Abstract
Conservation agriculture (CA) is widely promoted as a sustainable agricultural management strategy with the potential to alleviate some of the adverse effects of modern, industrial agriculture such as large‐scale soil erosion, nutrient leaching and overexploitation of water resources. Moreover, agricultural land managed under CA is proposed to contribute to climate change mitigation and adaptation through reduced emission of greenhouse gases, increased solar radiation reflection, and the sustainable use of soil and water resources. Due to the lack of official reporting schemes, the amount of agricultural land managed under CA systems is uncertain and spatially explicit information about the distribution of CA required for various modeling studies is missing. Here, we present an approach to downscale present‐day national‐level estimates of CA to a 5 arcminute regular grid, based on multicriteria analysis. We provide a best estimate of CA distribution and an uncertainty range in the form of a low and high estimate of CA distribution, reflecting the inconsistency in CA definitions. We also design two scenarios of the potential future development of CA combining present‐day data and an assessment of the potential for implementation using biophysical and socioeconomic factors. By our estimates, 122–215 Mha or 9%–15% of global arable land is currently managed under CA systems. The lower end of the range represents CA as an integrated system of permanent no‐tillage, crop residue management and crop rotations, while the high estimate includes a wider range of areas primarily devoted to temporary no‐tillage or reduced tillage operations. Our scenario analysis suggests a future potential of CA in the range of 533–1130 Mha (38%–81% of global arable land). Our estimates can be used in various ecosystem modeling applications and are expected to help identifying more realistic climate mitigation and adaptation potentials of agricultural practices.
Highlights
Present-day highly mechanized, industrial agricultural systems often come at the cost of irreversible impacts on the environment and related ecosystem services (Foley et al, 2005, 2011; Power, 2010), contributing to stagnating or even decreasing agricultural productivity in some regions (Alston, Beddow, & Pardey, 2009; Ray, Ramankutty, Mueller, West, & Foley, 2012)
The lack of independent data on cropland management strategies at the global scale makes it challenging to evaluate the accuracy of our maps and the allocation procedure
The overall spatial pattern at the subnational scale is depicted well, especially in Canada and Australia with 79% and 66% of conservation agriculture (CA) area allocated to the subnational unit reported in census data
Summary
Present-day highly mechanized, industrial agricultural systems often come at the cost of irreversible impacts on the environment and related ecosystem services (Foley et al, 2005, 2011; Power, 2010), contributing to stagnating or even decreasing agricultural productivity in some regions (Alston, Beddow, & Pardey, 2009; Ray, Ramankutty, Mueller, West, & Foley, 2012). Changing climatic conditions, which have the potential to severely impact agricultural production due to changes in mean climate and extreme events such as droughts and heatwaves (Porter et al, 2014; Seneviratne et al, 2012), call for revisiting current trends in the agricultural sector in the context of environmental sustainability (Foley et al, 2011; Lobell & Gourdji, 2012). Climatesmart agriculture (CSA) is promoted as a strategy to enhance the resilience of agricultural systems to climate change while reducing agricultural greenhouse gas (GHG) emissions (FAO, 2013) Both concepts are closely linked, the main difference being a focus on either the intensification or climate mitigation and adaptation aspect, respectively (Campbell, Thornton, Zougmore, van Asten, & Lipper, 2014). A notable example of SI and CSA in arable systems is conservation agriculture (CA) (Hobbs, 2007), which is considered an operational strategy to implement both sustainable and climate-smart agricultural practices across socioecological contexts (Hobbs, Sayre, & Gupta, 2008)
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