For example, compact plow pans, resulting from the application of pressure on wet soil by agricultural machinery, can strongly reduce rooting depth. When precipitation rates are higher than the infiltrative capacity of soils, water will flow laterally away over the soil surface, possibly leading to erosion and reducing the amount of water available for plant growth. The climate and varying weather conditions among the years govern biomass production. Rainfall varies in terms of quantities, intensities and patterns.
Radiation and temperature regimes vary as well.
In this context, definitions of location-specific potential yield Yp , water-limited yield Yw and actual yield Ya are important, as will be discussed later. However, not only the adsorptive character of the soil is important but also the flow rate of applied water that can be affected by climatic conditions or by management when irrigating. Rapid flow rates generally result in poor filtration as was demonstrated for viruses and fecal bacteria in sands and silt loam soils Bouma, The organic matter content of soils is highly affected by soil temperature and moisture regimes and soil chemical conditions.
This system can only be realized by process modeling, which requires the five parameters listed above, and is therefore an ideal vehicle to realize interdisciplinary cooperation. Simulation models of the soil—water—atmosphere—plant system are ideal to integrate these various aspects. When analyzing soil quality and soil health, emphasis must be on the dynamics of vital, living ecosystems requiring a dynamic approach that is difficult to characterize with static soil characteristics such as bulk density, organic matter content and texture except when these characteristics are used as input data into dynamic simulation models of the soil—water—plant—climate system.
Restricting attention to soil physical characteristics, hydraulic conductivity K and moisture retention properties O h of soils are applied in such dynamic models. Measurement procedures are complex and can only be made by specialists, making them unsuitable for general application in the context of soil quality and health. The latter soil characteristics are available in existing soil databases and are required information for the dynamic models predicting biomass production.
So-called water-limited yields Yw can be calculated, assuming optimal soil fertility and lack of pests and diseases e.ays.chipichipistudio.com/roars-of-the-gaijin-shogun-douglas-macarthur.php
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Yw reflects climate conditions at any given location in the world as it is derived from potential production Yp that reflects radiation, temperature and basic plant properties, assuming that water and nutrients are available and pests and diseases do not occur. Yw reflects local availability of water. Yw is usually, but not always, lower than Yp. Note that Yp and Yw, while providing absolute science-based points of reference, include assumptions on soil fertility and crop health. Actual yields Ya are often lower than Yw e.
When multiplied with , a number between 1 and is obtained as a quantitative measure of the yield gap for a given type of soil. Yw can be calculated for a non-degraded soil. Ya should ideally be measured but can also be calculated as was done in this exploratory study in terms of Yw values on the basis of the assumed effects of different forms of soil degradation, such as subsoil soil compaction, poor water infiltration at the soil surface due to surface compaction, or crusting and erosion. This requires the introduction of a compact layer plow pan in the soil, a reduction of rainfall amounts with the volume of estimated overland flow and the removal of topsoil.
Each variant of the analyzed soil series represents a phenoform. In this exploratory study Ya values were simulated but, ideally, field observations should be made in a given soil type to define effects of management as explored, for example, by Pulleman et al. Field work identifying phenoforms includes important interaction with farmers as also mentioned by Moebius-Clune et al.
Sometimes, soil degradation processes, such as erosion, may be so severe that the soil classification the soil genoform changes. Then, the soil quality and soil health discussion shifts to a different soil type. This approach will now be explored with a particular focus on the Mediterranean environment. This way a characteristic range of Yw values is obtained for each of the six soil series, reflecting positive and negative effects of soil management and representing a range of soil physical quality values of the particular soil series considered. Within this range an actual value of Ya will indicate the soil physical health of the particular soil at a given time and its position within the range of values will indicate the severity of the problem and potential for possible improvement.
Health is relatively low when real conditions occur in the lower part of the soil quality range for that particular soil and relatively high when it occurs in the upper range. Again, in this exploratory study measured values at current climate conditions for Ya have not been made, so Ya only applies to the three degraded soil forms being distinguished here where hypothetical effects of soil degradation have been simulated as related to the corresponding calculated Yw values.
Of course, actual measured Ya values cannot be determined at all when considering future climate scenarios and simulation is the only method allowing exploratory studies.
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We assume that climate change will not significantly affect soil formation processes until the year Soil properties will therefore stay the same. Obviously, only computer simulations can be used when exploring future conditions — another important reason to use dynamic simulation modeling in the context of characterizing soil quality and soil health. The approach in this paper extends earlier studies on soil quality for some major soil types in the world that did not consider aspects of soil health nor effects of climate change Bouma, ; Bouma et al.
