A dynamic, flexible and productive agricultural system is essential to achieving the goals of a sustainable development. Obviously, having such an agriculture needs a lot of changes. Creating such a change requires optimization of a wide range of agricultural, environmental and socio-economic factors in agricultural systems (from crop yield to biodiversity to human nutrition). However, these results are not independent of each other and interact with both positive and negative ways and create synergy and trade potential. In a broad study, Kanter and his colleagues have examined the contexts of agricultural trade analysis. They have divided it into four stages and examined: 1) Describe decision making and identify specific indicators required for agricultural sustainability, 2) Selection of methods for producing index values on different scales, 3) decision making on evaluation and communication of trade-offoptions with stakeholders and decision makers and 4) Improves the absorption of trade-offanalysis outputs by decision makers.

Overall look at the subject

Agriculture plays a key role in sustainable development. Its fundamental position as a supplier of human nutrition shapes the global economy and society’s relations with the natural world. Therefore, achieving a set of sustainable development goals (SDG) agreed by the United Nations in 2015 is important because of ending hunger and poverty to improve human well -being and reduce environmental impacts. Currently, more than one -third of the world’s land level and about three -quarters of its freshwater resources are dedicated to agriculture. In addition, almost three -quarters of the world’s poorest people live in rural areas, where agriculture is the main source of employment and income. Sustainable agricultural growth is not a specific set of methods, but rather provides a conceptual framework to guide the discussion of achieving balanced growth results. Therefore, several alternatives to sustainable agricultural systems can be considered that vary depending on the agricultural context, agriculture, cultural preferences, institutions and policies, and so on. Trade analysis was developed from cost and benefit analysis (CBA) and was first used during the Green Revolution in the 1970s to assess the economic impact of emerging agricultural technologies. These approaches are focused on maximizing profit margins in agriculture. When the researchers began to expand their focus on sustainability issues in the 1980s and 1990s, it was found that the CBA pattern was insufficient to address multiple monetary and non-monetary goals The initial applications of trade-off analysis in agricultural sustainability evaluate the biophysical data, and economic models to produce a more inclusive approach to this evaluation.

The purpose of this study:

Given that the international community focuses on how SDGs are implemented on local, national and global scales, it is more important than ever to understand how business analysis can help decision makers to develop balanced and integrated approaches. This integrated approach is particularly important for agriculture, as trying to create it sustainable, economically, environmentally, and socially, is very important for the success of the majority of SDGs. This article is set up with the distillation of the Agricultural Trade Analysis process in 4 sections: 1) Understanding the context of decision making and identifying the indicators needed to evaluate agricultural sustainability on a specific scale, 2) Select methods to produce index values at different scales, 3) Deciding on the means of evaluation and communication with stakeholders and decision makers, and 4) Improving the absorption of trade-offanalysis outputs by decision makers. Considering these steps helps to identify gaps in data sources, evaluation tools, and the relationship between systematic evaluations in the growth of agricultural sustainability.

How should we choose the indicators?

Each decision context consists of a unique combination of local parameters, conditions and actors that define the range and analysis results. Understanding a particular context determines the boundaries of what is socially, political, economic, and environmental and desirable for the system and is essential to define the main purpose of sustainability. Common cooperation with stakeholders is very important to create a conceptual model for trade-offanalysis. Important stakeholders in this process include policymakers, farmers, landowners, consumers, the environment and scientists. The purpose of this process is to define the key indicators under the analysis, to ensure that they are meaningful to end users and cover the multiple dimensions of stability related to a particular context. The criteria to consider in the selection of indicators include: 1) Ensure that the links between the index and what it shows are well understood and without ambiguity, 2) reliability and accuracy of the information conveyed, and 3) the ease and cost of monitoring the indicator over time. In addition, the indicators should be sensitive to the system’s natural and human pressures, anticipation of imminent changes, and forecast of changes that are prevented by management performance. The goals and concerns of stakeholders often change from local to region, then state and ultimately global. As a result, the selected spatial scale greatly affects the selection of index and the involvement of stakeholders. An interpretation in a scale cannot necessarily be generalized to other scales. Therefore, it is suggested that in order to be more comprehensive, the interpretation be expressed within a higher limit and one lower than the desired scale.

How to estimate the values of the indicators?

