2010年-世界发展银行全球_Economics_of_Adaptation_to_Climate_Change___Ghana_Volume_2_Annexes_48页_2mb

Summary of the Economics of Adaptation to Climate Change in Ghana

Core Content

The document discusses the Economics of Adaptation to Climate Change with a specific focus on agricultural adaptation strategies in Ghana. It outlines the use of CliCrop, a specialized crop model, to estimate the impacts of climate change on agricultural productivity and to support decision-making in the face of environmental challenges like droughts and floods.

Main Points

• Agricultural Adaptation Strategies:

  • Encourage and support crop and livestock diversification.
  • Include programs to conserve water use and reduce soil erosion.
  • Emphasize investment in agricultural extension services to transfer R&D findings to farmers.
  • Address rainfed and irrigated lands with tailored strategies.
  • Highlight the importance of high-level coordination with poverty reduction programs to optimize outcomes for vulnerable communities.

• CliCrop Model Overview:

  • Designed for large-scale yield calculations using historical and future weather data and soil properties.
  • Simulates the effects of climate change on crop yields and livestock production.
  • Incorporates two water management techniques: zai holes and mulching.
  • Includes two methods for calculating evapotranspiration (ET):
    • Single crop coefficient method.
    • Dual crop coefficient method (using basal crop coefficient, $K_{cb}$).

• Key Components of CliCrop:

  • Soil Layer Modeling:
    • The model uses 20 layers, each 10 cm deep, by default.
    • Water transport is simulated using equations from SWAT.
    • Percolation and ponding are calculated based on soil saturation and moisture excess.
  • Water Table:
    • Used to determine waterlogging losses.
    • Height is measured from the bottom soil layer to the saturated layer.
    • Waterlogging is modeled using the SEW30 coefficient and root growth hindrance.
  • Zai Holes:
    • Simulated with two soil profiles: catchment and zai-hole.
    • The zai ratio and catchment efficiency determine how much runoff is captured.
    • Runoff caught is calculated using a linear relationship.

• Mulching:

  • Simulated in three ways: reduction in evapotranspiration, runoff reduction, and organic matter increase.
  • Evapotranspiration reduction is modeled using a modified crop coefficient.
  • Runoff reduction is simulated by reducing the curve number based on the mulch coverage.

• Climate Forecasting and Data:

  • The model uses CHARM (Collaborative Historical African Rainfall Model) for precipitation data.
  • Potential Evapotranspiration (PET) is calculated using the modified Hargreaves equation.
  • Soil properties are estimated using FAO Soil Map of the World and NCAR methods.

Key Information

• Inputs:

  • Precipitation (daily, in mm).
  • Potential Evapotranspiration (PET) (calculated from temperature, latitude, and PET data).
  • Crop type (with specific crop coefficients and stage durations).
  • Soil properties (hydraulic conductivity, wilting point, field capacity, saturation).

• Model Outputs:

  • Yield reductions or improvements based on the ratio of actual ET to potential ET.
  • True yield is the lowest of the five yield values calculated for each development stage and the entire season.
  • Yield coefficient is used to determine the impact of water stress on yield.

• Special Considerations:

  • Waterlogging is modeled using the SEW30 function and root growth hindrance.
  • Soil moisture upward flow is estimated using DSSAT model equations.
  • Zai holes and mulching are critical adaptation techniques in Ghana, especially for low-income communities.

Conclusion

The CliCrop model is a vital tool for understanding and predicting the effects of climate change on agricultural productivity in Ghana. It integrates soil and weather data, simulates complex interactions between crops, soil, and water, and supports adaptation strategies such as zai holes and mulching. The model also accounts for poverty reduction and climate forecasting, ensuring that adaptation efforts are targeted and effective for vulnerable populations.