The soil—water—atmosphere—plant model Kroes et al. SWAP is an integrated physically based simulation model of water, solute and heat transport in the saturated—unsaturated zone in relation to crop growth. In this study only the water flow module was used; it assumes unidimensional vertical flow processes and calculates the soil water flow through the Richards equation.
The unit gradient was set as the condition at the bottom boundary.
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The upper boundary conditions of SWAP in agricultural crops are generally described by the potential evapotranspiration ET p , irrigation and daily precipitation. The water uptake and actual transpiration were modeled according to Feddes et al. The model was calibrated and validated by measured soil water content data at different depths for Italian conditions Bonfante et al. Simulations were run considering a soil without assumed degradation phenomena the reference and for three variants with a compacted plow layer, surface runoff and erosion, as discussed above.
Runoff from the soil surface was simulated removing ponded water resulting form intensive rainfall events. Variants were theoretical but based on local knowledge of the Sele Plain. Compaction is relevant considering the highly specialized and intensive horticulture land use of the Sele Plain which typically involves repetitive soil tillage at similar depth. The main agricultural production consists of irrigated crops maize, vegetables and fruit orchards , greenhouse-grown vegetables and mozzarella cheese from water buffalo herds.
The area can be divided into four different landform classes foothills, alluvial fans, fluvial terraces and dunes with heterogeneous parent materials in which 20 different soil series were distinguished within Inceptisol, Alfisol, Mollisol, Entisol and Vertisol soil orders Regione Campania, , according to Soil Taxonomy Soil Survey Staff, Six soil series were selected in the area to test application of the soil quality and soil health concepts.
Decision trees were developed to test whether the selection process of the soil series was based on stable criteria, allowing extrapolation of results from measured to unmeasured locations when considering effects of climate change. While extrapolation in space of soil series data has been a common procedure in soil survey e.
A basic principle of many taxonomic soil classification systems is a focus on stable soil characteristics when selecting diagnostic criteria for soil types. Also, emphasis on morphological features allows, in principle, a soil classification without requiring elaborate laboratory analyses e. A given soil classification should, in order to obtain permanent names, not change following traditional management measures, such as plowing.
This does, however, not apply to all soils and therefore a different name will have to be assigned. This way, soil classification results in an assessment of the semi permanent physical constitution of a given soil in terms of its horizons and textures. That is why soil quality is defined for each soil type as a characteristic range of Yw values, representing different effects of soil management that have not changed the soil classification.
The validations performed showed that these model data agree closely with different regional high-resolution observational datasets, in terms of both average temperature and precipitation in Bucchignani et al. In particular, the Representative Concentration Pathway 8. The simulations covered the period from to ; more specifically, the CMIP5 historical experiment based on historical greenhouse gas concentrations was used for the period — reference climate scenario — RC , while for the period —, a simulation was performed using the IPCC scenario mentioned.
The reliability of this equation in the study area was performed by Fagnano et al. The Yp potential yield is the maize production for the Destra Sele area assuming optimal irrigation and fertilization and no pests and diseases. Yp is only calculated for the reference climate. The differences in temperature between RC and the period — showed an average increase in minimum and maximum temperatures of about 6. The projected increase in temperatures produces an increase in the expected ET 0.
Considering current climate conditions, the Longobarda and Cifariello soils with loamy textures have the highest values, while the sandy soil Lazzaretto is lower. The effects of climate change are most pronounced and quite clear for the two periods after This follows from the important reduction of projected rainfall during the cropping season Fig. Only a Yp value is presented for current conditions because estimates for future climates involve too many unknown factors.
The projected effects of soil compaction are shown in Fig.
The effects of compaction are very strong in all soils, demonstrating that restricting the rooting depth has major effects on biomass production. Clearly, any effort to increase the effective rooting patterns of crops should be a key element when considering attempts to combat effects of climate change.
Data indicate that reactions are soil specific. Other terms are explained in Fig. Runoff occurs when rainfall intensity is higher than the assumed infiltrative capacity of the soil. Results presented in Fig. This implies that surface crusting or compaction of surface soil, leading to lower infiltration rates and more surface runoff, does not seem to have played a major role here in the assumed scenarios. Real field measurements may well produce different results.
Even though projected future climate scenarios predict rains with higher intensities that were reflected in the climate scenarios being run, the effects of lower precipitation, as shown in Fig. Soil quality for a given soil is thus represented by a characteristic range of values. Soil health is indicated by the particular location of an actual Ya within this range. Yw values are lower in all soils as compared with reference climate conditions, but loamy and clayey subsoils still can still provide moisture to plant roots, leading to relatively low reductions of Yw e.
