Assessment Of Agricultural Sustainability Research Articles

Agricultural sustainability assessment and national policy-making using an axiomatic mathematical model

Agriculture is a basic activity of human survival and its sustainability is of the utmost importance. In this paper, we present a novel mathematical model for the assessment of agricultural sustainability, based on certain intuitive and fundamental postulates. The resulting mathematical framework generalizes most existing models. We then rank 148 countries according to their agricultural sustainability, using data from the period 1995 to 2022 and pinpoint those aspects with the highest potential of improving sustainability using sensitivity analysis. Our findings demonstrate that farming is mostly unsustainable worldwide. The most crucial factors are connected with bad water use, destruction of biodiversity, and the prevalence of conventional chemical farming. As expected, poor countries make the bottom of the sustainability list. Surprisingly, several advanced countries such as Belgium, Japan, the Netherlands and Norway also occupy the bottom of the list because of very high emissions and bad performance concerning land and biodiversity. Finally, the highest score worldwide is of the order of only 70% and the median 50%, which exhibit a rather unsatisfactory farming state of affairs. Such findings ought to guide immediate action to make agriculture more sustainable.

Energy, economic, and environmental (3E) assessment of the major greenhouse crops: MFCA-LCA approach.

In order to develop sustainable production of greenhouse crops, the economic, energy, and environmental aspects of production should be considered. The purpose of this study was to evaluate the economic, energy, and environmental (3E) sustainability of cucumber, tomato, and bell pepper production in greenhouses by performing material flow cost accounting (MFCA) and life cycle assessment (LCA) material and methods. Calculating the economic and energy value of losses in agricultural sustainability assessment studies is not common. Using the LCA method alone does not allow us to calculate the monetary and energy value of waste. If this method is used simultaneously with MFCA, this gap will be filled. The system boundary for LCA was from cradle to farm, and for MFCA, foreground processes were considered. The production of each crop was compared at the level of 1000 m2 during 1year. Data were collected through questionnaire-based interviews. The gross value of production for cucumber, tomato, and bell pepper were 8982, 16387, and 17610 $/1000 m2, respectively. The negative production of cucumber, tomato, and bell pepper were 702, 718, and 449 $/1000 m2, respectively. The benefit-to-cost ratio in the production of cucumber, tomato, and bell pepper was calculated as 2.8, 5.17, and 5.8, respectively. The economic productivity in the production of cucumber, tomato, and bell pepper was calculated at 10.25, 7, and 4.4kg/$. Labor cost was the main cost in the production of all three crops. The total input energy for the production of cucumber, tomato, and bell pepper was estimated to be 99.4, 123.1, and 164.6 GJ/1000 m2, respectively. Negative products in the production of cucumber, tomato, and bell pepper were obtained at - 24.2, - 23.9, and - 13.5 GJ/1000 m2, respectively. The energy productivity of cucumber, tomato, and bell pepper was calculated as 0.23, 0.26, and 0.08kg/MJ, respectively. The specific energy indices were 4.32, 3.79, and 12.20MJ/kg for cucumber, tomato, and bell pepper, respectively. The energy ratio in the production of tomato (0.02) was higher than bell pepper (- 0.02) and cucumber (- 0.06). From the perspective of energy, electricity was recognized as the hotspot for the production of three crops. Global warming (GWP100a), ozone layer depletion (ODP), acidification (AC), and eutrophication (EP) indices were calculated for all three crops. Tomato production was ranked first in all impact categories. On-farm emissions and electricity consumption were identified as environmental hotspots. The subsidized price of electricity, natural gas, and chemical fertilizers has led to their excessive use in the production of greenhouse plants. It can be concluded that bell pepper has the best performance from an economic point of view. However, its production is not justified in terms of energy. Tomato was ranked first in terms of energy, and cucumber was ranked first in terms of low environmental impacts. The production of these plants with energy and chemical fertilizer subsidies is currently cost-effective. If the prices are corrected, the production of these plants will face serious challenges. Producing electricity from sunlight and mechanizing production processes can be a solution to these challenges.

