Climate change, increasing population, food-versus-fuel economics, pandemics, etc. pose a threat to food security to unprecedented levels. It has fallen upon the practitioners of agriculture and technologists of the world to innovate and become more productive to address the multi-pronged food security challenges. Agricultural innovation is key to managing food security concerns. The infusion of data science, artificial intelligence (AI), advanced analytics, satellites data, geospatial data, climatology, sensor technologies, and climate modeling with traditional agricultural practices such as soil engineering, fertilizers use, and agronomy are some of the best ways to achieve this. Data science helps farmers to unravel patterns in fertilizer pricing, equipment usage, transportation and storage costs, yield per hectare, and weather trends to better plan and spend resources. AI enables farmers to learn from fellow farmers to apply best techniques that are transferred learning from AI to improve agricultural productivity and to achieve financial sustainability. Sensor technologies play an important role in getting real-time farm field data and provide feedback loops to improve overall agricultural practices and can yield huge productivity gains. Advanced Analytics modeling is essential software technique that codifies farmers’ tacit knowledge such as better seed per soil, better feed for dairy cattle breed, or production practices to match weather pattern that was acquired over years of their hard work to share with worldwide farmers to improve overall production efficiencies, the best antidote to food security issue. In addition to the paradigm shift, economic sustainability of small farms is a major enabler of food security.
The book reviews all these technological advances and proposes macroeconomic pricing models that data mines macroeconomic signals and the influence of global economic trends on small farm sustainability to provide actionable insights to farmers to avert any financial disasters due to recurrent economic crises.
Author(s): Chandrasekar Vuppalapati
Publisher: CRC Press
Year: 2023
Language: English
Pages: 547
City: Boca Raton
Cover
Title Page
Copyright Page
Preface
Table of Contents
Section I: Advanced Analytics
1. Time Series and Advanced Analytics
Milk Pricing Linkage & Exploratory Data Analysis (EDA)
Auto Correlation
Partial Autocorrelation Function (PACF)
Stationarity Check: Augmented Dickey Fuller (ADF) & Kwiatkowski-Phillips-Schmidt-Shin (KPSS) Tests
Non-stationary Stochastic Time Series
Granger’s Causality Test
Transformation & Detrending by Differencing
Models
VAR Linkage Model: CPI Average Milk Prices, Imported Oil Prices, and All Dairy Products (milk-fat milk-equivalent basis): Supply and Use
Regressive Linkage Model: CPI Average Milk Prices, Imported Oil Prices, and All Dairy Products (milk-fat milk-equivalent basis): Supply and Use
Prophet Time Series Model: CPI Average Milk Prices, Imported Oil Prices, and All Dairy Products (milk-fat milk-equivalent basis): Supply and Use
References
2. Data Engineering Techniques for Artificial Intelligence and Advanced Analytics
Food Security Data
Data Encoding—Categorical to Numeric
Data Enrichment
Data Resampling
Synthetic Minority Oversampling Technique (SMOTE) & Adaptive Synthetic Sampling (ADASYN)
Machine Learning Model: Kansas Wheat Yield SMOTE Model
Machine Learning Model: Kansas Wheat Yield with Adaptive Synthetic (ADASYN) Sampling
References
Section II: Food Security & Machine Learning
3. Food Security
Domino Effect
Food Security is National Security!
Food Security Frameworks
The Food Security Bell Curve—Machine Learning (FS-BCML) Framework
Machine Learning Model: Who In the World is Food Insecure? Prevalence of Moderate or Severe Food Insecurity in the Population
Machine Learning Model: Who In the World is Food Insecure? Prevalence of Moderate or Severe Food Insecurity in the Population—Ordinary Least Squares (OLS) Model
Machine Learning Model: Who In the World is Food Insecure? Prevalence of Severe Food Insecurity in the Population (%)
Machine Learning Model: Who In the World is Food Insecure? Prevalence of Severe Food Insecurity in the Population—Ordinary Least Squares (OLS) Model
Guidance to Policy Makers
References
4. Food Security Drivers and Key Signal Pattern Analysis
Food Security Drivers & Signal Analysis
Afghanistan
Afghanistan Macroeconomic Key Drivers & Linkage Model - Prevalence of Undernourishment
Afghanistan Macroeconomic Key Drivers, Food Security Parameters, & Linkage Model - Prevalence of Undernourishment
Sri Lanka
Sri Lanka Macroeconomic Key Drivers & Linkage Model - Prevalence of Undernourishment
Guidance to Policy Makers
References
Section III: Prevalence of Undernourishment and Severe Food Insecurity in the Population Models
5. Commodity Terms of Trade and Food Security
Mechanics of Food Inflation
Machine Learning Model: Food Grain Producer Prices & Consumer Prices
Machine Learning Model: Prophet—Food Grain Producer Prices & Consumer Prices
FAO in Emergencies—CTOT & Food Inflation
Trade and Food Security
Food Security is National Security!
