Note: In this section of iSQAPERiS we use and develop concepts that have been developed in other parts of iSQAPER in order to upscale the results from the local to the regional and national scale. We draw on the analysis of farming systems presented in »Crop & livestock systems, the analysis of the effect of agricultural management practices carried out in »Management practices and soil quality and we select a small number of soil quality indicators from those reviewed in »Soil quality: a critical review. 

1. Selecting farming systems for upscaling

The objective of this section is to identify a set of farming systems to be considered in the upscaling model. We elaborate the results obtained in other sections of iSQAPER including information from other sources: previous projects and public databases. We first present the main conclusions of »Crop & livestock systems and then briefly review other farming system classifications developed in previous projects.

Farming systems iSQPAPER

One of the aims of iSQAPER is to develop a classification of crop and livestock farming systems in various pedo-climatic zones across Europe and China. »Crop & livestock systems adopted a definition of farming system based on the review of diverse approaches and proposed an operational classification for the Soil Quality Application (SQAPP). The results are summarized in Table 7.  

Table 7. Classification of Farming Systems adopted by iSQAPER

The classification of FSs is divided in four main categories: Arable Land, Permanent Crops, Pastures and Livestock. A total of 39 FSs were identified.

Farming systems in other projects

SmartSOIL The project SmartSOIL was carried out in the period 2011-2015. Its aim was to improve soil carbon management in European arable and mixed farming systems. They developed the SmartSOIL toolbox as an interactive platform with tools showing the impacts of field management practices on soil organic matter and soil organic carbon (SOC) content. As part of the project, they produced a deliverable on typical farming systems and trends in crop and soil management in Europe (SmartSOIL, 2015).

The farming systems of SmartSOIL were derived from the SEAMLESS project, where a classification was developed distinguishing 21 farm types (Andersen, 2010). For SmartSoil, these 21 farm types were aggregated into six main categories: Field crops, Permanent crops, Mixed farms, Pastures and grasslands, Industrial crops, and horticulture. The results are shown in Table 8.

Table 8. Farming systems considered in the SmartSOIL project.

Catch-C The Catch-C project aims at identifying and improving the farm compatibility of sustainable soil management practices for farm productivity, climate-change mitigation, and soil quality. The project developed an “agri-environment farm type” typology, by combining soil and climate data with farm specialization data. They identified agri-environmental zones based on climate, soil texture and slope, which were later combined with farming activities (as defined by the Farm Accountancy Data Network) and land uses to describe farm typologies. This typology was presented on Deliverable 2.242 (Catch-C, 2014) and is summarized in Table 9, which shows the classes adopted for specialization and land use. A farming system is a combination of two suitable classes, one from each column.  

Table 9. Classes adopted in the Catch-C project for farm specialization and farm land use. Adapted from Catch-C (2013).

2. Proposal of farming systems for upscaling in iSQAPER

One of the requirements of the iSQAPER upscaling model is simplicity. Farming system classifications have been developed for different purposes. In the upscaling model we need to balance model complexity and representatively. For this reason, farming systems have been grouped into seven categories, which represent a large fraction of the food produced globally. The categories are the following:

• Cereals: this farming system includes extensive cereals like wheat, barley, oats or rye. They are grown in temperate regions, usually rain fed, although they might require supplemental irrigation in some locations. Winter varieties may allow for growing another crop in the remaining season. Farming practices usually rely on machinery for harvesting and the use of herbicides and fertilizer is frequent.
• Rice: this farming system is represented by intensive rice wetland cultivation, with or without irrigation. Farming practices range from subsistence agriculture in small and fragmented fields to fairly advanced high-tech cultivation found in some areas of Europe.
• Maize: this farming system includes arable land devoted to maize cultivation.
• Soybean: this farming system includes arable land devoted to maize cultivation
• Vegetables: this farming system includes vegetable crops: legumes (beans, peas), root vegetables (carrot, potato, onion, beet), leafy greens (spinach, cabbage, cauliflower, broccoli) and fruit-bearing(tomato, cucumber, pumpkin, zucchini, eggplant). These are grown with a diversity of cultivation techniques: open field, plastic tunnels, glasshouses with or without heating, allowing production in different seasons.
• Pasture: this farming system includes grass-based livestock systems for meat and dairy production.
• Permanent crops: this farming system includes crops that are produced from plants that last for many seasons. It includes olive production for oil or table olives, fruit trees (apples, pears, citrus), vineyard, nuts (walnut, almonds) among others.

3. Selecting agricultural management practices for upscaling

This section is devoted to the identification of management practices to be considered in the upscaling model. It is based on several sources dealing with the characterization and study of agricultural practices, like public databases and research projects. We first present the analysis of the effect of agricultural management practices carried out in »Management practices and soil quality and then briefly review other AMP classifications developed in previous projects.

Agricultural practices in iSQAPER

In »Management practices and soil quality we chose five management practices to evaluate their long-term effect on soil quality indicators. This decision was based on practices commonly selected on previous EU projects, practices described in the iSQAPER long term experiment (LTE) documentation and the agreement reached in the iSQAPER group. The adopted management practices are listed in »Approach taken to evaluate the soil environment footprint, Table 1.

Table 10. Agricultural practices in »Management practices and soil quality

Agricultural practices in other projects

WOCAT The WOCAT initiative maintains the Global Database on Sustainable Land Management (SLM) that documents and assesses SLM practices with the objective of sharing and spreading valuable knowledge in land management, supporting evidence-based decision-making, and scaling up identified good practices. It includes a catalogue of 942 SLM technologies that control land degradation and enhance productivity or other ecosystem services. Not all measures are direcly linked to soil quality. They are classified according to different criteria. For instance, Table 11 shows categories according to three different classification criteria.

