Course

Syllabus

Theory

Introduction to computers and programming concepts; algorithms, flowcharts, variables, operators, input/output statements, conditional and looping statements; basic programming using R for agricultural applications; functions, lists, dictionaries, arrays, file handling, and data visualization.

Introduction to system approach and system boundaries in agriculture; soil–plant–atmosphere continuum; principles of system simulation; crop models, concepts and types; data requirements and relational diagrams; introduction to crop simulation models and decision support systems.

Weather and climate data handling using coding tools; data preprocessing, visualization, and statistical analysis; evaluation of crop responses to weather elements; elementary crop growth models; calibration, validation, verification, and sensitivity analysis.

Concepts of potential and achievable crop production; simulation under water- and nutrient-limited conditions; soil water and nutrient balance; weather forecasting methods, forecast verification, value-added weather forecasts, Indigenous Technical Knowledge (ITK) in weather forecasting; crop-weather calendars; preparation and dissemination of agro-advisory bulletins using coding tools, weather forecasts, and crop simulation model outputs.

Practical

Introduction to R programming and R-Studio; writing simple programs using variables, operators, conditional statements, loops, and functions; reading and processing agricultural datasets; importing and exporting CSV/Excel files; graphical visualization of weather and crop data.

Preparation of crop-weather calendars using coding tools; statistical analysis of meteorological data; simulation of crop growth using AquaCrop models; simulation under varying weather, irrigation, and nutrient management scenarios; sensitivity analysis using simulation models; yield forecasting using statistical and simulation approaches; introduction to pest and disease forecasting models; preparation of agro-advisory bulletins based on weather forecasts and simulation outputs; collection and analysis of farmers’ feedback on agro-advisories.

Objectives

1. To impart basic knowledge of computer programming and coding concepts for agricultural applications.

2. To familiarize students with system simulation techniques and their application in crop growth, yield prediction, and agro-advisory services.

3. To develop skills in integrating weather, crop, and field data using coding tools for preparation and dissemination of agro-advisory bulletins.

Course Outcome:

Any student who has undergone the course shall be able to:

Suggested Readings

1. R for Data Science: https://r4ds.hadley.nz

2. R Manuals (R Project): https://cran.r-project.org/manuals.html

3. Wallach, D., Makowski, D., Jones, J. W., & Brun, F. (2018). Working with dynamic crop models: methods, tools, and examples for agriculture and environment. Academic Press.

4. AquaCrop model: https://www.fao.org/aquacrop/en

5. Prasada Rao, G.S.L.H.V. (2008). Agricultural Meteorology. PHI Publisher, Delhi

6. Santanu K.B., Rao, K.V. Sarath Chandran, M.A., Subodh, Sasmal and Singh, V.K. (2021). Weather forecast, agriculture contingency plan and agromet-advisory services for climate resilient agriculture. Indian Journal of Agronomy 66 (5th IAC Special issue): S1-S14

7. ICAR Agro advisory: https://ccari.icar.gov.in/agroadvisory.html