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Bouma, J.; De Haan, J.; , . UN Sustainable Development Goals. Encyclopedia. Available online: https://encyclopedia.pub/entry/22198 (accessed on 25 June 2024).
Bouma J, De Haan J,  . UN Sustainable Development Goals. Encyclopedia. Available at: https://encyclopedia.pub/entry/22198. Accessed June 25, 2024.
Bouma, Johannes, Janjo De Haan,  . "UN Sustainable Development Goals" Encyclopedia, https://encyclopedia.pub/entry/22198 (accessed June 25, 2024).
Bouma, J., De Haan, J., & , . (2022, April 23). UN Sustainable Development Goals. In Encyclopedia. https://encyclopedia.pub/entry/22198
Bouma, Johannes, et al. "UN Sustainable Development Goals." Encyclopedia. Web. 23 April, 2022.
UN Sustainable Development Goals
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Reaching the land-related UN Sustainable Development Goals (SDGs) and similar goals articulated by the EU Green Deal (GD) by 2030 presents a major challenge and requires a pragmatic approach focused on joint learning by land users (mostly farmers), researchers and other stakeholders in “Living Labs” and system experiments at experimental farms of research organizations. Defining specific indicators and thresholds for ecosystem services in line with land-related SDGs is crucial to establish “Lighthouses” that can act as inspiring examples if they meet the various thresholds.

sustainable development modeling interdisciplinarity SDG

1. Introduction

1.1. Sustainable Development Goals

The Brundtland report of 1988, “Our Common Future”, has been instrumental in emphasizing the urgency of putting the issue of sustainable development on the international policy agenda, as the concept has remained rather vague. The need for an integrated approach combining economic, societal or environmental issues when dealing with societal development has changed the sustainability discourse. The introduction of seventeen sustainable development goals by the General Assembly of the United Nations (https://sdgs.un.org, assessed on 20 February 2022), approved in the 2030 Agenda for Sustainable Development by 193 countries in 2015, provided a welcome focus for the sustainability effort that was, in essence, also adopted by the Green Deal of the European Union of 2019 (https://ec.europa.eu/greendeal, assessed on 20 February 2022). Both policy documents emphasize the need for a practical approach resulting in visible results.
Green Deal targets and land-related SDGs are strongly affected by agricultural practices, and soils play an important role [1]. When focusing on agriculture, primary attention will not only be on the traditional role of producing healthy crops to combat hunger (SDG2 and SDG3), but also on clean ground and surface water (SDG6) on increasing carbon sequestration and limiting greenhouse gas emissions for climate mitigation (SDG13) and on the reduction in land degradation and biodiversity preservation (SDG15). Additionally, energy use (SDG7) and sustainable production and consumption (SDG12) are relevant, where the latter has much in common with SDG2 and SDG3. The indicators and thresholds of the Green Deal and the SDGs specify the required “clear and concrete objectives” of [2]. They are strongly interrelated. Some form of multifunctional soil use and management therefore has to be realized in agriculture, and this can be assessed in “Living Labs”, which will certainly be very different in different regions.
Focus on the SDGs serves to connect with the international discourse on sustainable development. Each of the five major land-related SDGs can be reached when adequate ecosystem services are provided that are defined in terms of “services provided by ecosystems to mankind”, as first proposed by the Millennium Ecosystem Assessment of 2005 (https://www.millenniumassessment.org, assessed on 10 January 2022)). Soil functions contribute to ecosystem services in line with the SDGs [3][4]. Man is a recipient of such services, which cannot be taken for granted. Adequate levels can only be reached by applying appropriate management measures. Ecosystem services can only be reached by an interdisciplinary effort, involving agronomists, hydrologists, climatologists, ecologists, economists, sociologists and others in addition to soil scientists. This represents a key message for all disciplinary researchers involved in the sustainability effort.
Each SDG is so far specified in terms of targets and indicators (https://unstats.un.org/sdgs/metadata, assessed on 20 February 2022) that do not, however, address operational methods and procedures by which these targets can be reached in the real world, presenting a key challenge to not only the scientific arena but also to society at large.
Where to start? The SDGs will only be reached when land users, most of them farmers, are willing to embrace management procedures that result in providing ecosystem services in line with the SDGs. Farmers have been interviewed many times and their questions, concerns and demands need particular attention before new activities are started. Their major concerns are about unsure economic prospects and unclear and dysfunctional environmental rules and regulations in their perception, as well as about not receiving independent advice [5][6][7]. These economic prospects are significantly affected by the Common Agricultural Policy (CAP) of the European Union, supporting farmers with EUR 350 billion for the period 2021–2027, including a provision now that 25% of the funds, and perhaps more in future, will be allocated to support the realization of ecosystem services. This justifies the need for an operational assessment of ecosystem services allowing a functional link with the CAP.
Considering the role of soils in this broad ecosystem context, soil health was defined in terms of: “the continued capacity of soils to contribute to ecosystem services in line with the SDGs and the Green Deal” [8]. At first sight, this may seem to be a rather politically inspired definition, but it rather emphasizes two key aspects: (i) soils cannot contribute to ecosystem services alone. Their importance is determined by their contributions, and (ii) By referring to the SDGs and the Green Deal, all environmental objectives beyond the classical production function are considered.

