We identify two principle ways in which climate-smartness can be measured: (i) metrics that directly measure a component of climate-smartness, e.g., a single pillar, or aspect of a pillar; (ii) indices, which are composite metrics that synthezise and summarize numerically information across or within pillars. Whilst the delineation between metrics and indices is not always clear in practice, since the two terms are often used interchangeably, the distinction is a useful one, since it clarifies what exactly is being measured–an element of CSA, or of a pillar (metrics), or the overall pillar or extent of climate-smartness (indices).

The “Assessment, monitoring and evaluation” section of the CSA sourcebook (FAO, 2013) presents an overview of methodological frameworks for assessing, monitoring, and evaluating CSA. Subsequently, a wide range of biophysical, social, and economic metrics of mitigation, adaption, and productivity have been developed (World Bank, 2016; Duffy et al., 2017; Christiansen et al., 2018). This work has led to a plethora of CSA-related indices. For example, Quinney et al. (2016) collected over 378 CSA-related indicators from several international development agencies and created an interactive database called “CSA Programming and Indicator Tool”. The tool examines the scope and CSA intentionality among different project designs and supports an appropriate selection of indicators to measure and monitor CSA-related outcomes.

In contrast to the more top-down approaches outlined above, participatory approaches to CSA metrics permit more grounded assessments. Examples of this include Sain et al. (2017), the rapid appraisal method of Mwongera et al. (2017); the multi-criteria and multi-perspective ranking system of Wassmann et al. (2019); and the participatory ranking of Kumar et al. (2018). The contribution of participatory approaches in the understanding of climate-smartness is limited to a geographical, political, or socio-economic context by the specificity of actors involved. Whilst such approaches are not generally suited to the assessment of trade-offs and synergies across CSA pillars, they can enable a quantitative measure of climate smartness that can be applied at the country level (World Bank, CIAT, 2015).

Indices are the result of equations that combine information, often across all three CSA pillars, in order to assess climate smartness. The water-based and soil-based climate-smartness indices (CSIs) published by Arenas-Calle et al. (2019, 2021) are indices that seek to provide a measure of climate-smartness from an agronomic perspective. Both indices represent trade-offs and synergies between all three CSA objectives (adaptation, mitigation, and productivity) by translating them into quantitative values. One index uses water productivity (yield per unit water used) and Greenhouse Gas Intensity (GHGI) to capture the extent to which water-based adaptation strategies can have the co-benefits of increased yields and low emissions. This index uses seasonal data, providing a short-term measure of climate-smartness. It can also be used in a long-term time series of seasonal records to track improvements in sustainability and adaptation. The soil-based index is aimed at measuring climate-smartness over multiple years. It measures the ability of a soil-based agronomic practice to increase productivity smoothly (i.e., without the year-to-year variations that can result from climate change) whilst also capturing carbon in the soil. Given that Soil Organic Carbon (SOC) is a key driver of agronomic soil function (e.g., water and nutrient retention, biological activity, or structural stability), it can measure resilience and adaptive capacity in agriculture. Moreover, cumulative changes in SOC in the soil in the middle and long term can indirectly indicate carbon storage.

Whilst not technically a metric or index, databases that can be interrogated for information on CSA effectiveness are clearly important in measuring climate-smartness. Meta-analysis based on extensive data provides another way to understand how climate-smartness varies across space and time. Evidence for Resilient Agriculture (ERA) provides such a database of publications, with analytical tools that enable dynamic interrogation (Nowak and Rosenstock, 2020).

The climate-smartness indices (CSIs) reviewed in Section Indices, Metrics and Participatory Approaches are composite indices that define the climate-smartness of certain cropping systems according to the synergies and trade-offs between mitigation, adaptation and productivity indicators. As such, they can act as an integrated attribute that goes beyond individual assessments of CSA pillars. For example, Arenas-Calle et al. (2021) used a soil-based CSI to assess how synergies in adaptation and mitigation evolve over time, demonstrating that maximum synergy in conservation agriculture practices tends to peak at around 5 and 10 years after the practices have been initiated. After 20 years, neither SOC nor yield show evidence of benefiting from the practices. Similarly, CSI-based assessment of Alternate Wetting and Drying (AWD) practices in rice (Arenas-Calle et al., 2019) expressed the extent to which synergistic adaptation and mitigation were achieved across a range of AWD studies.