But Van Ittersum et al. This provides important signals for the future. As discussed, the presented ranges are soil specific and are based on hypothetical conditions associated with different forms of land degradation. Field research may well result in different ranges also possibly considering different soil degradation factors beyond compaction, surface runoff and erosion.
Nevertheless, principles involved are identical. Ranges presented in Fig. Actual values Ya will fit somewhere in this range and will thus indicate how far they are removed from the maximum and minimum value, thereby presenting a quantitative measure for soil physical health. This cannot only be important for communication purposes but it also allows a judgment of the effects of different forms of degradation in different soils as well as potential for improvement.
Of course, different indicator crops will have to be defined for different areas in the world. Linking soil quality and health to specific and well-defined soil types is essential because soil types, such as the soil series presented in this paper, uniquely reflect soil-forming processes in a landscape context. They provide much more information than just a collection of soil characteristics, such as texture, organic matter content and bulk density.
They are well known to stakeholders and policymakers in many countries. A good example is the USA where state soils have been defined. Defining semipermanent soil quality for specific soil types, in terms of a characteristic range of Yw values reflecting effects of different forms of land management, represents a quantification of the more traditional soil survey interpretations or land evaluations where soil performance was judged by qualitative, empirical criteria.
In this exploratory study, hypothetical effects of three forms of soil degradation were tested. In reality, soil researchers should go to the field and assemble data for a given soil series as shown on soil maps, establishing a characteristic range of properties, following the example of Pulleman et al. This way, a characteristic series of phenoforms can be established.
Physical soil quality for a given soil type, which is the genoform has a characteristic range of Yw values, as shown in Fig. One could argue that this range acts as a thermometer for a particular type of soil allowing the determination of the physical health of a given soil by the placement of Ya. But calculating Yw has implications beyond defining physical soil quality and health.
As discussed, Yw not only reflects the effects of soil moisture regimes but also assumes that chemical conditions for crop growth are optimal and that pests and diseases do not occur. Defining Yw can thus function as a starting point of a general soil quality and soil health discussion. If Ya is lower than Yw the reasons must be found. Is it lack of water, nutrients, or occurrence of pests and diseases? Irrigation may be difficult to realize but fertility can be restored rather easily and many methods, biological or chemical, are available to combat pests and diseases.
This way, the Yw analysis can be a logical starting point for follow-up discussions defining appropriate forms of future soil management. This paper has focused on physical aspects but the proposed procedure has the potential to extend the discussion to chemical and biological aspects, to be further explored in future. Rather than consider the physical, chemical and biological aspects separately, each with their own indicators as proposed by Moebius-Clune et al.
This is more relevant because the definition of reproducible biological soil health parameters is still an object of study Wade et al. Recent tests of current soil health protocols have not resulted in the adequate expression of soil conditions in North Carolina Roper et al. Lack of widely accepted, operational criteria to express soil quality and soil health is a barrier for effective external communication of the importance of soil science.
Using well-defined soil types as carriers of information on soil quality and soil health can improve communication to stakeholders and the policy arena. A universal system defining soil quality and soil health is needed based on reproducible scientific principles that can be applied all over the world, avoiding a multitude of different local systems. Models of the soil—water—atmosphere—plant system can fulfil this role. The proposed system allows an extension of classical soil classification schemes, defining genoforms, by allowing estimates of effects of various forms of past and present soil management phenoforms within a given genoform that often strongly affects soil performance.
Quantitative information thus obtained can improve current empirical and qualitative soil survey interpretations and land evaluation. Rather than consider physical, chemical and biological aspects of soil quality and health separately, a combined approach starting with pedological and soil physical aspects followed by chemical and biological aspects, all to be manipulated by management, is to be preferred.
Only the proposed modeling approach allows exploration of possible effects of climate change on future soil behavior, which is a necessity considering societal concerns and questions. Field work, based on existing soil maps to select sampling locations for a given genoform, is needed to identify a characteristic range of phenoforms for a given genoform, which, in turn, can define a characteristic soil quality range by calculating Yw values. AB contributed the soil data and simulation results, FT contributed the background soil data and JB suggested the study.
Allen, R. Almajmaie, A. Basile, A. Bonfante, A. Dispersion Ratio DR correlated significantly with moisture content. There was significant positive correlation between CFI and BS as well as gravimetric moisture content. Parent material is one of the major soil forming factors. There were variations in the textural classes of the soil with respect to parent materials under the three land use types of the studied soils.
This observation was in line with the previous works of Adaikwu et al.
