CONSTRUCTION OF AN INDEX SYSTEM FOR SUSTAINABILITY ASSESSMENT IN SMALLHOLDER FARMING SYSTEMS

doi:10.15302/J-FASE-2022463

Front. Agr. Sci. Eng.

2022, Vol. 9

Issue (4) : 511-522 https://doi.org/10.15302/J-FASE-2022463

REVIEW

CONSTRUCTION OF AN INDEX SYSTEM FOR SUSTAINABILITY ASSESSMENT IN SMALLHOLDER FARMING SYSTEMS

Xiaoxia GUO, Chong WANG(

), Fusuo ZHANG

College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing 100193, China

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Abstract

● A five-step process for quantifying smallholder farming system sustainability is proposed.

● Definition of system boundary, functional unit, and indicators depends on research issues.

● Weighting, conversion, and aggregation methods tightly relates to the validity of assessment results.

Smallholder farming systems are important for global food security, but these faces multiple environmental challenges hindering sustainable development. Although sustainable smallholder agriculture issues have been widely discussed and addressed by scientists globally, harmonized approaches in evaluating sustainability are still lacking. This paper proposes a five-step process for constructing a sustainability assessment method for smallholder farming systems, namely definition of system boundary, indicator selection, indicator weighting, indicator conversion, and indicator aggregation. The paper summarizes the state-of-art progresses in agricultural sustainability assessment at different stages, and systematically discussed the benefits and limitations of weighting and aggregation methods. Overall, this evaluation process should be useful by providing rational and comprehensive results for quantifying the sustainability of smallholder farming systems, and will contribute to practice by providing decision-makers with directions for improving sustainable strategies.

Keywords indicator aggregation multi-indicator smallholder agriculture sustainability assessment weighting method

Corresponding Author(s): Chong WANG

Just Accepted Date: 22 July 2022 Online First Date: 15 August 2022 Issue Date: 07 November 2022

Cite this article:

Xiaoxia GUO,Chong WANG,Fusuo ZHANG. CONSTRUCTION OF AN INDEX SYSTEM FOR SUSTAINABILITY ASSESSMENT IN SMALLHOLDER FARMING SYSTEMS[J]. Front. Agr. Sci. Eng. , 2022, 9(4): 511-522.

URL:

https://academic.hep.com.cn/fase/EN/10.15302/J-FASE-2022463
https://academic.hep.com.cn/fase/EN/Y2022/V9/I4/511

Fig.1 Role of sustainability assessment in agricultural multi-objective development. Constructing a sustainable evaluation system would be helpful to evaluate the sustainable level when applying agricultural technology combinations to farmlands, and it can provide guidance for redesigning technology combination to achieve multi-objective development mode.

Fig.2 A five-step process for constructing a sustainability assessment method for smallholder farming systems, which includes the definition of system boundary & functional unit, indicator selection, indicator weighting, indicator conversion, and indicator aggregation.

Fig.3 System boundaries and functional unit of smallholder farming system. The system boundaries are set as extraction of minerals and fossil fuels through to crop harvest. Specifically, smallholder farming systems consist of agricultural material production subsystem and farming subsystem.

Tab.1 Typical evaluation dimensions and indicators of agricultural sustainable assessment

Method name	Formula	Explanation	Characteristic
Min-max normalization	$x ′ i = x i − m i n (x i) m a x (x i) − m i n (x i) (10)$ $x ′ i = m a x (x i) − x i m a x (x i) − m i n (x i) (11)$	Equation (10) for positive indicators and Eq. (11) for negative indicators. Max(x_i) and min(x_i) represent the best performance and the worst performance of indicator values, respectively^[⁷⁹^]	It is a unity-based normalization method, and it is used to bring all values into the range [0, 1]
Z-score standardization	$x ′ i = x − μ δ (12)$	In Eq. (12), μ represents the mean value, and $δ$ represents the standard deviation	Works well when populations are normally distributed

Tab.2 Common methods for indicator normalization

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