection ofdata• The data collection and the analysis seem to be promising tools to develop aSSCP for other crops or regions where there is neither information on climatenor on soils• This is the first time that this methodology has been implemented for under-researched crops in general and in Colombia in particular28
    26. 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 areconstant over the time• The results found here cannot be extrapolated outside the ranges of the variablevalues appearing in the collected datasets29
    27. 27. Contributions• Use of farmers’ production experiences (commercial data) for understandingvariability• 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 highlyunder-researched fruits• First formal research study that evidences the yield gap between farmers undersimilar climatic conditions in Colombia• More than 3000 farmers in Colombia are willing to increase productivity andtaking benefit of this doctoral research• Provides a sound basis for transferring technology between localities and farms30
    28. 28. Questions

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