This document discusses using self-organizing maps (SOMs) to cluster and analyze agro-ecological zones based on variables like climate, soil, and management factors to better understand sugar cane productivity. SOM component planes representing different variables are clustered based on distances in the SOM. This allows visualization of similar productivity zones and identification of relationships between variables like higher radiation before harvest being linked to increased sugar accumulation and medium-high productivity.

1. Classification of similar productivity zones in the sugar cane culture using clustering of SOM component planes based on the SOM distance matrix Miguel BARRETO Andrés Pérez-Uribe MINISTERIO DE AGRICULTURA Y DESARROLLO RURAL asocaña

4. A new approach Management Climate Genotype Experiment 1. Every crop is an experiment Sowing Growing Harvest Soil

5. A new approach 4 experiments Same cultivated zone For example: 1999 2000 2001 2002

6. A new approach 1358 experiments Management Climate Genotype 2. Each agroecological event is unique in time and space, but it is possible to find similar characteristics between events that allow finding similar behaviors permitting to discover why and how the agroecological variables affect the crop development and therefore the agricultural productivity. Sowing Growing Harvest Soil

8. The idea Soil type A, B etc Variety type A,B etc Management type A,B etc Weather condition Sunny, rainy etc 1. To construct a plane for each zone with its characteristics.

9. The idea 2. To find natural groups of experiments with similar characteristics (Without knowing the productivity). Conditions A Conditions B 3. Add labels and look for the more homogeneous groups Zone 1 Rainy B B C Zone 2 Sunny A B A Sunny A B A Zone 3 Sunny A B A Zone 5 Sunny A B A Zone 6 Rainy B B C Zone 7 Rainy B B C Zone 8 Rainy B B C Zone 9

10. The idea (Analyze the conditions) 4. To extract new knowledge about the relationship between the agro-ecological variables and productivity. Soil type B Variety type C Management type B Weather condition Rainy Soil type A Variety type A Management type B Weather condition Sunny Conditions A High productivity Conditions B Low productivity

12. SOM visualization of the variables Soil type Variety type Management type Weather condition Relative Humidity (RH) Before Harvest (BH) After Seeding (AS) Radiation (Ra) Before Harvest (BH) After Seeding (AS) Soil order 2 Sugarcane variety 1 Precipitation (P) Before Harvest (BH) After Seeding (AS) Temperature (T) Before Harvest (BH) After Seeding (AS)

13. Component planes To improve the analysis of the relationships between variables and/or their influence on the outputs of the system, it is possible to slice the Self-organizing maps in order to visualize their so-called component planes Zone 1 Zone 2 Zone 3 Zone 4 Zone n Variable 54 Variable 2 Variable 1 Zone 1358 Zone 3 Zone 2 Zone 1

14. SOM visualization of the variables Relative Humidity (RH) Before Harvest (BH) After Seeding (AS) Radiation (Ra) Before Harvest (BH) After Seeding (AS) Sugarcane variety 1 Precipitation (P) Before Harvest (BH) After Seeding (AS) Temperature (T) Before Harvest (BH) After Seeding (AS) Soil order 2 Relative Humidity (RH) Before Harvest (BH) After Seeding (AS) Radiation (Ra) Before Harvest (BH) After Seeding (AS) Soil order 2 Sugarcane variety 1 Precipitation (P) Before Harvest (BH) After Seeding (AS) Temperature (T) Before Harvest (BH) After Seeding (AS)

15. Correlation hunting The task of organizing similar components planes in order to find correlating components is called correlation hunting. However, when the number of components is large it is difficult to determine which planes are similar to each other.

16. Correlation hunting A new SOM can be used to reorganize the component planes in order to perform the correlation hunting. The main idea is to place correlated components close to each other. An advantage of using a SOM for component plane projection is that the placements of the component planes can be shown on a regular grid . In addition, an ordered presentation of similar components is automatically generated. A disadvantage is that the choice of grouping variables is left to the user .

17. Clustering of SOM component planes based on the SOM distance matrix The U-matrix had been used as an effective cluster distance function. The U-matrix visualizes distances between each map unit and its neighbors, thus it is possible to visualize the SOM cluster structure .

18. Clustering of SOM component planes based on the SOM distance matrix

19. Clusters with similar productivity Medium High Low Productivity 0 10 - 10

20. Prototypes from clusters with similar productivity Relative Humidity (RH) Before Harvest (BH) After Seeding (AS) Radiation (Ra) Before Harvest (BH) After Seeding (AS) Soil order 2 Sugarcane variety 1 Precipitation (P) Before Harvest (BH) After Seeding (AS) Temperature (T) Before Harvest (BH) After Seeding (AS)

21. Best Matching Units from radiation before harvest (RaBH) Ra1BH Ra2BH Ra3BH Ra4BH Ra5BH Best Matching Units

22. Analyzing the plots Radiation Relative Humidity Temperature