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1. INFORMS 2017
Agricultural Analytics
Challenges and opportunities of establishing
data-driven agronomic services to small-scale
farmers across the world (MD54)
362A. Monday Oct 23,
2017 4:30 PM - 6:00 PM
Session Summary
A wide range of services, mostly in developed coun-
tries, offer farmers the possibility of making better
data-driven decisions including what to plant the
next season, what is the most profitable crop, the
right amount of fertilizers, etc., Large producers
have often been able to capitalize on these services,
smaller producers often find themselves on wrong
side of the technology divide. In this session, we
seek to explore answers on how data-driven
approaches can support farmers to increase pro-
ductivity, provide farmers with climate services and
predict the occurrence of fungal diseases.

2. Agronomic Modeling Scientist. Drew graduated with honors from Yale
University having studied Economics with focus on the applied use of
econometrics. During his Master’s (Duke University) and PhD work
(North Carolina State University), Drew applied his skills to a number of
diverse areas of study. These topics include but are not limited to the
optimization of crop rotations in the Eastern Coastal Plain of North Caro-
lina in support of transitioning farmland to Organic usage, the develop-
ment of complex Bayesian based variable selection algorithms to better
understand the drivers of farmland loss in North Carolina, and economic
analyses of burgeoning specialty crop markets in the state with particu-
lar attention paid to the issue of rural development. Drew now leads
aWhere’s efforts to develop statistics based models to provide actiona-
ble and insightful information across the agricultural value chain. Drew’s work makes
extensive use of hierarchical regression analysis, data mining, machine learning, copula
modeling, and spatio-temporal analyses all implemented through a nuanced understan-
ding of agriculture, soil science, and ecology.
Drew Marticorena
P a n e l i s t s
Currently Strategy Lead for Sustainable Intensification in Latin America
at the International Maize and Wheat Improvement Center (CIMMYT).
He is dedicated to turning subsistence agriculture and failed farming
systems into productive and sustainable production units that make it
possible for farmers to escape hunger and poverty in the developing
world. At CIMMYT, Govaerts leads the centers’ research on conservation
agriculture, applying science to the development of sustainable
farming practices and methods that are specifically designed to meet
the challenges confronting the rural poor. Since 2007, Govaerts has led
the Mexico based conservation agriculture program in CIMMYT. From
2003-2007 he was Research Associate with the Katholieke Universiteit Leuven conducting
research for the Ph D dissertation. Bram Govaerts holds a Master and Bachelor of Science
in Bioscience Engineering, specialization Soil Conservation in combination with Tropical
Agriculture at the Katholieke Universiteit Leuven, Belgium. Bram Govaerts has authored
>58 peer reviewed journal articles, >19 book chapters and numerous papers in internatio-
nal conferences. Govaerts received the Development Cooperation Prize 2003 of the Bel-
gian Federal Government and was appointed Belgian Official Representative of the Inter-
national Youth Forum for the Food and Agriculture Organization of the United Nations in
1997. Bram Govaerts received the 2014 Norman Borlaug Award for Field Research and
Application, endowed by the Rockefeller Foundation on October 2014, in Des Moines,
Iowa.
Bram Govaerts

3. Julian Ramírez
Associate Professor and Extension Specialist, Crop, Soil, and Environ-
mental Sciences Department. Dr. Brenda Ortiz is an Associate Professor
at Auburn University. She has an extension and research appointment
in the area of Precision Agriculture. Her Ph.D. is in Biological and Agri-
cultural Engineering from The University of Georgia. Her main research
and extension interests include evaluate different management practi-
ces to reduce aflatoxin contamination in corn, the study the impact of
weather and climate on agriculture especially corn and wheat crops,
identification of adaptation strategies to reduce climate-related risk in
agriculture, the use of field studies and crop growth modeling to eva-
luate different management strategies for improving grain production, evaluation of irri-
gation scheduling strategies (sensor-based) for corn production, variable rate irrigation,
and the use of remote sensing technologies for variable rate application of nitrogen. In
2015, she was the leader of the Precision Agricultural Systems Community of the Ameri-
can Society of Agronomy. Because of her expertise in Precision Agriculture, she has been
invited by several Universities in Europe and South America (Brazil in particular) to mentor
their graduate student and to work on collaborative projects.
Brenda Ortiz
Holds a PhD in Environmental Science at University of Leeds. His work
is on climate, crop modelling as well as biodiversity conservation espe-
cially in the context of climate change, making use of statistics, crop
and species distributions models and geographic information systems
(GIS). Currently he focused mainly in spatial analysis of relationships
between environmental factors on both crop production systems and
biodiversity, including crop wild relatives and leads research on clima-
te services and climate change adaptation. He has published more
than 40 papers, iIncluding high impact journals such as Nature Clima-
te Change, Nature Plants, PNAS, amongst others.
Colombian agronomist Daniel Jiménez is a scientist at the International
Center for Tropical Agriculture (CIAT). He holds a PhD in Applied Biologi-
cal Sciences (Agriculture) from Ghent University, and is the coordinator
of the Data-Driven Agronomy Community of Practice of the CGIAR Plat-
form for Big Data in Agriculture. Daniel’s data-mining approach to agro-
nomy allows decision makers in agriculture to accelerate and enhance
the impact of agricultural research in the face of pressing challenges
such as yield gaps and climate change. In recognition of this work, the
United Nations selected Daniel as one of two winners of its Big Data
Climate Challenge in 2014. He was also selected by the World Bank as
one of the winners of the first World Bank Group Big Data Innovation Challenge in 2015;
and in 2017 he led one of the three teams whose work was selected by the United Nations
Climate Change secretariat as one of the winners of the Momentum for Change award.
Before joining CIAT, Daniel worked at Bioversity International and the University Of
Applied Sciences Of Western Switzerland (HEIG-VD), and was also a consultant for the
French Agricultural Research Centre for International Development (CIRAD).
Daniel Jiménez
Session Chair

