Development and application of ecological model in agriculture

The main issues in agricultural development in the future that require urgent attention, such as global climate change, declining in yield potential and degradation of the environment, are more complex in nature and the search for solutions has necessitates more focused yet interdisciplinary efforts. Organized thinking about future farming requires forecasting of the implications of alternative ways to farm and develop agriculture. Ecological model is an indispensable tool for such integration and exploration. This paper highlights our experiences in the development of rice growth model. It is a mechanistic model that simulates rice growth based on the interactions between rice characteristics and the various underlying environmental conditions. The model was developed in four levels of integration-potential, attainable, actual and ecosystem levels. Potential level describes crop growth performance under optimal conditions, where nutrient and water are not limited. In this level the rice growth and production are explained on the basis of processes at the organ level. The basic ecology process considered here is photosynthesis. At this level only the interactions between crop specific characteristics (genetic) and climate factor (light and temperatures) were considered. Parameters for the model were obtained under (well) controlled environment i.e. under phytotron or conviron. Attainable level explains the crop growth under constraint (limitation) of water and nutrients. Parameters were obtained from small plot experiments. The actual level considers crop growth under real environment, where the presence of crop growth reducing factor such as pest, weeds and pollutant are taken into account. The ecosystem level considers the effect of socio-economic factors on rice growth. This type of model is very useful to the researchers, extension workers and policy makers in assisting their works. In research, it enables researchers to integrate knowledge from various disciplines in a quantitative manner to help understanding the complex behaviour of agricultural systems on the basis of underlying processes. In field, it may be used to support problem-solving and decision-making processes. To policy maker, the model helps to predict the future outcomes based on the proposed current set of scenarios.