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Similarly, Onwudike et al. False bedded sand stone retained more moisture than coastal plain sand due to high sand fraction in the coastal plain sand that encourages runoff and leaching; hence increasing erodibility of soil to the impact of rainfall. Onwudike et al. Low pH in coastal plain sand under cassava cultivated land could be attributed to the particle size distribution of the soil with sand fraction dominating the soil fractions Table 1.
This encourages leaching of soil organic matter and basic cations thereby enabling acid forming cations to dominated the colloidal surface. This observation is in line with Onweremadu et al. False bedded sand stone recorded lower DR than coastal plain sand due to higher organic matter in false bedded sand stone which helps to bind soil aggregates together.
According to Onwudike and Mbonu 22 , the higher the value of CFI, the lower the stability of soil against rain drop. This could be attributed to agronomic practices like tillage and exposure of soil to climatic factors. Coastal plain sand influenced these parameters due to the particle sizes of the soil with poor organic matter content which made the soil porous hence resulting to poor aggregate formation.
The negative relationship between CDI with, BS, MC, OM, TEB, TN and soil pH indicated that an increase in the values of base saturation, moisture content , total exchangeable bases, total nitrogen and soil pH could reduce the value of clay dispersion ratio thereby increasing the stability of soil aggregates.
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Similarly, increase in soil gravimetric moisture content reduces the value of Clay Ratio CR and Dispersion Ratio DR showing improvement in soil aggregate formation. These interactions were possible due to higher accumulation of soil organic matter , increased soil pH and exchangeable bases as was observed in false bedded sand stone under plantain plantation and palm plantation.
Onwudike and Mbonu 23 recorded similar results on the contribution of agricultural wastes on soil aggregate stability and attributed the effect to high organic matter content in the added agricultural wastes. There were variations in the physico chemical properties of soil in the three land use types studied under different parent materials.
Establishment of plantain plantation and palm plantation on these parent materials have shown to help bind soil fractions together thereby increasing the stability of the soil. Aggregate stability of soils in cassava cultivated lands were low and therefore agronomic practices such as minimum tillage organic manure application and mulching should be practiced to increase soil organic matter content that helps to hold soil particles together. This study discovered the importance of land use system in improving the aggregate stability of soil of contrasting parent materials that can be beneficial for improving productivity and environmental sustainability.
This study will help the researchers to uncover the critical areas of improving soil aggregate stability under different land use system that many researchers were not able to explore. Thus a new theory on improving soil aggregation under different land use systems may be arrived at. The assistance and contribution of Mr Dike Onu who helped in locating sampling locations in Obowo is appreciated.
The technologists in Soil Science and Technology Laboratory, Federal University of Technology Owerri who helped in the laboratory analysis are acknowledged in the work. Subscribe Today. Science Alert. All Rights Reserved. Research Article. Similar Articles in this Journal. Search in Google Scholar. Journal of Applied Sciences, DOI: This is an open access article distributed under the terms of the creative commons attribution License , which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.
Effect of land use types and parent materials on Dispersion Ratio DR. Effect of land use types and parent materials on Clay Ratio CR. Correction between erodibility index and soil properties. Adaikwu, A. Obi and A. Ali, Soil Sci. Brady, N. Weil, The Nature and Properties of Soils. Carter, M. Soil quality for sustainable land management: Organic matter and aggregation interactions that maintain soil functions.
Land-use effects on organic matter and physical properties of soil in a Southern Mediterranean highland of Turkey. Soil Tillage Res. Schumacher, M. Lindstrom and D. Malo, Aggregate sizes and stability in cultivated South Dakota prairie ustolls and usterts. Or, Particle Size Analysis. Topp Eds. Gelaw, A. Singh and R. Lal, Organic carbon and nitrogen associated with soil aggregates and particle sizes under different land uses in Tigray, Northern Ethiopia.
Reinsch, Bulk Density and Linear Extensibility. Chapter 2. Herdershot, W. Lalande and M.
Healthy, high-quality soil has:
Duquette, Soil Reaction and Exchangeable Acidity. Igwe, C. Obalum, Stepniewski Eds. Akamigbo and J. Mbagwu, The use of some soil aggregate indices to assess potential soil loss in soils of South-Eastern Nigeria. Chang, Y. Qi, J. Liu and T. Chen, Aggregation and soil organic carbon fractions under different land uses on the tableland of the Loess Plateau of China. Soil pH and Lime Requirements.
Miller and D. Keeney Eds. Olsen, S. Sommers, Soil Available Phosphorus. In: Methods of Soil Analysis, Part 2. Chemical and Microbiological Properties, Sparks, D. Page, P. Hennke, K. Loppez, E. Solanpour and M.