MEV 024: Unit 04 – Uncertainties in climate change assessment

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 UNIT 4: UNCERTAINTIES IN CLIMATE CHANGE ASSESSMENT

4.1 Introduction

Climate change assessments aim to predict future impacts on natural and human systems. However, due to the inherent complexity of climate and biological systems, uncertainty remains a significant challenge. In agriculture, crop simulation models are commonly used to assess how climate change may affect crop growth, yield, and sustainability. These models depend on various input parameters—many of which are uncertain or variable over time and space.

Uncertainties arise from multiple sources: variability in weather data, imprecision in model structure, assumptions in agronomic management, and inaccuracies in soil or crop data. Understanding and managing these uncertainties is critical to ensuring that projections are realistic and useful for decision-making in climate-smart agriculture.

4.2 Objectives

After studying this unit, learners will be able to:

  • Understand the structure and role of crop simulation models in climate change assessments.
  • Identify the types of input data and associated uncertainties in crop modeling.
  • Recognize how model outputs such as phenology, growth, and yield are analyzed.
  • Learn techniques for uncertainty analysis including statistical and sensitivity analysis.
  • Evaluate a case study applying these concepts to a real-world crop model.

4.3 Crop Simulation Models

Crop simulation models are computer-based tools that simulate plant growth, development, and yield as influenced by environmental conditions and management practices. They are used extensively in climate impact studies, policy-making, and resource planning.

4.3.1 The Structure of Crop Model

A typical crop simulation model consists of the following modules:

  • Weather module: Inputs daily climate variables like temperature, rainfall, and solar radiation.
  • Soil module: Tracks water and nutrient dynamics.
  • Crop growth module: Simulates phenological stages and biomass accumulation.
  • Management module: Includes irrigation, sowing date, fertilizer application, etc.

4.3.2 Input Factors

4.3.2.1 Uncertainty in Input Factors

Model performance depends heavily on the accuracy of input data. Uncertainty can arise due to:

  • Measurement errors in climate or soil data.
  • Spatial variability in field conditions.
  • Inadequate resolution of climate data.
  • Assumptions in crop management practices.

4.3.3 Input Data for Validation of the Model

4.3.3.1 Weather and Climate Data

Includes daily minimum and maximum temperatures, solar radiation, humidity, wind speed, and rainfall. Sources of uncertainty:

  • Incomplete historical records.
  • Coarse resolution of global climate model outputs.

4.3.3.2 Crop and Varietal Data

Involves genotype-specific information like:

  • Growth duration.
  • Temperature thresholds.
  • Yield potential.

Lack of localized data for specific cultivars contributes to variability in model outcomes.

4.3.3.3 Soil Data

Includes physical and chemical properties such as:

  • Soil texture and structure.
  • Organic matter content.
  • Water holding capacity.

Errors or generalizations in soil maps can misrepresent conditions.

4.3.3.4 Agronomical Data

Relates to:

  • Planting dates.
  • Fertilizer and pesticide usage.
  • Irrigation schedules.

Such data can vary widely across regions and farming systems, influencing model performance.

4.3.4 Model Output Parameters

4.3.4.1 Phenology

Phenological development stages are critical for timing management interventions and predicting yield.

4.3.4.1.1 Days to Germination

Time between sowing and seedling emergence, influenced by soil temperature and moisture.

4.3.4.1.2 Days to 50% Flowering

Key milestone for assessing reproductive success and potential yield.

4.3.4.1.3 Days to Physiological Maturity

Marks the point when dry matter accumulation ceases and harvest becomes viable.

4.3.4.2 Growth Parameters

4.3.4.2.1 Leaf Area Index (LAI)

Measures canopy development; directly linked to photosynthetic activity and evapotranspiration.

4.3.4.2.2 Dry Matter Production and Its Partitioning

Tracks total biomass and its allocation to roots, shoots, and grains.

4.3.4.3 Economic Yield

Final output in terms of grain, fruit, or tuber yield. This is the most policy-relevant model output, used to assess economic impacts of climate change.

4.4 Model Uncertainty Analysis

Understanding and quantifying uncertainty improves confidence in model projections and highlights areas needing more data or refinement.

4.4.1 Statistical Analysis

Includes techniques like:

  • Mean Absolute Error (MAE)
  • Root Mean Square Error (RMSE)
  • Coefficient of Determination (R²)

These are used to compare simulated outputs with observed field data for validation.

4.4.2 Sensitivity Analysis

Assesses how changes in input parameters influence model outputs. Helps identify:

  • Most influential parameters.
  • Model robustness.
  • Priority areas for improving data quality.

4.5 Case Study: InfoCrop-Sorghum Model Uncertainty Analysis

InfoCrop is an Indian crop simulation model developed for a variety of crops including sorghum.

4.5.1 Phenology

4.5.1.1 Days to 50% Flowering

  • Field observations may vary ±3 days.
  • Model sensitivity to temperature data is high.

4.5.1.2 Days to Physiological Maturity

  • Highly affected by cumulative heat units and water availability.

4.5.2 Dry Matter Production

  • Influenced by light interception, LAI, and nutrient availability.
  • Uncertainty arises from variability in solar radiation and plant parameters.

4.5.3 Yield

  • Sensitive to water stress, pest damage, and nutrient deficits.
  • Errors in predicting yield often reflect cumulative uncertainties in all preceding stages.

4.5.4 Statistical Analysis

  • RMSE and MAE used to quantify deviations between simulated and actual yield.
  • An R² value of >0.80 is considered acceptable for research purposes.

4.5.5 Sensitivity Analysis

  • Simulations run under different rainfall and temperature scenarios.
  • Sensitivity charts illustrate which parameters (e.g., sowing date, fertilizer amount) most affect yield and phenology.

4.6 Let Us Sum Up

  • Crop simulation models are important tools for assessing the impacts of climate change on agriculture.
  • These models require a range of input data, each of which carries uncertainties.
  • Model outputs like phenological stages, biomass, and yield are used for decision-making and policy development.
  • Statistical and sensitivity analyses are essential to validate models and understand their reliability.
  • The InfoCrop-Sorghum case study demonstrates how models can be applied and refined through uncertainty analysis.

4.7 Key Words

  • Crop Simulation Model: Computer-based tool simulating crop development under given environmental and management conditions.
  • Uncertainty: Variability or lack of precision in model inputs, assumptions, or outputs.
  • Phenology: Developmental stages of a crop over time.
  • Leaf Area Index (LAI): Ratio of leaf surface area to ground area; influences growth.
  • Dry Matter Production: Accumulated plant biomass.
  • Statistical Analysis: Techniques to assess the accuracy of model predictions.
  • Sensitivity Analysis: Study of how variations in input affect model outputs.
  • InfoCrop: A crop simulation model developed in India for climate impact assessments.

 

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