References
6. Climate Change and Agricultural Yield Analytics
Wheat Phenological Stages
Climate Change & Wheat Yield
Wheat Futures
NOAA Star Global Vegetation Health (VH)
Coupled Model Intercomparison Project Climate Projections (CMIP)
Shared Socioeconomic Pathway (SSP) Projection Models
Kansas & Wheat Production
Mathematical Modeling
Machine Learning Model: Drought & Wheat Yield Production Linkage in Kansas
India & Wheat Production
Machine Learning Model: Heat Waves & Wheat Yield Production Linkage in India
Machine Learning Forecasting Model: Heat Waves & Wheat Yield Production in India
Machine Learning (Mid-century 2050) Projection Model: Heat Waves with Increased Frequencies and Intensities & Wheat Yield Production in India
Food Security and Climate Resilient Economy: Heatwaves and Dairy Productivity Signal Mining to create a Smart Climate Sensor for Enhanced Food Security
Machine Learning Model: Drought & Heatwave Signature Mining through the Application of Sensor, Satellite Data to reduce overall Food Insecurity
References
7. Energy Shocks and Macroeconomic Linkage Analytics
Climate Change and Energy Shocks Linkage
Unexpected Price Shocks—Gasoline, Natural Gas, and Electricity
Impact of Higher Gas prices on Macroeconomic Level
The Channels of Transmission & Behavioral Economy
Fertilizer Use and Price
The U.S. Dollar Index
U.S. Dollar and Global Commodity Prices Linkage
Farm Inputs & Fertilizer Linkage Model
Machine Learning Model Energy Prices & Fertilizer Costs—Urea
Machine Learning Model Energy Prices and Fertilizer Costs—Phosphate
Machine Learning Model—Commodities Demand and Energy Shocks on the Phosphate Model
Machine Learning Model—Prophet Time Series Commodities Demand and Energy Shocks on Phosphate Model
References
Section IV: Conclusion
8. Future
Appendices
Appendix A—Food Security & Nutrition
The 17 Sustainable Development Goals (SDGs)
Food Security Monitoring System (FSMS)
NHANES 2019–2020 Questionnaire Instruments—Food Security
Macroeconomic Signals that Could help Predict Economic Cycles
List of World Development Indicators (WDI)
Food Aids
The United Nations—17 Sustainable Development Goals (SDGs)
The Statistical Distributions of Commodity Prices in Both Real and Nominal Terms
Poverty Thresholds for 2019 by Size of Family and Number of Related Children Under 18 Years
Appendix B—Agriculture
Agricultural Data Surveys
USDA—The Foreign Agricultural Service (FAS) Reports and Databases
USDA Data Products
Data Sources
Conversion Factors
National Dairy Development Board (NDDB) India
Worldwide—Artificial Intelligence (AI) Readiness
Appendix C—Data World
Global Historical Climatology Network monthly (GHCNm)
Labor Force Statistics from the Current Population Survey
Appendix D—Data U.S.
U.S. Bureau of Labor Statistics
Dollars/Bushel : Dollars/Tonne Converter
NOAA - Storm Events Database
Consumer Price Index, 1913
Appendix E—Economic Frameworks & Macroeconomics
Macroeconomic Signals that could help Predict Economic Cycles
The U.S. Recessions
Labor Force Statistics from the Current Population Survey
United Nations Statistics Department (UNSD)
Reserve Bank of India—HANDBOOK OF STATISTICS ON INDIAN ECONOMY
Department of Commerce
United Nations Data Sources
Poverty Thresholds for 2019 by Size of Family and Number of Related Children Under 18 Years
Rice Production Manual
Index
