Conservation Agriculture: factors and drivers of adoption and scalable innovative practices in Indo-Gangetic plains of India– a review

H. Jat,A. Datta,Madhu Choudhary,P. C. Sharma,M. L. Jat

Published 2020 in International Journal of Agricultural Sustainability

ABSTRACT

ABSTRACT Conventional- till (CT) agriculture is known to be ecologically indiscreet, economically and environmentally unsustainable, and leads to degradation of soil and environment in Indo-Gangetic Plains (IGP). Conservation Agriculture (CA) approach was introduced to manage agro-ecosystems for improved and sustained productivity, and increased farmers’ profits while maintaining the natural resources. This comprises the management of natural resources at the farm, village, and landscape scales to increase synergies between food production and ecosystem conservation. CA-based rice-wheat (RW) system integrated with mungbean improved the system productivity by ∼10%, profitability by 20–30% using 15–30% less irrigation water, and 20–25% less energy input compared to conventional RW system in the IGP. However, the replacement of rice with maize improved the productivity by 10-15% and profitability by 40–50% using ∼70% less irrigation water. CA layered with subsurface drip irrigation (SDI) in CA-based rice/maize systems recorded ∼5% higher system productivity and saved ∼50% of irrigation water compared to flood irrigation in CT-based systems. CA-based systems are found more adapted to extreme climatic conditions and can mitigate the negative effects of climatic stresses like terminal heat, water stress and thereby helps in increasing crop yields to the tune of 0.4–0.8 t ha−1 per season over the conventional system.

PUBLICATION RECORD

  • Publication year

    2020

  • Venue

    International Journal of Agricultural Sustainability

  • Publication date

    2020-09-15

  • Fields of study

    Agricultural and Food Sciences, Geography, Environmental Science

  • Identifiers
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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