Table 11. Categories of management practices in WOCAT according to three classification criteria

SmartSOIL The focus of the SmartSOIL project was management of soil organic carbon (SOC). They identified key management practices affecting SOC flows and stocks and their applicability in various farming systems and agro-ecological zones in Europe. The project had also a dynamic orientation, because scenarios of future crop and soil management systems in Europe were developed to evaluate the potential for improved productivity and enhanced soil SOC sequestration. The management practices included in their analysis are listed on Table 12.

Table 12. Management practices analysed in SmartSOIL project

CATCH-C The Catch-C project compiled a standard list of management practices where they were classified in five categories: Rotation, Grassland management, Tillage, Crop protection and Water management, as discussed below.

• Rotation: divided in two subcategories: Crop rotation (Monoculture, Roration with cereals, Rotation with legume crops, Rotation with tuber or root crops, Rotation with fallow land and Rotation with grassland) and Intercropping/green manure/catch crop (Intercropping, Rotation with cover/catch crops, Rotation with green manures)
• Grassland management: Permanent grazing, Rotational grazing, Zero grazing
• Tillage: Conventional tillage, No/zero tillage, Shallow non inversion tillage/reduced tillage, Shallow non inversion tillage/minimum tillage, Deep non inversion tillage, Deep ploughing, Direct drilling, Contour ploughing, Terrace farming, Controlled traffic farming
• Crop protection: divided in two subcategories: Crop protection-weeds (Mechanical weeding, Herbicide application) and Crop protection-pests (Push-pull strategies, Patches or stripes or natural vegetation, Pheromones application, Insecticide application, Fungicide application, Nematode application, Soil fumigation and Soil solarization)
• Water management: divided in two subcategories: Water management-irrigation (Surface irrigation, Drip irrigation, Sprinkler irrigation) and Water management-drainage (Subsurface drainage)

4. Proposal of management practices for upscaling in iSQAPER

The management practices adopted for upscaling are the same categories adopted in Deliverable 3.2 to evaluate their effect on different soil quality indicators. This will allow us to take advantage of the results of the analyses performed on the LTE sites. The categories are the following:

• Organic matter addition: Addition of organic matter through different techniques, such as selection of a high-residue crop rotation that leaves surface residue or roots in the soil or application of livestock manure.
• No tillage: Grow crops without disturbing the soil through tillage or apply tillage without inversion at a reduced depth.
• Crop rotation: Growing of different species of crops in a crop rotation scheme.
• Irrigation: Application of water to the field through surface, sprinkler or drip irrigation.
• Organic agriculture: Combination of different management techniques to avoid synthetic substances. It includes fertilizers of organic origin such as compost or animal manure, crop rotation, companion planting, biological pest control, mixed cropping or fostering of insect predators.

5. Soil quality indicators linked to soil ecosystem services

Soil quality indicators in iSQAPER

iSQAPER has paid a lot of attention to the characterization of soil quality through indicators. In »Soil quality: a critical review we reviewed existing soil quality concepts and indicators, focusing on three categories: Chemical (Total organic C, Total N, Available P, CEC (incl. avail. K), Labile C)., Biological (Microbial Biomass, N mineralization, Molecular analyses, Earthworms, Disease incidence, Yield, Tea bag test) and Physical (Bulk density, Particle-size distribution, Soil depth, Aggregate stability, Water holding capacity, Penetration resistance; Spade diagnosis). In »Management practices and soil quality we focussed on six indicators to study the effect of management practices in LTEs:

• Yield: provides a good indication of soil quality and is of most concern to farmers. It is also an important ecosystem service.
• Soil organic matter/soil organic carbon: plays a central role in the maintenance of soil fertility and other soil functions. Its environmental and economic relevance is based on the capacity of soil organic matter (SOM) to limit physical damage and to improve nutrient availability.
• pH: is a measure of soil acidity, which controls nutrient availability to crops. If soil pH is too high, nutrients such as phosphorus, copper, manganese, iron and boron become unavailable to crops. If pH is too low, potassium, phosphorus, calcium, magnesium and molybdenum become unavailable.
• Aggregate stability/soil structure: is a key factor in the functioning of soil, its ability to support plant and animal life, and regulate environmental quality with particular emphasis on soil carbon sequestration and water holding capacity.
• Water holding capacity: is an important determinant of crop production. Soil texture, mineralogy and content of organic matter are key components that determine soil water holding capacity.
• Earthworms: Earthworms can increase soil porosity and improve soil structure; they can increase mineralization of SOM in the short-term by altering physical protection within aggregates and enhance microbial activity and nutrient cycling.

Soil quality indicators in CATCH-C

In the Catch-C project an analysis was performed to estimate the effect of management practices on a set of soil quality indicators linked to different factors. The selected soil indicators were grouped in five categories: Productivity, Climate Change, Soil Quality Chemical, Soil Quality Physical and Soil Quality Biological. The indicators are listed in Table 13.  

Table 13. Soil quality indicators adopted in »Catch-C project 

6. Proposal of soil quality indicators to represent soil ecosystem services for upscaling

Previous studies have used a diversity of soil quality indicators. The selection of indicators for upscaling is based on simplicity and data availability. The indicators selected for upscaling are the following:

• Yield: Yield is selected because it is the most relevant factor for the farmer and is also linked to basic soil functions and ecosystem services. Spatially disaggregated yield information is available for many crops.
• Soil organic carbon: SOC is selected because it is directly linked to soil productivity and to climate change mitigation. This quantity may be estimated from proxy data included in soil databases.
• Water holding capacity: WHC is selected because it is directly linked to soil functions of temperature (i.e., soils with higher water content regulate temperature better and are not exposed at risk of high temperature stress to crops and fauna) and flood regulation. This quantity may be estimated from proxy data included in soil databases.

Note: For full references to papers quoted in this article see

» References