Crop modelling for identifying climate change

Climate change is defined as “Any long term substantial deviation from present climate because of variations in weather and climatic elements”.

Crop modelling for identifying climate change

The causes of climate change

  1. The natural causes like changes in earth revolution, changes in area of continents, variations in solar system, etc.
  2. Due to human activities the concentrations of carbon dioxide and certain other harmful atmospheric gases have been increasing. The present level of carbon dioxide is 325 ppm and it is expected to reach 700 ppm by the end of this century, because of the present trend of burning forests grasslands and fossil fuels.

Green house effect

The effect because of which the earth is warmed more than expected due to the presence of atmospheric gases like carbon dioxide, methane and other tropospheric gases.

The shortwave radiation can pass through the atmosphere easily, but, the resultant outgoing terrestrial radiation cannot escape because atmosphere is opaque to this radiation and this acts to conserve heat which rises temperature

Effects of climate Change

  1. The increased concentration of carbon dioxide and other green house gases are expected to increase the temperature of earth.
  2. Crop production is highly dependent on variation in weather and therefore any change in global climate will have major effects on crop yields and productivity.
  3. Elevated temperature and carbon dioxide affects the biological processes like respiration, photosynthesis, plant growth, reproduction, water use etc. In case of rice increased carbon dioxide levels results in larger number of tillers, greater biomass and grain yield. Similarly, in groundnut increased carbon dioxide levels results in greater biomass and pod yields.
  4. However, in tropics and sub-tropics the possible increase in temperatures may offset the beneficial effects of carbon dioxide and results in significant yield losses and water requirements.
  5. Proper understanding of the effects of climate change helps scientists to guide farmers to make crop management decisions such as selection of crops, cultivars, sowing dates and irrigation scheduling to minimize the risks

Role of Climate Change in Crop Modeling

In recent years there has been a growing concern that changes in climate will lead to significant damage to both market and non-market sectors. The climate change will have a negative effect in many countries. But farmers adaptation to climate change-through changes in farming practices, cropping patterns, and use of new technologies will help to ease the impact.

The variability of our climate and especially the associated weather extremes is currently one of the concerns of the scientific as well as general community. The application of crop models to study the potential impact of climate change and climate variability provides a direct link between models, agro meteorology and the concerns of the society.

Tables 2 and 3 present the results of sensitivity analysis for different climate change scenarios for peanut in Hyderabad, India. As climate change deals with future issues, the use of General Circulation

Models (GCMs) and crop simulation models proves a more scientific approach to study the impact of climate change on agricultural production and world food security compared to surveys. Crop grow (DSSAT) is one of the first packages that modified weather simulation generators/or introduced a package to evaluate the performance of models for climate change situations.

Irrespective of the limitations of GCM sit would be in the larger interest of farming community of the world that these DSSAT modelers look at GCMs for more accurate and acceptable weather generators for use in models. This will help in finding solutions to crop production under climate changes conditions, especially in under developed and developing countries

Future Issues related to weather on crop modeling 

For any application of a crop model weather data is an essential input and it continues to play a key role. So:

  1. There is an urgent need to develop standards for weather station equipment and sensors installation and maintenance.
  2. It is also important that a uniform file format is defined for storage and distribution of weather data, so that they can easily be exchanged among agro meteorologists, crop modelers and others working in climate and weather aspects across the globe.
  3. Easy access to weather data, preferably through the internet and the worldwide web, will be critical for the application of crop models for yield forecasting and tactical decision making.
  4. Previously one of the limitations of the current crop simulation models was that they can only simulate crop yield for a particular site. At this site weather (soil and management) data also must be available. It is a known fact that the weather data (and all these other details) are not available at all locations where crops are grown.
  5. To solve these problems the Geographical Information System (GIS) approach has opened up a whole field of crop modeling applications at spatial scale. From the field level for site-specific management to the regional level for productivity analysis and food security the role of GIS is going to be tremendous

Applications ans Uses of Crop Growth Models in Agricultural Meteorology: 

The crop growth models are being developed to meet the demands under the following situations in agricultural meteorology.

  1. When the farmers have the difficult task of managing their crops on poor soils in harsh and risky climates.
  2. When scientists and research managers need tools that can assist them in taking an integrated approach to finding solutions in the complex problem of weather, soil and crop management.
  3. When policy makers and administrators need simple tools that can assist them in policy management in agricultural meteorology.

On farm decision-making and agronomic management

The models allow evaluation of one or more options that are available with respect to one or more agronomic management decisions like:

  • Determine optimum planting date.
  • Determine best choice of cultivars.
  • Evaluate weather risk.
  • Investment decisions.

The crop growth models can be used to predict crop performance in regions where the crop has not been grown before or not grown under optimal conditions. Such applications are of value for regional development and agricultural planning in developing countries).

A model can calculate probabilities of grain yield levels for a given soil type based on rainfall Investment decisions like purchase of irrigation systems  can be taken with an eye on long term usage of the equipment thus acquired.showed that for maize, both simulated and measured mean yields with weeds are 86% of the weed-free yields

Understanding of research

In agro-meteorological research the crop models basically helps in:

  • Testing scientific hypothesis.
  • Highlight where information is missing.
  • Organizing data.
  • Integrating across disciplines.
  • Assist in genetic improvement;
  • Evaluate optimum genetic traits for specific environments.
  • Evaluate cultivar stability under long term weather.

Penning de Vries (1977) emphasized that simulation models contribute to our understanding of the real system which in-turn helps to bridge areas and levels of knowledge. It is believed that in conversion of conceptual models into mathematical simulation models the agro meteorologists can understand the gaps in their knowledge.

So, the interdisciplinary nature of simulation modeling efforts leads to increased research efficacy and improved research direction through direct feedback. In this direction de Wit and Goudriaan(1978) developed Basic Crop growth Simulator (BACROS) which was used as a reference model for developing other models and as a basis for developing summary models.

Also described the potential of simulation models in assessing trait benefits of winter cereals and their capacity to survive and reproduce in stress-prone environment. Crop growth models have been used in plant breeding to simulate the effects of changes in themorphological and physiological characteristics of crops which aid in identification of types for different environments

Policy management

The policy management is one very useful application of crop simulation models. The issues range from global (impacts of climate change on crops) to field level (effect of crop rotation on soil quality) issues. Thornton et al. (1997) showed that in Burkina Faso.

Crop simulation modeling using satellite and ground-based data could be used to estimate millet production for famine early warning which can allow policy makers the time they need to take appropriate steps to ameliorate the effects of global food shortages on vulnerable urban and rural populations.

In Australia Meinke and Hammer (1997) found that when November-December SOI (Southern Oscillation Index) phase is positive, there is an 80% chance of exceeding average district yields.

Conversely,in years when the November-December SOI phase is either negative or rapidly falling, there is only a 5% chance of exceeding average district yields, but 95% chance of below average yields. This information allows the industry to adjust strategically for the expected volume of production.

Crop models can be used to understand the effects of climate change such as :

  1. a) Consequences of elevated carbon-dioxide, and
  2. b) Changes in temperature and rainfall on crop development, growth and yield. Ultimately, the breeders can anticipate future requirements based on the climate change

By Jaffar Iqbal

Welcome! I am passionate about Agriculture and food security, Graduated in Agriculture. I love to work in the field of Agronomy and sustainable farming.