1.2. Research on Wicked Problems

To reach the thresholds derived from SDGs, farmers need to adapt their management to fulfill at least five ecosystem services while also having to adapt to changing weather conditions that are unpredictable beyond at most a ten-day period. This is why farms are complex systems where no simple solutions are available to solve problems, but only a set of alternative options that produce acceptable overall results. The problems encountered when researching such complex systems are “wicked”. Studying “wicked” problems cannot follow the standard linear research protocol that produces a single answer based on reductionistic experiments with sufficient replicates to allow a statistical analysis resulting in “significant” results. The standard protocol has many shortcomings. As an example, in the comparison of ploughing and non-inversion tillage, many more differences are relevant than only replacing the plough with a cultivator. The timings of operations are different, but other aspects such as crop rotation, choice of cover crops, weed control and fertilization levels also need to be adapted to the new tillage method. It is not possible to take all aspects in to account in factorial experiments.
The European Commission recognizes the need for other forms of research by supporting the establishment of “Living Labs” and “Lighthouses” at the farm level, following the advice of the Mission Board of Soil Health and Food [8]. “Living Labs” are defined as “spaces for co-innovation, through participatory, transdisciplinary systemic research” that “contribute to Green Deal targets for sustainable farming, climate resilience, biodiversity and zero-pollution”, and “Lighthouses” are defined as “single sites, like a farm or a park, where to showcase good practices. These are places for demonstration and peer-to-peer learning”.
In Living Labs, farmers, researchers and other stakeholders are jointly creating knowledge [2][8] to develop suitable field-tested management methods that result in achieving several ecosystem services. Moreover, Living Labs function to inspire colleague farmers to adopt certain management options that fit their particular farming style.
In Living Labs, the practical feasibility of management options can be well-tested. However, possibilities for experiments on commercial farms are highly limited for financial and operational reasons. This aspect has not received adequate emphasis when promoting the “Living Lab” concept. Links with existing experimental farms of research organizations and universities can therefore be highly effective in designing and executing relevant experiments, including the development of operational monitoring methods that can be applied at the farm level. Next to more classical research, research projects and experiments on integrating management options into farming systems are important to test the feasibility of individual measures within systems and to assess the effects of the system on various ecosystem services. This can be carried out with more accuracy and precision on experimental farms than on commercial farms in “Living Labs”. In this type of research, farmers and other stakeholders also need to be involved in the set up and execution of the research. Methodologies for these types of research already exist in, e.g., the prototyping methodology [9][10]. In addition, the value of the combination of research on experimental farms together with commercial farms has been described previously [11]. These system experiments are also important in the dissemination of knowledge through field days, excursions and open discussions.

2. Implications for Environmental Rules and Regulations and Support Programs

The proposed system of evaluating ecosystem services in line with the SDGs can be the basis for an attractive and relatively simple regulatory system based on comparing indicator values, as discussed above, with thresholds that still need to be developed in most cases. Such a system should be based on measurements in system experiments on experimental farms and in “Living Labs”, applying relatively simple field methods, which farmers will welcome, as they complain about the current complex systems. This requires the development of new measuring methods that can produce a lot of data in a short period of time, allowing a scientifically sound evaluation of spatial variability. Soil health studies are important as soil health makes major contributions to achieving ecosystem services, as was shown with modeling studies for some Italian soils [12][13][14][15].
Of particular interest is a link with the future Common Agricultural Policy (2021–2027) where payments for ecosystem services are now one of the options being discussed. This could mean a substantial payment when all ecosystem services have a sufficiently high level. If one or more of the services are inadequate, a focused subsidy on the lacking services can be considered, as a case study in Switzerland has shown, where subsidies were based on introducing cover crops and on applying minimum tillage to enhance carbon sequestration. This program turned out to be highly successful [16].
The system presented in this entry is based on the selection of a limited set of relatively simple indicators directly coupled to an ecosystem service linked to SDGs or, separately, to soil health contributing to ecosystem services. Along these lines, the application of a soil indicator set is being explored in the Netherlands [17].