In Table 1, the eight categories of vW2020 (see section Review of Progress in CSA Research) were used to assess the way in which recently-published CSA assessment methods address the three pillars. On the whole, the CSA assessment methods reviewed here showed similar research gaps to the 15 CSA assessment frameworks reviewed by vW2020. For instance, productivity is addressed in all assessments, whereas other elements of food security are rarely assessed (and then only partially). Similarly, food security pathways, adoptability of technologies, and longer-term adaptation and mitigation all stand out as under-assessed elements. There are some exceptions to this general observation, including assessments of:

As a general rule, metrics and indices (as defined in Section Indices, Metrics, and Participatory Approaches) differ in that the former tends toward greater depth of analysis and the latter toward greater breadth across CSA dimensions. This distinction in scope becomes even more important when assessing the broader issue of climate-smart food systems (CSFS), in contrast to the narrower field of CSA. The extent to which multiple context-dependent aspects of CSFS can be integrated into a single indicator, or even a single meaningful and clear assessment, remains an open question. Such integration facilitates comparisons across multiple geographies, contexts and across time (see section Recent Progress in Assessing Trade-offs and Synergies). However, the clarity of a quantified indicators comes at the expense of greater difficulty in accounting for numerous important aspects of food systems, for which one must turn to others tools that assess climate-smartness beyond the farm gate. These tools and emergent research areas are explored below, and they inform the research needs identified in section Research Needs for Measuring CSA and CSFS.

Life Cycle Assessment (LCA) enables estimates of the environmental sustainability and adaptation potential of agricultural systems and subsequent value chains beyond farm. Emissions and pollution metrics assess sustainability, whilst adaptation is measured through various means, e.g., inventory of use of fertilizers, pesticides, energy, and water use. The use of LCA in the assessment of climate smartness was applied by Acosta-Alba et al. (2019) who designed the LCA4CSA assessment framework (Life Cycle Assessment for Climate Smart Agriculture) which intend to provide climate-smartness assessments by integrating life cycle analysis structure. More broadly, Iannetta et al. (2021) and Lemay et al. (2021) present examples of how to frame different elements of food systems connected with on-farm agronomic decisions that have off-farm implications (e.g., value-chain, diets, public health, culture).

Whilst clearly important and moderately well-researched for mitigation, livestock has received less attention than crops in the area of full climate-smart assessments. Gaitán et al. (2016) published an assessment of mitigation potential of some practices in livestock in Nicaragua and defined climate-smart livestock as those systems that could achieve higher efficiency in terms of GHG emissions per kg of milk produced, as well as increased capacity to store carbon in silvo-pastoral systems. Such mixed cropping and livestock systems are very common and thus it is important to assess them. March et al. (2021), discussed the use of different alternatives to feed livestock with human-inedible products, while Espitia Buitrago et al. (2021) explore the opportunities and constraints in the production and consumption of alternative protein sources for humans and livestock like forage-fed insects. This work points the way forward to smarter ways of distributing land and resources to produce food.

Nutrition security is another important aspect of food systems, since it goes beyond the narrower views offered by calorie- and protein- based assessments of food security. Mustafa et al. (2021) make reference to “climate-smart and nutrient dense crops” as a way of capturing this idea. The rise of atmospheric CO₂ and soil degradation (and subsequent depletion of micronutrients) represents a threat for crop nutrient quality. Whilst it is increasingly common in climate impact studies, the assessment of nutrient quality as an aspect of climate-smartness has been overlooked in climate-smartness assessments. There has, however, been some progress in assessing nutrition security at the country scale (e.g., Jennings et al., 2022).