Towards Sustainable Agriculture: A Critical Analysis of Agrobiodiversity Assessment Methods and Recommendations for Effective Implementation
Abstract
:1. Introduction
2. Materials and Methods
- Determine the initial research question and field of inquiry. This study aims to answer two main questions in the field of the development of assessment methods for agricultural systems: What methods were recently developed for the assessment of overall agrobiodiversity? And, what functionalities should be integrated in future investigations of agrobiodiversity performance assessment methods?
- Determine the timeframe. In the resolution taken on the 20 April 2012, for the EU Biodiversity Strategy 2020, the European Parliament gave a lot of attention to agriculture, highlighting the importance of ensuring the conservation of biodiversity and, according to what is feasible, repairing biodiversity damages [27]. For this reason, studies from 2012 to 2023 were selected for our analysis to identify current tendencies in the developed assessment methods.
- Finalize research questions to reflect the timeframe. The initial research questions were maintained.
- Develop a search strategy to find relevant manuscripts. The Web of Science database was consulted to select the relevant literature on the 3 January 2024. The search string used was TITLE: (biodiversity) AND (agriculture OR farm OR crop OR agrobiodiversity OR agro-biodiversity) AND (measure OR algorithm OR “decision support system” OR “decision support tool” OR “decision-making system” OR “decision-making tool” OR assessment OR index OR indicator). From the 70 results obtained, 14 references were selected for our analysis, corresponding to the ones mentioned in Results Section. Since the aim of this study is to identify comprehensive assessment methods of agrobiodiversity, studies on the evaluation of the conservation status of populations or exclusively related to the assessment of the diversity of plants, dietary diversity, or governmental initiatives were not included.
- Analysis of the selected articles. In the Results Section, the similarities across the articles as well as the gaps in the current methods are identified.
- Reflexivity. A state-of-the-art review should explain the subjectivity of the research team in the interpretation of the data by describing the applications of their expertise. Insights on the limitations of this study are described in the Discussion Section.
3. Results
3.1. Comparative Approach
3.2. Composite Indicator Approach
3.2.1. Goals of Assessment Methods
3.2.2. Covered Aspects of Biodiversity
3.2.3. Application Context
3.2.4. Standardization Methods
3.2.5. Aggregation Methods
3.2.6. Weighting Methods
4. Discussion
5. Conclusions
- The development of DSS, providing suggestions to enhance biodiversity performance to minimize interpretation difficulties regarding indicators’ values and priority action areas.
- The use of optimization algorithms, considering local constraints, for realistic guidance on sustainable practices’ implementation for the benefit of biodiversity.
- The inclusion of indicators to monitor the impacts of enhancement practices’ implementation, namely, environmental, social, and economic ones.
- The integration of users’ perceptions in the conception and operation of DSS to overcome communication gaps associated with quantitative methods by adding the experience and knowledge acquired by farmers. Considering the decision maker’s motivations allows for the identification of the key functionalities that effectively promote the implementation of more sustainable practices.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Love, B.; Spaner, D. Agrobiodiversity: Its Value, Measurement, and Conservation in the Context of Sustainable Agriculture. J. Sustain. Agric. 2007, 31, 53–82. [Google Scholar] [CrossRef]
- Kozicka, M.; Gotor, E.; Ocimati, W.; de Jager, T.; Kikulwe, E.; Groot, J.C.J. Responding to Future Regime Shifts with Agrobiodiversity: A Multi-Level Perspective on Small-Scale Farming in Uganda. Agric. Syst. 2020, 183, 102864. [Google Scholar] [CrossRef]
- Jackson, L.E.; Brussaard, L.; de Ruiter, P.C.; Pascual, U.; Perrings, C.; Bawa, K. Agrobiodiversity. Encycl. Biodivers. (Second Ed.) 2013, 1, 126–135. [Google Scholar] [CrossRef]
- FAO. What Is Happening to Agrobiodiversity? Available online: https://www.fao.org/3/y5609e/y5609e02.htm (accessed on 3 October 2023).