3. Need for Operational Measuring and Monitoring Methods

3.1. Ecosystem Services for Farming Systems

As discussed, production levels were well-documented and can be supported by modeling the soil–water–atmosphere–plant system. Several well-tested models are available [18][19][20]. Basic soil data, such as texture, bulk density and organic matter content, are used in so-called pedotransfer functions to predict hydraulic soil characteristics needed for modeling such as hydraulic conductivity and moisture retention [21][22]. However, aside from modeling, measurements of real yields are still also necessary to validate the models. Researchers advocate for attention to climate change, which will have major effects on food production. Obviously, only modelling can handle future climate scenarios (SDG2). Healthy food, based on healthy crops, can be assessed by existing health regulations’ defining thresholds. This is, however, a highly dynamic field of study as new pollutants arrive (SDG3). The quality of ground and surface waters (SDG6) could only be derived from national monitoring systems and are not yet part of monitoring systems at the farm level. This would be required when assessing “Living Labs” in future. Modern automated monitoring systems are available to obtain hard data that do not depend on debatable interpolations from current, often far-away, measurement locations. Energy use (SDG7) is less relevant from a national point of view but is important for individual farmers as a cost item to be reduced. The emission of greenhouse gases (SDG13) is important and is now being estimated by modeling, even though the particular soil being considered here was not yet covered. However, modeling is only justified when the models are properly validated with measured data. This validation process is still rather undefined, and direct measurements are therefore needed. The available measurement methods using small on-site chambers are cumbersome and costly while only providing point data at specific moments in time. Applying frequent satellite images would be highly attractive, as is being explored now by the European Space Agency (https://www.esa.int, assessed on 20 February 2022). This type of work needs a high priority and strong support. (SDG15) refers to land degradation, where the indicators for soil health are relevant to assess soil degradation. Biodiversity is, however, still undefined in terms of specific indicators for the entire farming system. Policy decisions on future land-use scenarios are therefore urgently needed. Innovative methods for measuring ecosystem services are summarized in Table 1.
Table 1. Summary of methods to be used for characterizing ecosystem services in line with the SDGs and the Green Deal and the contributions of soil health.
  Methods for Measuring Ecosystem Services Methods for Assessing Soil Contributions to Ecosystem Services
SDG 2/3
  • governmental statistics
  • remote sensing
  • modeling the soil–water–atmosphere–plant system, also considering climate change
  • new methods to characterize soil structure: radiation methods for bulk density; proximal sensing for organic matter
  • modeling the soil–water–atmosphere–plant system
SDG 6
  • automatic monitoring equipment
  • modeling soil nutrient regimes to support precision techniques
SDG13
  • satellite remote sensing for measurement of emissions
  • validated models estimating greenhouse gas emissions
  • proximal sensing of organic matter content focused on carbon capture
SDG 15
  • soil health
  • biodiversity on a landscape scale
  • all of the above

3.2. Soil Contributions to Ecosystem Services

Soil contributions to ecosystem services can be framed in terms of soil health, for which several indicators have been defined as discussed in this entry. Soil as a favorable environment for root growth is key for all ecosystem services contributing to the five SDGs being considered. Soil structure, the organic matter content and soil moisture regimes are key indicators for soil health, assuming a lack of pollutants and adequate levels of nutrients by fertilization. The current standard methods assessing soil structure, as reviewed above for bulk density, use relatively small soil samples and are costly and laborious as they require laboratory analysis, not providing instant data. Standard deviations among replicate measurements are relatively high due to small sample volumes and, considering 95% confidence intervals, hardly allow the distinction of differences among treatments, let alone among different soils. Measuring the penetration resistance is attractive, as many observations can be instantly made. An important factor causing variation among measurements is moisture content; so, measurements will have to be made at certain periods only, preferably only when the soil is at field capacity. Innovative techniques are available to allow rapid, multiple and cheap measurements for both organic matter content and bulk density once equipment has been obtained. The application of proximal sensing for organic matter [23][24][25] and further testing of radiation techniques for measuring bulk density (e.g., [26]) are highly recommended. Field research on the impact of present and past soil management on the organic matter content of a given type of soil can provide valuable insights on the effects of management that differ significantly among soils. Organic matter contents were sampled at fifty farms on two prominent Dutch soil types, and the study could relate actual organic matter content very well (R2 > 0.8) with current and past management, providing valuable suggestions for future management practices [27][28]. There are thousands of experiments out there in the field waiting to be discovered! Soil biodiversity is being studied widely, but so far, standard techniques have not been suggested or approved. Doing so is a high priority as soil biodiversity plays a key role in soil functioning. Applying proxies, such as the organic matter content, as in this study, needs improvement.
The simulation of soil moisture regimes combined with nutrient dynamics in the soil–water–atmosphere–plant system can provide important information for precision agriculture where nutrient inputs are fine-tuned to the needs of plants, optimizing the soil fertility regime. This can result in substantial savings of fertilizer input and costs of up to at least 10%, thereby also reducing leaching and groundwater pollution by excess nutrients. A study on precision fertilization consisted of the preparation of a functional soil map with four different soil units, derived from the interpolation of point data, with a distinct behavior in terms of water regimes and nitrogen dynamics. Modeling was applied to determine the critical moment when the available nitrogen reached a threshold. Then, fertilization was needed. This moment was different for the different soil units, providing a basis for applying precision techniques [29]. Again, robots can in future perform the task of fertilization, strongly reducing labor demand and reducing pressures on soil by traditional fertilization equipment.

References

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