Soil health is the capability of soil to provide ecosystem services. These can be quantified through multiple separate indicators (N-mineralization, water infiltration, aggregate stability, etc.) or by a single proxy that integrates many soil processes. Two commonly used integrative measurements are the soil 24h-respiration test (CO2burst) and the visual evaluation of soil structure (VESS). Both are fast, but capture only a part of whole phenomenon of soil health. Soil redox potential is a promising soil and plant health indicator. The redox potential is controlled by soil chemical oxidation-reduction reactions and therefore integrates several processes. However, this method has been tested only on a few soils. In this study, we evaluated redox by comparing it with other established soil health indicators on 35 fields in Finland. Based on the results, redox correlated well with soil biological activity, structure, and texture. Soils with good structure had an oxidized redox status. A low redox state was connected to high biological activity. The carbon farming practices resulted in lower oxidation. A combination of redox and pH could be used to classify soils. The analysis supports the use of redox as a soil health indicator, but further research is needed in identifying the processes and properties the redox is an indicator for.
Maintaining a healthy soil microbiome is important for key soil functions and plant growth. However, little is known about temporal changes in soil microbial communities across different soils and nitrogen fertilization in production soils. The aim of this investigation was to determine soil bacterial and fungal baseline communities and seasonal changes in cornfields, under contrasting soil orders with and without nitrogen fertilization. Three Missouri soil orders (Entisol, Alfisol, and Mollisol) and two nitrogen fertilizer rates (0 and 225 kg nitrogen ha-1) were used for this research. Soil samples (0-5 and 5-15 cm) were taken six times during the season, starting prior to planting up to the R2 corn growth stage. Samples were used to determine bacterial and fungal abundancies and biomass. Soil characteristics (e.g., CEC, pH, organic matter) and nitrogen fertilization showed significant but minor influence on bacterial abundance and biomass, while soil order and corn growth stage had major influence. Each soil order had a distinct and significantly different bacterial and fungal community. Soil depth significantly influenced all Beta diversity metrics, and bacterial and fungal biomass were greater in the 0-5 cm depth. No microbial interactions influenced corn growth more than nitrogen. Though strong relationships between microbes and soil and plant health have been shown, linkages of microbiome information to agronomic decisions are rare. Before developing soil microbial information based decision aids for farmers, longer temporal sampling in more growing environments are needed to identify links between management practices and microbial information.
A more vital soil science future 11Acceptance speech at the Presidential Award ceremony of the 2022 Soil Science Society of America (SSSA) meeting in BaltimoreAlfred E. HarteminkUniversity of Wisconsin-Madison, Department of Soil Science, FD Hole Soils Lab, 1525 Observatory Drive, Madison Wi 53711, USA. E-mail firstname.lastname@example.orgBy some measures, soil science is doing fine. We have gained a battery of aspiring young scientists, and became more gender-balanced although still not as diverse as we should be (Carter et al., 2021). The number of soil scientific papers and books are growing at an almost exponential rate reflecting increased research and funding and, hopefully, an expanding knowledge base. An enormous amount of soil information is available, and increasingly peer-reviewed publications are freely accessible. Soon there will be no barrier for humanity between what is known and what information can be retrieved about soil.Soil awareness has grown among policy makers and the general public, following enduring campaigns by national and international soil organizations. It is not uncommon to hear podcasts, talks on the tv, or read articles about soil health, regenerative agriculture, or the relationships between soil management, greenhouse gases and the changing climate. Scientific disciplines ranging from medical geology to urban planning recognize the relevance of soils and have embarked on soil research.Countless technological advances enable us to observe, measure, model and monitor soil attributes at accelerating speed (Wadoux and McBratney, 2021). The private sector and industry have entered the soil carbon market, discovered the commercial value of the soil microbiome, and continues to develop technologies to optimize soil water use. But as soil science is rapidly evolving with a particular focus to solve the grand environmental challenges, it is vital that the science is done efficiently and impactful, and that it stays well-ahead of the technology.Some soil research approaches are holding us back. First, a lot of soil science is conducted at the fringe of traditional soil science centers and departments by a community that has not had the benefit of primary schooling in soil science. Our discipline has always been enriched by approaches and theory from other scientific disciplines but, at the same time, a lot of fundamental knowledge about our soils, knowledge that was learned the hard way, is ignored (Hartemink, 2015; Schimel and Chadwick, 2013). This particularly refers to the lack of viewing soils as a four-dimensional system that cannot be reduced to measuring a limited set of attributes in one or a few timesteps and from shallow soil depth.Quite a lot of research purports to investigate systems and aims to derive knowledge from differences among the systems. For example, soils under different vegetation or cropping systems are sampled for certain fractions, say, soil organic carbon, and the differences are attributed to land use and management. Too often, samples are collected only from the uppermost 20 cm (Yost and Hartemink, 2020) and, in many studies, diverse vegetation or a ‘range of agricultural soils’ are sampled across the continent so as to maximize variation. No wonder that differences are unearthed, but such studies rarely progress beyond the description of loosely connected phenomena that are hard to extrapolate, or interpolate.With research framed within one or other grand environmental challenge (Wortman and Lovell, 2013), it remains unclear how the results translate or contribute to solving those challenges. Studying and understanding soils is noy easy. The best studies are those that investigate a multitude of soils and their attributes across a range of spatial and timescales using a wide range of tools and, above all are based on a solid framework and sound theory. We need to speculate more and think deeper and longer, and require more explorations that combine measurements with modelling and predictions across time and space.Next, there is soil anxiety – that might be described as the hidden apprehension of doing something new and novel. This seems to occur, for example, at the research proposal stage whereby new ideas are met with: ‘Why?’ instead of a firm: ‘Why not!’