Intensive agriculture is based on the use of high-energy inputs and quality planting materials with assured irrigation, but it has failed to assure agricultural sustainability because of creation of ecological imbalance and degradation of natural resources. On the other hand, intercropping systems, also known as mixed cropping or polyculture, a traditional farming practice with diversified crop cultivation, uses comparatively low inputs and improves the quality of the agro-ecosystem. Intensification of crops can be done spatially and temporally by the adoption of the intercropping system targeting future need. Intercropping ensures multiple benefits like enhancement of yield, environmental security, production sustainability and greater ecosystem services. In intercropping, two or more crop species are grown concurrently as they coexist for a significant part of the crop cycle and interact among themselves and agro-ecosystems. Legumes as component crops in the intercropping system play versatile roles like biological N fixation and soil quality improvement, additional yield output including protein yield, and creation of functional diversity. But growing two or more crops together requires additional care and management for the creation of less competition among the crop species and efficient utilization of natural resources. Research evidence showed beneficial impacts of a properly managed intercropping system in terms of resource utilization and combined yield of crops grown with low-input use. The review highlights the principles and management of an intercropping system and its benefits and usefulness as a low-input agriculture for food and environmental security.
Rice holds key importance in food and nutritional security across the globe. Nutrient management involving rice has been a matter of interest for a long time owing to the unique production environment of rice. In this research, an artificial neural network-based prediction model was developed to understand the role of individual nutrients (N, P, K, Zn, and S) on different plant parameters (plant height, tiller number, dry matter production, leaf area index, grain yield, and straw yield) of rice. A feed-forward neural network with back-propagation training was developed using the neural network (nnet) toolbox available in Matlab. For the training of the model, data obtained from two consecutive crop seasons over two years (a total of four crops of rice) were used. Nutrients interact with each other, and the resulting effect is an outcome of such interaction; hence, understanding the role of individual nutrients under field conditions becomes difficult. In the present study, an attempt was made to understand the role of individual nutrients in achieving crop growth and yield using an artificial neural network-based prediction model. The model predicts that growth parameters such as plant height, tiller number, and leaf area index often achieve their maximum performance at below the maximum applied dose, while the maximum yield in most cases is achieved at 100% N, P, K, Zn, and S dose. In addition, the present study attempted to understand the impact of individual nutrients on both plant growth and yield in order to optimize nutrient recommendation and nutrient management, thereby minimizing environmental pollution and wastage of nutrients.
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