- Kawtrakul, A.; Chanlekha, H.; Waiyamai, K.; Kangkachit, T.; D’Orazio, L.; Kotzinos, D.; Laurent, D.; Spyratos, N. Towards Data-and-Innovation Driven Sustainable and Productive Agriculture: BIO-AGRI-WATCH as a Use Case Study. In Proceedings of the 2021 IEEE International Conference on Big Data, Big Data 2021, Virtually, 15–18 December 2021; pp. 3407–3415. [Google Scholar] [CrossRef]
- Vera-Sánchez, K.S.; Parra-Quijano, M.; Nieto-ángel, R.; Barrientos-Pliego, A.F. Multi-Criteria Analysis for the Prioritization of Areas for the in Situ Conservation of Crataegus, L. an Underutilized Fruit Tree in Mexico. Plants 2021, 10, 2561. [Google Scholar] [CrossRef] [PubMed]
- Jones, S.K.; Estrada-Carmona, N.; Juventia, S.D.; Dulloo, M.E.; Laporte, M.A.; Villani, C.; Remans, R. Agrobiodiversity Index Scores Show Agrobiodiversity Is Underutilized in National Food Systems. Nat. Food 2021, 2, 712–723. [Google Scholar] [CrossRef] [PubMed]
- Maas, B.; Fabian, Y.; Kross, S.M.; Richter, A. Divergent Farmer and Scientist Perceptions of Agricultural Biodiversity, Ecosystem Services and Decision-Making. Biol. Conserv. 2021, 256, 109065. [Google Scholar] [CrossRef]
- FAO. Tracking Progress on Food and Agriculture-Related SDG Indicators 2022; FAO: Washington, DC, USA, 2022; ISBN 9789251380130. [Google Scholar]
- Cervelli, E.; Pindozzi, S.; Sacchi, M.; Capolupo, A.; Cialdea, D.; Rigillo, M.; Boccia, L. Supporting Land Use Change Assessment through Ecosystem Services and Wildlife Indexes. Land Use Policy 2017, 65, 249–265. [Google Scholar] [CrossRef]
- Castillo-Díaz, F.J.; Belmonte-Ureña, L.J.; López-Serrano, M.J.; Camacho-Ferre, F. Assessment of the Sustainability of the European Agri-Food Sector in the Context of the Circular Economy. Sustain. Prod. Consum. 2023, 40, 398–411. [Google Scholar] [CrossRef]
- López-Hernández, F.; Cortés, A.J. Whole Transcriptome Sequencing Unveils the Genomic Determinants of Putative Somaclonal Variation in Mint (Mentha L.). Int. J. Mol. Sci. 2022, 23, 5291. [Google Scholar] [CrossRef]
- Santillán-Fernández, A.; Salinas-Moreno, Y.; Valdez-Lazalde, J.R.; Bautista-Ortega, J.; Pereira-Lorenzo, S. Spatial Delimitation of Genetic Diversity of Native Maize and Its Relationship with Ethnic Groups in Mexico. Agronomy 2021, 11, 672. [Google Scholar] [CrossRef]
- Weise, S.; Lohwasser, U.; Oppermann, M. Document or Lose It—On the Importance of Information Management for Genetic Resources Conservation in Genebanks. Plants 2020, 9, 1050. [Google Scholar] [CrossRef] [PubMed]
- Scherf, B.; Baumung, R. Monitoring the Implementation of the Global Plan of Action for Animal Genetic Resources. Biodiversity 2015, 16, 149–156. [Google Scholar] [CrossRef]
- Agrawal, R.C.; Archak, S.; Tyagi, R.K. An Overview of Biodiversity Informatics with Special Reference to Plant Genetic Resources. Comput. Electron. Agric. 2012, 84, 92–99. [Google Scholar] [CrossRef]
- Chandora, R.; Paul, S.; RC, K.; Kumar, P.; Kumar, P.; Sharma, A.; Kumar, A.; Singh, D.; Negi, N.; Lata, S.; et al. Ecological Survey, Population Assessment and Habitat Distribution Modelling for Conserving Fritillaria Roylei—A Critically Endangered Himalayan Medicinal Herb. S. Afr. J. Bot. 2023, 160, 75–87. [Google Scholar] [CrossRef]
- Timler, C.; Alvarez, S.; DeClerck, F.; Remans, R.; Raneri, J.; Estrada Carmona, N.; Mashingaidze, N.; Abe Chatterjee, S.; Chiang, T.W.; Termote, C.; et al. Exploring Solution Spaces for Nutrition-Sensitive Agriculture in Kenya and Vietnam. Agric. Syst. 2020, 180, 102774. [Google Scholar] [CrossRef]
