Application of Precision Agriculture

Abstract

Improved standards of living in the world are expected to double the demand for livestock products by 2050. Meanwhile, anthropogenic climate change is threatening sustainable agriculture and livestock production by causing anomalies in temperature, rainfall, carbon dioxide levels, irrigation water availability, and other crucial plant growth factors. It has profound impacts on the food supply and livelihood of struggling economies because they have limited resources, abilities, and technologies to adapt to the austerities. There is a need to develop coherent fodder crop adaptation and climate change mitigation strategies to ensure sustainable livestock production and world food security. Forage quality, an essential factor to fulfill the nutritional requirements of livestock, is significantly affected by erratic rainfall and temperature variability, whose impact is predicted to be appalling at the end of the 21st century. Precision agriculture can develop a resilient global dairy industry by underpinning economically and environmentally sustainable crop production systems. Precision agriculture strategies ensure judicious and timely application of resources and cultivation practices for sustainable and quality fodder production. Incorporating the latest scientific techniques and reliable data in livestock-based agricultural systems is indispensable for the success of a diversified fodder production system. It improves growers’ management ability by tailoring decisions both spatially and temporally. Reliable data and the incorporation of artificial techniques have paved the way for sustainable livestock of the future. However, at present, adoption of precision agriculture strategies is geographically uneven. However, it is predicted that new technologies will be adopted depending upon their effectiveness in using the region’s scarce productive resources. It is a whole-farm management approach encompassing information technology, satellite positioning, remote sensing, and proximal data utilization. These must be adopted to optimize return on input while reducing potential impact on the environment.