## 4 Discussion  
This paper presented the design and implementation of an interactive platform for fitting experimental data to mathematical expressions and generating the spatial projection of the result at the local, regional, and continental scales. The platform was developed with the combination of R programming language, RCP architecture, and Udig, which makes it an interactive decision support tool with multiple components, as described in the literature [12; 13; 14]. The most important components of the platform are RComponent and the graphical user interface (GUI). However, the trigger of the iterative procedure is the acquisition of the experimental data used as input. The fitting procedure implemented in the platform applied the LM algorithm. The LM algorithm combines two approaches; it operates like the gradient descent method when the equation parameters are far from their optimal values, while it performs like the Gauss-Newton method when the parameter values are close to their optimum ([PERSON], 2001). The main advantage of the LM algorithm is its ability to converge to optimum values faster than the gradient descent or the Gauss-Newton method. This allows the LM algorithm to still find the optimal value of parameters when the initial values of equation parameters are distant from the optimum. This makes the LM algorithm very robust when compared to other minimization algorithms usually used in the fitting procedure ([PERSON], 2001). The GUI is considered as a key component of the tool as it is used directly by the end-user to interact and communicate with the system. The design of the software GUI satisfies the requirements for a software development interface as described in Reference [42]. The component-based approach  
Figure 9: Cameroon map of the potential efficacy of ICIPE 62 isolate when used against mustard aphid, modeled with the software. The level of efficacy varies between 0 and 1. Locations with 0% level of efficacy are displayed in white, values between 0 and 0.5 are displayed in blue, values between 0.5 and 0.75 are displayed in green, and values between 0.75 and 1 are displayed in red. Red indicates the highest efficacy levels. is adopted to rely on the possibilities to extend and reuse developed components. By employing such a development concept, the maintenance of the software architecture can be secured while new functionalities can be easily integrated. Component-based UIs further enable functional and logical decomposition of the software GUI into perspectives, which helps in defining features needed by the end-users. Component-based UIs accelerate the development process, which is contrary to the iterative or agile development approach that tends to slow the process of developing software. The modularity offered by the RCP component allows easily integrating additional features in the tool. In comparison to other fields such as education or finance, the need for such tools in agriculture and IPM research activities is lacking despite their importance. This is particularly required to assist in monitoring numerous processes in the crop production system, such as field release of biological control agents. In recent years, progress was perceptible with the proliferation of DSS [43; 44], which can be web-based [43] or standalone like the proposed DSS. Despite disparities among conceptual approaches, what they all have in common is the emphases put on input data and the user interface. One advantage of a web-based DSS is the possibility it offers to directly access real-time environmental data to carry out analytics [44], which make these categories of tools highly dependent on an internet connection that is not always available in most of Africa. A standalone DSS, once installed, only requires acquiring input data to be used anywhere and offline. Moreover, the uniqueness of this tool is the flexibility it offers to easily provide spatial distribution maps using gridded values of temperature for delimiting potential areas of field application of control measures.