. This goes along with the lack of study whether the research has already been done: it is easier to repeat than to formulate and invent. Part of the soil anxiety is the strictly regional approach that can be summed up as: ‘We have done in North Dakota. Now we do it in South Dakota.’ Science needs verification but there is quite a limit to progress in this direction. Progress comes from tirelessly chasing of new ideas, quite often with only partial success, but the lack of ideas - equivalent to maintaining a status quo - is regressive. It is not: ‘Go big or go home’ but: ‘Do something new or go home. Dare to fail - and dare to publish it.’And, finally, a lot more effort is needed to develop sound theory. Sound theory is the bedrock of all the soil science subdisciplines. This could be the last chapter of every PhD thesis. It is not the same thing as hypothesis formulation and testing. Research considered ‘high impact-high risk’ is no guarantee of novelty or an overture for theory development if that is not the overarching aim. Funding bodies might hesitate to support foundational soil science research but it is good to see that methodology development is advancing in most soil science subdisciplines. It seems to go hand in hand with increased technological availability.So, soil science is thriving, but it can do better. As much of our research findings are available, we have all the more responsibility to deliver the best. The future directions of our science are by no means solely determined by funding or the environmental challenges but, in particular, by the community, its education and willingness to strive for excellence. There is surely a role for the soil science journals to weed out problematic research approaches, but it is the task of the community, research centers and universities to guarantee that the research approaches are solid, reproducible, and innovative. Innovation should be the foundation of our research and not a special program defined as ‘high risk’ or ‘quick impact’. Concerted efforts should be made to force breakthroughs, for the growth of soil science theory and frameworks, and to think about the vitality of our discipline – now and in the future. And in all that, there is no need to think outside the box. There is no box.
The use of heavy machinery is increasing in agricultural industries in particular cotton farming systems, which induces an increased risk of soil compaction and yield reduction. Hence, there is a need for a technical solution to use available tools to measure projected soil compaction due to farm machinery traffics. The aim of this work was to compare the effects of static and dynamic loads on soil compaction. In this study, three vertosols (common soil for cotton production) were selected to examine soil compaction under a range of static and dynamic loads using uniaxial compression equipment and a modified proctor test, respectively. In general, soils behaved similarly under static and dynamic loads with no significant difference between bulk density values for all moisture contents with a high index of agreement (d=0.96, RMSE= 0.056). The results further indicate better agreement between soil compaction for static and dynamic loads Uniaxial compression test (static loads) produced higher compaction compared to the modified proctor test (dynamic loads) in particular at moisture contents lower than the plastic limit condition. The variation in soil compaction for static and loads was often evident for loads ≥600 kPa, with the highest soil compaction induced under loads ≥1200 kPa. The findings of this study confirm the suitability of a modified proctor method to assess soil compaction as an alternative tool under a range of moisture contents and machinery loads for vertosols.
Large and publicly available soil spectral libraries, such as the USDA National Soil Survey Center – Kellogg Soil Survey Laboratory (NSSC-KSSL) mid infrared (MIR) spectral library, are enormously valuable resources enabling laboratories around the world to make rapid estimates of a number of soil properties. A limitation to widespread sharing of soil spectral data is the need to ensure that spectra collected on a local spectrometer are compatible with the spectra in the primary or reference library. Various spectral preprocessing and calibration transfer techniques have been proposed to overcome this limitation. We tested the transferability of models developed using the USDA NSSC-KSSL MIR library to a secondary instrument. For the soil properties, total C (TC), pH and clay content, we found that good performance (RPD = 4.9, 2.0 and 3.6, respectively) could be achieved on an independent test set with Savitzky-Golay (SG) smoothing and first derivative preprocessing of the secondary spectra using a memory-based learning chemometric approach. We tested three calibration transfer techniques (direct standardization (DS), piecewise direct standardization (PDS), and spectral space transformation (SST)) using different size transfer sets selected to be representative of the entire NSSC-KSSL library. Of the transfer methods, SST consistently outperformed DS and PDS with 50 transfer samples being an optimal number for transfer model development. For TC and pH, performance was improved using the SST transfer (RPD = 7.7 and 2.2, respectively) primarily through the elimination of bias. Calibration transfer could not improve predictions for clay. These findings suggest calibration transfer may not always be necessary but users should test to confirm this assumption using a small set of representative samples scanned at both laboratories.
Growing media constituents have heterogeneous particle size and shape, and their physical properties are partly related to them. Particle size distribution is usually analyzed through sieving process, segregating the particles by their width. However, sieving techniques are best describing more granular shapes and are not as reliable for materials exhibiting large varieties of shapes, like growing media constituents. A dynamic image analysis has been conducted for a multidimensional characterization of particle size distribution of several growing media constituents (white and black peats, pine bark, coir, wood fiber, and perlite), from particles that were segregated and dispersed in water. Diameters describing individual particle width and length were analyzed, then compared to particle size distribution obtained by sieving DM and HM methods. This work suggests the relevance of two parameters, Feret MAX and Chord MIN diameters for assessing particle length and width, respectively. They largely varied among the growing media constituents, confirming their non-spherical (i.e. elongated) shapes, demonstrating the advantages in using dynamic image analysis tools over traditional sieving methods. Furthermore, large differences in particle size distribution were also observed between dynamic image analysis and sieving procedures, with a finer distribution for dynamic image analysis. The discrepancies observed between methodologies were discussed (particle segregation, distribution weighing, etc.), while describing in details methodological limitations of dynamic image analysis.