Developing a Composite Indicator for Agricultural Sustainability Assessment in the Fes-Meknes Region, Morocco

Developing a Composite Indicator for Agricultural Sustainability Assessment in the Fes-Meknes Region, Morocco

A water-land-energy-carbon nexus evaluation of agricultural sustainability under multiple uncertainties: The application of a multi-attribute group decision method determined by an interval-valued intuitionistic fuzzy set

The interactions between agricultural subsystems, resource scarcity and ecological degradation increase the need for agricultural sustainability assessments in relation to water, land, energy and carbon emissions. In addition, uncertainties caused by natural factors and human activities complicate sustainability assessments. Therefore, this study proposes a regional agricultural sustainability assessment method based on the water-land-energy-carbon (WLEC) nexus system under high uncertainty. This method couples the interval-valued intuitionistic fuzzy set (IVIFS) and multi-attribute group decision making (MAGDM) methods to quantitatively characterise and assess element management and corresponding relationships within the agricultural WLEC nexus system in a multidimensional manner. The paper is able to (1) clarify the role of the coupled agricultural “water-land-energy-carbon” nexus and (2) address the double ambiguity and uncertainty in the indicators and more objectively reflect the interval fluctuations of natural resources and social activities in both positive and negative scenarios. (3) Sustainability management of agricultural resources is carried out according to different regional conditions. The method was applied to Heilongjiang Province in northeast China, and the coupling of agricultural WLEC nexus system in 13 administrative regions of the study area was quantitatively analysed. The degree of agroecological sustainability from a multidimensional perspective is higher in Daxinganling and lower in the provincial capital, Harbin. The recommended range of groundwater use in the study area is [16%,28%] to ensure ecological sustainability when total agricultural output fluctuates between 285,395.18 and 404,495.33 billion yuan. This methodology helps policy makers understand the overall dynamics of agricultural sustainability in an environment of multiple uncertainties and gives suggestions for better assessing management.

Assessment of Agricultural Sustainability: Case Study of Baltic States

The agri-food sector is the world’s largest economic sector having the biggest impact on environment. According to the Food and Agriculture Organization of the United Nations (FAO), sustainable agriculture must meet the needs of present and future generations while ensuring profitability, environmental health and social and economic equity. Therefore, agricultural systems need transition to become more sustainable. The use of renewables in agriculture allows to reduce energy costs and GHG emissions and implement cyclic economy principles. Sustainability assessment of agriculture can be done by applying various sets of indicators. The sustainability assessment of agriculture can be performed on country and micro level. The paper presents assessment of agricultural sustainability of Baltic States based on indicators framework and applies MCDA tool for ranking of Baltic States in terms of agriculture sustainability.

Spatiotemporal Dynamics of Agricultural Sustainability Assessment: A Study across 30 Chinese Provinces

Agricultural sustainability is crucial for ensuring food security, promoting economic development, maintaining ecological balance, and achieving sustainable development goals. In this study, based on relevant theories of agricultural sustainability, an analytical framework is constructed for agricultural sustainability encompassing economic, resource, environmental, and social dimensions. The Analytic Network Process (ANP) method is employed to determine indicator weights and assess the spatiotemporal changes in agricultural sustainability levels across Chinese provinces. The findings reveal that environmental quality is the primary dimension for assessing agricultural sustainability, and the significance of the rural social development dimension is continuously increasing. Although the sustainability levels have significantly improved in various regions of China, there remain issues of development imbalance and instability. In conclusion, this paper offers a comprehensive understanding of the spatiotemporal changes in agricultural sustainability across Chinese provinces, providing valuable insights for policymakers and researchers.

Land Use Indicators in the Context of Land Use Efficiency

In recent decades, the land use changes induced by various economic activities in agricultural ecosystems have affected many aspects of human life. This is the reason why land use change is considered as one of the agriculture-related environmental impacts in a sustainability assessment of food and bio-based products. At the same time, the methodology applied for the quantification of land use change effects is still under intensive research, stimulating scientific discussions. The overall objective of this paper is to fill the gap in knowledge of responsible and sustainable land use management. Specifically, the research provides a comprehensive set of land use change indicators in the context of land use change and land use efficiency. The indicators can be measured based on publicly available databases with the applicability to agricultural sustainability assessment of land use change on a local, regional and global scale. The high share of artificial land and dominant agricultural use of land with low land use intensity were noted in Belgium, Luxemburg, Netherlands, Slovenia, Cyprus, Croatia, Finland, Germany, and United Kingdom. However, land use efficiency was also low. In turn, heterogeneous land cover (but less artificial areas than in other EU countries) and heterogeneous land uses with diverse land use intensity were noted in Austria, Bulgaria, Denmark, Estonia, France, Hungary, Ireland, Italy, Latvia, Lithuania, Malta, Poland, Portugal, Romania, Slovakia, Spain, and Sweden. The challenge in future research could be aggregation of different indicators in assessing the similarity of land use between countries.

