Abstract

Meeting future global staple crop demand requires continual productivity improvement. Many performance indicators have been proposed to track and measure the increase in productivity while minimizing environmental degradation. However, their use has lagged behind theory, and has not been uniform across crops in different geographies. The consequence is an uneven understanding of opportunities for sustainable intensification. Simple but robust key performance indicators (KPIs) are needed to standardize knowledge across crops and geographies. This paper defines a new term 'agronomic gain' based on an improvement in KPIs, including productivity, resource use efficiencies, and soil health that a specific single or combination of agronomic practices delivers under certain environmental conditions. We apply the concept of agronomic gain to the different stages of science-based agronomic innovations and provide a description of different approaches used to assess agronomic gain including yield gap assessment, meta-data analysis, on-station and on-farm studies, impact assessment, panel studies, and use of subnational and national statistics for assessing KPIs at different stages. We mainly focus on studies on rice in sub-Saharan Africa, where large yield gaps exist. Rice is one of the most important staple food crops and plays an essential role in food security in this region. Our analysis identifies major challenges in the assessment of agronomic gain, including differentiating agronomic gain from genetic gain, unreliable in-person interviews, and assessment of some KPIs at a larger scale. To overcome these challenges, we suggest to (i) conduct multi-environment trials for assessing variety × agronomic practice × environment interaction on KPIs, and (ii) develop novel approaches for assessing KPIs, through development of indirect methods using remote-sensing technology, mobile devices for systematized site characterization, and establishment of empirical relationships among KPIs or between agronomic practices and KPIs.

Highlights

  • Meeting the future global demand for staple crops produced sus­ tainably is a challenge

  • An example of a two-way analysis of variance model in randomized control block design is as follows: Yijk = μ + Gi + Mj + (GM)ij + eij where Yijk is the given indicator of a plot, μ is the experiment mean, Gi is the effect of the ith variety, Mj is the effect of the jth agro­ nomic practice, (GM)ij is the interaction of the ith variety and the jth agronomic practice, and eij is the residual

  • This study presented a definition of agronomic gain and described different approaches for its assessment in four different stages

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Summary

Introduction

Meeting the future global demand for staple crops produced sus­ tainably is a challenge. The traits of interest can differ among breeding programs based on their priorities, but typical traits considered in rice (Oryza spp.) breeding programs in SSA include yield, adaptation to climate change including tolerance to abiotic and biotic stresses, and grain quality (Saito et al, 2018; Futakuchi et al, 2021) They do not often include resource (i.e., nutrient and water) use efficiencies, weed competitive­ ness, and traits that could improve soil fertility (e.g., higher straw pro­ duction for improving soil fertility). The objectives of this paper are to: (1) define agronomic gain and provide a conceptual framework for its assessment; (2) describe different approaches for assessing agronomic gain at different stages of the research process on agronomic innovations from benchmarking of current situations to development and piloting of innovations, and impact assessment of innovations across different scales from field to subnational and national levels; (3) identify the challenges in assessment of agronomic gain; and (4) suggest further research areas for assessing the agronomic gain for rice in SSA in different stages of the research process for agronomic innovations at different scales

Prioritization of agronomic gain key performance indicators
Definition of agronomic gain
Agronomic vs genetic gain
Agronomic gain in relation to environmental conditions
Agronomic gain in relation to the social context
Stages of the research process for assessment of agronomic gain
Global Yield Gap and Water Productivity Atlas
Baseline and diagnostic surveys
Meta-analysis
Testing of improved agronomic practices in on-station and on-farm trials
Participatory on-farm trials
Ex-ante impact assessment study
Panel studies and ex-post adoption and impact assessment
Use of data from sub- or national statistics
Key challenges and opportunities for assessment of agronomic gain
Differentiating agronomic gain from genetic gain
Unreliable in-person interviews
Difficulty in assessing some KPIs at a larger scale
Potential use of indirect assessment for KPIs via agronomic practices
Limited data on rice agronomic gain KIPs in SSA
Findings
Conclusions
Full Text
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