- Boedecker, J.; Odhiambo Odour, F.; Lachat, C.; Van Damme, P.; Kennedy, G.; Termote, C. Participatory Farm Diversification and Nutrition Education Increase Dietary Diversity in Western Kenya. Matern. Child Nutr. 2019, 15, 12803. [Google Scholar] [CrossRef]
- Ribeiro, J.M.P.; Berchin, I.I.; da Silva Neiva, S.; Soares, T.; de Albuquerque Junior, C.L.; Deggau, A.B.; de Amorim, W.S.; Barbosa, S.B.; Secchi, L.; de Andrade Guerra, J.B.S.O. Food Stability Model: A Framework to Support Decision-Making in a Context of Climate Change. Sustain. Dev. 2021, 29, 13–24. [Google Scholar] [CrossRef]
- Santoso, M.V.; Bezner Kerr, R.N.; Kassim, N.; Martin, H.; Mtinda, E.; Njau, P.; Mtei, K.; Hoddinott, J.; Young, S.L. A Nutrition-Sensitive Agroecology Intervention in Rural Tanzania Increases Children’s Dietary Diversity and Household Food Security but Does Not Change Child Anthropometry: Results from a Cluster-Randomized Trial. J. Nutr. 2021, 151, 2010–2021. [Google Scholar] [CrossRef]
- Weerasekara, P.C.; Withanachchi, C.R.; Ginigaddara, G.A.S.; Ploeger, A. Understanding Dietary Diversity, Dietary Practices and Changes in Food Patterns in Marginalised Societies in Sri Lanka. Foods 2020, 9, 1659. [Google Scholar] [CrossRef]
- Valencia, V.; Wittman, H.; Jones, A.D.; Blesh, J. Public Policies for Agricultural Diversification: Implications for Gender Equity. Front. Sustain. Food Syst. 2021, 5, 718449. [Google Scholar] [CrossRef]
- Elmiger, B.N.; Finger, R.; Ghazoul, J.; Schaub, S. Biodiversity Indicators for Result-Based Agri-Environmental Schemes—Current State and Future Prospects. Agric. Syst. 2023, 204, 103538. [Google Scholar] [CrossRef]
- Grant, M.J.; Booth, A. A Typology of Reviews: An Analysis of 14 Review Types and Associated Methodologies. Health Inf. Libr. J. 2009, 26, 91–108. [Google Scholar] [CrossRef] [PubMed]
- Barry, E.S.; Merkebu, J.; Varpio, L. State-of-the-Art Literature Review Methodology: A Six-Step Approach for Knowledge Synthesis. Perspect. Med. Educ. 2022, 11, 281–288. [Google Scholar] [CrossRef] [PubMed]
- Bàrberi, P. Functional Agrobiodiversity. Agric. Sustain. 2013, 3–20. [Google Scholar] [CrossRef]
- Kikas, T.; Bunce, R.G.H.; Kull, A.; Sepp, K. New High Nature Value Map of Estonian Agricultural Land: Application of an Expert System to Integrate Biodiversity, Landscape and Land Use Management Indicators. Ecol. Indic. 2018, 94, 87–98. [Google Scholar] [CrossRef]
- Bassignana, C.F.; Merante, P.; Belliére, S.R.; Vazzana, C.; Migliorini, P. Assessment of Agricultural Biodiversity in Organic Livestock Farms in Italy. Agronomy 2022, 12, 607. [Google Scholar] [CrossRef]
- Switek, S.; Sawinska, Z.; Głowicka-Wołoszyn, R. A New Approach to Farm Biodiversity Assessment. Agronomy 2019, 9, 551. [Google Scholar] [CrossRef]
- Henke, R.; Vaquero-Piñeiro, C. Measuring Agro-Biodiversity through Leverage Factors: Land Use, Farmer Practices and Public Policies. Land 2023, 12, 1499. [Google Scholar] [CrossRef]
- Ruckli, A.K.; Dippel, S.; Durec, N.; Gebska, M.; Guy, J.; Helmerichs, J.; Leeb, C.; Vermeer, H.; Hörtenhuber, S. Environmental Sustainability Assessment of Pig Farms in Selected European Countries: Combining Lca and Key Performance Indicators for Biodiversity Assessment. Sustainability 2021, 13, 11230. [Google Scholar] [CrossRef]
- Pépin, A.; Guidoboni, M.V.; Jeanneret, P.; van der Werf, H.M.G. Using an Expert System to Assess Biodiversity in Life Cycle Assessment of Vegetable Crops. Ecol. Indic. 2023, 148, 110098. [Google Scholar] [CrossRef]
