Soil research challenges in response to emerging agricultural soil management practices

Techen, Anja-K.; Helming, Katharina; Brüggemann, Nicolas; Veldkamp, Edzo; Reinhold‐Hurek, Barbara; Lorenz, Marco; Bartke, Stephan; Heinrich, Uwe; Amelung, Wulf; Augustin, Katja; Boy, Jens; Corre, Marife D.; Duttman, Rainer; Gebbers, Robin; Gentsch, Norman; Grosch, Rita; Guggenberger, Georg; Kern, Jürgen; Kiese, Ralf; Kuhwald, Michael; Leinweber, Peter; Schloter, Michael; Wiesmeier, Martin; Winkelmann, Traud; Vogel, Hans‐Jörg

doi:10.1016/bs.agron.2020.01.002

Cited by 23 publications

(21 citation statements)

References 230 publications

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“…The CWBg was chosen as a suitable parameter to represent the climatic conditions for agricultural land use and because of its huge relevance for vegetation growth. Its impact may be even larger than that of temperature (Crimmins et al, 2011), and it may determine the growing season (Sattar et al, 2019). We used data from the German Meteorological Service (DWD) for the period 1981-2010 for the main growing season, defined from 1 May to 31 October (Ad-hoc-AG Boden, 2005).…”

Section: Methodsmentioning

confidence: 99%

“…Most of the indicators are sensitive to agricultural management, which makes the MSQR most useful for studying the effects of agricultural management on soil. The MSQR has been proven useful in other studies of geospatial representation (Askari et al, 2013;Hanauer et al, 2017;Smolentseva et al, 2014). Since no MSQR is available for grassland areas, the LTFEs on grassland were excluded in this analysis.…”

Section: Methodsmentioning

confidence: 99%

“…LTFEs are further important for research related to questions regarding the interannual variability of crop yield (i.e., yield stability) that can be associated with climate change (Berti et al, 2016;Reckling et al, 2018;Macholdt et al, 2019a) and respective adaptation options (Hamidov et al, 2018). Valuable data can also be delivered for the validation of models (Franko et al, 2011;Ellerbrock et al, 2005) and for concepts used to evaluate soil functions (Vogel et al, 2019;Techen et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…A general strategy starts out from the analysis model that would be used for a single year of data and then extends the model to account for variation across years. A specific challenge here is that during the course of the experiment several observations are made on the same experimental units, and this serial correlation needs to be taken into account (Payne et al, 2015;Richter and Kroschewski, 2006;Singh and Jones, 2002). Also, there may be heterogeneity of variance between years, which may be related to changes in stability of the investigated systems (Macholdt et al, 2019a, b).…”

Section: Introductionmentioning

confidence: 99%

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Long-term field experiments in Germany: classification and spatial representation

Grosse

Hierold

Ahlborn

et al. 2020

SOIL

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Abstract. The collective analysis of long-term field experiments (LTFEs), here defined as agricultural experiments with a minimum duration of 20 years and research in the context of sustainable soil use and yield, can be used for detecting changes in soil properties and yield such as those induced by climate change. However, information about existing LTFEs is scattered, and the research data are not easily accessible. In this study, meta-information on LTFEs in Germany is compiled and their spatial representation is analyzed. The study is conducted within the framework of the BonaRes project, which, inter alia, has established a central access point for LTFE information and research data. A total of 205 LTFEs which fit to the definition above are identified. Of these, 140 LTFEs are ongoing. The land use in 168 LTFEs is arable field crops, in 34 trials grassland, in 2 trials vegetables and in 1 trial pomiculture. Field crop LTFEs are categorized into fertilization (n=158), tillage (n=38) and crop rotation (n=32; multiple nominations possible) experiments, while all grassland experiments (n=34) deal with fertilization. The spatial representation is analyzed according to the climatic water balance of the growing season (1 May to 31 October) (CWBg), the Müncheberg Soil Quality Rating (MSQR) and clay content. The results show that, in general, the LTFEs well represent the area shares of both the CWBg and the MSQR classes. Eighty-nine percent of the arable land and 65 % of the grassland in Germany are covered by the three driest CWBg classes, hosting 89 % and 71 % of the arable and grassland LTFEs, respectively. LTFEs cover all six MSQR classes but with a bias towards the high and very high soil quality classes. LTFEs on arable land are present in all clay content classes according to the European Soil Data Centre (ESDAC) but with a bias towards the clay content class 4. Grassland LTFEs show a bias towards the clay content classes 5, 6 and 7, while well representing the other clay content classes, except clay content class 3, where grassland LTFEs are completely missing. The results confirm the very high potential of LTFE data for spatially differentiated analyses and modeling. However, reuse is restricted by the difficult access to LTFE research data. The common database is an important step in overcoming this restriction.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Long-term field experiments in Germany: classification and spatial representation

Grosse

Hierold

Ahlborn

et al. 2020

SOIL

Self Cite

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“…In this sense, creating, adopting or adapting innovations in the rural sector has become complex and must be combined locally through tacit and implicit knowledge and, on ther other hand, derived from specialization through explicit knowledge (Esparcia, 2014). For this reason, studies regarding both themes in the agricultural sector worldwide have been reflected in various scientific articles highlighting technical characteristics (Ho et al, 2019;Totin et al, 2020), such as the knowledge and study of soils (Chenu et al, 2019;Techen et al, 2020), pest management (Abtew et al, 2016;Khan and Damalas, 2015) and various economic tools, strategies and models (Baloch and Thapa, 2016;Djanibekov et al, 2015;Dolinska and d'Aquino, 2016).…”

Section: Introductionmentioning

confidence: 99%

Knowledge Management and Innovation in Agricultural Organizations:

2021

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Agricultural activity in the rural sector has been studied mainly in terms of technical questions regarding cultivation; however, the organizational aspects that generate added value, such as knowledge, have rarely been addressed. Therefore, the purpose of this paper is to investigate the relationship between knowledge management and innovation in rural agricultural organizations in northwestern Mexico. A questionnaire with composed of 36 items and a thorough interview was applied to seven leading organizations of the Yaqui Valley (YV). The results reveal a positive and significant relationship between knowledge management and innovation.

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Innovative Phosphorus Governance: How to Address Recurring Regulatory Shortfalls—The Example of Germany, Costa Rica and Nicaragua

Stubenrauch

2022

International Yearbook of Soil Law and Policy 2020/2021

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Soil research challenges in response to emerging agricultural soil management practices

Cited by 23 publications

References 230 publications

Long-term field experiments in Germany: classification and spatial representation

Long-term field experiments in Germany: classification and spatial representation

Knowledge Management and Innovation in Agricultural Organizations:

Innovative Phosphorus Governance: How to Address Recurring Regulatory Shortfalls—The Example of Germany, Costa Rica and Nicaragua

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