Stakeholder’s practices for the sustainability assessment of professional urban agriculture reveal numerous original criteria and indicators

With the rapid growth of professional intra-urban agriculture (PIUA) projects in the Global North, sponsors, projects leaders, and experts developing these projects are seeking to evaluate their sustainability. As existing assessment tools are not adapted to PIUA projects, they establish their own assessment practices. Our study examines these practices to identify their original features, criteria, and indicators used. To this end, we analysed 19 case studies of different PIUA projects. We identified four dimensions underpinning sustainability assessment, namely, internal sustainability, external sustainability, the project leader’s credibility, and the innovative nature of the project. We also shed light on the wide diversity of the 67 assessment criteria identified, as well as the qualitative nature of 78% of indicators used. In addition, our study highlights that assessment practices evolve over time as the project progresses from ideation to implementation, according to the variety of assessment situations. Our study is the first to provide an in-depth exploration of PIUA stakeholders’ sustainability assessment practices and to shed light on their specific features. Our results afford a better understanding of the way the sustainability of PIUA projects is assessed and contribute to reflection on the design of a flexible assessment tool, considering the diverse criteria and practices used by stakeholders to assess the sustainability of PIUA.

A non-compensatory approach to the creation of composite indices of agricultural sustainability of the European Union countries

The paper aims to calculate and evaluate the degree of achieved agricultural sustainability in the countries of the European Union. Agriculture is a specific activity that, on the one hand, must provide sufficient amounts of food for the population, while on the other hand, it can have a significant negative impact on the environment. The assessment of agricultural sustainability was made by selecting ten relevant indicators from the Eurostat and OECD databases, and by creating a composite index using the Mazziotta-Pareto method. The research results show that the Northern Europe countries (Sweden, Finland and Denmark) achieve the highest level of agricultural sustainability, mainly due to the high participation of organic agricultural production, as well as the low level of soil erosion, considering all European countries. On the contrary, countries with high agricultural economic performance show significantly worse results in terms of sustainability (such as Italy, Spain and France).

Sustainability assessment methodology oriented to soil-associated agricultural experiments

Summary A variety of established tools are available for agricultural sustainability assessment at global, regional, and farm geographical scales. However, no assessment has been reported in research literature to indicate their ability to provide insights about the most sustainable cropping system at plot level or experimental unit. Despite the environmental and social importance of soil in agricultural systems, many of the sustainability assessments use few or no indicators related to soil properties or processes. Hence, we propose a sustainability assessment methodology oriented to soil-associated agricultural experiments (SMAES) by defining its parameters through simulations and testing the methodology with real data from a fertilization tomato experiment with five treatments: chemical control (CR); organic control (OR); and organic:chemical ratios (OR) of 25:75, 50:50, and 75:25. The distance from the maximum, principal component analysis, and product of weighted indicator techniques were chosen for normalization, weighting, and aggregation in a single index process, respectively. Applying the SMAES methodology, the sustainability level of the treatments followed this sequence: CR (0.95) > O25:C75 (0.73) > O50:C50 (0.60) > O75:C25 (0.55) > OR (0.45). The proposed SMAES methodology allows soil researchers to define the best treatment through the interaction of the environmental, social, and economic dimensions of agricultural systems.

Assessing the sustainability of agricultural production - a cross-sectoral comparison of the blackberry, tomato and tree tomato sectors in Ecuador

ABSTRACT Achieving a sustainable agricultural system requires an effective assessment of economic, environmental, and social conditions. Existing research has mainly focused on a single sector and lacks critical comparisons of determinants and trade-offs between sustainability dimensions. This study adopts a cross-sectoral perspective to assess and compare the sustainability performance of fruit and vegetable farms in Ecuador. Furthermore, the study analyzes the relation between sustainability outcomes on the one hand and farm resources, farmer demographics, and farm size on the other. Principal Component Analysis and a General Linear Regression model are used in the analysis. The results show that clear disparities in sustainability performance exist between different fruit and vegetable sectors. Furthermore, on average, farms with a higher social- and economic-oriented sustainability performance are more proficient in exploiting conventional resources (land and labour). In contrast, farms with a higher environmental-oriented performance display better handling of ecological resources such as water and forests. A sustainable use of resources is needed to prevent conflicts between sustainability dimensions. The research provides novel insights to stakeholders on the integrated assessment of agricultural sustainability and its results.

Assessing the association of sustainable agriculture with rural household food security (considering ecological, economic, and social aspects).