4. Simulation Based Fungal Disease Modeling In Agriculture Using Big Data (Drew Marticorena): The FAO
estimates that over 1B MT of food are lost due to the fungal diseases yearly. In addition, associated toxins
because harmful health effects to both animals and people that consume the contaminated food. These
issues are especially prevalent amongst smallholder producers. The ability to predict where and when
fungal diseases are likely to occur would be of great value across the agricultural value chain and would
improve the lives of smallholder producers. Using field datasets and modern parameter optimization.
Increasing agricultural production and resilience through data services and decision support sys-
tems (Brenda Ortiz): The farming community is currently being challenged to build resilience to climate
change and to increase crop productivity through the adoption of practices and technology. Our extension
efforts have showed that low adoption of decision support systems is due to the lack of engagement with
farmers during the conceptualization and development process. A success farmer’s engagement story is
the Tri-state Climate Learning Network for Row Crop Agriculture. Through bi-annual meetings, we learned
that awareness on the limitations of the data, the applicability of climate information to crop management,
and the co-development of usable decision support tools are key elements of farmers’ adoption.
Data-driven agro-climatic services improve farmer responses to climate variability (Julian Ramírez):
Climate is one of the most important factors influencing the performance farming systems. In Colombia,
climate variability explains between 30-60 % of rice yield. Farmers, however, make decisions in their farms
including issues related to planting date, what variety to plant, or whether to plant, at best, on the basis of
no information. We develop data-driven seasonal crop-climate prediction analyses that feed into the deve-
lopment of a climate services platform. The analyses demonstrate that the combination of skillful seasonal
climate forecasts, calibrated crop models, and a forecast platform tailored to users’ needs can prove suc-
cessful in establishing a climate service for agriculture.
Crowed Sourced data for decision making and taking in resilient agrifood systems: (Bram Govaerts):
Managing the hugely complex risks that are associated with the food system of the 21st century is a major
challenge for decision makers in government, civil society and the private sector alike, and one that has
been neglected for the past 30 years. Therefore complex agriculture innovation systems (AIS) that can
support agrifood systems for nutrition, nature conservation and national and international security are
required. While Knowledge Management (KM) is an important component of AIS. Previous KM frameworks
did not account for the fact that agricultural systems are complex systems and did not integrate innovation
with KM. The results presented will show a real case of an AIS that was implemented in Mexico including
crowd sourcing of on plot data to develop complex decision support systems. The case presented shows
that these approached can boost performance and steer complex systems in ways that benefit all stakehol-
ders.
Abstracts
Robin Lougee is the IBM Research Lead for Consumer Products & Agri-
culture. Robin chairs the 2017 Syngenta Crop Challenge Award in
Analytics Prize Committee and serves on the Advisory Committee for
the World Agritech Investment Summit. She is an industrial research
scientist with a strong track record of delivering innovation to IBM and
its customers. Robin pioneered the creation of COIN-OR, an open-sour-
ce foundry for computational operations research, and led its growth to
an independent non-profit that has served the scientific and business
community for over 15 years. She was elected to the Board of INFORMS,
the largest society in the world for professionals in the field of opera-
tions research, management science and analytics, Chair of the INFOR-
MS Computing Society, and President of the Fora of Women in ORMS. She is an Associate
Editor of Surveys in Operations Research. Robin earned a Ph.D. in Mathematical Sciences
from Clemson University in 1993.
Robin Lougee
Track Chair