- Soulé, E.; Hawes, C.; Young, M.; Henckel, L.; Michel, N.; Michonneau, P.; Bockstaller, C. A Predictive Indicator Assessing Effect of Cropping System and Surrounding Landscape on Biodiversity. Ecol. Indic. 2023, 151, 289. [Google Scholar] [CrossRef]
- Xu, X.; Qin, C.; Zhu, Y. Developing the Agri-Environment Biodiversity Index for the Assessment of Eco-Friendly Farming Systems. Ecol. Indic. 2022, 142, 109220. [Google Scholar] [CrossRef]
- Tasser, E.; Rüdisser, J.; Plaikner, M.; Wezel, A.; Stöckli, S.; Vincent, A.; Nitsch, H.; Dubbert, M.; Moos, V.; Walde, J.; et al. A Simple Biodiversity Assessment Scheme Supporting Nature-Friendly Farm Management. Ecol. Indic. 2019, 107, 105649. [Google Scholar] [CrossRef]
- Roul, C.; Chand, P.; Pal, S.; Naik, K. Assessment of Agrobiodiversity in the Intensive Agriculture: A Case Study of the Indo-Gangetic Plains of India. Biodivers. Conserv. 2022, 31, 397–412. [Google Scholar] [CrossRef]
- Quintero, I.; Daza-cruz, Y.X. Main Agro-Ecological Structure: An Index for Evaluating Agro-Biodiversity in Agro-Ecosystems. Sustainability 2022, 14, 13738. [Google Scholar] [CrossRef]
- Leyva, Á.; Lores, A. Assessing Agroecosystem Sustainability in Cuba: A New Agrobiodiversity Index. Elementa 2018, 6, 80. [Google Scholar] [CrossRef]
- Turner, P.A.M.; Ximenes, F.A.; Penman, T.D.; Law, B.S.; Waters, C.M.; Grant, T.; Mo, M.; Brock, P.M. Accounting for Biodiversity in Life Cycle Impact Assessments of Forestry and Agricultural Systems—The BioImpact Metric. Int. J. Life Cycle Assess. 2019, 24, 1985–2007. [Google Scholar] [CrossRef]
- Blandi, M.L.; Gargoloff, N.A.; Iermanó, M.J.; Paleologos, M.F.; Sarandón, S.J. The Mental Map as an Instrument to Define Indicators in Complex Systems: An Application on the Environmental Knowledge of Local Horticultural Farmers. Rev. Econ. Sociol. Rural. 2021, 60, 233521. [Google Scholar] [CrossRef]
- Tamburini, G.; Bommarco, R.; Wanger, T.C.; Kremen, C.; van der Heijden, M.G.A.; Liebman, M.; Hallin, S. Agricultural Diversification Promotes Multiple Ecosystem Services without Compromising Yield. Sci. Adv. 2020, 6, eaba1715. [Google Scholar] [CrossRef]
- Dinar, A.; Quinn, N.W.T. Developing a Decision Support System for Regional Agricultural Nonpoint Salinity Pollution Management: Application to the San Joaquin River, California. Water 2022, 14, 2384. [Google Scholar] [CrossRef]
- Ma, K. Kunming-Montreal Global Biodiversity Framework: An Important Global Agenda for Biodiversity Conservation. Biodivers. Sci. 2023, 31, 23133. [Google Scholar] [CrossRef]
Reference | Aim | Covered Aspects of Biodiversity | Standardization Method | Aggregation Method | Weighting Method | Application Context |
---|---|---|---|---|---|---|
[7] | Capture the most relevant dimensions of agrobiodiversity contributing to food system sustainability | Consumption, contributing to healthy diets; agrobiodiversity in production, contributing to sustainable agriculture; and Agrobiodiversity in genetic resource conservation, contributing to current and future use options | Min–max scaling method | Arithmetic mean of the pillar scores | Equal weights | Eighty countries around the world, using globally available public datasets |
[30] | Assess farm biodiversity according to farmers’ perspectives | Farm attractiveness for: pollinators; wild game; birds; amphibians and reptiles; rodents; and non-crop plants | Values of 1 (very unattractive) and 5 (very attractive) and min–max scaling method | TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method | Equal weights | A total of 273 complete interviews with farmers across Poland were used for the analysis |