Due to the importance of sustainability in the world, we follow the missing pieces of the puzzle of sustainable agriculture and food security as a huge gap in the literature. To achieve this goal, a survey was analyzed to assess the linkage between these two concepts in the rural areas in Lorestan province in Iran. The status of food security was assessed using US Household Food Security Survey Module (HFSSM). A standard questionnaire extracted from the related literature was applied to calculate sustainability indicators. The results showed that the status of food security among households with no children (70%) was better than those with one or more children (28%). According to the Tobit model, none of the sustainability dimensions had a significant association with food insecurity. The results support the fact that the assessment of agricultural sustainability at the microeconomic level, short-term period, and a small, specified location cannot lead to reliable results due to the similar behaviors of farmers in these areas. The low level of agricultural sustainability is due to the lack of macroeconomic policies in the region to promote and disseminate the principles of sustainability, lack of plans and actions to promote sustainability by stakeholders and policymakers, ignorance of the target community, as subsistence producers who fall into a deprivation trap. The results suggest that policymakers should use two short-term and long-term strategies to improve the level of agricultural sustainability and increase food security status.

Assessment of Agricultural Sustainability in the Indo-Gangetic Plains of India: An Application of the Indicator Framework

Assessment of Agricultural Sustainability in the Indo-Gangetic Plains of India: An Application of the Indicator Framework

Assessment of Agricultural Sustainability of Bahariya Oasis using Geo-informatics techniques

Assessment of Agricultural Sustainability of Bahariya Oasis using Geo-informatics techniques

Regional agricultural sustainability assessment in China based on a developed model

Regional agricultural sustainability assessment in China based on a developed model

Using accounting dataset for agricultural sustainability assessment through a multi‐criteria approach: an Italian case study

AbstractSustainability has become a key issue for agricultural systems. In this context, sustainability assessment implies considering different aspects of agricultural activities, including environmental, economic and social issues, and it is usually based on practice‐based indicators. However, proposed methods and applications are still rare due to the difficulty to build a strong general framework and to find appropriate data. The present work develops a performance indicator framework using farm accounting data from the Italian Agricultural Accounting Information Network and proposes a multi‐criteria approach based on the multiple reference point weak‐strong composite indicators (MRP‐WSCI) method to assess the degree of sustainability at the farm level. This method, widely applied to sustainability assessment, calculates the relative rankings including the weak and strong approaches to sustainability and facilitates an understanding of the different indices used, but it is still unused in the agricultural sector. The proposed evaluation framework shows that MRP‐WSCI can handle a holistic set of indicators and rank the farms according to their level of sustainability, making it suitable for the agricultural farm sustainability assessment. Our results show that none of the farms in the case study sample can be classified as “good,” according to some of the indices produced, that is, the strong–strong and strong–weak index, while just two of them are good for the weak–weak index. Although the size of the sample used is small, the methodological approach proposed can be straightforwardly extended to larger samples. This methodology can provide information regarding the sustainability of farms to regional and/or national agricultural offices, agricultural extension workers, and farmers themselves, helping in understanding issues in specific dimensions.

Assessment of agricultural sustainability in European Union countries: a group-based multivariate trajectory approach

Sustainability of agriculture is difficult to measure and assess because it is a multidimensional concept that involves economic, social and environmental aspects and is subjected to temporal evolution and geographical differences. Existing studies assessing agricultural sustainability in the European Union (EU) are affected by several shortcomings that limit their relevance for policy makers. Specifically, most of them focus on farm level or cover a small set of countries, and the few exceptions covering a broad set of countries consider only a subset of the sustainable dimensions or rely on cross-sectional data. In this paper, we consider yearly data on 12 indicators (5 for the economic, 3 for the social and 4 for the environmental dimension) measured on 26 EU countries in the period 2004–2018 (15 years), and apply group-based multivariate trajectory modeling to identify groups of countries with common trends of sustainable objectives. An expectation-maximization algorithm is proposed to perform maximum likelihood estimation from incomplete data without relying on an explicit imputation procedure. Our results highlight three groups of countries with distinguished strong and weak sustainable objectives. Strong objectives common to all the three groups include improvement of productivity, increase of personal income in rural areas, reduction of poverty in rural areas, increase of production of renewable energy, rise of organic farming and reduction of nitrogen balance. Instead, enhancement of manager turnover and reduction of greenhouse gas emissions are weak objectives common to all the three groups of countries. Our findings represent a valuable resource to formulate new schemes for the attribution of subsidies within the Common Agricultural Policy (CAP).

A composite index for assessment of agricultural sustainability: the case of Iran.

