This document summarizes a case study applying data mining techniques to agricultural data from Mexico to provide actionable recommendations. Key points: 1) MasAgro has collected extensive farm data from over 36,000 cropping events to support sustainable intensification of maize and wheat production. 2) Models using this data explained a large portion of yield and profitability, identifying important limiting factors like nitrogen, cultivars, and weather patterns. 3) Recommendations validated by experts can provide personalized advice to farmers through MasAgro's mobile app on optimizing practices like fertilizer use. 4) Scaling the approach nationwide could define agro-ecological zones and connect to seasonal forecasts for enhanced

1. Data-Driven Agronomy
Case-study on MasAgro in Mexico
Executive summary
Sylvain Delerce | March 2018

2. Context
• MasAgro is a 10 years project lead by CIMMYT and funded by the Mexican
government. It aims at the sustainable intensification of maize production nationwide
by promoting conservation agriculture.
• Since year two (2013), MasAgro has been collecting extensive amounts of detailed
farms’ data using the Bitacora Electronica MasAgro (BEM) platform. By 2016 more
than 36,000 cropping events have been registered in maize and wheat at national
level
• In 2017, CIMMYT and CIAT start a collaboration to apply the data mining workflows
developed by CIAT to the BEM. The goal is to enrich the MasAgro initiative with
additional knowledge extracted from the BEM on the main limiting factors of yield and
profitability at site-specific scale.

3. Studyarea
rawdataavailable
Pacifico Norte (Sonora +Sinaloa)
• Wheat (irrigated): 194 records
• Maize (irrigated): 243 records
Guanajuato
• Irrigated maize: 631
• Rainfed maize: 660
Chiapas
• Maize (rainfed): 3589

4. Crops data
3 to 4 years of BEM records
• Cropping events data from the BEM
http://bem.cimmyt.org/Inicio/Default.aspx
• Detailed information on management practices
• Specific workflow designed to query the DB, filter for desired records,
calculate aggregated indicators like total N, P, K.

5. Weather data
specific association between cropping events and stations
Main source: INIFAP
Old nationwide network with
only part of the stations still
working. Open Access
http://clima.inifap.gob.mx/ln
mysr/Estaciones/MapaEstacio
nes
Specific additional source for
Sonora: CESAVESON-REMAS
Rich, state-of-the-art weather
stations network about 100
stations across the state.
Partly funded by farmers.
http://www.siafeson.com/rem
as/index.php
Automated workflow for quality control and estimation of
missing values
Sources Series QC and reconstruction

6. Weather data
Site-specific association between cropping events and stations.
Variable-wise multi-criterion algorithm for site-specific association of cropping events to the
most representative station
Guanajuato Sonora Sinaloa Chiapas
Pacifico Norte (Sonora +Sinaloa)
Records characterized with weather data
• Wheat (irrigated): 116 records
• Maize (irrigated): 226 records
Guanajuato
Records characterized with weather data
• Irrigated maize: 441
• Rainfed maize: 533
Chiapas
Records characterized with weather data
• Maize (rainfed): 383
Significant loss of data due to very sparse weather
stations network

7. Soil data
A fine blend of existing data
INEGI 250 original map
63794 polygons
23013 WRB soil classes
combinations
CIAT’s combined map with
polygons and soil properties
Low loss in coverage
INEGI 250 profiles II
4210 profiles
1317 WRB main soil classes
INEGI 250 profiles I
4316 profiles
1119 main WRB soil classes
INEGI 250 map resumed to main soil
WRB soil class
2257 polygons
2257 main WRB soil classes
Polygonsofsoil
classes
Soilfunctional
properties
+
Sources:
Open data from INEGI
http://www.inegi.org.mx/geo/contenidos/recnat/edafologia/default.aspx

8. Economic data
Double checked
Raw dataset: BEM
Automatic report on profitability of cropping
events. Economic indicators and profitability
available for ALL records cropping events
Validated dataset: A team o economists from
CIMMYT doubled checked for three years (2013 to
2015) a sample of the BEM
Automated workflow for quality control and estimation of
missing values
Sources
Evaluation of error in the raw
dataset
STATE TOTAL MAIZE TOTAL WHEAT
CHIAPAS 880 0
GUANAJUATO 582 133
SINALOA 149 10
SONORA 12 133
TOTAL 1623 276

