|Main authors:||Ana Iglesias, David Santillán, Luis Garrote and contributions from ISS (China)|
|iSQAPERiS editor:||Jane Brandt|
|Source document:||Iglesias, A. et al. (2018) Report on definition of typical combinations of farming systems and agricultural practices in Europe and China and their effects on soil quality. iSQAPER Project Deliverable 7.1, 87 pp|
|2. Ecosystem services considered|
|3. Indicators for upscaling|
|4. Definition of thresholds|
The focus of the dynamic analyses carried out in this section of iSQAPERiS is soil management environmental footprint. We analyse how agricultural management practices improve or deteriorate critical soil properties that are relevant to determine the beneficial or damaging impact of soils on the environment. The dynamic models developed here aim to determine the effect of the evolving physical and socio-economic context (climate, population, economic development, policies) on the implementation of dominant management practices that have an impact on soil quality. The complex interplay between physical, chemical and biological factors that affect soil quality needs to be simplified in order to produce global results at the continental scale. For this reason, this analysis is focused on a limited number of factors that are considered essential.
The benefits that are derived from ecosystems are collectively referred as ecosystem services. We have selected three main ecosystem services directly linked to soil quality: food, water and climate regulation. These basic ecosystem services are relevant in Europe and China and may be directly linked to social welfare. These ecosystem services may be affected by soil threats, like water quality problems, erosion, decrease of soil organic carbon, and others. We have identified four major soil threats that can be linked to soil ecosystem services, as presented in Figure 2. The linkages are defined based in the science developed in iSQAPER and discussed below.
Here is brief description of the ecosystem services that will be considered:
The productivity and production variability of main groups of crops will be evaluated based on simplified functions that respond to management practices, as analysed in »Management practices and soil quality from long term experiment site data. These groups will include: the main extensive field crops (wheat, maize and soybean), rice, horticulture, and permanent crops and pasture. These crops are also very relevant to livestock production, especially maize and soybeans. Soybeans are relevant to greenhouse gas mitigation since it is a leguminous crop, capable of fixing nitrogen and therefore does not require nitrogen fertilisation at the same level as cereals. Both organic and conventional agriculture will be taken into account. Food production may be affected by soil and water pollution originated by poor management practices.
Water availability and variability
Water is a key factor to be managed to enhance agricultural benefits. In rainfed farming systems, the objective of management may be to maximize soil infiltration and soil water holding capacity or to drain excess water to ensure good crop growth. In irrigation, the aim is to provide water from external sources to supplement rainfall at timely intervals for the crop. Irrigated agriculture has experienced a tremendous expansion during the second half of the twentieth century and water availability may be a limiting factor for such practice in the future. The water availability for agriculture will be estimated with the WAPAA model (Garrote et al. 2015) to represent the potential for irrigation and the water available for ecosystems. Water also plays a significant role as regulating service, dampening natural fluctuations. An evaluation of the risk of extreme events (floods and drought) will be included. Here the actual water consumption of existing irrigated agriculture will be evaluated. This indicator has direct linkage to the availability of water for ecosystem services, soil erosion and water logging.
Greenhouse gas emissions from agricultural land
The GHG emissions from agricultural land will be calculated on the basis of the SmartSOIL project methodology (SmartSOIL, D3.2; Olesen et al. 2014). The GHG emission mitigation management measures can be related with changes in soil organic carbon stocks and flows and to the nitrogen ferlitiser inputs, ultimately providing useful information on effectiveness of these measures under varying conditions and assumptions regarding their effect on nutrient availability and yield.
The objective is to identify and characterize a relatively limited number of representative farming systems, agricultural practices and pedoclimatic units that will inform on the environmental footprint at the Europe and China wide scale. These units of analysis and thresholds are defined:
- building on other work in iSQAPER;
- structuring the analysis as proposed in »Upscaling approaches in geospatial environmental studies;
- taking into account additional global sources of data; and
- exploring the possible validation and contribution of the case studies, that will be included in »Effect of management on soil quality.
The indicators in the iSQAPER databases regarding the typical farming systems and soil management practices are summarised in the following table (Table 1). These indicators will be made spatially explicit if enough information is available.
Table 1. Indicators summary. These indicators will also include the list of indicators proposed by other sections of iSQAPER for the local case studies, to ensure the harmonization of the methodologies included in the SQAPP
|Indicators||Description / Comment|
|Main farming systems||We aggregate into five main farming systems: Field crops, Rice, Permanent crops, Pasture and grasslands, Horticulture.|
|Total utilized agricultural area at the continental scale (UAA)||Area is expressed in 1000 ha and is based on the sum of all crop areas, including grazing.|
|Area of main farming system||The area of main farming system is expressed in 1000 ha of the total utilized agricultural area.|
|Crop yield in the main farming system||The average crop yield, in kg of dry matter per ha, is provided for the main farming systems. The unit of analysis will be 0.5 x 0.5 degree resolution|
|Main soil management practices of the main farming system||Main combinations of soil management practices relevant for soil ecosystem services for the main farming system.|
|Use of soil management practices based on areas relative to arable land||Agricultural management practices relevant for soil health. The implementation level is expressed as the percentage of land under a certain management practice, compared to the total area of arable land.|
|Irrigated area||The total irrigated area (in 1000 ha) may be derived from the SAPM 2010 survey from Eurostat.|
|Main limiting factor to attain potential production||The combinations of main limiting factors which affect potential production in the main farming system provided by partners for regions of case study countries (bottom-up assessment).|
|Nitrogen fertilizer use||The average nitrogen fertiliser use (kg N /ha), consisting of both animal manure and mineral fertilizer.|
|Organic farming percentage of the agricultural area||The area of organic farming is expressed as percentage of the utilized agricultural area, and it excludes the farms in conversion to organic farming.|
|Climate classification||We aggregate into five main climate zones: Boreal, Atlantic, Alpine, Continental and Mediterranean.|
|Pedoclimatic zones||Pedoclimatic zones as defined by »Pedoclimatic zones of Europe and »Pedoclimatic zones of China|
|Other aspects related to implementation at the farmer and policy levels||To be decided in »Soil management scenarios and »Policies and environmental footprint|
The upscaling approach is framed in qualitative terms. The objective is to identify management practices that have positive or negative effects on soil quality indicators linked to soil ecosystem services and thus assess the projected impact of alternative policies in future scenarios.
The upscaling approach will deal with qualitative variables formulated in a domain of five categories. The proposed methodology for identifying categories is inspired on the Likert scale. Likert scaling is a bipolar scaling method, measuring either positive or negative response to a statement. Experts will be asked to fill a questionnaire about the impact of management practices on soil quality. Based on their responses and on the analyses carried out in WP3, the effect of the management practice for every farming system will be classified in the following categories:
- Positive (++): This category means that the management practice will certainly improve the soil quality indicator, with effects larger than 10%
- Beneficial (+): This category means that the management practice has potential to improve the soil quality indicator, but the effects may depend on additional factors. The improvement will be between 5% and 10%
- Neutral (=): This category represents a neutral impact of the management practice on the soil quality indicator under analysis. It corresponds to a positive or negative effect of less than 5%.
- Unfavourable (-):This category means that the management practice may degrade the soil quality indicator, but the effects may depend on additional factors. The degradation will be between 5% and 10%
- Negative (--): This category means that the management practice will certainly degrade the soil quality indicator, with effects larger than 10%
Note: For full references to papers quoted in this article see