Nowadays, sustainable agriculture is globally considered a path to achieving the goals of sustainable development. Assessing the sustainability of agriculture can help develop better policies and plans that contribute to sustainable agriculture. Hence, the present study developed new composite indices to assess regional and national agricultural sustainability over time using a four-stage process. The indices were used to conduct an empirical assessment of agricultural sustainability in Iranian provinces during 2007-2017. Initially, 24 individual sustainability indicators were determined in economic, social, and environmental dimensions. Then, the indicators were normalized using the min-max method and were weighted using the pairwise comparisons and Analytic Hierarchy Process method. Finally, the composite economic, social, and environmental, and overall indices were constructed using the weighted sum of indicators and the most important results were analyzed using sustainability maps and diagrams. The individual indicators show the current status of resources used in the agriculture sector, and the constructed composite indices can be helpful tools for policymakers and planners to identify the strengths and weaknesses of agriculture in achieving sustainable development, comparing different regions in terms of agricultural sustainability status, evaluating agricultural sustainability over time, and directing agricultural policies. The empirical results indicated that sustainability statuses were different and following an irregular trend in Iranian provinces (except for the economic index in Kohgiluyeh and Boyer-Ahmad province and social index in Hamedan and Gilan) throughout the studied period. By adopting regular and proper policies, agricultural sustainability can be increased in Iran.

Behavioural insights for improved uptake of agricultural sustainability assessment tools

Abstract Changing human behaviour to enhance agricultural sustainability outcomes is an ongoing challenge that urgently needs addressing. It requires identifying the mechanisms for the successful uptake and application of tools and behavioural interventions to mitigate the detrimental impacts of farm activities on the environment. We combined a participatory action research and mixed‐methods approach (via online surveys, workshops, seminars, meetings and emails) to work with diverse stakeholders to understand their perspectives on the following: (a) sharing farm biodiversity data; (b) behavioural factors likely to influence farmer uptake and use of a farm‐level sustainability assessment tool developed for New Zealand farms; and (c) how to improve the tool. Respondents (n = 133) to an online survey were most willing to share data to gain advice on improving farm practices, but least willing when farmers were paid for the data, or companies used it to market products to farmers. Respondents were most comfortable with sharing data when an independent board decided the data‐use policies, or the contributor could change their sharing preferences and data were anonymised. Feedback on the tool prototypes was elicited via 27 events and more than 133 individual interactions. Of the 529 alignments between 255 discussion points and 14 behavioural factors, almost half were centred on the material context (e.g. infrastructures, technologies, time and schedules) but were often coupled with individual and social factors. Overall, a strong desire for a tool that empowers individual farmers to enhance their farm's biodiversity outcomes emerged, although, during the initial prototype tests, the tool's strengths were rarely acknowledged and concerns about weaknesses were more common. Ten strategies were identified for enhancing the tool. These aligned closely with three human‐thinking principles: four strategies focused on thinking automatically, three on thinking socially and seven on thinking using mental models. The system transformations required to implement such an integrated package of interventions to change behaviour at scale include a collective approach to developing a stronger evidence base for conservation actions, and behavioural interventions to enhance social and environmental capital in production landscapes. Read the free Plain Language Summary for this article on the Journal blog.

Application of Convolutional Neural Networks With Object-Based Image Analysis for Land Cover and Land Use Mapping in Coastal Areas: A Case Study in Ain Témouchent, Algeria

Land use and land cover (LULC) information is a fundamental component of environmental research relating to urban planning, agricultural sustainability, and natural hazards assessment. In particular, remote sensing technology has demonstrated a powerful capacity for LULC modeling with a corresponding increase in sensor number and type. Here, an advanced convolutional neural network (CNN) deep learning model was developed in combination with object-based image analysis (OBIA) to map LULC in Ain Témouchent coastal area, western Algeria, using Sentinel-2 and Pléiades imagery data. First, the CNN model was constructed based on convolution, hidden, and max pooling layers. The parameters of CNN architecture were optimized to improve the model for further processing. Then, based on high levels of CNN feature extraction, the OBIA was applied to classify the segmented objects, and detect the LULC features. Furthermore, machine learning methods, including random forest (RF) and support vector machines (SVM) were tested for comparison. The proposed method achieved a high overall accuracy (93.5%) using Pléiades imagery, revealing significant improvements compared to other machine learning techniques. Accordingly, it was concluded that the method proposed here is useful for LULC detection, and can be applied at larger scales in coastal areas. The derived maps can also inform regional and national-level decision making.