[31] | Evaluate agrobiodiversity through leverage factors at the territorial (regional) level | Land use strategies, agriculture practices, and common agricultural policy funds | Min–max scaling method | Arithmetic mean of the sub-indicators | Equal weights | Farm Account Data Network (FADN) 2020 database for Italian farms |
[32] | Combine life cycle assessment (LCA) with key performance indicator (KPI) assessment focusing on biodiversity in order to examine the environmental impacts of different pig farm types | Ecosystem (habitat) diversity; species (flora and fauna; number of species; and abundance) diversity; and genetic diversity | Benchmark method | Arithmetic mean of the sub-indicators | Expert weights | Different pig farm types (13 breeding, 23 finishing, and 27 breeding-to-finishing farms) in Austria, Finland, Germany, Italy, the Netherlands, the United Kingdom, and Poland |
[33] | Assess and compare impacts on the biodiversity of vegetable production systems as a function of farming practices and the local context | 11 indicator species groups (ISG) (crop flora, grassland flora, birds, small mammals, amphibians, snails, spiders, carabid beetles, butterflies, wild bees, and grasshoppers); coefficient of the habitat’s potential for hosting each ISG (Chabitat); coefficient of the influence of a management practice in each ISG (Cmanagement); and direct impact of each management option in a given habitat on the population of each of the 11 ISGs (R) | R × ((Chabitat + Cmanagement)/2), where Chabitat is on a scale from 0 to 10, Cmanagement is on a scale from 0 to 10, and R is on a scale from 0 to 5 | Additive aggregation method | Use of areas of the fields as weights | Case study of an organic vegetable farm in Brittany, France |
[34] | Develop a new indicator, I-BIO, aiming to predict the impacts of management practices on the overall biodiversity at the field level | Microorganisms; vegetation; invertebrates; and vertebrates | Indicators are converted to a qualitative class | “If–then” linguistic rules | DEXi-CSC model software calculated the weights by transforming qualitative classes (manually verified) into quantitative ones. The mean of the input variables corresponded to the relative weights of each basic indicators. | Three case studies at the field-level in Scotland and France |
[35] | Propose a simplified, rapid assessment method of biodiversity performance to guide the improvement of self-management capabilities in eco-friendly farms | Animal biodiversity; plant diversity; invasive species; habitat; and educational activities | Values for each indicator were scaled between 0 and 1 (dimensionless) | Additive | Equal weights | A total of 9 best-practice farms from a total of >300 eco-friendly farms in China |
[36] | Present a biodiversity assessment scheme for farmland to detect the impact associated with land-use practices, combining compositional (faunal and floral) and structural aspects, which can assist the monitoring of result-oriented measures | Flower color index; butterfly abundance; landscape structuring degree; and patch diversity index | Min–max scaling method | Additive | Not specified | Forty-four farms in five countries (France, Switzerland, Germany, Italy, and Austria) |