The numerical value of a selected index can be estimated in different ways. The strongest and most desirable method is to measure directly through the collection of basic data in the context. However, data collection costs can be very high, especially for assessments with multiple large -scale indicators. Modeling approaches to estimate index values fall into the following categories: process -based biophysical and economic models. When it comes to direct measurement or costly, process -based biophysical or economic models can be used. Process-based models, which rely on empirical or theoretical relationships, can be used to simulate environmental, biophysical and socio-economic processes. Economic process -based models are based on basic concepts such as supply and demand and maximize output that are applied to a specific scale to produce specific index values. Due to the complexity and multifaceted nature of agricultural systems, analyzing trade-offsrelated to different management practices requires models that combine several methods and use the output of other models as input to other models.

How to evaluate the trade-offs?

After estimating the values of the indicators, the trade-offsbetween them can be evaluated. Trade-offevaluation generally includes comparisons of indicators and changes in index values under different scenarios. Since there are limited models that are capable of evaluating and displaying the output of the indicators used, techniques are used to compensate for this. Some of them will be explained below.

5.1- Use the optimization approach:

Optimization approaches are widely used in trade-off analysis, especially in cases where land use is a key part of agricultural systems. Optimizing multiple goals in particular can be a valuable technique for the implementation of SDG in the agricultural sector with the help of decision makers to identify options that maximize the likelihood of SDGs at the same time. A wide range of techniques are available for optimization, from algorithms of a limited number of steps to duplicate methods, which converge in a solution, and the exploration, which offer more approximate solutions.

5.2- Select the scenario:

Scenarios describe sets of plausible and internally consistent possible futures that may consist of a discrete set of agriculture and landmanagement options, or sets of a priori defined shifts in key drivers such as agricultural commodity prices or climate forcings identified by scientists and other stakeholders at the outset of the process. Direct comparison is oftenmade between modeled indicator values based on a current baseline or business-as-usual scenario, and values fromone ormore alternative future scenarios. Ideally scenarios are developed in collaboration with relevant stakeholders to ensure that they are realistic and represent a number of shared or contested visions of future land management and intensification. The process of scenario development is interdisciplinary and involves: 1) developing narratives of present or future changes around identified drivers of change and plausible intervention pathways (defined collaboratively with stakeholders); and 2) translating these narratives into quantitative data to parameterize models.

A major conceptual and analytical challenge is to evaluate how trade-offs at the farm or (sub-national) regional levels will affect or be affected by local or national economic changes. Changes in prices and incomes in particular, can have important effects on both demand and supply of agricultural commodities and on the economic well-being of farm households as well as other members of society. Large-scale, aggregate economic models at the national or global scale can simulate how major changes, such as technology, policy or climate, can impact prices and incomes. However, these outputs are also aggregated to provide meaningful assessments of distributional impacts, e.g., impacts on poverty rates, food security or climate vulnerability of poor households. Research shows that integrating these economic impacts into trade-off analysis can significantly influence the results, as well as stakeholder decision-making. Thus, establishing linkages between disaggregated analyses, such as household or regional models that do not represent markets, and more aggregate market analyses is essential.

5.4- Visualize:

Presenting the information relevant to trade-off analysis in an easy to interpret format is critical for effective communication of results. Well-designed visualizations ofmultiple indicator values can be a powerful and intuitive means of conveying large amounts of complex data, facilitating deeper understanding of the interactions among indicators to support better decision-making. Visualizations can be effective for highlighting similarities and divergences in indicator values and temporal or spatial patterns among scenarios that are not as easily perceived when reviewing the rawdata. However,mostmodels that produce indicator values do not automatically generate a visual representation, creating the need for researchers to select an appropriate visualization technique. The choice of visualization approach depends on a number of factors including the type of trade-off scenarios being evaluated, the number of scenarios considered, the number of indicators in question, and the intended audience.

A few tips on using trade-off analysis to improve decision making:

Scientists often think that decision makers will automatically apply any results produced by science because of the formal logic and the reduction of uncertainty that models can provide to the non -complex nature of agricultural systems. The low level of absorption of trade-off analysis results is due to several factors: 1) lack of proper stakeholder involvement in design and research process, 2) often general advice that removes traditional decision -making processes, and 3) Lack of an end user or clear stakeholder for trade-off analysis outputs. Poor participation of stakeholders often leads to tools that prescribe action rather than facilitating learning. In order to address this challenge, some argue that scientists first need to explain what the knowledge created: Is it merely to advance a fundamental scientific understanding and without any clear use? Or is this for more practical purposes, decision -making and negotiation support?

For more information on this subject, be sure to see the “Evaluating agricultural trade-offs in the age of sustainable development” article, written by Kanter, D.R., et al.


Doi: 10.1016/j.agsy.2016.09.010

Link: https://www.sciencedirect.com/science/article/abs/pii/S0308521X1630542X

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