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New Tool To Help Cassava Growers Optimise Yield




The African Cassava Agronomy Initiative (ACAI) site-specific fertilizer recommendation (FR) tool is specifically designed to provide optimised and economically beneficial recommendations to cassava growers.

The tool considers local soil data, weather conditions, prices of available fertilizers and cassava root produce, planting and harvest dates, and the investment capacity of the farmer.

The Initiative has been conducting nutrient omission trials (NOTs) in Nigeria and Tanzania in collaboration with national research and development partners to find out how cassava responds to nutrients.

Currently, available results show a large variation in nutrient response indicating the need for site-specific fertilizer recommendations.

To provide site-specific recommendations, ACAI is developing an integrated system using machine learning techniques coupled with process-based crop models. The ACAI team is combining the Light Interception and Utilisation model (LINTUL), Quantitative Evaluation of the Fertility of Tropical Soils model (QUEFTS), and economic optimiser algorithms to calibrate the recommendations.

The mechanism put in place determines the soil nutrient supply capacity, yield potential, nutrient-limited yield, and fertilizer rates required to acquire a target yield maximising net revenue by combining observations from field trials, available GIS data, weather data, and farmers’ ability to invest in fertilizer.

Using the QUEFTS model, the soil NPK supply was accurately predicted using the observed yield response in the NOTs. At these locations, the relationship between apparent soil nutrient supply and soil properties obtained from GIS layers from the International Soil Reference and Information Centre (ISRIC) was modeled using machine learning techniques.

These models, in turn, were used to predict the soil NPK for the entire target intervention area. These soil properties can sufficiently explain the regional level of soil variation. To explain soil variation at a short range, however, the GIS layers need to be complemented with a local scale soil fertility indicator.

The use of common local soil fertility indicators, such as local soil name, soil depth/color, cropping history, perception of soil fertility, cropping history, manure/fertilizer use, etc., are not sufficiently generic as their predictive ability depends on the local context. Such indicators are therefore challenging to use in a standardised way. Within ACAI, the current yield was found to be the best generic fertility indicator to adjust the soil nutrient supply at a regional scale to local soil conditions.