Assessing trade-offs and synergies in climate smart agriculture across timescales

Climate-Smart Agriculture (CSA) aims for the transformation of agriculture -particularly in low- and middle-income countries- into sustainable, food secure and climate-resilient systems by the achievement of its three principles: increasing of sustainable productivity and food security, building climate resilience, and reducing Greenhouse Gas (GHG) emissions. Since strategies addressed in any of CSA pillars can potentially benefit (synergies) or hinder (trade-offs) the others, CSA focus on identifying such relations to enhance synergies and minimise trade-offs in each context. This holistic approach of CSA is widely accepted, and its uptake has been going faster than the availability of official methodological frameworks and metrics for its assessment. The lack of alignments for climate-smartness results controversial given its increasing relevance in agriculture policy. Moreover, several organizations have been raising concerns that CSA may narrowly address agronomic issues and overlook social issues like the underrepresentation of minorities, inequality, and resources access that constraint agricultural development. In this thesis, two CSA metrics are developed and assessed using existing data sets and process-based modelling simulations. The Climate-Smartness Index (CSI) and Soil-based Climate-Smartness Index (SCSI) were built from agronomic/biophysical indicators of mitigation, adaptation, and productivity to their represent trade-offs and synergies. The CSI represents the synergy between water use efficiency and GHG mitigation by the implementation of water-oriented adaptation practices in irrigated rice. The SCSI represents the synergy between the progressive improvement of soil and crop productivity under soil-oriented practices. CSI was first calculated for a dataset of existing experiments that assessed several irrigation strategies, and second, for output from a process-based model. SCSI was calculated for a dataset of conservation agriculture experiments. The CSI and SCSI are useful tools to identify and compare climate-smartness across spatial-temporal contexts. The CSI captured the temporal and spatial variability climates-smartness and evidenced the context-dependency of this attribute in so-called “climate-smart practices” (e.g., Alternate Wetting and Drying). SCSI results evidenced the temporal dynamic of climate-smartness in treatments under Conservation Agriculture management. The indices showed the potential to summarised information regarding the performance of soil and water adaptation strategies in cropping systems from existing evidence, both alone and when used with model output. The indices can help to monitor CSA interventions and be complementary in socio-economics assessments or scaling up projections. The results of this thesis contribute to the call to generate reliable and transparent measures of climate smartness. The results of this thesis contribute to the call to generate reliable and transparent measures of climate smartness.