[37] | Develop an agroecosystem diversity index to identify the status and challenges and offer suggestions to conserve and enrich agrobiodiversity | Landscape diversity; genetic and species diversity; agrobiodiversity threats; and societal response | Benchmark method | Additive | Average of three weights, namely, equal weights, expert weights, and PCA weights | Indo-Gangetic Plains of India (Punjab and Haryana) |
[38] | Describe the agrobiodiversity of agroecosystems, considering the management and conservation practices and the producer’s perceptions, awareness, and ability to promote sustainable practices in a farm context | Connection with the main ecological structure of the landscape; extension of external connectors; diversity of external connectors; extension of internal connectors; diversity of internal connectors, land use; management practices; conservation practices; perception, awareness, and knowledge; and action capacity | Values for each indicator were expressed on the ordinal scale, from 0 to 10 | The score for each category was obtained by the arithmetic mean of the indicators that composed it, and the composite index was obtained by summing the values of each category | Differential weights for each criterion could be considered, according to applicational needs | Not specified |
[39] | Development of a new index of agrobiodiversity (IDA) to identify the extent to which agroecosystems are sustainable, based on their agrobiodiversity | Biodiversity for human diet; biodiversity for animal feed; biodiversity to improve soils; and complementary and associated biodiversity for non-dietary measures | Max scaling method | Arithmetic mean of the sub-indicators | Equal weights | Agroecosystems in Cuba’s urban agriculture movement |
[40] | Propose a metric (BioImpact) that incorporates biodiversity and the complexity of ecological interactions and processes using dialogue and data, with the strength of the LCA framework | Connectivity (fragmentation, isolation, gene flow); interactions (invasive species, and natural disturbance regimes); anthropogenic disturbance regime impacts (frequency, duration, intensity, extent, recovery × frequency, and succession); habitat structure (ecosystem function, and resilience); and threatened communities and species | Six risk levels (from no risk to very high risk). | Additive aggregation method | Expert weights | Four agricultural production systems in Australia |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Marcelino, S.M.; Gaspar, P.D.; do Paço, A.; Lima, T.M.; Monteiro, A.; Franco, J.C.; Santos, E.S.; Campos, R.; Lopes, C.M. Towards Sustainable Agriculture: A Critical Analysis of Agrobiodiversity Assessment Methods and Recommendations for Effective Implementation. Appl. Sci. 2024, 14, 2622. https://doi.org/10.3390/app14062622
Marcelino SM, Gaspar PD, do Paço A, Lima TM, Monteiro A, Franco JC, Santos ES, Campos R, Lopes CM. Towards Sustainable Agriculture: A Critical Analysis of Agrobiodiversity Assessment Methods and Recommendations for Effective Implementation. Applied Sciences. 2024; 14(6):2622. https://doi.org/10.3390/app14062622
Chicago/Turabian StyleMarcelino, Sara M., Pedro Dinis Gaspar, Arminda do Paço, Tânia M. Lima, Ana Monteiro, José Carlos Franco, Erika S. Santos, Rebeca Campos, and Carlos M. Lopes. 2024. "Towards Sustainable Agriculture: A Critical Analysis of Agrobiodiversity Assessment Methods and Recommendations for Effective Implementation" Applied Sciences 14, no. 6: 2622. https://doi.org/10.3390/app14062622