Sustainable intensification of agriculture

Main thematic areas of sustainable intensification. Figure from Grewer & Rodriguez (2019).

Agriculture often negatively impacts on the natural environment. Sustainable intensification of agriculture, which aims to increase crop yields with fewer inputs and without expanding land use, seeks to balance these priorities.

Sustainable intensification is applicable to all agricultural systems including industrial agriculture in temperate climates and smallholder agriculture in the tropics. A large variety of sustainable intensification approaches and processes have been described in scientific literature.

Our research structurally reviews the sustainable intensification approaches available to date. With cross-disciplinarity being at the core of the sustainable intensification concept, we particularly review:

What scientific disciplines are identified in the research?
What combination of sustainability dimensions are considered?
What aspects of research design cross-disciplinarity are achieved?
Which type of empirical data are combined?

This review informs the future design of research on sustainable intensification processes.

Conceptual definitions and indicator frameworks

The multi-dimensional and cross-disciplinary nature of sustainable intensification has been well identified by scientific literature on conceptual definitions and indicator frameworks. The reviewed conceptual definitions focus on the traditional core aspects of productivity, environmental sustainability and resource efficiency, as well as including economic, social and institutional aspects. The figure above presents the wide range of thematic focal areas identified across the reviewed literature.

The reviewed indicator frameworks propose a comprehensive range of indicators that allow the assessment of farming systems across a diverse range of dimensions relevant for sustainable intensification.

Understanding current drivers – simulating future trends

Identification of core drivers of sustainable intensification processes in a cross-disciplinary and multi-dimensional manner was not well identified in the scientific literature.

Improved understanding of which current food system trends can enable or block sustainability is crucial for designing effective intervention strategies. However, the main interdisciplinary focus that was identified in the literature was limited to blending selected environmental issues with profitability of farming systems. Social and institutional outcomes are only seldom considered.

The majority of reviewed analyses are further limited by using a single research method from one scientific discipline. The most frequently strategy to achieve a cross-disciplinarity approach is the utilisation of variables from different disciplines within singular research methodologies.

The genuine combination of multiple research methodologies from different disciplines is rare. The integrated use of bio-physical and econometric simulation models is one of the few exemptions. The below tables from Grewer & Rodriguez (2019) provide an overview of the identified methodological approaches.

a) Explanatory studies: Explaining current drivers
Research method	Scientific disciplines	Form of cross-disciplinarity	Data types
Reduced-form regression models	Econometrics	Using economic, agronomic, environmental, and institutional variables within econometric models	Structured household survey data
ANOVA of agronomic field trial	Agronomy, statistics	Using experimental agronomic data within statistical analysis	Split-plot experimental data
Production function models	Econometrics	Using economic, agronomic, environmental, and institutional variables within econometric models	Structured household survey data
Qualitative farmer evaluation	Participatory research	Using a diversity of variables and sustainability dimensions within qualitative evaluation	Semi-structured and open-ended household surveys
Econometric household models	Econometrics	Using economic, agronomic, environmental, and institutional variables within econometric models	Structured household survey data
Theoretical economic models	Theoretical economics	Using economic, agronomic, environmental, and institutional variables within economic models	NA

b) Simulation studies: simulating future trends
Research method	Scientific disciplines	Form of cross-disciplinarity	Data types
Mathematical programming	Operations research	Using economic, agronomic, environmental, and institutional variables within optimization models	Structured household survey data
Bio-geo-chemical modelling	Bio-geo-chemical cycling	Using bio-physical and agronomic data within process-based models	Global bio-physical satellite data; local agricultural management data and agronomic field data
Computable General Equilibrium models	Agricultural economics	Using economic, agronomic, environmental, and institutional variables within equilibrium models	Global and national production, market and trade statistics
Bayesian networks analysis	Bayesian statistics	Using estimates of practice impacts from various sources within a Bayesian model	Mix of stakeholder evaluation, expert knowledge and secondary data
Econometric models	Econometrics	Using economic, agronomic, environmental, and institutional variables within econometric models	Structured household survey data
Participatory scenario development	Agronomy	Using a diversity of variables and sustainability dimensions within participatory simulation	Semi-structured and open-ended household surveys

Strengthening the dynamic feedbacks between such economic and bio-physical methodologies and extending methodological integration to other sustainability dimensions are promising avenues for future research in the field.

Contact Uwe Grewer for more information.