9. Results
High performances of the models | Most of the yield/profitability explained | Potential for prediction
Model R-squared explaining
YIELD
R-squared explaining
PROFITABILITY
Chiapas rainfed maize 75% 78%
Pacifico Norte irrigated maize 35% 77%
Sonora irrigated wheat 65% 64%
Guanajuato irrigated maize 60% 77%
Guanajuato rainfed maize 81% 70%

10. Main limiting factors validated by local experts | Actionable information for farmers/extensionists
Results
Guanajuato
Rainfed maize: 441
R-squared: 81%
1. Sowing density
2. Cultivar
3. Total Nitrogen
4. Total precipitation
Guanajuato
Irrigated maize: 533 events
R-squared: 60%
1. Total nitrogen
2. Cultivar
3. Accumulated solar
energy

11. Main limiting factors validated by local experts | Actionable information for farmers/extensionists
Results
Pacifico Norte
Irrigated wheat: 116 events
R-squared : 65%
1. Total precipitation
2. Accumulated solar
energy
3. Frequency of minimum
temperatures <8°C
Pacifico Norte
Irrigated maize: 226 events
R-squared : 35%
/! low R-squared
1. pH
2. SOC
3. Cultivar

12. Main limiting factors validated by local experts | Actionable information for farmers/extensionists
Results
Chiapas
Rainfed maize: 383 events
R-squared : 75%
1. Average max temperature
2. Cultivar
3. Estimated plant density
4. Total nitrogen applied

13. Average = 3.67 ton/ha
Std. desv = 5.44 ton/ha
Avarage = 5.44 ton/ha
Std. desv = 0.55 ton/ha1.77 ton/ha
Current
GHS Optimized
Global-best Harmony Search
Automatic optimization to find the optimal farming practices to increase crop yield in maize,
State Chiapas
Iterations
AptitudeYield(ton/ha)
Predictors Current Optimal
Number of post-sowing herbicides applications 2 3
Number of applications of insecticides 1 3
Total amount of nitrogen applied (kg/ha) 117.8 178.4
Total amount of phosphorus applied (kg/ha) 0 92
Total amount of potassium applied 0 74.1
Cultivars' group Others P4082W
Seed treatment No Yes
Conservation agriculture No Yes
… … …
Yield 5 7.2
Results enable personalized recomandations

14. Characterization of climate variability | CSA practices per weather pattern
Results
Chiapas: 11 different weather pattern
identified Significant
differences in yields
observed under
main patterns (clusters
eith less than 10 observations
are not pictured)
Patterns repeat
themselves in time:
here patterns 2, 6,
and 9

15. Expected gain corresponding to a change in a practice.
First derivative of the trend line in the partial dependence plot gives estimates of the expected gain corresponding to a change, for example in nitrogen fertilization.
Results
N↗ = $↗
N↗ = $↘
Average gain for an increase
in 10 units N: +400 $/ha
Average gain for an increase
in 10 units N: +250$/ha
Average loss for an increase
in 10 units N: -350$/ha
Partial dependence plot for the total nitrogen versus profitability
The blue dots represents the original data. The blue line represents the overall tendency
of the data based on a loess smoothing. The orange line represents the first derivative of
the tendency, emphasizing the sensitivity of the output variable to yield

16. Agronomic recommendations feeding
MasAgro’s mobile phone app
• To be filled with CIMMYT’s info

17. • BEM dataset holds coherent information
we can tap into
• The significant number of cropping events
and the good performances of the models
allow for solid recommendations to users,
and open the way for prediction/simulation
• Climate variability is affecting crops and
should be managed
• BEM economic data is usable. We can now
integrate profitability in the routine
workflow
Conclusions What is next ?
• Scale-up the data-driven approach to the
entire country and all MasAgro Hub to be
able to define agro-ecological zones and
make results even more specific
• Connect workflow with seasonal forecast
for enhanced climate services and climate
variability management