|Luis Garrote, David Santillán, Ana Iglesias
|Garrote L., Santillán D., Iglesias A. (2018) Report on key management practices affecting soil quality and their applicability in various farming systems. iSQAPER Project Deliverable 7.2 140 pp
Note: Here we draw on research results discussed in »Soil quality indicators from long-term experiments, »Crop & livestock systems and »Management practices and soil quality
|1. From local results to the continental scale
|2. Linking farming systems, management practices and soil quality indicators
The upscaling model intends to provide results of the scientific knowledge at the local level to a wider geographical context, to understand how agricultural management practices that mitigate soil threats also affect other ecosystem services. In order to perform this task, the model accounts for the basic processes that influence agricultural management of the soil. These processes are extremely complex at the physical, chemical, biological and socioeconomic levels and therefore they need to be simplified to become manageable. The basic approach is illustrated on Figure 2, where the relevant processes are represented.
The central actor in the process is the farmer, who is managing a plot of land where a certain crop is grown under a typical farming system. This plot of land is subject to a physical context, determined by soil type, climate, water availability and other factors that control biophysical processes. The farmer is also immersed in a socio-economic context that influences agricultural activity: Common Agricultural Policy, environmental policy, financial instruments, market conditions and socio-economic development determine management decisions regarding crop selection and management practices. The choice of management practices is also influenced by existing soil threats, like soil erosion, desertification, loss of organic matter and many others. The farmer intends to control local soil threats by applying suitable management practices.
Science developed iSQAPER determines that certain agricultural management practices may have a beneficial effect on agricultural soil conditions. These conditions are described through a set of suitable indicators, chosen because they represent the status of the soil. The analysis of Long Term Experiment (LTE) sites (see »Soil quality indicators from long-term experiments) proves that these effects can be objectively quantified in terms of such indicators. Under the upscaling approach, policy is considered to be a driver of change, motivating farmers to adopt beneficial management practices. The upscaling model intends to quantify the global effect of policies promoting beneficial agricultural practices. In order to do so, functional relations are established between the agricultural management practices and the soil quality indicators for different farming systems. The improved values of soil quality indicators can then be used to evaluate the soil environmental footprint by accounting for soil functions that support ecosystem services. For instance, the soil quality indicator “Yield” is linked to the soil function of provision of food, a basic ecosystem service for the soils. Through the upscaling model a spatial representation of soil environmental footprint may be generated under a set of policy scenarios. These upscaled maps can be used as a decision support tool for policy identification and implementation.
The dynamic models developed in WP7 aim to determine the effect of the evolving physical and socioeconomic 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, the analysis in WP7 is focused on a limited number of essential components that are introduced in this section. The components of the upscaling model are summarized in Figure 3. They are grouped in the three main categories described below.
2.1 Farming systems
Farming systems are complex and include multiple dimensions that cannot be easily mapped due to these complexities and data limitations. In iSQAPER, for upscaling of results farming systems were therefore represented by the typical cropping patterns (see »Crop & livestock systems). Seven cropping patterns were considered for the upscaling model of iSQAPER. These types represent a large fraction of the food produced globally and have been chosen to balance model complexity and representability. The categories are the following:
- Cereals: Extensive cereals, like wheat, barley, oats or rye, grown in temperate regions, usually rain fed, although they might require supplemental irrigation in some locations. Farming practices usually rely on machinery for harvesting. The use of herbicides and fertilizer is frequent.
- Rice: 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: Arable land devoted to maize cultivation.
- Soybean: Arable land devoted to soybean cultivation
- Vegetables: 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: Grass-based livestock systems for meat and dairy production.
- Permanent crops: 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.
2.2 Agricultural management practices
Four categories of management practices have been adopted for upscaling in iSQAPER. They are the same categories published in (Bai et al., 2018, see »Management practices and soil quality) to evaluate their effect on different soil quality indicators. They have been chosen to assimilate 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 or reduced 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.
- Organic farming: 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.
2.3 Soil Quality Indicators
Three soil quality indicators have been selected for the iSQAPER upscaling model. The selection was based on the indicators identified in (Bai et al., 2018, see »Management practices and soil quality), but reducing the number for considerations of simplicity, relevance 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 to 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