Crop suitability

Seamless Weather Forecasting for Farmers

Crop suitability analysis is a powerful tool to determine which crops are most likely to thrive in a specific region or under certain conditions. By combining climate data across various time scales, these analyses helps farmers and agribusiness make informed decisions. It is especially valuable in the face of changing weather patterns and provides guidance for selecting resilient crops and ensure food security.
This unique approach, to combine various data sources and models into one seamless product that is tailored to your location is called Seamless Forecasting.

Understanding Seamless forecasting

The risks related to the climate differ according to the time scales:

1.)   Present day: Is the current climate normal? What do the temperature and weather precipitation extremes look like over the last decade?

2.)   Seasonal weather forecasting: Will the coming season be drier than normal? What will the expected yield be this season? To act, one can plant drought-resistant crops, plant their crops later than normal or adjust their agricultural practices. When the price is expected to drop in the coming months it can be strategic to store or sell their products at other times.

3.)   Multi-year: How will the local climate change in the future? Will your area still be suitable to grow certain crops in 20 years? This is especially relevant for the planning of future production areas and the sustainability of existing plantations.

Figure 1: Seamless forecasting couples various data sets into one congruent data set, tailored to a specific location or region.

Below are some examples of crop suitability analysis

-Example 1: Status and seasonal weather services

It is important to knowing the status of the season in combination with a seasonal weather outlook, to plan farming activities and project the expected yield for the coming season. In Myanmar, we issue a monthly seasonal weather outlook.

An example, a subset of the seasonal status for July 2020, can be seen in Figure 2. In this particular case, the monsoon season of 2020 has started in almost the whole country. Yet the received rainfall is (much) lower than normal.

Figure 2: Rainfall conditions over Myanmar compared to historic average for July 2020.

-Example 2: Coffee yield in a changing climate

Coffee grows in semi-tropical regions, and 70% of all coffee grown worldwide is Arabica coffee. This makes coffee products extremely vulnerable to climate change. The most optimal growing conditions for Arabica coffee beans are temperatures between 18 – 21 degrees Celsius. But it can tolerate annual mean temperatures of around 24 degrees Celsius. However, higher temperatures cause the beans to ripen fast and reduce their quality. By combining historical yield data and seamless forecasting data one can find a relation between climatic conditions and coffee yield. These can be combined with climate projections to project the coffee yield in the future.

An example of such a product for Tanzania is shown in Figure 3. The shaded area is the range in projected coffee yield outcomes for the coming decades. This figure shows that the coffee sector in Tanzania has an urgent problem, as the projected yield is falling fast. Tanzanian farmers can adjust to global warming by switching to other coffee varieties before it is too late to change, and coffee companies find areas that are more suitable for growing coffee in 20 to 30 years.

Figure 3: Coffee yield prediction for Tanzania based on historical climate data and the range of future climate predicted by climate models. Please note this is a country average, regionally large differences are present.

-Example 3: Climate suitability for monsoon rice in Myanmar

Crop growth strongly depends on agro-climatic conditions such as precipitation, humidity, and temperature. For instance, rice is highly sensitive to both drought stress and prolonged flooding, which directly affect yields. Using this information, regional suitability for specific crops can be determined. Seamless forecasting is very relevant for large existent plantations, as climate variability can rapidly shift areas from suitable to unsuitable. On the other hand, when selecting new plantation sites, resilience to changing climate conditions is key. At Weather Impact we use (regional/global) climate models to locate suitable regions to grow specific crops. An example for monsoon rice in the Ayeyarwady region is shown in Figure 4.

Figure 4. Suitability map for monsoon rice cultivation in the Ayeyarwady region, Myanmar, based on climate and environmental data using a multi-criteria framework. Colors indicate areas classified as very suitable (dark green), suitable (light green), just suitable (yellow), and unsuitable (red) for rice production.