MEV 024: Unit 10 – Use of simulation models for analysing vulnerability of crops to climate change

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 UNIT 10: USE OF SIMULATION MODELS FOR ANALYSING VULNERABILITY OF CROPS TO CLIMATE CHANGE

10.1 Introduction

Climate change poses a major threat to global food security. Rising temperatures, altered precipitation patterns, and increased frequency of extreme weather events have significant impacts on crop productivity. To address this challenge, simulation models have become essential tools in assessing the vulnerability of crops to changing climatic conditions.

This unit explores how simulation models, including General Circulation Models (GCMs), Regional Climate Models (RCMs), and crop-specific models, are used to understand and predict the impact of climate change on agriculture. By focusing on real-life case studies, the unit also illustrates how these models are applied to assess yield variability, crop duration, water use, and potential adaptation strategies.

10.2 Objectives

After completing this unit, you will be able to:

  • Understand the role of simulation models in evaluating crop vulnerability.
  • Distinguish between GCMs and RCMs.
  • Analyze case studies involving sorghum, rice, and water budgets using various models.
  • Evaluate the effectiveness of adaptation strategies to climate change.
  • Identify the strengths and limitations of simulation-based crop vulnerability assessments.

10.3 General Circulation Models (GCMs)

GCMs are complex mathematical models that simulate the Earth's climate system. They include interactions between the atmosphere, oceans, land surface, and ice. GCMs are used to:

  • Project future climate scenarios based on greenhouse gas emissions.
  • Provide large-scale climate data (temperature, rainfall, radiation) at global or continental scales.
  • Serve as inputs for downscaling in crop vulnerability studies.

However, their coarse spatial resolution (100–250 km) often limits direct application to local-scale agricultural systems.

10.4 Regional Climate Models (RCMs)

RCMs offer higher-resolution climate projections (typically 20–50 km) by downscaling outputs from GCMs. They incorporate local topography, land use, and finer-scale processes.

Applications:

  • Project localized impacts of climate change.
  • Provide inputs for crop simulation models.
  • Evaluate the efficacy of climate adaptation measures.

Examples include:

  • PRECIS (Providing Regional Climates for Impacts Studies)
  • RegCM (Regional Climate Model)

10.5 Case Study: Vulnerability Assessment of KharifRainfed Sorghum to Climate Change in SAT Regions of India

Sorghum is a major rainfed crop in the semi-arid tropics (SAT). Assessing its vulnerability involves simulating crop growth under current and future climates using models like InfoCrop or DSSAT.

10.5.1 Calibration and Validation of Model

  • Historical yield and weather data used to calibrate the model.
  • Parameters adjusted for cultivar, soil, and management practices.
  • Validation ensures model replicates observed crop performance accurately.

10.5.2 Statistical Analysis

  • Root Mean Square Error (RMSE), R², and Mean Bias Error (MBE) used to evaluate model accuracy.

10.5.3 Sensitivity Analysis

  • Determines how changes in inputs (temperature, rainfall, CO₂) affect sorghum growth and yield.

10.5.4 Climate Change Impact Assessment

  • Simulations run using projected climate data (RCP 4.5, RCP 8.5).
  • Decline in yield under high-emission scenarios observed.

10.5.5 Model Validation

10.5.5.1 Phenology

  • Simulated flowering and maturity dates compared with observed field data.

10.5.5.2 Growth and Yield

  • Biomass and grain yield validated against experimental results.

10.5.6 Statistical Analysis of Model

  • Data visualization and statistical tests confirm robustness of simulations under current and future conditions.

10.5.7 Impact Assessment of CSH 16-Sorghum Hybrid

  • Hybrid shows better resilience to temperature increases and water stress.

10.5.8 Impact Assessment of CSV 15 Sorghum Variety

  • Traditional variety more susceptible to drought and heat stress.

10.5.9 Adaptation Strategies

  • Early sowing
  • Use of drought-tolerant varieties
  • Improved soil management (mulching, organic amendments)
  • Water conservation practices

10.5.10 Yield Gap

  • Quantified difference between potential yield (simulated) and actual farmer yield.
  • Helps identify technological and management constraints.

10.6 Case Study: Climate Change and Rice Crop Duration over the Cauvery Delta Zone

The Cauvery Delta is a vital rice-producing region sensitive to climate variations.

10.6.1 Impact on Crop Duration

  • Rising temperatures accelerate crop development.
  • Shorter growing periods reduce biomass accumulation and yield.

10.6.2 Duration and Yield for PRECIS

  • Using PRECIS projections, simulation shows 7–10 day reduction in crop duration.
  • Yields decline due to insufficient grain filling period.

10.6.3 Impact on Yield for RegCM3

  • RegCM3-based simulations indicate greater inter-annual variability in rice yields.
  • Late-maturing varieties may become non-viable without adaptation.

10.7 Case Study: DNDC Model for Water Budget

DeNitrification-DeComposition (DNDC) is a model used to simulate soil carbon and nitrogen dynamics, water movement, and greenhouse gas emissions.

10.7.1 Simulation of Crop Water Requirement

  • DNDC calculates crop evapotranspiration under various climatic scenarios.
  • Identifies periods of water stress and irrigation needs.

10.7.2 Water Use Efficiency

  • Enhancing irrigation efficiency, mulching, and crop rotation improves water productivity.
  • DNDC helps test water-saving techniques in virtual trials before field implementation.

10.8 Applications of Crop Model

Crop simulation models are used to:

  • Assess climate change impacts on crop yield, quality, and duration.
  • Evaluate adaptation strategies like changing sowing dates or using climate-resilient varieties.
  • Support policy development for climate-smart agriculture.
  • Guide research and extension by identifying vulnerable zones and recommending suitable practices.
  • Quantify yield gaps and resource use efficiency.

10.9 Limitations of Modelling

  • Data-intensive: Requires detailed weather, soil, crop, and management data.
  • Uncertainty in projections: Depends on climate scenarios and model assumptions.
  • Limited local calibration: May not reflect regional complexities if not properly adapted.
  • Computational demand: High-resolution simulations require substantial processing power.
  • Model bias: Different models may yield varying outcomes for the same scenario.

10.10 Let Us Sum Up

Simulation models are powerful tools for analyzing the vulnerability of crops to climate change. By integrating climate projections with crop physiology, they help researchers and policymakers assess risks, test adaptation strategies, and improve agricultural resilience. Through case studies on sorghum, rice, and water budgeting, this unit illustrates the practical applications of GCMs, RCMs, and crop-specific models. While modeling comes with limitations, its role in preparing agriculture for climate uncertainty is indispensable.

10.11 Key Words

  • GCM (General Circulation Model): Simulates large-scale global climate patterns.
  • RCM (Regional Climate Model): High-resolution climate projection for specific regions.
  • Calibration: Adjusting model parameters to match observed data.
  • Sensitivity Analysis: Testing model response to variable changes.
  • Crop Simulation Model: Tool for predicting crop growth under varying environmental conditions.
  • Yield Gap: Difference between potential and actual crop yield.
  • DNDC Model: Simulates soil and water processes including emissions.

 

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