|Main authors:||Zhanguo Bai, Thomas Casparia, Maria Ruiperez Gonzalez, Niels H. Batjes, Paul Mäder, Else K. Bünemann, Ron de Goede, Lijbert Brussaard, Minggang Xu, Carla Sofia Santos Ferreira, Endla Reintam, Hongzhu Fan, Rok Mihelič, Matjaž Glavan, Zoltán Tóth|
|iSQAPERiS editor:||Jane Brandt|
|Source document:||Bai, Z. et al. (2018) Effects of agricultural management practices on soil quality: a review of long-term experiments in Europe and China. Agriculture Ecosystems and Environment, 265, pp 1–7|
|1. Selection of soil quality indicators and agricultural management practices|
|2. Data collection and literature review|
|3. Data analysis and visualization|
Based on »Soil quality - a critical review and work by Spiegel et al. (2015), we have initially chosen six soil quality indicators. Main considerations in making this selection were:
- Changes in soil quality and fertility are gradual and significant effects of land use and management generally cannot be measured within at least five years after their introduction; hence, long-term experiments (LTEs) are of critical importance. Focus will be on “dynamic” over “static” indicators as only the former can reflect changes within a reasonable time span.
- Most indicators are soil and site specific (e.g. soil organic matter content and pH), so it is essential that experiments have been done under comparable conditions (e.g. LTEs with split-plot design, or at least with neighbouring parcels) under identical soil and climate conditions.
- It is necessary to distinguish between short-term effects and long-term changes in soil quality indicators.
- Indicators can be related to potential changes in soil functions and soil threats.
- It is important not only to identify the most appropriate bio-physical indicators, but also to ensure that farmers and land managers can easily understand and relate to these indicators so that they may be used to support on-farm management decisions.
The selected soil quality indicators were: soil organic matter (SOM) content, pH, aggregate stability, water-holding capacity and (number of) earthworms. Yield, although not a soil property, is also considered here as it is a good measure for soil quality and a primary concern to farmers.
Five agricultural management practices were chosen as “promising”: organic matter addition, no-tillage, crop rotation, irrigation, and – at the system level – organic agriculture. For each LTE, we compared results with respect to the corresponding “standard practice” (reference): no organic matter input, conventional tillage, monoculture, non-irrigation, and conventional farming.
LTEs are indispensable for assessing effects of agricultural management practices on changes in soil quality. We have collated data of 30 long-term experiments from the 13 iSQAPER project partners in Europe and China. Data collated for each LTE included: location, climate, land use, soil data, trial factors, management systems, assessments done, sample storage and analysis. The average duration of the LTEs under consideration was 19 years (range: 5 to 34 years). The earliest LTE began in 1964 and most of these LTE’s are still ongoing. Details on the trials included are provided as supplementary information in Table S1.
The above data were complemented with analytical data from 42 long-term agricultural experiments across China covering over 30 years of observations and various management practices (Xu et al., 2015a; 2015b).
To augment our database, we performed an extensive literature review, including over 900 publications and reports using web-based search engines Google Scholar, ScienceDirect, ISI Web of Science, ResearchGate, and Scopus. Publications in Chinese were retrieved using the »China Knowledge Resource Integrated (CNKI) database. Key search terms used included organic matter addition (crop residue, straw return, green manure, farmyard manure, compost, slurry), crop rotation, no-tillage, organic agriculture, organic farming, and combination with the chosen soil properties and yield.
The resulting publications were documented using an open source reference manager (Mendeley.com) and subsequently screened for their relevance for the present review. Key elements of the selected studies (402 observations) were entered into a Microsoft Excel database. The corresponding data and literature references are documented in supplementary Table S2.
Effects of management practices on the selected soil quality indicators were assessed on the basis of both the iSQAPER LTE data (supplementary Table S1), and the data extracted from the literature review including analytical results from the LTEs of China (supplementary Table S2).
For the LTEs, we calculated response ratios (RR) for each indicator under a paired practice. For example, SOM content under NT (Treatment 2) was divided by SOM content under conventional tillage (Treatment 1 as a reference). For some experiments, results were reported as soil organic carbon (SOM = 1.724 * SOC, according to van Bemmelen (1890)), so the ratios are comparable.
Measurements were made at variable intervals depending on the objective of each experiment. As indicated, for this study, the duration of each experiment should be at least five years. For this, we have first analysed the data using the following procedure; if there are:
- ≥ 3 measurements (92% of the LTE observations), then we calculated the average RR for the last three measurements (e.g. total 5 measurements over 14 years, period 2002-2015, last three measurements in 2008, 20012, 2015).
- two measurements (8% of the LTE observations), then we calculated the average RR for both measurements.
For data extracted from the literature review and the supplementary LTEs of China, we also calculated the RR for each soil quality indicator under a paired practice as indicated above, for example, aggregate stability under crop rotation divided by aggregate stability under monoculture for the given LTEs.
Due to a lack of data, the previously selected indicator of water-holding capacity was excluded as well as the paired practice of irrigation/non-irrigation.
In total, response ratios for 354 paired observations have been calculated (»Effects of management practices on soil quality indicators). Inherently, the number of observations was biased by relying on available data. For example, we found more data for changes in yield, SOM content and pH than for (number of) earthworms. This represents a known limitation for this type of descriptive studies.
To limit the influence of possible data outliers, medians instead of means were employed to visualise the response ratios per treatment. ‘Flower petal’ diagrams were generated for each paired management practices. All analyses and visualisations were performed using R scripts (R Development Core Team, 2008).
Note: For full references to papers quoted in this article see